phd climate model

ESE PhD Model Program

This description of the Environmental Science & Engineering PhD course expectations augments the school-wide  PhD course requirements .   Students should make themselves familiar with both.

Intellectual Scope

ESE is broadly conceived, covering science and related engineering applications for the atmosphere, oceans, land, cryosphere, hydrosphere, and biosphere. Areas of study include dynamics (weather, ocean circulation, physical climate, atmosphere-ocean interactions, glaciology, geomorphologic and hydrogeologic processes), chemistry (atmospheric composition, global and local pollution, aerosols, marine biogeochemistry), biology (atmosphere-biosphere interactions, microbial transformations in the environment), and engineering (water technology, geologic hazards).

Graduate Programs in ESE

Graduate programs in ESE are diverse and flexible, as needed for the wide range of subject matter, but nonetheless there are common elements shared by many students. Programs are built around introductory courses that apply principles of physics and chemistry to the atmosphere, oceans, surface and near-surface earth processes. Students build on a strong foundation in mathematics, physics, chemistry, and computational science, and study both the fundamentals and research frontiers of atmospheric, oceanic, and land surface processes and dynamics.

Climate phenomena and global changes in atmospheric and ocean composition are a strong part of the curriculum, including advanced applications of statistics and large-scale data analysis, and including transient/accelerated glacial processes, air and water pollution, the science of geologic hazards (earthquakes, volcanic eruptions, and landslides), energy-related geoscience, and engineering applications are a growing component.

Requirements 

The program below forms a starting point for a discussion with the faculty about courses of interest. Students should work in close consultation with their advisors to develop an appropriate program plan that is consistent with the SEAS Ph.D. program's overall  course requirements .   Courses provide the background knowledge that is often needed to successfully complete research and allow students to learn more broadly about their field of study or related fields.

Note: Many courses in ESE are taught by SEAS faculty jointly with the Earth and Planetary Sciences (EPS) department and are listed as EPS courses.  Students take 10 total courses in consultation with their advisor including:

• At least six science or engineering-based disciplinary courses at the 200 level, not including 299r courses.
• At most two 299r courses.
• Any remaining 200-level or 100-level science or engineering-based disciplinary courses to fulfill the 10-course requirement, with at most two at the 100 level.

Many students take EPS 200 or 208 during their first year.

SuggestedCore Courses

These courses have been identified by the ESE faculty as useful for many students.  Individual students should discuss choices with their advisors.

Mathematics


Preparation in mathematics and statistics is required for all ESE students. Since students have diverse backgrounds and a broad range of educational goals, they undertake mathematics at different levels. Generally students will take 2 courses in the mathematical sciences which include math, applied math, and statistics. Note that ordinarily a maximum of two 100-level courses can be applied to a Ph.D. program of study.

Minimum levels

• AM 105 Ordinary and Partial Differential Equations
• AM 115 Mathematical Modeling
• AM 120 Linear Algebra and Big Data

Typical programs include at least one of the following courses

• AM 201 Physical Mathematics I
• AM 202 Physical Mathematics II (partial differential equations)
• AM 205 Advanced Scientific Computing: Numerical Methods

Physics, Chemistry, Biology

Generally students must take at least one of the following courses. In most cases students take two of the following.

• ES 260 / EPS 200 Atmospheric Chemistry and Physics (includes computer laboratory)
• ES 268 / EPS 208 Physics of Climate

Sub-areas of ESE each have additional educational needs for foundation courses covering various aspects of physics, chemistry, engineering sciences or biology, such as fluid mechanics, spectroscopy, laser physics, ecosystem dynamics, etc. In consultation with his/her advisor, each student will develop a program that includes the relevant graduate courses of this type.

Physics/Dynamics-oriented courses

Students divide roughly by physics and chemistry foci, but many students do some of each.

• EPS 231 Climate Dynamics
• EPS 232 Dynamic Meteorology

Chemistry-oriented courses

• ES 266 / EPS 236 Environmental Modeling (includes computer laboratory)
• ES 268 Chemical Kinetics
• ES 267 Aerosol Science and Technology

Technical Breadth Courses

Most programs will have courses outside of the students direct research area. These courses ensure that there is technical breadth in the student’s education. A typical program will contain two courses which provide technical breadth. The following is a list of courses which will often satisfy the technical breadth requirement:

• ES 123 Introduction to Fluid Mechanics and Transport Processes, or ES 220 Fluid Dynamics
• ES 231 Energy Technology
• ES 237 Planetary Radiation and Climate
• ES 240 Solid Mechanics
• ES 265 Advanced Water Treatment
• ES 269 Environmental Nanotechnology
• OEB 157 Global Change Biology
• Physics 223 Electronics for Scientists

This is list is by no means comprehensive. Courses at the graduate level in Physics and Chemistry (e.g. quantum mechanics, molecular structure, electronics) are commonly included.

Please note that many students studying Geophysical Fluid Dynamics take 1 or 2 MIT courses.

Note that, for Program Plans in Engineering Sciences, Physics 223  Electronics for Scientists  is considered to be a 200-level SEAS-equivalent technical course.

In Environmental Science & Engineering

• Undergraduate Engineering at Harvard
• Concentration Requirements
• How to Declare
• Who are my Advisors?
• Sophomore Forum
• ABET Information
• Senior Thesis
• Research for Course Credit (ES 91R)
• AB/SM Information
• Peer Concentration Advisors (PCA) Program
• Student Organizations
• How to Apply
• PhD Timeline
• PhD Model Program (Course Guidelines)
• Qualifying Exam
• Committee Meetings
• Committee on Higher Degrees
• Guidelines for Advisor-Student Discussions
• Research Interest Comparison
• Collaborations
• Cross-Harvard Engagement
• Clubs & Organizations
• Centers & Initiatives
• Alumni Stories

Jump to navigation

Search form

• From the Director
• Job Opportunities

MIT Center for Global Change Science affiliates include: the  MIT School of Science ;  MIT School of Engineering ;  Earth, Atmospheric & Planetary Sciences (EAPS) ;  Civil & Evironmental Engineering (CEE) ; Electrical Engineering & Computer Science (EECS) ;  Engineering Systems Division (ESD) , and  Biology .

• Atmospheric Chemistry and Large-Scale Circulation
• Biogeochemistry of Greenhouse Gases and Reflective Aerosols
• Convection, Atmospheric Water Vapor, and Cloud Formation
• Land Surface Hydrology and Hydrology-Vegetation Coupling
• Oceans, Ocean-Atmospheric Coupling, and Carbon Cycling

Climate Modeling Initiative

• Joint Program on the Science and Policy of Global Change
• All Publications
• Journal Articles
• CGCS Reports
• Student Theses
• Conference Papers

Recent Publications

• Talks & Presentations
• MIT OpenCourseWare
• Climate Change FAQs
• Links & Resources

For MIT Students

• Suggested Courses & Degree Programs
• Independent Activities Period
• Student Groups & Activities

CGCS Directory

• All Personnel
• Research Staff
• Administration
• Collaborators

Featured Profile

MIT’s Climate Modeling Initiative is a collaboration between scientists at MIT, coordinated by the Center for Global Change Science, to develop a modeling infrastructure for the study of the atmosphere, ocean and climate of the Earth.

An approach that emphasizes modeling hierarchies is pursued, bridging from simple to complex, but based on a common set of modeling tools. The development focus of CMI is MITgcm , a hydrodynamical model that can be used to study both the atmosphere and ocean. Our approach is wide-ranging encompassing algorithmic, computational, physical, biogeochemical and technological innovations, drawing together elements of computational fluid dynamics, statistics, meteorology, oceanography and computer science.

The climate system is extremely complex, involving so many different components and interacting processes, that even with the biggest, fastest computers it is not possible to come close to representing all of them accurately.To make progress it is essential to improve the understanding of fundamental physical, chemical, and biological processes that control climate. We do this by studying key processes in isolation and then bringing them together. Current focus areas are:

• Climate Dynamics
• Self-Assembling Ecosystem Models
• Ice-ocean-climate interactions
• Ocean state estimation

The movie depicts the 3D temperature field evolving over a period of one year. The model is a zonally-periodic channel domain meant to represent the ACC, forced with wind stress and heating at the surface. The forcing leads to a baroclinically-unstable mean state, which produces the vigorous mesoscale eddies seen in the movie.

The Climate Modeling Initiative is directed by John Marshall .

Website:  mitgcm.org

Contact :  [email protected]

Phone: 617-253-9614

Principal Investigators : 

Prof. John C. Marshall CMI Director 

• Climate Analysis & Modeling

Climate analysis and modeling research seeks to refine our understanding of the functioning of the climate system from statistical and dynamical analysis of earth observations, and from numerical modeling of ocean-atmosphere-land-cryosphere interactions.

Physical and biophysical interactions operative in past and present climates are targeted.

Description

Climate analysis covers natural variability, anthropogenic climate change, and the effort to distinguish the two. Notable examples include the intriguing seasonal-cycles in the eastern tropical Pacific and Atlantic basins with coldest SSTs in the Northern summer/fall, and the well-known large-scale patterns of recurrent interannual variability El Nino Southern Oscillation, North Atlantic Oscillation, North Pacific Oscillation, and Pacific Decadal variability. The latter has spurred detection of the global warming fingerprints in surface temperature and hydroclimate as well as diagnosis of the structure and mechanisms of seasonal and interannual climate variability.

Climate model assessment is a recurring theme: Simulations produced by the state-of-the-art climate system models are scrutinized to assess the realism of the circulation and hydroclimate variability patterns. Representation of the atmospheric water-cycle and extreme hydrologic events (droughts and floods) in both regional and global reanalysis data sets and model simulations is a special focus.

Climate modeling activities target interactions of the physical (atmosphere, ocean, land, cryosphere) and biogeochemical (including vegetation and ocean biology) components and their interactions/feedbacks. Key feedbacks include those related to the carbon cycle. Diagnostic modeling is used to investigate the dynamical and thermodynamical interactions occurring in the troposphere and the exchanges beetween troposphere and stratosphere.

Research Areas

Global change.

• Global warming detection: trends in sea-ice, snow cover, and climate variability ( Farrell , Canty , Vinnikov, Salawitch , Carton , Chepurin )
• Seasonal and diurnal cycles of climate trends (Vinnikov)
• Impact of urban and land-use changes on climate trends ( Kalnay )
• Estimation of global and regional land-surface temperature trends from AVHRR (Jin)
• Analysis and modeling of climate sensitivity to greenhouse gas concentrations ( Lau , Kirk-Davidoff)
• Sampling issues in satellite climate monitoring (Kirk-Davidoff)
• Coupled atmosphere-land-vegetation modeling of Sahelian climate ( Lau , Zeng )
• Carbon cycle and climate change: physical-biochemical interactions ( Murtugudde , Salawitch , Zeng )
• Bio-climate feedbacks ( Murtugudde )
• Earth System Modeling: Past, present, and future climates ( Lau , Zeng , Murtugudde , Canty , Salawitch )
• Subseasonal to seasonal climate prediction ( Liang )
• Mesoscale regional climate model downscaling ( Liang )
• Climate change projection and impact assessment ( Liang )
• Machine learning for climate related decision support ( Liang )

Atmospheric and Oceanic Reanalyses

• Development of Global and Regional NCEP Reanalyses ( Kalnay )
• Historical global ocean and sea ice systems ( Carton , Chepurin , Chen)
• Diagnosis of 3D diabatic heating from ECMWF and NCEP Reanalyses ( Nigam )

Hydroclimate Studies

• Water and energy cycles and land-surface interactions (Arkin, Berbery , Jin, Lau , Nigam , Ruiz-Barradas )
• Detection and prediction of urban effects on water and energy cycles (Jin)
• Analysis of global soil moisture variability and its remote sensing (Vinnikov, Zeng , Yoon)
• Surface/atmosphere radiative fluxes: Diagnosis; Land/ocean energy budgets and LDAS ( Pinker , Berbery , Ruiz-Barradas )
• US Drought: Initiation, maintenance, linkage with Pacific SSTs ( Kalnay , Nigam , Ruiz-Barradas )
• Asian and African Droughts ( Zeng , Lau , Pinker , Yoon)
• Food-energy-water nexus and sustainability ( Liang )
• Climate-crop interactions and agricultural productivity ( Liang )

Ocean-Cryosphere-Atmosphere Interaction

• Analysis and modeling of ENSO air-sea interactions ( Canty , Carton , Murtugudde , Nigam , Salawitch , Zeng )
• Analysis and modeling of tropical Atlantic variability ( Canty , Carton , Chepurin , Grodsky, Murtugudde , Nigam , Ruiz-Barradas , Salawitch , Zeng )
• Arctic climate ( Carton , Chepurin , Farrell )
• Diagnosis and modeling of mid-latitude air-sea interaction ( Kalnay , Nigam )
• Evolution of eastern tropical Pacific climate: ocean-atmosphere coupling and stratus clouds ( Nigam , Murtugudde )
• Sea ice ( Farrell )
• Dynamical modeling of Asian summer-monsoon variability, including its linkage with ENSO ( Lau ,  Nigam , Zeng , Yoon, Murtugudde )
• Mesoscale and multiscale modeling of monsoon systems ( Berbery , Fox-Rabinovitz, Murtugudde )
• Seasonal evolution and interannual variability of North American and South American Monsoons ( Berbery , Nigam , Zeng , Yoon, Lau , Murtugudde )

Extratropical Interannual Variability

• Dynamical simulation of seasonal climate anomalies ( Nigam )
• Structure and dynamics of stormtrack variability ( Berbery , Nigam )
• Analysis of North Atlantic Oscillation (NAO) structure and dynamics ( Nigam )
• Excitation and forcing of PNA and NPO variability ( Nigam )

Clouds and Radiation

• Analysis of cloud-radiation interactions ( Li , Lau , Lee)
• Remote sensing of clouds, forest fires, aerosols, and the radiation budget ( Li )
• Analysis of spectrally resolved infrared radiance from space (Kirk-Davidoff)
• Multi-scale Radiation-Cloud-Convection-Circulation Interactions  ( Lau )

NWP methods in Climate Modeling

• Enhanced ocean-atmosphere predictability from coupled breeding (Lyapunov) vectors ( Kalnay )
• Hybrid coupled models for tropical climate variability ( Murtugudde )

Reduced Complexity Models for Climate Forecasting

• Quantification of the impacts of uncertainties in aerosol RF of climate and ocean heat uptake on projections of global warming ( Canty , Salawitch )
• Detection and attribution of global warming due to anthropogenic activities ( Canty , Salawitch )

Advising Faculty

Non-advising faculty, external links.

• Simple Ocean Data Assimilation (SODA)

• Atmospheric Chemistry & Physics
• Cryosphere, Glaciology & Climate
• Numerical Weather Prediction, Data Assimilation & Atmospheric Dynamics
• Remote Sensing
• Air-Sea Interaction & Physical Oceanography
• Carbon Cycle, Ecosystem & Climate
• Directed Research Section Numbers

New to Climate Change?

Climate models.

Climate models are computer programs that simulate weather patterns over time. By running these simulations, climate models can estimate the Earth’s average weather patterns—the climate —under different conditions. Scientists use climate models to predict how the climate might change in the future, especially as human actions, like adding greenhouse gases to the atmosphere, change the basic conditions of our planet.

Under the hood of a climate model

To simulate weather, climate models must reflect real properties of the Earth’s climate, including physical laws like the conservation of energy and the ideal gas law. They also include variables like air pressure, temperature, and wind. All of these are expressed as equations that a climate model must solve. Solving the equations produces a three-dimensional picture that shows natural climate patterns in action, like rainfall, ocean currents, and the changing of seasons.

Climate models agree on many important facts about our climate. For instance, models reliably show that adding more greenhouse gases to the atmosphere will cause average temperatures to rise. Models also try to predict how climate change will affect rainfall, sea levels , ice cover, and other parts of the natural world.

Principles, variables, and parameterizations

The dynamics of the climate system are governed by seven physical principles :

• conservation of air mass
• conservation of water mass
• conservation of energy
• conservation of momentum of air in three directions
• the ideal gas law applied to air

Climate models describe these principles as seven equations, which constrain seven variables :

air temperature

water vapor content

wind magnitude in three directions

By solving the equations, climate models can simulate all these variables in three dimensions and in time.

Other variables that affect the Earth’s climate are hard to model directly. Clouds are a good example: a cloud is much smaller than the smallest unit of distance in a typical climate model, so the model cannot “see” individual clouds, but taken together they have big effects on the Earth’s temperature.

For these factors, climate models use “parameterizations,” or simplified equations that behave roughly the same as the real thing. Rain, snow, and evaporation are other physical processes that have to be “parameterized” in climate models. These are important features of the Earth’s climate, so getting the parameterizations right is a huge part of designing a good climate model.

Global vs. regional models

Climate models can be global or regional. Global models cover the whole Earth. They usually have “resolutions” of hundreds of kilometers, meaning they can only show climate trends on a very large scale: for instance, they can model temperature changes in New England, but not in Rhode Island.

Regional climate models, which zoom in on specific areas, have much finer resolutions, usually a few tens of kilometers. This is much closer to the scale of real-world observations about topography, land cover and soil types, all of which affect the climate system. For this reason, regional climate models can use more real-life data than global models, and their simulations are generally more accurate. They are useful for studying natural variations in the Earth’s climate; studying how land use (like agriculture and deforestation ) can affect regional weather patterns; and making more detailed predictions about how climate change will affect the places where people live.

In general, global climate models are useful for understanding the consequences of human actions across the whole world. For example, when the Intergovernmental Panel on Climate Change evaluates the actions needed to meet the worldwide climate targets set in the Paris Agreement , they use data from global climate models. Regional climate models are better suited to studying how climate change affects things important to us, like agriculture, diseases, and specific ecosystems, and for making plans to adapt to future climate change.

A powerful tool

The Earth’s climate is too complex for even the most powerful computers to fully simulate. Just as modern weather models cannot tell us with certainty whether it will rain next week, climate models can only predict a likely range of outcomes.

Nonetheless, they are a crucial tool for understanding climate change, and are continually growing more detailed and accurate. New discoveries in climate science are improving our understanding of natural climate processes, and providing more real-world data about the Earth’s climate system, which allows for more accurate simulations of complex features like clouds and the water cycle. At the same time, advances in computer technology are making it possible to simulate weather patterns on a finer scale than ever before.

Published January 8, 2021.

More Resources for Learning

Want to learn more.

Listen to this episode of MIT's "Today I Learned: Climate" podcast on uncertainty and predicting climate change.

Keep exploring

With more Explainers from our library:

The Intergovernmental Panel on Climate Change

The National Climate Assessment

Climate Sensitivity

Mit climate news in your inbox.

• Graduate Program

Interdepartmental PhD Emphasis in Climate Sciences and Climate Change

Rationale for this phd emphasis.

Climate Sciences is the study of the physical processes that control climate on Earth including variations and interactions among the atmosphere, oceans, land and hydrosphere. Climate variations and changes are known to occur on broad ranges of spatial and temporal scales, ranging from decades, centuries, millennia and millions of years. Climate science can also inform the study of climate change, which is broadly defined as changes to the baseline of mean conditions and variability over long periods. Climate change since the beginning of the industrial revolution is one of the major issues affecting the environment and the future of humanity.

Anthropogenic influences on climate are already detectable and expected to continue into the future; examples of the impacts of climate change include extreme precipitation, droughts, heat waves, sea level rise, loss of habitats, food and water insecurity, economic and political stability to name just a few. Mitigation and Adaptation might involve economic regulations such as cap-and-trade or carbon tax, which put a price on carbon emissions.

Research in Climate Sciences and Climate Change requires specialized training in specific disciplines such as Atmospheric Sciences, Oceanography, Geology, Geography, Ecology, Economics, as well as interdisciplinary education across different areas. UCSB has a long tradition for carrying out research in Climate Sciences and Climate Change impacts. This research includes the study of the fundamental physical processes controlling climate on Earth and its response to human activities as well as the impacts of climate on humans and the environment. Research and teaching at UCSB is highly specialized as well as interdisciplinary.

This Interdepartmental PhD Emphasis in Climate Sciences and Climate Change provides doctoral students a broader understanding of the physical principles governing climate on Earth, climate changes associated with natural variability and anthropogenic forcings, and the impacts of climate change on the environment and society. The PhD emphasis provides graduate students with both core-training opportunities to gain access to methodological expertise across UCSB as well as to interact with Faculty, Researchers and graduate students in disciplines other than their own. Furthermore, the PhD Emphasis provides graduate students opportunities to learn how to effectively teach Climate Sciences and Climate Change. The Emphasis is administered in the Department of Geography. The PhD Emphasis formally acknowledges and builds upon existing collaborations among the departments and the Bren School listed herein.

Program of Study

Participation in this emphasis is optional and independent of the doctoral curriculum and degree requirements established by the student’s home department.

Admission to the Emphasis

Applications to the PhD Emphasis are accepted at any time during a graduate student’s academic tenure at UCSB. It is expected that most students will apply for admission between their first and third year of graduate study. Application materials consist of:

• Application form
• Student’s letter including research interests in climate sciences and climate change, expectations related to the emphasis and career goals
• Letter of support from PhD Advisor

The Director of the PhD Emphasis (see Faculty roster) reviews applications on a routine basis and informs applicants the outcome of their applications. Criteria for admission will include:

• Admission into a PhD program at UCSB
• Good academic standing
• Recommendation and strong support from the student’s PhD Advisor

Charles Jones

Departments & programs.

• Bren School of Environmental Science and Management

Earth Science

• Interdepartmental Graduate Program in Marine Science

  Related Faculty

• Elizabeth Ackert, Geography
• Diana Arya, Associate Professor, Education
• Kathy Baylis, Geography
• Leila Carvalho, Geography
• Kelly Caylor, Geography / Bren School
• Olivier Deschenes, Economics
• Timothy DeVries, Geography / IGPMS
• Qinghua Ding, Geography / IGPMS
• Steve Gaines, Bren School
• Vamsi Ganti, Geography
• Kostas Goulias, Geography
• Danielle Harlow, Professor and Associate Dean, Education
• Charles Jones, Geography - Director of the Emphasis
• David Lea, Earth Science / IGPMS
• Lorraine Lisiecki, Earth Science / IGPMS
• Hugo Loaciga, Geography
• Karin Lohwasser, Assistant Teaching Professor, Education
• David Lopez-Carr, Geography
• Joe McFadden, Geography
• Sally MacIntyre, IGPMS / Bren School
• Kyle Meng, Bren School / Economics
• Andrew Plantinga, Bren School
• Samantha Stevenson, Bren School
• Stuart Sweeney, Geography, Chair
• Naomi Tague, Bren School
• Anna Trugman, Geography
• Syee Weldeab, Earth Science / IGPMS
• Dave Siegel, Geography / IGPMS
• Ian Walker, Geography

Required Coursework

All students enrolled in this PhD Emphasis need to fulfill the following requirements:

Students are required to enroll and successfully pass a one-quarter, 4 Unit seminar course: GEOG 287 Seminar in Climate Sciences and Climate Change. The instructor for this course will be one of the Faculty participating in the Emphasis. This course covers key concepts and research methods related to climate, climate variability and change and impacts. Lectures consist of guest seminars primarily from Faculty participating in the Emphasis; the course serves as a venue to foster interaction among graduate students participating in the Emphasis, Researchers and Faculty.

Students are required to take two courses from the following list:

• GEOG 266 Introduction to Atmospheric Sciences Units: 4 – Prerequisite: graduate standing
• GEOG 263 Introduction to Physical Oceanography Units: 4 – Prerequisite: graduate standing
• GEOG 276 Geographical Time Series Analysis Units: 3 – Prerequisite: GEOG 172
• GEOG 213 Polar Environments Units:4 – Prerequisite: GEOG 3 or Geog4, ES 1 or 2, or EARTH1
• GEOG 243 Vegetation-Atmosphere Interactions Units: 4 – Prerequisite: graduate standing
• GEOG 246 Advanced Hydrologic Modeling Units: 4 – Prerequisite: GEOG 112 and 116
• GEOG 267 Chemical Oceanography Units: 4 (cross-listed with EARTH 276) – Prerequisite: CHEM 1C and graduate standing
• GEOG 281: Introduction to the Coupled Model Intercomparison Project
• GEOG 273: Trait-based Ecological Modeling
• EARTH 205 Earth’s Climate: Past and Present Units: 3 – Prerequisite: graduate standing
• EARTH 206 Introduction to Climate Modeling Units: 4 – Prerequisite: graduate standing
• EARTH 266 Chemical Oceanography Units:4 (cross-listed with GEOG 267) – Prerequisite: CHEM 1C and graduate standing
• EARTH 276 Geological Oceanography Units: 4 – Prerequisite: graduate standing

Bren School

• ESM 203 Earth System Science Units: 4 – Prerequisite: GEOG 3 or equivalent IGPMS
• EARTH 266/GEOG 267 Chemical Oceanography Units: 4 – Prerequisite: CHEM 1C and graduate standing
• GEOG 263 Introduction to Physical Oceanography Units: 4– Prerequisite: graduate standing

The total number of units will vary depending on which courses are selected from this list:

• Geog 244 Society and Hazards Units: 4 – Prerequisite: graduate standing
• Geog 254 Demography Units: 4 – Prerequisite: graduate standing
• Geog 288EA Urban Geography
• ESM 229 Economics and Policy of Climate Change Units: 4 – Prerequisite: ESM 204
• ESM 237 Climate Change Impacts and Adaptation Units: 4 – Prerequisite: graduate standing
• ECON 260F Demand for Environmental Goods Units: 2 – Prerequisite: graduate standing
• ECON 260G Environmental Externalities and Regulation Units: 2 – Prerequisite: graduate standing
• ECON 260H Climate Change, Adaptation, and Policy Units: 2 – Prerequisite: graduate standing
• ED 256 Technology and Education Contexts
• ED 287 Informal STEM Education
• ED 221H Design-based Research and Research-based design

Students are required to enroll and present their research in the GEOG 280 Geography Climate Research Meetings, which are a forum for researchers and students to discuss research topics in Climate Sciences and Climate Change. The meeting is held in the Earth Research Institute (ERI). Students are required to enroll in the Climate Research Meetings for a minimum of three quarters as a way to foment their participation in climate research topics.

The PhD dissertation of students participating in this Emphasis needs to have a strong focus in Climate Sciences and/or Climate Change. Furthermore, a member of the student’s PhD committee needs to be a member of the core Faculty participating in the Emphasis in Climate Sciences and Climate Change. No other limitations are set for the other members of the PhD committee.

Alumnus Testimonial

Emily Williams, PhD: “Participating in the Interdepartmental PhD Emphasis in Climate Sciences and Climate Change helped me build a robust interdisciplinary lens and toolbox through which to engage with climate science and policy. Through the emphasis, I was able to take courses across departments on climate sciences, policy, and impacts, providing me with foundational knowledge of the socio-political and physical dimensions of climate change. The emphasis also offered opportunities for professional development, such as presenting my graduate research to students and faculty in the climate seminar, thereby receiving invaluable feedback from distinguished scholars in the field. The rich training I gained has set me up to do both postgraduate research and advocacy, as I engage with academia and non-profits on issues of climate change and historical justice”.

Support Services

• Business Operations
• Technical Support
• Research Facilities
• Information
• Terms of Use

Department of Geography • 1832 Ellison Hall • UC Santa Barbara , CA 93106-4060 2024 © Regents of the University of California

Study at Cambridge

About the university, research at cambridge.

• Undergraduate courses
• Events and open days
• Fees and finance
• Postgraduate courses
• How to apply
• Postgraduate events
• Fees and funding
• International students
• Continuing education
• Executive and professional education
• Courses in education
• How the University and Colleges work
• Term dates and calendars
• Visiting the University
• Annual reports
• Equality and diversity
• A global university
• Public engagement
• Give to Cambridge
• For Cambridge students
• For our researchers
• Business and enterprise
• Colleges & departments
• Email & phone search
• Museums & collections
• Research Themes

• Cambridge Centre for Climate Science
• Research Themes overview
• Atmosphere-Biosphere Interactions
• Ocean and sea ice dynamics
• Cryosphere and Sea-Level Rise
• Earth system modelling
• Past Climate Change
• Events overview
• Past Events
• Jobs & Studentships
• Community overview

Mailing Lists

Climate scientists across ccfcs use and develop computer models to study the whole range of physical, chemical and biological processes that make up the earth's climate system..

Environmental Models

To help us understand how the Earth system works and to improve predictions of future environmental change, members of CCfCS work to develop and run a variety of state-of-the-art computer models in areas including:

• Atmosphere: dynamics and chemistry
• Ocean: dynamics, chemistry and biological activity
• Land-surface: forests, vegetation and soils
• Sea ice: understanding the complex processes at the atmosphere-ocean interface
• Glaciers and ice shelves: their role in global sea level rise
• Space weather: the effect on Earth's atmosphere

CCfCS researchers also use paleoclimate models to help unlock signals observed within sediment cores, ice cores and other preserved records to help us understand the Earth's past climate .  

A list of the computer models we develop and use, and details of who to contact for more information on each can be found here

A collaboration between the UK Met Office and the Natural Environmental Research Council (NERC) is helping to drive the development of a new Earth System Model, UKESM1. This is a truly joint effort, with many of the component systems being designed and coded by NERC research centres and University groups. This includes the British Antarctic Survey (BAS), who are playing a central role in improving the simulation of the cryosphere components (e.g., Southern Ocean, sea ice, ice shelves), and the Department of Chemistry who are building and testing the new atmospheric chemistry and aerosols component.

Of course, observational and theoretical research into the Earth system plays a crucial role in the model development process. To make sure the models incorporate the most up-to-date information from these fields, we maintain good communication channels between the CCfCS partners, and with the wider UK research community, to ensure information is passed onto the relevant developers.

[email protected]

Site privacy & cookie policies, participating groups.

Department of Applied Mathematics and Theoretical Physics

Department of Chemistry

Department of Earth Sciences

Department of Geography

Department of Plant Sciences

Division of Archaeology

Scott Polar Research Institute

Keep Updated

Seminars (RSS)

Cambridge Centre for Climate Change Mitigation Research

Cambridge Institute for Sustainability Leadership

Cambridge Climate and Sustainability Forum

© 2024 University of Cambridge

• Contact the University
• Accessibility
• Freedom of information
• Privacy policy and cookies
• Statement on Modern Slavery
• Terms and conditions
• University A-Z
• Undergraduate
• Postgraduate
• Research news
• About research at Cambridge
• Spotlight on...

We have 302 climate PhD Projects, Programmes & Scholarships

All disciplines

All locations

Institution

All Institutions

All PhD Types

All Funding

climate PhD Projects, Programmes & Scholarships

Super dtp: how are seminatural environments affected by climate change a study of climate impacts and future challenges to heathland soils., phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (UK Students Only)

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Can simulation models influence behavioural change towards climate change solutions in the fashion industry

Self-funded phd students only.

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Restoring Ecosystems Under Climate Change

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

UNRAVELLING THE CAUSES OF PLANETARY DARKENING Ref: 5198

Impacts of environmental climate change on plant and ecosystem carbon and nutrient cycling, and biodiversity, planning for the extremes: wildfire resilience of power system infrastructure, phd on the quantification of the impact of natural variability and possible volcanic futures on climate projections across the irish and british isles, risk and sustainability dimensions of renewable energy infrastructure in the uk and ireland in a changing climate, phd scholarship in integrating ecological and genomic diversity for climate resilient marine spatial planning – dtu aqua, awaiting funding decision/possible external funding.

This supervisor does not yet know if funding is available for this project, or they intend to apply for external funding once a suitable candidate is selected. Applications are welcome - please see project details for further information.

PhD in land-based CO2 removal strategies for climate change mitigation

Climate change implications for river restoration in global biodiversity hotspots, ai to the rescue of climate change, modelling air quality for cleaner urban planning, a smart ecosystem monitoring platform for long-term, multifunctional landscape climate adaptation research, advancing inclusive and equitable approaches for anticipatory climate change actions, climate change adaptation in construction organisations.

FindAPhD. Copyright 2005-2024 All rights reserved.

Unknown    ( change )

Have you got time to answer some quick questions about PhD study?

Select your nearest city

You haven’t completed your profile yet. To get the most out of FindAPhD, finish your profile and receive these benefits:

• Monthly chance to win one of ten £10 Amazon vouchers ; winners will be notified every month.*
• The latest PhD projects delivered straight to your inbox
• Access to our £6,000 scholarship competition
• Weekly newsletter with funding opportunities, research proposal tips and much more
• Early access to our physical and virtual postgraduate study fairs

Or begin browsing FindAPhD.com

or begin browsing FindAPhD.com

*Offer only available for the duration of your active subscription, and subject to change. You MUST claim your prize within 72 hours, if not we will redraw.

Do you want hassle-free information and advice?

Create your FindAPhD account and sign up to our newsletter:

• Find out about funding opportunities and application tips
• Receive weekly advice, student stories and the latest PhD news
• Hear about our upcoming study fairs
• Save your favourite projects, track enquiries and get personalised subject updates

Create your account

Looking to list your PhD opportunities? Log in here .

Filtering Results

You have a strong interest in climate research?

• You like studying complex environmental processes and dynamics?
• You have experience in working with numerical models of the Earth system or its components?
• You enjoy problem solving, numerical modelling, programming, working with large data sets, applying statistical methods, among other research strategies and tools?

Our call for applications is open 1 July - 15 September.

This is what you bring:

• A first class Master’s degree with written thesis (!) in physics, geophysical sciences (incl. meteorology and oceanography), ecology, mathematics, computer science, engineering, economics, or political science
• Students currently working on their MSc thesis are also encouraged to apply
• The study and work load for both your degrees (MSc and BSc) amount to (at least) 300 ECTS (or an equivalent to this)

This is what we offer:

• A large, diverse and international, high quality and inspiring research environment
• Being part of a group of doctoral candidates with similar research interests
• Ample opportunity to advance your academic training and skills, and to present your own science and research results
• Supervision through a personal scientific Advisory Panel
• Dedicated support through the office team
• A 3-year doctoral contract with social benefits; monthly net payment approximately EUR 2,000. This amount is sufficient to cover your basic living expenses
• Application

Dates (of a general annual call)

• All required documents must be submitted electronically
• Submit documents that follow the prescribed format only (see 2 nd step below)
• For reference letters use the prescribed reference form (link to be found at Step 4)

WE DO NOT ACCEPT - any hardcopies / paper copies sent by ordinary post or fax - any communication without the candidate’s ‘Registration ID’ - any reference letter without the candidate’s ‘Registration ID’

Step-by-step

Step 1: preparation of the required documents and certificates.

A complete application consists of the following documents:

• Letter of Motivation (see advice ‘General notes’ at the end of these guidelines)
• Curriculum Vitae (CV)
• title page of your thesis and an abstract
• copy of MSc certificate
• copy of MSc transcript (a list of all courses/lectures you took including marks and explanation of grading system)
• copy of BSc certificate
• copy of BSc transcript (a list of all courses/lectures you took including marks and explanation of grading system)
• Proof of English skills, if possible
• 2 Reference Letters (consider 2 senior scientists) – further details are found in step 4

Optionally you may submit any additional documents, which you consider important for your application (e.g. publications, job references, certificates of workshops or awards etc.).

Step 2: Arrange these documents into two pdf files

Note that both pdf files must be uploaded during the subsequent online registration process. 1. The first file ‘application documents.pdf’ should contain (in the following order and using these file names)

• Letter of Motivation
• MSc transcript
• BSc transcript  

2. The second file ‘complementing information.pdf’ should contain (in the following order)

• The title page and abstract of your MSc thesis (preliminary information in case the preparation of your thesis is on-going)
• Your MSc certificate (in case not yet available, please include instead an informal note which announces the expected date of completion of your MSc)
• Your BSc certificate
• If applicable : any additional documents related to your application such as e.g. publications, job references, certificates of workshop, etc

Step 3: Online registration

During the online registration (find the link at the bottom of this page while Call is open) you will need to complete our registration form and upload the required application files (thus, the two pdf files you prepared before - see step 2). We recommend to allow some time for the completion of the registration form as detailed information is requested. Be aware that once you exit the questionnaire, all entries are lost! Therefore, we suggest that you look at the questionnaire and study all solicited entries before starting to fill it in. Most answers are straightforward and short. However, some points need further elaboration. Perhaps you wish to first prepare more detailed answers off-line and then copy/paste text blocks directly into the online form. Upon successful registration, you will receive an automated e-mail containing your 6-digit ‘Registration ID’. Please state this ID for any correspondence with IMPRS-ESM as well as in the IMPRS-ESM reference sheet. Attention: unreferenced correspondence will be ignored.

Step 4: Arrange for two reference letters (submission until 20 September)

You require two reference letters to complete your application (always use the IMPRS-ESM template reference sheet).

• Download the template reference sheet AND our  letter to referees in which we explain our reference request
• Add your registration ID and name to the template reference sheet
• Forward both, template AND letter, to your chosen referees (preferably your former advisors)

A referee shall directly e-mail her/his recommendation to the IMPRS-ESM office:

Attn. Dr. Florian Mundt (Mr.) office.imprs@ we dont want spam mpimet.mpg.de

IMPORTANT: - We will consider those references only that are using our standardized reference sheet - Both reference letters have to be sent to us directly by the referees via e-mail from their official work account - Reference letters sent from private accounts as, e.g., Gmail or Yahoo or by yourself cannot be accepted - Your application can only be processed AFTER we have received the two reference letters

General notes

Advice for your ‘Letter of Motivation’ (to be written in English) :

We recommend that you prepare this document very carefully as this is a crucial component in the selection process! Apart from a brief, general introduction of yourself and your academic interests you should elaborate on:

• Your motivation for aiming to do PhD research
• Your reasons for choosing IMPRS-ESM in Hamburg for your advanced studies
• The research topic(s) in which you are interested

Language of your academic documents :

Application documents that are neither in German nor English must be accompanied by a translation into one of these two languages (please note that the 'Letter of Motivation' must be written in English). In this very first stage of application to the IMPRS-ESM, unofficial translations serve well. However, upon admission to our doctoral program and for registration as PhD candidate at the University of Hamburg officially certified translations are required.

Please allow yourself enough time to submit your application and do not wait until the last moment as technical difficulties or other problems might occur. The application period is also a busy time for us - therefore we are not always able to answer immediately.

Overview of possible supervisors per research area

Atmospheric processes and climate change.

Our research aims to understand how climate is influenced by the water content in the atmosphere. We study how clouds affect the reflection of sunlight, how moisture changes the structure of the lower atmosphere and how water-based weather systems interact with the Earth's surface and upper atmosphere. In recent years, we have developed new methods both in modeling and in our observational techniques.

Earth system coupled dynamics and climate change

Our objective is to elucidate the mechanisms governing large-scale climate patterns and climate change induced by greenhouse gases and aerosols. This investigation encompasses a detailed examination of the complex interactions among different components of the Earth's system, including atmospheric and ocean circulation, cloud radiation, and land processes. To achieve this, we conduct a series of hierarchical model experiments, ranging from the use of an aquaplanet slab ocean model to a storm-resolving coupled model.

In the application form, this research area is titled: o   Atmospheric and oceanic coupled dynamics and climate change 

Ocean Climate Physics and Climate Variability

We investigate climate and Earth-system variability on all timescales from seasonal to millennial. A particular focus lies on the role of the ocean in climate variability and change. The main research tools are coupled ocean-atmosphere and Earth-system models, but observations, statistical analysis, and data assimilation are also employed.

Economics and Sustainability Sciences

Expected research topics include the assessment of weather and climate extremes and their socio-economic impacts, as well as their interactions with sustainable development goals. Integrated studies on agriculture and forestry will also be conducted, focusing on reducing negative environmental impacts, developing sustainable adaptation strategies and analyzing conflicts and synergies between multiple land use objectives. Other research includes the development of methods to produce reliable climate policy recommendations using economic models that take uncertainties into account, with the aim of providing informed advice to policy makers.

Online Registration

Frequently asked questions, selection criteria.

By what criteria are PhD candidates chosen?

Since the IMPRS-ESM is focusing on Earth system modeling, a strong interest in mathematical modeling and a good background in mathematics and programming are very important. Besides the academic achievements, the Selection Committee will carefully consider the applicant's Letter of Motivation, reference letters, as well as his/her knowledge of the English language. Also, a candidate's academic interests are assessed with regard to research interests of potential supervisors in the collaborating institutions.

Confirmation email

I did not receive a confirmation email

There are two likely possibilities, why you did not receive the confirmation e-mail: 1) sometimes the e-mail ends up in the spam folder, did you also look there?

2) as the e-mail confirmation is automatically generated, your e-mail provider might have declared it as spam and rejected it immediately. This sometimes happens with public e-mail providers and they also do not generate an error message to the sender. Unfortunately, we cannot prevent this from happening. Nevertheless, you can just continue with contacting your referees. The templates for the reference sheets can be downloaded directly from our website:

• reference sheet

How do I apply?

Your application to the IMPRS-ESM consists of two principal steps, starting with the online application. It is best to allow some time for the completion of the online questionnaire as detailed information is requested, including the immediate upload of your application documents. In addition to this, two reference letters are required. Once you have completed our online application form and we have received the two standardised reference letters submitted to us directly from your referee, your application package is considered complete.

The deadline for an online application is September 15.

The letters of recommendation can be submitted until September 20.

English skills

What proof of my English skills can I provide?

A proof of your English skills is not mandatory for your application, nevertheless, it does help us to have some kind of reference with regard to your knowledge. Ideally you have passed an official English exam, such as the TOEFL, Cambridge Proficiency Certificate, or the British Council IELTS. Alternatively, an attestation from your University regarding your English skills or stays abroad where you had to speak English every day serve equally well.

What we will further evaluate is the letter of motivation (must be written in English) as well as, eventually, the personal interview which will be held in English. Most study at the IMPRS-ESM is in English, so you are expected to read, write, understand, and speak English very fluently.

When will I be informed about the outcome of my application?

It usually takes 6 weeks to set up a list of candidates who will be invited for an interview. If you have not heared from us within 2 months after the registration deadline, most likely, our Selection Committee was unable to add your name to the short list of interview candidates. Letters with the final outcome are sent to applicant pool only after we have filled the available PhD positions.

Funding of PhD candidates

Up until now, the IMPRS-ESM has been able to assign 5 to 10 new doctoral positions to promising applicants each year. These positions are generally financed through doctoral contracts.

A contract offers approximately EUR 2,000 monthly net payment. Additional benefits for children are possible.

Master studies

I will not finish my MSc before next year, can I still apply?

An application prior to finishing your MSc degree is possible as long as your MSc study reached its final phase and thus you can provide us with a clear completion target. We generally aim for a joint start of all our PhD candidates. This facilitates both administrative matters as well as the integration of new students. It is in some cases also possible to defer the starting date if someone is not able to complete their graduate studies in time, however, this is decided on an individual basis. Please mention your late starting date clearly in your Letter of Motivation.

Master certificate

My MSc certificate will not be issued before the deadline, can I still apply?

If your MSc certificates are not issued in time before the deadline it is no problem to forward them to us at a later date. Maybe you can ask your university for a brief statement which confirms that you are currently enrolled as an MSc student. Alternatively, if you have already been issued with any transcripts from previous terms, you can also send us those. Please specify in your letter of motivation when you expect to receive the official documents.

Multiple registration numbers

I received several e-mail confirmations with different registration numbers. Which one is correct?

Sometimes the system generates several automated e-mail responses with individual registration numbers after a successful registration. This can be caused by having hit the 'back' or 'send' buttons several times or after reloading the page while filling in the online questionnaire. Please contact us directly at [email protected] if you receive multiple registration numbers.

What happens after I've applied?

All complete applications will be forwarded to the IMPRS-ESM Selection Committee that gets explicitely appointed by the IMPRS-ESM Executive Committee for individual calls (recruitment processes). The Selection Committee evaluates the applications and identifies top candidates for an interview. Usually interviews take place in person in Hamburg on prescribed dates. However, candidates living too far away and can not travel to Hamburg in time will be interviewed by telephone or video call. Within a week after the interviews, successful candidates will be contacted to further advance the recruitment process.

Qualifications required

What qualifications do I need?

A first class MSc degree or upper second class MSc Hons. degree (or national equivalent) is required for access to the IMPRS-ESM PhD programme. Furthermore your transcripts should show proof that the study and work load for both your degrees (MSc and BSc) collectively amount to (at least) 300 ECTS (or an equivalent to this). It is important that a written thesis forms part of the requirements for the MSc degree. Because of the interdisciplinary nature of the subject, we accept students with backgrounds in a wide range of scientific disciplines.

May I change a referee after I have registered?

You are free to change any of your referees, if necessary. Please provide their respective details via via e-mail to [email protected]; you don’t need to go through the registration process again. In all correspondence, please remember to state your registration number.

Number of positions

How many applications will be accepted each year? What are my chances to be admitted to the IMPRS-ESM?

Usually we receive more than 100 complete applications each year of which we can only admit 5-10 suitable candidates.

• People and Academic Environment
• Structure and Support

More Content

About our institute.

Our institute is an internationally known institute for climate research. The goal of the Max Planck Institute for Meteorology is to understand how and why the climate on our planet is changing.

ORCESTRA: Exploring Tropical Clouds and Their Impact on Climate

The ORCESTRA started his performance on August 10th in Cap Verde. The PERCUSION gives the beat by flying back and forth across the belt of deep rain…

WASCAL Master's Students Attend ORCESTRA Campaign on Climate Research

Five master's students from Ghana, Senegal, Nigeria, Benin, and Guinea, participating in the WASCAL program, had the unique opportunity to gain…

Climate modeling at Princeton

Princeton’s vital research across the spectrum of environmental issues is today and will continue to be pivotal to solving some of humanity’s toughest problems. Our impact is built on a long, deep, broad legacy of personal commitment, intellectual leadership, perseverance and innovation. This article is the first in a series to present the sweep of Princeton’s environmental excellence over the past half-century.

Imagine you could experimentally manipulate the climate of the whole planet. You could sprinkle rain clouds where you want them, add or remove greenhouse gases from the atmosphere, change the intensity of incoming sunlight, rearrange mountains or forests at will … the possibilities are endless.

That is the promise of climate models, sophisticated computer simulations running on the world’s fastest computers. These behemoth undertakings — with over a million lines of computer code that would fill thousands of pages of printed text — replicate as many aspects of the Earth’s systems as possible. 

The effort is aimed at addressing one of humanity’s biggest challenges: climate change. Computer modeling, the beating heart of modern climate science, is fundamental to our understanding of human-induced global warming and is a singularly important tool cited by the Intergovernmental Panel on Climate Change (IPCC) in its climate change assessments. If international policymakers succeed in organizing to avert climate catastrophe, it will be largely due to the impact of these models — and the scientists who created them. 

The birthplace of modern climate modeling

Syukuro Manabe

Princeton’s history with climate modeling started more than half a century ago, with a remarkable partnership between the University and a national lab housed on Princeton’s Forrestal Campus. The Geophysical Fluid Dynamics Laboratory (GFDL) formed a collaboration with Princeton University in 1967 and moved to New Jersey in 1968 to take advantage of the University’s leadership in early computing and the proximity of the Institute for Advanced Study.

Princeton-GFDL luminary Syukuro “Suki” Manabe produced a series of studies that are widely attributed with launching the long-term study of global climate warming and quantitatively linking the warming of Earth’s climate with increasing carbon dioxide emissions. In particular, his 1967 model of the atmosphere, created with Richard Wetherald, is considered the first credible calculation of the Earth’s climate, and in 1969, Manabe partnered with groundbreaking oceanographer Kirk Bryan to create the first coupled ocean-atmosphere model.

“It was really the beginning of the climate modeling effort worldwide,” said Princeton ecologist Stephen Pacala , the Frederick D. Petrie Professor in Ecology and Evolutionary Biology , who collaborates extensively with GFDL to this day.

Manabe’s recent book, “ Beyond Global Warming: How Numerical Models Revealed the Secrets of Climate Change ,” published by Princeton University Press this year, is co-authored with his former student Anthony Broccoli. In it, they explain the stakes behind Manabe’s pathbreaking career. “Unless dramatic reductions of greenhouse gases are achieved, global warming is likely to exert far-reaching impacts upon human society and the ecosystem of our planet during the remainder of this century and for many centuries to come,” they wrote.

Michael Oppenheimer

Manabe’s early understanding of the reality of global warming drove his pioneering work. “Suki Manabe is certainly one of the great heroes of the scientific world of climate change,” said Michael Oppenheimer — an active member of the IPCC, the director of Princeton’s Center for Policy Research on Energy and the Environment , and the Albert G. Milbank Professor of Geosciences and International Affairs .

Even before the IPCC was founded, Oppenheimer noted, Manabe’s work mobilized climate action. A 1979 climate summary by the National Academy of Sciences affirmed the robustness of the overall predictions of his model and a later model produced by NASA. Oppenheimer believes the academy’s endorsement activated the scientific community and spurred governments to markedly increase funding for climate change research.

“So you could easily say,” Oppenheimer said, “that the Princeton model, which preceded the NASA model by about 10 years or so, was the basic building block of everything that followed.”

Research and teaching

Jorge Sarmiento

What followed at Princeton was 50 more years of modeling advances, research and teaching. In 1968, the University created the precursor to today’s Program in Atmospheric and Oceanic Sciences (AOS), a graduate and postdoctoral program dedicated to understanding key mechanisms driving global climate systems. 

“Right from the very beginning, the program had an exceptionally collegial atmosphere, where students felt free to seek out advice from the entire staff and were exposed to a wide range of ideas,” said Bryan, who like Manabe was a founding member of the program’s faculty. “This fostered independent thinkers and contributed to the success of our graduate students after leaving Princeton.”

Undergraduates, graduate students and postdoctoral researchers all had the opportunity to learn from GFDL scientists and work directly with their supercomputers. Soon, there was a steady pipeline of young researchers from the University who worked “across the street” both before and after graduation. Others seeded new climate science programs at universities and institutions around the world.

“Princeton’s contributions to climate research and education can’t be measured — and they can’t be separated,” said longtime AOS Director Jorge Sarmiento , the George J. Magee Professor of Geoscience and Geological Engineering, Emeritus. “To speak of only one wouldn’t give justice to the enormous influence that this institution has had. They educated the whole world.”

Adding complexity

Elena Shevliakova

Over the years, scientists have factored ever more variables into their simulations. “Climate modeling is a continuing story of increasing complexity,” explained Tom Delworth , a senior scientist at GFDL and a lecturer in AOS. “You’re increasing the number of components you’re trying to model and, hopefully, getting better at representing the physical processes of each component.”

Here again, Princeton played a leading role. The strong connections between the University and the national lab meant that the models could be supported by Princeton’s broad and deep bench of environmental researchers studying the processes underlying the global climate, including Sarmiento, who incorporated biogeochemical processes like the global carbon cycle and nitrogen cycle; Laure Resplandy , who tied ocean physics, biogeochemistry and ecosystems to climate models; and Gabriel Vecchi , who transformed our understanding of tropical cyclones — hurricanes and typhoons — in the world’s oceans.

“As the model became more elaborate, you needed more expertise to develop and interpret it,” said Isaac Held , a senior meteorologist in AOS who was one of Manabe’s first graduate students and who has become a world-renowned climatologist in his own right. “The connection to the University made that possible.”

Pacala had a particularly significant role, said Elena Shevliakova , a GFDL scientist and a visiting research scholar at the Princeton Environmental Institute who was a postdoctoral fellow in Pacala’s lab. “Steve had this vision that we could represent plants not just as boxes of carbon, but capturing the growth and development and death of each tree," she said. "He pioneered a mathematical way to show how plants compete for light, and that laid the foundation for every ‘Earth system model’ used in every U.S. climate center.”

She added, “Steve brought life into those physical climate models, in a mathematically rigorous way.”

The Intergovernmental Panel on Climate Change

In the 20 years after Manabe’s first models sounded the alarm about carbon dioxide raising Earth’s temperature, enough climate scientists — at Princeton and elsewhere — sounded the alarm about human-caused climate change that governments around the world began to pay attention. In 1988, the United Nations called for a team of experts who could deliver to the world’s policymakers periodic assessments on the state of the science. The first report of the IPCC came out in 1990, and its conclusions leaned heavily on two climate models: the Princeton-GFDL model and the model generated by NASA. Reports have continued to come out every five years, and all of them have used Princeton-GFDL models and had Princeton authors or editors.

In 2007, the Nobel Peace Prize was awarded jointly to Al Gore and the IPCC. Eleven Princeton faculty members — including Held, Oppenheimer and Sarmiento — and many alumni contributed to the IPCC reports cited in the prize.

This 1969 photograph shows AOS Senior Scientists Kirk Bryan (left) and Suki Manabe talking with GFDL Director Joseph Smagorinsky, who brought GFDL to Princeton because of the intellectual environment and computer resources available here.

Building on the legacy

In the five decades since Manabe and Bryan’s first ocean-atmosphere model, climate scientists at Princeton and GFDL have continued to push the limits of increasingly powerful supercomputers, with the goal of simulating the global climate with enough specificity to produce local predictions, on timescales from hours to centuries.

“When you look at all the pieces, we have genuine excellence here,” said Vecchi, a professor of geosciences and PEI, who is one of the world’s foremost hurricane climatologists.

Princeton: A half-century at the environmental forefront

Net-Zero America: How state policymakers can use data from Princeton's pioneering study to meet climate goals

Environmental research at Princeton is addressing the world's challenges

The future of food in a changing climate

Tough, timely and team-driven: 50 years of energy research

From muddy boots to mathematics: Advancing the science of ecosystems and biodiversity

Water, drought and flooding

• Skip to main navigation
• Skip to utility navigation
• Skip to main content

Department of the GeoPhysical Sciences

• Prospective Students
• Plan a Visit

Postdoctoral Scholar- Climate Extremes, Dynamics and AI

Postdoc and Scientist Positions at UChicago (flexible start date from 1/1/2024 to 1/1/2025)

The University of Chicago’s new Climate Extremes Theory and Data (C e TD) research group (PI: Pedram Hassanzadeh) has multiple positions for postdoctoral fellows and research scientists interested in working on multidisciplinary projects in the following general areas (and at their intersections):

• Extreme weather events and climate change (with a focus on dynamics and the extratropical atmospheric circulation),
• Scientific deep learning for multi-scale nonlinear dynamical systems (with a focus on developing general, rigorous frameworks),
• Applications of deep learning to improve analysis, modeling, and prediction of climate variability, weather extremes, and geophysical turbulence (with a focus on subgrid-scale modeling and spatio-temporal forecast/emulation).

Full financial support for these projects is available through funding from NSF, ONR, Schmidt Futures, and UChicago. Furthermore, outstanding applicants will be supported to apply for a number of prestigious opportunities at UChicago including the campus-wide Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, the T. C. Chamberlin Postdoctoral Fellowship at the Department of Geophysical Sciences, and Kruskal Instructor at the Committee of Computational and Applied Mathematics.

The topic of each project is flexible but is expected to be will be aligned with our current interests and future directions, which are as follows.

Regarding (1), we are especially interested in studying persistent extratropical circulation patterns, such as blocking events, wavy jet streams, and annular modes,, using a combination of theory, hierarchical modeling, and observational data analysis. Developing better eddy-mean flow interaction theories for blocks, constraining changes in their key characteristics with climate change, and understanding the implications for future extreme events (e.g., heat waves), are of particular interest. See, for example, this paper and news release . As for the annular modes, our work is focused on a new reduced-order model for the extratropical circulation, the recently discovered intrinsic 150-day periodicity of the Southern Annular Mode, and the implications for climate model evaluation and development (e.g., see this paper and Editor Highlight ).

Regarding (2), we are mainly focused on developing rigorous frameworks for applications of deep neural networks to multi-scale, nonlinear PDEs such as those governing the climate and turbulent systems. Explainability, stable spatio-temporal integration, generalizability, learning extreme events and high frequencies, and uncertainty quantification are of particular interest. We aim to combine fundamental concepts and tools from nonlinear dynamics, numerical analysis, and theoretical deep learning to address these challenges. For example, see this paper on the Fourier- wavelet analysis framework and this one on stable integrations.

Regarding (3), we aim to leverage the outcomes of (1)-(2) and employ physics-informed deep learning to improve analysis, modeling, and prediction of climate variability, weather extremes, and turbulence. Of particular interest are developing i) data-driven subgrid-scale parameterizations, and ii) data-driven spatio-temporal forecast models. As for (i), we are focused on using deep learning and equation-discovery methods applied to canonical geophysical turbulent flows, atmospheric gravity waves, and the quasi-biennial oscillation and polar vortex variability as a part of the Schmidt Futures-supported DataWave and NSF-supported GW-CSSI projects. For example, see this paper and this one . Note that DataWave and GW-CSSI involve a number of other institutions (NYU, Stanford U, NWRA, UK Met Office, MPI Hamburg, U Frankfurt, ENS Paris) and the former is also a part of Schmidt Futures Virtual Earth Systems Research Institutes . There are many opportunities for inter-institutional collaborations and visits for those involved in these projects. As for (ii), we are mainly interested in the short- and long-term stability and accuracy of such forecast models, with a focus on rare, extreme events. See, e.g., this paper and this one.

Furthermore, all postdocs and research scientists will benefit from our group’s involvement in multi-institutional, international collaborative projects such as the Schmidt Futures-supported DataWave and NSF-supported GW-CSSI . They will also benefit from UChicago’s thriving and expanding programs in Climate Science , AI+Science, Computational and Applied Math , Data Science, and Climate Systems Engineering .

Qualifications: We are looking for highly motivated applicants with Ph.D. degrees in Earth and climate sciences, applied math, physics, statistics, computer science, engineering, or a related field. The qualifications needed for each research area are:

Area 1) Required : Strong background in climate physics and/or geophysical fluid dynamics, extensive experience with climate models and data. Preferred : Skills in topics such as applied math, scientific computing, and statistical analysis for big data;

Area 2) Required : Strong background in applied math and/or theoretical deep learning. Preferred : Skills in topics such as nonlinear or fluid dynamics, numerical analysis, and scientific computing;

Area 3) Required : Strong background in climate science and/or geophysical fluid dynamics. Preferred : Skills in topics such as applied math, deep learning, climate models and data, turbulence physics, gravity waves, nonlinear dynamics, numerical analysis, and scientific computing.

Applications: The start date is flexible (anytime between 1/1/2024 and 1/1/2025). Applications will be reviewed starting 9/15/2023 on a rolling basis. The review will continue until all of the positions are filled. We encourage you to submit your application (or reach out) as soon as possible, even if you are interested in starting dates after the summer of 2024.

To apply, please send

• A complete CV that includes details of your education, research experience, publications, presentations, and contact information of at least 3 references,
• A brief (~1 page) description of your plans, qualifications, and research interests (particularly in the context of areas 1-3 and qualifications listed above),

to Prof. Pedram Hassanzadeh, [email protected] (use "Postdoc Position" as the email's subject).

Outstanding applicants for the above positions with more than 3 years of postdoc experience will be considered for appointment as research scientists.

Equal Employment Opportunity Statement:

All University departments and institutes are charged with building a faculty from a diversity of backgrounds and with diverse viewpoints; with cultivating an inclusive community that values freedom of expression; and with welcoming and supporting all their members.

We seek a diverse pool of applicants who wish to join an academic community that places the highest value on rigorous inquiry and encourages diverse perspectives, experiences, groups of individuals, and ideas to inform and stimulate intellectual challenge, engagement, and exchange. The University’s Statements on Diversity are at https://provost.uchicago.edu/statements- diversity

The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national or ethnic origin, age, status as an individual with a disability, protected veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.

Job seekers in need of a reasonable accommodation to complete the application process should call 773-834-3988 or email [email protected] with their request.

Research opportunities

Climate and atmospheric science.

Expertise of research area atmospheric chemistry; chemistry; climate; clouds; food security; ice sheets; Meteorology; palaeoclimate

We offer PhD projects in climate change, weather, air pollution, paleoclimates and impacts. We develop advanced computer models, lead major field campaigns, analyse satellite data and perform innovative laboratory experiments. Our research covers a wide area, from the surface of the planet to the upper atmosphere.

<p>The <a href="https://environment.leeds.ac.uk/institute-climate-atmospheric-science">Institute for Climate and Atmospheric Science</a> makes fundamental advances in our understanding of climate change, weather, atmospheric composition, palaeoclimates, and impacts on our planet and society. Our PhD student projects are fully integrated with this research programme. We have a total of around 30 academic staff, 30 post-doctoral researchers and about 50 PhD students, who develop advanced computer models, participate in major field campaigns, analyse satellite data and perform innovative laboratory experiments.</p> <p>Within ICAS our research is grouped into three broad areas, which span all areas of atmospheric and climate science:</p> <ul> <li><a href="https://environment.leeds.ac.uk/atmospheric-cloud-dynamics">Atmospheric and Cloud Dynamics</a></li> <li><a href="https://environment.leeds.ac.uk/atmospheric-chemistry-aerosols">Atmospheric Chemistry and Aerosols</a></li> <li><a href="https://environment.leeds.ac.uk/climate-science-impacts">Climate Science and Impacts</a></li> </ul> <p>A wide range of excellent atmospheric science research which takes place in Leeds, much of it within <a aria-label="Link ICAS" href="https://environment.leeds.ac.uk/institute-climate-atmospheric-science" rel="noreferrer noopener" target="_blank" title="https://environment.leeds.ac.uk/institute-climate-atmospheric-science">ICAS</a>. Many ICAS staff and students are also members of the new Priestley International Centre for Climate, which is located within our building.</p> <p>Within ICAS, PhD students play a full role in the institute activities. We have a lively programme of internal and external seminars, weekly weather briefings, annual science meetings and other events. Our students regularly win prizes for their work, including presentations at international conferences.</p> <h5>Why do your PhD at Leeds?</h5> <p><strong>Study in an active research environment </strong><br /> Studying your PhD with us means you’ll be working in a professional research environment, using UK-leading facilities to bring your project to life – alongside active researchers who are at the forefront of their area. <br /> <strong>A strong network of support  </strong><br /> The Leeds Doctoral College connects our community of researchers and can offer you the guidance, services and opportunities you’ll need to get the most out of your PhD. <br /> <strong>Close industry links </strong><br /> Our partnerships and links to companies and academic institutions give you the opportunity to network at industry talks, seminars and conferences, building connections that'll benefit your next steps after you complete your PhD. <br /> <strong>Professional skills development  </strong><br /> We think of the whole picture at Leeds. That’s why we offer a range of workshops and courses that'll enhance your skillset further and transfer into your professional career. <br /> <strong>Personal and wellbeing services </strong><br /> Mental health and wellbeing support are integral to who we are at Leeds and you’ll have access to the full range of services we offer to ensure you’re feeling your best – and reaching your potential in your studies. <br /> <strong>Join our global community </strong><br /> We welcome students, researchers, academics, partners and alumni from more than 140 countries, all over the world. This means, as a university, we’re bringing together different cultures and perspectives which helps strengthen our research – and societal impact.</p> <h3>Useful links and further reading:</h3> <ul> <li><a href="https://environment.leeds.ac.uk/see-research-degrees">Research degrees within the School of Earth and Environment</a></li> <li><a href="https://environment.leeds.ac.uk/institute-climate-atmospheric-science">Institute for Climate and Atmospheric Science</a></li> <li><a href="https://environment.leeds.ac.uk/see-research-innovation">School of Earth and Environment, Research and Innovation</a> </li> </ul> <h3>Leeds Doctoral College</h3> <p>Our <a aria-label="Link Doctoral College" href="https://www.leeds.ac.uk/research-leeds-doctoral-college" rel="noreferrer noopener" target="_blank" title="https://www.leeds.ac.uk/research-leeds-doctoral-college">Doctoral College</a> supports you throughout your postgraduate research journey. It brings together all the support services and opportunities to enhance your research, development and overall experience.</p>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>.</p>

<p>For queries relating to your research proposal or subject area, please contact <a href="https://environment.leeds.ac.uk/see/staff/1586/mollie-van-der-gucht">Mollie van der Gucht</a>.</p> <p>For general enquiries and details regarding the application process, please contact the Graduate School Office:<br /> e: <a href="mailto:[email protected]">[email protected]</a>, t: +44 (0)113 343 1314.</p>

Climate Energy College

• Visit us...
• PhD Projects
• Already started your PhD at Melbourne?
• Scholarships
• EU-AU Knowledge Network
• Australia's Paris Agreement Pathways
• Working Papers
• www.mitigation-contributions.org

NDC & INDC Factsheets

• Paris Equity Check Website
• Facts4COP21
• Climate Spirals
• Greenhouse Gas Factsheets

PHD SCHOLARSHIPS

Any student already doing a PhD at the University of Melbourne in the field of climate and energy transitions has an opportunity to join the College. Just get in touch and we'll discuss your suitability.

For new applicants, the PhD scholarship will need to be approved at two levels, first with the College (to ensure that the topic is relevant to themes of the College), and second within the faculty of your choice. The deadlines for PhD scholarships are different for each faculty; more details can be found  here . Further information about Central University Scholarships is provided below.

Please note:

• The cutoff for each faculty varies but in all cases is higher than the equivalent of a University of Melbourne 80% in a relevant degree. Successful applicants will meet the  University of Melbourne’s requirements for Research Higher Degree candidature
• The application process is highly competitive
• Students are encouraged to spend six months in Germany at a partner institution

College Scholarships

We are keen to hear from any candidates with strong academic and/or professional backgrounds that might be interested in studying at the College.

There are two ways to approach your PhD application. Both require you to think carefully about whether a PhD is right for you, and what PhD project might be suit your skills and interests.

1.   Develop your own research project

You are well suited to a PhD if you already have a topic that you are passionate about and a good idea of what you want to work on. In this case, you may develop your own research proposal;  ideally around 2000 words detailing the project and illustrating your understanding of the relevant academic literature.

The next step would be to scan the University of Melbourne's Find an Expert page to get some idea of a primary supervisor with whom you might like to work. Keep in mind that the more senior academics are likely to be inundated with similar such requests, and may only be available as secondary supervisors. So you are encouraged to identify early career researchers in your list of options as primary supervisors — they are also likely to have more time to work with you. 

Once you have prepared a proposal please proceed to the application page, where you will need to upload: 

• a completed College PhD Scholarship application form
• a copy of your research proposal
• a transcript of your results (including a grading scale)
• your curriculum vitae*

2.   Apply to work on a particular project

Our website lists projects that some of our supervisors are interested in. These are not PhD topics. You can apply to work on these projects but you will still need to develop a research proposal on a topic that relates to one of these projects. Before you approach the supervisor, do some research in the field and develop an idea of what aspect of the project you might like to work on.

Once you have identified a project please proceed to the application page , where you will need to upload the following documents:

• a covering letter which explains your interest in the project
• a research proposal (if you have one).

* Note: When we receive your documents we will keep them on file until scholarships are confirmed (and we will let you know when that is). Once College scholarships are confirmed, if you are identified as a potential College student you will need to follow the standard application procedure for admission to a PhD at the University of Melbourne — submit the application online (noting that you are applying to the Climate and Energy College) with other relevant documents, including getting your referees to submit the Referee Report Forms and (if applicable) the Employer Referee Report Form. 

Scholarship Entitlements

The scholarships provide:

• a living allowance of $30,600 per year pro rata (2019 full-time study rate) for up to 3.5 years
• a relocation grant of $2,000 for students who need to move from outside Victoria or $3,000 for students who need to move from outside Australia to study at the University of Melbourne
• overseas Student Health Cover (OSHC) Single Membership for international students who require a student visa to study in Australia
• limited paid sick, maternity and parenting leave

Central University Scholarships

The University has scholarships for Australian, New Zealand citizens, permanent residents of Australia and international students.

Details on Graduate Research Scholarships are available here

For further information about scholarships please click here

Other Information About Doing A PhD

Considering doing a PhD at Melbourne, please click here

For the University of Melbourne Graduate Research Hub, please click here

The steps for applying for a PhD at the University of Melbourne are outlined here

For information on English Language Requirements, please click here

Further Enquiries

Please read the information at the links provided above. If you have any other queries, please email the College Manager, Ms Anne Houston.

Web tools and Projects we developed

The live tracker of the Australian electricity market.

Paris Equity Check

This website is based on a Nature Climate Change study that compares Nationally Determined Contributions with equitable national emissions trajectories in line with the five categories of equity outlined by the IPCC.

liveMAGICC Climate Model

Run one of the most popular reduced-complexity climate carbon cycle models online. Used by IPCC, UNEP GAP reports and numerous scientific publications.

Check out our analysis of all the post-2020 targets that countries announced under the Paris Agreement.

To improve support for screen readers, please open this link. To deactivate improved support für screen readers, please open this link.

Begin of page section: Page sections:

• Go to contents (Accesskey 1)
• Go to position marker (Accesskey 2)
• Go to main navigation (Accesskey 3)
• Go to additional information (Accesskey 5)
• Go to page settings (user/language) (Accesskey 8)

End of this page section. Go to overview of page sections

Begin of page section: Page settings:

• Font size: small font size default font size large font size
• Contrast: default contrast blue-yellow contrast yellow-blue contrast black-yellow contrast yellow-black contrast black-white contrast
• User: Login
• Language: English Deutsch

Begin of page section: Main navigation:

Begin of page section: Contents:

Thesis Topics

The dissertation projects of the DK  (in the first phase from 2014 to 2018) contribute to finding answers to three questions:

• How do we understand and deal with climate change uncertainties in the natural and social sciences as well as from the perspective of normative theories?
• What are critical thresholds of environmental, social and economic systems considering their vulnerability and how are these thresholds related to the normative threshold of sufficiency, that is, the threshold of well-being below which persons’ basic rights are infringed or violated?
• What are scientifically sound, technologically and institutionally feasible, economically efficient, and ethically defensible and sustainable strategies to cope with climate change, particularly taking into account the problems of implementation in an environment characterized by uncertainties and thresholds?

Phd projects dealing with research question 1

Phd projects dealing with research question 2

Phd projects dealing with research question 3

• Coordinators
• Doctoral students
• Doctoral students (current)
• Project abstracts
• Poster: DK Topics
• Research Stays
• Guest Lectures
• Summer School "Coping with Climate Change"
• Winter School "Climate Change Thresholds"
• World Symposium "Climate Change Communication"
• Exhibition CliMatters
• Travel emissions statement

Begin of page section: Additional information:

Social media:

• Web Editors
• UNIGRAZonline
• Data Protection Declaration
• Accessibility Declaration

A cross-divisional department spanning

• Track in Environmental Sustainability, Resilience, and Health

Offered By: Department of Environmental Health and Engineering

In-person | Full-Time | 5 years

• Baltimore Community Outreach and Engagement Projects
• Northeast Market Patron Survey
• Evaluating the Impacts of Energy Options on Baltimore’s Air Quality
• One Health and Asthma Prevention in Baltimore
• Safer Urban Agriculture in Baltimore
• Diversity and Equity Initiatives
• Environmental Health and Engineering Student Organization (EHESO)
• Message from the Chair
• Postdoctoral Opportunity in Neuroscientist/(Neuro)toxicologist
• Postdoctoral Opportunity in Public Health Policy
• Research Assistant
• Bachelor of Science in Environmental Engineering
• Program Objectives and Outcomes
• Why Hopkins?
• Application Fee Waiver Requirements
• Areas of Focus
• Track in Exposure Sciences and Environmental Epidemiology
• Track in Health Security
• Track in Toxicology, Physiology and Molecular Mechanisms
• PhD in Geography and Environmental Engineering Faculty Advisers
• Graduate Student Resources
• Jensen Fellowship
• Master of Arts (MA) in Geography and Environmental Engineering
• Alumni Highlights
• Post-Baccalaureate Program in Environmental Health for Pre-Medicine Students
• Master of Science (MS) in Geography and Environmental Engineering
• Independent Professional Project and Final Essay 
• Internships
• ScM Faculty Advisers
• Master of Science in Engineering (MSE) in Geography and Environmental Engineering
• Postdoctoral Opportunity: Toxicology Policy, Law and Regulatory Analysis
• Quotes from our EHE Alumni
• Climate and Health Certificate Program
• Environmental and Occupational Health Certificate Program
• Food Systems, the Environment and Public Health Certificate Program
• Humane Sciences and Toxicology Policy Certificate Program
• Product Stewardship for Sustainability Certificate Program
• Public Health Preparedness Certificate Program
• EHE Research Retreat
• Aerosol Samplers
• Baltimore Healthy Schools: Impact of Indoor Air Quality on Health and Performance
• Exposure Assessment for Epidemiologic Studies
• Exposures from Oil Spills
• Kirsten Koehler's Research Team
• Occupational Hazard Mapping
• Particulate Exposures in Asthmatic Kids (PEAK)
• Personal Exposure Assessment
• Publications
• Spatiotemporal Exposure Assessment
• Within Baltimore Variability in Pollution
• Current Funding
• Honors and Awards
• Impact of Arsenic on Myocardial Ischemic Injury
• Meet Our Team...
• Postdoctoral Fellowship Opportunity
• Proteomic Methodologies to Assess Modifications
• Role for TRIM72 in Cardioprotection
• Sex-dependent S-nitrosothiol Signaling
• For Dog Handler Teams
• For Parents/Guardians
• Collaborators
• Dr. Meghan Davis Receives Canine Health Foundation Grant to Study the Health of Dogs
• GIRAPHE Activities
• GIRAPHE Core Faculty
• Summit on Animals, Public Health & Ethics Webcast
• News and Publications
• Eri Togami DVM, MPH
• Steven Sola, MSPH
• The COVET Study Research Goals
• The COVET Study Research Team
• The CoWelf Study
• The Minority Pet-Owner Health Project
• COVID-19 Human-Animal Interactions Survey (CHAIS)
• Climate Change, Pesticide Use, and Exposures
• Computed Tomography and Low-cost Sensor Networks to Reconstruct Spatial Pollutant Distributions
• Decision-making for Risk Management Using Small Data Sets, Mathematical Models, and Heuristics
• Infrastructure for Delivering and Characterizing Airborne Exposures in Exposure Chambers
• Mathematical Modeling of Chemical Exposures
• Meet Ram's Team...
• Modeling of Infectious Diseases
• Exposome Collaborative Investigators
• Exposome Projects
• Exposome Resources
• The Exposome Collaborative @ Johns Hopkins University
• The Exposome Collaborative Background
• The Exposome Collaborative Events and Activities
• Arsenic and other metals: association with cytokine dysregulation and risk of Hepatitis E infection in pregnant women
• Research Team
• Geyh-Bouwer Trainee Practice Award
• Mobile Air Pollution Measurement Laboratory
• Air Pollution and Cardiorespiratory Diseases
• Antimicrobial Resistance and Infectious Disease
• Biosecurity and Emerging Threats
• COVID-19 Research
• Carcinogens and Cancer
• Children's Environmental Health
• Chronic Disease Etiology and Prevention
• Community Sustainability, Resilience, and Preparedness
• Consumer Product Safety
• Energy Management and Alternative Technologies
• Environmental Chemistry, Microbiology and Ecology
• Environmental Engineering
• Environmental Epidemiology
• Environmental Inequities and Injustice
• Environmental Resource Quality
• Epigenetic Regulation in Environmental Diseases
• Food and Agricultural Systems
• Geomorphology, Geochemistry, and Hydrology
• About the Program
• Conceptual Framework
• Land Use and Energy Issues
• MPH Concentration in Global Environmental Sustainability & Health
• Projects and Research
• Recommended Reading
• Research on Land Use and Public Health
• What the Future Must Look Like
• Novel Exposure Assessment
• Risk Sciences and Public Policy
• Social and Behavioral Sciences
• Toxicology, Physiology, & Cell Biology
• Johns Hopkins University Water Institute
• Worker Health and Safety
• Teaching and Research Labs at WSE
• The INnovations to Generate Estimates of children's Soil/dust inTake (INGEST) Study
• Centers and Institutes
• Environmental Health and Engineering Doctoral Students
• Full-time Faculty Directory
• Postdoctoral Fellows
• News and Events
• Make a Gift

About the PhD Track in Environmental Sustainability, Resilience, and Health

The Environmental Sustainability, Resilience and Health (ESRH) track cultivates innovative public health scientists and engineers who address urgent challenges at the intersection of climate, sustainability, resilience, and equity.  ESRH students will be prepared for diverse careers, including work in academic institutions, government agencies, intergovernmental bodies, nongovernmental organizations, and private businesses.

Environmental Sustainability

Humans can safely co-exist within our planetary boundaries over a long time.

Environmental Resilience

Humans and their environment can quickly recover from negative shocks, such as extreme heat events or zoonotic pandemics.

Environmental Health

Humans shape their natural and built environments in ways that promote positive, equitable health outcomes for themselves and the planet.

The PhD Track in Environmental Sustainability, Resilience, and Health is Unique

Have you seen any other doctoral programs with a name like ours? There aren’t any. Yes, you can find programs in “environmental sustainability” and programs in “environmental health,” but you won’t find a program that combines those two emphases and includes a focus on resilience. The nexus between sustainability, health, and resilience is a critical frontier for research and practice in the 21st century. Our students are going to be leaders at that frontier.

Public Health and Engineering

One distinctive feature of this program is its joint delivery by the Bloomberg School of Public Health and the Whiting School of Engineering. Students benefit from an interdisciplinary cohort and the opportunity for coursework, mentorship, and research opportunities across both schools. There are slight differences in requirements at the two schools. Prospective students choose which school to apply through based on their primary interests. 

What you’ll study

The ESRH track trains students in systems thinking and the application of core public health and engineering tools. That training is supplemented with a deep understanding of the anthropogenic drivers of environmental change, and the consequences of that change for human health and well-being. Using methods and tools from public health and engineering, ESRH students design, implement and analyze research studies with relevance to both science and practice.

To provide a shared foundation in ESRH topics, all students take a sequence of core courses. Elective coursework is then customized to student interests. Each student identifies a primary focus area and a primary methodological area. Examples of focus areas include climate, food systems, energy, built environment, air, water, and equity. Examples of methodological areas include biostatistics, epidemiology, lifecycle assessment, engineering, economics, systems analysis, program evaluation, qualitative methods, risk policy and communication, and geography. 

Track Faculty

Meghan Davis, PhD, DVM  (BSPH) - Environmental microbiology, one health, asthma  Peter DeCarlo, PhD  (WSE) - Atmospheric aerosols (particulate matter), air quality, and climate Paul Ferraro, PhD  (WSE) - Behavioral science, causal inference, environmental policy Shima Hamidi , PhD  (BSPH) - Geospatial data, built environment, housing and transportation & health Ben Hobbs, PhD  (WSE) - Systems analysis, energy, water Ben Q. Huỳnh (BSPH) - AI, data science, environmental justice; planetary health Kirsten Koehler , PhD, MA  (BSPH) - Exposure assessment, aerosols, air quality Keeve Nachman , PhD, MHS  (BSPH) - Risk science, risk assessment, food systems Roni Neff , PhD, ScM  (BSPH) - Food system, wasted food, resilience, equity Scot Miller, PhD  (WSE) - Global change, greenhouse gases, air pollutants Carsten Prasse , PhD  (WSE) - Emerging contaminants, engineering processes, analytical detection methods Ana Rule, PhD  (BSPH) - Air pollution, bioaerosols, metal speciation Kellogg Schwab , PhD, MSPH  (BSPH) - Water, sanitation and hygiene, environmental microbiology, microbial fate and transport  Brian Schwartz , MD, MS  (BSPH) - Environmental epidemiology, sustainability, built environment, lead Genee Smith , PhD, MSPH  (BSPH) - Environmental epidemiology, health effects of climate change, infectious diseases

Browse an overview of this program's requirements in the JHU  Academic Catalogue  - See Track Requirements for Environmental Sustainability, Resilience, and Health , and explore all course offerings in the Bloomberg School  Course Directory .

Tuition and Funding

Per the Collective Bargaining Agreement (CBA) with the JHU PhD Union, the minimum guaranteed 2025-2026 academic year stipend is $50,000 for all PhD students with a 4% increase the following year. Tuition, fees, and medical benefits are provided, including health insurance premiums for PhD student’s children and spouses of international students, depending on visa type. The minimum stipend and tuition coverage is guaranteed for at least the first four years of a BSPH PhD program; specific amounts and the number of years supported, as well as work expectations related to that stipend will vary across departments and funding source. Please refer to the  CBA to review specific benefits, compensation, and other terms.

Need-Based Relocation Grants Students who  are admitted to PhD programs at JHU   starting in Fall 2023 or beyond can apply to receive a need-based grant to offset the costs of relocating to be able to attend JHU.   These grants provide funding to a portion of incoming students who, without this money, may otherwise not be able to afford to relocate to JHU for their PhD program. This is not a merit-based grant. Applications will be evaluated solely based on financial need.  View more information about the need-based relocation grants for PhD students .

Questions about the program? We're happy to help. [email protected]

Program Directors Paul Ferraro, PhD Keeve Nachman, PhD  Roni Neff, PhD  

• Quick Contacts

Faculty of Arts and Sciences

The ocean turbulence expert modeling earth’s climate with scientists around the globe.

By Michaela Herrmann

Every year, Yale’s Faculty of Arts and Sciences hires dozens of exceptional scholars in academic departments across the sciences, humanities, and social sciences. This series profiles six of the faculty joining the FAS in the 2024—25 academic year, highlighting their academic achievements, research ambitions, and the teaching they hope to do at Yale. Learn more about the incoming ladder faculty and multi-year instructional faculty joining the FAS.

Science is a fundamentally iterative, collaborative process: hypotheses are posited, theories developed, and both tested rigorously over many generations. It’s tough to imagine an academic discipline with more scientists working together in real-time than the field of climate modelling, of which new FAS faculty Elizabeth Yankovsky is a part.

Yankovsky  joins the Department of Earth and Planetary Sciences as Assistant Professor. At Yale,  she’ll  blend physics, computer science, and approaches from other fields to continue her research on ocean turbulence and improving global climate models—projects that require her to remotely log into supercomputing systems developed and refined by scientists in modeling centers around the globe.   

Her specialty is ocean turbulence, or how energy is added to the ocean through large-scale forcings like sun and wind, then removed by small-scale interactions between water molecules.  

“Turbulence really refers to the multi-scale interactions between all of these different scales,” she explains. “ So energy is put into the ocean, leading to the formation of large eddies, whirlpools, global scale currents. Eventually, there are different types of instabilities, waves, and other interesting fluid processes that transfer that energy to smaller scales where it’s dissipated and removed.” 

Yankovsky’s research on turbulence can in turn be used to improve the accuracy of climate modeling, which relies on numerical simulations to predict how Earth’s climate may change based on different inputs to the climate system—more atmospheric heat, or greater ocean alkalinity, for example. “Turbulence in the ocean is nonlinear and chaotic, which makes it really hard to study and predict how the ocean and the climate are going to evolve with time,” Yankovsky explains. That chaos means a robust understanding of turbulence is crucial for modeling the overarching climate system as accurately as possible.  

She would have joined the faculty a year earlier if not for an incredible opportunity to broaden her scientific horizons in Boulder, Colorado before moving to Yale. “Everyone here was very flexible about letting me defer my start date by a year, so I  spent the last year at  an organization that is working on carbon dioxide removal through various geoengineering projects in the ocean,” Yankovsky says.

Geoengineers are trying to take advantage of the weathering process to absorb some of the excess atmospheric CO2 that’s driving global climate change. “[With ocean carbon dioxide removal], essentially what you’re doing is putting an alkalinity source at the surface. One of the biggest questions is how that disperses both vertically and horizontally, because if that alkalinity sinks below the surface of the ocean, it’s no longer causing any CO2 uptake,” she explains. “I came into that project not really thinking that my past research experiences were that relevant, but understanding the vertical structure of mixing and turbulence ended up being one of the most important applications of my past research.”   

For a scientist who spends a lot of her time essentially conducting experiments in the virtual lab of climate modeling software, “it  has been very inspiring  to see all of these physical oceanographers and climate scientists getting involved in this field,” she says. “It’s a great application of the specific problems that we work on in the ocean modeling community, and it’s suddenly very relevant to society.”

That year of research in Boulder has already led Yankovsky to new connections on campus. “ I’ ve become a member of the Yale Center for Natural Carbon Capture, and talking to some of the other scientists in that space has been really interesting . Looking beyond the ocean alkalinity enhancement work that I’ve done already, I’m excited to collaborate more with people at the YCNCC and with other faculty.”  

The chance to work with scientists across Earth sciences and beyond was a large part of what drew Yankovsky to Yale. “I came into Earth sciences from geophysics originally, but sometimes it almost feels arbitrary to me which specific problem I’m working on. There are so many interesting questions in the broad field of Earth sciences, and I’m just really excited to be around geophysicists and paleontologists and people that study different components of the Earth system that are ultimately intimately linked.”  

“I’ve also been really pleased by how kind everyone is in this department,” she adds, excited at the prospect for thriving collaborations within Earth and Planetary Sciences. “I don’t think that’s necessarily a common feature , and through my interview process, negotiation process, and finally coming here, everyone has been super supportive and helpful.”  

As she looks forward to getting back into the cadence of the academic calendar, Yankovsky is planning to teach classes that introduce students to climate modeling, how numerical models can be used to understand fluid dynamics and geophysical systems, and some of the techniques and complexities of the field.  

Yankovsky hopes to mentor students and help guide them through a crucial yet occasionally confusing part of their academic careers. “When I was an undergraduate, I remember feeling a bit overwhelmed and lost with the amount of research options. It’s such a critical stage in your career development. Particularly for students that decide to pursue graduate studies, they have just a few short years to decide what sort of research and field inspires them,” she reflects. “I’m so grateful to the people who took the time to sit, code, work deeply with, and teach me during those years, and I’m excited to fulfill that role for my future students.”  

As Yankovsky anticipates a new chapter in New Haven, she reflects on one last thing Boulder gave her: a new love of its namesake activity. “I recently started bouldering, which I’ve never really done before,” she says. She’s eager to get to know the nature surrounding New Haven and volunteer at some local animal shelters. “I really love climbing, and I’m looking forward to getting to know the climbing areas around here and the climbing gyms,” she says , ready to reach new heights in her field, the classroom, and beyond.  

• Netherlands
• Academic Positions
• Posted on: 20 August 2024

PhD position: “Reconstructing and attributing regional sea-level change in the 20th century using climate models”

Job information, offer description.

The Department of Estuarine and Delta Systems (EDS) is looking for a highly motivated PhD candidate with a background in physical geography, oceanography or meteorology. The position is part of a larger project which addresses the challenge of reconstructing and attributing 20th century regional sea-level changes using observations and model simulations. The project was awarded to Dr. Slangen in the Vidi Talent Programme scheme funded by NWO. This 4-year PhD position, starting from 1 January 2025, is in close collaboration with Utrecht University (Department of Physical Geography) and NORCE (Bergen, Norway).

ROYAL NIOZ NWO-NIOZ Royal Netherlands Institute for Sea Research is the Dutch national oceanographic institute and principally performs academically excellent multidisciplinary, fundamental, and frontier applied marine research addressing important scientific and societal questions pertinent to the functioning of the ocean and seas. NIOZ includes the National Marine research Facilities (NMF) department that operates a fleet of research vessels and the national pool of large seagoing equipment, and supports excellence in multidisciplinary marine research, education, and policy development.

THE DEPARTMENT

The department of Estuarine and Delta Systems (EDS, NIOZ-Yerseke) studies how the interplay between biota, hydrodynamics, sediment dynamics and biochemistry shapes the estuarine, deltaic and coastal environments within the context of natural and human-induced environmental changes. Our department assigns central importance to a multidisciplinary approach that combines state-of-the-art biophysical, biochemical, ecological and physiological measurements and experiments with remote sensing and numerical modelling to create in-depth understanding of the processes that control estuarine and delta systems. One important focus for the department is how our research can create value for society.

THE PROJECT

Global mean sea level has been rising at a rate of 1.7 (1.3-2.1) mm/yr since 1900, increasing to 3.3 (2.9-3.6) mm/yr for the period 1993-2018. However, from satellite altimetry observations we know that regional changes can be up to three times faster than the global mean. Unfortunately, the drivers of regional sea-level changes in the 20th century are not accurately known yet. Therefore, there is an urgent need to reconstruct 20th century regional sea-level changes, such that we can unravel its causes and determine the part of the observed changes that is driven by man-made greenhouse gas emissions. This is the aim of the NWO-funded Vidi project ‘DARSea: Detecting and Attribution of 20th century Regional Sea-level change’. The outcomes of the project will help coastal societies to understand why the sea level has changed along their coast and which part is caused by man-made climate change. This knowledge will support policymakers to make well-informed decisions on coastal protection against future sea-level rise.

This position is the second of 2 PhD positions in the DARSea project (2024-2028). The project is led by NIOZ, with partners from Utrecht University, the UK Met Office, NORCE Bergen and the University of Bremen.

THE VACANCY

This PhD position is based at NIOZ Yerseke (The Netherlands), starting from January 2025. Your role in the DARSea project will be to reconstruct the 20th century regional sea-level changes using the output of various model simulations, for instance from climate models, ice sheet models or glacier models. You will work with state-of-the-art climate model data and set up a framework for analysing simulated 20th century regional sea-level changes. The model data will serve as your main research tool, to investigate regional sea-level budget closure, perform observation-model comparisons, and quantify the fraction of observed regional sea-level change caused by man-made greenhouse gas emissions and other climate drivers. You will lead and contribute to peer-reviewed publications, share your results online with the research community, as well as present the project results at international conferences.

This PhD candidate will closely work together with the other PhD candidate in the project (who is working on reconstructing the 20th century sea level budget using observations) to set up the model analysis framework. You will be enrolled in the graduate school of Utrecht University Earth Sciences and will be co-supervised by Prof. Roderik van de Wal (Utrecht University, Department of Physical Geography) and Dr. Kristin Richter (NORCE, Norway).

THE CANDIDATE

You must have completed an MSc degree in Oceanography, Physical Geography, Meteorology or a closely related relevant discipline, with demonstrated focus on the physical climate system.

Furthermore, you meet the following criteria:

• Strong analytical and computational skills.
• A keen interest in (marine) climate change related topics.
• An aptitude for multi-disciplinary work, problem-solving and teamwork.
• A good communicator with interest to share your knowledge outside academia.
• Excellent written and oral communication skills in English.
• Well-organized and methodical.

NIOZ wants to be a transparent institute with a healthy working climate and an inclusive culture, where people from diverse backgrounds and gender bring their talents and further develop these talents. We aim for inclusive decision-making processes and expect our leadership to show visible commitment, awareness of bias, and cultural intelligence.

• Employment of this full-time position at Royal NIOZ is by NWO-I, for a total duration of 4 years. You start with an appointment for the duration of 1 year and, after a positive evaluation in the 9th month (Go-No go), you will be extended to the full period of 4 years.
• Salary compliant with scales for PhD candidate (OIOs) CAO-WVOI (Collective Labour Agreement for Dutch Research Institutes).
• An appointment at NIOZ as a PhD candidate means working and learning simultaneously conform the NIOZ PhD policy.
• 338 annualized holiday hours for a full-time 40-hour work week.
• Pension scheme via ABP, 8% holiday allowance and a year-end bonus of 8.33%.
• 2nd class public transportation travel is reimbursed 100%.
• Employment benefits plan to exchange a portion of your salary for days off or vice versa, or can be used to purchase a bicycle with tax benefits.
• We offer relocation expenses for employees coming from abroad and support with finding accommodation. Yerseke is located on commuting distance from major cities such as Antwerpen, Breda, Bergen op Zoom and Middelburg.

MORE INFORMATION

For additional information about this vacancy, please contact Dr. Aimée Slangen . For additional information about the procedure, please send an e-mail to [email protected]

Closing date for this vacancy is 6 September 2024.

Where to apply

Requirements, additional information, work location(s), share this page.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 12 August 2024

Feasibility of peak temperature targets in light of institutional constraints

• Christoph Bertram   ORCID: orcid.org/0000-0002-0933-4395 1 , 2 ,
• Elina Brutschin   ORCID: orcid.org/0000-0001-7040-3057 3 ,
• Laurent Drouet   ORCID: orcid.org/0000-0002-4087-7662 4 , 5 ,
• Gunnar Luderer   ORCID: orcid.org/0000-0002-9057-6155 2 , 6 ,
• Bas van Ruijven   ORCID: orcid.org/0000-0003-1232-5892 3 ,
• Lara Aleluia Reis   ORCID: orcid.org/0000-0002-6676-7007 4 , 5 ,
• Luiz Bernardo Baptista   ORCID: orcid.org/0000-0002-7016-1796 7 ,
• Harmen-Sytze de Boer   ORCID: orcid.org/0000-0001-7376-2581 8 ,
• Ryna Cui   ORCID: orcid.org/0000-0002-1186-8230 1 ,
• Vassilis Daioglou   ORCID: orcid.org/0000-0002-6028-352X 8 , 9 ,
• Florian Fosse   ORCID: orcid.org/0000-0002-0239-1143 10 ,
• Dimitris Fragkiadakis   ORCID: orcid.org/0000-0001-5414-9570 11 ,
• Oliver Fricko   ORCID: orcid.org/0000-0002-6835-9883 3 ,
• Shinichiro Fujimori   ORCID: orcid.org/0000-0001-7897-1796 3 , 12 , 13 ,
• Nate Hultman   ORCID: orcid.org/0000-0003-0483-2210 1 ,
• Gokul Iyer   ORCID: orcid.org/0000-0002-3565-7526 1 , 14 ,
• Kimon Keramidas   ORCID: orcid.org/0000-0003-3231-5982 10 , 15 ,
• Volker Krey   ORCID: orcid.org/0000-0003-0307-3515 3 ,
• Elmar Kriegler   ORCID: orcid.org/0000-0002-3307-2647 2 , 16 ,
• Robin D. Lamboll   ORCID: orcid.org/0000-0002-8410-037X 17 ,
• Rahel Mandaroux   ORCID: orcid.org/0000-0001-5596-2571 2 ,
• Pedro Rochedo   ORCID: orcid.org/0000-0001-5151-0893 18 ,
• Joeri Rogelj   ORCID: orcid.org/0000-0003-2056-9061 3 , 17 ,
• Roberto Schaeffer   ORCID: orcid.org/0000-0002-3709-7323 7 ,
• Diego Silva   ORCID: orcid.org/0000-0001-5484-4442 13 ,
• Isabela Tagomori   ORCID: orcid.org/0000-0002-0469-6055 8 ,
• Detlef van Vuuren   ORCID: orcid.org/0000-0003-0398-2831 8 , 9 ,
• Zoi Vrontisi   ORCID: orcid.org/0000-0003-3767-0617 11 &
• Keywan Riahi   ORCID: orcid.org/0000-0001-7193-3498 3 , 19  

Nature Climate Change ( 2024 ) Cite this article

5992 Accesses

1 Citations

361 Altmetric

Metrics details

• Climate-change mitigation
• Energy modelling
• Energy policy

Despite faster-than-expected progress in clean energy technology deployment, global annual CO 2 emissions have increased from 2020 to 2023. The feasibility of limiting warming to 1.5 °C is therefore questioned. Here we present a model intercomparison study that accounts for emissions trends until 2023 and compares cost-effective scenarios to alternative scenarios with institutional, geophysical and technological feasibility constraints and enablers informed by previous literature. Our results show that the most ambitious mitigation trajectories with updated climate information still manage to limit peak warming to below 1.6 °C (‘low overshoot’) with around 50% likelihood. However, feasibility constraints, especially in the institutional dimension, decrease this maximum likelihood considerably to 5–45%. Accelerated energy demand transformation can reduce costs for staying below 2 °C but have only a limited impact on further increasing the likelihood of limiting warming to 1.6 °C. Our study helps to establish a new benchmark of mitigation scenarios that goes beyond the dominant cost-effective scenario design.

Similar content being viewed by others

A multi-model analysis of long-term emissions and warming implications of current mitigation efforts

Energy demand reduction options for meeting national zero-emission targets in the United Kingdom

1.5 °C degrowth scenarios suggest the need for new mitigation pathways

Global temperature rise is expected to peak around the time when global CO 2 emissions reach net-zero levels 1 , 2 . Reaching global net-zero CO 2 emissions quickly while limiting cumulative emissions therefore lies at the core of achieving the long-term goal of the Paris Agreement 3 , 4 . The level of peak temperature and the speed at which it is reached determines the adaptation needs for infrastructure and natural systems 5 . The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) 6 assessed a large number of scenarios and categorized them based on various metrics, including their projected peak temperature, and found a relatively large number (97) of scenarios still limiting warming to 1.5 °C with no or limited overshoot, defined as peak temperature below 1.6 °C with >50% likelihood. However, the feasibility of these have been questioned 7 , 8 , and recent emissions increases from 2020 to 2023 9 have underscored those doubts.

In addition, since AR6, continued measurements and advances in climate science have led to a downward correction of remaining carbon budgets for a given peak temperature target 10 . Furthermore, the understanding of the feasibility of near-term deployment of different mitigation options has improved with continued deployment (or lack thereof). Studies looking into feasibility aspects 8 , 11 , 12 , 13 have also highlighted the difficulty of fast emissions reductions as part of dedicated climate policies, especially in countries that lack the governance and institutional capabilities to enforce regulation in other policy domains (such as taxation or environmental regulation).

Our study thus explores the feasibility of ambitious peak temperature targets in the Paris Agreement target range, in light of the current state of knowledge, taking into account the observed emissions rebound after the COVID-19 pandemic 14 and the improved understanding of feasibility 8 , 11 , 12 , 13 along five relevant dimensions (Table 1 ): geophysical, technological, institutional, socio-cultural and economic. Using eight state-of-the-art global multi-regional process-based integrated assessment models (IAMs), we explore a set of 20 scenarios ( Methods and Supplementary Table 1 ), including both the cost-effective settings that dominate the IPCC scenario assessments and scenarios with harmonized variation of explicit feasibility considerations (Table 1 ). The choice for this treatment is informed by previous studies and the participating models’ capabilities and is not fully comprehensive in the sense that additional variables and aspects 15 could also be assessed. However, we use a more systematic approach than previous studies’ 16 , 17 scenarios and also assess the impact with and without regional differentiation, both of which have been identified as crucial missing pieces in previous studies 18 , 19 , 20 , 21 .

We explore the impact of explicit consideration of feasibility constraints on six scenarios that limit peak warming to less than 2 °C with more than 66% likelihood (defined as a 1,000 Gt CO 2 carbon budget from 2018) and additionally explore the lowest end of achievable peak temperature under variation of feasibility assumptions in 14 additional scenarios. Complementary to other studies looking at the role of short-lived climate forcers 22 or individual energy sectors or technologies 23 , 24 , 25 , 26 , we focus here on total CO 2 emissions and especially on the energy sector (details of the modelling in Methods and Supplementary Information sections 2–4 ). Thus, we only evaluate the warming implication via the link with cumulative CO 2 emissions. The models used in this analysis do include other greenhouse gases—including methane (CH 4 ), which is very important for understanding the trajectory of peak temperatures 27 . However, due to the lack of available evidence regarding the levels of CH 4 emissions reductions that are considered feasible and the large differences of representation across models, we limited this analysis to CO 2 . The key innovation beyond existing literature lies in the consideration of the institutional dimension with which we strive to proxy the capacity to effectively implement climate mitigation policies. We justify this key assumption based on past literature that has identified the quality of institutions as an important driver of many climate mitigation policies 28 , 29 , 30 , 31 and the credibility of their implementation 32 . We then operationalized institutional constraints via region-specific limits on both carbon prices and emissions-reduction rates, which are derived as a stylized function of the dynamically projected government effectiveness indicator 33 and historically observed reductions of sulfur dioxide emissions (Supplementary Information section 4A ). This constraint is thus region specific and changes over time as the government effectiveness improves (Fig. 1a ). Within this institutional dimension, we also analyse a more pessimistic alternative setting of ‘frozen’ governance indicators and a more optimistic setting in which only carbon prices and their spread are limited. Additional details provided in Fig. 1 , Methods and Supplementary Information section 4A .

a , Governance indicator data (based on Andrijevic et al. 33 ) for countries with a population of more than 25 million in 2020 (identified by ISO-3 country codes) and evolution for ten harmonized IPCC regions and the threshold values used for the binning of maximum carbon prices and reductions in dashed grey horizontal lines. The relationship between 2020 governance indicators and per capita gross domestic product (GDP) is shown in Extended Data Fig. 2 . b , Scaling of carbon prices relative to the maximum. c , Maximum carbon prices. d , Maximum relative yearly reduction of fossil fuel and industry CO 2 emissions. Regions with identical values are highlighted by additional region labels. The thin grey lines in each panel indicate the settings under the assumptions of frozen governance, so without improvement of the governance indicators across regions.

The logic behind this approach is to explicitly incorporate the most relevant feasibility considerations identified in previous studies 6 , 11 , making the scenarios more applicable for real-world interpretation and implementation. In other words, the inclusion of technological and institutional constraints helps scenarios to be closer to the fuzzy feasibility space, allowing them to also have a higher implicit likelihood of being realized (at least based on the assessment of aspects covered). Following recent critiques of the often narrow focus on mitigation costs in IAM studies 34 , we thus explicitly look at scenarios with higher narrowly defined mitigation costs but lower risk of failure 35 . The additional enablers of reduced demand 36 , 37 in high-income countries and increased electrification 38 in the combined ‘Tech and Enablers and Institutional’ scenarios create more flexibility on the supply side and thus further improve the feasibility of implementation. This approach is illustrated in Fig. 2 that situates our scenarios in the feasibility framework by Jewell and Cherp 39 , adapted to climate scenarios instead of single mitigation options.

The new scenarios in this study are closer to the (fuzzy) feasibility space than existing reference mitigation scenarios that target cost effectiveness, not feasibility. Scenarios that assume the socio-cultural enablers cannot unambiguously be assigned higher or lower feasibility as they are more doable in terms of their supply-side transformation but might be less doable regarding the assumed demand transformation. Figure adapted with permission from ref. 39 under a Creative Commons license CC BY 4.0.

Interaction of different feasibility dimensions

To explore implications of feasibility constraints on the cost and achievability of climate targets, we first explore carbon prices to limit cumulative CO 2 emissions from all sectors from 2018 until the time of net-zero to 1,000 Gt CO 2 (this section) and then explore minimum achievable cumulative CO 2 emissions across different feasibility-scenario variants (following section).

The technological constraints do not have a substantial impact in terms of overall difficulty and the relative effort required for reaching an ambitious decarbonization trajectory, which we estimate via the shadow price of carbon (Fig. 3 ). The relative change of the uniform carbon prices is for most models smaller than a factor of two and is also smaller than the difference across models for the same assumptions (Extended Data Fig. 1 ). The imposition of the institutional feasibility is assessed first in its default specification of both constrained prices and quantities. This leads to the differentiation of relative effort across regions (Fig. 3 shows the highest and lowest regional values). Countries with very low governance scores exhibit carbon prices below the Cost-effective-scenario level; for the highest-capacity countries, carbon prices increase between a factor 2 and 3 for most models compared with the Cost-effective scenario, leading to a shift of regional emissions. However, combining both the technological and institutional constraints leads to strong increases of carbon prices in high-capacity countries by a factor of 3–4 for most models. This strong nonlinear effect of adding both the technological and institutional constraint can be explained by the increased importance of fast upscaling of all mitigation technologies for the high government effectiveness regions that need to reach net-zero CO 2 earlier in scenarios with institutional constraint. Therefore, the regionalized constraint on solar and wind upscaling is more constraining for faster decarbonizing regions. And even the globally implemented constraints on the crucial technologies carbon capture and storage (CCS) and bioenergy for reaching net zero become more constraining compared with the scenario without institutional constraint, as overall reliance on carbon dioxide removal increases in such scenarios of differentiated speeds 17 .

Relative carbon prices compared with Cost-effective set-up for the regions with lowest and highest institutional capacities in different scenarios with a 1,000 Gt CO 2 budget from 2018 until the year of net-zero CO 2 (corresponding to a higher than 66% likelihood of limiting warming to below 2 °C). Carbon prices are harmonized across regions in the first two scenario variants (and shown in orange) and differentiated for the other four. For clarity, only the highest and lowest carbon prices for each model are shown (in red and yellow, respectively), with relative prices of the remaining regions being situated in between. The box plot shows the median (centre line), interquartile range (box) and full range (whiskers) across the models shown, with outliers further noted by a grey dot and arrow. Note that the outlier from the MESSAGE model is outside the plot range (‘ME’). The institutional dimension is using the default specification of dynamic governance scores and combined price and quantity constraints (Fig. 1 ).

Dedicated interventions on socio-cultural enablers (for example, the reduction of energy demand for high-income regions and more optimistic assumptions on electrification) substantially reduce CO 2 prices so that in some models even the highest-income regions have lower carbon prices compared with the Cost-effective case. Even with the additional technological constraints on the supply side, the combined scenario (Tech and Enablers and Institutional) achieves the target with only a doubling of carbon prices in high-institutional-capacity countries and reduced carbon prices in countries with the lowest governance scores (which closely coincides with lowest income; Extended Data Fig. 2 ). Absolute carbon prices in this scenario for regions with highest government effectiveness are still at a challenging and high level, but for four out of seven models below US$100 t −1  CO 2 in 2030 (Extended Data Fig. 1 ). Despite the lack of comprehensive global mitigation action and increasing global emissions in the past 15 years, the faster-than-expected technological progress has kept ambitious mitigation feasible at manageable efforts. This is in contrast to prominent earlier work that had not anticipated such fast progress and concluded that immediate fully harmonized participation from 2010 is required to stay below 2 °C (ref. 40 ). A comparison of the shadow carbon prices we find here (which measures the marginal cost of abating a ton of CO 2 ) with the social cost of carbon (which measures the monetized value of avoided damages of such abatement) should not be misinterpreted as a full cost–benefit analysis. Nevertheless, it is worth noting that recent literature has put the median social cost of carbon at values between of ~US$200 per ton of CO 2 in 2020, with substantially higher means 41 , 42 , 43 , higher than the 2030 carbon prices in regions with high government effectiveness, and much higher than those with low government effectiveness (Extended Data Fig. 1 ). The fact that the Tech and Enablers and Institutional scenario explicitly considers feasibility constraints implies that such a scenario represents a more plausible pathway towards climate-target achievement than the Cost-effective setting that so far has dominated most scenario analyses. The implications on regional emissions trajectories, including regional reductions until 2040 and net-zero dates, and technology choice of this difference are explored in detail in a parallel publication currently in preparation (E. B. et al., manuscript in preparation).

Lower bound of peak temperatures

If we assume that governance scores remain frozen at their 2020 levels, the ability to rapidly constrain emissions in most regions is sharply curtailed. In such a situation, and combined with technological constraints and the more pessimistic demand-side assumptions, more than 1,000 Gt CO 2 would still be emitted before net zero can be reached. With these pessimistic assumptions on feasibility constraints (not included in the previous section and Fig. 3 ), the maximum allowable policy ambition achieves peak temperature of 2 °C only with around 30–50% likelihood (left-hand side of Fig. 4a and comparison with Fig. 4b with identical y axis).

a , b , Minimum achievable carbon budget from 2023 until net-zero CO 2 across 14 different feasibility variants ( a ) and corresponding likelihood of staying below 1.5 °C, 1.6 °C, 1.8 °C and 2.0 °C at peak according to Table 7 in Forster et al. 10 ( b ). Likelihoods all assume the median of non-CO 2 contribution towards peak warming. In a , full symbols in the middle show the default assumption of combined differentiation of carbon prices and emissions-reduction quantities, whereas the four options on the left with open points show the results assuming no improvement of institutional capacity over time. The open points on the right side show the more optimistic assumption of only differentiated carbon prices but without the explicit emissions-reduction constraints.

Keeping the pessimistic frozen institutional constraints but relaxing the technological constraint or assuming faster demand-side transformation helps to lower achievable peak budgets and temperatures, with the models diverging on which effect is larger. The models do agree, however, that the combined relaxation of the technological and socio-economic dimension allows for peak budgets between 750 and 900 Gt CO 2 , corresponding to 40–55% likelihood of staying below 1.8 °C (Fig. 4a,b ).

Under the default specification of dynamically improving governance scores for the institutional constraint, results are more diverse across models, with MESSAGE, POLES and WITCH at the more pessimistic high end of the carbon budget and temperature range, and GEM-E3, IMAGE and REMIND at the lower end. With the most pessimistic assumptions on technological and socio-cultural constraints (Tech and Institutional), they cluster around 900–1,000 and 550–700 Gt CO 2 , respectively, which corresponds to either around 40% probability of staying below 1.8 °C or around 75%, respectively. With the more optimistic assumptions on technological and/or socio-cultural constraints, the range of likelihood to stay below 1.8 °C reaches 50–90%, which corresponds to a 15–50% likelihood of staying below 1.6 °C. Put differently, with these settings, some but not all models still reach the C1 class of scenarios from IPCC AR6 (defined as having >50% likelihood of a peak below 1.6 °C).

Even a more optimistic implementation of the institutional constraint, which differentiates carbon prices but does not explicitly constrain emissions reductions, leads to similar results. Not all models have run these scenario variants, but the comparison of scenarios from the same models (indicated by the connecting lines in Fig. 4 ) shows that the effect is slightly larger for scenarios with enablers (solid lines) than for scenarios without (dashed lines). Therefore, all models running scenarios with enablers and this more optimistic institutional constraint achieve scenarios in the C1 class.

For scenarios without any form of institutional constraint, nearly all models achieve C1 compatible scenarios, both with and without additional technological constraints. The exception is the AIM model in which, due to very strong growth of assumed electricity demand to 2030 (+ 1,900 TWh yr −1 from 2022 to 2030, compared with an average of +600 TWh yr −1 from 2010 to 2022; Extended Data Fig. 3 ), renewables scale up is not fast enough to allow for the necessary pace of fossil phase out in the electricity sector 44 . Therefore, in scenarios without Tech constraints, this model projects what are probably unrealistically high rates of growth of fossil CCS to 2030 based on the recent track record of those technologies. AIM thus projects a very slow phase out of unabated fossil fuels in electricity generation in the Tech scenario, causing most of the more than 300 Gt CO 2 higher emissions in the Tech scenario compared with the Cost-effective scenario.

Our results show that the most ambitious scenarios accounting for the institutional feasibility concern only allow for a likelihood of 5–45% of staying below 1.6 °C at peak warming, with considerable differences across models and assumptions around the institutional constraints. The world needs to be prepared for the possibility of an overshoot of the 1.5 °C limit by at least one and probably multiple tenths of a degree even under the highest possible ambition. Without much increased near-term climate policy ambition everywhere, and especially without dedicated efforts to improve institutional capacity to enact fast mitigation, in particular in countries with currently low government effectiveness scores, an even higher overshoot will soon become inevitable. Our study does not imply that the 1.5 °C target needs to be abandoned. Rather, it provides a nuanced picture of what needs to happen to peak temperatures at a minimal overshoot above 1.5 °C to decrease temperatures afterwards. However, given our focus on improved understanding of near- and medium-term feasibility constraints, we look only at the trajectories until peaking and do not discuss in detail strategies and trade-offs for temperature reductions after the peak 3 .

The analysis does, however, make clear that to bring temperatures down to below 1.5 °C after such an overshoot, a substantial amount of several hundreds of Gt CO 2 per 0.1 °C of overshoot will need to be removed from the atmosphere. Reducing demand and increasing electrification, while not being sufficient alone to avoid overshoot, will be very helpful when it comes to reducing temperatures from such an overshoot, as reduced demand for energy services leaves more energy and materials available for carbon dioxide removal. This is particularly important in the presence of technological, geophysical and institutional constraints limiting the availability of bioenergy and CCS and their viability in certain regions.

Our study provides an innovative addition to the scenario literature in that it explicitly considers harmonized feasibility constraints along various dimensions. The results show that technological constraints are not the most critical concern for mitigation, given the latest acceleration of observed deployment in key mitigation technologies. Enabling factors such as reduced demand, especially in high-income regions, and faster demand-side transformation towards electrification can help to lower the achievable lowest peak temperatures for a given set of assumptions.

The most important dimension studied, however, is the institutional dimension. Our results show that explicit consideration of institutional constraints allows for delineating a plausible, though fuzzy, lower limit of peak temperature increase. The nuanced results show that both the assumptions on the relationship between government effectiveness and feasible mitigation ambition and the built-in model difference have an impact on results.

When looking at scenarios with enablers, it is important to keep in mind that we have not considered the potential economic or political costs of faster technological transformation and reduced demand in high governance regions nor have we considered an explicit feedback of enablers on allowing for faster relaxation of the institutional constraints.

While our work goes beyond existing assessments of feasibility considerations, more work can be done to look at the dynamics between different aspects of feasibility and to link this work with frameworks of probabilistic policy outcomes 45 . Including feasibility assessments of methane abatement 46 , 47 , 48 , 49 will also be important for a more complete understanding of the feasibility of different peak temperatures as will be studies that link the general approach presented here with a scenario set-up based on detailed policy packages 16 instead of generic carbon pricing. A robust insight from this work, however, is that focusing on cost effectiveness without consideration of institutional feasibility and regional differentiation leads to important biases in benchmark scenarios. Our approach has been to identify scenarios that qualitatively move towards higher feasibility as an important innovation, helping to fill the scenario space and creating a bridge between pure cost-optimal benchmark scenarios and pure bottom-up prospective scenarios 50 , 51 , 52 .

Motivation for the chosen scenario set-up

The latest IPCC assessment report AR6 6 included an analysis of the feasibility of mitigation pathways, and we here use the same five dimensions (Fig. 1 ). On the basis of the results of the IPCC analysis (Fig. 3.43 in Riahi et al. 6 ), we put the largest emphasis on the Institutional dimension, which the analysis found to be of highest concern. We combine the Geophysical and Technological dimensions, which the IPCC analysis found to exhibit medium concern levels. The economic dimension is used as the diagnostic dimension, as this is kept unconstrained in the case of the 1,000 Gt CO 2 scenarios (below), though economic differentiation also is inherent to our treatment of the institutional dimension via the carbon price constraints. As the IPCC found that socio-cultural concerns are lowest across available mitigation scenarios (driven partly by limited explicit exploration of this dimension), we here use this dimension to explore a key enabling mechanism: assumptions on lowering energy demand and a faster demand transformation towards electrification (which both increase the concern level in the socio-cultural dimension) can reduce pressure across the other dimensions to arrive at overall more balanced levels of feasibility concerns (Fig. 2 ).

Scenario set-up

For the purpose of understanding the impact of feasibility assumptions on scenario characteristics and the lower level of achievable peak temperature, we run a protocol of 20 harmonized scenarios across eight global integrated assessment models (model descriptions of the used IAMs in Riahi et al. 4 , overview table of scenarios in Supplementary Table 1 ). The protocol differentiates between two different peak temperature objectives and six different assumptions about feasibility.

Net-zero carbon budgets

In terms of peak temperature objective, one set of scenarios constrains the net-zero CO 2 budget 3 , 4 (from all sectors) from 2018 until the year of net-zero CO 2 to 1,000 Gt, which corresponds to a slightly higher than 66% likelihood of limiting peak warming to below 2 °C based on the latest science on carbon budget 10 . The other set aims to constrain the net-zero CO 2 budget to 550 Gt, or the lowest possible value in case that this is not possible given the models’ default constraints, or any of the dedicated feasibility assumptions in the respective scenarios. All models implement equivalent mitigation ambition for non-CO 2 greenhouse gases, but we do not vary the feasibility assumptions around non-CO 2 abatement explicitly (however, we do note that non-CO 2 abatement is important for temperature outcomes 22 ). We thus translate net-zero CO 2 budget results into likelihoods of peak warming assuming a constant uncertainty of non-CO 2 impacts across scenarios and models. The scenarios are constructed such that after reaching net zero, global CO 2 emissions stay at net zero until the end of the modelling period (2100). This makes sure that net-zero budgets are aligned to 1,000 Gt CO 2 across models in the first case and provides a harmonized assumption for the evolution of mitigation ambition after net zero in the latter case. However, this is not meant to imply that net-zero CO 2 emissions and a mere stabilization of temperature at the peak level is desirable. Our study intends to inform the debate on feasible trajectories towards peak temperature but not about desirable pathways afterwards, including an eventual return to lower temperatures through sustained net-negative CO 2 emissions after passing net zero 3 . In a previous study comparing scenarios with and without net-negative CO 2 emissions after net zero, it was shown that there is no relevant impact of this choice on near-term mitigation trajectories 53 .

Feasibility assumptions

In terms of feasibility assumption, we consider 14 different variants, made up by six main variants explained in the following section, and the two alternative sensitivity settings for the institutional setting explained in the next paragraph (only for the highest ambition carbon budget): first, in the Cost-effective setting, globally harmonized carbon prices increasing at the model’s default rate are used for meeting the net-zero targets, and only model-default constraints are used. Second, a Tech constraint case considers technology-specific feasibility concerns for all energy supply technologies and for bioenergy and carbon capture and storage (CCS) 54 . In the case of wind, solar, nuclear and gas electricity generation and CCS, the annual rate of deployment (ramp up) is constrained, whereas bioenergy is subject to a limit of 100 EJ per year (ref. 55 ). Third, scenarios with Institutional constraints assume regionally differentiated 17 and time-varying maximum carbon prices and emissions-reduction rates, based on empirical work and government effectiveness indicators from the World Bank 33 , 56 (more details below). Fourth, the previous two constraints are combined in the Tech and Institutional setting. Fifth, the Enablers and Institutional case considers the combination of the institutional regional differentiation of maximum decarbonization rates with optimistic assumptions on socio-cultural enablers for demand-side electrification 38 and reduced energy demand with a focus on regions with high per capita demand 57 . Finally, the sixth variant, Tech and Enablers and Institutional , explores the combination of the institutional and technical constraints with the socio-cultural enablers.

Implementation of institutional constraint

Whereas there are many possible ways to measure the competence of governments, we focus on the ‘government effectiveness’ indicator, which is one of the six indicators proposed by the World Bank to measure governance and institutional quality. The specific indicator assesses the quality of policy formulation and implementation of a given country—that is, the ability of government to elaborate, implement and enforce policies 58 and has been estimated along Shared Socio Economic Pathways 59 for all countries until the end of the century, using projected levels of GDP per capita, gender equality and education levels 33 . Government effectiveness is a result of certain governance and institutional characteristics to which we, for simplicity, refer to as ‘institutional capacity’ given that many governance structures are driven by institutions.

This government effectiveness indicator is calculated for each model region as a weighted average across each region’s countries (which typically are clustered based on geographical proximity and socio-economic similarity) with population as weight and then linked to maximum carbon prices (both relative to the highest regional carbon price and in absolute terms) and emissions-reduction constraints in the default and pessimistic setting. The carbon price is used as a stylized representation of climate policy. In the real world, the various fiscal and non-fiscal policy instruments to reduce emissions would not necessarily take the form of an explicit pricing on carbon emissions but could also be achieved via regulation, subsidies or a combination of carbon pricing and other measures addressing mitigation options with abatement costs up to the carbon price level used in the models. Whereas various studies with IAMs explore more detailed policy packages 16 , 60 , we here use only carbon prices to have a more manageable transparent and easier reproducible harmonized scenario design across models. For the same reason, we use the same carbon price threshold levels across models, despite models differing in the price level required to reach a given target. Carbon prices, however, vary by model given that they are calculated based on the regionally average governance indicator using population as weight, and models’ regional resolution differ.

The four feasibility variants including the institutional constraints for the lowest carbon budget setting are further analysed in three different sensitivity settings: the default setting of both differentiated and constrained carbon prices and maximum emissions reductions, both as a function of dynamically improving governance scores; the more optimistic setting of only differentiated and constrained carbon prices based on dynamically improving governance scores; and the pessimistic setting of both differentiated and constrained carbon prices and maximum emissions reductions based on governance scores frozen at the 2020 level (Fig. 1 ).

Data availability

The underlying data are available via Zenodo at https://doi.org/10.5281/zenodo.11562539 (ref. 61 ). All scenarios are made accessible online also via the ENGAGE Scenario Portal at https://data.ece.iiasa.ac.at/engage .

Code availability

The models are documented on the common integrated assessment model documentation website ( https://www.iamcdocumentation.eu/index.php/IAMC_wiki ), and several have been published as open source code (for example, REMIND, https://github.com/remindmodel/remind ; MESSAGE, https://github.com/iiasa/message_ix ). A repository for the source code of the figures is available via Github at https://github.com/christophbertram/Feasibility-scenario-analysis .

MacDougall, A. H. et al. Is there warming in the pipeline? A multi-model analysis of the zero emissions commitment from CO 2 . Biogeosciences 17 , 2987–3016 (2020).

Article   Google Scholar  

Palazzo Corner, S. et al. The zero emissions commitment and climate stabilization. Front. Sci. 1 , 1170744 (2023).

Rogelj, J. et al. A new scenario logic for the Paris Agreement long-term temperature goal. Nature 573 , 357–363 (2019).

Article   CAS   Google Scholar  

Riahi, K. et al. Cost and attainability of meeting stringent climate targets without overshoot. Nat. Clim. Change 11 , 1063–1069 (2021).

Hoegh-Guldberg, O. et al. The human imperative of stabilizing global climate change at 1.5 °C. Science 365 , eaaw6974 (2019).

Riahi, K. et al. in Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) 295–408 (Cambridge Univ. Press, 2022).

Jewell, J. & Cherp, A. On the political feasibility of climate change mitigation pathways: is it too late to keep warming below 1.5 °C? WIREs Clim. Change 11 , e621 (2020).

Warszawski, L. et al. All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal. Environ. Res. Lett. 16 , 064037 (2021).

Friedlingstein, P. et al. Global Carbon Budget 2023. Earth Syst. Sci. Data 15 , 5301–5369 (2023).

Forster, P. M. et al. Indicators of global climate change 2022: annual update of large-scale indicators of the state of the climate system and human influence. Earth Syst. Sci. Data 15 , 2295–2327 (2023).

Brutschin, E. et al. A multidimensional feasibility evaluation of low-carbon scenarios. Environ. Res. Lett. 16 , 064069 (2021).

Steg, L. et al. A method to identify barriers to and enablers of implementing climate change mitigation options. One Earth 5 , 1216–1227 (2022).

van de Ven, D.-J. et al. A multimodel analysis of post-Glasgow climate targets and feasibility challenges. Nat. Clim. Change 13 , 570–578 (2023).

Statistical Review of World Energy (Energy Institute, 2023); https://www.energyinst.org/statistical-review/home

Iyer, G. C. et al. Improved representation of investment decisions in assessments of CO 2 mitigation. Nat. Clim. Change 5 , 436–440 (2015).

van Soest, H. L. et al. Global roll-out of comprehensive policy measures may aid in bridging emissions gap. Nat. Commun. 12 , 6419 (2021).

Bauer, N. et al. Quantification of an efficiency–sovereignty trade-off in climate policy. Nature 588 , 261–266 (2020).

Hickmann, T. et al. Exploring global climate policy futures and their representation in integrated assessment models. Polit. Gov. 10 , 171–185 (2022).

Google Scholar  

Pianta, S. & Brutschin, E. Emissions lock-in, capacity, and public opinion: how insights from political science can inform climate modeling efforts. Polit. Gov. 10 , 186–199 (2022).

Peng, W. et al. Climate policy models need to get real about people—here’s how. Nature 594 , 174–176 (2021).

Keppo, I. et al. Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models. Environ. Res. Lett. 16 , 053006 (2021).

Harmsen, M. et al. Uncertainty in non-CO 2 greenhouse gas mitigation contributes to ambiguity in global climate policy feasibility. Nat. Commun. 14 , 2949 (2023).

Muttitt, G., Price, J., Pye, S. & Welsby, D. Socio-political feasibility of coal power phase-out and its role in mitigation pathways. Nat. Clim. Change 13 , 140–147 (2023).

Vinichenko, V., Vetier, M., Jewell, J., Nacke, L. & Cherp, A. Phasing out coal for 2 °C target requires worldwide replication of most ambitious national plans despite security and fairness concerns. Environ. Res. Lett. 18 , 014031 (2023).

Hasegawa, T. et al. Land-based implications of early climate actions without global net-negative emissions. Nat. Sustain. 4 , 1052–1059 (2021).

Gidden, M. J. et al. Fairness and feasibility in deep mitigation pathways with novel carbon dioxide removal considering institutional capacity to mitigate. Environ. Res. Lett. 18 , 074006 (2023).

Rogelj, J. & Lamboll, R. D. Substantial reductions in non-CO 2 greenhouse gas emissions reductions implied by IPCC estimates of the remaining carbon budget. Commun. Earth Environ. 5 , 1–5 (2024).

Eskander, S. M. S. U. & Fankhauser, S. Reduction in greenhouse gas emissions from national climate legislation. Nat. Clim. Change 10 , 750–756 (2020).

Levi, S., Flachsland, C. & Jakob, M. Political economy determinants of carbon pricing. Glob. Environ. Polit. 20 , 128–156 (2020).

Meckling, J. & Nahm, J. Strategic state capacity: how states counter opposition to climate policy. Comp. Polit. Stud. 55 , 493–523 (2022).

Jewell, J., Vinichenko, V., Nacke, L. & Cherp, A. Prospects for powering past coal. Nat. Clim. Change 9 , 592–597 (2019).

Victor, D. G., Lumkowsky, M. & Dannenberg, A. Determining the credibility of commitments in international climate policy. Nat. Clim. Change 12 , 793–800 (2022).

Andrijevic, M., Crespo Cuaresma, J., Muttarak, R. & Schleussner, C.-F. Governance in socioeconomic pathways and its role for future adaptive capacity. Nat. Sustain. 3 , 35–41 (2020).

Köberle, A. C. et al. The cost of mitigation revisited. Nat. Clim. Change 11 , 1035–1045 (2021).

Gambhir, A. & Lempert, R. From least cost to least risk: Producing climate change mitigation plans that are resilient to multiple risks. Front. Clim . 5 (2023).

Gaur, A., Balyk, O., Glynn, J., Curtis, J. & Daly, H. Low energy demand scenario for feasible deep decarbonisation: whole energy systems modelling for Ireland. Renew. Sustain. Energy Transit. 2 , 100024 (2022).

Nemet, G. & Greene, J. Innovation in low-energy demand and its implications for policy. Oxford Open Energy 1 , oiac003 (2022).

Luderer, G. et al. Impact of declining renewable energy costs on electrification in low-emission scenarios. Nat. Energy 7 , 32–42 (2022).

Jewell, J. & Cherp, A. The feasibility of climate action: bridging the inside and the outside view through feasibility spaces. WIREs Clim. Change 14 , e838 (2023).

Clarke, L. et al. International climate policy architectures: overview of the EMF 22 International Scenarios. Energy Econ. 31 , S64–S81 (2009).

Rennert, K. et al. Comprehensive evidence implies a higher social cost of CO 2 . Nature 610 , 687–692 (2022).

EPA Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances (EPA, 2023).

Moore, F. C. et al. Synthesis of Evidence Yields High Social Cost of Carbon Due to Structural Model Variation and Uncertainties (NBER, 2024); https://doi.org/10.3386/w32544

Bertram, C. et al. COVID-19-induced low power demand and market forces starkly reduce CO 2 emissions. Nat. Clim. Change 11 , 193–196 (2021).

Moore, F. C. et al. Determinants of emissions pathways in the coupled climate–social system. Nature 603 , 103–111 (2022).

Harmsen, M. et al. The role of methane in future climate strategies: mitigation potentials and climate impacts. Clim. Change 163 , 1409–1425 (2020).

Ou, Y. et al. Deep mitigation of CO 2 and non-CO 2 greenhouse gases toward 1.5 °C and 2 °C futures. Nat. Commun. 12 , 6245 (2021).

Malley, C. S. et al. A roadmap to achieve the global methane pledge. Environ. Res. Clim. 2 , 011003 (2023).

Sun, T., Ocko, I. B., Sturcken, E. & Hamburg, S. P. Path to net zero is critical to climate outcome. Sci. Rep. 11 , 22173 (2021).

Meinshausen, M. et al. Realization of Paris Agreement pledges may limit warming just below 2 °C. Nature 604 , 304–309 (2022).

Ou, Y. et al. Can updated climate pledges limit warming well below 2 °C? Science 374 , 693–695 (2021).

Rogelj, J. et al. Credibility gap in net-zero climate targets leaves world at high risk. Science 380 , 1014–1016 (2023).

Bertram, C. et al. Energy system developments and investments in the decisive decade for the Paris Agreement goals. Environ. Res. Lett. 16 , 074020 (2021).

Grant, N., Gambhir, A., Mittal, S., Greig, C. & Köberle, A. C. Enhancing the realism of decarbonisation scenarios with practicable regional constraints on CO 2 storage capacity. Int. J. Greenhouse Gas Control 120 , 103766 (2022).

Creutzig, F. et al. Bioenergy and climate change mitigation: an assessment. GCB Bioenergy 7 , 916–944 (2015).

Kaufmann, D. & Kraay, A. Worldwide Governance Indicators 2023 Update (World Bank, 2023); www.govindicators.org

Soergel, B. et al. A sustainable development pathway for climate action within the UN 2030 Agenda. Nat. Clim. Change 11 , 656–664 (2021).

Kaufmann, D., Kraay, A. & Mastruzzi, M. The worldwide governance indicators: methodology and analytical issues. Hague J. Rule Law 3 , 220–246 (2011).

Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42 , 153–168 (2017).

Kriegler, E. et al. Short term policies to keep the door open for Paris climate goals. Environ. Res. Lett. 13 , 074022 (2018).

Bertram, C. et al. ENGAGE feasibility scenarios. V1.0 Zenodo https://doi.org/10.5281/zenodo.11562539 (2024).

Download references

Acknowledgements

This research received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 821471 (ENGAGE) (C.B., E.B., L.D., G.L., B.v.R., L.A.R., L.B.B., H.-S.d.B., R.C., V.D., F.F., D.F., O.F., S.F., N.H., G.I., K.K., V.K., E.K., R.D.L., R.M., P.R., J.R., R.S., D.S., I.T., D.v.V., Z.V., K.R.). We thank A. Cherp for permission to use Fig. 2 , the entire modelling teams for the development of the used IAMs and participants of the IAMC 2023 conference for helpful feedback. S.F. and D.S. are supported by the Environment Research and Technology Development Fund (JPMEERF20241001) of the Environmental Restoration and Conservation Agency of Japan and JST ASPIRE project grant number JPMJAP2331.

Author information

Authors and affiliations.

Center for Global Sustainability (CGS), School of Public Policy, University of Maryland, College Park, MD, USA

Christoph Bertram, Ryna Cui, Nate Hultman & Gokul Iyer

Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany

Christoph Bertram, Gunnar Luderer, Elmar Kriegler & Rahel Mandaroux

International Institute for Applied System Analysis (IIASA), Laxenburg, Austria

Elina Brutschin, Bas van Ruijven, Oliver Fricko, Shinichiro Fujimori, Volker Krey, Joeri Rogelj & Keywan Riahi

CMCC Foundation–Euro-Mediterraneaen Center on Climate Change, Milan, Italy

Laurent Drouet & Lara Aleluia Reis

RFF-CMCC European Institute of Economics and the Environment, Milan, Italy

Global Energy Systems Analysis, Technische Universität Berlin, Berlin, Germany

Gunnar Luderer

Centre for Energy and Environmental Economics (Cenergia), Energy Planning Program (PPE), COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

Luiz Bernardo Baptista & Roberto Schaeffer

Netherland’s Environmental Agency (PBL), The Hague, The Netherlands

Harmen-Sytze de Boer, Vassilis Daioglou, Isabela Tagomori & Detlef van Vuuren

Copernicus Institute for Sustainable Development, Utrecht University, Utrecht, The Netherlands

Vassilis Daioglou & Detlef van Vuuren

European Commission, Joint Research Centre (JRC), Seville, Spain

Florian Fosse & Kimon Keramidas

E3Modelling, Athens, Greece

Dimitris Fragkiadakis & Zoi Vrontisi

Department of Environmental Engineering, Kyoto University, Kyoto, Japan

Shinichiro Fujimori

Social Systems Division, National Institute for Environmental Studies (NIES), Tsukuba, Japan

Shinichiro Fujimori & Diego Silva

Joint Global Change Research Institute, Pacific Northwest National Laboratory and University of Maryland, College Park, MD, USA

Grenoble Applied Economics Lab, Université Grenoble Alpes, Grenoble, France

Kimon Keramidas

Faculty of Economics and Social Sciences, University of Potsdam, Potsdam, Germany

Elmar Kriegler

Centre for Environmental Policy and Grantham Institute–Climate Change and Environment, Imperial College London, London, UK

Robin D. Lamboll & Joeri Rogelj

Research and Innovation Center on CO₂ and Hydrogen (RICH Center) and Management Science and Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates

Pedro Rochedo

Graz University of Technology, Graz, Austria

Keywan Riahi

You can also search for this author in PubMed   Google Scholar

Contributions

C.B., E.B. and K.R. designed the study with input by L.D., E.K., G.L., B.v.R., R.S., D.v.V. and Z.V.; E.B. and C.B. prepared the governance input data for the IAMs; C.B., E.B., L.D., B.v.R., L.A.R., L.B.B., H.-S.d.B., V.D., F.F., D.F., O.F., S.F., K.K., V.K., R.M., P.R., R.S., D.S., I.T. and Z.V. produced the IAM scenario results; R.C., G.I. and N.H. provided a review of results and framing; R.D.L. and J.R. provided temperature probabilities as a function of carbon budgets; C.B. performed the data analysis and produced the plots with input by E.B. and L.D.; C.B. wrote the first draft and all authors contributed to writing the paper.

Corresponding author

Correspondence to Christoph Bertram .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Climate Change thanks Shiyan Chang, Debra Davidson and Gernot Wagner for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended data fig. 1 absolute carbon prices in the highest and lowest capacity regions in the 1000 gt scenarios in 2030 (top panel) and 2050 (bottom panel)..

The horizontal lines in the upper panel show the median 2020 social cost of carbon estimates in dashed, solid and dotted lines from Rennert et al. 41 , EPA 42 , and Moore et al. 43 respectively. Please note that for highest capacity regions the POLES datapoint for 2030 in the “Tech & Enablers & Institutional” scenario covers the datapoint for AIM at the same value below. Furthermore the REMIND datapoint for 2050 in the “Tech & Enablers & Institutional” scenario partially covers the MESSAGE and IMAGE datapoints at very similar values below.

Extended Data Fig. 2 Relationship between governance indicators from Andrijevic et al. 33 , and GDP per capita (in PPP).

The countries with a population of more than 25 million are shown in large ISO code labels, while the smaller ones are shown in semi-transparent, smaller labels. Note the logarithmic x-Axis.

Extended Data Fig. 3 Global final energy use in 2030 and 2050.

The dashed lines in the background show the model’s 2020 values (which due to different calibration routines do not all coincide).

Supplementary information

Supplementary information.

Scenario overview table, Implementation of institutional feasibility constraint, Information on differences in model implementation of institutional feasibility constraint and Scenario protocol.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Bertram, C., Brutschin, E., Drouet, L. et al. Feasibility of peak temperature targets in light of institutional constraints. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-02073-4

Download citation

Received : 28 February 2024

Accepted : 20 June 2024

Published : 12 August 2024

DOI : https://doi.org/10.1038/s41558-024-02073-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

What counts as climate mitigation costs.

• Gernot Wagner

Nature Climate Change (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

NSF Grant to Help BU PhD Students Attack Climate Change

$3 million over five years for multidisciplinary efforts to convert and store renewable energy.

By Doug Most

If the extreme heat and droughts, catastrophic flooding, and devastating wildfires of recent years have proven anything, it’s that the world needs better solutions for sustainable energy in order to help combat the damaging effects of climate change.

That’s why the National Science Foundation has awarded a Boston University team led by Associate Professor Malika Jeffries-EL (Chemistry, MSE ) a five-year, $3 million NSF Research Traineeship (NRT) grant to help PhD students collaborate across disciplines to develop new ways to convert and store sustainable energy. The award will create a new training program that unifies resources in engineering, chemistry, computer science, and data sciences to provide participating students with a broad exposure to energy-related issues.

The NRT grant is a collaborative award between BU’s Institute for Global Sustainability and the Rafik B. Hariri Center for Computing and Computational Science & Engineering . Along with Jeffries-EL, the project’s co–principal investigators are College of Engineering faculty Associate Professor Emily Ryan ( ME , MSE), Assistant Professor James Chapman (ME), and Associate Professor Brian Kulis ( ECE ), as well as College of Arts & Sciences Professor of Chemistry and Computing & Data Sciences David Coker.

“We are at a point where we need to be intentional with problems we are tackling,” says Jeffries-EL, who is also associate dean of the Graduate School of Arts & Sciences. “It’s all interconnected. These are complicated problems, and it requires an interdisciplinary approach and interdisciplinary science.”

And getting PhD students involved in the work is critical.

Read the full story at BU’s The Brink

Wildfire haze photo by Thom Milkov on Unsplas h

Related posts:

• NSF Training Grant Funds New Neurophotonics Initiative Using Light to Explore the Brain
• How Can Synthetic Biology Combat Climate Change?
• Densmore and Yazicigil Win NSF Grant
• Chemistry and Materials Professor Named ACS Fellow

Share this:

• Click to email this to a friend
• Click to share on LinkedIn
• Click to share on Twitter
• Share on Facebook
• Click to print
• Click to Press This!

View all posts

Study at Cambridge

About the university, research at cambridge.

• Undergraduate courses
• Events and open days
• Fees and finance
• Postgraduate courses
• How to apply
• Postgraduate events
• Fees and funding
• International students
• Continuing education
• Executive and professional education
• Courses in education
• How the University and Colleges work
• Term dates and calendars
• Visiting the University
• Annual reports
• Equality and diversity
• A global university
• Public engagement
• Give to Cambridge
• For Cambridge students
• For our researchers
• Business and enterprise
• Colleges & departments
• Email & phone search
• Museums & collections
• Course Directory

MPhil in Quantitative Climate and Environmental Science

Postgraduate Study

• Why Cambridge overview
• Chat with our students
• Cambridge explained overview
• The supervision system
• Student life overview
• In and around Cambridge
• Leisure activities
• Student unions
• Music awards
• Student support overview
• Mental health and wellbeing
• Disabled students
• Accommodation
• Language tuition
• Skills training
• Support for refugees
• Courses overview
• Department directory
• Qualification types
• Funded studentships
• Part-time study
• Research degrees
• Visiting students
• Finance overview
• Fees overview
• What is my fee status?
• Part-time fees
• Application fee
• Living costs
• Funding overview
• Funding search
• How to apply for funding
• University funding overview
• Research Councils (UKRI)
• External funding and loans overview
• Funding searches
• External scholarships
• Charities and the voluntary sector
• Funding for disabled students
• Widening participation in funding
• Colleges overview
• What is a College?
• Choosing a College
• Applying overview
• Before you apply
• Entry requirements
• Application deadlines
• How do I apply? overview
• Application fee overview
• Application fee waiver
• Life Science courses
• Terms and conditions
• Continuing students
• Disabled applicants
• Supporting documents overview
• Academic documents
• Finance documents
• Evidence of competence in English
• AI and postgraduate applications
• Terms and Conditions
• Applicant portal and self-service
• After you apply overview
• Confirmation of admission
• Student registry
• Previous criminal convictions
• Deferring an application
• Updating your personal details
• Appeals and Complaints
• Widening participation
• Postgraduate admissions fraud
• International overview
• Immigration overview
• ATAS overview
• Applying for an ATAS certificate
• Current Cambridge students
• International qualifications
• Competence in English overview
• What tests are accepted?
• International events
• International student views overview
• Akhila’s story
• Alex’s story
• Huijie’s story
• Kelsey’s story
• Nilesh’s story
• Get in touch!
• Events overview
• Upcoming events
• Postgraduate Open Days overview
• Discover Cambridge: Master’s and PhD Study webinars
• Virtual tour
• Research Internships
• How we use participant data
• Postgraduate Newsletter

Primary tabs

• Overview (active tab)
• Requirements
• How To Apply

Course closed:

Quantitative Climate and Environmental Science is no longer accepting new applications.

The MPhil in Quantitative Climate and Environmental Sciences is a 10-month cross-departmental programme in the School of the Physical Sciences which aims to provide education of the highest quality in the analysis and modelling of Earth's climate and environment at a master’s level. The programme covers a range of skills required for the acquisition and assessment of laboratory and field data, and for the understanding through quantitative modelling of climate and environmental processes. 

The course structure has been designed to provide students with the theoretical knowledge, practical experience, and transferable skills required to undertake world-leading quantitative scientific research in Climate and Environmental Sciences. The course will train a new generation of scientists to work with environmental data to address the myriad of challenges associated with climate change. Multidisciplinary skills will be developed through diverse topics addressed in the course combined with a research project which will prepare students for further academic research and careers in many sectors of the economy dealing with climate and environmental impacts.

The course responds to the growing:

• demand for highly trained quantitative research scientists in climate and environmental modelling to better understand global and local climate change and its consequences,
• societal demand to find solutions to climate-related challenges and develop new sustainable technologies,
• importance of interdisciplinary expertise to better respond to the complexity of challenges faced by societies in a changing climate.

The objectives of the course are to give students:

• a deep knowledge in scientific areas related to climate and environmental change,
• a familiarity and facility with acquiring and assessing climate and environmental data,
• a knowledge and practical experience in climate and environmental modelling, an awareness of the impacts and possible solutions to climate change.

Learning Outcomes

By the end of this course, students will have:

• a thorough knowledge of state-of-the-art climate and environmental science;
• a comprehensive understanding of, and the ability to develop or interrogate, models of climate and environmental systems;
• demonstrated abilities for the critical evaluation of scientific analysis on climate and environmental processes.

Students wishing to progress to PhD study after passing the Masters degree should reapply for admission to a PhD through the University admissions website, taking the funding and application deadlines into consideration.

The Department of Applied Mathematics and Theoretical Physics, the Department of Earth Sciences, and other MPhil participating Departments contribute to the University of Cambridge's Postgraduate Open Day.

The Postgraduate Virtual Open Day usually takes place at the end of October. It’s a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the  Postgraduate Open Day  page for more details.

Details of activities hosted by the Faculty of Mathematics can be found on the  Faculty website .

Departments

This course is advertised in the following departments:

• Department of Earth Sciences
• Department of Applied Mathematics and Theoretical Physics

Key Information

10 months full-time, study mode : taught, master of philosophy, department of applied mathematics and theoretical physics this course is advertised in multiple departments. please see the overview tab for more details., course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, michaelmas 2024 (closed).

Some courses can close early. See the Deadlines page for guidance on when to apply.

Funding Deadlines

These deadlines apply to applications for courses starting in Michaelmas 2024, Lent 2025 and Easter 2025.

Similar Courses

• Holocene Climates MPhil
• Climate, Environmental and Urban Policy MSt
• Politics and International Studies PhD
• Geography PhD
• Geography MPhil

Postgraduate Admissions Office

• Admissions Statistics
• Start an Application
• Applicant Self-Service

At a glance

• Bringing a family
• Current Postgraduates
• Cambridge Students' Union (SU)

University Policy and Guidelines

Privacy Policy

Information compliance

Equality and Diversity

Terms of Study

About this site

About our website

Privacy policy

© 2024 University of Cambridge

• Contact the University
• Accessibility
• Freedom of information
• Privacy policy and cookies
• Statement on Modern Slavery
• University A-Z
• Undergraduate
• Postgraduate
• Research news
• About research at Cambridge
• Spotlight on...

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Assessing climate change projections through high-resolution modelling: a comparative study of three european cities.

1. Introduction

2. data and methods, 2.1. study areas, 2.2. the modelling setup and evaluation, 2.3. climate change indices, 3. results and discussion, 3.1. model evaluation for recent past, 3.2. daily mean, maximum and minimum temperature, 3.3. climate change indices, 4. nature-based solutions potential, 5. summary and conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Pörtner, D.C.; Roberts, M.; Tignor, E.S.; Poloczanska, K.; Mintenbeck, A.; Alegría, M.; Craig, S.; Langsdorf, S.; Löschke, V.; Möller, A.; et al. (Eds.) IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability: Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change ; Cambridge University Press: Cambridge, UK, 2022; ISBN 978-92-9169-159-3. [ Google Scholar ]
• WMO-No. 1320: State of the Climate in Europe 2022 ; World Meteorological Organization: Geneva, Switzerland, 2023. [ Google Scholar ]
• IPCC Sixth Assessment Report. Available online: https://www.ipcc.ch/assessment-report/ar6/ (accessed on 2 August 2024).
• Mbow, C.; Rosenzweig, C.; Barioni, L.G.; Benton, T.G.; Herrero, M.; Krishnapillai, M.; Liwenga, E.; Pradhan, P.; Rivera-Ferre, M.G.; Sapkota, T.; et al. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems ; Shukla, P.R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D.C., Zhai, P., Slade, R., Connors, S., van Diemen, R., et al., Eds.; Cambridge University Press: Cambridge, UK, 2019. [ Google Scholar ]
• Dugord, P.A.; Lauf, S.; Schuster, C.; Kleinschmit, B. Land Use Patterns, Temperature Distribution, and Potential Heat Stress Risk—The Case Study Berlin, Germany. Comput. Environ. Urban Syst. 2014 , 48 , 86–98. [ Google Scholar ] [ CrossRef ]
• Gill, S.E.; Handley, J.F.; Ennos, A.R.; Pauleit, S. Adapting Cities for Climate Change: The Role of the Green Infrastructure. Built Environ. Environ. 2007 , 33 , 115–133. [ Google Scholar ] [ CrossRef ]
• Kim, S.; Ryu, Y. Describing the Spatial Patterns of Heat Vulnerability from Urban Design Perspectives. Int. J. Sustain. Dev. World Ecol. 2015 , 22 , 189–200. [ Google Scholar ] [ CrossRef ]
• Barros, V.R.; Field, C.B.; Dokken, D.J.; Mastrandrea, M.D.; Mach, K.J.; Bilir, T.E.; Chatterjee, M.; Yuka, K.L.E.; Estrada, O.; Genova, R.C. (Eds.) IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability Part A: Global and Sectoral Aspects Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014; 1132p, ISBN 978107641655. [ Google Scholar ]
• Rafael, S.; Augusto, B.; Ascenso, A.; Borrego, C.; Miranda, A.I. Re-Naturing Cities: Evaluating the Effects on Future Air Quality in the City of Porto. Atmos. Environ. 2020 , 222 , 117123. [ Google Scholar ] [ CrossRef ]
• Hobbie, S.E.; Grimm, N.B. Nature-Based Approaches to Managing Climate Change Impacts in Cities. Philos. Trans. R. Soc. B 2020 , 375 , 1794. [ Google Scholar ] [ CrossRef ]
• Jacob, D.; Petersen, J.; Eggert, B.; Alias, A.; Christensen, O.B.; Bouwer, L.M.; Braun, A.; Colette, A.; Déqué, M.; Georgievski, G.; et al. EURO-CORDEX: New High-Resolution Climate Change Projections for European Impact Research. Reg. Environ. Change 2014 , 14 , 563–578. [ Google Scholar ] [ CrossRef ]
• Van Vuuren, D.P.; Edmonds, J.; Kainuma, M.; Riahi, K.; Thomson, A.; Hibbard, K.; Hurtt, G.C.; Kram, T.; Krey, V.; Lamarque, J.-F.; et al. The Representative Concentration Pathways: An Overview. Clim. Change 2011 , 109 , 5–31. [ Google Scholar ] [ CrossRef ]
• Schär, C.; Fuhrer, O.; Arteaga, A.; Ban, N.; Charpilloz, C.; Di Girolamo, S.; Hentgen, L.; Hoefler, T.; Lapillonne, X.; Leutwyler, D.; et al. Kilometer-Scale Climate Models: Prospects and Challenges. Bull. Am. Meteorol. Soc. 2020 , 101 , E567–E587. [ Google Scholar ] [ CrossRef ]
• Tölle, M.H.; Schefczyk, L.; Gutjahr, O. Scale Dependency of Regional Climate Modeling of Current and Future Climate Extremes in Germany. Theor. Appl. Climatol. 2018 , 134 , 829–848. [ Google Scholar ] [ CrossRef ]
• Keppas, S.C.; Papadogiannaki, S.; Parliari, D.; Kontos, S.; Poupkou, A.; Tzoumaka, P.; Kelessis, A.; Zanis, P.; Casasanta, G.; de’Donato, F.; et al. Future Climate Change Impact on Urban Heat Island in Two Mediterranean Cities Based on High-Resolution Regional Climate Simulations. Atmosphere 2021 , 12 , 884. [ Google Scholar ] [ CrossRef ]
• Qiu, L.; Im, E.-S.; Hur, J.; Shim, K.-M. Added Value of Very High Resolution Climate Simulations over South Korea Using WRF Modeling System. Clim. Dyn. 2020 , 54 , 173–189. [ Google Scholar ] [ CrossRef ]
• Coelho, S.; Rafael, S.; Lopes, D.; Miranda, A.I.; Ferreira, J. How Changing Climate May Influence Air Pollution Control Strategies for 2030? Sci. Total Environ. 2021 , 758 , 143911. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Thomson, A.M.; Calvin, K.V.; Smith, S.J.; Kyle, G.P.; Volke, A.; Patel, P.; Delgado-Arias, S.; Bond-Lamberty, B.; Wise, M.A.; Clarke, L.E.; et al. RCP4.5: A Pathway for Stabilization of Radiative Forcing by 2100. Clim. Change 2011 , 109 , 77–94. [ Google Scholar ] [ CrossRef ]
• Ascenso, A.; Gama, C.; Roebeling, P.; Miranda, A.I. How Effective Are Nature-Based Solutions in Different Environments? Proceedings of the WIT Transactions on Ecology and the Environment. Online, 26–28 October 2021; WIT Press: Billerica MA, USA, 2021; Volume 252, pp. 3–14. [ Google Scholar ] [ CrossRef ]
• Chausson, A.; Turner, B.; Seddon, D.; Chabaneix, N.; Girardin, C.A.J.; Kapos, V.; Key, I.; Roe, D.; Smith, A.; Woroniecki, S.; et al. Mapping the Effectiveness of Nature-Based Solutions for Climate Change Adaptation. Glob. Change Biol. 2020 , 26 , 6134–6155. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Seddon, N.; Smith, A.; Smith, P.; Key, I.; Chausson, A.; Girardin, C.; House, J.; Srivastava, S.; Turner, B. Getting the Message Right on Nature-Based Solutions to Climate Change. Glob. Change Biol. 2021 , 27 , 1518–1546. [ Google Scholar ] [ CrossRef ]
• Pereira, P.; Yin, C.; Hua, T. Nature-Based Solutions, Ecosystem Services, Disservices, and Impacts on Well-Being in Urban Environments. Curr. Opin. Environ. Sci. Health 2023 , 33 , 100465. [ Google Scholar ] [ CrossRef ]
• Coelho, S.; Rafael, S.; Coutinho, M.; Monteiro, A.; Medina, J.; Figueiredo, S.; Cunha, S.; Lopes, M.; Miranda, A.I.; Borrego, C. Climate-Change Adaptation Framework for Multiple Urban Areas in Northern Portugal. Environ. Manag. 2020 , 66 , 395–406. [ Google Scholar ] [ CrossRef ]
• Municipality of Eindhoven Eindhoven | Population & Governance. Available online: https://onderzoek.eindhoven.nl/basisinfo/bevolking-bestuur (accessed on 2 August 2022).
• KNMI Climatology, Daily Data of the Weather in the Netherlands—Download. Available online: http://projects.knmi.nl/klimatologie/daggegevens/selectie.cgi (accessed on 2 August 2022).
• Ascenso, A.; Augusto, B.; Silveira, C.; Rafael, S.; Coelho, S.; Monteiro, A.; Ferreira, J.; Menezes, I.; Roebeling, P.; Miranda, A.I. Impacts of Nature-Based Solutions on the Urban Atmospheric Environment: A Case Study for Eindhoven, The Netherlands. Urban For. Urban Green. 2021 , 57 , 126870. [ Google Scholar ] [ CrossRef ]
• Augusto, B.; Roebeling, P.; Rafael, S.; Ferreira, J.; Ascenso, A.; Bodilis, C. Short and Medium- to Long-Term Impacts of Nature-Based Solutions on Urban Heat. Sustain. Cities Soc. 2020 , 57 , 102122. [ Google Scholar ] [ CrossRef ]
• Costa, S.; Peters, R.; Martins, R.; Postmes, L.; Keizer, J.J.; Roebeling, P. Effectiveness of Nature-Based Solutions on Pluvial Flood Hazard Mitigation: The Case Study of the City of Eindhoven (The Netherlands). Resources 2021 , 10 , 24. [ Google Scholar ] [ CrossRef ]
• Roebeling, P.; Saraiva, M.; Gneco, I.; Palla, A.; Alves, H.; Rocha, J.; Martins, F. Sustainable Urbanizing Landscape Development (SULD) Decision Support Tool: Report on Other Aqua Cases ; Aqua-Add Project, Aqua-Add Technical Report No.04.; Open Repository of the University of Porto: Porto, Portugal, 2014; Volume 33. [ Google Scholar ]
• Saraiva, M.; Roebeling, P.; Sousa, S.; Teotónio, C.; Palla, A.; Gnecco, I. Dimensions of Shrinkage: Evaluating the Socio-Economic Consequences of Population Decline in Two Medium-Sized Cities in Europe, Using the SULD Decision Support Tool. Environ. Plan. B Urban Anal. City Sci. 2017 , 44 , 1122–1144. [ Google Scholar ] [ CrossRef ]
• Istat—Istituto Nazionale di Statistica Resident Population on 1st January. Available online: http://dati.istat.it/?lang=en (accessed on 2 August 2022).
• Acquaotta, F.; Faccini, F.; Fratianni, S.; Paliaga, G.; Sacchini, A.; Vilímek, V. Increased Flash Flooding in Genoa Metropolitan Area: A Combination of Climate Changes and Soil Consumption? Meteorol. Atmos. Phys. 2019 , 131 , 1099–1110. [ Google Scholar ] [ CrossRef ]
• Weatherbase Genoa, Italy Koppen Climate Classification. Available online: https://www.weatherbase.com/weather/weather-summary.php3?s=2161&cityname=Genoa,+Italy,%202021 (accessed on 20 June 2022).
• Rodrigues, V.; Gama, C.; Ascenso, A.; Oliveira, K.; Coelho, S.; Monteiro, A.; Hayes, E.; Lopes, M. Assessing Air Pollution in European Cities to Support a Citizen Centered Approach to Air Quality Management. Sci. Total Environ. 2021 , 799 , 149311. [ Google Scholar ] [ CrossRef ]
• Laikari, A.; Dubovik, M.; Rinta-Hiiro, V.; Wendling, L.; Postmes, L.; Van Dinter, M.; Den Hollander, M.; Van Der Putten, P.; Särkilahti, M.; Leppänen, S.; et al. NBS Demonstration Site Start-Up Report ; Urban Natures Lab: Brussels, Belgium, 2021. [ Google Scholar ]
• Acquaotta, F.; Faccini, F.; Fratianni, S.; Paliaga, G.; Sacchini, A. Rainfall Intensity in the Genoa Metropolitan Area: Secular Variations and Consequences. Weather 2018 , 73 , 356–362. [ Google Scholar ] [ CrossRef ]
• Statistics Finland Key Figures on Population by Area, Information and Year. Available online: https://pxweb2.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__vaerak/statfin_vaerak_pxt_11ra.px/ (accessed on 2 August 2022).
• Tampere City Board. Carbon Neutral Tampere 2030 Roadmap . 2020. Available online: https://www.tampere.fi/sites/default/files/2022-06/Carbon_Neutral_Tampere_2030_Roadmap.pdf (accessed on 2 August 2024).
• Skamarock, C.; Klemp, B.; Dudhia, J.; Gill, O.; Barker, D.; Duda, G.; Huang, X.; Wang, W.; Powers, G. A Description of the Advanced Research WRF Version 3 ; No. NCAR/TN-475+STR; University Corporation for Atmospheric Research: Boulder, CO, USA, 2008. [ Google Scholar ] [ CrossRef ]
• Fita, L.; Fernández, J.; García-Díez, M. CLWRF: WRF Modifications for Regional Climate Simulation under Future Scenarios. In Proceedings of the 11th WRF Users’ Workshop, Boulder, CO, USA, 22–24 June 2010. [ Google Scholar ]
• Fita, L.; Fernández, J.; García-Díez, M. CORINE Land Cover Technical Guide—Addendum 2000 ; European Environment Agency: Copenhagen, Denmark, 2000.
• Pineda, N.; Jorba, O.; Jorge, J.; Baldasano, J.M. Using NOAA AVHRR and SPOT VGT Data to Estimate Surface Parameters: Application to a Mesoscale Meteorological Model. Int. J. Remote Sens. 2004 , 25 , 129–143. [ Google Scholar ] [ CrossRef ]
• Regione Liguria Consultazione Dati Meteoclimatici. Available online: https://ambientepub.regione.liguria.it/SiraQualMeteo/script/PubAccessoDatiMeteo.asp (accessed on 2 August 2022).
• Finnish Meteorological Institute Weather and Sea—Download Observations. Available online: https://en.ilmatieteenlaitos.fi/download-observations (accessed on 3 August 2022).
• Borrego, C.; Monteiro, A.; Ferreira, J.; Miranda, A.I.; Costa, A.M.; Carvalho, A.C.; Lopes, M. Procedures for Estimation of Modelling Uncertainty in Air Quality Assessment. Environ. Int. 2008 , 34 , 613–620. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Schlünzen, K.H.; Sokhi, R.S. Overview of Tools and Methods for Meteorological and Air Pollution Mesoscale Model Evaluation and User Training ; WMO/TD; WMO: Geneva, Switzerland, 2008; Volume 41, ISBN 978-1-905313-59-4. [ Google Scholar ]
• Hong, S.Y.; Lim, J.O.J. The WRF Single Moment 6 Class Microphysics Scheme (WSM6). J. Korean Meteorol. Soc. 2006 , 42 , 129–151. [ Google Scholar ]
• Dudhia, J. Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model. J. Atmos. Sci. 1989 , 46 , 3077–3107. [ Google Scholar ] [ CrossRef ]
• Mlawer, E.J.; Taubman, S.J.; Brown, P.D.; Iacono, M.J.; Clough, S.A. Radiative Transfer for Inhomogeneous Atmospheres: RRTM, a Validated Correlated-k Model for the Longwave. J. Geophys. Res. D Atmos. 1997 , 102 , 16663–16682. [ Google Scholar ] [ CrossRef ]
• Zhang, D.; Anthes, R.A. A High-Resolution Model of the Planetary Boundary Layer—Sensitivity Tests and Comparisons with SESAME-79 Data. J. Appl. Meteorol. 1982 , 21 , 1594–1609. [ Google Scholar ] [ CrossRef ]
• Tewari, M.; Chen, F.; Wang, W.; Dudhia, J.; LeMone, M.A.; Mitchell, K.; Ek, M.; Gayno, G.; Wegiel, J.; Cuenca, R.H. Implementation and Verification of the Unified Noah Land Surface Model in the WRF Model. In Proceedings of the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, WA, USA, 12–16 January 2004. [ Google Scholar ]
• Hong, S.Y.; Noh, Y.; Dudhia, J. A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. Mon. Weather Rev. 2006 , 134 , 2318–2341. [ Google Scholar ] [ CrossRef ]
• Grell, G.A.; Freitas, S.R. A Scale and Aerosol Aware Stochastic Convective Parameterization for Weather and Air Quality Modeling. Atmos. Chem. Phys. 2014 , 14 , 5233–5250. [ Google Scholar ] [ CrossRef ]
• Giorgetta, M.A.; Jungclaus, J.; Reick, C.H.; Legutke, S.; Bader, J.; Böttinger, M.; Brovkin, V.; Crueger, T.; Esch, M.; Fieg, K.; et al. Climate and Carbon Cycle Changes from 1850 to 2100 in MPI-ESM Simulations for the Coupled Model Intercomparison Project Phase 5. J. Adv. Model Earth Syst. 2013 , 5 , 572–597. [ Google Scholar ] [ CrossRef ]
• Riahi, K.; Grübler, A.; Nakicenovic, N. Scenarios of Long-Term Socio-Economic and Environmental Development under Climate Stabilization. Technol. Forecast Soc. Change 2007 , 74 , 887–935. [ Google Scholar ] [ CrossRef ]
• Rafael, S.; Martins, H.; Marta-Almeida, M.; Sá, E.; Coelho, S.; Rocha, A.; Borrego, C.; Lopes, M. Quantification and Mapping of Urban Fluxes under Climate Change: Application of WRF-SUEWS Model to Greater Porto Area (Portugal). Environ. Res. 2017 , 155 , 321–334. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Klein Tank, A.M.; Zwiers, F.W.; Zhang, X.; Canada, E. Guidelines on Analysis of Extremes in a Changing Climate in Support of Informed Decisions for Adaptation ; World Meteorological Organization: Geneva, Switzerland, 2009. [ Google Scholar ]
• European Commission; Directorate-General for Research and Innovation. Evaluating the Impact of Nature-Based Solutions: A Handbook for Practitioners ; Dumitru, A., Wendling, L., Eds.; Publications Office of the European Union: Luxembourg, 2021; ISBN 978-92-76-22821-9. [ Google Scholar ]
• Lee, S.H.; Lee, K.S.; Jin, W.C.; Song, H.K. Effect of an Urban Park on Air Temperature Differences in a Central Business District Area. Landsc. Ecol. Eng. 2009 , 5 , 183–191. [ Google Scholar ] [ CrossRef ]
• Hamouda, M.E.; Pasquero, C. European Extreme Precipitation: The Effects of Spatio-Temporal Resolution of the Data. Weather Clim. Extrem. 2021 , 33 , 100337. [ Google Scholar ] [ CrossRef ]
• Marta-Almeida, M.; Teixeira, J.C.; Carvalho, M.J.; Melo-Gonçalves, P.; Rocha, A.M. High Resolution WRF Climatic Simulations for the Iberian Peninsula: Model Validation. Phys. Chem. Earth Parts A/B/C 2016 , 94 , 94–105. [ Google Scholar ] [ CrossRef ]
• Pieri, A.B.; von Hardenberg, J.; Parodi, A.; Provenzale, A. Sensitivity of Precipitation Statistics to Resolution, Microphysics, and Convective Parameterization: A Case Study with the High-Resolution WRF Climate Model over Europe. J. Hydrometeorol. 2015 , 16 , 1857–1872. [ Google Scholar ] [ CrossRef ]
• Yang, B.; Qian, Y.; Lin, G.; Leung, R.; Zhang, Y. Some Issues in Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model. Atmos. Chem. Phys. 2012 , 12 , 2409–2427. [ Google Scholar ] [ CrossRef ]
• Strangeways, I. Measuring Global Temperature: Their Analysis and Interpretation ; Cambridge University Press: Cambridge, UK, 2010. [ Google Scholar ]
• Wang, X.; Hou, X.; Piao, Y.; Feng, A.; Li, Y. Climate Change Projections of Temperature Over the Coastal Area of China Using SimCLIM. Front. Environ. Sci. 2021 , 9 , 548. [ Google Scholar ] [ CrossRef ]
• Cholakian, A.; Colette, A.; Coll, I.; Ciarelli, G.; Beekmann, M. Future Climatic Drivers and Their Effect on PM10 Components in Europe and the Mediterranean Sea. Atmos. Chem. Phys. 2019 , 19 , 4459–4484. [ Google Scholar ] [ CrossRef ]
• Warscher, M.; Wagner, S.; Marke, T.; Laux, P.; Smiatek, G.; Strasser, U.; Kunstmann, H. A 5 Km Resolution Regional Climate Simulation for Central Europe: Performance in High Mountain Areas and Seasonal, Regional and Elevation-Dependent Variations. Atmosphere 2019 , 10 , 682. [ Google Scholar ] [ CrossRef ]
• Ruosteenoja, K.; Markkanen, T.; Räisänen, J. Thermal Seasons in Northern Europe in Projected Future Climate. Int. J. Climatol. 2020 , 40 , 4444–4462. [ Google Scholar ] [ CrossRef ]
• KNMI KNMI’14: Climate Change Scenarios for the 21st Century–A Netherlands Perspective ; Hurk, B.; van den Siegmund, P.; Tank, A.K. (Eds.) KNMI: De Bilt, The Netherlands, 2014. [ Google Scholar ]
• Lecœur, È.; Seigneur, C.; Pagé, C.; Terray, L. A Statistical Method to Estimate PM2.5 Concentrations from Meteorology and Its Application to the Effect of Climate Change. J. Geophys. Res. Atmos. 2014 , 119 , 3537–3585. [ Google Scholar ] [ CrossRef ]
• D’Oria, M.; Cozzi, C.; Tanda, M.G. Future Precipitation and Temperature Changes over the Taro, Parma and Enza River Basins in Northern Italy. Ital. J. Eng. Geol. Environ. 2018 , 2018 , 49–63. [ Google Scholar ] [ CrossRef ]
• D’Oria, M.; Ferraresi, M.; Tanda, M.G. Historical Trends and High-Resolution Future Climate Projections in Northern Tuscany (Italy). J. Hydrol. 2017 , 555 , 708–723. [ Google Scholar ] [ CrossRef ]
• Ruosteenoja, K.; Jylhä, K.; Kämäräinen, M. Climate Projections for Finland Under the RCP Forcing Scenarios. Geophysica 2016 , 51 , 17–50. [ Google Scholar ]
• Keuler, K.; Radtke, K.; Kotlarski, S.; Lüthi, D. Regional Climate Change over Europe in COSMO-CLM: Influence of Emission Scenario and Driving Global Model. Meteorol. Z. 2016 , 25 , 121–136. [ Google Scholar ] [ CrossRef ]
• Collins, M.; Knutti, R.; Arblaster, J.; Dufresne, J.-L.; Fichefet, T.; Friedlingstein, P.; Gao, X.; Gutowski, W.J.; Johns, T.; Krinner, G.; et al. Long-Term Climate Change: Projections, Commitments and Irreversibility. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Midgley, V.B., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [ Google Scholar ]
• Maule, C.F.; Mendlik, T.; Christensen, O.B. The Effect of the Pathway to a Two Degrees Warmer World on the Regional Temperature Change of Europe. Clim. Serv. 2017 , 7 , 3–11. [ Google Scholar ] [ CrossRef ]
• Ban, N.; Schmidli, J.; Schär, C. Evaluation of the Convection-Resolving Regional Climate Modeling Approach in Decade-Long Simulations. J. Geophys. Res. Atmos. 2014 , 119 , 7889–7907. [ Google Scholar ] [ CrossRef ]
• Fernández, J.; Frías, M.D.; Cabos, W.D.; Cofiño, A.S.; Domínguez, M.; Fita, L.; Gaertner, M.A.; García-Díez, M.; Gutiérrez, J.M.; Jiménez-Guerrero, P.; et al. Consistency of Climate Change Projections from Multiple Global and Regional Model Intercomparison Projects. Clim. Dyn. 2019 , 52 , 1139–1156. [ Google Scholar ] [ CrossRef ]
• Giorgi, F.; Gutowski, W.J. Regional Dynamical Downscaling and the CORDEX Initiative. Annu. Rev. Environ. Resour. 2015 , 40 , 467–490. [ Google Scholar ] [ CrossRef ]
• Prein, A.F.; Gobiet, A.; Suklitsch, M.; Truhetz, H.; Awan, N.K.; Keuler, K.; Georgievski, G. Added Value of Convection Permitting Seasonal Simulations. Clim. Dyn. 2013 , 41 , 2655–2677. [ Google Scholar ] [ CrossRef ]
• Kim, S.; Sinclair, V.A.; Räisänen, J.; Ruuhela, R. Heat Waves in Finland: Present and Projected Summertime Extreme Temperatures and Their Associated Circulation Patterns. Int. J. Climatol. 2018 , 38 , 5253. [ Google Scholar ] [ CrossRef ]
• Jylhä, K.; Jokisalo, J.; Ruosteenoja, K.; Pilli-Sihvola, K.; Kalamees, T.; Seitola, T.; Mäkelä, H.M.; Hyvönen, R.; Laapas, M.; Drebs, A. Energy Demand for the Heating and Cooling of Residential Houses in Finland in a Changing Climate. Energy Build. 2015 , 99 , 104–116. [ Google Scholar ] [ CrossRef ]
• Pepin, N.; Bradley, R.S.; Diaz, H.F.; Baraer, M.; Caceres, E.B.; Forsythe, N.; Fowler, H.; Greenwood, G.; Hashmi, M.Z.; Liu, X.D.; et al. Elevation-Dependent Warming in Mountain Regions of the World. Nat. Clim. Change 2015 , 5 , 424–430. [ Google Scholar ] [ CrossRef ]
• Oswald, S.M.; Hollosi, B.; Žuvela-Aloise, M.; See, L.; Guggenberger, S.; Hafner, W.; Prokop, G.; Storch, A.; Schieder, W. Using Urban Climate Modelling and Improved Land Use Classifications to Support Climate Change Adaptation in Urban Environments: A Case Study for the City of Klagenfurt, Austria. Urban Clim. 2020 , 31 , 100582. [ Google Scholar ] [ CrossRef ]
• De Wit, R.; Kainz, A.; Goler, R.; Žuvela-Aloise, M.; Hahn, C.; Zuccaro, G.; Leone, M.; Loibl, W.; Tötzer, T.; Hager, W.; et al. Supporting Climate Proof Planning with CLARITY’s Climate Service and Modelling of Climate Adaptation Strategies—The Linz Use-Case. Urban Clim. 2020 , 34 , 100675. [ Google Scholar ] [ CrossRef ]
• Gál, T.; Mahó, S.I.; Skarbit, N.; Unger, J. Numerical Modelling for Analysis of the Effect of Different Urban Green Spaces on Urban Heat Load Patterns in the Present and in the Future. Comput. Environ. Urban Syst. 2021 , 87 , 101600. [ Google Scholar ] [ CrossRef ]
• He, C.; He, L.; Zhang, Y.; Kinney, P.L.; Ma, W. Potential Impacts of Cool and Green Roofs on Temperature-Related Mortality in the Greater Boston Region. Environ. Res. Lett. 2020 , 15 , 094042. [ Google Scholar ] [ CrossRef ]
• Rafael, S.; Correia, L.P.; Ascenso, A.; Augusto, B.; Lopes, D.; Miranda, A.I. Are Green Roofs the Path to Clean Air and Low Carbon Cities? Sci. Total Environ. 2021 , 798 , 149313. [ Google Scholar ] [ CrossRef ]
• Zhao, J.; Zhao, X.; Liang, S.; Wang, H.; Liu, N.; Liu, P.; Wu, D. Dynamic Cooling Effects of Permanent Urban Green Spaces in Beijing, China. Remote Sens. 2021 , 13 , 3282. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Ascenso, A.; Augusto, B.; Coelho, S.; Menezes, I.; Monteiro, A.; Rafael, S.; Ferreira, J.; Gama, C.; Roebeling, P.; Miranda, A.I. Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities. Sustainability 2024 , 16 , 7276. https://doi.org/10.3390/su16177276

Ascenso A, Augusto B, Coelho S, Menezes I, Monteiro A, Rafael S, Ferreira J, Gama C, Roebeling P, Miranda AI. Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities. Sustainability . 2024; 16(17):7276. https://doi.org/10.3390/su16177276

Ascenso, Ana, Bruno Augusto, Sílvia Coelho, Isilda Menezes, Alexandra Monteiro, Sandra Rafael, Joana Ferreira, Carla Gama, Peter Roebeling, and Ana Isabel Miranda. 2024. "Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities" Sustainability 16, no. 17: 7276. https://doi.org/10.3390/su16177276

Article Metrics

Article access statistics, supplementary material.

ZIP-Document (ZIP, 100 KiB)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals