Article, 2024
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
Geoscientific Model Development,
ISSN
1991-9603,
1991-959X,
Volume 17,
6,
Pages 2387-2417,
10.5194/gmd-17-2387-2024
Contributors
Fiedler, Stephanie
0000-0001-8898-9949
(Corresponding author)
[1]
[2]
Naik, Vaishali
0000-0002-2254-1700
[3]
O'Connor, Fiona M
0000-0003-2893-4828
[4]
[5]
Smith, Christopher J
0000-0003-0599-4633
[6]
[7]
Griffiths, Paul Thomas
0000-0002-1089-340X
[8]
Kramer, Ryan J
0000-0002-9377-0674
[3]
[9]
[10]
Takemura, Toshihiko
0000-0002-2859-6067
[11]
Allen, Robert J
0000-0003-1616-9719
[12]
Im, Ulas
0000-0001-5177-5306
[13]
[14]
Kasoar, Matthew R
0000-0001-5571-8843
[15]
Modak, Angshuman
0000-0002-6527-6142
[16]
Turnock, Steven T
0000-0002-0036-4627
[5]
[7]
Voulgarakis, Apostolos E
0000-0002-6656-4437
[15]
[17]
Watson-Parris, Duncan Thomas Stephens
0000-0002-5312-4950
[18]
[19]
Westervelt, Daniel M
[20]
[21]
Wilcox, Laura J
0000-0001-5691-1493
[22]
Zhao, Alcide D
0000-0002-8300-5872
[22]
Collins, William J
0000-0002-7419-0850
[22]
Schulz, Michael
0000-0003-4493-4158
[23]
Myhre, Gunnar
[24]
Forster, Piers Maxwell De Ferranti
0000-0002-6078-0171
[7]
Affiliations
- [1]
GEOMAR Helmholtz Centre for Ocean Research Kiel
[NORA names:
Germany; Europe, EU; OECD];
- [2]
Kiel University
[NORA names:
Germany; Europe, EU; OECD];
- [3]
Geophysical Fluid Dynamics Laboratory
[NORA names:
United States; America, North; OECD];
- [4]
University of Exeter
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [5]
Met Office
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
(... more)
- [6]
International Institute for Applied Systems Analysis
[NORA names:
Austria; Europe, EU; OECD];
- [7]
University of Leeds
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [8]
University of Cambridge
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [9]
Goddard Space Flight Center
[NORA names:
United States; America, North; OECD];
- [10]
University of Maryland, Baltimore County
[NORA names:
United States; America, North; OECD];
- [11]
Kyushu University
[NORA names:
Japan; Asia, East; OECD];
- [12]
University of California, Riverside
[NORA names:
United States; America, North; OECD];
- [13]
Aarhus University
[NORA names:
AU Aarhus University;
University; Denmark; Europe, EU; Nordic; OECD];
- [14]
Interdisciplinary Centre for Climate Change (iCLIMATE), Roskilde, Denmark
[NORA names:
Denmark; Europe, EU; Nordic; OECD];
- [15]
Imperial College London
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [16]
Stockholm University
[NORA names:
Sweden; Europe, EU; Nordic; OECD];
- [17]
Technical University of Crete
[NORA names:
Greece; Europe, EU; OECD];
- [18]
Scripps Institution of Oceanography
[NORA names:
United States; America, North; OECD];
- [19]
University of California, San Diego
[NORA names:
United States; America, North; OECD];
- [20]
Goddard Institute for Space Studies
[NORA names:
United States; America, North; OECD];
- [21]
Lamont-Doherty Earth Observatory
[NORA names:
United States; America, North; OECD];
- [22]
University of Reading
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [23]
Norwegian Meteorological Institute
[NORA names:
Norway; Europe, Non-EU; Nordic; OECD];
- [24]
CICERO Center for International Climate Research
[NORA names:
Norway; Europe, Non-EU; Nordic; OECD]
(less)
Abstract
Abstract. The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts.
Keywords
AerChemMIP,
Assessment Report,
Earth,
Earth System Model,
Earth's surface,
IPCC,
Intercomparison Project,
Intergovernmental,
Intergovernmental Panel,
aerosol,
air,
air quality,
anthropogenic influences,
approach,
assessment,
atmospheric composition,
big data,
challenges,
change progresses,
changes,
climate,
climate change,
climate change progresses,
climate interactions,
climate model results,
climate science community,
community,
complex,
composition,
constraints,
current challenges,
data,
dataset,
diversity,
ensemble of Earth system models,
experiment protocol,
experiments,
expert views,
focus,
future,
guidance,
impact,
influence,
interaction,
interest,
learning approach,
length,
leverage multiple datasets,
limitations,
machine,
machine learning approach,
model,
model constraints,
model diversity,
model results,
multi-model ensemble,
multiple datasets,
near future,
observed product,
opportunities,
optimal trade-off,
overlap,
panel,
paradigm,
parameterization,
partial overlap,
pattern recognition,
patterns,
perspective,
precipitation,
process,
production,
progression,
project,
protocol,
quality,
quantify model diversity,
radiation,
recognition,
reports,
representational limits,
resolution,
results,
science community,
scientific focus,
simulation,
specialization,
subgrid-scale parameterizations,
surface,
system model,
trade-offs,
uncertainty,
views
Funders
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