TY - JOUR
T1 - Regional climate change
T2 - consensus, discrepancies, and ways forward
AU - Shaw, Tiffany A.
AU - Arias, Paola A.
AU - Collins, Mat
AU - Coumou, Dim
AU - Diedhiou, Arona
AU - Garfinkel, Chaim I.
AU - Jain, Shipra
AU - Roxy, Mathew Koll
AU - Kretschmer, Marlene
AU - Leung, L. Ruby
AU - Narsey, Sugata
AU - Martius, Olivia
AU - Seager, Richard
AU - Shepherd, Theodore G.
AU - Sörensson, Anna A.
AU - Stephenson, Tannecia
AU - Taylor, Michael
AU - Wang, Lin
N1 - Publisher Copyright:
Copyright © 2024 Shaw, Arias, Collins, Coumou, Diedhiou, Garfinkel, Jain, Roxy, Kretschmer, Leung, Narsey, Martius, Seager, Shepherd, Sörensson, Stephenson, Taylor and Wang.
PY - 2024
Y1 - 2024
N2 - Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.
AB - Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.
KW - climate communication
KW - climate dynamics
KW - climate modeling
KW - climate prediction and projection
KW - regional climate change
UR - http://www.scopus.com/inward/record.url?scp=85193509611&partnerID=8YFLogxK
U2 - 10.3389/fclim.2024.1391634
DO - 10.3389/fclim.2024.1391634
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AN - SCOPUS:85193509611
SN - 2624-9553
VL - 6
JO - Frontiers in Climate
JF - Frontiers in Climate
M1 - 1391634
ER -