Regional climate change: consensus, discrepancies, and ways forward

• Published in: Frontiers in Climate
• Main Authors: Shaw, Tiffany A., Arias, Paola A., Collins, Mat, Coumou, Dim, Diedhiou, Arona, Garfinkel, Chaim I., Jain, Shipra, Roxy, Mathew Koll, Kretschmer, Marlene, Leung, L. Ruby, Narsey, Sugata, Martius, Olivia, Seager, Richard, Shepherd, Theodore G., Sörensson, Anna A., Stephenson, Tannecia, Taylor, Michael, Wang, Lin
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers 2024
• Subjects: Climate change
• Online Access: https://centaur.reading.ac.uk/116335/; https://centaur.reading.ac.uk/116335/2/Shaw%20Frontiers.pdf; https://centaur.reading.ac.uk/116335/1/Shaw_etal_2024_revised.pdf

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 ...