Eurasian Cooling Patterns in the CMIP5 Climate Models (1911.08178v1)

Abstract: The Arctic has warmed dramatically compared to the global average over the last few decades. During this same period, there have been strong cooling trends observed in the wintertime, near-surface air temperature over central Eurasia, a phenomenon known as Eurasian cooling. Many studies have suggested that the loss of sea ice, especially in the Barents and Kara Seas, is related to the cooling over Eurasia, although this connection and its possible mechanism is still a source of heated debate. Observations and reanalyses show a clear pattern of co-variability between Arctic sea ice and Eurasian wintertime temperatures. However, there is an open question regarding how robustly this teleconnection pattern is reproduced in the current generation of climate models. This study has examined Eurasian cooling in twenty models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), both in terms of temperature trends and by using singular value decomposition to identify patterns of co-variability between Arctic sea ice concentrations and near-surface temperatures over Eurasia. These are compared to trends and patterns of co-variability found in the ERA-Interim reanalysis. While most of the CMIP5 models have a robust pattern of co-variability that is similar to that found in the reanalysis, many fail to reproduce the wintertime cooling that has been observed. An examination of Arctic sea ice extent in the models suggests one possible explanation is the inability of the models to accurately simulate the wintertime sea ice changes over critical locations such as the Barents and Kara Seas.

• The paper analyzes CMIP5 models using SVD to examine the teleconnection between Arctic sea ice loss and observed Eurasian winter cooling trends.
• The study found that while most CMIP5 models show the correct pattern of sea ice/temperature co-variability, many fail to reproduce the observed magnitude of Eurasian winter cooling.
• This inability of models to accurately simulate Eurasian cooling highlights deficiencies in representing complex sea ice and atmospheric interactions crucial for climate projections.

Analysis of Eurasian Cooling Patterns in CMIP5 Climate Models

The paper conducted by Outten, Davy, and Chen focuses on the examination of Eurasian cooling patterns within the context of the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. This investigation is essential due to the observed phenomenon where the Arctic region has undergone significant warming, yet the wintertime near-surface air temperatures over central Eurasia have experienced cooling trends—an occurrence referred to as 'Eurasian cooling.'

Key Outcomes and Methodology

The paper utilizes data from 20 models incorporated within the CMIP5, with analyses extending from both reanalysis data (ERA-Interim) and simulations. The researchers employ singular value decomposition (SVD) to delineate patterns of co-variability between Arctic sea ice concentrations and Eurasian surface air temperatures. The project primarily assesses how accurately these models reproduce the teleconnection patterns observed in real-world data, specifically focusing on Arctic sea ice's implications on Eurasian winter conditions.

Evaluations from the ERA-Interim reanalysis confirm strong patterns in Arctic warming coupled with Eurasian cooling. For instance, warming rates in localized Arctic regions were found to reach up to 6.2 K per decade, while Eurasian cooling peaked at -4.9 K per decade. The paper suggests these phenomena are interrelated, likely influenced by changes in sea ice, particularly over the Barents and Kara Seas.

Findings in Climate Modeling

A significant revelation from this paper is the disparity in the models' ability to accurately replicate observed cooling trends. Although most models show patterns of co-variability akin to the real-world teleconnection, several fail to replicate the observed Eurasian wintertime cooling adequately. Notably, among the models, CanEMS2 and NorESM1-M were able to show pronounced cooling over Eurasia corresponding with real-world observations, while models like FIO-ESM and MIROC-ESM diverged significantly by showing warming trends instead.

The SVD analyses highlight that the primary mode of co-variability in most CMIP5 models reflects the ERA-Interim patterns, implying a decrease in sea ice concentration correlating with temperature changes—both warming over the Arctic Ocean and cooling over central Eurasia. Yet, models such as ACCESS1.3, CSIRO-Mk3.6.0, and FGOALS-g2 displayed discrepancies, either due to lack of clear separation in their SVD modes or unrealistic sea ice variability.

Implications and Future Directions

The observed failure of several CMIP5 models to capture the cooling trend calls into question their ability to accurately simulate complex climate dynamics, particularly concerning sea ice's roles in influencing atmospheric patterns. The paper posits that this limitation could arise from deficiencies in the models' ability to accurately simulate regional sea ice patterns, especially in critical areas such as the Barents and Kara Seas.

In terms of future development, the paper hints at the necessity for enhanced modeling capabilities that capture the nuanced dynamics of Northern Hemisphere atmospheric flows, sea-ice interactions, and their broader climatic impacts. Progress in these areas will likely contribute to more accurate depictions of mid-latitude weather phenomena in climate projections.

Conclusion

The outcomes of this research offer insightful perspectives into the representation of capricious climate phenomena within state-of-the-art climate models. While some models exhibit commendable fidelity to real-world teleconnections, the universal reproduction of such phenomena remains an ongoing challenge. This research underscores the need for continuous refinement of climate models to more accurately represent both historical trends and future climate variability and extremes. Such advancements are indispensable for enhancing our understanding and predictive capabilities regarding climate change and its diverse manifestations.