Assessing arctic wetting: Performances of CMIP6 models and projections of precipitation changes
Assessing arctic wetting: Performances of CMIP6 models and projections of precipitation changes
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DOI：
10.1016/j.atmosres.2023.107124
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发表时间：
2023-11
影响因子：
5.5
通讯作者：
Ziyi Cai;Qinglong You;Hans W. Chen;Ruonan Zhang;Z. Zuo;Deliang Chen;Judah Cohen;J. Screen
中科院分区：
文献类型：
--
作者：
Ziyi Cai;Qinglong You;Hans W. Chen;Ruonan Zhang;Z. Zuo;Deliang Chen;Judah Cohen;J. Screen
在北极变暖的背景下，北极地区正在经历显著的降水增加，这被称为北极湿润。这一现象对北极水文循环和许多社会生态系统都有影响。然而，气候模式准确模拟北极湿润变化的能力尚未得到全面评估。在这项研究中，我们利用站资料、多次再分析和35个参与耦合模式比对项目第6阶段（CMIP6）的模式来分析北极的总降水。以ERA5再分析为参考，采用水分收支方程和模式性能评价方法，对模式对北极过去湿润型的再现能力进行了评价。我们的发现表明，大多数再分析和模型都能够复制北极的湿润过程。然而，与ERA5再分析相比，CMIP6模式普遍高估了1979 - 2014年暖季的北极湿度，而低估了冷季的北极湿度。进一步的研究表明，暖季湿润度的高估在北冰洋北部，特别是加拿大北极群岛最为严重，这与高估大气水分输送有关。相反，这些模式明显低估了巴伦支-喀拉海在寒冷季节的湿润程度，这可归因于模式没有充分代表该地区的海冰减少，从而低估了蒸发量。在模拟历史北极湿化方面表现最好的模式表明预估的北极湿化会加剧，与原始模式相比，最优模式显著降低了未来预估的不确定性，特别是在寒冷季节和海洋地区。我们的研究强调了CMIP6模式模拟北极降水的显著偏差，提高模式模拟历史北极降水的能力可以减少未来预估的不确定性。
The Arctic region is experiencing a notable increase in precipitation, known as Arctic wetting, amidst the backdrop of Arctic warming. This phenomenon has implications for the Arctic hydrological cycle and numerous socio-ecological systems. However, the ability of climate models to accurately simulate changes in Arctic wetting has not been thoroughly assessed. In this study, we analyze total precipitation in the Arctic using station data, multiple reanalyses, and 35 models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). By employing the moisture budget equation and an evaluation method for model performance with ERA5 reanalysis as a reference, we evaluated the models' capability to reproduce past Arctic wetting patterns. Our findings indicate that most reanalyses and models are able to replicate Arctic wetting. However, the CMIP6 models generally exhibit an overestimation of Arctic wetting during the warm season and an underestimation during the cold season from 1979 to 2014 when compared to the ERA5 reanalysis. Further investigation reveals that the overestimation of wetting during the warm season is largest over the Arctic Ocean's northern part, specifically the Canadian Arctic Archipelago, and is associated with an overestimation of atmospheric moisture transport. Conversely, the models significantly underestimate wetting over the Barents-Kara Sea during the cold season, which can be attributed to an underestimation of evaporation resulting from the models' inadequate representation of sea ice reduction in that region. The models with the best performance in simulating historical Arctic wetting indicate a projected intensification of Arctic wetting, and optimal models significantly reduce uncertainties in future projections compared to the original models, particularly in the cold season and oceanic regions. Our study highlights significant biases in the CMIP6 models' simulation of Arctic precipitation, and improving the model's ability to simulate historical Arctic precipitation could reduce uncertainties in future projections.