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Processes shaping the spatial pattern and seasonality of the surface air temperature response to anthropogenic forcing

Tian, F., Dong, B., Robson, J., Sutton, R. and Wilcox, L. (2020) Processes shaping the spatial pattern and seasonality of the surface air temperature response to anthropogenic forcing. Climate Dynamics, 54 (9-10). pp. 3959-3975. ISSN 0930-7575

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To link to this item DOI: 10.1007/s00382-020-05211-8


In the period 1960-2010, the land surface air temperature (SAT) warmed more rapidly over some regions relative to the global mean. Using a set of time-slice experiments, we highlight how different physical processes shape the regional pattern of SAT warming. The results indicate an essential role of anthropogenic forcing in regional SAT changes from the 1970s to 2000s, and show that both surface-atmosphere interactions and large-scale atmospheric circulation changes can shape regional responses to forcing. Single forcing experiments show that an increase in greenhouse gases (GHG) can lead to regional changes in land surface warming in winter (DJF) due to snow-albedo feedbacks, and in summer (JJA) due to soil-moisture and cloud feedbacks. Changes in anthropogenic aerosol and precursor (AA) emissions induce large spatial variations in SAT, characterized by warming over western Europe, Eurasia, and Alaska. In western Europe, SAT warming is stronger in JJA than in DJF due to substantial increases in clear sky shortwave radiation over Europe, associated with decreases in local AA emissions since the 1980s. In Alaska, the amplified SAT warming in DJF is due to increased downward longwave radiation, which is related to increased water vapor and cloud cover. In this case, although the model was able to capture the regional pattern of SAT change, and the associated local processes, it did not simulate all processes and anomalies correctly. For the Alaskan warming, the model is seen to achieve the correct regional response in the context of a wider North Pacific anomaly that is not consistent with observations. This demonstrates the importance of model evaluation that goes beyond the target variable in detection and attribution studies.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
ID Code:89561


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