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Understanding Climate Change through Cloud Response
Team from the Chinese Academy of Meteorological Sciences formulated a method to assess and improve current climate models in simulating cloud feedback.

Clouds are key in understanding climate change and also help in reducing errors when predicting future global warming patterns from climate models. According to Professor Zhang Hua, a scientist at the Chinese Academy of Meteorological Sciences, estimates from short-term cloud feedback based on observations provide a method of assessing and improving climate models.

"Cloud radiative forcing in the East Asian monsoon region has unique characteristics, and the current deviation and uncertainty in simulating radiation budgets in East Asia are all related to its feedback, which greatly constrains our understanding of climate change in the region using climate models," explains Professor Zhang.

"Using observational data to study short-term cloud feedback in East Asia can provide an observational constraint on long-term feedback there, and thus help us to assess the contribution of cloud feedback to climate sensitivity."

From a study published in Advances in Atmospheric Sciences, a new set of cloud radiative kernels were developed by Professor Zhang and her team, taking reference from the BCC_RAD radiation model. Cloud feedback in response to inter-annual climate variability in East Asia was estimated using these kernels and combined with cloud measurements from MODIS onboard NASA’s Aqua satellite from July 2002 to June 2018.

In order to reveal the spatial distribution and seasonal variation of feedbacks due to different cloud types, the team adopted the cloud classification method provided by the ISCCP (International Satellite Cloud Climatology Project) dataset and selected four subregions according to the surface and monsoon types. The strongest cloud feedback was located in the subtropical monsoon region, mainly due to the contributions of nimbostratus and stratus.

They found that short-term cloud feedback in East Asia is mainly driven by decreases in mid- and low-cloud fraction, resulting from changes in the thermodynamic structure of atmosphere, and a decrease in low-cloud optical thickness, related to changes in cloud water content.

Professor Zhang concludes by sharing that, "Short-term cloud feedback is a useful variable for estimating the uncertainties relating to clouds, and it can provide a reference for the study of long-term cloud feedback and narrowing the inter-model uncertainties in long-term cloud feedbacks through the relationship between long- and short-term cloud feedbacks in East Asia.”


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