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Researchers Explore Challenges, Barriers to Achieving Realistic Rainfall Modeling

Heavy precipitation can result in great losses to human life, ecology, and economy. However, both its intensity and frequency have raised as a result of climate change impacts.

Heavy rain in Shenzhen on April 19th, 2019, caused extensive flight delays, affecting thousands of passengers. Image Credit: Sicheng He.

Hence, it is turning out to be highly crucial to precisely model and forecast heavy precipitation events. But current global climate models (GCMs) strive to model tropical precipitation properly, especially heavy rainfall. Atmospheric scientists are working to determine and reduce model biases that emerge when an attempt is made to model large-scale and convective precipitation.

Unrealistic convective and large-scale precipitation components essentially contribute to the biases of simulated precipitation.

Jing Yang, Professor and Faculty Member, Geographical Science Department, Beijing Normal University

Professor Yang and her postgraduate student Sicheng He, together with Qing Bao from the Institute of Atmospheric Physics at the Chinese Academy of Sciences, have examined the obstacles and difficulties to attain realistic rainfall modeling from the convective and large-scale precipitation point of view.

Although sometimes total rainfall amounts can be simulated well, the convective and large-scale precipitation partitions are incorrect in the models.

Jing Yang, Professor and Faculty Member, Geographical Science Department, Beijing Normal University

Scientists have extensively classified 16 CMIP6 models concentrating on tropical heavy rainfall to clear the status of convective and large-scale precipitation components in present GCMs. In a majority of the cases, findings display much more rainfall resolved from large-scale rainfall instead of convective components of CMIP6 model simulations, which is unrealistic.

The model components were segregated into three different groups by the team to better evaluate based on the percentage of large-scale precipitation: (1) whole mid-to-lower tropospheric wet biases (60% to 80% large-scale rainfall); (2) mid-tropospheric wet peak (50% convective/large-scale rainfall); and (3) lower tropospheric wet peak (90% to 100% large-scale rainfall).

Such classifications are strongly linked with the vertical distribution of clouds and moisture inside the tropical atmosphere. Since the radiative impacts of low and high clouds tend to vary, the associated variations in vertical cloud distributions can possibly lead to distinct climate responses, and thus significant uncertainties in climate projections.

The associated vertical distribution of unique clouds potentially causes different climate feedback, suggesting accurate convective/large-scale rainfall partitions are necessary to reliable climate projection.

Jing Yang, Professor and Faculty Member, Geographical Science Department, Beijing Normal University

Journal Reference:

Yang, J., et al. (2021) Convective/Large-scale Rainfall Partitions of Tropical Heavy Precipitation in CMIP6 Atmospheric Models. Advances in Atmospheric Sciences. doi.org/10.1007/s00376-021-0238-4.

Source: http://english.iap.cas.cn/

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