Climate change attribution of the 2021 Henan extreme precipitation: Impacts of convective organization
IMAGE: THE BLUE TRIANGLES AND FILLED CIRCLES IN (A) DENOTE THE SIMULATIONS OF WEAK SHEAR AND REVERSED SHEAR (SHEARHALF AND SHEARREV, 301 K AND 303 K RUNS). (C) SHOWS THE DYNAMIC (SOLID LINES) AND THERMODYNAMIC (DASHED LINES) COMPONENTS OF THE SENSITIVITY. THE DOTTED LINES IN (C) INDICATE THE RESIDUAL TERM (WHICH ALSO MAY BE INTERPRETED AS THE PRECIPITATION EFFICIENCY TERM). IN (A)–(C), THE BLACK, RED, AND BLUE COLORS DENOTE THE EXPERIMENTS IN THE HOMOSST, HETESST, AND SHEAR GROUPS, RESPECTIVELY. view more
CREDIT: ©SCIENCE CHINA PRESS
This study is led by Dr. Ji Nie (assistant professor at Department of Atmospheric and Oceanic Sciences, Peking University). The research group collaborates with other researchers in using a conditional storyline attribution method based on a column quasi-geostrophic modeling framework. The results show that warming over the past century has significantly intensified the extreme precipitation event. As to the role of convective organization, shear-organized convection contributed greatly to the extreme precipitation on both regional and station scale.
Since climate change arouses wide concern, people ask how does climate change affect this kind of extreme event? The storyline attribution method is used here to avoid dealing with the uncertain nature of large-scale dynamics associated with extremes events under climate change. It is more desirable to regard the circulation regime related to the event as unchanged with climate warming, and see how do the changes in thermodynamic states (such as sea surface temperature and water vapor) affect the particular event.
A column quasi-geostrophic (CQG) framework based on the quasi-geostrophic omega equation is introduced to interactively simulate the large-scale dynamics. This framework allows researchers to prescribe the large-scale forcing associated with the synoptic regime and simulate the interaction between convective and large-scale dynamics.
Three groups of experiments are designed to investigate the impacts of different convective organization. Driven by uniform surface temperature, heterogeneous surface temperature and background low-level wind shear, the three groups of experiments show unorganized convection, clustered convection and squall-line convection respectively. For each group, the large-scale forcing (temperature and vorticity advection) is prescribed and surface temperature is increased to represent climate warming.
The result shows that global warming contributed significantly to the intensification of “21·7” Henan extreme precipitation event. The cloud-resolving simulations coupled to CQG method capture the extreme event quite well. It is shown that warming and moistening of the atmosphere led to strengthened regional-scale (10–14% K-1, depending on the convective organization, Figure 1) and station-scale precipitation extremes (7–9% K-1, Figure 2). When considering regional-scale precipitation, the shear-organized convection is much more sensitive to large-scale forcing and causes much higher precipitation compared with other two groups. The shear-organized convection also has a larger sensitivity to warming due to its stronger diabatic heating feedback. When considering station-scale precipitation, the shear-organized convection also generates much larger probabilities in extreme precipitation. No systematic dependence on convective organization is observed for the climate sensitivity of station-scale extreme precipitation.
The innovative CQG method allows researchers to manipulate the large-scale forcing and convective organizations with flexibility. This work clearly indicates the importance of mesoscale convective organization in coupling large-scale and convective-scale dynamics. Also, this study takes a step forward in addressing the station-scale precipitation extremes in a real extreme weather event.
(a) Precipitation extremes for the 301 K cases (solid lines) and 303 K cases (dashed lines) in the three groups; (b) the fractional increases in precipitation for each percentile; (c) the fractional increases in probabilities for each precipitation intensity.
CREDIT
©Science China Press
For more information, see the article:
Qin H, Yuan W, Wang J, Chen Y, Dai P, Sobel A H, Meng Z, Nie J. 2022. Climate change attribution of the 2021 Henan extreme precipitation: Impacts of convective organization. Science China Earth Sciences, 65(10): 1837–1846, https://doi.org/10.1007/s11430-022-9953-0
JOURNAL
Science China Earth Sciences
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