Chinese Manufacturers' Association of Hong Kong

Abstract Significant variability of raindrop size distributions (DSDs) has been observed in the “21·7” Henan extremely heavy rainfall event (the “21·7” Henan EHR event), while the capability of model to reproduce such complicated heavy rainfall DSDs is yet unclear. This study primarily evaluates the simulated DSDs of multiple microphysics schemes by comparing with the observations from a network of 50 disdrometers. Constrained DSD variability is identified in most schemes that the simulated raindrop mean sizes are gradually restricted around specific values as the growth of heavy rainfall intensity. The schemes are also incapable of reproducing the different raindrop mean sizes from deep convection and shallow convection. Moreover, simulations show unrealistic evolutions of raindrop mean size standard deviations as height declines. By investigating the empirical formula and performing sensitivity experiment, the constrained DSD variability in heavy rainfall is largely blamed on the insufficient parameterizations of the self‐collection (breakup) processes.

Abstract. The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow, and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth System Models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode ("LMIP", building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework ("LFMIP", building upon the GLACE-CMIP blueprint).