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).
| Original language | English |
|---|---|
| Pages (from-to) | 2809-2832 |
| Number of pages | 24 |
| Journal | Geoscientific Model Development |
| Volume | 9 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 24 2016 |
Funding
The authors thank the CMIP panel of the WCRP Working Group on Climate Modelling for their efforts in coordinating the CMIP6 enterprise. Graham P. Weedon was supported by the Joint UK DECC/Defra Met Office Hadley Climate Centre Programme (GA01101). Jiafu Mao is supported by the Biogeochemistry-Climate Feedbacks Scientific Focus Area project funded through the Regional and Global Climate Modeling Program in Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the U.S. Department of Energy (DOE) Office of Science. Oak Ridge National Laboratory is managed by UT-BATTELLE for DOE under contract DE-AC05-00OR22725. H. Kim and T. Oki were supported by Japan Society for the Promotion of Science KAKENHI (16H06291). Hanna Lee (NorESM) has expressed intention to participate in LS3MIP when feasible, but has not contributed to this manuscript.