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LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome

Authors :
van den Hurk, Bart
Kim, Hyungjun
Krinner, Gerhard
Seneviratne, Sonia I.
Derksen, Chris
Oki, Taikan
Douville, Hervé
Colin, Jeanne
Ducharne, Agnès
Cheruy, Frederique
Viovy, Nicholas
Puma, Michael J.
Wada, Yoshihide
Li, Weiping
Jia, Binghao
Alessandri, Andrea
Lawrence, Dave M.
Weedon, Graham P.
Ellis, Richard
Hagemann, Stefan
Mao, Jiafu
Flanner, Mark G.
Zampieri, Matteo
Materia, Stefano
Law, Rachel M.
Sheffield, Justin
van den Hurk, Bart
Kim, Hyungjun
Krinner, Gerhard
Seneviratne, Sonia I.
Derksen, Chris
Oki, Taikan
Douville, Hervé
Colin, Jeanne
Ducharne, Agnès
Cheruy, Frederique
Viovy, Nicholas
Puma, Michael J.
Wada, Yoshihide
Li, Weiping
Jia, Binghao
Alessandri, Andrea
Lawrence, Dave M.
Weedon, Graham P.
Ellis, Richard
Hagemann, Stefan
Mao, Jiafu
Flanner, Mark G.
Zampieri, Matteo
Materia, Stefano
Law, Rachel M.
Sheffield, Justin
Publication Year :
2016

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).

Details

Database :
OAIster
Notes :
text, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1363208379
Document Type :
Electronic Resource