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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

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

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).

1991-959X
2809-2832
Van Den Hurk, Bart
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Kim, Hyungjun
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Krinner, Gerhard
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Seneviratne, Sonia I.
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Derksen, Chris
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Oki, Taikan
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Douville, Hervé
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Colin, Jeanne
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Ducharne, Agnès
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Cheruy, Frederique
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Viovy, Nicholas
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Puma, Michael J.
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Wada, Yoshihide
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Li, Weiping
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Jia, Binghao
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Alessandri, Andrea
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Lawrence, Dave M.
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Weedon, Graham P.
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Ellis, Richard
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Hagemann, Stefan
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Mao, Jiafu
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Flanner, Mark G.
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Zampieri, Matteo
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Materia, Stefano
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Law, Rachel M.
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Sheffield, Justin
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Van Den Hurk, Bart
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Kim, Hyungjun
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Krinner, Gerhard
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Seneviratne, Sonia I.
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Derksen, Chris
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Oki, Taikan
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Douville, Hervé
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Colin, Jeanne
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Ducharne, Agnès
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Cheruy, Frederique
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Viovy, Nicholas
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Puma, Michael J.
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Wada, Yoshihide
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Li, Weiping
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Jia, Binghao
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Alessandri, Andrea
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Lawrence, Dave M.
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Weedon, Graham P.
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Ellis, Richard
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Hagemann, Stefan
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Mao, Jiafu
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Flanner, Mark G.
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Zampieri, Matteo
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Materia, Stefano
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Law, Rachel M.
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Sheffield, Justin
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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. and Sheffield, Justin (2016) LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome. Geoscientific Model Development, 9 (8), 2809-2832. (doi:10.5194/gmd-9-2809-2016).

Record type: Article

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).

This record has no associated files available for download.

More information

Accepted/In Press date: 28 July 2016
Published date: 24 August 2016
Additional Information: A correction has been attached to this output located at https://doi.org/:10.5194/gmd-9-2809-2016-corrigendum

Identifiers

Local EPrints ID: 479574
URI: http://eprints.soton.ac.uk/id/eprint/479574
ISSN: 1991-959X
PURE UUID: a052c46a-7f9b-482e-865d-50d599312233
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

Catalogue record

Date deposited: 26 Jul 2023 16:38
Last modified: 06 Jun 2024 01:54

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Contributors

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

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