2016
DOI: 10.5194/gmd-9-2809-2016
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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

Abstract: 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 cy… Show more

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Cited by 177 publications
(168 citation statements)
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“…Löfver-ström et al, 2014Löfver-ström et al, , 2016, but also at a larger scale if the changes in large-scale circulation are sufficiently large to have an impact on the North Atlantic Ocean circulation (e.g. Roberts et al, 2014;Ullman et al, 2014;Zhang et al, 2014;Beghin et al, 2016). Several studies have shown that changes in vegetation cover and increases in dust loading affect LGM climates (e.g.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Löfver-ström et al, 2014Löfver-ström et al, , 2016, but also at a larger scale if the changes in large-scale circulation are sufficiently large to have an impact on the North Atlantic Ocean circulation (e.g. Roberts et al, 2014;Ullman et al, 2014;Zhang et al, 2014;Beghin et al, 2016). Several studies have shown that changes in vegetation cover and increases in dust loading affect LGM climates (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…Klockmann et al, 2016;Brady et al, 2013;Pausata et al, 2011). We also expect analyses of the impacts of these LGM forcings to strongly benefit from diagnostics developed by the Modelling Intercomparison Projects (MIPs) dedicated to these components and processes, such as OMIP for the ocean (Griffies et al, 2016;Orr et al, 2017), SIMIP for sea-ice processes (Notz et al, 2016), LS3MIP for the land surface (van den Hurk et al, 2016), AerChemMIP for dust (Collins et al, 2017), CFMIP for clouds (Webb et al, 2017), and RFMIP for radiative forcing diagnostics (Pincus et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…These efforts dovetail with expanding emphasis in CMIP6 on model performance metrics. Several recent studies have utilized various methodologies to infer observationally based historical change in land surface variables impacted by LULCC or divergences in surface response between different land-cover types Lee et al, 2011;Lejeune et al, 2016;Li et al, 2015;Teuling et al, 2010;Williams et al, 2012).…”
Section: Land-use Metricsmentioning
confidence: 99%
“…Careful assessment will be necessary to validate the inferred relationships between LULCC and extremes, given partly contradicting results with respect to the effects of LULCC on climate extremes in models and observations (Lejeune et al, 2016;Teuling et al, 2010).…”
Section: Extremesmentioning
confidence: 99%
“…These regions have been experiencing the strongest surface warming over the last century globally (IPCC, 2014), a trend which is expected to exacerbate in the future and to significantly change hydrological patterns (AMAP, 2017). Therefore, solid understanding of present hydrological processes and variations is crucial, yet the effect of complex snow dynamics on other storages and water resources is relatively unknown (van den Hurk et al, 2016;Kug et al, 2015). While it has been shown that snow mass is the primary component of seasonal variations of TWS in large northern 15 basins (Niu et al, 2007;Rangelova et al, 2007), it is not known what drives the TWS variations on inter-annual or longer time scales in these regions.…”
Section: Introductionmentioning
confidence: 99%