A CENTURY-LONG GLOBAL OFFLINE SIMULATION TOWARD LAND SURFACE, SNOW, SOIL-MOISTURE MODEL INTERCOMPARISON PROJECT (LS3MIP)

2018 ◽  
Vol 74 (5) ◽  
pp. I_43-I_48
Author(s):  
Yukihiko ONUMA ◽  
Hyungjun KIM ◽  
Kei YOSHIMURA ◽  
Tomoko NITTA ◽  
Ryouta O’ISHI ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Intercomparison Project

scholarly journals CAS-LSM Datasets for the CMIP6 Land Surface Snow and Soil Moisture Model Intercomparison Project

2021 ◽  
Author(s):  
Binghao Jia ◽  
Longhuan Wang ◽  
Yan Wang ◽  
Ruichao Li ◽  
Xin Luo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Grid Point ◽  
Global Ocean ◽  
Surface Model ◽  
Past Century ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Intercomparison Project

AbstractThe datasets of the five Land-offline Model Intercomparison Project (LMIP) experiments using the Chinese Academy of Sciences Land Surface Model (CAS-LSM) of CAS Flexible Global-Ocean-Atmosphere-Land System Model Grid-point version 3 (CAS FGOALS-g3) are presented in this study. These experiments were forced by five global meteorological forcing datasets, which contributed to the framework of the Land Surface Snow and Soil Moisture Model Intercomparison Project (LS3MIP) of CMIP6. These datasets have been released on the Earth System Grid Federation node. In this paper, the basic descriptions of the CAS-LSM and the five LMIP experiments are shown. The performance of the soil moisture, snow, and land-atmosphere energy fluxes was preliminarily validated using satellite-based observations. Results show that their mean states, spatial patterns, and seasonal variations can be reproduced well by the five LMIP simulations. It suggests that these datasets can be used to investigate the evolutionary mechanisms of the global water and energy cycles during the past century.

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

2016 ◽  
Vol 9 (8) ◽  
pp. 2809-2832 ◽  
Author(s):  
Bart van den Hurk ◽  
Hyungjun Kim ◽  
Gerhard Krinner ◽  
Sonia I. Seneviratne ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Radiation ◽  
The Arctic ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Systematic Biases ◽  
Intercomparison Project

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).

scholarly journals The Land Surface, Snow and Soil moisture Model Intercomparison Program (LS3MIP): aims, set-up and expected outcome

2016 ◽  
Author(s):  
Bart van den Hurk ◽  
Hyungjun Kim ◽  
Gerhard Krinner ◽  
Sonia I. Seneviratne ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Surface Radiation ◽  
The Arctic ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Systematic Biases

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).

scholarly journals Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results

2011 ◽  
Vol 12 (5) ◽  
pp. 869-884 ◽  
Author(s):  
Ingjerd Haddeland ◽  
Douglas B. Clark ◽  
Wietse Franssen ◽  
Fulco Ludwig ◽  
Frank Voß ◽  
...  
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Water Model ◽  
Hydrological Models ◽  
Model Intercomparison ◽  
Land Surface Models ◽  
Surface Models ◽  
Terrestrial Water ◽  
Intercomparison Project ◽  
Energy Balance Approach

Abstract Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

scholarly journals The AMMA Land Surface Model Intercomparison Project (ALMIP)

2009 ◽  
Vol 90 (12) ◽  
pp. 1865-1880 ◽  
Author(s):  
Aaron Boone ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo ◽  
Anton Beljaars ◽  
Franck Chopin ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Intercomparison ◽  
Intercomparison Project

scholarly journals Intermodel Variability and Mechanism Attribution of Central and Southeastern U.S. Anomalous Cooling in the Twentieth Century as Simulated by CMIP5 Models

2013 ◽  
Vol 26 (17) ◽  
pp. 6215-6237 ◽  
Author(s):  
Zaitao Pan ◽  
Xiaodong Liu ◽  
Sanjiv Kumar ◽  
Zhiqiu Gao ◽  
James Kinter
Keyword(s):  
United States ◽  
Twentieth Century ◽  
Land Surface ◽  
Regional Scale ◽  
Coupled Model ◽  
The United States ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Central United States ◽  
Intercomparison Project

Abstract Some parts of the United States, especially the southeastern and central portion, cooled by up to 2°C during the twentieth century, while the global mean temperature rose by 0.6°C (0.76°C from 1901 to 2006). Studies have suggested that the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) may be responsible for this cooling, termed the “warming hole” (WH), while other works reported that regional-scale processes such as the low-level jet and evapotranspiration contribute to the abnormity. In phase 3 of the Coupled Model Intercomparison Project (CMIP3), only a few of the 53 simulations could reproduce the cooling. This study analyzes newly available simulations in experiments from phase 5 of the Coupled Model Intercomparison Project (CMIP5) from 28 models, totaling 175 ensemble members. It was found that 1) only 19 out of 100 all-forcing historical ensemble members simulated negative temperature trend (cooling) over the southeast United States, with 99 members underpredicting the cooling rate in the region; 2) the missing of cooling in the models is likely due to the poor performance in simulating the spatial pattern of the cooling rather than the temporal variation, as indicated by a larger temporal correlation coefficient than spatial one between the observation and simulations; 3) the simulations with greenhouse gas (GHG) forcing only produced strong warming in the central United States that may have compensated the cooling; and 4) the all-forcing historical experiment compared with the natural-forcing-only experiment showed a well-defined WH in the central United States, suggesting that land surface processes, among others, could have contributed to the cooling in the twentieth century.

scholarly journals Summer U.S. Surface Air Temperature Variability: Controlling Factors and AMIP Simulation Biases

2016 ◽  
Vol 29 (14) ◽  
pp. 5123-5139 ◽  
Author(s):  
Anna L. Merrifield ◽  
Shang-Ping Xie
Keyword(s):  
United States ◽  
Heat Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Sensible Heat ◽  
Model Intercomparison ◽  
Central United States ◽  
Intercomparison Project ◽  
Atmosphere Interaction

Abstract This study documents and investigates biases in simulating summer surface air temperature (SAT) variability over the continental United States in the Atmospheric Model Intercomparison Project (AMIP) experiment from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Empirical orthogonal function (EOF) and multivariate regression analyses are used to assess the relative importance of circulation and the land surface feedback at setting summer SAT over a 30-yr period (1979–2008). Regions of high SAT variability are closely associated with midtropospheric highs, subsidence, and radiative heating accompanying clear-sky conditions. The land surface exerts a spatially variable influence on SAT through the sensible heat flux and is a second-order effect in the high-variability centers of action (COAs) in observational estimates. The majority of the AMIP models feature high SAT variability over the central United States, displaced south and/or west of observed COAs. SAT COAs in models tend to be concomitant and strongly coupled with regions of high sensible heat flux variability, suggesting that excessive land–atmosphere interaction in these models modulates U.S. summer SAT. In the central United States, models with climatological warm biases also feature less evapotranspiration than ERA-Interim but reasonably reproduce observed SAT variability in the region. Models that overestimate SAT variability tend to reproduce ERA-Interim SAT and evapotranspiration climatology. In light of potential model biases, this analysis calls for careful evaluation of the land–atmosphere interaction hot spot region identified in the central United States. Additionally, tropical sea surface temperatures play a role in forcing the leading EOF mode for summer SAT in models. This relationship is not apparent in observations.

scholarly journals The Performance of Multiple Model-Simulated Soil Moisture Datasets Relative to ECV Satellite Data in China

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1384 ◽  
Author(s):  
Wenkui Bai ◽  
Xiling Gu ◽  
Shenlin Li ◽  
Yihan Tang ◽  
Yanhu He ◽  
...  
Keyword(s):  
Soil Moisture ◽  
South China ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coupled Model ◽  
Temporal Correlation ◽  
Multiple Model ◽  
Model Intercomparison ◽  
Reanalysis Dataset ◽  
Intercomparison Project

Reliability and accuracy of soil moisture datasets are essential for understanding changes in regional climate such as precipitation and temperature. Soil moisture datasets from the Essential Climate Variable (ECV), the Coupled Model Intercomparison Project Phase 5 (CMIP5), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), the Global Land Data Assimilation System (GLDAS), and reanalysis products are widely used. These datasets generated by different techniques are compared in a common framework over China in this study. The comparison focuses on four aspects: spatial pattern, temporal correlation, long-term trend, and the relationships with precipitation and the Normalized Difference Vegetation Index (NDVI). The results indicate that all soil moisture datasets reach a good agreement on the spatial patterns of wet and dry soil. These patterns are also consistent with that of precipitation. However, there are considerable discrepancies in the absolute values of soil moisture among these datasets. In terms of unbiased Root-Mean-Square Difference (unRMSE, i.e., removing the differences in absolute values), all modeled datasets obtain performances comparable with ECV observations. Our results also suggest that a multi-model ensemble of soil moisture datasets can improve the representation of soil moisture conditions. The optimal dataset from which the wetting/drying trends in soil moisture have the highest consistency in terms of changes in precipitation and NDVI varies by season. Specifically, in spring, CMIP5 in northwest China shows that the trends in soil moisture are consistent with the changes in precipitation and NDVI. In summer, ECV presents the most identical performance compared to the changes in precipitation and NDVI. In autumn, GLDAS and Reanalysis have better performance in south China and parts of north China. In winter, GLDAS performs the best in the east of south China, followed by the Reanalysis dataset. These discrepancies among the datasets present various changes in different regions, which should be well noted and discussed before use.

Evaluation of the WAMME model surface fluxes using results from the AMMA land-surface model intercomparison project

2009 ◽  
Vol 35 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Aaron Anthony Boone ◽  
Isabelle Poccard-Leclercq ◽  
Yongkang Xue ◽  
Jinming Feng ◽  
Patricia de Rosnay
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Fluxes ◽  
Surface Model ◽  
Model Intercomparison ◽  
Model Surface ◽  
Intercomparison Project

Complementing ERA5 and E-OBS20 with high resolution river discharge

2020 ◽  
Author(s):  
Stefan Hagemann ◽  
Tobias Stacke
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Land Surface ◽  
River Discharge ◽  
River Mouth ◽  
Water Model ◽  
Model Intercomparison ◽  
Station Data ◽  
Intercomparison Project ◽  
Subsurface Runoff

<p>The 0.5° resolution of many global observational datasets is not sufficient for the requirements of current state-of-the-art regional climate model (RCM) simulations over Europe. Here, the ERA5 reanalysis of the ECMWF (C3S 2017) and E-OBS data (Cornes et al. 2018) are frequently used as reference datasets when RCM results are evaluated on resolutions higher than 0.5°. In addition, ERA5 data are also commonly used to force regional ocean models. As ERA data do not comprise river discharges, the lateral forcing of freshwater inflow from land is taken from other data sources, such as station data, runoff climatologies, etc. However, these data are not necessarily consistent with the ERA5 forcing over the ocean surface. If such data are derived from station data, they are only available for specific rivers and not spatially homogeneously distributed for all coastal areas. In addition, they might not be representative for the river mouth if the respective station location is too far away from the river mouth, which is often the case.</p><p>In order to allow a consistent forcing of river discharges and evaluation of simulated hydrological fluxes, we extended ERA5 and E-OBS v20.0e with high resolution river discharge. This also allows a consistent assessment of hydrological changes from these two datasets. The discharge was simulated with the recently developed 5 Min. version of the Hydrological discharge (HD) model (Hagemann et al., submitted). Note that for the development of this HD model version, no river specific parameter adjustments were conducted so that the HD model is generally applicable for climate change studies and over ungauged catchments.</p><p>The HD model requires gridded fields of surface and subsurface runoff as input with a daily temporal resolution or higher. As no large-scale observations of these variables exist, they need to be calculated by a land surface scheme or hydrology model using observed or re-analyzed meteorological data. Here, we used the HydroPy global hydrological model, which is the successor of the MPI-HM model (Stacke and Hagemann 2012). The latter has contributed to the WATCH Water Model Intercomparison Project (WaterMIP; Haddeland et al. 2011) and the inter-sectoral impact model intercomparison project (ISIMIP; Warszawski et al. 2014). Note that ERA5 also comprises archived fields of surface and subsurface runoff, but it turned out that its separation of total runoff is not suitable to generate adequate river discharges with the HD model. In our presentation, we evaluate the simulated discharge using various metrics and consider significant discharge trends over Europe.</p><p><strong>References</strong></p><p>C3S (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS)</p><p>Cornes, R., et al. (2018) J. Geophys. Res. Atmos. 123, doi:10.1029/2017JD028200</p><p>Haddeland, I., et al. (2011). J. Hydrometeorol. 12, doi: 10.1175/2011jhm1324.1</p><p>Hagemann, S., T. Stacke and H. Ho-Hagemann, High resolution discharge simulations over Europe and the Baltic Sea catchment. Frontiers in Earth Sci., submitted.</p><p>Stacke, T. and Hagemann, S. (2012). Hydrol. Earth Syst. Sci. 16, doi: 10.5194/hess-16-2915-2012</p><p>Warszawski, L., et al. (2014) Proc. Natl. Acad. Sci. USA 111, doi: 10.1073/pnas.1312330110</p>

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