scholarly journals Use of agricultural statistics to verify the interannual variability in land surface models: a case study over France with ISBA-A-gs

2012 ◽  
Vol 5 (1) ◽  
pp. 37-54 ◽  
Author(s):  
J.-C. Calvet ◽  
S. Lafont ◽  
E. Cloppet ◽  
F. Souverain ◽  
V. Badeau ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Land Surface Model ◽  
Grid Cell ◽  
Warming Trend ◽  
Daily Maximum ◽  
Surface Model ◽  
Growing Period ◽  
The Impact

Abstract. In order to verify the interannual variability of the above-ground biomass of herbaceous vegetation simulated by the ISBA-A-gs land surface model, within the SURFEX modelling platform, French agricultural statistics for C3 crops and grasslands were compared with the simulations for the 1994–2008 period. While excellent correlations are obtained for grasslands, representing the interannual variability of crops is more difficult. It is shown that, the Maximum Available soil Water Capacity (MaxAWC) has a large influence on the correlation between the model and the agricultural statistics. In particular, high values of MaxAWC tend to reduce the impact of the climate interannual variability on the simulated biomass. Also, high values of MaxAWC allow the simulation of a negative trend in biomass production, in relation to a marked warming trend, of about 0.12 Kyr−1 on average, affecting the daily maximum air temperature during the growing period (April–June). This trend is particularly acute in Northern France. The estimates of MaxAWC for C3 crops and grasslands, currently used in SURFEX, are about 129 mm and do not vary much. Therefore, more accurate grid-cell values of this parameter are needed.

scholarly journals Use of agricultural statistics to verify the internannual variability in land surface models: a case study over France with ISBA-A-gs

2011 ◽  
Vol 4 (3) ◽  
pp. 1477-1512
Author(s):  
J.-C. Calvet ◽  
S. Lafont ◽  
E. Cloppet ◽  
F. Souverain ◽  
V. Badeau ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Land Surface Model ◽  
Grid Cell ◽  
Warming Trend ◽  
Daily Maximum ◽  
Surface Model ◽  
Growing Period ◽  
The Impact

Abstract. In order to verify the interannual variability of the above-ground biomass of herbaceous vegetation simulated by the ISBA-A-gs land surface model, within the SURFEX modelling platform, French agricultural statistics for C3 crops and grasslands were compared with the simulations for the 1994–2008 period. While excellent correlations are obtained for grasslands, representing the interannual variability of crops is more difficult. It is shown that, the Maximum Available soil Water Capacity (MaxAWC) has a large influence on the correlation between the model and the agricultural statistics. In particular, high values of MaxAWC tend to reduce the impact of the climate interannual variability on the simulated biomass, and to allow the simulation of a negative trend in biomass production, in relation to a marked warming trend, of about 0.12 Ky−1 on average, affecting the daily maximum air temperature during the growing period (April–June), especially in northern France. The estimates of MaxAWC for C3 crops and grasslands, currently used in SURFEX, are about 129 mm and do not vary much. Therefore, more accurate grid-cell values of this parameter are needed.

scholarly journals Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system

2015 ◽  
Vol 8 (6) ◽  
pp. 1857-1876 ◽  
Author(s):  
J. J. Guerrette ◽  
D. K. Henze
Keyword(s):  
Land Surface ◽  
Vertical Mixing ◽  
Model Performance ◽  
Land Surface Model ◽  
Sensitivity Study ◽  
Climate Impacts ◽  
Surface Model ◽  
Time Period ◽  
Develop Land ◽  
The Impact

Abstract. Here we present the online meteorology and chemistry adjoint and tangent linear model, WRFPLUS-Chem (Weather Research and Forecasting plus chemistry), which incorporates modules to treat boundary layer mixing, emission, aging, dry deposition, and advection of black carbon aerosol. We also develop land surface and surface layer adjoints to account for coupling between radiation and vertical mixing. Model performance is verified against finite difference derivative approximations. A second-order checkpointing scheme is created to reduce computational costs and enable simulations longer than 6 h. The adjoint is coupled to WRFDA-Chem, in order to conduct a sensitivity study of anthropogenic and biomass burning sources throughout California during the 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. A cost-function weighting scheme was devised to reduce the impact of statistically insignificant residual errors in future inverse modeling studies. Results of the sensitivity study show that, for this domain and time period, anthropogenic emissions are overpredicted, while wildfire emission error signs vary spatially. We consider the diurnal variation in emission sensitivities to determine at what time sources should be scaled up or down. Also, adjoint sensitivities for two choices of land surface model (LSM) indicate that emission inversion results would be sensitive to forward model configuration. The tools described here are the first step in conducting four-dimensional variational data assimilation in a coupled meteorology–chemistry model, which will potentially provide new constraints on aerosol precursor emissions and their distributions. Such analyses will be invaluable to assessments of particulate matter health and climate impacts.

scholarly journals Interannual variability in Australia's terrestrial carbon cycle constrained by multiple observation types

2016 ◽  
Vol 13 (23) ◽  
pp. 6363-6383 ◽  
Author(s):  
Cathy M. Trudinger ◽  
Vanessa Haverd ◽  
Peter R. Briggs ◽  
Josep G. Canadell
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Net Primary Production ◽  
Land Surface Model ◽  
Heterotrophic Respiration ◽  
Arid Ecosystems ◽  
Surface Model ◽  
Calibration Data ◽  
Terrestrial Carbon

Abstract. Recent studies have shown that semi-arid ecosystems in Australia may be responsible for a significant part of the interannual variability in the global concentration of atmospheric carbon dioxide. Here we use a multiple constraints approach to calibrate a land surface model of Australian terrestrial carbon and water cycles, with a focus on interannual variability. We use observations of carbon and water fluxes at 14 OzFlux sites, as well as data on carbon pools, litterfall and streamflow. We include calibration of the function describing the response of heterotrophic respiration to soil moisture. We also explore the effect on modelled interannual variability of parameter equifinality, whereby multiple combinations of parameters can give an equally acceptable fit to the calibration data. We estimate interannual variability of Australian net ecosystem production (NEP) of 0.12–0.21 PgC yr−1 (1σ) over 1982–2013, with a high anomaly of 0.43–0.67 PgC yr−1 in 2011 relative to this period associated with exceptionally wet conditions following a prolonged drought. The ranges are due to the effect on calculated NEP anomaly of parameter equifinality, with similar contributions from equifinality in parameters associated with net primary production (NPP) and heterotrophic respiration. Our range of results due to parameter equifinality demonstrates how errors can be underestimated when a single parameter set is used.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

scholarly journals Evaluation of root water uptake in the ISBA-A-gs land surface model using agricultural yield statistics over France

2014 ◽  
Vol 11 (5) ◽  
pp. 5421-5461
Author(s):  
N. Canal ◽  
J.-C. Calvet ◽  
B. Decharme ◽  
D. Carrer ◽  
S. Lafont ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Water Uptake ◽  
Land Surface ◽  
Land Surface Model ◽  
Root Water Uptake ◽  
Surface Model ◽  
Above Ground Biomass ◽  
Soil Layers ◽  
Ground Biomass ◽  
Cereal Grain

Abstract. The interannual variability of cereal grain yield and permanent grassland dry matter yield is simulated over French sites by the Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) generic Land Surface Model (LSM). The two soil profile schemes available in the model are used to simulate the above-ground biomass (Bag) of cereals and grasslands: a 2-layer force-restore (FR-2L) bulk reservoir model and a multi-layer diffusion (DIF) model. The DIF model is implemented with or without deep soil layers below the root-zone. The evaluation of the various root water uptake models is achieved by using the French agricultural statistics of Agreste over the 1994–2010 period at 45 cropland and 48 grassland sites, for a range of rooting depths. The number of sites where the simulated annual maximum Bag presents a significant correlation with the yield observations is used as a metric to benchmark the root water uptake models. Significant correlations (p value < 0.01) are found for up to 29% of the cereal sites and 77% of the grassland sites. It is found that modelling additional subroot zone base flow soil layers does not improve (and may even degrade) the representation of the interannual variability of the vegetation above-ground biomass. These results are particularly robust for grasslands as calibrated simulations are able to represent the extreme 2003 and 2007 years corresponding to unfavourable and favourable fodder production, respectively.

Comparing Assimilation of Soil Moisture and C-band Backscatter in High Resolution Land Surface Model

2020 ◽  
Author(s):  
Leqiang Sun ◽  
Stéphane Belair ◽  
Marco Carrera ◽  
Bernard Bilodeau
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Open Loop ◽  
In Situ Measurements ◽  
Lookup Table ◽  
Surface Model ◽  
Loop Model ◽  
The Impact

&lt;p&gt;Canadian Space Agency (CSA) has recently started receiving and processing the images from the recently launched C-band RADARSAT Constellation Mission (RCM). The backscatter and soil moisture retrievals products from the previously launched RADARSAT-2 agree well with both in-situ measurements and surface soil moisture modeled with land surface model Soil, Vegetation, and Snow (SVS). RCM will provide those products at an even better spatial coverage and temporal resolution. In preparation of the potential operational application of RCM products in Canadian Meteorological Center (CMC), this paper presents the scenarios of assimilating either soil moisture retrieval or outright backscatter signal in a 100-meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on field scale with time interval of three hours. The soil moisture retrieval map was synthesized by extrapolating the regression relationship between in-situ measurements and open loop model output based on soil texture lookup table. Based on this, the backscatter map was then generated with the surface roughness retrieved from RADARSAT-2 images using a modified Integral Equation Model (IEM) model. Bias correction was applied to the Ensemble Kalman filter (EnKF) to mitigate the impact of nonlinear errors introduced by multi-sourced perturbations. Initial results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals. Compared to open loop, both can improve the analysis of surface moisture, particularly in terms of reducing bias. &amp;#160;&lt;/p&gt;

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