ALBERT

Description: The sensitivity of an operational CO2-responsive land surface model (the ISBA-A-gs model of Météo-France) to the atmospheric CO2 concentration, [CO2], is investigated for 3 vegetation types (winter wheat, irrigated corn, coniferous forest). Past (1960) and future (2050) scenarios of [CO2] corresponding to 320 ppm and 550 ppm, respectively, are explored. The sensitivity study is performed for 4 annual cycles presenting contrasting conditions of precipitation regime and air temperature, based on continuous measurements performed on the SMOSREX site near Toulouse, in southwestern France. A significant CO2-driven reduction of canopy conductance is simulated for the irrigated corn and the coniferous forest. The reduction is particularly large for corn, from 2000 to 2050 (–18%), and triggers a drop in optimum irrigation (–30 mm y−1). In the case of wheat, the response is more complex, with an equal occurrence of enhanced or reduced canopy conductance.

Description: In order to evaluate the uncertainty associated with the impact model in climate change studies, a CO2 responsive version of the land surface model ISBA (ISBA-A-gs) is compared with its standard version in a climate impact assessment study. The study is performed over the French Mediterranean basin using the Safran-Isba-Modcou chain. A downscaled A2 regional climate scenario is used to force both versions of ISBA, and the results of the two land surface models are compared for the present climate and for that at the end of the century. Reasonable agreement is found between models and with discharge observations. However, ISBA-A-gs has a lower mean evapotranspiration and a higher discharge than ISBA-Standard. Results for the impact of climate change are coherent on a yearly basis for evapotranspiration, total runoff, and discharge. However, the two versions of ISBA present contrasting seasonal variations. ISBA-A-gs develops a different vegetation cycle. The growth of the vegetation begins earlier and reaches a slightly lower maximum than in the present climate. This maximum is followed by a rapid decrease in summertime. In consequence, the springtime evapotranspiration is significantly increased when compared to ISBA-Standard, while the autumn evapotranspiration is lower. On average, discharge changes are more significant at the regional scale with ISBA-A-gs.

Description: In 2009 and 2010 the L-band microwave Cooperative Airborne Radiometer for Ocean and Land Studies (CAROLS) campaign was performed in southwestern France to support the calibration and validation of the new Soil Moisture and Ocean Salinity (SMOS) satellite mission. The L-band Microwave Emission of the Biosphere (L-MEB) model was used to retrieve surface soil moisture (SSM) and the vegetation optical depth (VOD) from the CAROLS brightness temperature measurements. The CAROLS SSM was compared with in situ observations at 11 sites of the SMOSMANIA (Soil Moisture Observing System-Meteorological Automatic Network Integrated Application) network of Météo-France. For eight of them, significant correlations were observed (0.51 ≤ r ≤ 0.82), with standard deviation of differences ranging from 0.039 m3 m−3 to 0.141 m3 m−3. Also, the CAROLS SSM was compared with SSM values simulated by the A-gs version of the Interactions between Soil, Biosphere and Atmosphere (ISBA-A-gs) model along 20 flight lines, at a resolution of 8 km × 8 km. A significant spatial correlation between these two datasets was observed for all the flights (0.36 ≤ r ≤ 0.85). The CAROLS VOD presented significant spatial correlations with the vegetation water content (VWC) derived from the spatial distribution of vegetation types used in ISBA-A-gs and from the Leaf Area Index (LAI) simulated for low vegetation. On the other hand, the CAROLS VOD presented little temporal changes, and no temporal correlation was observed with the simulated LAI. For low vegetation, the ratio of VOD to VWC tended to decrease, from springtime to summertime. The ISBA-A-gs grid cells (8 km × 8 km) were sampled every 5 m by CAROLS observations, at a spatial resolution of about 2 km. For 83% of the grid cells, the standard deviation of the sub-grid CAROLS SSM was lower than 0.05 m3 m−3. The presence of small water bodies within the ISBA-A-gs grid cells tended to increase the CAROLS SSM spatial variability, up to 0.10 m3 m−3. Also, the grid cells characterised by a high vegetation cover heterogeneity presented higher standard deviation values, for both SSM and VOD.

Description: The first products derived over France in 2010 from the L-band brightness temperatures (Tb) measured by the SMOS (Soil Moisture and Ocean Salinity) satellite, launched in November 2009, were compared with the surface soil moisture (SSM) estimates produced by the C-band Advanced Scatterometer, ASCAT, launched in 2006 on board METOP-A. SMOS and ASCAT SSM products were compared with the simulations of the ISBA-A-gs model and with in situ measurements from the SMOSMANIA network, including 21 stations located in southern France. ASCAT tended to correlate better than SMOS with ISBA-A-gs. The significant anomaly correlation coefficients between in situ observations and the SMOS (ASCAT) product ranged from 0.23 to 0.48 (0.35 to 0.96). However, in wet conditions, similar results between the two satellite products were found. An attempt was made to derive SSM from regressed empirical logarithmic equations using a combination of SMOS Tb at different incidence angles and different polarizations, and the Leaf Area Index (LAI) modeled by ISBA-A-gs. The analysis of the intercept coefficient of the regression showed an impact of topography. A similar analysis applied to ASCAT and SMOS SSM values showed a more limited impact of topography on the intercept coefficient of the SMOS SSM product, while fewer residual geographic patterns were found for the ASCAT SSM.

Description: The simulation of root water uptake in land surface models is affected by large uncertainties. The difficulty in mapping soil depth and in describing the capacity of plants to develop a rooting system is a major obstacle to the simulation of the terrestrial water cycle and to the representation of the impacts of drought. In this study, long time series of agricultural statistics are used to evaluate and constrain root water uptake models. The inter-annual variability of cereal grain yield and permanent grassland dry matter yield is simulated over France 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 two-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 départements, for a range of rooting depths. The number of départements 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 and 77% of the départements for cereals and grasslands, respectively. A rather neutral impact of the most refined versions of the model is found with respect to the simplified soil hydrology scheme. This shows that efforts should be made in future studies to reduce other sources of uncertainty, e.g. by using a more detailed soil and root density profile description together with satellite vegetation products. It is found that modelling additional subroot-zone base flow soil layers does not improve (and may even degrade) the representation of the inter-annual 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.

Description: The SMOSMANIA soil moisture network in Southwestern France is used to evaluate modelled and remotely sensed soil moisture products. The surface soil moisture (SSM) measured in situ at 5 cm permits to evaluate SSM from the SIM operational hydrometeorological model of Météo-France and to perform a cross-evaluation of the normalised SSM estimates derived from coarse-resolution (25 km) active microwave observations from the ASCAT scatterometer instrument (C-band, onboard METOP), issued by EUMETSAT and resampled to the Discrete Global Grid (DGG, 12.5 km gridspacing) by TU-Wien (Vienna University of Technology) over a two year period (2007–2008). A downscaled ASCAT product at one kilometre scale is evaluated as well, together with operational soil moisture products of two meteorological services, namely the ALADIN numerical weather prediction model (NWP) and the Integrated Forecasting System (IFS) analysis of Météo-France and ECMWF, respectively. In addition to the operational SSM analysis of ECMWF, a second analysis using a simplified extended Kalman filter and assimilating the ASCAT SSM estimates is tested. The ECMWF SSM estimates correlate better with the in situ observations than the Météo-France products. This may be due to the higher ability of the multi-layer land surface model used at ECMWF to represent the soil moisture profile. However, the SSM derived from SIM corresponds to a thin soil surface layer and presents good correlations with ASCAT SSM estimates for the very first centimetres of soil. At ECMWF, the use of a new data assimilation technique, which is able to use the ASCAT SSM, improves the SSM and the root-zone soil moisture analyses.

Description: A Land Data Assimilation System (LDAS) able to ingest surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested at local scale to increase prediction accuracy for water and carbon fluxes. The ISBA-A-gs Land Surface Model (LSM) is used together with LAI and the soil water content observations of a grassland at the SMOSREX experimental site in southwestern France for a seven-year period (2001–2007). Three configurations corresponding to contrasted model errors are considered: (1) best case (BC) simulation with locally observed atmospheric variables and model parameters, and locally observed SSM and LAI used in the assimilation, (2) same as (1) but with the precipitation forcing set to zero, (3) real case (RC) simulation with atmospheric variables and model parameters derived from regional atmospheric analyses and from climatological soil and vegetation properties, respectively. In configuration (3) two SSM time series are considered: the observed SSM using Thetaprobes, and SSM derived from the LEWIS L-band radiometer located 15m above the ground. Performance of the LDAS is examined in the three configurations described above with either one variable (either SSM or LAI) or two variables (both SSM and LAI) assimilated. The joint assimilation of SSM and LAI has a positive impact on the carbon, water, and heat fluxes. It represents a greater impact than assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to counterbalance large errors in the precipitation forcing given as input to the model.

Description: This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

Description: An evaluation of the global ECMWF atmospheric reanalysis ERA-Interim (with a 0.5° grid) is performed over France, based on the high resolution (8 km) SAFRAN atmospheric reanalysis. The ERA-Interim precipitation, Incoming Solar Radiation (ISR), air temperature, air humidity, and wind speed, are compared with their SAFRAN counterparts. Also, interpolated in situ ISR observations are used in order to consolidate the evaluation of this variable. The daily precipitation estimates produced by ERA-Interim over France correlate very well with SAFRAN. However, the values are underestimated by 27%. A GPCP-corrected version of ERA-Interim is less biased (13%). The ERA-Interim estimates of ISR correlate very well with SAFRAN and with in situ observations on a daily basis. Whereas SAFRAN underestimates the ISR by 6 Wm−2, ERA-Interim overestimates the ISR by 10 Wm−2. In order to assess the impact of the ERA-Interim errors, simulations of the ISBA-A-gs land surface model are performed over the SMOSREX grassland site in southwestern France using ERA-Interim (with and without GPCP rescaling) and SAFRAN. Latent and sensible heat fluxes are simulated, together with carbon dioxide fluxes. The rescaled ERA-Interim performs better than the original ERA-Interim and permits to achieve flux scores similar to those obtained with SAFRAN.

Description: Land surface models (LSM) have improved considerably in the last two decades. In this study, the Interactions between Surface, Biosphere, and Atmosphere (ISBA) LSM soil diffusion scheme is used (with 11 soil layers represented). A simplified extended Kalman filter (SEKF) allows ground observations of surface soil moisture (SSM) to be assimilated in the multilayer LSM in order to constrain deep soil moisture. In parallel, the same simulations are performed using the ISBA LSM with 2 soil layers (a thin surface layer and a bulk reservoir). Simulations are performed over a 3 yr period (2003–2005) for a bare soil field in southwestern France, at the SMOSREX (Surface Monitoring Of the Soil Reservoir Experiment) site. Analyzed soil moisture values correlate better with soil moisture observations when the ISBA LSM soil diffusion scheme is used. The Kalman gain is greater from the surface to 45 cm than below this limit. For dry periods, corrections introduced by the assimilation scheme mainly affect the first 15 cm of soil whereas weaker corrections impact the total soil column for wet periods. Such seasonal corrections cannot be described by the two-layer ISBA LSM. Sensitivity studies performed with the multilayer LSM show improved results when SSM (0–6 cm) is assimilated into the second layer (1–5 cm) than into the first layer (0–1 cm). The introduction of vertical correlations in the background error covariance matrix is also encouraging. Using a yearly cumulative distribution function (CDF)-matching scheme for bias correction instead of matching over the three years permits the seasonal variability of the soil moisture content to be better transcribed. An assimilation experiment has also been performed by forcing ISBA-DF (diffusion scheme) with a local forcing, setting precipitation to zero. This experiment shows the benefit of the SSM assimilation for correcting inaccurate atmospheric forcing.