Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites

International audienceThe present article is a contribution to the CLARIS WorkPackage "Climate and Agriculture", and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions. © Springer Science + Business Media B.V. 2009

AMMA Land Surface Model Intercomparison experiment coupled to the Community Microwave Emission Model: ALMIP-MEM

International audienceThis paper presents the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Models Intercomparison Project (ALMIP) for Microwave Emission Models (ALMIP-MEM). ALMIP-MEM comprises an ensemble of simulations of C-band brightness temperatures over West Africa for 2006. Simulations have been performed for an incidence angle of 55°, and results are evaluated against C-band satellite data from the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E). The ensemble encompasses 96 simulations, for 8 Land Surface Models (LSMs) coupled to 12 configurations of the Community Microwave Emission Model (CMEM). CMEM has a modular structure which permits combination of several parameterizations with different vegetation opacity and soil dielectric models. ALMIP-MEM provides the first intercomparison of state-of-the-art land surface and microwave emission models at regional scale. Quantitative estimates of the relative importance of land surface modeling and radiative transfer modeling for the monitoring of low-frequency passive microwave emission on land surfaces are obtained. This is of high interest for the various users of coupled land surface microwave emission models. Results show that both LSMs and microwave model components strongly influence the simulated top of atmosphere (TOA) brightness temperatures. For most of the LSMs, the Kirdyashev opacity model is the most suitable to simulate TOA brightness temperature in best agreement with the AMSR-E data. When this best microwave modeling configuration is used, all the LSMs are able to reproduce the main temporal and spatial variability of measured brightness temperature. Averaged among the LSMs, correlation is 0.67 and averaged normalized standard deviation is 0.98

Variance Based Sensitivity Analysis of FLake Lake Model for Global Land Surface Modeling

International audienceGiven the ever increasing spatial resolution of climate models and the significant role of lakes on the regional climate, it becomes important to represent water bodies in climate models. Such developments have started in the IPSL (Institut Pierre Simon Laplace) climate model and its land surface component, ORganizing Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE), with the Freshwater Lake model, FLake. To answer the questions raised by these new developments, such as the lake differentiation and related model parameters, we analyze spatial distributions of lake characteristics in the whole world to perform a global sensitivity analysis of the FLake parameters. As a result, three different climates and four lake depth configurations were selected as test cases. The Sobol method as sensitivity analysis based on variance decomposition was chosen to rank parameters impact on the model output, that is, lake surface water temperature, latent and sensible heat fluxes. We focus on the 11 parameters of the FLake model, which are the lake depth, the albedo and light extinction coefficient of water, snow, and ice respectively, the fetch, and the relaxation coefficient of the thermocline shape factor. The results show different sensitivity features according to the lake type and climate. The dominant role and time varying contribution of the lake depth, radiative parameters (albedo, light extinction coefficient) and thermocline relaxation coefficient linked to the atmospheric conditions, were clearly highlighted. These findings will lead us to distinguish between different lake categories in each grid cell of ORCHIDEE in the future implementation

Evaluation of nine large-scale hydrological models with respect to the seasonal runoff climatology in Europe

Large-scale hydrological models, simulating the terrestrial water cycle on continental and global scales, are fundamental for many studies in earth system sciences. However, due to imperfect knowledge of real world systems, the models cannot be expected to capture all aspects of large-scale hydrology equally well. To gain insights in the strengths and shortcomings of nine large-scale hydrological models, we assessed their ability to capture the mean annual runoff cycle. Unlike most other studies that rely on discharge observations from continental scale river basins, our study is based on observed runoff from a large number of small, near-natural catchments in Europe. We evaluated the models' ability to capture the average magnitude, the amplitude, as well as the timing of the mean annual runoff cycle. Our study revealed large uncertainties when modeling runoff from these small catchments. We identified large differences in model performance, however, the ensemble mean (mean of all model simulations) yielded rather robust predictions. Model performance varied systematically with climatic conditions and was best in regions with little influence of snow. In cold regions, many models exhibited low correlations between observed and simulated mean annual cycles, which can be associated with shortcomings in simulating the timing of snow accumulation and melt. Local (grid cell) scale differences between observed and simulated runoff can be large and local biases often exceeded 100%. These local uncertainties are contrasted by a relatively good regional average performance, ultimately reflecting the purpose of the models, i.e., to capture regional hydroclimatology.</p