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Breon F.-M.,French Climate and Environment Sciences Laboratory | Vermeulen A.,Lille University of Science and Technology | Descloitres J.,Lille University of Science and Technology
Remote Sensing of Environment | Year: 2011

Because atmospheric aerosols scatter sunlight back to space, reflectance measurements from spaceborne radiometers can be used to estimate the aerosol load and its optical properties. Several aerosol products have been generated in a systematic way, and are available for further studies. In this paper, we evaluate the accuracy of such aerosol products derived from the measurements of POLDER, MODIS, MERIS, SEVIRI and CALIOP, through a statistical comparison with Aerosol Optical Depth (AOD) measurements from the AERONET sunphotometer network. Although this method is commonly used, this study is, to our knowledge, among the most extensive of its type since it compares the performance of the products from 5 different sensors using up to five years of data for each of them at global scale. The choice of these satellite aerosol datasets was based on their availability at the ICARE Data and Service Centre (www.icare.univ-lille1.fr).We distinguish between retrievals over land and ocean and between estimates of total and fine mode AOD. Over the oceans, POLDER and MODIS retrievals are of similar quality, with RMS difference lower than 0.1 and a correlation with AERONET of around 0.9. The POLDER estimates suffer from a small positive bias for clean atmospheres, which weakens its statistics. The other aerosol products are of lesser quality, although the SEVIRI products may be of interest for some applications that require a high temporal resolution. The MERIS product shows a very high bias. Over land, only the MODIS product offers a reliable estimate of the total AOD. On the other hand, the polarization-based retrieval using POLDER data allows a better fine mode estimate than that from MODIS. These results suggest the need for a product combining POLDER and MODIS products over land. The paper also analyses how the statistics change with the spatial and temporal thresholds that are used. Spatio-temporal averaging improves the statistics only slightly, which indicates that random errors are not dominant in the error budget. The paper includes various statistical indicators at global scale, and detailed results at individual ground stations can be obtained on request from the authors. © 2011 Elsevier Inc. Source


Paillard D.,French Climate and Environment Sciences Laboratory
Comptes Rendus - Geoscience | Year: 2010

The discovery of glacial ages in the 19th century triggered the first scientific questions on the evolution of climate through time, and thus corresponds to the dawn of palaeoclimatology. Since then, scientists have attempted to reconstruct past climatic changes and to understand their physical basis. Two competing theories have been suggested to explain the sequence of glacial-interglacial epochs: either the variations of the Earth orbital elements, or the atmospheric composition in carbon dioxide. If the astronomical theory has been largely confirmed since the last 30 years, a physical modeling of the climatic processes at work is still in its infancy. Besides, the most recent results of palaeoclimatology are clearly demonstrating that, more than never, a synthesis of these two old hypotheses is needed. © 2010 Académie des sciences. Source


Carreau J.,Montpellier University | Vrac M.,French Climate and Environment Sciences Laboratory
Water Resources Research | Year: 2011

We present a new class of stochastic downscaling models, the conditional mixture models (CMMs), which builds on neural network models. CMMs are mixture models whose parameters are functions of predictor variables. These functions are implemented with a one-layer feed-forward neural network. By combining the approximation capabilities of mixtures and neural networks, CMMs can, in principle, represent arbitrary conditional distributions. We evaluate the CMMs at downscaling precipitation data at three stations in the French Mediterranean region. A discrete (Dirac) component is included in the mixture to handle the "no-rain" events. Positive rainfall is modeled with a mixture of continuous densities, which can be either Gaussian, log-normal, or hybrid Pareto (an extension of the generalized Pareto). CMMs are stochastic weather generators in the sense that they provide a model for the conditional density of local variables given large-scale information. In this study, we did not look for the most appropriate set of predictors, and we settled for a decent set as the basis to compare the downscaling models. The set of predictors includes the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalyses sea level pressure fields on a 6 × 6 grid cell region surrounding the stations plus three date variables. We compare the three distribution families of CMMs with a simpler benchmark model, which is more common in the downscaling community. The difference between the benchmark model and CMMs is that positive rainfall is modeled with a single Gamma distribution. The results show that CMM with hybrid Pareto components outperforms both the CMM with Gaussian components and the benchmark model in terms of log-likelihood. However, there is no significant difference with the log-normal CMM. In general, the additional flexibility of mixture models, as opposed to using a single distribution, allows us to better represent the distribution of rainfall, both in the central part and in the upper tail. © 2011 by the American Geophysical Union. Source


Dueri S.,IRD Montpellier | Bopp L.,French Climate and Environment Sciences Laboratory | Maury O.,IRD Montpellier | Maury O.,University of Cape Town
Global Change Biology | Year: 2014

Climate-induced changes in the physical, chemical, and biological environment are expected to increasingly stress marine ecosystems, with important consequences for fisheries exploitation. Here, we use the APECOSM-E numerical model (Apex Predator ECOSystem Model - Estimation) to evaluate the future impacts of climate change on the physiology, spatial distribution, and abundance of skipjack tuna, the worldwide most fished species of tropical tuna. The main novelties of our approach lie in the mechanistic link between environmental factors, metabolic rates, and behavioral responses and in the fully three dimensional representation of habitat and population abundance. Physical and biogeochemical fields used to force the model are provided by the last generation of the IPSL-CM5 Earth System Model run from 1990 to 2100 under a 'business-as-usual' scenario (RCP8.5). Our simulations show significant changes in the spatial distribution of skipjack tuna suitable habitat, as well as in their population abundance. The model projects deterioration of skipjack habitat in most tropical waters and an improvement of habitat at higher latitudes. The primary driver of habitat changes is ocean warming, followed by food density changes. Our projections show an increase of global skipjack biomass between 2010 and 2050 followed by a marked decrease between 2050 and 2095. Spawning rates are consistent with population trends, showing that spawning depends primarily on the adult biomass. On the other hand, growth rates display very smooth temporal changes, suggesting that the ability of skipjack to keep high metabolic rates in the changing environment is generally effective. Uncertainties related to our model spatial resolution, to the lack or simplification of key processes and to the climate forcings are discussed. © 2013 John Wiley & Sons Ltd. Source


Roche D.M.,VU University Amsterdam | Roche D.M.,French Climate and Environment Sciences Laboratory
Geoscientific Model Development | Year: 2013

A new 18O stable water isotope scheme is developed for three components of the iLOVECLIM coupled climate model: atmospheric, oceanic and land surface. The equations required to reproduce the fractionation of stable water isotopes in the simplified atmospheric model ECBilt are developed consistently with the moisture scheme. Simplifications in the processes are made to account for the simplified vertical structure including only one moist layer. Implementation of these equations together with a passive tracer scheme for the ocean and a equilibrium fractionation scheme for the land surface leads to the closure of the (isotopic-) water budget in our climate system. Following the implementation, verification of the existence of usual δ18O to climatic relationships are performed for the Rayleigh distillation, the Dansgaard relationship and the δ18O -salinity relationship. Advantages and caveats of the approach taken are outlined. The isotopic fields simulated are shown to reproduce most expected oxygen-18-climate relationships with the notable exception of the isotopic composition in Antarctica. © Author(s) 2013. Source

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