A hybrid statistical dynamical downscaling method intended to emulate regional climate models is described and applied on Western Europe. The method is based on a constructed analogues algorithm, already used for statistical downscaling. For emulation, the statistical downscaling relationship is not derived from observations but from climate projections at low and high resolution. The hybrid approach therefore does not rely on the stationarity assumption inherent to conventional statistical downscaling.Within a perfect model framework, and using a large number of regional projections, the hybrid method is shown to very well reproduce climate change signals, and to clearly outperforms conventional statistical downscaling. The hybrid approach remains skillful even when applied to very low-resolution climate data.In practice, two emulation modes exist. In the GCM / RCM mode, the downscaling relationship is built between a RCM and its forcing GCM. In the RCM / RCM mode, the relationship is built between a RCM and the same RCM after aggregation of its results to a low resolution grid.The large-scale climate change signal of the downscaled GCM is generally retained with the RCM / RCM mode, but not with the GCM /RCM mode. Additionally, the choice of the GCM / RCM pair used for learning leads to large differences in downscaling results at large scale with the GCM /RCM mode, but not with the RCM / RCM mode. These results are explained by the differences that exist at large scale between projected changes by current RCMs and their forcing GCMs. Whether these differences are a testimony of a real added value of RCMs at large scale in the climate change context, or whether they have other causes, is therefore a crucial question.
<p>This contribution presents early results on the effects of biomass burning aerosols (BBA) on fog and low clouds (FLC) in the Namib, using data from multiple satellite platforms and station measurements.</p> <p>Fog, which is the most relevant non-rainfall water source for plants and animals in the coastal parts of the Namib Desert, may become increasingly important for local ecosystems as regional climate simulations predict a warmer and drier climate for southern Africa in the future. Previous studies showed the role of BBA on cloud development over the ocean off the Namibian coast. The same processes are likely to influence Namib-region FLC formation and persistence as well. However, the potential effects of aerosols on FLC in the Namib Desert, a direct extension of the South-East Atlantic cloud system, have yet to be investigated.</p> <p>A clear seasonal cycle of FLC dissipation is found in a satellite-based product of FLC formation and dissipation times, with longer FLC persistence during the BBA season. Using a BBA reanalysis product in combination with the satellite data, it is found that during this season, FLC dissipation times are positively correlated to BBA loading (higher aerosol loading coinciding with later FLC dissipation). It is assumed that semi-direct and indirect BBA effects contribute to this pattern, with further analyses aimed at isolating aerosol effects from possible confounders.</p> <p>These findings are a first step in a better understanding of the Namib-region FLC system and will help in the development of a statistical model to quantify the sensitivities of FLC lifetime in the region in the next step of the project.</p>
<p>To characterize the impacts of climate change, robust high-resolution climate change information is generally needed. The resolution of global climate models is currently too coarse to provide directly such information. A specific spatial downscaling step is therefore generally needed, either (1) dynamical downscaling with regional climate models or (2) statistical downscaling.</p><p>In this study, we present a new hybrid statistical-dynamical downscaling approach, intended to combine the respective strengths of statistical and dynamical downscaling, while overcoming their respective limitations. This hybrid method aims to emulate regional climate models and is based on a constructed analogues method.</p><p>Contrary to dynamical downscaling, the computational cost of the method is low, allowing to downscale a large number of global climate projections and therefore to correctly assess the climate uncertainties in impact studies. Contrary to statistical downscaling, the method does not rely on the assumption that the downscaling relationship established in the present climate with observations remains valid in the future climate perturbed by anthropogenic forcings. Therefore, the hybrid approach should be as robust as regional climate models in projecting future climate change.</p><p>In this presentation, the hybrid statistical-dynamical downscaling method is first presented. Elements of evaluation, in a perfect model framework based on an ensemble of regional climate models over Europe, are then shown and discussed to demonstrate the interest of the method and its applicability to study future climate changes over western Europe. Finally, results of the application of the method to downscale global climate projections over western Europe are shown, and important implications of the results are discussed. &#160;&#160;</p>
A hybrid statistical dynamical downscaling method intended to emulate regional climate models is described and applied on Western Europe. The method is based on a constructed analogues algorithm, already used for statistical downscaling. For emulation, the statistical downscaling relationship is not derived from observations but from climate projections at low and high resolution. The hybrid approach therefore does not rely on the stationarity assumption inherent to conventional statistical downscaling.Within a perfect model framework, and using a large number of regional projections, the hybrid method is shown to very well reproduce climate change signals, and to clearly outperforms conventional statistical downscaling. The hybrid approach remains skillful even when applied to very low-resolution climate data. In practice, two emulation modes exist. In the GCM / RCM mode, the downscaling relationship is built between a RCM and its forcing GCM. In the RCM / RCM mode, the relationship is built between a RCM and the same RCM after aggregation of its results to a low resolution grid. The large-scale climate change signal of the downscaled GCM is generally retained with the RCM / RCM mode, but not with the GCM /RCM mode. Additionally, the choice of the GCM / RCM pair used for learning leads to large differences in downscaling results at large scale with the GCM /RCM mode, but not with the RCM / RCM mode. These results are explained by the differences that exist at large scale between projected changes by current RCMs and their forcing GCMs. Whether these differences are a testimony of a real added value of RCMs at large scale in the climate change context, or whether they have other causes, is therefore a crucial question.
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