Regional climate downscaling with prior statistical correction of the global climate forcing

International audienceA novel climate downscaling methodology that attempts to correct climate simulation biases is proposed. By combining an advanced statistical bias correction method with a dynamical downscaling it constitutes a hybrid technique that yields nearly unbiased, high-resolution, physically consistent, three-dimensional fields that can be used for climate impact studies. The method is based on a prior statistical distribution correction of large-scale global climate model (GCM) 3-dimensional output fields to be taken as boundary forcing of a dynamical regional climate model (RCM). GCM fields are corrected using meteorological reanalyses. We evaluate this methodology over a decadal experiment. The improvement in terms of spatial and temporal variability is discussed against observations for a past period. The biases of the downscaled fields are much lower using this hybrid technique, up to a factor 4 for the mean temperature bias compared to the dynamical downscaling alone without prior bias correction. Precipitation biases are subsequently improved hence offering optimistic perspectives for climate impact studies

An RCM multi-physics ensemble over Europe: Multi-variable evaluation to avoid error compensation

ABSTRACT:Regional Climate Models (RCMs) are widely used tools to add detail to the coarse resolution of global simulations. However, these are known to be affected by biases. Usually, published model evaluations use a reduced number of variables, frequently precipitation and temperature. Due to the complexity of the models, this may not be enough to assess their physical realism (e.g. to enable a fair comparison when weighting ensemble members). Furthermore, looking at only a few variables makes difficult to trace model errors. Thus, in many previous studies, these biases are de- scribed but their underlying causes and mechanisms are often left unknown. In this work the ability of a multi-physics ensemble in reproducing the observed climatologies of any variables over Europe is analysed. These are temperature, precipitation, cloud cover, ra- diative fluxes and total soil moisture content. It is found that, during winter, the model suffers a significant cold bias over snow covered regions. This is shown to be re- lated with a poor representation of the snow-atmosphere interaction, and is amplified by an albedo feedback. It is shown how two members of the ensemble are able to alleviate this bias, but by generating a too large cloud cover. During summer, a large sensitivity to the cumulus parameterization is found, related to large differences in the cloud cover and short wave radiation flux. Results also show that small errors in one variable are sometimes a result of error compensation, so the high dimensionality of the model evaluation problem cannot be disregarded.This work was partially supported by Projects EXTREMBLES (CGL2010-21869) and CORWES (CGL2010-22158-C02), funded by the Spanish R&D Programme. WRF4G (CGL2011-28864) provided the framework to run the model; this Spanish R&D project is co-funded by the European Regional Development Fund (ERDF). Partial support from the 7th European Framework Programme (FP7) through Grant 308291 (EUPORIAS) is also acknowledged

Acclimatization across space and time in the effects of temperature on mortality: a time-series analysis

Background: Climate change has increased the days of unseasonal temperature. Although many studies have examined the association between temperature and mortality, few have examined the timing of exposure where whether this association varies depending on the exposure month even at the same temperature. Therefore, we investigated monthly differences in the effects of temperature on mortality in a study comprising a wide range of weather and years, and we also investigated heterogeneity among regions. Methods: We analyzed 38,005,616 deaths from 148 cities in the U.S. from 1973 through 2006. We fit city specific Poisson regressions to examine the effect of temperature on mortality separately for each month of the year, using penalized splines. We used cluster analysis to group cities with similar weather patterns, and combined results across cities within clusters using meta-smoothing. Results: There was substantial variation in the effects of the same temperature by month. Heat effects were larger in the spring and early summer and cold effects were larger in late fall. In addition, heat effects were larger in clusters where high temperatures were less common, and vice versa for cold effects. Conclusions: The effects of a given temperature on mortality vary spatially and temporally based on how unusual it is for that time and location. This suggests changes in variability of temperature may be more important for health as climate changes than changes of mean temperature. More emphasis should be placed on warnings targeted to early heat/cold temperature for the season or month rather than focusing only on the extremes. Electronic supplementary material The online version of this article (doi:10.1186/1476-069X-13-89) contains supplementary material, which is available to authorized users

Pollution atmosphérique et climat

National audienceClimate change and air quality are closely related: through the policy measures implemented to mitigate these major environmental threats but also through the geophysical processes that drive them. We designed, developed and implemented a comprehensive regional air quality and climate modelling system to investigate future air quality in Europe taking into account the combined pressure of future climate change and long range transport. Using the prospective scenarios of the last generation of pathways for both climate change (emissions of well mixed greenhouse gases) and air pollutants, we can provide a quantitative view into the possible future air quality in Europe. We find that ozone pollution will decrease substantially under the most stringent scenario but the efforts of the air quality legislation will be adversely compensated by the penalty of global warming and long range transport for the business as usual scenario. For particulate matter, the projected reduction of emissions efficiently reduces exposure levels.Changement climatique et qualité de l'air sont intimement liés : à travers les politiques de gestion mises en oeuvre pour atténuer ces menaces environnementales majeures mais aussi à travers les processus géophysiques qui les gouvernent. Afin de pouvoir étudier l'évolution de la pollution atmosphérique en Europe en prenant en compte l'influence conjointe du changement climatique et du transport à longue distance, nous avons conçu, développé et mis en oeuvre un système complet de modélisation régionale du climat et de la qualité de l'air. En utilisant des scénarios prospectifs de dernière génération relatifs au changement climatique (émissions de gaz à effet de serre) mais aussi pour les polluants à courte durée de vie, nous avons pu proposer une quantification de l'évolution future de la qualité de l'air en Europe. D'après le scénario le plus volontariste, la pollution liée à l'ozone sera réduite de manière substantielle mais les efforts positifs induits par les politiques de gestion de la qualité de l'air seront contrebalancés par le changement climatique et le transport à longue distance pour le scénario statu-quo. En ce qui concerne les particules, les réductions d'émissions futures réduiront de manière efficace les niveaux d'exposition

The PREV'AIR system, an operational system for large scale air quality forecasts over Europe : applications at the local scale

International audienceSince Summer 2003, the PREV'AIR system has been delivering through the Internet daily air quality forecasts over Europe. This is the visible part of a wider collaborative project - the PREV'AIR project - launched by the French Ministry for Ecology and Sustainable Development (MEDD), aiming at: (1) Providing technical support on atmospheric pollution management in Europe, in the framework of negotiations on trans-boundary air pollution. (2) Providing large scale national air quality information based on numerical simulations and observations. The PREV'AIR system is a complementary monitoring tool with respect to the local information delivered by the French qualified associations in charge of regional air quality monitoring (AASQA). PREV'AIR relies on a chain of numerical tools: air quality simulation models, modules ensuring the provision of meteorological and air quality input data to these models, modules enabling the extraction and use of the numerical data computed by the system. The outputs of the PREV'AIR system (secondary pollutants forecasts and maps) are archived to build up a large scale air quality simulation data base over Europe

The PREV’AIR system, an operational system for large scale air quality forecasts over Europe; applications at the local scale

International audienceNumerical simulations of pollution events with deterministic models have become easier for the last decade thanks to increasing computer skills. Hence three-dimensional chemistry-transport-runs can be performed on a single workstation for long-term simulation or real-time forecast over large scale areas. Furthermore, fast Internet download and high file storage capacity in data processing make it possible to use a wide database of meteorological parameters and pollutant concentration measurements. The PREV'AIR System rests on those technological progresses for delivering daily air qualiry forecasts in operational conditions

Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains

Copyright 2011 Elsevier B.V., All rights reserved.Over twenty modeling groups are participating in the Air Quality Model Evaluation International Initiative (AQMEII) in which a variety of mesoscale photochemical and aerosol air quality modeling systems are being applied to continental-scale domains in North America and Europe for 2006 full-year simulations for model inter-comparisons and evaluations. To better understand the reasons for differences in model results among these participating groups, each group was asked to use the same source of emissions and boundary concentration data for their simulations. This paper describes the development and application of the boundary concentration data for this AQMEII modeling exercise. The European project known as GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data) has produced global-scale re-analyses of air quality for several years, including 2006 (http://gems.ecmwf.int). The GEMS trace gas and aerosol data were made available at 3-hourly intervals on a regular latitude/longitude grid of approximately 1.9° resolution within 2 "cut-outs" from the global model domain. One cut-out was centered over North America and the other over Europe, covering sufficient spatial domain for each modeling group to extract the necessary time- and space-varying (horizontal and vertical) concentrations for their mesoscale model boundaries. Examples of the impact of these boundary concentrations on the AQMEII continental simulations are presented to quantify the sensitivity of the simulations to boundary concentrations. In addition, some participating groups were not able to use the GEMS data and instead relied upon other sources for their boundary concentration specifications. These are noted, and the contrasting impacts of other data sources for boundary data are presented. How one specifies four-dimensional boundary concentrations for mesoscale air quality simulations can have a profound impact on the model results, and hence, this aspect of data preparation must be performed with considerable care.Peer reviewedFinal Accepted Versio

Two methods for estimating limits to large-scale wind power generation

Wind turbines remove kinetic energy from the atmospheric flow, which reduces wind speeds and limits generation rates of large wind farms. These interactions can be approximated using a vertical kinetic energy (VKE) flux method, which predicts that the maximum power generation potential is 26% of the instantaneous downward transport of kinetic energy using the preturbine climatology. We compare the energy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped with a wind turbine parameterization over a 105 km2 region in the central United States. The WRF simulations yield a maximum generation of 1.1 We⋅m−2, whereas the VKE method predicts the time series while underestimating the maximum generation rate by about 50%. Because VKE derives the generation limit from the preturbine climatology, potential changes in the vertical kinetic energy flux from the free atmosphere are not considered. Such changes are important at night when WRF estimates are about twice the VKE value because wind turbines interact with the decoupled nocturnal low-level jet in this region. Daytime estimates agree better to 20% because the wind turbines induce comparatively small changes to the downward kinetic energy flux. This combination of downward transport limits and wind speed reductions explains why large-scale wind power generation in windy regions is limited to about 1 We⋅m−2, with VKE capturing this combination in a comparatively simple way

Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities

Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction