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A major source of uncertainty in regional climate model simulations arises from the convection parameterisation. Increasing spatial resolution to the so-called convection-permitting scale allows switching off most of the convective parameterisations. Several studies prove the benefits of this spatial scale, but none of them is based on climatological time-scale (i.e. 30 years) as this research.
regional climate model --- convection --- Regionale Klimamodelle --- Konvektion --- atmosphärische Prozesse --- convection-permitting scale --- atmospheric processes --- climate change signal --- Klimawandel-Signal
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A major source of uncertainty in regional climate model simulations arises from the convection parameterisation. Increasing spatial resolution to the so-called convection-permitting scale allows switching off most of the convective parameterisations. Several studies prove the benefits of this spatial scale, but none of them is based on climatological time-scale (i.e. 30 years) as this research.
regional climate model --- convection --- Regionale Klimamodelle --- Konvektion --- atmosphärische Prozesse --- convection-permitting scale --- atmospheric processes --- climate change signal --- Klimawandel-Signal
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A major source of uncertainty in regional climate model simulations arises from the convection parameterisation. Increasing spatial resolution to the so-called convection-permitting scale allows switching off most of the convective parameterisations. Several studies prove the benefits of this spatial scale, but none of them is based on climatological time-scale (i.e. 30 years) as this research.
regional climate model --- convection --- Regionale Klimamodelle --- Konvektion --- atmosphärische Prozesse --- convection-permitting scale --- atmospheric processes --- climate change signal --- Klimawandel-Signal
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Melt takes place where the surface of glaciers or ice sheets interacts with the atmosphere. While the processes governing surface melt are fairly well understood, the pathways of the meltwater, from its origin to the moment it leaves a glacier system, remain enigmatic. It is not even guaranteed that meltwater leaves a glacier or ice sheet. On Greenland, for example, only slightly more than 50% of the meltwater runs off. The remainder mostly refreezes within the so-called firn cover of the ice sheet. This eBook contains 11 studies which tackle the challenge of understanding meltwater retention in snow and firn from various angles. The studies focus both on mountain glaciers and on the Greenland ice sheet and address challenges such as measuring firn properties, quantifying their influence on meltwater retention, modelling firn processes and meltwater refreezing as well as unravelling the mechanisms within the recently discovered Greenland firn aquifers.
firn --- regional climate model --- expert elicitation --- refreezing --- mass balance --- Meltwater retention --- snow --- Greenland firn aquifer --- glacier and ice sheet --- sea level rise
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Melt takes place where the surface of glaciers or ice sheets interacts with the atmosphere. While the processes governing surface melt are fairly well understood, the pathways of the meltwater, from its origin to the moment it leaves a glacier system, remain enigmatic. It is not even guaranteed that meltwater leaves a glacier or ice sheet. On Greenland, for example, only slightly more than 50% of the meltwater runs off. The remainder mostly refreezes within the so-called firn cover of the ice sheet. This eBook contains 11 studies which tackle the challenge of understanding meltwater retention in snow and firn from various angles. The studies focus both on mountain glaciers and on the Greenland ice sheet and address challenges such as measuring firn properties, quantifying their influence on meltwater retention, modelling firn processes and meltwater refreezing as well as unravelling the mechanisms within the recently discovered Greenland firn aquifers.
firn --- regional climate model --- expert elicitation --- refreezing --- mass balance --- Meltwater retention --- snow --- Greenland firn aquifer --- glacier and ice sheet --- sea level rise
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Melt takes place where the surface of glaciers or ice sheets interacts with the atmosphere. While the processes governing surface melt are fairly well understood, the pathways of the meltwater, from its origin to the moment it leaves a glacier system, remain enigmatic. It is not even guaranteed that meltwater leaves a glacier or ice sheet. On Greenland, for example, only slightly more than 50% of the meltwater runs off. The remainder mostly refreezes within the so-called firn cover of the ice sheet. This eBook contains 11 studies which tackle the challenge of understanding meltwater retention in snow and firn from various angles. The studies focus both on mountain glaciers and on the Greenland ice sheet and address challenges such as measuring firn properties, quantifying their influence on meltwater retention, modelling firn processes and meltwater refreezing as well as unravelling the mechanisms within the recently discovered Greenland firn aquifers.
firn --- regional climate model --- expert elicitation --- refreezing --- mass balance --- Meltwater retention --- snow --- Greenland firn aquifer --- glacier and ice sheet --- sea level rise
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MAR (Modèle Atmosphérique Régional) meets the needs to update its surface data to take better account of relations between surface and atmosphere in the framework of its actual projects. In this master thesis, vegetation (GlobCover, ESA) and soil texture (HWSD, IIASA) data has been integrated to MAR to update and improve the spatial resolution and cover in regard to the previous data. The impact of this new data has been assessed in Belgium during the 2008-2014 period at a resolution of 7.5 km. Moreover, the sensitivity of MAR to dynamic NDVI has also been appraised to reveal its significance on the surface radiative budget. Results show that MAR reproduce very well the variability of temperature over Belgium but it is not the case for precipitation. In general, the model is too cold and too moist in regard of the IRM (Institut Royal Météorologique) observations. The sensitivity test to vegetation and soil texture data did not show big differences. These results may be explained in several ways : the cold bias has hidden a part of the data effects ; the size of our domain may force too strongly the model at its frontiers ; the surface scheme of the model may not be sensitive enough to this kind of data. Nevertheless, this master thesis has given rise to other update track as the vegetation classification of the surface scheme or the NDVI data. Furthermore, the integration of global data will increase the efficiency of MAR in regions which had not or bad vegetation and soil texture data. Le Modèle Atmosphérique Régional (MAR) rencontre le besoin de mettre à jour ses données de surface afin de mieux prendre en compte les relations surface- atmosphère dans le cadre de ses nouveaux projets. Dans ce travail, des données de végétation (GlobCover, ESA) et de texture de sol (HWSD, IIASA) ont été intégrées au modèle afin d’actualiser et d’améliorer la résolution ainsi que la couverture spatiale des informations qui étaient présentes dans MAR. L’impact de ce changement de données a été évalué sur la Belgique au cours de la période 2008-2014 à une résolution de 7,5 km. De plus, la sensibilité aux données de NDVI mensuel a été évaluée afin de démontrer l’importance de la saisonnalité de cette variable dans le bilan radiatif. Les résultats des simulations nous montrent que MAR reproduit bien la variabilité des températures, mais peine à reproduire le cycle des précipitations. De manière générale, le modèle est trop froid et trop humide par rapport aux observations de l’Institut Royal Météorologique belge. Les tests de sensibilité aux données de végétation et de texture n’ont montré que de faibles différences. Ceci peut être dû à plusieurs raisons : le biais froid a masqué une partie des effets des données ; la taille du domaine peut contraindre trop fortement le modèle aux frontières ; le module de surface peut ne pas être suffisamment sensible à ce type de données. Toutefois, ce travail a permis de soulever d’autres pistes d’actualisation comme les classes de végétation du module de surface ou les données de NDVI. De plus, l’intégration de données globales permettra d’utiliser MAR plus efficacement dans les régions qui ne disposait pas ou peu de données de végétation et de texture.
vegetation --- soil texture --- modelisation --- MAR model --- Belgium --- regional climate model --- texture de sol --- Belgique --- modèle MAR --- modélisation climatique régionale --- Physique, chimie, mathématiques & sciences de la terre > Sciences de la terre & géographie physique
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- Water resources management should be assessed under climate change conditions, as historic data cannot replicate future climatic conditions. - Climate change impacts on water resources are bound to affect all water uses, i.e., irrigated agriculture, domestic and industrial water supply, hydropower generation, and environmental flow (of streams and rivers) and water level (of lakes). - Bottom-up approaches, i.e., the forcing of hydrologic simulation models with climate change models’ outputs, are the most common engineering practices and considered as climate-resilient water management approaches. - Hydrologic simulations forced by climate change scenarios derived from regional climate models (RCMs) can provide accurate assessments of the future water regime at basin scales. - Irrigated agriculture requires special attention as it is the principal water consumer and alterations of both precipitation and temperature patterns will directly affect agriculture yields and incomes. - Integrated water resources management (IWRM) requires multidisciplinary and interdisciplinary approaches, with climate change to be an emerging cornerstone in the IWRM concept.
Research & information: general --- California --- hydrologic regions --- warming --- drought --- regional climate modeling --- hydrological modeling --- bias correction --- multivariate --- pseudo reality --- rainfall --- trend analysis --- Mann–Kendall --- kriging interpolation --- multiple climate models --- standardized precipitation index (SPI) --- droughts --- weights --- Vu Gia-Thu Bon --- climate change --- optimal control --- geoengineering --- climate manipulation --- GCM --- RCM --- CMIP5 --- CORDEX --- climate model selection --- upper Indus basin --- NDVI --- ENSO --- wavelet --- time series analysis --- Hluhluwe-iMfolozi Park --- Google Earth Engine --- Mediterranean climate --- cluster analysis --- objective classification --- ERA5 --- mega-fires --- Bayesian-model averaging --- model uncertainty --- climate-fire models --- Mono River watershed --- climate --- temperature --- heat wave --- excess heat factor --- acclimatization --- Greece --- precipitations --- Hurst exponent --- persistence --- spatial correlation --- Caucasian region --- Regional Climate Model --- climate classification --- bias correction methods --- precipitation --- terrestrial ecosystems --- GPP --- LAI --- CO2 fertilization effect --- feedback --- sassandra watershed --- Côte d’Ivoire --- boreal region --- extreme wind speed --- wind climate --- soil frost --- wind damage risk management --- wind multiplier --- downscaling --- topography --- surface roughness --- VIIRS --- MODIS --- OLCI --- RSB --- SNPP --- Terra --- Aqua --- Sentinel-3A --- reflective solar bands --- intersensor comparison --- intercalibration --- SNO --- climate indices --- climate change and Conakry
Choose an application
- Water resources management should be assessed under climate change conditions, as historic data cannot replicate future climatic conditions. - Climate change impacts on water resources are bound to affect all water uses, i.e., irrigated agriculture, domestic and industrial water supply, hydropower generation, and environmental flow (of streams and rivers) and water level (of lakes). - Bottom-up approaches, i.e., the forcing of hydrologic simulation models with climate change models’ outputs, are the most common engineering practices and considered as climate-resilient water management approaches. - Hydrologic simulations forced by climate change scenarios derived from regional climate models (RCMs) can provide accurate assessments of the future water regime at basin scales. - Irrigated agriculture requires special attention as it is the principal water consumer and alterations of both precipitation and temperature patterns will directly affect agriculture yields and incomes. - Integrated water resources management (IWRM) requires multidisciplinary and interdisciplinary approaches, with climate change to be an emerging cornerstone in the IWRM concept.
Research & information: general --- California --- hydrologic regions --- warming --- drought --- regional climate modeling --- hydrological modeling --- bias correction --- multivariate --- pseudo reality --- rainfall --- trend analysis --- Mann–Kendall --- kriging interpolation --- multiple climate models --- standardized precipitation index (SPI) --- droughts --- weights --- Vu Gia-Thu Bon --- climate change --- optimal control --- geoengineering --- climate manipulation --- GCM --- RCM --- CMIP5 --- CORDEX --- climate model selection --- upper Indus basin --- NDVI --- ENSO --- wavelet --- time series analysis --- Hluhluwe-iMfolozi Park --- Google Earth Engine --- Mediterranean climate --- cluster analysis --- objective classification --- ERA5 --- mega-fires --- Bayesian-model averaging --- model uncertainty --- climate-fire models --- Mono River watershed --- climate --- temperature --- heat wave --- excess heat factor --- acclimatization --- Greece --- precipitations --- Hurst exponent --- persistence --- spatial correlation --- Caucasian region --- Regional Climate Model --- climate classification --- bias correction methods --- precipitation --- terrestrial ecosystems --- GPP --- LAI --- CO2 fertilization effect --- feedback --- sassandra watershed --- Côte d’Ivoire --- boreal region --- extreme wind speed --- wind climate --- soil frost --- wind damage risk management --- wind multiplier --- downscaling --- topography --- surface roughness --- VIIRS --- MODIS --- OLCI --- RSB --- SNPP --- Terra --- Aqua --- Sentinel-3A --- reflective solar bands --- intersensor comparison --- intercalibration --- SNO --- climate indices --- climate change and Conakry
Choose an application
- Water resources management should be assessed under climate change conditions, as historic data cannot replicate future climatic conditions. - Climate change impacts on water resources are bound to affect all water uses, i.e., irrigated agriculture, domestic and industrial water supply, hydropower generation, and environmental flow (of streams and rivers) and water level (of lakes). - Bottom-up approaches, i.e., the forcing of hydrologic simulation models with climate change models’ outputs, are the most common engineering practices and considered as climate-resilient water management approaches. - Hydrologic simulations forced by climate change scenarios derived from regional climate models (RCMs) can provide accurate assessments of the future water regime at basin scales. - Irrigated agriculture requires special attention as it is the principal water consumer and alterations of both precipitation and temperature patterns will directly affect agriculture yields and incomes. - Integrated water resources management (IWRM) requires multidisciplinary and interdisciplinary approaches, with climate change to be an emerging cornerstone in the IWRM concept.
California --- hydrologic regions --- warming --- drought --- regional climate modeling --- hydrological modeling --- bias correction --- multivariate --- pseudo reality --- rainfall --- trend analysis --- Mann–Kendall --- kriging interpolation --- multiple climate models --- standardized precipitation index (SPI) --- droughts --- weights --- Vu Gia-Thu Bon --- climate change --- optimal control --- geoengineering --- climate manipulation --- GCM --- RCM --- CMIP5 --- CORDEX --- climate model selection --- upper Indus basin --- NDVI --- ENSO --- wavelet --- time series analysis --- Hluhluwe-iMfolozi Park --- Google Earth Engine --- Mediterranean climate --- cluster analysis --- objective classification --- ERA5 --- mega-fires --- Bayesian-model averaging --- model uncertainty --- climate-fire models --- Mono River watershed --- climate --- temperature --- heat wave --- excess heat factor --- acclimatization --- Greece --- precipitations --- Hurst exponent --- persistence --- spatial correlation --- Caucasian region --- Regional Climate Model --- climate classification --- bias correction methods --- precipitation --- terrestrial ecosystems --- GPP --- LAI --- CO2 fertilization effect --- feedback --- sassandra watershed --- Côte d’Ivoire --- boreal region --- extreme wind speed --- wind climate --- soil frost --- wind damage risk management --- wind multiplier --- downscaling --- topography --- surface roughness --- VIIRS --- MODIS --- OLCI --- RSB --- SNPP --- Terra --- Aqua --- Sentinel-3A --- reflective solar bands --- intersensor comparison --- intercalibration --- SNO --- climate indices --- climate change and Conakry
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