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- Author or Editor: Lauriane Batté x
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Abstract
Early indication of an increased risk of extremely warm conditions could help alleviate some of the consequences of severe heat waves on human health. This study focuses on boreal spring heat wave events over West Africa and the Sahel and examines the long-range predictability and forecast quality of these events with two coupled forecasting systems designed at Météo-France, both based on the CNRM-CM coupled global climate model: the operational seasonal forecasting System 5 and the experimental contribution to the World Weather Research Programme/World Climate Research Programme (WWRP/WCRP) subseasonal-to-seasonal (S2S) project. Evaluation is based on past reforecasts spanning 22 years, from 1993 to 2014, compared to reference data from reanalyses. On the seasonal time scale, skill in reproducing interannual anomalies of heat wave duration is limited at a gridpoint level but is significant for regional averages. Subseasonal predictability of daily humidity-corrected apparent temperature drops sharply beyond the deterministic range. In addition to reforecast skill measures, the analysis of real-time forecasts for 2016, both in terms of anomalies with respect to the reforecast climatology and using a weather-type approach, provides additional insight on the systems’ performance in giving relevant information on the possible occurrence of such events.
Abstract
Early indication of an increased risk of extremely warm conditions could help alleviate some of the consequences of severe heat waves on human health. This study focuses on boreal spring heat wave events over West Africa and the Sahel and examines the long-range predictability and forecast quality of these events with two coupled forecasting systems designed at Météo-France, both based on the CNRM-CM coupled global climate model: the operational seasonal forecasting System 5 and the experimental contribution to the World Weather Research Programme/World Climate Research Programme (WWRP/WCRP) subseasonal-to-seasonal (S2S) project. Evaluation is based on past reforecasts spanning 22 years, from 1993 to 2014, compared to reference data from reanalyses. On the seasonal time scale, skill in reproducing interannual anomalies of heat wave duration is limited at a gridpoint level but is significant for regional averages. Subseasonal predictability of daily humidity-corrected apparent temperature drops sharply beyond the deterministic range. In addition to reforecast skill measures, the analysis of real-time forecasts for 2016, both in terms of anomalies with respect to the reforecast climatology and using a weather-type approach, provides additional insight on the systems’ performance in giving relevant information on the possible occurrence of such events.
Abstract
Soil moisture anomalies are expected to be a driver of summer predictability for the U.S. Great Plains since this region is prone to intense and year-to-year varying water and energy exchange between the land and the atmosphere. However, dynamical seasonal forecast systems struggle to deliver skillful summer temperature forecasts over that region, otherwise subject to a consistent warm-season dry bias in many climate models. This study proposes two techniques to mitigate the impact of this precipitation deficit on the modeled soil water content in a forecast system based on the CNRM-CM6-1 model. Both techniques lead to increased evapotranspiration during summer and reduced temperature and precipitation bias. However, only the technique based on a correction of the precipitation feeding the land surface throughout the forecast integration enables skillful summer prediction. Although this result cannot be generalized for other parts of the globe, it confirms the link between bias and skill over the U.S. Great Plains and pleads for continued efforts of the modeling community to tackle the summer bias affecting that region.
Abstract
Soil moisture anomalies are expected to be a driver of summer predictability for the U.S. Great Plains since this region is prone to intense and year-to-year varying water and energy exchange between the land and the atmosphere. However, dynamical seasonal forecast systems struggle to deliver skillful summer temperature forecasts over that region, otherwise subject to a consistent warm-season dry bias in many climate models. This study proposes two techniques to mitigate the impact of this precipitation deficit on the modeled soil water content in a forecast system based on the CNRM-CM6-1 model. Both techniques lead to increased evapotranspiration during summer and reduced temperature and precipitation bias. However, only the technique based on a correction of the precipitation feeding the land surface throughout the forecast integration enables skillful summer prediction. Although this result cannot be generalized for other parts of the globe, it confirms the link between bias and skill over the U.S. Great Plains and pleads for continued efforts of the modeling community to tackle the summer bias affecting that region.
Abstract
The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.
Abstract
The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.
