Ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique
Over the last twenty years, research in ensemble predictions at a seasonal timescale using general circulation models has undergone a considerable development due to the exponential growth rate of computing capacities, the improved model resolution and the introduction of more and more components (o...
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ftecoleponts:oai:HAL:pastel-00795478v1 2024-09-09T20:07:53+00:00 Ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique Prévisions d'ensemble à l'échelle saisonnière : mise en place d'une dynamique stochastique Saunier-Batté, Lauriane Centre d'Enseignement et de Recherche en Environnement Atmosphérique (CEREA) École des Ponts ParisTech (ENPC)-EDF R&D (EDF R&D) EDF (EDF)-EDF (EDF) Université Paris-Est Marc Bocquet 2013-01-23 https://pastel.hal.science/pastel-00795478 https://pastel.hal.science/pastel-00795478/document https://pastel.hal.science/pastel-00795478/file/TH2013PEST1001_complete.pdf fr fre HAL CCSD NNT: 2013PEST1001 pastel-00795478 https://pastel.hal.science/pastel-00795478 https://pastel.hal.science/pastel-00795478/document https://pastel.hal.science/pastel-00795478/file/TH2013PEST1001_complete.pdf info:eu-repo/semantics/OpenAccess https://pastel.hal.science/pastel-00795478 Sciences de la Terre. Université Paris-Est, 2013. Français. ⟨NNT : 2013PEST1001⟩ Seasonal forecast Stochastic physics Ensemble forecast Prévision saisonnière du climat Physique stochastique Prévision d'ensemble [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/doctoralThesis Theses 2013 ftecoleponts 2024-07-24T07:39:32Z Over the last twenty years, research in ensemble predictions at a seasonal timescale using general circulation models has undergone a considerable development due to the exponential growth rate of computing capacities, the improved model resolution and the introduction of more and more components (ocean, atmosphere, land surface and sea-ice) that have an impact on climate at this time scale. Regardless of these efforts, predicting temperature and precipitation for the upcoming season is a difficult task, not only over mid-latitudes but also over regions subject to high climate risk, like West Africa during the monsoon season. One key to improving predictions is to represent model uncertainties (due to resolution, parametrizations, approximations and model error). The multimodel approach is a well-tried method which consists in pooling members from different individual coupled models into a single superensemble. This approach was undertaken as part of the European Commission funded ENSEMBLES project, and we find that it usually improves seasonal precipitation re-forecasts over several regions of Africa with respect to individual model predictions. The main goal of this thesis is to study another approach to addressing model uncertainty in the global coupled model CNRM-CM5, by adding stochastic perturbations to the dynamics of the atmospheric model ARPEGE-Climat. Our method, called “stochastic dynamics”, consists in adding additive perturbations to the temperature, specific humidity and vorticity fields, thus correcting estimations of model initial tendency errors. In this thesis, two initial tendency error estimation techniques were studied, based on nudging the model towards reference data. They yield different results in terms of re-forecast scores, depending on the regions studied. If the initial tendency error corrections are estimated using an iterative nudging method towards the ERA-Interim reanalysis, seasonal prediction scores over the Northern Hemisphere in winter are significantly improved by drawing ... Doctoral or Postdoctoral Thesis Sea ice École des Ponts ParisTech: HAL |
institution |
Open Polar |
collection |
École des Ponts ParisTech: HAL |
op_collection_id |
ftecoleponts |
language |
French |
topic |
Seasonal forecast Stochastic physics Ensemble forecast Prévision saisonnière du climat Physique stochastique Prévision d'ensemble [SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
spellingShingle |
Seasonal forecast Stochastic physics Ensemble forecast Prévision saisonnière du climat Physique stochastique Prévision d'ensemble [SDU.STU]Sciences of the Universe [physics]/Earth Sciences Saunier-Batté, Lauriane Ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique |
topic_facet |
Seasonal forecast Stochastic physics Ensemble forecast Prévision saisonnière du climat Physique stochastique Prévision d'ensemble [SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
description |
Over the last twenty years, research in ensemble predictions at a seasonal timescale using general circulation models has undergone a considerable development due to the exponential growth rate of computing capacities, the improved model resolution and the introduction of more and more components (ocean, atmosphere, land surface and sea-ice) that have an impact on climate at this time scale. Regardless of these efforts, predicting temperature and precipitation for the upcoming season is a difficult task, not only over mid-latitudes but also over regions subject to high climate risk, like West Africa during the monsoon season. One key to improving predictions is to represent model uncertainties (due to resolution, parametrizations, approximations and model error). The multimodel approach is a well-tried method which consists in pooling members from different individual coupled models into a single superensemble. This approach was undertaken as part of the European Commission funded ENSEMBLES project, and we find that it usually improves seasonal precipitation re-forecasts over several regions of Africa with respect to individual model predictions. The main goal of this thesis is to study another approach to addressing model uncertainty in the global coupled model CNRM-CM5, by adding stochastic perturbations to the dynamics of the atmospheric model ARPEGE-Climat. Our method, called “stochastic dynamics”, consists in adding additive perturbations to the temperature, specific humidity and vorticity fields, thus correcting estimations of model initial tendency errors. In this thesis, two initial tendency error estimation techniques were studied, based on nudging the model towards reference data. They yield different results in terms of re-forecast scores, depending on the regions studied. If the initial tendency error corrections are estimated using an iterative nudging method towards the ERA-Interim reanalysis, seasonal prediction scores over the Northern Hemisphere in winter are significantly improved by drawing ... |
author2 |
Centre d'Enseignement et de Recherche en Environnement Atmosphérique (CEREA) École des Ponts ParisTech (ENPC)-EDF R&D (EDF R&D) EDF (EDF)-EDF (EDF) Université Paris-Est Marc Bocquet |
format |
Doctoral or Postdoctoral Thesis |
author |
Saunier-Batté, Lauriane |
author_facet |
Saunier-Batté, Lauriane |
author_sort |
Saunier-Batté, Lauriane |
title |
Ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique |
title_short |
Ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique |
title_full |
Ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique |
title_fullStr |
Ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique |
title_full_unstemmed |
Ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique |
title_sort |
ensemble predictions at the seasonal time scale : implementation of a stochastic dynamics technique |
publisher |
HAL CCSD |
publishDate |
2013 |
url |
https://pastel.hal.science/pastel-00795478 https://pastel.hal.science/pastel-00795478/document https://pastel.hal.science/pastel-00795478/file/TH2013PEST1001_complete.pdf |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
https://pastel.hal.science/pastel-00795478 Sciences de la Terre. Université Paris-Est, 2013. Français. ⟨NNT : 2013PEST1001⟩ |
op_relation |
NNT: 2013PEST1001 pastel-00795478 https://pastel.hal.science/pastel-00795478 https://pastel.hal.science/pastel-00795478/document https://pastel.hal.science/pastel-00795478/file/TH2013PEST1001_complete.pdf |
op_rights |
info:eu-repo/semantics/OpenAccess |
_version_ |
1809941564925935616 |