Modeling climate trends and variability in High Mountain Asia to understand cryosphere changes

The High Mountain Asia (HMA) is hosting the largest ice stock after the polar regions. This resource provides a freshwater supply to nearly 1.4 billion people, making it a particularly vulnerable region to climate change. HMA includes the highest mountain ranges on Earth, including the Himalayas, th...

Full description

Bibliographic Details
Main Author: Lalande, Mickaël
Other Authors: Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Université Grenoble Alpes 2020-., Gerhard Krinner
Format: Doctoral or Postdoctoral Thesis
Language:French
Published: HAL CCSD 2023
Subjects:
Online Access:https://theses.hal.science/tel-04368947
https://theses.hal.science/tel-04368947/document
https://theses.hal.science/tel-04368947/file/LALANDE_2023_archivage.pdf
id ftunigrenoble:oai:HAL:tel-04368947v1
record_format openpolar
spelling ftunigrenoble:oai:HAL:tel-04368947v1 2024-04-14T08:08:40+00:00 Modeling climate trends and variability in High Mountain Asia to understand cryosphere changes Modélisation de la variabilité et des tendances climatiques dans les Hautes Montagnes d'Asie pour une meilleure compréhension de leurs impacts sur la cryosphère Lalande, Mickaël Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) Université Grenoble Alpes 2020-. Gerhard Krinner 2023-02-28 https://theses.hal.science/tel-04368947 https://theses.hal.science/tel-04368947/document https://theses.hal.science/tel-04368947/file/LALANDE_2023_archivage.pdf fr fre HAL CCSD NNT: 2023GRALU005 tel-04368947 https://theses.hal.science/tel-04368947 https://theses.hal.science/tel-04368947/document https://theses.hal.science/tel-04368947/file/LALANDE_2023_archivage.pdf info:eu-repo/semantics/OpenAccess https://theses.hal.science/tel-04368947 Sciences de la Terre. Université Grenoble Alpes [2020-.], 2023. Français. ⟨NNT : 2023GRALU005⟩ High Mountain Asia Mountain Areas General Circulation Models CMIP6 Snow Cover Parameterizations Hautes Montagnes d'Asie Région de Montagne Modèles de Circulation Générale Couverture de Neige Paramétrisations [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/doctoralThesis Theses 2023 ftunigrenoble 2024-03-21T16:09:22Z The High Mountain Asia (HMA) is hosting the largest ice stock after the polar regions. This resource provides a freshwater supply to nearly 1.4 billion people, making it a particularly vulnerable region to climate change. HMA includes the highest mountain ranges on Earth, including the Himalayas, the Karakoram, and the Hindu Kush, which surround the Tibetan Plateau (TP), an area of nearly 2.5 million km² with an average elevation of about 4000~m.Studying climate change in HMA is challenging because of its complex topography which makes difficult the application of climate models in this area, and limits the possibility to collect observations. The aim of this thesis is to study the variability and trends of the climate in HMA. It is based on two main objectives: (1) studying and quantifying the climate change in HMA with general circulation models (GCMs) experiments and observation datasets, and (2) improving the simulated snow cover in mountainous regions in GCMs.Current GCMs simulate a cold bias in HMA reaching an annual average value of -1.9 °C, associated with an overestimation of snow cover of 12 % and an excess of precipitation of 1.5 mm d-1 (relative biases of 52 % and 143 % as compared to observations). Model biases and their ability to simulate trends do not show a clear link, suggesting that model bias is not a robust criterion to discard models in trend analysis.The simulated median warming in HMA over 2081-2100 as compared to 1995-2014 reaches respectively 1.9 and 6.5 °C on the low (SSP1-2.6) and the high (SSP5-8.5) greenhouse gas emission scenarios. This warming is associated with a relative decrease in the snow cover extent of -9.4 to -32.2 % and a relative increase in precipitation of 8.5 to 24.9 % in these two respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces, excluding the Arctic area.5 parameterizations of the snow cover fraction (SCF) are tested, calibrated, and validated using a snow reanalysis in HMA. The relationship ... Doctoral or Postdoctoral Thesis Arctic Climate change Université Grenoble Alpes: HAL Arctic
institution Open Polar
collection Université Grenoble Alpes: HAL
op_collection_id ftunigrenoble
language French
topic High Mountain Asia
Mountain Areas
General Circulation Models
CMIP6
Snow Cover
Parameterizations
Hautes Montagnes d'Asie
Région de Montagne
Modèles de Circulation Générale
Couverture de Neige
Paramétrisations
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences
spellingShingle High Mountain Asia
Mountain Areas
General Circulation Models
CMIP6
Snow Cover
Parameterizations
Hautes Montagnes d'Asie
Région de Montagne
Modèles de Circulation Générale
Couverture de Neige
Paramétrisations
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences
Lalande, Mickaël
Modeling climate trends and variability in High Mountain Asia to understand cryosphere changes
topic_facet High Mountain Asia
Mountain Areas
General Circulation Models
CMIP6
Snow Cover
Parameterizations
Hautes Montagnes d'Asie
Région de Montagne
Modèles de Circulation Générale
Couverture de Neige
Paramétrisations
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences
description The High Mountain Asia (HMA) is hosting the largest ice stock after the polar regions. This resource provides a freshwater supply to nearly 1.4 billion people, making it a particularly vulnerable region to climate change. HMA includes the highest mountain ranges on Earth, including the Himalayas, the Karakoram, and the Hindu Kush, which surround the Tibetan Plateau (TP), an area of nearly 2.5 million km² with an average elevation of about 4000~m.Studying climate change in HMA is challenging because of its complex topography which makes difficult the application of climate models in this area, and limits the possibility to collect observations. The aim of this thesis is to study the variability and trends of the climate in HMA. It is based on two main objectives: (1) studying and quantifying the climate change in HMA with general circulation models (GCMs) experiments and observation datasets, and (2) improving the simulated snow cover in mountainous regions in GCMs.Current GCMs simulate a cold bias in HMA reaching an annual average value of -1.9 °C, associated with an overestimation of snow cover of 12 % and an excess of precipitation of 1.5 mm d-1 (relative biases of 52 % and 143 % as compared to observations). Model biases and their ability to simulate trends do not show a clear link, suggesting that model bias is not a robust criterion to discard models in trend analysis.The simulated median warming in HMA over 2081-2100 as compared to 1995-2014 reaches respectively 1.9 and 6.5 °C on the low (SSP1-2.6) and the high (SSP5-8.5) greenhouse gas emission scenarios. This warming is associated with a relative decrease in the snow cover extent of -9.4 to -32.2 % and a relative increase in precipitation of 8.5 to 24.9 % in these two respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces, excluding the Arctic area.5 parameterizations of the snow cover fraction (SCF) are tested, calibrated, and validated using a snow reanalysis in HMA. The relationship ...
author2 Institut des Géosciences de l’Environnement (IGE)
Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )
Université Grenoble Alpes (UGA)
Université Grenoble Alpes 2020-.
Gerhard Krinner
format Doctoral or Postdoctoral Thesis
author Lalande, Mickaël
author_facet Lalande, Mickaël
author_sort Lalande, Mickaël
title Modeling climate trends and variability in High Mountain Asia to understand cryosphere changes
title_short Modeling climate trends and variability in High Mountain Asia to understand cryosphere changes
title_full Modeling climate trends and variability in High Mountain Asia to understand cryosphere changes
title_fullStr Modeling climate trends and variability in High Mountain Asia to understand cryosphere changes
title_full_unstemmed Modeling climate trends and variability in High Mountain Asia to understand cryosphere changes
title_sort modeling climate trends and variability in high mountain asia to understand cryosphere changes
publisher HAL CCSD
publishDate 2023
url https://theses.hal.science/tel-04368947
https://theses.hal.science/tel-04368947/document
https://theses.hal.science/tel-04368947/file/LALANDE_2023_archivage.pdf
geographic Arctic
geographic_facet Arctic
genre Arctic
Climate change
genre_facet Arctic
Climate change
op_source https://theses.hal.science/tel-04368947
Sciences de la Terre. Université Grenoble Alpes [2020-.], 2023. Français. ⟨NNT : 2023GRALU005⟩
op_relation NNT: 2023GRALU005
tel-04368947
https://theses.hal.science/tel-04368947
https://theses.hal.science/tel-04368947/document
https://theses.hal.science/tel-04368947/file/LALANDE_2023_archivage.pdf
op_rights info:eu-repo/semantics/OpenAccess
_version_ 1796306087349059584