Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 2. Formulation of experiments and practical applications

Global glacier models provide a new way for studying glaciers on the regional and global scales. They make it possible to perform predictive experiments - for example, to forecast changes in glaciation and river runoff, and diagnostic ones – to identify regularities in the behavior of glaciers (a re...

Full description

Bibliographic Details
Published in:Diversity
Main Authors: T. Postnikova N., O. Rybak O., Т. Постникова Н., О. Рыбак О.
Other Authors: This work was supported by the Russian Foundation for Basic Research, RFBR grant № 20-35-90042. O. O. Rybak was supported by the.Governmental Order to Water Problems Institute of RAS, subject № FMWZ-2022-0001., Работа поддержана РФФИ, грант № 20-35-90042. О. О. Рыбак получил поддержку в рамках темы № FMWZ-2022-0001 Государственного задания ИП РАН.
Format: Article in Journal/Newspaper
Language:Russian
Published: IGRAS 2022
Subjects:
mountain glaciers;glacier modeling;numerical experiments;methods of prediction;climate change
горные ледники;гляциологическое моделирование;численные эксперименты;методы прогнозирования;изменения климата
Annals of Glaciology
The Cryosphere
Online Access:https://ice-snow.igras.ru/jour/article/view/987
https://doi.org/10.31857/S2076673422020133
id ftjias:oai:oai.ice.elpub.ru:article/987
record_format openpolar
institution Open Polar
collection Ice and Snow
op_collection_id ftjias
language Russian
topic mountain glaciers;glacier modeling;numerical experiments;methods of prediction;climate change
горные ледники;гляциологическое моделирование;численные эксперименты;методы прогнозирования;изменения климата
spellingShingle mountain glaciers;glacier modeling;numerical experiments;methods of prediction;climate change
горные ледники;гляциологическое моделирование;численные эксперименты;методы прогнозирования;изменения климата
T. Postnikova N.
O. Rybak O.
Т. Постникова Н.
О. Рыбак О.
Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 2. Formulation of experiments and practical applications
topic_facet mountain glaciers;glacier modeling;numerical experiments;methods of prediction;climate change
горные ледники;гляциологическое моделирование;численные эксперименты;методы прогнозирования;изменения климата
description Global glacier models provide a new way for studying glaciers on the regional and global scales. They make it possible to perform predictive experiments - for example, to forecast changes in glaciation and river runoff, and diagnostic ones – to identify regularities in the behavior of glaciers (a response time to climate change) taking account of their characteristics. The characteristics and design of global glacier models were described in the first part of the review (see Postnikova T.N., Rybak O.O. Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 1. General approach and model architecture. Led i Sneg. Ice and Snow. 2021, 61 (4): 620–636. [In Russian]. doi:10.31857/S2076673421040111.). In the second part, we present the methods for setting up of numerical experiments with these models, including model initialization, climate forcing, calibration, and validation procedures. The only way to provide the climate forcing of a glaciological model on a regional or global scale is to use low-resolution reanalysis or output of climate modeling on GCMs or RCMs that needs to use a process of scaling to reproduce the local climate in a complex topography where glaciers are usually located. Calibration of mass balance complements the downscaling of climate forcing for each glacier, and usually it includes parameters responsible for the glacier's response to climate change. Sampling from the Latin hypercube and Bayesian inversion are some of the methods discussed in this connection. In this review we present a comparative description of the selected global glaciological models, the results obtained by both diagnostic and prognostic ones, as well as scale and significance of them. We discuss also ways for further development of global glacier models, in particular the inclusion of 3D-modeling and the moraine (debris cover) block. The difficulties arising in a process of modeling glaciation of a particular mountain region or several regions are noted. Глобальные ...
author2 This work was supported by the Russian Foundation for Basic Research, RFBR grant № 20-35-90042. O. O. Rybak was supported by the.Governmental Order to Water Problems Institute of RAS, subject № FMWZ-2022-0001.
Работа поддержана РФФИ, грант № 20-35-90042. О. О. Рыбак получил поддержку в рамках темы № FMWZ-2022-0001 Государственного задания ИП РАН.
format Article in Journal/Newspaper
author T. Postnikova N.
O. Rybak O.
Т. Постникова Н.
О. Рыбак О.
author_facet T. Postnikova N.
O. Rybak O.
Т. Постникова Н.
О. Рыбак О.
author_sort T. Postnikova N.
title Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 2. Formulation of experiments and practical applications
title_short Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 2. Formulation of experiments and practical applications
title_full Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 2. Formulation of experiments and practical applications
title_fullStr Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 2. Formulation of experiments and practical applications
title_full_unstemmed Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 2. Formulation of experiments and practical applications
title_sort global glaciological models: a new stage in the development of methods for predicting glacier evolution. part 2. formulation of experiments and practical applications
publisher IGRAS
publishDate 2022
url https://ice-snow.igras.ru/jour/article/view/987
https://doi.org/10.31857/S2076673422020133
genre Annals of Glaciology
The Cryosphere
genre_facet Annals of Glaciology
The Cryosphere
op_source Ice and Snow; Том 62, № 2 (2022); 287-304
Лёд и Снег; Том 62, № 2 (2022); 287-304
2412-3765
2076-6734
op_relation https://ice-snow.igras.ru/jour/article/view/987/617
Farinotti D., Huss M., Fürst J.J., Landmann J., Machguth H., Maussion F., Pandit A. A consensus estimate for the ice thickness distribution of all glaciers on Earth Nature Geoscience 2019, 12 (3): 168–173 https://doi.org/10.1038/s41561-019-0300-3
Cogley J.G. Area of the ocean Marine Geodesy 2012, 35: 379–388 doi: 10.688.1080/01490419.2012.709476
Marzeion B., Hock R., Anderson B., Bliss A., Champollion N., Fujita K., Huss M., Immerzeel W.W., Kraaijenbrink P., Malles J.­H., Maussion F, Radić V., Rounce D.R., Sakai A., Shannon S., van de Wal R., Zekollari H. Partitioning the Uncertainty of Ensemble Projections of Global Glacier Mass Change Earth's Future 2020, 8 (7): e2019EF001470 https://doi.org/10.1029/2019EF001470
Marzeion B., Kaser G., Maussion F., Champollion N. Limited influence of climate change mitigation on short-term glacier mass loss Nature Climate Change 2018, 8: 305–308 doi:10.1038/s41558-018-0093-1
Zekollari H., Huss M., Farinotti D. On the imbalance and response time of glaciers in the European Alps Geophys Research Letter 2020, 47 (2): e2019GL085578 https://doi.org/10.1029/2019GL085578
Huss M., Hock R. Global-scale hydrological response to future glacier mass loss Nature Climate Change 2018, 8: 135–140 https://doi.org/10.1038/s41558-017-0049-x
Rounce D.R., Hock R., Shean D. Glacier mass change in high mountain Asia through 2100 using the opensource Python Glacier Evolution Model (PyGEM) Frontiers in Earth Science 2020, 7: 331 https://doi.org/10.3389/feart.2019.00331
Maussion F., Butenko A., Champollion N., Dusch M., Eis J., Fourteau K., Gregor P., Jarosch A.H., Landmann J., Oesterle F., Recinos B., Rothenpieler T., Vlug A., Wild C.T., Marzeion B. The Open Global Glacier Model (OGGM) v11 Geoscientific Model Development 2019, 12: 909– 931 https://doi.org/10.5194/gmd-12-909-2019
Zekollari H., Huss M., Farinotti D. Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble The Cryosphere 2019, 13: 1125–1146 https://doi.org/10.5194/tc-13-1125-2019
Huss M., Hock R. A new model for global glacier change and sea-level rise Frontiers in Earth Science 2015, 3: 54 https://doi.org/10.3389/feart.2015.00054
Rounce D.R., Khurana T., Short M.B., Hock R., Shean D.E., Brinkerhoff D.J. Quantifying parameter uncertainty in a large-scale glacier evolution model using Bayesian inference: application to High Mountain Asia Journ of Glaciology 2020, 66 (256):175–187
Shannon S., Smith R., Wiltshire A., Payne T., Huss M., Betts R., Caesar J., Koutroulis A., Jones D., Harrison S. Global glacier volume projections under high-end climate change scenarios The Cryosphere 2019, 13: 325–350 https://doi.org/10.5194/tc-2019-35
Hirabayashi Y., Zang Y., Watanabe S., Koirala S., Kanae S. Projection of glacier mass changes under a high-emission climate scenario using the global glacier model HYOGA2 Hydrol Research Letter 2013, 7 (1): 6–11 https://doi.org/10.3178/hrl.7.6
Marzeion B., Jarosch A., Hofer M. Past and future sealevel change from the surface mass balance of glaciers The Cryosphere 2012, 6 (6): 1295–1322 https://doi.org/10.5194/tc-6-1295-2012
Bahr D.B., Meier M.F., Peckham S.D. The physical basis of glacier volume-area scaling Journ of Geophys Reseasrch 1997, 102 (B9): 20355– 20362 https://doi.org/10.1029/97JB01696
Van de Wal R.S.W., Wild M Modelling the response of glaciers to climate change by applying volume-area scaling in combination with a high resolution GSM Climate Dynamics 2001, 18 (3–4): 359–366 doi:10.1007/s003820100184
Huss M., Jouvet G., Farinotti D., Bauder A. Future highmountain hydrology: a new parameterization of glacier retreat // Hydrology and Earth System Sciences 2010, 14: 815–829 https://doi.org/10.5194/hess-14-815-2010
Hofer M., Marzeion B., Mölg T. A statistical downscaling method for daily air temperature in data sparse, glaciated mountain environments Geosci Model Dev 2015, 8: 579–593 https://doi.org/10.5194/gmd-8-5792015
Morozova P.A., Rybak O.O. Regionalization of global climate modeling data for calculating the mass balance of mountain glaciers Led i Sneg. Ice and Snow 2017, 57 (4): 437–452 https://doi.org/10.15356/2076-67342017-4-437-452 [In Russian]
Murphy J An Evaluation of Statistical and Dynamical Techniques for Downscaling Local Climate Journ of Climate 1999, 12 (8): 2256–2284
Taylor K.E., Stouffer R.J., Meehl G.A. An overview of CMIP5 and the experiment design Bull Amer Meteor Soc 2012, 93: 485–498 https://doi.org/10.1175/ BAMS-D-11-00094 1
Eyring V., Bony S., Meehl G.A., Senior C.A., Stevens B., Stouffer B., Taylor K.E. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization Geoscientific Model Development 2016, 9 (5): 1937–1958 doi:10.5194/gmd-9-1937-2016.
Dee D.P., Uppala S.M., Simmons A.J., Berrisford P., Poli P., Kobayashi S., Andrae U., Balmaseda M.A., Balsamo G., Bauer P., Bechtold P., Beljaars A.C.M., van de Berg L., Bidlot J., Bormann N., Delsol C., Dragani R., Fuentes M., Geer A.J., Haimberger L., Healy S.B., Hersbach H., Hólm E.V., Isaksen L., Kållberg P., Köhler M., Matricardi M., McNally A.P., Monge­Sanz B.M., Morcrette J.­J., Park B.­K., Peubey C., de Rosnay P., Tavolato C., Thépaut J.­N., Vitart F. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system Quarterly Journ of the Royal Meteorological Society 2011, 137 (656): 553–597 https://doi.org/10.1002/qj.828
Gutowski Jr.W.J., Giorgi F., Timbal B., Frigon A., Jacob D., Kang H.S. Raghavan K., Lee B., Lennard Ch., Nikulin G., O’Rourke E., Rixen M., Solman S., Stephenson T., Tangang F. WCRP coordinated regional downscaling experiment (CORDEX): a diagnostic MIP for CMIP6 Geoscientific Model Development 2016, 9 (11): 4087–4095 https://doi.org/10.5194/gmd-9-4087-2016
Jacob D., Petersen J., Eggert B., Alias A., Bøssing O., Bouwer L.M., Braun A., Colette A., Georgopoulou E., Gobiet A., Menut L., Nikulin G., Haensler A., Kriegsmann A., Martin E., van Meijgaard E., Moseley C., Pfeifer S., Preuschmann S., Radermacher C., Radtke K., Rechid D., Roundsevell M., Samuelsson P., Somot S., Soussana J.­F., Teichmann C., Valentini R., Vautard R., Weber B., Yiou P. EUROCORDEX: new high-resolution climate change projections for European impact research Reg Environ Change 2014, 14: 563–578 https://doi.org/10.1007/s10113-013-0499-2
Van Vuuren D.P., Edmonds J., Kainuma M., Riahi K., Thomson A., Hibbard K., Hurtt G.C., Kram T., Krey V., Lamarque J.­F., Masui T., Meinshausen M., Nakicenovic N., Smith S.J., Rose S.K. The representative concentration pathways: an overview Climatic Change 2011, 109: 5–31 doi:10.1007/s10584-011-0148-z
O’Neill B.C., Kriegler E., Riahi K., Ebi K.L., Hallegatte S., Carter T.R., Mathur R., Detlef P., van Vuuren D.P. A new scenario framework for climate change research: the concept of shared socioeconomic pathways Climatic Сhange 2014, 122 (3): 387–400 https://doi.org/10.1007/s10584-013-0905-2
Edwards T. Quantifying uncertainties in the land ice contribution to sea level from ISMIP6 and GlacierMIP EGU General Assembly Conference Abstracts 2020: 11241
https://www.climate-cryosphere.org/mips/glaciermip/activities-experiments
Rybak O.O., Rybak E.A., Kutuzov S.S., Lavrentyev I.I., Morozova P.A. Calibration of the mathematical model of the dynamics of the Marukh glacier, Western Caucasus Led i Sneg. Ice and Snow 2015, 55 (2): 9–20 https://doi.org/10.15356/2076-6734-2015-2-9-20 [In Russian]
Radić V., Hock R. Regionally differentiated contribution of mountain glaciers and ice caps to future sealevel rise Nature Geoscience 2011, 4 (2): 91–94 https://doi.org/10.1038/ngeo1052
Jennings K.S., Winchell T.S., Livneh B., Molotch N.P. Spatial variation of the rain-snow temperature threshold across the Northern Hemisphere Nat Commun 2018, 9: 1148 https://doi.org/10.1038/s41467-018-03629-7
Pattyn F. The paradigm shift in Antarctic ice sheet modelling Nat Commun 2018, 9: 2728 https://doi.org/10.1038/s41467-018-05003-z
RGI Consortium Randolph Glacier Inventory (RGI) – A dataset of global glacier outlines: Version 6 0 Technical Report Global Land Ice Measurements from Space, Boulder, Colorado, USA, 2017 https://doi.org/10.7265/N5-RGI-60
Eis J., Maussion F., Marzeion B. Initialization of a global glacier model based on present-day glacier geometry and past climate information: an ensemble approach The Cryosphere 2019, 13: 3317–3335 doi:10.5194/tc-13-3317-2019
Shahgedanova M., Afzal M., Hagg W., Kapitsa V., Kasatkin N., Mayr E., Rybak O., Saidaliyeva Z., Severskiy I., Usmanova Z., Wade A., Yaitskaya N., Zhumabayev D. Emptying Water Towers? Impacts of Future Climate and Glacier Change on River Discharge in the Northern Tien Shan, Central Asia Water 2020, 12: 627 https://doi.org/10.3390/w12030627
WGMS: Fluctuations of Glaciers Database World Glacier Monitoring Service, Zürich, Switzerland 2017 https://doi.org/10.5904/wgmsfog-2017-10
Cogley J.G. Geodetic and direct mass-balance measurements: comparison and joint analysis Annals of Glaciology 2009, 50 (50): 96–100 doi:10.3189/172756409787769744
Scherler D., Bookhagen B., Strecker M.R. Spatially variable response of himalayan glaciers to climate change affected by debris cover Nature Geoscience 2011, 4 (3): 156–159 doi:10.1038/ngeo1068
Gardelle J., Berthier E., Arnaud Y., Kääb A. Region-wide glacier mass balances over the Pamir-Karakoram-Himalaya during 1999–2011 The Cryosphere 2013, 7 (4): 1263–1286 https://doi.org/10.5194/tc-7-1263-2013
Gardner A.S., Moholdt G., Cogley J.G., Wouters B., Arendt A.A., Wahr J., Berthier E., Hock R., Pfeffer W.T., Kaser G., Ligtenberg S.R.M., Bolch T., Sharp M.J., Hagen J.O., van den Broeke M.R., Paul F. A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009 Science 2013, 340: 852–857 doi:10.1126/science.1234532
Zemp M., Huss M., Thilbert E., Eckert N., McNabb R., Huber J., Barandun M., Machguth H., Nussbaumer S.U., Gärtner­Roer I., Thomson L., Paul F., Maussion F., Kutuzov S., Cogley J.G. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016 Nature 2019, 568 (7752): 382–386 https://doi.org/10.1038/s41586-019-1071-0
Shean D.E., Bhushan S., Montesano P., Rounce D.R., Arendt A., Osmanoglu B. A systematic, regional assessment of High Mountain Asia glacier mass balance Frontiers in Earth Science 2020, 7: 363 https://doi.org/10.3389/feart.2019.00363
Zemp M, Frey H., Gärtner­Roer I., Nussbaumer S., Hoelzle M., Paul F., Haeberli W., Denzinger F., Ahlstrøm A.P., Anderson B., Bajracharya S., Baroni C., Braun L.N., Cáceres B.E., Casassa G., Cobos G., Dávila L.R., Delgado Granados H., Demuth M.N., Espizua L., Fischer A., Fujita K., Gadek B., Ghazanfar A., Ove Hagen J., Holmlund P., Karimi N., Li Z., Pelto M., Pitte P., Popovnin V.V., Portocarrero C.A., Prinz R., Sangewar C.V., Severskiy I., Sigurđsson O., Soruco A., Usubaliev R., Vincent C. Historically unprecedented global glacier decline in the early 21st century Journ of Glaciology 2015, 61 (228): 745–762 doi:10.3189/2015JoG15J017
Radić V., Bliss A., Beedlow A.C., Hock R., Miles E., Cogley J.G. Regional and global projections of twenty-first century glacier mass changes in response to climate scenarios from global climate models Climate Dynamics 2014, 42 (1–2): 37–58 https://doi.org/10.1007/s00382-013-1719-7
Berg B.A. Introduction to Markov chain Monte Carlo simulations and their statistical analysis Markov Chain Monte Carlo Lect Notes Ser Inst Math Sci Natl Univ Singap 2005, 7: 1–52.
Renard B., Kavetski D., Kuczera G., Thyer M., Franks S.W. Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors Water Resources Research 2010, 46 (5): 1–22 doi:10.1029/2009WR008328
Stone E.J., Lunt D.J., Rutt I.C., Hanna E. Investigating the sensitivity of numerical model simulations of the modern state of the Greenland ice-sheet and its future response to climate change The Cryosphere 2010, 4: 397–417 https://doi.org/10.5194/tc-4-397-2010
McKay M.D., Beckman R.J., Conover W.J. Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code Technometrics 1979, 21: 239–245
op_rights Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Авторы, публикующие статьи в данном журнале, соглашаются на следующее:Авторы сохраняют за собой авторские права и предоставляют журналу право первой публикации работы, которая по истечении 6 месяцев после публикации автоматически лицензируется на условиях Creative Commons Attribution License , что позволяет другим распространять данную работу с обязательным сохранением ссылок на авторов оригинальной работы и оригинальную публикацию в этом журнале.Редакция журнала будет размещать принятую для публикации статью на сайте журнала до выхода её в свет (после утверждения к печати редколлегией журнала). Авторы также имеют право размещать их работу в сети Интернет (например в институтском хранилище или персональном сайте) до и во время процесса рассмотрения ее данным журналом, так как это может привести к продуктивному обсуждению и большему количеству ссылок на данную работу (См. The Effect of Open Access).
op_doi https://doi.org/10.31857/S207667342202013310.1038/s41561-019-0300-310.1029/2019EF00147010.1038/s41558-018-0093-110.1029/2019GL08557810.1038/s41558-017-0049-x10.3389/feart.2019.0033110.5194/gmd-12-909-201910.5194/tc-13-1125-201910.3389/feart.2015.0005410.5
container_title Diversity
container_volume 14
container_issue 6
container_start_page 429
_version_ 1810484280613142528
spelling ftjias:oai:oai.ice.elpub.ru:article/987 2024-09-15T17:40:03+00:00 Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 2. Formulation of experiments and practical applications Глобальные гляциологические модели: новый этап в развитии методов прогнозирования эволюции ледников. Часть 2. Постановка экспериментов и практические приложения T. Postnikova N. O. Rybak O. Т. Постникова Н. О. Рыбак О. This work was supported by the Russian Foundation for Basic Research, RFBR grant № 20-35-90042. O. O. Rybak was supported by the.Governmental Order to Water Problems Institute of RAS, subject № FMWZ-2022-0001. Работа поддержана РФФИ, грант № 20-35-90042. О. О. Рыбак получил поддержку в рамках темы № FMWZ-2022-0001 Государственного задания ИП РАН. 2022-05-31 application/pdf https://ice-snow.igras.ru/jour/article/view/987 https://doi.org/10.31857/S2076673422020133 rus rus IGRAS https://ice-snow.igras.ru/jour/article/view/987/617 Farinotti D., Huss M., Fürst J.J., Landmann J., Machguth H., Maussion F., Pandit A. A consensus estimate for the ice thickness distribution of all glaciers on Earth Nature Geoscience 2019, 12 (3): 168–173 https://doi.org/10.1038/s41561-019-0300-3 Cogley J.G. Area of the ocean Marine Geodesy 2012, 35: 379–388 doi: 10.688.1080/01490419.2012.709476 Marzeion B., Hock R., Anderson B., Bliss A., Champollion N., Fujita K., Huss M., Immerzeel W.W., Kraaijenbrink P., Malles J.­H., Maussion F, Radić V., Rounce D.R., Sakai A., Shannon S., van de Wal R., Zekollari H. Partitioning the Uncertainty of Ensemble Projections of Global Glacier Mass Change Earth's Future 2020, 8 (7): e2019EF001470 https://doi.org/10.1029/2019EF001470 Marzeion B., Kaser G., Maussion F., Champollion N. Limited influence of climate change mitigation on short-term glacier mass loss Nature Climate Change 2018, 8: 305–308 doi:10.1038/s41558-018-0093-1 Zekollari H., Huss M., Farinotti D. On the imbalance and response time of glaciers in the European Alps Geophys Research Letter 2020, 47 (2): e2019GL085578 https://doi.org/10.1029/2019GL085578 Huss M., Hock R. Global-scale hydrological response to future glacier mass loss Nature Climate Change 2018, 8: 135–140 https://doi.org/10.1038/s41558-017-0049-x Rounce D.R., Hock R., Shean D. Glacier mass change in high mountain Asia through 2100 using the opensource Python Glacier Evolution Model (PyGEM) Frontiers in Earth Science 2020, 7: 331 https://doi.org/10.3389/feart.2019.00331 Maussion F., Butenko A., Champollion N., Dusch M., Eis J., Fourteau K., Gregor P., Jarosch A.H., Landmann J., Oesterle F., Recinos B., Rothenpieler T., Vlug A., Wild C.T., Marzeion B. The Open Global Glacier Model (OGGM) v11 Geoscientific Model Development 2019, 12: 909– 931 https://doi.org/10.5194/gmd-12-909-2019 Zekollari H., Huss M., Farinotti D. Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble The Cryosphere 2019, 13: 1125–1146 https://doi.org/10.5194/tc-13-1125-2019 Huss M., Hock R. A new model for global glacier change and sea-level rise Frontiers in Earth Science 2015, 3: 54 https://doi.org/10.3389/feart.2015.00054 Rounce D.R., Khurana T., Short M.B., Hock R., Shean D.E., Brinkerhoff D.J. Quantifying parameter uncertainty in a large-scale glacier evolution model using Bayesian inference: application to High Mountain Asia Journ of Glaciology 2020, 66 (256):175–187 Shannon S., Smith R., Wiltshire A., Payne T., Huss M., Betts R., Caesar J., Koutroulis A., Jones D., Harrison S. Global glacier volume projections under high-end climate change scenarios The Cryosphere 2019, 13: 325–350 https://doi.org/10.5194/tc-2019-35 Hirabayashi Y., Zang Y., Watanabe S., Koirala S., Kanae S. Projection of glacier mass changes under a high-emission climate scenario using the global glacier model HYOGA2 Hydrol Research Letter 2013, 7 (1): 6–11 https://doi.org/10.3178/hrl.7.6 Marzeion B., Jarosch A., Hofer M. Past and future sealevel change from the surface mass balance of glaciers The Cryosphere 2012, 6 (6): 1295–1322 https://doi.org/10.5194/tc-6-1295-2012 Bahr D.B., Meier M.F., Peckham S.D. The physical basis of glacier volume-area scaling Journ of Geophys Reseasrch 1997, 102 (B9): 20355– 20362 https://doi.org/10.1029/97JB01696 Van de Wal R.S.W., Wild M Modelling the response of glaciers to climate change by applying volume-area scaling in combination with a high resolution GSM Climate Dynamics 2001, 18 (3–4): 359–366 doi:10.1007/s003820100184 Huss M., Jouvet G., Farinotti D., Bauder A. Future highmountain hydrology: a new parameterization of glacier retreat // Hydrology and Earth System Sciences 2010, 14: 815–829 https://doi.org/10.5194/hess-14-815-2010 Hofer M., Marzeion B., Mölg T. A statistical downscaling method for daily air temperature in data sparse, glaciated mountain environments Geosci Model Dev 2015, 8: 579–593 https://doi.org/10.5194/gmd-8-5792015 Morozova P.A., Rybak O.O. Regionalization of global climate modeling data for calculating the mass balance of mountain glaciers Led i Sneg. Ice and Snow 2017, 57 (4): 437–452 https://doi.org/10.15356/2076-67342017-4-437-452 [In Russian] Murphy J An Evaluation of Statistical and Dynamical Techniques for Downscaling Local Climate Journ of Climate 1999, 12 (8): 2256–2284 Taylor K.E., Stouffer R.J., Meehl G.A. An overview of CMIP5 and the experiment design Bull Amer Meteor Soc 2012, 93: 485–498 https://doi.org/10.1175/ BAMS-D-11-00094 1 Eyring V., Bony S., Meehl G.A., Senior C.A., Stevens B., Stouffer B., Taylor K.E. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization Geoscientific Model Development 2016, 9 (5): 1937–1958 doi:10.5194/gmd-9-1937-2016. Dee D.P., Uppala S.M., Simmons A.J., Berrisford P., Poli P., Kobayashi S., Andrae U., Balmaseda M.A., Balsamo G., Bauer P., Bechtold P., Beljaars A.C.M., van de Berg L., Bidlot J., Bormann N., Delsol C., Dragani R., Fuentes M., Geer A.J., Haimberger L., Healy S.B., Hersbach H., Hólm E.V., Isaksen L., Kållberg P., Köhler M., Matricardi M., McNally A.P., Monge­Sanz B.M., Morcrette J.­J., Park B.­K., Peubey C., de Rosnay P., Tavolato C., Thépaut J.­N., Vitart F. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system Quarterly Journ of the Royal Meteorological Society 2011, 137 (656): 553–597 https://doi.org/10.1002/qj.828 Gutowski Jr.W.J., Giorgi F., Timbal B., Frigon A., Jacob D., Kang H.S. Raghavan K., Lee B., Lennard Ch., Nikulin G., O’Rourke E., Rixen M., Solman S., Stephenson T., Tangang F. WCRP coordinated regional downscaling experiment (CORDEX): a diagnostic MIP for CMIP6 Geoscientific Model Development 2016, 9 (11): 4087–4095 https://doi.org/10.5194/gmd-9-4087-2016 Jacob D., Petersen J., Eggert B., Alias A., Bøssing O., Bouwer L.M., Braun A., Colette A., Georgopoulou E., Gobiet A., Menut L., Nikulin G., Haensler A., Kriegsmann A., Martin E., van Meijgaard E., Moseley C., Pfeifer S., Preuschmann S., Radermacher C., Radtke K., Rechid D., Roundsevell M., Samuelsson P., Somot S., Soussana J.­F., Teichmann C., Valentini R., Vautard R., Weber B., Yiou P. EUROCORDEX: new high-resolution climate change projections for European impact research Reg Environ Change 2014, 14: 563–578 https://doi.org/10.1007/s10113-013-0499-2 Van Vuuren D.P., Edmonds J., Kainuma M., Riahi K., Thomson A., Hibbard K., Hurtt G.C., Kram T., Krey V., Lamarque J.­F., Masui T., Meinshausen M., Nakicenovic N., Smith S.J., Rose S.K. The representative concentration pathways: an overview Climatic Change 2011, 109: 5–31 doi:10.1007/s10584-011-0148-z O’Neill B.C., Kriegler E., Riahi K., Ebi K.L., Hallegatte S., Carter T.R., Mathur R., Detlef P., van Vuuren D.P. A new scenario framework for climate change research: the concept of shared socioeconomic pathways Climatic Сhange 2014, 122 (3): 387–400 https://doi.org/10.1007/s10584-013-0905-2 Edwards T. Quantifying uncertainties in the land ice contribution to sea level from ISMIP6 and GlacierMIP EGU General Assembly Conference Abstracts 2020: 11241 https://www.climate-cryosphere.org/mips/glaciermip/activities-experiments Rybak O.O., Rybak E.A., Kutuzov S.S., Lavrentyev I.I., Morozova P.A. Calibration of the mathematical model of the dynamics of the Marukh glacier, Western Caucasus Led i Sneg. Ice and Snow 2015, 55 (2): 9–20 https://doi.org/10.15356/2076-6734-2015-2-9-20 [In Russian] Radić V., Hock R. Regionally differentiated contribution of mountain glaciers and ice caps to future sealevel rise Nature Geoscience 2011, 4 (2): 91–94 https://doi.org/10.1038/ngeo1052 Jennings K.S., Winchell T.S., Livneh B., Molotch N.P. Spatial variation of the rain-snow temperature threshold across the Northern Hemisphere Nat Commun 2018, 9: 1148 https://doi.org/10.1038/s41467-018-03629-7 Pattyn F. The paradigm shift in Antarctic ice sheet modelling Nat Commun 2018, 9: 2728 https://doi.org/10.1038/s41467-018-05003-z RGI Consortium Randolph Glacier Inventory (RGI) – A dataset of global glacier outlines: Version 6 0 Technical Report Global Land Ice Measurements from Space, Boulder, Colorado, USA, 2017 https://doi.org/10.7265/N5-RGI-60 Eis J., Maussion F., Marzeion B. Initialization of a global glacier model based on present-day glacier geometry and past climate information: an ensemble approach The Cryosphere 2019, 13: 3317–3335 doi:10.5194/tc-13-3317-2019 Shahgedanova M., Afzal M., Hagg W., Kapitsa V., Kasatkin N., Mayr E., Rybak O., Saidaliyeva Z., Severskiy I., Usmanova Z., Wade A., Yaitskaya N., Zhumabayev D. Emptying Water Towers? Impacts of Future Climate and Glacier Change on River Discharge in the Northern Tien Shan, Central Asia Water 2020, 12: 627 https://doi.org/10.3390/w12030627 WGMS: Fluctuations of Glaciers Database World Glacier Monitoring Service, Zürich, Switzerland 2017 https://doi.org/10.5904/wgmsfog-2017-10 Cogley J.G. Geodetic and direct mass-balance measurements: comparison and joint analysis Annals of Glaciology 2009, 50 (50): 96–100 doi:10.3189/172756409787769744 Scherler D., Bookhagen B., Strecker M.R. Spatially variable response of himalayan glaciers to climate change affected by debris cover Nature Geoscience 2011, 4 (3): 156–159 doi:10.1038/ngeo1068 Gardelle J., Berthier E., Arnaud Y., Kääb A. Region-wide glacier mass balances over the Pamir-Karakoram-Himalaya during 1999–2011 The Cryosphere 2013, 7 (4): 1263–1286 https://doi.org/10.5194/tc-7-1263-2013 Gardner A.S., Moholdt G., Cogley J.G., Wouters B., Arendt A.A., Wahr J., Berthier E., Hock R., Pfeffer W.T., Kaser G., Ligtenberg S.R.M., Bolch T., Sharp M.J., Hagen J.O., van den Broeke M.R., Paul F. A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009 Science 2013, 340: 852–857 doi:10.1126/science.1234532 Zemp M., Huss M., Thilbert E., Eckert N., McNabb R., Huber J., Barandun M., Machguth H., Nussbaumer S.U., Gärtner­Roer I., Thomson L., Paul F., Maussion F., Kutuzov S., Cogley J.G. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016 Nature 2019, 568 (7752): 382–386 https://doi.org/10.1038/s41586-019-1071-0 Shean D.E., Bhushan S., Montesano P., Rounce D.R., Arendt A., Osmanoglu B. A systematic, regional assessment of High Mountain Asia glacier mass balance Frontiers in Earth Science 2020, 7: 363 https://doi.org/10.3389/feart.2019.00363 Zemp M, Frey H., Gärtner­Roer I., Nussbaumer S., Hoelzle M., Paul F., Haeberli W., Denzinger F., Ahlstrøm A.P., Anderson B., Bajracharya S., Baroni C., Braun L.N., Cáceres B.E., Casassa G., Cobos G., Dávila L.R., Delgado Granados H., Demuth M.N., Espizua L., Fischer A., Fujita K., Gadek B., Ghazanfar A., Ove Hagen J., Holmlund P., Karimi N., Li Z., Pelto M., Pitte P., Popovnin V.V., Portocarrero C.A., Prinz R., Sangewar C.V., Severskiy I., Sigurđsson O., Soruco A., Usubaliev R., Vincent C. Historically unprecedented global glacier decline in the early 21st century Journ of Glaciology 2015, 61 (228): 745–762 doi:10.3189/2015JoG15J017 Radić V., Bliss A., Beedlow A.C., Hock R., Miles E., Cogley J.G. Regional and global projections of twenty-first century glacier mass changes in response to climate scenarios from global climate models Climate Dynamics 2014, 42 (1–2): 37–58 https://doi.org/10.1007/s00382-013-1719-7 Berg B.A. Introduction to Markov chain Monte Carlo simulations and their statistical analysis Markov Chain Monte Carlo Lect Notes Ser Inst Math Sci Natl Univ Singap 2005, 7: 1–52. Renard B., Kavetski D., Kuczera G., Thyer M., Franks S.W. Understanding predictive uncertainty in hydrologic modeling: the challenge of identifying input and structural errors Water Resources Research 2010, 46 (5): 1–22 doi:10.1029/2009WR008328 Stone E.J., Lunt D.J., Rutt I.C., Hanna E. Investigating the sensitivity of numerical model simulations of the modern state of the Greenland ice-sheet and its future response to climate change The Cryosphere 2010, 4: 397–417 https://doi.org/10.5194/tc-4-397-2010 McKay M.D., Beckman R.J., Conover W.J. Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code Technometrics 1979, 21: 239–245 Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access). Авторы, публикующие статьи в данном журнале, соглашаются на следующее:Авторы сохраняют за собой авторские права и предоставляют журналу право первой публикации работы, которая по истечении 6 месяцев после публикации автоматически лицензируется на условиях Creative Commons Attribution License , что позволяет другим распространять данную работу с обязательным сохранением ссылок на авторов оригинальной работы и оригинальную публикацию в этом журнале.Редакция журнала будет размещать принятую для публикации статью на сайте журнала до выхода её в свет (после утверждения к печати редколлегией журнала). Авторы также имеют право размещать их работу в сети Интернет (например в институтском хранилище или персональном сайте) до и во время процесса рассмотрения ее данным журналом, так как это может привести к продуктивному обсуждению и большему количеству ссылок на данную работу (См. The Effect of Open Access). Ice and Snow; Том 62, № 2 (2022); 287-304 Лёд и Снег; Том 62, № 2 (2022); 287-304 2412-3765 2076-6734 mountain glaciers;glacier modeling;numerical experiments;methods of prediction;climate change горные ледники;гляциологическое моделирование;численные эксперименты;методы прогнозирования;изменения климата info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2022 ftjias https://doi.org/10.31857/S207667342202013310.1038/s41561-019-0300-310.1029/2019EF00147010.1038/s41558-018-0093-110.1029/2019GL08557810.1038/s41558-017-0049-x10.3389/feart.2019.0033110.5194/gmd-12-909-201910.5194/tc-13-1125-201910.3389/feart.2015.0005410.5 2024-06-28T03:05:47Z Global glacier models provide a new way for studying glaciers on the regional and global scales. They make it possible to perform predictive experiments - for example, to forecast changes in glaciation and river runoff, and diagnostic ones – to identify regularities in the behavior of glaciers (a response time to climate change) taking account of their characteristics. The characteristics and design of global glacier models were described in the first part of the review (see Postnikova T.N., Rybak O.O. Global glaciological models: a new stage in the development of methods for predicting glacier evolution. Part 1. General approach and model architecture. Led i Sneg. Ice and Snow. 2021, 61 (4): 620–636. [In Russian]. doi:10.31857/S2076673421040111.). In the second part, we present the methods for setting up of numerical experiments with these models, including model initialization, climate forcing, calibration, and validation procedures. The only way to provide the climate forcing of a glaciological model on a regional or global scale is to use low-resolution reanalysis or output of climate modeling on GCMs or RCMs that needs to use a process of scaling to reproduce the local climate in a complex topography where glaciers are usually located. Calibration of mass balance complements the downscaling of climate forcing for each glacier, and usually it includes parameters responsible for the glacier's response to climate change. Sampling from the Latin hypercube and Bayesian inversion are some of the methods discussed in this connection. In this review we present a comparative description of the selected global glaciological models, the results obtained by both diagnostic and prognostic ones, as well as scale and significance of them. We discuss also ways for further development of global glacier models, in particular the inclusion of 3D-modeling and the moraine (debris cover) block. The difficulties arising in a process of modeling glaciation of a particular mountain region or several regions are noted. Глобальные ... Article in Journal/Newspaper Annals of Glaciology The Cryosphere Ice and Snow Diversity 14 6 429

Similar Items