Targeted model evaluations for climate services: A case study on heat waves in Bangladesh

Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptati...
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Autor*in:	Hannah Nissan [verfasserIn] Ángel G. Muñoz [verfasserIn] Simon J. Mason [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Übergeordnetes Werk:	In: Climate Risk Management - Elsevier, 2016, 28(2020), Seite -
Übergeordnetes Werk:	volume:28 ; year:2020 ; pages:-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.crm.2020.100213

Katalog-ID:	DOAJ06568432X

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520			\|a Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly.The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability.
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spelling	10.1016/j.crm.2020.100213 doi (DE-627)DOAJ06568432X (DE-599)DOAJ26432d1f6f7b41e08e54f07509ff3ed9 DE-627 ger DE-627 rakwb eng QC851-999 Hannah Nissan verfasserin aut Targeted model evaluations for climate services: A case study on heat waves in Bangladesh 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly.The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability. Meteorology. Climatology Ángel G. Muñoz verfasserin aut Simon J. Mason verfasserin aut In Climate Risk Management Elsevier, 2016 28(2020), Seite - (DE-627)776855387 (DE-600)2751138-8 22120963 nnns volume:28 year:2020 pages:- https://doi.org/10.1016/j.crm.2020.100213 kostenfrei https://doaj.org/article/26432d1f6f7b41e08e54f07509ff3ed9 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212096320300036 kostenfrei https://doaj.org/toc/2212-0963 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 28 2020 -
allfields_unstemmed	10.1016/j.crm.2020.100213 doi (DE-627)DOAJ06568432X (DE-599)DOAJ26432d1f6f7b41e08e54f07509ff3ed9 DE-627 ger DE-627 rakwb eng QC851-999 Hannah Nissan verfasserin aut Targeted model evaluations for climate services: A case study on heat waves in Bangladesh 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly.The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability. Meteorology. Climatology Ángel G. Muñoz verfasserin aut Simon J. Mason verfasserin aut In Climate Risk Management Elsevier, 2016 28(2020), Seite - (DE-627)776855387 (DE-600)2751138-8 22120963 nnns volume:28 year:2020 pages:- https://doi.org/10.1016/j.crm.2020.100213 kostenfrei https://doaj.org/article/26432d1f6f7b41e08e54f07509ff3ed9 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212096320300036 kostenfrei https://doaj.org/toc/2212-0963 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 28 2020 -
allfieldsGer	10.1016/j.crm.2020.100213 doi (DE-627)DOAJ06568432X (DE-599)DOAJ26432d1f6f7b41e08e54f07509ff3ed9 DE-627 ger DE-627 rakwb eng QC851-999 Hannah Nissan verfasserin aut Targeted model evaluations for climate services: A case study on heat waves in Bangladesh 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly.The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability. Meteorology. Climatology Ángel G. Muñoz verfasserin aut Simon J. Mason verfasserin aut In Climate Risk Management Elsevier, 2016 28(2020), Seite - (DE-627)776855387 (DE-600)2751138-8 22120963 nnns volume:28 year:2020 pages:- https://doi.org/10.1016/j.crm.2020.100213 kostenfrei https://doaj.org/article/26432d1f6f7b41e08e54f07509ff3ed9 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212096320300036 kostenfrei https://doaj.org/toc/2212-0963 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 28 2020 -
allfieldsSound	10.1016/j.crm.2020.100213 doi (DE-627)DOAJ06568432X (DE-599)DOAJ26432d1f6f7b41e08e54f07509ff3ed9 DE-627 ger DE-627 rakwb eng QC851-999 Hannah Nissan verfasserin aut Targeted model evaluations for climate services: A case study on heat waves in Bangladesh 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly.The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability. Meteorology. Climatology Ángel G. Muñoz verfasserin aut Simon J. Mason verfasserin aut In Climate Risk Management Elsevier, 2016 28(2020), Seite - (DE-627)776855387 (DE-600)2751138-8 22120963 nnns volume:28 year:2020 pages:- https://doi.org/10.1016/j.crm.2020.100213 kostenfrei https://doaj.org/article/26432d1f6f7b41e08e54f07509ff3ed9 kostenfrei http://www.sciencedirect.com/science/article/pii/S2212096320300036 kostenfrei https://doaj.org/toc/2212-0963 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 28 2020 -
language	English
source	In Climate Risk Management 28(2020), Seite - volume:28 year:2020 pages:-
sourceStr	In Climate Risk Management 28(2020), Seite - volume:28 year:2020 pages:-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Meteorology. Climatology
isfreeaccess_bool	true
container_title	Climate Risk Management
authorswithroles_txt_mv	Hannah Nissan @@aut@@ Ángel G. Muñoz @@aut@@ Simon J. Mason @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	776855387
id	DOAJ06568432X
language_de	englisch
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callnumber-first	Q - Science
author	Hannah Nissan
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topic_title	QC851-999 Targeted model evaluations for climate services: A case study on heat waves in Bangladesh
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title	Targeted model evaluations for climate services: A case study on heat waves in Bangladesh
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title_full	Targeted model evaluations for climate services: A case study on heat waves in Bangladesh
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title_sort	targeted model evaluations for climate services: a case study on heat waves in bangladesh
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title_auth	Targeted model evaluations for climate services: A case study on heat waves in Bangladesh
abstract	Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly.The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability.
abstractGer	Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly.The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability.
abstract_unstemmed	Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model’s climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly.The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability.
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title_short	Targeted model evaluations for climate services: A case study on heat waves in Bangladesh
url	https://doi.org/10.1016/j.crm.2020.100213 https://doaj.org/article/26432d1f6f7b41e08e54f07509ff3ed9 http://www.sciencedirect.com/science/article/pii/S2212096320300036 https://doaj.org/toc/2212-0963
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