Development of an Updated Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes: HadEX3

Robert J.H. Dunn; Lisa V. Alexander; Markus G. Donat; Xuebin Zhang; Margot Bador; Nicholas Herold; Tanya Lippmann; Rob Allan; Enric Aguilar; Abdoul Aziz Barry; Manola Brunet; John Caesar; Guillaume Chagnaud; Vincent Cheng; Thelma Cinco; Imke Durre; Rosaline de Guzman; Tin Mar Htay; Wan Maisarah Wan Ibadullah; Muhammad Khairul Izzat Bin Ibrahim; Mahbobeh Khoshkam; Andries Kruger; Hisayuki Kubota; Tan Wee Leng; Gerald Lim; Lim Li-Sha; Jose Marengo; Sifiso Mbatha; Simon McGree; Matthew Menne; Maria de los Milagros Skansi; Sandile Ngwenya; Francis Nkrumah; Chalump Oonariya; Jose Daniel Pabon-Caicedo; Gérémy Panthou; Cham Pham; Fatemeh Rahimzadeh; Andrea Ramos; Ernesto Salgado; Jim Salinger; Youssouph Sané; Ardhasena Sopaheluwakan; Arvind Srivastava; Ying Sun; Bertrand Timbal; Nichanun Trachow; Blair Trewin; Gerard van der Schrier; Jorge Vazquez-Aguirre; Ricardo Vasquez; Claudia Villarroel; Lucie Vincent; Theo Vischel; Russ Vose; Mohd Noor Arifin Bin Hj Yussof

doi:10.1029/2019JD032263

Development of an Updated Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes: HadEX3

Robert J.H. Dunn^*, Lisa V. Alexander, Markus G. Donat, Xuebin Zhang, Margot Bador, Nicholas Herold, Tanya Lippmann, Rob Allan, Enric Aguilar, Abdoul Aziz Barry, Manola Brunet, John Caesar, Guillaume Chagnaud, Vincent Cheng, Thelma Cinco, Imke Durre, Rosaline de Guzman, Tin Mar Htay, Wan Maisarah Wan Ibadullah, Muhammad Khairul Izzat Bin IbrahimMahbobeh Khoshkam, Andries Kruger, Hisayuki Kubota, Tan Wee Leng, Gerald Lim, Lim Li-Sha, Jose Marengo, Sifiso Mbatha, Simon McGree, Matthew Menne, Maria de los Milagros Skansi, Sandile Ngwenya, Francis Nkrumah, Chalump Oonariya, Jose Daniel Pabon-Caicedo, Gérémy Panthou, Cham Pham, Fatemeh Rahimzadeh, Andrea Ramos, Ernesto Salgado, Jim Salinger, Youssouph Sané, Ardhasena Sopaheluwakan, Arvind Srivastava, Ying Sun, Bertrand Timbal, Nichanun Trachow, Blair Trewin, Gerard van der Schrier, Jorge Vazquez-Aguirre, Ricardo Vasquez, Claudia Villarroel, Lucie Vincent, Theo Vischel, Russ Vose, Mohd Noor Arifin Bin Hj Yussof

^*Corresponding author for this work

Earth and Climate

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

We present the second update to a data set of gridded land-based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude-latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”-average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global-scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.

Original language	English
Article number	e2019JD032263
Pages (from-to)	1-28
Number of pages	28
Journal	Journal of Geophysical Research: Atmospheres
Volume	125
Issue number	16
Early online date	2 Jul 2020
DOIs	https://doi.org/10.1029/2019JD032263
Publication status	Published - 27 Aug 2020

Funding

Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project ( https://www.ecad.eu ), the SACA&D project ( https://saca-bmkg.knmi.nl ), and the LACA&D project ( https://ciifen.knmi.nl ). We thank the three anonymous reviewers for their detailed comments which improved the manuscript. Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ram?n y Cajal 2017 Grant Reference RYC-2017-22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014-1, FAPESP Grants 2014/50848-9 and 2015/03804-9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402-00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA-2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP-2), 25?29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada-Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorol?gico Nacional (SMN) of Comisi?n Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.

Funders	Funder number
Atmospheric Science and Meteorological Organization Research Center
Comisi?n Nacional del Agua
Department for International Development DFID) Future Climate For Africa	NE/M020428/1, ARCDAP‐2, NE/M019969/1
I.R. of Iranian Meteorological Organization
IRIMO
Met Office Hadley Centre Climate Programme
Meteorological Service Singapore
National Coordination for High Level Education and Training
National Institute of Science and Technology
Servicio Meteorol?gico Nacional
Spanish Ministry for the Economy, Industry and Competitiveness Ram?n y Cajal
Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal	2018YFA0605604, 2018YFC1507702, RYC‐2017‐22964
Thai Meteorological Department
UK's National Environment Research Council
Department for Business, Energy and Industrial Strategy, UK Government
Natural Environment Research Council
Department for Environment, Food and Rural Affairs, UK Government	GA01101
Australian Research Council	DP160103439, CE170100023
Fundação de Amparo à Pesquisa do Estado de São Paulo	2015/03804‐9, 2014/50848‐9
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior	88887.136402‐00INCT
Ulsan National Institute of Science and Technology
Conselho Nacional de Desenvolvimento Científico e Tecnológico	465501/2014‐1
Kementerian Pendidikan
World Meteorological Organization
Economy and Environment Program for Southeast Asia
Thailand Science Research and Innovation	RDG6030003

Keywords

climate extremes
global-gridded data set
observations
precipitation
temperature

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1029/2019JD032263

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Dunn, R. J. H., Alexander, L. V., Donat, M. G., Zhang, X., Bador, M., Herold, N., Lippmann, T., Allan, R., Aguilar, E., Barry, A. A., Brunet, M., Caesar, J., Chagnaud, G., Cheng, V., Cinco, T., Durre, I., de Guzman, R., Htay, T. M., Wan Ibadullah, W. M., ... Bin Hj Yussof, M. N. A. (2020). Development of an Updated Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes: HadEX3. Journal of Geophysical Research: Atmospheres, 125(16), 1-28. Article e2019JD032263. https://doi.org/10.1029/2019JD032263

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abstract = "We present the second update to a data set of gridded land-based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude-latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”-average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global-scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.",

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Dunn, RJH, Alexander, LV, Donat, MG, Zhang, X, Bador, M, Herold, N, Lippmann, T, Allan, R, Aguilar, E, Barry, AA, Brunet, M, Caesar, J, Chagnaud, G, Cheng, V, Cinco, T, Durre, I, de Guzman, R, Htay, TM, Wan Ibadullah, WM, Bin Ibrahim, MKI, Khoshkam, M, Kruger, A, Kubota, H, Leng, TW, Lim, G, Li-Sha, L, Marengo, J, Mbatha, S, McGree, S, Menne, M, de los Milagros Skansi, M, Ngwenya, S, Nkrumah, F, Oonariya, C, Pabon-Caicedo, JD, Panthou, G, Pham, C, Rahimzadeh, F, Ramos, A, Salgado, E, Salinger, J, Sané, Y, Sopaheluwakan, A, Srivastava, A, Sun, Y, Timbal, B, Trachow, N, Trewin, B, van der Schrier, G, Vazquez-Aguirre, J, Vasquez, R, Villarroel, C, Vincent, L, Vischel, T, Vose, R & Bin Hj Yussof, MNA 2020, 'Development of an Updated Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes: HadEX3', Journal of Geophysical Research: Atmospheres, vol. 125, no. 16, e2019JD032263, pp. 1-28. https://doi.org/10.1029/2019JD032263

TY - JOUR

T1 - Development of an Updated Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes

T2 - HadEX3

AU - Dunn, Robert J.H.

AU - Alexander, Lisa V.

AU - Donat, Markus G.

AU - Zhang, Xuebin

AU - Bador, Margot

AU - Herold, Nicholas

AU - Lippmann, Tanya

AU - Allan, Rob

AU - Aguilar, Enric

AU - Barry, Abdoul Aziz

AU - Brunet, Manola

AU - Caesar, John

AU - Chagnaud, Guillaume

AU - Cheng, Vincent

AU - Cinco, Thelma

AU - Durre, Imke

AU - de Guzman, Rosaline

AU - Htay, Tin Mar

AU - Wan Ibadullah, Wan Maisarah

AU - Bin Ibrahim, Muhammad Khairul Izzat

AU - Khoshkam, Mahbobeh

AU - Kruger, Andries

AU - Kubota, Hisayuki

AU - Leng, Tan Wee

AU - Lim, Gerald

AU - Li-Sha, Lim

AU - Marengo, Jose

AU - Mbatha, Sifiso

AU - McGree, Simon

AU - Menne, Matthew

AU - de los Milagros Skansi, Maria

AU - Ngwenya, Sandile

AU - Nkrumah, Francis

AU - Oonariya, Chalump

AU - Pabon-Caicedo, Jose Daniel

AU - Panthou, Gérémy

AU - Pham, Cham

AU - Rahimzadeh, Fatemeh

AU - Ramos, Andrea

AU - Salgado, Ernesto

AU - Salinger, Jim

AU - Sané, Youssouph

AU - Sopaheluwakan, Ardhasena

AU - Srivastava, Arvind

AU - Sun, Ying

AU - Timbal, Bertrand

AU - Trachow, Nichanun

AU - Trewin, Blair

AU - van der Schrier, Gerard

AU - Vazquez-Aguirre, Jorge

AU - Vasquez, Ricardo

AU - Villarroel, Claudia

AU - Vincent, Lucie

AU - Vischel, Theo

AU - Vose, Russ

AU - Bin Hj Yussof, Mohd Noor Arifin

PY - 2020/8/27

Y1 - 2020/8/27

N2 - We present the second update to a data set of gridded land-based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude-latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”-average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global-scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.

AB - We present the second update to a data set of gridded land-based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude-latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”-average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global-scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.

KW - climate extremes

KW - global-gridded data set

KW - observations

KW - precipitation

KW - temperature

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JO - Journal of Geophysical Research: Atmospheres

JF - Journal of Geophysical Research: Atmospheres

IS - 16

M1 - e2019JD032263

ER -

Development of an Updated Global Land In Situ-Based Data Set of Temperature and Precipitation Extremes: HadEX3

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UN SDGs

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