Observed global changes in sector-relevant climate extremes indices—an extension to HadEX3

DATA AVAILABILITY STATEMENT : The gridded dataset are available at www.metoffice.gov.uk/hadobs/hadex3 and at www.climdex.org. In addition, a version is available on the CEDA archive (https://dx.doi.org/10.5285/2bfbdba03d9b423f99cadf404ca2daab). The underlying station indices will be made available on www.climdex.org where we are allowed to do so. For some collections we are not allowed to make the underlying station data public under terms of their licence.Please read abstract in the article.PLAIN LANGUAGE SUMMARY : To be able to assess changes in extreme temperature and rainfall events across the globe, data sets which capture characteristics of these extreme events are required. The use of indices for these characteristics further enables both data sharing and the comparison of events across the world. Extreme events have impacts across human health, our infrastructure and the natural environment. So far there has not been a global product which presents indices which are relevant for different sectors of our society, including health, agriculture and water resources. In this work we present an extension to an existing data set of extremes indices, HadEX3, by including indices defined by the World Meteorological Organization which were developed with sector specific applications in mind. We have used the same approach and methodology, and where possible the same underlying daily temperature and rainfall observations. The temperature indices show changes consistent with global scale warming, with heat wave characteristics showing increases in the number, duration and intensity of these extreme events in most places. The data files are available for use by interested researchers in their work.The Met Office Hadley Centre Climate Programme funded by DSIT and by the UK-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China under the International Science Partnerships Fund (ISPF); Australian Research Council Grant; the Horizon 2020 LANDMARC project; the RED-CLIMA (Red Española e Iberoamericana sobre Variabilidad Climática y Servicios Climáticos en Ecosistemas Terrestres y Marinos: RED-CLIMA) Project from the Consejo Superior de Investigaciones Científicas LINCGLOBAL CSIC from Spain; National Institute of Science and Technology for Climate Change Phase 2; the National Coordination for Higher Education and Training (CAPES).https://agupubs.onlinelibrary.wiley.com/journal/23335084hj2024Geography, Geoinformatics and MeteorologySDG-13:Climate actio

Practical predictability of the 17 December 2014 heavy rainfall event over East Coast of Peninsular Malaysia using WRF model

An investigation on the practical predictability aspects of heavy rainfall event in the east coast states of Peninsular Malaysia was carried out by simulating the 17 December 2014 episode using the Weather Research and Forecasting (WRF). The WRF model was configured with three nested domains of 36 km, 12 km, and 4 km horizontal resolution for 36 h simulations. It was found that the cumulative rainfall amount and the location of the heavy rainfall centre are sensitive to the choices of Cumulus Parameterisation Scheme (CPS). The experiment with a resolution of 4 km that used the multiscale Kain-Fritsch for the outer domains and no cumulus scheme in the inner domain reasonably well simulated the case. Further analysis suggests that the CPS and initialisation gave larger impact to the forecast quality compared to boundary conditions. Grid resolution contributed the least error

Klimatologi hujan diurnal dan bayu laut-darat di Semenanjung Malaysia

Kajian ini bertujuan memahami klimatologi kitaran hujan diurnal dan mekanismanya di Semenanjung Malaysia berdasarkan analisis data cerapan. Analisis jumlah, kekerapan dan keamatan hujan diurnal memperlihatkan 4 peristiwa hujan diurnal yang utama, iaitu: kejadian hujan petang maksimum semasa musim peralihan monsun di kaki bukit Banjaran Titiwangsa adalah lebat dan sering berlaku; kejadian hujan sepanjang hari di pantai timur semenanjung (subrantauan PT) semasa monsun timur laut adalah lebat dan sering berlaku; kejadian hujan pada pagi di lembah-pedalaman semenanjung (sub-rantauan LP) semasa monsun timur laut adalah rendah walaupun hujan lebat sering berlaku pada petangnya; dan kejadian hujan pagi maksimum di pantai barat semenanjung (sub-rantauan PB) semasa Jun-Julai-OgosSeptember-Oktober-November (JJOSON) merupakan kejadian hujan yang melampau namun kekerapannya adalah rendah. Di sub-rantauan PB, kelajuan angin permukaan yang kuat berbanding lemah pada aras troposfera yang lebih tinggi semasa musim peralihan monsun menyebabkan ricih angin bergerak ke atas. Sebaliknya, kombinasi angin monsun dan bayu laut semasa monsun barat daya menyebabkan ricih angin bergerak ke troposfera bawah. Semasa monsun timur laut, luluran angin sejuk dan bayu laut menyebabkan kelajuan angin adalah kuat pada permukaan sehingga ke aras 2 km tinggi di timur semenanjung. Ini menyebabkan zon perolakan yang tertumpu di sub-rantauan PT pada pagi meluas sehingga ke sub-rantauan LP pada waktu petang

Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3

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.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.Peer ReviewedPostprint (published version

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

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.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.Peer ReviewedPostprint (published version