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.

[1]  B. Trewin,et al.  An updated long‐term homogenized daily temperature data set for Australia , 2020, Geoscience Data Journal.

[2]  S. Seneviratne,et al.  On the use of indices to study extreme precipitation on sub-daily and daily timescales , 2019, Environmental Research Letters.

[3]  R. Dunn,et al.  Changes in statistical distributions of sub-daily surface temperatures and wind speed , 2019 .

[4]  N. Bhattarai,et al.  Forest loss in Brazil increases maximum temperatures within 50 km , 2019, Environmental Research Letters.

[5]  L. Alexander,et al.  Recent Changes in Mean and Extreme Temperature and Precipitation in the Western Pacific Islands , 2019, Journal of Climate.

[6]  Éva Mekis,et al.  An Overview of the Second Generation Adjusted Daily Precipitation Dataset for Trend Analysis in Canada , 2011, Data, Models and Analysis.

[7]  W. Paul Menzel,et al.  State of the Climate in 2018 , 2019, Bulletin of the American Meteorological Society.

[8]  J. Blunden,et al.  State of the Climate in 2017 , 2018, Bulletin of the American Meteorological Society.

[9]  Hilary James Oliver,et al.  Cylc: A Workflow Engine for Cycling Systems , 2018, J. Open Source Softw..

[10]  A. Diongue‐Niang,et al.  Rainfall intensification in tropical semi-arid regions: the Sahelian case , 2018, Environmental Research Letters.

[11]  Stefan Hunziker,et al.  Atlas - Clima y eventos extremos del Altiplano Central perú-boliviano , 2018 .

[12]  T. Butler,et al.  Assessment of an extended version of the Jenkinson–Collison classification on CMIP5 models over Europe , 2018, Climate Dynamics.

[13]  Elizabeth C. Kent,et al.  Toward an Integrated Set of Surface Meteorological Observations for Climate Science and Applications , 2017 .

[14]  A. Kruger,et al.  Historical rainfall trends in South Africa: 1921–2015 , 2017 .

[15]  A. Kruger,et al.  Surface temperature trends from homogenized time series in South Africa: 1931–2015 , 2017 .

[16]  P. Huybers,et al.  The changing shape of Northern Hemisphere summer temperature distributions , 2016 .

[17]  J. A. Guijarro Automatización de la homogeneización de series climáticas: nuevas funciones del paquete Climatol 3.0 , 2016 .

[18]  Claude N. Williams,et al.  Reassessing changes in diurnal temperature range: Intercomparison and evaluation of existing global data set estimates , 2016 .

[19]  Claude N. Williams,et al.  Reassessing changes in diurnal temperature range: A new data set and characterization of data biases , 2016 .

[20]  Lisa V. Alexander,et al.  ClimPACT2: Indices and software , 2016 .

[21]  L. Vogt Statistics For Spatial Data , 2016 .

[22]  Peter John Huybers,et al.  Decoding the precision of historical temperature observations , 2015 .

[23]  L. Alexander,et al.  How Well Do Gridded Datasets of Observed Daily Precipitation Compare over Australia , 2015 .

[24]  L. Alexander,et al.  Systematic investigation of gridding-related scaling effects on annual statistics of daily temperature and precipitation maxima: A case study for south-east Australia , 2015 .

[25]  L. Alexander,et al.  Multi‐dataset comparison of gridded observed temperature and precipitation extremes over China , 2015 .

[26]  Sunaryo,et al.  International Climate Assessment & Dataset: Climate Services across Borders , 2015 .

[27]  R. Bradley,et al.  Winter Climate Extremes over the Northeastern United States and Southeastern Canada and Teleconnections with Large-Scale Modes of Climate Variability* , 2015 .

[28]  R. Dunn,et al.  Investigating uncertainties in global gridded datasets of climate extremes , 2014 .

[29]  Francis W. Zwiers,et al.  Consistency of Temperature and Precipitation Extremes across Various Global Gridded In Situ and Reanalysis Datasets , 2014 .

[30]  F. Rahimzadeh,et al.  Effects of adjustment for non‐climatic discontinuities on determination of temperature trends and variability over Iran , 2014 .

[31]  F. Sima,et al.  West Africa climate extremes and climate change indices , 2014 .

[32]  J. Houghton,et al.  Climate Change 2013 - The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , 2014 .

[33]  Thomas C. Peterson,et al.  Changes in extreme temperature and precipitation in the Arab region: long‐term trends and variability related to ENSO and NAO , 2014 .

[34]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[35]  T. Stocker,et al.  Climate Change 2013: The Physical Science Basis. An overview of the Working Group 1 contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). , 2013 .

[36]  Wenhui Xu,et al.  Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices , 2013 .

[37]  F. Zwiers,et al.  Global increasing trends in annual maximum daily precipitation , 2013 .

[38]  Anuj Srivastava,et al.  Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset , 2013 .

[39]  F. Zwiers,et al.  Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate , 2013 .

[40]  R. Vose,et al.  Global Land-Based Datasets for Monitoring Climatic Extremes , 2013 .

[41]  P. Jones,et al.  Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America , 2013 .

[42]  J. Curry,et al.  Berkeley Earth Temperature Averaging Process , 2013 .

[43]  Xiaolan L. Wang,et al.  A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis , 2012 .

[44]  T. Lebel,et al.  Extreme rainfall in West Africa: A regional modeling , 2012 .

[45]  L. Alexander,et al.  The shifting probability distribution of global daytime and night‐time temperatures , 2012 .

[46]  R. Vose,et al.  An Overview of the Global Historical Climatology Network-Daily Database , 2012 .

[47]  Gerald A. Meehl,et al.  Mechanisms Contributing to the Warming Hole and the Consequent U.S. East–West Differential of Heat Extremes , 2012 .

[48]  G. Hegerl,et al.  Indices for monitoring changes in extremes based on daily temperature and precipitation data , 2011 .

[49]  Roger Stone,et al.  The International Atmospheric Circulation Reconstructions over the Earth (ACRE) Initiative , 2011 .

[50]  Peter Domonkos,et al.  Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT) , 2011 .

[51]  G. Jumaux,et al.  Observed trends in indices of daily and extreme temperature and precipitation for the countries of the western Indian Ocean, 1961–2008 , 2011 .

[52]  L. Alexander,et al.  Changes in temperature and precipitation extremes over the Indo‐Pacific region from 1971 to 2005 , 2011 .

[53]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[54]  G. Hegerl,et al.  Human contribution to more-intense precipitation extremes , 2011, Nature.

[55]  M. New,et al.  The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data , 2010 .

[56]  M. New,et al.  Spatial variability in correlation decay distance and influence on angular‐distance weighting interpolation of daily precipitation over Europe , 2009 .

[57]  E. J. Klok,et al.  Updated and extended European dataset of daily climate observations , 2009 .

[58]  Susan Solomon,et al.  Spatial and seasonal patterns in climate change, temperatures, and precipitation across the United States , 2009, Proceedings of the National Academy of Sciences.

[59]  J. Sillmann,et al.  Present and future atmospheric blocking and its impact on European mean and extreme climate , 2009 .

[60]  A. Mhanda,et al.  Changes in temperature and precipitation extremes in western central Africa, Guinea Conakry, and Zimbabwe, 1955–2006 , 2009 .

[61]  Temperature extremes in the south of South America in relation to Atlantic Ocean surface temperature and Southern Hemisphere circulation , 2008 .

[62]  P. Jones,et al.  A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006 , 2008 .

[63]  Thomas C. Peterson,et al.  Monitoring Changes in Climate Extremes: A Tale of International Collaboration , 2008 .

[64]  G. Hegerl,et al.  Influence of Modes of Climate Variability on Global Temperature Extremes , 2008 .

[65]  Thomas C. Peterson,et al.  Changes in North American extremes derived from daily weather data , 2008 .

[66]  T. Knutson,et al.  NOTES AND CORRESPONDENCE On the Verification and Comparison of Extreme Rainfall Indices from Climate Models , 2008 .

[67]  Adam A. Scaife,et al.  European Climate Extremes and the North Atlantic Oscillation , 2008 .

[68]  D. Lister,et al.  The development of a new dataset of Spanish Daily Adjusted Temperature Series (SDATS) (1850–2003) , 2006 .

[69]  Thomas C. Peterson,et al.  Changes in daily temperature and precipitation extremes in central and south Asia , 2006 .

[70]  P. Jones,et al.  Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850 , 2006 .

[71]  R. Vose,et al.  Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set , 2006 .

[72]  J. V. Revadekar,et al.  Global observed changes in daily climate extremes of temperature and precipitation , 2006 .

[73]  Francis W. Zwiers,et al.  Avoiding Inhomogeneity in Percentile-Based Indices of Temperature Extremes , 2005 .

[74]  D. Easterling,et al.  Trends in Intense Precipitation in the Climate Record , 2005 .

[75]  R. Arritt,et al.  Altered hydrologic feedback in a warming climate introduces a “warming hole” , 2004 .

[76]  K. Trenberth,et al.  The changing character of precipitation , 2003 .

[77]  Anders Moberg,et al.  Daily dataset of 20th‐century surface air temperature and precipitation series for the European Climate Assessment , 2002 .

[78]  Myles R. Allen,et al.  Towards objective probabalistic climate forecasting , 2002, Nature.

[79]  M. Allen,et al.  Constraints on future changes in climate and the hydrologic cycle , 2002, Nature.

[80]  M. Haylock,et al.  Observed coherent changes in climatic extremes during the second half of the twentieth century , 2002 .

[81]  P. Jones,et al.  Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate , 2000 .

[82]  Elizabeth C. Kent,et al.  A Statistical Determination of the Random Observational Errors Present in Voluntary Observing Ships Meteorological Reports , 1999 .

[83]  Neville Nicholls,et al.  Clivar/GCOS/WMO Workshop on Indices and Indicators for Climate Extremes Workshop Summary , 1999 .

[84]  John R. Lanzante,et al.  Resistant, Robust and Non-Parametric Techniques for the Analysis of Climate Data: Theory and Examples, Including Applications to Historical Radiosonde Station Data , 1996 .

[85]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[86]  R. Vose,et al.  Daily temperature and precipitation data for 223 USSR Stations , 1993 .

[87]  P. Sen Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[88]  D. Shepard A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[89]  H. Theil A Rank-Invariant Method of Linear and Polynomial Regression Analysis , 1992 .