HadISDH.extremes is an annually updated global gridded monthly monitoring product of wet and dry bulb temperature–based extremes indices,from January 1973 to December 2022.Data quality,including spatial and temporal ...HadISDH.extremes is an annually updated global gridded monthly monitoring product of wet and dry bulb temperature–based extremes indices,from January 1973 to December 2022.Data quality,including spatial and temporal stability,is a key focus.The hourly data are quality controlled.Homogeneity is assessed on monthly means and used to score each gridbox according to its homogeneity rather than to apply adjustments.This enables user-specific screening for temporal stability and avoids errors from inferring adjustments from monthly means for the daily maximum values.For general use,a score(HQ Flag)of 0 to 6 is recommended.A range of indices are presented,aligning with existing standardised indices.Uniquely,provision of both wet and dry bulb indices allows exploration of heat event character—whether it is a“humid and hot”,“dry and hot”or“humid and warm”event.It is designed for analysis of long-term trends in regional features.HadISDH.extremes can be used to study local events,but given the greater vulnerability to errors of maximum compared to mean values,cross-validation with independent information is advised.展开更多
Since 2007,the Intergovernmental Panel on Climate Change(IPCC)has heavily relied on the comparison between global climate model hindcasts and global surface temperature(ST)estimates for concluding that post-1950s glob...Since 2007,the Intergovernmental Panel on Climate Change(IPCC)has heavily relied on the comparison between global climate model hindcasts and global surface temperature(ST)estimates for concluding that post-1950s global warming is mostly human-caused.In Connolly et al.,we cautioned that this approach to the detection and attribution of climate change was highly dependent on the choice of Total Solar Irradiance(TSI)and ST data sets.We compiled 16 TSI and five ST data sets and found by altering the choice of TSI or ST,one could(prematurely)conclude anything from the warming being“mostly human-caused”to“mostly natural.”Richardson and Benestad suggested our analysis was“erroneous”and“flawed”because we did not use a multilinear regression.They argued that applying a multilinear regression to one of the five ST series re-affirmed the IPCC's attribution statement.They also objected that many of the published TSI data sets were out-of-date.However,here we show that when applying multilinear regression analysis to an expanded and updated data set of 27 TSI series,the original conclusions of Connolly et al.are confirmed for all five ST data sets.Therefore,it is still unclear whether the observed warming is mostly human-caused,mostly natural or some combination of both.展开更多
基金supported by the UK–China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund
文摘HadISDH.extremes is an annually updated global gridded monthly monitoring product of wet and dry bulb temperature–based extremes indices,from January 1973 to December 2022.Data quality,including spatial and temporal stability,is a key focus.The hourly data are quality controlled.Homogeneity is assessed on monthly means and used to score each gridbox according to its homogeneity rather than to apply adjustments.This enables user-specific screening for temporal stability and avoids errors from inferring adjustments from monthly means for the daily maximum values.For general use,a score(HQ Flag)of 0 to 6 is recommended.A range of indices are presented,aligning with existing standardised indices.Uniquely,provision of both wet and dry bulb indices allows exploration of heat event character—whether it is a“humid and hot”,“dry and hot”or“humid and warm”event.It is designed for analysis of long-term trends in regional features.HadISDH.extremes can be used to study local events,but given the greater vulnerability to errors of maximum compared to mean values,cross-validation with independent information is advised.
基金financial support from the Center for Environmental Research and Earth Sciences(CERES,www.ceres-science.com)while carrying out the research for this paperlong-term support from NASA,NSF,Tennessee State University,and the State of Tennessee through its Centers of Excellence Programthe support of the grant PID-5265TC of the National Technological University of Argentina。
文摘Since 2007,the Intergovernmental Panel on Climate Change(IPCC)has heavily relied on the comparison between global climate model hindcasts and global surface temperature(ST)estimates for concluding that post-1950s global warming is mostly human-caused.In Connolly et al.,we cautioned that this approach to the detection and attribution of climate change was highly dependent on the choice of Total Solar Irradiance(TSI)and ST data sets.We compiled 16 TSI and five ST data sets and found by altering the choice of TSI or ST,one could(prematurely)conclude anything from the warming being“mostly human-caused”to“mostly natural.”Richardson and Benestad suggested our analysis was“erroneous”and“flawed”because we did not use a multilinear regression.They argued that applying a multilinear regression to one of the five ST series re-affirmed the IPCC's attribution statement.They also objected that many of the published TSI data sets were out-of-date.However,here we show that when applying multilinear regression analysis to an expanded and updated data set of 27 TSI series,the original conclusions of Connolly et al.are confirmed for all five ST data sets.Therefore,it is still unclear whether the observed warming is mostly human-caused,mostly natural or some combination of both.
