A trend-preserving bias correction – the ISI-MIP approach

Hempel, Sabrina; Frieler, Katja; Warszawski, Lila; Schewe, Jacob; Piontek, Franziska

doi:10.5194/esd-4-219-2013

Cited by 1,036 publications

(873 citation statements)

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“…The correction of the wet-day frequency is also considered in the ISI-MIP approach. Additional methodological details on the ISI-MIP approach are in Hempel et al [2013].…”

Section: Selected Bias-correction Methods 221 Isi-mip Approachmentioning

confidence: 99%

“…The temperature data in the ISI-MIP database used here are only available for two GCMs (GFDL-ESM2M and IPSL-CM5A-LR), whereas the daily precipitation data are available for five GCMs (GFDL-ESM2M, IPSL-CM5A-LR, MIROC-ESM-CHEM, HadGEM2-ES, and NorESM1-M) (Tables 1 and S4 in the supporting information). These data are bias corrected using the ISI-MIP approach and the reference climatology of WFD, as elaborated in Hempel et al [2013]. Therefore, one bias-correction method and one reference forcing data set are available in the ISI-MIP database.…”

Section: /2017jd026613mentioning

confidence: 99%

“…The This study a The bias-corrected daily maximum and minimum temperature data were only available for two GCMs (GFDL-ESM2M and IPSL-CM5A-LR) in the ISI-MIP database used in this study, and the bias-corrected precipitation data were available for five GCMs. b The ISI-MIP first-track approach is used for temperature, and the ISI-MIP extended approach [Hempel et al, 2013] was used for precipitation. c Eight GCMs in the S14 database were bias corrected using CDFDM, but the same five GCMs as in the ISI-MIP database were used for this study.…”

Section: Cdfdmmentioning

confidence: 99%

“…Here we used two bias-correction methods, one that was used in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) [Hempel et al, 2013] and the cumulative distribution function-based downscaling method (CDFDM) [Iizumi et al, 2010[Iizumi et al, , 2012[Iizumi et al, , 2014; three forcing data sets, the Water and Global Change project forcing data set (WFD) [Weedon et al, 2011], Princeton's global meteorological forcing data set (PFD) [Sheffield et al, 2006], and a new forcing data set S14FD that is elaborated in Appendix A; five GCMs used in the ISI-MIP that were originally provided in the Coupled Model Intercomparison Project phase 5 (CMIP5) [Taylor et al, 2012]; and four RCPs (2.6, 4.5, 6.0, and 8.5 W m À2 ) [van Vuuren et al, 2011]. The name of the forcing data set, S14, is derived from the project number of the Environmental Research and Technology Development Fund, Strategic Research 14, Global Mitigation and Local Adaptation to Climate Change (http:// s-14.iis.u-tokyo.ac.jp/eng/).…”

Section: Introductionmentioning

confidence: 99%

“…Although bias correction is an unavoidable procedure for using GCM outputs as the inputs of impact models, it is an additional source of uncertainty in climate risk assessments because different bias-correction methods and reference daily weather data sets often lead to different impact outcomes Hawkins et al, 2013;Seaby et al, 2015;Baker et al, 2016]. Global retrospective meteorological forcing data sets, a hybrid of reanalysis data and gridded observations [e.g., Sheffield et al, 2006;Hanasaki et al, 2008;Hirabayashi et al, 2008a;Weedon et al, 2011Weedon et al, , 2014Iizumi et al, 2014], are used as the reference climatology in impact communities when bias correction is conducted at the global scale [e.g., Hempel et al, 2013]. Earlier work examined the uncertainties in the projected climate extremes associated with Representative Concentration Pathways IIZUMI ET AL.…”

Section: Introductionmentioning

confidence: 99%

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Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

et al. 2017

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The use of different bias‐correction methods and global retrospective meteorological forcing data sets as the reference climatology in the bias correction of general circulation model (GCM) daily data is a known source of uncertainty in projected climate extremes and their impacts. Despite their importance, limited attention has been given to these uncertainty sources. We compare 27 projected temperature and precipitation indices over 22 regions of the world (including the global land area) in the near (2021–2060) and distant future (2061–2100), calculated using four Representative Concentration Pathways (RCPs), five GCMs, two bias‐correction methods, and three reference forcing data sets. To widen the variety of forcing data sets, we developed a new forcing data set, S14FD, and incorporated it into this study. The results show that S14FD is more accurate than other forcing data sets in representing the observed temperature and precipitation extremes in recent decades (1961–2000 and 1979–2008). The use of different bias‐correction methods and forcing data sets contributes more to the total uncertainty in the projected precipitation index values in both the near and distant future than the use of different GCMs and RCPs. However, GCM appears to be the most dominant uncertainty source for projected temperature index values in the near future, and RCP is the most dominant source in the distant future. Our findings encourage climate risk assessments, especially those related to precipitation extremes, to employ multiple bias‐correction methods and forcing data sets in addition to using different GCMs and RCPs.

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“…The correction of the wet-day frequency is also considered in the ISI-MIP approach. Additional methodological details on the ISI-MIP approach are in Hempel et al [2013].…”

Section: Selected Bias-correction Methods 221 Isi-mip Approachmentioning

confidence: 99%

Section: /2017jd026613mentioning

confidence: 99%

Section: Cdfdmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

et al. 2017

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Assessing the Burden of Suicide Death Associated With Nonoptimum Temperature in a Changing Climate

et al. 2023

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ImportanceFew studies have projected future suicide burden associated with daily temperatures in a warming climate.ObjectivesTo assess the burden of suicide death associated with daily nonoptimal temperature and to project the change of suicide burden associated with nonoptimal temperature in different regions and seasons under various climate change scenarios.Design, Setting, and ParticipantsBetween January 1, 2013, and December 31, 2019, we conducted a time-stratified, case-control study among more than 430 000 individual suicide decedents from all counties in mainland China.ExposuresDaily meteorological data were obtained from the European Centre for Medium-Range Weather Forecasts Reanalysis Fifth Generation (ERA5) reanalysis product. Historical and future temperature series were projected under 3 scenarios of greenhouse-gas emissions from 1980 to 2099, with 10 general circulation models.Main Outcomes and MeasuresThe relative risk (RR) and burden of suicide death associated with nonoptimal temperature (ie, temperatures greater than or less than minimum-mortality temperature); the change of suicide burden associated with future climate warming in different regions and seasons under various climate change scenarios.ResultsOf 432 008 individuals (mean [SD] age; 57.6 [19.0] years; 253 093 male [58.6%]) who died by suicide, 85.8% (370 577) had a middle school education or less. The temperature-suicide associations were approximately linear, with increasing death risks at higher temperatures. The excess risk was more prominent among older adults (ie, ≥75 years; RR, 1.71; 95% CI, 1.46-1.99) and those with low education level (ie, middle school education or less; RR, 1.46; 95% CI, 1.36-1.57). There were 15.2% suicide deaths (95% estimated CI [eCI], 14.6%-15.6%) associated with nonoptimal temperature nationally. Consistent and drastic increases in excess suicide deaths over this century were predicted under the high-emission scenario, whereas a leveling-off trend after the mid–21st century was predicted under the medium- and low-emission scenarios. Nationally, compared with the historical period (1980-2009), excess suicide deaths were predicted to increase by 8.3% to 11.4% in the 2050s and 8.5% to 21.7% in the 2090s under the 3 scenarios. The projected percentage increments of excess suicide deaths were predicted to be greater in the South (55.0%; 95% eCI, 30.5%-85.6%) and in winter (54.5%; 95% eCI, 30.4%-77.0%) in the 2090s under the high-emission scenario.Conclusions and RelevanceFindings of this nationwide case-control study suggest that higher temperature may be associated with the risk and burden of suicide death in China. These findings highlight the importance of implementing effective climate policies to reduce greenhouse gas emissions and tailoring public health policies to adapt to global warming.

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Meeting the radiative forcing targets of the representative concentration pathways in a world with agricultural climate impacts

et al. 2014

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This study assesses how climate impacts on agriculture may change the evolution of the agricultural and energy systems in meeting the end-of-century radiative forcing targets of the representative concentration pathways (RCPs). We build on the recently completed Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) exercise that has produced global gridded estimates of future crop yields for major agricultural crops using climate model projections of the RCPs from the Coupled Model Intercomparison Project Phase 5 (CMIP5). For this study we use the bias-corrected outputs of the HadGEM2-ES climate model as inputs to the LPJmL crop growth model, and the outputs of LPJmL to modify inputs to the GCAM integrated assessment model. Our results indicate that agricultural climate impacts generally lead to an increase in global cropland, as compared with corresponding emissions scenarios that do not consider climate impacts on agricultural productivity. This is driven mostly by negative impacts on wheat, rice, other grains, and oil crops. Still, including agricultural climate impacts does not significantly increase the costs or change the technological strategies of global, whole-system emissions mitigation. In fact, to meet the most aggressive climate change mitigation target (2.6 W/m 2 in 2100), the net mitigation costs are slightly lower when agricultural climate impacts are considered. Key contributing factors to these results are (a) low levels of climate change in the low-forcing scenarios, (b) adaptation to climate impacts simulated in GCAM through inter-regional shifting in the production of agricultural goods, and (c) positive average climate impacts on bioenergy crop yields.

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A trend-preserving bias correction – the ISI-MIP approach

Cited by 1,036 publications

References 30 publications

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

Assessing the Burden of Suicide Death Associated With Nonoptimum Temperature in a Changing Climate

Meeting the radiative forcing targets of the representative concentration pathways in a world with agricultural climate impacts

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