A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison

Iizumi, Toshichika; Okada, Masashi; Yokozawza, Masayuki

doi:10.1002/2013jd020130

Cited by 40 publications

(48 citation statements)

References 71 publications

(172 reference statements)

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“…In addition, two forcing data sets, WFDEI [Weedon et al, 2014] and GRASP [Iizumi et al, 2014], were used as examples of newer forcing data sets to compare with HadEX2 in recent years . However, these forcing data sets were not used as the reference climatology in the bias correction because WFDEI does not cover the calibration period of 1961-2000, although its grid size (0.5°) is the same as the other forcing data sets.…”

Section: Selected Retrospective Forcing Data Setsmentioning

confidence: 99%

“…data (JRA-25) [Onogi et al, 2007] and ERA-40 [Uppala et al, 2005], to cover 1961-2010 [Iizumi et al, 2014]. GRASP was used after interpolation onto 0.5°grid cells.…”

Section: 1002/2017jd026613mentioning

confidence: 99%

“…The CDFDM [Iizumi et al, 2010[Iizumi et al, , 2012[Iizumi et al, , 2014, a nonparametric bias-correction method, was used in the development of the S14 database (section 3.2). In this method, the error in the GCM data of a climatic variable is defined for each percentile of empirical cumulative distribution functions (CDFs) derived from GCM and reference daily data for a calibration period.…”

Section: Cdfdmmentioning

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%

“…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%

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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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Section: Selected Retrospective Forcing Data Setsmentioning

confidence: 99%

“…data (JRA-25) [Onogi et al, 2007] and ERA-40 [Uppala et al, 2005], to cover 1961-2010 [Iizumi et al, 2014]. GRASP was used after interpolation onto 0.5°grid cells.…”

Section: 1002/2017jd026613mentioning

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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Modeling irrigation‐based climate change adaptation in agriculture: Model development and evaluation in Northeast China

Okada

Iizumi

Sakurai

et al. 2015

J Adv Model Earth Syst

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Replacing a rainfed cropping system with an irrigated one is widely assumed to be an effective measure for climate change adaptation. However, many agricultural impact studies have not necessarily accounted for the space-time variations in the water availability under changing climate and land use. Moreover, many hydrologic and agricultural assessments of climate change impacts are not fully integrated. To overcome this shortcoming, a tool that can simultaneously simulate the dynamic interactions between crop production and water resources in a watershed is essential. Here we propose the regional production and circulation coupled model (CROVER) by embedding the PRYSBI-2 (Process-based Regional Yield Simulator with Bayesian Inference version 2) large-area crop model into the global water resources model (called H08), and apply this model to the Songhua River watershed in Northeast China. The evaluation reveals that the model's performance in capturing the major characteristics of historical change in surface soil moisture, river discharge, actual crop evapotranspiration, and soybean yield relative to the reference data during the interval 1979-2010 is satisfactory accurate. The simulation experiments using the model demonstrated that subregional irrigation management, such as designating the area to which irrigation is primarily applied, has measurable influences on the regional crop production in a drought year. This finding suggests that reassessing climate change risk in agriculture using this type of modeling is crucial not to overestimate potential of irrigation-based adaptation.

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The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data

Weedon

Balsamo

Bellouin

et al. 2014

Water Resources Research

1,002

863

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The WFDEI meteorological forcing data set has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA-Interim reanalysis data. We discuss the specifics of how changes in the reanalysis and processing have led to improvement over the WFD. We attribute improvements in precipitation and wind speed to the latest reanalysis basis data and improved downward shortwave fluxes to the changes in the aerosol corrections. Covering 1979-2012, the WFDEI will allow more thorough comparisons of hydrological and Earth System model outputs with hydrologically and phenologically relevant satellite products than using the WFD.

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A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison

Cited by 40 publications

References 71 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

Modeling irrigation‐based climate change adaptation in agriculture: Model development and evaluation in Northeast China

The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data

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