Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation

Ruane, Alex C.; Goldberg, R. A.; Chryssanthacopoulos, James

doi:10.1016/j.agrformet.2014.09.016

Cited by 350 publications

(265 citation statements)

References 45 publications

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“…Future assessments may find greater adherence to historical conditions by utilising the agricultural-impacts oriented version of the MERRA dataset for gap-filling (AgMERRA; Ruane et al, 2014). The Delta climate change projection downscaling technique is relatively unsophisticated.…”

Section: Methodsology Issuesmentioning

confidence: 99%

Simulated impacts of climate change on water use and yield of irrigated sugarcane in South Africa

Jones

Singels

Ruane

2015

Agricultural Systems

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Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated sugarcane production sites in South Africa (Malelane, Pongola and La Mercy) for current and future (2070-2100) climate scenarios, using an approach based on the Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating climate change impacts on sugarcane production. 2Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models (GCMs) assuming atmospheric CO 2 concentration [CO 2 ] of 734 ppm (A2 emissions scenario). Yield and water use were simulated using the DSSAT-Canegro v4.5 model.Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected to decline because of increased consumption of photo-assimilate for structural growth and maintenance respiration. Crop responses in canopy development and yield formation differed markedly between the crop cycles investigated.Possible agronomic implications of these results include reduced weed control costs due to shortened periods of partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to ripening and harvest practices to counter decreased cane quality and optimise productivity.Although the Delta climate data downscaling method is considered robust, accurate and easily-understood, it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications ought to be explored in future work.Shortcomings of the DSSAT-Canegro model include the simulated responses of phenological development, photosynthesis and respiration processes to high temperatures, and the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology refinements should improve the reliability of predicted climate change impacts on sugarcane yield.

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Section: Methodsology Issuesmentioning

confidence: 99%

Simulated impacts of climate change on water use and yield of irrigated sugarcane in South Africa

Jones

Singels

Ruane

2015

Agricultural Systems

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“…First, our analysis does not include the latest forcing data sets, AgMERRA and AgCFSR [Ruane et al, 2015], used in the Agricultural Model Intercomparison and Improvement Project. A sophisticated retrospective forcing data set for precipitation (MSWEP), which is a hybrid of gauge observations, satellite estimates, and reanalysis data, has become recently available [Beck et al, 2017], although this study only considered forcing data sets that cover the many climatic variables necessary to run impact models.…”

Section: /2017jd026613mentioning

confidence: 99%

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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“…However, they all show a similar relative decreasing trend over the 21st century. The main perspective of this work is to go on exploring the uncertainty linked to bias-correction methods and their associated reference data in RCP climate scenarios by producing a second version of this bias-corrected 29-GCM ensemble over Africa using more recent reference data like EWEMBI or others like those used in AgMIP based on other reanalyses (AgMERRA or AgCFSR; Ruane et al, 2015). The main divergence among all those reference datasets is probably expected from rsds.…”

Section: Resultsmentioning

confidence: 99%

A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

et al. 2018

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Abstract. The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950-2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, nearsurface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders. It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century.

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Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation

Cited by 350 publications

References 45 publications

Simulated impacts of climate change on water use and yield of irrigated sugarcane in South Africa

Simulated impacts of climate change on water use and yield of irrigated sugarcane in South Africa

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

A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

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