Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

Mearns, Linda O.; Sain, Stephan R.; Leung, L. Ruby; Bukovsky, Melissa; McGinnis, Seth; Biner, S.B.; Caya, Daniel; Arritt, R. W.; Gutowski, William J.; Takle, Eugene S.; Snyder, Mark A.; Jones, R. G.; Nunes, Ana M. B.; Tucker, Simon; Herzmann, Daryl; McDaniel, Larry; Sloan, L. C.

doi:10.1007/s10584-013-0831-3

Cited by 237 publications

(215 citation statements)

References 29 publications

Supporting

Mentioning

197

Contrasting

Order By: Relevance

“…3 and Figs. S6 and S7) are themselves not well understood (41). It is therefore not yet possible to evaluate whether any given RCM improves yield forecasts over GCM output.…”

Section: Resultsmentioning

confidence: 99%

Evaluating the utility of dynamical downscaling in agricultural impacts projections

Glotter¹,

Elliott

McInerney³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscalingnested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output-to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO 2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM-and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections.agriculture | food security | NARCCAP | CORDEX

show abstract

“…3 and Figs. S6 and S7) are themselves not well understood (41). It is therefore not yet possible to evaluate whether any given RCM improves yield forecasts over GCM output.…”

Section: Resultsmentioning

confidence: 99%

Evaluating the utility of dynamical downscaling in agricultural impacts projections

Glotter¹,

Elliott

McInerney³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…One approach that has been applied to climate models is that of analysis of variance, as suggested by Yip et al (2011) and used in the context of regional and global climate models by Mearns et al (2013). (Wallach et al 2016) propose a random effects analysis of variance for crop model uncertainty, with analytical expressions for the contributions from model structure, parameters and inputs.…”

Section: Evaluating the Separate Contributions To Overall Uncertaintymentioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

View full text Add to dashboard Cite

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically Climatic Change (2016) 139:551-564 DOI 10.1007/s10584-016-1803-1 Highlights ?•Ensembles of crop models can improve uncertainty estimates, impact projections, and collaboration.•The climate modeling community has identified and studied many of the questions related to design and analysis of ensembles.•Much of the climate experience could be adapted to crop modeling. oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

show abstract

“…The North American Regional Climate Change Assessment Program-NARCCAP- (Mearns et al 2007) includes twelve combinations of RCMs driven by GCMs (see supplementary material for details). Our application used daily values of maximum temperatures from the Canadian global climate model 3.1 (CGCM3.1) and the Canadian regional climate model 4.2 (CRCM4.2) as a proof of concept.…”

Section: Datasetsmentioning

confidence: 99%

Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada

Gaitán

2016

Nat Hazards

View full text Add to dashboard Cite

Statistical downscaling techniques are often used to generate finer-scale projections of climate variables affected by local-scale processes not resolved by coarse resolution numerical models like global climate models (GCMs). Statistical downscaling models rely on several assumptions in order to produce finer-/local-scale projections of the variable of interest; one of these assumptions is the time-invariance of the relationships between predictors (e.g. coarse resolution GCM output) and the local-scale predictands (e.g. gridded observation-based time-series or weather station observations). However, in the absence of future observations, statistical downscaling studies use historical data to evaluate their models and assume that these historical simulation skills will be retained in the future. In addition, regression-based downscaling models fail to reproduce the observed variance, and hence their projections need to be adjusted accordingly. Two approaches are usually employed to perform this adjustment: randomization and variance inflation. Here, we study the effect of the stationarity assumption when downscaling daily maximum temperatures and using the downscaled information to estimate historical and future metrics like return periods and heat waves durations over Montreal, Canada; and the effect of the two variance adjustment techniques on the historical and future time-series. To do so, we used regional climate model (RCM) output from the Canadian RCM 4.2, as proxies of historical and future local climates, and daily maximum temperatures obtained from the Canadian GCM 3.1. The results show that the root-mean-squared errors between the pseudo-observations and the statistically downscaled time-series (historical and future) varied over time, with higher errors in the future period; and the effects of randomization and variance inflation on the tails of the statistically downscaled time-series.

show abstract

Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

Cited by 237 publications

References 29 publications

Evaluating the utility of dynamical downscaling in agricultural impacts projections

Evaluating the utility of dynamical downscaling in agricultural impacts projections

Lessons from climate modeling on the design and use of ensembles for crop modeling

Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada

Contact Info

Product

Resources

About