Improving Climate Projections to Better Inform Climate Risk Management

Keller, Klaus; Nicholas, Robert E.

doi:10.1093/oxfordhb/9780199856978.013.0002

Cited by 6 publications

(6 citation statements)

References 44 publications

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“…Based on a simple empirical analysis, the 10th percentile corresponds to approximately the 1in-10-year event, while the lowest observed yield corresponds to approximately the 1-in-50-year event (from a 50-year sample). From a risk management perspective, it is important to properly characterize the distribution tails and thus the magnitude of these events 39 ; but exactly how reliable the models need to be at different thresholds ultimately depends on the decision context of the end-use application.…”

Section: Resultsmentioning

confidence: 99%

Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

Sriver

Haqiqi

Hertel

et al. 2021

Commun Earth Environ

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Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

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Section: Resultsmentioning

confidence: 99%

Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

Sriver

Haqiqi

Hertel

et al. 2021

Commun Earth Environ

Self Cite

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show abstract

“…By contrast, every sample generated by the CMIP ensemble exhibits variability, measured either by the standard deviation or median absolute deviation, larger than that observed. Decision-making strategies that are robust against low-probability, high-impact events require information about the entire range of outcomes 34 . Here, the largest negative yield shock (Figure 1.d) represents the most extreme event observed throughout the historical period.…”

Section: Resultsmentioning

confidence: 99%

Statistically bias-corrected and downscaled climate models underestimate the severity of U.S. maize yield shocks

Sriver

Haqiqi

Hertel

et al. 2021

Preprint

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Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Previous studies typically focus their uncertainty analyses on climatic variables and are silent on how these uncertainties propagate into human systems through their subsequent incorporation into econometric models. Here, we use a multi-model ensemble of statistically downscaled and bias-corrected climate models, as well as the corresponding CMIP5 parent models, to analyze uncertainty surrounding annual maize yield variability in the United States. We find that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest historically observed yield shocks. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

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“…We have not yet investigated the spread of the ensemble, which is as least as important, especially for impact-and risk-related fields. As an example, the potential danger of having a too-narrow ensemble spread (overconfident projections) by neglecting important uncertainties is highlighted in Keller and Nicholas (2015).…”

Section: Application To the Future 421 Testing Out-of-sample Skillmentioning

confidence: 99%

Selecting a climate model subset to optimise key ensemble properties

et al. 2018

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Abstract. End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history.Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

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Improving Climate Projections to Better Inform Climate Risk Management

Cited by 6 publications

References 44 publications

Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

Statistically bias-corrected and downscaled climate models underestimate the severity of U.S. maize yield shocks

Selecting a climate model subset to optimise key ensemble properties

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