An alternate approach to assessing climate risks

Brown, Casey; Wilby, Robert L.

doi:10.1029/2012eo410001

Cited by 195 publications

(113 citation statements)

References 14 publications

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“…Uncertainties do not need to be an obstacle; rather, they are themselves useful information about what we know and what we do not know. One way to deal with uncertainties is to explore the importance of different factors, or to carry out sensitivity tests (Brown and Wilby 2012). Furthermore, a range of statistical methods can be applied to study the quality of model results and the dependency of these results on the choices made in the model set-up.…”

Section: Uncertaintiesmentioning

confidence: 99%

Reconsidering the Quality and Utility of Downscaling

Takayabu¹,

Kanamaru

Dairaku

et al. 2016

Journal of the Meteorological Society of Japan

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Dynamical downscaling (DDS) is performed using regional climate models (RCMs) with global atmospheric states as the input, but there is no consensus among researchers on how to define and estimate the resolvable scale of the various climatic variables obtained by DDS. Sources of RCM uncertainties, including both internal model and intermodel variability, have been assessed by performing ensemble simulations and model intercomparisons, sometimes under the controversial assumption that model bias is independent of the climatic state. Compared with low-resolution global climate simulations, DDS can add value in several ways. For example, because they consider high-resolution topographic data, RCMs can often capture mesoscale phenomena and can better represent climate dynamics. Another downscaling method, empirical statistical downscaling (ESD), is complementary to DDS because it is based on a different philosophy (i.e., sources of information) and on a mostly different set of assumptions. More collaboration and communication should be encouraged among those who develop models, those who use models and perform downscaling, those who use downscaling data, and those who make decisions

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Section: Uncertaintiesmentioning

confidence: 99%

Reconsidering the Quality and Utility of Downscaling

Takayabu¹,

Kanamaru

Dairaku

et al. 2016

Journal of the Meteorological Society of Japan

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“…Changes other that the ones currently projected by climate models are plausible, particularly at impacts relevant spatial scales. Therefore decision makers should use a variety of scenarios for their planning, and not restrict their analysis exclusively to model projected ranges of uncertainties (Risbey 1998;Brown and Wilby 2012).…”

Section: Discussionmentioning

confidence: 99%

Towards a typology for constrained climate model forecasts

et al. 2014

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In recent years several methodologies have been developed to combine and interpret ensembles of climate models with the aim of quantifying uncertainties in climate projections. Constrained climate model forecasts have been generated by combining various choices of metrics used to weight individual ensemble members, with diverse approaches to sampling the ensemble. The forecasts obtained are often significantly different, even when based on the same model output. Therefore, a climate model forecast classification system can serve two roles: to provide a way for forecast producers to self-classify their forecasts; and to provide information on the methodological assumptions underlying the forecast generation and its uncertainty when forecasts are used for impacts studies. In this review we propose a possible classification system based on choices of metrics and sampling strategies. We illustrate the impact of some of the possible choices in the uncertainty quantification of large scale projections of temperature and precipitation changes, and briefly discuss possible connections between climate forecast uncertainty quantification and decision making approaches in the climate change context.

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“…They argue that this is a more inclusive way of assessing risks, including from climate variability and climate change, than using the outcome vulnerability approach adopted by the Intergovernmental Panel on Climate Change (IPCC). Brown and Wilby (2012) assess climate risks using a form of sensitivity analysis ('stresstesting') to determine what climate characteristics a water supply system serving a metropolitan area in Massachusetts might be most sensitive to/at risk from. First, they demonstrated that GCM projections underestimated the variability statistics (standard deviation and lag-one autocorrelation) of streamflow for a region of the northeast United States based on a widely used, bias-corrected and statistically downscaled source of GCM projections (Maurer et al 2007).…”

Section: Risk-based Assessments Of Climate Change Impacts and Adaptatmentioning

confidence: 99%

Towards Adaptation of Water Resource Systems to Climatic and Socio-Economic Change

O’Connell

2017

Water Resour Manage

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Climate change is viewed as the major threat to the security of water supplies in most parts of the world in the coming decades, and the water resources literature continues to be dominated by impact and risk assessments based on the latest climate projections from General Circulation Models (GCMs). However, the evidence for anthropogenic changes in precipitation and streamflow records continues to be elusive which, together with the known high uncertainty in GCM ensemble projections, has led to the development of risk assessment methods which are not driven exclusively by GCMs. It is argued that a baseline risk assessment should retain the assumption of climatic stationarity, and be based on the modelling of observed interannual variability as a dominant process in determining water resource system reliability, augmented where justifiable by reliable information from GCMs. However, irrespective of what the climate does in the future, globalization and socio-economic changes are the major drivers for increases in water demand and threats to water security, as exemplified by the burgeoning economies of the BRIC and MINT countries, and the large population increases and economic growth seen in many developing countries. It is suggested that more attention needs to be paid to adaptation to socio-economic change which is arguably more predictable than climatic change, based on what is already known about population and economic growth, lifestyle changes and human choices. More focus is needed on economic analyses that can inform the major investments in water use efficiency measures which can deliver the water savings needed to avert widespread water scarcity. The effectiveness of water use efficiency measures is largely determined by (a) the potential of modern information technology to achieve more efficient water resources management and water use and (b) human responses and choices in the uptake of measures. To assess the potential efficiency gains, it is argued that water resource systems modelling needs to evolve to incorporate the human dimension more explicitly, through Coupled Human and Natural Systems (CHANS) modelling. A CHANS modelling framework is outlined which incorporates agent-based Water Resour Manage

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An alternate approach to assessing climate risks

Cited by 195 publications

References 14 publications

Reconsidering the Quality and Utility of Downscaling

Reconsidering the Quality and Utility of Downscaling

Towards a typology for constrained climate model forecasts

Towards Adaptation of Water Resource Systems to Climatic and Socio-Economic Change

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