The Practitioner's Dilemma: How to Assess the Credibility of Downscaled Climate Projections

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101803/1/eost2013EO460005.pd

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections

Exploring user needs for climate risk assessment in the transport sector: how could global high-resolution climate models help?

Climate change is an issue relevant for all modes of transport. The interconnected nature of transport systems – and their dependence on other key services such as energy – mean that the transport sector must account for both direct and indirect effects of climate change in sector-focused climate risk assessments."br" To respond to sector-focused climate information needs in Europe, the “PRIMAVERA” project aims to provide useful and usable climate information, derived from high-resolution, global climate models. The model simulations will be evaluated to assess their ability to simulate key climate processes and hence to add value to existing climate risk assessment methods."br" PRIMAVERA is engaging with users and stakeholders across sectors, including transport. Here we give information on transport users’ needs for climate risk assessment. We outline how PRIMAVERA could address these needs, and how it will share relevant outcomes with users and stakeholders, including those in transport. This article is © Crown copyright Met Office, 2018

Climate scenario development and applications for local/regional climate change impact assessments: An overview for the non-climate scientist. Geography Compass 5: 301–28

Abstract Although downscaling methods for deriving local ⁄ regional climate change scenarios have been extensively studied, little guidance exists on how to use the downscaled scenarios in applications such as impact assessments. In this second part of a two-part communication, we review for nonclimate scientists a number of practical considerations when utilizing climate change scenarios. The issues discussed are drawn from questions frequently asked by our colleagues on assessment teams and include sources of observational data for scenario evaluation, the advantages of scenario ensembles, adjusting for scenario biases, and the availability of archived downscaled scenarios. Together with Part I, which reviews various downscaling methods, Part II is intended to improve the communication between suppliers and users of local ⁄ regional climate change scenarios, with the overall goal of improving the utility of climate impact assessments through a better understanding by all assessment team members of the strengths and limitations of local ⁄ regional climate change scenarios

Climate scenario development and applications for local/regional climate change impact assessments: An overview for the non-climate scientist. Geography Compass 5: 301–28

Abstract The majority of climate change impact assessments focus on potential impacts at the local ⁄ regional scale. Climate change scenarios with a fine spatial resolution are essential components of these assessments. Scenarios must be designed with the goals of the assessment in mind. Often the scientists and stakeholders leading, or participating in, impact assessments are unaware of the challenging and time-consuming nature of climate scenario development. The intent of this review, presented in two parts, is to strengthen the communication between the developers and users of climate scenarios and ultimately to improve the utility of climate impact assessments. In Part I, approaches to climate downscaling are grouped into three broad categoriesdynamic downscaling, empirical-dynamic downscaling and disaggregation downscaling methods -and the fundamental considerations of the different methods are highlighted and explained for non-climatologists. Part II focuses on the application of climate change scenarios

Climate scenario development and applications for local / regional climate change impact assessments : anoverview for the non-climate scientist

Abstract The majority of climate change impact assessments focus on potential impacts at the local ⁄ regional scale. Climate change scenarios with a fine spatial resolution are essential components of these assessments. Scenarios must be designed with the goals of the assessment in mind. Often the scientists and stakeholders leading, or participating in, impact assessments are unaware of the challenging and time-consuming nature of climate scenario development. The intent of this review, presented in two parts, is to strengthen the communication between the developers and users of climate scenarios and ultimately to improve the utility of climate impact assessments. In Part I, approaches to climate downscaling are grouped into three broad categoriesdynamic downscaling, empirical-dynamic downscaling and disaggregation downscaling methods -and the fundamental considerations of the different methods are highlighted and explained for non-climatologists. Part II focuses on the application of climate change scenarios

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella.

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971-2000--a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections