Use of Representative Climate Futures in impact and adaptation assessment
A key challenge for climate projection science is to serve the rapidly growing needs of impact and adaptation assessments (hereafter risk assessments) in an environment where there are substantial differences in the regional projections of climate models, an expanding number of potentially relevant climate model results, and a desire amongst many users to limit the number of future climate scenarios in their assessments. While it may be attractive to select a small number of climate models based on their ability to replicate current climate, there is no robust method for doing this. We outline and illustrate a method that addresses this challenge in a different way. The range of plausible future climates simulated by climate models is classified into a small set of Representative Climate Futures (RCFs) and the relative likelihood of these estimated. For each region, the RCFs are then used as a framework in which to classify more detailed information, such as available climate model and downscaled data sets. Researchers wishing to apply the RCFs in risk assessments can then choose to use a subset of RCFs, such as the “most likely”, “high risk” and “least change” cases for their impact system. Preparation and analysis of future climate data sets can therefore be confined to those models whose simulations best represent the selected RCFs. This significantly reduces the number of models involved, and potentially the effort required to undertake the risk assessment. Consistently applied within a region, RCFs, rather than individual climate models, can become the boundary objects which anchor discussion between the climate science and risk assessment communities, simplifying communication. Since the RCF descriptions need not change as new climate model results emerge, they can also provide a stable framework for assimilating risk assessments undertaken at different times with different sets of climate models. Systematic application of this approach requires various challenges to be addressed, such as robustly classifying future regional climates into a small set and estimating likelihoods. Copyright Springer Science+Business Media B.V. 2012
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 115 (2012)
Issue (Month): 3 (December)
|Contact details of provider:|| Web page: http://www.springer.com/economics/journal/10584|
|Order Information:||Web: http://link.springer.de/orders.htm|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ian Watterson, 2012. "Understanding and partitioning future climates for Australian regions from CMIP3 using ocean warming indices," Climatic Change, Springer, vol. 111(3), pages 903-922, April.
When requesting a correction, please mention this item's handle: RePEc:spr:climat:v:115:y:2012:i:3:p:433-442. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)or (Christopher F Baum)
If references are entirely missing, you can add them using this form.