Online climate change projections report 4.8 Variable not provideded
For certain variables (soil moisture, latent heat flux and snowfall rate) it was not possible to provide probabilistic projections of future changes in UKCP09.
In the case of soil moisture, different definitions of this variable are used by different modelling groups, making it impossible to construct PDFs combining results from variants of Met Office models with those from other climate models. Without this key aspect of our methodology, it was not possible to provide probabilistic projections.
In the case of latent heat flux we found that projected changes from two of the alternative climate models were often well outside the range of the Met Office model variants (see Chapter 3, Section 3.2.10). In this situation, our method of combining results from the Met Office model variants and the alternative models could not be guaranteed to provide a robust indication of the probabilities of different outcomes, and hence PDFs were not provided.
In the case of snowfall rate, the models sometimes project small but non-zero values in the future, implying changes relative to the baseline climate that are close to the absolute lower bound of –100%. Under these conditions, statistical contributions to the uncertainties captured in the UKCP09 methodology were found to become unrealistically large, and hence probabilistic projections were not provided.
In the absence of a UKCP09 probabilistic projection for these three variables, there are three possible alternative sources of projections of transient changes during the 21st century:
- the 17-member ensemble of variants of the Met Office GCM,
- the 11-member ensemble of variants of the Met Office RCM
- the ensemble of other global climate models, available from the PCMDI website
Data from the first two (Met Office GCM and RCM variants) is available from the Climate Impacts LINK project, operated by BADC. Data from alternative global climate models can be accessed from the Program for Climate Model Diagnosis and Intercomparison (PCMDI), based in California, which has collected model output from simulations contributed by modelling centres around the world, as part of the Coupled Model Intercomparison Project (CMIP3) of the World Climate Research Programme. The CMIP3 multi-model dataset can be freely accessed for non-commercial purposes here .
Each type of data has advantages and disadvantages. The data from other global climate models, and that from the 17-member Met Office GCM ensemble, is at a relatively coarse resolution. The Met Office RCM has a finer resolution (25 km) and hence provides more information on possible regional variations across the UK. The range of modelling uncertainties explored in the 17-member Met Office GCM ensemble, and the 11-member Met Office RCM ensemble, is not as wide as that explored in the variables for which probabilistic projections are provided in UKCP09. The RCM data is only available for the Medium emissions scenario.
In the case of snow, we recommend the use of changes from the 11-member Met Office RCM ensemble in the first instance. Changes by the 2080s in the winter mean snowfall rate, averaged over the 11-RCM ensemble are shown in Figure 4.33; typically there are reductions of 65–80% over mountain areas and 80–95% elsewhere. Chapter 5 gives details of the data available from the RCM ensemble, its advantages and limitations. Of course, users may wish to extend their analysis, and investigate the robustness of any adaptation decisions, using data from other global climate models. We have not looked at possible alternative projections of soil moisture and latent heat flux, although both are available from the 11-member Met Office RCM ensemble via LINK. It is recommended that users do not revert to UKCIP02 scenarios in isolation, for any of the variables that are not available in UKCP09.
Figure 4.31: Percentage average changes in mean snowfall rate in winter, by the 2080s (relative to 1961–1990) under the Medium emissions scenario, averaged over the 11 members of the Met Office RCM ensemble.
- Last updated: Sunday, 11 March 2012