What can I use to assess current and near-term vulnerabilities, impacts, risks and adaptation?

To support near-term assessments (up to the year for which observed data is available), users should use the historical observational data available in the UKCP09 Observed trends report and within the observed datasets available on the Met Office website.

To extend this assessment into the near-future (i.e. next 5–10 years), users should consider combining the historical climate information with one (or more) of the following methods which can provide alternative plausible near-future climate information.

In detail

Use of information from the earliest projection period (2020s)

One possible approach for identifying possible near-future climate change information is to use the probabilistic projections from the first 30-year time period (2020s). The available probabilistic projections are an average for that 30-year time period and therefore can be used with caution to inform selection of a set of possible climate projections for the near-term period.

Users should exercise particular care when using these projections in this manner as for the early periods of the projected climate (first 1 to 2 30-year time periods), uncertainties associated with natural variability dominate (See Section 2.3 of the Climate change projections report). Users should not to unduly limit the range of probability levels when selecting the projections to be considered to inform the development of possible near-term climate projections. This selection should also be tempered with a consideration of the users’ aversion to risk in light of the associated uncertainties.

Use of statistically defined scenarios for the near-future time period

An additional approach worth considering is based on using historical observed data and defining potential future climate regimes for the period of interest based on observed trends and informed decisions about those trends. 

This could include such things as  changes in variables based on trends from observed data (e.g. 5, 10 and 20% decrease in mean precipitation in summer), based on projections as per the 2020s and 2030s, and/or based on short-term information such as trends coming from the North Atlantic Oscillation. This type of approach has shown to have particular merit in terms of providing a basis for effective decision making for the near-term period and may warrant further consideration by users.

If adopting this approach, user should explore the sensitivity of the results of the assessment in terms of their risk tolerance and to their relative confidence in each of the scenarios developed and used.

Use of seasonal and decadal forecast

Supporting near-term assessments of vulnerabilities, impacts and risks with the necessary climate information is something the climate research community is also addressing. As mentioned earlier, the stumbling block is the inevitable and dominating internal variability. Recent research results, however, suggest there is potential to predict some aspects of internal variability out to a decade or more ahead (see Chapter 3 of the Climate change projections report). Efforts to provide this capability include the NERC and Met Office Joint Climate Research Programme and the European Commission’s 6th Framework Programme project Ensembles.

Find out more

• Risbey, J.S., Hamza, K. and Marsden, J.S. 2007. Use of climate scenarios to aid in decision analysis for interannual water supply planning, Water Resource Management 21pp. 919–932.

• Advancing decadal-scale climate prediction in the North Atlantic sector; N. S. Keenlyside, M. Latif, J. Jungclaus, L. Kornblueh & E. Roeckner; Nature; 453, 1 May 2008. doi:10.1038/nature06921.

• Prospects for downscaling seasonal precipitation variability using conditioned weather generator parameters; R. L. Wilby  D. Conway,  and P. D. Jones. Hydrol. Process. 16, 1215–1234 (2002); www.interscience.wiley.com). doi: 10.1002/hyp.1058.

• Improved Surface Temperature Prediction for the Coming Decade from a Global Climate Model, Doug M. Smith Stephen Cusack, Andrew W. Colman, Chris K. Folland, Glen R. Harris, James M. Murphy. Science, 317, 796 (2007).