An Observationally-Centred Method to Quantify the Changing Shape of Local Temperature Distributions

Monday, 15 December 2014
David Alan Stainforth, London School of Economics, CATS, and GRI, London, United Kingdom, Sandra C Chapman, University of Warwick, Coventry, United Kingdom and Nicholas Wynn Watkins, Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
For climate sensitive decisions and adaptation planning, guidance on how local climate is changing is needed at the specific thresholds relevant to particular impacts or policy endeavours. This requires the quantification of how the distributions of variables, such as daily temperature, are changing at specific quantiles. These temperature distributions are non-normal and vary both geographically and in time. We present a method[1,2] for analysing local climatic time series data to assess which quantiles of the local climatic distribution show the greatest and most robust changes. We have demonstrated this approach using the E-OBS gridded dataset[3] which consists of time series of local daily temperature across Europe over the last 60 years. Our method extracts the changing cumulative distribution function over time and uses a simple mathematical deconstruction of how the difference between two observations from two different time periods can be assigned to the combination of natural statistical variability and/or the consequences of secular climate change. The change in temperature can be tracked at a temperature threshold, at a likelihood, or at a given return time, independently for each geographical location. Geographical correlations are thus an output of our method and reflect both climatic properties (local and synoptic), and spatial correlations inherent in the observation methodology. We find as an output many regionally consistent patterns of response of potential value in adaptation planning. For instance, in a band from Northern France to Denmark the hottest days in the summer temperature distribution have seen changes of at least 2°C over a 43 year period; over four times the global mean change over the same period. We discuss methods to quantify the robustness of these observed sensitivities and their statistical likelihood. This approach also quantifies the level of detail at which one might wish to see agreement between climate models and observations if such models are to be used directly as tools to assess climate change impacts at local scales.

[1] S C Chapman, D A Stainforth, N W Watkins, 2013, Phil. Trans. R. Soc. A, 371 20120287.

[2] D A Stainforth, S C Chapman, N W Watkins, 2013, Environ. Res. Lett. 8, 034031

[3] Haylock, M.R. et al., 2008, J. Geophys. Res (Atmospheres), 113, D20119