This SPP will study changes in the variability of the weather using daily climate data. An important reason to study variability is that changes in extremes can be caused by changes in the mean and in the variability. Variability is more important for extreme extremes and the mean is more important for moderate extremes. It is thus not clear whether results for moderate extremes, which are most studied, extrapolate to true extremes, which are important for many climate change impacts. Furthermore, the mean state has been studied in much more detail and is thus likely more reliable. Also for modelling nonlinear processes in climate models, variability is important.
In the first three-year period the SPP will study weather variability with a focus on the quality of observational data and on the performance of the modelling and analysis tools. This phase will concentrate on Europe, which has the longest and best observations available. In the second phase, the focus will be on understanding the mechanisms that cause these changes. Such studies need to be performed on the global scale.
First phase
1. Weather variability needs to be analysed in a range of difference climatic variables and phenomena at various temporal time scales and spatial ranges, as well as for different measures of variability, seasons and regions and their relations with climate modes. To learn most from these studies, they should be performed in a way that eases intercomparisons.
2. We will improve the analysis methods to study changes in the spatial and temporal dependence of variability over a range of spatio-temporal scales. Important for the comparability of studies is that the range of spatio-temporal scales is well defined. These methods will analyse the full probability distribution or multiple variability measures and not just one or a few.
3. Non-climatic changes due to changes in monitoring practices are especially important when it comes to changes in variability. We will thus develop quality control and (stochastic) homogenization methods for the probability distribution of daily data and estimate uncertainties due to remaining data problems.
4. We will investigate the properties of inhomogeneities in the essential climatic variables in various climate regions. Two methods for this are 1) using parallel measurements with historical and modern set-ups and 2) by studying the adjustments made by homogenisation methods.
5. An attractive alternative to creating homogenized datasets is the development of analysis methods that use data from homogeneous subperiods (similar to what the Berkeley project (BEST) has done for the mean temperature).
6. We will validate climate models with respect to variability at various temporal and spatial scales. Because of the differing spatial averaging scales, this includes the study of the downscaling or gridding methods.
Second phase
7. The methods developed in the first phase will have to be made robust to be applied to large global datasets to be able to study changes in weather variability for all climate regions of the Earth.
8. We will validate climate models globally, for various climate regions, with respect to variability at various temporal and spatial scales.
9. The mechanisms that determine natural and man-made changes in variability will be studied in global models and datasets.
Climatologists, statisticians and time series analysts working on extreme weather, quality control, homogenization, model validation or downscaling likely have the skills to participate in this SPP.
The SPP is focused on our understanding of the climate system. While impact studies will strongly benefit from the results, they are not part of this SPP. Studies on changes in extremes are welcome if they analyse the extremes together with other variability measures. Research on long-term (climatic) changes in the mean does not fit in this SPP on weather variability.
No comments:
Post a Comment