A first skill assessments for seasonal forecasts of climate information indices has been completed in D22.1.
This deliverable provides exemplary maps of climate information indices (CIIs) that are relevant for a variety of economic sectors and a large variety of stakeholders. For the observation based E-OBS data set, maps of over 20 indices covering the years 1950-2013 are now available to the EUPORIAS community. We present analyses of the skill of seasonal forecasts for the following indices:
- the heating and cooling degree days (HDD, CDD), i.e. two quantities serving as a proxy for energy demand and therefore of major importance for the energy sector
- various precipitation or drought-related measures with relevance to agriculture and hydrology (e.g. cumulative precipitation over growing season, water requirement satisfaction index, intense precipitation index)
- a wild fire index (relevant to forestryand civil protection)
- a heat mortality index (public health)
- wind speed related indices tailored to renewable energy producers
Common to all examples is the finding of limited forecast skill over Europe, highlighting the challenge for providing added-value services to stakeholders operating in Europe. The reasons for the lack of forecast skill vary: often we find little skill in the underlying variable(s) precisely in those areas that are relevant for the CII, in other cases the nature of the CII is particularly demanding for predictions, as seen in the case of counting measures such as frost days or cool nights. On the other hand, several results suggest there may be some predictability in sub-regions for certain indices. Several of the exemplary analyses show potential for skilful forecasts and prospect for improvements by investing in post-processing. Furthermore, those cases for which CII forecasts showed similar skill values as those of the underlying meteorological variables (e.g. HDD), forecasts of CIIs provide added value from a user perspective: The results of WP12 clearly indicate that users appreciate forecasts of such impact oriented quantities. Therefore, the effort in deriving more sophisticated and - at the same time – robust bias correction methods or downscaling approaches is a crucial task to improve seasonal forecasts of climate information indices. It is foreseen to further explore and deepen the understanding of the issues presented in this deliverable by considering new seasonal forecast data provided through SPECS and to improve the statistical post-processing in the second part of the project. Various of the CII forecasts will directly link into EUPORIAS’ prototype and case studies thereby strengthening the links of this work package to project partners and stakeholders.