MiKlip - a National Research Project on Decadal Climate Prediction

A German national project coordinates research on improving a global decadal climate prediction system for future operational use. MiKlip, an eight-year German national research project on decadal climate prediction, is organized around a global prediction system comprising the climate model MPI-ESM together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that targets future operational use. Three prediction-system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multi-year-mean European summer surface temperatures, extra-tropical cyclone tracks, the Quasi-Biennial Oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind-energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption. MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction-system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization. Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of two to three years

SPI - Standardized Precipitation Index from CRU / ECAD for EU and USA

The "Standardized Precipitation Index" (SPI) is used to describe extremely dry or wet climate situations. The World Meteorological Organization (WMO) recommends, that all national meteorological and hydrological services should use the SPI for monitoring of dry spells (Press report December 2009, WMO No. 872). The advantages of SPI usage are: Only precipitation data are needed for the calculation of the index. The index is a standardized measure for precipitation in different climatic regions and for seasonal differences. Calculated for different time scales: meteorological, agricultural-economic and hydrological. SPI Classes: SPI ≤ -2: Extremely dry, -2 < SPI ≤ -1.5: Severely dry, -1.5 < SPI ≤ -1: Moderately dry, -1 < SPI ≤ 1: Near normal, 1 < SPI ≤ 1.5: Moderately wet, 1.5 < SPI ≤ 2: Severely wet, SPI ≥ 2: Extremely wet. Calculation: The SPI, presented here, is different from the original SPI definition of McKee et al. 1993. An enhanced SPI is used, that significantly reduces errors resulting from the determination of the precipitation's distribution (Sienz et al. 2011). MC Kee et al. 1993 shifted the time series of the SPI one time step into the future, but this is not done for the calculation of the SPI presented here. The SPI was calculated from two precipitation data sets: European Climate and Data Assessment (ECA&D), E-OBS gridded dataset Version 4.0 (1951 - 2010) for Europe Climate Research Unit (CRU), Version: CRU TS 2.1 (1901 - 2002) for Europe and US

Extreme dry and wet events in Iceland: Observations, simulations and scenarios

Monthly extremes of dryness and wetness in Iceland are analysed based on the standardised precipitation index (SPI). The analysis is performed for observations and four sets of coupled atmosphere-ocean climate model simulations (ECHAM5.MPI-OM) to link water cycle extremes in Iceland with regional atmospheric flow patterns and to estimate and evaluate future changes. The following results are obtained: (i) SPI extremes are linked with a Europe-Greenland Index (EGI) describing south-westerly flow anomalies by a dipole and the related geopotential height differences. The good agreement between the observed statistics and transient 20th century simulations encourages analysis of future climate projections. (ii) Comparison of the 21st century A1B-scenario with the pre-industrial climate reveals significant and large differences: While extremes of dryness hardly change, extremely wet conditions increase in winter and spring. As there is no flow intensification and cyclone density decreases, the cause maybe found in air moisture raising in a warmer climate