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User-oriented global predictions of the GPCC drought index for the next decade

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Pohlmann,  Holger
Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Modali,  Kameswarrao
Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Citation

Paxian, A., Ziese, M., Kreienkamp, F., Pankatz, K., Brand, S., Pasternack, A., et al. (2019). User-oriented global predictions of the GPCC drought index for the next decade. Meteorologische Zeitschrift, 28, 3-21. doi:10.1127/metz/2018/0912.


Cite as: http://hdl.handle.net/21.11116/0000-0002-96B8-5
Abstract
Multi-year droughts strongly impact food production and water management. Thus, predictions for the next decade are required for decision makers. This study analyzes the decadal prediction skill of the Global Precipitation Climatology Centre Drought Index (GPCC‑DI) and its components, namely the Standardized Precipitation Index (SPI‑DWD) adapted by the German Meteorological Service (Deutscher Wetterdienst, DWD) and the Standardized Precipitation Evapotranspiration Index (SPEI) within the German global decadal prediction system. The decadal predictions are recalibrated. The prediction skills of the two prediction types ensemble mean predictions and probabilistic predictions are evaluated against those of the commonly applied reference predictions observed climatology and uninitialized simulations. The evaluation of 4‑year mean droughts for the lead-year period 1–4 at 5° spatial resolution shows high prediction skills for the SPEI in the tropics, especially northern Africa, and several heterogeneously distributed hot spots for the SPI‑DWD. The advantage of GPCC‑DI is its global coverage, but it hardly enhances the SPI‑DWD and SPEI skills. The recalibration clearly enhances ensemble mean prediction skills in slightly improving correlations and in strongly reducing standard deviations as well as large conditional biases in decadal predictions. For probabilistic predictions, impacts of conditional biases and recalibration are less prominent. To meet user requirements decadal drought predictions with higher temporal and spatial resolutions are analyzed. 1‑year mean droughts for lead year 1 mostly show smaller prediction skills than 4‑year means because of larger small-scale noise, but some regions reveal improved skills due to regional processes predictable at the 1‑year time scale, e.g. over the western United States. Drought predictions at 2° resolution show similar spatial skill patterns with enhanced fine-scale structures mostly without losing prediction skill. A user-oriented evaluation of the decadal GPCC‑DI prediction for the severe North African drought of 2008–2011 reproduces most observed drought index tendencies in both prediction types, but intensities are often underestimated. Finally, the decadal GPCC‑DI prediction for 2018–2021 presents a drought over North Africa and Arabia and wetting over the Northern Hemisphere in both prediction types. For 2018, predicted patterns are similar but with smoothed intensities. In summary, decadal drought prediction skill depends on the indices, time periods, and areas considered. However, the analyzed drought indices can provide skillful high-resolution information for several future time periods and regions meeting user needs for decadal drought predictions.