The project aims at providing practical knowledge and tools to water managers affected by climate change to better cope with climate projections and near-term climate predictions (decadal predictions). The work package led by the Institute of Meteorology of Freie Universität Berlin will particularly address hydrological extreme conditions within climate change scenarios and decadal climate predictions, focusing on six representative areas across Europe.
Development of Indices for large scale atmospheric blocking situations and their impact on climate variability
This project aims at developing and implementing post-processing approaches for (re-)calibrating the MiKlip decadal prediction ensemble; addressing the typical problems encountered for decadal predictions, i.e. relatively small ensemble sizes and limited availability of hindcast-observation pairs. Starting with normally distributed variables, strategies will be specifically tailored to problems encounter for decadal predictions (model drift, climate trend). In a later stage, non-normally distributed quantities will be considered, as variables relevant to the end-user do not necessarily follow a Gaussian distribution (e.g. precipitation, humidity or wind gusts). Moreover calibration methods for probabilistic forecasts of dichotomous and countable events (e.g., droughts) will be also considered. An implementation into the central evaluation system (CES) allows all other MiKlip projects to (re-)calibrate the ensemble predictions and prepares the calibration for operational use.
ECO covers the coordination of the Module E; it integrates the individual evaluation efforts and organizes scientific exchanges with the other modules and WPs in MiKlip II. Stakeholder/end-user interaction required for the orientation of the evaluation system to end-user needs will be ensured in cooperation with module D, involving individual work packages as needed. Beside the coordination of Module E, ECO has following scientific contributions: As bias and drift correction become more relevant for all validation activities, ECO will implement a drift correction framework based on generalized linear models (GLMs). The framework includes a drift correction which is initialization time dependent. It shall be developed and implemented into the Central Evaluation System (CES). Together with WP E-6 DROUGHTCLIP, the GLM-based framework will be extended to skewed and positive variables, to be particularly suited for precipitation (WP E-2 DAPAGLOCO, WP E-5 PROMISA), humidity (WP E-1 MOSQUITO) or wind. Furthermore, calibration of probabilistic forecasts to increase reliability is an important issue in MiKlip II. Different calibration approaches will be developed in Module E within WP E-8 PROVESIMAC and WP E-9 CALIBRATION. Together with bias and drift correction, calibration of forecast will contribute to general post-processing methodologies which will be coordinated by ECO. Predictive skill is assumed not only to vary with lead time but also with season and/or with the phase of low-frequency climate modes, like Atlantic multidecadal variability (AMV), Atlantic meridional overturning circulation (AMOC), and Pacific decadal oscillation (PDO). Skill estimation stratified along these influences will be a valuable contribution to the general skill estimation but also for processoriented validation. Therefore ECO will implement CES plug-ins for stratified verification.
A graduate research school with the University of Potsdam, Potsdam Institute for Climate Impact Research (PIK), Helmholtz Centre Potsdam German Research Centre for Geosciences GFZ. P3: Spatio-temporal response of extreme precipitation to climate change and decadal climate variability I1: Natural hazards in a changing climate – What causes the variability?
Coupling a multiscale stochastic precipitation model to large-scale atmospheric flow is part of the Collaborative Research Center SFB1114: Scaling Cascades in Complex Systems. A stochastic precipitation model is investigated for means to be linked to atmospheric flow via the use of the Dynamic State Index (DSI).
This project investigates the relationship between the ocean state and European windstorms in the MiKlip decadal prediction system. Relevant oceanic variables and the associated time scales for windstorm development are identified. These results are the basis for the model validation of oceanic processes leading to European winter windstorm development (In cooperation with project VALOCEAN). STOC analyses how far the representation of the identified oceanic variables influences the forecast skill for winter windstorms and extratropical cyclones. This helps understanding and communicating skill found in the operational forecast system, and to explore possibilities to identify oceanic states leading to improved skill.
Current master's theses:
Quantifying and adjusting drift in decadal predictions (Igor Kröner)
How to represent orographic effects in spatial extreme value statistics (Katharina Roth)