Regional decadal climate predictions for Europe – Feasibility & Skill

The climate is highly variable on all time-scales. External forcings like the addition of anthropoghenic carbon dioxid into the climate system as well as internal feedbacks within the climate system contribute this variability. To understand the variability and to predict its changes is a major scientific field of research and in the interest of many users. For the time frame up to a hundred years ahead the IPCC performs climate projections to estimate the effects of the changing forced boundary conditions due to the anthropogenic green-house gas emissions on the climate trends. For the short time-scales the numerical weather prediction is well established. Such predictions based on initial conditions have been extended to seasonal scales in the last two decades. In between these time frames emerges a new research field of the decadal predictions, which is located in the transition between the initial value and the forced boundary condition problem. Decadal climate predictions cover the time frame of interest for many decision support activities and impacts. Only in the last few years, predictions over a few decades have become feasibly due to the availability of large observational data sets and advancements in climate modelling. To be able to develop mitigation strategies for potential negative impacts of the changing climate, as well as to take advantage of opportunities for adapting, it is neccessary to understand the mechanism behind the natural variations and how much they contribute to regional variations of the trend. While seasonal climate predictions and climate projections have made great advancements, decadal climate prediction is still in its early stages. There are many open questions and topics especially in regard to regional applications. This works aims to close some of the knowledge gaps in regards to climate variability on a decadal time-scale and the prediction thereof on a regional scale in Europe. The German research program MiKlip aims at the development of a decadal ensemble predictions system. A module within MiKlip is dedicated to develop a regional downscaling system for the global predictions. The regional focuses of the downscaling experiments are Europe, which is the main focus of this work, and Africa. Previous studies indicate some potential predictability for both regions but many questions have yet to be answered. With the use of the global prediction system consisting of the Max-Planck-Institute for Meteorology Earth System Model MPI-ESM and the downscaling by two regional models (RCM) COSMO-CLM and REMO, a regional decadal climate ensemble is established. The predictions are compared to gridded observational data (E-OBS) in a comprehensive analysis. More to the point, this work will answer three questions. First, what is the potential of a prediction in Europe on a decadal scale? To answer, the prediction potential of several variables in Europeis assessed using only observations. Decadal predictability in Europe originates from low frequency climate variations, especially in the North Atlantic. One possibility to quantify a variable’s predictive potential could be to compare the distribution of a decadal forecast to a climatological one. How much and for how long the predicted probability density function (PDF) can be distinguished from the corresponding climatological PDF will then give a measure of predictability. I use the relative entropy from information theory to measure the difference between the PDF’s. Second, how should regional decadal predictions be evaluated? And, in conjunction with that, third, how can the predictions and their analysis be improved? Various analysis techniques including skill metrics are applied to determine if there is decadal variability and the representation thereof in the global and regional ensemble. Additional to basic skill the following questions are posed in this study: At which temporal and spatial scales can predictive skill be achieved? And is the skill of the global hindcasts preserved or even improved with regionalisation? Previous studies indicate that extremes might partly show a higher predictive skill than mean precipitation or temperatures. Skilful predictions of decadal tendencies of extremes (like droughts, heat waves or storms) also exhibit a higher value to potential users than variations of mean quantities. Therefore, the decadal variations of extremes and their predictability are considered and found in the case of moderate temperature extremes and combined extremes of temperature and precipitation as well as variables that contain some memory to have the most potential to be predictable with a 10-year forecast. A positive skill for near surface temperature and precipitation was found especially for multi-year averages. This skill varies on season and region. The same can be found for the added value of downscaling global decadal climate predictions. Though this work makes use of only one prediction system, some of the results can be generalised, especially regarding the potential predictability and the optimal length of multi-year windows to determine decadal signals. So, a few recommendations for regional decadal prediction systems can be made, as so far that annual data is too noisy to exhibit significant skill and that the size of the ensemble has a huge impact on the skill as well. It has also emerged that the linear trend in temperature in particular is a large contributor to the skill, and, when included, improved the performance of the models significantly. A first try into prediction large, extreme climate anomalies (climate events), has shown that the MiKlip model is able to reproduce 9 out of 11 anomalous hot summer events. Subsuming, a positive outlook into the future of predicting events with great impact on decadal time scales emerges from the overall skill of the MiKlip prediction system, the potential predictability for many variables, as well as continuing advances in climate modelling.