Renate Hagedorn

DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER)

A multi-model ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multimodel Ensemble Prediction System for Seasonal to Interannual Prediction). The DEMETER system comprises seven global atmosphere–ocean coupled models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, innovative examples of the application of seasonal ensemble forecasts in malaria and crop yield prediction are discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information in the applications sector, illustrating the economic value of seasonal-to-interannual prediction for society as a whole.

The rationale behind the success of multi‐model ensembles in seasonal forecasting – I. Basic concept

The DEMETER multi-model ensemble system is used to investigate the rationale behind the multi-model concept. A comprehensive documentation of the differences in the single and multi-model performance in the DEMETER hindcast data set is given. Both deterministic and probabilistic diagnostics are used and a variety of analyses demonstrate the improvements achieved by using multi-model instead of single-model ensembles. In order to understand the reason behind the multi-model superiority, basic scenarios describing how the multi-model approach can improve over singlemodel skill are discussed. It is demonstrated that multi-model superiority is caused not only by error compensation but in particular by its greater consistency and reliability.

Malaria early warnings based on seasonal climate forecasts from multi-model ensembles.

The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate variability is an important determinant of epidemics in parts of Africa where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean–atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation.

The rationale behind the success of multi-model ensembles in seasonal forecasting – II. Calibration and combination

This invention relates to a selection mechanism for a postage meter in which a single Thomas-type mutilated drum actuator is rigidly mounted on a hollow cylindrical drive shaft on which is also mounted the postage meter printing head, and comprises a series of selection bars lying longitudinally of the drive shaft and angularly therearound, each of which bars is set by a suitable manual positioning means, such as a selection wheel, each such bar carrying a yoke to position a driven gear of a first register to be driven by the actuator, a like series of auxiliary setting bars, each of which is rigidly yoked to the respective first setting bar and which carries a yoke for setting a register drive gear of another register of a postage meter. The main setting bar, at its forward end, is provided with gear teeth which drive a gear train that meshes with a rack carried by the actuator drive shaft to set the mechanism in the print wheel.

Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation

Abstract As a companion to Part I, which discussed the calibration of probabilistic 2-m temperature forecasts using large training datasets, Part II discusses the calibration of probabilistic forecasts of 12-hourly precipitation amounts. Again, large ensemble reforecast datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Forecast System (GFS) were used for testing and calibration. North American Regional Reanalysis (NARR) 12-hourly precipitation analysis data were used for verification and training. Logistic regression was used to perform the calibration, with power-transformed ensemble means and spreads as predictors. Forecasts were produced and validated for every NARR grid point in the conterminous United States (CONUS). Training sample sizes were increased by including data from 10 nearby grid points with similar analyzed climatologies. “Raw” probabilistic forecasts from each system were considered, in which probabilities were set according to ensemble relative ...

Strategies: Revolution in Climate Prediction is Both Necessary and Possible: A Declaration at the World Modelling Summit for Climate Prediction

Thanks to the assessments by the IPCC, the world recognizes that humans are contributing to a global climate change that is among the most important threats we face. The climate science community now taces a majornew challenge ofproviding society with reliable regional climate predictions. Adapting to climate change while pursuing sustainable development will require accurate and reliable predictions ot changes in regional weather systems, especially extremes. Investments ot trillions of dollars worldwide may be necessary to avoid the worst consequences of climate change. Yet, current climate models have serious limitations in simulating regional weather variations and therefore in generating the requisite information about regional changes with a level of confidence required by society. Use of high-resoluti on regional models to downscale regional climate change is questionable if the global models from which lateral boundary conditions are prescribed are not realistic. In short, the limitations of current modeling methods are forcing the climate

Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part I: Two-Meter Temperatures

Abstract Recently, the European Centre for Medium-Range Weather Forecasts (ECMWF) produced a reforecast dataset for a 2005 version of their ensemble forecast system. The dataset consisted of 15-member reforecasts conducted for the 20-yr period 1982–2001, with reforecasts computed once weekly from 1 September to 1 December. This dataset was less robust than the daily reforecast dataset produced for the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), but it utilized a much higher-resolution, more recent model. This manuscript considers the calibration of 2-m temperature forecasts using these reforecast datasets as well as samples of the last 30 days of training data. Nonhomogeneous Gaussian regression was used to calibrate forecasts at stations distributed across much of North America. Significant observations included the following: (i) although the “raw” GFS forecasts (probabilities estimated from ensemble relative frequency) were commonly unskillful as measured in conti...

Probabilistic prediction of climate using multi-model ensembles: from basics to applications.

The development of multi-model ensembles for reliable predictions of inter-annual climate fluctuations and climate change, and their application to health, agronomy and water management, are discussed.

Toward a new generation of world climate research and computing facilities

The impending threat of global climate change and its regional manifestations is among the most important and urgent problems facing humanity. Society needs accurate and reliable estimates of changes in the probability of regional weather variations to develop science-based adaptation and mitigation strategies. Recent advances in weather prediction and in our understanding and ability to model the climate system suggest that it is both necessary and possible to revolutionize climate prediction to meet these societal needs. However, the scientific workforce and the computational capability required to bring about such a revolution is not available in any single nation. Motivated by the success of internationally funded infrastructure in other areas of science, this paper argues that, because of the complexity of the climate system, and because the regional manifestations of climate change are mainly through changes in the statistics of regional weather variations, the scientific and computational requireme...

A forecast quality assessment of an end‐to‐end probabilistic multi‐model seasonal forecast system using a malaria model

We discuss a novel three-tier hierarchical approach to the validation of an end-to-end seasonal climate forecast system. We present a malaria transmission simulation model (MTSM) driven with output from the DEMETER multi-model seasonal climate predictions, to produce probabilistic hindcasts of malaria prevalence. These prevalence hindcasts are second-tier validated against estimates from the MTSM driven with ERA-40 gridded analyses. The DEMETER’MTSM prevalence hindcasts are shown to be (tier-2) skilful for the one-month lead seasonal predictions as well as for the period covering the seasonal malaria peak with a 4–6 month forecast window for the event prevalence above the median. Interestingly, the tier-2 Brier skill score for the forecast window of the hindcasts starting in February, for the event prevalence above the median, is higher than for either the tier-1 precipitation or temperature forecasts, which were the MTSM driving variables.