Mark A. Liniger

The role of increasing temperature variability in European summer heatwaves.

Instrumental observations and reconstructions of global and hemispheric temperature evolution reveal a pronounced warming during the past ∼150 years. One expression of this warming is the observed increase in the occurrence of heatwaves. Conceptually this increase is understood as a shift of the statistical distribution towards warmer temperatures, while changes in the width of the distribution are often considered small. Here we show that this framework fails to explain the record-breaking central European summer temperatures in 2003, although it is consistent with observations from previous years. We find that an event like that of summer 2003 is statistically extremely unlikely, even when the observed warming is taken into account. We propose that a regime with an increased variability of temperatures (in addition to increases in mean temperature) may be able to account for summer 2003. To test this proposal, we simulate possible future European climate with a regional climate model in a scenario with increased atmospheric greenhouse-gas concentrations, and find that temperature variability increases by up to 100%, with maximum changes in central and eastern Europe.

Risks of Model Weighting in Multimodel Climate Projections

Multimodel combination is a pragmatic approach to estimating model uncertainties and to making climate projections more reliable. The simplest way of constructing a multimodel is to give one vote to each model (‘‘equal weighting’’), while more sophisticated approaches suggest applying model weights according to some measure of performance (‘‘optimum weighting’’). In this study, a simple conceptual model of climate change projections is introduced and applied to discuss the effects of model weighting in more generic terms. The results confirm that equally weighted multimodels on average outperform the single models, and that projection errors can in principle be further reduced by optimum weighting. However, this not only requires accurate knowledge of the single model skill, but the relative contributions of the joint model error and unpredictable noise also need to be known to avoid biased weights. If weights are applied that do not appropriatelyrepresentthetrueunderlyinguncertainties,weightedmultimodelsperformonaverageworsethan equally weighted ones, which is a scenario that is not unlikely, given that at present there is no consensus on how skill-basedweights can be obtained.Particularly when internal variabilityis large, more information may be lost by inappropriate weighting than could potentially be gained by optimum weighting. These results indicate that for many applications equal weighting may be the safer and more transparent way to combine models. However, also within the presented framework eliminating models from an ensemble can be justified if they are known to lack key mechanisms that are indispensable for meaningful climate projections.

Dynamical aspects of the life cycle of the winter storm ‘Lothar’ (24–26 December 1999)

Dynamical aspects of the life cycle of the winter storm ‘Lothar’ (24–26 December 1999) are investigated with the aid of the European Centre for Medium-Range Weather Forecasts analysis data and mesoscale model simulations. Neither of these datasets capture the full amplitude of the observed extreme pressure fall and surface wind speeds, but they do help identify a range of key dynamical and physical features that characterize the development of this unusual event. The analysis and interpretation is primarily based upon the evolution of the lower- and upper-level potential vorticity (PV) field complemented by three-dimensional trajectory calculations. ‘Lothar’ originated in the western Atlantic and travelled as a shallow low-level cyclone of moderate intensity towards Europe. This translation took place below and slightly to the south of a very intense upper-level jet and was accompanied by continuous and intense condensational heating that sustained a pronounced positive low-level PV anomaly (not unlike the concept of a ‘diabatic Rossby wave’). No significant PV anomalies were evident at the tropopause level during this early phase of the life cycle. The surface cyclone intensified rapidly when the shallow cyclone approached the jet-stream axis. The circulation induced by the diabatically produced low-tropospheric PV anomaly on steeply sloping isentropic surfaces that transect the intense upper-level jet contributed significantly to the rapid formation of a narrow and deep tropopause fold. This stratospheric PV anomaly virtually merged with the diabatically produced ephemeral PV feature to form a vertically aligned tower of positive PV at the time of maximum storm intensity. A sensitivity study with a dry adiabatic hindcast simulation shows no PV-tower configuration (and only a very weak surface development) and confirms the primary importance of the cloud diabatic heating for the tropopause fold formation and the rapid ‘bottom-up’ intensification of ‘Lothar’. A comparison of the anomalously high Atlantic sea surface temperatures in December 1999 with the water-vapour source regions for the latent-heat release that accompanied the rapid intensification phase of ‘Lothar’ shows a close relationship. This is of importance when discussing the possible implications of climate variability and change on the development of North Atlantic winter storms. Copyright

The Discrete Brier and Ranked Probability Skill Scores

Abstract The Brier skill score (BSS) and the ranked probability skill score (RPSS) are widely used measures to describe the quality of categorical probabilistic forecasts. They quantify the extent to which a forecast strategy improves predictions with respect to a (usually climatological) reference forecast. The BSS can thereby be regarded as the special case of an RPSS with two forecast categories. From the work of Muller et al., it is known that the RPSS is negatively biased for ensemble prediction systems with small ensemble sizes, and that a debiased version, the RPSSD, can be obtained quasi empirically by random resampling from the reference forecast. In this paper, an analytical formula is derived to directly calculate the RPSS bias correction for any ensemble size and combination of probability categories, thus allowing an easy implementation of the RPSSD. The correction term itself is identified as the “intrinsic unreliability” of the ensemble prediction system. The performance of this new formula...

MAP D-PHASE: Real-Time Demonstration of Weather Forecast Quality in the Alpine Region

Demonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/ fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system.

A Debiased Ranked Probability Skill Score to Evaluate Probabilistic Ensemble Forecasts with Small Ensemble Sizes

Abstract The ranked probability skill score (RPSS) is a widely used measure to quantify the skill of ensemble forecasts. The underlying score is defined by the quadratic norm and is comparable to the mean squared error (mse) but it is applied in probability space. It is sensitive to the shape and the shift of the predicted probability distributions. However, the RPSS shows a negative bias for ensemble systems with small ensemble size, as recently shown. Here, two strategies are explored to tackle this flaw of the RPSS. First, the RPSS is examined for different norms L (RPSSL). It is shown that the RPSSL=1 based on the absolute rather than the squared difference between forecasted and observed cumulative probability distribution is unbiased; RPSSL defined with higher-order norms show a negative bias. However, the RPSSL=1 is not strictly proper in a statistical sense. A second approach is then investigated, which is based on the quadratic norm but with sampling errors in climatological probabilities conside...

Seasonal Ensemble Forecasts: Are Recalibrated Single Models Better than Multimodels?

Abstract Multimodel ensemble combination (MMEC) has become an accepted technique to improve probabilistic forecasts from short- to long-range time scales. MMEC techniques typically widen ensemble spread, thus improving the dispersion characteristics and the reliability of the forecasts. This raises the question as to whether the same effect could be achieved in a potentially cheaper way by rescaling single model ensemble forecasts a posteriori such that they become reliable. In this study a climate conserving recalibration (CCR) technique is derived and compared with MMEC. With a simple stochastic toy model it is shown that both CCR and MMEC successfully improve forecast reliability. The difference between these two methods is that CCR conserves resolution but inevitably dilutes the potentially predictable signal while MMEC is in the ideal case able to fully retain the predictable signal and to improve resolution. Therefore, MMEC is conceptually to be preferred, particularly since the effect of CCR depend...

Calibrated Precipitation Forecasts for a Limited-Area Ensemble Forecast System Using Reforecasts

Abstract The calibration of numerical weather forecasts using reforecasts has been shown to increase the skill of weather predictions. Here, the precipitation forecasts from the Consortium for Small Scale Modeling Limited Area Ensemble Prediction System (COSMO-LEPS) are improved using a 30-yr-long set of reforecasts. The probabilistic forecasts are calibrated on the exceedance of return periods, independently from available observations. Besides correcting for systematic model errors, the spatial and temporal variability in the amplitude of rare precipitation events is implicitly captured when issuing forecasts of return periods. These forecast products are especially useful for issuing warnings of upcoming events. A way to visualize those calibrated ensemble forecasts conveniently for end users and to present verification results of the return period–based forecasts for Switzerland is proposed. It is presented that, depending on the lead time and return period, calibrating COSMO-LEPS with reforecasts inc...

Improved Estimates of the European Winter Windstorm Climate and the Risk of Reinsurance Loss Using Climate Model Data

Abstract Current estimates of the European windstorm climate and their associated losses are often hampered by either relatively short, coarse resolution or inhomogeneous datasets. This study tries to overcome some of these shortcomings by estimating the European windstorm climate using dynamical seasonal-to-decadal (s2d) climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The current s2d models have limited predictive skill of European storminess, making the ensemble forecasts ergodic samples on which to build pseudoclimates of 310–396 yr in length. Extended winter (October–April) windstorm climatologies are created using scalar extreme wind indices considering only data above a high threshold. The method identifies up to 2363 windstorms in s2d data and up to 380 windstorms in the 40-yr ECMWF Re-Analysis (ERA-40). Classical extreme value analysis (EVA) techniques are used to determine the windstorm climatologies. Differences between the ERA-40 and s2d windstorm climatol...

Application of long-range weather forecasts to agricultural decision problems in Europe

Agriculture can benefit substantially from long-range weather forecasts, for the month or the season, which can help to optimize farming operations and deal more effectively with the adverse impacts of climate variability, including extreme weather events. In the context of climate change, long-range weather forecasts also represent key elements for the development of adaptation strategies. In spite of an undeniable potential, long-range forecasts issued for instance by the European Centre for Medium-Range Weather Forecasts (ECMWF) have yet to find widespread application in European agriculture. To address partially the question of why this is the case, the performance of the ECMWF monthly ensemble forecasting system was examined. It was noted that predictability is currently limited to about 3 weeks for temperature and 2 weeks for precipitation and solar radiation. This may appear deceptive at first sight, but it was noticed that precipitation forecasts over a month are, overall, at least as valuable as information obtained from observed climatology. Encouraged by this finding, the possibility of using monthly forecasts to predict soil water availability was tested. In an operational context, this could serve as a basis for scheduling irrigation. Positive skills were found for lead times of up to 1 month. It was concluded that more systematic investigations of the possibilities offered by long-range forecasts should be undertaken in the future. However, this will require additional efforts to increase the quality of the forecasts, design appropriate application tools and promote the dissemination of the outcome within the agriculture community.