Extreme precipitation in the Netherlands: An event attribution case study

Attributing the change in likelihood of extreme weather events, particularly those occurring at small spatiotemporal scales, to anthropogenic forcing is a key challenge in climate science. While a warmer world is associated with an increase in atmospheric moisture on a global scale, the impact on the magnitude of extreme precipitation episodes has substantial regional variability. Analysis of individual cases is important in understanding the extent of these changes on spatial scales relevant to stakeholders. Here, we present a probabilistic attribution analysis of the extreme precipitation that fell in large parts of the Netherlands on 28 July 2014. Using a step-by-step approach, we aim to identify changes in intensity and likelihood of such an event as a result of anthropogenic global warming while highlighting the challenges in performing robust event attribution on high-impact precipitation events that occur at small scales. A method based on extreme value theory is applied to observational data in addition to global and regional climate model ensembles that pass a robust model evaluation process. Results based on observations suggest a strong and significant increase in the intensity and frequency of a 2014-type event as a result of anthropogenic climate change but trends in the model ensembles used are considerably smaller. Our results are communicated alongside considerable uncertainty, highlighting the difficulty in attributing events of this nature. Application of our approach to convection-resolving models may produce a more robust attribution.</p

Using nudging to improve global-regional dynamic consistency in limited-area climate modeling: What should we nudge?

International audienceRegional climate modelling sometimes requires that the regional model be nudged towards the large-scale driving data to avoid the development of inconsistencies between them. These inconsistencies are known to produce large surface temperature and rainfall artefacts. Therefore, it is essential to maintain the synoptic circulation within the simulation domain consistent with the synoptic circulation at the domain boundaries. Nudging techniques, initially developed for data assimilation purposes, are increasingly used in regional climate modeling and offer a workaround to this issue. In this context, several questions on the "optimal" use of nudging are still open. In this study we focus on a specific question which is: What variable should we nudge? in order to maintain the consistencies between the regional model and the driving fields as much as possible. For that, a "Big Brother Experiment", where a reference atmospheric state is known, is conducted using the weather research and forecasting (WRF) model over the Euro-Mediterranean region. A set of 22 3-month simulations is performed with different sets of nudged variables and nudging options (no nudging, indiscriminate nudging, spectral nudging) for summer and winter. The results show that nudging clearly improves the model capacity to reproduce the reference fields. However the skill scores depend on the set of variables used to nudge the regional climate simulations. Nudging the tropospheric horizontal wind is by far the key variable to nudge to simulate correctly surface temperature and wind, and rainfall. To a lesser extent, nudging tropospheric temperature also contributes to significantly improve the simulations. Indeed, nudging tropospheric wind or temperature directly impacts the simulation of the tropospheric geopotential height and thus the synoptic scale atmospheric circulation. Nudging moisture improves the precipitation but the impact on the other fields (wind and temperature) is not significant. As an immediate consequence, nudging tropospheric wind, temperature and moisture in WRF gives by far the best results with respect to the Big-Brother simulation. However, we noticed that a residual bias of the geopotential height persists due to a negative surface pressure anomaly which suggests that surface pressure is the missing quantity to nudge. Nudging the geopotential has no discernible effect. Finally, it should be noted that the proposed strategy ensures a dynamical consistency between the driving field and the simulated small-scale field but it does not ensure the best "observed" fine scale field because of the possible impact of incorrect driving large-scale field

Optimal nudging strategies in regional climate modelling: Investigation in a Big-Brother experiment over the European and Mediterranean regions

International audienceThe objective of this work is to gain a general insight into the key mechanisms involved in the impact of nudging on the large scales and the small scales of a regional climate simulation. A "Big Brother experiment" (BBE) approach is used where a "reference atmosphere" is known, unlike when regional climate models are used in practice. The main focus is on the sensitivity to nudging time, but the BBE approach allows to go beyond a pure sensitivity study by providing a reference which model outputs try to approach, defining an optimal nudging time. Elaborating upon previous idealized studies, this work introduces key novel points. The BBE approach to optimal nudging is used with a realistic model, here the weather research and forecasting model over the European and Mediterranean regions. A winter simulation (1 December 1989-28 February 1990) and a summer simulation (1 June 1999-31 August 1999) with a 50 km horizontal mesh grid have been performed with initial and boundary conditions provided by the ERA-interim reanalysis of the European Center for Medium-range Weather Forecast to produce the "reference atmosphere". The impacts of spectral and indiscriminate nudging are compared all others things being equal and as a function of nudging time. The impact of other numerical parameters, specifically the domain size and update frequency of the large-scale driving fields, on the sensitivity of the optimal nudging time is investigated. The nudged simulations are also compared to non-nudged simulations. Similarity between the reference and the simulations is evaluated for the surface temperature, surface wind and for rainfall, key variables for climate variability analysis and impact studies. These variables are located in the planetary boundary layer, which is not subject to nudging. Regarding the determination of a possible optimal nudging time, the conclusion is not the same for indiscriminate nudging (IN) and spectral nudging and depends on the update frequency of the driving large-scale fields ta. For IN, the optimal nudging time is around t = 3 h for almost all cases. For spectral nudging, the best results are for the smallest value of t used for the simulations (t = 1 h) for frequent update of the driving large-scale fields (3 and 6 h). The optimal nudging time is 3 for 12 h interval between two consecutive driving large-scale fields due to time sampling errors. In terms of resemblance to the reference fields, the differences between the simulations performed with IN and spectral nudging are small. A possible reason for this very similar performance is that nudging is active only above the planetary boundary layer where small-scale features are less energetic. As expected from previous studies, the impact of nudging is weaker for a smaller domain size. However the optimal nudging time itself is not sensitive to domain size. The proposed strategy ensures a dynamical consistency between the driving field and the simulated small-scale field but it does not ensure the best "observed" fine scale field because of the possible impact of incorrect driving large-scale field. This type of downscaling provides an upper bound on the skill possible for recent historical past and twenty-first century projections. The optimal nudging strategy with respect to dynamic downscaling could add skill whenever the parent global model has some level of skill. © 2012 Springer-Verlag Berlin Heidelberg

Spatial and temporal variability of wind energy resource and production over the North Western Mediterranean Sea: Sensitivity to air-sea interactions

International audienceThis work assesses the sensitivity of the offshore wind energy density and production to SST biases and air-sea feedbacks along the French and Spanish coast in the North-Western Mediterranean. It makes use of a set of three 20-years simulations from atmosphere stand-alone and atmosphere/ocean coupled models. In this numerical experiment, the effects of SST bias and air-sea feedbacks are isolated without any interference with other sources of error and uncertainty propagation, meaning that the same model and hence the same physics are used, all other things being equal. This study shows that the effects of SST bias or air-sea feedbacks on wind energy density and production estimation can reach up to 10% and 5%, respectively. Especially, accounting for air-sea coupled processes on sub-monthly time scales weakens systematically the energy density and production by 6.5% and 2.4% with respect to the configuration where these effects are neglected. The relative variability over the 20 years of simulation does not exceed 20% so the impact of air-sea feedbacks is very robust in time in terms of wind energy density and production assessment. Uncertainties up to 6.5 and 2.5% in the evaluation of the potential in terms of wind energy density and production potential can have severe consequences on the whole industry by lowering the projects profitability. This study shows that the effects of SST bias and air-sea feedbacks usually extend vertically up to the hub-height but their magnitude depends on the stability of the atmosphere. This study concludes that reducing the SST bias at the lower boundary of numerical atmospheric models and accounting for rapid interactions and feedbacks between the ocean and the atmosphere are key to improve the reliability of offshore wind energy density and production assessment

Sensitivity of the sea circulation to the atmospheric forcing in the Sicily Channel

International audienceWe investigate the sensitivity of the sea surface circulation in the Sicily Channel to surface winds, using a15-year long (1994–2008) air-sea coupled numerical simulation. Analysis is based on the clustering of sixmain wind regimes over the Sicily Channel domain. The analysis of the corresponding sea current clustersshows that sea circulation in this area is sensitive to surface wind patterns. This wind modulates thestrength of the two main branches of the sea circulation in the Sicily Channel (i.e. the AtlanticTunisian Current and the Atlantic Ionian Stream). The modulation of these two currents depends onthe wind regime, and displays a strong seasonal variability. It is also shown that the sea circulation inthe Sicily Channel is strongly controlled by the thermohaline circulation and the bathymetry (geostrophiccurrent). However, the contribution to the total current of its ageostrophic component forced by thesurface winds is significant, with a correlation coefficient varying from 0.3 to 0.7

Statistical Downscaling to Improve the Subseasonal Predictions of Energy-Relevant Surface Variables

International audienceOwing to the increasing share of variable renewable energies in the electricity mix, the European energy sector is becoming more weather sensitive. In this regard, skillful subseasonal predictions of essential climate variables can provide considerable socioeconomic benefits to the energy sector. The aim of this study is therefore to improve the European subseasonal predictions of 100-m wind speed and 2-m temperature, which we achieve through statistical downscaling. We employ redundancy analysis (RDA) to estimate spatial patterns of variability from large-scale fields that allow for the best prediction of surface fields. We compare explanatory powers between the patterns obtained using RDA against those derived using principal component analysis (PCA), when used as predictors in multilinear regression models to predict surface fields, and show that the explanatory power of the former is superior to that of the latter. Subsequently, we employ the estimated relationship between RDA patterns and surface fields to produce statistical probabilistic predictions of gridded surface fields using dynamical ensemble predictions of RDA patterns. We finally demonstrate how a simple combination of dynamical and statistical predictions of surface fields significantly improves the accuracy of subseasonal predictions of both variables over a large part of Europe. We attribute the improved accuracy of these combined predictions to improvements in reliability and resolution

Statistical Downscaling to Improve the Subseasonal Predictions of Energy-Relevant Surface Variables

International audienceOwing to the increasing share of variable renewable energies in the electricity mix, the European energy sector is becoming more weather sensitive. In this regard, skillful subseasonal predictions of essential climate variables can provide considerable socioeconomic benefits to the energy sector. The aim of this study is therefore to improve the European subseasonal predictions of 100-m wind speed and 2-m temperature, which we achieve through statistical downscaling. We employ redundancy analysis (RDA) to estimate spatial patterns of variability from large-scale fields that allow for the best prediction of surface fields. We compare explanatory powers between the patterns obtained using RDA against those derived using principal component analysis (PCA), when used as predictors in multilinear regression models to predict surface fields, and show that the explanatory power of the former is superior to that of the latter. Subsequently, we employ the estimated relationship between RDA patterns and surface fields to produce statistical probabilistic predictions of gridded surface fields using dynamical ensemble predictions of RDA patterns. We finally demonstrate how a simple combination of dynamical and statistical predictions of surface fields significantly improves the accuracy of subseasonal predictions of both variables over a large part of Europe. We attribute the improved accuracy of these combined predictions to improvements in reliability and resolution

How Skillful Are the European Subseasonal Predictions of Wind Speed and Surface Temperature?

International audienceSubseasonal forecasts of 100-m wind speed and surface temperature, if skillful, can be beneficial to the energy sector as they can be used to plan asset availability and maintenance, assess risks of extreme events, and optimally trade power on the markets. In this study, we evaluate the skill of the European Centre for Medium-Range Weather Forecasts' subseasonal predictions of 100-m wind speed and 2-m temperature. To the authors' knowledge, this assessment is the first for the 100-m wind speed, which is an essential variable of practical importance to the energy sector. The assessment is carried out on both forecasts and reforecasts over European domain gridpoint wise and also by considering several spatially averaged domains, using several metrics to assess different attributes of forecast quality. We propose a novel way of synthesizing the continuous ranked probability skill score. The results show that the skill of the forecasts and reforecasts depends on the choice of the climate variable, the period of the year, and the geographical domain. Indeed, the predictions of temperature are better than those of wind speed, with enhanced skill found for both variables in the winter relative to other seasons. The results also indicate significant differences between the skill of forecasts and reforecasts, arising mainly due to the differing ensemble sizes. Overall, depending on the choice of the geographical domain and the forecast attribute, the results show skillful predictions beyond 2 weeks, and in certain cases, up to 6 weeks for both variables, thereby encouraging their implementation in operational decision-making

Dynamical and Statistical downscaling of Mediterranean climate: comparison and uncertainty assessment in the MED-CORDEX and HYMEX context. EGU conference, Vienna, april 2011

This study presents the assessment and comparison of the two downscaling methods: Dynamic Downscaling (DD) and Statistical Downscaling (SD). Both methods are applied on a climatological study on the Mediterranean region. Hence, for the years 1989-2008, the ERA-Interim re-analyzes of mean daily values of 2-meter temperature and rainfall are dynamically downscaled using the Whether Research and Forecasting (WRF) model and statistically downscaled using the Cumulative Distribution Functions-transform model (CDF-t). In particular, the WRF model is evaluated in regards of two different horizontal resolutions (50km and 20km) and two different integrated Land Surface Models (Noah and RUC). Observations for the models evaluation are taken from the European Climate Assessment & Database (ECAD) and from the labeled hydro-meteorological stations attached to the HYdrological cycle in the Mediterranean Experiment (HYMEX)