Omar Bellprat

Attribution of extreme weather and climate events overestimated by unreliable climate simulations

Event attribution aims to estimate the role of an external driver after the occurrence of an extreme weather and climate event by comparing the probability that the event occurs in two counterfactual worlds. These probabilities are typically computed using ensembles of climate simulations whose simulated probabilities are known to be imperfect. The implications of using imperfect models in this context are largely unknown, limited by the number of observed extreme events in the past to conduct a robust evaluation. Using an idealized framework, this model limitation is studied by generating large number of simulations with variable reliability in simulated probability. The framework illustrates that unreliable climate simulations are prone to overestimate the attributable risk to climate change. Climate model ensembles tend to be overconfident in their representation of the climate variability which leads to systematic increase in the attributable risk to an extreme event. Our results suggest that event attribution approaches comprising of a single climate model would benefit from ensemble calibration in order to account for model inadequacies similarly as operational forecasting systems.

Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe

Land surfaces and soil conditions are key sources of climate predictability at the seasonal time scale. In order to estimate how the initialization of the land surface affects the predictability at seasonal time scale, we run two sets of seasonal hindcasts with the general circulation model EC-Earth2.3. The initialization of those hindcasts is done either with climatological or realistic land initialization in May using the ERA-Land re-analysis. Results show significant improvements in the initialized run occurring up to the last forecast month. The prediction of near-surface summer temperatures and precipitation at the global scale and over Europe are improved, as well as the warm extremes prediction. As an illustration, we show that the 2010 Russian heat wave is only predicted when soil moisture is initialized. No significant improvement is found for the retrospective prediction of the 2003 European heat wave, suggesting this event to be mainly large-scale driven. Thus, we confirm that late-spring soil moisture conditions can be decisive in triggering high-impact events in the following summer in Europe. Accordingly, accurate land-surface initial conditions are essential for seasonal predictions.

Using climate models to estimate the quality of global observational data sets

Models and data: A two-way street Data are used to drive models of climate and other complex systems, but is the relationship between data and models a one-way process? Massonnet et al. used climate models to assess the quality of the observations that such models use. Starting with a simple model and progressing to more complex ones, the authors show that models are better when they are assessed against the most recent, most advanced, and most independent observational references. These findings should help to evaluate the quality of observational data sets and provide guidance for more objective data set selection. Science, this issue p. 452 Climate models can be used to assess the quality of the observational data sets they use. Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection.

Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth

AbstractResolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (~1° and ~60 km in the ocean and atmosphere models, respectively), intermediate resolution (~0.25° and ~60 km), and high resolution (~0.25° and ~39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-m temperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is bet...

Detecting Improvements in Forecast Correlation Skill: Statistical Testing and Power Analysis

AbstractThe skill of weather and climate forecast systems is often assessed by calculating the correlation coefficient between past forecasts and their verifying observations. Improvements in forecast skill can thus be quantified by correlation differences. The uncertainty in the correlation difference needs to be assessed to judge whether the observed difference constitutes a genuine improvement, or is compatible with random sampling variations. A widely used statistical test for correlation difference is known to be unsuitable, because it assumes that the competing forecasting systems are independent. In this paper, appropriate statistical methods are reviewed to assess correlation differences when the competing forecasting systems are strongly correlated with one another. The methods are used to compare correlation skill between seasonal temperature forecasts that differ in initialization scheme and model resolution. A simple power analysis framework is proposed to estimate the probability of correctly...

The Role of Arctic Sea Ice and Sea Surface Temperatures on the Cold 2015 February Over North America

The cold spell of February 2015 in North America was predominantly internally generated; reduced Arctic sea ice and anomalous sea surface temperatures may have contributed in establishing and sustaining the anomalous flow.

Record Low Northern Hemisphere Sea Ice Extent in March 2015

The record low Northern Hemisphere (NH) winter sea ice maximum stemmed from a strong interannual surface anomaly in the Pacific sector, but it would not have been reached without long-term climate change.

Investigating the Effects of Pacific Sea Surface Temperatures on the Wind Drought of 2015 Over the United States

This work was funded by the EU projects S2S4E (GA 776787), EUCP (GA 776613), CLIM4ENERGY (C3S_441_Lot2_CEA), EUCLEIA (GA 607085), INDECIS (GA 690462), and MEDSCOPE (GA 690462). Omar Bellprat has been funded by the European Space Agency (ESA) Living Planet Fellowship Programme under the project VERITAS-CCI.We thank Daniel Cabezon for helping and informing on the wind drought episode and Javier Garcia-Serrano for the insights on the interpretation of the physical processes. All the analyses have been performed using the statistical software R (R Core Team, 2015). The startR and s2dverification R packages have been used to read data sets, compute EOFs, and plot maps. Model output from the numerical experiments can be accessed through EUDAT facilities at the link http://doi.org/10.23728/b2share.71078 a8618414e9b906cfa6bb7d2cdab. The reanalysis data and models used are listed in the references. We also acknowledge Javier Vegas and Nicolau Manubens for technical support, as well as the EC-Earth consortium for the model development.