Francisco J. Doblas-Reyes

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.

Seasonal Climate Prediction: A New Source of Information for the Management of Wind Energy Resources

AbstractClimate predictions tailored to the wind energy sector represent an innovation in the use of climate information to better manage the future variability of wind energy resources. Wind energy users have traditionally employed a simple approach that is based on an estimate of retrospective climatological information. Instead, climate predictions can better support the balance between energy demand and supply, as well as decisions relative to the scheduling of maintenance work. One limitation for the use of the climate predictions is the bias, which has until now prevented their incorporation in wind energy models because they require variables with statistical properties that are similar to those observed. To overcome this problem, two techniques of probabilistic climate forecast bias adjustment are considered here: a simple bias correction and a calibration method. Both approaches assume that the seasonal distributions are Gaussian. These methods are linear and robust and neither requires parameter...

Barriers to Using Climate Information: Challenges in Communicating Probabilistic Forecasts to Decision-Makers

Despite the strong dependence of certain sectors (e.g. energy, health, agriculture, tourism and insurance) on weather and climate variability, and several initiatives towards demonstrating the added benefits of integrating probabilistic climate forecasts into decision-making processes, such information is still underutilised. Improved communication is fundamental to stimulate the use of climate products by end users. This chapter evaluates current approaches to the visual communication of probabilistic seasonal climate forecast information. The overall aim of this study is to establish a visual communication protocol for such forecasts, which does not currently exist. Global Producing Centres (GPCs) show their own probabilistic forecasts with limited consistency in communication between different centres, which complicates how end users understand and interpret the products. A communication protocol that encompasses both the visualisation and description of climate forecasts can help to introduce a standard format and message to end users across different climate-sensitive sectors. It is hoped that this work will facilitate the improvement of decision-making processes that rely on climate forecast information and enable their wide-range dissemination via new climate services.

Linking crop yield anomalies to large-scale atmospheric circulation in Europe

Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used: North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.

Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state

Decadal prediction exploits sources of predictability from both the internal variability through the initialisation of the climate model from observational estimates, and the external radiative forcings. When a model is initialised with the observed state at the initial time step (Full Field Initialisation—FFI), the forecast run drifts towards the biased model climate. Distinguishing between the climate signal to be predicted and the model drift is a challenging task, because the application of a-posteriori bias correction has the risk of removing part of the variability signal. The anomaly initialisation (AI) technique aims at addressing the drift issue by answering the following question: if the model is allowed to start close to its own attractor (i.e. its biased world), but the phase of the simulated variability is constrained toward the contemporaneous observed one at the initialisation time, does the prediction skill improve? The relative merits of the FFI and AI techniques applied respectively to the ocean component and the ocean and sea ice components simultaneously in the EC-Earth global coupled model are assessed. For both strategies the initialised hindcasts show better skill than historical simulations for the ocean heat content and AMOC along the first two forecast years, for sea ice and PDO along the first forecast year, while for AMO the improvements are statistically significant for the first two forecast years. The AI in the ocean and sea ice components significantly improves the skill of the Arctic sea surface temperature over the FFI.

Decadal climate prediction with a refined anomaly initialisation approach

In decadal prediction, the objective is to exploit both the sources of predictability from the external radiative forcings and from the internal variability to provide the best possible climate information for the next decade. Predicting the climate system internal variability relies on initialising the climate model from observational estimates. We present a refined method of anomaly initialisation (AI) applied to the ocean and sea ice components of the global climate forecast model EC-Earth, with the following key innovations: (1) the use of a weight applied to the observed anomalies, in order to avoid the risk of introducing anomalies recorded in the observed climate, whose amplitude does not fit in the range of the internal variability generated by the model; (2) the AI of the ocean density, instead of calculating it from the anomaly initialised state of temperature and salinity. An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

Observed modes of sea surface temperature variability in the South Pacific region

The South Pacific (SP) region exerts large control on the climate of the Southern Hemisphere at many times scales. This paper identifies the main modes of interannual sea surface temperature (SST) variability in the SP which consist of a tropical-driven mode related to a horseshoe structure of positive/negative SST anomalies within midlatitudes and highly correlated to ENSO and Interdecadal Pacific Oscillation (IPO) variability, and another mode mostly confined to extratropical latitudes which is characterized by zonal propagation of SST anomalies within the South Pacific Gyre. Both modes are associated with temperature and rainfall anomalies over the continental regions of the Southern Hemisphere. Besides the leading mode which is related to well known warmer/cooler and drier/moister conditions due to its relationship with ENSO and the IPO, an inspection of the extratropical mode indicates that it is associated with distinct patterns of sea level pressure and surface temperature advection. These relationships are used here as plausible and partial explanations to the observed warming trend observed within the Southern Hemisphere during the last decades.