Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe

There is now a wide range of forecasts and observations of seasonal climatic conditions that can be used across a range of application sectors, including hydrological risk forecasting, planning and management. As we rely more on seasonal climate forecasts, it becomes essential to also assess its quality to ensure its intended use. In this study, we provide the most comprehensive assessment of seasonal temperature and precipitation ensemble forecasts of the EUROSIP multi-model forecasting system over Europe. The forecasts from the four individual climate models within the EUROSIP are assessed using both deterministic and probabilistic approaches. One equally and two unequally Weighted Multi-Models (WMMs) are also constructed from the individual models, for both climate variables, and their respective forecasts are also assessed. Consistent with existing literature, we find limited seasonal climate prediction skill over Europe. A simple equally WMM system performs better than both unequally WMM combination systems. However, the equally WMM system does not always outperform the single best model within the EUROSIP multi-model. Based on the results, it is recommended to assess seasonal temperature and precipitation forecast of individual climate models as well as their multi-model mean for a comprehensive overview of the forecast skill.The authors thank Prof. Francisco J. Doblas Reyes, Alicia Sanchez Lorente, Veronica Torralba and Louis-Philippe Caron for discussions and suggestions during the forming of this paper. Thanks to Nicolau Manubens, whose incredible technical support allowed steady implementation of the experiments in this study. Thanks are also due to the anonymous reviewers and their critical reviews. Their comments and suggestions improved the content of this paper. The research leading to these results has received funding from the EU H2020 Framework Programme under grant agreement #641811 (IMPREX).Peer ReviewedPostprint (author's final draft

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

The final publication is available at Springer via http://dx.doi.org/10.1007/s00382-015-2879-4Land 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.The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under grant agreements 308378 (SPECS), 282378 (DEN-FREE) and 607085 (EUCLEIA), and from the Spanish Ministerio de Economía y Competitividad (MINECO) under the project CGL2013-41055-R. We acknowledge the s2dverification R-based package (http://cran.r-project.org/web/packages/s2dverification/index.html). We also thank ECMWF for providing the ERA-Land initial conditions and computing resources through the SPICCF Special Project.Peer ReviewedPostprint (author's final draft

Summer drought predictability over Europe: empirical versus dynamical forecasts

Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts—System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction.We acknowledge the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for making the data available on their website http://www.esrl.noaa.gov/psd/. This work was partially funded by the Projects IMPREX (EU–H2020 PE024400) and SPECS (FP7-ENV-2012-308378). Marco Turco was supported by the Spanish Juan de la Cierva Programme (IJCI-2015-26953).Peer ReviewedPostprint (published version

Sensitivity of winter North Atlantic-European climate to resolved atmosphere and ocean dynamics

Northern Hemisphere western boundary currents, like the Gulf Stream, are key regions for cyclogenesis affecting large-scale atmospheric circulation. Recent observations and model simulations with high-temporal and -spatial resolution have provided evidence that the associated ocean fronts locally affect troposphere dynamics. A coherent view of how this affects the mean climate and its variability is, however, lacking. In particular the separate role of resolved ocean and atmosphere dynamics in shaping the atmospheric circulation is still largely unknown. Here we demonstrate for the first time, by using coupled seasonal forecast experiments at different resolutions, that resolving meso-scale oceanic variability in the Gulf Stream region strongly affects mid-latitude interannual atmospheric variability, including the North Atlantic Oscillation. Its impact on climatology, however, is minor. Increasing atmosphere resolution to meso-scale, on the other hand, strongly affects mean climate but moderately its variability. We also find that regional predictability relies on adequately resolving small-scale atmospheric processes, while resolving small-scale oceanic processes acts as an unpredictable source of noise, except for the North Atlantic storm-track where the forcing of the atmosphere translates into skillful predictions

Role of wind stress in driving SST biases in the tropical Atlantic

Coupled climate models used for long-term future climate projections and seasonal or decadal predictions share a systematic and persistent warm sea surface temperature (SST) bias in the tropical Atlantic. This study attempts to better understand the physical mechanisms responsible for the development of systematic biases in the tropical Atlantic using the so-called Transpose-CMIP protocol in a multi-model context. Six global climate models have been used to perform seasonal forecasts starting both in May and February over the period 2000-2009. In all models, the growth of SST biases is rapid. Significant biases are seen in the first month of forecast and, by six months, the root-mean-square SST bias is 80% of the climatological bias. These control experiments show that the equatorial warm SST bias is not driven by surface heat flux biases in all models, whereas in the south-eastern Atlantic the solar heat flux could explain the setup of an initial warm bias in the first few days. A set of sensitivity experiments with prescribed wind stress confirm the leading role of wind stress biases in driving the equatorial SST bias, even if the amplitude of the SST bias is model dependent. A reduced SST bias leads to a reduced precipitation bias locally, but there is no robust remote effect on West African Monsoon rainfall. Over the south-eastern part of the basin, local wind biases tend to have an impact on the local SST bias (except in the high resolution model). However, there is also a non-local effect of equatorial wind correction in two models. This can be explained by sub-surface advection of water from the equator, which is colder when the bias in equatorial wind stress is corrected. In terms of variability, it is also shown that improving the mean state in the equatorial Atlantic leads to a beneficial intensification of the Bjerknes feedback loop. In conclusion, we show a robust effect of wind stress biases on tropical mean climate and variability in multiple climate models

Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe

There is now a wide range of forecasts and observations of seasonal climatic conditions that can be used across a range of application sectors, including hydrological risk forecasting, planning and management. As we rely more on seasonal climate forecasts, it becomes essential to also assess its quality to ensure its intended use. In this study, we provide the most comprehensive assessment of seasonal temperature and precipitation ensemble forecasts of the EUROSIP multi-model forecasting system over Europe. The forecasts from the four individual climate models within the EUROSIP are assessed using both deterministic and probabilistic approaches. One equally and two unequally Weighted Multi-Models (WMMs) are also constructed from the individual models, for both climate variables, and their respective forecasts are also assessed. Consistent with existing literature, we find limited seasonal climate prediction skill over Europe. A simple equally WMM system performs better than both unequally WMM combination systems. However, the equally WMM system does not always outperform the single best model within the EUROSIP multi-model. Based on the results, it is recommended to assess seasonal temperature and precipitation forecast of individual climate models as well as their multi-model mean for a comprehensive overview of the forecast skill.The authors thank Prof. Francisco J. Doblas Reyes, Alicia Sanchez Lorente, Veronica Torralba and Louis-Philippe Caron for discussions and suggestions during the forming of this paper. Thanks to Nicolau Manubens, whose incredible technical support allowed steady implementation of the experiments in this study. Thanks are also due to the anonymous reviewers and their critical reviews. Their comments and suggestions improved the content of this paper. The research leading to these results has received funding from the EU H2020 Framework Programme under grant agreement #641811 (IMPREX).Peer Reviewe

Factors controlling the Indian summer monsoon onset in a coupled model

International audienceThe observed Indian Summer Monsoon (ISM) onset occurs around 30 May and 2 June, with a standard deviation of 8 to 9 days, according to the estimates. The relationship between interannual variability of the ISM onset and SSTs (Sea Surface Temperature) remains controversial. The role of Indian Ocean SSTs remain unclear, some studies have shown a driving role while other suggests a passive relation between Indian Ocean SSTs and ISM. The intrinsic impact of ENSO (El Nino-Southern Oscillation) is also difficult to estimate from observations alone. Finally, the predictability of the ISM onset remains drastically limited by the inability of both forced and coupled model to reproduce a realistic onset date. In order to measure objectively the ISM onset, different methods have been developed based on rainfall or dynamical indices (Ananthakrishnan and Soman, 1988 ; Wang and Ho 2002 ; Joseph et al. 2006). In the study we use the Tropospheric Temperature Gradient (TTG), which is the difference between the tropospheric temperature in a northern and a southern box in the Indian areas (Xavier et al. 2007). This index measures the dynamical strength of the monsoon and provides a stable and precise onset date consistent with rainfall estimates. In the SINTEX-F2 coupled model, the ISM onset measured with the TTG is delayed of approximately 10 days and is in advance of 6 days in the atmosphere-only (ECHAM) model. The 16 days lag between atmospheric-only and coupled runs suggests a crucial role of the coupling, especially SST biases on the delayed onset. With the help of several sensitivity experiments, this study tries to identify the keys regions influencing the ISM onset. Many studies have shown a strong impact of the Arabian Sea and Indian Ocean SST on the ISM onset. Nevertheless, the correction of the SSTs, based on AVHRR, in the tropical Indian Ocean only slightly corrects the delayed onset in the coupled model, which suggests an impact of SST in others regions on the ISM onset. During May and June, the main tropical SST biases in the coupled model are a strong warm bias in the Atlantic Ocean and a warm bias in the tropical Pacific Ocean, except along the equator around 140°W-100°W, where there is a cold tongue bias. The correction of the warm bias in the Atlantic Ocean slightly improves the onset date. Conversely, the correction of SST biases in the tropical and equatorial Pacific Oceans advances the onset date of 12 and 10 days, respectively, compared to the control coupled run. This result suggests that, at least in this model, the ISM onset is mainly control by the Pacific Ocean SSTs. Even if ENSO has an impact on the onset date it does not explain the delay, which is related to the biased SST mean state in the Pacific Ocean

Anatomy of the Indian Summer Monsoon and ENSO relationships in state-of-the-art CGCMs: Role of the tropical Indian Ocean

International audienceIndian Summer Monsoon (ISM) rainfall and El Niño-Southern Oscillation (ENSO) exhibit an inverse relationship during boreal summer, which is one of the roots of ISM interannual variability and its seasonal predictability. Here we document how current climate and seasonal prediction models simulate the timing and amplitude of this ISM-ENSO teleconnection. Many Coupled General Circulation Models (CGCMs) do simulate a simultaneous inverse relationship between ENSO and ISM, though with a large spread. However, most of them show significant negative correlations before ISM, which are at odd with observations. Consistent with this systematic error, simulated Niño-3.4 Sea Surface Temperature (SST) variability has erroneous high amplitude during boreal spring and ISM rainfall variability is also too strong during the first part of ISM. The role of the Indian Ocean (IO) in modulating the ISM-ENSO relationships is further investigated using dedicated experiments with the SINTEX-F2 CGCM. Decoupled tropical Pacific and IO experiments are conducted to assess the direct relationship between ISM and IO SSTs on one hand, and the specific role of IO feedback on ENSO on the other hand. The direct effect of IO SSTs on ISM is weak and insignificant at the interannual time scale in the Pacific decoupled experiment. On the other hand, IO decoupled experiments demonstrate that El Niño shifts rapidly to La Niña when ocean–atmosphere coupling is active in the whole IO or only in its western part. This IO negative feedback is mostly active during the decaying phase of El Niño, which is accompanied by a basin-wide warming in the IO, and significantly modulates the length of ENSO events in our simulations. This IO feedback operates through a modulation of the Walker circulation over the IO, which strengthens and shifts eastward an anomalous anticyclone centered on the Philippine Sea and associated easterly wind anomalies in the equatorial western Pacific during boreal winter. In turn, these atmospheric anomalies lead to a fast ENSO turnabout via oceanic adjustement processes mediated by eastward propagating upwelling Kelvin waves. An experiment in which only the SouthEast Indian Ocean (SEIO) is decoupled, demonstrates that the equatorial SST gradient in the IO during boreal winter plays a fundamental role in the efficiency of IO feedback. In this experiment, simulated ISM-ENSO lead-lag correlations match closely the observations. This success is associated with removal of erroneous SEIO SST variability during boreal winter in the SEIO decoupled experiment. Finally, it is illustrated that most CMIP5 CGCMs exhibit similar SST errors in the SEIO during boreal winter in addition to an exagerated SEIO SST variability during boreal fall

Precipitation response to extreme soil moisture conditions over the Mediterranean

International audienceAbstract The intimate link between soil moisture and precipitation makes it a “chicken-and-egg situation” that challenges climate studies of the continental water cycle. This association is particularly acute over the Mediterranean, increasingly exposed to droughts with climate change. This study aims at deciphering the impact of spring soil moisture state in the Mediterranean on subsequent warm season precipitation. In an idealized setup, two distinct climate models are used to generate extreme dry or wet soil conditions, and run climate simulations initialized and/or forced by these conditions. Changes in precipitation distribution and persistence are analyzed and where applicable compared to composites from a reanalysis. Spring soil moisture anomalies are found to be very persistent, but the precipitation response is largely model dependent. Overall, dry soils lead to a reduction of precipitation for early summer months and conversely for wet soils although with a fainter and less robust signal. On the other hand, wet soils tend to favor the persistence of precipitation throughout summer over several sub-regions. Our results highlight the stringent need to reduce the wide array of uncertainties associated to soil moisture, land-atmosphere coupling and convection in climate models, before ascertaining that soil moisture initialization could provide more skillful sub-seasonal to seasonal precipitation prediction

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

The final publication is available at Springer via http://dx.doi.org/10.1007/s00382-015-2879-4Land 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.The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under grant agreements 308378 (SPECS), 282378 (DEN-FREE) and 607085 (EUCLEIA), and from the Spanish Ministerio de Economía y Competitividad (MINECO) under the project CGL2013-41055-R. We acknowledge the s2dverification R-based package (http://cran.r-project.org/web/packages/s2dverification/index.html). We also thank ECMWF for providing the ERA-Land initial conditions and computing resources through the SPICCF Special Project.Peer Reviewe