Skilful climate predictions on seasonal-to-decadal timescales can generate large socio-economic benefits, but current forecast quality is relatively low over Europe and the Mediterranean Sea. In general, our limited understanding of the mechanisms and processes responsible for predictability and model systematic errors hamper our capability to simulate and forecast seasonal-to-decadal climate variability, especially over the Euro–Mediterranean region. Improved global climate model calibration and regionalization techniques, as well as better forecast verification methods need to be developed specifically for this region to both extract as much climate information as possible from operational forecast systems, and tailor this information to produce top–quality climate services for sectors with high societal impacts in the Mediterranean.
This Session is devoted to research and development aimed at improving climate prediction capabilities and related services on seasonal-to-decadal timescales in the Euro–Mediterranean region, and it invites contributions on (but not only restricted to):
- understanding of sources and mechanisms for predictability in the target region,
- novel, process-based methods for bias correction, downscaling, optimal combination of different sources of information,
- innovative empirical forecasting models,
- climate services based on end-user tailored climate forecasts, in relevant socio–economic sectors for the Mediterranean (e.g., water management, renewable energy, agriculture, tourism, forestry and fire risk)
The Session will act as a workshop organized by the JPI–ERA4CS EU–project MEDSCOPE (www. medscope–project.eu)
Chairperson: Lauriane Batté
Although most operational seasonal forecasting systems are based on dynamical models, empirical forecasting systems, built on statistical relationships between present and future at seasonal time horizons conditions of the climate system, provide a feasible and realistic alternative and a source of supplementary information. Here, a new empirical model based on partial least squares regression is presented. Originally designed as a flexible tool, the model can be run with many configurations including different predictands, resolutions, leads and aggregation times. To be able of producing forecast for any selected configuration, the model automatically selects predictors from an initial pool, containing global climate indices and specific predictors for the Mediterranean region unveiled in the frame of the MEDSCOPE project. Additionally, the model explores spatial fields, generating time series based on spatial averages of areas well correlated with the predictand. These time series are added to the initial pool of candidate predictors. We present here results from a configuration producing probabilistic forecasts of seasonal (3 month averages) temperature and precipitation, their verification and comparison against a selection of state-of-the-art seasonal forecast systems based on dynamical models in a hindcast period (1994-2015). The model is able to produce spatially coherent anomaly patterns, and reach levels of skill comparable to those based on dynamical models. As predictors can be easily removed or incorporated, the model can provide information on the impact of a particular predictor on skill, so it can be used to help in the search and understanding of new sources of predictability. Evaluation of soil moisture impact on summer temperature predictability is shown as an example
How to cite: Rodríguez-Guisado, E. and Rodríguez-Camino, E.: Sources of predictability over the Mediterranean at seasonal time-scale: building up an empirical forecasting model, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-39, https://doi.org/10.5194/ems2021-39, 2021.
As a result of the recent progress in the performance of seasonal prediction systems, forecasts of the mid-latitude weather at seasonal time scales are becoming increasingly important for societal decision making, as in risk estimate and management of meteorological extreme events. The predictability of the Northern-Hemisphere winter troposphere, especially in the Euro-Atlantic region, stems from the representation of a number of sources of predictability, notably El Nino Southern Oscillation, the stratospheric polar vortex, Arctic sea-ice extent, Eurasian snow cover. Among these, the stratospheric polar vortex is known to play a paramount role in seasonal forecasts of the winter tropospheric flow.
Here, we investigate the performance in the stratosphere of five seasonal prediction systems taking part in the Copernicus Climate Change Service (C3S), with a focus on the seasonal forecast skill and variability, and on the assessment of stratospheric processes. We show that dynamical forecasts of the stratosphere initialised at the beginning of November are considerably more skilful than empirical forecasts based on observed October or November anomalies. Advances in the representation of stratospheric seasonal variability and extremes, i.e. sudden stratospheric warming frequency, are identified with respect to previous generations of climate models running roughly a decade ago. Such results display, however, a large model dependence. Finally, we stress the importance of the relation between the stratospheric wave activity and the stratospheric polar vortex (i.e. the wave—mean-flow interaction), applied both to the variability and to the predictability of the stratospheric mean flow. Indeed, forecasts of the winter stratospheric polar vortex are closely connected to the prediction of November-to-February stratospheric wave activity, in particular in the Eurasian sector.
How to cite: Portal, A., Ruggieri, P., Palmeiro, F. M., Garcı́a-Serrano, J., Domeisen, D. I. V., and Gualdi, S.: Predictions of the boreal winter stratosphere with the C3S multi-model seasonal forecast system, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-42, https://doi.org/10.5194/ems2021-42, 2021.
El Niño Southern Oscillation (ENSO) represents the major driver of interannual climate variability at global scale. Observational and model-based studies have fostered a long-standing debate on the shape and intensity of the ENSO influence over the Euro-Mediterranean sector. Indeed, the detection of this signal is strongly affected by the large internal variability that characterizes the atmospheric circulation in the North Atlantic-European (NAE) region. This study explores if and how the low-frequency variability of North Pacific sea surface temperature (SST) may impact the El Niño-NAE teleconnection in late winter, which consists of a dipolar pattern between middle and high latitudes. A set of idealized atmosphere-only experiments, prescribing different phases of the anomalous SST linked to the Pacific Decadal Oscillation (PDO) superimposed onto an El Niño-like forcing in the tropical Pacific, has been performed in a multi-model framework, in order to assess the potential modulation of the positive ENSO signal. The modelling results suggest, in agreement with observational estimates, that the PDO negative phase (PDO-) may enhance the amplitude of the El Niño-NAE teleconnection, while the dynamics involved appear to be unaltered. On the other hand, the modulating role of the PDO positive phase (PDO+) is not reliable across models. This finding is consistent with the atmospheric response to the PDO itself, which is robust and statistically significant only for PDO-. Its modulation seems to rely on the enhanced meridional SST gradient and the related turbulent heat-flux released along the Kuroshio-Oyashio extension. PDO- weakens the North Pacific jet, whereby favoring more poleward propagation of wave activity, strengthening the El Niño-forced Rossby wave-train.
These results imply that there might be conditional predictability for the interannual Euro-Mediterranean climate variability depending on the background state.
How to cite: Benassi, M., Conti, G., Gualdi, S., Ruggieri, P., Materia, S., García-Serrano, J., Palmeiro, F. M., Batté, L., and Ardilouze, C.: El Niño teleconnection to the Euro-Mediterranean late-winter: the role of extratropical Pacific modulation , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-104, https://doi.org/10.5194/ems2021-104, 2021.
Seasonal forecasts are increasingly employed as sources of information on the expected evolution of climate in the few months ahead by various end-users. This study provides an overall assessment of the skills of the main seasonal forecast systems available in the Copernicus Climate Data Store (C3S) in representing temperature and precipitation anomalies at the monthly time scale. The focus area is the Mediterranean, a densely populated region identified as a hotspot for climate change, where seasonal forecasts could be useful to a variety of economic sectors, including water management, hydropower production, agriculture.
In this study, seasonal forecast systems issued by 5 European institutions (ECMWF, Météo-France, UKMO, DWD, CMCC), together with two different Multi-Model Ensembles (MME) derived from them, have been analysed. The added value of these forecast systems with respect to simpler forecast approaches based on climatology and persistence has been investigated.
Different deterministic (Anomaly Correlation Coefficient) and probabilistic scores (Ranked Probability Score, Continuous Ranked Probability Score and Receiver Operating Characteristic Curve) have been employed to obtain an overall assessment of the quality of the forecasts (as of Murphy, 1993 and WMO, 2018), using ERA5 dataset as a reference. We performed the analysis using 6-month forecasts starting in May and November to reproduce the following summer and the winter seasons.
In general, temperature patterns and respective skill scores are better reproduced than those regarding precipitation. The anomaly correlation coefficients for MME reach the best agreement values for each season and variable except for winter temperature. Different behaviours are found for the different skill scores; their high spatial variability suggests that smaller regions could perform better for a single variable or starting date. Seasonal forecast systems, despite some limitations, show an added value with respect to simple forecast approaches based on the climatology or persistence.
How to cite: Calì Quaglia, F., Terzago, S., and von Hardenberg, J.: Assessment of seasonal forecast skills of temperature and precipitation: a comparison of 5 different models over the Mediterranean region, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-168, https://doi.org/10.5194/ems2021-168, 2021.
Seasonal climate predictions leverage on many predictable or persistent components of the Earth system that can modify the state of the atmosphere and of relant weather related variable such as temprature and precipitation. With a dominant role of the ocean, the land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between land surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere both locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been investigated and documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of Autumn Eurasian snow in recent dynamical seasonal forecasts is therefore unclear. In this study we assess the role of Eurasian snow cover in a set of 5 operational seasonal forecast system characterized by a large ensemble size and a high atmospheric and oceanic resolution. Results are compemented with a set of targeted idealised simulations with atmospheric general circulation models forced by different snow cover conditions. Forecast systems reproduce realistically regional changes of the surface energy balance associated with snow cover variability. Retrospective forecasts and idealised sensitivity experiments converge in identifying a coherent change of the circulation in the Northern Hemisphere. This is compatible with a lagged but fast feedback from the snow to the Arctic Oscillation trough a tropospheric pathway.
How to cite: Ruggieri, P., Benassi, M., Materia, S., Peano, D., Ardilouze, C., Batté, L., and Gualdi, S.: On the role of Eurasian autumn snow cover in dynamical seasonal predictions , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-192, https://doi.org/10.5194/ems2021-192, 2021.
El Niño-Southern Oscillation (ENSO) is known to affect the Northern Hemisphere tropospheric circulation in late-winter (January–March), but whether El Niño and La Niña lead to symmetric impacts and with the same underlying dynamics remains unclear, particularly in the North Atlantic. Three state-of-the-art atmospheric models forced by symmetric anomalous sea surface temperature (SST) patterns, mimicking strong ENSO events, are used to robustly diagnose symmetries and asymmetries in the extra-tropical ENSO response. Asymmetries arise in the sea-level pressure (SLP) response over the North Pacific and North Atlantic, as the response to La Niña tends to be weaker and shifted westward with respect to that of El Niño. The difference in amplitude can be traced back to the distinct energy available for the two ENSO phases associated with the non-linear diabatic heating response to the total SST field. The longitudinal shift is embedded into the large-scale Rossby wave train triggered from the tropical Pacific, as its anomalies in the upper troposphere show a similar westward displacement in La Niña compared to El Niño. To fully explain this shift, the response in tropical convection and the related anomalous upper-level divergence have to be considered together with the climatological vorticity gradient of the subtropical jet, i.e. diagnosing the tropical Rossby wave source. In the North Atlantic, the ENSO-forced SLP signal is a well-known dipole between middle and high latitudes, different from the North Atlantic Oscillation, whose asymmetry is not indicative of distinct mechanisms driving the teleconnection for El Niño and La Niña.
The multi-model assessment, with 50 members for each experiment, contributes to the ERA4CS-funded MEDSCOPE project and includes: EC-EARTH/IFS (L91, 0.01hPa), CNRM/ARPEGE (L91, 0.01hPa), CMCC/CAM (L46, 0.3hPa).
How to cite: Mezzina, B., García-Serrano, J., Bladé, I., Palmeiro, F. M., Batté, L., Ardilouze, C., Benassi, M., and Gualdi, S.: Multi-model assessment of the late-winter tropospheric response to El Niño and La Niña, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-265, https://doi.org/10.5194/ems2021-265, 2021.
The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC-2). The main objective of this work is to establish a comparison among five statistical downscaling methods developed at AEMET: 1) Analog, 2) Regression, 3) Artificial Neural Networks, 4) Support Vector Machines and 5) Kernel Ridge Regression. All the five methods have been applied with a Perfect Prog approach to downscale daily maximum/minimum temperatures and daily precipitation on a high resolution observational grid (0.05o) over mainland Spain and the Balearic Islands, a region particularly challenging due to its large regional spatio-temporal variabilities. The comparison has been carried out under present conditions and with perfect predictors, based on the framework established by the VALUE network, in particular, on its perfect predictor experiment. The evaluation here performed is focused on marginal aspects, through an analysis of the four seasonal distributions of each variable. In order to enable a manageable comparison among all methods three indexes commonly used for climate change adaptation and impact studies have been used: the mean value and the 10th and 90th percentiles for daily maximum/minimum temperatures and the total precipitation amount (PRCPTOT), the total precipitation on very wet days (R95p) and the number of wet days (R01) for precipitation. For maximum/minimum temperatures, all methods display a similar behavior. They capture very satisfactorily the mean values although slight biases are detected on the extremes. In general, results for maximum temperature appear to be more accurate than for minimum temperature, and the non-linear methods display certain added value. For precipitation, remarkable differences are found among all methods: most of them are capable of reproducing the total precipitation amount quite satisfactorily, while other aspects such as intense precipitations and the precipitation occurrence are captured with more accuracy by the Analog method.
How to cite: Hernanz, A., García-Valero, J.-A., Domínguez, M., Ramos-Calzado, P., Pastor-Saavedra, M.-A., and Rodríguez-Camino, E.: Evaluation of statistical downscaling methods for climate change projections over Spain: present conditions with perfect predictors., EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-46, https://doi.org/10.5194/ems2021-46, 2021.
Climate forecasts need to be postprocessed to obtain user-relevant climate information, to develop and implement strategies of adaptation to climate variability and to trigger decisions. Several postprocessing methods are gathered into CSTools (short for Climate Service Tools) for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products.
Besides an overview of the methods and documentation available in CSTools, a practical example is demonstrated. The objective of this practical example is to postprocess a seasonal forecast with a set of CSTools functions in order to obtain the required data to produce forecasts of mountain snow resources. Quantile mapping bias-correction and RainFARM stochastic downscaling methods are applied to raw seasonal forecast daily precipitation data to derive 1 km resolution fields. Bias-adjusted and downscaled precipitation data are then employed to drive a snow model, SNOWPACK, and generate snow depth seasonal forecasts at selected high-elevation sites in North-Western Italian Alps.
The computational resources required by CSTools to process the forecasts will be discussed. This assessment is relevant given the memory requirements for the use case: while seasonal forecast data occupies ~10MB (8 x 8 grid cells, 215 forecast time steps for 30 different initializations with 25 members each), the data post-processed reaches ~1TB (the RainFARM downscaling requires a refinement factor 100 for the SNOWPACK model increasing the spatial resolution to 800 x 800 grid cells and creating 10 stochastic realizations for each ensemble member). In addition to one strategy using conventional loops, startR is introduced as an efficient alternative. startR is an R package that allows implementing the MapReduce paradigm, i.e. chunking the data and processing them either locally or remotely on high-performance computing systems, leveraging multi-node and multi-core parallelism where possible.
How to cite: Pérez-Zanón, N., Caron, L.-P., Terzago, S., Van Schaeybroeck, B., Batté, L., Alvarez-Castro, M. C., Corti, S., Dominguez, M., Fabiano, F., Gualdi, S., von Hardenberg, J., Lledó, L., Manubens, N., Marson, P., Materia, S., Sánchez, E., Torralba, V., Verfaillie, D., and Volpi, D.: CSTools: the MEDSCOPE Toolbox for Climate Forecasts postprocessing, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-204, https://doi.org/10.5194/ems2021-204, 2021.
Chairperson: Silvio Gualdi
In the framework of the MEDSCOPE project, Météo-France has initiated the development of new prototypes for seasonal water resource management in the Mediterranean region, addressing different scientific and technical challenges essential for a future operationalization of the services . In order to have a replicable result on the Mediterranean area, we decided first to consider the three large watersheds onof the Rhone river in France, the Ebro river in Spain and the Po river in Italy.
Our first challenge was to use a new hydrologic model SURFEX-CTRIP, covering the whole Mediterranean area. Another point was to perfect and evaluate a new downscaling tool named ADAMONT permitting to debiase all seasonal forecast input variables needed for hydrology applications and not only (temperature and, precipitation and 5 other surface meteorological parameters). We decided also to assess the new UERRA hydrological analyse available on these three countries. Lthe last challenge was to identify local end users facing with decision making process at seasonal scale for water resources management and develop decision help products adapted to their needs.
The evaluation of these prototypes, carried out over the period 2019-2020 using the MF Syst 6 and then Syst 7 seasonal forecasting model, has highlighted a significant potential in a future operational application but also difficulties to be overcome.
The communication will present the main results of this work and discuss the lessons to be learnet from this experience
How to cite: Dayon, G., Besson, F., Viel, C., Soubeyroux, J.-M., and Etchevers, P.: Assessment of climate services prototypes on seasonal water management for the Mediterranean regions , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-26, https://doi.org/10.5194/ems2021-26, 2021.
Following the need of winter cereal farmers from the main producing region (Castilla y León) in Spain to estimate crop yield with at least one season of anticipation, we have developed a climate service based essentially on current and historical meteorological observations, on spring seasonal forecasts from ECMWF System 5 and on the crop growth model AquaCrop. Different experiments have been designed to produce both a synthetic yield database serving as observed truth and three different seasonal forecasting strategies. Calculation of objective verification scores for deterministic and probabilistic crop yield forecasts -including an assessment of their potential economic value- in hindcast mode determines the quality of this service and the differences among forecasting strategies. We demonstrate that the three compared strategies show good skill of wheat yield forecasts at the beginning of July, although the meteorological forcing for Aquacrop simulations between 1st April and 30th June is very different for the three compared strategies. The important role of the memory from previous (autumn and winter) climate conditions carried by the crop growth model is analysed and discussed. A yearly assessment also allows some preliminary estimation of the value and possible benefits of the service for final users. Finally, we conclude that the simulation synthetically producing the observed truth compares rather well –especially the interannual variability- with other yield data based on surveys and experts estimations although it overestimates yield. Users have played a decisive role in co-design and co-development phases of this climate service. They have also actively intervened in the analysis and evaluation of results.
How to cite: Garrido, M. N., Villarino, J. I., Sánchez, E., Abia, I., Dominguez, M., García, P., Rodriguez-Camino, E., Navascues, B., Nafría, D., and Gutierrez, A.: May spring seasonal forecasts improve a climate service providing cereal yield predictions?, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-76, https://doi.org/10.5194/ems2021-76, 2021.
In this paper we present the upgrade of a web tool designed to help in the decision making process for water reservoirs management in Spain. The tool, called S-ClimWaRe (Seasonal Climate predictions in support of Water Reservoirs management) is organized in two main displaying panels. The first one -diagnostic panel- allows the user to explore, for any water reservoir or grid point over continental Spain, the existing hydrological variability and risk linked to climate variability. The second one -forecasting panel- provides probabilistic seasonal predictions for some variables of interest. Following users’ need the tool initially covers the extended winter season (from November to March), when the North Atlantic Oscillation pattern strongly influences the hydrological interannual variability in South-Western Europe. This climate service is fully user driven with a strong commitment of users and stakeholders that has allowed continuous improvement of this tool, meeting users requirements and incorporating latest scientific progress.
The latest S-ClimWaRe version -developed in the framework of the MEDSCOPE project within the European Research Area for Climate Services (ERA4CS) initiative- includes some technical enhancements requested by customers and new seasonal predictions obtained through application of two post-processing steps to ECMWF System-5 forecasts. These two steps consist of a downscaling statistical procedure and a new methodology that combines different skilful NAO forecasts to create an optimal NAO pdf that is then used to weight the ensemble members forecasts of hydrological variables. The new upgraded S-ClimWaRe web tool enriches the forecasting panel with precipitation and water inflow forecast skill, and provides additional forecasts for accumulated snowfall and temperature. A prototype based on two different hydrological models to produce the seasonal forecasts of water inflow has also been tested over a pilot dam. These hydrological models are driven by the downscaled precipitation and temperature forecasts also introduced in the web viewer. The assessment of this downscaling procedure shows promising results with respect to the existing seasonal forecasts based on a statistical approach.
How to cite: Sánchez-García, E., Abia, I., Domínguez, M., Voces, J., Sánchez-Perrino, J. C., Navascués, B., Rodríguez-Camino, E., Garrido, M. N., Pastor, F., García-Gómez, M. C., Dimas, M., Barranco, L., and Ruíz, C.: Upgrade of a climate service tailored to water reservoirs management , EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-154, https://doi.org/10.5194/ems2021-154, 2021.
Warming trends in the past decades in mountain regions have resulted in glacier shrinking, seasonal snow cover reduction, changes in the amount and seasonality of meltwater runoff (IPCC, 2019), and we expect droughts to become more severe in the future (Haslinger et al., 2014) with consequences for both mountain and downstream economies. Effective adaptation strategies to address and reduce negative climate change impacts involve multiple time scales, from the long-term support of mountain water resource management and the diversification of mountain tourism activities, to the seasonal scale, for the optimization of the available snow resources.
In the frame of the MEDSCOPE project we developed a prototype to generate seasonal forecasts of mountain snow resources, in order to estimate the temporal evolution of the depth and the water content of the snowpack with lead times of several months. The prototype has been tailored on the needs of water and hydropower plant managers and of mountain ski resorts managers. We present the modelling chain, based on the seasonal forecasts of ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S). Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and humidity are bias-corrected and downscaled to the site of Bocchetta delle Pisse 2410 m a.s.l. in the North-Western Italian Alps, and finally used as input for a physically-based multi-layer snow model (SNOWPACK, Bartelt and Lehning, 2002). The RainFARM stochastic downscaling procedure (Terzago et al., 2018) is used for precipitation data in order to allow an estimate of uncertainties linked to small-scale variability in the forcing.
The skills of the prototype in predicting the snow depth evolution from November 1st to May 31st in each season of the hindcast period 1995-2015 are demonstrated using station measurements as a reference. We show the correlation between forecast and observed snow depth anomalies and we quantify the forecast quality in terms of reliability, resolution, discrimination and sharpness using a set of probabilistic measures (Brier Skill Score, the Area Under the ROC Curve Skill Score and the Continuous Ranked Probability Skill Score). Implications of the forecast quality at different lead times on climate services are discussed.
Real-time snow forecasts for the current season (2020-2021) are available at this link: http://wilma.to.isac.cnr.it/diss/snowpack/snowseas-eng.html
How to cite: Terzago, S., Bongiovanni, G., and von Hardenberg, J.: Seasonal forecasting of mountain snow resources: evaluation of a climate service prototype, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-170, https://doi.org/10.5194/ems2021-170, 2021.
OSA3.5 runs as via Zoom. The live session page on EMS2021 allows you to: