scholarly journals Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment

2021 ◽  
Vol 2 (3) ◽  
pp. 893-912
Author(s):  
Cedric G. Ngoungue Langue ◽  
Christophe Lavaysse ◽  
Mathieu Vrac ◽  
Philippe Peyrillé ◽  
Cyrille Flamant
Keyword(s):  
West African Monsoon ◽  
Seasonal Forecast ◽  
Lead Times ◽  
Model Assessment ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Modes Of Variability ◽  
Forecast Models ◽  
Sahel Region ◽  
Heat Low

Abstract. The Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extremes SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times.

scholarly journals Seasonal Forecasts of the Saharan Heat Low characteristics: A multi-model assessment

2021 ◽  
Author(s):  
Cedric G. Ngoungue Langue ◽  
Christophe Lavaysse ◽  
Mathieu Vrac ◽  
Philippe Peyrille ◽  
Cyrille Flamant
Keyword(s):  
Monsoon Season ◽  
West African Monsoon ◽  
Warming Trend ◽  
Seasonal Forecast ◽  
Model Assessment ◽  
Seasonal Forecasts ◽  
Modes Of Variability ◽  
Forecast Models ◽  
Sahel Region ◽  
Heat Low

Abstract. The Saharan Heat Low (SHL) is a key component of the West African monsoon system at synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecasts models. This is investigated using the last versions of two seasonal forecast systems namely the SEAS5 and MF7 systems respectively from the European Center of Medium range Weather Forecasts (ECMWF) and Meteo-France. The SHL characteristics in the seasonal forecast models is assessed based on a comparison with the fifth ECMWF ReAnalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing of the SHL pulsations and the intensities when compared to ERA5. SEAS5 and MF7 show a cooling trend centered on the Sahara and a warming trend located in the eastern part of the Sahara, respectively. Both models tend to under-estimate the inter-annual variability of the SHL. We also show that the seasonal forecast models detect the eastward and westward shift of the SHL during the monsoon season. The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the forecast score. Despite an improvement of prediction score, the SHL-related forecast skills of SEAS5 and MF7 remain weak for a lead time beyond 1 month.

scholarly journals Seasonal Forecasts of the Twentieth Century

2020 ◽  
Vol 101 (8) ◽  
pp. E1413-E1426 ◽  
Author(s):  
Antje Weisheimer ◽  
Daniel J. Befort ◽  
Dave MacLeod ◽  
Tim Palmer ◽  
Chris O’Reilly ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Large Scale ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Climate Anomalies ◽  
Global Circulation Models ◽  
Skill Scores ◽  
Physically Based ◽  
Forecast Models

Abstract Forecasts of seasonal climate anomalies using physically based global circulation models are routinely made at operational meteorological centers around the world. A crucial component of any seasonal forecast system is the set of retrospective forecasts, or hindcasts, from past years that are used to estimate skill and to calibrate the forecasts. Hindcasts are usually produced over a period of around 20–30 years. However, recent studies have demonstrated that seasonal forecast skill can undergo pronounced multidecadal variations. These results imply that relatively short hindcasts are not adequate for reliably testing seasonal forecasts and that small hindcast sample sizes can potentially lead to skill estimates that are not robust. Here we present new and unprecedented 110-year-long coupled hindcasts of the next season over the period 1901–2010. Their performance for the recent period is in good agreement with those of operational forecast models. While skill for ENSO is very high during recent decades, it is markedly reduced during the 1930s–1950s. Skill at the beginning of the twentieth century is, however, as high as for recent high-skill periods. Consistent with findings in atmosphere-only hindcasts, a midcentury drop in forecast skill is found for a range of atmospheric fields, including large-scale indices such as the NAO and the PNA patterns. As with ENSO, skill scores for these indices recover in the early twentieth century, suggesting that the midcentury drop in skill is not due to a lack of good observational data. A public dissemination platform for our hindcast data is available, and we invite the scientific community to explore them.

Determination of seasonal forecast skill in identifying extreme events of temperature, wind speed, and SPI

2021 ◽  
Author(s):  
Massimiliano Palma ◽  
Franco Catalano ◽  
Irene Cionni ◽  
Marcello Petitta
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Climate Variability ◽  
Extreme Events ◽  
Bias Correction ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Energy Sector ◽  
Seasonal Forecasts ◽  
Weather Forecasts

<p>Renewable energy is the fastest-growing source of electricity globally, but climate variability and impacting events affecting the potential productivity of plants are obstacles to its integration and planning. Knowing a few months in advance the productivity of plants and the impact of extreme events on productivity and infrastructure can help operators and policymakers make the energy sector more resilient to climate variability, promoting the deployment of renewable energy while maintaining energy security.</p><p>The energy sector already uses weather forecasts up to 15 days for plant management; beyond this time horizon, climatologies are routinely used. This approach has inherent weaknesses, including the inability to predict extreme events, the prediction of which is extremely useful to decision-makers. Information on seasonal climate variability obtained through climate forecasts can be of considerable benefit in decision-making processes. The Climate Data Store of the Copernicus Climate Change Service (C3S) provides seasonal forecasts and a common period of retrospective simulations (hindcasts) with equal spatial temporal resolution for simulations from 5 European forecast centres (European Centre for Medium-Range Weather Forecasts (ECMWF), Deutscher Wetterdienst (DWD), Meteo France (MF), UK Met Office (UKMO) and Euro-Mediterranean Centre on Climate Change (CMCC)), one US forecasting centre (NCEP) plus the Japan Meteorological Agency (JMA) model.</p><p>In this work, we analyse the skill and the accuracy of a subset of the operational seasonal forecasts provided by Copernicus C3S, focusing on three relevant essential climate variables for the energy sector: temperature (t2m), wind speed (sfcWind, relevant to the wind energy production), and precipitation. The latter has been analysed by taking the Standard Precipitation Index (SPI) into account.</p><p>First, the methodologies for bias correction have been defined. Subsequently, the reliability of the forecasts has been assessed using appropriate reliability indicators based on comparison with ERA5 reanalysis dataset. The hindcasts cover the period 1993-2017. For each of the variables considered, we evaluated the seasonal averages based on monthly means for two seasons: winter (DJF) and summer (JJA). Data have been bias corrected following two methodologies, one based on the application of a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation; the other based on the correction of the bias, the overall forecast variance and the ensemble spread as described in Doblas-Reyes et al. (2005).</p><p>Predictive ability has been assessed by calculating binary (Brier Skill Score, BSS hereafter, and Ranked Probability Skill Score, RPSS hereafter) and continuous (Continuous Ranked Probability Skill Score, CRPSS hereafter) scores. Forecast performance has been assessed using ERA 5 reanalysis as pseudo-observations. </p><p>In this work we discuss the results obtained with different bias correction techniques highlighting the outcomes obtained analyzing the BSS for the first and the last terciles and the first and the last percentiles (10th and 90th). This analysis has the goal to identify the regions in which the seasonal forecast can be used to identify potential extreme events.</p>

scholarly journals Development of seasonal climate outlooks for agriculture in Finland

2020 ◽  
Vol 17 ◽  
pp. 269-277
Author(s):  
Andrea Vajda ◽  
Otto Hyvärinen
Keyword(s):  
Management Practices ◽  
Seasonal Forecast ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Crop Choice ◽  
Temperature Forecast ◽  
Dry Conditions

Abstract. Seasonal climate forecast products offer useful information for farmers supporting them in planning and making decisions in their management practices, such as crop choice, planting and harvesting time, and water management. Driven by the need of stakeholders for tailored seasonal forecast products, our goal was to assess the applicability of seasonal forecast outputs in agriculture and to develop and pilot with stakeholders a set of seasonal climate outlooks for this sector in Finland. Finnish end users were involved in both the design and testing of the outlooks during the first pilot season of 2019. The seasonal climate outlooks were developed using the SEAS5 seasonal forecast system provided by ECMWF. To improve the prediction skill of the seasonal forecast data, several bias adjustment approaches were evaluated. The tested methods increased the quality of temperature forecast, but no suitable approach was found for eliminating the biases from precipitation data. Besides the widely applied indices, such as mean temperature, growing degree days, cold spell duration, total precipitation and dry conditions, new sector-oriented indices (such as progress of growing season) have been implemented and issued for various lead times (up to 3 months). The first result of forecast evaluation, the development of seasonal forecast indices and the first pilot season of May–October 2019 are presented. We found that the temperature-based outlooks performed well, with better performance skills for short lead times, providing useful information for the farmers in activity management. Precipitation indices had poor skills for each forecasted month, and further research is needed for improving the quality of forecast for Finland. The farmers who have tested the seasonal climate outlooks considered those beneficial and valuable, helping them in planning their activities. Following the first pilot season, further research and implementation work took place to improve our understanding of the skill of seasonal forecasts and increase the quality of tailored seasonal climate services.

Did the ECMWF Seasonal Forecast Model Outperform Statistical ENSO Forecast Models over the Last 15 Years?

2005 ◽  
Vol 18 (16) ◽  
pp. 3240-3249 ◽  
Author(s):  
Geert Jan van Oldenborgh ◽  
Magdalena A. Balmaseda ◽  
Laura Ferranti ◽  
Timothy N. Stockdale ◽  
David L. T. Anderson
Keyword(s):  
El Niño ◽  
Statistical Models ◽  
El Nino ◽  
Southern Oscillation ◽  
Forecast Model ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Forecast Models ◽  
System 2 ◽  
System 1

Abstract The European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts since 1997 with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification. Seasonal predictability is to a large extent due to the El Niño–Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of statistical models, some of which are used operationally. The relative skill depends strongly on the season. The dynamical models are better at forecasting the onset of El Niño or La Niña in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.

scholarly journals Supplementary material to "Seasonal Forecasts of the Saharan Heat Low characteristics: A multi-model assessment"

2021 ◽  
Author(s):  
Cedric G. Ngoungue Langue ◽  
Christophe Lavaysse ◽  
Mathieu Vrac ◽  
Philippe Peyrille ◽  
Cyrille Flamant
Keyword(s):  
Model Assessment ◽  
Seasonal Forecasts ◽  
Supplementary Material ◽  
Heat Low

scholarly journals The Performance of ECMWF sub-seasonal forecasts to predict the Rainy Season Onset Dates in Vietnam

2021 ◽  
Author(s):  
Roderick van der Linden ◽  
Andreas H. Fink
Keyword(s):  
Rainy Season ◽  
Model Performance ◽  
Agricultural Practices ◽  
Absolute Error ◽  
Skill Score ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Almost All ◽  
Ecmwf Model

Abstract The onset of the rainy season is an important date for the mostly rain-fed agricultural practices in Vietnam. Sub-seasonal to seasonal (S2S) ensemble hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to evaluate the predictability of the rainy season onset dates (RSODs) over five climatic sub-regions of Vietnam. The results show that the ECMWF model reproduces well the observed inter-annual variability of RSODs, with a high correlation ranging from 0.60 to 0.99 over all sub-regions at all lead times (up to 40 days) using five different RSOD definitions. For increasing lead times, forecasted RSODs tend to be earlier than the observed ones. Positive skill score values for almost all cases examined in all sub-regions indicate that the model outperforms the observed climatology in predicting the RSOD at sub-seasonal lead times (~28–35 days). However, the model is overall more skilful at shorter lead times. The choice of the RSOD criterion should be considered because it can significantly influence the model performance. The result of analysing the highest skill score for each sub-region at each lead time shows that criteria with higher 5-day rainfall thresholds tend to be more suitable for the forecasts at long lead times. However, the values of mean absolute error are approximately the same as the absolute values of the mean error, indicating that the prediction could be improved by a simple bias correction. The present study shows a large potential to use S2S forecasts to provide meaningful predictions of RSODs for farmers.

scholarly journals Statistical forecast of seasonal discharge in Central Asia for water resources management: development of a generic linear modelling tool for operational use

2017 ◽  
Author(s):  
Heiko Apel ◽  
Zharkinay Abdykerimova ◽  
Marina Agalhanova ◽  
Azamat Baimaganbetov ◽  
Nedejda Gavrilenko ◽  
...  
Keyword(s):  
Water Resources ◽  
Central Asia ◽  
River Discharge ◽  
Cross Validation ◽  
Seasonal Forecast ◽  
Tien Shan ◽  
Generic Model ◽  
Seasonal Forecasts ◽  
Forecast Models ◽  
Seasonal Discharge

Abstract. The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan, Pamir and Altai mountains. During the summer months the snow and glacier melt dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for a sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims at the development of a generic tool for deriving statistical forecast models of seasonal river discharge. The generic model is kept as simple as possible in order to be driven by available meteorological and hydrological data, and be applicable for all catchments in the region. As snowmelt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite based snow cover data and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to 3 or 4 predictors. A user selectable number of best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross validation. Based on the cross validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month starting from January until June. The application of the model for several catchments in Central Asia – ranging from small to the largest rivers – for the period 2000–2015 provided skilful forecasts for most catchments already in January. The skill of the prediction increased every month, with R2 values often in the range 0.8–0.9 in April just before the prediction period. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of operational implementation.

10–25-Day Intraseasonal Variability of Convection over the Sahel: A Role of the Saharan Heat Low and Midlatitudes

2011 ◽  
Vol 24 (22) ◽  
pp. 5863-5878 ◽  
Author(s):  
R. Roehrig ◽  
F. Chauvin ◽  
J.-P. Lafore
Keyword(s):  
Rossby Wave ◽  
Intraseasonal Variability ◽  
West African Monsoon ◽  
Relevant Information ◽  
Disaster Mitigation ◽  
Dry Spells ◽  
Sahel Region ◽  
Equatorial Rossby Wave ◽  
Wet Spells ◽  
Heat Low

Abstract The understanding and forecasting of persistent dry or wet periods of the West African monsoon (WAM), especially those that occur at the intraseasonal time scale, are crucial to improve food management and disaster mitigation in the Sahel region. In the present study, the authors assess how the 10–25-day intraseasonal variability of convection over the Sahel is related to the recently documented intraseasonal variability of the Saharan heat low (SHL) and the associated extratropical circulation. Strongest and most frequent interactions occur when the SHL intraseasonal fluctuations lead those of convection over the Sahel with a 5-day lag. Using a nonlinear event-based approach, such a combination is shown to concern about one-third of Sahelian dry and wet spells and, in the case of dry spells, to yield convective anomalies that are stronger, last longer by at least 2 days, and reach a larger spatial scale. It is then argued that the 10–25-day intraseasonal variability of convection over the Sahel can be partly explained by the midlatitude intraseasonal variability, through a major role played by the SHL. The anomalous midlevel circulations observed during Sahelian wet and dry events can be shifted from the midlatitudes, which provide a complementary mechanism to that invoking equatorial Rossby wave dynamics. These two mechanisms are likely to interfere together in a constructive or destructive way, leading to high temporal and spatial variability of the Sahelian dry and wet spells. As a particular intraseasonal event, the WAM onset is shown to be clearly favored by phases of the SHL intraseasonal variability, when the Mediterranean ventilation is weakened and the SHL is able to strengthen. Conversely, the formation of a strong cold air surge over Libya and Egypt and its propagation toward the Sahel lead to the collapse of the SHL, which inhibits the WAM onset. From these extratropical–tropical interactions, more skillful forecasts of the Sahelian wet and dry spells and of the WAM onset can be expected. In particular, the monitoring of both the SHL intraseasonal activity and that of the equatorial Rossby wave should provide relevant information to forecast at least two-thirds of such high-impact events.

scholarly journals Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Winter Season ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Regimes ◽  
Start Dates ◽  
Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

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