Assessment of seasonal forecast skills of temperature and precipitation: a comparison of 5 different models over the Mediterranean region

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, M&#233;t&#233;o-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.

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Temperature and precipitation seasonal forecasts over the Mediterranean region: added value compared to simple forecasting methods

Climate Dynamics ◽

10.1007/s00382-021-05895-6 ◽

2021 ◽

Author(s):

Filippo Calì Quaglia ◽

Silvia Terzago ◽

Jost von Hardenberg

Keyword(s):

Lead Time ◽

Mediterranean Region ◽

Added Value ◽

Lead Times ◽

Model Ensemble ◽

Seasonal Forecasts ◽

Anomaly Correlation ◽

Temperature And Precipitation ◽

Precipitation Anomalies ◽

The Mediterranean

AbstractThis study considers a set of state-of-the-art seasonal forecasting systems (ECMWF, MF, UKMO, CMCC, DWD and the corresponding multi-model ensemble) and quantifies their added value (if any) in predicting seasonal and monthly temperature and precipitation anomalies over the Mediterranean region compared to a simple forecasting method based on the ERA5 climatology (CTRL) or the persistence of the ERA5 anomaly (PERS). This analysis considers two starting dates, May 1st and November 1st and the forecasts at lead times up to 6 months for each year in the period 1993–2014. Both deterministic and probabilistic metrics are employed to derive comprehensive information on the forecast quality in terms of association, reliability/resolution, discrimination, accuracy and sharpness. We find that temperature anomalies are better reproduced than precipitation anomalies with varying spatial patterns across different forecast systems. The Multi-Model Ensemble (MME) shows the best agreement in terms of anomaly correlation with ERA5 precipitation, while PERS provides the best results in terms of anomaly correlation with ERA5 temperature. Individual forecast systems and MME outperform CTRL in terms of accuracy of tercile-based forecasts up to lead time 5 months and in terms of discrimination up to lead time 2 months. All seasonal forecast systems also outperform elementary forecasts based on persistence in terms of accuracy and sharpness.

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Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation

Monthly Weather Review ◽

10.1175/mwr-d-18-0156.1 ◽

2019 ◽

Vol 147 (2) ◽

pp. 607-625 ◽

Cited By ~ 17

Author(s):

Sarah Strazzo ◽

Dan C. Collins ◽

Andrew Schepen ◽

Q. J. Wang ◽

Emily Becker ◽

...

Keyword(s):

North American ◽

Seasonal Forecast ◽

Forecast Skill ◽

Precipitation Forecast ◽

Dynamical Model ◽

Temperature Pattern ◽

Seasonal Forecasts ◽

Temperature And Precipitation ◽

Skill Scores ◽

Calibration Models

Abstract Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO–temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America.

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Seasonal Forecasts of the Twentieth Century

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-19-0019.1 ◽

2020 ◽

Vol 101 (8) ◽

pp. E1413-E1426 ◽

Cited By ~ 2

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.

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Information Content of Seasonal Forecasts in a Changing Climate

Advances in Meteorology ◽

10.1155/2013/480210 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 11

Author(s):

Nir Y. Krakauer ◽

Michael D. Grossberg ◽

Irina Gladkova ◽

Hannah Aizenman

Keyword(s):

Information Gain ◽

Seasonal Forecasts ◽

Temperature And Precipitation ◽

Skill Scores ◽

The Usa ◽

Precipitation Anomalies ◽

The Mean ◽

Climate Prediction Center

We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill.

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Assessing reasons for skilful predictions of winter windstorms over the Atlantic/European region

10.5194/egusphere-egu2020-10991 ◽

2020 ◽

Author(s):

Lisa Degenhardt ◽

Gregor Leckebusch ◽

Adam Scaife

Keyword(s):

Large Scale ◽

Surface Wind ◽

Forecast Model ◽

Surface Wind Speed ◽

Seasonal Forecast ◽

Forecast Skill ◽

Seasonal Forecasts ◽

Financial Industry ◽

Near Surface ◽

Skill Scores

Severe Atlantic winter storms are affecting densely populated regions of Europe (e.g. UK, France, Germany, etc.). Consequently, different parts of the society, financial industry (e.g., insurance) and last but not least the general public are interested in skilful forecasts for the upcoming storm season (usually December to March). To allow for a best possible use of steadily improved seasonal forecasts, the understanding which factors contribute to realise forecast skill is essential and will allow for an assessment whether to expect a forecast to be skilful or not.This study analyses the predictability of the seasonal forecast model of the UK MetOffice, the GloSea5. Windstorm events are identified and tracked following Leckebusch et al. (2008) via the exceedance of the 98th percentile of the near surface wind speed.Seasonal predictability of windstorm frequency in comparison to observations (based e.g., on ERA5 reanalysis) are calculated and different statistical methods (skill scores) are compared.Large scale patterns (e.g., NAO, AO, EAWR, etc.) and dynamical factors (e.g., Eady Growth Rate) are analysed and their predictability is assessed in comparison to storm frequency forecast skill. This will lead to an idea how the forecast skill of windstorms is depending on the forecast skill of forcing factors conditional to the phase of large-scale variability modes. Thus, we deduce information, which factors are most important to generate seasonal forecast skill for severe extra-tropical windstorms.The results can be used to get a better understanding of the resulting skill for the upcoming windstorm season.

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Downscaling and bias correction of seasonal forecasts to support climate services for the Alpine regions

10.5194/egusphere-egu2020-10109 ◽

2020 ◽

Author(s):

Alice Crespi ◽

Mattia Callegari ◽

Felix Greifeneder ◽

Claudia Notarnicola ◽

Marcello Petitta ◽

...

Keyword(s):

Energy Production ◽

Production Management ◽

Seasonal Forecast ◽

Accurate Information ◽

Sources Of Information ◽

Seasonal Forecasts ◽

Climate Services ◽

Mapping Procedure ◽

Alpine Regions ◽

Wide Range

The interest in trustable and accurate information about climate and its variability at local scale is currently increasing not only within the scientific community, but also by local stakeholders, political administrators and private companies. Clear, operative and close to the users&#8217; needs climate information represent relevant support tools for a wide range of decision-making policies, including vulnerability assessment, risk management and energy production.Seasonal forecasts, in particular, allow to provide predictions of the climate up to several months ahead and therefore they could represent precious sources of information for a wide range of activities, such as for the optimization of renewable energy sector. However, specific approaches are needed to deal with the probabilistic nature of seasonal forecasts and post-processing methods are required to adapt their large spatial resolution to the local scales of specific applications. This is particularly true for orographically complex areas, such as the Alpine regions, where coarse-resolution data could lead to remarkable under or overestimations in the predicted variables.In this framework, we present a downscaled and bias-corrected version of seasonal forecasts provided by the ECMWF&#8217;s seasonal forecasting system (SEAS5) for temperature, precipitation and wind speed over the Alpine area and spanning the period 1983 &#8211; 2018. The approach is based on the bilinear interpolation of the 1&#176;x1&#176; original fields onto the target 0.25&#176;x0.25&#176; resolution and on the quantile-mapping procedure using ERA-5 reanalysis data for the calibration. The ERA-5 reanalysis dataset is chosen as reference in order to allow the application of the implemented scheme over different areas. The accuracy and skills of the post-processed seasonal forecast fields are evaluated, also in comparison with observations and the performance of alternative downscaling schemes.The presented study supports the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.

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Seasonal forecast verification and application in times of change

10.5194/esd-2016-60 ◽

2016 ◽

Author(s):

Yoav Levi ◽

Itzhak Carmona

Keyword(s):

Warming Trend ◽

Seasonal Forecast ◽

Forecast Skill ◽

Decision Makers ◽

Economic Environment ◽

Boreal Summer ◽

Seasonal Forecasts ◽

Climate Services ◽

The Past ◽

Skill Scores

Abstract. Seasonal forecast is being promoted as one of the climate services given to the public and decision makers also in the extra-tropics. However seasonal forecast is a scientific challenge. Rapid changes in climate and the socio-economic environment in the past 30 years introduce even a bigger challenge for the end-users of seasonal forecasts based on the past 30 years. Decision makers should relay on a forecast only if they fully understand the forecast skill and the forecast will not be a completely erroneous.Therefore, the percentage of forecasts for above normal condition that realized to be below normal conditions and vice versa is measured straightforwardly by the "Fiasco score". To overcome the climate and socio-economic environment changes an attempt to relate the next seasonal forecast to the previous season forecast and observed values was tested.The findings indicate that ECMWF system-4 seasonal forecast skill for June-July-August (JJA) temperatures for the marine tropics is very promising as indicated by all the skill scores, including using the previous JJA forecast as the base for the next JJA season. However for the boreal summer temperatures forecast over land, the main source of the model predictability originates from the warming trend along the hindcast period. Over the Middle East and Mongolia removing the temperature trend eliminated the high forecast skill. Evaluation of the ability of the next season forecast to predict the changes relative to the previous year's season has shown a positive skill in some areas compared to the traditional 30 years based climatology after both forecasts and observed data were de-trend.

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Toward a Regional-Scale Seasonal Climate Prediction System over Central Italy based on Dynamical Downscaling

Climate ◽

10.3390/cli7100120 ◽

2019 ◽

Vol 7 (10) ◽

pp. 120 ◽

Cited By ~ 2

Author(s):

Sangelantoni ◽

Ferretti ◽

Redaelli

Keyword(s):

Climate Model ◽

Dynamical Downscaling ◽

Regional Scale ◽

Central Italy ◽

Seasonal Forecast ◽

Added Value ◽

Seasonal Forecasts ◽

Adaptation Measures ◽

Seasonal Climate ◽

Forecast Lead Time

Anticipating seasonal climate anomalies is essential for defining short-term adaptation measures. To be actionable, many stakeholders require seasonal forecasts at the regional scale to be properly coupled to region-specific vulnerabilities. In this study, we present and preliminarily evaluate a regional-scale Seasonal Forecast System (SFS) over Central Italy. This system relies on a double dynamical downscaling performed through the Regional-scale Climate Model (RCM) RegCM4.1. A twelve-member ensemble of the NCEP-CFSv2 provides driving fields for the RegCM. In the first step, the RegCM dynamically downscales NCEP-CFSv2 predictions from a resolution of 100 to 60 km over Europe (RegCM-d1). This first downscaling drives a second downscaling over Central Italy at 12 km (RegCM-d2). To investigate the added value of the downscaled forecasts compared to the driving NCEP-CFSv2, we evaluate the driving CFS, and the two downscaled SFSs over the same (inner) domain. Evaluation involves winter temperatures and precipitations over a climatological period (1982–2003). Evaluation for mean bias, statistical distribution, inter-annual anomaly variability, and hit-rate of anomalous seasons are shown and discussed. Results highlight temperature physical values reproduction benefiting from the downscaling. Downscaled inter-annual variability and probabilistic metrics show improvement mainly at forecast lead-time 1. Downscaled precipitation shows an improved spatial distribution with an undegraded but not improved seasonal forecast quality.

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Seasonal prediction of European summer heatwaves

Climate Dynamics ◽

10.1007/s00382-021-05828-3 ◽

2021 ◽

Author(s):

Chloé Prodhomme ◽

Stefano Materia ◽

Constantin Ardilouze ◽

Rachel H. White ◽

Lauriane Batté ◽

...

Keyword(s):

Forest Fires ◽

Linear Trend ◽

Seasonal Prediction ◽

Seasonal Forecast ◽

Added Value ◽

Prediction Skill ◽

Seasonal Forecasts ◽

Forecast System ◽

Predictive Capacity ◽

Promising Tool

AbstractUnder the influence of global warming, heatwaves are becoming a major threat in many parts of the world, affecting human health and mortality, food security, forest fires, biodiversity, energy consumption, as well as the production and transportation networks. Seasonal forecasting is a promising tool to help mitigate these impacts on society. Previous studies have highlighted some predictive capacity of seasonal forecast systems for specific strong heatwaves such as those of 2003 and 2010. To our knowledge, this study is thus the first of its kind to systematically assess the prediction skill of heatwaves over Europe in a state-of-the-art seasonal forecast system. One major prerequisite to do so is to appropriately define heatwaves. Existing heatwave indices, built to measure heatwave duration and severity, are often designed for specific impacts and thus have limited robustness for an analysis of heatwave variability. In this study, we investigate the seasonal prediction skill of European summer heatwaves in the ECMWF System 5 operational forecast system by means of several dedicated metrics, as well as its added-value compared to a simple statistical model based on the linear trend. We are able to show, for the first time, that seasonal forecasts initialized in early May can provide potentially useful information of summer heatwave propensity, which is the tendency of a season to be predisposed to the occurrence of heatwaves.

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Seasonal forecast of water resources over the Euro-Mediterranean region

10.5194/egusphere-egu2020-20246 ◽

2020 ◽

Author(s):

Gildas Dayon ◽

François Besson ◽

Jean-Michel Soubeyroux ◽

Chrisitian Viel ◽

Paola Marson

Keyword(s):

Mediterranean Region ◽

Snow Water Equivalent ◽

Seasonal Forecast ◽

Added Value ◽

Seasonal Forecasts ◽

Forecast System ◽

Physically Based Model ◽

Physically Based ◽

Snow Water

In the framework of the MEDSCOPE project, a forecasting chain is developed at M&#233;t&#233;o-France for hydrological long term predictions over the Euro-Mediterranean region, from one month up to seven months. This new prototype is based on the M&#233;t&#233;o-France System 6 global seasonal forecast system. Atmospheric forecasts are interpolated to 5.5 km and corrected by the statistical method ADAMONT using the UERRA regional atmospheric reanalysis as reference. These high resolution forecasts drive the physically-based model SURFEX coupled to CTRIP providing seasonal forecasts of surface variables : river discharges, soil wetness indices, snow water equivalent.A forecast using the climatology (ESP approach) has been produced on the period 1993-2016. It is use to explore the sources of predictability in the different watersheds (Ebro, Po, Rh&#244;ne). Predictability is mostly coming from the snow pack built during the winter and the soil moisture evolution in spring and summer. A hindcast on the period 1993-2016 is produced to assess the added value of the seasonal forecast compared to the climatology for the end-users in agriculture and energy.

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