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.

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

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

<p>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.</p><p>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 98<sup>th</sup> percentile of the near surface wind speed.</p><p>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.</p><p>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.</p><p>The results can be used to get a better understanding of the resulting skill for the upcoming windstorm season.</p>

scholarly journals Seasonal forecast verification and application in times of change

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.

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

2019 ◽  
Vol 147 (2) ◽  
pp. 607-625 ◽  
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.

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.

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 Impact of Sea Surface Temperature Forcing on Weeks 3 and 4 Forecast Skill in the NCEP Global Ensemble Forecasting System

2017 ◽  
Vol 32 (6) ◽  
pp. 2159-2174 ◽  
Author(s):  
Yuejian Zhu ◽  
Xiaqiong Zhou ◽  
Malaquias Peña ◽  
Wei Li ◽  
Christopher Melhauser ◽  
...  
Keyword(s):  
North America ◽  
Forecast Skill ◽  
Ensemble Forecasting ◽  
Superior Performance ◽  
Seasonal Forecasts ◽  
Skill Scores ◽  
Short Period ◽  
Forecasting System ◽  
Sst Forcing ◽  
The Impact

Abstract The Global Ensemble Forecasting System (GEFS) is being extended from 16 to 35 days to cover the subseasonal period, bridging weather and seasonal forecasts. In this study, the impact of SST forcing on the extended-range land-only global 2-m temperature, continental United States (CONUS) accumulated precipitation, and MJO skill are explored with version 11 of the GEFS (GEFSv11) under various SST forcing configurations. The configurations consist of 1) the operational GEFS 90-day e-folding time of the observed real-time global SST (RTG-SST) anomaly relaxed to climatology, 2) an optimal AMIP configuration using the observed daily RTG-SST analysis, 3) a two-tier approach using the CFSv2-predicted daily SST, and 4) a two-tier approach using bias-corrected CFSv2-predicted SST, updated every 24 h. The experimental period covers the fall of 2013 and the winter of 2013/14. The results indicate that there are small differences in the ranked probability skill scores (RPSSs) between the various SST forcing experiments. The improvements in forecast skill of the Northern Hemisphere 2-m temperature and precipitation for weeks 3 and 4 are marginal, especially for North America. The bias-corrected CFSv2-predicted SST experiment generally delivers superior performance with statistically significant improvement in spatially and temporally aggregated 2-m temperature RPSSs over North America. Improved representation of the SST forcing (AMIP) increased the forecast skill for MJO indices up through week 2, but there is no significant improvement of the MJO forecast skill for weeks 3 and 4. These results are obtained over a short period with weak MJO activity and are also subject to internal model weaknesses in representing the MJO. Additional studies covering longer periods with upgraded model physics are warranted.

How well do seasonal forecasts reproduce European winter storm clustering and its relationship to large-scale climate drivers?

2020 ◽  
Author(s):  
Michael Angus ◽  
Gregor Leckebusch
Keyword(s):  
Statistical Model ◽  
Large Scale ◽  
Seasonal Forecast ◽  
Decision Makers ◽  
Seasonal Forecasts ◽  
Annual Variability ◽  
The North ◽  
Explained Variance ◽  
The North Atlantic Oscillation ◽  
Insight Into

<div>The inter-annual variability of the European windstorm season is dependent on a number of large-scale climate drivers and conditions, for example the North Atlantic Oscillation. For seasonal forecasts to provide valuable information to decision makers about the potential severity of the winter windstorm season, they must capture this relationship between large-scale climate drivers and seasonal windstorm frequency in advance. Here, we examine the performance of the latest state of the art ECMWF seasonal forecast product (SEAS5) in capturing this climate response. We apply a statistical model previously shown to well reproduce the explained behaviour of European windstorms from large-scale climate drivers (Walz et al. 2018) to SEAS5, and examine the choice of statistically significant drivers. The model applied is a stepwise Poisson regression approach to account for serial clustering within inter-annual variability of windstorms, the resultant of which categorizes each windstorm season as either active, neutral or inactive. In particular, we focus on the European region where the explained variance of the statistical model in observations is highest (Walz et al. 2018), the British Isles. In addition to comparing the performance of the model in SEAS5 and in observations, we examine which relationships are not recreated in the seasonal forecast successfully from a dynamical perspective, to provide further insight into the current ability of seasonal forecasts to represent European windstorm inter-annual variability.</div><div> </div><div>Reference:</div><div>Walz, M. A., Befort, D. J., Kirchner‐Bossi, N. O., Ulbrich, U., & Leckebusch, G. C. (2018). Modelling serial clustering and inter‐annual variability of European winter windstorms based on large‐scale drivers. <em>International Journal of Climatology</em>, <em>38</em>(7), 3044-3057.</div>

scholarly journals Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts

2022 ◽  
Author(s):  
Peter Hitchcock ◽  
Amy Butler ◽  
Andrew Charlton-Perez ◽  
Chaim Garfinkel ◽  
Tim Stockdale ◽  
...  
Keyword(s):  
Winter Season ◽  
Polar Vortex ◽  
Forecast Skill ◽  
The Arctic ◽  
Seasonal Forecasts ◽  
Stratospheric Polar Vortex ◽  
Near Surface ◽  
Forecast Models ◽  
The Impact

Abstract. Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.

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.

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