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The probabilistic prediction skill of the weekly forecasts of extreme cold events (ECE) is illustrated and measured in the form of the Brier Skill Score (BSS) and the area under Relative Operating Characteristics (ROC) curves based on the subseasonal-to-seasonal (S2S) prediction project database. The ROC scores show that six S2S models have the good potential predictability skill required for use in ECE probabilistic forecasts, and they were more useful than climatologic probabilistic models in creating forecasts of about 3–4 weeks in length. However, the BSS results show that the actual prediction skill of six models used in ECE probabilistic forecasts are different. The ECMWF model has a good performance, and its actual probabilistic prediction skill of ECE for forecasts of about 3–4 weeks in length was higher than those of climatology, which operates close to its potential predictability. The actual probabilistic prediction skill of the NCEP model for ECE was only about 2 weeks over the extra-tropics, and no skill was recorded over the tropics given its bad reliability, especially over the tropics. BoM, JMA, and CNRM models only have a 1-week actual prediction skill over the Northern Hemisphere extra-tropics, and they have no skill over the rest of the world's land area. The CNR-ISAC model has a 1-week actual prediction skill over the extra-tropics and about 4 weeks over the tropics. There is still much room for improvement in the prediction ability of models used for ECE. MJO in tropical regions has an important influence on the probabilistic prediction skill of ECE required at middle and high latitudes. When there is an MJO in the initial conditions, the potential predictability and actual prediction skill of ECE probabilistic forecasts over North America in the 3rd week and over Europe in the 3rd–4th weeks are higher than those without MJO. [ABSTRACT FROM AUTHOR]

Record‐breaking above‐normal temperatures were observed across western North America in June–July 2021. In this study, our ability to predict this heatwave 2–3 weeks in advance is assessed based on 10 Subseasonal‐to‐Seasonal prediction models. It is found that the above normal temperature in Western Canada during June 28–July 4 was predicted by most models as early as June 10. However, for the forecasts initialized earlier than June 17, not a single ensemble member of all the models is able to capture the magnitude of the observed temperature anomaly. We identify two important processes: an upper tropospheric wave train associated with the boreal summer intraseasonal oscillation in Southeast Asia and an anomalous North Pacific atmospheric river leading to high moisture conditions. Most models are able to predict the wave train across the North Pacific. A realistic representation of moisture transport and its pattern appears crucial for the extended‐range forecast of this heatwave. Plain Language Summary: Record‐breaking high temperatures were observed across western Canada and the U.S. Pacific Northwest from late June to early July 2021. The heatwave caused more than 1,400 deaths, and led to numerous extensive wildfires and widespread smoke pollution. An accurate long‐range prediction of such heatwaves is needed and remains an enormous challenge. In this study, we assess our current ability in predicting this heatwave 2–3 weeks in advance. We identify two important processes for this heatwave: influence of the boreal summer monsoon variability in Southeast Asia and moisture transport across the North Pacific. We investigate why some forecasts are more successful than others and how the forecast could be improved. Key Points: The ability of current state‐of‐the‐art operational Subseasonal‐to‐Seasonal systems to predict an extreme heat wave 2–3 weeks in advance is assessedTwo important dynamical processes for the extreme heatwave are identifiedA realistic representation of moisture transport and its pattern is important for the extended‐range forecast of heatwaves [ABSTRACT FROM AUTHOR]

Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between daily weather forecasts and seasonal climate outlooks. Recent scientific advances have identified sources of predictability on this time range, and modeling advances are leading to better forecasts. However, much remains to be done to further improve their skill and to develop new climate service forecast products to help countries and sectorial decision makers better manage weather risks and extremes and to adapt to climate change. This paper reviews the history and describes the main challenges and opportunities for the modeling and forecast‐applications communities to improve subseasonal to seasonal (S2S) forecasts and products, along with current developments catalyzed by the World Weather Research Programme and World Climate Research Programme's joint Sub‐Seasonal to Seasonal Prediction Project. The case of tropical cyclones is highlighted as an illustrative example of the points discussed. Plain Language Summary: The forecast range between weather forecasts and seasonal outlooks was long thought to be a "predictability desert" with little forecast skill. However, many management decisions in agriculture and food security, water, disaster risk reduction, and health fall into this gap in prediction capabilities, so that developing forecast capabilities for this time range would be of considerable societal value. New research and better models have begun to close this gap through increased international collaboration between weather and climate forecasting centers, national research programs, and the academic and user communities. Better understanding of the coupled ocean‐atmosphere‐land‐cryosphere system has identified multiple sources of S2S predictability that are starting to be exploited to fill the prediction gap spurred by creation of new forecast databases. Key Points: Subseasonal to seasonal prediction is a new and rapidly developing area of forecasting capabilitiesCollaborative research on interactions between weather and climate has been accelerated by new multimodel databases of forecastsSubseasonal tropical cyclone forecasts have improved substantially with good prospects for developing useful early warning systems [ABSTRACT FROM AUTHOR]

Producing probabilistic subseasonal forecasts of extreme events up to six weeks in advance is crucial for many economic sectors. In agribusiness, this time scale is particularly critical because it allows for mitigation strategies to be adopted for counteracting weather hazards and taking advantage of opportunities. For example, spring frosts are detrimental for many nut trees, resulting in dramatic losses at harvest time. To explore subseasonal forecast quality in boreal spring, identified as one of the most sensitive times of the year by agribusiness end users, we build a multisystem ensemble using four models involved in the Subseasonal to Seasonal Prediction project (S2S). Two-meter temperature forecasts are used to analyze cold spell predictions in the coastal Black Sea region, an area that is a global leader in the production of hazelnuts. When analyzed at the global scale, the multisystem ensemble probabilistic forecasts for near-surface temperature are better than climatological values for several regions, especially the tropics, even many weeks in advance; however, in the coastal Black Sea, skill is low after the second forecast week. When cold spells are predicted instead of near-surface temperatures, skill improves for the region, and the forecasts prove to contain potentially useful information to stakeholders willing to put mitigation plans into effect. Using a cost–loss model approach for the first time in this context, we show that there is added value of having such a forecast system instead of a business-as-usual strategy, not only for predictions released 1–2 weeks ahead of the extreme event, but also at longer lead times. [ABSTRACT FROM AUTHOR]

A method has been developed to forecast seasonal landfall risk using ensembles of cyclone tracks generated by ECMWF's seasonal forecast system 4. The method has been applied to analyze and retrospectively forecast the landfall risk along the North American coast. The main result is that the method can be used to forecast landfall for some parts of the coast, but the skill is lower than for basinwide forecasts of activity. The rank correlations between forecasts issued on 1 May and observations are 0.6 for basinwide tropical cyclone number and 0.5 for landfall anywhere along the coast. When the forecast period is limited to the peak of the hurricane season, the landfall correlation increases to 0.6. Moreover, when the forecast issue date is pushed forward to 1 August, basinwide tropical cyclone and hurricane correlations increase to 0.7 and 0.8, respectively, whereas landfall correlations improve less. The quality of the forecasts is in line with that obtained by others. [ABSTRACT FROM AUTHOR]

The Stochastically Perturbed Parametrization Tendencies (SPPT) scheme is used in ensemble forecast systems to represent uncertainties due to the atmospheric physics parametrizations. The scheme works by introducing random perturbations to the tendencies from the physics schemes. The size of the perturbations is modulated by the size of the net tendencies from the full physics suite. Recent work has identified an inconsistency in this approach to attributing model uncertainty: greater uncertainty is attributed to the night‐time sky than to the daytime sky in clear‐sky conditions, which is not deemed physically realistic. This paper documents the details of a revised configuration of SPPT, which became active in the recent operational version of the ensemble forecast system ("CY45R1") at the European Centre for Medium‐Range Weather Forecasts (ECMWF) — released in June 2018. In this work, a modification to SPPT is outlined such that the clear‐sky contribution from the radiation scheme is subtracted from the physics tendencies that are acted on by SPPT. Inspection of individual vertical profiles of physics tendencies provides a greater understanding of the interactions between processes, and helps demonstrate that the revision gives a more realistic description of the associated uncertainties. Forecast experiments with the revised version of SPPT demonstrate neutral to positive impacts on the skill of the ensemble forecasts in the medium‐range; and in the extended‐range, a significantly improved match between the spread and error of the principal components describing the Madden–Julian Oscillation. In addition, it is shown that including the revisions in the ensemble of data assimilations leads to increases in spread in the boundary layer. [ABSTRACT FROM AUTHOR]

Several monsoon indices have been applied to multiple models from the sub-seasonal to seasonal ( S2S) prediction project database during the period May to October 1999-2010 to assess their ability to simulate the Asian monsoon. The bivariate anomaly correlation ( BAC) of the Boreal Summer IntraSeasonal Oscillation ( BSISO) index suggests that the operational models can predict the BSISO1 and BSISO2 events up to 6-24.5 and 6.5-14 days in advance respectively, although the models tend to underestimate the amplitude of BSISO as the lead time increases. For the strong BSISO events, BSISO1 ( BSISO2) display lower skill mostly in phases 3-5 for all the models, suggesting that the BSISO1 ( BSISO2) is not easy to predict when it is located over India and the Maritime Continent (South China Sea and Bay of Bengal). On the other hand, the higher skills appear in different phases for different models. For instance, the limit of predictive skill of strong BSISO1 and BSISO2 events in phases 6-7 for the ECMWF ensemble forecast could exceed 30 and 28 days, respectively. The comparisons of the BSISO life cycle among the ECMWF, NCEP and CMA models also indicate that the ECMWF model can better predict the evolution of strong BSISO events. Predictions of additional monsoon circulation indices including the Webster-Yang index ( WY), Indian Summer Monsoon index ( ISM), South Asian Monsoon index ( SAM), and SouthEast Asian Monsoon index ( SEAM) in the S2S models have statistically significant skill over the corresponding monsoon regions up to 9-31, 3-17, 7-13 and 7-14 days, respectively. However, the significant skill of summer monsoon precipitation over the SAM and SEAM regions varies significantly among the models, with the skill ranging from 2 days to 2 weeks lead time. [ABSTRACT FROM AUTHOR]

ABSTRACT While seasonal outlooks have been operational for many years, until recently the extended-range timescale referred to as subseasonal-to-seasonal ( S2S) has received little attention. S2S prediction fills the gap between short-range weather prediction and long-range seasonal outlooks. Decisions in a range of sectors are made in this extended-range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications-relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision-making and valuation of forecasts, to enhance the benefits of 'climate services' approaches for extended-range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application-ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon. [ABSTRACT FROM AUTHOR]

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3-6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean. [ABSTRACT FROM AUTHOR]

This paper provides a summary of the Workshop on Sub-Seasonal to Seasonal (S2S) Predictability of Extreme Weather and Climate, held at Columbia University, December 6–7, 2016. The 2-day workshop was attended by over 100 people and took stock of recent developments in Sub-seasonal to Seasonal predictability, S2S extreme weather phenomena, and real world predictions and use of forecasts. Workshop motivations, new findings, and outstanding questions discussed are described. [ABSTRACT FROM AUTHOR]