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In 2008, the International Federation of Red Cross and Red Crescent Societies (IFRC) used a seasonal forecast for West Africa for the first time to implement an Early Warning, Early Action strategy for enhanced flood preparedness and response. Interviews with disaster managers suggest that this approach improved their capacity and response. Relief supplies reached flood victims within days, as opposed to weeks in previous years, thereby preventing further loss of life, illness, and setbacks to livelihoods, as well as augmenting the efficiency of resource use. This case demonstrates the potential benefits to be realised from the use of medium-to-long-range forecasts in disaster management, especially in the context of potential increases in extreme weather and climate-related events due to climate variability and change. However, harnessing the full potential of these forecasts will require continued effort and collaboration among disaster managers, climate service providers, and major humanitarian donors., (© 2013 The Author(s). Journal compilation © Overseas Development Institute, 2013.)

The difficulty of forecasting “normal” climate conditions is demonstrated in the context of bivariate normally distributed forecasts and observations. Deterministic and probabilistic skill scores for the normal category are less than for the outer category for all-but-perfect models. There are two important mathematical properties of the normal category in a three-category climatologically equiprobable forecast system that affect the scores for this category. First, the normal category can achieve the highest probability less frequently than the outer categories, and far less frequently in contexts of weak to moderate skill. Second, there are upper limits to the probability the normal category can reach. These mathematical constraints suggest that summary measures of skill may underestimate the predictability and forecast-skill of extreme events, and that subjective inputs to probabilistic forecasts may need to take greater account of limitations to the predictability of normal conditions. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) is the dominant source of seasonal climate predictability. This study quantifies the historical impact of ENSO on seasonal precipitation through an update of the global ENSO teleconnection maps of Mason and Goddard. Many additional teleconnections are detected due to better handling of missing values and 20 years of additional, higher quality data. These global teleconnection maps are used as deterministic and probabilistic empirical seasonal forecasts in a verification study. The probabilistic empirical forecast model outperforms climatology in the tropics demonstrating the value of a forecast derived from the expected precipitation anomalies given the ENSO phase. Incorporating uncertainty due to SST prediction shows that teleconnection maps are skillful in predicting tropical precipitation up to a lead time of 4 months. The historical IRI seasonal forecasts generally outperform the empirical forecasts made with the teleconnection maps, demonstrating the additional value of state-of-the-art dynamical-based seasonal forecast systems. Additionally, the probabilistic empirical seasonal forecasts are proposed as reference forecasts for future skill assessments of real-time seasonal forecast 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]

This study describes a systematic approach to selecting optimal statistical recalibration methods and hindcast designs for producing reliable probability forecasts on seasonal-to-decadal time scales. A new recalibration method is introduced that includes adjustments for both unconditional and conditional biases in the mean and variance of the forecast distribution and linear time-dependent bias in the mean. The complexity of the recalibration can be systematically varied by restricting the parameters. Simple recalibration methods may outperform more complex ones given limited training data. A new cross-validation methodology is proposed that allows the comparison of multiple recalibration methods and varying training periods using limited data. Part I considers the effect on forecast skill of varying the recalibration complexity and training period length. The interaction between these factors is analyzed for gridbox forecasts of annual mean near-surface temperature from the CanCM4 model. Recalibration methods that include conditional adjustment of the ensemble mean outperform simple bias correction by issuing climatological forecasts where the model has limited skill. Trend-adjusted forecasts outperform forecasts without trend adjustment at almost 75% of grid boxes. The optimal training period is around 30 yr for trend-adjusted forecasts and around 15 yr otherwise. The optimal training period is strongly related to the length of the optimal climatology. Longer training periods may increase overall performance but at the expense of very poor forecasts where skill is limited. [ABSTRACT FROM AUTHOR]

The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM forecasts may render them more sufficient in their own right. [ABSTRACT FROM AUTHOR]

The skill of global-scale sea surface temperature forecasts using a statistically based linear forecasting technique is investigated. Canonical variates are used to make monthly sea surface temperature anomaly forecasts using evolutionary and steady-state features of antecedent sea surface temperatures as predictors. Levels of forecast skill are investigated over several months' lead time by comparing the model performance with a simple forecast strategy involving the persistence of sea surface temperature anomalies. Forecast skill is investigated over an independent test period of 18 yr (1982/83–1999/2000), for which the model training period was updated after every 3 yr. Forecasts for the equatorial Pacific Ocean are a significant improvement over a strategy of random guessing, and outscore forecasts of persisted anomalies beyond lead times of about one season during the development stages of the El Niño–Southern Oscillation phenomenon, but only outscore forecasts of persisted anomalies beyond 6 months' lead time during its most intense phase. Model predictions of the tropical Indian Ocean outscore persistence during the second half of the boreal winter, that is, from about December or January, with maximum skill during the March–May spring season, but poor skill during the autumn months from September to November. Some loss in predictability of the equatorial Pacific and Indian Oceans is evident during the early and mid-1990s, but forecasts appear to have improved in the last few years. The tropical Atlantic Ocean forecast skill has generally been poor. There is little evidence of forecast skill over the midlatitudes in any of the oceans. However, during the spring months significant skill has been found over the Indian Ocean as far south as 20°S and over the southern North Atlantic as far north as 30°N, both of which outscore persistence beyond a lead time of less than about one season. [ABSTRACT FROM AUTHOR]