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Gillian M. Mimmack, Simon J. Mason, and Jacqueline S. Galpin

Abstract

Cluster analysis is a technique frequently used in climatology for grouping cases to define classes (synoptic types or climate regimes, for example), or for grouping stations or grid points to define regions. Cluster analysis is based on some form of distance matrix, and the most commonly used metric in the climatological field has been Euclidean distances. Arguments for the use of Euclidean distances are in some ways similar to arguments for using a covariance matrix in principal components analysis: the use of the metric is valid if all data are measured on the same scale. When using Euclidean distances for cluster analysis, however, the additional assumption is made that all the variables are uncorrelated, and this assumption is frequently ignored. Two possible methods of dealing with the correlation between the variables are considered: performing a principal components analysis before calculating Euclidean distances, and calculating Mahalanobis distances using the raw data. Under certain conditions calculating Mahalanobis distances is equivalent to calculating Euclidean distances from the principal components. It is suggested that when cluster analysis is used for defining regions, Mahalanobis distances are inappropriate, and that Euclidean distances should be calculated using the unstandardized principal component scores based on only the major principal components.

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Esther Capó, Alejandro Orfila, Evan Mason, and Simón Ruiz

Abstract

Energy conversion routes are investigated in the western Mediterranean Sea from the eddy–mean flow interactions. The sources of eddy kinetic energy are analyzed by applying a regional formulation of the Lorenz energy cycle to 18 years of numerical simulation at eddy-resolving resolution (3.5 km), which allows for identifying whether the energy exchange between the mean and eddy flow is local or nonlocal. The patterns of energy conversion between the mean and eddy kinetic and potential energy are estimated in three subregions of the domain: the Alboran Sea, the Algerian Basin, and the northern basin. The spatial characterization of the energy routes hints at the physical mechanisms involved in maintaining the balance, suggesting that flow–topography interaction is strongly linked to eddy growth in most of the domain.

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Nathan J. L. Lenssen, Lisa Goddard, and Simon Mason

Abstract

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.

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Mark R. Jury, Henry M. Mulenga, and Simon J. Mason

Abstract

Teleconnection predictors are explored using multivariate regression models in an effort to estimate southern African summer rainfall and climate impacts one season in advance. The preliminary statistical formulations include many variables influenced by the El Niño–Southern Oscillation (ENSO) such as tropical sea surface temperatures (SST) in the Indian and Atlantic Oceans. Atmospheric circulation responses to ENSO include the alternation of tropical zonal winds over Africa and changes in convective activity within oceanic monsoon troughs. Numerous hemispheric-scale datasets are employed to extract predictors and include global indexes (Southern Oscillation index and quasi-biennial oscillation), SST principal component scores for the global oceans, indexes of tropical convection (outgoing longwave radiation), air pressure, and surface and upper winds over the Indian and Atlantic Oceans. Climatic targets include subseasonal, area-averaged rainfall over South Africa and the Zambezi river basin, and South Africa’s annual maize yield. Predictors and targets overlap in the years 1971–93, the defined training period. Each target time series is fitted by an optimum group of predictors from the preceding spring, in a linear multivariate formulation. To limit artificial skill, predictors are restricted to three, providing 17 degrees of freedom. Models with colinear predictors are screened out, and persistence of the target time series is considered. The late summer rainfall models achieve a mean r 2 fit of 72%, contributed largely through ENSO modulation. Early summer rainfall cross validation correlations are lower (61%). A conceptual understanding of the climate dynamics and ocean–atmosphere coupling processes inherent in the exploratory models is outlined.

Seasonal outlooks based on the exploratory models could help mitigate the impacts of southern Africa’s fluctuating climate. It is believed that an advance warning of drought risk and seasonal rainfall prospects will improve the economic growth potential of southern Africa and provide additional security for food and water supplies.

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Simon J. Mason, Michael K. Tippett, Andreas P. Weigel, Lisa Goddard, and Balakanapathy Rajaratnam
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Hannah Nissan, Katrin Burkart, Erin Coughlan de Perez, Maarten Van Aalst, and Simon Mason

Abstract

This paper proposes a heat-wave definition for Bangladesh that could be used to trigger preparedness measures in a heat early warning system (HEWS) and explores the climate mechanisms associated with heat waves. A HEWS requires a definition of heat waves that is both related to human health outcomes and forecastable. No such definition has been developed for Bangladesh. Using a generalized additive regression model, a heat-wave definition is proposed that requires elevated minimum and maximum daily temperatures over the 95th percentile for 3 consecutive days, confirming the importance of nighttime conditions for health impacts. By this definition, death rates increase by about 20% during heat waves; this result can be used as an argument for public-health interventions to prevent heat-related deaths. Furthermore, predictability of these heat waves exists from weather to seasonal time scales, offering opportunities for a range of preparedness measures. Heat waves are associated with an absence of normal premonsoonal rainfall brought about by anomalously strong low-level westerly winds and weak southerlies, detectable up to approximately 10 days in advance. This circulation pattern occurs over a background of drier-than-normal conditions, with below-average soil moisture and precipitation throughout the heat-wave season from April to June. Low soil moisture increases the odds of heat-wave occurrence for 10–30 days, indicating that subseasonal forecasts of heat-wave risk may be possible by monitoring soil-moisture conditions.

Open access
Simon J. Mason, Lisa Goddard, Nicholas E. Graham, Elena Yulaeva, Liqiang Sun, and Philip A. Arkin

The International Research Institute for Climate Prediction (IRI) was formed in late 1996 with the aim of fostering the improvement, production, and use of global forecasts of seasonal to interannual climate variability for the explicit benefit of society. The development of the 1997/98 El Niño provided an ideal impetus to the IRI Experimental Forecast Division (IRI EFD) to generate seasonal climate forecasts on an operational basis. In the production of these forecasts an extensive suite of forecasting tools has been developed, and these are described in this paper. An argument is made for the need for a multimodel ensemble approach and for extensive validation of each model's ability to simulate interannual climate variability accurately. The need for global sea surface temperature forecasts is demonstrated. Forecasts of precipitation and air temperature are presented in the form of “net assessments,” following the format adopted by the regional consensus forums. During the 1997/98 El Niño, the skill of the net assessments was greater than chance, except over Europe, and in most cases was an improvement over a forecast of persistence of the latest month's climate anomaly.

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Stefano Materia, Ángel G. Muñoz, M. Carmen Álvarez-Castro, Simon J. Mason, Frederic Vitart, and Silvio Gualdi

Abstract

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.

Open access
Stefano Materia, Ángel G. Muñoz, M. Carmen Álvarez-Castro, Simon J. Mason, Frederic Vitart, and Silvio Gualdi

Abstract

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.

Open access
Anthony G. Barnston, Simon J. Mason, Lisa Goddard, David G. DeWitt, and Stephen E. Zebiak

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.

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