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South America is a vast continent characterized by diverse atmospheric phenomena and climate regimes. In this context, seasonal climate predictions are helpful for decision-making in several relevant socioeconomic segments in this territory, such as agriculture and energy generation. Thus, the present work evaluates the performance of ECMWF-SEAS5 in simulating the South American precipitation regimes by applying a non-hierarchical clustering technique. In addition, the study describes the main atmospheric systems that cause precipitation in each cluster and updates a previous work performed in South America in 2010. As a result, ECMWF-SEAS5 simulates (with good correspondence) the eight climate regimes identified in the analysis of precipitation from the Climate Prediction Center (CPC). Moreover, ECMWF-SEAS5 has a satisfactory ability in representing the rainfall regime in low and medium climate predictability regions, such as central and southern South America. ECMWF-SEAS5 has good performance in the climate characterization of South America and it gives us confidence in using its seasonal climate predictions throughout the continent. [ABSTRACT FROM AUTHOR]

This study aims to assess the contribution to prediction performance of dynamically downscaling seasonal precipitation for the Brazilian Northeast region (NEB) produced in February with a global climate model for the austral autumn (March-April-May). This season includes the peak precipitation period of a large portion of this region. To achieve this aim, 32 years of hindcasts for the period 1979–2010 were produced with a regional model known as the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) run at 30 km spatial resolution, which was unidirectional nested to a global model run at approximately 200 km spatial resolution. The used global model was the Atmospheric General Circulation Models of the Brazilian Center for Weather Forecast and Climate Studies (CPTEC/AGCM), which provided initial and lateral boundary conditions for BRAMS. Hindcast ensembles of 10 members were produced with both the global and the regional models, simulating an operational prediction environment using persisted sea surface temperature (SST) anomalies observed in the previous January of each of the 32 hindcast years as oceanic boundary conditions. A comparative deterministic and probabilistic performance assessment of the two models was conducted. The deterministic assessment included the investigation of the mean error (bias) of the produced seasonal precipitation hindcasts, the degree of linear association between the observed and predicted precipitation anomalies measuring phase error through the correlation of the ensemble means with the corresponding observations, and amplitude error investigated through the ratio between the hindcast ensemble mean and observed variances. The probabilistic assessment was performed investigation the reliability, resolution, and discrimination characteristics of tercile probability predictions for the above normal and below normal categories, and also for probabilistic predictions for the occurrence of the symmetric event positive (or negative) precipitation anomaly. The two investigated modeling approaches showed similar performance, with an indication for dynamically downscaled predictions to have reduced amplitude error and slightly improved reliability and resolution compared to the predictions produced with the global model.

Seasonal predictions leverage on predictable or persistent components of the Earth system that can modify the state of the atmosphere. The land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of the snow in recent dynamical seasonal forecasts is therefore unclear. Here we assess the role of Autumn Eurasian snow cover in a set of five operational seasonal forecasts with large ensemble size and high resolution and with the help of targeted idealised simulations. Forecast systems reproduce realistically regional changes of the surface energy balance. Retrospective forecasts and idealised sensitivity experiments identify a coherent change of the circulation in the Northern Hemisphere. The main features of the atmospheric response are a wave-train downstream over the Pacific and North America and a signal in the Arctic. The latter does not emerge in reanalysis data but is compatible with a lagged but weak and fast feedback from the snow to the Arctic Oscillation. [ABSTRACT FROM AUTHOR]

Summary: We investigate the viability of using El Niño–Southern Oscillation (ENSO) and sea surface temperature (SST) data to predict seasonal streamflow for one of the major rivers in Sri Lanka, the Kelani, using correlation analysis, contingency tables, and principal component analysis. The agricultural seasons in Sri Lanka are Yala (April–September) and Maha (October–March). The correlation between the Kelani River streamflow during Yala and ENSO indices (r =−0.41) is significant at 99% level. In addition, the Kelani streamflow during Yala has a correlation with the Central Indian Ocean SST (r =−0.40) that is also significant at the 99% level. The first principal component of the Indo-Pacific Ocean SST is reminiscent of the SST associated with the ENSO mode. A prediction scheme based on this mode for the streamflow during Yala has a skill characterized by a correlation of 0.5 in a cross-validated mode. The prediction of streamflow during Maha is best carried out separately for the two halves of the season. During the El Niño phase, the rainfall during Maha is enhanced during the first half of the season (October–December) and diminished in the second half (January–February). Rainfall rather than streamflow has a better relationship with ENSO from October to December. During the second half of the Maha season, rainfall declines with both warm and cold ENSO phases and any prediction scheme has to take into account this non-linear relationship. Overall, useful skill for seasonal streamflow predictions has been demonstrated for the Yala season and skill for seasonal rainfall predictions for the first and second half of the Maha season has been elucidated. [Copyright &y& Elsevier]