Introduction

Institutions/Projects in the region working on S2S

• Climate Services Through Knowledge Co-Production (CLIMAX): An inter- and trans-disciplinary framework based on a European-South American research cooperation is implemented to underpin climate services in South America. Climate variability patterns linking the South American Monsoon region, including Amazonia, with south eastern South America, influence climate extremes and impact several societal sectors. More than 200 million people live in the region that is one of the world largest agricultural producing region and where the second world largest hydroelectric power plant is situated. Besides recent progress, further efforts are needed to better understand and predict regional climate variability. The project contributes to the implementation of the "Southern South America Regional Climate Centre (CRC-SAS)", and it includes actors from the national meteorological services, agriculture and energy stakeholders and organizations. Innovative technologies will be codeveloped to produce tools and products for the CRC-SAS, with focus on both agriculture and hydropower sectors. More information: http://www.climax-sa.org/index.php

• Drought Information System for southern South America (SISSA): SISSA provides tools and information on droughts and their impacts to governments, non-governmental and private institutions, and individuals. SISSA is a virtual institution that operates within the framework of the Regional Climate Center for southern South America (CRC-SAS). More information and forecasts: https://sissa.crc-sas.org/pronosticos-de-lluvia-y-sequia/pronostico-de-sequia-a-15-dias-chirps-gefs/ 

• The Chilean NextGen: Chile’s National Meteorological Service (DMC, as per its initials in Spanish) had implemented the Next Generation of Seasonal Climate Forecasts (NextGen), developed by the International Research Institute for Climate and Society (IRI). NextGen is a multi-model, statistically calibrated seasonal forecast system that takes advantage of the expertise of forecasters and local scientists. It is a modern forecast methodology that validates global models based on local experiences and climate data to provide more robust and fine-tuned forecasts for specific areas of interest. The predictions are to be used by decision-makers in sectors such as agriculture and food security, energy, water management, disaster prevention, health, and others. More information: https://globalcenters.columbia.edu/news/chile-preparing-implement-columbia-led-nextgen-forecast-system

  The seasonal forecast is updated every month and presented in a public discussion and a bulletin. Monthly bulletin (in spanish): https://climatologia.meteochile.gob.cl/application/publicaciones/boletinTendenciasClimaticas

• The Colombian NextGen: Climate scientists from the International Research Institute for Climate and Society and Colombia’s national meteorological service (called the Institute of Hydrology, Meteorology and Environmental Studies and known by its Spanish acronym, IDEAM develop a more advanced seasonal forecasting system called ‘the Next Generation of Seasonal Climate Forecasts,’ or NextGen. NextGen is a new set of high-quality, flexible, and tailored predictions made by Colombians for Colombians, assisted and funded by the IRI-led Adapting Agriculture to Climate Today, for Tomorrow (ACToday) project, part of Columbia World Projects. More information: https://iri.columbia.edu/news/developing-colombias-next-generation-of-seasonal-forecasts/

• Regional Climate Centers:

  Regional Climate Center for Western South-America (CRC-OSA): https://crc-osa.ciifen.org/

  Regional Climate Center for Southern South-America (CRC-SAS): https://www.crc-sas.org/en/

• Seasonal Climate Forecast System for Bolivia: Developed by the International Center for Research on the El Niño Phenomenon (CIIFEN) and the Bolivian Ministry of the Environment and Water (MMAyA), the Bolivian Seasonal Climate Forecasting System (SistPronClim-Bolivia) uses statistical calibration processes of the global model products, based onspatio-temporal patterns, capable of timely identification of risk situations due to deficit and excess precipitation, in the same way, temperature values ​​that are below or above threshold values ​​through indicators adapted to the Bolivian context, in order to feed the systems surveillance, early warning and information to the general public at the local and regional level, supporting preparedness plans and risk management policies.The project is based on the fact that climate prediction is used as an essential tool for takingmedium and long-term decisions, planning, early warning of potential threats such as drought, andadaptation to climate variability and change. More information and forecasts: http://prediccion.senamhi.gob.bo:3001/

  More information about CIIFEN: https://ciifen.org/

• Subseasonal to Seasonal Forecasts of the Argentine National Meteorological Service:  The SMN (for its acronym in Spanish) periodically conducts climate monitoring and forecasting from the sub-seasonal to the seasonal scale. More information (in Spanish): https://www.smn.gob.ar/clima/informes-anteriores

  Subseasonal (weekly) forecast: https://www.smn.gob.ar/clima/perspectiva

Publications

• Barrett, B. S., Carrasco, J. F., & Testino, A. P. (2012). Madden–Julian oscillation (MJO) modulation of atmospheric circulation and Chilean winter precipitation. Journal of Climate, 25(5), 1678-1688.

• Barrett, B. S., & Hameed, S. (2017). Seasonal variability in precipitation in central and southern Chile: Modulation by the South Pacific high. Journal of Climate, 30(1), 55-69.

• Barros, V. R., Grimm, A. M., & Doyle, M. E. (2002). Relationship between temperature and circulation in Southeastern South America and its influence from El Ninño and La Ninña events. Journal of the Meteorological Society of Japan. Ser. II, 80(1), 21-32.

• Barrucand, M., Rusticucci, M., & Vargas, W. (2008). Temperature extremes in the south of South America in relation to Atlantic Ocean surface temperature and Southern Hemisphere circulation. Journal of Geophysical Research: Atmospheres, 113(D20).

• Blázquez, J., & Solman, S. A. (2017). Interannual variability of the frontal activity in the Southern Hemisphere: relationship with atmospheric circulation and precipitation over southern South America. Climate Dynamics, 48(7-8), 2569-2579.

• Cerne, S. B., & Vera, C. S. (2011). Influence of the intraseasonal variability on heat waves in subtropical South America. Climate Dynamics, 36(11), 2265-2277.

• Coelho, C. A. (2013). Comparative skill assessment of consensus and physically based tercile probability seasonal precipitation forecasts for Brazil. Meteorological Applications, 20(2), 236-245.

• Collazo, S., Barrucand, M., & Rusticucci, M. (2019). Summer seasonal predictability of warm days in Argentina: statistical model approach. Theoretical and Applied Climatology, 138(3), 1853-1876.

• Collazo, S., Barrucand, M., & Rusticucci, M. (2019). Variability and predictability of winter cold nights in Argentina. Weather and Climate Extremes, 26, 100236.

• Delorit, J., Gonzalez Ortuya, E. C., & Block, P. (2017). Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile. Hydrology and Earth System Sciences, 21(9), 4711-4725.

• Gubler, S., Sedlmeier, K., Bhend, J., Avalos, G., Coelho, C. A. S., Escajadillo, Y., Jacques-Coper, M., Martinez, R., Schwierz, C., de Skansi, M., & Spirig, Ch. (2020). Assessment of ECMWF SEAS5 seasonal forecast performance over South America. Weather and Forecasting, 35(2), 561-584.

• Jacques‐Coper, M., Veloso‐Aguila, D., Segura, C., & Valencia, A. (2021). Intraseasonal teleconnections leading to heat waves in central Chile. International Journal of Climatology.

• Marín, J. C., & Barrett, B. S. (2017). Seasonal and intraseasonal variability of precipitable water vapour in the Chajnantor plateau, Chile. International Journal of Climatology, 37, 958-971.

• Meza, F. J., & Wilks, D. S. (2003). Value of operational forecasts of seasonal average sea surface temperature anomalies for selected rain-fed agricultural locations of Chile. Agricultural and Forest Meteorology, 116(3-4), 137-158.

• Montecinos, A., Díaz, A., & Aceituno, P. (2000). Seasonal diagnostic and predictability of rainfall in subtropical South America based on tropical Pacific SST. Journal of Climate, 13(4), 746-758.

• Montecinos, A., & Aceituno, P. (2003). Seasonality of the ENSO-related rainfall variability in central Chile and associated circulation anomalies. Journal of climate, 16(2), 281-296.

• Montecinos, A., Purca, S., & Pizarro, O. (2003). Interannual‐to‐interdecadal sea surface temperature variability along the western coast of South America. Geophysical Research Letters, 30(11).

• Osman, M., & Vera, C. S. (2017). Climate predictability and prediction skill on seasonal time scales over South America from CHFP models. Climate Dynamics, 49(7), 2365-2383.

• Rusticucci, M., & Vargas, W. (2002). Cold and warm events over Argentina and their relationship with the ENSO phases: risk evaluation analysis. International Journal of Climatology: A Journal of the Royal Meteorological Society, 22(4), 467-483.

• Rusticucci, M., Barrucand, M., & Collazo, S. (2017). Temperature extremes in the Argentina central region and their monthly relationship with the mean circulation and ENSO phases. International Journal of Climatology, 37(6), 3003-3017.

• Rutllant, J., & Fuenzalida, H. (1991). Synoptic aspects of the central Chile rainfall variability associated with the Southern Oscillation. International Journal of Climatology, 11(1), 63-76.

• Verbist, K., Robertson, A. W., Cornelis, W. M., & Gabriels, D. (2010). Seasonal predictability of daily rainfall characteristics in central northern Chile for dry-land management. Journal of Applied Meteorology and Climatology, 49(9), 1938-1955.

• Xue, J., Luo, J. J., Yuan, C., & Yamagata, T. (2020). Discovery of Chile Niño/Niña. Geophysical Research Letters, 47(5), no-no.