This dataset series refers to the information on long-term fire weather forecast provided by the European Forest Fire Information System (EFFIS). ▷_How to cite: see below_◁
Long-term fire weather forecast attempts to provide useful information about the near-time climate that can be expected in the coming months. The seasonal forecast is not a weather forecast: weather can be considered as a snapshot of continually changing atmospheric conditions, whereas climate is better considered as the statistical summary of the weather events occurring in a longer time interval, up to several years. Near-time climate focuses on statistics about a given season instead of multiple years. In particular, long-term monthly forecast estimates temperature and rainfall anomalies that are expected to prevail over European and Mediterranean areas during the next four weeks (including the current week). Long-term seasonal forecast, instead, focuses on temperature and rainfall anomalies that are expected to prevail over European and Mediterranean areas during the next 7 months (including the current month). Despite the chaotic nature of the atmosphere, long term predictions are possible to some degree thanks to a number of components which themselves show variations on long time scales (seasons and years) and, to a certain extent, are predictable. The most important of these components is the ENSO (El Niño Southern Oscillation) cycle, which refers to the coherent, large-scale fluctuation of ocean temperatures, rainfall, atmospheric circulation, vertical motion and air pressure across the tropical Pacific. El Niño episodes (also called Pacific warm episodes) and La Niña episodes (also called Pacific cold episodes) represent opposite extremes of the ENSO cycle. The ENSO cycle is the largest known source of year-to-year climate variability.
Changes in Pacific sea surface temperature (SST) are not the only cause of predictable changes in the weather patterns. There are other causes of seasonal climate variability. Unusually warm or cold sea surface temperatures in the tropical Atlantic or Indian Ocean can cause major shifts in seasonal climate in nearby continents.
In addition to the tropical oceans, other factors that may influence seasonal climate are snow cover and soil wetness. All these factors affecting the atmospheric circulation constitute the basis of long-term predictions.
Overall, seasonal forecasting is justified by the long predictability of the oceanic circulation (of the order of several months) and by the fact that the variability in tropical SSTs has a significant global impact on the atmospheric circulation.
Seasonal forecasts provide a range of possible climate changes that are likely to occur in the season ahead. It is important to bear in mind that, because of the chaotic nature of the atmospheric circulation, it is not possible to predict the daily weather variations at a specific location months in advance. It is not even possible to predict exactly the average weather, such as the average temperature for a given month.
How to cite - When using these data, please cite the relevant data sources. A suggested citation is included in the following:
San-Miguel-Ayanz, J., Houston Durrant, T., Boca, R., Libertà, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Artés Vivancos, T., Schulte, E., Loffler, P., Benchikha, A., Abbas, M., Humer, F., Konstantinov, V., Pešut, I., Petkoviček, S., Papageorgiou, K., Toumasis, I., Kütt, V., Kõiv, K., Ruuska, R., Anastasov, T., Timovska, M., Michaut, P., Joannelle, P., Lachmann, M., Pavlidou, K., Debreceni, P., Nagy, D., Nugent, C., Di Fonzo, M., Leisavnieks, E., Jaunķiķis, Z., Mitri, G., Repšienė, S., Assali, F., Mharzi Alaoui, H., Botnen, D., Piwnicki, J., Szczygieł, R., Janeira, M., Borges, A., Sbirnea, R., Mara, S., Eritsov, A., Longauerová, V., Jakša, J., Enriquez, E., Lopez, A., Sandahl, L., Reinhard, M., Conedera, M., Pezzatti, B., Dursun, K. T., Baltaci, U., Moffat, A., 2017. Forest fires in Europe, Middle East and North Africa 2016. Publications Office of the European Union, Luxembourg. ISBN:978-92-79-71292-0, https://doi.org/10.2760/17690
San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., 2013. The European Forest Fire Information System in the context of environmental policies of the European Union. Forest Policy and Economics 29, 19-25. https://doi.org/10.1016/j.forpol.2011.08.012
San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., Strobl, P., Libertà, G., Giovando, C., Boca, R., Sedano, F., Kempeneers, P., McInerney, D., Withmore, C., de Oliveira, S. S., Rodrigues, M., Houston Durrant, T., Corti, P., Oehler, F., Vilar, L., Amatulli, G., 2012. Comprehensive monitoring of wildfires in Europe: the European Forest Fire Information System (EFFIS). In: Tiefenbacher, J. (Ed.), Approaches to Managing Disaster - Assessing Hazards, Emergencies and Disaster Impacts. InTech, Ch. 5. http://doi.org/10.5772/28441
Rodwell, M. J., Doblas-Reyes, F. J., 2006. Medium-range, monthly, and seasonal prediction for Europe and the use of forecast information. Journal of Climate 19 (23), 6025-6046. https://doi.org/10.1175/jcli3944.1
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- Former Yugoslav Republic of Macedonia, San Marino, Armenia, Iraq, Mali, Monaco, Saudi Arabia, Romania, Slovakia, Belgium, Portugal, Algeria, Norway, Bosnia and Herzegovina, Montenegro, Slovenia, Serbia, Tunisia, Belarus, Croatia, Sweden, Mauritania, Czech Republic, Faroe Islands, Switzerland, Vatican City, France, Estonia, Georgia, Poland, Albania, Denmark, Austria, United Kingdom, Germany, Cyprus, Jersey, Greenland, Gibraltar, Italy, Iceland, Lebanon, Egypt, Western Sahara, Bulgaria, Hungary, Finland, Libya, Jordan, Greece, Netherlands, Luxembourg, Liechtenstein, Andorra, Ireland, Malta, Azerbaijan, Isle of Man, Guernsey, Åland Islands, Iran, Morocco, Israel, Latvia, Lithuania, Ukraine, Russia, Palestinian territory, Moldova, Turkey, Syria, Kosovo, Spain