The fascination of seasonal climate forecasting, of which El Niño forecasting is the prime example, comes from its multi-faceted character. Not only does it pose interesting new challenges for the climate scientific community but also it is naturally linked to a great variety of socio-economic applications. Seasonal climate forecasts are indeed becoming a most important element in some policy/decision making systems, especially within the context of climate change adaptation. Thus, seriously considering the management of risks posed by the variability of climate on the seasonal to interannual time scale is key to achieving the longer terms goals of climate change adaptation strategy. This review paper explores the main components needed to construct a seasonal forecasting system, from the physical basis of climate seasonal predictions, to the tools used for producing them, to the importance of assessing their skill, to their use in risk management decision-making. Future challenges are also examined. Copyright \copyright 2010 Royal Meteorological Society
(private-note)Discusses distinction between stat and dyn fcs, gives a good historical overview, and uses ENSO as a prime example.
Also uses MO seas fc of 2005-2006 winter as an important example, looking at impact on energy.
%0 Journal Article
%1 Troccoli2010Seasonal
%A Troccoli, Alberto
%D 2010
%I John Wiley & Sons, Ltd.
%J Met. Apps
%K ENSO MyECEMpaper communication energy review seasonal skill verification
%N 3
%P 251--268
%R 10.1002/met.184
%T Seasonal climate forecasting
%U http://dx.doi.org/10.1002/met.184
%V 17
%X The fascination of seasonal climate forecasting, of which El Niño forecasting is the prime example, comes from its multi-faceted character. Not only does it pose interesting new challenges for the climate scientific community but also it is naturally linked to a great variety of socio-economic applications. Seasonal climate forecasts are indeed becoming a most important element in some policy/decision making systems, especially within the context of climate change adaptation. Thus, seriously considering the management of risks posed by the variability of climate on the seasonal to interannual time scale is key to achieving the longer terms goals of climate change adaptation strategy. This review paper explores the main components needed to construct a seasonal forecasting system, from the physical basis of climate seasonal predictions, to the tools used for producing them, to the importance of assessing their skill, to their use in risk management decision-making. Future challenges are also examined. Copyright \copyright 2010 Royal Meteorological Society
@article{Troccoli2010Seasonal,
abstract = {The fascination of seasonal climate forecasting, of which El Ni\~{n}o forecasting is the prime example, comes from its multi-faceted character. Not only does it pose interesting new challenges for the climate scientific community but also it is naturally linked to a great variety of socio-economic applications. Seasonal climate forecasts are indeed becoming a most important element in some policy/decision making systems, especially within the context of climate change adaptation. Thus, seriously considering the management of risks posed by the variability of climate on the seasonal to interannual time scale is key to achieving the longer terms goals of climate change adaptation strategy. This review paper explores the main components needed to construct a seasonal forecasting system, from the physical basis of climate seasonal predictions, to the tools used for producing them, to the importance of assessing their skill, to their use in risk management decision-making. Future challenges are also examined. Copyright {\copyright} 2010 Royal Meteorological Society},
added-at = {2018-06-18T21:23:34.000+0200},
author = {Troccoli, Alberto},
biburl = {https://www.bibsonomy.org/bibtex/286b69d29d6ef1db08e97cb30afc16b76/pbett},
citeulike-article-id = {11732013},
citeulike-linkout-0 = {http://dx.doi.org/10.1002/met.184},
comment = {(private-note)Discusses distinction between stat and dyn fcs, gives a good historical overview, and uses ENSO as a prime example.
Also uses MO seas fc of 2005-2006 winter as an important example, looking at impact on energy.},
day = 1,
doi = {10.1002/met.184},
interhash = {f2e6f7372a2decafa5d13f713cb5d5c8},
intrahash = {86b69d29d6ef1db08e97cb30afc16b76},
journal = {Met. Apps},
keywords = {ENSO MyECEMpaper communication energy review seasonal skill verification},
month = sep,
number = 3,
pages = {251--268},
posted-at = {2016-05-06 22:34:10},
priority = {2},
publisher = {John Wiley \& Sons, Ltd.},
timestamp = {2020-04-15T09:29:49.000+0200},
title = {Seasonal climate forecasting},
url = {http://dx.doi.org/10.1002/met.184},
volume = 17,
year = 2010
}