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centers seasonal forecasts are done in a “burst” mode, and a large ensemble of seasonal forecasts is initiated on a particular calendar day. Examples of operational centers that run seasonal prediction systems in burst mode include the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Australian Bureau of Meteorology (BoM), and the ensemble size of such prediction systems is typically 30–50. In contrast with the seasonal forecasts run in the burst mode, at some other operational
centers seasonal forecasts are done in a “burst” mode, and a large ensemble of seasonal forecasts is initiated on a particular calendar day. Examples of operational centers that run seasonal prediction systems in burst mode include the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Australian Bureau of Meteorology (BoM), and the ensemble size of such prediction systems is typically 30–50. In contrast with the seasonal forecasts run in the burst mode, at some other operational
1. Introduction The future state of the atmosphere is influenced by chaotic internal dynamics (e.g., Lorenz 1969 ; Lau 1981 ; Hendon and Hartmann 1985 ; Branstator 1995 ) that can amplify the uncertainties in forecast system initialization and formulation to produce different possible seasonal climate states. Seasonal forecasting systems employ ensembles of simulations to sample these uncertainties, and the prediction of a meteorological quantity is most appropriately viewed as a
1. Introduction The future state of the atmosphere is influenced by chaotic internal dynamics (e.g., Lorenz 1969 ; Lau 1981 ; Hendon and Hartmann 1985 ; Branstator 1995 ) that can amplify the uncertainties in forecast system initialization and formulation to produce different possible seasonal climate states. Seasonal forecasting systems employ ensembles of simulations to sample these uncertainties, and the prediction of a meteorological quantity is most appropriately viewed as a
austral summer season, with early (delayed) onset dates observed during both phases of ENSO. Though these studies made an effort to understand the physical mechanisms for the onset of rains over South Africa and its variability, there have been few efforts to forecast the onset of the summer rains over South Africa at seasonal time scales. We try to fill this gap by using seasonal precipitation forecasts from the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2
austral summer season, with early (delayed) onset dates observed during both phases of ENSO. Though these studies made an effort to understand the physical mechanisms for the onset of rains over South Africa and its variability, there have been few efforts to forecast the onset of the summer rains over South Africa at seasonal time scales. We try to fill this gap by using seasonal precipitation forecasts from the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2
1. Introduction Tropical cyclones (TCs; see the appendix for a list of the acronyms used in this paper) are one of the most devastating types of natural disasters. Seasonal forecasts of TC activity could help the preparedness of coastal populations for an upcoming TC season and reduce economical and human losses. Currently, many institutions issue operational seasonal TC forecasts for various regions. In most cases, these are statistical forecasts, such as the Atlantic hurricane outlooks
1. Introduction Tropical cyclones (TCs; see the appendix for a list of the acronyms used in this paper) are one of the most devastating types of natural disasters. Seasonal forecasts of TC activity could help the preparedness of coastal populations for an upcoming TC season and reduce economical and human losses. Currently, many institutions issue operational seasonal TC forecasts for various regions. In most cases, these are statistical forecasts, such as the Atlantic hurricane outlooks
1. Introduction Seasonal predictions using comprehensive coupled dynamical models of the atmosphere, oceans, and land surface have now been operational for nearly a decade. These predictions have been shown to exhibit useful skill for certain regions and seasons ( Graham et al. 2005 ) and socioeconomic benefits are derived from them in areas such as food production and health ( Morse et al. 2005 ; Challinor et al. 2005 ). Furthermore, seasonal and decadal forecast systems are crucial in the
1. Introduction Seasonal predictions using comprehensive coupled dynamical models of the atmosphere, oceans, and land surface have now been operational for nearly a decade. These predictions have been shown to exhibit useful skill for certain regions and seasons ( Graham et al. 2005 ) and socioeconomic benefits are derived from them in areas such as food production and health ( Morse et al. 2005 ; Challinor et al. 2005 ). Furthermore, seasonal and decadal forecast systems are crucial in the
1. Introduction Seasonal forecasting of precipitation in the austral summer months, from December to February (DJF), is beneficial for the agro-based regions of northern Australia (landmass to the north of 25°S). During DJF, northern parts of Australia experience monsoon climate ( Wheeler and McBride 2005 ; Hendon et al. 2012 ) with reversal of low-level winter winds from easterlies to westerlies and increased precipitation. In fact, northern Australia receives most of its annual rainfall
1. Introduction Seasonal forecasting of precipitation in the austral summer months, from December to February (DJF), is beneficial for the agro-based regions of northern Australia (landmass to the north of 25°S). During DJF, northern parts of Australia experience monsoon climate ( Wheeler and McBride 2005 ; Hendon et al. 2012 ) with reversal of low-level winter winds from easterlies to westerlies and increased precipitation. In fact, northern Australia receives most of its annual rainfall
months of the heat wave when monthly means are used. Although the extreme nature of the heat wave means that this is not necessarily an indication of a general lack of skill, we will highlight the potential for future forecast improvements at these ranges. (These monthly averages come from the ECMWF “seasonal” coupled ocean–atmosphere ensemble forecasting system.) At forecast ranges beyond a few days, predictability of the timing and existence of individual atmospheric synoptic systems becomes less
months of the heat wave when monthly means are used. Although the extreme nature of the heat wave means that this is not necessarily an indication of a general lack of skill, we will highlight the potential for future forecast improvements at these ranges. (These monthly averages come from the ECMWF “seasonal” coupled ocean–atmosphere ensemble forecasting system.) At forecast ranges beyond a few days, predictability of the timing and existence of individual atmospheric synoptic systems becomes less
mean from the forecast and verification separately and then compare the corresponding anomalies. Numerous studies have confirmed that anomalies forecasted by dynamical models have skill even on seasonal time scales [see the July 2000 issue of the Quarterly Journal of the Royal Meteorological Society and Palmer and Hagedorn (2006) ]. In most prediction studies, however, the specific climatological mean that is subtracted from the forecast often is discarded without commenting on how close it is
mean from the forecast and verification separately and then compare the corresponding anomalies. Numerous studies have confirmed that anomalies forecasted by dynamical models have skill even on seasonal time scales [see the July 2000 issue of the Quarterly Journal of the Royal Meteorological Society and Palmer and Hagedorn (2006) ]. In most prediction studies, however, the specific climatological mean that is subtracted from the forecast often is discarded without commenting on how close it is
1. Introduction The North American Multi-Model Ensemble (NMME; Kirtman et al. 2014 ) system incorporates seasonal forecasts of different hydroclimatic variables from multiple U.S. and Canadian models. These forecasts are invaluable for a plethora of scientific and operational applications, including precipitation and temperature forecasting ( Setiawan et al. 2017 ; Wang 2014 ; Krakauer 2017 ; Cash et al. 2019 ; Wanders et al. 2017 ), prediction of extremes ( Yuan et al. 2015 ), atmospheric
1. Introduction The North American Multi-Model Ensemble (NMME; Kirtman et al. 2014 ) system incorporates seasonal forecasts of different hydroclimatic variables from multiple U.S. and Canadian models. These forecasts are invaluable for a plethora of scientific and operational applications, including precipitation and temperature forecasting ( Setiawan et al. 2017 ; Wang 2014 ; Krakauer 2017 ; Cash et al. 2019 ; Wanders et al. 2017 ), prediction of extremes ( Yuan et al. 2015 ), atmospheric
1. Introduction Seasonal climate forecasts, typically issued up to several months in advance, have a wide range of applications, including agricultural planning ( Klemm and McPherson 2017 ; Parton et al. 2019 ), predicting the risk of extreme events and environmental hazards ( Hao et al. 2018 ; Turco et al. 2018 ), and anticipating energy supply and demand ( Clark et al. 2017 ; Thornton et al. 2019 ). The skill in seasonal climate forecasts originates from processes with significant memory
1. Introduction Seasonal climate forecasts, typically issued up to several months in advance, have a wide range of applications, including agricultural planning ( Klemm and McPherson 2017 ; Parton et al. 2019 ), predicting the risk of extreme events and environmental hazards ( Hao et al. 2018 ; Turco et al. 2018 ), and anticipating energy supply and demand ( Clark et al. 2017 ; Thornton et al. 2019 ). The skill in seasonal climate forecasts originates from processes with significant memory
