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) based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) record ( Uppala et al. 2005 ). The much higher daily observed values for summer 2003 are shown by the dotted curve. Horizontal dashed and dotted lines show the corresponding weekly mean and monthly mean values. Important questions are how well was this 2003 event “predicted,” and what impact did the predictions have on decision making? The solid black curve shows a single high-resolution forecast
) based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) record ( Uppala et al. 2005 ). The much higher daily observed values for summer 2003 are shown by the dotted curve. Horizontal dashed and dotted lines show the corresponding weekly mean and monthly mean values. Important questions are how well was this 2003 event “predicted,” and what impact did the predictions have on decision making? The solid black curve shows a single high-resolution forecast
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
1. Introduction Several operational forecast centers issue predictions of weather and climate. The availability of multiple forecasts from different institutions raises the question of whether the forecasts can be combined to increase skill and reliability. Although several multimodel prediction systems have been proposed in the past, the relatively short datasets available for calibration lead to serious problems with overfitting. Consequently, a variety of approaches have been proposed to
1. Introduction Several operational forecast centers issue predictions of weather and climate. The availability of multiple forecasts from different institutions raises the question of whether the forecasts can be combined to increase skill and reliability. Although several multimodel prediction systems have been proposed in the past, the relatively short datasets available for calibration lead to serious problems with overfitting. Consequently, a variety of approaches have been proposed to
1. Introduction Skilful climate predictions provide useful scientific information to planners and operational agencies to plan and develop contingency measures and strategies to deal with the adverse conditions. In this regard, seasonal to interannual (long lead) climate forecasts are issued on a regular basis by various national and international agencies using both coupled ocean–atmosphere general circulation models (CGCMs) ( Saha et al. 2006 ; Weisheimer et al. 2009 ; Palmer et al. 2004
1. Introduction Skilful climate predictions provide useful scientific information to planners and operational agencies to plan and develop contingency measures and strategies to deal with the adverse conditions. In this regard, seasonal to interannual (long lead) climate forecasts are issued on a regular basis by various national and international agencies using both coupled ocean–atmosphere general circulation models (CGCMs) ( Saha et al. 2006 ; Weisheimer et al. 2009 ; Palmer et al. 2004
1. Introduction Seasonal climate forecasts are in high demand in Australia, and seasonal rainfall forecasts in particular are sought by farmers, water managers, and others throughout the year. The Australian Bureau of Meteorology provides probabilistic forecasts of seasonal rainfall using a statistical prediction system based on sea surface temperature (SST) anomaly patterns over the Indian and Pacific Oceans ( Drosdowsky and Chambers 2001 ; Fawcett et al. 2005 ). Different forecast models are
1. Introduction Seasonal climate forecasts are in high demand in Australia, and seasonal rainfall forecasts in particular are sought by farmers, water managers, and others throughout the year. The Australian Bureau of Meteorology provides probabilistic forecasts of seasonal rainfall using a statistical prediction system based on sea surface temperature (SST) anomaly patterns over the Indian and Pacific Oceans ( Drosdowsky and Chambers 2001 ; Fawcett et al. 2005 ). Different forecast models are
1. Introduction Ensemble forecasts of seasonal precipitation from coupled ocean–atmosphere general circulation models (GCMs) have mostly replaced traditional statistical forecasts as the basis of operational outlooks issued by many national weather services. For example, the National Centers for Environmental Prediction (NCEP) in the United States has operated its Climate Forecast System (CFS) since 2004 ( Saha et al. 2014 ), the European Centre for Medium-Range Weather Forecasts (ECMWF) has
1. Introduction Ensemble forecasts of seasonal precipitation from coupled ocean–atmosphere general circulation models (GCMs) have mostly replaced traditional statistical forecasts as the basis of operational outlooks issued by many national weather services. For example, the National Centers for Environmental Prediction (NCEP) in the United States has operated its Climate Forecast System (CFS) since 2004 ( Saha et al. 2014 ), the European Centre for Medium-Range Weather Forecasts (ECMWF) has
al. 2003 ). Especially, a general circulation model (GCM) simulation with prognostic SST anomalies by Waliser et al. (1999) confirms that the frictional wave–conditional instability of the second kind (CISK) process is operative on the equator as the maintenance and propagation mechanisms of the MJO. Compared to the worldwide influence of the MJO on local weather and climate, only limited success has been realized in skillfully forecasting the oscillation evolution, especially using GCMs
al. 2003 ). Especially, a general circulation model (GCM) simulation with prognostic SST anomalies by Waliser et al. (1999) confirms that the frictional wave–conditional instability of the second kind (CISK) process is operative on the equator as the maintenance and propagation mechanisms of the MJO. Compared to the worldwide influence of the MJO on local weather and climate, only limited success has been realized in skillfully forecasting the oscillation evolution, especially using GCMs
1. Introduction Linear regression has long played an important role in weather and climate forecasting, both in empirical prediction models and statistical postprocessing of physics-based prediction model output (e.g., Glahn and Lowry 1972 ; Penland and Magorian 1993 ). Here we focus on the use of regression to correct and calibrate climate forecasts, although our findings are generally applicable to all regression-based forecasts. A regression between past model output and observations
1. Introduction Linear regression has long played an important role in weather and climate forecasting, both in empirical prediction models and statistical postprocessing of physics-based prediction model output (e.g., Glahn and Lowry 1972 ; Penland and Magorian 1993 ). Here we focus on the use of regression to correct and calibrate climate forecasts, although our findings are generally applicable to all regression-based forecasts. A regression between past model output and observations
a , hereafter CS2009) . Such weights are termed dynamic model combination weights [or dynamic weights (DW)]. The improvement resulting from dynamic weight for 3-month-ahead forecasts of the Niño-3.4 index in contrast to temporally invariant weights [or static weights (SW)] has been documented in CS2009 . While the CS2009 study was limited to the prediction of a univariate response (Niño-3.4), this paper extends the method for prediction of gridded global sea surface temperature anomalies
a , hereafter CS2009) . Such weights are termed dynamic model combination weights [or dynamic weights (DW)]. The improvement resulting from dynamic weight for 3-month-ahead forecasts of the Niño-3.4 index in contrast to temporally invariant weights [or static weights (SW)] has been documented in CS2009 . While the CS2009 study was limited to the prediction of a univariate response (Niño-3.4), this paper extends the method for prediction of gridded global sea surface temperature anomalies
that the characteristics of synoptic fluctuations about the mean flow depend on the mean flow itself ( Chang et al. 2002 ; DelSole 2004b ), suggesting that a more accurate mean flow would translate into more accurate synoptic forecasts. Similarly, idealized models of ENSO consistently show that ENSO variability depends on the structure of the mean thermocline ( Kirtman and Schopf 1998 ; Fedorov et al. 2003 ), suggesting that coupled atmosphere–ocean models that simulate the mean thermocline more
that the characteristics of synoptic fluctuations about the mean flow depend on the mean flow itself ( Chang et al. 2002 ; DelSole 2004b ), suggesting that a more accurate mean flow would translate into more accurate synoptic forecasts. Similarly, idealized models of ENSO consistently show that ENSO variability depends on the structure of the mean thermocline ( Kirtman and Schopf 1998 ; Fedorov et al. 2003 ), suggesting that coupled atmosphere–ocean models that simulate the mean thermocline more
