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1. Introduction Decadal climate prediction is an evolving branch of climate science that fills the gap between seasonal climate forecasts and multidecadal-to-century projections of climate change. For seasonal climate forecasts, climate models are initialized to observation-based current conditions and are run out from months to a year. For multidecadal-to-century projections of climate change, climate models are run from a randomly selected preindustrial state and extended into the future
1. Introduction Decadal climate prediction is an evolving branch of climate science that fills the gap between seasonal climate forecasts and multidecadal-to-century projections of climate change. For seasonal climate forecasts, climate models are initialized to observation-based current conditions and are run out from months to a year. For multidecadal-to-century projections of climate change, climate models are run from a randomly selected preindustrial state and extended into the future
times of one to eight months over the Americas, with the goal of understanding the strengths and weaknesses of current models, and possibly more fundamental limitations to the ability to predict. We will distinguish present-day prediction skill (what we can do now) from “predictability” (what we can do ultimately). To our knowledge, the notion of predictability under perfect model assumptions has not yet been applied to extremes. Extreme climate events are a subject of increasing attention
times of one to eight months over the Americas, with the goal of understanding the strengths and weaknesses of current models, and possibly more fundamental limitations to the ability to predict. We will distinguish present-day prediction skill (what we can do now) from “predictability” (what we can do ultimately). To our knowledge, the notion of predictability under perfect model assumptions has not yet been applied to extremes. Extreme climate events are a subject of increasing attention
1. Introduction Because of the imperfect model structures such as model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters, a coupled climate model is biased so that the model climate tends to drift away from the real world ( Delworth et al. 2006 ; Collins et al. 2006 ). To obtain the initial conditions from which a model prediction can be started with the observed state, the measured data in the climate observing system are
1. Introduction Because of the imperfect model structures such as model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters, a coupled climate model is biased so that the model climate tends to drift away from the real world ( Delworth et al. 2006 ; Collins et al. 2006 ). To obtain the initial conditions from which a model prediction can be started with the observed state, the measured data in the climate observing system are
1. Introduction Prediction of the Asian summer monsoon, which affects about half of the population of the world and is linked to many natural disasters such as droughts and floods, has long been a challenging component in operational weather and climate predictions, both statistical and dynamical, in many countries. The challenge comes from the complex variations of the monsoon, which interacts strongly with oceanic and land surface processes and large-scale atmospheric patterns of natural
1. Introduction Prediction of the Asian summer monsoon, which affects about half of the population of the world and is linked to many natural disasters such as droughts and floods, has long been a challenging component in operational weather and climate predictions, both statistical and dynamical, in many countries. The challenge comes from the complex variations of the monsoon, which interacts strongly with oceanic and land surface processes and large-scale atmospheric patterns of natural
decadal prediction experiments are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012 ) and have shown an improved prediction skill in several regions around the globe compared with the predictions of the CMIP5’s long-term climate projection experiments ( Doblas-Reyes et al. 2013 ; Müller et al. 2012 ; Pohlmann et al. 2013 ; Müller et al. 2014 ). A demand for decadal climate predictions also exists ( Cane 2010 ). To provide meaningful information, climate model
decadal prediction experiments are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012 ) and have shown an improved prediction skill in several regions around the globe compared with the predictions of the CMIP5’s long-term climate projection experiments ( Doblas-Reyes et al. 2013 ; Müller et al. 2012 ; Pohlmann et al. 2013 ; Müller et al. 2014 ). A demand for decadal climate predictions also exists ( Cane 2010 ). To provide meaningful information, climate model
1. Introduction Creating skillful decadal climate predictions represents a major challenge ( Meehl et al. 2009 ) because decadal prediction lies at the intersection of initial-value problems (as in seasonal forecasts) and boundary-value problems (as in long-term climate projections). To predict near-term climate changes, we have to capture both the externally forced and the internally generated climate signals. For this, the coupled climate models need to be initialized from the best estimate
1. Introduction Creating skillful decadal climate predictions represents a major challenge ( Meehl et al. 2009 ) because decadal prediction lies at the intersection of initial-value problems (as in seasonal forecasts) and boundary-value problems (as in long-term climate projections). To predict near-term climate changes, we have to capture both the externally forced and the internally generated climate signals. For this, the coupled climate models need to be initialized from the best estimate
As Earth’s climate changes rapidly due to anthropogenic activity, predictive climate information has become increasingly crucial to help society prepare for these changes and attendant worsening of weather and climate extremes. Such information has been available from internationally coordinated sources in the form of seasonal to multiseasonal predictions issued monthly, and long-term climate projections to 2100 and beyond. The latter encompass the current epoch but do not constrain the
As Earth’s climate changes rapidly due to anthropogenic activity, predictive climate information has become increasingly crucial to help society prepare for these changes and attendant worsening of weather and climate extremes. Such information has been available from internationally coordinated sources in the form of seasonal to multiseasonal predictions issued monthly, and long-term climate projections to 2100 and beyond. The latter encompass the current epoch but do not constrain the
1. Introduction The newborn exercise of near-term climate prediction, also termed decadal prediction ( Smith et al. 2007 ; Keenlyside et al. 2008 ; Pohlmann et al. 2009 ; Meehl et al. 2009 ; Mochizuki et al. 2010 ; Murphy et al. 2010 ; Doblas-Reyes et al. 2011 ), offers a great opportunity to revisit the available observational datasets from a new angle and compare further the physical processes represented in our models against these observational datasets. At the edge between seasonal
1. Introduction The newborn exercise of near-term climate prediction, also termed decadal prediction ( Smith et al. 2007 ; Keenlyside et al. 2008 ; Pohlmann et al. 2009 ; Meehl et al. 2009 ; Mochizuki et al. 2010 ; Murphy et al. 2010 ; Doblas-Reyes et al. 2011 ), offers a great opportunity to revisit the available observational datasets from a new angle and compare further the physical processes represented in our models against these observational datasets. At the edge between seasonal
Yip et al. (2011 , hereafter YFSH11 ) use analysis of variance (ANOVA) to partition uncertainty in multimodel climate ensembles, illustrating their framework using predictions of twenty-first-century global temperature from the archive of phase 3 of the Coupled Model Intercomparison Project (CMIP3). They extract from the data a subset with a convenient structure: seven models, each with two ensemble runs under each of the emissions scenarios A1B, A2, and B1. Having equal numbers of runs per
Yip et al. (2011 , hereafter YFSH11 ) use analysis of variance (ANOVA) to partition uncertainty in multimodel climate ensembles, illustrating their framework using predictions of twenty-first-century global temperature from the archive of phase 3 of the Coupled Model Intercomparison Project (CMIP3). They extract from the data a subset with a convenient structure: seven models, each with two ensemble runs under each of the emissions scenarios A1B, A2, and B1. Having equal numbers of runs per
1. Introduction Tremendous effort and expense have been put into improving the accuracy, readability, and applicability of weather and climate predictions, and most experts would agree that these weather and climate forecasts can benefit agricultural production if used effectively. However, despite correspondingly tremendous efforts to inform farmers about the availability and potential usefulness of such products ( HPRCC 1994 ), farmers’ attitudes toward and use of weather and climate
1. Introduction Tremendous effort and expense have been put into improving the accuracy, readability, and applicability of weather and climate predictions, and most experts would agree that these weather and climate forecasts can benefit agricultural production if used effectively. However, despite correspondingly tremendous efforts to inform farmers about the availability and potential usefulness of such products ( HPRCC 1994 ), farmers’ attitudes toward and use of weather and climate
