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- Author or Editor: Arthur M. Greene x
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Abstract
Regional temperature change projections for the twenty-first century are generated using a multimodel ensemble of atmosphere–ocean general circulation models. The models are assigned coefficients jointly, using a Bayesian linear model fitted to regional observations and simulations of the climate of the twentieth century. Probability models with varying degrees of complexity are explored, and a selection is made based on Bayesian deviance statistics, coefficient properties, and a classical cross-validation measure utilizing temporally averaged data. The model selected is shown to be superior in predictive skill to a naïve model consisting of the unweighted mean of the underlying atmosphere–ocean GCM (AOGCM) simulations, although the skill differential varies regionally. Temperature projections for the A2 and B1 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios are presented.
Abstract
Regional temperature change projections for the twenty-first century are generated using a multimodel ensemble of atmosphere–ocean general circulation models. The models are assigned coefficients jointly, using a Bayesian linear model fitted to regional observations and simulations of the climate of the twentieth century. Probability models with varying degrees of complexity are explored, and a selection is made based on Bayesian deviance statistics, coefficient properties, and a classical cross-validation measure utilizing temporally averaged data. The model selected is shown to be superior in predictive skill to a naïve model consisting of the unweighted mean of the underlying atmosphere–ocean GCM (AOGCM) simulations, although the skill differential varies regionally. Temperature projections for the A2 and B1 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios are presented.
Abstract
A Bayesian hidden Markov model (HMM) for climate downscaling of multisite daily precipitation is presented. A generalized linear model (GLM) component allows exogenous variables to directly influence the distributional characteristics of precipitation at each site over time, while the Markovian transitions between discrete states represent seasonality and subseasonal weather variability. Model performance is evaluated for station networks of summer rainfall over the Punjab region in northern India and Pakistan and the upper Yangtze River basin in south-central China. The model captures seasonality and the marginal daily distributions well in both regions. Extremes are reproduced relatively well in the Punjab region, but underestimated for the Yangtze. In terms of interannual variability, the combined GLM–HMM with spatiotemporal averages of observed rainfall as a predictor is shown to exhibit skill (in terms of reduced RMSE) at the station level, particularly for the Punjab region. The skill is largest for dry-day counts, moderate for seasonal rainfall totals, and very small for the number of extreme wet days.
Abstract
A Bayesian hidden Markov model (HMM) for climate downscaling of multisite daily precipitation is presented. A generalized linear model (GLM) component allows exogenous variables to directly influence the distributional characteristics of precipitation at each site over time, while the Markovian transitions between discrete states represent seasonality and subseasonal weather variability. Model performance is evaluated for station networks of summer rainfall over the Punjab region in northern India and Pakistan and the upper Yangtze River basin in south-central China. The model captures seasonality and the marginal daily distributions well in both regions. Extremes are reproduced relatively well in the Punjab region, but underestimated for the Yangtze. In terms of interannual variability, the combined GLM–HMM with spatiotemporal averages of observed rainfall as a predictor is shown to exhibit skill (in terms of reduced RMSE) at the station level, particularly for the Punjab region. The skill is largest for dry-day counts, moderate for seasonal rainfall totals, and very small for the number of extreme wet days.
Distinguishing the Roles of Natural and Anthropogenically Forced Decadal Climate Variability
Implications for Prediction
Abstract
Given that over the course of the next 10–30 years the magnitude of natural decadal variations may rival that of anthropogenically forced climate change on regional scales, it is envisioned that initialized decadal predictions will provide important information for climate-related management and adaptation decisions. Such predictions are presently one of the grand challenges for the climate community. This requires identifying those physical phenomena—and their model equivalents—that may provide additional predictability on decadal time scales, including an assessment of the physical processes through which anthropogenic forcing may interact with or project upon natural variability. Such a physical framework is necessary to provide a consistent assessment (and insight into potential improvement) of the decadal prediction experiments planned to be assessed as part of the IPCC's Fifth Assessment Report.
Abstract
Given that over the course of the next 10–30 years the magnitude of natural decadal variations may rival that of anthropogenically forced climate change on regional scales, it is envisioned that initialized decadal predictions will provide important information for climate-related management and adaptation decisions. Such predictions are presently one of the grand challenges for the climate community. This requires identifying those physical phenomena—and their model equivalents—that may provide additional predictability on decadal time scales, including an assessment of the physical processes through which anthropogenic forcing may interact with or project upon natural variability. Such a physical framework is necessary to provide a consistent assessment (and insight into potential improvement) of the decadal prediction experiments planned to be assessed as part of the IPCC's Fifth Assessment Report.
Decadal Prediction
Can It Be Skillful?
A new field of study, “decadal prediction,” is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10–30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence time-evolving regional climate at the decadal time scale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally generated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internally generated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global time-evolving three-dimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated Coupled Model Intercomparison Model, phase 5 (CMIP5) experiments, some of which will be assessed for the IPCC Fifth Assessment Report (AR5). These experiments will likely guide work in this emerging field over the next 5 yr.
A new field of study, “decadal prediction,” is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10–30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence time-evolving regional climate at the decadal time scale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally generated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internally generated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global time-evolving three-dimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated Coupled Model Intercomparison Model, phase 5 (CMIP5) experiments, some of which will be assessed for the IPCC Fifth Assessment Report (AR5). These experiments will likely guide work in this emerging field over the next 5 yr.
