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This article discusses the interplay between computational experiments and scientific advancement in dynamical meteorology and climate dynamics. In doing so, the emphasis is on the dual role of computations in prediction and experimentation, permitting the development of physical insight and confidence in the mechanistic insight through verification. Modern climate dynamics has steadily evolved because of the ready access to computational power that has developed over the past quarter century.
The landscape for state-of-the-art computational climate science is changing rapidly, however, with the drive toward greater complexity in climate models in order to more fully represent the interactions among components, the need for higher-resolution atmospheric and oceanic models to fully capture critical aspects of the variability in these components, and the advent of petascale and (eventually) exascale computing facilities. Finally, the manner in which the combination of these changes will likely alter the planning and execution of grand-challenge computational experiments and what this might mean in terms of collaborative climate science is discussed.
This article discusses the interplay between computational experiments and scientific advancement in dynamical meteorology and climate dynamics. In doing so, the emphasis is on the dual role of computations in prediction and experimentation, permitting the development of physical insight and confidence in the mechanistic insight through verification. Modern climate dynamics has steadily evolved because of the ready access to computational power that has developed over the past quarter century.
The landscape for state-of-the-art computational climate science is changing rapidly, however, with the drive toward greater complexity in climate models in order to more fully represent the interactions among components, the need for higher-resolution atmospheric and oceanic models to fully capture critical aspects of the variability in these components, and the advent of petascale and (eventually) exascale computing facilities. Finally, the manner in which the combination of these changes will likely alter the planning and execution of grand-challenge computational experiments and what this might mean in terms of collaborative climate science is discussed.
In this article, the authors describe an innovative multimodel system developed within the Climate Change and Impact Research: The Mediterranean Environment (CIRCE) European Union (EU) Sixth Framework Programme (FP6) project and used to produce simulations of the Mediterranean Sea regional climate. The models include high-resolution Mediterranean Sea components, which allow assessment of the role of the basin and in particular of the air–sea feedbacks in the climate of the region.
The models have been integrated from 1951 to 2050, using observed radiative forcings during the first half of the simulation period and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario during the second half.
The projections show a substantial warming (about 1.5°–2°C) and a significant decrease of precipitation (about 5%) in the region for the scenario period. However, locally the changes might be even larger. In the same period, the projected surface net heat loss decreases, leading to a weaker cooling of the Mediterranean Sea by the atmosphere, whereas the water budget appears to increase, leading the basin to lose more water through its surface than in the past. Overall, these results are consistent with the findings of previous scenario simulations, such as the Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects (PRUDENCE), Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES), and phase 3 of the Coupled Model Intercomparison Project (CMIP3). The agreement suggests that these findings are robust to substantial changes in the configuration of the models used to make the simulations.
Finally, the models produce a 2021–50 mean steric sea level rise that ranges between +7 and +12 cm, with respect to the period of reference.
In this article, the authors describe an innovative multimodel system developed within the Climate Change and Impact Research: The Mediterranean Environment (CIRCE) European Union (EU) Sixth Framework Programme (FP6) project and used to produce simulations of the Mediterranean Sea regional climate. The models include high-resolution Mediterranean Sea components, which allow assessment of the role of the basin and in particular of the air–sea feedbacks in the climate of the region.
The models have been integrated from 1951 to 2050, using observed radiative forcings during the first half of the simulation period and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario during the second half.
The projections show a substantial warming (about 1.5°–2°C) and a significant decrease of precipitation (about 5%) in the region for the scenario period. However, locally the changes might be even larger. In the same period, the projected surface net heat loss decreases, leading to a weaker cooling of the Mediterranean Sea by the atmosphere, whereas the water budget appears to increase, leading the basin to lose more water through its surface than in the past. Overall, these results are consistent with the findings of previous scenario simulations, such as the Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects (PRUDENCE), Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES), and phase 3 of the Coupled Model Intercomparison Project (CMIP3). The agreement suggests that these findings are robust to substantial changes in the configuration of the models used to make the simulations.
Finally, the models produce a 2021–50 mean steric sea level rise that ranges between +7 and +12 cm, with respect to the period of reference.
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.
This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.
This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.
