A National Strategy for Advancing Climate Modeling (2012) / Chapter Skim
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3 Strategies for Developing Climate Models:Model Hierarchy, Resolution, and Complexity
3 Strategies for Developing Climate Models:Model Hierarchy, Resolution, and Complexity
Pages 63-80
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From page 63... ...
Through the 1970s and 1980s, high-end general circulation models became progressively more comprehensive to better capture climate change feedback processes and to provide more credible regional information on climate change. The incorporation of dynamic ocean and sea-ice models in the 1980s and 1990s to create coupled climate models allowed some of the first estimates of the transient response of the climate system to changing greenhouse gases.
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From page 64... ...
Although comprehensive climate models are becoming more complex, an increasing range of other models has helped to evaluate and understand their results and to address problems that require different tradeoffs between process complexity and grid resolution. Uncoupled component models, often run at higher resolution or with idealized configuration, allow a more controlled focus on individual processes such as clouds, vegetation feedbacks, or ocean mixing and enable the behaviors of the uncoupled components to be studied in more detail.
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From page 65... ...
Because of computational requirements for running GCMs at high resolution, nested regional atmospheric and oceanic models forced at the lateral boundaries have become attractive tools for addressing problems requiring locally high spatial resolutions, such as orographic snowfall and runoff, or oceanic eddies and coastal upwelling. Regional atmospheric models were mainly adapted in the past two decades from regional weather-prediction models and have attracted a somewhat different and more diverse user and model development community than global models (Giorgi and Mearns, 1999)
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From page 66... ...
These models, called integrated assessment models, could be useful tools for exploring climate mitigation and adaptation where human systems play an important role. The landscape of climate models that developed naturally in the past will continue to evolve.
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From page 67... ...
Because of the dependence on large-scale circulation, biases in global climate simulations used to provide lateral boundary conditions propagate into the nested regional climate simulations. Similar to global models, regional models are sensitive to model resolution and physics parameterizations.
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From page 68... ...
There is considerable evidence that refining the horizontal spatial resolution of climate models improves the fidelity of their simulations. At the most fundamental level, increasing resolution should improve the accuracy of the approximate numerical solutions of the governing equations that are at the heart of climate simulation.
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From page 69... ...
Most current climate models divide the atmospheric column into 20-30 vertical layers, but some models include more than 50 layers with the increased vertical levels mostly added near the surface (to better resolve boundary-layer processes) or near the tropopause (to better simulate atmospheric waves and moisture advection)
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From page 70... ...
regional climate models nested in the global models or empirical statistical downscaling of projections developed from global climate model output and observational data sets. Neither downscaling approach can reduce the large uncertainties in climate projections, which derive in large part from global-scale feedbacks and circulation changes, and it is important to base such downscaling on model output from a representative set of global climate models to propagate some of these uncertainties into the downscaled predictions.
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From page 71... ...
Finding 3.2: Although different approaches to achieving high resolution in climate models have been explored for more than two decades, there remains a need for more systematic evaluation and comparison of the various downscaling methods, including how different grid refinement approaches interact with model resolution and physics parameterizations to influence the simulation of critical regional climate phenomena. MODEL COMPLEXITY The climate system includes a wide range of complex processes, involving spatial and temporal scales that span many orders of magnitude.
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From page 72... ...
. Besides increasing the complexity of individual model components, the scope of Earth system interactions that are represented has continued to increase to capture the feedbacks among Earth system components and to provide more complete depictions of the energy, water, and biogeochemical cycles.
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From page 73... ...
Finding 3.3: Climate models have evolved to include more components in order to more completely depict the complexity of the Earth system; future challenges include more complete depictions of Earth's energy, water, and biogeochemical cycles, as well as integrating models of human activities with natural Earth system models.
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From page 74... ...
In 2012, systems with ~500,000 processors that deliver a peak computational performance rate of 10 petaflops are becoming available. In theory such computers would already allow a global climate model with a grid spacing as fine as 6-10 km to simulate 5-10 years per day of computer time, a throughput rate suitable for decadal climate prediction or centennial-scale climate change projection.
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From page 75... ...
One example is the Department of Energyfunded Climate Science for a Sustainable Energy Future project, which involves nine U.S. institutions working on regional predictive capabilities in global models for 20152020 in a strategic, multidisciplinary effort, including development of observational data sets into specialized data sets for model testing and improvement, development of model development testbeds, enhancement of numerical methods and computational science to take advantage of future computing architectures, and research on uncertainty quantification of climate model simulations.
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From page 76... ...
MODEL EVALUATION To guide future investments in model development, careful assessment of the additional benefits of increasing model resolution and complexity will be important. Model evaluation, in the context of predictability and uncertainty, will be increasingly critical to improve understanding of model strengths and weaknesses.
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From page 77... ...
Objective measures of model skill are actively being developed to guide model development and implementation. More advanced methods of evaluating climate models are coming into use that take advantage of more recent statistical analysis methods and the growing use of ensembles of climate simulations to estimate uncertainty and reliability and ensembles of climate models confronted with the same experimental protocols to evaluate the relative performance of different models.
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From page 78... ...
The choice of model for a given problem would ideally be optimized to various length and time scales and with different degrees of complexity in their representation of the Earth system. This hierarchy of models is necessary to advance climate science and improve climate predictions from intraseasonal to millennial time scales.
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From page 79... ...
As noted above, the uncertainties in climate models even at local scales derive in large part from global-scale processes such as cloud and carbon-cycle feedbacks, as well as uncertainties in how future human greenhouse gas and aerosol emissions unfold. Recommendation 3.1: To address the increasing breadth of issues in climate science, the climate modeling community should vigorously pursue a full spectrum of models and evaluation approaches, including further systematic comparisons of the value added by various downscaling approaches as the resolution of climate models increases.
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