A National Strategy for Advancing Climate Modeling (2012) / Chapter Skim
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1 Introduction
1 Introduction
Pages 19-46
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Some concrete examples of current users of climate information are farmers, city planners, water managers, and insurance companies, and details about their use of climate information are described in Box 1.1. BOX 1.1 EXAMPLES OF CLIMATE DATA USERS Climate data are needed by many individuals and companies.
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. Longer-term regional climate projections of precipitation, temperature, and soil moisture will allow farmers to decide which crops to focus on in the future and to prepare for investments in new technologies needed to successfully grow new crops.
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Improved climate data (Figure 2) can help cities make more informed decisions on long-term infrastructure investments that will help to protect the health and economic interests of their constituents.
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. FIGURE 3 Managers of hydropower systems such as those of the Federal Columbia River Power System require climate information for both short-term operational decisions and long-term infrastructure planning.
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To realistically assess the probabilities of weather- and climate-related natural disasters, insurance companies have been using climate data on past weather events for many years to develop specific risk models for different regions and operations (e.g., transportation, farming, and construction)
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. In order to use climate model projections to inform its decisions, the Navy would need high-spatial-resolution regional climate models on decadal time scales, uncertainty quantification of the models, and probability distribution functions in the model output.
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Adaptation of the built environment to climate change is particularly important because it has significant resource implications.
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These types of infrastructure decisions can have implications for decades. As the climate changes, information from climate models is being used as a guide to future climate conditions.
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, these climate predictions allowed the government to coordinate "with local, state and federal agencies before and during the flooding, so that emergency officials could make important decisions to best protect life and limit property damage."2 Such decisions included evacuations and destruction of levees in some locations to allow excess waters to flow into floodways. In looking at longer time scales, climate models can provide information on projected rainfall runoff for the coming decades (Figure 1.2)
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Climate models are mathematical representations of physical, chemical, and biological processes in Earth's climate system (Figure 1.3)
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SOURCE: USGCRP, 2009. The many different kinds of climate models are all derived from fundamental physical laws such as Newton's laws of motion and the chemistry and thermodynamics of gases, liquids, solids, and electromagnetic radiation.
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The atmospheric part of a climate model is functionally identical to a weather forecast model, but the climate model is run far longer to simulate interactions between atmosphere, land, ocean, and cryosphere on time scales of months to millennia. In these projections, individual simulated weather systems are not expected to match reality; only statistics of the C L O U D S & W A T ER V A P O R RA D I A TI VE EX CHA NGE W IN D EX CHA NGE OF HEA T & GA SB ETWEEN A TM OSPHERE, SEA I CE OCEA N EVA PORA TI ON EV A P O T R A N S P IR A T IO N COND ENSA TI ON & CONVECTI ON WAT E R S T OR AG E A IR P O L L U T IO N IN I CE & S NOW GL A CI ER M EL T T ERRESTRI A L CA RB ON CYCL E S UR FACE R UN-OFF OCEA N OCEA N CA RB ON CYCL E CI RCUL A TI ON FIGURE 1.3 Climate models are mathematical representations of the physical, chemical, and biological processes in the Earth system.
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Climate Modeling in the United States Climate modeling activities that examine the entire planet are referred to as "global models," and those that focus on specific parts of the globe are called "regional models." Global modeling activities are generally larger and more resource-intensive efforts. The current U.S.
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supports efforts at both the Goddard Institute for Space Studies and the Goddard Space Flight Center. Global climate models are run on supercomputers and use data storage facilities housed both inside and outside the labs (Figure 1.5)
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In the United States, most regional modeling is focused around a few basic modeling codes such as the Weather Research and Forecasting model, but each group typically customizes important details of such a model to its own region and applications. Regional climate models are often run on small, cheap, widely available computer clusters rather than supercomputers.
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Regional climate modeling efforts are more widespread internationally, including in several Latin American and European countries. Several global weather forecasting centers (e.g., NCEP in the United States, the UK Met Office and the European Centre for Medium-Range Weather Forecasts in England)
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The DOE-funded Program for Climate Model Diagnosis FIGURE 1.6 This figure shows the relationship among IPCC, CMIP, and PCMDI with respect to the larger climate research community. CMIP and IPCC are managed by separate organizations within the United Nations but are coordinated in the timing of their activities.
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, especially in models using grid spacings of less than 100 km. In many ways climate models have become remarkably accurate tools for simulating observable statistical aspects of the Earth system (see Chapter 3 for more details of historical model improvements)
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Tropical rainfall and many cloud processes rely on interactions between very small scale air motions and other processes such as condensation or freezing that are also not straightforward to represent in current climate models. Other limitations include a lack of fully coupled land-ice or ocean biogeochemistry models in many simulations, which are areas of active research but which are just starting to be included in climate simulations.
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. FIGURE 1 Climate model development and testing involves multiple stages and the contributions of the model development community, the model user/evaluation community, and the data community.
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Because of the uncertain cooling by aerosols the current warming cannot be used to constrain the "climate sensitivity." Thus, the simulated 21st-century global-average warming varies across the international suite of climate models with a range of approximately 30 percenta as is further discussed in Chapter 4. Models provide quantitative estimates of future climate change, but with significant sources of uncertainty -- lack of knowledge, or imperfect knowledge about specific quantities or the behavior of a system.
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They agree that the polar regions will become wetter and that the subtropics will become drier, but they do not agree on which regions of the subtropics will experience strong drying. As climate models become more comprehensive and their grid scale becomes finer, they can provide meaningful projections of more parts of the climate response and their possible feedbacks on the overall climate system, but this does not necessar FIGURE 1.7 Time and space scales of key climate phenomena.
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, now is an appropriate time to examine the capabilities of the nation's climate modeling enterprise to ensure that it is advancing adequately. The modeling community has already developed plans to make continued progress over the next 3-5 years.
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4 One cannot consider advancing climate modeling without attention to the supporting climate obser vations, both space-based and in situ, needed to initialize, force, and validate climate models, as well as for monitoring climate variability and change. The United States currently does not have a coordinated climate observing system, or a strategy that could lead to a coherent system, across both in situ and remotely sensed observations.
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climate modeling enterprise where the committee presents its primary recommendations and an overarching national strategy for advancing climate modeling in the United States over the next two decades. These issues include the challenges and opportunities related to computational infrastructure (Chapter 10)
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Common modeling framework: A group of programs that provides a high-performance, flexible software infrastructure, which enables climate models to run on very large parallel computers and that supports coupling diverse, modular climate model components. Data assimilation: The process of making best use of observational data to provide an estimate of the state of the system that is compatible with a given model and that is better than could be obtained using just the data or the model alone.
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Regional climate models sometimes include greater scientific complexity that can inform particular applications and decision makers. Seamless prediction: Viewing weather and climate prediction as problems that share common processes and dynamics and that can be addressed using modeling approaches that span a broad range of time scales and spatial resolutions.
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