Strategic Plan for the
Climate Change
Science Program
Final Report, July 2003

   This chapter also available as PDF file.

Authors of this Chapter

Final Report also is available as single PDF file (8.8 Mb)

See also:
 

Vision for the Program and Highlights of the Scientific Strategic Plan

Acronyms, Abbreviations,
and Units

Climate variability and change profoundly influence social and natural environments throughout the world, with consequent impacts on natural resources and industry that could be large and far-reaching. For example, seasonal to interannual climate fluctuations strongly affect the success of agriculture, the abundance of water resources, and the demand for energy, while long-term climate change may alter agricultural productivity, land and marine ecosystems, and the resources that these ecosystems supply. Recent advances in climate science are beginning to provide information for decisionmakers and resource managers to better anticipate and plan for potential impacts of climate variability and change. Further advances will serve the nation by providing improved knowledge to enable more scientifically informed decisions across a broad array of climate-sensitive sectors.

Climate research has indicated that, globally, it is very likely that the 1990s were the warmest decade in the instrumental record, which extends back to the 1860s (see Figure 4-1); large climate changes can occur within decades or less, yet last for centuries or longer; and the increase in Northern Hemisphere surface temperatures during the 20^th century likely exceeds the natural variability of the past 1,000 years (IPCC, 2001a, d). Placing instrumental records in the context of longer-term variability through paleoclimate analyses has played a key role in these findings. Moreover, observational evidence together with model simulations incorporating a comprehensive suite of natural and anthropogenic forcings indicate that "...the changes observed over the last several decades are likely mostly due to human activities, but we cannot rule out that some significant part of these changes is also a reflection of natural variability" (see Figure 4-2) (NRC, 2001a). All climate models used in the most recent Intergovernmental Panel on Climate Change (IPCC) assessment project that global mean temperatures will continue to increase in the 21^st century and will be accompanied by other important environmental changes, such as sea level rise, although the magnitudes of the projected changes vary significantly depending on the specific models and emissions scenarios (IPCC, 2001a, d).

Figure 4-1: Top Panel: Changes in the Earth's surface temperature over the period of direct temperature measurements (1860 -- 2000). The global mean surface temperature is shown each year by the red bars (with very likely ranges as thin black lines) and approximately decade-by-decade by the continuous red line. Bottom Panel: Proxy data (year-by-year blue line with very likely ranges as gray band, 50-year-average purple line) merged with the direct temperature measurements (red line) for the Northern Hemisphere. The proxy data consist of tree rings, corals, ice cores, and historical records that have been calibrated against thermometer data. Source: IPCC (2001d). For more information, see Annex C.

Climate research has also significantly advanced our knowledge of the temporal and spatial patterns of climate variability. Substantial improvements in our ability to monitor the upper tropical Pacific Ocean now provide the world with an "early warning" system that shows the development and evolution of El Nino-Southern Oscillation (ENSO) events as they occur. This improved observational system, together with an increased understanding of the mechanisms that produce ENSO, have led to useful climate forecasts at lead times of up to several months. This developing capability has given the world an unprecedented opportunity to prepare for, and reduce vulnerabilities to the impacts of ENSO, and thereby provided direct social and economic benefits as returns on climate science investments.

Research supported by the U.S. Global Change Research Program (USGCRP) has played a leading role in these scientific advances, which have provided new climate information to help society better anticipate and prepare for potential effects of climate variability and change. While progress in this area has been impressive, there still remain many unresolved questions about key aspects of the climate system, including some that have enormous societal and environmental implications. For example, we are just beginning to understand how climate variability and change influence the local and regional occurrence and severity of extreme events such as hurricanes, floods, droughts, and wildfires. In many parts of the world, including the United States, such events are tied to ENSO variability, which has undergone significant changes in the past, perhaps in response to relatively subtle changes in forcing. A better understanding of ENSO behavior under different climate states is therefore needed.

We have also identified several major recurrent natural patterns of climate variability other than ENSO, but do not yet know to what extent they are predictable. Our predictive capabilities at local and regional scales show promise in some regions and for some phenomena, but in many instances are still quite poor. We have yet to obtain confident estimates of the likelihood of abrupt climate transitions, although such events have occurred in the past (NRC, 2002). Perhaps most fundamentally, we do not yet have a clear understanding of how these natural climate variations may be modified in the future by human-induced changes in the climate, particularly at regional and local scales, and how emerging information about such changes can be used most effectively to evaluate the vulnerability and sustainability of human and natural systems (see, e.g., Figure 4-3).

Figure 4-3: Wind-blown dust buried farms and equipment, killed livestock, and caused human death and misery during the Dust Bowl drought of the 1930s. Source: Monthly Weather Review, June 1936 (courtesy NOAA).

The transformation of knowledge gained from climate research into information that is useful for societal decisions presents many challenges, as well as significant new opportunities. The process of understanding climate impacts and using climate information requires a detailed understanding of the interactions of climate, natural systems, and human institutions. Thus, to obtain maximum benefits from advances in knowledge of climate variability and change it will be essential to forge new relationships between the climate research community, social scientists, and the rapidly expanding base of public and private sector users of climate information. For continued progress over the next decade, research on climate variability and change will focus on answering two overarching questions:

Providing decision-relevant answers to these questions will require a research infrastructure that includes: a sustained, long-term observing system of a quality necessary for climate research and assessments (Chapter 12); a highly focused and adequately funded modeling activity to analyze and integrate climate observations and support climate predictions and projections (Chapter 10); and a research-based infrastructure to develop partnerships among climate scientists, other natural scientists (e.g., biologists), social scientists, and public/private-sector decisionmakers to accelerate the production and applications of climate knowledge (Chapter 11). In addition, coordinated research management will be required to ensure a broad-based and collaborative research program spanning academic institutions, government and private laboratories, and public and private sector expertise, to sustain research into climate variability and change and to provide advanced graduate and post-doctoral training for the next generation of scientists (Chapter 16).

In developing a research strategy, it is vital to recognize that the problems of climate variability and change are intrinsically connected: for example, regional impacts of climate change will depend directly on the variability of the global climate system. Moreover, future climate variability (e.g., frequency of ENSO events) will depend in part on changes in the mean climate. Therefore, problems of climate variability and change cannot be cleanly separated, and the success of understanding each will require improved understanding of both. The overall scientific strategy described in this chapter includes:

Advances will require improvements in paleoclimatic data as well as modern observational data systems, because in general the latter have been present for too short a time to extract robust features of climate variability on decadal or longer time scales. For example, in the Arctic, few climate stations have records extending back beyond 50 years, but paleo-environmental analyses indicate that both the magnitude and spatial extent of 20^th century Arctic warming may be unprecedented over the past 400 years. Paleoclimatic analyses also reveal the occurrence of decades-long mega-droughts at lower latitudes, including large portions of the United States (NRC, 2002).

The Climate Variability and Change element will play a central integrating role in the Climate Change Science Program (CCSP). As indicated by the numerous linkages, Climate Variability and Change will provide the array of advanced climate prediction and projection products that the other CCSP elements will utilize. This can be achieved only through the continued development of core climate system models that integrate the observational, analytical, and specialized modeling capabilities planned within the other CCSP elements, in order to provide improved information necessary to respond to the scientific and decisionmaking needs of the overall program. The overarching questions in the areas of climate variability and change can be addressed most effectively by focusing attention on five key science questions and their associated research objectives, as described below.

Climate system feedbacks, such as from clouds, water vapor, atmospheric convection, ocean circulation, ice albedo, and vegetation, produce large uncertainties in climate change projections by modulating the direct response to radiative perturbations that result from changing greenhouse gas concentrations, solar variability, or land-cover changes. State-of-the-art climate models exhibit a large range in the cumulative strengths of these feedbacks, with major U.S. models used in recent IPCC assessments lying at nearly the opposite ends of this range (IPCC, 2001a). A key issue for climate science is the extent to which the range in model projections resulting from the differences in climate system feedbacks can be quantified and reduced. Important feedbacks include relatively fast processes on time scales of minutes to months, e.g., clouds and turbulent ocean mixing. Such rapid processes also affect models used for seasonal-to-interannual climate predictions, which can be used as effective test beds for research in this area.

All major U.S. climate models fail to accurately simulate certain climate system processes and their associated feedbacks in response to natural or anthropogenic perturbations. The oceans store and transport energy, carbon, nutrients, salt, and freshwater on multiple time scales and help to regulate and determine climate changes on a continuum of time scales. Yet some critical ocean phenomena, including ocean mixing and large-scale circulation features that determine the rate of storage and transport, remain as key challenges to understand, assess, and model. Other critical processes that are inadequately represented in climate models include atmospheric convection, the hydrological cycle, and cloud radiative forcing processses. Although observed changes in incoming solar radiation, a natural climate forcing, are small relative to changes in net radiative forcing by greenhouse gases (IPCC, 2001a, b) there is some evidence that feedbacks within the climate system may magnify otherwise weak solar variability. In spite of many research efforts over the past several decades and longer, the physical processes responsible for such feedbacks remain uncertain.

The cumulative effect of these processes influences the magnitude, rate, and spatial distributions of the climate response to natural or anthropogenic forcing. Modeling deficiencies are related both to limits in understanding the physics of the climate system and insufficient fine-scale treatment of the key processes. They contribute to uncertainties in projections of climate change, and thereby hinder the development of adequate response strategies and formulation of environmental and energy policies. High priority research will focus on several sub-questions:

US research into climate forcing, feedbacks, and sensitivity is conducted at a few major modeling centers, federal and private laboratories, and universities. The intellectual quality of the research is outstanding, with many new and innovative ideas for model development and applications. However, the infrastructure and the observational data are currently inadequate to implement and evaluate these ideas cooperatively among the various modeling groups. Steps are being taken to address these deficiencies, but more must be done.

In order to optimize modeling resources and enable meaningful collaborations among modelers, it is necessary to develop and maintain a common and flexible infrastructure at the major modeling centers. By adopting common coding standards and system software, researchers will be able to test ideas at any of the major modeling centers, and the centers themselves will be able to easily exchange parameterizations as well as entire modules so that all groups benefit. The CCSP-supported Earth System Modeling Framework (ESMF) is an important start in this direction.

Additional infrastructure needs include: continuing enhancements of computational resources to keep pace with increasing model complexity (e.g., chemistry, biology); higher-resolution, multi-century climate model simulations run from many different initial states (i.e., as ensembles) to help understand climate variability and change of the 18^th, 19^th, and 20^th centuries and quantify probabilities of future climate events; additional software engineers for developing and managing model codes and building common and flexible infrastructure; and modeling center outreach scientists to aid and enable collaborations with external researchers. Benefits will include more efficient and rapid transfer of research results into applications, thereby achieving savings in human resource and dollar costs (Chapter 10).

Climate research also requires sustained, high quality environmental observations. Long-term climate observing systems (e.g., ARGO floats and ocean profilers, aerosol-radiation-cloud observatories), satellite data, retrospective data (instrumental and paleoclimatic), field observations, and increased fidelity of current operational data streams and improved reanalyses of historical data will all be needed to produce data sets designed for climate change detection studies, trend analyses, process research, and model development and testing (see also Question 4.2 and Chapter 12). Moreover, incorporation of observational data into modeling through improved data assimilation methods and more advanced models will address the reliability and uncertainties of these frameworks as well as facilitate the design of observing networks.

Further modeling research is required to improve simulations of seasonal-to-interannual variability in global models used for climate projections and to apply these models to improve seasonal-to-interannual climate predictions. Because many of the most important effects of global change will be felt at regional to local scales, improved capabilities of the global models to simulate and predict seasonal-to-interannual variability at these scales will be important both for validating the credibility of the models and building confidence among decisionmakers regarding the use of these models in global change projections.

A new research mode for accelerating improvements in climate models will be tested and evaluated with Climate Process and Modeling Teams (CPTs, see Box 4-1 below). CPTs are intended to complement rather than replace single investigators or other collaborative research on climate sensitivity and feedbacks. Pilot CPTs will begin in FY 2003-2004.

• Refined estimates of the role of climate feedback processes in affecting climate sensitivity and improvements in their representation in climate models, leading to a narrowing of the range of climate model projections (Questions 4.1.1 and 4.1.2) [2-4 years and beyond].
  • For cloud and water vapor feedbacks, the Water Cycle element will provide theoretically-based cloud resolving models, mesoscale models of cloud processes, cloud/precipitation process research, cloud energy budgets and satellite data sets (e.g., CloudSat). The Climate Variability and Change element will be responsible for cloud/water vapor feedback processes in the context of the coupled climate system models (e.g., the use of cloud resolving models to test cloud parameterizations in climate models).
• Improved estimates of the climate response to different emissions (e.g., carbon dioxide (CO₂), aerosols, black soot) and land-use scenarios (Question 4.1.3) [2-4 years and beyond].
  • The capabilities of current climate models to link emissions to global atmospheric distributions of concentrations of pollution, including the chemical and heating and cooling properties of imbedded atmospheric aerosols, will be addressed in cooperation with the Atmospheric Composition element, as will assessments of the ability of the models to simulate observed radiative forcing of chemically-active greenhouse gases, including improved uncertainty ranges. Aspects of this research will also be conducted cooperatively with the Land-Use/Land-Cover Change element.
• New and improved climate data products, including: assimilated data from satellite retrievals and other remotely sensed and in situ data for model development and testing; consistent and regularly updated reanalysis data sets suitable for climate studies; centuries-long retrospective and projected climate system model data sets; high-resolution data sets for regional studies (e.g., Atmospheric Radiation Measurement (ARM) site data to initialize and evaluate cloud resolving models); and assimilated aerosol, radiation, and cloud microphysical data for areas with high air pollution, such as urban centers throughout the world (Question 4.1.4) [2-4 years and beyond].

• Some of these data will be collected, quality-controlled, and integrated in cooperation with the Atmospheric Composition and Water Cycle elements, and will support these science elements and the Carbon Cycle element.

• Increased understanding and confidence in attribution of the causes of recent and historical changes in the climate (Questions 4.1.4 and 4.1.5) [2-4 years and beyond].
  • The Atmospheric Composition element will provide emission and atmospheric concentration data and the Land-Use/Land-Cover Change element will provide historical land-use change time series and land use scenarios (see second bullet above). Selected Climate Variability and Change data and analyses will be provided for the Atmospheric Composition element's assessment of the impacts of tropospheric ozone on radiative forcing brought about by clean air regulations enacted during the last decade.
• Targeted paleoclimatic time series as needed, for example, to establish key time series of observations and natural forcing mechanisms as benchmarks of climate variability and change (Question 4.1.5) [2-4 years and beyond]
• Improved effectiveness of global and regional observing systems, including deployment of new systems and re-deployment of existing systems, based on guidance provided by modeled climate sensitivities and feedbacks (Question 4.1.6) [2-4 years and beyond].
• Policy-relevant information on climate sensitivities and the uncertainties in climate model projections due to climate system feedbacks, in support of the IPCC and other national and international assessments (all 4.1 Questions) [2-4 years and beyond].

• More efficient and rapid transfer of research results into applications [2-4 years and beyond].

• Increased confidence in estimates of the global and regional manifestations of future changes in climate [beyond 4 years].

One of the major advances in climate science over the past decade has been the recognition that much of the climate variability is associated with a relatively small number of recurrent spatial patterns, or climate modes. These include, in addition to ENSO, the North Atlantic Oscillation (NAO), the northern and southern hemisphere annular modes (NAM, SAM), Pacific Decadal Variability (PDV), Tropical Atlantic Variability (TAV), the Tropical Intra-Seasonal Oscillation (TISO), and monsoon systems. At present, there is limited understanding of the physical mechanisms that produce and maintain natural climate modes, the extent to which these modes interact, and how they may be modified in the future by human-induced climate changes. These limitations in knowledge introduce major uncertainties in climate predictions, climate change projections, and estimates of the limits of climate predictability, especially for regional climate (see also Question 4.1). They directly hinder our capabilities to address many of the "If..., then..." questions posed by decisionmakers.

Simulations of past climate conditions for which forcing estimates have been obtained provide an effective and practical means for assessing the scientific credibility of climate models. They enable detailed investigations of whether climate models realistically reproduce past climate states and responses in key environmental variables, such as sea level. They may also be used to evaluate how well the models simulate the various naturally recurring modes of climate variability. Process research that includes enhanced and extended observations, such as over the Pacific, Atlantic, and Indian Oceans basins and adjacent land and ice regions, provides a critical means for evaluating physical mechanisms and feedbacks, validating models, and assessing the corresponding effects on regional climate.

The extent to which skillful regionally-specific climate predictions and climate change projections can be provided is an issue of fundamental practical importance. Various approaches have been proposed, including high-resolution global models, nested global-regional models, probabilistic information derived from ensembles with either individual or multiple climate models, and statistical downscaling. Much additional work is required to determine optimal methods and the feasibility of downscaling climate information to regional-to-local levels. Here, research on short-term climate variability, for example, due to ENSO, can provide valuable insights. Developing capabilities to reproduce regional manifestations of interannual climate variability in climate models will also be crucial for establishing credibility with scientists and decisionmakers regarding longer-term climate change scenarios (see, e.g., Figure 4-4).

Figure 4-4: Left Panel: Multi-model forecast of (a) precipitation and (b) temperature for January-March 2003 (models run December 2002). Positive values project above-normal activity, and negative below normal. Right Panel: Ensemble predictions with a coupled model system of eastern Pacific sea surface temperature (SST) anomalies 9 months in advance, with predictions starting in April (top) and September (bottom) of each year. The spread of the six-member ensembles highlights the relatively large uncertainties in predicting SST anomalies so far in advance. Sources: (a) International Research Institute for Climate Prediction, and (b) NASA Seasonal to Interannual Prediction Project.

High priority research will seek to answer the following questions:

Essential research needs include the development and support for long-term, sustained climate modeling and observing capabilities (see also Question 4.1 and Chapters 10 and 12). These include remote sensing data sets, global and regional reanalyses, and retrospective data including new high-resolution paleoclimate datasets. Field observations and process studies are necessary for improving understanding and model representations of the physical mechanisms responsible for climate feedbacks, evaluating the extent to which climate models successfully replicate these mechanisms, and determining observational requirements for critical processes. Additional research is required to develop improved methodologies to determine from global model projections changes in regional climate and seasonal-to-interannual variability. Vital constraints that must be considered include the water cycle (Chapter 5) and global energy balance.

Focused research efforts, such as the CPTs described earlier in this chapter, can play an important role in accelerating improvements in global climate models. Sea level observations, geodetic reference frame measurements, ice sheet and glacier volume estimates, as well as advances in modeling are required to further refine sea level change projections. Other research needs include producing data sets from ensembles of extended model simulations, and an updated, consistent reanalysis suitable for climate diagnostic analysis, attribution, and detection, including, if feasible, all of the 20^th century. Moreover, access to model products, predictions, and tailored value-added products/information must be provided to the decisionmaking community to foster progress in utilizing prediction capabilities (see Question 4.5 and Chapter 11).

Analyses of the paleoclimate record -- the record of the Earth's environmental history derived from sources such as ice cores, tree rings, and lake and ocean sediments -- provide compelling evidence for past abrupt climate changes. In some locations, changes of up to 16°C in temperature and a factor of two in precipitation have occurred within decades to years, yet lasted for centuries and longer (NRC, 2002). Paleoclimate data indicate that these changes have been manifested by significant shifts in the baseline climate and in the character and patterns of variations about average conditions. The rapidity of such changes poses major challenges to the vulnerability and adaptability of societies and ecosystems.

Previous paleoclimate research has provided significant advances in our understanding of the general structure and geographic extent of past abrupt climate changes. Much past research has focused on colder climate conditions, and a challenge for the future will be to understand the potential for abrupt change in the context of an overall warming climate. Abrupt climate changes may be associated with the crossing of a climatic threshold, the onset of nonlinear responses, or feedbacks in the climate system. To date, however, the causes of past abrupt changes are not fully explained or understood. In addition, present climate models fail to adequately capture the magnitude, rapidity, and geographical extent of past abrupt changes. Consequently, at this time climate models cannot be used with confidence to estimate the potential for future abrupt changes (NRC, 2002). Improved knowledge of the causes for abrupt changes, and the ability to project their future probabilities, will provide policymakers with an improved scientific basis to evaluate risks of future abrupt changes and, as needed, to develop strategies to reduce vulnerabilities. Major questions include:

(a, above) and (b, below) Figure 4-5: The retreat of the South Cascade Glacier in Washington's Cascade Mountains shown in photographs from (a) 1928 and (b) 2000. Since 1957, the glacier has retreated about 700 meters, losing about one-fifth of its length and one-third of its volume. Source: USGS.

Improved paleoclimatic data sets, expanded observing and monitoring systems and rigorous paleoclimate modeling studies will be required to identify the causes and mechanisms of past abrupt changes. Efforts should be focused on key regions or phenomena that may be especially vulnerable or contribute most strongly to abrupt climate change, such as the tropics, the Arctic and Antarctic regions, and the ocean thermohaline circulation. Significant research into how to numerically model the full three-dimensional circulation of the ocean will be required in order to accurately project impacts and time scales for abrupt changes, which range from interannual ENSO variability to centennial-millennial fluctuations in the ocean circulation. Key research needs include better understanding of the relationships between abrupt change and:

One of the highest priorities for decisionmakers is to determine how climate variations, whether natural or human-induced, alter the frequencies, intensities, and locations of extreme events (NRC, 1999a). There is now compelling evidence that some natural climate variations, such as ENSO, PDV, and the NAO / NAM, can significantly alter the behavior of extreme events, including floods, droughts, hurricanes, and cold waves (IPCC, 2001a, b). Studies of long-term trends in extreme events show that in many regions where average rainfall has been increasing, these trends are evident in extreme precipitation events (there continues to be debate on how to define an extreme precipitation event). For other high-impact phenomena, such as tropical storms/hurricanes, no compelling evidence yet exists for significant trends in frequency of occurrence (IPCC, 2001a, b).

A question central to both short-term climate predictions and longer-term climate change is how climate variability and change will alter the probability distributions of various quantities, such as of temperature and precipitation, as well as related temporal characteristics (e.g., persistence), and hence the likelihood of extreme events (see Figure 4-6). A key challenge is to develop improved methods for modeling or downscaling climate information to the scales required for extreme event analysis (IPCC, 2001a). Further, understanding of the processes by which climate variability and change modulate extreme event behavior is incomplete. Major research questions include:

Figure 4-6: Heavy rains and high surf from storms associated with the 1998 El Nino event produced severe erosion along the California coast, leading to major property losses. Source: Paul Neiman, Environmental Technology Laboratory, NOAA

Progress in this area will require two key steps. First, it will be necessary to advance scientific understanding and quantitative estimates of how natural climate variations such as ENSO, NAO/NAM, SAM, or PDV alter the probabilities of extreme events (e.g., floods, droughts, hurricanes, or storm surges). Second, it will be essential to improve understanding of how human-induced climate change may alter natural variations of the atmosphere, ocean, land surface, and cryosphere, and hence the behavior of extreme events in different regions.

Key data requirements include the development of improved climate-quality data and reference data sets and higher-resolution model reanalyses to support analyses of extreme event variability and trends. High-resolution observations together with focused process studies will be essential for scientific evaluation of regional model simulations, especially in regions with significant topographic variations, such as mountainous and coastal regions. Higher-resolution paleoclimatic data will also be necessary to improve descriptions and understanding of how natural climate variations have in the past altered drought, mega-drought, flood, and tropical storm variability (see question 4.3). Improving hydrological extreme event risk estimates will require improved hydrological data sets and advances in coupled climate-land surface-hydrology models (see Chapters 5, 6, 11, and 12).

Empirical and diagnostic research will be required to ascertain relationships between natural climate modes, boundary forcing mechanisms (e.g., sea surface temperature variations, land surface and cryospheric changes), and extreme events; to clarify the physical bases for these relationships; and to evaluate the veracity of model simulations and projections. Model sensitivity experiments will significantly advance understanding of how natural climate modes, boundary variations, and human-induced climate trends alter the probabilities of extreme events. Further development of regional climate modeling and improved downscaling techniques will be necessary to provide information at the scales needed by resource managers and decisionmakers.

Continuing development of ensemble-based approaches, and the capabilities to produce large ensembles from climate models, will be essential in order to improve probability estimates of extreme events for either short-term climate predictions or longer-term climate projections. Because extreme events can have societal and environmental impacts, it will be essential to identify key climate information needed to better anticipate and plan for such events (see Question 4.5 and Chapters 9 and 11).

MILESTONES, PRODUCTS AND PAYOFFS

Research in this area focuses on making climate knowledge more useful and responsive to the needs of decisionmakers, policymakers, and the public. Climate information, when integrated together with knowledge of non-climatic factors, can reduce costs and risks related to climate variability and change while increasing management and decisionmaking opportunities across a broad range of sectors, from local and regional to global scales (NRC, 1999a; IPCC, 2001b).

For example, pilot efforts in sustained regional integrated science research, such as the NOAA-supported Regional Integrated Science and Assessments projects (RISAs), NASA Regional Earth Science Application Centers (RESACs), and NOAA-supported International Research Institute for Climate Prediction (IRI) for areas outside of the United States, have provided opportunities to apply climate information in decision processes in climate-sensitive sectors, including agriculture, water, energy (e.g., hydropower), and forest (wildfire) management. USEPA also sponsors regional science and assessment projects. Finally, specialized entities outside the research domain, such as state climatologists, regional climate centers, and agricultural cooperative networks, have served as partners and liaisons by identifying and communicating climate information needs and requirements between the climate research and service communities and a broad array of users.

With continuing population growth and increasing demands on environmental resources, the need to more effectively identify, develop, and provide climate information useful for society will become ever more vital. Even in the absence of human-induced climate changes, further research in this area provides new opportunities for resource managers and policymakers to develop strategies to reduce vulnerabilities to natural climate variability. Major questions include:

In recent reports the National Research Council (NRC) identifies the "region" as a key scale for decisionmaking, and stresses the critical need to improve regional scientific capabilities and user interactions to better inform such decisions (NRC, 1999a, 2001e). As these reports emphasize, the impacts of climate variability and change will continue to be felt most directly at regional to local scales, for example, within natural boundaries associated with coastlines, mountains, or watersheds, within the context of demographics, ecosystems, and land use, and within the context of its economic and technological wherewithal. A central goal of research over the next decade will be to improve capabilities to identify, develop, and deliver climate information at regional to local scales in order to better meet societal needs.

A key challenge in this area is to continue developing the observational, diagnostic, and modeling expertise required to determine the impacts of climate variability and change at global and regional scales. The required basic science research must be complemented by a strong applied research component to ensure identification of key regional issues and impacts of multiple stresses on resource management, determine responsiveness to user needs, and develop objective means for measuring success. To be developed most effectively, these research efforts should be conducted as sustained, two-way partnerships that directly involve decisionmakers and other regional stakeholders. This will help to ensure that results of climate research are made most useful for applications. Further, user needs can provide important guidance for developing future research directions. Regional "test beds" or "enterprises" can serve as important foci for developing such partnerships, evaluating potential uses of climate information at regional scales, and performing analyses of regional climate impacts, vulnerabilities, adaptation and mitigation options related to climate variability and change. They can also be used as demonstration projects for providing end-to-end delivery of climate information and evaluations of its uses, and for establishing an improved national decision support capability.

Because of the difficulties in evaluating the effectiveness of decisions with long lead times, important initial steps toward building confidence in the use of climate information can be made through focused research on shorter-term decisions, such as those that occur on monthly and seasonal-to-interannual time scales (e.g., agriculture, water management, energy distribution, wildfire management). Improved information for supporting climate-sensitive decisions on these time frames will be critical for building credibility on the uses of climate data and projections to better inform difficult, long-term decisions. A focus on shorter-term climate variability also provides opportunities to try various decision options, e.g., through adaptive management strategies. Evaluating the effectiveness of strategies on shorter time scales will be useful for developing longer-term policy options and decisions.

As climate knowledge improves, evaluation can be extended to multi-year and decadal time scales, which will provide an important bridge to policy and decision options related to longer-term changes. In this regard, the decadal timescale offers a valuable bridge for research on climate-sensitive adaptive strategies across timescales. It links the management of the impacts of individual extreme events and interannual variations to longer-term variations and can provide tangible observational regional analogs for climatic change.

To ensure that these efforts are efficient and cost-effective, it will be crucial to involve existing regional experts in climate information, applications, and user needs, such as state climatologists, regional climate centers, university extension agents, local weather service offices, and members of the private sector. These regional efforts must ultimately be coordinated effectively in order to provide information that will serve the needs of policymakers and decisionmakers at the national level (see Chapter 11).

Internationally coordinated research programs such as the World Climate Research Programme (WCRP) and its projects Climate Variability and Predictability (CLIVAR), Stratospheric Processes and their Role in Climate (SPARC), Climate and Cryosphere (CliC), the Global Energy and Water Cycle Experiment (GEWEX), as well as the International Geosphere -- Biosphere Programme (IGBP) PAGES paleoscience project are critical for developing global infrastructure and research activities designed to ensure that global aspects of climate variability and change are addressed in a coordinated manner.

In particular, CLIVAR, the broadest of the WCRP programs (WCRP, 1995), has a suite of vigorous activities that address numerous facets of the climate problem. For example, its Working Group on Seasonal to Interannual Prediction leads worldwide development and assessment of prediction approaches and forecast systems while the CLIVAR -- WCRP Working Group on Coupled Modeling is fostering advancements in coupled modeling. In the Atlantic region, CLIVAR is actively coordinating and encouraging international (e.g., U.S., European, and South American) observational, analysis, and modeling activities that will advance understanding and predictions of the puzzling climate changes that impact this region. These research activities are identifying how the regional climate changes are manifested through features such as TAV and NAO / NAM. Furthermore, CLIVAR investigations are elucidating critical ocean-atmosphere-land-cryosphere (and with WCRP -- SPARC, stratosphere-troposphere) coupled processes as well as critical inherent features such as the Atlantic Ocean thermohaline circulation that must be correctly modeled to project future climate changes. CLIVAR and WCRP are fostering numerous activities in the Americas that are addressing global issues (e.g., process studies focusing on the evolution and dynamics of the monsoons) as well as regional issues (e.g., extreme events, paleo-environmental variability) and their implications for the global climate system.

Within the United States there are a number of partners that will coordinate implementation of the Climate Variability and Change strategic vision. U.S. CLIVAR has in place a nucleus of scientific and programmatic elements, but will need to strengthen ties with additional WCRP groups (e.g., U.S. CliC) as well as with focused model and assimilation system development (e.g., at the National Center for Atmospheric Research, Geophysical Fluid Dynamics Laboratory, and the Goddard Space Flight Center). The deep-ocean observation program, a joint U.S. CLIVAR -- Carbon Cycle Study Program effort, is fostering a complementary international component that is providing an example of the benefits of program coordination. Additionally, NOAA's International Research Institute of Climate Prediction (IRI) is leading international development of climate predictions and their applications. Finally, NOAA's National Centers for Environmental Prediction (NCEP) will also contribute in many key areas including climate monitoring and diagnostics, forecasting, reanalyses, and high-resolution weather modeling. Stronger linkages with NCEP and other members of the weather modeling community will be most helpful in advancing capabilities to address issues of high societal relevance, such as downscaling information to regional to local scales and improving predictions and projections of extreme events. Finally, involving the cadre of regional climate centers and state climatologists will help ensure that regional and user expertise is represented in the development of effective frameworks for developing useful information.