Anthropogenic changes in climate have been the topic of many publications, and have received considerable public attention. An important part of the research strategy for understanding such changes depends on defining the natural state and variability of earth's climate, but natural climate changes are not yet well enough understood to constitute a baseline against which we might realistically measure human-induced effects. A broad spectrum of observations, including both instrumental records and paleoclimate data (the former possibly contaminated by anthropogenic change, the latter not) has revealed substantial variability in the earth's climate on time scales of decades to centuries. This natural variability alone has considerable socioeconomic impact, particularly as it affects agriculture, fisheries, and water resources. To discover possible anthropogenic effects, we must assess variations of the modern climate, anticipate those of the future, and identify the regions or variables that are indicative of change.
The participants in the Dec-Cen workshop presented papers in a range of areas of climate research. These papers bear witness to substantial progress in our ability to describe, understand, and model the spatial and temporal structure, the magnitude, and the patterns of natural variability. However, it is clear that considerable effort is still required both to establish a real baseline of climate variability (natural and anthropogenically forced) and to determine the mechanisms controlling it. To achieve this dual objective, particularly in a time of limited available funding, will require careful consideration of proposed research efforts. On several occasions since the workshop, the CRC has discussed goals, community concerns, and available tools and information. We have identified four fundamental scientific questions that can give direction to work in the field:
How can data, theory, and models best be combined to permit us to separate natural climate variability from anthropogenic change on the decade-to-century time scale'?
Once we can distinguish between natural variability and anthropogenic change, can we understand their interaction, and thus decade-to-century-scale variability?
To what extent is the climate state predictable on the decade-to-century time scale?
What types of observations would be most helpful in achieving the goal of understanding decade-to-century-scale variability and forecasting climate change on that scale?
To obtain a clear picture of the causes of climate variability, both modeling and real-world observations must be employed, and the traces left by past changes must be uncovered. National and international efforts to explore and document climate variability on decade-to-century time scales have begun to provide the necessary foundation for examining human influences on climate. In addition to inspiring the fundamental questions above, the workshop's presentations and wide-ranging discussions gave the CRC the basis for formulating the following set of recommendations for the conduct of future research.
Criteria must be established to ensure that key variables are identified and observations are made in such a way that their results will yield the most useful data base for future studies of climate variability on decade-to-century time scales. For instance:
Minimal quality standards that exceed those required for measuring diurnal and seasonal cycles must be implemented.
The quality and continuity of data acquisition must be maintained over time. (Converting successful, appropriate research programs to sustained operational ones is one way to provide this continuity.)
Critical forcings and internal climate variables must be monitored as well, to complete the data base.
Multiple quantities should be monitored simultaneously, to permit cross-checking and provide statistical control.
Models should be consulted to help design optimal sampling strategies for monitoring systems.
Modeling studies must be actively pursued in order to improve our skill in simulating and predicting the climate state. The use of many types of models, and closer links between models and observational studies (such as data assimilation), will be necessary. Specific recommendations are:
Different model types must be intercalibrated to establish levels of confidence.
Models of the individual climate system components must be improved in order to facilitate the development of better coupled models, which integrate these components.
Known weaknesses in both models and existing data