• short-term climate prediction on scales of months to years,
• study of climate variability and predictability on decadal to centennial time scales,
• national and international assessments of anthropogenic climate change,
• national and international ozone assessments, and
• assessment of the regional impacts of climatic change.
These needs were identified decades ago, remain today, and motivate the current committee.
Many interviewees felt that, until the climate modeling community committed to support provision of user-driven predictive products, there would be little chance of garnering additional sponsor support. It is fully realized by both interviewees and the committee that there remain formidable challenges in climate prediction with open fundamental questions. However, given that the present state of knowledge about our future climate is sufficient to identify risk and to motivate the need for profound societal response, there is a need for the development of the routine production and evaluation of experimental products, with the development of operational capabilities as experimental products mature.
Finding 2.3: Previous reports highlight the need for routine and reliable climate information, products, and services. In addition, the view outside the modeling community is that more of these products are needed.
Challenges of Institutional Reorganizations
Climate modeling needs to be considered as a part of a broader enterprise, existing in a balance with climate observations, high-performance computing, and disciplinespecific information systems that support analysis, access, and interpretation of climate information. Currently, the U.S. climate modeling enterprise that addresses this suite of activities is spread across a number of different modeling groups; in particular weather and climate modeling are largely being done in separate institutions. Our interviewees strongly and broadly valued maintaining a diversity of approaches within the suite of climate modeling activities, offering justifications based on scientific, organizational, and mission-related reasons. Several also noted that diversity poses risks to the effectiveness of the climate modeling enterprise ranging from systematic fragmentation and the potential perception of uncertainty regarding climate information from outside the science community.