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Uncertainty Management in Remote Sensing of Climate Data: Summary of a Workshop 3 Concluding Thoughts As conveyed by many participants at the National Academies’ workshop on uncertainty management for remotely sensed climate data, it would be helpful if the climate research community as a whole could settle on priority questions where collaboration with the statistics community would be most beneficial. The advancement of statistical techniques could then focus on these fundamental science questions. For example, the importance of climate models for policy making suggests that improved statistical techniques for improving their parameterizations and analyzing their output could have substantial benefits for both the scientific community and society as a whole. There are historical precedents for this type of progress; for example, in the 1970s and 1980s the community recognized that it was critical to gain a better understanding of how clouds affect the earth’s radiation budget. Today, we have a much clearer picture of how clouds alter the transfer of radiation through the atmosphere on a variety of timescales, and progress on this topic has allowed scientists to develop a more comprehensive, although still incomplete, understanding of the feedbacks between clouds and other aspects of the climate system. As described throughout the workshop, the components of the climate system are coupled and interact in multiple ways and at multiple scales, which makes it difficult to discern which components are contributing the most to the uncertainties in our knowledge. In addition, the challenges associated with quantifying uncertainties in a certain geophysical parameter (e.g., precipitation) are typically unique, and cannot necessarily
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Uncertainty Management in Remote Sensing of Climate Data: Summary of a Workshop be readily translated to other system components. However, a statistical framework for evaluating uncertainties throughout the system can help lead to a logical roadmap for a research enterprise. As conveyed by many workshop participants, stronger collaboration between the earth science and statistics communities would likely result in many benefits. Workshop participants suggested that additional, more focused workshops might be organized as a way of spurring progress in understanding uncertainties associated with particular geophysical processes and with some particular data-collection challenges (e.g., measuring aerosols or cloud cover). These follow-on workshops could probe more deeply into particular models, research, and challenges. Examples might include the advancement of statistical techniques to address spatial and temporal autocorrelation in large datasets and methods to incorporate more physical knowledge and physical modeling into the statistical models that will help improve calibration and validation studies. With the rich collection of remotely sensed data, the workshop participants discussed that considerable progress could be made by going beyond simple monthly and annual averages to describe the climate system, and that modern statistical methods had much to offer in the area of representing the physical processes that make up the climate system. There is a wealth of data to be processed, and analysis of this data requires both good physical models and modern statistical methods to fully understand the biases and residual errors. In the era of advanced earth science collection and processing techniques, the fusion of multiple datasets is a challenge in the remote sensing community and represents another rich area for collaboration, as does data assimilation. The goal for a statistical framework is to account for uncertainty not only in individual parameters but also in the entire modeling framework used to predict the processes of interest. An overall statistical framework for accounting for uncertainty in remotely sensed climate data and in climate models might also assist in the development of an integrated strategy for communicating uncertainties. Such progress would not only aid research in the earth science and statistics communities, but will result in more useful information for the climate policy community.