Abstract
Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean–land–atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.
Highlights
One of the largest scientific gaps in climate change studies is the inability to develop credible projections of extremes with the degree of precision required for adaptation decisions and policy (Fischer et al, 2013)
The 2014 Climate Data Initiative (Lehmann, 2014) launched by the White House (United States President’s Office) points to big data as a solution for climate adaptation and lends further urgency of the theme discussed in this manuscript
This paper emphasizes the need to intelligently combine an understanding of physics with data mining, not just to avoid the risk of generating misleading insights, and to produce novel results that may not have been possible otherwise
Summary
Physical science-based analyses tend to emphasize mechanistic understanding and attribution; statistical analyses generally develop data-driven techniques for descriptive and predictive analyses (for example, recent applications of extreme value theory, change detection and sparse regression to climate extremes); and impact studies tend to focus on exposure, vulnerability and consequence assessments. The wealth of data continues to increase, as does our conceptual understanding of processes that may generate extremes, such as the influence of oceans and climate oscillators, and local or regional terrestrial drivers. Dependence characterization and data-driven predictive modeling may be conditioned on the results of physics-based models, and further based on physical or process understanding, that in turn may be difficult to capture within the current set of model parameterizations. Ocean or atmospheric temperatures from climate models may generate better characterizations and projections of precipitation extremes statistics with.
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