Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

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.

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

“…For example, the use of physical principles to constrain spatiotemporal pattern mining algorithms has been explored in [81], [82] for finding ocean eddies from satellite data. The need to explore TGDS models for uncertainty quantification is discussed in [33] in the context of understanding and projecting climate extremes. Scientific knowledge can also be used to advance other aspects of data science, e.g., the design of scientific work-flows [83], [84] or the generation of model simulations [85].…”

“…Some examples include the discovery of novel climate patterns and relationships [18], [19], closure of knowledge gaps in turbulence modeling efforts [20], [21], discovery of novel compounds in material science [22], [23], [24], design of density functionals in quantum chemistry [25], improved imaging technologies in bio-medical science [26], [27], discovery of genetic biomarkers [28], and the estimation of surface water dynamics at a global scale [29], [30]. These efforts have been complemented with recent review papers [8], [31], [32], [33], workshops (e.g., a 2016 conference on physics informed machine learning [34]) and industry initiatives (e.g., a recent IBM Research initiative on "physical analytics" [35]).…”

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

“…Domain expertise will be needed to frame questions, identify inputs, construct suitable model architectures, and interpret results. There is also evidence that domain expertise of physical principles can improve machine learning outcomes (Ganguly et al, 2014;Karpatne et al, 2017).…”

Evaluating wind extremes in CMIP5 climate models

“…Several studies have been performed on extreme precipitation and temperature under climate change (Solomon et al 2007; Kao and Ganguly 2011;Coumou and Rahmstorf 2012;Field et al 2012;Stocker et al 2013;Ganguly et al 2014;Kodra and Ganguly 2014). However, the impact of climate change on wind extremes has not received similar attention even though they have effects on energy sectors Barthelmie 2010, 2013), design and safety of buildings and bridges (ASCE 7-05: Minimum Design Loads for Building and Structures), insurance industry (Born and Viscusi 2006), and coastal ecosystems (Iles et al 2012).…”