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

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Title Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques
 
Creator GANGULY, AR
KODRA, EA
AGRAWAL, A
BANERJEE, A
BORIAH, S
CHATTERJEE, S
CHATTERJEE, S
CHOUDHARY, A
DAS, D
FAGHMOUS, J
GANGULI, P
GHOSH, S
HAYHOE, K
HAYS, C
HENDRIX, W
FU, Q
KAWALE, J
KUMAR, D
KUMAR, V
LIAO, W
LIESS, S
MAWALAGEDARA, R
MITHAL, V
OGLESBY, R
SALVI, K
SNYDER, PK
STEINHAEUSER, K
WANG, D
WUEBBLES, D
 
Subject PRECIPITATION EXTREMES
PART I
DOWNSCALING EXTREMES
WARMING ENVIRONMENT
TROPICAL CYCLONES
REGRESSION-MODELS
CLUSTER-ANALYSIS
TYPHOON TRACKS
CARBON-CYCLE
OCEAN MODEL
 
Description 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.
 
Publisher COPERNICUS GESELLSCHAFT MBH
 
Date 2014-12-29T06:37:19Z
2014-12-29T06:37:19Z
2014
 
Type Article
 
Identifier NONLINEAR PROCESSES IN GEOPHYSICS, 21(4)777-795
1023-5809
http://dx.doi.org/10.5194/npg-21-777-2014
http://dspace.library.iitb.ac.in/jspui/handle/100/17327
 
Language English