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Toward Enhanced Understanding and Projections of Climate Extremes Using Physics-guided Data Mining Techniques : Volume 21, Issue 4 (28/07/2014)

By Ganguly, A. R.

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Book Id: WPLBN0004013920
Format Type: PDF Article :
File Size: Pages 19
Reproduction Date: 2015

Title: Toward Enhanced Understanding and Projections of Climate Extremes Using Physics-guided Data Mining Techniques : Volume 21, Issue 4 (28/07/2014)  
Author: Ganguly, A. R.
Volume: Vol. 21, Issue 4
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Agrawal, A., Kodra, E. A., Hays, C., Banerjee, A., Kawale, J., Chatterjee, S.,...Oglesby, R. (2014). Toward Enhanced Understanding and Projections of Climate Extremes Using Physics-guided Data Mining Techniques : Volume 21, Issue 4 (28/07/2014). Retrieved from http://hawaiilibrary.net/


Description
Description: Northeastern University, Boston, MA, USA. 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.

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

Excerpt
Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame, D. J., Kettleborough, J. A., Knight, S., Martin, A., Murphy, J. M., Piani, C., Sexton, D., Smith, L. A., Spicer, R. A., Thorpe, A. J., and Allen, M. R.: Uncertainty in predictions of the climate response to rising levels of greenhouse gases, Nature, 433, 403–406, doi:10.1038/nature03301, 2005.; Stainforth, D., Kettleborough, J., Allen, M., Collins, M., Heaps, A., and Murphy, J.: Distributed computing for public-interest climate modeling research, Comput. Sci. Eng., 4, 82–89, doi:10.1109/5992.998644, 2002.; Steinhaeuser, K. and Tsonis, A. A.: A climate model intercomparison at the dynamics level, Clim. Dynam., 42, 1665–1670, doi:10.1007/s00382-013-1761-5, 2013.; Steinhaeuser, K., Chawla, N. V.,and Ganguly, A. R.: Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science, Stat. Anal. Data Min., 4, 497–511, doi:10.1002/sam.10100, 2011a.; Steinhaeuser, K., Ganguly, A. R., and Chawla, N. V.: Multivariate and multiscale dependence in the global climate system revealed through complex networks, Clim. Dynam., 39, 889–895, doi:10.1007/s00382-011-1135-9, 2011b.; Steinhaeuser, K., Ganguly, A. R., and Chawla, N. V.: Multivariate and multiscale dependence in the global climate system revealed through complex networks, Clim. Dynam., 39, 3–4, doi:10.1007/s00382-011-1135-9, 2012.; Sterk, A. E., Holland, M. P., Rabassa, P., Broer, H. W., and Vitolo, R.: Predictability of extreme values in geophysical models, Nonlin. Processes Geophys., 19, 529–539, doi:10.5194/npg-19-529-2012, 2012.; Sugiyama, M., Shiogama, H., and Emori, S.: Precipitation extreme changes exceeding moisture content increases in MIROC and IPCC climate models, P. Natl. Acad. Sci. USA, 107, 571–575, doi:10.1073/pnas.0903186107, 2010.; Taylor, K. E.: An overview of CMIP5 and the experiment design, available from: http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1 (last access: 1 January 2014), 2012.; Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, doi:10.1175/BAMS-D-11-00094.1, 2012.; Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective, J. R. Stat. Soc. Ser. B, 73, 273–282, doi:10.1111/j.1467-9868.2011.00771.x, 2011.; Tippett, M. K., Sobel, A. H., and Camargo, S. J.: Association of U.S. tornado occurrence with monthly environmental parameters, Geophys. Res. Lett., 39, L02801, doi:10.1029/2011GL050368, 2012.; Towler, E., Rajagopalan, B., Gilleland, E., Summers, R. S., Yates, D., and Katz, R. W.: Modeling hydrologic and water quality extremes in a changing climate: A statistical approach based on extreme value theory, Water Resour. Res., 46, W11504, doi:10.1029/2009WR008876, 2010.; Trapp, R. J., Robinson, E. D., Baldwin, M. E., Diffenbaugh, N. S., and Schwedler, B. R. J.: Regional climate of hazardous convective weather through high-resolution dynamical downscaling, Clim. Dynam., 37, 677–688, doi:10.1007/s00382-010-08

 

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