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| Titel |
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques |
| VerfasserIn |
A. R. Ganguly, E. A. Kodra, A. Agrawal, A. Banerjee, S. Boriah, Sn. Chatterjee, So. Chatterjee, A. Choudhary, D. Das, J. Faghmous, P. Ganguli, S. Ghosh, K. Hayhoe, C. Hays, W. Hendrix, Q. Fu, J. Kawale, D. Kumar, V. Kumar, W. Liao, S. Liess, R. Mawalagedara, V. Mithal, R. Oglesby, K. Salvi, P. K. Snyder, K. Steinhaeuser, D. Wang, D. Wuebbles |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 4 ; Nr. 21, no. 4 (2014-07-28), S.777-795 |
| Datensatznummer |
250120929
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| Publikation (Nr.) |
 copernicus.org/npg-21-777-2014.pdf |
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| Zusammenfassung |
| 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. |
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