1 / 8

Challenges in climate change research

Challenges in climate change research. Ramakrishna Tipireddy, Maud Comboul and Maarten Arnst. Challenges. Data Physics-based modeling Predictability and UQ Decision support Others. Small and Big Data.

ashlyn
Download Presentation

Challenges in climate change research

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Challenges in climate change research Ramakrishna Tipireddy, Maud Comboul and Maarten Arnst

  2. Challenges • Data • Physics-based modeling • Predictability and UQ • Decision support • Others

  3. Small and Big Data • Scarce and short time histories of “climate” dataVery large volumes of recent “weather” data • Y2008 GCMs generate tera/peta bytes of output

  4. Small and Big Data • http://www.ncdc.noaa.gov/paleo/icecore/antarctica/domec/domec_epica_data.html • http://www.earthsystemgrid.org/ (IPCC Working Group 1 data, Dataset Catalogus, IPCC) • http://ingrid.ldeo.columbia.edu/ • http://data.giss.nasa.gov/ • http://earthobservatory.nasa.gov/ • http://ecmwf.int/research/era/ERA-40_Atlas/docs/index.html • http://www.metoffice.gov.uk/research/hadleycentre/google/

  5. Physics-based modeling • Multiscale, multifacetal (physical, economical,…), high-dimensional, very nonlinear, and data driven. • How to deal with transients? • Strong nonlinearities: e.g. phase changes. • Multiple time scales: e.g. weather/climate, atmosphere/oceans. • Multiple spatial scales: flow over topography, cloud fronts, tornados, hurricanes, eddies, turbulence, mixing. • Feedback mechanisms. • Discretization. • Develop mathematical theory to direct/detect model improvement. • Sensor placement problem: where to take extra measurements to improve understanding/accuracy/…?

  6. Predictability and UQ • If Lorenz63 is already chaotic, how can we forecast in 10^10 variables? • Weather/climate is a fast/slow system.It is tractable because of the slow variations. • “Climate is a statistical collection of weather conditions during a specified interval of time (usually several decades).”Climate change is a change in the statistical distribution of the weather. • How to determine trends in extreme values? Intense rain, heat waves,…Climate change models were designed for means, not for extremes.

  7. J. Hacker, J. Hansen, J. Berner, Y. Chen, G. Eshel, G. Hakim, S. Lazarus, S. Majumdar, R. Morssi, A. Poje, V. Sheremet, Y. Tang, and C. Webb. Predictability. Bulletin of the American Meteoro-logical Society, 86:1733-1737, 2005. • B. Khouider, A.J. Majda, and M.A. Katsoulakis. Coarse-grained stochastic models for tropical convection and climate. Proceedings of the National Academy of Science, 21:11941-11946, 2003. • A.J. Majda, I. Timofeyev, and E. Vanden Eijnden. A mathematical framework for stochastic climate models. Communications on Pure and Applied Mathematics, 54:891-974, 2001. • A.J. Majda, C. Franzke, and R. Khouider. An applied mathematics perspective on stochastic modelling for climate. Philosophical Transactins of the Royal Society A, 366:2429-2455, 2008. • G.H. Roe and M.B. Baker. Why is climate sensitivity so unpredictable? Science, 318:629-632, 2007. • E.N. Lorenz. Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 20:130-141,1963. • E.N. Lorenz. Reflections on the conception, birth, and childhood of numerical weather prediction. Annual Reviews of Earth and Planetary Sciences, 34:37-45, 2006. • T.N. Palmer. Extended-range atmospheric prediction and the Lorenz model. Bulletin of the American Meteorological Society, 74:49-65, 1993. • T.N. Palmer, F.J. Doblas-Reyes, R. Hagedorn, and A.Weisheimer. Probabilistic predictin of climate using multi-model ensembles: from basics to applications. Philosophical Transactions of the Royal Society B, 360:1991-1998, 2005. • T.N. Palmer, G.J. Shutts, R. Hagedorn, F.J. Doblas-Reyes, T. Jung, and M. Leutbecher. Representing model uncertainty in weather and climate prediction. Annual Reviews of Earth and Planetary Sciences, 33:163-193, 2005. • T.N. Palmer, F.J. Doblas-Reyes, A. Weisheimer, and M.J. Rodwell. Toward seamless prediction. Bulletin of the American Meteorological Society, 89:459-470, 2008. • E. Evans, N. Bhatti, J. Kinney, L. Pann, M. Pena, S.-C. Yang, E. Kalnay, and J. Hansen. Rise undergraduates find that regime changes in Lorenz's model are predictable. Bulletin of the American Meteorological Society, 85:520-524, 2004. • M. Pena and E. Kalnay. Separating fast and slow modes in coupled chaotic systems. Nonlinear Processes in Geophysics, 11:319-327, 2004. • D. A. Stainforth, T. Aina, C. Christensen, M. Collins, N. Faull, D. J. Frame, J. A. Kettleborough, S. Knight, A. Martin, J. M. Murphy, C. Piani, D. Sexton, L. A. Smith, R. A. Spicer, A. J. Thorpe, and M. R. Allen. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433:403-406, 2005. • A. Lopez, C. Tebaldi, M. New, D. Stainforth, M. Allen, and J. Kettleborough. Two approaches to quantifying uncertainty in global temperature changes. Journal of Climate, 19:4785-4796, 2006. • M. Allen and D.A. Stainforth. Towards objective probabilistic climate forecasting. Nature, 419:228-232, 2002.

  8. Decision support • Decision support from imperfect models. Methods for packaging information/uncertainty so that it can better inform policy and decision support. • Thresholds: when do feedbacks become smaller than forcing? • On which space and time scales do we have decision-relevant information? • Sensitivity analysis.

More Related