A hybrid approach to interseasonal precipitation prediction ... - IMAGe

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A hybrid approach to interseasonal precipitation prediction ... - IMAGe

automated fitting and prediction algorithm to provide postdictions of seasonal rainfallanomalies for 2011 and spring 2012. The resulting 3 rd season ahead (minimally 6 monthahead) predictions showed an overall Heidke skill score of 56, vs that for the ½ monthahead predictions by the CPC for the same regions and times of 12 (100 is perfectprediction, 0 is chance, -50 is perfectly wrong). Figure 1 shows the results of runningthese experiments.A fifth experiment is currently being run with an aim to produce predictions fortime periods near 1970, 1990, and 2010 in Northern California. Preliminary results forthis new experiment, as well as more statistical details will be discussed at the Climateinformatics workshop.Deviations from standardized averagesSouthern California Region, observations and predictions by seasonPredictive R 5 season= 0.666 P= 3.4 e -6-2 -1 0 1 2OOXOXO2011 2012OXOXOOOO18 19 20 21 22 23 24 25OXOOXXXFigure 1standardized deviations from seasonal means-2 0 2 4New Jersey Region, observations and predictions by seasonPredictive R 5 season= 0.848 P= 6.76 e -13OOXOOX2011 2012OO Oobservations, Opredictions, XOX50% bounds -80% bounds -OOOOXOXO18 19 20 21 22 23 24 25XXXseasonseasonstandardized deviations from seasonal means-2 0 2 4Atlanta Region, observations and predictions by season,Predictive R 5 Season= 0.61 P= 1.02 e -4OOXOOOOXOO2011 2012OOOXOOXOO18 19 20 21 22 23 24 25XOOOOXXXstandardized deviations from seasonal means-2 -1 0 1 2 3Jacksonville Region, observations and predictions by seasonPredictive R 5 season= 0.646 P= 3.22 e -5OXOOOX O2011 2012OXOOOXO18 19 20 21 22 23 24 25OOXOOOX X Xseasonseason3. ConclusionA hybrid method of interseasonal to interannual prediction of regional climatefunctionals is described and appears to perform reasonably well across regions at a giventime period, and will be tested by the workshop within a given region sampling yearsacross multiple decades. To date the method seems simple (running entirely on a laptop)and to perform remarkably well given that regional precipitation prediction is consideredone of the hard problems in climate science.Acknowledgements: I would like to thank my director, David DiGiovanni, for allowingme time to work on this, Chris Anderson at Iowa State for providing early guidance onclimate science, and Claudia Tebaldi at Climate Central and UBC who first pointed me tothe fluctuation dissipation theorem as applied to climate.


References1. Weller, R. Anderson, J., Arribas,A., Goddard, L. Kalnay, E. Kerman, B., Koster,R. Richman, M., Saravanan, R., Waliser,D., and Wang, B., (2010), Assessmentof Interaseasonal to Interannual Climate Prediction and Predictability, NationalResearch Council.2. Hansen, J., Sato, M., Ruedy, R., Lo, K., Lea,D., and Medina-Elizade, M.,(2006),“Global Temperature Change”, PNAS, V103, #39, 14288-142933. Eckmann, J.P., and Ruelle, D. Ergodic Theory of chaos and strange attractors,Reviews of modern Physics, 57, 617-656, (1985)4. Sauer, T., Yoreck, J. and Casdagli, M. “Embedology”, Journal of Statistical Physics,65, 579-616 (1991)5. Leith C. E., (1975), “Climate response and fluctuation dissipation”, Journal ofAtmospheric Science, v32, 2022-20266. Gritsun, A., and Branstator, G., (2007), “Climate Response Using a ThreeDimensional Operator Based on the Fluctuation Dissipation Theorem”, Journal ofthe Atmospheric Sciences, V64, 2558-25757. Gritsun, A., Branstator, G., and Majda, A.,(2008),“Climate response of linear andquadratic functionals using the Fluctuation Dissipation Theorem”. Journal of theAtmospheric Sciences, V65, 2824-2841

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