Articles | Volume 6, issue 2
https://doi.org/10.5194/esd-6-637-2015
https://doi.org/10.5194/esd-6-637-2015
Research article
 | 
29 Sep 2015
Research article |  | 29 Sep 2015

The ScaLIng Macroweather Model (SLIMM): using scaling to forecast global-scale macroweather from months to decades

S. Lovejoy, L. del Rio Amador, and R. Hébert

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Cited articles

Ammann, C. M. and Wahl, E. R.: The importance of the geophysical context in statistical evaluations of climate reconstruction procedures, Climatic Change, 85, 71–88, https://doi.org/10.1007/s10584-007-9276-x, 2007.
Baillie, R. T. and Chung, S.-K.: Modeling and forecasting from trend-stationary long memory models with applications to climatology, Int. J. Forecast., 18, 215–226, 2002a.
Baillie, R. T. and Chung, S.-K.: Modeling and forecasting from trend-stationary long memory models with applications to climatology, Int. J. Forecast., 18, 215–226, 2002b.
Biagini, F., Hu, Y., Øksendal, B., and Zhang, T.: Stochastic Calculus for Fractional Brownian Motion and Applications, Springer-Verlag, London, 2008.
Blender, R., Fraedrich, K., and Hunt, B.: Millennial climate variability: GCM-simulation and Greenland ice cores, Geophys. Res. Lett., 33, L04710, https://doi.org/10.1029/2005GL024919, 2006.
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Short summary
Numerical climate models forecast the weather well beyond the deterministic limit. In this “macroweather” regime, they are random number generators. Stochastic models can have more realistic noises and can be forced to converge to the real-world climate. Existing stochastic models do not exploit the very long atmospheric and oceanic memories. With skill up to decades, our new ScaLIng Macroweather Model (SLIMM) exploits this to make forecasts more accurate than GCMs.
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