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

Related authors

Geographic variability of dust and temperature in climate scaling regimes over the Last Glacial Cycle
Nicolás Acuña Reyes, Elwin van ’t Wout, Fabrice Lambert, and Shaun Lovejoy
EGUsphere, https://doi.org/10.5194/egusphere-2023-1858,https://doi.org/10.5194/egusphere-2023-1858, 2023
Short summary
Review article: Scaling, dynamical regimes, and stratification. How long does weather last? How big is a cloud?
Shaun Lovejoy
Nonlin. Processes Geophys., 30, 311–374, https://doi.org/10.5194/npg-30-311-2023,https://doi.org/10.5194/npg-30-311-2023, 2023
Short summary
Fractional relaxation noises, motions and the fractional energy balance equation
Shaun Lovejoy
Nonlin. Processes Geophys., 29, 93–121, https://doi.org/10.5194/npg-29-93-2022,https://doi.org/10.5194/npg-29-93-2022, 2022
Short summary
The fractional energy balance equation for climate projections through 2100
Roman Procyk, Shaun Lovejoy, and Raphael Hébert
Earth Syst. Dynam., 13, 81–107, https://doi.org/10.5194/esd-13-81-2022,https://doi.org/10.5194/esd-13-81-2022, 2022
Short summary
The half-order energy balance equation – Part 1: The homogeneous HEBE and long memories
Shaun Lovejoy
Earth Syst. Dynam., 12, 469–487, https://doi.org/10.5194/esd-12-469-2021,https://doi.org/10.5194/esd-12-469-2021, 2021
Short summary

Related subject area

Earth system change: climate prediction
Past and future response of the North Atlantic warming hole to anthropogenic forcing
Saïd Qasmi
Earth Syst. Dynam., 14, 685–695, https://doi.org/10.5194/esd-14-685-2023,https://doi.org/10.5194/esd-14-685-2023, 2023
Short summary
Performance-based sub-selection of CMIP6 models for impact assessments in Europe
Tamzin E. Palmer, Carol F. McSweeney, Ben B. B. Booth, Matthew D. K. Priestley, Paolo Davini, Lukas Brunner, Leonard Borchert, and Matthew B. Menary
Earth Syst. Dynam., 14, 457–483, https://doi.org/10.5194/esd-14-457-2023,https://doi.org/10.5194/esd-14-457-2023, 2023
Short summary
Emergent constraints for the climate system as effective parameters of bulk differential equations
Chris Huntingford, Peter M. Cox, Mark S. Williamson, Joseph J. Clarke, and Paul D. L. Ritchie
Earth Syst. Dynam., 14, 433–442, https://doi.org/10.5194/esd-14-433-2023,https://doi.org/10.5194/esd-14-433-2023, 2023
Short summary
Ensemble forecast of an index of the Madden–Julian Oscillation using a stochastic weather generator based on circulation analogs
Meriem Krouma, Riccardo Silini, and Pascal Yiou
Earth Syst. Dynam., 14, 273–290, https://doi.org/10.5194/esd-14-273-2023,https://doi.org/10.5194/esd-14-273-2023, 2023
Short summary
Reconstructions and predictions of the global carbon budget with an emission-driven Earth system model
Hongmei Li, Tatiana Ilyina, Tammas Loughran, Aaron Spring, and Julia Pongratz
Earth Syst. Dynam., 14, 101–119, https://doi.org/10.5194/esd-14-101-2023,https://doi.org/10.5194/esd-14-101-2023, 2023
Short summary

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.
Download
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.
Altmetrics
Final-revised paper
Preprint