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| Titel |
The ScaLIng Macroweather Model (SLIMM): using scaling to forecast global-scale macroweather from months to decades |
| VerfasserIn |
S. Lovejoy, L. Rio Amador, R. Hébert |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2190-4979
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| Digitales Dokument |
URL |
| Erschienen |
In: Earth System Dynamics ; 6, no. 2 ; Nr. 6, no. 2 (2015-09-29), S.637-658 |
| Datensatznummer |
250115481
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| Publikation (Nr.) |
 copernicus.org/esd-6-637-2015.pdf |
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| Zusammenfassung |
On scales of ≈ 10 days (the lifetime
of planetary-scale structures), there is a drastic transition from
high-frequency weather to low-frequency macroweather. This scale is close to
the predictability limits of deterministic atmospheric models; thus, in GCM
(general circulation model) macroweather forecasts, the
weather is a high-frequency noise. However, neither the GCM noise nor the GCM
climate is fully realistic. In this paper we show how simple stochastic
models can be developed that use empirical data to force the statistics and
climate to be realistic so that even a two-parameter model can perform as
well as GCMs for annual global temperature forecasts.
The key is to exploit the scaling of the dynamics and the large stochastic
memories that we quantify. Since macroweather temporal (but not spatial)
intermittency is low, we propose using the simplest model based on fractional
Gaussian noise (fGn): the ScaLIng Macroweather Model (SLIMM). SLIMM is based
on a stochastic ordinary differential equation, differing from usual linear
stochastic models (such as the linear inverse modelling – LIM) in that it is
of fractional rather than integer order. Whereas LIM implicitly assumes that
there is no low-frequency memory, SLIMM has a huge memory that can be
exploited. Although the basic mathematical forecast problem for fGn has been
solved, we approach the problem in an original manner, notably using the
method of innovations to obtain simpler results on forecast skill and on the
size of the effective system memory.
A key to successful stochastic forecasts of natural macroweather variability
is to first remove the low-frequency anthropogenic component. A previous
attempt to use fGn for forecasts had disappointing results because this was
not done. We validate our theory using hindcasts of global and Northern
Hemisphere temperatures at monthly and annual resolutions. Several
nondimensional measures of forecast skill – with no adjustable parameters –
show excellent agreement with hindcasts, and these show some skill even on
decadal scales. We also compare our forecast errors with those of several GCM
experiments (with and without initialization) and with other stochastic
forecasts, showing that even this simplest two parameter SLIMM is somewhat
superior. In future, using a space–time (regionalized) generalization of
SLIMM, we expect to be able to exploit the system memory more extensively and
obtain even more realistic forecasts. |
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