The temporal cascade structure of reanalyses and global circulation models

Stolle, Jonathan; Lovejoy, S.; Schertzer, D.

doi:10.1002/qj.1916

Cited by 11 publications

(7 citation statements)

References 37 publications

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“…AchutaRao and Sperber 2006) as an Orenstein-Uhlenbeck process i.e. with β w = 2, β mw = 0, corresponding to H w = 1/2, H mw = −1/2, although this is not a very accurate approximation and can be misleading (Stolle 2012). Finally, (Vallis 2010) proposed a (nonscaling) mechanism by suggesting that τ w is determined by the lifetimes of baroclinic instabilities.…”

Section: Scaling In the Weather Macroweather And Climate Regimesmentioning

confidence: 95%

A voyage through scales, a missing quadrillion and why the climate is not what you expect

Lovejoy

2014

Clim Dyn

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116

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with the help of the new, pedagogical "H model". Both deterministic General Circulation Models (GCM's) with fixed forcings ("control runs") and stochastic turbulencebased models reproduce weather and macroweather, but not the climate; for this we require "climate forcings" and/ or new slow climate processes. Averaging macroweather over periods increasing to ≈30-50 yrs yields apparently converging values: macroweather is "what you expect". Macroweather averages over ≈30-50 yrs have the lowest variability, they yield well defined climate states and justify the otherwise ad hoc "climate normal" period. However, moving to longer periods, these states increasingly fluctuate: just as with the weather, the climate changes in an apparently unstable manner; the climate is not what you expect. Moving to time scales beyond 100 kyrs, to the macroclimate regime, we find that averaging the varying climate increasingly converges, but ultimately-at scales beyond ≈0.5 Myr in the megaclimate, we discover that the apparent point of convergence itself starts to "wander", presumably representing shifts from one climate to another.

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Section: Scaling In the Weather Macroweather And Climate Regimesmentioning

confidence: 95%

A voyage through scales, a missing quadrillion and why the climate is not what you expect

Lovejoy

2014

Clim Dyn

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116

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“…Stolle et al . [, ] undertook a wide survey of the spatial and temporal scaling behavior of turbulent fluxes associated with temperature, humidity, and wind speed in hydrostatic global models and reanalysis products. Note that one important caveat in reanalysis fields is that they typically are submitted to significant adjustments via data assimilation, which typically does not require that scaling properties of model simulations be preserved, or that the scaling properties of observations be matched.…”

Section: Introductionmentioning

confidence: 99%

The nonconvective/convective structural transition in stochastic scaling of atmospheric fields

Nogueira

Barros

2014

JGR Atmospheres

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High-resolution numerical weather prediction simulations are able to reproduce observed stochastic scale invariant behavior of atmospheric wind and water fields down to the effective model resolution, which is shown to be a process-dependent transient property that varies with the underlying dynamics. The effective resolution gain in dynamical downscaling of convective regimes is substantially smaller than the grid size decrease indicating that improvements in the model's capacity to resolve small-scale processes require consistent adjustments including both numerical formulation and physical parameterizations. Instantaneous realizations of simulated atmospheric wind and water fields exhibit robust multifractal properties with intrinsically transient scaling behavior depending on the underlying atmospheric state. In particular, a sharp transition in the scaling parameters between nonconvective and convective conditions is found, which explains different scaling regimes reported in the literature for atmospheric wind, temperature, and moisture observations. Spectral slopes around 2-2.3 arise under nonconvective or very weak convective conditions, tightly related to the scaling behavior of the underlying topography. In convective situations the transient scaling exponents remain under 5/3 in agreement with the Kolmogorov turbulent regime accounting for the intermittency correction. These findings have important implications for stochastic downscaling and the implementation of stochastic subgrid scale parameterizations using fractal methods. Specifically, it is shown that, based on scaling arguments, subgrid scale probability distributions of atmospheric moisture can be obtained from the coarse resolution information alone. Our results suggest that fractal methods can be used for estimating temporally and spatially varying regime-based subgrid scale statistics (and realizations of moisture fields) in real time and in a computationally efficient manner that could be useful in climate models.

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“…In the left column (weather regime), the figure visually displays the strong turbulent (weather) intermittency that culminated in the 1980s with the discovery of multifractals and an understanding of their generic process, cascades. In multifractals, the variability builds up scale after scale, so that while numerical weather models do capture the cascades and multifractality with some accuracy (Stolle et al, 2009(Stolle et al, , 2012, although they lack a wide enough range of scales to accurately reproduce the extremes. Fortunately, for macroweather in time (top middle), the intermittency is much smaller so that Gaussian approximations may be adequate for many purposes.…”

Section: Intermittency Multifractals and Parisi's Contributionmentioning

confidence: 99%

The 2021 “Complex Systems” Nobel Prize: The Climate, With and Without Geocomplexity

Lovejoy

2022

AGU Advances

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This year's physics Nobel prize marks history as the first for geophysics since 1947 when Edward Appleton won it for ionospheric research. Half-shared by statistical physicist Georgio Parisi and half by climate scientists Syukoro Manabe and Klauss Hasselmann, the prize was nominally for "complex systems," yet the two halves of the work were so disparate that at least one of the winners (Manabe) reportedly confessed to never having heard of another (Parisi). Ironically, Parisi's important contribution to multifractals was not even mentioned in the committee's 18 page report (Nobel Committee for Physics, 2021) in spite of its significant atmospheric and climate applications (see below). In addition, nonagenarians Manabe and Hasselmann were honored primarily for work in the 1960s and 1970s-before the Nonlinear revolution and before complexity science even existed. In this commentary, I focus on the climate half of the prize giving a succinct update on complexity applied to geoscience: geocomplexity.Complexity science in general-and geocomplexity in particular-emerged in the wake of the 1980s nonlinear revolution: notably deterministic chaos, fractals, nonlinear waves, self organized criticality and somewhat later, network theory. Complexity physics took shape in the 1990s (see the review, Nicolis & Nicolis, 2012) whereas nonlinear geoscience can be roughly dated from the workshops on Nonlinear VAriability in Geophysics (NVAG 1-4, 1986(NVAG 1-4, -1997, the establishment of the Nonlinear Processes division at the European Geophysical Society (now European Geophysical Union, EGU, 1989), the Nonlinear Geophysics focus group at the American Geophysical Union (AGU, 1997) and in 2009, an AGU session with accompanying geocomplexity workshop (Lovejoy et al., 2009). Recently, a group of AGU and EGU geocomplexity scientists collaborated in the establishment of the ongoing Climate Variability Across-Scales working group of PAGES (

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The temporal cascade structure of reanalyses and global circulation models

Cited by 11 publications

References 37 publications

A voyage through scales, a missing quadrillion and why the climate is not what you expect

A voyage through scales, a missing quadrillion and why the climate is not what you expect

The nonconvective/convective structural transition in stochastic scaling of atmospheric fields

The 2021 “Complex Systems” Nobel Prize: The Climate, With and Without Geocomplexity

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