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  • 1
    Publication Date: 2017-03-01
    Description: The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.
    Print ISSN: 0003-0007
    Electronic ISSN: 1520-0477
    Topics: Geography , Physics
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  • 2
    Publication Date: 2019-01-28
    Description: Parameterizations are developed for each of the subgrid turbulence interaction classes in fully three-dimensional global atmospheric flows over topography, typical of January and July climate states. Stochastic and deterministic parameterizations are developed for the eddy–eddy interactions and deterministic parameterizations for eddy–mean field, eddy–topographic, mean field–mean field, and mean field–topographic interactions. All parameterizations are calculated from the statistics of higher-resolution reference direct numerical simulations (DNSs) truncated into resolved and subgrid scales and employed without tuning coefficients. This parameterization framework is validated by performing large-eddy simulations (LESs) that closely agree with the reference DNSs in terms of time-averaged kinetic energy spectra, zonal jet structure, and nonzonal streamfunction fields. Both the DNSs and LESs are formulated in such a way that the usual problem of a long artificial dissipation range does not occur. Successful LESs are produced with truncation wavenumbers 31 and 15, using, respectively, only 11.9% and 1.3% of the DNS computational effort at truncation wavenumber 63. The lower-resolution LESs show that the parameterizations are successful even when the energy injection due to baroclinic instability is not completely resolved. The contribution of each of the parameterized interaction classes to the quality of the LES is identified. The best agreement is achieved when all subgrid components are included. There is a very high level of agreement between the LESs and DNSs with typical streamfunction pattern correlations of r = 0.962 for the nonzonal components and r = 0.999 for the total fields when the complete sets of parameterizations are used.
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
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  • 3
  • 4
    Publication Date: 2020-02-17
    Description: Recent studies have shown that regardless of model configuration, skill in predicting El Niño–Southern Oscillation (ENSO), in terms of target month and forecast lead time, remains largely dependent on the temporal characteristics of the boreal spring predictability barrier. Continuing the 2019 study by O’Kane et al., we compare multiyear ensemble ENSO forecasts from the Climate Analysis Forecast Ensemble (CAFE) to ensemble forecasts from state-of-the-art dynamical coupled models in the North American Multimodel Ensemble (NMME) project. The CAFE initial perturbations are targeted such that they are specific to tropical Pacific thermocline variability. With respect to individual NMME forecasts and multimodel ensemble averages, the CAFE forecasts reveal improvements in skill when predicting ENSO at lead times greater than 6 months, in particular when predictability is most strongly limited by the boreal spring barrier. Initial forecast perturbations generated exclusively as disturbances in the equatorial Pacific thermocline are shown to improve the forecast skill at longer lead times in terms of anomaly correlation and the random walk sign test. Our results indicate that augmenting current initialization methods with initial perturbations targeting instabilities specific to the tropical Pacific thermocline may improve long-range ENSO prediction.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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  • 5
    Publication Date: 2019-07-25
    Description: The Madden–Julian oscillation (MJO) is presented as a series of interacting Rossby and inertial gravity waves of varying vertical scales and meridional extents. These components are isolated by decomposing reanalysis fields into a set of normal mode functions (NMF), which are orthogonal eigenvectors of the linearized primitive equations on a sphere. The NMFs that demonstrate spatial properties compatible with the MJO are inertial gravity waves of zonal wavenumber k = 1 and the lowest meridional index n = 0, and Rossby waves with (k, n) = (1, 1). For these horizontal scales, there are multiple small vertical-scale baroclinic modes that have temporal properties indicative of the MJO. On the basis of one such eastward-propagating inertial gravity wave (i.e., a Kelvin wave), composite averages of the Japanese 55-year Reanalysis demonstrate an eastward propagation of the velocity potential, and oscillation of outgoing longwave radiation and precipitation fields over the Maritime Continent, with an MJO-appropriate temporal period. A cross-spectral analysis indicates that only the MJO time scale is coherent between this Kelvin wave and the more energetic modes. Four mode clusters are identified: Kelvin waves of correct phase period and direction, Rossby waves of correct phase period, energetic Kelvin waves of larger vertical scales and meridional extents extending into the extratropics, and energetic Rossby waves of spatial scales similar to that of the energetic Kelvin waves. We propose that within this normal mode framework, nonlinear interactions between the aforementioned mode groups are required to produce an energetic MJO propagating eastward with an intraseasonal phase period. By virtue of the selected mode groups, this theory encompasses both multiscale and tropical–extratropical interactions.
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
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  • 6
    Publication Date: 2012-03-30
    Description: Subgrid-scale parameterizations with self-similar scaling laws are developed for large-eddy simulations (LESs) of atmospheric flows. The key new contribution is the development of scaling laws that govern how these parameterizations depend on the LES resolution and flow strength. Both stochastic and deterministic representations of the effects of subgrid-scale eddies on the retained scales are considered. The stochastic subgrid model consists of a backscatter noise term and a drain eddy viscosity, while in the deterministic subgrid model the net effect of these two terms is represented by a net eddy viscosity. In both cases the subgrid transfers are calculated self-consistently from the statistics of higher-resolution-reference direct numerical simulations (DNSs). The dependence of the subgrid parameterizations on the resolution of the LESs is determined for DNSs having resolutions up to triangular 504 wavenumber truncations. The subgrid parameterizations are developed for typical large-scale atmospheric flows and for different strengths and spectra of kinetic energy within a quasigeostrophic spectral model. LESs using the stochastic and deterministic subgrid parameterizations are shown to replicate the kinetic energy spectra of the reference DNS at the scales of the LESs. It is found that the maximum strengths of the drain, net, and backscatter viscosities satisfy scaling laws dependent on the LES truncation wavenumber and that the dependence of these eddy viscosities on total wavenumber can also be written as essentially universal functions that depend on flow strength and resolution. The scaling laws make the subgrid-scale parameterizations more generally applicable in LESs and remove the need to generate them from reference DNSs.
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
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  • 7
    Publication Date: 2016-04-14
    Description: Due to the massive disparity between the largest and smallest eddies in the atmosphere and ocean, it is not possible to simulate these flows by explicitly resolving all scales on a computational grid. Instead the large scales are explicitly resolved, and the interactions between the unresolved subgrid turbulence and large resolved scales are parameterised. If these interactions are not properly represented then an increase in resolution will not necessarily improve the accuracy of the large scales. This has been a significant and long-standing problem since the earliest climate simulations. Historically subgrid models for the atmosphere and ocean have been developed in isolation, with the structure of each motivated by different physical phenomena. Here we solve the turbulence closure problem by determining the parameterisation coefficients (eddy viscosities) from the subgrid statistics of high-resolution quasi-geostrophic atmospheric and oceanic simulations. These subgrid coefficients are characterised into a set of simple unifying scaling laws, for truncations made within the enstrophy-cascading inertial range. The ocean additionally has an inverse energy cascading range, within which the subgrid model coefficients have different scaling properties. Simulations adopting these scaling laws are shown to reproduce the statistics of the reference benchmark simulations across resolved scales, with orders of magnitude improvement in computational efficiency. This reduction in both resolution dependence and computational effort will improve the efficiency and accuracy of geophysical research and operational activities that require data generated by general circulation models, including weather, seasonal, and climate prediction; transport studies; and understanding natural variability and extreme events.
    Print ISSN: 1023-5809
    Electronic ISSN: 1607-7946
    Topics: Geosciences , Physics
    Published by Copernicus on behalf of European Geosciences Union.
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  • 8
    Publication Date: 2020-05-21
    Description: Data assimilation (DA) experiments are performed to assess impacts of observations in climate model state estimation through the cross-domain ocean–atmosphere forecast error covariances (cross covariances). Specifically, we explore strongly and weakly coupled DA variants using the Climate Analysis Forecast Ensemble (CAFE) system. This comprises 96 ensemble members of the Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 climate model assimilating observational data from the ocean, atmosphere, and sea ice realms with the ensemble Kalman filter (EnKF). Sequences of atmospheric synoptic time-scale coupled forecasts (7 days) are carried out with model consistent initialization. Unassimilated forward-independent observations are used to quantify forecast innovation error-growth rates. The results show benefit for the slow components of the atmosphere and ocean subsurface when strongly coupling ocean observations to the atmosphere. In the present system, projecting fast atmospheric observations onto the ocean subsurface through the cross covariances benefits the oceanic and atmospheric near-surface layers; however, this leads to deterioration in the ocean subsurface. Particular variants of coupled DA are able to constrain the ocean and atmosphere. The forecasts initialized with these variants have predictability at intraseasonal time scales. Errors associated with the dominant intraseasonal mode of variability, the Madden–Julian oscillation (MJO), are decomposed into normal mode functions. Consistent with recent studies showing large MJO events are concurrent with rapid error growth associated with nonlinear interactions, we find a clear relationship between the strength of a given MJO event and the related forecast innovations. Our results demonstrate consistent system behavior in relation to capturing real-world disturbances that affect climate predictability.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
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  • 9
    Publication Date: 2008-10-01
    Print ISSN: 0894-1777
    Electronic ISSN: 1879-2286
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Elsevier
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  • 10
    Publication Date: 2020-02-19
    Description: The basis and challenge of strongly coupled data assimilation (CDA) is the accurate representation of cross-domain covariances between various coupled subsystems with disparate spatio-temporal scales, where often one or more subsystems are unobserved. In this study, we explore strong CDA using ensemble Kalman filtering methods applied to a conceptual multiscale chaotic model consisting of three coupled Lorenz attractors. We introduce the use of the local attractor dimension (i.e. the Kaplan–Yorke dimension, dimKY) to prescribe the rank of the background covariance matrix which we construct using a variable number of weighted covariant Lyapunov vectors (CLVs). Specifically, we consider the ability to track the nonlinear trajectory of each of the subsystems with different variants of sparse observations, relying only on the cross-domain covariance to determine an accurate analysis for tracking the trajectory of the unobserved subdomain. We find that spanning the global unstable and neutral subspaces is not sufficient at times where the nonlinear dynamics and intermittent linear error growth along a stable direction combine. At such times a subset of the local stable subspace is also needed to be represented in the ensemble. In this regard the local dimKY provides an accurate estimate of the required rank. Additionally, we show that spanning the full space does not improve performance significantly relative to spanning only the subspace determined by the local dimension. Where weak coupling between subsystems leads to covariance collapse in one or more of the unobserved subsystems, we apply a novel modified Kalman gain where the background covariances are scaled by their Frobenius norm. This modified gain increases the magnitude of the innovations and the effective dimension of the unobserved domains relative to the strength of the coupling and timescale separation. We conclude with a discussion on the implications for higher-dimensional systems.
    Print ISSN: 1023-5809
    Electronic ISSN: 1607-7946
    Topics: Geosciences , Physics
    Published by Copernicus on behalf of European Geosciences Union.
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