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JournalISSN: 0148-0227

Journal of Geophysical Research 

About: Journal of Geophysical Research is an academic journal. The journal publishes majorly in the area(s): Solar wind & Magnetosphere. It has an ISSN identifier of 0148-0227. Over the lifetime, 111092 publication(s) have been published receiving 5900202 citation(s).

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Journal ArticleDOI
Abstract: [1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1° latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5° latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reduced-space optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month-to-month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.

7,777 citations

Journal ArticleDOI
Abstract: A rapid and accurate radiative transfer model (RRTM) for climate applications has been developed and the results extensively evaluated. The current version of RRTM calculates fluxes and cooling rates for the longwave spectral region (10–3000 cm−1) for an arbitrary clear atmosphere. The molecular species treated in the model are water vapor, carbon dioxide, ozone, methane, nitrous oxide, and the common halocarbons. The radiative transfer in RRTM is performed using the correlated-k method: the k distributions are attained directly from the LBLRTM line-by-line model, which connects the absorption coefficients used by RRTM to high-resolution radiance validations done with observations. Refined methods have been developed for treating bands containing gases with overlapping absorption, for the determination of values of the Planck function appropriate for use in the correlated-k approach, and for the inclusion of minor absorbing species in a band. The flux and cooling rate results of RRTM are linked to measurement through the use of LBLRTM, which has been substantially validated with observations. Validations of RRTM using LBLRTM have been performed for the midlatitude summer, tropical, midlatitude winter, subarctic winter, and four atmospheres from the Spectral Radiance Experiment campaign. On the basis of these validations the longwave accuracy of RRTM for any atmosphere is as follows: 0.6 W m−2 (relative to LBLRTM) for net flux in each band at all altitudes, with a total (10–3000 cm−1) error of less than 1.0 W m−2 at any altitude; 0.07 K d−1 for total cooling rate error in the troposphere and lower stratosphere, and 0.75 K d−1 in the upper stratosphere and above. Other comparisons have been performed on RRTM using LBLRTM to gauge its sensitivity to changes in the abundance of specific species, including the halocarbons and carbon dioxide. The radiative forcing due to doubling the concentration of carbon dioxide is attained with an accuracy of 0.24 W m−2, an error of less than 5%. The speed of execution of RRTM compares favorably with that of other rapid radiation models, indicating that the model is suitable for use in general circulation models.

5,946 citations

Journal ArticleDOI
Abstract: A diagram has been devised that can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their variances. Although the form of this diagram is general, it is especially useful in evaluating complex models, such as those used to study geophysical phenomena. Examples are given showing that the diagram can be used to summarize the relative merits of a collection of different models or to track changes in performance of a model as it is modified. Methods are suggested for indicating on these diagrams the statistical significance of apparent differences and the degree to which observational uncertainty and unforced internal variability limit the expected agreement between model-simulated and observed behaviors. The geometric relationship between the statistics plotted on the diagram also provides some guidance for devising skill scores that appropriately weight among the various measures of pattern correspondence.

4,471 citations

Journal ArticleDOI
Abstract: A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computational load for reasonable accuracy is only a fraction of what is required for the extended Kalman filter and is given by the storage of, say, 100 model states for an ensemble size of 100 and thus CPU requirements of the order of the cost of 100 model integrations. The proposed method can therefore be used with realistic nonlinear ocean models on large domains on existing computers, and it is also well suited for parallel computers and clusters of workstations where each processor integrates a few members of the ensemble.

4,357 citations

Journal ArticleDOI
Abstract: An earthquake model is derived by considering the effective stress available to accelerate the sides of the fault. The model describes near- and far-field displacement-time functions and spectra and includes the effect of fractional stress drop. It successfully explains the near- and far-field spectra observed for earthquakes and indicates that effective stresses are of the order of 100 bars. For this stress, the estimated upper limit of near-fault particle velocity is 100 cm/sec, and the estimated upper limit for accelerations is approximately 2g at 10 Hz and proportionally lower for lower frequencies. The near field displacement u is approximately given by u(t) = (σ/μ) βr(1 - e−t/r) where. σ is the effective stress, μ is the rigidity, β is the shear wave velocity, and τ is of the order of the dimension of the fault divided by the shear-wave velocity. The corresponding spectrum is Ω(ω)=σβμ1ω(ω2+τ−2)1/2(1) The rms average far-field spectrum is given by 〈 Ω(ω) 〉=〈 Rθϕ 〉σβμrRF(e)1ω2+α2(2) where 〈Rθϕ〉 is the rms average of the radiation pattern; r is the radius of an equivalent circular dislocation surface; R is the distance; F(e) = {[2 – 2e][1 – cos (1.21 eω/α)] +e2}1/2; e is the fraction of stress drop; and α = 2.21 β/r. The rms spectrum falls off as (ω/α)−2 at very high frequencies. For values of ω/α between 1 and 10 the rms spectrum falls off as (ω/α)−1 for e < ∼0.1. At low frequencies the spectrum reduces to the spectrum for a double-couple point source of appropriate moment. Effective stress, stress drop and source dimensions may be estimated by comparing observed seismic spectra with the theoretical spectra.

4,176 citations

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