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M. J. Harrison and R. W. Hallberg

Abstract

Equatorial turbulent diffusivities resulting from breaking gravity waves may be more than a factor of 10 less than those in the midlatitudes. A coupled general circulation model with a layered isopycnal coordinate ocean is used to assess Pacific climate sensitivity to a latitudinally varying background diapycnal diffusivity with extremely low values near the equator.

The control experiments have a minimum upper-ocean diffusivity of 10−5 m2 s−1 and are initialized from present-day conditions. The average depth of the σθ = 26.4 interface (z 26.4) in the Pacific increases by ∼140 m after 500 yr of coupled model integration. This corresponds to a warming trend in the upper ocean. Low equatorial diffusivities reduce the z 26.4 bias by ∼30%. Isopycnal surfaces are elevated from the eastern boundary up to midlatitudes by cooling in the upper several hundred meters, partially compensated by freshening. Entrainment of intermediate water masses from below σθ = 26.4 decreases by ∼1.5 Sv (1 Sv ≡ 106 m3 s−1), mainly in the western tropical Pacific. The Pacific heat uptake (30°S–30°N) from the atmosphere reduces by ∼0.1 PW. This is associated with warmer entrainment temperatures in the eastern equatorial Pacific upwelling region. Equatorward heat transport from the Southern Ocean increases by ∼0.07 PW.

Reducing the upper-ocean background diffusivity uniformly to 10−6 m2 s−1 cools the upper ocean from the tropics, but warms and freshens from the midlatitudes. Enhanced convergence into the Pacific of water lighter than σθ = 26.4 compensates the reduction in upwelling of intermediate waters in the tropics. Basin-averaged z 26.4 bias increases in the low background case.

These results demonstrate basin-scale sensitivity to the observed suppression of equatorial background dissipation. This has clear implications for understanding oceanic heat uptake in the Pacific as well as other important aspects of the climate system. Diapycnal diffusivities due to truncation errors and other numerical artifacts in ocean models may need to be less than 10−6 m2 s−1 in order to accurately represent this effect in climate models.

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S. Zhang, M. J. Harrison, A. Rosati, and A. Wittenberg

Abstract

A fully coupled data assimilation (CDA) system, consisting of an ensemble filter applied to the Geophysical Fluid Dynamics Laboratory’s global fully coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members, which can be used directly to initialize probabilistic climate forecasts. Here, the CDA is evaluated using a series of perfect model experiments, in which a particular twentieth-century simulation—with temporally varying greenhouse gas and natural aerosol radiative forcings—serves as a “truth” from which observations are drawn, according to the actual ocean observing network for the twentieth century. These observations are then assimilated into a coupled model ensemble that is subjected only to preindustrial forcings. By examining how well this analysis ensemble reproduces the “truth,” the skill of the analysis system in recovering anthropogenically forced trends and natural climate variability is assessed, given the historical observing network. The assimilation successfully reconstructs the twentieth-century ocean heat content variability and trends in most locations. The experiments highlight the importance of maintaining key physical relationships among model fields, which are associated with water masses in the ocean and geostrophy in the atmosphere. For example, when only oceanic temperatures are assimilated, the ocean analysis is greatly improved by incorporating the temperature–salinity covariance provided by the analysis ensemble. Interestingly, wind observations are more helpful than atmospheric temperature observations for constructing the structure of the tropical atmosphere; the opposite holds for the extratropical atmosphere. The experiments indicate that the Atlantic meridional overturning circulation is difficult to constrain using the twentieth-century observational network, but there is hope that additional observations—including those from the newly deployed Argo profiles—may lessen this problem in the twenty-first century. The challenges for data assimilation of model systematic biases and evolving observing systems are discussed.

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G. M. B. DOBSON, D. N. HARRISON, and J. LAWRENCE

Abstract

No Abstract Available.

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R. E. Evans, M. S. J. Harrison, R. J. Graham, and K. R. Mylne

Abstract

One possible method of incorporating model sensitivities into ensemble forecasting systems is to combine ensembles run from two or more models. Furthermore, the use of more than one analysis, to which perturbations are added, may provide further unstable directions for error growth not present with a single analysis.

Results are presented from recent investigations into the potential benefit of combining ensembles from the systems of the European Centre for Medium-Range Weather Forecasts and The Met. Office of the United Kingdom. The multimodel and multianalysis ensemble significantly outperforms either individual system in many performance aspects, including deterministic and probabilistic forecast skill, spread–skill correlations, and breadth of synoptic information. It is demonstrated that these improvements are achieved through the combination of independent, useful information contained in the individual systems, and not through simple cancellation of biases that could occur when ensembles from two different forecast systems are combined. In addition, results indicate that model dependencies are at least comparable with analysis dependencies on medium-range timescales, and so in general both models and both analyses are required in the joint ensemble for the largest benefits.

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S. Zhang, M. J. Harrison, A. T. Wittenberg, A. Rosati, J. L. Anderson, and V. Balaji

Abstract

As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing.

A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980–2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF’s utilization of anisotropic background error covariances that may vary in time.

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M. J. Harrison, A. Rosati, B. J. Soden, E. Galanti, and E. Tziperman

Abstract

This paper presents a quantitative methodology for evaluating air–sea fluxes related to ENSO from different atmospheric products. A statistical model of the fluxes from each atmospheric product is coupled to an ocean general circulation model (GCM). Four different products are evaluated: reanalyses from the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF), satellite-derived data from the Special Sensor Microwave/Imaging (SSM/I) platform and the International Satellite Cloud Climatology Project (ISCCP), and an atmospheric GCM developed at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Atmospheric Model Intercomparison Project (AMIP) II. For this study, comparisons between the datasets are restricted to the dominant air–sea mode.

The stability of a coupled model using only the dominant mode and the associated predictive skill of the model are strongly dependent on which atmospheric product is used. The model is unstable and oscillatory for the ECMWF product, damped and oscillatory for the NCEP and GFDL products, and unstable (nonoscillatory) for the satellite product. The ocean model is coupled with patterns of wind stress as well as heat fluxes. This distinguishes the present approach from the existing paradigm for ENSO models where surface heat fluxes are parameterized as a local damping term in the sea surface temperature (SST) equation.

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Colm Sweeney, Anand Gnanadesikan, Stephen M. Griffies, Matthew J. Harrison, Anthony J. Rosati, and Bonita L. Samuels

Abstract

The impact of changes in shortwave radiation penetration depth on the global ocean circulation and heat transport is studied using the GFDL Modular Ocean Model (MOM4) with two independent parameterizations that use ocean color to estimate the penetration depth of shortwave radiation. Ten to eighteen percent increases in the depth of 1% downwelling surface irradiance levels results in an increase in mixed layer depths of 3–20 m in the subtropical and tropical regions with no change at higher latitudes. While 1D models have predicted that sea surface temperatures at the equator would decrease with deeper penetration of solar irradiance, this study shows a warming, resulting in a 10% decrease in the required restoring heat flux needed to maintain climatological sea surface temperatures in the eastern equatorial Atlantic and Pacific Oceans. The decrease in the restoring heat flux is attributed to a slowdown in heat transport (5%) from the Tropics and an increase in the temperature of submixed layer waters being transported into the equatorial regions. Calculations were made using a simple relationship between mixed layer depth and meridional mass transport. When compared with model diagnostics, these calculations suggest that the slowdown in heat transport is primarily due to off-equatorial increases in mixed layer depths. At higher latitudes (5°–40°), higher restoring heat fluxes are needed to maintain sea surface temperatures because of deeper mixed layers and an increase in storage of heat below the mixed layer. This study offers a way to evaluate the changes in irradiance penetration depths in coupled ocean–atmosphere GCMs and the potential effect that large-scale changes in chlorophyll a concentrations will have on ocean circulation.

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J. Zavala-Garay, C. Zhang, A. M. Moore, A. T. Wittenberg, M. J. Harrison, A. Rosati, Jérôme Vialard, and R. Kleeman

Abstract

A common practice in the design of forecast models for ENSO is to couple ocean general circulation models to simple atmospheric models. Therefore, by construction these models (known as hybrid ENSO models) do not resolve various kinds of atmospheric variability [e.g., the Madden–Julian oscillation (MJO) and westerly wind bursts] that are often regarded as “unwanted noise.” In this work the sensitivity of three hybrid ENSO models to this unresolved atmospheric variability is studied. The hybrid coupled models were tuned to be asymptotically stable and the magnitude, and spatial and temporal structure of the unresolved variability was extracted from observations. The results suggest that this neglected variability can add an important piece of realism and forecast skill to the hybrid models. The models were found to respond linearly to the low-frequency part of the neglected atmospheric variability, in agreement with previous findings with intermediate models. While the wind anomalies associated with the MJO typically explain a small fraction of the unresolved variability, a large fraction of the interannual variability can be excited by this forcing. A large correlation was found between interannual anomalies of Kelvin waves forced by the intraseasonal MJO and the Kelvin waves forced by the low-frequency part of the MJO. That is, in years when the MJO tends to be more active it also produces a larger low-frequency contribution, which can then resonate with the large-scale coupled system. Other kinds of atmospheric variability not related to the MJO can also produce interannual anomalies in the hybrid models. However, when projected on the characteristics of Kelvin waves, no clear correlation between its low-frequency content and its intraseasonal activity was found. This suggests that understanding the mechanisms by which the intraseasonal MJO interacts with the ocean to modulate its low-frequency content may help to better to predict ENSO variability.

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Kevin G. Harrison, Richard J. Norby, Wilfred M. Post, and Emily L. Chapp

Abstract

After four growing seasons, soil below white oak trees exposed to elevated atmospheric carbon dioxide levels (ambient + 300 ppm) had an average of 14% more soil carbon than soil below trees exposed to ambient levels of carbon dioxide. The soil carbon inventories in five soil cores collected from ambient chambers and six soil cores collected from elevated chambers at the Global Change Field Research Site, Oak Ridge, Tennessee, were measured. The authors conclude that the increase in soil carbon was due to an increase in belowground soil carbon input, because aboveground litter inputs were excluded by experimental design. These findings are consistent with the hypothesis that elevated carbon dioxide levels are increasing the amount of carbon stored in soil.

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Andrew W. Colman, Erika J. Palin, Michael G. Sanderson, Robert T. Harrison, and Ian M. Leggett

Abstract

The height of waves at North Sea oil and gas installations is an important factor governing the degree to which operational activities may be undertaken at those facilities. A link between the North Atlantic Oscillation (NAO) and winter (defined as December–February) wave heights at North Sea oil and gas installations has been established. A tool has been developed that uses a forecast NAO index to predict the proportions of wave heights in four categories that could be used to assess the operational downtime that will be experienced in the coming winter. The wave height forecasting system is shown to have useful skill in predicting the probability of occurrence of a stormy winter, and therefore probability forecasts provide a potentially useful guide to whether more or less disruption than the “climatological mean” might be experienced. The main limit on the skill of the wave forecasts is our very limited ability to accurately predict the NAO index on seasonal time scales.

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