Bibliography - Matthew J Harrison

1. Chang, Y-S, Anthony Rosati, Shoaqing Zhang, and Matthew J Harrison, February 2009: Objective analysis of monthly temperature and salinity for the world ocean in the 21st century: Comparison with World Ocean Atlas and application to assimilation validation. Journal of Geophysical Research, 114, C02014, doi:10.1029/2008JC004970.
   [ Abstract ]

   A new World Ocean atlas of monthly temperature and salinity, based on individual profiles for 2003–2007 (WOA21c), is constructed and compared with the World Ocean Atlas 2001 (WOA01), the World Ocean Atlas 2005 (WOA05), and the data assimilation analysis from the Coupled Data Assimilation (CDA) system developed by the Geophysical Fluid Dynamics Laboratory (GFDL). First, we established a global data management system for quality control (QC) of oceanic observed data both in real time and delayed mode. Delayed mode QC of Argo floats identified about 8.5% (3%) of the total floats (profiles) up to December 2007 as having a significant salinity offset of more than 0.05. Second, all QCed data were gridded at 1° by 1° horizontal resolution and 23 standard depth levels using six spatial scales (large and small longitudinal, latitudinal, and cross-isobath) and a temporal scale. Analyzed mean temperature in WOA21c is warm with respect to WOA01 and WOA05, while salinity difference is less evident. Consistent differences among WOA01, WOA05, and WOA21c are found both in the fully and subsampled data set, which indicates a large impact of recent observations on the existing climatologies. Root mean square temperature and salinity differences and offsets of the GFDL's CDA results significantly decrease in the order of WOA01, WOA05, and WOA21c in most oceans and depths as well. This result suggests that the WOA21c is of use for the collocated assessment approach especially for high-performance assimilation models on the global scale.

2. Adcroft, Alistair, Robert W Hallberg, and Matthew J Harrison, 2008: A finite volume discretization of the pressure gradient force using analytic integration. Ocean Modelling, 22(3-4), doi:10.1016/j.ocemod.2008.02.001.
   [ Abstract ]

   Layered ocean models can exhibit spurious thermobaric instability if the compressibility of sea water is not treated accurately enough. We find that previous solutions to this problem are inadequate for simulations of a changing climate. We propose a new discretization of the pressure gradient acceleration using the finite volume method. In this method, the pressure gradient acceleration is exhibited as the difference of the integral “contact” pressure acting on the edges of a finite volume. This integral “contact” pressure can be calculated analytically by choosing a tractable equation of state. The result is a discretization that has zero truncation error for an isothermal and isohaline layer and does not exhibit the spurious thermobaric instability.

3. Harrison, Matthew J., and Robert W Hallberg, 2008: Pacific subtropical cell response to reduced equatorial dissipation. Journal of Physical Oceanography, 38(9), 1894-1912.
   [ Abstract PDF ]

   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 σ_θ = 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 σ_θ = 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 σ_θ = 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.

4. Zavala-Garay, J, C Zhang, A M Moore, Andrew T Wittenberg, Matthew J Harrison, Anthony Rosati, J Vialard, and R Kleeman, 2008: Sensitivity of hybrid ENSO models to unresolved atmospheric variability. Journal of Climate, 21(15), doi:10.1175/2007JCLI1188.1.
   [ 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.

5. Griffies, Stephen, Matthew J Harrison, Ronald C Pacanowski, and Anthony Rosati, 2007: Ocean modelling with MOM. Clivar Exchanges, 12(3), 3-5, 13.
   [ PDF ]
6. Sun, C, M M Rienecker, Anthony Rosati, Matthew J Harrison, Andrew T Wittenberg, C L Keppenne, J P Jacob, and R M Kovach, June 2007: Comparison and sensitivity of ODASI ocean analyses in the Tropical Pacific. Monthly Weather Review, 135(6), doi:10.1175/MWR3405.1.
   [ Abstract ]

   Two global ocean analyses from 1993 to 2001 have been generated by the Global Modeling and Assimilation Office (GMAO) and Geophysical Fluid Dynamics Laboratory (GFDL), as part of the Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI) consortium efforts. The ocean general circulation models (OGCM) and assimilation methods in the analyses are different, but the forcing and observations are the same as designed for ODASI experiments. Global expendable bathythermograph and Tropical Atmosphere Ocean (TAO) temperature profile observations are assimilated. The GMAO analysis also assimilates synthetic salinity profiles based on climatological T–S relationships from observations (denoted "TS scheme"). The quality of the two ocean analyses in the tropical Pacific is examined here. Questions such as the following are addressed: How do different assimilation methods impact the analyses, including ancillary fields such as salinity and currents? Is there a significant difference in interpretation of the variability from different analyses? How does the treatment of salinity impact the analyses? Both GMAO and GFDL analyses reproduce the time mean and variability of the temperature field compared with assimilated TAO temperature data, taking into account the natural variability and representation errors of the assimilated temperature observations. Surface zonal currents at 15 m from the two analyses generally agree with observed climatology. Zonal current profiles from the analyses capture the intensity and variability of the Equatorial Undercurrent (EUC) displayed in the independent acoustic Doppler current profiler data at three TAO moorings across the equatorial Pacific basin. Compared with independent data from TAO servicing cruises, the results show that 1) temperature errors are reduced below the thermocline in both analyses; 2) salinity errors are considerably reduced below the thermocline in the GMAO analysis; and 3) errors in zonal currents from both analyses are comparable. To discern the impact of the forcing and salinity treatment, a sensitivity study is undertaken with the GMAO assimilation system. Additional analyses are produced with a different forcing dataset, and another scheme to modify the salinity field is tested. This second scheme updates salinity at the time of temperature assimilation based on model T–S relationships (denoted "T scheme"). The results show that both assimilated field (i.e., temperature) and fields that are not directly observed (i.e., salinity and currents) are impacted. Forcing appears to have more impact near the surface (above the core of the EUC), while the salinity treatment is more important below the surface that is directly influenced by forcing. Overall, the TS scheme is more efective than the T scheme in correcting model bias in salinity and improving the current structure. Zonal currents from the GMAO control run where no data are assimilated are as good as the best analysis.

7. Vecchi, G A., and Matthew J Harrison, July 2007: An observing system simulation experiment for the Indian Ocean. Journal of Climate, 20(13), doi:10.1175/JCLI4147.1.
   [ Abstract ]

   An integrated in situ Indian Ocean observing system (IndOOS) is simulated using a high-resolution ocean general circulation model (OGCM) with daily mean forcing, including an estimate of subdaily oceanic variability derived from observations. The inclusion of subdaily noise is fundamental to the results; in the mixed layer it is parameterized as Gaussian noise with an rms of 0.1°C; below the mixed layer a Gaussian interface displacement with an rms of 7 m is used. The focus of this assessment is on the ability of an IndOOS—comprising a 3° × 3° Argo profiling float array, a series of frequently repeated XBT lines, and an array of moored buoys—to observe the interannual and subseasonal variability of subsurface Indian Ocean temperature. The simulated IndOOS captures much of the OGCM interannual subsurface temperature variability. A fully deployed Argo array with 10-day sampling interval is able to capture a significant part of the Indian Ocean interannual temperature variability; a 5-day sampling interval degrades its ability to capture variability. The proposed moored buoy array and frequently repeated XBT lines provide complementary information in key regions, particularly the Java/Sumatra and Somali upwelling and equatorial regions. Since the subdaily noise is of the same order as the subseasonal signal and since much of the variability is submonthly, a 5-day sampling interval does not drastically enhance the ability of Argo to capture the OGCM subseasonal variability. However, as sampling intervals are decreased, there is enhanced divergence of the Argo floats, diminished ability to quality control data, and a decreased lifetime of the floats; these factors argue against attempting to resolve subseasonal variability with Argo by shortening the sampling interval. A moored array is essential to capturing the subseasonal and near-equatorial variability in the model, and the proposed moored buoy locations span the region of strong subseasonal variability. On the whole, the proposed IndOOS significantly enhances the ability to capture both interannual and subseasonal variability in the Indian Ocean.

8. Zhang, Shoaqing, Matthew J Harrison, Anthony Rosati, and Andrew T Wittenberg, 2007: System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies. Monthly Weather Review, 135(10), doi:10.1175/MWR3466.1.
   [ 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.

9. Delworth, Thomas L., Anthony J Broccoli, Anthony Rosati, Ronald J Stouffer, Ventakramani Balaji, J A Beesley, W F Cooke, Keith W Dixon, John Dunne, Krista A Dunne, J W Durachta, Kirsten L Findell, Paul Ginoux, Anand Gnanadesikan, C Tony Gordon, Stephen Griffies, Rich Gudgel, Matthew J Harrison, Isaac Held, Richard S Hemler, Larry Horowitz, Stephen A Klein, Thomas R Knutson, P J Kushner, A R Langenhorst, H C Lee, Shian-Jiann Lin, Jian Lu, S Malyshev, P C D Milly, V Ramaswamy, J L Russell, M Daniel Schwarzkopf, Elena Shevliakova, Joseph J Sirutis, Michael J Spelman, William F Stern, Michael Winton, Andrew T Wittenberg, Bruce Wyman, Fanrong Zeng, and Rong Zhang, 2006: GFDL's CM2 Global Coupled Climate Models. Part I: Formulation and Simulation Characteristics. Journal of Climate, 19(5), doi:10.1175/JCLI3629.1.
   [ Abstract ]

   The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved. Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments. The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic. Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/). Manuscript received 8 December 2004, in final form 18 March 2005

10. Gnanadesikan, Anand, Keith W Dixon, Stephen Griffies, Ventakramani Balaji, M Barreiro, J A Beesley, W F Cooke, Thomas L Delworth, R Gerdes, Matthew J Harrison, Isaac Held, William J Hurlin, H C Lee, Z Liang, G Nong, Ronald C Pacanowski, Anthony Rosati, J L Russell, Bonita L Samuels, Qian Song, Michael J Spelman, Ronald J Stouffer, C Sweeney, G A Vecchi, Michael Winton, Andrew T Wittenberg, Fanrong Zeng, Rong Zhang, and John Dunne, 2006: GFDL's CM2 Global Coupled Climate Models. Part II: The baseline ocean simulation. Journal of Climate, 19(5), doi:10.1175/JCLI3630.1.
    [ Abstract ]

    The current generation of coupled climate models run at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Climate Change Science Program contains ocean components that differ in almost every respect from those contained in previous generations of GFDL climate models. This paper summarizes the new physical features of the models and examines the simulations that they produce. Of the two new coupled climate model versions 2.1 (CM2.1) and 2.0 (CM2.0), the CM2.1 model represents a major improvement over CM2.0 in most of the major oceanic features examined, with strikingly lower drifts in hydrographic fields such as temperature and salinity, more realistic ventilation of the deep ocean, and currents that are closer to their observed values. Regional analysis of the differences between the models highlights the importance of wind stress in determining the circulation, particularly in the Southern Ocean. At present, major errors in both models are associated with Northern Hemisphere Mode Waters and outflows from overflows, particularly the Mediterranean Sea and Red Sea.

11. Vecchi, G A., Brian J Soden, Andrew T Wittenberg, Isaac Held, Ants Leetma, and Matthew J Harrison, 2006: Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing. Nature, 441(7089), 73-76.
    [ Abstract PDF ]

    Since the mid-nineteenth century the Earth's surface has warmed1, 2, 3, and models indicate that human activities have caused part of the warming by altering the radiative balance of the atmosphere1, 3. Simple theories suggest that global warming will reduce the strength of the mean tropical atmospheric circulation4, 5. An important aspect of this tropical circulation is a large-scale zonal (east–west) overturning of air across the equatorial Pacific Ocean—driven by convection to the west and subsidence to the east—known as the Walker circulation6. Here we explore changes in tropical Pacific circulation since the mid-nineteenth century using observations and a suite of global climate model experiments. Observed Indo-Pacific sea level pressure reveals a weakening of the Walker circulation. The size of this trend is consistent with theoretical predictions, is accurately reproduced by climate model simulations and, within the climate models, is largely due to anthropogenic forcing. The climate model indicates that the weakened surface winds have altered the thermal structure and circulation of the tropical Pacific Ocean. These results support model projections of further weakening of tropical atmospheric circulation during the twenty-first century4, 5, 7.

12. Zhang, Shoaqing, Anthony Rosati, and Matthew J Harrison, in press: Detection of multi-decadal oceanic variability within a coupled ensemble data assimilation system. Journal of Geophysical Research. 11/06.
    [ Abstract ]

    This study examines the detectability of long time scale variability of oceanic heat content and salinity, based on the 20th-century (temperature only) and 21st-century (ARGO deploy for temperature and salinity) oceanic observing networks (OONs) by an oceanic data assimilation approach within the GFDL coupled data assimilation system. The assimilation algorithm is an ensemble filter. As an implementation of stochastic estimate theory, the filter solves for a temporally-varying joint probability density function (joint-PDF) of oceanic states by combining the observational PDF and a prior PDF derived from an oceanic general circulation model (GCM) that is coupled with an atmospheric GCM. A series of perfect-model experiments has been performed to examine the impact of temporally-varying radiative forcings, initial conditions (ICs) and OONs. A 20th-century simulation with temporally-varying greenhouse gas and natural aerosol (GHGNA) radiative forcings serves as the "truth" from which observations are drawn by the 20th-21st-century OONs. These oceanic observations were assimilated into the coupled climate model for targeting a 25-year climate variation (corresponding to 1976-2000 historical GHGNA records) starting from different ICs and with fixed-year/temporally-varying GHGNA forcings. Two sets of ICs called the controlled and the forced are used here, in which the former/latter was produced from a long time model integration with fixed-year/temporally-varying GHGNA radiative forcings.

13. Griffies, Stephen, Anand Gnanadesikan, Keith W Dixon, John Dunne, R Gerdes, Matthew J Harrison, Anthony Rosati, J L Russell, Bonita L Samuels, Michael J Spelman, Michael Winton, and Rong Zhang, 2005: Formulation of an ocean model for global climate simulations. Ocean Science, 1, 45-79.
    [ Abstract PDF ]

    This paper summarizes the formulation of the ocean component to the Geophysical Fluid Dynamics Laboratory's (GFDL) climate model used for the 4th IPCC Assessment (AR4) of global climate change. In particular, it reviews the numerical schemes and physical parameterizations that make up an ocean climate model and how these schemes are pieced together for use in a state-of-the-art climate model. Features of the model described here include the following: (1) tripolar grid to resolve the Arctic Ocean without polar filtering, (2) partial bottom step representation of topography to better represent topographically influenced advective and wave processes, (3) more accurate equation of state, (4) three-dimensional flux limited tracer advection to reduce overshoots and undershoots, (5) incorporation of regional climatological variability in shortwave penetration, (6) neutral physics parameterization for representation of the pathways of tracer transport, (7) staggered time stepping for tracer conservation and numerical efficiency, (8) anisotropic horizontal viscosities for representation of equatorial currents, (9) parameterization of exchange with marginal seas, (10) incorporation of a free surface that accomodates a dynamic ice model and wave propagation, (11) transport of water across the ocean free surface to eliminate unphysical "virtual tracer flux" methods, (12) parameterization of tidal mixing on continental shelves. We also present preliminary analyses of two particularly important sensitivities isolated during the development process, namely the details of how parameterized subgridscale eddies transport momentum and tracers.

14. Sweeney, C, Anand Gnanadesikan, Stephen Griffies, Matthew J Harrison, Anthony Rosati, and Bonita L Samuels, 2005: Impacts of shortwave penetration depth on large-scale ocean circulation and heat transport. Journal of Physical Oceanography, 35(6), 1103-1119.
    [ Abstract PDF ]

    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.

15. Zhang, Shoaqing, Matthew J Harrison, Andrew T Wittenberg, Anthony Rosati, Jeffrey L Anderson, and Ventakramani Balaji, 2005: Initialization of an ENSO Forecast System using a parallelized ensemble filter. Monthly Weather Review, 133(11), doi:10.1175/MWR3024.1.
    [ 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.

16. Griffies, Stephen, Matthew J Harrison, Ronald C Pacanowski, and Anthony Rosati, 2004: A Technical Guide to MOM4, GFDL Ocean Group Technical Report No. 5, Princeton, NJ:: NOAA/Geophysical Fluid Dynamics Laboratory, 342 pp.
    [ Abstract PDF ]

    This manual provides a detailed description of the analytical, numerical, and computational aspects of the MOM4 ocean model.

17. Zhang, Shoaqing, Jeffrey L Anderson, Anthony Rosati, Matthew J Harrison, S P Khare, and Andrew T Wittenberg, 2004: Multiple time level adjustment for data assimilation. Tellus A, 56A(1), 2-15.
    [ Abstract PDF ]

    Time-stepping schemes in ocean-atmosphere models can involve multiple time levels. Traditional data assimilation implementation considers only the adjustment of the current state using observations available, i.e. the one time level adjustment. However, one time level adjustment introduces an inconsistency between the adjusted and unadjusted states into the model time integration, which can produce extra assimilation errors. For time-dependent assimilation approaches such as ensemble-based filtering algorithms, the persistent introduction of this inconsistency can give rise to computational instability and requires extra time filtering to maintain the assimilation. A multiple time level adjustment assimilation scheme is thus proposed, in which the states at times t and t- 1, t- 2, ... , if applicable, are adjusted using observations at time t. Given a leap frog time-stepping scheme, a low-order (Lorenz-63) model and a simple atmospheric (global barotropic) model are used to demonstrate the impact of the two time level adjustment on assimilation results in a perfect model framework with observing/assimilation simulation experiments. The assimilation algorithms include an ensemble-based filter (the ensemble adjustment Kalman filter, EAKF) and a strong constraint four-dimensional variational (4D-Var) assimilation method. Results show that the two time level adjustment always reduces the assimilation errors for both filtering and variational algorithms due to the consistency of the adjusted states at times t and t- 1 that are used to produce the future state in the leap frog time-stepping. The magnitude of the error reduction made by the two time level adjustment varies according to the availability of observations, the nonlinearity of the assimilation model and the strength of the time filter used in the model. Generally the sparser the observations in time, the larger the error reduction. In particular, for the EAKF when the model uses a weak time filter and for the 4D-Var method when the model is strongly nonlinear, two time level adjustment can significantly improve the performance of these assimilation algorithms.

18. Galanti, E, E Tziperman, Matthew J Harrison, Anthony Rosati, and Z Sirkes, 2003: A study of ENSO Prediction using a hybrid coupled model and the adjoint method for data assimilation. Monthly Weather Review, 131(11), 2748-2764.
    [ Abstract PDF ]

    An experimental ENSO prediction system is presented, based on an ocean general circulation model (GCM) coupled to a statistical atmosphere and the adjoint method of 4D variational data assimilation. The adjoint method is used to initialize the coupled model, and predictions are performed for the period 1980–99. The coupled model is also initialized using two simpler assimilation techniques: forcing the ocean model with observed sea surface temperature and surface fluxes, and a 3D variational data assimilation (3DVAR) method, similar to that used by the National Centers for Environmental Prediction (NCEP) for operational ENSO prediction. The prediction skill of the coupled model initialized by the three assimilation methods is then analyzed and compared. The effect of the assimilation period used in the adjoint method is studied by using 3-, 6-, and 9-month assimilation periods. Finally, the possibility of assimilating only the anomalies with respect to observed climatology in order to circumvent systematic model biases is examined. It is found that the adjoint method does seem to have the potential for improving over simpler assimilation schemes. The improved skill is mainly at prediction intervals of more than 6 months, where the coupled model dynamics start to influence the model solution. At shorter prediction time intervals, the initialization using the forced ocean model or the 3DVAR may result in a better prediction skill. The assimilation of anomalies did not have a substantial effect on the prediction skill of the coupled model. This seems to indicate that in this model the climatology bias, which is compensated for by the anomaly assimilation, is less significant for the predictive skill than the bias in the model variability, which cannot be eliminated using the anomaly assimilation. Changing the optimization period from 6 to 3 to 9 months showed that the period of 6 months seems to be a near-optimal choice for this model.

19. Galanti, E, E Tziperman, Matthew J Harrison, Anthony Rosati, R Giering, and Z Sirkes, 2002: The equatorial thermocline outcropping--A seasonal control on the tropical Pacific Ocean-Atmosphere instability strength. Journal of Climate, 15(19), 2721-2739.
    [ Abstract PDF ]

    One of the major factors determining the strength and extent of ENSO events is the instability state of the equatorial Pacific coupled ocean–atmosphere system and its seasonal variations. This study analyzes the coupled instability in a hybrid coupled model of the Indo–Pacific region, using the adjoint method for sensitivity studies. It is found that the seasonal changes in the ocean–atmosphere instability strength in the model used here are related to the outcropping of the thermocline in the east equatorial Pacific. From July to December, when the thermocline outcrops over a wide area in the east Pacific, there is a strong surface–thermocline connection and anomalies that arrive as Kelvin waves from the west along the thermocline can reach the surface and affect the SST and thus the coupled system. Conversely, from February to June, when the thermocline outcropping is minimal, the surface decouples from the thermocline and temperature anomalies in the thermocline depth range do not affect the surface and dissipate within the thermocline. The role of vertical mixing rather than upwelling in linking vertical thermocline movements to SST changes is emphasized. It is therefore suggested that the seasonal ocean–atmosphere instability strength in the equatorial Pacific is strongly influenced by the thermocline outcropping and its seasonal modulation, a physical mechanism that is often neglected in intermediate coupled models and that can be represented properly only in models that employ the full dynamics of the mixed layer.

20. Harrison, Matthew J., Anthony Rosati, Brian J Soden, E Galanti, and E Tziperman, 2002: An evaluation of air-sea flux products for ENSO simulation and prediction. Monthly Weather Review, 130(3), 723-732.
    [ Abstract PDF ]

    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 ocillatory 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.

21. Harrison, Matthew J., and Anthony Rosati, 1999: Coupled model simulation and prediction of the tropical Pacific - impact of ocean model physics In COARE-98 - Proceedings of a Conference on the TOGA Coupled Ocean-atmosphere Response Experiment (COARE), WMO/TD-No. 940, WCRP-107, Geneva, Switzerland, World Meteorological Organization, 381-382.
22. Harrison, Matthew J., and Anthony Rosati, 1999: Simulating the tropical Pacific ocean using prescribed forcing In COARE-98 - Proceedings of a Conference on the TOGA Coupled Ocean-atmosphere Response Experiment (COARE),, WMO/TD-No. 940, WCRP-107, Geneva, Switzerland, World Meteorological Organization, 377-378.
23. Rosati, Anthony, and Matthew J Harrison, 1997: Ocean modelling and data assimilation at GFDL In CAS/JSC Working Group on Numerical Experimentation - Research Activities in Atmospheric & Oceanic Modelling, WMO/ICSU/IOC World Climate Research Programme, Report No. 25, WMO/TD-No. 792, Geneva, Switzerland, World Meteorological Organization, 8.59-8.60.
24. Harrison, Matthew J., Anthony Rosati, Rich Gudgel, and Jeffrey L Anderson, 1996: Initialization of coupled model forecasts using an improved ocean data assimilation system In 11th Conference on Numerical Weather Prediction, Boston, MA, American Meteorological Society, 7.

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