Bibliography - Anthony Rosati

1. Chang, You-Soon, Shaoqing Zhang, Anthony Rosati, Thomas L Delworth, and William F Stern, February 2013: An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation. Climate Dynamics, 40(3-4), DOI:10.1007/s00382-012-1412-2.
   [ Abstract ]

   The Geophysical Fluid Dynamics Laboratory has developed an ensemble coupled data assimilation (ECDA) system based on the fully coupled climate model, CM2.1, in order to provide reanalyzed coupled initial conditions that are balanced with the climate prediction model. Here, we conduct a comprehensive assessment for the oceanic variability from the latest version of the ECDA analyzed for 51 years, 1960–2010. Meridional oceanic heat transport, net ocean surface heat flux, wind stress, sea surface height, top 300 m heat content, tropical temperature, salinity and currents are compared with various in situ observations and reanalyses by employing similar configurations with the assessment of the NCEP’s climate forecast system reanalysis (Xue et al. in Clim Dyn 37(11):2511–2539, 2011). Results show that the ECDA agrees well with observations in both climatology and variability for 51 years. For the simulation of the Tropical Atlantic Ocean and global salinity variability, the ECDA shows a good performance compared to existing reanalyses. The ECDA also shows no significant drift in the deep ocean temperature and salinity. While systematic model biases are mostly corrected with the coupled data assimilation, some biases (e.g., strong trade winds, weak westerly winds and warm SST in the southern oceans, subsurface temperature and salinity biases along the equatorial western Pacific boundary, overestimating the mixed layer depth around the subpolar Atlantic and high-latitude southern oceans in the winter seasons) are not completely eliminated. Mean biases such as strong South Equatorial Current, weak Equatorial Under Current, and weak Atlantic overturning transport are generated during the assimilation procedure, but their variabilities are well simulated. In terms of climate variability, the ECDA provides good simulations of the dominant oceanic signals associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and Atlantic Meridional Overturning Circulation during the whole analyzed period, 1960–2010.

2. Doi, Takeshi, Gabriel A Vecchi, Anthony Rosati, and Thomas L Delworth, in press: Response to CO2 doubling of the Atlantic Hurricane Main Development Region in a High-Resolution Climate Model. Journal of Climate. DOI:10.1175/JCLI-D-12-00110.1. 2/13.
   [ Abstract ]

   Response of climate conditions in the Atlantic Hurricane Main Development Region (MDR) to doubling of atmospheric CO2 has been explored, using the new high-resolution coupled Climate Model version 2.5 developed at the Geophysical Fluid Dynamics Laboratory (GFDL-CM2.5). In the annual mean, the SST in the MDR warms by about 2°C in the CO2 doubling run relative to the Control run, the trade winds become weaker in the northern tropical Atlantic, and the rainfall increases over the ITCZ and its northern region. The amplitude of the annual cycle of the SST over the MDR is not significantly changed by CO2 doubling. However, we find that the interannual variations show significant responses to CO2 doubling: the seasonal maximum peak of the interannual variations of the SST over the MDR is about 25% stronger than in the Control run. The enhancement of the interannual variations of the SST in the MDR is due to changes in effectiveness of the Wind-Evaporation-SST (WES) positive feedback: WES remains a positive feedback until boreal early summer in the CO2 doubling run. The enhancement of the interannual variability of the SST over the MDR in boreal early summer due to CO2 doubling could lead to serious damages associated with the Atlantic Hurricane count and drought (or flood) in the Sahel and South America in a future climate.

3. Lee, Hyun-Chul, Thomas L Delworth, Anthony Rosati, Rong Zhang, Whit G Anderson, Fanrong Zeng, Charles A Stock, Anand Gnanadesikan, Keith W Dixon, and Stephen M Griffies, January 2013: Impact of climate warming on upper layer of the Bering Sea. Climate Dynamics, 40(1-2), DOI:10.1007/s00382-012-1301-8.
   [ Abstract ]

   The impact of climate warming on the upper layer of the Bering Sea is investigated by using a high-resolution coupled global climate model. The model is forced by increasing atmospheric CO2 at a rate of 1% per year until CO2 reaches double its initial value (after 70 years), after which it is held constant. In response to this forcing, the upper layer of the Bering Sea warms by about 2�C in the southeastern shelf and by a little more than 1�C in the western basin. The wintertime ventilation to the permanent thermocline weakens in the western Bering Sea. After CO2 doubling, the southeastern shelf of the Bering Sea becomes almost ice-free in March, and the stratification of the upper layer strengthens in May and June. Changes of physical condition due to the climate warming would impact the pre-condition of spring bio-productivity in the southeastern shelf.

4. Msadek, Rym, W E Johns, S G Yeager, G Danabasoglu, Thomas L Delworth, and Anthony Rosati, in press: The Atlantic Meridional Heat transport at 26.5° N and its relationship with the MOC in the RAPID array and the GFDL and NCAR coupled models. Journal of Climate. DOI:10.1175/JCLI-D-12-00081.1. 1/13.
   [ Abstract ]

   The link at 26.5° N between the Atlantic meridional heat transport (MHT) and the Atlantic meridional overturning circulation (MOC) is investigated in two climate models, GFDL CM2.1 and NCAR CCSM4, and compared with the recent observational estimates from the RAPID-MOCHA array. Despite a stronger than observed MOC magnitude, both models underestimate the mean MHT at 26.5° N due to an overly diffuse thermocline. Biases result from errors in both overturning and gyre components of the MHT. The observed linear relationship between MHT and MOC at 26.5° N is realistically simulated by the two models and is mainly due to the overturning component of the MHT. Fluctuations in overturning MHT are dominated by Ekman transport variability in CM2.1 and CCSM4, whereas baroclinic geostrophic transport variability plays a larger role in RAPID. CCSM4 which has a parameterization of Nordic Sea overflows and thus a more realistic North Atlantic Deep Water (NADW) penetration shows smaller biases in the overturning heat transport than CM2.1 due to deeper NADW at colder temperatures. The horizontal gyre heat transport and its sensitivity to the MOC are poorly represented in both models. The wind-driven gyre heat transport is northward in observations at 26.5° N whereas it is weakly southward in both models, reducing the total MHT. We emphasize model biases that are responsible for the too weak MHT, particularly at the western boundary. The use of direct MHT observations through RAPID allows us to identify the source of the too weak MHT in the two models, a bias shared by a number of CMIP5 coupled models.

5. Vecchi, Gabriel A., Rym Msadek, Whit G Anderson, You-Soon Chang, Thomas L Delworth, Keith W Dixon, Rich Gudgel, Anthony Rosati, William F Stern, G Villarini, Andrew T Wittenberg, Xiaosong Yang, Fanrong Zeng, Rong Zhang, and Shaoqing Zhang, in press: Multi-year Predictions of North Atlantic Hurricane Frequency: Promise and limitations. Journal of Climate. DOI:10.1175/JCLI-D-12-00464.1. 2/13.
   [ Abstract ]

   Retrospective predictions of multi-year North Atlantic hurricane frequency are explored, by applying a hybrid statistical-dynamical forecast system to initialized and non-initialized multi-year forecasts of tropical Atlantic and tropical mean sea surface temperatures (SSTs) from two global climate model forecast systems. By accounting for impacts of initialization and radiative forcing, retrospective predictions of five-year mean and nine-year mean tropical Atlantic hurricane frequency show significant correlation relative to a null hypothesis of zero correlation. The retrospective correlations are increased in a two-model average forecast and by using a lagged-ensemble approach, with the two-model ensemble decadal forecasts hurricane frequency over 1961-2011 yielding correlation coefficients that approach 0.9. These encouraging retrospective multi-year hurricane predictions, however, should be interpreted with care: although initialized forecasts have higher nominal skill than uninitialized ones, the relatively short record and large autocorrelation of the time series limits our confidence in distinguishing between the skill due to external forcing and that added by initialization. The nominal increase in correlation in the initialized forecasts relative to the uninitialized experiments is due to improved representation of the multi-year tropical Atlantic SST anomalies. The skill in the initialized forecasts comes in large part from the persistence of a mid-1990s shift by the initialized forecasts, rather than from predicting its evolution. Predicting shifts like that observed in 1994-1995 remains a critical issue for the success of multi-year forecasts of Atlantic hurricane frequency. The retrospective forecasts highlight the possibility that changes in observing system impact forecast performance.

6. Yang, Xiaosong, Anthony Rosati, Shaoqing Zhang, Thomas L Delworth, Rich Gudgel, Rong Zhang, Gabriel A Vecchi, Whit G Anderson, You-Soon Chang, T DelSole, Keith W Dixon, Rym Msadek, William F Stern, Andrew T Wittenberg, and Fanrong Zeng, January 2013: A predictable AMO-like pattern in GFDL's fully-coupled ensemble initialization and decadal forecasting system. Journal of Climate, 26(2), DOI:10.1175/JCLI-D-12-00231.1.
   [ Abstract ]

   The decadal predictability of sea surface temperature (SST) and 2m air temperature (T2m) in Geophysical Fluid Dynamics Laboratory (GFDL)'s decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL's Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model, allows identification of internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an inter-hemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bi-polar seesaw, with warm anomalies centered in Greenland, and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observations, indicates that the IMP of SST may be predictable up to 4 (10) year lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) years at 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments, in which radiative forcing is returned to 1961 values. These results point towards the possibility of meaningful decadal climate outlooks using dynamical coupled models, if they are appropriately initialized from a sustained climate observing system.

7. Zhang, Rong, Thomas L Delworth, R Sutton, D Hodson, Keith W Dixon, Isaac M Held, Y Kushnir, D Marshall, Yi Ming, Rym Msadek, J Robson, Anthony Rosati, Mingfang Ting, and Gabriel A Vecchi, in press: Have Aerosols Caused the Observed Atlantic Multidecadal Variability?. Journal of the Atmospheric Sciences. DOI:10.1175/JAS-D-12-0331.1. 1/13.
   [ Abstract ]

   Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting broad impacts on weather and precipitation patterns around the globe. Recently Booth et al (2012) showed that the HadGEM2-ES climate model closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the 20th century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al (2012) concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anti-correlation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability.

8. Brantstator, G, H Teng, G A Meehl, M Kimoto, J R Knight, M Latif, and Anthony Rosati, March 2012: Systematic estimates of initial-value decadal predictability for six AOGCMs. Journal of Climate, 25(6), DOI:10.1175/JCLI-D-11-00227.1.
   [ Abstract ]

   Initial-value predictability measures the degree to which the initial state can influence predictions. In this paper, the initial-value predictability of six atmosphere–ocean general circulation models in the North Pacific and North Atlantic is quantified and contrasted by analyzing long control integrations with time invariant external conditions. Through the application of analog and multivariate linear regression methodologies, average predictability properties are estimated for forecasts initiated from every state on the control trajectories. For basinwide measures of predictability, the influence of the initial state tends to last for roughly a decade in both basins, but this limit varies widely among the models, especially in the North Atlantic. Within each basin, predictability varies regionally by as much as a factor of 10 for a given model, and the locations of highest predictability are different for each model. Model-to-model variations in predictability are also seen in the behavior of prominent intrinsic basin modes. Predictability is primarily determined by the mean of forecast distributions rather than the spread about the mean. Horizontal propagation plays a large role in the evolution of these signals and is therefore a key factor in differentiating the predictability of the variousmodels.

9. Delworth, Thomas L., Anthony Rosati, Whit G Anderson, Alistair Adcroft, Ventakramani Balaji, Rusty Benson, Keith W Dixon, Stephen M Griffies, Hyun-Chul Lee, Ronald C Pacanowski, Gabriel A Vecchi, Andrew T Wittenberg, Fanrong Zeng, and Rong Zhang, April 2012: Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. Journal of Climate, 25(8), DOI:10.1175/JCLI-D-11-00316.1.
   [ Abstract ]

   We present results for simulated climate and climate change from a newly developed high-resolution global climate model (GFDL CM2.5). The GFDL CM2.5 model has an atmospheric resolution of approximately 50 Km in the horizontal, with 32 vertical levels. The horizontal resolution in the ocean ranges from 28 Km in the tropics to 8 Km at high latitudes, with 50 vertical levels. This resolution allows the explicit simulation of some mesoscale eddies in the ocean, particularly at lower latitudes. We present analyses based on the output of a 280 year control simulation; we also present results based on a 140 year simulation in which atmospheric CO2 increases at 1% per year until doubling after 70 years. Results are compared to the GFDL CM2.1 climate model, which has somewhat similar physics but coarser resolution. The simulated climate in CM2.5 shows marked improvement over many regions, especially the tropics, including a reduction in the double ITCZ and an improved simulation of ENSO. Regional precipitation features are much improved. The Indian monsoon and Amazonian rainfall are also substantially more realistic in CM2.5. The response of CM2.5 to a doubling of atmospheric CO2 has many features in common with CM2.1, with some notable differences. For example, rainfall changes over the Mediterranean appear to be tightly linked to topography in CM2.5, in contrast to CM2.1 where the response is more spatially homogeneous. In addition, in CM2.5 the near-surface ocean warms substantially in the high latitudes of the Southern Ocean, in contrast to simulations using CM2.1.

10. Doi, Takeshi, Gabriel A Vecchi, Anthony Rosati, and Thomas L Delworth, August 2012: Biases in the Atlantic ITCZ in seasonal-interannual variations for a coarse and a high resolution coupled climate model. Journal of Climate, 25(16), DOI:10.1175/JCLI-D-11-00360.1.
    [ Abstract ]

    Using two fully coupled ocean-atmosphere models of CM2.1 (the Climate Model version 2.1 developed at the Geophysical Fluid Dynamics Laboratory) and CM2.5 (a new high-resolution climate model based on CM2.1), the characteristics and sources of SST and precipitation biases associated with the Atlantic ITCZ have been investigated. CM2.5 has an improved simulation of the annual mean and the annual cycle of the rainfall over the Sahel and the northern South America, while CM2.1 shows excessive Sahel rainfall and lack of northern South America rainfall in boreal summer. This marked improvement in CM2.5 is due to not only high-resolved orography, but also a significant reduction of biases in the seasonal meridional migration of the ITCZ. In particular, the seasonal northward migration of the ITCZ in boreal summer is coupled to the seasonal variation of the SST and a subsurface doming of the thermocline in the northeastern tropical Atlantic, known as the Guinea Dome. Improvements in the ITCZ allow for better representation of the coupled processes that are important for an abrupt seasonally phase-locked decay of the interannual SST anomaly in the northern tropical Atlantic. Nevertheless, the differences between CM2.5 and CM2.1 were not sufficient to reduce the warm SST biases in the eastern equatorial region and Angola-Benguela Area. The weak bias of southerly winds along the southwestern African coast associated with the excessive southward migration bias of the ITCZ may be a key to improve the warm SST biases there.

11. Vecchi, Gabriel A., Rym Msadek, Thomas L Delworth, Keith W Dixon, E Guilyardi, E Hawkins, A R Karspeck, J Mignot, J Robson, Anthony Rosati, and Rong Zhang, November 2012: Comment on "Multiyear Prediction of Monthly Mean Atlantic Meridional Overturning Circulation at 26.5�N". Science, 338(6107), DOI:10.1126/science.1222566.
    [ Abstract ]

    Matei et al. (Reports, 6 January 2012, p. 76) claim to show skillful multiyear predictions of the Atlantic Meridional Overturning Circulation (AMOC). However, these claims are not justified, primarily because the predictions of AMOC transport do not outperform simple reference forecasts based on climatological annual cycles. Accordingly, there is no justification for the "confident" prediction of a stable AMOC through 2014.

12. Wu, X, Shaoqing Zhang, Z Liu, Anthony Rosati, and Thomas L Delworth, in press: A study of impact of the geographic dependence of observing system on parameter estimation with an intermediate coupled model. Climate Dynamics. DOI:10.1007/s00382-012-1385-1. 4/12.
    [ Abstract ]

    Observational information has a strong geographic dependence that may directly influence the quality of parameter estimation in a coupled climate system. Using an intermediate atmosphere-ocean-land coupled model, the impact of geographic dependent observing system on parameter estimation is explored within a ‘‘twin’’ experiment framework. The ‘‘observations’’ produced by a ‘‘truth’’ model are assimilated into an assimilation model in which the most sensitive model parameter has a different geographic structure from the ‘‘truth’’, for retrieving the ‘‘truth’’ geographic structure of the parameter. To examine the influence of data-sparse areas on parameter estimation, the twin experiment is also performed with an observing system in which the observations in some area are removed. Results show that traditional single-valued parameter estimation (SPE) attains a global mean of the ‘‘truth’’, while geographic dependent parameter optimization (GPO) can retrieve the ‘‘truth’’ structure of the parameter and therefore significantly improves estimated states and model predictability. This is especially true when an observing system with data-void areas is applied, where the error of state estimate is reduced by 31 % and the corresponding forecast skill is doubled by GPO compared with SPE.

13. Wu, X, Shaoqing Zhang, Z Liu, Anthony Rosati, Thomas L Delworth, and Y Liu, December 2012: Impact of Geographic Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model. Monthly Weather Review, 140(12), DOI:10.1175/MWR-D-11-00298.1.
    [ Abstract ]

    Due to the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere-ocean-land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. Four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root mean square errors as 41%, 23%, 62% and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature and land surface temperature respectively. Consistently, the new parameter optimization greatly improves the model predictability due to the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.

14. Zhang, Shaoqing, Z Liu, Anthony Rosati, and Thomas L Delworth, January 2012: A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model. Tellus A, 64, 10963, DOI:10.3402/tellusa.v64i0.10963.
    [ Abstract ]

    Uncertainties in physical parameters of coupled models are an important source of model bias and adversely impact initialisation for climate prediction. Data assimilation using error covariances derived from model dynamics to extract observational information provides a promising approach to optimise parameter values so as to reduce such bias. However, effective parameter estimation in a coupled model is usually difficult because the error covariance between a parameter and the model state tends to be noisy due to multiple sources of model uncertainties. Using a simple coupled model consisting of the 3-variable Lorenz model and a slowly varying slab ‘ocean’, this study first investigated how to enhance the signal-to-noise ratio in covariances between model states and parameters, and then designed a data assimilation scheme for enhancive parameter correction (DAEPC). In DAEPC, parameter estimation is facilitated after state estimation reaches a ‘quasiequilibrium’ where the uncertainty of coupled model states is sufficiently constrained by observations so that the covariance between a parameter and the model state is signal dominant. The observation-updated parameters are applied to improving the next cycle of state estimation and the refined covariance of parameter and model state further improves parameter correction. Performing dynamically adaptive state and parameter estimations with speedy convergence, DAEPC provides a systematic way to estimate the whole array of coupled model parameters using observations, and produces more accurate state estimates. Forecast experiments show that the DAEPC initialisation with observation-estimated parameters greatly improves the model predictability - while valid ‘atmospheric’ forecasts are extended two times longer, the ‘oceanic’ predictability is almost tripled. The simple model results here provide some insights for improving climate estimation and prediction with a coupled general circulation model.

15. Zhang, Shaoqing, Michael Winton, Anthony Rosati, Thomas L Delworth, and B Huang, in press: Impact of Enthalpy-Based Ensemble Filtering Sea-Ice Data Assimilation on Decadal Predictions: Simulation with a Conceptual Pycnocline Prediction Model. Journal of Climate. DOI:10.1175/JCLI-D-11-00714.1. 10/12.
    [ Abstract ]

    The non-Gaussian probability distribution of sea-ice concentration makes difficulties for directly assimilating sea-ice observations into a climate model. Because of the strong impact of the atmospheric and oceanic forcing on the sea-ice state, any direct assimilation adjustment on sea-ice states is easily overridden by model physics.A new approach implements sea-ice data assimilation in enthalpy space where a sea-ice model represents a nonlinear function that transforms a positive-definite space into the sea-ice concentration subspace.Results from observation-assimilation experiments using a conceptual pycnocline prediction model that characterizes the influences of sea-ice on the decadal variability of the climate system show that the new scheme efficiently assimilates “sea-ice observations” into the model – while improving “sea-ice” variability itself, it consistently improves the estimates of all “climate” components.The resulted coupled initialization that is physically consistent among all coupled components significantly improves decadal-scale predictability of the coupled model.

16. Chang, You-Soon, Anthony Rosati, and Shaoqing Zhang, February 2011: A construction of pseudo salinity profiles for the global ocean: Method and evaluation. Journal of Geophysical Research, 116, C02002, DOI:10.1029/2010JC006386.
    [ Abstract ]

    This study demonstrates a reconstruction of salinity profiles for the global ocean for the period 1993-2008. All available T-S profiles from the GTSPP and Argo data are divided in two subsets; one half used for producing the vertical coupled T-S EOF modes and the other for the verification. We employ a weighted least square method that minimizes the misfits between the predetermined EOF structures and independent observed temperature and altimetry data. Verification shows that the South Indian and North Atlantic Oceans maintain good correlations to 900 m depth between the observed and reconstructed salinity with altimetry data. Meanwhile, the Pacific and Antarctic Oceans below 500 m show significant negative correlations, which is associated with the relationship between steric height and salinity variability in these basins. In order to guarantee general agreements with observations for all ocean depths, we calculate a regional correlation index considering the impact of altimetry data and employ it for our final products. Except for the surface ocean, the pseudo salinity profiles show general improvements compared to the existing climatology and the reanalysis outputs from the GFDL’s ensemble coupled data assimilation system. Near the surface layer, reanalysis outputs show a relatively high performance due to the coupling between the atmosphere and ocean. Assimilation system produces reliable surface flux variability not accounted for the construction of the global pseudo salinity profiles. These results encourage the application of the global pseudo salinity profiles into an assimilation system for the 20th century when the observed salinity data are sparse.

17. Chang, You-Soon, Shaoqing Zhang, and Anthony Rosati, July 2011: Improvement of salinity representation in an ensemble coupled data assimilation system using pseudo salinity profiles. Geophysical Research Letters, 38, L13609, DOI:10.1029/2011GL048064.
    [ Abstract ]

    The scarcity of salinity observations prior to the Argo period makes it tremendously difficult to estimate ocean states. By using the so-called pseudo salinity profiles constructed from temperature and altimetry information, here we show the improvement of salinity representation estimated by the ensemble coupled data assimilation system of the Geophysical Fluid Dynamics Laboratory. The comparisons with climatology and independent observations show that the pseudo salinity data considerably improve the assimilation skill for the pre-Argo period (1993- 2001). For the Argo period (2002-2007), there is little degradation of the assimilation skill using pseudo salinity instead of Argo observations. This result ensures the robustness of the new assimilation fields with pseudo salinity for the pre-Argo period when salinity observations are sparse. We also suggest that the interannual variability of the existing reanalysis products could suffer from erroneously-estimated discontinuities due to the non-stationary nature of the salinity observing system.

18. Mahajan, S, Rong Zhang, Thomas L Delworth, Shaoqing Zhang, Anthony Rosati, and You-Soon Chang, September 2011: Predicting Atlantic meridional overturning circulation (AMOC) variations using subsurface and surface fingerprints. Deep-Sea Research, Part II, 58(17-18), DOI:10.1016/j.dsr2.2010.10.067.
    [ Abstract ]

    Recent studies have suggested that the leading modes of North Atlantic subsurface temperature (T_sub) and sea surface height (SSH) anomalies are induced by Atlantic meridional overturning circulation (AMOC) variations and can be used as fingerprints of AMOC variability. Based on these fingerprints of the AMOC in the GFDL CM2.1 coupled climate model, a linear statistical predictive model of observed fingerprints of AMOC variability is developed in this study. The statistical model predicts a weakening of AMOC strength in a few years after its peak around 2005. Here, we show that in the GFDL coupled climate model assimilated with observed subsurface temperature data, including recent Argo network data (2003–2008), the leading mode of the North Atlantic T_sub anomalies is similar to that found with the objectively analyzed T_sub data and highly correlated with the leading mode of altimetry SSH anomalies for the period 1993–2008. A statistical auto-regressive (AR) model is fit to the time-series of the leading mode of objectively analyzed detrended North Atlantic T_sub anomalies (1955–2003) and is applied to assimilated T_sub and altimetry SSH anomalies to make predictions. A similar statistical AR model, fit to the time-series of the leading mode of modeled T_sub anomalies from the 1000-year GFDL CM2.1 control simulation, is applied to predict modeled T_sub, SSH, and AMOC anomalies. The two AR models show comparable skills in predicting observed T_sub and modeled T_sub, SSH and AMOC variations.

19. Mehta, V M., and Anthony Rosati, et al., May 2011: Decadal climate predictability and prediction: Where are we? Bulletin of the American Meteorological Society, 92(5), DOI:10.1175/2010BAMS3025.1.
20. Vecchi, Gabriel A., Ming Zhao, H Wang, G Villarini, Anthony Rosati, A Kumar, Isaac M Held, and Rich Gudgel, April 2011: Statistical-dynamical predictions of seasonal North Atlantic hurricane activity. Monthly Weather Review, 139(4), DOI:10.1175/2010MWR3499.1.
    [ Abstract ]

    Skillfully predicting North Atlantic hurricane activity months in advance is of potential societal significance and a useful test of our understanding of the factors controlling hurricane activity. We describe a statistical-dynamical hurricane forecasting system, based on a statistical hurricane model, with explicit uncertainty estimates, built from a suite of high-resolution global atmospheric dynamical model integrations spanning a broad range of climate states. The statistical model uses two climate predictors: the sea surface temperature (SST) in the tropical North Atlantic and SST averaged over the global tropics. The choice of predictors is motivated by physical considerations, results of high-resolution hurricane modeling and of statistical modeling of the observed record. The statistical hurricane model is applied to a suite of initialized dynamical global climate model forecasts of SST to predict North Atlantic hurricane frequency, which peaks in the August-October season, from different starting dates. Retrospective forecasts of the 1982-2009 period indicate that skillful predictions can be made from as early as November of the previous year – that is, skillful forecasts for the coming North Atlantic hurricane season could be made as the current one is closing. Based on forecasts initialized between November 2009 and March 2010, the model system predicts that the upcoming 2010 North Atlantic hurricane season will likely be more active than the 1982- 2009 climatology, with the forecasts initialized in March 2010 predicting an expected hurricane count of eight and a 50% probability of counts between six (the 1966-2009 median) and nine.

21. Zhang, Rong, Thomas L Delworth, Anthony Rosati, Whit G Anderson, Keith W Dixon, Hyun-Chul Lee, and Fanrong Zeng, December 2011: Sensitivity of the North Atlantic Ocean circulation to an abrupt change in the Nordic Sea overflow in a high resolution global coupled climate model. Journal of Geophysical Research, 116, C12024, DOI:10.1029/2011JC007240.
    [ Abstract ]

    The sensitivity of the North Atlantic Ocean Circulation to an abrupt change in the Nordic Sea overflow is investigated for the first time using a high resolution eddy-permitting global coupled ocean-atmosphere model (GFDL CM2.5). The Nordic Sea overflow is perturbed through the change of the bathymetry in GFDL CM2.5. We analyze the Atlantic Meridional Overturning Circulation (AMOC) adjustment process and the downstream oceanic response to the perturbation. The results suggest that north of 34N, AMOC changes induced by changes in the Nordic Sea overflow propagate on the slow tracer advection time scale, instead of the fast Kelvin wave time scale, resulting in a time lead of several years between subpolar and subtropical AMOC changes. The results also show that a stronger and deeper-penetrating Nordic Sea overflow leads to stronger and deeper AMOC, stronger northward ocean heat transport, reduced Labrador Sea deep convection, stronger cyclonic Northern Recirculation Gyre (NRG), westward shift of the North Atlantic Current (NAC) and southward shift of the Gulf Stream, warmer sea surface temperature (SST) east of Newfoundland and colder SST south of the Grand Banks, stronger and deeper NAC and Gulf Stream, and stronger oceanic eddy activities along the NAC and the Gulf Stream paths. A stronger/weaker Nordic Sea overflow also leads to a contracted/expanded subpolar gyre (SPG). This sensitivity study points to the important role of the Nordic Sea overflow in the large scale North Atlantic ocean circulation, and it is crucial for climate models to have a correct representation of the Nordic Sea overflow.

22. Chang, You-Soon, Anthony Rosati, and Gabriel A Vecchi, February 2010: Basin patterns of global sea level changes for 2004-2007. Journal of Marine Systems, 80(1-2), DOI:10.1016/j.jmarsys.2009.11.003.
    [ Abstract ]

    Based on independent observations, we estimate the sea level budget and linear trends for individual ocean basins and the world ocean during 2004–2007. Even though it is confirmed that the seasonal variation of global sea level is balanced by the different sea level components (total sea level change from satellite altimetry equals to the sum of the steric height contribution obtained by Argo profiles and any variability in ocean mass observed from GRACE), basin-scale sea level budgets show very different characteristics. Sea level budgets over the South Pacific and Antarctic Ocean maintain a good balance both on seasonal to interannual time scales. Meanwhile, only the satellite altimeter data exhibits a large 4-year trend over the South Indian Ocean. This basin significantly impacts the magnitude of the disagreement for the global sea level budget. Large differences among the 3 different gravity fields related to the hydrologic signals in the Atlantic and Indian Ocean could be one of the major causes of the imbalance in the global sea level budget.

23. Farneti, Riccardo, Thomas L Delworth, Anthony Rosati, Stephen M Griffies, and Fanrong Zeng, July 2010: The role of mesoscale eddies in the rectification of the Southern Ocean response to climate change. Journal of Physical Oceanography, 40(7), DOI:10.1175/2010JPO4353.1.
    [ Abstract ]

    Simulations from a fine-resolution global coupled model, the Geophysical Fluid Dynamics Laboratory Climate Model, version 2.4 (CM2.4), are presented, and the results are compared with a coarse version of the same coupled model, CM2.1, under idealized climate change scenarios. A particular focus is given to the dynamical response of the Southern Ocean and the role played by the eddies—parameterized or permitted— in setting the residual circulation and meridional density structure. Compared to the case in which eddies are parameterized and consistent with recent observational and idealized modeling studies, the eddy-permitting integrations of CM2.4 show that eddy activity is greatly energized with increasing mechanical and buoyancy forcings, buffering the ocean to atmospheric changes, and the magnitude of the residual oceanic circulation response is thus greatly reduced. Although compensation is far from being perfect, changes in poleward eddy fluxes partially compensate for the enhanced equatorward Ekman transport, leading to weak modifications in local isopycnal slopes, transport by the Antarctic Circumpolar Current, and overturning circulation. Since the presence of active ocean eddy dynamics buffers the oceanic response to atmospheric changes, the associated atmospheric response to those reduced ocean changes is also weakened. Further, it is hypothesized that present numerical approaches for the parameterization of eddy-induced transports could be too restrictive and prevent coarse-resolution models from faithfully representing the eddy response to variability and change in the forcing fields.

24. Hurrell, J W., Thomas L Delworth, Stephen M Griffies, and Anthony Rosati, et al., September 2010: Decadal Climate Prediction: Opportunities and Challenges, 2010 In OceanObs’09: Sustained Ocean Observations and Information for Society, Vol. 2, ESA Publication, DOI:10.5270/OceanObs09.cwp.45.
25. Lee, J-Y, B Wang, I-S Kang, J Shukla, A Kumar, J-S Kug, C E Schemm, J-J Luo, T Yamagata, X Fu, O Alves, William F Stern, Anthony Rosati, and C-K Park, August 2010: How are seasonal prediction skills related to models’ performance on mean state and annual cycle? Climate Dynamics, 35(2-3), DOI:10.1007/s00382-010-0857-4.
    [ Abstract ]

    Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.

26. Lee, T, and Anthony Rosati, et al., August 2010: Consistency and fidelity of Indonesian-throughflow total volume transport estimated by 14 ocean data assimilation products. Dynamics of Atmospheres and Oceans, 50(2), DOI:10.1016/j.dynatmoce.2009.12.004.
    [ Abstract ]

    Monthly averaged total volume transport of the Indonesian throughflow (ITF) estimated by 14 global ocean data assimilation (ODA) products that are decade to multi-decade long are compared among themselves and with observations from the INSTANT Program (2004–2006). The main goals of the comparisons are to examine the consistency and evaluate the skill of different ODA products in simulating ITF transport. The ensemble averaged, time-mean value of ODA estimates is 13.6 Sv (1 Sv = 106 m3/s) for the common 1993–2001 period and 13.9 Sv for the 2004–2006 INSTANT Program period. These values are close to the 15-Sv estimate derived from INSTANT observations. All but one ODA time-mean estimate fall within the range of uncertainty of the INSTANT estimate. In terms of temporal variability, the scatter among different ODA estimates averaged over time is 1.7 Sv, which is substantially smaller than the magnitude of the temporal variability simulated by the ODA systems. Therefore, the overall “signal-to-noise” ratio for the ensemble estimates is larger than one. The best consistency among the products occurs on seasonal-to-interannual time scales, with generally stronger (weaker) ITF during boreal summer (winter) and during La Nina (El Nino) events. The scatter among different products for seasonal-to-interannual time scales is approximately 1 Sv. Despite the good consistency, systematic difference is found between most ODA products and the INSTANT observations. All but the highest-resolution (18 km) ODA product show a dominant annual cycle while the INSTANT estimate and the 18-km product exhibit a strong semi-annual signal. The coarse resolution is an important factor that limits the level of agreement between ODA and INSTANT estimates. Decadal signals with periods of 10–15 years are seen. The most conspicuous and consistent decadal change is a relatively sharp increase in ITF transport during 1993–2000 associated with the strengthening tropical Pacific trade wind. Most products do not show a weakening ITF after the mid-1970s’ associated with the weakened Pacific trade wind. The scatter of ODA estimates is smaller after than before 1980, reflecting the impact of the enhanced observations after the 1980s. To assess the representativeness of using the average over a three-year period (e.g., the span of the INSTANT Program) to describe longer-term mean, we investigate the temporal variations of the three-year low-pass ODA estimates. The average variation is about 3.6 Sv, which is largely due to the increase of ITF transport from 1993 to 2000. However, the three-year average during the 2004–2006 INSTANT Program period is within 0.5 Sv of the long-term mean for the past few decades.

27. Rienecker, M M., Stephen M Griffies, and Anthony Rosati, et al., September 2010: Synthesis and Assimilation Systems: Essential Adjuncts to the Global Ocean Observing System In OceanObs’09: Sustained Ocean Observations and Information for Society, Vol. 2, ESA Publication, DOI:doi:10.5270/OceanObs09.pp.31.
28. Zhang, Shaoqing, Anthony Rosati, and Thomas L Delworth, October 2010: The adequacy of observing systems in monitoring AMOC and North Atlantic climate. Journal of Climate, 23(19), DOI:10.1175/2010JCLI3677.1.
    [ Abstract ]

    The Atlantic Meridional Overturning Circulation (AMOC) has an important influence on climate, and yet we lack adequate observations of this circulation. Here we assess the adequacy of past and current widely deployed routine observing systems for monitoring the AMOC and associated North Atlantic climate. To do so we draw on two independent simulations of the 20th century using an IPCC AR4 coupled climate model. We treat one simulation as “truth” and sample it according to the observing system we are evaluating. We then assimilate these synthetic “observations” into the second simulation within a fully-coupled system that instantaneously exchanges information among all coupled components and produces a nearly balanced and coherent estimate for global climate states including the North Atlantic climate system. The degree to which the assimilation recovers the “truth” is an assessment of the adequacy of the observing system being evaluated. As the coupled system responds to the constraint of the atmosphere or ocean, the assessment of the recovery for climate quantities such as Labrador Sea Water (LSW) and the North Atlantic Oscillation increases our understanding for the factors that determine AMOC variability. For example, we found the low-frequency sea-surface forcings provided by the atmospheric and sea-surface temperature observations can excite a LSW variation that governs the long time scale variability of the AMOC. When we use the most complete modern observing system consisting of atmospheric winds and temperature, along with Argo ocean temperature and salinity down to 2000 meters, a skill estimate of AMOC reconstruction is 90% (out of 100% maximum). Similarly encouraging results hold for other quantities, such as LSW. The past XBT observing system, in which deep ocean temperature and salinity were not available, has a lesser ability to recover the “truth” AMOC (the skill is reduced to 52%). While these results raise concerns about our ability to properly characterize past variations of the AMOC, they also hold promise for future monitoring of the AMOC and for initializing prediction models.

29. Zhang, Shaoqing, and Anthony Rosati, October 2010: An inflated ensemble filter for ocean sata assimilation with a biased coupled GCM. Monthly Weather Review, 138(10), DOI:10.1175/2010MWR3326.1.
    [ Abstract ]

    A “biased twin” experiment using two coupled general circulation models (CGCMs) that are biased with respect to each other is used to study the impact of deep ocean bias on ensemble ocean data assimilation. The “observations” drawn from one CGCM based on the Argo network are assimilated into the other. Traditional ensemble filtering can successfully recover the upper-ocean temperature and salinity of the target model but it usually fails to converge in the deep ocean where the model bias is large compared to the ocean’s intrinsic variability. The inconsistency between the well-constrained upper ocean and poorly constrained deep ocean generates spurious assimilation currents. An adaptively inflated ensemble filter is designed to enhance the consistency of upper- and deep-ocean adjustments, based on “climatological” standard deviations being adaptively updated by observations. The new algorithm reduces deep-ocean errors greatly, in particular, reducing current errors up to 70% and vertical motion errors up to 50%. Specifically, the tropical circulation is greatly improved with a better representation of the undercurrent, upwelling, and Western Boundary Current systems. The structure of the subtropical gyre is also substantially improved. Consequently, the new algorithm leads to better estimates of important global hydrographic features such as global overturning and pycnocline depth. Based on these improved estimates, decadal trends of basin-scale heat content and salinity as well as the seasonal–interannual variability of the tropical ocean are constructed coherently. Interestingly, the Indian Ocean (especially the north Indian Ocean), which is associated with stronger atmospheric feedbacks, is the most sensitive basin to the covariance formulation used in the assimilation. Also, while reconstruction of the local thermohaline structure plays a leading-order role in estimating the decadal trend of the Atlantic meridional overturning circulation (AMOC), more accurate estimates of the AMOC variability require coupled assimilation to produce coherently improved external forcings as well as internal heat and salt transport.

30. Chang, You-Soon, Anthony Rosati, Shaoqing 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.

31. Wang, B, Ngar-Cheung Lau, Anthony Rosati, and William F Stern, et al., July 2009: Advance and prospectus of seasonal prediction: Assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004). Climate Dynamics, 33(1), DOI:10.1007/s00382-008-0460-0.
    [ Abstract ]

    We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 onetier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Ni ńo 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Nińo 3.4 SST variation. The forecasts for 2 m air temperature are better in El Nińo years than in La Nińa years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the onetier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.

32. Zhang, Shaoqing, Anthony Rosati, and Matthew J Harrison, December 2009: Detection of multidecadal oceanic variability by ocean data assimilation in the context of a “perfect” coupled model. Journal of Geophysical Research, 114, C12018, DOI:10.1029/2008JC005261.
    [ Abstract ]

    The impact of oceanic observing systems, external radiative forcings due to greenhouse gas and natural aerosol (GHGNA), and oceanic initial conditions on long time variability of oceanic heat content and salinity is assessed by the assimilation of oceanic “observations” in the context of a “perfect” Intergovernmental Panel on Climate Change Fourth Assessment Report model. According to times and locations at which observations are available, the 20th century expendable bathythermograph (XBT) temperature and 21st century Argo temperature and salinity observations are drawn from a model simulation (set as the “truth”) with historical GHGNA radiative forcings. These model observations are assimilated into another coupled model simulation based on temporally varying or fixed year GHGNA values and different oceanic initial conditions. The degree to which the assimilation recovers the truth variability of oceanic heat content and salinity is an assessment of the impact of each factor on the detection of the oceanic “climate.” Results show that both the 20th century XBT and 21st century Argo observations adequately capture the basin-scale variability of heat content. The Argo salinity observations appear to be necessary to reproduce the North Atlantic thermohaline structure and variability. The addition of historical radiative forcings does not make a significant contribution to the detection skill. The initial conditions spun up by historical GHGNA produce better detection skill than the initial conditions spun up by preindustrial fixed year GHGNA due to reduced assimilation shocks. While the 20th century XBT temperature observations alone capture some basic features of salinity variations of the tropical ocean due to the strong T-S relationship from tropical air-sea interactions, the Argo salinity observations are important for global state estimation, particularly in high latitudes where haline effects on ocean density are greater.

33. Song, Qian, Gabriel A Vecchi, and Anthony Rosati, 2008: Predictability of the Indian Ocean sea surface temperature anomalies in the GFDL coupled model. Geophysical Research Letters, 35, L02701, DOI:10.1029/2007GL031966.
    [ Abstract ]

    We explore the predictability of the sea surface temperature anomalies associated with the Indian Ocean Dipole/Zonal Mode (IODZM) at a three-season lead, within the Geophysical Fluid Dynamics Laboratory (GFDL) coupled general circulation model (CGCM). In both control simulations and retrospective forecasts of the 1990's in the CGCM, we find that the occurrence of some IODZM events is preconditioned by oceanic conditions and potentially predictable three seasons in advance, while other IODZM events appear to be triggered by weather noise and have low predictability. The results highlight the necessity for future studies to distinguish periods when the IODZM is more or less predictable and search for its precursory pattern in the ocean.

34. 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.

35. Griffies, Stephen M., Matthew J Harrison, Ronald C Pacanowski, and Anthony Rosati, 2007: Ocean modelling with MOM. Clivar Exchanges, 12(3), 3-5, 13.
    [ PDF ]
36. Song, Qian, Gabriel A Vecchi, and Anthony Rosati, June 2007: The role of Indonesian throughflow in the Indo-Pacific climate variability in the GFDL coupled climate model. Journal of Climate, 20(11), DOI:10.1175/JCLI4133.1.
    [ Abstract ]

    The impacts of the Indonesian Throughflow (ITF) on the tropical Indo–Pacific climate, particularly on the character of interannual variability, are explored using a coupled general circulation model (CGCM). A pair of CGCM experiments—a control experiment with an open ITF and a perturbation experiment in which the ITF is artificially closed—is integrated for 200 model years, with the 1990 values of trace gases. The closure of the ITF results in changes to the mean oceanic and atmospheric conditions throughout the tropical Indo–Pacific domain as follows: surface temperatures in the eastern tropical Pacific (Indian) Ocean warm (cool), the near-equatorial Pacific (Indian) thermocline flattens (shoals), Indo–Pacific warm-pool precipitation shifts eastward, and there are relaxed trade winds over the tropical Pacific and anomalous surface easterlies over the equatorial Indian Ocean. The character of the oceanic changes is similar to that described by ocean-only model experiments, though the amplitude of many features in the tropical Indo–Pacific is amplified in the CGCM experiments. In addition to the mean-state changes, the character of tropical Indo–Pacific interannual variability is substantially modified. Interannual variability in the equatorial Pacific and the eastern tropical Indian Ocean is substantially intensified by the closure of the ITF. In addition to becoming more energetic, El Niño–Southern Oscillation (ENSO) exhibits a shorter time scale of variability and becomes more skewed toward its warm phase (stronger and more frequent warm events). The structure of warm ENSO events changes; the anomalies of sea surface temperature (SST), precipitation, and surface westerly winds are shifted to the east and the meridional extent of surface westerly anomalies is larger. In the eastern tropical Indian Ocean, the interannual SST variability off the coast of Java–Sumatra is noticeably amplified by the occurrence of much stronger cooling events. Closing the ITF shoals the eastern tropical Indian Ocean thermocline, which results in stronger cooling events through enhanced atmosphere–thermocline coupled feedbacks. Changes to the interannual variability caused by the ITF closure rectify into mean-state changes in tropical Indo–Pacific conditions. The modified Indo–Pacific interannual variability projects onto the mean-state differences between the ITF open and closed scenarios, rectifying into mean-state differences. These results suggest that CGCMs need to reasonably simulate the ITF in order to successfully represent not just the mean climate, but its variations as well.

37. Song, Qian, Gabriel A Vecchi, and Anthony Rosati, July 2007: Indian Ocean Variability in the GFDL Coupled Climate Model. Journal of Climate, 20(13), DOI:10.1175/JCLI4159.1.
    [ Abstract ]

    The interannual variability of the Indian Ocean, with particular focus on the Indian Ocean dipole/zonal mode (IODZM), is investigated in a 250-yr simulation of the GFDL coupled global general circulation model (CGCM). The CGCM successfully reproduces many fundamental characteristics of the climate system of the Indian Ocean. The character of the IODZM is explored, as are relationships between positive IODZM and El Niño events, through a composite analysis. The IODZM events in the CGCM grow through feedbacks between heat-content anomalies and SST-related atmospheric anomalies, particularly in the eastern tropical Indian Ocean. The composite IODZM events that co-occur with El Niño have stronger anomalies and a sharper east–west SSTA contrast than those that occur without El Niño. IODZM events, whether or not they occur with El Niño, are preceded by distinctive Indo-Pacific warm pool anomaly patterns in boreal spring: in the central Indian Ocean easterly surface winds, and in the western equatorial Pacific an eastward shift of deep convection, westerly surface winds, and warm sea surface temperature. However, delayed onsets of the anomaly patterns (e.g., boreal summer) are often not followed by IODZM events. The same anomaly patterns often precede El Niño, suggesting that the warm pool conditions favorable for both IODZM and El Niño are similar. Given that IODZM events can occur without El Niño, it is proposed that the observed IODZM–El Niño relation arises because the IODZM and El Niño are both large-scale phenomena in which variations of the Indo-Pacific warm pool deep convection plays a central role. Yet each phenomenon has its own dynamics and life cycle, allowing each to develop without the other. The CGCM integration also shows substantial decadal modulation of the occurrence of IODZM events, which is found to be not in phase with that of El Niño events. There is a weak, though significant, negative correlation between the two. Moreover, the statistical relationship between the IODZM and El Niño displays strong decadal variability.

38. 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.

39. Zhang, Shaoqing, 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.

40. Delworth, Thomas L., Anthony J Broccoli, Anthony Rosati, Ronald J Stouffer, Ventakramani Balaji, J A Beesley, W F Cooke, Keith W Dixon, John P Dunne, Krista A Dunne, J W Durachta, Kirsten L Findell, Paul Ginoux, Anand Gnanadesikan, C Tony Gordon, Stephen M Griffies, Rich Gudgel, Matthew J Harrison, Isaac M Held, Richard S Hemler, Larry W Horowitz, Stephen A Klein, Thomas R Knutson, P J Kushner, Amy R Langenhorst, Hyun-Chul Lee, Shian-Jiann Lin, Jian Lu, Sergey 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

41. Gnanadesikan, Anand, Keith W Dixon, Stephen M Griffies, Ventakramani Balaji, M Barreiro, J A Beesley, W F Cooke, Thomas L Delworth, R Gerdes, Matthew J Harrison, Isaac M Held, William J Hurlin, Hyun-Chul 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, Gabriel A Vecchi, Michael Winton, Andrew T Wittenberg, Fanrong Zeng, Rong Zhang, and John P 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.

42. Lee, Hyun-Chul, Anthony Rosati, and Michael J Spelman, 2006: Barotropic tidal mixing effects in a coupled climate model: Oceanic conditions in the Northern Atlantic. Ocean Modelling, 11(3-4), DOI:10.1016/j.ocemod.2005.03.003.
    [ Abstract ]

    Impacts of mixing driven by barotropic tides in a coupled climate model are investigated by using an atmosphere–ocean–ice–land coupled climate model, the GFDL CM2.0. We focus on oceanic conditions of the Northern Atlantic. Barotropic tidal mixing effects increase the surface salinity and density in the Northern Atlantic and decrease the RMS error of the model surface salinity and temperature fields related to the observational data.

43. Vecchi, Gabriel A., Andrew T Wittenberg, and Anthony Rosati, 2006: Reassessing the role of stochastic forcing in the 1997–1998 El Niño. Geophysical Research Letters, 33, L01706, DOI:10.1029/2005GL024738.
    [ Abstract ]

    We explore the extent to which stochastic atmospheric variability was fundamental to development of extreme sea surface temperature anomalies (SSTAs) during the 1997–8 El Niño. The observed western equatorial Pacific westerly zonal stress anomalies (τ a x ), which appeared between Nov. 1996 and May 1997 as a series of episodic bursts, were largely reproducible by an atmospheric general circulation model (AGCM) ensemble forced with observed SST. Retrospective forecasts using a hybrid coupled model (HCM) indicate that coupling only the part of τ a x linearly related to large-scale tropical Pacific SSTA is insufficient to capture the observed 1997 warming; but, accounting in the HCM for all the τ a x that was connected to SST, recovers most of the strong SSTA warming. The AGCM-estimated range of stochastic τ a x forcing induces substantial dispersion in the forecasts, but does not obscure the large-scale warming in most HCM ensemble members.

44. Wittenberg, Andrew T., Anthony Rosati, Ngar-Cheung Lau, and Jeff J Ploshay, 2006: GFDL's CM2 Global Coupled Climate Models. Part III: Tropical Pacific Climate and ENSO. Journal of Climate, 19(5), DOI:10.1175/JCLI3631.1.
    [ Abstract ]

    Multicentury integrations from two global coupled ocean–atmosphere–land–ice models [Climate Model versions 2.0 (CM2.0) and 2.1 (CM2.1), developed at the Geophysical Fluid Dynamics Laboratory] are described in terms of their tropical Pacific climate and El Niño–Southern Oscillation (ENSO). The integrations are run without flux adjustments and provide generally realistic simulations of tropical Pacific climate. The observed annual-mean trade winds and precipitation, sea surface temperature, surface heat fluxes, surface currents, Equatorial Undercurrent, and subsurface thermal structure are well captured by the models. Some biases are evident, including a cold SST bias along the equator, a warm bias along the coast of South America, and a westward extension of the trade winds relative to observations. Along the equator, the models exhibit a robust, westward-propagating annual cycle of SST and zonal winds. During boreal spring, excessive rainfall south of the equator is linked to an unrealistic reversal of the simulated meridional winds in the east, and a stronger-than-observed semiannual signal is evident in the zonal winds and Equatorial Undercurrent. Both CM2.0 and CM2.1 have a robust ENSO with multidecadal fluctuations in amplitude, an irregular period between 2 and 5 yr, and a distribution of SST anomalies that is skewed toward warm events as observed. The evolution of subsurface temperature and current anomalies is also quite realistic. However, the simulated SST anomalies are too strong, too weakly damped by surface heat fluxes, and not as clearly phase locked to the end of the calendar year as in observations. The simulated patterns of tropical Pacific SST, wind stress, and precipitation variability are displaced 20°–30° west of the observed patterns, as are the simulated ENSO teleconnections to wintertime 200-hPa heights over Canada and the northeastern Pacific Ocean. Despite this, the impacts of ENSO on summertime and wintertime precipitation outside the tropical Pacific appear to be well simulated. Impacts of the annual-mean biases on the simulated variability are discussed.

45. Griffies, Stephen M., Anand Gnanadesikan, Keith W Dixon, John P 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.

46. Sweeney, C, Anand Gnanadesikan, Stephen M 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.

47. Zhang, Shaoqing, 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.

48. Griffies, Stephen M., 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.

49. Zhang, Shaoqing, 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.

50. 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.

51. Schneider, E K., D G DeWitt, Anthony Rosati, B P Kirtman, L Ji, and J J Tribbia, 2003: Retrospective ENSO forecasts: sensitivity to atmospheric model and ocean resolution. Monthly Weather Review, 131(12), 3038-3060.
    [ Abstract PDF ]

    Results are described from a series of 40 retrospective forecasts of tropical Pacific SST, starting 1 January and 1 July 1980–99, performed with several coupled ocean–atmosphere general circulation models sharing the same ocean model—the Modular Ocean Model version 3 (MOM3) OGCM—and the same initial conditions. The atmospheric components of the coupled models were the Center for Ocean–Land–Atmosphere Studies (COLA), ECHAM, and Community Climate Model version 3 (CCM3) models at T42 horizontal resolution, and no empirical corrections were applied to the coupling. Additionally, the retrospective forecasts using the COLA and ECHAM atmospheric models were carried out with two resolutions of the OGCM. The high-resolution version of the OGCM had 1° horizontal resolution (1/3° meridional resolution near the equator) and 40 levels in the vertical, while the lower-resolution version had 1.5° horizontal resolution (1/2° meridional resolution near the equator) and 25 levels. The initial states were taken from an ocean data assimilation performed by the Geophysical Fluid Dynamics Laboratory (GFDL) using the high-resolution OGCM. Initial conditions for the lower-resolution retrospective forecasts were obtained by interpolation from the GFDL ocean data assimilation. The systematic errors of the mean evolution in the coupled models depend strongly on the atmospheric model, with the COLA versions having a warm bias in tropical Pacific SST, the CCM3 version a cold bias, and the ECHAM versions a smaller cold bias. Each of the models exhibits similar levels of skill, although some statistically significant differences are identified. The models have better retrospective forecast performance from the 1 July initial conditions, suggesting a spring prediction barrier. A consensus retrospective forecast produced by taking the ensemble average of the retrospective forecasts from all of the models is generally superior to any of the individual retrospective forecasts. One reason that averaging across models appears to be successful is that the averaging reduces the effects of systematic errors in the structure of the ENSO variability of the different models. The effect of reducing noise by averaging ensembles of forecasts made with the same model is compared to the effects from multimodel ensembling for a subset of the cases; however, the sample size is not large enough to clearly distinguish between the multimodel consensus and the single-model ensembles. There are obvious problems with the retrospective forecasts that can be connected to the various systematic errors of the coupled models in simulation mode, and which are ultimately due to model error (errors in the physical parameterizations and numerical truncation). These errors lead to initial shock and a “spring variability barrier” that degrade the retrospective forecasts.

52. 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.

53. 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.

54. Anderson, D L., T N Stockdale, M K Davey, M Fischer, M Ji, Anthony Rosati, N R Smith, and S E Zebiak, 2001: Ocean data needs for ENSO and seasonal forecast systems In Observing the Oceans in the 21st Century, Melbourne, Australia, Uniprint Pty. Ltd., 546-560.
    [ Abstract ]

    In this paper we consider the ocean data requirements of comprehensive coupled models used for seasonal forecasting. These are not independent of the measurements and procedures needed to produce analyses of the winds and surface heat and freshwater fluxes which are used to force the ocean models and to provide vital information on the ocean initial conditions. To further put ocean data requirements in context, brief descriptions of a few current ocean analysis systems are given. The way in which different data types are used in practice is then discussed and future requirements assessed.

55. Gudgel, Rich, Anthony Rosati, and C Tony Gordon, 2001: The sensitivity of a coupled atmospheric-oceanic GCM in prescribed low-level clouds over the ocean and tropical landmasses. Monthly Weather Review, 129(8), 2103-2115.
    [ Abstract PDF ]

    The sensitivity of a coupled general circulation model (CGCM) to tropical marine stratocumulus (MSc) clouds and low-level clouds over the tropical land is examined. The hypothesis that low-level clouds play an important role in determining the strength and position of the Walker circulation and also on the strength and phase of the El Niño–Southern Oscillation (ENSO) is studied using a Geophysical Fluid Dynamics Laboratory (GFDL) experimental prediction CGCM. In the Tropics, a GFDL experimental prediction CGCM exhibits a strong bias in the western Pacific where an eastward shift in the ascending branch of the Walker circulation diminishes the strength and expanse of the sea surface temperature (SST) warm pool, thereby reducing the east–west SST gradient, and effectively weakening the trade winds. These model features are evidence of a poorly simulated Walker circulation, one that mirrors a “perpetual El Niño” state. One possible factor contributing to this bias is a poor simulation of MSc clouds in the eastern equatorial Pacific (which are essential to a proper SST annual cycle). Another possible contributing factor might be radiative heating biases over the land in the Tropics, which could, in turn, have a significant impact on the preferred locations of maximum convection in the Tropics. As a means of studying the sensitivity of a CGCM to both MSc clouds and to varied radiative forcing over the land in the Tropics, low-level clouds obtained from the International Satellite Cloud Climatology Project (ISCCP) are prescribed. The experiment sets consist of one where clouds are fully predicted, another where ISCCP low-level clouds are prescribed over the oceans alone, and a third where ISCCP low-level clouds are prescribed both over the global oceans and over the tropical landmasses. A set of ten 12-month hindcasts is performed for each experiment. The results show that the combined prescription of interannually varying global ocean and climatological tropical land low-level clouds into the CGCM results in a much improved simulation of the Walker circulation over the Pacific Ocean. The improvement to the tropical circulation was also notable over the Indian and Atlantic basins as well. These improvements in circulation led to a considerable increase in ENSO hindcast skill in the first year by the CGCM. These enhancements were a function of both the presence of MSc clouds over the tropical oceans and were also due to the more realistic positioning of the regions of maximum convection in the Tropics. This latter model feature was essentially a response to the change in radiative forcing over tropical landmasses associated with a reduction in low cloud fraction and optical depth when ISCCP low-level clouds were prescribed there. These results not only underscore the importance of a reasonable representation of MSc clouds but also point out the considerable impact that radiative forcing over the tropical landmasses has on the simulated position of the Walker circulation and also on ENSO forecasting.

56. Zhang, C, H H Hendon, W S Kessler, and Anthony Rosati, 2001: A Workshop on the MJO and ENSO. Bulletin of the American Meteorological Society, 82(5), 971-976.
    [ Abstract PDF ]

    A workshop was held 15-17 March 2000 to discuss the possibility that the Madden-Julian oscillation (MJO) interacts with El Niño-Southern Oscillation (ENSO). The workshop explored a number of topics related to the MJO-ENSO problem, proposed a set of competing hypotheses, and made recommendations for future studies on this issue.

57. Gordon, C T., Anthony Rosati, and Rich Gudgel, 2000: Tropical sensitivity of a coupled model to specified ISCCP low clouds. Journal of Climate, 13(13), 2239-2260.
    [ Abstract PDF ]

    The seasonal cycle of SST observed in the eastern equatorial Pacific is poorly simulated by many ocean-atmosphere coupled GCMs. This deficiency may be partly due to an incorrect prediction of tropical marine stratocumulus (MSc). To explore this hypothesis, two basic multiyear simulations have been performed using a coupled GCM with seasonally varying solar radiation. The model's cloud prediction scheme, which under-predicts tropical marine stratocumulus, is used for all clouds in the control run. In contrast, in the "ISCCP" run, the climatological monthly mean low cloud fraction is specified over the open ocean, utilizing C2 data from the International Satellite Cloud Climatology Project (ISCCP). In this manner, the treatment of MSc clouds, including the annual cycle, is more realistic than in previous sensitivity studies. Robust surface and subsurface thermodynamical and dynamical responses to the specified MSc are found in the Tropics, especially near the equator. In the annual mean, the equatorial cold tongue extends farther west and intensifies, while the east-west SST gradient is enhanced. A double SST maximum flanking the cold tongue becomes asymmetric about the equator. The SST annual cycle in the eastern equatorial Pacific strengthens, and the equatorial SST seasonal anomalies migrate farther westward. MSc-induced local shortwave radiative cooling enhances dynamical cooling associated with the southeast trades. The surface meridional wind stress in the extreme eastern equatorial Pacific remains southerly all year, while the surface zonal wind stress and equatorial upwelling intensify, as does the seasonal cycle of evaporation, in better agreement with observation. Within the ocean, the thermocline steepens and the Equatorial Undercurrent intensifies. When the low clouds are entirely removed, the SST warms by about 5.5 K in the western and central tropical Pacific, relative to "ISCCP," and the model's SST bias there reverses sign. ENSO-like interannual variability with a characteristic timescale of 3-5 yr is found in all simulations, though its amplitude varies. The "ISCCP" equatorial cold tongue inhibits the eastward progression of ENSO-like warm events east of the date line. When the specified low cloud fraction in "ISCCP' is reduced by 20%, the interannual variability amplifies somewhat and the coupled model responds more like a delayed oscillator. The apparent sensitivity in the equatorial Pacific to a 20% relative change in low cloud fraction may have some cautionary implications for seasonal prediction by coupled GCMs.

58. Gordon, C T., Anthony Rosati, and Rich Gudgel, 1999: Tropical interannual variability response of a coupled model to specified low clouds In Proceedings of the Twenty-Fourth Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 331-334.
59. Gudgel, Rich, Anthony Rosati, and C Tony Gordon, 1999: The impact of prescribed tropical land and ocean clouds on the Walker circulation in the GFDL coupled ocean-atmosphere GCM In Proceedings of the Twenty-Fourth Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 307-310.
    [ Abstract ]

    The role that tropical land and ocean clouds play in the GFDL coupled ocean-atmosphere GCM is studied through a series of 10 one-year model runs. In the tropics, a strong bias in the GFDL coupled GCM is evident in the western Pacific where excessive convection erodes the SST warm pool, reducing the SST pacific gradient, and effectively weakening the trade winds. This bias is exacerbated by the poor simulation of eastern equatorial Pacific marine stratus clouds which are essential to a proper seasonal cycle (annual as opposed to biannual) of the trade winds and the SST's. As a means to better understand the importance of ocean-only versus ocean and land tropical cloud prediction, low-level ISCCP clouds are used to study the effects on the GFDL atmosphere-only and coupled model simulation. The prescription of tropical low-level ocean and land clouds into the GCM resulted in a better simulation of the Walker circulation in both coupled and uncoupled modes. This climatological improvement to the Walker circulation corresponded with an improvement in the ability of the coupled GCM to simulate ENSO (El Niño/Southern Oscillation). The more reasonably represented land surface heating in the tropics led to more well-defined and positioned regions of convergence and divergence both at the surface and aloft. The GCM appears to be quite sensitive to the pronounced horizontal and vertical topographical structure in the Indonesian Archipelago and in tropical South America. This is most notable in the sensitivity of the model to small cloud fraction changes over these regions. This emphasizes the importance of reasonably representing the land surface heating in these regions. Whereas this sensitivity is evident in both the coupled and uncoupled simulations not only in terms of the model's climate but also in term of the model's ability to simulate ENSO, it underscores the importance of producing reasonable heating profiles over the land regions in the tropics.

60. 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.
61. 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.
62. Gordon, C T., Anthony Rosati, and Rich Gudgel, 1998: Tropical sensitivity to specified ISCCP low clouds in a coupled model In Proceedings of the Twenty-Second Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 232-235.
63. Latif, M, D Anderson, T P Barnett, M Cane, R Kleeman, Ants Leetma, J O'Brien, Anthony Rosati, and E K Schneider, 1998: A review of the predictability and prediction of ENSO. Journal of Geophysical Research, 103(C7), 14,375-14,393.
    [ Abstract PDF ]

    A hierarchy of El Niño-Southern Oscillation (ENSO) prediction schemes has been developed during the Tropical Ocean-Global Atmosphere (TOGA) program which includes statistical schemes and physical models. The statistical models are, in general, based on linear statistical techniques and can be classified into models which use atmospheric (sea level pressure or surface wind) or oceanic (sea surface temperature or a measure of upper ocean heat content) quantities or a combination of oceanic and atmospheric quantities as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the "shallow water" type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast in predicting typical indices of ENSO on lead times of 6 to 12 months. The TOGA program can be regarded as a success from this perspective. However, despite the demonstrated predictability, little is known about ENSO predictability limits and the predictability of phenomena outside the tropical Pacific. Furthermore, the predictability of anomalous features known to be associated with ENSO (e.g., Indian monsoon and Sahel rainfall, southern African drought, and off-equatorial sea surface temperature) needs to be addressed in more detail. As well, the relative importance of different physical mechanisms (in the ocean or atmosphere) has yet to be established. A seasonal dependence in predictability is seen in many models, but the processes responsible for it are not fully understood, and its meaning is still a matter of scientific discussion. Likewise, a marked decadal variation in skill is observed, and the reasons for this are still under investigation. Finally, the different prediction models yield similar skills, although they are initialized quite differently. The reasons for these differences are also unclear.

64. Stern, William F., and Anthony Rosati, 1998: Issues of orographic adjustment of SSTs in a spectral/coupled GCM In Research Activities in Atmospheric and Oceanic Modelling, Report No. 27, WMO/TD-No. 865, Geneva, Switzerland, World Meteorological Organization, 6.21-6.22.
65. Anderson, Jeffrey L., Anthony Rosati, and Rich Gudgel, 1997: Potential predictability in an ensemble of coupled atmosphere-ocean general circulation model seasonal forecasts In Proceedings of the Twenty-First Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 18-21.
66. Miyakoda, Kikuro, Jeff J Ploshay, and Anthony Rosati, 1997: Preliminary study on SST forecast skill associated with the 1982/83 El Niño process, using coupled model data assimilation. Atmosphere-Ocean, 35(1), 469-486.
    [ Abstract PDF ]

    A previous study by Rosati et al. (1997) has concluded that the specification of an adequate thermocline structure along the equatorial Pacific ocean is most crucial for El Niño forecasts. In that paper, the oceanic initial condition was generated by a data assimilation (DA) system (Derber and Rosati, 1989). However, the initial condition for the atmospheric part was taken from the National Meteorological Center's (NMC) operational analysis, which was simply attached to the oceanic part for the coupled model forecasts. In the present paper, both the atmospheric and oceanic initial conditions are generated by a coupled DA system applied to a coupled air-sea general circulation model (GCM). The assimilation for the ocean is performed by the same system as mentioned above, in which the SST (sea surface temperature) and the subsurface temperatures are injected into a 15 vertical level oceanic GCM. The upper boundary condition, such as surface wind stress, is specified by the atmospheric DA. The assimilation for the atmosphere is performed by the continuous injection method of Stern and Ploshay (1992), using an 18 vertical level atmospheric GCM. The lower boundary condition, such as SST, is specified by the oceanic DA. The coupled model assimilations are carried out by switching the DA processes alternately every 6 hours between the ocean and the atmosphere. The emphases of this study are: firstly, the effect of coupled air-sea model DA on the performance of subsequent forecsts; secondly, the impact of the coupled assimilation on improvement of the "spin-up" behavior of forecasts, i.e., to see whether a smooth start to the forecast is achieved by the coupled model DA process; and thirdly, investigation of the effect that the "spring barrier" has on predictability in the coupled GCM system. Preliminary results indicate that, in order to answer these questions, ensemble forecasts are necessary. Besides, the coupled assimilation could be important in improving the overall behavior of El Niño and La Niña forecasts.

67. 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.
68. Rosati, Anthony, Kikuro Miyakoda, and Rich Gudgel, May 1997: The impact of ocean initial conditions on ENSO forecasting with a coupled model. Monthly Weather Review, 125(5), 754-772.
    [ Abstract PDF ]

    A coupled atmosphere-ocean GCM (general circulation model) has been developed for climate predictions on seasonal to interannual timescales. The atmosphere model is a global spectral GCM T30L18 and the ocean model is global on a 1 degree grid. Initial conditions for the atmosphere were obtained from National Meteorological Center (now known as the National Centers for Environmental Prediction) analyses, while those for the ocean came from three ocean data assimilation (DA) systems. One system is a four-dimensional DA scheme that uses conventional SST observations and vertical temperature profiles inserted into the ocean model and is forced from winds from an operational analysis. The other two initialization schemes are based on the coupled model, both nudging the surface temperature toward observed SSTs and one nudging surface winds from an operational analysis. all three systems were run from 1979 to 1988, saving the state of the ocean every month, thus initial conditions may be obtained for any month during this period. The ocean heat content from the three systems was examined, and it was found that a strong lag correlation between Niño-3 SST anomalies and equatorial thermocline displacements exists. This suggests that, based on subsurface temperature field only, eastern tropical Pacific SST changes are possibly predictable at lead times of a year or more. It is this "memory" that is the physical basis for ENSO predictions. Using the coupled GCM, 13-month forecasts were made for seven January and seven July cases, focusing on ENSO (El Niño-Southern Oscillation) prediction. The forecasts, whose ocean initial conditions contained subsurface thermal data, were successful in predicting the two El Niño and two La Niña events during the decade, whereas the forecasts that utilized ocean initial conditions from the coupled model that were nudged toward surface wind fields and SST only, failed to predict the events. Despite the coupled model's poor simulation of the annual cycle in the tropical Pacific, the ENSO forecasts from the full DA were remarkably good.

69. Gordon, C T., Anthony Rosati, and Rich Gudgel, 1996: The impact of specified ISCCP low clouds in coupled model integrations In 11th Conference on Numerical Weather Prediction, Boston, MA, American Meteorological Society, 8-10.
70. Gordon, C T., Anthony Rosati, and Rich Gudgel, 1996: The impact of specified ISCCP low clouds in coupled model integrations In Research Activities in Atmospheric and Oceanic Modelling, CAS/JSC Working Group on Numerical Experimentation, Report No. 23 WMO/TD No. 734, World Meteorological Organization, 9.12-9.13.
71. 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.
72. Rosati, Anthony, Rich Gudgel, and Kikuro Miyakoda, 1996: Global ocean data assimilation system In Modern Approaches to Data Assimilation in Ocean Modeling, The Netherlands, Elsevier Science Publishers, 181-203.
    [ Abstract ]

    A global oceanic four-dimensional data assimilation system has been developed for use in initializing coupled ocean-atmosphere general circulation models and also to study interannual variability. The data inserted into a high resolution global ocean model consists only of conventional sea surface temperature observations and vertical temperature profiles. The data are inserted continuously into the model by updating the model's temperature solution every timestep. This update is created using a statistical interpolation routine applied to all data in a 30-day window for three consecutive timesteps and then the correction is held constant for nine timesteps. Not updating every timestep allows for a more computational efficient system without affecting the quality of the analysis. The data assimilation system was run over a ten year period from 1979-1988. The resulting analysis product was compared with independent analysis including model derived fields like velocity. The large scale features seem consistent with other products based on observations. Using the mean of the ten-year period as a climatology, the data assimilation system was compared with the Levitus climatological atlas. Looking at the sea surface temperature and the seasonal cycle, as represented by the mixed layer depth, the agreement is quite good, however, some systematic differences do emerge. Special attention is given to the tropical Pacific examining the El Niño signature. Two other assimilation schemes based on using Newtonian nudging of SST, are compared to the full data assimilation system. The heat content variability in the data assimilation seemed faithful to the observations. Overall, the results are encouraging, demonstrating that the data assimilation system seems to be able to capture many of the large scale general circulation features that are observed, both in a climatological sense and in the temporal variability.

73. Sirutis, Joseph J., and Anthony Rosati, 1996: The impact of cumulus convection parameterization in coupled air-sea models In 11th Conference on Numerical Weather Prediction, Boston, MA, American Meteorological Society, 348-350.
74. Miyakoda, Kikuro, Joseph J Sirutis, Anthony Rosati, C Tony Gordon, Rich Gudgel, William F Stern, Jeffrey L Anderson, and A Navarra, 1995: Atmospheric parameterizations in coupled air-sea models used for forecasts of ENSO In Proceedings of the International Scientific Conference on the Tropical Ocean Global Atmosphere (TOGA) Programme, WCRP-91, WMO/TD No. 717, Geneva, Switzerland, World Meteorological Organization, 802-806.
    [ Abstract ]

    In order to investigate the feasibility of seasonal forecasts, a prediction system is developed. Here the main theme is the study of atmospheric physics parameterization for coupled air-sea modeling. The oceanic GCM uses 1 degree global grid with a finer resolution in the equatorial belt. The atmospheric GCM has the spectral T30 representation, which includes all of the usual physics parameterizations. Using a first version of the model (Coupled Model I) and a set of appropriate initial conditions, the capability of El Niño and La Niña forecasting with a 13 month lead time was tested, resulting in successful forecasts of the 1982/83 and 1988/89 events (Rosati et al., 1995b). However, longer runs of this system have revealed a sizable systematic error in simulations with a tendency to cool most of the world ocean, particularly the western tropical Pacific, and also without an adequate annual cycle of the SST in the eastern tropical Pacific. In order to improve some of these features, particularly the ENSO phenomena, various versions of the atmospheric parameterizations and mountain representation are incorporated into the atmospheric GCM, and the model simulations are examined. The experiments are divided into two steps: one is with the uncoupled atmospheric model, and the other is with the coupled model. In the first step, five year simulations are carried out with the observed SST prescribed, and the results are compared with observations, which enables one to make the critical validation of the model. The second step is to couple the atmospheric and oceanic models, and integrate them from a January 1982 initial condition for 7 years, and also for another initial condition, i.e., January, 1988 for 13 months. Compared with the boundary forced simulation, the coupling process introduces more degree of freedom, with increase of the sensitivity as well as the complexity considerably. In particular, the El Niño simulation is sensitive to any change of physics. For this reason, the objective of the simulation is focused only on the equatorial Pacific process and secondly the Indian monsoon, as opposed to the overall improvement of the general circulation. In other words, the approach is close to that of mechanistic modeling with specific targets rather than that of a GCM with broader objectives. The research is proceeding in two directions. One is: investigating the model's sensitivity for El Niño and La Niña processes to variation in a coupling parameter. The second is: after a number of trial-and-error experiments on various combinations of the parameterizations, the second atmospheric model, i.e., Model II, is selected. It is shown that Coupled Model II performs substantially better in some aspects but worse in other aspects than Coupled Model I. The improvement is found in the SST: warming occurs not only over the equatorial Pacific but also over the whole globe. The SST increase is achieved by the strong effect of the cumulus convection. On the other hand, some deficiencies remain the same in both models, i.e., the large positive errors of the SST in the eastern oceans, the lack of an annual cycle of the SST in the eastern equatorial Pacific, and the failure in forecast of the second El Niño. In summary, the prediction of the Southern Oscillation has been achieved by the two models for a full first cycle but not for the second cycle .

75. Pinardi, N, Anthony Rosati, and Ronald C Pacanowski, 1995: The sea surface pressure formulation of rigid lid models. Implications for altimetric data assimilation studies. Journal of Marine Systems, 6, 109-119.
    [ Abstract PDF ]

    The sea surface pressure formulation of the rigid lid primitive equation oceanic problem is reviewed and clarified. The geostrophic limit for the sea surface pressure equation is then considered and a new diagnostic relationship is found that relates the surface pressure to the barotropic and baroclinic components of the subsurface flow field. We demonstrate that a direct insertion in the model equations of sea surface information, such as that provided by satellite altimetry, does not produce changes in the subsurface dynamics due to the divergenceless nature of the barotropic flow field. The geostrophic limit of the sea surface pressure field computed from a standard general circulation model of the world ocean is presented and the barotropic/baroclinic components of the asolute dynamic topography of the global general circulation are discussed.

76. Rosati, Anthony, Rich Gudgel, and Kikuro Miyakoda, 1995: Decadal analysis produced from an ocean data assimilation system. Monthly Weather Review, 123(7), 2206-2228.
    [ Abstract PDF ]

    A global oceanic four-dimensional data assimilation system has been developed for use in initializing coupled ocean-atmosphere general circulation models and also to study interannual variability. The data inserted into a high-resolution global ocean model consist of conventional sea surface temperature observations and vertical temperature profiles. The data are inserted continuously into the model by updating the model's temperature solution every time step. This update is created using a statistical interpolation routine applied to all data in a 30-day window for three consecutive time steps and then the correction is held constant for nine time steps. Not updating every time step allows for a more computationally efficient system without affecting the quality of the analysis. The data assimilation system was run over a 10-yr period from 1979 to 1988. The resulting analysis product was compared with independent analysis including model-derived fields like velocity. The large-scale features seem consistent with other products based on observations. Using the mean of the 10-yr period as a climatology, the data assimilation system was compared with the Levitus climatological atlas. Looking at the sea surface temperature and the seasonal cycle, as represented by the mixed-layer depth, the agreement is quite good, however, some systematic differences do emerge. Special attention is given to the tropical Pacific examining the El Niño signature. Two other assimilation schemes based on the coupled model using Newtonian nudging of SST and then SST and surface winds are compared to the full data assimilation system. The heat content variability in the data assimilation seemed faithful to the observations. Overall, the results are encouraging, demonstrating that the data assimilation system seems to be able to capture many of the large-scale general circulation features that are observed, both in a climatological sense and in the temporal variability.

77. Miyakoda, Kikuro, Anthony Rosati, and Rich Gudgel, 1994: Air-sea coupling experiments: ENSO forecasting. Part I In Proceedings of the 18th Annual Climate Diagnostics Workshop, U. S. Dept. of Commerce/NOAA/NWS, 153-156.
78. Rosati, Anthony, Kikuro Miyakoda, and Rich Gudgel, 1994: Air-sea coupling experiments: ENSO forecasting. Part II In Proceedings of the 18th Annual Climate Diagnostics Workshop, U. S. Dept. of Commerce/NOAA/NWS, 358-361.
79. Kantha, L H., and Anthony Rosati, 1990: The effect of curvature on turbulence in stratified fluids. Journal of Geophysical Research, 95(C11), 20,313-20,330.
    [ Abstract PDF ]

    The influence of streamline curvature on small-scale turbulence and vertical mixing in stratified fluids is the subject of this study. The roles of curvature and stratification in enhancing and suppressing turbulent mixing are explored using second-moment closure for turbulence. Governing equations for second moments are expressed in generalized orthogonal curvilinear coordinates, from which, through a series of approximations, simplified expressions are derived for second moments in the limit of small streamline curvature. The governing equations are then used to obtain a quasi- equilibrium turbulence model suited for application to atmospheric and oceanic mixed layers. A typical model application is illustrated by simulation of stratified flows over two-dimentional, idealized mountains and valleys. The limit of local equilibrium is further invoked to derive semi- analytical results for the enhancement and suppression of vertical turbulent mixing under the combined influence of stratification and curvature. It is shown that stabilizing curvature can drastically suppress turbulence even when the stratification is strongly destabilizing. Conversely, under strong stable ratification that would otherwise lead to total suppression of turbulence, destabilizing curvature can keep turbulence alive. Streamline curvature is also shown to significantly modify the Monin-Obukhov similarity laws for momentum and heat fluxes in the constant flux region of the atmospheric boundary layer. Finally, the need for observational data on curvature effects on mixing in stratified flows either in the laboratory or in flows over topography in the oceans and the atmosphere is highlighted.

80. Miyakoda, Kikuro, Joseph J Sirutis, Anthony Rosati, and J Derber, 1990: Experimental forecasts with an air-sea model: Preliminary results In Air-Sea Interaction in Tropical Western Pacific, Beijing, China, China Ocean Press, 417-432.
    [ Abstract ]

    An air-sea model has been applied to the seasonal forecasting problem for a single case beginning 1 October 1979. The model consists of an atmospheric model and a global 1° x 1° resolution oceanic model, with a higher latitudinal resolution in the equatorial zone. The initial conditions are obtained by the 4-dimensional data assimilation system for the atmosphere and the ocean. The experiments reveal that strong air-sea interaction is evident, manifested in a close connection between the predicted sea temperature and the sea level pressure anomaly patterns. There is a certain degree of predictive skill up to 5 months for the ocean and beyond 9 months for the atmosphere. However, the systematic bias in the sea temperature prediction is pronounced.

81. Derber, J, and Anthony Rosati, 1989: A global oceanic data assimilation system. Journal of Physical Oceanography, 19(9), 1333-1347.
    [ Abstract PDF ]

    A global oceanic four-dimensional data assimilation system has been developed for use in initializing coupled ocean-atmosphere general circulation models and many other applications. The data assimilation system uses a high resolution global ocean model to extrapolate the information forward in time. The data inserted into the model currently consists only of conventional sea surface temperature observations and vertical temperature profiles. The data are inserted continuously into the model by updating the model's temperature solution every timestep. This update is created using a statistical interpolation routine applied to all data in a 30-day window centered on the present timestep. Large scale features in the sea surface temperature analyses are consistent with those from independent analyses. Subsurface fields created from the assimilation are much more realistic than those produced without the insertion of data. Furthermore, information contained in the assimilation field is shown to be retained in the model solution after the assimilation procedure is terminated. The results are encouraging but further improvements can be made.

82. Galperin, B, Anthony Rosati, L H Kantha, and George L Mellor, 1989: Modeling rotating stratified turbulent flows with application to oceanic mixed layers. Journal of Physical Oceanography, 19(7), 901-916.
    [ Abstract PDF ]

    Rotational effects on turbulence structure and mixing are investigated using a second-moment closure model. Both explicit and implicit Coriolis terms are considered. A general Criterion for rotational effects to be small is established in terms of local turbulent Rossby numbers. Characteristic length scales are determined for rotational effects and Monin-Obukhov type similarity theory is developed for rotating stratified flows. A one-dimensional version of the closure model is then applied to simulate oceanic mixed layer evolution. It is shown that the effects of rotation onmixed layer depth tend to be small because of the influence of stable stratification. These findings contradict a hypothesis of Garwood et al. that rotational effects on turbulence are responsible for the disparity in the mixed-layer depths between the eastern and western regions of the equatorial Pacific Ocean. The model is also applied to neutrally stratified flows to demonstrate that rotation can either stabilize or destabilize the flow.

83. Kantha, L H., Anthony Rosati, and B Galperin, 1989: Effect of rotation on vertical mixing and associated turbulence in stratified fluids. Journal of Geophysical Research, 94(C4), 4843-4854.
    [ Abstract PDF ]

    Combined effects of stratification and rotation on vertical mixing and the characteristics of associated small-scale turbulence are explored using second-moment closure methodology; the rotational terms in the equations for Reynolds stresses and turbulent heat fluxes are retained, not ignored as in earlier works. Semianalytical results valid for arbitrary values of rotation and stratification are derived by further invoking the local equilibrium limit of closure. Two cases are considered: nonzero vertical rotation and nonzero meridional rotation; the latter case is of more general interest in geophysics because of its potential application to equatorial mixed layers. In both cases the influence of rotation on mixing coefficients and Monin-Obukhov constant flux layer similarity relations is investigated for arbitrary values of rotation and stratification. In both cases, turbulent mixing coefficients assume tensorial properties. However, meridional rotation appears to have a stronger influence on vertical mixing and turbulence characteristics than does vertical rotation. These results, along with perturbation expansions for weak rotation, suggest that for geophysical flows, in most cases, the direct effect of rotation on vertical turbulent mixing itself is but a small correction, a few tens of percent at best. It is seldom large, although it might not be negligible in some particular cases. Nevertheless, the study of rotational effects on small-scale turbulence provides a fascinating insight into the direct impact of rotation on the characteristics of small-scale turbulence and mixing in stratified fluids; the results are also of interest in other fields such as engineering.

84. Galperin, B, L H Kantha, S Hassid, and Anthony Rosati, 1988: A quasi-equilibrium turbulent energy model for geophysical flows. Journal of the Atmospheric Sciences, 45(1), 55-62.
    [ Abstract PDF ]

    The Mellor-Yamada hierarchy of turbulent closure models is reexamined to show that the elimination of a slight inconsistency in their analysis leads to a quasi-equilibrium model that is somewhat simpler than their level 2 1/2 model. Also the need to impose realizability conditions restricting the dependence of exchange coefficients on shearing rates is eliminated. The model is therefore more robust while the principal advantage of the level 2 1/2 model, namely the solution of a prognostic equation for turbulent kinetic energy is retained. Its performance is shown to be not much different than that of level 2 1/2.

85. Rosati, Anthony, and Kikuro Miyakoda, 1988: A general circulation model for upper ocean simulation. Journal of Physical Oceanography, 18(11), 1601-1626.
    [ Abstract PDF ]

    A general circulation model (GCM) of the ocean that emphasizes the simulation of the upper ocean has been developed. This emphasis is in keeping with its future intent, that of an air-sea coupled model. The basic model is the primitive equation model of Bryan and Cox with the additions, of optional usage, of the Mellor-Yamada level 2.5 turbulence closure scheme and horizontal nonlinear viscosity. These modifications are intended to improve the upper ocean simulations, particularly sea surface temperature and heat content. The horizontal grid spacing is 1° latitude x 1° longitude and is global in domain. The equatorial region between 10°N and 10°S is further refined in the north-south direction to 1/3° resolution. There are 12 vertical levels, with six levels in the top 70 m. The model incorporates varying bottom topography. Prior to coupling the ocean model to an atmospheric GCM, experiments have been carried out to determine the ocean GCM's performance using atmospheric forcing from observed data. The data source was the National Meteorological Center twice daily 1000 mb analysis for winds, temperature, and relative humidity for 1982 and 1983. From these data, wind stress and total heat flux were calculated from bulk formulas and used as surface boundary conditions for the ocean model. The response of the ocean GCM to mixing parameterization schemes and frequency of atmospheric forcing have been examined. In particular, the use of constant eddy coefficients for both horizontal and vertical mixing (A-model) versus nonlinear horizontal viscosity and turbulence closure schemes (E-model) have been examined, along with comparisons of monthly mean versus 12-hourly forcing. It was found that, in general, the E-physics produces a more realistic mixed-layer structure as compared to A-physics. Using the monthly mean values produces sea surface temperatures that are too warm, presumably because the evaporative flux, which is proportional to the wind speed, is underestimated. The 12-h forcing improves appreciably both the A and E model since the heat flux is better represented; the E-case shows an even greater improvement due to its sensitivity to wind stirring. The near surface heat budget, along with more traditional variables, is examined for a short period during the 1982-83 El Niño event. These results are encouraging considering the many possible sources of error, including those in forcing data, initial conditions, radiative fluxes, and bulk exchange coefficients.

86. Miyakoda, Kikuro, and Anthony Rosati, 1984: Variation of sea surface temperature in 1976 and 1977, Pt. 2: Simulation with mixed layer models. Journal of Geophysical Research, 89(C4), 6533-6542.
    [ Abstract PDF ]

    In connection with a study of the extreme weather events over the North American continent in January 1977, analyses were performed to investigate characteristic properties of spatial and temporal variations of sea surface temperature for the years 1976 and 1977 by using the world distribution of sea surface temperature described in the accompanying paper, Pt. 1. The time evolutions of ocean temperature patterns for these years are displayed by latitudinal distribution diagrams of sea surface temperature and by longitude-time (Hovmoller) diagrams. Gill-Turner's integral model and Mellor-Durbin's turbulence closure model of the mixed layer were applied to calculate the sea surface temperature anomaly in the Northern Hemisphere by using realistic atmospheric forcing. An increase of time variability of the external forcing leads to an appreciably improved simulation of the sea surface temperature anomaly fields. Both models gave reasonable predictions for <> 5 months in wintertime if the realistic external forcings were specified.

87. Miyakoda, Kikuro, and Anthony Rosati, 1982: The variation of sea surface temperature in 1976 and 1977, 1: The data analysis. Journal of Geophysical Research, 87(C8), 5667-5680.
    [ Abstract PDF ]

    To study the spatial distribution of the sea surface temperature (SST) for the years of 1976 and 1977, ship and satellite data at 1 degree quadrangles were collected. Two points were investigated: (1) the difference of monthly mean SST data between the two sources, and (2) map analyses over the globe. The study shows that without satellite data, an adequate coverage of world ocean is not possible and that there is a large difference in values between the ship and satellite data. The standard deviation of the difference between the satellite and merchant ship SST data for monthly and 1 degree quadrangle mean was plus or minus 1.49 degrees C, where the sampling errors were not subtracted. Using these data, analyses were created and compared with independent analyses. The comparisons included large-scale analyses and two small-scale analyses. Attention was focussed specially on (1) the utility of the satellite SST data and (2) the data quality control. The large-scale analyses agreed well with the independent analyses. However, both of the small-scale analyses did not compare well.

88. Miyakoda, Kikuro, and Anthony Rosati, 1977: One-way nested grid models: The interface conditions and the numerical accuracy. Monthly Weather Review, 105(9), 1092-1107.
    [ Abstract PDF ]

    Tests of several interface conditions in a one-way nested grid model were undertaken, where the ratio of grid size for the coarse mesh in the large domain and the fine mesh in the small domain was 4:1. The interface values for all parameters are specified by the solutions of the larger domain model, although they are modified in some cases. Scheme A includes "a boundary adjustment" and the consideration of mountain effect for the surface pressure along the interface. Scheme B uses, in addition to Scheme A, a "radiation condition" at the outward propagation boundaries. Scheme C uses viscous damping along five rows adjacent to the border lines in addition to Scheme A. The solutions for the fine-mesh models obtained by these schemes are compared quantitatively with the solution of a control model. The results show how quickly the effect at the interface propagates into the interior. The proper treatment of the mountain effect on the surface pressure along the interface, and the boundary adjustment are important for obtaining reasonable solutions. Schemes A, B, and C are all acceptable, though not entirely satisfactory. Scheme B was useful in reducing the false reflection at the interface. Scheme C gave smooth fields of predicted variables, but false reflection sometimes occurred. A combination of these conditions optimally chosen was applied to a 34 km mesh model for a domain covering the whole mainland of the United States. The resulting maps of the time integration show the formation of a front and the detailed structure of intense rainbands associated with the front.

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