Search Results

You are looking at 1 - 10 of 39 items for

  • Author or Editor: A. Rosati x
  • Refine by Access: All Content x
Clear All Modify Search
K. Miyakoda and A. Rosati

Abstract

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.

Full access
S. Zhang and A. Rosati

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.

Full access
A. Rosati and K. Miyakoda

Abstract

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 × 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 ⅓° 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.

Full access
A. Rosati, R. Gudgel, and K. Miyakoda

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 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 Nin˜o 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.

Full access
C. T. Gordon, A. Rosati, and R. Gudgel

Abstract

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 underpredicts 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.

Full access
A. Rosati, K. Miyakoda, and R. Gudgel

Abstract

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° 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 Nino-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.

Full access
S. Zhang, A. Rosati, and T. Delworth

Abstract

The Atlantic meridional overturning circulation (AMOC) has an important influence on climate, and yet adequate observations of this circulation are lacking. Here, the authors assess the adequacy of past and current widely deployed routine observing systems for monitoring the AMOC and associated North Atlantic climate. To do so, this study draws on two independent simulations of the twentieth century using an Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) coupled climate model. One simulation is treated as “truth” and is sampled according to the observing system being evaluated. The authors 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 the understanding of the factors that determine AMOC variability. For example, the low-frequency sea surface forcings provided by the atmospheric and sea surface temperature observations are found to excite a LSW variation that governs the long-time-scale variability of the AMOC. When the most complete modern observing system, consisting of atmospheric winds and temperature, is used along with Argo ocean temperature and salinity down to 2000 m, a skill estimate of AMOC reconstruction is 90% (out of 100% maximum). Similarly encouraging results hold for other quantities, such as the 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 the ability to properly characterize past variations of the AMOC, they also hold promise for future monitoring of the AMOC and for initializing prediction models.

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

Abstract

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

Full access
Qian Song, Gabriel A. Vecchi, and Anthony J. Rosati

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.

Full access
S. Zhang, M. Winton, A. Rosati, T. Delworth, and B. Huang

Abstract

The non-Gaussian probability distribution of sea ice concentration makes it difficult to directly assimilate 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.

Full access