Wallcraft, A. J., Kara, A. B., Hurlburt, H. E., Chassignet, E. P., & Halliwell, G. H. (2008). Value of bulk heat flux parameterizations for ocean SST prediction.
Journal of Marine Systems, 74(1-2), 241–258.
Wei, J., Dirmeyer, P. A., Guo, Z., Zhang, L., & Misra, V. (2010). How Much Do Different Land Models Matter for Climate Simulation? Part I: Climatology and Variability.
J. Climate, 23(11), 3120–3134.
Winterbottom, H. (2010).
The Development of a High-Resolution Coupled Atmosphere-Ocean Model and Applications Toward Understanding the Limiting Factors for Tropical Cyclone Intensity Prediction. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: The prediction of tropical cyclone (TC) motion has improved greatly in recent decades. However, similar trends remain absent with respect to TC intensity prediction. Several hypotheses have been proposed attempting to explain why dynamical NWP models struggle to predict TC intensity. The leading candidates are as follows: (1) the lack of an evolving ocean (i.e., sea-surface temperature) boundary condition which responds as a function of the atmosphere (e.g., TC) forcing, (2) inappropriate initial conditions for the TC vortex (e.g., lack of data assimilation methods), (3) NWP model grid-length resolutions which are unable to resolve the temporal and length scale for the features believed responsible for TC vortex intensity. modulations (i.e., eye-wall dynamics, momentum transport, vortex Rossby wave interactions, etc.), and (4) physical parametrization which do not adequately represent the air-sea interactions observed during TC passage. In this study, a coupling algorithm for two independent, high-resolution, and state-of-the-art atmosphere and ocean models is developed. The atmosphere model -- the Advanced Weather Research and Forecasting (WRF-ARW) model is coupled to the HYbrid Coordinate Ocean Model (HYCOM) using a (UNIX) platform independent and innovative coupling methodology. Further, within the WRF-ARW framework, a dynamic initialization algorithm is developed to specify the TC vortex initial condition while preserving the synoptic-scale environment. Each of the tools developed in this study is implemented for a selected case-study: TC Bertha (2008) and TC Gustav (2008) for the coupled-model and TC vortex initialization, respectively. The experiment results suggest that the successful prediction (with respect to the observations) for both the ocean response and the TC intensity cannot be achieved by simply incorporating (i.e., coupling) an ocean model and/or by improving the initial structure for the TC. Rather the physical parametrization governing the air-sea interactions is suggested as the one of the weaknesses for the NWP model. This hypothesis is (indirectly) supported through a diagnostic evaluation of the synoptic-scale features (e.g., sea-level pressure and the deep-layer mean wind beyond the influence of the TC) while the assimilated TC vortex is nudged toward the observed intensity value. It is found -- in the case of TC Gustav (2008) using WRF-ARW, that as the assimilated TC vortex intensity approaches that of the observed, the balance between the mass and momentum states for WRF-ARW is compromised leading to unrealistic features for the environmental sea-level pressure and deep-layer (800- to 200-hPa) mean wind surrounding the TC. Forcing WRF-ARW to assimilate a TC vortex of the observed maximum wind-speed intensity may ultimately compromise the prediction for the TC's motion and subsequently mitigate any gains for the corresponding intensity prediction.Suggestions for additions to the coupled atmosphere-ocean model include a wave-model (WAVEWATCH3), the assimilation of troposphere thermodynamic observations, and modifications to the existing atmospheric boundary-layer parametrization. The current suite of atmosphere model parametrizations do not accurately simulate the observed azimuthal and radial variations for the exchange coefficients (e.g., drag and enthalpy) that have been indicated as potentialpredictor variables for TC intensity modulation. However, these modifications should be implemented only after the limitations for the current coupled-model and TC vortex initialization methods are fully evaluated.
Xue, W., Xin, X., Zhang, J., Zhang, W., Wu, H., Huang, Z., et al. (2016). Development and Testing of a Multi-model Ensemble Coupling Framework. In
Development and Evaluation of High Resolution Climate System Models (pp. 163–208). Springer.
Yu, P. (2006).
Development of New Techniques for Assimilating Satellite Altimetry Data into Ocean Models. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: State of the art fully three-dimensional ocean models are very computationally expensive and their adjoints are even more resource intensive. However, many features of interest are approximated by the first baroclinic mode over much of the ocean, especially in the lower and mid latitude regions. Based on this dynamical feature, a new type of data assimilation scheme to assimilate sea surface height (SSH) data, a reduced-space adjoint technique, is developed and implemented with a three-dimensional model using vertical normal mode decomposition. The technique is tested with the Navy Coastal Ocean Model (NCOM) configured to simulate the Gulf of Mexico. The assimilation procedure works by minimizing the cost function, which generalizes the misfit between the observations and their counterpart model variables. The “forward” model is integrated for the period during which the data are assimilated. Vertical normal mode decomposition retrieves the first baroclinic mode, and the data misfit between the model outputs and observations is calculated. Adjoint equations based on a one-active-layer reduced gravity model, which approximates the first baroclinic mode, are integrated backward in time to get the gradient of the cost function with respect to the control variables (velocity and SSH of the first baroclinic mode). The gradient is input to an optimization algorithm (the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used for the cases presented here) to determine the new first baroclinic mode velocity and SSH fields, which are used to update the forward model variables at the initial time. Two main issues in the area of ocean data assimilation are addressed: 1. How can information provided only at the sea surface be transferred dynamically into deep layers? 2. How can information provided only locally, in limited oceanic regions, be horizontally transferred to ocean areas far away from the data-dense regions, but dynamically connected to it? The first problem is solved by the use of vertical normal mode decomposition, through which the vertical dependence of model variables is obtained. Analyses show that the first baroclinic mode SSH represents the full SSH field very closely in the model test domain, with a correlation of 93% in one of the experiments. One common way to solve the second issue is to lengthen the assimilation window in order to allow the dynamic model to propagate information to the data-sparse regions. However, this dramatically increases the computational cost, since many oceanic features move very slowly. An alternative solution to this is developed using a mapping method based on complex empirical orthogonal functions (EOF), which utilizes data from a much longer period than the assimilation cycle and deals with the information in space and time simultaneously. This method is applied to map satellite altimeter data from the ground track observation locations and times onto a regular spatial and temporal grid. Three different experiments are designed for testing the assimilation technique: two experiments assimilate SSH data produced from a model run to evaluate the method, and in the last experiment the technique is applied to TOPEX/Poseidon and Jason-1 altimeter data. The assimilation procedure converges in all experiments and reduces the error in the model fields. Since the adjoint, or “backward”, model is two-dimensional, the method is much more computationally efficient than if it were to use a fully three-dimensional backward model.
Yu, P., Morey, S. L., & O'Brien, J. J. (2009). A reduced-dynamics variational approach for the assimilation of altimeter data into eddy-resolving ocean models.
Ocean Modelling, 27(3-4), 215–229.
Zavala-Hidalgo, J., Pares-Sierra, A., & Ochoa, J. (2002). Seasonal variability of the temperature and heat fluxes in the Gulf of Mexico.
Atmosfera, 15(2), 81–104.
Zeng, H., Chambers, J. Q., Negron-Juarez, R. I., Hurtt, G. C., Baker, D. B., & Powell, M. D. (2009). Impacts of tropical cyclones on U.S. forest tree mortality and carbon flux from 1851 to 2000.
Proc Natl Acad Sci U S A, 106(19), 7888–7892.
Abstract: Tropical cyclones cause extensive tree mortality and damage to forested ecosystems. A number of patterns in tropical cyclone frequency and intensity have been identified. There exist, however, few studies on the dynamic impacts of historical tropical cyclones at a continental scale. Here, we synthesized field measurements, satellite image analyses, and empirical models to evaluate forest and carbon cycle impacts for historical tropical cyclones from 1851 to 2000 over the continental U.S. Results demonstrated an average of 97 million trees affected each year over the entire United States, with a 53-Tg annual biomass loss, and an average carbon release of 25 Tg y(-1). Over the period 1980-1990, released CO(2) potentially offset the carbon sink in forest trees by 9-18% over the entire United States. U.S. forests also experienced twice the impact before 1900 than after 1900 because of more active tropical cyclones and a larger extent of forested areas. Forest impacts were primarily located in Gulf Coast areas, particularly southern Texas and Louisiana and south Florida, while significant impacts also occurred in eastern North Carolina. Results serve as an important baseline for evaluating how potential future changes in hurricane frequency and intensity will impact forest tree mortality and carbon balance.