Publications

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2018
Mazloff, MR, Cornuelle BD, Gille ST, Verdy A.  2018.  Correlation lengths for estimating the large-scale carbon and heat content of the Southern Ocean. Journal of Geophysical Research-Oceans. 123:883-901.   10.1002/2017jc013408   AbstractWebsite

The spatial correlation scales of oceanic dissolved inorganic carbon, heat content, and carbon and heat exchanges with the atmosphere are estimated from a realistic numerical simulation of the Southern Ocean. Biases in the model are assessed by comparing the simulated sea surface height and temperature scales to those derived from optimally interpolated satellite measurements. While these products do not resolve all ocean scales, they are representative of the climate scale variability we aim to estimate. Results show that constraining the carbon and heat inventory between 35 degrees S and 70 degrees S on time-scales longer than 90 days requires approximately 100 optimally spaced measurement platforms: approximately one platform every 20 degrees longitude by 6 degrees latitude. Carbon flux has slightly longer zonal scales, and requires a coverage of approximately 30 degrees by 6 degrees. Heat flux has much longer scales, and thus a platform distribution of approximately 90 degrees by 10 degrees would be sufficient. Fluxes, however, have significant subseasonal variability. For all fields, and especially fluxes, sustained measurements in time are required to prevent aliasing of the eddy signals into the longer climate scale signals. Our results imply a minimum of 100 biogeochemical-Argo floats are required to monitor the Southern Ocean carbon and heat content and air-sea exchanges on time-scales longer than 90 days. However, an estimate of formal mapping error using the current Argo array implies that in practice even an array of 600 floats (a nominal float density of about 1 every 7 degrees longitude by 3 degrees latitude) will result in nonnegligible uncertainty in estimating climate signals.

2013
Hoteit, I, Hoar T, Gopalakrishnan G, Collins N, Anderson J, Cornuelle B, Kohl A, Heimbach P.  2013.  A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico. Dynamics of Atmospheres and Oceans. 63:1-23.   10.1016/j.dynatmoce.2013.03.002   AbstractWebsite

This paper describes the development of an advanced ensemble Kalman filter (EnKF)-based ocean data assimilation system for prediction of the evolution of the loop current in the Gulf of Mexico (GoM). The system integrates the Data Assimilation Research Testbed (DART) assimilation package with the Massachusetts Institute of Technology ocean general circulation model (MITgcm). The MITgcm/DART system supports the assimilation of a wide range of ocean observations and uses an ensemble approach to solve the nonlinear assimilation problems. The GoM prediction system was implemented with an eddy-resolving 1/10th degree configuration of the MITgcm. Assimilation experiments were performed over a 6-month period between May and October during a strong loop current event in 1999. The model was sequentially constrained with weekly satellite sea surface temperature and altimetry data. Experiments results suggest that the ensemble-based assimilation system shows a high predictive skill in the GoM, with estimated ensemble spread mainly concentrated around the front of the loop current. Further analysis of the system estimates demonstrates that the ensemble assimilation accurately reproduces the observed features without imposing any negative impact on the dynamical balance of the system. Results from sensitivity experiments with respect to the ensemble filter parameters are also presented and discussed. (C) 2013 Elsevier B.V. All rights reserved.

1999
Miller, AJ, Cornuelle BD.  1999.  Forecasts from fits of frontal fluctuations. Dynamics of Atmospheres and Oceans. 29:305-333.   10.1016/s0377-0265(99)00009-3   AbstractWebsite

A primitive equation ocean model is fit with strong constraints to non-synoptic hydrographic surveys in an unstable frontal current region, the Iceland-Faeroe Front. The model is first initialized from a time-independent objective analysis of non-synoptic data (spanning 2 to 6 days). A truncated set of eddy-scale basis functions is used to represent the initial error in temperature, salinity, and velocity. A series of model integrations, each perturbed with one basis function for one dependent variable in one layer, is used to determine the sensitivity to the objective-analysis initial state of the match to the non-synoptic hydrographic data. A new initial condition is then determined from a generalized inverse of the sensitivity matrix and the process is repeated to account for non-linearity. The method is first tested in 'identical twin' experiments to demonstrate the adequacy of the basis functions in representing initial condition error and the convergence of the method to the true solution. The approach is then applied to observations gathered in August 1993 in the Iceland-Faeroe Front. Model fits are successful in improving the match to the true data, leading to dynamically consistent evolution scenarios. However, the forecast skill (here defined as the variance of the model-data differences) of the model runs from the optimized initial condition is not superior to less sophisticated methods of initialization, probably due to inadequate initialization data. The limited verification data in the presence of strong frontal slopes may not be sufficient to establish Forecast skill, so that it must be judged subjectively or evaluated by other quantitative measures. (C) 1999 Elsevier Science B.V. All rights reserved.