Data Assimilation for Interannual Climate Change Prediction. Ph.D. Thesis

Thesis/Dissertation · Sat Jan 01 04:00:00 UTC 1994

OSTI ID:237298

Hao, Z

A good numerical prediction requires an accurate initial state. One way to accurately estimate the initial state is to integrate present and past observations with a numerical model, by a procedure called data assimilation. Data assimilation has been used to produce a continuous description of the atmosphere`s or oceans` time evolution. Such a description is studied here for the El Nino/Southern Oscillation (ENSO) phenomenon, which dominates interannual variability in the tropics. Studies concerning the dynamics of and data assimilation for the ENSO are carried out using a coupled ocean-atmosphere model consisting of an upper ocean of the tropical Pacific and a steady-state atmospheric response to it. Model errors arise from the uncertainty in atmospheric wind stress. To better understand ENSO dynamics, the coupled system`s linear and nonlinear behavior are explored in the fast-wave limit, in which the time scale of the oceanic wave dynamics is much faster than the sea-surface temperature. This simple model produces a rich variety of flow regimes that show many features noted in various coupled general circulation models. Data-assimilation problem is studied first for a linear, uncoupled tropical ocean model with the optimal interpolation method. Limited improvements are obtained when not correcting the wind-stress error directly. The assimilation problem for the coupled system is investigated next using more advanced sequential estimators. The dynamical structure of forecast errors is estimated by a linearized Kalman filter (KF). The coupling processes produce large error correlations with a seesaw feature, i.e. having opposite sign in the western and eastern part of the basin. Similar results are obtained with a Monte Carlo method, which provides an independent estimate of KF performance. The extended KF (EKF) is used to blend various combinations of synthetic oceanic and atmospheric data with the coupled model.

• Research Organization: California Univ., Los Angeles, CA (United States). Dept. of Mechanical, Aerospace and Nuclear Engineering
• OSTI ID: 237298
• Report Number(s): N--96-22501; NIPS--96-34049
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
AIR-WATER INTERACTIONS
ANNUAL VARIATIONS
CLIMATIC CHANGE
PREDICTION EQUATIONS
SOUTHERN OSCILLATION
TROPICAL REGIONS