An ocean reanalysis system for the joining area of Asia and Indian-Pacific Ocean (AIPO) has been developed and is currently delivering reanalysis data sets for study on the air-sea interaction over AIPO and its climate variation over China in the inter-annual time scale.This system consists of a nested ocean model forced by atmospheric reanalysis,an ensemble-based multivariate ocean data assimilation system and various ocean observations.The following report describes the main components of the data assimilation system in detail.The system adopts an ensemble optimal interpolation scheme that uses a seasonal update from a free running model to estimate the background error covariance matrix.In view of the systematic biases in some observation systems,some treatments were performed on the observations before the assimilation.A coarse resolution reanalysis dataset from the system is preliminarily evaluated to demonstrate the performance of the system for the period 1992 to 2006 by comparing this dataset with other observations or reanalysis data.
Recent studies have found cold biases in a fraction of Argo profiles (hereinafter referred to as bad Array for Real-time Geostrophic Oceanography (Argo) profiles) due to the pressure drifts during 2003 and 2006. These bad Argo profiles have had an important impact on in situ observation-based global ocean heat content esti- mates. This study investigated the impact of bad Argo profiles on ocean data assimilation results that were based on observations from diverse ocean observation systems, such as in situ profiles (e.g., Argo, expendable bathy- thermograph (XBT), and Tropical Atmosphere Ocean (TAO), remote-sensing sea surface temperature products and satellite altimetry between 2004 and 2006. Results from this work show that the upper ocean heat content analysis is vulnerable to bad Argo profiles and demon- strate a cooling trend in the studied period despite the multiple independent data types that were assimilated. When the bad Argo profiles were excluded from the as- similation, the decreased heat content disappeared and a warming occurred. Combination of satellite altimetry and mass variation data from gravity satellite demonstrated an increase, which agrees well with the increased heat con- tent. Additionally, when an additional Argo profile quality control procedure was utilized that simply removed the profiles that presented static unstable water columns, the results were very similar to those obtained when the bad Argo profiles were excluded from the assimilation. This indicates that an ocean data assimilation that uses multiple data sources with improved quality control could be less vulnerable to a major observation system failure, such as a bad Argo event.
The vapor deposition chemical reaction processes, which are of extremely extensive applications, can be classified as a mathematical model by the following governing nonlinear partial differential equations containing velocity vector, temperature field, pressure field, and gas mass field. The mixed finite element (MFE) method is employed to study the system of equations for the vapor deposition chemical reaction processes. The semidiscrete and fully discrete MFE formulations are derived. And the existence and convergence (error estimate) of the semidiscrete and fully discrete MFE solutions are demonstrated. By employing MFE method to treat the system of equations for the vapor deposition chemical reaction processes, the numerical solutions of the velocity vector, the temperature field, the pressure field, and the gas mass field can be found out simultaneously. Thus, these researches are not only of important theoretical means, but also of extremely extensive applied vistas.
A nonlinear Galerkin mixed element (NGME) method for the stationary incompressible magnetohydrodynamics equations is presented. And the existence and error estimates of the NGME solution are derived.
The Galerkin-Petrov least squares method is combined with the mixed finite element method to deal with the stationary, incompressible magnetohydrodynamics system of equations with viscosity. A Galerkin-Petrov least squares mixed finite element format for the stationary incompressible magnetohydrodynamics equations is presented. And the existence and error estimates of its solution are derived. Through this method, the combination among the mixed finite element spaces does not demand the discrete Babuska-Brezzi stability conditions so that the mixed finite element spaces could be chosen arbitrartily and the error estimates with optimal order could be obtained.
The seasonal cycle of atmospheric CO2 at surface observation stations in the northern hemisphere is driven primarily by net ecosystem production (NEP) fluxes from terrestrial ecosystems. In addition to NEP from terrestrial ecosystems, surface fluxes from fossil fuel combustion and ocean exchange also contribute to the seasonal cycle of atmospheric CO2. Here the authors use the Goddard Earth Observing System-Chemistry (GEOS-Chem) model (version 8-02-01), with modifications, to assess the impact of these fluxes on the seasonal cycle of atmospheric CO2 in 2005. Modifications include monthly fossil and ocean emission inventories. CO2 simulations with monthly varying and annual emission inventories were carried out separately. The sources and sinks of monthly averaged net surface flux are different from those of annual emission inventories for every month. Results indicate that changes in monthly averaged net surface flux have a greater impact on the average concentration of atmospheric CO2 in the northern hemisphere than on the average concentration for latitudes 30-90°S in July. The concentration values differ little between both emission inventories over the latitudinal range from the equator to 30°S in January and July. The accumulated impacts of the monthly averaged fossil and ocean emissions contribute to an increase of the total global monthly average of CO2 from May to December.An apparent discrepancy for global average CO2 concentration between model results and observation was because the observation stations were not sufficiently representative. More accurate values for monthly varying net surface flux will be necessary in future to run the CO2 simulation.
