Seasonally different carbon flux changes in the Southern Ocean in response to the southern annular mode

Stratospheric ozone depletion and emission of greenhouse gases lead to a trend of the Southern Annular Mode (SAM) towards its high-index polarity. The positive phase of the SAM is characterised by stronger than usual westerly winds that induce changes in the physical carbon transport. Changes in the natural carbon budget of the upper 100 m of the Southern Ocean in response to a positive SAM phase are explored with a coupled ecosystem-general circulation model and regression analysis. Previously overlooked processes that are important for the upper ocean carbon budget during a positive SAM period are identified, namely export production and downward transport of carbon north of the Polar Front (PF) as large as the upwelling in the south. The limiting micronutrient iron is brought into the surface layer by upwelling and stimulates phytoplankton growth and export production, but only in summer. This leads to a drawdown of carbon and less summertime outgassing (or more uptake) of natural CO2. In winter, biological mechanisms are inactive and the surface ocean equilibrates with the atmosphere by releasing CO2. In the annual mean, the upper ocean region south of the PF loses more carbon by additional export production than by the release of CO2 into the atmosphere, highlighting the role of the biological carbon pump in response to a positive SAM event

On the sensitivity of field reconstruction and prediction using Empirical Orthogonal Functions derived from gappy data

Empirical Orthogonal Function (EOF) analysis is commonly used in the climate sciences and elsewhere to describe, reconstruct, and predict highly dimensional data fields. When data contain a high percentage of missing values (i.e. ’gappy’), alternate approaches must be used in order to correctly derive EOFs. The aims of this paper are to assess the accuracy of several EOF approaches in the reconstruction and prediction of gappy data fields, using the Galapagos Archipelago as a case study area. EOF approaches included least-squares estimations via a covariance matrix decomposition (EIGEN, SVD), ’Data Interpolating Empirical Orthogonal Functions’ (DINEOF), and a novel approach called ’Recursively-Subtracted Empirical Orthogonal Functions’ (RSEOF). Model-derived data of historical surface Chlorophyll a concentrations and sea surface temperature, combined with a mask derived from gaps in remote sensing estimates, allowed for the creation of ’true’ and ’observed’ fields by which to gauge the performance of EOF approaches. Only DINEOF and RSEOF were found to be appropriate for gappy data reconstruction and prediction. DINEOF proved to be the superior approach in terms of accuracy, especially for data with a high Noise/Signal ratio, although RSEOF may be preferred for larger data fields due to its relatively faster computation time

Ocean state estimation from hydrography and velocity observations during EIFEX with a regional biogeochemical ocean circulation model

In the European Iron Fertilization Experiment (EIFEX), the iron hypothesis was tested by an open ocean perturbation experiment. The success of EIFEX owes to the applied experimental strategy; namely to use the closed core of a mesoscale eddy for the iron injection. This strategy not only allowed tracking the phytoplankton bloom within the fertilized patch of mixed-layer water, but also allowed the export of biologically fixed carbon to the deep ocean to be quantified. In this present study, least-squares techniques are used to fit a regional numerical ocean circulation model with four open boundaries to temperature, salinity, and velocity observations collected during EIFEX. By adjusting the open boundary values of temperature, salinity and velocity, an optimized model is obtained that clearly improves the simulated eddy and its mixed layer compared to a first guess representation of the cyclonic eddy. A biogeochemical model, coupled to the optimized circulation model, simulates the evolution of variables such as chlorophyll a and particular organic carbon in close agreement with the observations. The estimated carbon export, however, is lower than the estimates obtained from observations without numerical modeling support. Tuning the sinking parameterization in the model increases the carbon export at the cost of unrealistically high sinking velocities. Repeating the model experiment without adding iron allows more insight into the effects of the iron fertilization. In the model this effect is about 40% lower than in previous estimates in the context of EIFEX. The likely causes for these discrepancies are potentially too high remineralization, inaccurate representation of the bloom-termination in the model, and ambiguity in budget computations and averaging. The discrepancies are discussed and improvements are suggested for the parameterization used in the biogeochemical model components