Global surface-ocean pCO2 and sea-air CO2 flux variability from an observation-driven ocean mixed-layer scheme
نویسندگان
چکیده
A temporally and spatially resolved estimate of the global surface-ocean CO2 partial pressure field and the sea–air CO2 flux is presented, obtained by fitting a simple data-driven diagnostic model of ocean mixed-layer biogeochemistry to surface-ocean CO2 partial pressure data from the SOCAT v1.5 database. Results include seasonal, interannual, and short-term (daily) variations. In most regions, estimated seasonality is well constrained from the data, and compares well to the widely used monthly climatology by Takahashi et al. (2009). Comparison to independent data tentatively supports the slightly higher seasonal variations in our estimates in some areas. We also fitted the diagnostic model to atmospheric CO2 data. The results of this are less robust, but in those areas where atmospheric signals are not strongly influenced by land flux variability, their seasonality is nevertheless consistent with the results based on surface-ocean data. From a comparison with an independent seasonal climatology of surface-ocean nutrient concentration, the diagnostic model is shown to capture relevant surface-ocean biogeochemical processes reasonably well. Estimated interannual variations will be presented and discussed in a companion paper.
منابع مشابه
Prevalence of strong vertical CO2 and O2 variability in the top meters of the ocean
[1] The gradient in the partial pressure of carbon dioxide (pCO2) across the air-sea boundary layer is the main driving force for the air-sea CO2 flux. Global data bases for surface seawater pCO2 are actually based on pCO2 measurements from several meters below the sea surface, assuming a homogeneous distribution between the diffusive boundary layer and the upper top meters of the ocean. Compil...
متن کاملSimulation and assimilation of global ocean pCO2 and air–sea CO2 fluxes using ship observations of surface ocean pCO2 in a simplified biogeochemical offline model
We used an offline tracer transport model, driven by reanalysis ocean currents and coupled to a simple biogeochemical model, to synthesize the surface ocean pCO2 and air–sea CO2 flux of the global ocean from 1996 to 2004, using a variational assimilation method. This oceanic CO2 flux analysis system was developed at the National Institute for Environmental Studies (NIES), Japan, as part of a pr...
متن کاملWhat does chlorophyll variability tell us about export and air-sea CO2 flux variability in the North Atlantic?
[1] The importance of biology to the ocean carbon sink is often quantified in terms of export, the removal of carbon from the ocean surface layer. Satellite images of sea surface chlorophyll indicate variability in biological production, but how these variations affect export and air-sea carbon fluxes is poorly understood. We investigate this in the North Atlantic using an ocean general circula...
متن کاملData-based estimates of the ocean carbon sink variability – First results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM)
Using measurements of the surface-ocean CO2 partial pressure (pCO2) and 14 different pCO2 mapping methods recently collated by the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative, variations in regional and global sea–air CO2 fluxes are investigated. 5 Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCO2 sea...
متن کاملDenitrification effects on air-sea CO2 flux in the coastal ocean: Simulations for the northwest North Atlantic
[1] The contribution of coastal oceans to the global air-sea CO2 flux is poorly quantified due to insufficient availability of observations and inherent variability of physical, biological and chemical processes. We present simulated air-sea CO2 fluxes from a high-resolution biogeochemical model for the North American east coast continental shelves, a region characterized by significant sedimen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017