China Sea Air CO2 Flux Dataset - Marine Carbon Satellite Data

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Information

Producer	Second Institute of Oceanography, MNR
Data Type	Remote Sensing
Data Abbreviation	FCO2; pCO₂; pCO₂_air
Time Range	2003-2023
Product Level	L3B
Spatial Coverage	The China Sea
Spatial resolution	1 km
Temporal Resolution	Monthly
Reference Coordinate System	Equal Latitude-Longitude Projection

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Description

Sea-air CO2 flux (FCO2): The total net exchange of carbon dioxide absorbed or emitted by seawater at the air-sea interface within a given spatial range and time range. The sea-to-air CO2 flux unit of this product is mmol/m2/d. A positive value represents the release of CO2 from seawater to the atmosphere, and a negative value represents the absorption of CO2 by seawater from the atmosphere. This dataset is based on the theoretical diffusion model of CO2 at the water-air interface and is calculated using the sea-air CO2 partial pressure difference method. That is, the sea-air CO2 flux is equal to the gas transfer rate at the sea-air interface, the solubility coefficient of carbon dioxide in seawater and the sea-air CO2 flux. The product of the difference in carbon dioxide partial pressure.

First, the atmospheric CO2 mole fraction (xCO2), sea surface atmospheric pressure (SLP), sea surface temperature (SST) and sea surface salinity (SSS) are used to calculate the atmospheric CO2 partial pressure; the seawater CO2 partial pressure uses the China Sea water provided by the SATCO2 website. CO2 partial pressure product, calculates the difference between seawater and atmospheric CO2 partial pressure; the solubility coefficient of carbon dioxide in seawater is a function of temperature and salinity; the gas transfer rate at the sea-air interface is mainly determined by wind speed.

Seawater CO2 partial pressure (pCO2): At a certain temperature and salinity, the CO2 partial pressure of seawater and air reaches equilibrium. The unit is μatm. This algorithm combines the seawater CO2 partial pressure semi-analytical remote sensing model framework (MeSAA) based on control factor analysis and the machine learning algorithm based on big data mining. Through a large number of training and testing experiments, the machine learning algorithm with the best performance (XGBOOST algorithm) is determined. and the optimal data parameter combination strategy (sea surface temperature (SST), thermodynamic pCO2 (pCO2therm), three-band remote sensing reflectance (Rrs413, 443, 488nm), chlorophyll concentration (Chl), mixed layer depth (MLD), and simultaneous Latitude temperature difference (SST_lat), a total of 9 input parameters).

The selection of input parameters takes into account the regulatory mechanisms of pCO2 by various biogeochemical processes. pCO2therm is a theoretical pCO2 value controlled only by temperature-related thermodynamic effects and sea-air CO2 exchange. It represents the thermodynamic effects and the forcing mechanism of increased atmospheric CO2 on seawater.

Mixed layer depth (MLD) and same-latitude temperature difference (SST_lat) represent the impact of seasonal mixing and upwelling on seawater pCO2 parameters, and remote sensing reflectance (Rrs) and chlorophyll concentration (Chl) represent the impact of biological effects; respectively, and South China Sea modeling. Finally, a pCO2 remote sensing model suitable for the China Sea was constructed, and a 1 km monthly average long-term series gridded product from 2003 to 2019 was produced.

Atmospheric CO2 partial pressure (pCO2air): At a certain temperature, the pressure when CO2 gas exists alone and occupies the same volume as the atmosphere. The unit of atmospheric CO2 partial pressure of this product is μatm. The partial pressure of atmospheric CO2 is the number of moles of carbon dioxide (xCO2) in dry air, the sea surface atmospheric pressure (SLP) and the saturated water vapor pressure (pCO2air): at a certain temperature, the pressure when CO2 gas exists alone and occupies the same volume as the atmosphere. The unit of atmospheric CO2 partial pressure of this product is μatm. The atmospheric CO2 partial pressure is the product of the moles of carbon dioxide in dry air (xCO2) and the difference between the sea surface atmospheric pressure (SLP) and the saturated water vapor pressure (pH2O). The moles of carbon dioxide in dry air and the sea surface atmospheric pressure are from the model of CarbonTracker, version CT2019, saturated vapor pressure is calculated from sea surface temperature and salinity.

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Naming Convention

GML_MERGE_MERGE_YYYYMMDDTOYYYYMMDD_L3B_SCS_4KM_FCO2_XGB2021

GML_MERGE_MERGE_YYYYMMDDTOYYYYMMDD_L3B_ECS_4KM_FCO2_XGB2021

GML_MERGE_MERGE_YYYYMMDDTOYYYYMMDD_L3B_SCS_4KM_PCO2AIR_XGB2021

GML_MERGE_MERGE_YYYYMMDDTOYYYYMMDD_L3B_ECS_4KM_PCO2AIR_XGB2021

GML_MERGE_MERGE_YYYYMMDDTOYYYYMMDD_L3B_SCS_4KM_PCO2SW_XGB2021

GML_MERGE_MERGE_YYYYMMDDTOYYYYMMDD_L3B_ECS_4KM_PCO2SW_XGB2021

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Related Website

This dataset can be downloaded and used in the scientific research database Zenodo (East China Sea data: https://zenodo.org/records/8042265; South China Sea data: https://zenodo.org/records/7743187)

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Reference

[1] Z. Song et al., "Construction of a High Spatiotemporal Resolution Dataset of Satellite-Derived pCO2 and Air–Sea CO2 Flux in the South China Sea (2003–2019)," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023, Art no. 4207015, doi: 10.1109/TGRS.2023.3306389.

[2] S. Yu et al.,“Satellite-estimated air-sea CO2 fluxes in the Bohai Sea, Yellow Sea, and East China Sea: Patterns and variations during 2003–2019,” in Science of The Total Environment, vol 904,2023, 1166804 ,doi: 10.1016/j.scitotenv.2023.166804.

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National Earth System Science Data Center State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography, MNR

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