A Study of CO2 Variation in China with Multi-Algorithm GOSAT Products

Abstract:

Satellite remote sensing is the most efficient way to monitoring global CO₂ flux. “Full physics” retrieval algorithms that applied on GOSAT observation were introduced in several studies publications. The ACOS, NIES-FP, RemoTeC and UoL are the most accuracy algorithm, and provide the product of GOSAT with global measurements. The differences among those algorithms lead to the difference uncertain in product. In this study, the quantity of retrieval data of each algorithm was analyzed, and the spatial coverage indicated that only one dataset was not enough for XCO₂ (column-averaged CO₂ dry-air mixing ratio) study. Therefore an ensemble average method that combines four datasets was investigated in this study, which aimed to increasing the data spatial coverage indirectly. The method was mainly about use the frequency of high quality data to decide which algorithm was appropriate in observation of specification time and location. Using the ensemble average, the spatial and temporal distribution of XCO₂ in China was studied. The results indicated a strong variation of XCO₂ in spatial and temporal. A seasonal trend with maximum and minimum appeared in spring and summer respectively all over the China, and the most of area indicated a large XCO₂ (>380ppmv). However there was a significant difference between East and West. In the East of China, strong CO₂ source and sink respectively due to strong human activities and large area of vegetation coverage made a large variation of XCO₂ (8ppmv). In the West of China, sparsely populated and vegetation covered leaded to a small variation (5ppmv).