Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data
M. InoueIsamu MorinoOsamu UchinoTakahiro NakatsuruYukio YoshidaTatsuya YokotaDebra WunchP. O. WennbergColeen M. RoehlDavid GriffithVoltaire A. VelazcoNicholas M. DeutscherThorsten WarnekeJustus NotholtJohn RobinsonV. SherlockFrank HaseThomas BlumenstockMarkus RettingerRalf SussmannE. KyröRigel KiviKei ShiomiS. KawakamiMartine De MazièreSabrina G. ArnoldDietrich G. FeistErica A. BarrowJames BarneyM. K. DubeyMatthias SchneiderLaura T. IraciJames R. PodolskePatrick W. HillyardToshinobu MachidaYousuke SawaKazuhiro TsuboiHidekazu MatsuedaColm SweeneyPieter P. TansA. E. AndrewsSébastien BiraudY. FukuyamaJ. V. PittmanE. A. KortT. Tanaka
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Abstract. We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO2 and XCH4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO2/XCH4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.Abstract Lakes, especially shallow lakes, contribute disproportionately to greenhouse gas (GHG; particularly CO 2 and CH 4 ) emissions and have received global attention due to their high potential to contribute to global warming and future climate change. Recent studies have identified eutrophication as a critical factor in GHG emissions. However, the role of lake trophic state index (TSI) and the impact of important water quality parameters (WQP) such as pH, Chl‐a, total nitrogen, total phosphorus and organic carbon on GHG emissions are still a subject of debate and an area of intense research. To further understand the relationship between GHG and lake eutrophication, datasets (GHG and WQP) from the scientific literature have been compiled, and statistical analyses of these secondary data were performed to determine the influence of eutrophication on GHG emissions. In this review, GHG emissions from Chinese lakes are quantified, and the important factors affecting these emissions are analysed systematically. The statistical analysis reveals that chlorophyll a and carbon (as TOC) are the key factors of lake eutrophication and have a significant effect on the GHG potential (mainly CH 4 fluxes). In addition, the proposed mitigation measures could serve as a guide for scientists and young researchers to reduce future climatic risks.
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