Does GOSAT capture the true seasonal cycle of carbon dioxide?

Abstract

Abstract. The seasonal cycle accounts for a dominant mode of total column CO 2 (XCO 2 ) annual variability and is connected to CO 2 uptake and release; it thus represents an important quantity to test the accuracy of the measurements from space. We quantitatively evaluate the XCO 2 seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO 2 Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO 2 , as well as the regional growth rates in XCO 2 through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO 2 between ACOS and TCCON, but ACOS generally infers a date of maximum XCO 2 2-3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO 2 . In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO 2 depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO 2 doubles from western USA to East Asia at 45-50 • N, which is only partially shown by the models. In general, we find that model-tomodel differences can be larger than GOSAT-to-model difPublished by Copernicus Publications on behalf of the European Geosciences Union. ferences. These results suggest that GOSAT/ACOS retrievals of the XCO 2 seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.

Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data

“…In addition, this method is not applicable to XCH 4 . Guerlet et al (2013) and Lindqvist et al (2015) were based on the distribution of XCO 2 simulated by atmospheric transport model (e.g., the region where there is a modeled XCO 2 value within ±0.5 ppm of standard deviation for the modeled value at the observation site). This can lead to much larger matched data and be applied to the entire globe including the Southern Hemisphere.…”

A comparison of atmospheric CO<sub>2</sub> flux signals obtained from GEOS-Chem flux inversions constrained by in situ or GOSAT observations

“…The improved ability of inversions constrained by GOSAT data to capture the seasonal cycle was also found by previous analyses (e.g., Deng et al, 2014;Liu et al, 2014;and Reuter et al, 2014). Butz et al (2011) and Lindqvist et al (2015) showed that GOSAT/ACOS data alone can match the seasonal cycle at TCCON locations (typically within 1 ppm in the Lindqvist study). In addition to better capturing the seasonal cycle, the GOSAT-based simulations result 25 in lower mean residuals at many of the northern hemisphere sites in June, July and August (Figures 9).…”

A Comparison of the Atmospheric CO2Concentrations Obtained by an Inverse Modeling System and Passenger Aircraft Based Measurement

“…서 론 대기와 육지 및 해양 간 탄소 교환을 이해하기 위 해 대기 중 이산화탄소 몰농도 관측 자료로부터 지표 면 탄소 플럭스를 추정하는 대기 인버스 모델링 (atmospheric inverse modeling), 즉 "top-down" 방법이 널리 이용되고 있다(e.g., Peters et al, 2007Peters et al, , 2010Feng et al, 2009;Chevallier et al, 2010 (Heymann et al, 2015;Lindqvist et al, 2015). 하지만 인버스 모델링 결과 또한 부족한 관측 수와 수송 모델에 의한 오차로 인해 불확실성이 존재한다 (Stephens et al, 2007;Chevallier et al, 2010).…”