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Intercomparison of atmospheric CO2 and CH4 abundances on regional scales in boreal areas using Copernicus Atmosphere Monitoring Service (CAMS) analysis, COllaborative Carbon Column Observing Network (COCCON) spectrometers, and Sentinel-5 Precursor satellite observations
Atmospheric Measurement Techniques ( IF 3.8 ) Pub Date : 2020-09-09 , DOI: 10.5194/amt-13-4751-2020
Qiansi Tu , Frank Hase , Thomas Blumenstock , Rigel Kivi , Pauli Heikkinen , Mahesh Kumar Sha , Uwe Raffalski , Jochen Landgraf , Alba Lorente , Tobias Borsdorff , Huilin Chen , Florian Dietrich , Jia Chen

We compare the atmospheric column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) measured with a pair of COllaborative Carbon Column Observing Network (COCCON) spectrometers at Kiruna and Sodankylä (boreal areas). We compare model data provided by the Copernicus Atmosphere Monitoring Service (CAMS) between 2017 and 2019 with XCH4 data from the recently launched Sentinel-5 Precursor (S5P) satellite between 2018 and 2019. In addition, measured and modeled gradients of XCO2 and XCH4 (ΔXCO2 and ΔXCH4) on regional scales are investigated. Both sites show a similar and very good correlation between COCCON retrievals and the modeled CAMS XCO2 data, while CAMS data are biased high with respect to COCCON by 3.72 ppm (±1.80 ppm) in Kiruna and 3.46 ppm (±1.73 ppm) in Sodankylä on average. For XCH4, CAMS values are higher than the COCCON observations by 0.33 ppb (±11.93 ppb) in Kiruna and 7.39 ppb (±10.92 ppb) in Sodankylä. In contrast, the S5P satellite generally measures lower atmospheric XCH4 than the COCCON spectrometers, with a mean difference of 9.69 ppb (±20.51 ppb) in Kiruna and 3.36 ppb (±17.05 ppb) in Sodankylä. We compare the gradients of XCO2 and XCH4 (ΔXCO2 and ΔXCH4) between Kiruna and Sodankylä derived from CAMS analysis and COCCON and S5P measurements to study the capability of detecting sources and sinks on regional scales. The correlations in ΔXCO2 and ΔXCH4 between the different datasets are generally smaller than the correlations in XCO2 and XCH4 between the datasets at either site. The ΔXCO2 values predicted by CAMS are generally higher than those observed with COCCON with a slope of 0.51. The ΔXCH4 values predicted by CAMS are mostly higher than those observed with COCCON with a slope of 0.65, covering a larger dataset than the comparison between S5P and COCCON. When comparing CAMS ΔXCH4 with COCCON ΔXCH4 only in S5P overpass days (slope = 0.53), the correlation is close to that between S5P and COCCON (slope = 0.51). CAMS, COCCON, and S5P predict gradients in reasonable agreement. However, the small number of observations coinciding with S5P limits our ability to verify the performance of this spaceborne sensor. We detect no significant impact of ground albedo and viewing zenith angle on the S5P results. Both sites show similar situations with the average ratios of XCH4 (S5P/COCCON) of 0.9949±0.0118 in Kiruna and 0.9953±0.0089 in Sodankylä. Overall, the results indicate that the COCCON instruments have the capability of measuring greenhouse gas (GHG) gradients on regional scales, and observations performed with the portable spectrometers can contribute to inferring sources and sinks and to validating spaceborne greenhouse gas sensors. To our knowledge, this is the first published study using COCCON spectrometers for the validation of XCH4 measurements collected by S5P.

中文翻译:

使用哥白尼大气监测服务(CAMS)分析,碳柱观测碳柱观测网络(COCCON)光谱仪和Sentinel-5前驱卫星观测值比较北方地区大气中的CO 2和CH 4丰度。

我们比较了在基律纳和Sodankylä(北方地区)使用一对COllaborative碳柱观测网络(COCCON)光谱仪测得的二氧化碳(XCO 2)和甲烷(XCH 4)的大气柱平均干空气摩尔分数。我们将Copernicus大气监测服务(CAMS)在2017年至2019年之间提供的模型数据与最近发射的Sentinel-5前驱卫星(S5P)卫星在2018年至2019年之间的XCH 4数据进行了比较。此外,还对XCO 2XCH 4Δ XCO 2Δ XCH 4)在区域范围内进行了调查。这两个站点都显示出COCCON检索结果与建模的CAMS XCO 2数据之间的相似性和很好的相关性,而相对于COCCON  ,基律纳的CAMS数据偏高3.72 ppm(±1.80 ppm),而 Sodankylä偏高3.46 ppm(±1.73 ppm)。一般。对于XCH 4, 基律纳的CAMS值比COCCON观测值高0.33 ppb(±11.93 ppb),而 Sodankylä则为7.39 ppb(±10.92 ppb)。相反,S5P卫星测量的大气XCH 4通常比COCCON光谱仪低,平均差为9.69 ppb(±20.51 ppb)(基律纳)和3.36 ppb(±17.05  ppb)的Sodankylä。我们比较的梯度XCO 2XCH 4Δ XCO 2Δ XCH 4)基律纳和索丹基拉之间从CAMS分析和COCCON和S5P测量中导出的,研究的检测区域范围的源和汇的能力。在相关性Δ XCO 2Δ XCH 4的不同数据集之间通常比的相关性较小的XCO 2XCH 4任一站点的数据集之间。的Δ XCO 2个由CAMS预测值通常比那些具有COCCON观察到的0.51的斜率更高。该Δ XCH 4通过CAMS预测值大多是比那些COCCON观察到0.65的斜率,覆盖除S5P和COCCON之间的比较的较大的数据集更高。当比较CAMS Δ XCH 4与COCCON Δ XCH 4仅在S5P立交桥天(斜率 =  0.53),相关性是接近S5P和COCCON之间(斜率 = 0.51)。CAMS,COCCON和S5P可以合理地预测梯度。但是,与S5P一致的少量观测结果限制了我们验证该星载传感器性能的能力。我们没有发现地面反照率和天顶角对S5P结果有显着影响。两个站点都显示出类似的情况,基律纳的XCH 4(S5P / COCCON)平均比 为0.9949±0.0118,而0.9953±0.0089在Sodankylä。总体而言,结果表明COCCON仪器具有在区域范围内测量温室气体(GHG)梯度的能力,并且使用便携式光谱仪进行的观测可有助于推断源和汇,并有助于验证星载温室气体传感器。就我们所知,这是首次发表使用COCCON光谱仪进行的研究,以验证S5P收集的 XCH 4测量值。
更新日期:2020-09-10
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