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Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES)

机译:通过多个卫星数据集(MOPITT,AIRS,SCIAMACHY,TES)的伴随反演,获得高分辨率CO来源的全球估算

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摘要

We combine CO column measurements from the MOPITT, AIRS, SCIAMACHY, and TES satellite instruments in a full-year (May 2004-April 2005) global inversion of CO sources at 4°×5° spatial resolution and monthly temporal resolution. The inversion uses the GEOS-Chem chemical transport model (CTM) and its adjoint applied to MOPITT, AIRS, and SCIAMACHY. Observations from TES, surface sites (NOAA/GMD), and aircraft (MOZAIC) are used for evaluation of the a posteriori solution. Using GEOS-Chem as a common intercomparison platform shows global consistency between the different satellite datasets and with the in situ data. Differences can be largely explained by different averaging kernels and a priori information. The global CO emission from combustion as constrained in the inversion is 1350 Tg a~1. This is much higher than current bottom-up emission inventories. A large fraction of the correction results from a seasonal underestimate of CO sources at northern mid-latitudes in winter and suggests a larger-than-expected CO source from vehicle cold starts and residential heating. Implementing this seasonal variation of emissions solves the long-standing problem of models underestimating CO in the northern extratropics in winter-spring. A posteriori emissions also indicate a general underestimation of biomass burning in the GFED2 inventory. However, the tropical biomass burning constraints are not quantitatively consistent across the different datasets.
机译:我们将MOPITT,AIRS,SCIAMACHY和TES卫星仪器的CO柱测量值结合了全年(2004年5月至2005年4月)在4°×5°空间分辨率和每月时间分辨率下对CO源进行的全球反演。反演使用GEOS-Chem化学传输模型(CTM),其伴随项适用于MOPITT,AIRS和SCIAMACHY。使用TES,地面站点(NOAA / GMD)和飞机(MOZAIC)的观测值来评估后验解决方案。使用GEOS-Chem作为通用的比对平台,可以显示不同卫星数据集之间以及与原位数据之间的整体一致性。差异可以通过不同的平均内核和先验信息来很大程度上解释。反演所约束的燃烧产生的全球CO排放量为1350 Tg a〜1。这远高于当前的自下而上的排放清单。校正的很大一部分是由于冬季北半球冬季CO的季节性低估所导致的,并且表明来自汽车冷启动和住宅取暖的CO来源比预期大。实施这种季节性的排放变化解决了长期存在的模型,该模型低估了冬季冬季北方热带地区的CO。后验排放还表明,GFED2清单中的生物质燃烧普遍被低估了。然而,热带生物量燃烧限制在不同数据集之间在数量上不一致。

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