The Arctic region is experiencing notable warming as well as more lightning. Lightning is the dominant source of upper tropospheric nitrogen oxides (NO x ), which are precursors for ozone and hydroxyl radicals. In this study, we combine the nitrogen dioxide (NO2) observations from the TROPOspheric Monitoring Instrument (TROPOMI) with Vaisala Global Lightning Dataset 360 to evaluate lightning NO2 (LNO2) production in the Arctic. By analyzing consecutive TROPOMI NO2 observations, we determine the lifetime and production efficiency of LNO2 during the summers of 2019–2021. Our results show that the LNO2 production efficiency over the ocean is ∼6 times higher than over continental regions. Additionally, we find that a higher LNO2 production efficiency is often correlated with lower lightning rates. The summertime lightning NO x emission in the Arctic (north of 70° N) is estimated to be 219 ± 116 Mg of N, which is equal to 5% of anthropogenic NO x emissions. However, for the span of a few hours, the Arctic LNO2 density can even be comparable to anthropogenic NO2 emissions in the region. These new findings suggest that LNO2 can play an important role in the upper-troposphere/lower-stratosphere atmospheric chemical processes in the Arctic, particularly during the summer.
Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S-5P) satellite, launched in 2017, measures the total column concentration of the trace gas Carbon Monoxide (CO) daily on a global scale and at a high spatial resolution of 7 x 7 km2, improved to 5.5 x 7 km2 in August 2019. The TROPOMI observations show plumes of CO due to localized CO emissions from industrial sources and biomass burning. In this paper, we quantify these CO emissions for biomass burning by an automated algorithm, APE, to detect plumes and quantify the CO emission rate using cross-sectional flux method. Furthermore, the influence of a constant and a varying plume height in downwind direction on emissions is investigated and algorithm uncertainties are quantified. The VIIRS active fire data in conjunction with the TROPOMI CO datasets is used to identify fires and the fire locations. Then, an automated plume detection algorithm using traditional image processing algorithms is developed and utilized to identify plumes. For these plumes, the emission rate is estimated by the cross-section flux method at three different plume heights. The first two are constant plume heights at a 100 m and an IS4FIRES injection height from Global Fire Assimilation System. And the last one is a varying plume height in downwind direction. A 3D Lagrangian model is used to simulate tracer particles where the source locations for the simulation are based on the VIIRS fire counts and IS4FIRES injection height. 3D velocities at 137 model levels (ERA5) are utilized to simulate tracer particles. We demonstrate the quality and validity of our automated approach by investigating biomass burning events and their emissions for Australia on Oct 2019 and the US on Sept 2020. A total of 110 and 31 individual fire plumes in Australia and the US, respectively were detected and their emissions estimated. The emissions were severely under-predicted and negative for 11 cases when based on constant plume height of 100 m compared to emissions based on varying plume height. Furthermore, the effect of the changing plume height in downwind direction on the emission estimate compared to emissions from constant IS4FIRES plume height was minor as 124 cases are found to have emission variation less than 10 %. However, we were able to identify several cases where the flux estimates become more reliable with varying plume height. Thus, the varying plume height in downwind direction is considered for the automated algorithm. The cross-section flux method is found to have an uncertainty of 38 % in one of the idealized cases. However, overall uncertainty of the algorithm is difficult to quantify as conditions for each fire are unique.
<p>The implementation of land management is widely included in national climate mitigation strategies as negative carbon technology. The effectiveness of these land mitigation techniques to extract atmospheric carbon is however highly uncertain. The H2020 LANDMARC, Land Use Based Mitigation for Resilient Climate Pathways, project monitors actual land mitigation sites to improve the understanding of their impact on the carbon cycle and focuses on the development of accurate and cost-effective monitoring techniques. Here we aim to assess the ability of satellite-based solar-induced fluorescence (SIF) observations to quantify the impact of land cover changes on the terrestrial gross primary production (GPP) &#8211; the carbon fixated during photosynthesis.</p><p>We use SIF measurements from the European TROPOMI and GOME-2A sensors to monitor the GPP dynamics following land cover change. We evaluate the impact of changed land cover on GPP for two distinct case studies with (1) an increasing trend in GPP (negative carbon emission) and (2) a decreasing trend in GPP (positive carbon emission) by examining the time-series of SIF signal over both cases. The positive carbon emission case concerns a massive wildfire in South-East Australia in which 220 km<sup>2</sup> of Eucalypt Forest burned down from January to February 2019. The negative emission case examines China&#8217;s large scale afforestation project, the Three-North Shelterbelt Program (TNSP), which started in the 1980&#8217;s to combat desertification.</p><p>We analysed the TROPOMI SIF signal over burned and surrounding unburned area to elucidate the reduction in GPP following the destruction of vegetation in the positive carbon emission case. We detected a strong reduction in SIF (70%) immediately after the fire and smaller reductions in SIF (22%) over the winter period, June&#8211;July, when vegetation is mostly dormant. The reduction in SIF signal was scaled to loss in GPP via an obtained empirical linear SIF&#8212;GPP relation. Namely, positive agreement (R<sup>2</sup>=0.89) was discerned between TROPOMI SIF and GPP from a neighbouring flux site (Tumbarumba), located in a similar ecosystem. Overall, we identified a GPP deficit of ~9.05 kgCm<sup>-2</sup>, or 2TgC, for the first 10 months after the fire. This deficit is 1-2 magnitudes larger than the anomalies linked to intense summer droughts, indicating the significant long-term effects of local wildfires on the carbon cycle.</p><p>For the negative carbon emission case, we analyse long timeseries of GOME-2A SIF (2007&#8212;2020) over the TNSP region. We use statistical data on local afforestation in synergy with the SIF observations and compare yearly and seasonal trends for different sub-regions in the area in order to reveal the impact of the implementations on the regional carbon sink. Large scale monitoring of different land management strategies, especially in difficult dryland areas such as the TNSP region, and their success rate is an important step to support policy makers in designing and upscaling of land mitigation techniques.</p>
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