Direct retrieval of isoprene from satellite-based infrared measurements

Fu, Dejian; Millet, Dylan B.; Wells, Kelley C.; Payne, Vivienne H.; Yu, Shanshan; Eldering, A.

doi:10.1038/s41467-019-11835-0

Cited by 63 publications

(71 citation statements)

References 70 publications

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“…This class of models can efficiently interact in a hypothesis-testing mode in at least three ways: 1) One can use the models for predicting the expected patterns of variability among important ecological variables as simulated by the models. These predictions include a priori estimation of expected composition of forests (Asner et al 2012), remote sensing observations implying physical structure of forests and their relationships to biomass (Köhler and Huth 2010;Le Toan et al 2011;Saatchi et al 2011;Lobo and Dalling 2014), productivity (Yang et al 2015) and VOCs (Fu et al 2019). 2) One can use remote sensing and gap models to predict how ecosystem processes produce patterns across scales from micro to global.…”

Section: Discussionmentioning

confidence: 99%

Gap models across micro- to mega-scales of time and space: examples of Tansley’s ecosystem concept

et al. 2020

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Background: Gap models are individual-based models for forests. They simulate dynamic multispecies assemblages over multiple tree-generations and predict forest responses to altered environmental conditions. Their development emphases designation of the significant biological and ecological processes at appropriate time/space scales. Conceptually, they are with consistent with A.G. Tansley's original definition of "the ecosystem". Results: An example microscale application inspects feedbacks among terrestrial vegetation change, air-quality changes from the vegetation's release of volatile organic compounds (VOC), and climate change effects on ecosystem production of VOC's. Gap models can allocate canopy photosynthate to the individual trees whose leaves form the vertical leaf-area profiles. VOC release depends strongly on leaf physiology by species of these trees. Leaf-level VOC emissions increase with climate-warming. Species composition change lowers the abundance of VOC-emitting taxa. In interactions among ecosystem functions and biosphere/atmosphere exchanges, community composition responses can outweigh physiological responses. This contradicts previous studies that emphasize the warming-induced impacts on leaf function. As a mesoscale example, the changes in climate (warming) on forests including pest-insect dynamics demonstrates changes on the both the tree and the insect populations. This is but one of many cases that involve using a gap model to simulate changes in spatial units typical of sampling plots and scaling these to landscape and regional levels. As this is the typical application scale for gap models, other examples are identified. The insect/climatechange can be scaled to regional consequences by simulating survey plots across a continental or subcontinental zone. Forest inventories at these scales are often conducted using independent survey plots distributed across a region. Model construction that mimics this sample design avoids the difficulties in modelling spatial interactions, but we also discuss simulation at these scales with contagion effects. Conclusions: At the global-scale, successful simulations to date have used functional types of plants, rather than tree species. In a final application, the fine-scale predictions of a gap model are compared with data from micrometeorological eddy-covariance towers and then scaled-up to produce maps of global patterns of evapotranspiration, net primary production, gross primary production and respiration. New active-remote-sensing instruments provide opportunities to test these global predictions.

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Section: Discussionmentioning

confidence: 99%

Gap models across micro- to mega-scales of time and space: examples of Tansley’s ecosystem concept

et al. 2020

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“…CC BY 4.0 License. identified the Amazon to be the world most prominant isoprene source region, with mixing ratios exceeding 6 ppb isoprene in the boundary layer by end of the dry season in September 2014 (Gu et al, 2017;Fu et al, 2019). These elevated mixing ratios are a consequence of the large isoprene emission from the rain forest combined with a significantly prolonged atmospheric lifetime of up to 36 hours (GEOS-Chem model simulations) over the Amazon Basin compared to the global mean of less than 4 hours (Fu et al (2019), fig.…”

Section: 1)mentioning

confidence: 97%

Profiling of formaldehyde, glyoxal, methylglyoxal, and CO over the Amazon: Normalised excess mixing ratios and related emission factors in biomass burning plumes

Kluge¹,

Hüneke²,

Knecht³

et al. 2020

Preprint

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Abstract. We report on airborne measurements of tropospheric mixing ratios and vertical profiles of CH2O, C2H2O2, C3H4O2*, and CO over the Amazon Basin during the ACRIDICON-CHUVA campaign from the German High Altitude and Long-range research aircraft (HALO) in fall 2014. The joint observation of in situ CO and remotely measured CH2O, C2H2O2, C3H4O2*, together with visible imagery and air mass back trajectory modelling using NOAA HYSPLIT (National Oceanic Atmospheric Administration, HYbrid Single-Particle Lagrangian Integrated Trajectory) allow us to discriminate between the probing of background tropical air, in which the concentration of the measured species results from the oxidation of biogenically emitted VOCs (mostly isoprene), and measurements of moderately to strongly polluted air masses affected by biomass burning emissions or the city plume of Manaus. For twelve near surface measurements of fresh biomass burning plumes, normalized excess mixing ratios of C2H2O2 and C3H4O2* with respect to CH2O are inferred and compared to recent studies. The mean RGF = 0.07 (range 0.02–0.11) is in good agreement with recent reports which suggest RGF to be significantly lower than previously assumed in global CTM models. The mean RMF = 0.98 (range 0.09–1.50) varies significantly during the different observational settings, but overall appears to be much larger (up to a factor of 5) than previous reports suggest when applaying a correction factor of 2.0 ± 0.5 to account for the additional dicarbonyls included in the C3H4O2* measurements. Using recently reported emission factors of CH2O for tropical forests, our observations suggest emission factors of EFG = 0.25 (range 0.11 to 0.52) g per kg for C2H2O2, and EFM = 4.7 (range 0.5 to 8.64) g per kg for C3H4O2*. While EFG agrees well with recent reports, EFM is (like RMF) slightly larger than reported in other studies, presumably due to the different plume ages or fuels studied. Our observations of these critical carbonyls and intermediate oxidation products may support future photochemical modelling of air pollution over tropical vegetation, as well as validate past and present space-borne observations of the respective species.

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“…This discrepancy between results of Klinger et al (2002) and this study is mainly due to the differences in vegetation distribution and Klinger et al (2002) lower than emissions in this study by 33.7 %-76.9 %. Aside from the influence of difference meteorological conditions and land cover changes during the past years, the reliability of satellite-based constraints also needs to be improved because that the HCHO is affected by non-isoprene sources plus uncertainty and spatial smearing in isoprene-formaldehyde relationship (Fu et al, 2019).…”

Section: Comparisons With Field Measurements and Previous Budgets Estmentioning

confidence: 99%

Estimation of biogenic volatile organic compound (BVOC) emissions in China using WRF–CLM–MEGAN coupled model

Yin

Liu

et al. 2020

Preprint

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<p><strong>Abstract.</strong> Biogenic volatile organic compounds (BVOCs) emitted by terrestrial vegetation significantly influence the atmospheric chemistry and global climate. Previous studies calculated BVOC emissions outside the comprehensive terrestrial ecosystem processes and oversimplified the representation of canopy environments which exert substantial impacts on BVOC emissions. The sophisticated land surface model CLM (Community Land Model) reproduces essential ground and canopy characteristics and can calculate BVOC emissions as a step of integrated biogeochemical processes. In this study, the land surface scheme CLM version4 (CLM4) of WRF (Weather Research and Forecasting model) was used to estimate BVOC emissions in China. Based on highly-resolved meteorological outputs derived from WRF, CLM4 computed real-time physical and biological variables to drive MEGAN (Model of Emissions of Gases and Aerosols from Nature), a BVOC estimation algorithm embedded within biogeochemistry component of CLM4, to calculate plant emission flux. MODIS (Moderate Resolution Imaging Spectroradiometer) land-use data with high resolutions was introduced in WRF to replace the outdated land surface parameters. An emission inventory of isoprene and monoterpenes (including &#945;-pinene, &#946;-pinene, 3-carene, t-&#946;-ocimene, limonene, sabinene and myrcene) with high spatiotemporal resolution (12&#8201;&#215;&#8201;12&#8201;km, hourly) was established for the year 2018. The annual BVOC emission in China was 14.7&#8201;Tg&#8201;C, in which isoprene contributes about 78.3&#8201;% (11.5&#8201;Tg&#8201;C), followed by &#945;-pinene (1.2&#8201;Tg&#8201;C) and &#946;-pinene (0.7&#8201;Tg&#8201;C). Due to the strong emission capacity and large areas, broadleaf forests contribute to 76.8&#8201;% of total isoprene emission and 72.1&#8201;% of monoterpenes emission, respectively. BVOC emissions showed marked seasonal and diurnal patterns with the peak emission occurring in summer and midday. Spatially, high emissions of BVOC were mainly concentrated in southern and northeastern China, as well as the Qinling Mountains in central China, accounting for 91.4&#8201;% of national emission. Guangxi, Yunnan and Hunan provinces are significant emitters due to large area of vegetation with high emission rate and favored environmental conditions. The emission estimates are compared to past modeling results, field measurements and further evaluated against top-down isoprene emission estimates. Generally, the coupled mode produced a reasonable simulation in both emission amounts and the spatiotemporal distribution of BVOCs. The WRF&#8211;CLM&#8211;MEGAN coupling framework could be further integrated with atmospheric chemistry model to investigate BVOC chemistry and their effects on regional pollution and climate.</p>

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Direct retrieval of isoprene from satellite-based infrared measurements

Cited by 63 publications

References 70 publications

Gap models across micro- to mega-scales of time and space: examples of Tansley’s ecosystem concept

Gap models across micro- to mega-scales of time and space: examples of Tansley’s ecosystem concept

Profiling of formaldehyde, glyoxal, methylglyoxal, and CO over the Amazon: Normalised excess mixing ratios and related emission factors in biomass burning plumes

Estimation of biogenic volatile organic compound (BVOC) emissions in China using WRF–CLM–MEGAN coupled model

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