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Satellite isoprene retrievals constrain emissions and atmospheric oxidation

Nature volume 585, pages 225–233 (2020)Cite this article

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Abstract

Isoprene is the dominant non-methane organic compound emitted to the atmosphere1,2,3. It drives ozone and aerosol production, modulates atmospheric oxidation and interacts with the global nitrogen cycle4,5,6,7,8. Isoprene emissions are highly uncertain1,9, as is the nonlinear chemistry coupling isoprene and the hydroxyl radical, OH—its primary sink10,11,12,13. Here we present global isoprene measurements taken from space using the Cross-track Infrared Sounder. Together with observations of formaldehyde, an isoprene oxidation product, these measurements provide constraints on isoprene emissions and atmospheric oxidation. We find that the isoprene–formaldehyde relationships measured from space are broadly consistent with the current understanding of isoprene–OH chemistry, with no indication of missing OH recycling at low nitrogen oxide concentrations. We analyse these datasets over four global isoprene hotspots in relation to model predictions, and present a quantification of isoprene emissions based directly on satellite measurements of isoprene itself. A major discrepancy emerges over Amazonia, where current underestimates of natural nitrogen oxide emissions bias modelled OH and hence isoprene. Over southern Africa, we find that a prominent isoprene hotspot is missing from bottom-up predictions. A multi-year analysis sheds light on interannual isoprene variability, and suggests the influence of the El Niño/Southern Oscillation.

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Fig. 1: Global distribution of ΔTb and isoprene columns.
Fig. 2: Comparison of the CrIS ANN isoprene columns with other datasets.
Fig. 3: Dependence of atmospheric isoprene columns on emissions and lifetime.
Fig. 4: Global distribution of the isoprene:HCHO ratio as a function of isoprene and NOx.
Fig. 5: Seasonality of space-based isoprene over Amazonia and southern Africa.
Fig. 6: Seasonality of space-based isoprene over the southeast United States and Australia.

Data availability

The CrIS Level 1B data used in this work are publicly available at https://snpp-sounder.gesdisc.eosdis.nasa.gov/data/SNPP_Sounder_Level1/SNPPCrISL1BNSR.1/. The isoprene column data employed in this work are available at https://doi.org/10.13020/v959-dr15. The airborne data are publicly available for SENEX at http://esrl.noaa.gov/csd/projects/senex/ and for SEAC4RS at http://www-air.larc.nasa.gov/missions/seac4rs/index.html. OMI QA4ECV HCHO and NO2 data are publicly available at http://www.qa4ecv.eu/ecvs.

Code availability

GEOS-Chem model code is publicly available at http://www.geos-chem.org. The LBLRTM80,81, which is used to calculate the molecular absorption look-up tables employed in ELANOR85, is publicly available at http://rtweb.aer.com/lblrtm.html.

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Acknowledgements

This work was supported by the NASA Atmospheric Composition Modeling and Analysis Program (Grant Number NNX17AF61G) and by the Minnesota Supercomputing Institute. We thank D. Fu for providing optimal estimation isoprene retrievals over Amazonia and input on this manuscript; C. Barnet, E. Manning and R. Monarrez for providing CLIMCAPS HNO3 retrievals; M. Alvarado, K. Cady-Pereira, D. Gombos, J. Hegarty and I. Strickland for generating and testing isoprene absorption look-up tables employed here; and E. Edgerton for providing isoprene data from the SouthEastern Aerosol Research and CHaracterization (SEARCH) network. The SEARCH network was sponsored by the Southern Company and the Electric Power Research Institute. Isoprene measurements aboard the NASA DC-8 during SEAC4RS were supported by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit) through the Austrian Space Applications Programme (ASAP) of the Austrian Research Promotion Agency (FFG). T. Mikoviny is acknowledged for his support during SEAC4RS. We thank S. Springston for GoAmazon T3 data, which were supported by the ARM Climate Research Facility, the Central Office of the Large-Scale Biosphere Atmosphere Experiment in Amazonia (LBA), the Instituto Nacional de Pesquisas da Amazonia (INPA) and the Universidade do Estado do Amazonia (UEA). Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract to NASA.

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  1. M. Julian Deventer

    Present address: Bioclimatology, University of Göttingen, Göttingen, Germany

Authors and Affiliations

  1. Department of Soil, Water, and Climate, University of Minnesota, St Paul, MN, USA

    Kelley C. Wells, Dylan B. Millet & M. Julian Deventer

  2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Vivienne H. Payne

  3. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Kelvin H. Bates

  4. Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA

    Joost A. de Gouw & Carsten Warneke

  5. Department of Chemistry, University of Colorado, Boulder, CO, USA

    Joost A. de Gouw

  6. Department of Atmospheric and Cryogenic Sciences, University of Innsbruck, Innsbruck, Austria

    Martin Graus

  7. NOAA Chemical Sciences Laboratory, Boulder, CO, USA

    Carsten Warneke

  8. Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria

    Armin Wisthaler

  9. Department of Chemistry, University of Oslo, Oslo, Norway

    Armin Wisthaler

  10. Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USA

    Jose D. Fuentes

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D.B.M. planned the project and oversaw the scientific interpretation. K.C.W. performed the ΔTb calculations, ANN training, CrIS isoprene retrievals and evaluation, lifetime calculations, emission optimization, and synthesis of results for major source regions. V.H.P. performed radiative transfer model simulations and provided guidance with CrIS data analysis. M.J.D. assisted with ANN training and application. D.B.M. and K.C.W. conducted the GEOS-Chem model simulations. K.H.B. worked on the development of RCIM and Mini-CIM and incorporated them into GEOS-Chem. J.A.d.G., M.G., C.W. and A.W. carried out the aircraft measurements used for evaluation. J.D.F. provided ground-based isoprene measurements from Amazonia. K.C.W. and D.B.M. wrote the manuscript. All authors reviewed and commented on the paper.

Corresponding author

Correspondence to Dylan B. Millet.

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The authors declare no competing interests.

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Peer review information Nature thanks Klaas Boersma, Jean-Francois Müller and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Simulated spectral signals near 900 cm−1 for the CrIS sensor.

a, Brightness temperature (Tb) difference for simulated spectra with and without isoprene (black), nitric acid (red), ammonia (violet) and CFC-12 (yellow) and a 10% perturbation in water vapour (cyan). Red and blue arrows indicate the ν28 on-peak and off-peak spectral points used to calculate ΔTb. Simulations were performed with LBLRTM80,81 for an isoprene profile with 5 ppb in the boundary layer (P > 800 hPa) that decays exponentially aloft, and US standard atmosphere profiles of temperature, water vapour and nitric acid79. b, Relationship between ΔTb and isoprene column density, shaded by thermal contrast, for the full synthetic dataset used in this work. c, Global distribution of surface-atmosphere thermal contrast at the time of the CrIS overpass. Maps are derived from time-interpolated GMAO temperatures for January, April, July and October.

Extended Data Fig. 2 Global distribution of isoprene columns, emissions and lifetime as predicted by GEOS-Chem.

Predicted columns (left), emissions (middle) and lifetime (z < 500 m; right) are shown at 13:30 lt for January, April, July and October 2013 (top to bottom).

Extended Data Fig. 3 Statistical uncertainty in the global distribution of monthly mean isoprene:HCHO ratios as a function of isoprene and NOx regime.

a, Relative 95% confidence interval in the mean ratio for each isoprene and tropospheric NO2 bin. b, Number of observations in each bin.

Extended Data Fig. 4 Global distribution of isoprene column densities derived from CrIS.

Plotted are the mean (left) and relative standard deviation (right) across the 10 ANNs for January, April, July and October 2013 (top to bottom).

Extended Data Fig. 5 Boundaries of the four regions examined in the seasonal bar plots shown in Figs. 5, 6.

.

Extended Data Fig. 6 Measured and simulated HCHO columns.

Plotted are the HCHO columns measured by OMI (left) and simulated by GEOS-Chem (right) at ~13:30 lt for January, April, July and October 2013 (top to bottom).

Extended Data Fig. 7 CrIS cloud screening and ANN performance.

a, Function used for cloud screening CrIS L1B data before ΔTb calculation. The black line shows the modelled clear-sky difference between the 900 cm−1 brightness temperature and surface skin temperature, as a function of water vapour column density (calculated using LBLRTM80,81). The solid red line is the linear approximation used here, and the dashed red line represents a less stringent threshold used to test the sensitivity of the results to our cloud screening approach. b, c, Sensitivity of the CrIS brightness temperature differences (b) and isoprene columns (c) to cloud screening. Data shown represent the median relative differences between the base-case results (derived using the solid red line in a) and those derived using the less stringent cloud screening threshold (dashed red line in a). d, e, Scatterplots of the predicted versus true isoprene columns for the six-predictor ANN (d) and an ANN in which ΔTb is withheld as a predictor variable (e). Red dots show the mean of the ten ANN predictions, and blue error bars show the standard deviation across the predictions. f, The relative uncertainty (based on the difference between the mean ANN predicted value and the true value) for the six-predictor ANN, binned as a function of thermal contrast and isoprene column density.

Extended Data Fig. 8 CrIS isoprene measurements over Amazonia.

The maps were derived using ANN- (left) and optimal estimation- (right) based approaches. Data are shown for September 2014 and displayed as absolute columns.

Extended Data Table 1 Spatial correlation between monthly mean CrIS ΔTb and monthly mean 13:30 lt isoprene columns predicted by GEOS-Chem at 2° × 2.5° resolution for select regions
Full size table
Extended Data Table 2 Data sources for the six input parameters used for ANN training and retrievals
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Notes 1-8, Supplementary References, Supplementary Table 1 and Supplementary Figures 1-22.

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Wells, K.C., Millet, D.B., Payne, V.H. et al. Satellite isoprene retrievals constrain emissions and atmospheric oxidation. Nature 585, 225–233 (2020). https://doi.org/10.1038/s41586-020-2664-3

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