Articles | Volume 18, issue 24
https://doi.org/10.5194/bg-18-6579-2021
https://doi.org/10.5194/bg-18-6579-2021
Research article
 | 
23 Dec 2021
Research article |  | 23 Dec 2021

Extreme events driving year-to-year differences in gross primary productivity across the US

Alexander J. Turner, Philipp Köhler, Troy S. Magney, Christian Frankenberg, Inez Fung, and Ronald C. Cohen

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Cited articles

Badgley, G., Field, C. B., and Berry, J. A.: Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv., 3, e1602244, https://doi.org/10.1126/sciadv.1602244, 2017. a
Badgley, G., Anderegg, L. D. L., Berry, J. A., and Field, C. B.: Terrestrial Gross Primary Production: Using NIRV to Scale from Site to Globe, Glob. Change Biol., 25, 3731–3740, https://doi.org/10.1111/gcb.14729, 2019. a, b
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities, Bull. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:fantts>2.3.co;2, 2001. a
Baldocchi, D. D., Hicks, B. B., and Meyers, T. P.: Measuring Biosphere-Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods, Ecology, 69, 1331–1340, https://doi.org/10.2307/1941631, 1988. a
Bishop, C. M.: Pattern Recognition and Machine Learning, Springer, 1st Edn., New York, NY, 2007. a, b
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This work builds a high-resolution estimate (500 m) of gross primary productivity (GPP) over the US using satellite measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) between 2018 and 2020. We identify ecosystem-specific scaling factors for estimating gross primary productivity (GPP) from TROPOMI SIF. Extreme precipitation events drive four regional GPP anomalies that account for 28 % of year-to-year GPP differences across the US.
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