Atmospheric CO2 Observations Reveal Strong Correlation Between Regional Net Biospheric Carbon Uptake and Solar‐Induced Chlorophyll Fluorescence

Recent studies have shown the promise of remotely sensed solar-induced chlorophyll fluorescence (SIF) in informing terrestrial carbon exchange, but analyses have been limited to either plot level (~1 km) or hemispheric/global (~10 km) scales due to the lack of a direct measure of carbon exchange at intermediate scales. Here we use a network of atmospheric CO2 observations over North America to explore the value of SIF for informing net ecosystem exchange (NEE) at regional scales. We find that SIF explains space-time NEE patterns at regional (~100 km) scales better than a variety of other vegetation and climate indicators. We further show that incorporating SIF into an atmospheric inversion leads to a spatial redistribution of NEE estimates over North America, with more uptake attributed to agricultural regions and less to needleleaf forests. Our results highlight the synergy of ground-based and spaceborne carbon cycle observations.

[1]  D. Lobell,et al.  Improving the monitoring of crop productivity using spaceborne solar‐induced fluorescence , 2016, Global change biology.

[2]  Atul K. Jain,et al.  Global Carbon Budget 2016 , 2016 .

[3]  L. Guanter,et al.  Satellite chlorophyll fluorescence measurements reveal large‐scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests , 2016, Global change biology.

[4]  Peter E. Thornton,et al.  Remote Sensing Evaluation of CLM4 GPP for the Period 2000–09* , 2012 .

[5]  A. Michalak,et al.  Detecting fossil fuel emissions patterns from subcontinental regions using North American in situ CO2 measurements , 2014, Geophysical research letters.

[6]  David P. Turner,et al.  Atmospheric inverse modeling to constrain regional-scale CO2 budgets at high spatial and temporal resolution , 2010 .

[7]  Natasha Miles,et al.  Atmospheric CO 2 monitoring with single-cell NDIR-based analyzers , 2011 .

[8]  Vineet Yadav,et al.  Using atmospheric observations to evaluate the spatiotemporal variability of CO 2 fluxes simulated by terrestrial biospheric models , 2014 .

[9]  W. Post,et al.  The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design , 2013 .

[10]  John C. Lin,et al.  A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model , 2003 .

[11]  Philippe Ciais,et al.  Weak Northern and Strong Tropical Land Carbon Uptake from Vertical Profiles of Atmospheric CO2 , 2007, Science.

[12]  Nicholas C. Parazoo,et al.  Interpreting seasonal changes in the carbon balance of southern Amazonia using measurements of XCO2 and chlorophyll fluorescence from GOSAT , 2013 .

[13]  Shamil Maksyutov,et al.  A very high-resolution (1 km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights , 2011 .

[14]  Pierre Friedlingstein,et al.  Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks , 2014 .

[15]  Kevin R. Gurney,et al.  A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of results , 2014 .

[16]  Anthony W. King,et al.  The Potential Response of Terrestrial Carbon Storage to Changes in Climate and Atmospheric CO2 , 1997 .

[17]  I. Fung,et al.  Observational Contrains on the Global Atmospheric Co2 Budget , 1990, Science.

[18]  L. Guanter,et al.  Consistency Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production of Vegetation in North America , 2016 .

[19]  Atul K. Jain,et al.  A globally aggregated reconstruction of cycles of carbon and its isotopes , 1996 .

[20]  Xiaoliang Lu,et al.  Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest , 2017, Global change biology.

[21]  S S I T C H,et al.  Evaluation of Ecosystem Dynamics, Plant Geography and Terrestrial Carbon Cycling in the Lpj Dynamic Global Vegetation Model , 2022 .

[22]  W. Post,et al.  Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length , 2011 .

[23]  Eric R. Crosson,et al.  Field Testing of Cavity Ring-Down Spectroscopy Analyzers Measuring Carbon Dioxide and Water Vapor , 2012 .

[24]  M. S. Moran,et al.  Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence , 2014, Proceedings of the National Academy of Sciences.

[25]  Peter E. Thornton,et al.  Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests , 2002 .

[26]  Yinghai Ke,et al.  Evaluating runoff simulations from the Community Land Model 4.0 using observations from flux towers and a mountainous watershed , 2011 .

[27]  Atul K. Jain,et al.  Climate‐driven uncertainties in modeling terrestrial gross primary production: a site level to global‐scale analysis , 2014, Global change biology.

[28]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[29]  John C. Lin,et al.  North American CO 2 exchange: inter-comparison of modeled estimates with results from a fine-scale atmospheric inversion , 2011 .

[30]  D. Baldocchi.  ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .

[31]  John M. Baker,et al.  Direct measurement of biosphere‐atmosphere isotopic CO2 exchange using the eddy covariance technique , 2008 .

[32]  E. Davidson,et al.  On the variability of respiration in terrestrial ecosystems: moving beyond Q10 , 2006 .

[33]  Yuanyuan Fang,et al.  Atmospheric observations inform CO2 flux responses to enviroclimatic drivers , 2015 .

[34]  Thomas Kaminski,et al.  On aggregation errors in atmospheric transport inversions , 2001 .

[35]  S. Wofsy,et al.  Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest , 2007 .

[36]  I. C. Prentice,et al.  Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) , 2008 .

[37]  John C. Lin,et al.  Coupled weather research and forecasting–stochastic time-inverted lagrangian transport (WRF–STILT) model , 2010 .

[38]  Vineet Yadav,et al.  Regional-scale geostatistical inverse modeling of North American CO 2 fluxes: a synthetic data study , 2009 .

[39]  Alessandro Anav,et al.  Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 , 2013, Remote. Sens..

[40]  Federico Magnani,et al.  Let’s exploit available knowledge on vegetation fluorescence , 2014, Proceedings of the National Academy of Sciences.

[41]  John C. Lin,et al.  Toward constraining regional‐scale fluxes of CO2 with atmospheric observations over a continent: 1. Observed spatial variability from airborne platforms , 2003 .

[42]  J. Randerson,et al.  Technical Description of version 4.0 of the Community Land Model (CLM) , 2010 .

[43]  William C. Skamarock,et al.  A time-split nonhydrostatic atmospheric model for weather research and forecasting applications , 2008, J. Comput. Phys..

[44]  Daniel Cooley,et al.  Evaluating atmospheric CO2 inversions at multiple scales over a highly inventoried agricultural landscape , 2011, Global change biology.

[45]  David D. Parrish,et al.  NORTH AMERICAN REGIONAL REANALYSIS , 2006 .

[46]  Nicolas Gruber,et al.  Trends and regional distributions of land and ocean carbon sinks , 2009 .

[47]  H. Tian,et al.  Net exchanges of CO2, CH4, and N2O between China's terrestrial ecosystems and the atmosphere and their contributions to global climate warming , 2011 .

[48]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[49]  C. Frankenberg,et al.  Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. , 2014, Journal of experimental botany.

[50]  Colm Sweeney,et al.  Constraining the CO 2 budget of the corn belt: exploring uncertainties from the assumptions in a mesoscale inverse system , 2011 .

[51]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[52]  P. Tans,et al.  A geostatistical approach to surface flux estimation of atmospheric trace gases , 2004 .

[53]  W. Oechel,et al.  Tundra photosynthesis captured by satellite‐observed solar‐induced chlorophyll fluorescence , 2017 .

[54]  P. Bakwin,et al.  What is the concentration footprint of a tall tower , 2001 .

[55]  A. J. Dolman,et al.  Radiation, temperature, and leaf area explain ecosystem carbon fluxes in boreal and temperate European forests , 2005 .

[56]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[57]  Wout Verhoef,et al.  A model for chlorophyll fluorescence and photosynthesis at leaf scale , 2009 .

[58]  C. Frankenberg,et al.  Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2 , 2013 .

[59]  Carl Ekdahl,et al.  Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii , 1976 .

[60]  Akihiko Ito,et al.  Changing ecophysiological processes and carbon budget in East Asian ecosystems under near-future changes in climate: implications for long-term monitoring from a process-based model , 2010, Journal of Plant Research.

[61]  John C. Lin,et al.  How can mountaintop CO 2 observations be used to constrain regional carbon fluxes , 2016 .

[62]  Y. Xue,et al.  Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis , 2012 .

[63]  Taro Takahashi,et al.  Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models , 2002, Nature.

[64]  M. Rossini,et al.  Solar‐induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest , 2015 .

[65]  Robert B. Cook,et al.  The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project – Part 2: Environmental driver data , 2013 .

[66]  Scot M. Miller,et al.  Spatio-temporal approach to moving window block kriging of satellite data v 1 . 0 , 2016 .

[67]  Vineet Yadav,et al.  Attributing the variability of eddy‐covariance CO2 flux measurements across temporal scales using geostatistical regression for a mixed northern hardwood forest , 2010 .

[68]  A. Mariotti,et al.  Terrestrial mechanisms of interannual CO2 variability , 2005 .

[69]  Dylan B. A. Jones,et al.  Terrestrial gross primary production inferred from satellite fluorescence and vegetation models , 2014, Global change biology.

[70]  Takeshi Ise,et al.  The global-scale temperature and moisture dependencies of soil organic carbon decomposition: an analysis using a mechanistic decomposition model , 2006 .

[71]  W. Shen,et al.  Are variations in heterotrophic soil respiration related to changes in substrate availability and microbial biomass carbon in the subtropical forests? , 2015, Scientific Reports.

[72]  Atul K. Jain,et al.  Impact of large‐scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data , 2013 .

[73]  Jovan M. Tadić,et al.  Spatio-temporal approach to moving window block kriging of satellite data v1.0 , 2016 .

[74]  C. Frankenberg,et al.  New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity , 2011, Geophysical Research Letters.

[75]  Vineet Yadav,et al.  Mapping of satellite Earth observations using moving window block kriging , 2014 .

[76]  Atul K. Jain,et al.  Climate‐driven uncertainties in modeling terrestrial energy and water fluxes: a site‐level to global‐scale analysis , 2014, Global change biology.

[77]  K. Davis,et al.  Large amplitude spatial and temporal gradients in atmospheric boundary layer CO2mole fractions detected with a tower-based network in the U.S. upper Midwest , 2012 .

[78]  C. Frankenberg,et al.  Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests , 2017 .

[79]  Scot M. Miller,et al.  Anthropogenic emissions of methane in the United States , 2013, Proceedings of the National Academy of Sciences.

[80]  H. Tian,et al.  Century-Scale Responses of Ecosystem Carbon Storage and Flux to Multiple Environmental Changes in the Southern United States , 2012, Ecosystems.

[81]  Kazuhiro Ohtani,et al.  Bootstrapping R2 and adjusted R2 in regression analysis , 2000 .

[82]  L. Guanter,et al.  The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange , 2014 .

[83]  W. Post,et al.  NACP MsTMIP: Global and North American Driver Data for Multi-Model Intercomparison , 2014 .