Recent Changes in Global Photosynthesis and Terrestrial Ecosystem Respiration Constrained From Multiple Observations

To assess global carbon cycle variability, we decompose the net land carbon sink into the sum of gross primary productivity (GPP), terrestrial ecosystem respiration (TER), and fire emissions and apply a Bayesian framework to constrain these fluxes between 1980 and 2014. The constrained GPP and TER fluxes show an increasing trend of only half of the prior trend simulated by models. From the optimization, we infer that TER increased in parallel with GPP from 1980 to 1990, but then stalled during the cooler periods, in 1990–1994 coincident with the Pinatubo eruption, and during the recent warming hiatus period. After each of these TER stalling periods, TER is found to increase faster than GPP, explaining a relative reduction of the net land sink. These results shed light on decadal variations of GPP and TER and suggest that they exhibit different responses to temperature anomalies over the last 35 years.

[1]  R. Woods,et al.  Towards the representation of groundwater in the Joint UK Land Environment Simulator , 2020, Hydrological Processes.

[2]  Atul K. Jain,et al.  Global Carbon Budget 2018 , 2014, Earth System Science Data.

[3]  B. Poulter,et al.  Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration , 2017 .

[4]  Atul K. Jain,et al.  Compensatory water effects link yearly global land CO2 sink changes to temperature , 2017, Nature.

[5]  I. Noble,et al.  On the direct effect of clouds and atmospheric particles on the productivity and structure of vegetation , 2001, Oecologia.

[6]  Youngryel Ryu,et al.  Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS) , 2016 .

[7]  Atul K. Jain,et al.  Increased light‐use efficiency in northern terrestrial ecosystems indicated by CO2 and greening observations , 2016 .

[8]  I. C. Prentice,et al.  Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake , 2016, Nature Communications.

[9]  J. Canadell,et al.  Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets , 2016, Proceedings of the National Academy of Sciences.

[10]  Fabienne Maignan,et al.  A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle , 2016 .

[11]  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.

[12]  Gregory Duveiller,et al.  Spatially downscaling sun-induced chlorophyll fluorescence leads to an improved temporal correlation with gross primary productivity , 2016 .

[13]  Markus Reichstein,et al.  Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms , 2016 .

[14]  R. Betts,et al.  El Nino and a record CO2 rise , 2016 .

[15]  J. Canadell,et al.  Greening of the Earth and its drivers , 2016 .

[16]  Steven W. Running,et al.  Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization , 2016 .

[17]  Yujie He,et al.  Toward more realistic projections of soil carbon dynamics by Earth system models , 2016 .

[18]  J. Shutler,et al.  Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM) , 2015 .

[19]  Atul K. Jain,et al.  Global Carbon Budget 2015 , 2015 .

[20]  P. Ciais,et al.  Spatiotemporal patterns of terrestrial gross primary production: A review , 2015 .

[21]  J. Willis,et al.  Recent hiatus caused by decadal shift in Indo-Pacific heating , 2015, Science.

[22]  Yan Sun,et al.  Change in terrestrial ecosystem water‐use efficiency over the last three decades , 2015, Global change biology.

[23]  Ranga B. Myneni,et al.  Recent trends and drivers of regional sources and sinks of carbon dioxide , 2015 .

[24]  R. Houghton,et al.  Audit of the global carbon budget: estimate errors and their impact on uptake uncertainty , 2014 .

[25]  M. Heimann,et al.  Interannual sea-air CO2 flux variability from an observation-driven ocean mixed-layer scheme , 2014 .

[26]  P. Landschützer,et al.  Recent variability of the global ocean carbon sink , 2014 .

[27]  J. Houghton,et al.  Climate Change 2013 - The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , 2014 .

[28]  P. Jones,et al.  Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset , 2014 .

[29]  Kuno M. Strassmann,et al.  Past and future carbon fluxes from land use change, shifting cultivation and wood harvest , 2014 .

[30]  A. Manning,et al.  Studies of Recent Changes in Atmospheric O 2 Content , 2014 .

[31]  Jindi Wang,et al.  Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[32]  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 .

[33]  Yu Kosaka,et al.  Recent global-warming hiatus tied to equatorial Pacific surface cooling , 2013, Nature.

[34]  N. Gruber,et al.  A joint atmosphere‐ocean inversion for the estimation of seasonal carbon sources and sinks , 2013 .

[35]  V. Brovkin,et al.  Representation of natural and anthropogenic land cover change in MPI‐ESM , 2013 .

[36]  J. Randerson,et al.  Analysis of daily, monthly, and annual burned area using the fourth‐generation global fire emissions database (GFED4) , 2013 .

[37]  Are Olsen,et al.  Global surface-ocean p CO 2 and sea–air CO 2 flux variability from an observation-driven ocean mixed-layer scheme , 2013 .

[38]  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..

[39]  Yoshiki Yamagata,et al.  Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model , 2013 .

[40]  Ronggao Liu,et al.  Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data , 2012 .

[41]  Wenche Aas,et al.  Introduction to the European Monitoring and Evaluation Programme (EMEP) and observed atmospheric composition change during 1972–2009 , 2012 .

[42]  Philip Lewis,et al.  Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements , 2012 .

[43]  A. Ito,et al.  Use of a process-based model for assessing the methane budgets of global terrestrial ecosystems and evaluation of uncertainty , 2012 .

[44]  Mathew Barlow,et al.  Asymmetric seasonal temperature trends , 2012 .

[45]  E. Davidson,et al.  Excess Nitrogen in the U.S. Environment: Trends, Risks, and Solutions , 2011 .

[46]  Y. Malhi,et al.  The allocation of ecosystem net primary productivity in tropical forests , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[47]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

[48]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics , 2011 .

[49]  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.

[50]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes , 2011 .

[51]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[52]  M. Razinger,et al.  Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power , 2011 .

[53]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[54]  Yi Y. Liu,et al.  Error characterisation of global active and passive microwave soil moisture datasets. , 2010 .

[55]  J. Randerson,et al.  Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009) , 2010 .

[56]  F. Woodward,et al.  Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate , 2010, Science.

[57]  Ben Bond-Lamberty,et al.  Temperature-associated increases in the global soil respiration record , 2010, Nature.

[58]  Andrew D. Friend,et al.  Carbon and nitrogen cycle dynamics in the O‐CN land surface model: 1. Model description, site‐scale evaluation, and sensitivity to parameter estimates , 2010 .

[59]  S. Khatiwala,et al.  Reconstruction of the history of anthropogenic CO2 concentrations in the ocean , 2009, Nature.

[60]  J. Lamarque,et al.  Emissions of gases and particles from biomass burning during the 20th century using satellite data and an historical reconstruction , 2009 .

[61]  P. Cox,et al.  Impact of changes in diffuse radiation on the global land carbon sink , 2009, Nature.

[62]  Benjamin Smith,et al.  Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space , 2008 .

[63]  J. Pereira,et al.  Global wildland fire emissions from 1960 to 2000 , 2008 .

[64]  H. V. D. Dool,et al.  A global monthly land surface air temperature analysis for 1948-present , 2008 .

[65]  J. Sarmiento,et al.  Correction to “A joint atmosphere‐ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global‐scale fluxes” , 2007 .

[66]  Maosheng Zhao,et al.  Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses , 2006 .

[67]  R. Giering,et al.  Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS) , 2005 .

[68]  Atul K. Jain,et al.  Modeling the effects of two different land cover change data sets on the carbon stocks of plants and soils in concert with CO2 and climate change , 2005 .

[69]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

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

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

[72]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[73]  Huug van den Dool,et al.  Climate Prediction Center global monthly soil moisture data set at 0.5 resolution for 1948 to present , 2004 .

[74]  Dennis D. Baldocchi,et al.  Response of a Deciduous Forest to the Mount Pinatubo Eruption: Enhanced Photosynthesis , 2003, Science.

[75]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[76]  J. Sarmiento,et al.  Anthropogenic CO2 Uptake by the Ocean Based on the Global Chlorofluorocarbon Data Set , 2003, Science.

[77]  I. C. Prentice,et al.  Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect , 2002, Science.

[78]  G. Orians,et al.  Global biogeochemical cycles , 1992 .