Optimal design of surface CO2 observation network to constrain China's land carbon sink.

[1]  Matthew S. Johnson,et al.  National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake , 2023, Earth System Science Data.

[2]  Matthew S. Johnson,et al.  Constraining China's land carbon sink from emerging satellite CO2 observations: Progress and challenges , 2022, Global change biology.

[3]  S. Piao,et al.  Perspectives on the role of terrestrial ecosystems in the ‘carbon neutrality’ strategy , 2022, Science China Earth Sciences.

[4]  H. Tian,et al.  The size of the land carbon sink in China , 2022, Nature.

[5]  S. Piao,et al.  Estimation of China’s terrestrial ecosystem carbon sink: Methods, progress and prospects , 2022, Science China Earth Sciences.

[6]  F. Deng,et al.  Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7 , 2022, Atmospheric Chemistry and Physics.

[7]  A. Ito,et al.  Estimated regional CO2 flux and uncertainty based on an ensemble of atmospheric CO2 inversions , 2021, Atmospheric Chemistry and Physics.

[8]  F. Chevallier.  Fluxes of Carbon Dioxide From Managed Ecosystems Estimated by National Inventories Compared to Atmospheric Inverse Modeling , 2021, Geophysical Research Letters.

[9]  P. Ciais,et al.  PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data , 2020, Geoscientific Model Development.

[10]  P. Palmer,et al.  Large Chinese land carbon sink estimated from atmospheric carbon dioxide data , 2020, Nature.

[11]  Atul K. Jain,et al.  Global Carbon Budget 2020 , 2020, Earth System Science Data.

[12]  C. Sweeney,et al.  Estimating US fossil fuel CO2 emissions from measurements of 14C in atmospheric CO2 , 2020, Proceedings of the National Academy of Sciences.

[13]  P. Jones,et al.  Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset , 2020, Scientific Data.

[14]  G. Janssens‑Maenhout,et al.  Evaluating China's fossil-fuel CO2 emissions from a comprehensive dataset of nine inventories , 2020, Atmospheric Chemistry and Physics.

[15]  J. Ardö,et al.  Greenhouse gas observation network design for Africa , 2020, Tellus B: Chemical and Physical Meteorology.

[16]  P. Ciais,et al.  The regional European atmospheric transport inversion comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes for the period 2006–2015 , 2019, Atmospheric Chemistry and Physics.

[17]  S. Sijikumar,et al.  Designing surface CO2 monitoring network to constrain the Indian land fluxes , 2019 .

[18]  F. Chevallier,et al.  Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal , 2019, Nature Communications.

[19]  Xiaobing Feng,et al.  An Adjoint-Free CNOP–4DVar Hybrid Method for Identifying Sensitive Areas Targeted Observations: Method Formulation and Preliminary Evaluation , 2019, Advances in Atmospheric Sciences.

[20]  P. Campbell,et al.  Updates to the Noah Land Surface Model in WRF‐CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition , 2019, Journal of advances in modeling earth systems.

[21]  L. Merbold,et al.  Towards a feasible and representative pan-African research infrastructure network for GHG observations , 2018, Environmental Research Letters.

[22]  P. Ciais,et al.  Potential of European 14 CO 2 observation network to estimate the fossil fuel CO 2 emissions via atmospheric inversions , 2017 .

[23]  Daniel S. Goll,et al.  ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation , 2017 .

[24]  Thomas Kaminski,et al.  Reviews and syntheses: Flying the satellite into your model: on the role of observation operators in constraining models of the Earth system and the carbon cycle , 2017 .

[25]  P. Ciais,et al.  Estimation of observation errors for large-scale atmospheric inversion of CO2 emissions from fossil fuel combustion , 2017 .

[26]  D. Higdon,et al.  Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example , 2016 .

[27]  P. Ciais,et al.  What would dense atmospheric observation networks bring to the quantification of city CO 2 emissions , 2016 .

[28]  John B. Miller,et al.  Separation of biospheric and fossil fuel fluxes of CO 2 by atmospheric inversion of CO 2 and 14 CO 2 measurements: Observation System Simulations , 2016 .

[29]  P. Ciais,et al.  A comprehensive estimate of recent carbon sinks in China using both top-down and bottom-up approaches , 2016, Scientific Reports.

[30]  R. Parker,et al.  Estimates of European uptake of CO2 inferred from GOSAT XCO2 retrievals: sensitivity to measurement bias inside and outside Europe , 2016 .

[31]  P. Ciais,et al.  On the potential of the ICOS atmospheric CO 2 measurement network for estimating the biogenic CO 2 budget of Europe , 2015 .

[32]  W. Peters,et al.  Net terrestrial CO2 exchange over China during 2001–2010 estimated with an ensemble data assimilation system for atmospheric CO2 , 2014 .

[33]  P. M. Lang,et al.  CO 2 , CO, and CH 4 measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts , 2014 .

[34]  Y. Niwa,et al.  Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions. , 2013 .

[35]  A. E. Schuh,et al.  Network design for mesoscale inversions of CO2 sources and sinks , 2012 .

[36]  Fabienne Maignan,et al.  CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements , 2010 .

[37]  D. Sculley,et al.  Web-scale k-means clustering , 2010, WWW '10.

[38]  Philippe Ciais,et al.  The carbon balance of terrestrial ecosystems in China , 2009, Nature.

[39]  J. Randerson,et al.  An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker , 2007, Proceedings of the National Academy of Sciences.

[40]  U. Karstens,et al.  Inferring high-resolution fossil fuel CO2 records at continental sites from combined 14CO2 and CO observations , 2007 .

[41]  Shamil Maksyutov,et al.  TransCom 3 CO2 inversion intercomparison: 1. Annual mean control results and sensitivity to transport and prior flux information , 2003 .

[42]  Shamil Maksyutov,et al.  Incremental approach to the optimal network design for CO2 surface source inversion , 2002 .

[43]  Ian G. Enting,et al.  Optimizing the CO2 observing network for constraining sources and sinks , 1996 .