Respiration driven CO2 pulses dominate Australia's flux variability
暂无分享,去创建一个
Atul K. Jain | Tokyo | Exeter | Devon | Engineering | Jena | Nasa | Nanjing | Mathematics | Boulder | France | Canberra | Maryland. | Hamburg | Urbana | Toulouse | Analysis | Canada. | College of Engineering | China | Department of Astrophysical Sciences | Japan. | Cnrs | H. Tian | S. Zaehle | B. Poulter | P. Friedlingstein | U. Maryland | D. Griffith | S. Sitch | U. Exeter | School of Materials Science | M. Jung | V. Arora | S. Basu | P. Briggs | W. Yuan | Etsushi Kato | U. Illinois | United Kingdom. | Uk | X. Yue | Lsceipsl | A. Wiltshire | D. Goll | D. Lombardozzi | J. Nabel | N. Deutscher | T. El-Madany | B. Ahrens | Usa | U. Toulouse | National Center for Atmospheric Research | G. Center | S. O. Sciences | Environment | School of Chemistry | Germany | H. University | Paris | Environmental Sciences | Interdisciplinary Center for Scientific Computing | Climate | Max-Planck-Institute for Biogeochemistry | P. Sciences | Climate Change Canada | S. Vardag | Zhuhai | Victoria | Atmosphere | Eva-Marie Schomann | Roland S'ef'erian | A. S. I. O. Physics | Heidelberg Center for the Environment | Earth System Science Interdisciplinary Center | College of Life | Canadian Centre for Climate Modelling | Climate Science Centre | Csiro Oceans | Australia | College of Electronic Engineering | Laboratoire de M'et'eorologie Dynamique | Institut Pierre-Simon Laplace | CNRS-ENS-UPMC-X | U. Saclay | CEA-CNRS-UVSQ | Institute of Applied Energy | Global Dynamics Laboratory | Max Planck Institute for Meteorology | Biospheric Sciences Laboratory | Cnrm | M'et'eo-France | International Center for Climate | Global Change Research | School of Forestry | Wildlife Sciences | A. University | Met Office Hadley Centre | Southern Marine Science | Engineering Guangdong Laboratory | Nanjing University of Information ScienceTechnology | Centre for Atmospheric Chemistry | University of Wollongong | Wollongong
[1] D. Griffith,et al. Interannual variability in the Australian carbon cycle over 2015–2019, based on assimilation of OCO-2 satellite data , 2022 .
[2] M. Dubey,et al. An 11-year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm , 2022, Earth System Science Data.
[3] B. Poulter,et al. Bias-correcting carbon fluxes derived from land-surface satellite data for retrospective and near-real-time assimilation systems , 2021, Atmospheric Chemistry and Physics.
[4] Atul K. Jain,et al. Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models , 2021, Environmental Research Letters.
[5] S. Seneviratne,et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability , 2021, Nature.
[6] Atul,et al. Assessing the representation of the Australian carbon cycle in global vegetation models , 2021 .
[7] Daniel S. Goll,et al. JSBACH 3 - The land component of the MPI Earth System Model: documentation of version 3.2 , 2021 .
[8] S. Zaehle,et al. Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems , 2021, Environmental Research Letters.
[9] M. Duniway,et al. Drought resistance and resilience: The role of soil moisture–plant interactions and legacies in a dryland ecosystem , 2020, Journal of Ecology.
[10] Atul K. Jain,et al. Global Carbon Budget 2020 , 2020, Earth System Science Data.
[11] C. Delire,et al. The Global Land Carbon Cycle Simulated With ISBA‐CTRIP: Improvements Over the Last Decade , 2020, Journal of Advances in Modeling Earth Systems.
[12] P. Rayner,et al. The potential of Orbiting Carbon Observatory-2 data to reduce the uncertainties in CO2 surface fluxes over Australia using a variational assimilation scheme , 2020, Atmospheric Chemistry and Physics.
[13] Atul K. Jain,et al. Sources of Uncertainty in Regional and Global Terrestrial CO2 Exchange Estimates , 2020, Global Biogeochemical Cycles.
[14] Atul K. Jain,et al. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach , 2019, Biogeosciences.
[15] Nathan Collier,et al. The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty , 2019, Journal of Advances in Modeling Earth Systems.
[16] J. Canadell,et al. Interannual variation of terrestrial carbon cycle: Issues and perspectives , 2019, Global change biology.
[17] E. Chan,et al. CLASSIC v1.0: the open-source community successor to the Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM) – Part 1: Model framework and site-level performance , 2019, Geoscientific Model Development.
[18] F. Chevallier,et al. Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions , 2019 .
[19] D. Wunch,et al. Improved Constraints on Northern Extratropical CO2 Fluxes Obtained by Combining Surface‐Based and Space‐Based Atmospheric CO2 Measurements , 2019, Journal of Geophysical Research: Atmospheres.
[20] A. Huete,et al. Multi-climate mode interactions drive hydrological and vegetation responses to hydroclimatic extremes in Australia , 2019, Remote Sensing of Environment.
[21] S. Prober,et al. TERN, Australia’s land observatory: addressing the global challenge of forecasting ecosystem responses to climate variability and change , 2019, Environmental Research Letters.
[22] F. Chevallier,et al. Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal , 2019, Nature Communications.
[23] Nuno Carvalhais,et al. Memory effects of climate and vegetation affecting net ecosystem CO2 fluxes in global forests , 2019, PloS one.
[24] J. Ghattas,et al. Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): multi-scale evaluation of gross primary production , 2018, Geoscientific Model Development.
[25] J. Schimel. Life in Dry Soils: Effects of Drought on Soil Microbial Communities and Processes , 2018, Annual Review of Ecology, Evolution, and Systematics.
[26] P. Ciais,et al. Low Phosphorus Availability Decreases Susceptibility of Tropical Primary Productivity to Droughts , 2018, Geophysical Research Letters.
[27] Benjamin Smith,et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis , 2018, Geoscientific Model Development.
[28] Markus Reichstein,et al. Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product , 2018, Earth System Science Data.
[29] P. Sellers,et al. Observing carbon cycle–climate feedbacks from space , 2018, Proceedings of the National Academy of Sciences.
[30] M. Reichstein,et al. Drought, Heat, and the Carbon Cycle: a Review , 2018, Current Climate Change Reports.
[31] F. Joos,et al. A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions , 2018 .
[32] P. Cox,et al. Emergent constraint on equilibrium climate sensitivity from global temperature variability , 2018, Nature.
[33] Robert Joseph Andres,et al. The Open-source Data Inventory for Anthropogenic Carbon dioxide (CO2), version 2016 (ODIAC2016): A global, monthly fossil-fuel CO2 gridded emission data product for tracer transport simulations and surface flux inversions. , 2017, Earth system science data.
[34] Ying Sun,et al. The Orbiting Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes , 2017, Science.
[35] F. Pappenberger,et al. Combining fire radiative power observations with the fire weather index improves the estimation of fire emissions , 2017 .
[36] J. Randerson,et al. Global fire emissions estimates during 1997–2016 , 2017 .
[37] F. Woodward,et al. The impact of alternative trait-scaling hypotheses for the maximum photosynthetic carboxylation rate (Vcmax ) on global gross primary production. , 2017, The New phytologist.
[38] Benjamin Smith,et al. Carbon cycle responses of semi‐arid ecosystems to positive asymmetry in rainfall , 2017, Global change biology.
[39] Atul K. Jain,et al. Compensatory water effects link yearly global land CO2 sink changes to temperature , 2017, Nature.
[40] B. Poulter,et al. Drought rapidly diminishes the large net CO2 uptake in 2011 over semi-arid Australia , 2016, Scientific Reports.
[41] Jeffrey P. Walker,et al. An introduction to the Australian and New Zealand flux tower network - OzFlux , 2016 .
[42] P. Cox,et al. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2 , 2016, Nature.
[43] Markus Reichstein,et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms , 2016 .
[44] Henry Buijs,et al. Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space , 2016 .
[45] Natascha Kljun,et al. The importance of interacting climate modes on Australia’s contribution to global carbon cycle extremes , 2016, Scientific Reports.
[46] Luis Guanter,et al. Anomalous carbon uptake in Australia as seen by GOSAT , 2015 .
[47] Atul K. Jain,et al. Increased influence of nitrogen limitation on CO2 emissions from future land use and land use change , 2015 .
[48] Nadine Unger,et al. The Yale Interactive terrestrial Biosphere model version 1.0: description, evaluation and implementation into NASA GISS ModelE2 , 2015 .
[49] Atul K. Jain,et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink , 2015, Science.
[50] Ranga B. Myneni,et al. Recent trends and drivers of regional sources and sinks of carbon dioxide , 2015 .
[51] Wouter Peters,et al. ObsPack: a framework for the preparation, delivery, and attribution of atmospheric greenhouse gas measurements , 2014 .
[52] Yi Y. Liu,et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle , 2014, Nature.
[53] Jianping Huang,et al. Multiyear precipitation reduction strongly decreases carbon uptake over northern China , 2014 .
[54] H. Tian,et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget , 2014, Climatic Change.
[55] F. Moyano,et al. Responses of soil heterotrophic respiration to moisture availability: An exploration of processes and models , 2013 .
[56] Yoshiki Yamagata,et al. Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model , 2013 .
[57] A. Butz,et al. Global CO2 fluxes estimated from GOSAT retrievals of total column CO2 , 2013 .
[58] Niklaus E. Zimmermann,et al. Plant functional type mapping for earth system models , 2011 .
[59] A. Carrara,et al. Autotrophic and heterotrophic contributions to short‐term soil CO2 efflux following simulated summer precipitation pulses in a Mediterranean dehesa , 2011 .
[60] Akihiko Kuze,et al. Toward accurate CO2 and CH4 observations from GOSAT , 2011 .
[61] Justus Notholt,et al. The Total Carbon Column Observing Network , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[62] 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 .
[63] S. K. Akagi,et al. The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning , 2010 .
[64] Fabienne Maignan,et al. CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements , 2010 .
[65] Pierre Friedlingstein,et al. Carbon and nitrogen cycle dynamics in the O‐CN land surface model: 2. Role of the nitrogen cycle in the historical terrestrial carbon balance , 2010 .
[66] W. Borken,et al. Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils , 2009 .
[67] J. Randerson,et al. An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker , 2007, Proceedings of the National Academy of Sciences.
[68] Franco Miglietta,et al. Drying and wetting of Mediterranean soils stimulates decomposition and carbon dioxide emission: the "Birch effect". , 2007, Tree physiology.
[69] Philippe Peylin,et al. The contribution of AIRS data to the estimation of CO2 sources and sinks , 2005 .
[70] I. C. Prentice,et al. A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .
[71] H. Birch. Mineralisation of plant nitrogen following alternate wet and dry conditions , 1964, Plant and Soil.
[72] W. Pockman,et al. Precipitation pulses and carbon fluxes in semiarid and arid ecosystems , 2004, Oecologia.
[73] Philippe Ciais,et al. Transcom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks , 2004, Global Biogeochemical Cycles.
[74] Christopher B. Field,et al. Substrate limitations for heterotrophs: Implications for models that estimate the seasonal cycle of atmospheric CO2 , 1996 .