Junjie Liu

Organization

Jet Propulsion Laboratory

Email

Contact person by email

Business Address

California Institute of Technology
4500 Oak Grove Dr.
MS 233-200
Pasadena, CA 91109
United States

First Author Publications

Liu, J., et al. (2021), Carbon Monitoring System Flux Net Biosphere Exchange 2020 (CMS-Flux NBE 2020), Earth Syst. Sci. Data, 13, 299-330, doi:10.5194/essd-13-299-2021.
Liu, J. (2018), Detecting drought impact on terrestrial biosphere carbon fluxes over contiguous US with satellite observations, Environmental Research Letters, 13, 095003.
Liu, J., et al. (2017), R ES E A RC H | R E MO T E S E NS I NG, Science, 358, eaam5690, doi:10.1126/science.aam5690.
Liu, J., and K. Bowman (2016), A method for independent validation of surface fluxes from atmospheric inversion: Application to CO2, Geophys. Res. Lett., 43, 3502-3508, doi:10.1002/2016GL067828.
Liu, J., et al. (2016), Comparison between the Local Ensemble Transform Kalman Filter (LETKF) and 4D-Var in atmospheric CO2 flux inversion with the Goddard Earth Observing System-Chem model and the observation impact diagnostics from the LETKF, J. Geophys. Res., 121, 13,066-13,087, doi:10.1002/2016JD025100.
Liu, J., et al. (2015), Source-receptor relationships of column-average CO2 and implications for the impact of observations on flux inversions, J. Geophys. Res., 120, Atmos., doi:10.1002/2014JD022914.
Liu, J., et al. (2014), Carbon monitoring system flux estimation and attribution: impact of ACOS-GOSAT XCO2 sampling on the inference of terrestrial biospheric sources and sinks, Tellus, 66, 22486, doi:10.3402/tellusb.v66.22486.
Liu, J., et al. (2012), Simultaneous assimilation of AIRS Xco2 and meteorological observations in a carbon climate model with an ensemble Kalman filter, J. Geophys. Res., 117, D05309, doi:10.1029/2011JD016642.
Liu, J., et al. (2011), CO2 transport uncertainties from the uncertainties in meteorological fields, Geophys. Res. Lett., 38, L12808, doi:10.1029/2011GL047213.
Liu, J., et al. (2009), Analysis sensitivity calculation in an ensemble Kalman filter, Q. J. R. Meteorol. Soc., 135, 1842-1851, doi:10.1002/qj.511.
Liu, J., et al. (2009), Univariate and Multivariate Assimilation of AIRS Humidity Retrievals with the Local Ensemble Transform Kalman Filter, Mon. Wea. Rev., 137, 3918-3932, doi:10.1175/2009MWR2791.1.
Liu, J., et al. (2008), Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM – perfect model experiments, Nonlin. Processes Geophys., 15, 645-659.
Liu, J., and E. Kalnay (2008), Estimating observation impact without adjoint model in an ensemble Kalman filter, Q. J. R. Meteorol. Soc., 134, 1327-1335, doi:10.1002/qj.280.
Liu, J., and E. Kalnay (2007), Simple Doppler Wind Lidar adaptive observation experiments with 3D-Var and an ensemble Kalman filter in a global primitive equations model, Geophys. Res. Lett., 34, L19808, doi:10.1029/2007GL030707.

Note: Only publications that have been uploaded to the ESD Publications database are listed here.

Co-Authored Publications

Gaubert, B., et al. (2024), Neutral Tropical African CO2 Exchange Estimated From Aircraft and Satellite Observations, Global Biogeochem. Cycles, 37, e2023GB007804, doi:10.1029/2023GB007804.
Wang, Y., et al. (2023), Elucidating climatic drivers of photosynthesis by tropical forests,  wileyonlinelibrary.com/journal/gcb, 1-15, doi:10.1111/gcb.16837.
Barkhordarian, A., et al. (2022), Emergent Constraints on Tropical Atmospheric Aridity{\textendash}carbon Feedbacks and the Future of Carbon Sequestration, October., doi:10.1088/1748-9326/ac2ce8.
Feng, S., et al. (2022), Covariation of Airborne Biogenic Tracers (CO2, COS, and CO) Supports Stronger Than Expected Growing Season Photosynthetic Uptake in the Southeastern US Nicholas C. Parazoo1 , Kevin W. Bowman1 , Bianca C. Baier2,3 , Junjie Liu1 , Meemong Lee1 , Le Kuai1 , , Global Biogeochem. Cycles.
Friedlingstein, P., et al. (2022), Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917-2005, doi:10.5194/essd-14-1917-2022.
He, W., et al. (2022), China's Terrestrial Carbon Sink Over 2010–2015 Constrained by Satellite Observations of Atmospheric CO2 and Land Surface Variables, J. Geophys. Res..
Laughnera, J.L., et al. (2022), Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change, Proc. Natl. Acad. Sci., doi:10.1073/pnas.2109481118.
Peiro, H., et al. (2022), 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, Atmos. Chem. Phys., doi:10.5194/acp-22-1097-2022.
Byrne, B.K.A., et al. (2021), The Carbon Cycle of Southeast Australia During 2019-2020: Drought, Fires, and Subsequent Recovery, AGU Advances, 2, e2021AV000469., doi:10.1029/2021AV000469.
Chen, Z., et al. (2021), Linking global terrestrial CO2 fluxes and environmental drivers using OCO-2 and a geostatistical inverse model, Atmos. Chem. Phys., 21, 6663-6680, doi:10.5194/acp-21-6663-2021.
Chen, Z., et al. (2021), Five years of variability in the global carbon cycle: Comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models, Environmental Research Letters, 16, 054041, doi:10.1088/1748-9326/abfac1.
Chen, Z., et al. (2021), Linking global terrestrial CO2 fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models, Atmos. Chem. Phys., 21, 6663-6680, doi:10.5194/acp-21-6663-2021.
Liao, E., et al. (2021), Future weakening of the ENSO ocean carbon buffer under anthropogenic forcing, Geophys. Res. Lett., 48, e2021GL094021, doi:10.1029/2021GL094021.
Park, C., et al. (2021), Evaluation of the Potential Use of Satellite-Derived XCO2 in Detecting CO2 Enhancement in Megacities with Limited Ground Observations: A Case Study in Seoul Using Orbiting Carbon Observatory-2. Asia-Pacific, J. Atmos. Sci., 57, 289-299, doi:10.1007/s13143-020-00202-5.
Byrne, B.K.A., et al. (2020), 2 fluxes obtained by combining surface-based and 3 space-based atmospheric CO2 measurements, J. Geophys. Res., doi:10.1029/2019JD032029.
Jones, S., et al. (2020), The impact of a simple representation of non-structural carbohydrates on the simulated response of tropical forests to drought, Biogeosciences, 17, 3589-3612, doi:10.5194/bg-17-3589-2020.
Yin, Y., et al. (2020), Cropland carbon uptake delayed and reduced by 2019 Midwest floods, AGU Advances, 1, 1-15, doi:10.1029/2019AV000140.
Crowell, S., et al. (2019), The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network, Atmos. Chem. Phys., 19, 9797-9831, doi:10.5194/acp-19-9797-2019.
Konings, A.G., et al. (2019), Global satellite-driven estimates of heterotrophic respiration, Biogeosciences, 16, 2269-2284, doi:10.5194/bg-16-2269-2019.
Konings, A.G., et al. (2019), Global satellite-driven estimates of heterotrophic respiration, Biogeosciences, 16, 2269-2284.
Philip, S., et al. (2019), Prior biosphere model impact on global terrestrial CO2 fluxes estimated from OCO-2 retrievals, Atmos. Chem. Phys., 19, 13267-13287, doi:10.5194/acp-19-13267-2019.
Schimel, D., et al. (2019), Flux towers in the sky: global ecology from space, New Phytologist, 224, 570-584, doi:10.1111/nph.15934.
Schuh, A., et al. (2019), Quantifying the impact of atmospheric transport uncertainty on CO2 surface flux estimates, Global Biogeochem. Cycles, 33, 484-500.
Shi, M., et al. (2019), The 2005 Amazon drought legacy effect delayed the 2006 wet season onset, Geophys. Res. Lett., 46, 9082-9090, doi:10.1029/2019GL083776.
Shi, ., et al. (2019), The 2005 Amazon Drought Legacy Effect Delayed the 2006 Wet Season Onset, Geophys. Res. Lett..
Basu, S., et al. (2018), The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2, Atmos. Chem. Phys., 18, 7189-7215, doi:10.5194/acp-18-7189-2018.
Basu, S., et al. (2018), The impact of transport model differences on CO2 surface flux estimates from OCO-2 retrievals of column average CO2, Atmos. Chem. Phys., 18, 7189-7215, doi:10.5194/acp-18-7189-2018.
Hedelius, J.K., et al. (2018), Southern California megacity CO2, CH4, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model, Atmos. Chem. Phys., 18, 16271-16291, doi:10.5194/acp-18-16271-2018.
Sellers, P.J., et al. (2018), Observing Carbon Cycle-climate feedbacks from space, Proc. Natl. Acad. Sci., 115, 7860-7868, doi:10.1073/pnas.1716613115.
Bowman, K.W., et al. (2017), Global and Brazilian carbon response to El Niño Modoki 2011-2010, Earth and Space Science, 4, 637-660, doi:10.1002/2016EA000204.
Byrne, B.K.A., et al. (2017), Sensitivity of CO2 surface flux constraints to observational coverage, J. Geophys. Res., 122, 6672-6694, doi:10.1002/2016JD026164.
Eldering, A., et al. (2017), The Orbiting Carbon Observatory‐2 early science investigations of regional carbon dioxide fluxes, Science, 358, eaam5745.
Fischer, M.L., et al. (2017), Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations, J. Geophys. Res., 122, doi:10.1002/2016JD025617.
Mueller, K.J., et al. (2017), An Adjoint-Based Forecast Impact from Assimilating MISR Winds into the GEOS-5 Data Assimilation and Forecasting System, Mon. Wea. Rev., 145, 4937-4947, doi:10.1175/MWR-D-17-0047.1.
Bousserez, N., et al. (2015), Improved analysis-error covariance matrix for high-dimensional variational inversions: application to source estimation using a 3D atmospheric transport model, Q. J. R. Meteorol. Soc., doi:10.1002/qj.2495.
Miller, S.M., et al. (2015), Biases in atmospheric CO2 estimates from correlated meteorology modeling errors, Atmos. Chem. Phys., 15, 2903-2914, doi:10.5194/acp-15-2903-2015.
Ott, L., et al. (2015), Assessing the magnitude of CO2 flux uncertainty in atmospheric CO2 records using products from NASA’s Carbon Monitoring Flux Pilot Project, J. Geophys. Res., 120, 734-765, doi:10.1002/2014JD022411.
Kang, J., et al. (2012), Estimation of surface carbon fluxes with an advanced data assimilation methodology, J. Geophys. Res., 117, D24101, doi:10.1029/2012JD018259.
Kang, J., et al. (2011), “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation, J. Geophys. Res., 116, D09110, doi:10.1029/2010JD014673.

Note: Only publications that have been uploaded to the ESD Publications database are listed here.

National Aeronautics and Space Administration

National Aeronautics and
Space Administration