Organization:
Jet Propulsion Laboratory
Business Address:
California Institute of Technology
4500 Oak Grove Dr.
MS 233-200
Pasadena, CA 91109
United StatesFirst 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., K. W. Bowman, and M. Lee (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., K. Bowman, and D. Henze (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.
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., 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., 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, M., 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., 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.