Anna M. Michalak

Organization

Carnegie Institution for Science

Stanford University

Contact person by email

Business Phone

Work

(650) 353-4247

Business Address

Department of Global Ecology
260 Panama Street
Stanford, CA 94305
United States

Website

Michalak lab page

First Author Publications

Michalak, A., et al. (2017), Diagnostic methods for atmospheric inversions of long-lived greenhouse gases, Atmos. Chem. Phys., 17, 7405-7421, doi:10.5194/acp-17-7405-2017.
Michalak, A. (2013), Atmospheric observations and inverse modeling approaches for identifying geographical sources and sinks of carbon.
Michalak, A. (2008), Technical Note: Adapting a fixed-lag Kalman smoother to a geostatistical atmospheric inversion framework, Atmos. Chem. Phys., 8, 6789-6799, doi:10.5194/acp-8-6789-2008.

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

Co-Authored Publications

Frankenberg, C., et al. (2024), Data Drought in the Humid Tropics: How to Overcome the Cloud Barrier in Greenhouse Gas Remote Sensing, Geophys. Res. Lett., 51, e2024GL108791, doi:10.1029/2024GL108791.
Sun, W., et al. (2021), Midwest US croplands determine model divergence in North American carbon fluxes, AGU Advances, 2, doi:10.1029/2020AV000310.
He, Y., et al. (2020), Global vegetation biomass production efficiency constrained by models and observations, Global Change Biology, 26, 1474-1484, doi:10.1111/gcb.14816.
Huntzinger, ., et al. (2020), Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems, Environmental Research Letters, 15, 1-14, doi:10.1088/1748-9326/ab6784.
Miller, S.M., and A. Michalak (2020), The impact of improved satellite retrievals on estimates of biospheric carbon balance, Atmos. Chem. Phys., 20, 323-331, doi:10.5194/acp-20-323-2020.
Randazzo, N.A., et al. (2020), Synoptic Meteorology Explains Temperate Forest Carbon Uptake, J. Geophys. Res., 125, doi:10.1029/2019JG005476.
Schwalm, C.R., et al. (2020), Modeling suggests fossil fuel emissions have been driving increased land carbon uptake since the turn of the 20th Century, Scientific Reports, 10, 1-9, doi:10.1038/s41598-020-66103-9.
Cui, E., et al. (2019), Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region, Global Biogeochem. Cycles, 33, doi:10.1029/2018GB005909.
Hu, L., et al. (2019), Enhanced North American carbon uptake associated with El Nino, Science Advances, 5, doi:10.1126/sciadv.aaw0076.
Kolus, H.R., et al. (2019), Land carbon models underestimate the severity and duration of drought's impact on plant productivity, Scientific Reports, 9, doi:10.1038/s41598-019-39373-1.
Liu, Y., et al. (2019), Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization, Nature Geoscience, doi:10.1038/s41561-019-0436-1.
Masri, E.B., et al. (2019), Carbon and Water Use Efficiencies: A Comparative Analysis of Ten Terrestrial Ecosystem Models under Changing Climate, Scientific Reports, 9, 14680, doi:10.1038/s41598-019-50808-7.
Miller, S.M., et al. (2019), China's coal mine methane regulations have not curbed growing emissions, Nature Communications, 10, doi:10.1038/s41467-018-07891.
Ryoo, J., et al. (2019), Quantification of CO2 and CH4 emissions over Sacramento, California, based on divergence theorem using aircraft measurements, Atmos. Meas. Tech., 12, 2949-2966, doi:10.5194/amt-12-2949-2019.
Schwalm, C.R., et al. (2019), Divergence in land surface modeling: linking spread to structure, Environmental Research Communications, 1, 111004, doi:10.1088/2515-7620/ab4a8a.
Huang, K., et al. (2018), Enhanced peak growth of global vegetation and its key mechanisms, Nature Ecology & Evolution, 2, 1897-1905, doi:10.1038/s41559-018-0714-0.
Jeong, S., et al. (2018), Accelerating rates of Arctic carbon cycling revealed by long-term atmospheric CO2 measurements, Science Advances, 4, eaao1167, doi:10.1126/sciadv.aao1167.
Miller, S.M., et al. (2018), Characterizing biospheric carbon balance using CO2 observations from the OCO-2 satellite, Atmos. Chem. Phys., 18, 6785-6799, doi:10.5194/acp-18-6785-2018.
Shiga, Y., et al. (2018), Atmospheric CO2 observations reveal strong correlation between regional net biospheric carbon uptake and solar-induced chlorophyll fluorescence, Geophys. Res. Lett., 45, doi:10.1002/2017GL076630.
Shiga, Y., et al. (2018), Forests dominate the interannual variability of the North American carbon sink, Environmental Research Letters, 13, doi:10.1088/1748-9326/aad505.
Fang, Y., et al. (2017), Global land carbon sink response to temperature and precipitation varies with ENSO phase, Environmental Research Letters, 12, 064007, doi:10.1088/1748-9326/aa6e8e.
Huntzinger, D.N., et al. (2017), Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions, Scientific Reports, 7, doi:10.1038/s41598-017-03818-2.
Kim, J., et al. (2017), Reduced North American terrestrial primary productivity linked to anomalous Arctic warming, Nature Geoscience, 10, 572-576, doi:10.1038/ngeo2986.
Schwalm, C.R., et al. (2017), Global patterns of drought recovery, Nature, 548, 202-205, doi:10.1038/nature23021.
Tadić, J.M., et al. (2017), Spatio-temporal approach to moving window block kriging of satellite data v1.0, Geosci. Model. Dev., 10, 709-720, doi:10.5194/gmd-10-709-2017.
Tadic, J.M., et al. (2017), Elliptic Cylinder Airborne Sampling and Geostatistical Mass Balance Approach for Quantifying Local Greenhouse Gas Emissions, Environ. Sci. Technol., 51, 10012-10021, doi:10.1021/acs.est.7b03100.
Zhou, S., et al. (2017), Response of water use efficiency to global environmental change based on output from terrestrial biosphere models, Global Biogeochem. Cycles, 31, doi:10.1002/2017GB005733.
Ito, A., et al. (2016), Decadal trends in the seasonal-cycle amplitude of terrestrial CO2 exchange resulting from the ensemble of terrestrial biosphere models, Tellus, 68, 28968, doi:10.3402/tellusb.v68.28968.
Miller, S.M., et al. (2016), A multi-year estimate of methane fluxes in Alaska form CARVE atmospheric observations, Global Biogeochem. Cycles, 30, 1441-1453, doi:10.1002/2016GB005419.
Miller, S.M., et al. (2016), Evaluation of wetland methane emissions across North America using atmospheric data and inverse modeling, Biogeosciences, 13, 1329-1339, doi:10.5194/bg-13-1329-2016.
Shao, J., et al. (2016), Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005, J. Geophys. Res., 121, 1372-1393, doi:10.1002/2015JG003062.
Thomas, R.T., et al. (2016), Increased light-use efficiency in northern terrestrial ecosystems indicated by CO2 and greening observations, Geophys. Res. Lett., 43, 11,339-11,349, doi:10.1002/2016GL070710.
Yadav, V., and A. Michalak (2016), Technical Note: Improving the computational efficiency of sparse matrix multiplication in linear atmospheric inverse problems, Geosci. Model. Dev., doi:10.5194/gmd-2016-204.
Yadav, V., et al. (2016), A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems, J. Geophys. Res., 121, 12,490-12,504, doi:10.1002/2016JD025642.
Fang, Y., and A. Michalak (2015), Atmospheric observations inform CO2 flux responses to enviroclimatic drivers, Global Biogeochem. Cycles, 29, 555-566, doi:10.1002/2014GB005034.
Hammerling, D.M., et al. (2015), Detectability of CO2 flux signals by a space-based lidar mission, J. Geophys. Res., 120, 1794-1807, doi:10.1002/2014JD022483.
Mao, J.M., et al. (2015), Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends, Environmental Research Letters, 10, 094008, doi:10.1088/1748-9326/10/0/094008.
Schwalm, C.R., et al. (2015), Toward optimal integration of terrestrial biosphere models, Geophys. Res. Lett., 42, 4418-4428, doi:10.1002/2015GL064002.
Tadić, J.M., et al. (2015), Mapping of satellite Earth observations using moving window block kriging,”, Geosci. Model. Dev., 8, 3311-3319, doi:10.5194/gmd-8-3311-2015.
Tian, H., et al. (2015), Global patterns and controls of soil carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions, Global Biogeochem. Cycles, 29, 775-792, doi:10.1002/2014GB005021.
Fang, Y., et al. (2014), Using atmospheric observations to evaluate the spatiotemporal variability of CO2 fluxes simulated by terrestrial biospheric models, Biogeosciences, 11, 6985-6997, doi:10.5194/bg-11-6985-2014.
Shiga, Y., et al. (2014), Detecting fossil fuel emissions patterns from subcontinental regions using North American in situ CO2 measurements, Geophys. Res. Lett., 41, 4381-4388, doi:10.1002/2014GL059684.
Wei, Y., et al. (2014), The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project: Part 2 - Environmental driver data, Geosci. Model. Dev., 7, 2875-2893, doi:10.5194/gmd-7-2875-2014.
Zscheischler, J., et al. (2014), Impact of large-scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data, Global Biogeochem. Cycles, 28, 585-600, doi:10.1002/2014GB004826.
Chatterjee, A., and A. Michalak (2013), Technical note: Comparison of ensemble Kalman filter and variational approaches for CO2 data assimilation, Atmos. Chem. Phys., 13, 11643-11660, doi:10.5194/acp-13-11643-2013.
Chatterjee, A., et al. (2013), Background error covariance estimation for atmospheric CO2 data assimilation, J. Geophys. Res., 118, 10140-10154, doi:10.1002/jgrd.50654.
Huntzinger, D.N., et al. (2013), The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project - Part I: Overview and experimental design, Geosci. Model. Dev., 6, 2121-2133, doi:10.5194/gmd-6-2121-2013.
Schwalm, C.R., et al. (2013), Sensitivity of inferred climate model skill to evaluation decisions: a case study using CMIP5 evapotranspiration, Environmental Research Letters, 8, doi:10.1088/1748-9326/8/2/024028.
Shiga, Y., et al. (2013), In-situ CO2 monitoring network evaluation and design: A criterion based on atmospheric CO2 variability, J. Geophys. Res., 118, 2007-2018, doi:10.1002/jgrd.50168.
Yadav, ., and A. Michalak (2013), Improving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimation, Geosci. Model Dev., 6, 583-590, doi:10.5194/gmd-6-583-2013.
Yadav, ., et al. (2013), A backward elimination discrete optimization algorithm for model selection in spatio-temporal regression models, Environmental Modelling & Software, 42, 88-98.
Chatterjee, A., et al. (2012), Toward reliable ensemble Kalman filter estimates of CO2 fluxes, J. Geophys. Res., 117, D22306, doi:10.1029/2012JD018176.
Gourdji, S.M., et al. (2012), North American CO2 exchange: inter-comparison of modeled estimates with results from a fine-scale atmospheric inversion, Biogeosciences, 9, 457-475, doi:10.5194/bg-9-457-2012.
Hammerling, D.M., et al. (2012), Global CO2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT), Geophys. Res. Lett., 39, L08804, doi:10.1029/2012GL051203.
Hammerling, D.M., et al. (2012), Mapping of CO2 at high spatiotemporal resolution using satellite observations: Global distributions from OCO-2, J. Geophys. Res., 117, D06306, doi:10.1029/2011JD017015.
Huntzinger, D.N., et al. (2012), North American Carbon Program (NACP) regional interim synthesis: Terrestrial biospheric model intercomparison, Ecological Modelling, 232, 144-157, doi:10.1016/j.ecolmodel.2012.02.004.
Erickson, T.A., et al. (2011), “A data system for visualizing 4-D atmospheric CO2 models and data”, OSGeo Journal, 8, 37-47.
Huntzinger, ., et al. (2011), A systematic approach for comparing modeled biospheric carbon fluxes across regional scales, Biogeosciences, 8, 1579-1593, doi:10.5194/bg-8-1579-2011.
Huntzinger, D.N., et al. (2011), The utility of continuous atmospheric measurements for identifying biospheric CO2 flux variability, J. Geophys. Res., 116, D06110, doi:10.1029/2010JD015048.
Chatterjee, A., et al. (2010), A geostatistical data fusion technique for merging remote sensing and ground‐based observations of aerosol optical thickness, J. Geophys. Res., 115, D20207, doi:10.1029/2009JD013765.
Gourdji, S.M., et al. (2010), Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study, Atmos. Chem. Phys., 10, 6151-6167, doi:10.5194/acp-10-6151-2010.
Mueller, K.L., et al. (2010), Attributing the variability of eddy‐covariance CO2 flux measurements across temporal scales using geostatistical regression for a mixed northern hardwood forest, Global Biogeochem. Cycles, 24, GB3023, doi:10.1029/2009GB003642.
Yadav, ., et al. (2010), A geostatistical synthesis study of factors affecting gross primary productivity in various ecosystems of North America, Biogeosciences, 7, 2655-2671, doi:10.5194/bg-7-2655-2010.
Alkhaled, A.A., et al. (2008), Using CO2 spatial variability to quantify representation errors of satellite CO2 retrievals, Geophys. Res. Lett., 35, L16813, doi:10.1029/2008GL034528.
Alkhaled, A.A., et al. (2008), A global evaluation of the regional spatial variability of column integrated CO2 distributions, J. Geophys. Res., 113, D20303, doi:10.1029/2007JD009693.
Gourdji, S.M., et al. (2008), Global monthly averaged CO2 fluxes recovered using a geostatistical inverse modeling approach: 2. Results including auxiliary environmental data, J. Geophys. Res., 113, D21115, doi:10.1029/2007JD009733.
Mueller, K.L., et al. (2008), Global monthly averaged CO2 fluxes recovered using a geostatistical inverse modeling approach: 1. Results using atmospheric measurements, J. Geophys. Res., 113, D21114, doi:10.1029/2007JD009734.
Miller, C.E., et al. (2007), Precision requirements for space-based XCO2 data, J. Geophys. Res., 112, D10314, doi:10.1029/2006JD007659.

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

Anna M. Michalak

National Aeronautics and Space Administration

National Aeronautics and
Space Administration