Anna M. Michalak
Organization:
Carnegie Institution for Science
Stanford University
Email:
Business Phone:
Work:
(650) 353-4247
Business Address:
Department of Global Ecology
260 Panama Street
Stanford, CA 94305
United StatesWebsite:
First Author Publications:
- Michalak, A. M., N. A. Randazzo, and F. Chevallier (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. M. (2013), Atmospheric observations and inverse modeling approaches for identifying geographical sources and sinks of carbon.
- Michalak, A. M. (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.
Co-Authored Publications:
- 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, D. N., 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. M. 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., A. M. Michalak, and A. R. Desai (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. P., et al. (2018), Forests dominate the interannual variability of the North American carbon sink, Environmental Research Letters, 13, doi:10.1088/1748-9326/aad505.
- Shiga, Y. P., 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.
- 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., 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.
- Yadav, V., and A. M. 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.
- Fang, Y., and A. M. 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. P., 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. M. 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. P., 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, V., and A. M. 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, V., K. L. Mueller, and A. M. Michalak (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., A. M. Michalak, and S. R. Kawa (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., A. M. Michalak, and J. C. Lin (2011), “A data system for visualizing 4-D atmospheric CO2 models and data”, OSGeo Journal, 8, 37-47.
- Huntzinger, D. N., 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, V., 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., A. M. Michalak, and S. R. Kawa (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., S. M. Gourdji, and A. M. Michalak (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.