TY - JOUR
T1 - Development of a Mesoscale Inversion System for Estimating Continental-Scale CO2 Fluxes
AU - Wesloh, Daniel
AU - Lauvaux, Thomas
AU - Davis, Kenneth J.
N1 - Funding Information:
Many thanks to Sha Feng and Klaus Keller for extensive feedback improving the focus and presentation of the figures. Plots were generated using Matplotlib, Cartopy, and XArray on Python 3.6.4, with coastlines and borders using Natural Earth data (Hoyer & Hamman, 2017 ; Hunter, 2007 ; Met Office, 2010 ; van Rossum, 1995 ). Extensive use was made of Numpy and Scipy for the low‐level arithmetic and linear algebra and of FFTW via pyFFTW for Fourier transforms (Frigo & Johnson, 2018 , 2005 ; Gomersall, 2016 ; Jones et al., 2001 ; Oliphant, 2006b ; Virtanen et al., 2020 ). This work was funded by the NASA ACT‐America project, the Gulf Coast Intensive (GCI) project, the Dr. Dennis W. and Dr. Joan S. Thomson Distinguished Graduate Fellowship, and the Anne C. Wilson Graduate Fellowship. ACT‐America project is a NASA Earth Venture Suborbital 2 project funded by NASA's Earth Science Division, Grant NNX15AG76G to Penn State. The GCI was supported by NASA Terrestrial Carbon Cycle program, Grant NNX14AJ17G.
Funding Information:
Many thanks to Sha Feng and Klaus Keller for extensive feedback improving the focus and presentation of the figures. Plots were generated using Matplotlib, Cartopy, and XArray on Python 3.6.4, with coastlines and borders using Natural Earth data (Hoyer & Hamman,?2017; Hunter,?2007; Met Office,?2010; van Rossum,?1995). Extensive use was made of Numpy and Scipy for the low-level arithmetic and linear algebra and of FFTW via pyFFTW for Fourier transforms (Frigo & Johnson,?2018, 2005; Gomersall,?2016; Jones et?al.,?2001; Oliphant,?2006b; Virtanen et?al.,?2020). This work was funded by the NASA ACT-America project, the Gulf Coast Intensive (GCI) project, the Dr. Dennis W. and Dr. Joan S. Thomson Distinguished Graduate Fellowship, and the Anne C. Wilson Graduate Fellowship. ACT-America project is a NASA Earth Venture Suborbital 2 project funded by NASA's Earth Science Division, Grant NNX15AG76G to Penn State. The GCI was supported by NASA Terrestrial Carbon Cycle program, Grant NNX14AJ17G.
Publisher Copyright:
© 2020. The Authors.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - Computational requirements often impose limitations on the spatial and temporal resolutions of atmospheric CO2 inversions, increasing aggregation and representation errors. This study enables higher spatial and temporal resolution inversions with spatial and temporal error structures similar to those used in other published inversions by representing the prior flux error covariances as a Kronecker product of spatial and temporal covariances and by using spectral methods for the spatial correlations. Compared to existing inversion systems that are forced to degrade the resolution of the problem in order to bring the dimensionality down to computationally tractable levels, this inversion framework is able to take advantage of mesoscale transport simulations and more of the complexity of spatial and temporal covariances in the surface CO2 fluxes. This approach was successfully implemented over one month with an identical-twin observing system simulation experiment (OSSE) using a set of assumptions about the prior flux uncertainties compatible both with continental-scale uncertainty estimates and with comparisons of vegetation models to flux towers. The demonstration illustrates the potential of the newly developed inversion system to use high-temporal-resolution information from the North American tower network, to extract high-resolution information about CO2 fluxes that is inaccessible to coarser resolution inversion systems, and to simultaneously optimize an ensemble of prior estimates. This demonstration sets the stage for regional flux inversions that can take full advantage of the high-resolution data available in tower CO2 records and mesoscale atmospheric transport reanalyses, include more realistic prior error structures, and explore specifying prior fluxes with ensembles.
AB - Computational requirements often impose limitations on the spatial and temporal resolutions of atmospheric CO2 inversions, increasing aggregation and representation errors. This study enables higher spatial and temporal resolution inversions with spatial and temporal error structures similar to those used in other published inversions by representing the prior flux error covariances as a Kronecker product of spatial and temporal covariances and by using spectral methods for the spatial correlations. Compared to existing inversion systems that are forced to degrade the resolution of the problem in order to bring the dimensionality down to computationally tractable levels, this inversion framework is able to take advantage of mesoscale transport simulations and more of the complexity of spatial and temporal covariances in the surface CO2 fluxes. This approach was successfully implemented over one month with an identical-twin observing system simulation experiment (OSSE) using a set of assumptions about the prior flux uncertainties compatible both with continental-scale uncertainty estimates and with comparisons of vegetation models to flux towers. The demonstration illustrates the potential of the newly developed inversion system to use high-temporal-resolution information from the North American tower network, to extract high-resolution information about CO2 fluxes that is inaccessible to coarser resolution inversion systems, and to simultaneously optimize an ensemble of prior estimates. This demonstration sets the stage for regional flux inversions that can take full advantage of the high-resolution data available in tower CO2 records and mesoscale atmospheric transport reanalyses, include more realistic prior error structures, and explore specifying prior fluxes with ensembles.
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U2 - 10.1029/2019MS001818
DO - 10.1029/2019MS001818
M3 - Article
AN - SCOPUS:85091650500
VL - 12
JO - Journal of Advances in Modeling Earth Systems
JF - Journal of Advances in Modeling Earth Systems
SN - 1942-2466
IS - 9
M1 - e2019MS001818
ER -