Auxiliary Material for
Detecting fossil fuel emissions patterns from sub-continental regions using North American in-situ CO2 measurements
Yoichi P. Shiga (a),(b) , Anna M. Michalak (b), Sharon M. Goudji (c), Kim L. Mueller (d), Vineet Yadav (b)
a Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA
b Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA
c Department of Environmental and Earth Systems Science, Stanford University, Stanford, CA, USA
d Science and Technology Policy Institute, Washington, D.C., 20006, USA
Journal of Geophysical Research, Geophysical Research Letters, 2014
Introduction
Auxiliary materials include additional information on the VULCAN-ODIAC inventory, Atmospheric CO2 data, Synthetic CO2 data, Model Selection Approach, and Model Performance statistics in the file “text01.txt”. A figure showing the seasonal differences in measurement sensitivity over North America for 2008 is shown in “Supp_fig_1.tiff”. An additional figure showing the compounding effects hindering detectability throughout the year and the worsening of these effects in the summer months is shown in “Supp_fig_2.tiff”. Tables showing details of the towers where atmospheric CO2 data was collected “ts01.xlxs”, the parameters of the "Q" error-covariance matrix "ts02.xlxs", and the model performance statistics "ts03.xlxs" are also included.
1. text01.txt Auxiliary Materials detailing the VULCAN-ODIAC inventory, Atmospheric CO2 data, Synthetic CO2 data, Model Selection Approach, and Model Performance statistics.
2. Supp_fig_1.tiff (Figure S1) Monthly-averaged sensitivity of the 2008 atmospheric CO2 observations to surface fluxes, for January and July 2008, based on STILT transport model (see Section 2).
3. Supp_fig_2.tiff (Figure S2) Seasonality of factors affecting detectability of fossil fuel emissions. All factors are normalized to a mean of zero and variance of one, making them unitless and directly comparable. H_daily represents the sensitivity of the measurement network to the surface averaged monthly (Figure S1) and spatially over the study area. N_obs represents the average number of observations per day where the smaller number of observations in summer is due primarily to data filtering that leads to a larger number of data points being eliminated during months with large variability in observed concentrations. (1/sigm_R^2) represents the inverse of the average model-data-mismatch variance across the observation locations, such that higher variances correspond to lower constraint provided by observations in a given month. h_o represents the information scale of the underlying natural fluxes [e.g., Alkhaled et al., 2008], such that smaller values/distances represent higher variability and therefore a higher influence from biospheric fluxes. This is due to the fact that the majority of the seasonality in the fluxes is due to biospheric fluxes. Note that all factors/metrics are lowest in the summer, meaning that all factors contribute to making the fossil fuel signal more difficult to identify during the summer months.
4. ts01.xlsx (Table S1) Towers where continuous atmospheric CO2 concentration data were collected during 2008, including the times of day for which 3 hourly average CO2 concentration data were used in this study. Also listed are the square root of the annual average monthly model-data mismatch variances.
4.1 Column "Tower", tower name abbreviation
4.2 Column "Name", full tower name
4.3 Column "Latitude", degrees, latitude of tower
4.4 Column "Longitude", degrees, longitude of tower
4.5 Column "Time of Day (Local Time hours)", time of day in hours when data were used for specific tower
4.6 Column "Height [m]", meters, height above ground of tower
4.7 Column "Sigma_bar_R [ppm]", ppm, the square root of the annual average monthly model-data mismatch variances
5. ts02.xlsx (Table S2) Parameters that define the prior error covariance matrix, "Q" , including the spatial correlation length, which represents the separation distance beyond which two points become essentially uncorrelated, the temporal correlation length, which represents the time after which two fluxes become essentially uncorrelated, and the variance parameter, which represents the variance of fluxes at large separation distances, all optimized from the CASA-GFEDv2 model.
5.1 Column "Parameter", name of error covariance parameter
5.2 Column "Jan", month
5.3 Column "Feb", month
5.4 Column "Mar", month
5.5 Column "Apr", month
5.6 Column "May", month
5.7 Column "Jun", month
5.8 Column "July", month
5.9 Column "Aug", month
5.10 Column "Sept", month
5.11 Column "Oct", month
5.12 Column "Nov", month
5.13 Column "Dec", month
6. ts03.xlsx (Table S3) Variance explained by the models with the lowest BIC and the full models for each case study. Full model includes all possible variables (i.e. region-months of FFCO2 emissions).
6.1 Column "Case", case study name
6.2 Column "p", number of variables (FFCO2 region-months) included in the BIC model
6.3 Column "R^2", the variance explained as described by equation S2
6.4 Column "p", number of variables (FFCO2 region-months) included in the full model
6.5 Column "R^2", the variance explained as described by equation S2
7. References
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