Optimal estimation of regional N2O emissions using a three-dimensional global model

Conference Proceedings Paper
Optimal estimation of regional N2O emissions using a three-dimensional global model
Huang, J., A. Golombek and R. Prinn (2004)
Eos Transactions, 85(47) ABSTRACT A13A-0086

Abstract/Summary:

In this study, we use the MATCH (Model of Atmospheric Transport and Chemistry) model and Kalman filtering techniques to optimally estimate N2O emissions from seven source regions around the globe. The MATCH model was used with NCEP assimilated winds at T62 resolution (192 longitude by 94 latitude surface grid, and 28 vertical levels) from July 1st 1996 to December 31st 2000. The average concentrations of N$_{2}$O in the lowest four layers of the model were then compared with the monthly mean observations from six national/global networks (AGAGE, CMDL (HATS), CMDL (CCGG), CSIRO, CSIR and NIES), at 48 surface sites. A 12-month-running-mean smoother was applied to both the model results and the observations, due to the fact that the model was not able to reproduce the very small observed seasonal variations. The Kalman filter was then used to solve for the time-averaged regional emissions of N$_{2}$O for January 1st 1997 to June 30th 2000. The inversions assume that the model stratospheric destruction rates, which lead to a global N$_{2}$O lifetime of 130 years, are correct. It also assumes normalized emission spatial distributions from each region based on previous studies. We conclude that the global N$_{2}$O emission flux is about 16.2 TgN/yr, with ${34.9\pm1.7%}$ from South America and Africa, ${34.6\pm1.5%}$ from South Asia, ${13.9\pm1.5%}$ from China/Japan/South East Asia, ${8.0\pm1.9%}$ from all oceans, ${6.4\pm1.1%}$ from North America and North and West Asia, ${2.6\pm0.4%}$ from Europe, and ${0.9\pm0.7%}$ from New Zealand and Australia. The errors here include the measurement standard deviation, calibration differences among the six groups, grid volume/measurement site mis-match errors estimated from the model, and a procedure to account approximately for the modeling errors.

Citation:

Huang, J., A. Golombek and R. Prinn (2004): Optimal estimation of regional N2O emissions using a three-dimensional global model. Eos Transactions, 85(47) ABSTRACT A13A-0086 (http://www.agu.org/meetings/fm04/)
  • Conference Proceedings Paper
Optimal estimation of regional N2O emissions using a three-dimensional global model

Huang, J., A. Golombek and R. Prinn

85(47) ABSTRACT A13A-0086

Abstract/Summary: 

In this study, we use the MATCH (Model of Atmospheric Transport and Chemistry) model and Kalman filtering techniques to optimally estimate N2O emissions from seven source regions around the globe. The MATCH model was used with NCEP assimilated winds at T62 resolution (192 longitude by 94 latitude surface grid, and 28 vertical levels) from July 1st 1996 to December 31st 2000. The average concentrations of N$_{2}$O in the lowest four layers of the model were then compared with the monthly mean observations from six national/global networks (AGAGE, CMDL (HATS), CMDL (CCGG), CSIRO, CSIR and NIES), at 48 surface sites. A 12-month-running-mean smoother was applied to both the model results and the observations, due to the fact that the model was not able to reproduce the very small observed seasonal variations. The Kalman filter was then used to solve for the time-averaged regional emissions of N$_{2}$O for January 1st 1997 to June 30th 2000. The inversions assume that the model stratospheric destruction rates, which lead to a global N$_{2}$O lifetime of 130 years, are correct. It also assumes normalized emission spatial distributions from each region based on previous studies. We conclude that the global N$_{2}$O emission flux is about 16.2 TgN/yr, with ${34.9\pm1.7%}$ from South America and Africa, ${34.6\pm1.5%}$ from South Asia, ${13.9\pm1.5%}$ from China/Japan/South East Asia, ${8.0\pm1.9%}$ from all oceans, ${6.4\pm1.1%}$ from North America and North and West Asia, ${2.6\pm0.4%}$ from Europe, and ${0.9\pm0.7%}$ from New Zealand and Australia. The errors here include the measurement standard deviation, calibration differences among the six groups, grid volume/measurement site mis-match errors estimated from the model, and a procedure to account approximately for the modeling errors.