34 Global CO Emission Estimates Inferred …
223
sions between the a priori and the a posteriori assimilating MOPITT-only, and MSA
run. The single instrument run (MOPITT CO assimilation in this case) showed
increased emissions more than factor over 2.5 in Europe, the eastern and western
United States, and East Asia. The biomass burning emissions assimilating MOPITT
CO showed that the emissions over southern Africa, northern Australia, and northwestern North America were also increased around by a factor of 2. When assimilating all chemical species, the optimized emission changes over both anthropogenic
and biomass burning emission regions were consistent with MOPITT assimilation.
The all instrument assimilation showed larger emissions in Europe but lower emissions in East Asia, comparing to the single instrument run. These differences on CO
emissions in MSA were due to other observations affecting the modeled OH budget.
34.4 Conclusion
In this study, we found that the 4D-Var scheme seeks to obtain a model trajectory
in state space that best matches all the available observations over the assimilation
period. It thus provides consistent chemical state with all available observations over
the assimilation period. The multi-species assimilation reduced the absolute mean
bias in modeled O 3 , relative to Atom-1 data. We also found the global mean tropospheric OH has been decreased from 13.89 to 12.38 (in 10
5 mol/cm
3 ). The resulting
MOPITT CO-only and multi-species assimilation produced similar CO emission
estimates for the major source regions (except for southern Africa), suggesting large
increases in the emissions compared to the a priori. The emission was increased by
50–120% in both major anthropogenic emission hotspots such as North America,
Europe, East Asia, and biomass burning regions such as southern Africa.
Questions
Pablo Saide:
Question: Can we use the emission statistics from these assimilation studies to avoid
biases from CO surface flux?
Answer: CO data assimilation can quantify the biases of the bottom-up emission
inventories. In our case, we showed that the bottom up emission inventories over
the northern extratropics were still underestimated. However, the surface flux biases
cannot be fully removed even we use the optimized emission statistics since they
were still affected by model and observation error.
Shuzhan Ren:
Question: How much confidence do you have on your constrained OH concentrations?
Answer: Our comparison between our assimilated results and Spivakovsky et al. [3]
showed that the modeled global mean tropospheric OH has been reduced by 10%
223
sions between the a priori and the a posteriori assimilating MOPITT-only, and MSA
run. The single instrument run (MOPITT CO assimilation in this case) showed
increased emissions more than factor over 2.5 in Europe, the eastern and western
United States, and East Asia. The biomass burning emissions assimilating MOPITT
CO showed that the emissions over southern Africa, northern Australia, and northwestern North America were also increased around by a factor of 2. When assimilating all chemical species, the optimized emission changes over both anthropogenic
and biomass burning emission regions were consistent with MOPITT assimilation.
The all instrument assimilation showed larger emissions in Europe but lower emissions in East Asia, comparing to the single instrument run. These differences on CO
emissions in MSA were due to other observations affecting the modeled OH budget.
34.4 Conclusion
In this study, we found that the 4D-Var scheme seeks to obtain a model trajectory
in state space that best matches all the available observations over the assimilation
period. It thus provides consistent chemical state with all available observations over
the assimilation period. The multi-species assimilation reduced the absolute mean
bias in modeled O 3 , relative to Atom-1 data. We also found the global mean tropospheric OH has been decreased from 13.89 to 12.38 (in 10
5 mol/cm
3 ). The resulting
MOPITT CO-only and multi-species assimilation produced similar CO emission
estimates for the major source regions (except for southern Africa), suggesting large
increases in the emissions compared to the a priori. The emission was increased by
50–120% in both major anthropogenic emission hotspots such as North America,
Europe, East Asia, and biomass burning regions such as southern Africa.
Questions
Pablo Saide:
Question: Can we use the emission statistics from these assimilation studies to avoid
biases from CO surface flux?
Answer: CO data assimilation can quantify the biases of the bottom-up emission
inventories. In our case, we showed that the bottom up emission inventories over
the northern extratropics were still underestimated. However, the surface flux biases
cannot be fully removed even we use the optimized emission statistics since they
were still affected by model and observation error.
Shuzhan Ren:
Question: How much confidence do you have on your constrained OH concentrations?
Answer: Our comparison between our assimilated results and Spivakovsky et al. [3]
showed that the modeled global mean tropospheric OH has been reduced by 10%