Chapter 6. OCS flux inversion 87 sites vary from 10 to 30 ppt, which in not consistent. The differences with simulation with FTIR inversed fluxes get smaller, but still different from site to site. Therefore, the FTIR measurements can not be included directly in the inversion. The OCS retrieval need to be calibrated using independent measurements.
Table 6.2: Annual global atmospheric OCS budget from inversions with FTIR mea-surements (fluxes in Gg S year-1)
Prior Inversion
with flask only Inversion with FTIR
Ocean 800 844 773
Land 938 927 865
Anthropogenic 256 267 287
2 4 6 8 10 12
−110
−100
−90
−80
−70
−60
−50
Global
Month
Land OCS (GgS)
2 4 6 8 10 12
−50
−40
−30
−20
−10 0
30°N−90°N
Month
Land OCS (GgS)
2 4 6 8 10 12
−65
−60
−55
−50
−45
−40
30°S−30°N
Month
Land OCS (GgS)
2 4 6 8 10 12
−4
−3
−2
−1 0
90°S−30°S
Month
Land OCS (GgS)
2 4 6 8 10 12
55 60 65 70 75 80 85 90
Global
Month
Ocean OCS (GgS)
2 4 6 8 10 12
−2 0 2 4 6 8 10 12
30°N−90°N
Month
Ocean OCS (GgS)
2 4 6 8 10 12
45 50 55 60 65 70 75 80
30°S−30°N
Month
Ocean OCS (GgS)
2 4 6 8 10 12
−2 0 2 4 6 8 10
Month
Ocean OCS (GgS)
90°S−30°S prior posterior 1 posterior 2
Figure 6.11: Monthly totals of OCS land (top) and ocean (bottom) fluxes of the posteriors from inversion with and without FTIR measurements for global, 30◦ N - 90◦ N, 30◦N - 30◦ S, and 30◦S - 90◦S. The prior is shown in red; the posterior from inversion without FTIR measurements is shown in green; the posterior from inversion with FTIR
measurements is blue.
−50 0 50 0
10 20 30 40 50
Latitude
Offset (ppt)
EUR NYA BRE
JFJ TOR
TSU PMBMLO WOLRUN
LAU ARH
Figure 6.12: Differences between FTIR measurements with simulations with optimized fluxes. The green line is the differences with inversion with only flask measurements, and the blue line is the differences with inversion with both flask and FTIR measurements.
NOAA flask measurements using different flux fields as prior. Then the inclusion of FTIR data into the inversion was tested.
The inversion with K2002 increases the land uptake, ocean sources, and the anthropogenic emissions. The simulation with the optimized fluxes agrees with the measurements better than that with the original fluxes, but still underestimated the seasonal amplitude at Northern temperate region. The comparison with HIPPO shows big mismatches in the tropics. Replacing the K2002 land fluxes with SiB improves the comparison with the measurements, and reproduces better latitudinal gradient. The inversion with Campbell anthropogenic emission further improves the simulation at Northern mid to low latitude sites, and decreases the differences with HIPPO measurements in that region.
The OCS inversions with different priors result in different distributions of the optimized fluxes. However, the seasonal variations of the fluxes are similar in the Northern Hemi-sphere. The disagreements between inversions are concentrated in the tropics and the Southern Hemisphere, where the measurements are sparse.
The inversion with both flask and FTIR measurements does not improve the results, because there are offsets between these two data sets. To exploit the full potential use of the FTIR measurements, the errors must be evaluated by independent measurements as discussed in Chapter 4.
Chapter 7
Using OCS to study the biospheric processes of CO 2
In this Chapter, the application of OCS as a photosyntheses tracer is utilized to investigate the carbon cycle. Although there are still uncertainties in the OCS sources and sinks, apart from the land uptake, their effect on the seasonal cycle in the northern high latitudes is small. Therefore we focus on the Northern Hemisphere in the following study. Two parts of work have been done: evaluate the GPP and Respiration estimation in SiB from the mean seasonal cycles of OCS and CO2; understanding the biosphere responses to climate factors from analyzing the inter-annual variations.
SiB calculates OCS and CO2 uptake simultaneously. Through using the coupled land fluxes of OCS and CO2 from SiB, we simulate the atmosphere concentration of OCS and CO2 with their seasonal cycles connected via the same modeled processes. By looking at the comparison of both species to the measurements, we can evaluate the GPP and Re in the biosphere model.
The carbon processes in the biosphere are sensitive to the environmental factors, espe-cially the climate extremes, and contribute significant uncertainties in the climate models.
Therefore it is important to understand the biosphere feedbacks to climate change. The IAV in the atmospheric CO2 concentrations, which is mainly driven by the biosphere re-sponses to the climate variability, provide a way to quantifying the biosphere feedbacks.
However, the analysis of CO2 alone can only determine the effect of NEP. As mentioned in Chapter 2, the photosynthetic uptake and respiration emission acts independently to different climate drivers. Studies of photosynthesis have identified canopy development
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and nutrient status, light, temperature, ambient humidity, CO2 concentration, and soil moisture as controlling factors; while respiration is controlled by temperature, soil mois-ture, nutrient availability, living and dead biomass, ecosystem productivity, and seasonal carbon allocation. Therefore the separation of photosynthesis and respiration is one of the key points for improving the understanding of the biospheric processes.
In 2010, Europe and Russia was stricken by outstanding heatwave. Atmospheric CO2
from the measurements shows weaker drawdown in the growing season. Previous studies (Guerlet et al., 2013, Wunch et al., 2013) have indicated that the biosphere has a large contribution to the IAV. However, how the photosynthesis and respiration responded to the extreme conditions respectively is unclear. In this study, the year 2010 is taken for a case study to investigate the biosphere behaver under extremes with the help of OCS.