From a coarse spatial and temporal resolution methodology based on synthesis inversion, we should progress to full scale 4D-variational assimilation which will have the ability to track the complete fate of CO2 emitted from anywhere on the globe. The refinement of biogeochemical models carbon, nitrogen, silica, phosphorous, iron cycles in the various earth-system components atmosphere, land and ocean should be a major focus of activity in the XII Plan.
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This will involve a close synergy between different observation programmes biological and chemical oceanographic cruises, remote sensing of marine and terrestrial productivity and development of terrestrial and marine ecosystem models. These will finally be integrated into Earth System Models ESM which will have the ability to predict future climate impacts which may include ocean acidification, loss of biodiversity and productivity. The ESMs will also assist us in evaluating strategies for climate-change mitigation such as ocean fertilisation and other futuristic schemes.
An analysis centre with state of the art GC and Mass specs which can deliver above accuracy. Fossil fuel emission estimates used here are from Boden et al.
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The most robust information provided by atmospheric CO 2 concentration records is the global atmospheric inventory and how it changes over time. This reveals, for example, very clearly the well-known rapid increase of atmospheric CO 2 over the last decades.
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Thus, in principle, these patterns should allow us to trace back the spatial distribution and strength of regional surface fluxes, provided the relationship between fluxes and the concentration patterns they cause can be established. This relationship involves the representation of the processes of atmospheric advection and dispersion, which can be estimated fairly well using numerical fluid dynamics models of the atmospheric flow atmospheric transport models e.
The relation to the actual atmospheric flow is established by using wind and cloud convection transport fields derived from regular observations of the state of the atmosphere for the purpose of weather prediction. Flux estimation reduces then to a least-squares minimization problem of the difference between a linear combination of concentration fields resulting from localized fluxes in space and time sampled at the same time and location as the observations and the actual observations.
B is the a priori flux error covariance matrix, c is a vector containing the observed atmospheric concentrations and H is the transport-model-calculated matrix, which relates surface fluxes to the atmospheric concentration signal they cause at the sampling sites. This approach solves for small deviations from a prescribed flux model. We resolve fluxes monthly and spatially on a grid 5. The prior flux model includes three components: i annually changing fossil fuel emissions, ii monthly net land gains or losses which do not change from year to year, based on the CASA Carnegie—Ames—Stanford land biosphere model average climatology for — , and iii air—sea fluxes.
Second State of the Carbon Cycle Report
Respiration is estimated using a carbon cycle model that includes soils . We have chosen annually repeating and balanced land vegetation—atmosphere CO 2 flux prior estimates because our interest is in extracting the information on inter-annual variations contained in atmospheric data. For our prior estimates of air—sea fluxes, we treat separately the fluxes associated with the pre-industrial carbon cycle two hemispherical loops with CO 2 outgassing in the tropics and CO 2 uptake at high latitudes and uptake of carbon induced by the anthropogenic perturbation of atmospheric CO 2 with fluxes steadily increasing and located primarily in the northern Atlantic and Southern Ocean [ 24 , 25 ].
For the former, we use the monthly resolved climatology based on air—sea partial pressure differences and an air—sea gas exchange coefficient parameterization, compiled by Takahashi et al. For the latter, we use the spatial air—sea flux pattern of Khatiwala et al.
This approach leads to improved a posteriori data model fits compared with inversions which use air—sea flux prior compilations based on interpolation algorithms of air-to-sea partial pressure measurement differences alone specifically [ 26 ]. Observational data have an uncertainty of 1 ppm plus an estimate of representation error derived by averaging the absolute prior concentration variation between the model grid cell containing the measurement location and the surrounding grid cells.
This leads to overall observational uncertainties of between 1 and 6 ppm, depending on the measurement location. To assess the influence of the Amazon vertical profile data, we have also performed an inversion without these data. One cause of plant water stress is negative deviations or anomalies of the abundance of soil water or soil water content from the climatological mean representative for a region.
The Effects of Carbon Dioxide Removal on the Carbon Cycle
A proxy for soil water content over large spatial scales can be measured from satellites because large-scale land surface water content anomalies cause Earth gravity anomalies. Such gravity anomalies are being estimated from space by the twin satellite mission GRACE Gravity Recovery and Climate Experiment [ 29 ] , with the satellites following each other closely on a polar orbit. Instruments on the satellites measure the distance between the two satellites, which increases when the front satellite is approaching a positive gravity anomaly and decreases again once the front satellite has passed the anomaly and the rear satellite is approaching the anomaly.
To confirm the realism of the gravity anomaly data measured from space, we compare gravity anomaly anomalies with precipitation anomalies measured by TRMM Tropical Rainfall Measuring Mission [ 30 ]; electronic supplementary material, figure S3. The signatures of the two data types are very consistent taking into account that gravity anomalies are to first order equal to cumulative precipitation anomalies.
To calculate monthly gravity anomaly anomalies, we subtract monthly mean values calculated using the full — record from the continuous record of monthly mean values. To do so, we first estimate carbon monoxide fluxes from monthly CO air column anomalies. Nonetheless, because of the non-uniform weighting function of the retrievals, column content estimates may contain a bias an underestimate of column CO if signals are concentrated to the lower troposphere electronic supplementary material, figure S5.
Breaching a “carbon threshold” could lead to mass extinction
The retrieval calculations include a time-invariant prior and so retrieved anomalies stem entirely from the radiometric data. Photosynthesis is associated with fluorescence. A small fraction of solar radiation trapped by chlorophyll escapes instead of being used to fix CO 2. This fraction is re-emitted into the atmosphere from the leaf at larger wavelengths in the range of and nm, e. Fluorescence has been shown to be related to productivity [ 35 , 36 ] and thus we use it here as a proxy for productivity.
The bias correction procedure, which is an essential part of the post-retrieval processing, was performed using the European Space Agency Climate Change Initiative land cover maps [ 39 ]. GOSAT measurements over permanently non-vegetated areas where zero fluorescence can be assumed were identified using these maps in order to derive radiance-dependent calibration curves on a monthly basis.
skyscypfooseap.tk Based on these monthly curves, a two-dimensional spline interpolation was used along time and radiance dimensions to obtain the bias correction term for any given GOSAT sounding. This ensures that the time dependence of the instrument-related bias is taken into account. The retrievals are available for the period April to September Monthly compilations of SIF retrievals exhibit missing values. To obtain sufficient data coverage we, therefore, calculate quarterly three-monthly means and similarly calculated anomalies for three-month periods.
Despite lumping three months together, there are still pixels with no retrievals. To calculate quarterly anomalies we kept track of the number of existing retrievals on an individual pixel basis and averaged on a pixel basis. For the comparison of anomalies for specific areas and three-monthly periods, we calculated region mean anomalies including only those pixels for which retrievals exist. The largest annual global atmospheric CO 2 increase rates recorded with modern analytical tools i. The anomalies in and were positive and when summed up were approximately 1.
Figure 2. The first and primary measure is plant water stress caused by negative deviations or anomalies of the abundance of soil water or soil water content from the climatological mean representative for a region.
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Figure 3. The deficit reached its peak and covers the entire basin by the first quarter of , with water deficit remaining high throughout the basin until the final quarter of ; the most pronounced negative precipitation anomaly occurred during the final quarter of all across the basin. A second notable feature is excessively hot conditions in the Congo Basin west equatorial Africa; host to most of the remaining African humid forests around February , while according to gravity anomalies and precipitation estimated by TRMM there were no very clear indications of drought conditions but this will need further investigation.
Land gravity anomaly anomalies and monthly means of daily minimum and maximum temperatures, respectively, for tropical land regions. Among the three continents, the climate anomalies for the Amazon seem to be the strongest, with temperature and precipitation anomalies centred around the last three months of and first three months of , and with the effects of precipitation anomalies on soil moisture lasting over nearly all of , reflecting the time it takes for water deficits to propagate through the catchment electronic supplementary material, figure S4.
The possible exception is the Congo Basin, which was excessively hot during the first quarter of What do atmospheric inversion results suggest? The flux anomalies are calculated from the net flux estimates, which include all processes, also including in particular fossil fuel emissions. This result demonstrates the inversion's ability to detect and attribute expected flux anomalies from the atmosphere data. According to our calculations, four regions released significant amounts of carbon during the — period: tropical South America, tropical Africa, southern Africa and tropical East Asia.
The timing of peak carbon losses differs between the regions, with a peak in October for tropical East Asia, February for southern Africa, February and March for tropical Africa, November to December and March to April for tropical South America. Figure 5. Tropical South America to atmosphere CO 2 flux anomalies estimated with inverse modelling of atmospheric transport and atmospheric CO 2 concentration observations estimates indicated in black include vertical profile data available from onwards , and tropical South American gravity anomaly anomalies estimated by GRACE satellite mission.
Figure 6. Time series of land-to-atmosphere carbon flux estimates low-pass filtered for tropical land regions. The portion for which Amazon vertical profile data are available and included in the atmospheric transport inversion calculations is coloured in black. Dashed lines show estimates that do not include tropical South American data. Online version in colour.
Tropical land carbon flux anomalies. CO 2 estimates based just on atmospheric CO 2 concentration data and inversion of atmospheric transport provide net fluxes but cannot discern between the different underlying processes, such as biomass burning, or changes in vegetation productivity and respiration processes e.
Figure 7. Figure 8. Solar-induced fluorescence anomalies measured from GOSAT satellite [ 37 ] and based on retrievals at nm. Oschlies et al. This phenomenon lasts until it is countered by a decreasing sequestration efficacy i. They also show that in the fertilized region there is a decline in pH and an increase in pCO 2. Carbon cycling is also affected in other regions of the ocean because of reduced nutrient availability following enhanced biological production.
Other studies have estimated the carbon sequestration potential of nitrogen and phosphorus fertilization [ 88 , 89 , 90 ]. While these macro-nutrient fertilization studies explore some of the factors that would determine carbon sequestration and estimate ocean carbon uptake, they do not fully explore the response of the global carbon cycle. Artificial ocean upwelling Table 1 as a biological pump enhancement has been investigated by Oschlies et al.
Many of the impacts are the same as for ocean fertilization see above since it is the goal of this method to fertilize upper ocean biology with upwelled nutrients. However, upwelling also directly affects the carbon cycle since in most places the deep water that is pumped up contains more CO 2 than the surface ocean, part of which may outgas into the atmosphere if the additional C is not consumed by phytoplankton.