数据说明
Atmospheric Carbon Dioxide Pressure (ACP)
上传时间：2019-05-22 14:05:26 浏览次数：作者与来源：admin
ACP means spatially interpolated CO2 mole fractions in the atmosphere (after the correction of air and vapor pressure), which reflect the sum of all the CO2 exchange at the surface, they form the ultimate record of the combined human and natural influence on greenhouse gas levels.
Unit: ppm
Resolution: 25km, daily
Duration: Jan. 2000 – Dec. 2016
Source: NOAA/ESRL
Version: 2017
Institution: NOAA Earth System Research Laboratory
Processing:
This file contains CarbonTracker CT2017 mole fractions sampled at 1330 local time. Take care when inteprreting time axis; absolute time varies as a function of longitude.
Model simulations may drift off from reality for a number of reasons. Some models are highly nonlinear, and depend sensitively on knowing the system state with high accuracy. Weather models fall into this category, and as a result reliable forecast systems depend on having a constant stream of meteorological data to correct their simulations. In contrast, models like CarbonTracker need data assimilation not because the controlling dynamics are nonlinear, but because those dynamics are not well known. CarbonTracker uses approximate or estimated rules about the evolution of surface CO2 fluxes, then corrects these approximate projections using observational constraints. The resulting optimal surface flux estimates can then be used to better understand the functioning of the carbon cycle.
CO2 fluxes F(x,y,t) in CarbonTracker are parameterized according to
Unit: ppm
Resolution: 25km, daily
Duration: Jan. 2000 – Dec. 2016
Source: NOAA/ESRL
Version: 2017
Institution: NOAA Earth System Research Laboratory
Processing:
This file contains CarbonTracker CT2017 mole fractions sampled at 1330 local time. Take care when inteprreting time axis; absolute time varies as a function of longitude.
Model simulations may drift off from reality for a number of reasons. Some models are highly nonlinear, and depend sensitively on knowing the system state with high accuracy. Weather models fall into this category, and as a result reliable forecast systems depend on having a constant stream of meteorological data to correct their simulations. In contrast, models like CarbonTracker need data assimilation not because the controlling dynamics are nonlinear, but because those dynamics are not well known. CarbonTracker uses approximate or estimated rules about the evolution of surface CO2 fluxes, then corrects these approximate projections using observational constraints. The resulting optimal surface flux estimates can then be used to better understand the functioning of the carbon cycle.
CO2 fluxes F(x,y,t) in CarbonTracker are parameterized according to
|
where Fland, Focean, FFF, and Fbio are prior flux model predictions for land biosphere, ocean, fossil fuel and wildfire emissions respectively, and λ represents a set of unknown multiplicative scaling factors applied to the fluxes, to be estimated in the assimilation. These scaling factors are the final product of our assimilation and together with the prior flux models determine CarbonTracker optimized fluxes. Note that no scaling factors are applied to the fossil fuel and fire modules. The fossil fuel and wildfire fluxes are relatively well-known from prior flux models compared to highly-uncertain land biosphere and ocean fluxes, and as a result we impose those emissions without modification in our model.
Each 1° × 1° pixel of our domain was assigned one of the categories above based on the Olson category that was most prevalent in the 0.5°×0.5° underlying area.
Measured are the result of upstream surface fluxes and atmospheric transport, which includes both advective movement and diffusive mixing. Near-field surface fluxes can cause significant changes in CO2 mole fractions, whereas flux signals from further upstream become spread out and diluted. Generally speaking, the longer in the past a flux event occurred, the smaller its impact will be on a given sample of air (although it will be spread out through a larger volume of the atmosphere). Thus we choose an "assimilation window" that represents how far back in time we expect to be able to pinpoint a given flux signal from available measurements.
Interpolating the CO2 mole fractions after the correction of air and vapor pressure, the resolution of spatially interpolated CO2 mole fractions is about 25km.
Known issues:
The formal "internal" error estimates produced by CarbonTracker are unrealistically large. This is largely a result of the dynamical model that introduces a fresh prior covariance matrix with every new week entering the assimilation window. Uncertainties using the new 12-week assimilation window are smaller that those from previous CarbonTracker releases that used a much shorter five-week assimilation window.
Uncertainties in CarbonTracker tend to increase as larger regions are considered; regional errors mostly just add in quadrature without any cancellation from dipole anticorrelation. Whereas many inversions yield smaller errors as the spatial extent of the region being considered increases, CarbonTracker acts in the opposite fashion. This is perhaps most obvious in the estimate of CarbonTracker's global annual surface flux of carbon dioxide. While CT2017 estimates a one-sigma error of more than 5 PgC yr−1on its global flux, this quantity is in actuality much more well-constrained. This is evident from CarbonTracker's excellent agreement with observational estimates of atmospheric growth rate.
In CT2017, error estimates are about a factor of two larger than in previous releases, mainly due to the retuning of the land prior covariance discussed above. However, uncertainties presented for CT2017 take into account not only the "internal" flux uncertainty generated by a single inversion, but also the across-model "external" uncertainty representing the spread of the inversion models due to the choice of prior flux.
Reference
Olson, J.S., J.A. Watts, and L.J. Allison (1985), Major world ecosystem complexes ranked by carbon in live vegetation: A Database, NDP-017
Loveland, T.R. et al. (2000), Development of a global land cover characteristics database and IGB6 DISCover from the 1km AVHRR data, Int. J. Remote Sensing, 211303-1330
Peters, W. et al. (2005), An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations, J. Geophys. Res., 110(D24304), doi:10.1029/2005JD006157
Peters, W. et al. (2007), An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, PNAS, November 27, 2007 , vol. 104, no. 48, 18925-18930
All the contents of this page are quoted from National Oceanic and Atmospheric Administration Earth System Research Laboratory. All project documentation and related publications can be found at the website:
https://www.esrl.noaa.gov/gmd/ccgg/carbontrack
Each 1° × 1° pixel of our domain was assigned one of the categories above based on the Olson category that was most prevalent in the 0.5°×0.5° underlying area.
Measured are the result of upstream surface fluxes and atmospheric transport, which includes both advective movement and diffusive mixing. Near-field surface fluxes can cause significant changes in CO2 mole fractions, whereas flux signals from further upstream become spread out and diluted. Generally speaking, the longer in the past a flux event occurred, the smaller its impact will be on a given sample of air (although it will be spread out through a larger volume of the atmosphere). Thus we choose an "assimilation window" that represents how far back in time we expect to be able to pinpoint a given flux signal from available measurements.
Interpolating the CO2 mole fractions after the correction of air and vapor pressure, the resolution of spatially interpolated CO2 mole fractions is about 25km.
Known issues:
The formal "internal" error estimates produced by CarbonTracker are unrealistically large. This is largely a result of the dynamical model that introduces a fresh prior covariance matrix with every new week entering the assimilation window. Uncertainties using the new 12-week assimilation window are smaller that those from previous CarbonTracker releases that used a much shorter five-week assimilation window.
Uncertainties in CarbonTracker tend to increase as larger regions are considered; regional errors mostly just add in quadrature without any cancellation from dipole anticorrelation. Whereas many inversions yield smaller errors as the spatial extent of the region being considered increases, CarbonTracker acts in the opposite fashion. This is perhaps most obvious in the estimate of CarbonTracker's global annual surface flux of carbon dioxide. While CT2017 estimates a one-sigma error of more than 5 PgC yr−1on its global flux, this quantity is in actuality much more well-constrained. This is evident from CarbonTracker's excellent agreement with observational estimates of atmospheric growth rate.
In CT2017, error estimates are about a factor of two larger than in previous releases, mainly due to the retuning of the land prior covariance discussed above. However, uncertainties presented for CT2017 take into account not only the "internal" flux uncertainty generated by a single inversion, but also the across-model "external" uncertainty representing the spread of the inversion models due to the choice of prior flux.
Reference
Olson, J.S., J.A. Watts, and L.J. Allison (1985), Major world ecosystem complexes ranked by carbon in live vegetation: A Database, NDP-017
Loveland, T.R. et al. (2000), Development of a global land cover characteristics database and IGB6 DISCover from the 1km AVHRR data, Int. J. Remote Sensing, 211303-1330
Peters, W. et al. (2005), An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations, J. Geophys. Res., 110(D24304), doi:10.1029/2005JD006157
Peters, W. et al. (2007), An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, PNAS, November 27, 2007 , vol. 104, no. 48, 18925-18930
All the contents of this page are quoted from National Oceanic and Atmospheric Administration Earth System Research Laboratory. All project documentation and related publications can be found at the website:
https://www.esrl.noaa.gov/gmd/ccgg/carbontrack
