Interannual variation of atmospheric CO2 concentration

Thompson, M. L.; Enting, I. G.; Pearman, G. I.; Hyson, P.

doi:10.1007/bf00053775

Cited by 49 publications

(33 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study of the influence of land and ocean carbon fluxes on the atmospheric CO 2 concentration, both in terms of seasonal cycle and of interannual variability, is well documented [ Pearman and Hyson , 1981; Cleveland et al , 1983; Keeling et al , 1984; Bacastow et al , 1985; Peterson et al , 1986; Thompson et al , 1986; Enting , 1987; Thoning et al , 1989; Keeling et al , 1996; Jones et al , 2001; Buermann et al , 2007]. We define here for each time scale (seasonal, interannual, and longer term) a series of traits.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking coupled climate‐carbon models against long‐term atmospheric CO₂ measurements

Cadule¹,

Friedlingstein²,

Bopp

et al. 2010

Global Biogeochemical Cycles

114

124

View full text Add to dashboard Cite

[1] We evaluated three global models of the coupled carbon-climate system against atmospheric CO 2 concentration measured at a network of stations. These three models, HadCM3LC, IPSL-CM2-C, and IPSL-CM4-LOOP, participated in the C 4 MIP experiment and in various other simulations of the future climate impacts on the land and ocean carbon cycle. A new set of performance metrics is defined and applied to quantify each model's ability to reproduce the global growth rate, the seasonal cycle, the El Niño-Southern Oscillation (ENSO)-forced interannual variability of atmospheric CO 2 , and the sensitivity to climatic variations. Knowing that the uncertainty on the amplitude, in 2100, of the climate-carbon feedback is mainly due to the uncertainty of the response of the terrestrial biosphere to the climate change, our new metrics primarily target the evaluation of the land parameterization of the carbon cycle. The modeled fluxes are prescribed to the same global atmospheric transport model LMDZ4, and the simulated concentrations are compared to available observations. We found that the IPSL-CM4-LOOP model is best able to reproduce the phase and amplitude of the atmospheric CO 2 seasonal cycle in the Northern Hemisphere, while the other two models generally underestimate the seasonal amplitude. This points to some shortcomings in describing the vegetation phenology and heterotropic respiration response to climate. We also found that IPSL-CM2-C produces a climate-driven abnormal source of CO 2 to the atmosphere in response to El Niño anomalies. Here a good model performance rests upon a realistic simulation of ENSO-type climate variability and the subsequent tropical carbon cycle response. The three climate models underestimate the sea surface temperature warm anomaly during an El Niño, but HadCM3LC does best in reproducing the interannual CO 2 variability. More efforts are needed to further develop metrics for assessing the sensitivity of the carbon cycle to climate change, and this work should now be extended to assess ocean carbon models against observations.

show abstract

Section: Methodsmentioning

confidence: 99%

Benchmarking coupled climate‐carbon models against long‐term atmospheric CO₂ measurements

Cadule¹,

Friedlingstein²,

Bopp

et al. 2010

Global Biogeochemical Cycles

114

124

View full text Add to dashboard Cite

show abstract

“…Bacastow et al, 1985;Thompson et al, 1986;Keeling et al, 1996;Piao et al, 2008;Barichivich et al, 2012Barichivich et al, , 2013 and spectral filtering methods such as complex demodulation (Thompson and Clark, 2008;Thompson, 2011). The most commonly used method is a combination of curve-fitting and spectral filtering (e.g.…”

Section: J M Barlow Et Al: Spectral Analysis Of Atmospheric Comentioning

confidence: 99%

Analysis of CO<sub>2</sub> mole fraction data: first evidence of large-scale changes in CO<sub>2</sub> uptake at high northern latitudes

2015

View full text Add to dashboard Cite

Abstract. Atmospheric variations of carbon dioxide (CO 2 ) mole fraction reflect changes in atmospheric transport and regional patterns of surface emission and uptake. Here we present a study of changes in the observed high northern latitude CO 2 seasonal cycle. We report new estimates for changes in the phase and amplitude of the seasonal variations, indicative of biospheric changes, by spectrally decomposing multi-decadal records of surface CO 2 mole fraction using a wavelet transform to isolate the changes in the observed seasonal cycle. We also perform similar analysis of the first derivative of CO 2 mole fraction, t CO 2 , that is a crude proxy for changes in CO 2 flux. Using numerical experiments, we quantify the aliasing error associated with independently identifying trends in phase and peak uptake and release to be 10-25 %, with the smallest biases in phase associated with the analysis of t CO 2 . We report our analysis from Barrow, Alaska (BRW), during 1973-2013, which is representative of the broader Arctic region. We determine an amplitude trend of 0.09 ± 0.02 ppm yr −1 , which is consistent with previous work. Using t CO 2 we determine estimates for the timing of the onset of net uptake and release of CO 2 of −0.14±0.14 and −0.25±0.08 days yr −1 respectively and a corresponding net uptake period of −0.11±0.16 days yr −1 , which are significantly different to previously reported estimates. We find that the wavelet transform method has significant skill in characterizing changes in the peak uptake and release. We find a trend of 0.65±0.34 % yr −1 (p < 0.01) and 0.42 ± 0.34 % yr −1 (p < 0.05) for rates of peak uptake and release respectively. Our analysis does not provide direct evidence about the balance between uptake and release of carbon when integrated throughout the year, but the increase in the seasonal amplitude of CO 2 together with an invariant net carbon uptake period provides evidence that high northern latitude ecosystems are progressively taking up more carbon during spring and early summer.

show abstract

“…A digital ®ltering approach, where components are separated by an application of desired ®lters (Thoning et al 1989, Enting 1987a, Thompson et al 1986, Cleveland et al 1983, usually requires regularly spaced data in time. In the curve ®tting technique developed at Tohoku University, the original unevenly spaced data are detrended and seasonal cycles removed by ®tting a smoothing spline and Fourier components to the data; the resulting`residual' values are then interpolated onto an equally spaced data format and smoothed by passing through digital ®lters to derive interannual and intraannual variations.…”

Section: Digital Filtering Approachmentioning

confidence: 99%

Two Curve Fitting Methods Applied to Co2 Flask Data

et al. 1997

View full text Add to dashboard Cite

Digital ®ltering and harmonic regression curve ®tting techniques are applied to CO 2¯a sk data from four stations in North America (Pt. Barrow, Alert, Sable Island and Cape St. James) to evaluate these two dierent methodologies in terms of growth rate and seasonal cycle in the atmospheric CO 2 concentration. Both methods agree relatively well in producing long-term atmospheric CO 2 trend at each of the monitoring stations, as well as in capturing relatively large interannual variations in the annual growth rate. Furthermore, they both agree in indicating the dependency of the variation in the seasonal amplitude on the seasonal minimum concentration. The digital ®ltering technique is able to capture the local temporal variation in CO 2 measurements much better than the harmonic regression method, although in some cases this variability is exaggerated in the digital ®ltering approach. The harmonic regression approach tends to smooth out the data, with much of the power in the very long period oscillations. Also, the timing of the occurrence of the seasonal minimum calculated by the digital ®ltering method tends to be earlier than that calculated by the harmonic regression method, although both methods do not indicate any major secular change in the timing. The overall assessment of the two methods applied to the CO 2¯a sk data underscores the importance of using more than one curve ®tting method before any conclusions can be drawn from thē ask data about the interannual variability in the trend and seasonal cycle of the atmospheric CO 2 concentration. #

show abstract

Interannual variation of atmospheric CO2 concentration

Cited by 49 publications

References 30 publications

Benchmarking coupled climate‐carbon models against long‐term atmospheric CO₂ measurements

Benchmarking coupled climate‐carbon models against long‐term atmospheric CO₂ measurements

Analysis of CO<sub>2</sub> mole fraction data: first evidence of large-scale changes in CO<sub>2</sub> uptake at high northern latitudes

Two Curve Fitting Methods Applied to Co2 Flask Data

Contact Info

Product

Resources

About

Interannual variation of atmospheric CO2 concentration

Cited by 49 publications

References 30 publications

Benchmarking coupled climate‐carbon models against long‐term atmospheric CO2 measurements

Benchmarking coupled climate‐carbon models against long‐term atmospheric CO2 measurements

Analysis of CO&lt;sub&gt;2&lt;/sub&gt; mole fraction data: first evidence of large-scale changes in CO&lt;sub&gt;2&lt;/sub&gt; uptake at high northern latitudes

Two Curve Fitting Methods Applied to Co2 Flask Data

Contact Info

Product

Resources

About

Benchmarking coupled climate‐carbon models against long‐term atmospheric CO₂ measurements

Benchmarking coupled climate‐carbon models against long‐term atmospheric CO₂ measurements

Analysis of CO<sub>2</sub> mole fraction data: first evidence of large-scale changes in CO<sub>2</sub> uptake at high northern latitudes