Constraining predictions of the carbon cycle using data

Rayner, P. J.; Koffi, Ernest N.; Scholze, Marko; Kaminski, Thomas W.; Dufresne, Jean‐Louis

doi:10.1098/rsta.2010.0378

Cited by 21 publications

(18 citation statements)

References 25 publications

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“…For example, surface CO 2 fluxes are typically calculated using surface or near-surface CO 2 measurements along with aircraft data (Law and Rayner, 1999;Bousquet et al, 2000;Rayner and O'Brien, 2001;Gurney et al, 2002;Rayner et al, 2008Rayner et al, , 2011Baker et al, 2010;Chevallier et al, 2010Chevallier et al, , 2011Keppel-Aleks et al, 2012). More recently it has been shown that total column CO 2 measurements derived from ground-based or satellite observations can be used to place constraints on continental-scale flux estimates (O'Brien and2010;Keppel-Aleks et al, 2011); variations in the total column are only partly driven by local surface fluxes, because the total column depends on CO 2 from remote locations.…”

Section: Introductionmentioning

confidence: 99%

Profiling tropospheric CO<sub>2</sub> using Aura TES and TCCON instruments

et al. 2013

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Abstract.Monitoring the global distribution and long-term variations of CO 2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO 2 concentrations from groundbased near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO 2 column amount. Estimates of tropospheric CO 2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO 2 estimates into the GEOS-Chem model. We find that quantifying lower tropospheric CO 2 by subtracting free tropospheric CO 2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of −5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO 2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO 2 with the precision and accuracy needed to quantify lower tropospheric CO 2 variability.

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Section: Introductionmentioning

confidence: 99%

Profiling tropospheric CO<sub>2</sub> using Aura TES and TCCON instruments

et al. 2013

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“…Further research has typically added more details to these models but has rarely gone back and characterised the consistency of the initial assumptions, or the overall model, with empirical data. Recent work has shown that explicitly constraining parameters of terrestrial carbon models with empirical data can lead to better understanding of uncertainty in their parameterisations and of the importance of that uncertainty for predictions (Knorr and Heimann, 2001;Scholze et al, 2007;Zhou and Luo, 2008;Rayner et al, 2011;Ricciuto et al, 2011). Recent systematic comparisons of alternative carbon models or their components have also shown how differences and inconsistencies between different models can be identified more precisely (Keenan et al, 2012;van Oijen et al, 2011;Randerson et al, 2009;Kloster et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties

et al. 2013

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Abstract.The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System (the thin layer that contains and supports life) over the coming decades and centuries. However, Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their terrestrial carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Utilising empirical data to constrain and assess component processes in terrestrial carbon cycle models will be essential to achieving this goal. We used a new model construction method to data-constrain all parameters of all component processes within a global terrestrial carbon model, employing as data constraints a collection of 12 empirical data sets characterising global patterns of carbon stocks and flows. Our goals were to assess the climate dependencies inferred for all component processes, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model and ultimately to identify a methodology by which alternative component models could be compared within the same framework in the future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies, although it was not possible to data-constrain all parameters, indicating some potentially redundant details. Similar climate dependencies were obtained for most processes, whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70 % of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models to make better constrained projections.

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“…An initial range of values for each parameter and standard deviation was obtained from previous settings and the CMIP3 calibrated values (from Meinshausen et al 2011). The difference in the year 2100 temperature change results, derived based on the SRES A1FI emission scenario, was then used to provide a measurement of the uncertainty from the model outputs for each of the input parameters (the Jacobian of the model, Rayner et al 2010). The variance of each parameter provides the content for the covariance matrix C(v) and the calculated (a) Global average ocean temperature change profiles as a function of depth from observations, multi-model mean dedrifted CMIP3 models (1950dedrifted CMIP3 models ( -1999, and standard and revised MAGICC model Gupta et al (2010).…”

Section: Identifying Key Climate Parametersmentioning

confidence: 99%