scholarly journals Trivial improvements in predictive skill due to direct reconstruction of the global carbon cycle

2021 ◽  
Vol 12 (4) ◽  
pp. 1139-1167
Author(s):  
Aaron Spring ◽  
István Dunkl ◽  
Hongmei Li ◽  
Victor Brovkin ◽  
Tatiana Ilyina
Keyword(s):  
Carbon Cycle ◽  
Initial Conditions ◽  
Co2 Flux ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
State Variables ◽  
Predictive Skill ◽  
Perfect Model ◽  
Prediction Systems ◽  
Global Carbon

Abstract. State-of-the art climate prediction systems have recently included a carbon component. While physical-state variables are assimilated in reconstruction simulations, land and ocean biogeochemical state variables adjust to the state acquired through this assimilation indirectly instead of being assimilated themselves. In the absence of comprehensive biogeochemical reanalysis products, such an approach is pragmatic. Here we evaluate a potential advantage of having perfect carbon cycle observational products to be used for direct carbon cycle reconstruction. Within an idealized perfect-model framework, we reconstruct a 50-year target period from a control simulation. We nudge variables from this target onto arbitrary initial conditions, mimicking an assimilation simulation generating initial conditions for hindcast experiments of prediction systems. Interested in the ability to reconstruct global atmospheric CO2, we focus on the global carbon cycle reconstruction performance and predictive skill. We find that indirect carbon cycle reconstruction through physical fields reproduces the target variations. While reproducing the large-scale variations, nudging introduces systematic regional biases in the physical-state variables to which biogeochemical cycles react very sensitively. Initial conditions in the oceanic carbon cycle are sufficiently well reconstructed indirectly. Direct reconstruction slightly improves initial conditions. Indirect reconstruction of global terrestrial carbon cycle initial conditions are also sufficiently well reconstructed by the physics reconstruction alone. Direct reconstruction negligibly improves air–land CO2 flux. Atmospheric CO2 is indirectly very well reconstructed. Direct reconstruction of the marine and terrestrial carbon cycles slightly improves reconstruction while establishing persistent biases. We find improvements in global carbon cycle predictive skill from direct reconstruction compared to indirect reconstruction. After correcting for mean bias, indirect and direct reconstruction both predict the target similarly well and only moderately worse than perfect initialization after the first lead year. Our perfect-model study shows that indirect carbon cycle reconstruction yields satisfying initial conditions for global CO2 flux and atmospheric CO2. Direct carbon cycle reconstruction adds little improvement to the global carbon cycle because imperfect reconstruction of the physical climate state impedes better biogeochemical reconstruction. These minor improvements in initial conditions yield little improvement in initialized perfect-model predictive skill. We label these minor improvements due to direct carbon cycle reconstruction “trivial”, as mean bias reduction yields similar improvements. As reconstruction biases in real-world prediction systems are likely stronger, our results add confidence to the current practice of indirect reconstruction in carbon cycle prediction systems.

scholarly journals Trivial improvements of predictive skill due to direct reconstruction of global carbon cycle

2021 ◽  
Author(s):  
Aaron Spring ◽  
István Dunkl ◽  
Hongmei Li ◽  
Victor Brovkin ◽  
Tatiana Ilyina
Keyword(s):  
Carbon Cycle ◽  
Initial Conditions ◽  
Co2 Flux ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
State Variables ◽  
Predictive Skill ◽  
Perfect Model ◽  
Prediction Systems ◽  
Global Carbon

Abstract. State-of-the-art carbon cycle prediction systems are initialized from reconstruction simulations in which state variables of the climate system are assimilated. While currently only the physical state variables are assimilated, biogeochemical state variables adjust to the state acquired through this assimilation indirectly instead of being assimilated themselves. In the absence of comprehensive biogeochemical reanalysis products, such approach is pragmatic. Here we evaluate a potential advantage of having perfect carbon cycle observational products to be used for direct carbon cycle reconstruction. Within an idealized perfect-model framework, we define 50 years of a control simulation under pre-industrial CO2 levels as our target representing observations. We nudge variables from this target onto arbitrary initial conditions 150 years later mimicking an assimilation simulation generating initial conditions for hindcast experiments of prediction systems. We investigate the tracking performance, i.e. bias, correlation and root-mean-square-error between the reconstruction and the target, when nudging an increasing set of atmospheric, oceanic and terrestrial variables with a focus on the global carbon cycle explaining variations in atmospheric CO2. We compare indirect versus direct carbon cycle reconstruction against a resampled threshold representing internal variability. Afterwards, we use these reconstructions to initialize ensembles to assess how well the target can be predicted after reconstruction. Interested in the ability to reconstruct global atmospheric CO2, we focus on the global carbon cycle reconstruction and predictive skill. We find that indirect carbon cycle reconstruction through physical fields reproduces the target variations on a global and regional scale much better than the resampled threshold. While reproducing the large scale variations, nudging introduces systematic regional biases in the physical state variables, on which biogeochemical cycles react very sensitively. Global annual surface oceanic pCO2 initial conditions are indirectly reconstructed with an anomaly correlation coefficient (ACC) of 0.8 and debiased root mean square error (RMSE) of 0.3 ppm. Direct reconstruction slightly improves initial conditions in ACC by +0.1 and debiased RMSE by −0.1 ppm. Indirect reconstruction of global terrestrial carbon cycle initial conditions for vegetation carbon pools track the target by ACC of 0.5 and debiased RMSE of 0.5 PgC. Direct reconstruction brings negligible improvements for air-land CO2 flux. Global atmospheric CO2 is indirectly tracked by ACC of 0.8 and debiased RMSE of 0.4 ppm. Direct reconstruction of the marine and terrestrial carbon cycles improves ACC by 0.1 and debiased RMSE by −0.1 ppm. We find improvements in global carbon cycle predictive skill from direct reconstruction compared to indirect reconstruction. After correcting for mean bias, indirect and direct reconstruction both predict the target similarly well and only moderately worse than perfect initialization after the first lead year. Our perfect-model study shows that indirect carbon cycle reconstruction yields satisfying initial conditions for global CO2 flux and atmospheric CO2. Direct carbon cycle reconstruction adds little improvements in the global carbon cycle, because imperfect reconstruction of the physical climate state impedes better biogeochemical reconstruction. These minor improvements in initial conditions yield little improvement in initialized perfect-model predictive skill. We label these minor improvements due to direct carbon cycle reconstruction trivial, as mean bias reduction yields similar improvements. As reconstruction biases in real-world prediction systems are even stronger, our results add confidence to the current practice of indirect reconstruction in carbon cycle prediction systems.

Earth system predictions of the carbon sinks and atmospheric CO2 growth: new insights and lessons from DCPP

2021 ◽  
Author(s):  
Tatiana Ilyina ◽  
Hongmei Li ◽  
Wolfgang Müller ◽  
Aaron Spring
Keyword(s):  
Carbon Cycle ◽  
Time Scales ◽  
Carbon Sink ◽  
Global Carbon Cycle ◽  
Carbon Sinks ◽  
Model Framework ◽  
Predictive Skill ◽  
Ocean Carbon ◽  
Prediction Systems ◽  
Global Carbon

<p>Initialized predictions of near-term future climate have proven successful and predictive power for the global carbon cycle is also emerging. Through extending ESM-based decadal prediction systems, i.e. those contributing to Decadal Climate Prediction Project (DCPP) with the ocean and land carbon cycle components, it becomes possible to establish predictability of the carbon sinks and variations of atmospheric CO<sub>2</sub> concentrations. However, such predictions of the global carbon cycle still remain a cutting-edge activity of only a few modeling groups.</p><p>On interannual to decadal time-scales, atmospheric CO<sub>2</sub> growth rates exhibit pronounced anomalies driven by varying strengths of the land and ocean carbon sinks; these anomalies are linked to climate variability on decadal and interannual time scales. Is it possible to predict if atmospheric CO<sub>2</sub> changes slower of faster as expected from changes in emissions? This question is examined in a multi-model framework comprising prediction systems initialized by the observed state of the physical climate. The multi-model framework comprises ESM-based prediction systems that contributed to DCPP within CMIP6, as well as those which run with the CMIP5 forcing.</p><p>A predictive skill for the global ocean carbon sink of up to 6 years is found for some models. Longer regional predictability horizons are found across single models. On land, a predictive skill of up to 2 years is primarily maintained in the tropics and extra-tropics enabled by the initialization of the physical climate. Furthermore, anomalies of atmospheric CO<sub>2</sub> growth rate inferred from natural variations of the land and ocean carbon sinks are predictable at lead time of 2 years and the skill is limited by the land carbon sink predictability horizon. These predictions of the global carbon cycle and the planet’s breath maintained by variations of atmospheric CO<sub>2</sub> are essential to understand where the anthropogenic carbon would go in response to emission reduction efforts addressing global warming mitigation. Such information is useful to verify the effectiveness of fossil fuel emissions reduction measures.</p>

Time-scale dependence of climate-carbon cycle feedbacks for weak perturbations in CMIP5 models

2021 ◽  
Author(s):  
Guilherme Torres Mendonça ◽  
Julia Pongratz ◽  
Christian Reick
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Time Scales ◽  
Radiative Forcing ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Ice Age ◽  
Cmip5 Models ◽  
Global Carbon ◽  
Different Time Scales

<p>The increase in atmospheric CO2 driven by anthropogenic emissions is the main radiative forcing causing climate change. But this increase is not only a result from emissions, but also from changes in the global carbon cycle. These changes arise from feedbacks between climate and the carbon cycle that drive CO2 into or out of the atmosphere in addition to the emissions, thereby either accelerating or buffering climate change. Therefore, understanding the contribution of these feedbacks to the global response of the carbon cycle is crucial in advancing climate research. Currently, this contribution is quantified by the α-β-γ framework (Friedlingstein et al., 2003). But this quantification is only valid for a particular perturbation scenario and time period. In contrast, a recently proposed generalization (Rubino et al., 2016) of this framework for weak perturbations quantifies this contribution for all scenarios and at different time scales. </p><p>Thereby, this generalization provides a systematic framework to investigate the response of the global carbon cycle in terms of the climate-carbon cycle feedbacks. In the present work we employ this framework to study these feedbacks and the airborne fraction in different CMIP5 models. We demonstrate (1) that this generalization of the α-β-γ framework consistently describes the linear dynamics of the carbon cycle in the MPI-ESM; and (2) how by this framework the climate-carbon cycle feedbacks and airborne fraction are quantified at different time scales in CMIP5 models. Our analysis shows that, independently of the perturbation scenario, (1) the net climate-carbon cycle feedback is negative at all time scales; (2) the airborne fraction generally decreases for increasing time scales; and (3) the land biogeochemical feedback dominates the model spread in the airborne fraction at all time scales. This last result therefore emphasizes the need to improve our understanding of this particular feedback.</p><p><strong>References:</strong></p><p>P. Friedlingstein, J.-L. Dufresne, P. Cox, and P. Rayner. How positive is the feedback between climate change and the carbon cycle? Tellus B, 55(2):692–700, 2003.</p><p>M. Rubino, D. Etheridge, C. Trudinger, C. Allison, P. Rayner, I. Enting, R. Mulvaney, L. Steele, R. Langenfelds, W. Sturges, et al. Low atmospheric CO2 levels during the Little Ice Age due to cooling-induced terrestrial uptake. Nature Geoscience, 9(9):691–694, 2016.</p>

scholarly journals Comment on "Carbon farming in hot, dry coastal areas: an option for climate change mitigation" by Becker et al. (2013)

2013 ◽  
Vol 4 (2) ◽  
pp. 869-873
Author(s):  
M. Heimann
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Reference Scenario ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Sink ◽  
Carbon Dioxide Co2 ◽  
Global Carbon

Abstract. Becker et al. (2013) argue that an afforestation of 0.73 109 ha with Jatropha curcas plants would generate an additional terrestrial carbon sink of 4.3 PgC yr−1, enough to stabilise the atmospheric mixing ratio of carbon dioxide (CO2) at current levels. However, this is not consistent with the dynamics of the global carbon cycle. Using a well established global carbon cycle model, the effect of adding such a hypothetical sink leads to a reduction of atmospheric CO2 levels in the year 2030 by 25 ppm compared to a reference scenario. However, the stabilisation of the atmospheric CO2 concentration requires a much larger additional sink or corresponding reduction of anthropogenic emissions.

scholarly journals The validity of floating chambers in quantifying CO2 flux from headwater streams

2020 ◽  
Author(s):  
M. J. Rawitch ◽  
G. L. Macpherson ◽  
A. E. Brookfield
Keyword(s):  
Carbon Cycle ◽  
Headwater Streams ◽  
Co2 Flux ◽  
Field Measurements ◽  
Global Carbon Cycle ◽  
Low Velocity ◽  
Flume Experiments ◽  
Moderate Range ◽  
Co2 Degassing ◽  
Global Carbon

Abstract The amount of CO2 exiting headwater streams through degassing plays an important role in the global carbon cycle, yet quantification of CO2 degassing remains challenging because of the morphology of headwater streams and because of uncertainty about whether floating or suspended chambers provide valid measurements in moving water. We show that experiments using large and small floating chambers in flowing water over a moderate range of water velocities (0.13–0.23 m s−1) in a laboratory flume resulted in similar k600s to published field measurements with similar water velocities. We confirmed the flume experiments with paired stirred-still beaker experiments, where resulting k600s fell within the extrapolated trend of the flume experiments. We propose that the floating chambers can provide good estimates of CO2 degassing, particularly in shallow, low-velocity, morphologically complex headwater streams, permitting quantification of this important contributor to the global carbon cycle.

scholarly journals The global carbon budget 1959–2011

2013 ◽  
Vol 5 (1) ◽  
pp. 165-185 ◽  
Author(s):  
C. Le Quéré ◽  
R. J. Andres ◽  
T. Boden ◽  
T. Conway ◽  
R. A. Houghton ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Carbon Cycle ◽  
Co2 Emissions ◽  
Carbon Budget ◽  
Co2 Concentration ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Global Carbon Budget ◽  
Atmospheric Co2 Concentration ◽  
Global Carbon

Abstract. Accurate assessments of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics, while emissions from Land-Use Change (ELUC), including deforestation, are based on combined evidence from land cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. Finally, the global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms. For the last decade available (2002–2011), EFF was 8.3 ± 0.4 PgC yr−1, ELUC 1.0 ± 0.5 PgC yr−1, GATM 4.3 ± 0.1 PgC yr−1, SOCEAN 2.5 ± 0.5 PgC yr−1, and SLAND 2.6 ± 0.8 PgC yr−1. For year 2011 alone, EFF was 9.5 ± 0.5 PgC yr−1, 3.0 percent above 2010, reflecting a continued trend in these emissions; ELUC was 0.9 ± 0.5 PgC yr−1, approximately constant throughout the decade; GATM was 3.6 ± 0.2 PgC yr−1, SOCEAN was 2.7 ± 0.5 PgC yr−1, and SLAND was 4.1 ± 0.9 PgC yr−1. GATM was low in 2011 compared to the 2002–2011 average because of a high uptake by the land probably in response to natural climate variability associated to La Niña conditions in the Pacific Ocean. The global atmospheric CO2 concentration reached 391.31 ± 0.13 ppm at the end of year 2011. We estimate that EFF will have increased by 2.6% (1.9–3.5%) in 2012 based on projections of gross world product and recent changes in the carbon intensity of the economy. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future. All data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_V2013). Global carbon budget 2013

scholarly journals Comment on "Carbon farming in hot, dry coastal areas: an option for climate change mitigation" by Becker et al. (2013)

2014 ◽  
Vol 5 (1) ◽  
pp. 41-42 ◽  
Author(s):  
M. Heimann
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Reference Scenario ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Sink ◽  
Carbon Dioxide Co2 ◽  
Global Carbon

Abstract. Becker et al. (2013) argue that an afforestation of 0.73 × 109 ha with Jatropha curcas plants would generate an additional terrestrial carbon sink of 4.3 PgC yr−1, enough to stabilise the atmospheric mixing ratio of carbon dioxide (CO2) at current levels. However, this is not consistent with the dynamics of the global carbon cycle. Using a well-established global carbon cycle model, the effect of adding such a hypothetical sink leads to a reduction of atmospheric CO2 levels in the year 2030 by 25 ppm compared to a reference scenario. However, the stabilisation of the atmospheric CO2 concentration requires a much larger additional sink or corresponding reduction of anthropogenic emissions.

Reconstructions and predictions of the global carbon cycle with an emission-driven Earth System Model

2021 ◽  
Author(s):  
Hongmei Li ◽  
Tatiana Ilyina ◽  
Tammas Loughran ◽  
Julia Pongratz
Keyword(s):  
Growth Rate ◽  
Carbon Cycle ◽  
Carbon Budget ◽  
Global Carbon Cycle ◽  
System Model ◽  
Earth System ◽  
Global Carbon Budget ◽  
Predictive Skill ◽  
Atmospheric Carbon ◽  
Global Carbon

<p>The global carbon budget including CO<sub>2</sub> fluxes among different reservoirs and atmospheric carbon growth rate vary substantially in interannual to decadal time-scales. Reconstructing and predicting the variable global carbon cycle is of essential value of tracing the fate of carbon and the corresponding climate and ecosystem changes. For the first time, we extend our prediction system based on the Max Planck Institute Earth system model (MPI-ESM) from concentration-driven to emission-driven taking into account the interactive carbon cycle and hence enabling prognostic atmospheric carbon increment. </p><p>By assimilating atmospheric and oceanic observational data products into MPI-ESM decadal prediction system, we can reproduce the observed variations of the historical global carbon cycle globally. The reconstruction from the fully coupled model enables quantification of global carbon budget within a close Earth system and therefore avoids the budget imbalance term of budgeting the carbon with standalone models. Our reconstructions of carbon budget provide a novel approach for supporting global carbon budget and understanding the dominating processes. Retrospective predictions based on the  emission-driven hindcasts, which are initiated from the reconstructions, show predictive skill in the atmospheric carbon growth rate, air-sea CO<sub>2</sub> fluxes, and air-land CO<sub>2</sub> fluxes. The air-sea CO<sub>2</sub> fluxes have higher predictive skill up to 5 years, and the air-land CO<sub>2</sub> fluxes and atmospheric carbon growth rate show predictive skill of 2 years. Our results also suggest predictions based on Earth system models enable reproducing and further predicting the evolution of atmospheric CO<sub>2</sub> concentration changes. The earth system predictions will provide valuable inputs for understanding the global carbon cycle and supporting climate relevant policy development. </p>

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