scholarly journals Natural Variability in a Stable, 1000-Yr Global Coupled Climate–Carbon Cycle Simulation

2006 ◽  
Vol 19 (13) ◽  
pp. 3033-3054 ◽  
Author(s):  
Scott C. Doney ◽  
Keith Lindsay ◽  
Inez Fung ◽  
Jasmin John
Keyword(s):  
Carbon Cycle ◽  
Surface Temperature ◽  
Time Scale ◽  
Co2 Flux ◽  
Natural Variability ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Climate System Model ◽  
Control Simulation ◽  
Ocean Carbon

Abstract A new 3D global coupled carbon–climate model is presented in the framework of the Community Climate System Model (CSM-1.4). The biogeochemical module includes explicit land water–carbon coupling, dynamic carbon allocation to leaf, root, and wood, prognostic leaf phenology, multiple soil and detrital carbon pools, oceanic iron limitation, a full ocean iron cycle, and 3D atmospheric CO2 transport. A sequential spinup strategy is utilized to minimize the coupling shock and drifts in land and ocean carbon inventories. A stable, 1000-yr control simulation [global annual mean surface temperature ±0.10 K and atmospheric CO2 ± 1.2 ppm (1σ)] is presented with no flux adjustment in either physics or biogeochemistry. The control simulation compares reasonably well against observations for key annual mean and seasonal carbon cycle metrics; regional biases in coupled model physics, however, propagate clearly into biogeochemical error patterns. Simulated interannual-to-centennial variability in atmospheric CO2 is dominated by terrestrial carbon flux variability, ±0.69 Pg C yr−1 (1σ global net annual mean), which in turn reflects primarily regional changes in net primary production modulated by moisture stress. Power spectra of global CO2 fluxes are white on time scales beyond a few years, and thus most of the variance is concentrated at high frequencies (time scale <4 yr). Model variability in air–sea CO2 fluxes, ±0.10 Pg C yr−1 (1σ global annual mean), is generated by variability in sea surface temperature, wind speed, export production, and mixing/upwelling. At low frequencies (time scale >20 yr), global net ocean CO2 flux is strongly anticorrelated (0.7–0.95) with the net CO2 flux from land; the ocean tends to damp (20%–25%) slow variations in atmospheric CO2 generated by the terrestrial biosphere. The intrinsic, unforced natural variability in land and ocean carbon storage is the “noise” that complicates the detection and mechanistic attribution of contemporary anthropogenic carbon sinks.

scholarly journals Deglacial carbon cycle changes observed in a compilation of 127 benthic <i>δ</i><sup>13</sup>C time series (20–6 ka)

2018 ◽  
Vol 14 (8) ◽  
pp. 1229-1252 ◽  
Author(s):  
Carlye D. Peterson ◽  
Lorraine E. Lisiecki
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Carbon Storage ◽  
Large Scale ◽  
Deep Ocean ◽  
Atmospheric Co2 ◽  
Last Deglaciation ◽  
Terrestrial Biosphere ◽  
Spatial Coverage ◽  
Ocean Carbon

Abstract. We present a compilation of 127 time series δ13C records from Cibicides wuellerstorfi spanning the last deglaciation (20–6 ka) which is well-suited for reconstructing large-scale carbon cycle changes, especially for comparison with isotope-enabled carbon cycle models. The age models for the δ13C records are derived from regional planktic radiocarbon compilations (Stern and Lisiecki, 2014). The δ13C records were stacked in nine different regions and then combined using volume-weighted averages to create intermediate, deep, and global δ13C stacks. These benthic δ13C stacks are used to reconstruct changes in the size of the terrestrial biosphere and deep ocean carbon storage. The timing of change in global mean δ13C is interpreted to indicate terrestrial biosphere expansion from 19–6 ka. The δ13C gradient between the intermediate and deep ocean, which we interpret as a proxy for deep ocean carbon storage, matches the pattern of atmospheric CO2 change observed in ice core records. The presence of signals associated with the terrestrial biosphere and atmospheric CO2 indicates that the compiled δ13C records have sufficient spatial coverage and time resolution to accurately reconstruct large-scale carbon cycle changes during the glacial termination.

scholarly journals A fishy tale: A missing part of the inorganic ocean carbon cycle

2011 ◽  
Vol 33 (3) ◽  
pp. 30-34
Author(s):  
Rod W. Wilson ◽  
Erin E. Reardon ◽  
Christopher T. Perry
Keyword(s):  
Carbon Cycle ◽  
Ocean Acidification ◽  
Fossil Fuels ◽  
Sea Water ◽  
Atmospheric Co2 ◽  
Co2 Production ◽  
Ph Change ◽  
Ocean Carbon Cycle ◽  
Sea Levels ◽  
Ocean Carbon

Human activities, such as burning fossil fuels, are playing an important role in the rising levels of carbon dioxide (CO2) in the Earth's atmosphere1. The oceans may store a large portion of CO2 that we are releasing into the atmosphere, with up to 40% already taken up by the oceans. Although this absorption helps to offset some of the greenhouse effect of atmospheric CO2, it also contributes to ocean acidification, or a fall in the pH of sea water. The historical global mean pH of oceanic sea water is about 8.2, and this has already declined by 0.1 pH units (a 30% increase in H+ concentration) and is predicted to reach pH ~7.7 by the end of the century if current rates of fossil fuel use continue, leading to an atmospheric CO2 level of 800 p.p.m.1,2. Even this extreme potential fall in pH would still leave seawater above the neutral point (pH 7.0), so technically it is more accurate to say that the ocean is becoming less alkaline, rather than truly acidic (i.e. below pH 7.0). However, the magnitude is perhaps less important than the speed of pH change which is occurring faster than at any time during the previous 20 million years. Over this time, the average ocean pH has probably never fallen below pH 8.02,3. It is only during the last decade that the importance of ocean acidification has come to the forefront of concerns for scientists1,2. Consequences of these changes in global CO2 production are predicted to include elevated global temperatures, rising sea levels, more unpredictable and extreme weather patterns, and shifts in ecosystems1. In order to more fully understand the implications of ocean acidification, teams of researchers, including fisheries scientists, physiologists, geologists, oceanographers, chemists and climate modellers, are working to refine current understanding of the ocean carbon cycle.

scholarly journals The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

2014 ◽  
Vol 14 (16) ◽  
pp. 23681-23709
Author(s):  
S. M. Miller ◽  
I. Fung ◽  
J. Liu ◽  
M. N. Hayek ◽  
A. E. Andrews
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Meteorological Model ◽  
Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

scholarly journals Sensitivity of atmospheric CO<sub>2</sub> and climate to explosive volcanic eruptions

2011 ◽  
Vol 8 (8) ◽  
pp. 2317-2339 ◽  
Author(s):  
T. L. Frölicher ◽  
F. Joos ◽  
C. C. Raible
Keyword(s):  
Global Warming ◽  
Carbon Cycle ◽  
Surface Temperature ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Time Scales ◽  
Volcanic Eruptions ◽  
Stratospheric Aerosol ◽  
Atmospheric Co2 ◽  
Explosive Volcanic Eruptions

Abstract. Impacts of low-latitude, explosive volcanic eruptions on climate and the carbon cycle are quantified by forcing a comprehensive, fully coupled carbon cycle-climate model with pulse-like stratospheric aerosol optical depth changes. The model represents the radiative and dynamical response of the climate system to volcanic eruptions and simulates a decrease of global and regional atmospheric surface temperature, regionally distinct changes in precipitation, a positive phase of the North Atlantic Oscillation, and a decrease in atmospheric CO2 after volcanic eruptions. The volcanic-induced cooling reduces overturning rates in tropical soils, which dominates over reduced litter input due to soil moisture decrease, resulting in higher land carbon inventories for several decades. The perturbation in the ocean carbon inventory changes sign from an initial weak carbon sink to a carbon source. Positive carbon and negative temperature anomalies in subsurface waters last up to several decades. The multi-decadal decrease in atmospheric CO2 yields a small additional radiative forcing that amplifies the cooling and perturbs the Earth System on longer time scales than the atmospheric residence time of volcanic aerosols. In addition, century-scale global warming simulations with and without volcanic eruptions over the historical period show that the ocean integrates volcanic radiative cooling and responds for different physical and biogeochemical parameters such as steric sea level or dissolved oxygen. Results from a suite of sensitivity simulations with different magnitudes of stratospheric aerosol optical depth changes and from global warming simulations show that the carbon cycle-climate sensitivity γ, expressed as change in atmospheric CO2 per unit change in global mean surface temperature, depends on the magnitude and temporal evolution of the perturbation, and time scale of interest. On decadal time scales, modeled γ is several times larger for a Pinatubo-like eruption than for the industrial period and for a high emission, 21st century scenario.

scholarly journals Sensitivity of atmospheric CO<sub>2</sub> and climate to explosive volcanic eruptions

2011 ◽  
Vol 8 (2) ◽  
pp. 2957-3007 ◽  
Author(s):  
T. L. Frölicher ◽  
F. Joos ◽  
C. C. Raible
Keyword(s):  
Global Warming ◽  
Carbon Cycle ◽  
Surface Temperature ◽  
Time Scales ◽  
Radiative Forcing ◽  
Volcanic Eruptions ◽  
Atmospheric Co2 ◽  
Dynamical Response ◽  
Steric Sea Level ◽  
Explosive Volcanic Eruptions

Abstract. Impacts of low-latitude, explosive volcanic eruptions on climate and the carbon cycle are quantified by forcing a comprehensive, fully coupled carbon cycle-climate model with pulse-like stratospheric sulfur release. The model represents the radiative and dynamical response of the climate system to volcanic eruptions and simulates a decrease of global and regional atmospheric surface temperature, regionally distinct changes in precipitation, a positive phase of the North Atlantic Oscillation, and a decrease in atmospheric CO2 after volcanic eruptions. The volcanic-induced cooling reduces overturning rates in tropical soils, which dominates over reduced litter input due to soil moisture decrease, resulting in higher land carbon inventories for several decades. The perturbation in the ocean carbon inventory changes sign from an initially weak carbon sink to a carbon source. Positive carbon and negative temperature anomalies in subsurface waters last up to several decades. The multi-decadal decrease in atmospheric CO2 yields an additional radiative forcing that amplifies the cooling and perturbs the Earth System on much longer time scales than the atmospheric residence time of volcanic aerosols. In addition, century-scale global warming simulations with and without volcanic eruptions over the historical period show that the ocean integrates volcanic radiative cooling and responds for different physical and biogeochemical parameters such as steric sea level or dissolved oxygen. Results from a suite of sensitivity simulations with different amounts of sulfur released and from global warming simulations show that the carbon cycle-climate sensitivity γ, expressed as change in atmospheric CO2 per unit change in global mean surface temperature, depends on the perturbation. On decadal time scales, modeled γ is several times larger for a Pinatubo-like eruption than for the industrial period and for a high emission, 21st century scenario.

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.

scholarly journals Seasonal variations of sea–air CO<sub>2</sub> fluxes in the largest tropical marginal sea (South China Sea) based on multiple-year underway measurements

2013 ◽  
Vol 10 (11) ◽  
pp. 7775-7791 ◽  
Author(s):  
W.-D. Zhai ◽  
M.-H. Dai ◽  
B.-S. Chen ◽  
X.-H. Guo ◽  
Q. Li ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
River Estuary ◽  
Co2 Flux ◽  
Pearl River Estuary ◽  
Sea Surface ◽  
Atmospheric Co2 ◽  
Pearl River ◽  
Co2 Fluxes ◽  
The Pearl River

Abstract. Based upon 14 field surveys conducted between 2003 and 2008, we showed that the seasonal pattern of sea surface partial pressure of CO2 (pCO2) and sea–air CO2 fluxes differed among four different physical–biogeochemical domains in the South China Sea (SCS) proper. The four domains were located between 7 and 23° N and 110 and 121° E, covering a surface area of 1344 × 103 km2 and accounting for ~ 54% of the SCS proper. In the area off the Pearl River estuary, relatively low pCO2 values of 320 to 390 μatm were observed in all four seasons and both the biological productivity and CO2 uptake were enhanced in summer in the Pearl River plume waters. In the northern SCS slope/basin area, a typical seasonal cycle of relatively high pCO2 in the warm seasons and relatively low pCO2 in the cold seasons was revealed. In the central/southern SCS area, moderately high sea surface pCO2 values of 360 to 425 μatm were observed throughout the year. In the area west of the Luzon Strait, a major exchange pathway between the SCS and the Pacific Ocean, pCO2 was particularly dynamic in winter, when northeast monsoon induced upwelling events and strong outgassing of CO2. These episodic events might have dominated the annual sea–air CO2 flux in this particular area. The estimate of annual sea–air CO2 fluxes showed that most areas of the SCS proper served as weak to moderate sources of the atmospheric CO2, with sea–air CO2 flux values of 0.46 ± 0.43 mol m−2 yr−1 in the northern SCS slope/basin, 1.37 ± 0.55 mol m−2 yr−1 in the central/southern SCS, and 1.21 ± 1.48 mol m−2 yr−1 in the area west of the Luzon Strait. However, the annual sea–air CO2 exchange was nearly in equilibrium (−0.44 ± 0.65 mol m−2 yr−1) in the area off the Pearl River estuary. Overall the four domains contributed (18 ± 10) × 1012 g C yr−1 to the atmospheric CO2.

scholarly journals Twentieth-Century Oceanic Carbon Uptake and Storage in CESM1(BGC)*

2013 ◽  
Vol 26 (18) ◽  
pp. 6775-6800 ◽  
Author(s):  
Matthew C. Long ◽  
Keith Lindsay ◽  
Synte Peacock ◽  
J. Keith Moore ◽  
Scott C. Doney
Keyword(s):  
Climate Variability ◽  
Carbon Cycle ◽  
North Atlantic ◽  
Carbon Uptake ◽  
Dissolution Profile ◽  
Co2 Fluxes ◽  
Zone Formation ◽  
The North ◽  
Ocean Carbon ◽  
And Storage

Abstract Ocean carbon uptake and storage simulated by the Community Earth System Model, version 1–Biogeochemistry [CESM1(BGC)], is described and compared to observations. Fully coupled and ocean-ice configurations are examined; both capture many aspects of the spatial structure and seasonality of surface carbon fields. Nearly ubiquitous negative biases in surface alkalinity result from the prescribed carbonate dissolution profile. The modeled sea–air CO2 fluxes match observationally based estimates over much of the ocean; significant deviations appear in the Southern Ocean. Surface ocean pCO2 is biased high in the subantarctic and low in the sea ice zone. Formation of the water masses dominating anthropogenic CO2 (Cant) uptake in the Southern Hemisphere is weak in the model, leading to significant negative biases in Cant and chlorofluorocarbon (CFC) storage at intermediate depths. Column inventories of Cant appear too high, by contrast, in the North Atlantic. In spite of the positive bias, this marks an improvement over prior versions of the model, which underestimated North Atlantic uptake. The change in behavior is attributable to a new parameterization of density-driven overflows. CESM1(BGC) provides a relatively robust representation of the ocean–carbon cycle response to climate variability. Statistical metrics of modeled interannual variability in sea–air CO2 fluxes compare reasonably well to observationally based estimates. The carbon cycle response to key modes of climate variability is basically similar in the coupled and forced ocean-ice models; however, the two differ in regional detail and in the strength of teleconnections.

scholarly journals Sea–air CO<sub>2</sub> fluxes in the Indian Ocean between 1990 and 2009

2013 ◽  
Vol 10 (11) ◽  
pp. 7035-7052 ◽  
Author(s):  
V. V. S. S. Sarma ◽  
A. Lenton ◽  
R. M. Law ◽  
N. Metzl ◽  
P. K. Patra ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Carbon Cycle ◽  
Interannual Variability ◽  
Southern Annular Mode ◽  
Atmospheric Co2 ◽  
Co2 Uptake ◽  
Co2 Fluxes ◽  
Biogeochemical Models ◽  
The Indian Ocean ◽  
Atmospheric Inversions

Abstract. The Indian Ocean (44° S–30° N) plays an important role in the global carbon cycle, yet it remains one of the most poorly sampled ocean regions. Several approaches have been used to estimate net sea–air CO2 fluxes in this region: interpolated observations, ocean biogeochemical models, atmospheric and ocean inversions. As part of the RECCAP (REgional Carbon Cycle Assessment and Processes) project, we combine these different approaches to quantify and assess the magnitude and variability in Indian Ocean sea–air CO2 fluxes between 1990 and 2009. Using all of the models and inversions, the median annual mean sea–air CO2 uptake of −0.37 ± 0.06 PgC yr−1 is consistent with the −0.24 ± 0.12 PgC yr−1 calculated from observations. The fluxes from the southern Indian Ocean (18–44° S; −0.43 ± 0.07 PgC yr−1 are similar in magnitude to the annual uptake for the entire Indian Ocean. All models capture the observed pattern of fluxes in the Indian Ocean with the following exceptions: underestimation of upwelling fluxes in the northwestern region (off Oman and Somalia), overestimation in the northeastern region (Bay of Bengal) and underestimation of the CO2 sink in the subtropical convergence zone. These differences were mainly driven by lack of atmospheric CO2 data in atmospheric inversions, and poor simulation of monsoonal currents and freshwater discharge in ocean biogeochemical models. Overall, the models and inversions do capture the phase of the observed seasonality for the entire Indian Ocean but overestimate the magnitude. The predicted sea–air CO2 fluxes by ocean biogeochemical models (OBGMs) respond to seasonal variability with strong phase lags with reference to climatological CO2 flux, whereas the atmospheric inversions predicted an order of magnitude higher seasonal flux than OBGMs. The simulated interannual variability by the OBGMs is weaker than that found by atmospheric inversions. Prediction of such weak interannual variability in CO2 fluxes by atmospheric inversions was mainly caused by a lack of atmospheric data in the Indian Ocean. The OBGM models suggest a small strengthening of the sink over the period 1990–2009 of −0.01 PgC decade−1. This is inconsistent with the observations in the southwestern Indian Ocean that shows the growth rate of oceanic pCO2 was faster than the observed atmospheric CO2 growth, a finding attributed to the trend of the Southern Annular Mode (SAM) during the 1990s.

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.

Sign in / Sign up

Export Citation Format

Share Document