scholarly journals Sources of Uncertainty in Future Projections of the Carbon Cycle

2016 ◽  
Vol 29 (20) ◽  
pp. 7203-7213 ◽  
Author(s):  
Alan J. Hewitt ◽  
Ben B. B. Booth ◽  
Chris D. Jones ◽  
Eddy S. Robertson ◽  
Andy J. Wiltshire ◽  
...  

Abstract The inclusion of carbon cycle processes within CMIP5 Earth system models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way analysis of variance (ANOVA) approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between representative concentration pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modeled processes that determine ocean fluxes are currently better constrained than those of land fluxes; thus, one can be more confident in linking different future socioeconomic pathways to consequences of ocean carbon uptake than for land carbon uptake. The contribution of internal variance is negligible for ocean fluxes and small for land fluxes, indicating that there is little dependence on the initial conditions. The apparent agreement in atmosphere–ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modeled processes represent an important source of variability in projected regional fluxes.

2013 ◽  
Vol 9 (3) ◽  
pp. 1111-1140 ◽  
Author(s):  
M. Eby ◽  
A. J. Weaver ◽  
K. Alexander ◽  
K. Zickfeld ◽  
A. Abe-Ouchi ◽  
...  

Abstract. Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate–carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2 × and 4 × CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate–carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from palaeoclimate reconstructions. This in turn could be a result of unforced variability within the climate system, uncertainty in the reconstructions of temperature and CO2, errors in the reconstructions of forcing used to drive the models, or the incomplete representation of certain processes within the models. Given the forcing datasets used in this study, the models calculate significant land-use emissions over the pre-industrial period. This implies that land-use emissions might need to be taken into account, when making estimates of climate–carbon feedbacks from palaeoclimate reconstructions.


2013 ◽  
Vol 26 (18) ◽  
pp. 6775-6800 ◽  
Author(s):  
Matthew C. Long ◽  
Keith Lindsay ◽  
Synte Peacock ◽  
J. Keith Moore ◽  
Scott C. Doney

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.


2015 ◽  
Vol 29 (1) ◽  
pp. 259-272 ◽  
Author(s):  
Mátyás Herein ◽  
János Márfy ◽  
Gábor Drótos ◽  
Tamás Tél

Abstract A time series resulting from a single initial condition is shown to be insufficient for quantifying the internal variability in a climate model, and thus one is unable to make meaningful climate projections based on it. The authors argue that the natural distribution, obtained from an ensemble of trajectories differing solely in their initial conditions, of the snapshot attractor corresponding to a particular forcing scenario should be determined in order to quantify internal variability and to characterize any instantaneous state of the system in the future. Furthermore, as a simple measure of internal variability of any particular variable of the model, the authors suggest using its instantaneous ensemble standard deviation. These points are illustrated with the intermediate-complexity climate model Planet Simulator forced by a CO2 scenario, with a 40-member ensemble. In particular, the leveling off of the time dependence of any ensemble average is shown to provide a much clearer indication of reaching a steady state than any property of single time series. Shifts in ensemble averages are indicative of climate changes. The dynamical character of such changes is illustrated by hysteresis-like curves obtained by plotting the ensemble average surface temperature versus the CO2 concentration. The internal variability is found to be the most pronounced on small geographical scales. The traditionally used 30-yr temporal averages are shown to be considerably different from the corresponding ensemble averages. Finally, the North Atlantic Oscillation (NAO) index, related to the teleconnection paradigm, is also investigated. It is found that the NAO time series strongly differs in any individual realization from each other and from the ensemble average, and climatic trends can be extracted only from the latter.


2018 ◽  
Vol 10 (1) ◽  
pp. 609-626 ◽  
Author(s):  
Rebecca Latto ◽  
Anastasia Romanou

Abstract. In this paper, we present a database of the basic regimes of the carbon cycle in the ocean, the “ocean carbon states”, as obtained using a data mining/pattern recognition technique in observation-based as well as model data. The goal of this study is to establish a new data analysis methodology, test it and assess its utility in providing more insights into the regional and temporal variability of the marine carbon cycle. This is important as advanced data mining techniques are becoming widely used in climate and Earth sciences and in particular in studies of the global carbon cycle, where the interaction of physical and biogeochemical drivers confounds our ability to accurately describe, understand, and predict CO2 concentrations and their changes in the major planetary carbon reservoirs. In this proof-of-concept study, we focus on using well-understood data that are based on observations, as well as model results from the NASA Goddard Institute for Space Studies (GISS) climate model. Our analysis shows that ocean carbon states are associated with the subtropical–subpolar gyre during the colder months of the year and the tropics during the warmer season in the North Atlantic basin. Conversely, in the Southern Ocean, the ocean carbon states can be associated with the subtropical and Antarctic convergence zones in the warmer season and the coastal Antarctic divergence zone in the colder season. With respect to model evaluation, we find that the GISS model reproduces the cold and warm season regimes more skillfully in the North Atlantic than in the Southern Ocean and matches the observed seasonality better than the spatial distribution of the regimes. Finally, the ocean carbon states provide useful information in the model error attribution. Model air–sea CO2 flux biases in the North Atlantic stem from wind speed and salinity biases in the subpolar region and nutrient and wind speed biases in the subtropics and tropics. Nutrient biases are shown to be most important in the Southern Ocean flux bias. All data and analysis scripts are available at https://data.giss.nasa.gov/oceans/carbonstates/ (DOI: https://doi.org/10.5281/zenodo.996891).


2012 ◽  
Vol 8 (4) ◽  
pp. 4121-4181 ◽  
Author(s):  
M. Eby ◽  
A. J. Weaver ◽  
K. Alexander ◽  
K. Zickfeld ◽  
A. Abe-Ouchi ◽  
...  

Abstract. Both historical and idealized climate model experiments are performed with a variety of Earth System Models of Intermediate Complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land-use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes seem to be underestimated. It is possible that recent modelled climate trends or climate-carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2x and 4x CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate-carbon feedbacks. The values from EMICs generally fall within the range given by General Circulation Models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows considerable synergy between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from paleoclimate reconstructions. This in turn could be a result of errors in the reconstructions of volcanic and/or solar radiative forcing used to drive the models or the incomplete representation of certain processes or variability within the models. Given the datasets used in this study, the models calculate significant land-use emissions over the pre-industrial. This implies that land-use emissions might need to be taken into account, when making estimates of climate-carbon feedbacks from paleoclimate reconstructions.


2013 ◽  
Vol 10 (9) ◽  
pp. 15033-15076 ◽  
Author(s):  
K. B. Rodgers ◽  
O. Aumont ◽  
S. E. Mikaloff Fletcher ◽  
Y. Plancherel ◽  
L. Bopp ◽  
...  

Abstract. Here we test the hypothesis that winds have an important role in determining the rate of exchange of CO2 between the atmosphere and ocean through wind stirring over the Southern Ocean. This is tested with a sensitivity study using an ad hoc parameterization of wind stirring in an ocean carbon cycle model. The objective is to identify the way in which perturbations to the vertical density structure of the planetary boundary in the ocean impacts the carbon cycle and ocean biogeochemistry. Wind stirring leads to reduced uptake of CO2 by the Southern Ocean over the period 2000–2006, with differences of order 0.9 Pg C yr−1 over the region south of 45° S. Wind stirring impacts not only the mean carbon uptake, but also the phasing of the seasonal cycle of carbon and other species associated with ocean biogeochemistry. Enhanced wind stirring delays the seasonal onset of stratification, and this has large impacts on both entrainment and the biological pump. It is also found that there is a strong sensitivity of nutrient concentrations exported in Subantarctic Mode Water (SAMW) to wind stirring. This finds expression not only locally over the Southern Ocean, but also over larger scales through the impact on advected nutrients. In summary, the large sensitivity identified with the ad hoc wind stirring parameterization offers support for the importance of wind stirring for global ocean biogeochemistry, through its impact over the Southern Ocean.


2011 ◽  
Vol 24 (16) ◽  
pp. 4255-4275 ◽  
Author(s):  
Kirsten Zickfeld ◽  
Michael Eby ◽  
H. Damon Matthews ◽  
Andreas Schmittner ◽  
Andrew J. Weaver

Abstract Coupled climate–carbon models have shown the potential for large feedbacks between climate change, atmospheric CO2 concentrations, and global carbon sinks. Standard metrics of this feedback assume that the response of land and ocean carbon uptake to CO2 (concentration–carbon cycle feedback) and climate change (climate–carbon cycle feedback) combine linearly. This study explores the linearity in the carbon cycle response by analyzing simulations with an earth system model of intermediate complexity [the University of Victoria Earth System Climate Model (UVic ESCM)]. The results indicate that the concentration–carbon and climate–carbon cycle feedbacks do not combine linearly to the overall carbon cycle feedback. In this model, the carbon sinks on land and in the ocean are less efficient when exposed to the combined effect of elevated CO2 and climate change than to the linear combination of the two. The land accounts for about 80% of the nonlinearity, with the ocean accounting for the remaining 20%. On land, this nonlinearity is associated with the different response of vegetation and soil carbon uptake to climate in the presence or absence of the CO2 fertilization effect. In the ocean, the nonlinear response is caused by the interaction of changes in physical properties and anthropogenic CO2. These findings suggest that metrics of carbon cycle feedback that postulate linearity in the system’s response may not be adequate.


2014 ◽  
Vol 11 (15) ◽  
pp. 4077-4098 ◽  
Author(s):  
K. B. Rodgers ◽  
O. Aumont ◽  
S. E. Mikaloff Fletcher ◽  
Y. Plancherel ◽  
L. Bopp ◽  
...  

Abstract. Here we test the hypothesis that winds have an important role in determining the rate of exchange of CO2 between the atmosphere and ocean through wind stirring over the Southern Ocean. This is tested with a sensitivity study using an ad hoc parameterization of wind stirring in an ocean carbon cycle model, where the objective is to identify the way in which perturbations to the vertical density structure of the planetary boundary in the ocean impacts the carbon cycle and ocean biogeochemistry. Wind stirring leads to reduced uptake of CO2 by the Southern Ocean over the period 2000–2006, with a relative reduction with wind stirring on the order of 0.9 Pg C yr−1 over the region south of 45° S. This impacts not only the mean carbon uptake, but also the phasing of the seasonal cycle of carbon and other ocean biogeochemical tracers. Enhanced wind stirring delays the seasonal onset of stratification, and this has large impacts on both entrainment and the biological pump. It is also found that there is a strong reduction on the order of 25–30% in the concentrations of NO3 exported in Subantarctic Mode Water (SAMW) to wind stirring. This finds expression not only locally over the Southern Ocean, but also over larger scales through the impact on advected nutrients. In summary, the large sensitivity identified with the ad hoc wind stirring parameterization offers support for the importance of wind stirring for global ocean biogeochemistry through its impact over the Southern Ocean.


2017 ◽  
Vol 21 (11) ◽  
pp. 5747-5762 ◽  
Author(s):  
Rachel Bazile ◽  
Marie-Amélie Boucher ◽  
Luc Perreault ◽  
Robert Leconte

Abstract. Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.


2016 ◽  
Author(s):  
Davide Zanchettin ◽  
Myriam Khodri ◽  
Claudia Timmreck ◽  
Matthew Toohey ◽  
Anja Schmidt ◽  
...  

Abstract. The enhancement of the stratospheric aerosol layer by volcanic eruptions induces a complex set of responses causing global and regional climate effects on a broad range of timescales. Uncertainties exist regarding the climatic response to strong volcanic forcing identified in coupled climate simulations that contributed to the fifth phase of the Climate Model Intercomparison Project (CMIP5). In order to better understand the sources of these model diversities, the model intercomparison project on the climate response to volcanic forcing (VolMIP) has defined a coordinated set of idealized volcanic perturbation experiments to be carried out in alignment with the CMIP6 protocol. VolMIP provides a common stratospheric aerosol dataset for each experiment to eliminate differences in the applied volcanic forcing, and defines a set of initial conditions to determine how internal climate variability contributes to determining the response. VolMIP will assess to what extent volcanically-forced responses of the coupled ocean-atmosphere system are robustly simulated by state-of-the-art coupled climate models and identify the causes that limit robust simulated behavior, especially differences in the treatment of physical processes. This paper illustrates the design of the idealized volcanic perturbation experiments in the VolMIP protocol and describes the common aerosol forcing input datasets to be used.


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