scholarly journals Limitations of the 1 % experiment as the benchmark idealized experiment for carbon cycle intercomparison in C<sup>4</sup>MIP

2019 ◽  
Vol 12 (2) ◽  
pp. 597-611 ◽  
Author(s):  
Andrew Hugh MacDougall
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Smooth Transition ◽  
Model Intercomparison ◽  
Carbon Cycle Model ◽  
Atmospheric Co2 Concentration ◽  
Benchmark Experiment ◽  
Intercomparison Project

Abstract. Idealized climate change simulations are used as benchmark experiments to facilitate the comparison of ensembles of climate models. In the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the 1 % per yearly compounded change in atmospheric CO2 concentration experiment was used to compare Earth system models with full representations of the global carbon cycle in the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP). However, this “1 % experiment” was never intended for such a purpose and implies a rise in atmospheric CO2 concentration at double the rate of the instrumental record. Here, we examine this choice by using an intermediate complexity climate model to compare the 1 % experiment to an idealized CO2 pathway derived from a logistic function. The comparison shows three key differences in model output when forcing the model with the logistic experiment. (1) The model forced with the logistic experiment exhibits a transition of the land biosphere from a carbon sink to a carbon source, a feature absent when forcing the model with the 1 % experiment. (2) The ocean uptake of carbon comes to dominate the carbon cycle as emissions decline, a feature that cannot be captured when forcing a model with the 1 % experiment, as emissions always increase in that experiment. (3) The permafrost carbon feedback to climate change under the 1 % experiment forcing is less than half the strength of the feedback seen under logistic experiment forcing. Using the logistic experiment also allows smooth transition to zero or negative emissions states, allowing these states to be examined without sharp discontinuities in CO2 emissions. The protocol for the CMIP6 iteration of C4MIP again sets the 1 % experiment as the benchmark experiment for model intercomparison; however, clever use of the Tier 2 experiments may alleviate some of the limitations outlined here. Given the limitations of the 1 % experiment as the benchmark experiment for carbon cycle intercomparisons, adding a logistic or similar idealized experiment to the protocol of the CMIP7 iteration of C4MIP is recommended.

scholarly journals Limitations of the 1 % experiment as the benchmark idealized experiment for carbon cycle intercomparison in C<sup>4</sup>MIP

2018 ◽  
Author(s):  
Andrew H. MacDougall
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Climate Models ◽  
Climate Model ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Smooth Transition ◽  
Atmospheric Co2 Concentration ◽  
Benchmark Experiment

Abstract. Idealized climate change simulations are used as benchmark experiments to facilitate the comparison of ensembles of climate models. In the Fifth Assessment Report of the IPCC the 1 % per yearly compounded change in atmospheric CO2 concentration experiment was used to compare Earth System Models with full representations of the global carbon cycle (C4MIP). However this ``1 % experiment'' was never intended for such a purpose and implies a rise in atmospheric CO2 concentration at double the rate of the instrumental record. Here we examine this choice by using an intermediate complexity climate model to compare the 1 % experiment to an idealized CO2 pathway derived from a logistic function. The comparison shows that the logistic experiment has three key differences from the 1 % experiment. (1) The Logistic experiment exhibits a transition of the land biosphere from a carbon sink to a carbon source, a feature absent from the 1 % experiment. (2) The ocean uptake of carbon comes to dominate the carbon cycle as emissions decelerate, a feature that cannot be captured by the 1 % experiment as emissions always accelerate in that experiment. (3) The permafrost carbon feedback to climate change in the 1 % experiment is less than half the strength of the feedback seen in the logistic experiment. The logistic experiment also allows smooth transition to zero or negative emission states, allowing these states to be examined without sharp discontinuities in CO2 emissions. The protocol for the CMIP6 iteration of C4MIP again sets the 1 % experiment as the benchmark experiment for model intercomparison, however clever use of the Tier 2 experiments may alleviate some of the limitations outlined here. Given the limitations of the 1 % experiment as the benchmark experiment for carbon cycle intercomparisons, adding a logistic or similar idealized experiment to the protocol of the CMIP7 iteration of C4MIP is recommended.

scholarly journals Quantifying Carbon Cycle Feedbacks

2009 ◽  
Vol 22 (19) ◽  
pp. 5232-5250 ◽  
Author(s):  
J. M. Gregory ◽  
C. D. Jones ◽  
P. Cadule ◽  
P. Friedlingstein
Keyword(s):  
Carbon Cycle ◽  
Co2 Emissions ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Climate Feedback ◽  
Climate Response ◽  
Carbon Cycle Model ◽  
Atmospheric Co2 Concentration ◽  
Intercomparison Project ◽  
Earth System Models

Abstract Perturbations to the carbon cycle could constitute large feedbacks on future changes in atmospheric CO2 concentration and climate. This paper demonstrates how carbon cycle feedback can be expressed in formally similar ways to climate feedback, and thus compares their magnitudes. The carbon cycle gives rise to two climate feedback terms: the concentration–carbon feedback, resulting from the uptake of carbon by land and ocean as a biogeochemical response to the atmospheric CO2 concentration, and the climate–carbon feedback, resulting from the effect of climate change on carbon fluxes. In the earth system models of the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP), climate–carbon feedback on warming is positive and of a similar size to the cloud feedback. The concentration–carbon feedback is negative; it has generally received less attention in the literature, but in magnitude it is 4 times larger than the climate–carbon feedback and more uncertain. The concentration–carbon feedback is the dominant uncertainty in the allowable CO2 emissions that are consistent with a given CO2 concentration scenario. In modeling the climate response to a scenario of CO2 emissions, the net carbon cycle feedback is of comparable size and uncertainty to the noncarbon–climate response. To quantify simulated carbon cycle feedbacks satisfactorily, a radiatively coupled experiment is needed, in addition to the fully coupled and biogeochemically coupled experiments, which are referred to as coupled and uncoupled in C4MIP. The concentration–carbon and climate–carbon feedbacks do not combine linearly, and the concentration–carbon feedback is dependent on scenario and time.

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 C4MIP – The Coupled Climate–Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6

2016 ◽  
Vol 9 (8) ◽  
pp. 2853-2880 ◽  
Author(s):  
Chris D. Jones ◽  
Vivek Arora ◽  
Pierre Friedlingstein ◽  
Laurent Bopp ◽  
Victor Brovkin ◽  
...  
Keyword(s):  
Experimental Design ◽  
Carbon Cycle ◽  
Boundary Conditions ◽  
Anthropogenic Activity ◽  
Climate Modelling ◽  
Model Intercomparison ◽  
Cycle Model ◽  
Carbon Cycle Model ◽  
Intercomparison Project ◽  
Set Up

Abstract. Coordinated experimental design and implementation has become a cornerstone of global climate modelling. Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models, by reducing the influence of ad hoc differences in model set-up or experimental boundary conditions. As it enters its 6th phase, the Coupled Model Intercomparison Project (CMIP6) has grown significantly in scope with the design and documentation of individual simulations delegated to individual climate science communities. The Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation, and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading-order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilize climate or avoid dangerous climate change. For over a decade, C4MIP has coordinated coupled climate–carbon cycle simulations, and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate–carbon cycle community has created this experimental design, the simulations also fit within the wider CMIP activity, conform to some common standards including documentation and diagnostic requests, and are designed to complement the CMIP core experiments known as the Diagnostic, Evaluation and Characterization of Klima (DECK). C4MIP has three key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation, (2) idealized coupled and partially coupled simulations with 1 % per year increases in CO2 to enable diagnosis of feedback strength and its components, (3) future scenario simulations to project how the Earth system will respond to anthropogenic activity over the 21st century and beyond. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set up and run the simulations. Particular attention is paid to boundary conditions, input data, and requested output diagnostics. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design.

scholarly journals The influence of vegetation dynamics on anthropogenic climate change

2012 ◽  
Vol 3 (2) ◽  
pp. 233-243 ◽  
Author(s):  
U. Port ◽  
V. Brovkin ◽  
M. Claussen
Keyword(s):  
Climate Change ◽  
Vegetation Cover ◽  
Vegetation Dynamics ◽  
Global Climate ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Tree Cover ◽  
Earth System ◽  
Atmospheric Co2 Concentration ◽  
Global Mean

Abstract. In this study, vegetation–climate and vegetation–carbon cycle interactions during anthropogenic climate change are assessed by using the Earth System Model of the Max Planck Institute for Meteorology (MPI ESM) that includes vegetation dynamics and an interactive carbon cycle. We assume anthropogenic CO2 emissions according to the RCP 8.5 scenario in the time period from 1850 to 2120. For the time after 2120, we assume zero emissions to evaluate the response of the stabilising Earth System by 2300. Our results suggest that vegetation dynamics have a considerable influence on the changing global and regional climate. In the simulations, global mean tree cover extends by 2300 due to increased atmospheric CO2 concentration and global warming. Thus, land carbon uptake is higher and atmospheric CO2 concentration is lower by about 40 ppm when considering dynamic vegetation compared to the static pre-industrial vegetation cover. The reduced atmospheric CO2 concentration is equivalent to a lower global mean temperature. Moreover, biogeophysical effects of vegetation cover shifts influence the climate on a regional scale. Expanded tree cover in the northern high latitudes results in a reduced albedo and additional warming. In the Amazon region, declined tree cover causes a regional warming due to reduced evapotranspiration. As a net effect, vegetation dynamics have a slight attenuating effect on global climate change as the global climate cools by 0.22 K due to natural vegetation cover shifts in 2300.

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 &amp;pm; 0.4 PgC yr−1, ELUC 1.0 &amp;pm; 0.5 PgC yr−1, GATM 4.3 &amp;pm; 0.1 PgC yr−1, SOCEAN 2.5 &amp;pm; 0.5 PgC yr−1, and SLAND 2.6 &amp;pm; 0.8 PgC yr−1. For year 2011 alone, EFF was 9.5 &amp;pm; 0.5 PgC yr−1, 3.0 percent above 2010, reflecting a continued trend in these emissions; ELUC was 0.9 &amp;pm; 0.5 PgC yr−1, approximately constant throughout the decade; GATM was 3.6 &amp;pm; 0.2 PgC yr−1, SOCEAN was 2.7 &amp;pm; 0.5 PgC yr−1, and SLAND was 4.1 &amp;pm; 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 &amp;pm; 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 &amp;pm;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 The C4MIP experimental protocol for CMIP6

2016 ◽  
Author(s):  
Chris D. Jones ◽  
Vivek Arora ◽  
Pierre Friedlingstein ◽  
Laurent Bopp ◽  
Victor Brovkin ◽  
...  
Keyword(s):  
Experimental Design ◽  
Carbon Cycle ◽  
Boundary Conditions ◽  
Atmospheric Composition ◽  
Anthropogenic Activity ◽  
Climate Modelling ◽  
Model Intercomparison ◽  
Carbon Cycle Model ◽  
Intercomparison Project ◽  
Set Up

Abstract. Coordinated experimental design and implementation has become a cornerstone of global climate modelling. So-called Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models to identify common signals and understand model similarities and differences without being hindered by ad-hoc differences in model set-up or experimental boundary conditions. The activity known as the Coupled Model Intercomparison Project (CMIP) has thus grown significantly in scope and as it enters its 6th phase, CMIP6, the design and documentation of individual simulations has been devolved to individual climate science communities. The Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilise climate or avoid dangerous climate change. For over a decade C4MIP has coordinated coupled climate-carbon cycle simulations and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate-carbon cycle community has formed this experimental design the simulations also fit into the wider CMIP activity and conform to some common standards such as documentation and diagnostic requests and are designed to complement the CMIP core experiments known as the DECK. C4MIP has 3 key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation; (2) idealised coupled and partially-coupled simulations with 1 % per year increases in CO2 to enable diagnosis of feedback strength and its components; (3) future scenario simulations to project how the Earth System will respond over the 21st century and beyond to anthropogenic activity. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set-up and run the simulations. Particular attention is paid to boundary conditions and input data required, and also the output diagnostics requested. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design.

scholarly journals What was the source of the atmospheric CO<sub>2</sub> increase during the Holocene?

2019 ◽  
Vol 16 (13) ◽  
pp. 2543-2555 ◽  
Author(s):  
Victor Brovkin ◽  
Stephan Lorenz ◽  
Thomas Raddatz ◽  
Tatiana Ilyina ◽  
Irene Stemmler ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Ice Core ◽  
Carbon Sources ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Ion Concentration ◽  
Model Framework ◽  
Atmospheric Co2 Concentration ◽  
Transient Simulations

Abstract. The atmospheric CO2 concentration increased by about 20 ppm from 6000 BCE to the pre-industrial period (1850 CE). Several hypotheses have been proposed to explain mechanisms of this CO2 growth based on either ocean or land carbon sources. Here, we apply the Earth system model MPI-ESM-LR for two transient simulations of climate and carbon cycle dynamics during this period. In the first simulation, atmospheric CO2 is prescribed following ice-core CO2 data. In response to the growing atmospheric CO2 concentration, land carbon storage increases until 2000 BCE, stagnates afterwards, and decreases from 1 CE, while the ocean continuously takes CO2 out of the atmosphere after 4000 BCE. This leads to a missing source of 166 Pg of carbon in the ocean–land–atmosphere system by the end of the simulation. In the second experiment, we applied a CO2 nudging technique using surface alkalinity forcing to follow the reconstructed CO2 concentration while keeping the carbon cycle interactive. In that case the ocean is a source of CO2 from 6000 to 2000 BCE due to a decrease in the surface ocean alkalinity. In the prescribed CO2 simulation, surface alkalinity declines as well. However, it is not sufficient to turn the ocean into a CO2 source. The carbonate ion concentration in the deep Atlantic decreases in both the prescribed and the interactive CO2 simulations, while the magnitude of the decrease in the prescribed CO2 experiment is underestimated in comparison with available proxies. As the land serves as a carbon sink until 2000 BCE due to natural carbon cycle processes in both experiments, the missing source of carbon for land and atmosphere can only be attributed to the ocean. Within our model framework, an additional mechanism, such as surface alkalinity decrease, for example due to unaccounted for carbonate accumulation processes on shelves, is required for consistency with ice-core CO2 data. Consequently, our simulations support the hypothesis that the ocean was a source of CO2 until the late Holocene when anthropogenic CO2 sources started to affect atmospheric CO2.

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