GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-11-1133-2018The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP):
rationale and experimental protocol for CMIP6The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP)KellerDavid P.dkeller@geomar.deLentonAndrewScottVivianVaughanNaomi E.BauerNicoJiDuoyinghttps://orcid.org/0000-0002-1887-887XJonesChris D.https://orcid.org/0000-0002-7141-9285KravitzBenhttps://orcid.org/0000-0001-6318-1150MuriHelenehttps://orcid.org/0000-0003-4738-493XZickfeldKirstenhttps://orcid.org/0000-0001-8866-6541GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, GermanyCSIRO Oceans and Atmosphere, Hobart, AustraliaAntarctic Climate and Ecosystems Cooperative Research Centre, Hobart,
AustraliaSchool of GeoSciences, University of Edinburgh, Edinburgh, UKTyndall Centre for Climate Change Research, School of Environmental
Sciences, University of East Anglia, Norwich, UKPotsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, GermanyCollege of Global Change and Earth System Science, Beijing Normal
University, Beijing, ChinaMet Office Hadley Centre, Exeter, UKAtmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USADepartment of Geosciences, University of Oslo, Oslo, NorwayDepartment of Geography, Simon Fraser University, Burnaby, British Columbia, CanadaDavid P. Keller (dkeller@geomar.de)29March20181131133116011July201717August20177December201719December2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://gmd.copernicus.org/articles/11/1133/2018/gmd-11-1133-2018.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/11/1133/2018/gmd-11-1133-2018.pdf
The recent IPCC reports state that continued anthropogenic greenhouse gas
emissions are changing the climate, threatening “severe, pervasive and
irreversible” impacts. Slow progress in emissions reduction to mitigate
climate change is resulting in increased attention to what is called
geoengineering, climate engineering, or climate intervention –
deliberate interventions to counter climate change that seek to either modify
the Earth's radiation budget or remove greenhouse gases such as CO2 from
the atmosphere. When focused on CO2, the latter of these categories is
called carbon dioxide removal (CDR). Future emission scenarios that stay well
below 2 ∘C, and all emission scenarios that do not exceed
1.5 ∘C warming by the year 2100, require some form of CDR. At
present, there is little consensus on the climate impacts and atmospheric
CO2 reduction efficacy of the different types of proposed CDR. To
address this need, the Carbon Dioxide Removal Model Intercomparison Project
(or CDRMIP) was initiated. This project brings together models of the Earth
system in a common framework to explore the potential, impacts, and
challenges of CDR. Here, we describe the first set of CDRMIP experiments,
which are formally part of the 6th Coupled Model Intercomparison Project
(CMIP6). These experiments are
designed to address questions concerning CDR-induced climate
“reversibility”, the response of the Earth system to direct atmospheric
CO2 removal (direct air capture and storage), and the CDR potential and
impacts of afforestation and reforestation, as well as ocean alkalinization.
Introduction
The Earth system is sensitive to the concentration of
atmospheric greenhouse gases (GHGs) because they have a direct impact on the
planetary energy balance (Hansen, 2005) and in many cases also on
biogeochemical cycling (IPCC, 2013). The concentration of one particularly
important GHG, carbon dioxide (CO2), has increased from approximately
277 ppm in the year 1750 to over 400 ppm today as a result of anthropogenic
activities (Dlugokencky and Tans, 2016; Le Quéré et al., 2015). This
CO2 increase, along with other GHG increases and anthropogenic
activities (e.g., land use change), has perturbed the Earth's energy balance,
leading to an observed global mean surface air temperature increase of around
0.8 ∘C above preindustrial (year 1850) levels in the year 2015
(updated from Morice et al., 2012). Biogeochemistry on land and in the ocean
has also been affected by the increase in CO2, with a well-observed
decrease in ocean pH being one of the most notable results
(Gruber, 2011; Hofmann and
Schellnhuber, 2010). Many of the changes attributed to this rapid
temperature increase and perturbation of the carbon cycle have been
detrimental for natural and human systems (IPCC, 2014a).
While recent trends suggest that the atmospheric CO2 concentration is
likely to continue to increase (Peters et al., 2013; Riahi et al., 2017), the
Paris Agreement of the 21st session of the Conference of Parties (COP21) on
climate change (UNFCCC, 2016) has set the goal of limiting anthropogenic
warming to well below 2 ∘C (ideally no more than 1.5 ∘C)
relative to the global mean preindustrial temperature. To do this a massive
climate change mitigation effort to reduce the sources or enhance the sinks
of greenhouse gases (IPCC, 2014b) must be undertaken. Even if significant
efforts are made to reduce CO2 emissions, it will likely take decades
before net emissions approach zero (Bauer et al., 2017; Riahi et al., 2017;
Rogelj et al., 2015a), a level that is likely required to reach and maintain
such temperature targets (Rogelj et al., 2015b). Changes in the climate will
therefore continue for some time, with future warming strongly dependent on
cumulative CO2 emissions (Allen et al., 2009; IPCC, 2013; Matthews et
al., 2009), and there is the possibility that “severe, pervasive and
irreversible” impacts will occur if too much CO2 is emitted (IPCC,
2013, 2014a). The lack of agreement on how to sufficiently reduce CO2
emissions in a timely manner and the magnitude of the task required to
transition to a low carbon world has led to increased attention to what is
called geoengineering, climate engineering, or
climate intervention. These terms are all used to define actions
that deliberately manipulate the climate system in an attempt to
ameliorate or reduce the impact of climate change by either modifying the
Earth's radiation budget (solar radiation management, or SRM) or removing
the primary greenhouse gas, CO2, from the atmosphere (carbon dioxide
removal, or CDR; National Research Council, 2015). In particular, there is
an increasing focus and study on the potential of carbon dioxide removal
(CDR) methods to offset emissions and eventually enable “net negative
emissions”, whereby more CO2 is removed via CDR than is emitted by
anthropogenic activities, to complement emissions reduction efforts. CDR has
also been proposed as a means of “reversing” climate change if too much
CO2 is emitted; i.e., CDR may be able to reduce atmospheric CO2 to
return radiative forcing to some target level.
All integrated assessment model (IAM) scenarios of the future state that some
form of CDR will be needed to prevent the mean global surface temperature
from exceeding 2 ∘C (Bauer et al., 2017; Fuss et al., 2014; Kriegler
et al., 2016; Rogelj et al., 2015a). Most of these limited warming scenarios
feature overshoots in radiative forcing around mid-century, which is closely
related to the amount of cumulative CDR until the year 2100 (Kriegler et
al., 2013). Despite the prevalence of CDR in these scenarios and its
increasing utilization in political and economic discussions, many of the
methods by which this would be achieved at this point rely on immature
technologies (National Research Council, 2015; Schäfer et al., 2015). Large-scale CDR methods are not yet a commercial product, and hence questions
remain about their feasibility, realizable potential, and risks
(Smith et al., 2015; Vaughan and Gough, 2016).
Overall, knowledge about the potential climatic, biogeochemical,
biogeophysical, and other impacts in response to CDR is still quite limited,
and large uncertainties remain, making it difficult to comprehensively
evaluate the potential and risks of any particular CDR method and make
comparisons between methods. This information is urgently needed to allow us
to assess the following:
the degree to which CDR could help mitigate or perhaps reverse climate
change;
the potential risks and benefits of different CDR proposals; and
how climate and carbon cycle responses to CDR could be included
when calculating and accounting for the contribution of CDR in mitigation
scenarios, i.e., so that CDR is better constrained when it is included in IAM-generated scenarios.
To date, modeling studies of CDR focusing on the carbon cycle and climatic
responses have been undertaken with only a few Earth system models (Arora and
Boer, 2014; Boucher et al., 2012; Cao and Caldeira, 2010; Gasser et al.,
2015; Jones et al., 2016a; Keller et al., 2014; MacDougall, 2013; Mathesius
et al., 2015; Tokarska and Zickfeld, 2015; Zickfeld et al., 2016). However,
as these studies all use different experimental designs, their results are
not directly comparable, and consequently building a consensus on responses is
challenging. A model intercomparison study with Earth system models of
intermediate complexity (EMICS) that addresses climate reversibility, among
other things, has recently been published (Zickfeld et al., 2013), but the
focus was on the very distant future rather than this century. Moreover, in
many of these studies, atmospheric CO2 concentrations were prescribed
rather than being driven by CO2 emissions, and thus the projected
changes were independent of the strength of feedbacks associated with the
carbon cycle.
Given that Earth system models are one of the few tools available for making
quantifications on these scales and for making projections into the future,
CDR assessments must include emissions-driven modeling studies to capture the
carbon cycle feedbacks. However, such an assessment cannot be done with one
or two models alone, since this will not address uncertainties due to model
structure and internal variability. Below we describe the scientific foci and
several experiments (Table 1) that comprise the initial phase of the CMIP6-endorsed Carbon Dioxide
Removal Model Intercomparison Project (CDRMIP).
Overview of CDRMIP experiments. Note that each experiment is
comprised of several individually named simulations (Tables 2–7). In the
“Forcing methods” column, “All” means “all anthropogenic, solar, and
volcanic forcing”. Anthropogenic forcing includes aerosol emissions,
non-CO2 greenhouse gas emissions, and land use changes.
Short nameLong nameTierExperiment descriptionForcing methodsMajor purposeCDR-reversibilityClimate and carbon1CO2 prescribed to increaseCO2Evaluate climatecycle reversibilityat 1 % yr-1 to 4×concentrationreversibilityexperimentpreindustrial CO2 andprescribedthen decrease at 1 % yr-1until again at a preindustriallevel, after which the simulationcontinues for as long as possibleCDR-pi-pulseInstantaneous CO21100 Gt C is instantly removedCO2Evaluate climate and C-cycleremoval and/or addition(negative pulse) from a steady-stateconcentrationresponse of an unperturbedfrom an unperturbedpreindustrial atmosphere; 100 Gt Ccalculatedsystem to atmospheric CO2climateis instantly added (positive pulse)(i.e., freelyremoval; comparison withexperimentto a steady-stateevolving)the positive pulse responsepreindustrial atmosphereCDR-yr2010-pulseInstantaneous CO23100 Gt C is instantly removedAll; CO2Evaluate climate and C-cycleremoval and/or addition(negative pulse) from a near 100 Gt Cconcentrationresponse of a perturbed systemfrom a perturbedis instantly removed (negative pulse)calculatedto atmospheric CO2 removal;climatefrom a near present day atmosphere;(i.e., emissioncomparison with the positiveexperiment100 Gt C is instantly addeddriven)*pulse response(positive pulse) to a near presentday atmosphereCDR-overshootEmission-driven2SSP5-3.4 overshoot scenarioAll; CO2Evaluate the Earth systemSSP5-3.4-OSin which CO2 emissionsconcentrationresponse to CDR inscenarioare initially high and thencalculatedan overshoot climateexperimentrapidly reduced,(i.e., emissionchange scenariobecoming negativedriven)CDR-afforestationAfforestation–2Long-term extension of an experimentAll; CO2Evaluate the long-termreforestationwith forcing from a highconcentrationEarth system responseexperimentCO2 emission scenariocalculatedto afforestation and(SSP5-8.5), but with land use(i.e., emissionreforestation duringprescribed from a scenariodriven)a high CO2 emissionwith high levels of afforestationclimate change scenarioand reforestation (SSP1-2.6)CDR-ocean-alkOcean alkalinization2A high CO2 emission scenarioAll; CO2Evaluate the Earth systemin a high CO2 world(SSP5-8.5) with 0.14 Pmol yr-1concentrationresponse to oceanexperimentalkalinity added to ice-free oceancalculated (i.e.,alkalinization duringsurface waters fromemission driven)a high CO2 emissionthe year 2020 onwardclimate change scenario
* In this experiment CO2 is first prescribed to diagnose
emissions; however, the key simulations calculate the CO2
concentration.
CDRMIP scientific foci
There are three principal science motivations behind CDRMIP. First and
foremost, CDRMIP will provide information that can be used to help assess the
potential and risks of using CDR to address climate change. A thorough
assessment will need to look at both the impacts of CDR upon the Earth system
and human society. CDRMIP will focus primarily on Earth system impacts, with
the anticipation that this information will also be useful for understanding
potential impacts upon society. The scientific outcomes will lead to more
informed decisions about the role CDR may play in climate change mitigation
(defined here as a human intervention to reduce the sources or enhance the
sinks of greenhouse gases). CDRMIP experiments will also provide an
opportunity to better understand how the Earth system responds to
perturbations, which is relevant to many of the Grand Science Challenges
posed by the World Climate Research Program (WCRP;
https://www.wcrp-climate.org/grand-challenges/grand-challenges-overview).
CDRMIP experiments provide a unique opportunity because the perturbations are
often opposite in sign to previous CMIP perturbation experiments (CO2 is
removed instead of added). Second, CDRMIP results may also be able to provide
information that helps to understand how model resolution and complexity
cause systematic model bias. In this instance, CDRMIP experiments may be
especially useful for gaining a better understanding of the similarities and
differences between global carbon cycle models because we invite a diverse
group of models to participate in CDRMIP. Finally, CDRMIP results can help to
quantify uncertainties in future climate change scenarios, especially those
that include CDR. In this case CDRMIP results may be useful for calibrating
CDR inclusion in IAMs during the scenario development process.
The initial foci that are addressed by CDRMIP include (but are not limited
to) the following.
Climate “reversibility” by assessing the efficacy of using CDR to
return high future atmospheric CO2 concentrations to lower levels. This
topic is highly idealized, as the technical ability of CDR methods to remove
such enormous quantities of CO2 on relatively short timescales (i.e.,
this century) is doubtful. However, the results will provide information on
the degree to which a changing and changed climate could be returned to a
previous state. This knowledge is especially important since socioeconomic
scenarios that limit global warming to well below 2 ∘C often feature
radiative forcing overshoots that must be ”reversed” using CDR. Specific
questions on reversibility will address the following.
What components of the Earth's climate system exhibit “reversibility” when
CO2 increases and then decreases? On what timescales do these
“reversals” occur? And if reversible, is this complete reversibility or
just on average (are there spatial and temporal aspects)?
Which, if any, changes are irreversible?
What role does hysteresis play in these responses?
The potential efficacy, feedbacks, and side effects of specific CDR
methods. Efficacy is defined here as CO2 removed from the atmosphere
over a specific time horizon as a result of a specific unit of CDR action.
This topic will help to better constrain the carbon sequestration potential
and risks and/or benefits of selected methods. Together, a rigorous analysis
of the nature, sign, and timescales of these CDR-related topics will provide
important information for the inclusion of CDR in climate mitigation
scenarios and in resulting mitigation and adaptation policy strategies.
Specific questions on individual CDR methods will address the following.
How much CO2 would have to be removed to return to a specified
concentration level, for example present day or preindustrial?
What are the short-term carbon cycle feedbacks (e.g., rebound) associated
with the method?
What are the short- and longer-term physical, chemical, and biological impacts
and feedbacks and the potential side effects of the method?
For methods that enhance natural carbon uptake, for example afforestation or
ocean alkalinization, where is the carbon stored (land and ocean) and for how
long (i.e., issues of permanence; at least as much as this can be calculated
with these models)?
Structure of this paper
Our motivation for preparing this paper is to lay out in detail the CDRMIP
experimental protocol, which we request all modeling groups to follow as
closely as possible. Firstly, in Sect. 2, we review the scientific background
and motivation for CDR in more detail than covered in this introduction.
Section 3 describes some requirements and recommendations for participating
in CDRMIP and describes links to other CMIP6 activities. Section 4 describes
each CDRMIP simulation in detail. Section 5 describes the model output and
data policy. Section 6 presents an outlook of potential future CDRMIP
activities and a conclusion. Section 7 describes how to obtain the model code
and data used during the production of this paper.
Background and motivation
At present, there are two main proposed CDR approaches, which we briefly
introduce here. The first category encompasses methods that are primarily
designed to enhance the Earth's natural carbon sequestration mechanisms.
Enhancing natural oceanic and terrestrial carbon sinks is suggested because
these sinks have already each taken up over one-quarter of the carbon
emitted as a result of anthropogenic activities (Le Quéré et al.,
2016) and have the capacity to store additional carbon, although this is
subject to environmental limitations. Some prominent proposed sink
enhancement methods include afforestation or reforestation, enhanced
terrestrial weathering, biochar, land management to enhance soil carbon
storage, ocean fertilization, ocean alkalinization, and coastal management of
blue carbon sinks.
The second general CDR category includes methods that rely primarily on
technological means to directly remove carbon from the atmosphere, ocean, or
land and isolate it from the climate system, for example storage in a geological
reservoir (Scott et al., 2015). Methods that are primarily technological are
suggested because they may not be as limited by environmental constraints.
Some prominent proposed technological methods include direct CO2 air
capture with storage and seawater carbon capture (and storage). One other
proposed CDR method, bioenergy with carbon capture and storage (BECCS),
relies on both natural processes and technology. BECCS is thus constrained
by some environmental limitations (e.g., suitable land area), but because the
carbon is removed and ultimately stored elsewhere, it may have a higher CDR
potential than if the same deployment area were used for a sink-enhancing CDR
method like afforestation that stores carbon permanently above ground and
reaches a saturation level for a given area
(Smith et al., 2015).
From an Earth system perspective, the potential and impacts of proposed CDR
methods have only been investigated in a few individual studies; see recent
climate intervention assessments for a broad overview of the state of CDR
research (National Research Council, 2015; Rickels et al., 2011; The Royal
Society, 2009; Vaughan and Lenton, 2011) and references therein. These
studies agree that CDR application on a large scale
(≥ 1 Gt CO2 yr-1) would likely have a substantial impact on
the climate, biogeochemistry, and the ecosystem services that the Earth
provides (i.e., the benefits humans obtain from ecosystems; Millennium
Ecosystem Assessment, 2005). Idealized Earth system model simulations suggest
that CDR does appear to be able to limit or even reverse warming and changes
in many other key climate variables (Boucher et al., 2012; Tokarska and
Zickfeld, 2015; Wu et al., 2014; Zickfeld et al., 2016). However, less
idealized studies, for example when some environmental limitations are accounted
for, suggest that many methods have only a limited individual mitigation
potential (Boysen et al., 2016, 2017; Keller et al., 2014; Sonntag et al.,
2016).
Studies have also focused on the carbon cycle response to the deliberate
redistribution of carbon between dynamic carbon reservoirs or permanent
(geological) carbon removal. Understanding and accounting for the feedbacks
between these reservoirs in response to CDR is particularly important for
understanding the efficacy of any method (Keller et al., 2014). For example,
when CO2 is removed from the atmosphere in simulations, the rate of
oceanic CO2 uptake, which has historically increased in response to
increasing emissions, is reduced and might eventually reverse (i.e., net
outgassing) because of a reduction in the air–sea flux disequilibrium (Cao
and Caldeira, 2010; Jones et al., 2016a; Tokarska and Zickfeld, 2015; Vichi
et al., 2013). Equally, the terrestrial carbon sink also weakens in response
to atmospheric CO2 removal and can also become a source of CO2 to
the atmosphere
(Cao and Caldeira, 2010; Jones et al., 2016a; Tokarska
and Zickfeld, 2015). This “rebound” carbon flux response that weakens or
reverses carbon uptake by natural carbon sinks would oppose CDR and needs to
be accounted for if the goal is to limit or reduce atmospheric CO2
concentrations to some specified level (IPCC, 2013).
In addition to the climatic and carbon cycle effects of CDR, most methods
appear to have side effects (Keller et al., 2014). The impacts of these side
effects tend to be method specific and may amplify or reduce the climate
change mitigation potential of the method. Some significant side effects are
caused by the spatial scale (e.g., millions of km2) on which many
methods would have to be deployed to have a significant impact upon CO2
and global temperatures (Boysen et al., 2016; Heck et al., 2016; Keller et
al., 2014). Side effects can also potentially alter the natural environment
by disrupting biogeochemical and hydrological cycles, ecosystems, and
biodiversity (Keller et al., 2014). For example, large-scale afforestation
could change regional albedo and evapotranspiration and have a
biogeophysical impact on the Earth's energy budget and climate (Betts, 2000;
Keller et al., 2014). Additionally, if afforestation were done with
non-native plants or monocultures to increase carbon removal rates, this could
impact local biodiversity. For human societies, this means that CDR-related
side effects could potentially impact the ecosystem services provided by the
land and ocean (e.g., food production), with the information so far
suggesting that there could be both positive and negative impacts on these
services. Such effects could change societal responses and strategies for
climate change adaptation if large-scale CDR were to be deployed.
CDR deployment scenarios have focused on both preventing climate change and
reversing it. While there is some understanding of how the Earth system may
respond to CDR, as described above, another dynamic comes into play if CDR
were to be applied to “reverse” climate change. This is because if CDR were
deployed for this purpose, it would deliberately change the climate, i.e.,
drive it in another direction, rather than just prevent it from changing by
limiting CO2 emissions. Few studies have investigated how the Earth
system may respond if CDR is applied in this manner. The link between
cumulative CO2 emissions and global mean surface air temperature change
has been extensively studied (IPCC, 2013). Can this change simply be reversed
by removing the CO2 that has been emitted since the preindustrial era?
Little is known about how reversible this relationship is or whether it
applies to other Earth system properties (e.g., net primary productivity, sea
level, etc.). Investigations of CDR-induced climate reversibility have
suggested that many Earth system properties are “reversible”, but often
with nonlinear responses (Armour et al., 2011; Boucher et al., 2012;
MacDougall, 2013; Tokarska and Zickfeld, 2015; Wang et al., 2014; Wu et al.,
2014; Zickfeld et al., 2016). However, these analyses were generally limited
to global annual mean values, and most models did not include potentially
important components such as permafrost or terrestrial ice sheets. Thus,
there are many unknowns and much uncertainty about whether it is possible to
“reverse” climate change. Obtaining knowledge about climate
“reversibility” is especially important as it could be used to direct or
change societal responses and strategies for adaptation and mitigation.
Why a model intercomparison study on CDR?
Although ideas for controlling atmospheric CO2 concentrations were
proposed in the middle of the last century, it is only recently that CDR
methods have received widespread attention as climate intervention
strategies
(National Research Council, 2015; Schäfer et al., 2015; The Royal
Society, 2009; Vaughan and Lenton, 2011). While some proposed CDR methods do
build upon substantial knowledge bases (e.g., soil and forest carbon, and
ocean biogeochemistry), little research into large-scale CDR has been
conducted and limited research resources applied (National Research Council,
2015; Oschlies and Klepper, 2017). The small number of existing laboratory
studies and small-scale field trials of CDR methods were not designed to
evaluate climate or carbon cycle responses to CDR. At the same time it is
difficult to conceive how such an investigation could be carried out without
scaling a method up to the point at which it would essentially be
“deployment”. The few natural analogues that exist for some methods (e.g.,
weathering or reforestation) only provide limited insight into the
effectiveness of deliberate large-scale CDR. As such, beyond syntheses of
resource requirements and availabilities (e.g., Smith, 2016), there is a lack
of observational constraints that can be applied to the assessment of the
effectiveness of CDR methods. Lastly, many proposed CDR methods are
premature at this point and technology deployment strategies would be
required to overcome this barrier (Schäfer et al., 2015), which means that
they can only be studied in an idealized manner, i.e., through model
simulations.
Understanding the response of the Earth system to CDR is urgently needed
because CDR is increasingly being utilized to inform policy and economic
discussions. Examples of this include scenarios that are being developed with
GHG emission forcing that exceeds (or overshoots) what is required to limit
global mean temperatures to 2 or 1.5 ∘C, with the assumption that
reversibility is possible with the future deployment of CDR. These scenarios
are generated using integrated assessment models, which compute the emissions
of GHGs, short-lived climate forcers, and land cover change associated with
economic, technological, and policy drivers to achieve climate targets. Most
integrated assessment models represent BECCS as the only CDR option, with
only a few also including afforestation (IPCC, 2014b). During scenario
development and calibration the output from the IAMs is fed into climate
models of reduced complexity, for example MAGICC (Model for the Assessment of
Greenhouse-gas Induced Climate Change; Meinshausen et al., 2011), to
calculate the global mean temperature achieved through the scenario choices,
for example those in the Shared Socioeconomic Pathways (SSPs; Riahi et al., 2017).
These climate models are calibrated to Earth system models or based on
modeling intercomparison exercises like the Coupled Model Intercomparison
Phase 5 (CMIP5), in which much of the climate–carbon cycle information comes
from the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP).
However, since the carbon cycle feedbacks of large-scale negative CO2
emissions have not been explicitly analyzed in projects like CMIP5, with the
exception of Jones et al. (2016a), many assumptions have been made about the
effects of CDR on the carbon cycle and climate. Knowledge of these short-term
carbon cycle feedbacks is needed to better constrain the effectiveness of the
CDR technologies assumed in the IAM-generated scenarios.
This relates to the policy-relevant question of whether in a regulatory
framework CO2 removals from the atmosphere should be treated like
emissions except for the opposite (negative) sign or if specific methods,
which may or may not have long-term consequences (e.g.,
afforestation and reforestation vs. direct CO2 air capture with geological
carbon storage), should be treated differently. The lack of these kinds of
analyses is a knowledge gap in current climate modeling (Jones et al., 2016a)
and relevant for IAMs and political decisions. There is an urgent need
to close this gap since additional CDR options like the enhanced weathering
of rocks on land or direct air capture continue to be included in IAMs (e.g.,
Chen and Tavoni, 2013). For the policy-relevant questions it is also
important to analyze the carbon cycle effects given realistic policy
scenarios rather than idealized perturbations.
Requirements and recommendations for participation in CDRMIP
The CDRMIP initiative is designed to bring together a suite of Earth system
models, Earth system models of intermediate complexity (EMICs), and
potentially even box models in a common framework. Note that only models that
meet certain requirements (https://pcmdi.llnl.gov/CMIP6/Guide/) can
participate in an official CMIP6 capacity. Models of differing complexities are invited to participate
because the questions posed above cannot be answered with any single class of
models. For example, ESMs are primarily suited for investigations spanning
only the next century because of the computational expense, while EMICs and
box models are well suited to investigate the long-term questions surrounding
CDR, but are often highly parameterized and may not include important
processes, for example cloud feedbacks. The use of differing models will also
provide insight into how model resolution and complexity controls modeled
short- and long-term climate and carbon cycle responses to CDR.
All groups that are running models with an interactive carbon cycle are
encouraged to participate in CDRMIP. We desire diversity and encourage groups
to use older models with well-known characteristics, biases, and established
responses (e.g., previous CMIP model versions), as well as state-of-the-art
CMIP6 models. For longer model simulations, we would encourage modelers when
possible to include additional carbon reservoirs, such as ocean sediments or
permafrost, as these are not always implemented for short simulations. Models
that only include atmospheric and oceanic carbon reservoirs are welcome and
will be able to participate in some experiments. All models wishing to
participate in CDRMIP must provide clear documentation that details the model
version, components, and key run-time and initialization information (model
time stepping, spin-up state at initialization, etc.). Furthermore, all model
output must be standardized to facilitate analyses and public distribution
(see Sects. 4 and 5).
Relations to other MIPs
There are no existing MIPs with experiments focused on climate
“reversibility”, direct CO2 air capture (with storage), or ocean
alkalinization. However, this does not mean that there are no links between
CDRMIP and other MIPs. CMIP6 and CMIP5 experiments, analyses, and assessments
both provide a valuable baseline and model sensitivities that can be used to
better understand CDRMIP results and we highly recommend that participants in
CDRMIP also conduct other MIP experiments. Further, to maximize the use of
computing resources, CDRMIP may use experiments from other MIPs as a control run
for a CDRMIP experiment or to provide a pathway from which a CDRMIP
experiment branches (Sects. 3.2 and 4, Tables 2–7). Principal among these is
the CMIP Diagnostic, Evaluation, and Characterization of Klima (DECK) and
historical experiments as detailed in Eyring et al. (2016) for CMIP6, since
they provide the basis for many experiments with almost all MIPs leveraging
these in some way.
Here, we additionally describe links to ongoing MIPs that are endorsed by
CMIP6, noting that earlier versions of many of these MIPs were part of CMIP5
and provide a similar synergy for any CMIP5 models participating in CDRMIP.
Given the emphasis on carbon cycle perturbations in CDRMIP, there is a strong
synergy with C4MIP that provides a baseline, standard protocols, and
diagnostics for better understanding the relationship between the carbon
cycle and the climate in CMIP6 (Jones et al., 2016b). For example, the C4MIP
emissions-driven SSP5-8.5 scenario (a high CO2 emission scenario with a
radiative forcing of 8.5 W m-2 in year 2100) simulation,
esm-ssp585, is a control run and branching pathway for several
CDRMIP experiments. CDRMIP experiments may equally be valuable for
understanding model responses during related C4MIP experiments. For example,
the C4MIP experiment ssp534-over-bgc is a concentration-driven
“overshoot” scenario simulation that is run in a partially coupled mode.
The simulation required to analyze this experiment is a fully coupled
CO2-concentration-driven simulation of this scenario,
ssp534-over, from the Scenario Model Intercomparison Project
(ScenarioMIP). The novel CDRMIP experiment,
CDR-overshoot, which is a
fully coupled CO2-emission-driven version of this scenario, will provide
additional information that can be used to extend the analyses to better
understand climate–carbon cycle feedbacks.
The Land Use Model Intercomparison Project (LUMIP) is designed to better
understand the impacts of land use and land cover change on the climate
(Lawrence et al., 2016). The three main LUMIP foci overlap with some of the
CDRMIP foci, especially in regards to land management as a CDR method (e.g.,
afforestation–reforestation). To facilitate land use and land cover change
investigations LUMIP provides standard protocols and diagnostics for the
terrestrial components of CMIP6 Earth system models. The inclusion of these
diagnostics will be important for all CDRMIP experiments performed with CMIP6
models. The CDRMIP experiment on afforestation and reforestation,
CDR-afforestation (esm-ssp585-ssp126Lu-ext), is also an
extension of the LUMIP esm-ssp585-ssp126Lu simulation beyond 2100 to
investigate the long-term consequences of afforestation and reforestation in
a high CO2 world (Sect. 4.3).
ScenarioMIP is designed to provide multi-model climate projections for
several scenarios of future anthropogenic emissions and land use changes
(O'Neill et al., 2016) and provides baselines or branching for many MIP
experiments. The ScenarioMIP SSP5-3.4-OS experiments, ssp534-over
and ssp534-over-ext, which prescribe atmospheric CO2 to follow
an emission overshoot pathway that is followed by aggressive mitigation to
reduce emissions to zero by about 2070 with substantial negative global
emissions thereafter, are used as control runs for the CDRMIP
CO2-emission-driven version of this scenario. Along with the partially
coupled C4MIP version of this experiment, these experiments will allow for
qualitative comparative analyses to better understand climate–carbon cycle
feedbacks in an “overshoot” scenario with negative emissions (CDR). If it
is found that the carbon cycle effects of CDR are improperly accounted for in
the scenarios, then this information can be used to recalibrate older
CDR-including IAM scenarios and be used to better constrain CDR when it is
included in new scenarios.
The Ocean Model Intercomparison Project (OMIP), which primarily investigates
the ocean-related origins and consequences of systematic model biases, will
help to provide an understanding of ocean component functioning for models
participating in CMIP6 (Griffies et al., 2016). OMIP will also establish
standard protocols and output diagnostics for ocean model components. The
biogeochemical protocols and diagnostics of OMIP (Orr et al.,
2016) are particularly
relevant for CMIP6 models participating in CDRMIP. While the inclusion of
these diagnostics will be important for all CDRMIP experiments, these
standards will be particularly important for facilitating the analysis of our
marine CDR experiment on ocean alkalinization,
CDR–cean-alk
(Sect. 4.4).
Prerequisite and recommended CMIP simulations
The following CMIP experiments are considered prerequisites for specified
CDRMIP experiments (Tables 2–7) and analyses.
The CMIP prescribed atmospheric CO2 preindustrial control
simulation, piControl, is required for all CDRMIP experiments (many
control runs and experiment prerequisites branch from this), and it is usually
done as part of the spin-up process.
The CMIP6 preindustrial control simulation with interactively simulated
atmospheric CO2 (i.e., the CO2 concentration is internally
calculated, but emissions are zero), esm-piControl, is required for
CDRMIP experiments CDR-pi-pulse, CDR-overshoot,
CDR-afforestation, and CDR-ocean-alk.
The CMIP 1 % per year increasing CO2 simulation,
1pctCO2, is initialized from a preindustrial CO2
concentration with CO2 then increasing by 1 % per year until the
CO2 concentration has quadrupled (approximately 139 years). This is
required for CDRMIP experiment CDR-reversibility.
The CMIP6 historical simulation, historical, in which historical
atmospheric CO2 forcing is prescribed along with land use, aerosols, and
non-CO2 greenhouse gas forcing, is required for CDRMIP experiment
CDR-yr2010-pulse.
The CMIP6 emissions-driven historical simulation, esm-hist,
in which the atmospheric CO2 concentration is internally calculated in
response to historical anthropogenic CO2 emissions forcing (other
forcing such as land use, aerosols, and non-CO2 greenhouse gases are
prescribed), is required for CDRMIP experiments CDR-overshoot,
CDR-afforestation, and CDR-ocean-alk.
The LUMIP esm-ssp585-ssp126Lu simulation, which simulates
afforestation in a high CO2 emission scenario, is the basis for CDRMIP
experiment esm-ssp585-ssp126Lu-ext.
The C4MIP esm-ssp585 simulation is a high emission
scenario and serves as a control run and branching pathway for the CDRMIP
CDR-ocean-alk experiment.
We also highly recommend that groups run these additional C4MIP and
ScenarioMIP simulations.
The ScenarioMIP ssp534-over and ssp534-over-ext
simulations, which prescribe the atmospheric CO2 concentration to follow
an emission overshoot pathway that is followed by aggressive mitigation to
reduce emissions to zero by about 2070, with substantial negative global
emissions thereafter. These results can be qualitatively compared to CDRMIP
experiment CDR-overshoot, which is the same scenario but driven by
CO2 emissions.
The C4MIP ssp534-over-bgc and ssp534-over-bgcExt
simulations, which are biogeochemically coupled versions of the
ssp534-over and ssp534-over-ext simulations, i.e., only the
carbon cycle components (land and ocean) see the prescribed increase in the
atmospheric CO2 concentration; the model's radiation scheme sees a fixed
preindustrial CO2 concentration. These results can be qualitatively
compared to CDRMIP experiment CDR-overshoot, which is a fully
coupled version of this scenario.
Simulation ensembles
We encourage participants whose models have internal variability to conduct
multiple realizations, i.e., ensembles, for all experiments. While these are
highly desirable, they are neither mandatory nor a prerequisite for
participation in CDRMIP. Therefore, the number of ensemble members is at the
discretion of each modeling group. However, we strongly encourage groups to
submit at least three ensemble members if possible.
Climate sensitivity calculation
Knowing the climate sensitivity of each model participating in CDRMIP is
important for interpreting the results. For modeling groups that have not
already calculated their model's climate sensitivity, the required CMIP
1pctCO2 simulation can be used to calculate both the transient
and equilibrium climate sensitivities. The transient climate sensitivity can
be calculated as the difference in the global annual mean surface temperature
between the start of the experiment and a 20-year period centered on the time
of CO2 doubling. The equilibrium response can be diagnosed following
Gregory (2004), Frölicher et al. (2013), or if possible (desirable) by
running the model to an equilibrium state at 2 × CO2 or
4 × CO2.
Model drift
Model drift (Gupta et al., 2013; Séférian et al.,
2016) is a concern for all
CDRMIP experiments because if a model is not at an equilibrium state when the
experiment or prerequisite CMIP experiment begins, then the response to any
experimental perturbations could be confused by drift. Thus, before beginning
any of the experiments a model must be spun up to eliminate long-term drift
in carbon reservoirs or fluxes. Groups participating in CMIP6 should follow
the C4MIP protocols described in Jones et al. (2016b) to ensure that drift is
acceptably small. This means that land, ocean, and atmosphere carbon stores
should each vary by less than 10 Gt C per century (long-term average ≤ 0.1 Gt C yr-1). We leave it to individual groups to determine the
length of the run required to reach such a state. If older model versions,
for example CMIP5, are used for any experiments, any known drift should be
documented.
Experimental design and protocols
To facilitate multiple model needs, the experiments described below have been
designed to be relatively simple to implement. In most cases, they were also
designed to have high signal-to-noise ratios to better understand how the
simulated Earth system responds to significant CDR perturbations. While there
are many ways in which such experiments could be designed to address the
questions surrounding climate reversibility and each proposed CDR method, the
CDRMIP, like all MIPs, must be limited to a small number of practical
experiments. Therefore, after careful consideration, one experiment was
chosen specifically to address climate reversibility and several more were
chosen to investigate CDR through the idealized direct air capture of
CO2 (DAC), afforestation and reforestation, and ocean alkalinization
(Table 1). Experiments are prioritized based on a tiered system, although we
encourage modeling groups to complete the full suite of experiments.
Unfortunately, limiting the number of experiments means that a number of
potentially promising or widely utilized CDR methods or combinations of
methods must wait until a later time, i.e., a second phase, to be
investigated in a multi-model context. In particular, the exclusion of
biomass energy with carbon capture and storage (BECCS) is unfortunate, as
this is the primary CDR method in the Representative Concentration Pathway
(RCP) and Shared Socioeconomic Pathway (SSP) scenarios used in CMIP5 and 6,
respectively. However, there was no practical way to design a less idealized
BECCS experiment as most state-of-the-art models are either incapable of
simulating a biomass harvest with permanent removal or would require a
substantial amount of reformulating to do so in a manner that allows for
comparable multi-model analyses.
In some of the experiments described below we ask that non-CO2 forcing
(e.g., land use change, radiative forcing from other greenhouse gases, etc.)
be held constant, for example at that of a specific year, so that only changes in
other forcing, like CO2 emissions, drive the main model response. For
some forcing, for example aerosol emissions, this may mean that monthly changes in
forcing are repeated throughout the rest of the simulation as if it was
always one particular year. However, we recognize that models apply forcing
in different ways and leave it to individual modeling groups to determine
the best way to hold forcing constant. We request that the methodology for
holding forcing constant be documented for each model.
Climate and carbon cycle reversibility experiment
(CDR-reversibility) simulations. All simulations are required to
complete the experiment.
CMIP6 ExperimentSimulation descriptionOwning MIPRun lengthInitialized usingID(years)a restart frompiControlPreindustrial prescribed CO2 control simulationCMIP6 DECK100athe model spin-up1pctCO2Prescribed 1 % yr-1 CO2 increaseCMIP6 DECK140bpiControlto 4 × the preindustrial levelCMIP6 DECK140bpiControl1pctCO2-cdr1 % yr-1 CO2 decrease from 4 × theCDRMIP200 min.1pctCO2preindustrial level until the preindustrial CO25000 max.level is reached and held for as long as possible
a This CMIP6 DECK should have been run for at least
500 years. Only the last 100 years are needed as a control for
CDR-reversibility. b This CMIP6 DECK experiment is
150 years long. A restart for CDR-reversibility should be generated
after 139 years when CO2 is 4 times that of piControl.
Schematic of the CDRMIP climate and carbon cycle reversibility
experimental protocol (CDR-reversibility). From a preindustrial run
at steady state atmospheric CO2 is prescribed to increase and then
decrease over a ∼ 280-year period, after which it is held constant for
as long as computationally possible.
Climate and carbon cycle reversibility experiment (CDR-reversibility)
If CO2 emissions are not reduced quickly enough and more warming occurs
than is desirable or tolerable, then it is important to understand if CDR has
the potential to “reverse” climate change. Here we propose an idealized
Tier 1 experiment that is designed to investigate CDR-induced climate
“reversibility” (Fig. 1, Table 2). This experiment investigates the
“reversibility” of the climate system by leveraging the prescribed
1 % yr-1 CO2 concentration increase experiment that was done
for prior CMIPs and is a key run for CMIP6 (Eyring et al., 2016; Meehl et
al., 2014). The CDRMIP experiment starts from the 1 % yr-1 CO2
concentration increase experiment, 1pctCO2, and then at the
4 × CO2 concentration level prescribes a -1 % yr-1
removal of CO2 from the atmosphere to preindustrial levels (Fig. 1; this
is also similar to experiments in Boucher et al., 2012, and Zickfeld et al.,
2016). This approach is analogous to an unspecified CDR application or DAC,
in which CO2 is removed to permanent storage to return atmospheric
CO2 to a prescribed level, i.e., a preindustrial concentration. To do
this, CDR would have to counter emissions (unless they have ceased) and
changes in atmospheric CO2 due to the response of the ocean and
terrestrial biosphere. We realize that the technical ability of CDR methods
to remove such enormous quantities of CO2 on such a relatively short
timescale (i.e., in a few centuries) is unrealistic. However, branching from
the existing CMIP 1pctCO2 experiment provides a relatively
straightforward opportunity, with a high signal-to-noise ratio, to explore
the effect of large-scale removal of CO2 from the atmosphere and issues
involving reversibility (Fig. 2 shows exemplary CDR-reversibility
results from two models).
Exemplary climate and carbon cycle reversibility experiment
(CDR-reversibility) results with the Mk3L-COAL Earth system model
and the University of Victoria (UVic) Earth system model of intermediate
complexity (models are described in Appendix D). The left panels show annual
global mean (a) temperature anomalies (∘C; relative to
pre-industrial temperatures) and (c) the atmosphere to ocean carbon
fluxes (Pg C yr-1) versus the atmospheric CO2 (ppm) during the
first 280 years of the experiment (i.e., when CO2 is increasing and
decreasing). The right panels show the same (b) temperature
anomalies and (d) the atmosphere to ocean carbon fluxes versus time.
Note that the Mk3L-COAL simulation was only 400 years long.
Protocol for CDR-reversibility
Prerequisite simulations. Perform the CMIP piControl and
the 1pctCO2 experiments. The 1pctCO2 experiment branches
from the DECK piControl experiment, which should ideally represent a
near-equilibrium state of the climate system under imposed year 1850
conditions. Starting from year 1850 conditions (piControl global
mean atmospheric CO2 should be 284.7 ppm) the 1pctCO2
simulation prescribes a CO2 concentration increase at a rate of
1 % yr-1 (i.e., exponentially). The only externally imposed
difference from the piControl experiment is the change in CO2;
i.e., all other forcing is kept at that of year 1850. A restart must be
generated when atmospheric CO2 concentrations are 4 times that of the
piControl simulation (1138.8 ppm; this should be 140 years into the
run). Groups that have already performed the piControl and
1pctCO2 simulations for CMIP5 or CMIP6 may provide a link to them if
they are already on the Earth System Grid Federation (ESGF) that hosts CMIP
data.
The 1pctCO2-cdr simulation. Use the 4 × CO2 restart from
1pctCO2 and prescribe a 1 % yr-1 removal of CO2 from
the atmosphere (start removal at the beginning of the 140th year: 1 January)
until the CO2 concentration reaches 284.7 ppm (140 years of removal).
As in 1pctCO2 the only externally imposed forcing should be the
change in CO2 (all other forcing is kept at that of year 1850). The
CO2 concentration should then be held at 284.7 ppm for as long as
possible (a minimum of 60 years is required), with no change in other
forcing. EMICs and box models are encouraged to extend runs for at least
1000 years (and up to 5000 years) at 284.7 ppm CO2 to investigate
long-term climate system and carbon cycle reversibility (see Fig. 2b and d
for examples of why it is important to understand the long-term response).
Direct CO2 air capture with permanent storage experiments
(CDR-pi-pulse, CDR-year2010-pulse, CDR-overshoot)
The idea of directly removing excess CO2 from the atmosphere (i.e.,
concentrations above preindustrial levels) and permanently storing it in
some reservoir, such as a geological formation, is appealing because such an
action would theoretically address the main cause of climate change:
anthropogenically emitted CO2 that remains in the atmosphere.
Laboratory studies and small-scale pilot plants have demonstrated that
atmospheric CO2 can be captured by several different methods that are
often collectively referred to as direct air capture (DAC) technology
(Holmes and Keith, 2012; Lackner et al., 2012; Sanz-Pérez et al., 2016). Technology
has also been developed that can place captured carbon in permanent
reservoirs, i.e., carbon capture and storage (CCS) methods
(Matter et al., 2016; Scott et al., 2013, 2015). DAC technology is currently prohibitively
expensive to deploy on large scales and may be technically difficult to scale
up (National Research Council, 2015), but it does appear to be a potentially
viable CDR option. However, aside from the technical questions involved in
developing and deploying such technology, there remain questions about how
the Earth system would respond if CO2 were removed from the atmosphere.
Here we propose a set of experiments that are designed to investigate and
quantify the response of the Earth system to idealized large-scale DAC. In
all experiments, atmospheric CO2 is allowed to freely evolve to
investigate carbon cycle and climate feedbacks in response to DAC. The first
two idealized experiments described below use the approach of an instantaneous
(pulse) CO2 removal from the atmosphere for this
investigation. Instantaneous CO2 removal perturbations were chosen since
pulsed CO2 addition experiments have already been proven useful
for diagnosing carbon cycle and climate feedbacks in response to CO2
perturbations. For example, previous positive CO2 pulse experiments have
been used to calculate global warming potential (GWP) and global temperature
change potential (GTP) metrics (Joos et al., 2013). The experiments described
below build upon the previous positive CO2 pulse experiments, i.e., the
PD100 and PI100 impulse experiments described in Joos et al. (2013), in which
100 Gt C is instantly added to preindustrial and near present day simulated
climates. However, our experiments also prescribe a negative CDR pulse as
opposed to just adding CO2 to the atmosphere. Two experiments are
desirable because the Earth system response to CO2 removal will be
different when starting from an equilibrium state versus starting from a
perturbed state (Zickfeld et al., 2016). One particular goal of these
experiments is to estimate a global cooling potential (GCP) metric based on a
CDR impulse response function (IRFCDR). Such a metric will be
useful for calculating how much CO2 is removed by DAC and how much DAC
is needed to achieve a particular climate target.
The third experiment, which focuses on “negative emissions”, is based on
the Shared Socioeconomic Pathway (SSP) 5-3.4 overshoot scenario and its
long-term extension (Kriegler et al., 2016; O'Neill et al., 2016). This
scenario is of interest to CDRMIP because after an initially high level of
emissions, which follows the SSP5-8.5 unmitigated baseline scenario until
2040, CO2 emissions are rapidly reduced with net CO2 emissions
becoming negative after the year 2070 and continuing to be so until the year
2190 when they reach zero. In the original SSP5-3.4-OS scenario, the negative
emissions are achieved using BECCS. However, as stated earlier there is
currently no practical way to design a good multi-model BECCS experiment.
Therefore, in our experiments negative emissions are achieved by simply
removing CO2 from the atmosphere and assuming that it is permanently
stored in a geological reservoir. While this may violate the economic
assumptions underlying the scenario, it still provides an opportunity to
explore the response of the climate and carbon cycle to potentially
achievable levels of negative emissions.
According to calculations done with a simple climate model, MAGICC version
6.8.01 BETA (Meinshausen et al., 2011; O'Neill et al., 2016), the SSP5-3.4-OS
scenario considerably overshoots the 3.4 W m-2 forcing level, with a
peak global mean temperature of about 2.4 ∘C, before returning to
3.4 W m-2 at the end of the century. Eventually in the long-term
extension of this scenario, the forcing stabilizes just above 2 W m-2,
with a global mean temperature that should equilibrate at about
1.25 ∘C above preindustrial temperatures. Thus, in addition to
allowing for an investigation into the response of the climate and carbon
cycle to negative emissions, this scenario also provides the opportunity to
investigate issues of reversibility, albeit on a shorter timescale and with
less of an “overshoot” than in experiment CDR-reversibility.
Schematic of the CDRMIP instantaneous CO2 removal and addition from
an unperturbed climate experimental protocol (CDR-pi-pulse). Models
are spun up for as long as possible with a prescribed preindustrial
atmospheric CO2 concentration. Then atmospheric CO2 is allowed to
freely evolve for at least 100 years as a control run. The negative–positive
pulse experiments are conducted by instantly removing or adding 100 Gt C to
the atmosphere of a simulation in which the atmosphere is at steady state and
CO2 can freely evolve. These runs continue for as long as
computationally possible.
Instantaneous CO2 removal and addition from an
unperturbed climate experimental protocol (CDR-pi-pulse)
This idealized Tier 1 experiment is designed to investigate how the Earth
system responds to DAC when perturbed from an equilibrium state (Fig. 3,
Table 3). The idea is to provide a baseline system response that can later be
compared to the response of a perturbed system, i.e., experiment
CDR-yr2010-pulse (Sect. 4.2.3). By also performing another
simulation in which the same amount of CO2 is added to the system, it
will be possible to diagnose if the system responds in an inverse manner when
the CO2 pulse is positive. Many modeling groups will have already
conducted the prerequisite simulation for this experiment in preparation for
other modeling research, for example during model spin-up or for CMIP, which
should minimize the effort needed to perform the complete experiment. The
protocol is as follows.
Instantaneous CO2 removal from an unperturbed climate experiment
(CDR-pi-pulse) simulation. All simulations are required to complete
the experiment.
CMIP6 ExperimentSimulation descriptionOwning MIPRun lengthInitialized usingID(years)a restart fromesm-piControlPreindustrial freely evolvingCMIP6 DECK100*the modelCO2 control simulationspin-upesm-pi-cdr-pulse100 Gt C is instantly removed (negative pulse)CDRMIP100 min.esm-piControlfrom a preindustrial atmosphere5000 max.esm-pi-CO2pulse100 Gt C is instantly added (positive pulse)CDRMIP100 min.esm-piControlto a preindustrial atmosphere5000 max.
* This CMIP6 DECK should have been run for at least 500 years.
Only the last 100 years are needed as a control for CDR-pi-pulse.
Prerequisite simulation. This is a control simulation under preindustrial
conditions with freely evolving CO2. All boundary conditions (solar
forcing, land use, etc.) are expected to remain constant. This is also the
CMIP5 esmControl simulation (Taylor et al., 2012) and the CMIP6
esm-piControl simulation (Eyring et al., 2016). Note that this is
exactly the same as PI100 run 4 in Joos et al. (2013).
The esm-pi-cdr-pulse simulation. This is as in esm-Control or
esm-piControl, but with 100 Gt C instantaneously (within 1 time
step) removed from the atmosphere in year 10. If models have CO2
spatially distributed throughout the atmosphere, we suggest removing this
amount in a uniform manner. After the negative pulse, ESMs should continue the
run for at least 100 years, while EMICs and box models are encouraged to
continue the run for at least 1000 years (and up to 5000 years if possible).
Figure 4 shows example esm-pi-cdr-pulse model responses.
Exemplary instantaneous CO2 removal from a preindustrial climate
experiment (CDR-pi-pulse) results from the esm-pi-cdr-pulse
simulation with the Mk3L-COAL Earth system model and the University of
Victoria (UVic) Earth system model of intermediate complexity (models are
described in Appendix D). (a) Atmospheric CO2 vs. time,
(b) the land to atmosphere carbon flux vs. time, and
(c) the ocean to atmosphere carbon flux vs. time. Note that the
Mk3LCOAL simulation was only 184 years long.
The esm-pi-CO2pulse simulation. This is the same as
esm-pi-cdr-pulse, but add a positive 100 Gt C pulse (within 1 time
step) as in Joos et al. (2013) instead of a negative one. If models have
CO2 spatially distributed throughout the atmosphere, we suggest adding
CO2 in a uniform manner. Note that this would be exactly the same as the
PI100 run 5 in Joos et al. (2013) and can thus be compared to this earlier
study.
Instantaneous CO2 removal from a perturbed
climate experimental protocol (CDR-yr2010-pulse)
This Tier 3 experiment is designed to investigate how the Earth system
responds when CO2 is removed from an anthropogenically altered climate
not in equilibrium (Fig. 5, Table 4). Many modeling groups will have already
conducted part of the first run of this experiment in preparation for other
modeling research, for example CMIP, and may be able to use a “restart” file to
initialize the first run, which should reduce the effort needed to perform
the complete experiment.
Schematic of the CDRMIP instantaneous CO2 removal and addition
from a perturbed climate experimental protocol (CDR-yr2010-pulse).
(a) Initially historical CO2 forcing is prescribed and then held
constant at 389 ppm (∼ year 2010) while CO2 emissions are
diagnosed. (b) A control simulation is conducted using the diagnosed
emissions. The negative–positive pulse experiments are conducted by instantly
removing or adding 100 Gt C to the atmosphere of the CO2-emission-driven
simulation 5 years after CO2 reaches 389 ppm. Another control
simulation is also conduced that sets emissions to zero at the time of the
negative pulse. The emission-driven simulations continue for as long as
computationally possible.
Instantaneous CO2 removal from a perturbed climate experiment
(CDR-yr2010-pulse) simulation. All simulations are required to
complete the experiment.
CMIP6 ExperimentSimulation descriptionOwning MIPRun lengthInitialized usingID(years)a restart fromhistoricalHistorical atmospheric CO2 (andCMIP6 DECK160*piControlother forcing) is prescribed untila concentration of 389 ppm CO2 is reachedyr2010CO2Branching from historical, atmosphericCDRMIP105 min.historicalCO2 is held constant (prescribed)5000 max.at 389 ppm; other forcing is also heldconstant at the 2010 levelesm-yr2010CO2-controlControl run forced using CO2 emissionsCDRMIP265 min.esm-piControldiagnosed from historical and5160 max.or piControlyr2010CO2 simulations;other forcing as in historicaluntil 2010 after which it is constantesm-yr2010CO2-noemitControl run that branches fromCDRMIP105 min.esm-yr2010CO2-controlesm-yr2010CO2-control5000 max.in year 2010 with CO2 emissionsset to zero 5 years afterthe start of the simulationesm-yr2010CO2-cdr-pulseBranches from esm-yr2010CO2-controlCDRMIP105 min.esm-yr2010CO2-controlin year 2010 with 100 Gt C instantly removed5000 max.(negative pulse) from the atmosphere 5 yearsafter the start of the simulationesm-yr2010CO2-CO2pulseBranches from esm-yr2010CO2-controlCDRMIP105 min.esm-yr2010CO2-controlin year 2010 with 100 Gt C instantly added5000 max.(positive pulse) to the atmosphere 5 yearsafter the start of the simulation
* This CMIP6 DECK continues until the year 2015 but only the
first 160 years are need for CDR-yr2010-pulse.
Prerequisite simulation. This is a prescribed CO2 run.
Historical atmospheric CO2 is prescribed until a concentration of
389 ppm is reached (∼ year 2010; Fig. 5a). Other historical forcing,
i.e., from CMIP, should also be applied. An existing run or setup from CMIP5
or CMIP6 may also be used to reach a CO2 concentration of 389 ppm, for
example the RCP 8.5 CMIP5 simulation or the CMIP6 historical
experiment. During this run, compatible emissions should be frequently
diagnosed (at least annually).
The yr2010CO2 simulation. Atmospheric CO2 should be held
constant at 389 ppm with other forcing, like land use and aerosol emissions,
also held constant (Fig. 5a). ESMs should continue the run at 389 ppm for at
least 105 years, while EMICs and box models are encouraged to continue the
run for as long as needed for the subsequent simulations (e.g., 1000+
years). During this run, compatible emissions should be frequently diagnosed
(at least annually). Note that when combined with the prerequisite simulation
described above this is exactly the same as the PD100 run 1 in Joos et
al. (2013).
The esm-yr2010CO2-control simulation. This is a diagnosed emissions
control run. The model is initialized from the preindustrial period (i.e.,
using a restart from either piControl or esm-piControl)
with the emissions diagnosed in the historical and
yr2010CO2 simulations, i.e., year 1850 to approximately year 2115
for ESMs and longer for EMICs and box models (up to 5000 years). All other
forcing should be as in the historical and yr2010CO2
simulations. Atmospheric CO2 must be allowed to freely evolve. The
results should be quite close to those in the historical and
yr2010CO2 simulations. If there are significant differences, for
example due to climate–carbon cycle feedbacks that become evident when
atmospheric CO2 is allowed to freely evolve, then they must be diagnosed
and used to adjust the CO2 emission forcing. In some cases it may be
necessary to perform an ensemble of simulations to diagnose compatible
emissions. Note that this is exactly the same as the PD100 run 2 in Joos et
al. (2013). As in Joos et al. (2013), if computational time is an issue and
if a group is sure that CO2 remains at a nearly constant value with the
emissions diagnosed in yr2010CO2, the esm-yr2010CO2-control
simulation may be skipped. This may only apply to ESMs and it is strongly
recommended to perform the esm-yr2010CO2-control simulation to avoid
model drift.
The esm-yr2010CO2-cdr-pulse simulation. This is a CO2 removal
simulation. Setup is initially as in the esm-yr2010CO2-control
simulation. However, a “negative” emissions pulse of 100 Gt C is
subtracted instantaneously (within 1 time step) from the atmosphere 5 years
after the time at which CO2 was held constant in the
esm-yr2010CO2-control simulation (this should be at the beginning of
the year 2015), with the run continuing thereafter for at least 100 years (up
to 5000 years if possible). If models have CO2 spatially distributed
throughout the atmosphere, we suggest removing this amount in a uniform
manner. It is crucial that the negative pulse be subtracted from a constant
background concentration of ∼ 389 ppm. All forcing, including CO2
emissions, must be exactly as in the esm-yr2010CO2-control
simulation so that the only difference between these runs is that this one
has had CO2 instantaneously removed from the atmosphere.
The esm-yr2010CO2-noemit simulation. This is a zero CO2
emissions control run. Setup is initially as in the
esm-yr2010CO2-cdr-pulse simulation. However, at the time of the
“negative” emissions pulse in the esm-yr2010CO2-cdr-pulse
simulation, emissions are set to zero with the run continuing thereafter for
at least 100 years. If possible, extend the runs for at least 1000 years (and
up to 5000 years). All other forcing must be exactly as in the
esm-yr2010CO2-control simulation. This experiment will be used to
isolate the Earth system response to the negative emissions pulse in the
esm-yr2010CO2-cdr-pulse simulation, which convolves the response to
the negative emissions pulse with the lagged response to the preceding
positive CO2 emissions (diagnosed with the zero emissions simulation).
The response to the negative emissions pulse will be calculated as the
difference between esm-yr2010CO2-cdr-pulse and
esm-yr2010CO2-noemit simulations.
The esm-yr2010CO2-CO2pulse simulation. This is a CO2 addition
simulation. Setup is initially as in the esm-yr2010CO2-cdr-pulse
simulation. However, a “positive” emissions pulse of 100 Gt C is added
instantaneously (within 1 time step), with the run continuing thereafter for
a minimum of 100 years. If models have CO2 spatially distributed
throughout the atmosphere, we suggest adding CO2 in a uniform manner. If
possible, extend the runs for at least 1000 years (and up to 5000 years). It
is crucial that the positive pulse be added to a constant background
concentration of ∼ 389 ppm. All forcing, including CO2 emissions,
must be exactly as in the esm-yr2010CO2-control simulation so that
the only difference between these runs is that this one has had CO2
instantaneously added to the atmosphere. Note that this would be exactly the
same as the PD100 run in Joos et al. (2013). This will be used to investigate
if, after positive and negative pulses, carbon cycle and climate feedback
responses, which are expected to be opposite in sign, differ in magnitude and
temporal scale. The results can also be compared to Joos et al. (2013).
Schematic of the CDRMIP emission-driven SSP5-3.4-OS scenario
experimental protocol (CDR-overshoot). A CO2-emission-driven
historical simulation is conducted until the year 2015. Then an
emission-driven simulation with SSP5-3.4-OS scenario forcing is conducted.
This simulation is extended until the year 2300 using SSP5-3.4-OS scenario
long-term extension forcing. Thereafter, runs may continue for as long as
computationally possible with constant forcing after the year
2300.
This Tier 2 experiment explores CDR in an “overshoot” climate change
scenario, the SSP5-3.4-OS scenario (Fig. 6, Table 5). To start, groups must
perform the CMIP6 emission-driven historical simulation, esm-hist.
Then using this as a starting point, conduct an emissions-driven SSP5-3.4-OS
scenario simulation, esm-ssp534-over (starting on 1 January 2015),
that includes the long-term extension to the year 2300. All non-CO2 forcing should be identical to
that in the ScenarioMIP ssp534-over and
ssp534-over-ext simulations. If computational resources are
sufficient, we recommend that the esm-ssp534-over simulation be
continued for at least another 1000 years with year 2300 forcing; i.e., the
forcing is held constant at year 2300 levels as the simulation continues for
as long as possible (up to 5000 years) to better understand processes that
are slow to equilibrate, for example ocean carbon and heat exchange or permafrost
dynamics.
Emission-driven SSP5-3.5-OS scenario experiment
(CDR-overshoot) simulations. All simulations are required to
complete the experiment.
Enhancing the terrestrial carbon sink by restoring or extending forest cover,
i.e., reforestation and afforestation, has often been suggested as a
potential CDR option (National Research Council, 2015; The Royal Society,
2009). Enhancing this sink is appealing because terrestrial ecosystems have
cumulatively absorbed over one-quarter of all fossil fuel emissions (Le
Quéré et al., 2016) and could potentially sequester much more. Most
of the key questions concerning land use change are being addressed by LUMIP
(Lawrence et al., 2016).
These include investigations into the potential and side effects of
afforestation and reforestation to mitigate climate change, for which they
have designed four experiments (LUMIP Phase 2 experiments). However, three of
these experiments are CO2 concentration driven and thus are unable to
fully investigate the climate–carbon cycle feedbacks that are important for
CDRMIP. The LUMIP experiment in which CO2 emissions force the
simulation, esm-ssp585-ssp126Lu, will allow for climate–carbon
cycle feedbacks to be investigated. Unfortunately, since this experiment ends
in the year 2100 it is too short to answer some of the key CDRMIP questions
(Sect. 1.2). We have therefore decided to extend this LUMIP experiment within
the CDRMIP framework as a Tier 2 experiment (Table 6) to better investigate
the longer-term CDR potential and risks of afforestation and reforestation.
Afforestation–reforestation experiment (CDR-afforestation)
simulations. All simulations are required to complete the experiment.
CMIP6 ExperimentSimulation descriptionOwning MIPRun lengthInitialized usingID(years)a restart fromesm-ssp585CO2-emission-driven SSP5-8.5 scenarioC4MIP85esm-histesm-ssp585-ssp126LuCO2-emission-driven SSP5-8.5 scenarioLUMIP85esm-histwith SSP1-2.6 land use forcingesm-ssp585-ssp126Lu-extLong-term extension of theCDRMIP200 min.esm-ssp585-ssp126Luesm-ssp585-ssp126Lu simulation5000 max.esm-ssp585extLong-term extension of the CO2-emission-drivenCDRMIP200 min.esm-ssp585SSP5-8.5 scenario5000 max.
The LUMIP experiment, esm-ssp585-ssp126Lu, simulates afforestation and reforestation by combining a
high SSP CO2 emission scenario, SSP5-8.5, with a future land use change
scenario from an alternative SSP scenario, SSP1-2.6, which has much greater
afforestation and reforestation
(Kriegler et al., 2016; Lawrence et al., 2016). By comparing this combination to the
SSP5-8.5 baseline scenario, it will be possible to determine the CDR
potential of this particular afforestation–reforestation scenario in a high
CO2 world. This is similar to the approach of Sonntag et al. (2016)
using RCP 8.5 emissions combined with prescribed RCP 4.5 land use.
CDR-afforestation experimental protocol
Prerequisite simulations. Conduct the C4MIP emission-driven
esm-ssp585 simulation, which is a control run, and the LUMIP Phase 2
experiment esm-ssp585-ssp126Lu (Lawrence et al., 2016). Generate
restart files in the year 2100.
The esm-ssp585-ssp126Lu-ext simulation. Using the year 2100 restart from
the esm-ssp585-ssp126Lu experiment, it continues the run with the same
LUMIP protocol (i.e., an emission-driven SSP5-8.5 simulation with SSP1-2.6
land use instead of SSP5-8.5 land use) until the year 2300 using the SSP5-8.5
and SSP1-2.6 long-term extension data
(O'Neill et al., 2016). If computational
resources are sufficient, we recommend that the simulation be continued for
at least another 1000 years with year 2300 forcing (i.e., forcing is held at
year 2300 levels as the simulation continues for as long as possible; up to
5000 years). This is to better understand processes that are slow to
equilibrate, for example ocean carbon and heat exchange or permafrost dynamics, and
the issue of permanence.
The esm-ssp585ext simulation. The emission-driven esmSSP5-8.5 simulation
must be extended beyond the year 2100 to serve as a control run for the
esm-ssp585-ssp126Lu-ext simulation. This will require using the
ScenarioMIP ssp585-ext forcing, but driving the model with CO2
emissions instead of prescribing the CO2 concentration. If computational
resources are sufficient, the simulation should be extended even further than
in the official SSP scenario, which ends in year 2300, by keeping forcing
constant after this time (i.e., forcing is held at year 2300 levels as the
simulation continues for as long as possible; up to 5000 years).
Ocean alkalinization experiment (CDR-ocean-alk)
Enhancing the natural process of weathering, which is one of the key negative
climate–carbon cycle feedbacks that removes CO2 from the atmosphere on
long timescales (Colbourn et al., 2015; Walker et al., 1981), has been
proposed as a potential CDR method (National Research Council, 2015; The
Royal Society, 2009). Enhanced weathering ideas have been proposed for both
the terrestrial environment (Hartmann et al., 2013) and the ocean (Köhler
et al., 2010; Schuiling and Krijgsman, 2006). We focus on the alkalinization
of the ocean given its capacity to take up vast quantities of carbon over
relatively short time periods and its potential to reduce the rate and
impacts of ocean acidification (Kroeker et al., 2013). The idea is to
dissolve silicate or carbonate minerals in seawater to increase total
alkalinity. Total alkalinity, which can chemically be defined as the excess
of proton acceptors over proton donors with respect to a certain zero level
of protons, is a measurable quantity that is related to the concentrations of
species of the marine carbonate system (Wolf-Gladrow et al., 2007). It plays
a key role in determining the air–sea gas exchange of CO2 (Egleston et
al., 2010). When total alkalinity is artificially increased in surface
waters, it basically allows more CO2 to dissolve in the seawater and be
stored as ions such as bicarbonate or carbonate; i.e., the general
methodology increases the carbon storage capacity of seawater.
Theoretical work and idealized modeling studies have suggested that ocean
alkalinization may be an effective CDR method that is more limited by
logistic constraints (e.g., mining, transport, and mineral processing) rather
than natural ones, such as available ocean area, although chemical
constraints and side effects do exist (González and Ilyina, 2016; Ilyina
et al., 2013; Keller et al., 2014; Köhler et al., 2010, 2013). One
general side effect of ocean alkalinization is that it increases the
buffering capacity and pH of the seawater. While such a side effect could be
beneficial or even an intended effect to counter ocean acidification (Feng et
al., 2016), high levels of alkalinity may also be detrimental to some
organisms (Cripps et al., 2013). Ocean alkalinization likely also has
method-specific side effects. Many of these side effects are related to the
composition of the alkalizing agent, for example olivine may contain
nutrients or toxic heavy metals, which could affect marine organisms and
ecosystems
(Hauck et al., 2016; Köhler et al., 2013).
Other side effects could be caused by the mining, processing, and transport
of the alkalizing agent, which in some cases may offset the CO2
sequestration potential of specific ocean alkalinization methods (e.g.,
through CO2 release by fossil fuel use or during the calcination of
CaCO3; Kheshgi, 1995; Renforth et al., 2013).
Ocean alkalinization (CDR-ocean-alk) experiment
simulations. “Pr” in the Tier column indicates a prerequisite experiment.
CMIP6 ExperimentTierSimulation descriptionOwning MIPRun lengthInitialized usingID(years)a restart fromesm-ssp585PrCO2-emission-drivenC4MIP85esm-histPrSSP5-8.5 scenarioesm-ssp585-ocn-alk2SSP5-8.5 scenario with 0.14 Pmol yr-1CDRMIP65 min.esm-ssp585alkalinity added to ice-free ocean5000 max.surface waters fromthe year 2020 onwardesm-ssp585-ocn-alk-stop3Termination simulation to investigateCDRMIP30*esm-ssp585-ocn-alkan abrupt stop in oceanalkalinization in the year 2070esm-ssp585ext3Long-term extension ofCDRMIP200 min.esm-ssp585the CO2-emission-driven5000 max.SSP5-8.5 scenario
* If the esm-ssp585ext simulation is being conducted this may be extended for more than
200 more years (up to 5000 years).
Although previous modeling studies have suggested that ocean alkalinization
may be a viable CDR method, these studies are not comparable due to different
experimental designs. Here we propose an idealized Tier 2 experiment
(Table 7) that is designed to investigate the response of the climate system
and carbon cycle to ocean alkalinization. The amount of any particular
alkalizing agent that could be mined, processed, transported, and delivered
to the ocean in a form that would easily dissolve and enhance alkalinity is
poorly constrained (Köhler et al., 2013; Renforth et al., 2013).
Therefore, the amount of alkalinity that is to be added in our experiment is
set (based on exploratory simulations conducted with the CSIRO-Mk3L-COAL
model) to have a cumulative effect on atmospheric CO2 by the year 2100
that is comparable to the amount removed in the CDRMIP instantaneous DAC
simulations, i.e., an atmospheric reduction of ∼ 100 Gt C;
experiments CDR-pi-pulse and CDR-yr2010-pulse. The idea
here is not to test the maximum potential of such a method, which would be
difficult given the still relatively coarse resolution of many models and the
way in which ocean carbonate chemistry is simulated, but rather to compare
the response of models to a significant alkalinity perturbation. We have also
included an additional “termination” simulation that can be used to
investigate an abrupt stop in ocean alkalinization deployment.
CDR-ocean-alk experimental protocol
Prerequisite simulation. Conduct the C4MIP emission-driven
esm-ssp585 simulation as described by Jones et al. (2016b). This is
the SSP5-8.5 high CO2 emission scenario, and it serves as the control
run and branching point for the ocean alkalinization experiment. A restart
must be generated at the end of the year 2019.
The esm-ssp585-ocn-alk simulation. Begin an 81+-year run using the
esm-ssp585 year 2020 restart (starting on 1 January 2020) and add
0.14 Pmol total alkalinity (TA) yr-1 to the upper grid boxes of each
model's ocean component, i.e., branch from the C4MIP esm-ssp585
simulation in 2020. The alkalinity additions should be limited to
mostly ice-free, year-round ship-accessible waters, which for simplicity
should be set between 70∘ N and 60∘ S (note that this
ignores the presence of seasonal sea ice in some small regions). For many
models, this will in practice result in an artificial TA flux at the air–sea
interface with realized units that might, for example, be something like
µmol TA s-1 cm-2. Adding 0.14 Pmol TA yr-1 is
equivalent to adding 5.19 Pg yr-1 of an alkalizing agent like
Ca(OH)2 or 4.92 Pg yr-1 of forsterite (Mg2SiO4), a form
of olivine (assuming theoretical net instant dissolution reactions, which for
every mole of Ca(OH)2 or Mg2SiO4 added sequesters 2 or 4 mol,
respectively, of CO2; Ilyina et al., 2013; Köhler et al., 2013). As
not all models include marine iron or silicate cycles, the addition of these
nutrients, which could occur if some form of olivine were used as the
alkalizing agent, is not considered here. All other forcing is as in the
esm-ssp585 control simulation. If the ocean alkalinization
termination simulation (below) is to be conducted, generate a restart at the
beginning of the year 2070.
Optional (Tier 3) esm-ssp585-ocn-alk-stop simulation. Use the year
2070 restart from the esm-ssp585-ocn-alk simulation and start a
simulation (beginning on 1 January 2070) with the SPP5-8.5 forcing, but
without adding any additional alkalinity. Continue this run until the year
2100, or beyond, if conducting a long esm-ssp585-ocn-alk simulation.
The following are optional (Tier 3) ocean alkalinization extension
simulations.
The esm-ssp585ext simulation. If groups desire to extend the ocean
alkalinization experiment beyond the year 2100, an optional simulation may be
conducted to extend the control run using forcing data from the ScenarioMIP
ssp585ext simulation; i.e., conduct a longer emission-driven control
run, esm-ssp585ext. This extension is also a control run for those
conducting the CDRMIP CDR-afforestation
simulation (Sect. 4.3). If
computational resources are sufficient, the simulation should be extended
even further than in the official SSP scenario, which ends in year 2300, by
keeping the forcing constant after this time (i.e., forcing is held at year
2300 levels as the simulation continues for as long as possible; up to
5000 years).
Model output, data availability, and data use policyGridded model output
Models capable of generating gridded data must use a NetCDF format. The
output (see Appendix A web link for the list of requested variables) follows
the CMIP6 output requirements in frequency and structure. This allows groups
to use CMOR software (Climate Model Rewriter Software, available at
http://cmor.llnl.gov/) to generate the files that will be
available for public download (Sect. 5.5). The resolution of the data should
be as close to native resolution as possible, but on a regular grid. Please
note that as different models have different formulations, only applicable
outputs need be provided. However, groups are encouraged to generate
additional output, i.e., whatever their standard output variables are, and
can also make these data available (preferably following the CMIP6 CMOR
standardized naming structure).
Conversion factor Gt C to ppm
For experiments in which carbon must be converted between Gt C (or Pg) and ppm
CO2, please use a conversion factor of 2.12 Gt C per ppm CO2 to
be consistent with global carbon budget (Le Quéré et al., 2015) conversion
factors.
Box model output
For models that are incapable of producing gridded NetCDF data (i.e., box
models), output is expected to be in an ASCII format (Appendix B). All ASCII
files are expected to contain tabulated values (at a minimum global mean
values), with at least two significant digits for each run. Models must be
able to calculate key carbon cycle variables (Appendix C) to participate in
CDRMIP experiments CDR-reversibility, CDR-pi-pulse, and
CDR-yr2010-pulse.
Please submit these files directly to the corresponding author, who will make
them available for registered users to download from the CDRMIP website.
Model output frequency
Model output frequency for 3-D models with seasonality. Box models
and EMICs without seasonality are expected to generate annual global mean
output for the duration of all experiments. For longer simulations (right
column), if possible, 3-D monthly data should be written out for 1 year every
100 years. For models with interannual variability, for example ESMs, monthly
data should be written out for a 10-year period every 100 years so that a
climatology may be developed. The years referred to in the table indicate
simulations years, for example years from the start of the run, and not those
of any particular scenario.
CDRMIP ExperimentIndividual simulation short nameoutput frequency Monthly gridded 3-D outputAnnual global mean output + climatologicaloutput at 100-year intervalsCDR-reversibilitypiControl (last 100 years)1pctCO21pctCO2-cdr (from year 200 onward)1pctCO2-cdr (initial 200 years)CDR-pi-pulseaesm-piControlesm-pi-cdr-pulse (from year 100 onward)esm-pi-cdr-pulse (initial 100 years)esm-pi-CO2pulse (from year 100 onward)esm-pi-CO2pulse (initial 100 years)CDR-yr2010-pulseesm-yr2010CO2-control (initial 105 years)esm-yr2010CO2-controlesm-yr2010CO2-noemitesm-yr2010CO2-noemitesm-yr2010CO2-cdr-pulseesm-yr2010CO2-cdr-pulseesm-yr2010CO2-CO2pulseesm-yr2010CO2-CO2pulseCDR-overshootesm-histesm-ssp534-over (from year 200 onward)besm-ssp534-over (initial 285 years)CDR-afforestationesm-ssp585ext (initial 200 years)esm-ssp585ext (from year 200 onward)besm-ssp585-ssp126Luesm-ssp585-ssp126Lu-ext (from year 200 onward)besm-ssp585-ssp126Lu-ext (initial 200 years)CDR-ocean-alkesm-ssp585esm-ssp585-ocn-alk-stop (from year 200 onward)besm-ssp585-ocn-alk (initial 280 years)esm-ssp585ext(from year 200 onward)besm-ssp585-ocn-alk-stop (initial 200 years)esm-ssp585-ocn-alk (from year 200 onward)besm-ssp585ext (initial 200 years)
a In the historical and yr2010CO2
simulations output is needed only to diagnose (at least annually) CO2
emissions. b This is from scenario year 2300 onward.
The model output frequency is listed in Table 8. In all experiments box
models and EMICs without seasonality are expected to generate annual mean
output for the duration of the experiment, while models with seasonality are
expected to generate higher-spatial-resolution data, i.e., monthly, for most
simulations.
In experiment CDR-reversibility for the control run,
piControl, we request that 100 years of 3-D model output be written
monthly (this should be the last 100 years if conducting a 500+ year run
for CMIP6). For the 1pctCO2 and 1pctCO2-cdr simulations 3-D
model output should also be written monthly, i.e., as the atmospheric
CO2 concentration is changing. We suggest that groups that have already
performed the piControl and 1pctCO2 simulations for CMIP5
or CMIP6 with an even higher output resolution (e.g., daily) continue to use
this resolution for the 1pctCO2-cdr simulation, as this will
facilitate the analysis. For groups continuing the simulations for up to
5000 years after CO2 has returned to 284.7 ppm, at a minimum annual
global mean values (non-gridded output) should be generated after the initial
minimum 60 years of higher-resolution output.
For experiment CDR-pi-pulse, if possible, 3-D model output should be
written monthly for 10 years before the negative pulse and for 100 years
following the pulse. For groups that can perform longer simulations, for
example thousands of years, at a minimum annual global mean values
(non-gridded output) should be generated. Data for the control run, i.e., the
equilibrium simulation esm-piControl, must also be available for
analytical purposes. CMIP participants may provide a link to the
esm-Control or esm-piControl data on the ESGF.
For experiment CDR-yr2010-pulse the historical and
yr2010CO2 simulation output is only needed to diagnose annual
CO2 emissions and will not be archived on the ESGF, unless the historical run is being conducted for CMIP6. Gridded 3-D monthly
mean output for the esm-yr2010CO2-control (starting in the year
2010), esm-yr2010CO2-cdr-pulse, esm-yr2010CO2-noemit, and
esm-yr2010CO2-CO2pulse simulations should be written for the initial
100 years of the simulation. Thereafter, for groups that can perform longer
simulations (up to 5000 years), at a minimum annual global mean values
(non-gridded output) should be generated. CMIP participants are requested to
provide a link to the historical simulation data on the ESGF.
For experiment CDR-overshoot, if possible, 3-D model output should
be written monthly until the year 2300. We suggest that groups that have
already performed the ScenarioMIP ssp534-over and
ssp534-over-ext and C4MIP ssp534-over-bgc and
ssp534-over-bgcExt CMIP6 simulations with an even higher
output resolution (e.g., daily) continue to use this resolution as this will
facilitate analyses. For groups that can perform longer simulations, for
example thousands of years, at a minimum annual global mean values
(non-gridded output) should be generated for every year beyond 2300. We
recommend that CMIP participants provide a link to the esm-hist data
on the ESGF. For analytical purposes, we also request that ScenarioMIP and
C4MIP participants provide links to any completed ssp534-over,
ssp534-over-ext, ssp534-over-bgc, and
ssp534-over-bgcExt simulation data on the ESGF.
For experiment CDR-afforestation, if possible, 3-D model output
should be written monthly until the year 2300. LUMIP participants may provide
a link to the esm-hist and esm-ssp585-ssp126Lu data on the
ESGF for the first portions of this run (until the year 2100). For groups
that can perform longer simulations, for example thousands of years, at a
minimum annual global mean values (non-gridded output) should be generated
for every year beyond 2300.
For experiment CDR-ocean-alk, if possible, 3-D gridded model output
should be written monthly for all simulations. For groups that can perform
longer simulations, for example thousands of years, at a minimum annual
global mean values (non-gridded output) should be generated for every year
beyond 2300.
Data availability and use policy
The model output from the CDRMIP experiments described in this paper will be
publically available. All gridded model output will, to the extent possible,
be distributed through the Earth System Grid Federation (ESGF). Box model
output will be available via the CDRMIP website
(http://www.kiel-earth-institute.de/cdr-mip-data.html). The CDRMIP
policy for data use is that if you use output from a particular model, you
should contact the modeling group and offer them the opportunity to
contribute as authors. Modeling groups will possess detailed understanding of
their models and the intricacies of performing the CDRMIP experiments, so
their perspectives will undoubtedly be useful. At a minimum, if the offer of
author contribution is not taken up, CDRMIP and the model groups should be
credited in acknowledgments with, for example, a statement like the
following: “We acknowledge the Carbon Dioxide Removal Model Intercomparison
Project leaders and steering committee who are responsible for CDRMIP and we
thank the climate modeling groups (listed in Table XX of this paper) for
producing and making their model output available.”
The natural and anthropogenic forcing data that are required for some
simulations are described in several papers in the Geoscientific Model
Development CMIP6 special issue. These data will be available on the ESGF.
Links to all forcing data can also be found on the CMIP6 Panel website
(https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6). CMIP6 and CMIP5
data should be acknowledged in the standard way.
CDRMIP outlook and conclusion
It is anticipated that this will
be the first stage of an ongoing project exploring CDR. CDRMIP welcomes input
on the development of other (future) experiments and scenarios. Potential
future experiments could include biomass energy with carbon capture and
storage (BECCS) or ocean fertilization. Future experiments could also include
the removal of non-CO2 greenhouse gases, for example methane, as these
in many cases have a much higher global warming potential (de Richter et al.,
2017; Ming et al., 2016). We also envision that it will be necessary to
investigate the simultaneous deployment of several CDR or other greenhouse
gas removal methods since early studies suggest that there is likely not an
individually capable method (Keller et al., 2014). It is also anticipated
that scenarios will be developed that might combine solar radiation
management (SRM) and CDR in the future, such as a joint GeoMIP
(Geoengineering Model Intercomparison Project) CDRMIP experiment.
In addition to reductions in anthropogenic CO2 emissions, it is very
likely that CDR will be needed to achieve the climate change mitigation goals
laid out in the Paris Agreement. The potential and risks of large-scale CDR
are poorly quantified, raising important questions about the extent to which
large-scale CDR can be depended upon to meet Paris Agreement goals. As an
endorsed CMIP6 activity, CDRMIP is designed to help us better understand how the Earth system
might respond to CDR. Over the past 2 years the CDRMIP team has developed a
set of numerical experiments to be performed with Earth system models of
varying complexity. The aim of these experiments is to provide coordinated
simulations and analyses that addresses several key CDR uncertainties,
including
the degree to which CDR could help mitigate climate change or even
reverse it;
the potential effectiveness and risks and benefits of different CDR proposals
with a focus on direct CO2 air capture, afforestation and reforestation, and
ocean alkalinization; and
how CDR might be appropriately accounted for within an Earth
system framework and during scenario development.
We anticipate that there will be numerous forthcoming studies that utilize
CDRMIP data. The model output from the CDRMIP experiments will be publically
available and we welcome and encourage interested parties to download these
data and utilize them to further investigate CDR.
As described in Sect. 5.5, the output from models
participating in CDRMIP will be made publically available. This will include
data used in exemplary Figs. 2 and 4. All gridded model output will be
distributed through the Earth System Grid Federation (ESGF) with digital
object identifiers (DOIs) assigned. Box model output will be available via the CDRMIP website
(http://www.kiel-earth-institute.de/cdr-mip-data.html). The code from
the models used to generate the exemplary figures in this paper (Figs. 2 and
4, Appendix D) is available at
http://thredds.geomar.de/thredds/catalog/open_ access/keller_
et_al_2018_gmd/catalog.html (Keller and Lenton, 2018).
To obtain code from modeling groups
participating in CDRMIP, please contact the modeling group using the contact
information that accompanies their data.
Requested model output variables
A spreadsheet of the requested model output variables and their format can be
found at
www.kiel-earth-institute.de/files/media/downloads/CDRMIP_model_output_requirements.pdf.
Please note that as different models have different formulations, only
applicable outputs need be provided. However, groups are encouraged to
generate additional output, i.e., whatever their standard output variables
are, and can also make these data available.
Box model output formatting
Box model ASCII formatting example.File name format:RUNNAME_MODELNAME_Modelversion.datC1_MYBOXMODEL_V1.0_.dat
Headers and formats example.
Start each header comment line with a #
Line 1: indicate run name, e.g., #
esm-pi-cdr-pulse
Line 2: provide contact address, e.g., # B. Box, Uni of Box
Models, CO2 Str., BoxCity 110110, BoxCountry
Line 3: provide a contact email address, e.g., #
bbox@unibox.bx
Line 4: indicate model name, version, e.g., # MyBoxModel
Version 2.2
Line 5: concisely indicate main components, e.g., # two
ocean boxes (upper and lower), terrestrial biosphere, and one atmospheric
box
Line 6: indicate climate sensitivity of model; the abbreviation TCS
may be used for transient climate sensitivity and ECS for equilibrium climate
sensitivity, e.g., # TCS=3.2 [deg C], ECS=8.1 [deg C]
Line 7: description of non-CO2 forcing applied, e.g.,
# Forcing: solar
Line 8: indicate the output frequency and averaging, e.g., #
Output: global mean values
Line 9: list tabulated output column headers with their units in
brackets (see table below), e.g., # year tas[K]
Complete header example.
# esm-pi-cdr-pulse
# B. Box, Uni. of Box Models, CO2 Str., BoxCity 110110,
BoxCountry
# bbox@unibox.bx
# MyBoxModel Version 2.2
# two ocean boxes (upper and lower), terrestrial
biosphere, and one atmospheric box
# TCS=3.2 deg C, ECS=8.1 deg C
# Forcing: solar
# Output: global mean values
# year tas[K] co2[Gt C] nep[Gt C yr-1] fgco2[Gt C
yr-1]
Requested box model output variables
Table of requested box model output (at a minimum as global mean values). To
participate in CDRMIP, at a minimum the variables tas, xco2, and
fgco2 must be provided.
Requested box model output for CDRMIP.
LongColumn headerUnitsCommentsnamename∗Relative yearyearyearNear-surface air temperaturetasKAtmospheric CO2xco2ppmSurface downward CO2 flux into the oceanfgco2kg m-2This is the net air to ocean carbon flux (positive flux is into the ocean)Total atmospheric mass of CO2co2masskgNet carbon mass flux out of atmosphere due to net ecosystem productivity on landnepkg m-2This is the net air to land carbon flux (positive flux is into the land)Total ocean carboncOceanGt CIf the ocean contains multiple boxes this output can also be provided, for example as cOcean_up and cOcean_low for upper and lower ocean boxesTotal land carboncLandGt CThis is the sum of all C poolsOcean potential temperaturethetaoKPlease report a mean value if there are multiple ocean boxesUpper ocean pHpH1Negative log of hydrogen ion concentration with the concentration expressed as mol H kg-1Carbon mass flux out of atmosphere due to net primary production on landnppkg m-2This is calculated as gross primary production–autotrophic respiration (gpp-ra)Carbon mass flux into atmosphere due to heterotrophic respiration on landrhkg m-2Ocean net primary production by phytoplanktonintppkg m-2
* Column header names follow the CMIP CMOR notation when possible.
Model descriptions
The two models used to develop and test CDRMIP experimental protocols and
provide example results (Figs. 2 and 4) are described below.
The University of Victoria Earth System Climate Model (UVic) version 2.9
consists of three dynamically coupled components: a three-dimensional general
circulation model of the ocean that includes a dynamic–thermodynamic sea ice
model, a terrestrial model, and a simple one-layer atmospheric
energy–moisture balance model (Eby et al., 2013). All components have a
common horizontal resolution of
3.6∘ longitude × 1.8∘ latitude. The oceanic
component, which is in the configuration described by Keller et al. (2012),
has 19 levels in the vertical with thicknesses ranging from 50 m near the
surface to 500 m in the deep ocean. The terrestrial model of vegetation and
carbon cycles (Meissner et al., 2003) is based on the Hadley Centre model
TRIFFID (Top-down Representation of Interactive Foliage and Flora Including
Dynamics). The atmospheric energy–moisture balance model interactively
calculates heat and water fluxes to the ocean, land, and sea ice. Wind
velocities, which are used to calculate the momentum transfer to the ocean
and sea ice model, surface heat and water fluxes, and the advection of water
vapor in the atmosphere, are determined by adding wind and wind stress
anomalies. These are determined from surface pressure anomalies that are
calculated from deviations in preindustrial surface air temperature to
prescribed NCAR/NCEP monthly climatological wind data (Weaver et al., 2001).
The model has been extensively used in climate change studies and is also
well validated under preindustrial to present day conditions (Eby et al.,
2009, 2013; Keller et al., 2012).
The CSIRO-Mk3L-COAL Earth system model consists of a climate model, Mk3L
(Phipps et al., 2011), coupled to a biogeochemical model of carbon, nitrogen,
and phosphorus cycles on land (CASA-CNP) in the Australian community land
surface model, CABLE (Mao et al., 2011; Wang et al., 2010), and an ocean
biogeochemical cycle model (Duteil et al., 2012; Matear and Hirst, 2003). The
atmospheric model has a horizontal resolution of
5.6∘ longitude × 3.2∘ latitude and 18 vertical
layers. The land carbon model has the same horizontal resolution as the
atmosphere. The ocean model has a resolution of
2.8∘ longitude × 1.6∘ latitude and 21 vertical
levels. Mk3L simulates the historical climate well compared to the models
used for earlier IPCC assessments (Phipps et al., 2011). Furthermore, the
simulated response of the land carbon cycle to increasing atmospheric
CO2 and warming are consistent with those from the Coupled Model
Intercomparison Project Phase 5 (CMIP5; Zhang et al., 2014). The ocean
biogeochemical model was also shown to realistically simulate the global
ocean carbon cycle (Duteil et al., 2012; Matear and Lenton, 2014).
The authors declare that they have no conflict of
interest.
Acknowledgements
David P. Keller and Nico Bauer acknowledge funding
received from the German Research Foundation's Priority Program 1689 “Climate
Engineering” (project CDR-MIA; KE 2149/2-1). Kirsten Zickfeld acknowledges support
from the Natural Sciences and Engineering Research Council of Canada (NSERC)
Discovery grant program. The Pacific Northwest National Laboratory is
operated for the US Department of Energy by Battelle Memorial Institute
under contract DE-AC05-76RL01830. Duoying Ji acknowledges support from the
National Basic Research Program of China under grant number 2015CB953600. CDJ
was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate
Programme (GA01101) and by the European Union's Horizon 2020 research and
innovation program under grant agreement no. 641816 (CRESCENDO). Helene Muri
was supported by Norwegian Research Council grant
261862/E10. Vivian Scott acknowledges funding received from UK Natural Environment Research Council GHG Removal Programme (NE/P019749/1).
Edited by: Claire Levy
Reviewed by: Ben Sanderson and two anonymous referees
ReferencesAllen, M. R., Frame, D. J., Huntingford, C., Jones, C. D., Lowe, J. A.,
Meinshausen, M., and Meinshausen, N.: Warming caused by cumulative carbon
emissions towards the trillionth tonne, Nature, 458, 1163–1166,
10.1038/nature08019, 2009.Armour, K. C., Eisenman, I., Blanchard-Wrigglesworth, E., McCusker, K.
E.,
and Bitz, C. M.: The reversibility of sea ice loss in a state-of-the-art
climate model, Geophys. Res. Lett., 38, 1–5, 10.1029/2011GL048739,
2011.Arora, V. K. and Boer, G. J.: Terrestrial ecosystems response to future changes in climate and atmospheric CO2
concentration, Biogeosciences, 11, 4157–4171, 10.5194/bg-11-4157-2014, 2014.Bauer, N., Calvin, K., Emmerling, J., Fricko, O., Fujimori, S., Hilaire, J.,
Eom, J., Krey, V., Kriegler, E., Mouratiadou, I., Sytze de Boer, H., van den
Berg, M., Carrara, S., Daioglou, V., Drouet, L., Edmonds, J. E., Gernaat,
D., Havlik, P., Johnson, N., Klein, D., Kyle, P., Marangoni, G., Masui, T.,
Pietzcker, R. C., Strubegger, M., Wise, M., Riahi, K., and van Vuuren, D. P.:
Shared Socio-Economic Pathways of the Energy Sector – Quantifying the
Narratives, Global Environ. Chang., 42, 316–330,
10.1016/j.gloenvcha.2016.07.006, 2017.Betts, R. A.: Offset of the potential carbon sink from boreal forestation by
decreases in surface albedo, Nature, 409, 187–190, 2000.Boucher, O., Halloran, P. R., Burke, E. J., Doutriaux-Boucher, M., Jones, C.
D., Lowe, J., Ringer, M. A., Robertson, E., and Wu, P.: Reversibility in an
Earth System model in response to CO2 concentration changes, Environ. Res.
Lett., 7, 24013, 10.1088/1748-9326/7/2/024013, 2012.Boysen, L. R., Lucht, W., Gerten, D., and Heck, V.: Impacts devalue the
potential of large-scale terrestrial CO 2 removal through biomass
plantations, Environ. Res. Lett., 11, 95010,
10.1088/1748-9326/11/9/095010, 2016.Boysen, L. R., Lucht, W., Gerten, D., Heck, V., Lenton, T. M., and
Schellnhuber, H. J.: The limits to global-warming mitigation by terrestrial
carbon removal, Earth's Future, 1–12, 10.1002/2016EF000469,
2017.Cao, L. and Caldeira, K.: Atmospheric carbon dioxide removal: long-term
consequences and commitment, Environ. Res. Lett., 5, 24011,
10.1088/1748-9326/5/2/024011, 2010.Chen, C. and Tavoni, M.: Direct air capture of CO2 and climate
stabilization: A model based assessment, Climatic Change, 118, 59–72,
10.1007/s10584-013-0714-7, 2013.Colbourn, G., Ridgwell, A., and Lenton, T. M.: The time scale of the silicate
weathering negative feedback on atmospheric CO2, 29, 583–596,
10.1002/2014GB005054, 2015.Cripps, G., Widdicombe, S., Spicer, J. I., and Findlay, H. S.: Biological
impacts of enhanced alkalinity in Carcinus maenas, Mar. Pollut. Bull.,
71, 190–198, 10.1016/j.marpolbul.2013.03.015, 2013.de Richter, R., Ming, T., Davies, P., Liu, W., and Caillol, S.: Removal of
non-CO2 greenhouse gases by large-scale atmospheric solar photocatalysis,
Prog. Energ. Combust., 60, 68–96, 10.1016/j.pecs.2017.01.001, 2017.Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, National Oceanic & Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), available at: http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html, last access: 28 October
2016.Duteil, O., Koeve, W., Oschlies, A., Aumont, O., Bianchi, D., Bopp, L.,
Galbraith, E., Matear, R., Moore, J. K., Sarmiento, J. L., and Segschneider,
J.: Preformed and regenerated phosphate in ocean general circulation models:
can right total concentrations be wrong?, Biogeosciences, 9, 1797–1807,
10.5194/bg-9-1797-2012, 2012.Eby, M., Zickfeld, K., Montenegro, A., Archer, D., Meissner, K. J., and
Weaver, A. J.: Lifetime of Anthropogenic Climate Change: Millennial Time
Scales of Potential CO2 and Surface Temperature Perturbations, J. Climate,
22, 2501–2511, 10.1175/2008JCLI2554.1, 2009.Eby, M., Weaver, A. J., Alexander, K., Zickfeld, K., Abe-Ouchi, A.,
Cimatoribus, A. A., Crespin, E., Drijfhout, S. S., Edwards, N. R., Eliseev,
A. V., Feulner, G., Fichefet, T., Forest, C. E., Goosse, H., Holden, P. B.,
Joos, F., Kawamiya, M., Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov,
I. I., Monier, E., Olsen, S. M., Pedersen, J. O. P., Perrette, M.,
Philippon-Berthier, G., Ridgwell, A., Schlosser, A., Schneider von Deimling,
T., Shaffer, G., Smith, R. S., Spahni, R., Sokolov, A. P., Steinacher, M.,
Tachiiri, K., Tokos, K., Yoshimori, M., Zeng, N., and Zhao, F.: Historical
and idealized climate model experiments: an intercomparison of Earth system
models of intermediate complexity, Clim. Past, 9, 1111–1140,
10.5194/cp-9-1111-2013, 2013.Egleston, E. S., Sabine, C. L., and Morel, F. M. M.: Revelle revisited:
Buffer factors that quantify the response of ocean chemistry to changes in
DIC and alkalinity, Global Biogeochem. Cy., 24, GB1002, 10.1029/2008GB003407,
2010.Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R.
J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project
Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9,
1937–1958, 10.5194/gmd-9-1937-2016, 2016.Feng, E. Y., Keller, D. P., Koeve, W., and Oschlies, A.: Could artificial
ocean alkalinization protect tropical coral ecosystems from ocean
acidification?, Environ. Res. Lett., 11, 74008,
10.1088/1748-9326/11/7/074008, 2016.Frölicher, T. L., Winton, M., and Sarmiento, J. L.: Continued global
warming after CO2 emissions stoppage, Nat. Clim. Chang, 4, 40–44,
10.1038/nclimate2060, 2013.Fuss, S., Canadell, J. G., Peters, G. P., Tavoni, M., Andrew, R. M., Ciais,
P., Jackson, R. B., Jones, C. D., Kraxner, F., Nakicenovic, N.,
Quéré, C. Le, Raupach, M. R., Sharifi, A., Smith, P., and Yamagata,
Y.: Betting on negative emissions, Nat. Publ. Gr., 4, 850–853,
10.1038/nclimate2392, 2014.Gasser, T., Guivarch, C., Tachiiri, K., Jones, C. D., and Ciais, P.: Negative
emissions physically needed to keep global warming below 2? ∘C,
Nat. Commun., 6, 7958, 10.1038/ncomms8958, 2015.González, M. F. and Ilyina, T.: Impacts of artificial ocean
alkalinization on the carbon cycle and climate in Earth system simulations,
Geophys. Res. Lett., 43, 6493–6502, 10.1002/2016GL068576, 2016.Gregory, J. M.: A new method for diagnosing radiative forcing and climate
sensitivity, Geophys. Res. Lett., 31, 2–5, 10.1029/2003GL018747, 2004.Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V.,
Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H.,
Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W.,
Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H.,
Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S.,
McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F.,
Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P.,
Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to
CMIP6: experimental and diagnostic protocol for the physical component of the
Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296,
10.5194/gmd-9-3231-2016, 2016.Gruber, N.: Warming up, turning sour, losing breath: ocean biogeochemistry
under global change, Philos. T. Roy. Soc. A, 369, 1980–1996,
10.1098/rsta.2011.0003, 2011.Gupta, A. Sen, Jourdain, N. C., Brown, J. N., and Monselesan, D.: Climate
Drift in the CMIP5 Models, J. Climate, 26, 8597–8615,
10.1175/JCLI-D-12-00521.1, 2013.Hansen, J.: Efficacy of climate forcings, J. Geophys. Res., 110,
D18104, 10.1029/2005JD005776, 2005.Hartmann, J., West, J., Renforth, P., Köhler, P., De La Rocha, C. L.,
Wolf-Gladrow, D., Dürr, H., and Scheffran, J.:
Enhanced chemical weathering as a geoengineering strategy to reduce atmospheric carbon dioxide, supply nutrients, and mitigate ocean acidification, Rev. Geophys., 51, 113–149,
10.1002/rog.20004, 2013.Hauck, J., Köhler, P., Wolf-Gladrow, D., and Völker, C.: Iron
fertilisation and century-scale effects of open ocean dissolution of olivine
in a simulated CO2 removal experiment, Environ. Res. Lett., 11, 24007,
10.1088/1748-9326/11/2/024007, 2016.Heck, V., Gerten, D., Lucht, W., and Boysen, L. R.: Is extensive terrestrial
carbon dioxide removal a “green” form of geoengineering? A global modelling
study, Global Planet. Change, 137, 123–130,
10.1016/j.gloplacha.2015.12.008, 2016.Hofmann, M. and Schellnhuber, H. J.: Ocean acidification: a millennial
challenge, Energy Environ. Sci., 3, 1883–1896, 2010.Holmes, G. and Keith, D. W.: An air-liquid contactor for large-scale capture
of CO2 from air, Philos. T. Roy. Soc. A, 370, 4380–4403,
10.1098/rsta.2012.0137, 2012.Ilyina, T., Wolf-Gladrow, D., Munhoven, G., and Heinze, C.: Assessing the
potential of calcium-based artificial ocean alkalinization to mitigate rising
atmospheric CO2 and ocean acidification, Geophys. Res. Lett., 40,
5909–5914, 10.1002/2013GL057981, 2013.IPCC: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D.,
Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535
pp., 2013.IPCC: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A:
Global and Sectoral Aspects, Contribution of Working Group II to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Field, C. B., Barros, V. R., Dokken, D. J., Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 2014a.IPCC: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Edenhofer,
O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A.,
Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C.,
Zwickel, T., and Minx, J. C., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2014b.Jones, C. D., Ciais, P., Davis, S. J., Friedlingstein, P., Gasser, T.,
Peters, G. P., Rogelj, J., van Vuuren, D. P., Canadell, J. G., Cowie, A.,
Jackson, R. B., Jonas, M., Kriegler, E., Littleton, E., Lowe, J. A., Milne,
J., Shrestha, G., Smith, P., Torvanger, A., and Wiltshire, A.: Simulating the
Earth system response to negative emissions, Environ. Res. Lett., 11, 95012,
10.1088/1748-9326/11/9/095012, 2016a.Jones, C. D., Arora, V., Friedlingstein, P., Bopp, L., Brovkin, V., Dunne,
J., Graven, H., Hoffman, F., Ilyina, T., John, J. G., Jung, M., Kawamiya, M.,
Koven, C., Pongratz, J., Raddatz, T., Randerson, J. T., and Zaehle, S.: C4MIP
– The Coupled Climate-Carbon Cycle Model Intercomparison Project:
experimental protocol for CMIP6, Geosci. Model Dev., 9, 2853–2880,
10.5194/gmd-9-2853-2016, 2016b.Joos, F., Roth, R., Fuglestvedt, J. S., Peters, G. P., Enting, I. G., von
Bloh, W., Brovkin, V., Burke, E. J., Eby, M., Edwards, N. R., Friedrich, T.,
Frölicher, T. L., Halloran, P. R., Holden, P. B., Jones, C., Kleinen, T.,
Mackenzie, F. T., Matsumoto, K., Meinshausen, M., Plattner, G.-K., Reisinger,
A., Segschneider, J., Shaffer, G., Steinacher, M., Strassmann, K., Tanaka,
K., Timmermann, A., and Weaver, A. J.: Carbon dioxide and climate impulse
response functions for the computation of greenhouse gas metrics: a
multi-model analysis, Atmos. Chem. Phys., 13, 2793–2825,
10.5194/acp-13-2793-2013, 2013.Keller, D. P. and Lenton, A.: keller_et_al_2018_gmd, available at: http://thredds.geomar.de/thredds/catalog/open_access/keller_et_
al_2018_gmd/catalog.html, 2018.Keller, D. P., Oschlies, A., and Eby, M.: A new marine ecosystem model for
the University of Victoria Earth System Climate Model, Geosci. Model Dev., 5,
1195–1220, 10.5194/gmd-5-1195-2012, 2012.Keller, D. P., Feng, E. Y., and Oschlies, A.: Potential climate engineering
effectiveness and side effects during a high carbon dioxide-emission
scenario, Nat. Commun., 5, 1–11, 10.1038/ncomms4304, 2014.Kheshgi, H. S.: Sequestering atmospheric carbon dioxide by increasing ocean
alkalinity, Energy, 20, 915–922, 10.1016/0360-5442(95)00035-F, 1995.Köhler, P., Hartmann, J., and Wolf-Gladrow, D. A.: Geoengineering
potential of artificially enhanced silicate weathering of olivine, P. Natl.
Acad. Sci. USA, 107, 20228–20233, 10.1073/pnas.1000545107, 2010.Köhler, P., Abrams, J. F., Völker, C., Hauck, J., and Wolf-Gladrow,
D. A.: Geoengineering impact of open ocean dissolution of olivine on
atmospheric CO2, surface ocean pH and marine biology, Environ. Res. Lett.,
8, 14009, available at: http://stacks.iop.org/1748-9326/8/i=1/a=014009 (last access: 21 January 2013),
2013.Kriegler, E., Tavoni, M., Aboumahboub, T., Luderer, G., Calvin, K., Demaere,
G., Krey, V., Riahi, K., Rösler, H., Schaeffer, M., and Van Vuuren, D. P.:
What Does The 2∘C Target Imply For A Global Climate Agreement In
2020? The Limits Study On Durban Platform Scenarios, Climate Change
Economics, 4, 1340008, 10.1142/S2010007813400083, 2013.Kriegler, E., Bauer, N., Popp, A., Humpenöder, F., Leimbach, M.,
Strefler, J., Baumstark, L., Bodirsky, B. L., Hilaire, J., Klein, D.,
Mouratiadou, I., Weindl, I., Bertram, C., Dietrich, J.-P., Luderer, G., Pehl,
M., Pietzcker, R., Piontek, F., Lotze-Campen, H., Biewald, A., Bonsch, M.,
Giannousakis, A., Kreidenweis, U., Müller, C., Rolinski, S., Schultes,
A., Schwanitz, J., Stevanovic, M., Calvin, K., Emmerling, J., Fujimori, S.,
and Edenhofer, O.: Fossil-fueled development (SSP5): An energy and resource
intensive scenario for the 21st century, Global Environ. Chang., 42,
297–315,
10.1016/j.gloenvcha.2016.05.015, 2016.Kroeker, K. J., Kordas, R. L., Crim, R., Hendriks, I. E., Ramajo, L., Singh,
G. S., Duarte, C. M., and Gattuso, J.-P.: Impacts of ocean acidification on
marine organisms: quantifying sensitivities and interaction with warming,
Glob. Change Biol., 19, 1884–1896, 10.1111/gcb.12179, 2013.Lackner, K. S., Brennan, S., Matter, J. M., Park, A.-H. A., Wright, A., and
van der Zwaan, B.: The urgency of the development of CO2 capture from ambient
air, P. Natl. Acad. Sci. USA, 109, 13156–13162, 10.1073/pnas.1108765109,
2012.Lawrence, D. M., Hurtt, G. C., Arneth, A., Brovkin, V., Calvin, K. V., Jones,
A. D., Jones, C. D., Lawrence, P. J., de Noblet-Ducoudré, N., Pongratz, J.,
Seneviratne, S. I., and Shevliakova, E.: The Land Use Model Intercomparison
Project (LUMIP) contribution to CMIP6: rationale and experimental design,
Geosci. Model Dev., 9, 2973–2998, 10.5194/gmd-9-2973-2016,
2016.Le Quéré, C., Moriarty, R., Andrew, R. M., Canadell, J. G., Sitch, S.,
Korsbakken, J. I., Friedlingstein, P., Peters, G. P., Andres, R. J., Boden,
T. A., Houghton, R. A., House, J. I., Keeling, R. F., Tans, P., Arneth, A.,
Bakker, D. C. E., Barbero, L., Bopp, L., Chang, J., Chevallier, F., Chini, L.
P., Ciais, P., Fader, M., Feely, R. A., Gkritzalis, T., Harris, I., Hauck,
J., Ilyina, T., Jain, A. K., Kato, E., Kitidis, V., Klein Goldewijk, K.,
Koven, C., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A.,
Lima, I. D., Metzl, N., Millero, F., Munro, D. R., Murata, A., Nabel, J. E.
M. S., Nakaoka, S., Nojiri, Y., O'Brien, K., Olsen, A., Ono, T., Pérez, F.
F., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Rödenbeck, C., Saito,
S., Schuster, U., Schwinger, J., Séférian, R., Steinhoff, T., Stocker, B.
D., Sutton, A. J., Takahashi, T., Tilbrook, B., van der Laan-Luijkx, I. T.,
van der Werf, G. R., van Heuven, S., Vandemark, D., Viovy, N., Wiltshire, A.,
Zaehle, S., and Zeng, N.: Global Carbon Budget 2015, Earth Syst. Sci. Data,
7, 349-396, 10.5194/essd-7-349-2015, 2015.Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J.
I., Peters, G. P., Manning, A. C., Boden, T. A., Tans, P. P., Houghton, R.
A., Keeling, R. F., Alin, S., Andrews, O. D., Anthoni, P., Barbero, L., Bopp,
L., Chevallier, F., Chini, L. P., Ciais, P., Currie, K., Delire, C., Doney,
S. C., Friedlingstein, P., Gkritzalis, T., Harris, I., Hauck, J., Haverd, V.,
Hoppema, M., Klein Goldewijk, K., Jain, A. K., Kato, E., Körtzinger, A.,
Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D.,
Melton, J. R., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R.,
Nabel, J. E. M. S., Nakaoka, S.-I., O'Brien, K., Olsen, A., Omar, A. M., Ono,
T., Pierrot, D., Poulter, B., R”odenbeck, C., Salisbury, J., Schuster, U.,
Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Sutton, A. J.,
Takahashi, T., Tian, H., Tilbrook, B., van der Laan-Luijkx, I. T., van der
Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., and Zaehle, S.:
Global Carbon Budget 2016, Earth Syst. Sci. Data, 8, 605–649,
10.5194/essd-8-605-2016, 2016.MacDougall, A. H.: Reversing climate warming by artificial atmospheric
carbon-dioxide removal: Can a Holocene-like climate be restored?, Geophys.
Res. Lett., 40, 5480–5485, 10.1002/2013GL057467, 2013.Mao, J., Phipps, S. J., Pitman, A. J., Wang, Y. P., Abramowitz, G., and Pak,
B.: The CSIRO Mk3L climate system model v1.0 coupled to the CABLE land
surface scheme v1.4b: evaluation of the control climatology, Geosci. Model
Dev., 4, 1115-1131, 10.5194/gmd-4-1115-2011, 2011.Matear, R. J. and Hirst, A. C.: Long-term changes in dissolved oxygen
concentrations in the ocean caused by protracted global warming, Global
Biogeochem. Cy., 17, 1125, 10.1029/2002GB001997, 2003.Matear, R. J. and Lenton, A.: Quantifying the impact of ocean acidification
on our future climate, Biogeosciences, 11, 3965–3983,
10.5194/bg-11-3965-2014, 2014.Mathesius, S., Hofmann, M., Caldeira, K., and Schellnhuber, H. J.: Long-term
response of oceans to CO2 removal from the atmosphere, Nat. Clim. Chang., 5,
1107–1113,
10.1038/nclimate2729, 2015.Matter, J. M., Stute, M., Snaebjornsdottir, S. O., Oelkers, E. H., Gislason,
S. R., Aradottir, E. S., Sigfusson, B., Gunnarsson, I., Sigurdardottir, H.,
Gunnlaugsson, E., Axelsson, G., Alfredsson, H. A., Wolff-Boenisch, D.,
Mesfin, K., Taya, D. F. d. L. R., Hall, J., Dideriksen, K., and Broecker, W.
S.: Rapid carbon mineralization for permanent disposal of anthropogenic
carbon dioxide emissions, Science, 352, 1312–1314,
10.1126/science.aad8132, 2016.Matthews, H. D., Gillett, N. P., Stott, P. A., and Zickfeld, K.: The
proportionality of global warming to cumulative carbon emissions, Nature,
459, 829–32, 10.1038/nature08047, 2009.Meehl, G. A., Moss, R., Taylor, K. E., Eyring, V., Stouffer, R. J., Bony,
S., and Stevens, B.: Climate Model Intercomparisons: Preparing for the Next
Phase, Eos Trans. Am. Geophys. Union, 95, 77–78, 10.1002/2014EO090001,
2014.Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled
atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 –
Part 1: Model description and calibration, Atmos. Chem. Phys., 11,
1417–1456, 10.5194/acp-11-1417-2011, 2011.Meissner, K. J., Weaver, A. J., Matthews, H. D., and Cox, P. M.: The role of
land surface dynamics in glacial inception: A study with the UVic Earth
System Model, Clim. Dynam., 21, 515–537, 2003.Millennium Ecosystem Assesment: Ecosystems and Human Well-Being, Synthesis,
Island Press, Washington, DC, 2005.Ming, T., de Richter, R., Shen, S., and Caillol, S.: Fighting global warming
by greenhouse gas removal: destroying atmospheric nitrous oxide thanks to
synergies between two breakthrough technologies, Environ. Sci. Pollut. R., 23, 6119–6138,
10.1007/s11356-016-6103-9, 2016.Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 data set, J. Geophys. Res.-Atmos., 117,
1–22, 10.1029/2011JD017187, 2012.National Research Council: Climate Intervention, National Academies Press,
Washington, DC, 2015.O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein,
P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G.
A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model
Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9,
3461–3482, 10.5194/gmd-9-3461-2016, 2016.Orr, J. C., Najjar, R. G., Aumont, O., Bopp, L., Bullister, J. L., Danabasoglu, G., Doney, S. C., Dunne, J. P., Dutay, J.-C., Graven, H., Griffies, S. M., John, J. G., Joos, F., Levin, I., Lindsay, K., Matear, R. J., McKinley, G. A., Mouchet, A., Oschlies, A., Romanou, A., Schlitzer, R., Tagliabue, A., Tanhua, T., and Yool, A.: Biogeochemical protocols and diagnostics for the CMIP6
Ocean Model Intercomparison Project (OMIP), Geosci. Model Dev., 10, 2169–2199, 10.5194/gmd-10-2169-2017, 2017.Oschlies, A. and Klepper, G.: Research for assessment, not deployment, of
Climate Engineering: The German Research Foundation's Priority Program SPP
1689, Earth's Future, 5, 128–134, 10.1002/2016EF000446, 2017.Peters, G. P., Andrew, R. M., Boden, T., Canadell, J. G., Ciais, P., Le
Quéré, C., Marland, G., Raupach, M. R., and Wilson, C.: The challenge
to keep global warming below 2 ∘C, Nat. Clim. Chang., 3, 4–6,
10.1038/nclimate1783, 2013.Phipps, S. J., Rotstayn, L. D., Gordon, H. B., Roberts, J. L., Hirst, A. C.,
and Budd, W. F.: The CSIRO Mk3L climate system model version 1.0 – Part 1:
Description and evaluation, Geosci. Model Dev., 4, 483–509,
10.5194/gmd-4-483-2011, 2011.Renforth, P., Jenkins, B. G., and Kruger, T.: Engineering challenges of ocean
liming, Energy, 60, 442–452, 10.1016/j.energy.2013.08.006, 2013.Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp,
A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S.,
Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da
Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D.,
Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G.,
Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M.,
Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A.,
and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land use,
and greenhouse gas emissions implications: An overview, Global Environ.
Chang., 42, 153–168, 10.1016/j.gloenvcha.2016.05.009, 2017.Rickels, W., Klepper, G., Dovern, J., Betz, G., Brachatzek, N., Cacean, S.,
Güssow, K., Heintzenberg, J., Hiller, S., Hoose, C., Leisner, T.,
Oschlies, A., Platt, U., Proelß, A., Renn, O., Schäfer, S., and
Zürn, M.: Large-Scale Intentional Interventions into the Climate System?
Assessing the Climate Engineering Debate, 2011.Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M.,
Krey, V., and Riahi, K.: Energy system transformations for limiting
end-of-century warming to below 1.5 ∘C, Nat. Clim. Chang., 5,
519–527, 10.1038/nclimate2572, 2015a.Rogelj, J., Schaeffer, M., Meinshausen, M., Knutti, R., Alcamo, J., Riahi,
K., and Hare, W.: Zero emission targets as long-term global goals for climate
protection, Environ. Res. Lett., 10, 105007,
10.1088/1748-9326/10/10/105007, 2015b.Sanz-Pérez, E. S., Murdock, C. R., Didas, S. A., and Jones, C. W.: Direct
Capture of CO 2 from Ambient Air, Chem. Rev., 116, 11840–11876,
10.1021/acs.chemrev.6b00173, 2016.Schäfer, S., Lawrence, M., Stelzer, H., Born, W., Low,
S., Aaheim, A., Adriaìzola, P., Betz, G., Boucher, O., Carius, A.,
Devine-Right, P., Gullberg, A. T., Haszeldine, S., Haywood, J., Houghton, K.,
Ibarrola, R., Irvine, P., Kristjansson, J.-E., Lenton, T., Link, J. S. A.,
Maas, A., Meyer, L., Muri, H., Oschlies, A., Proelß, A., Rayner, T.,
Rickels, W., Ruthner, L., Scheffran, J., Schmidt, H., Schulz, M., Scott, V.,
Shackley, S., Tänzler, D., Watson, M. and Vaughan, N.: The European
Transdisciplinary Assessment of Climate Engineering (EuTRACE): Removing
Greenhouse Gases from the Atmosphere and Reflecting Sunlight away from Earth,
2015.Schuiling, R. D. and Krijgsman, P.: Enhanced weathering: An effective and
cheap tool to sequester CO2, Clim. Change, 74, 349–354,
10.1007/s10584-005-3485-y, 2006.Scott, V., Gilfillan, S., Markusson, N., Chalmers, H., and Haszeldine, R. S.:
Last chance for carbon capture and storage, Nat. Clim. Chang., 3, 105–111,
10.1038/Nclimate1695, 2013.Scott, V., Haszeldine, R. S., Tett, S. F. B., and Oschlies, A.: Fossil fuels
in a trillion tonne world, Nat. Clim. Chang., 5, 419–423,
10.1038/nclimate2578, 2015.Séférian, R., Gehlen, M., Bopp, L., Resplandy, L., Orr, J. C., Marti, O.,
Dunne, J. P., Christian, J. R., Doney, S. C., Ilyina, T., Lindsay, K.,
Halloran, P. R., Heinze, C., Segschneider, J., Tjiputra, J., Aumont, O., and
Romanou, A.: Inconsistent strategies to spin up models in CMIP5: implications
for ocean biogeochemical model performance assessment, Geosci. Model Dev., 9,
1827–1851, 10.5194/gmd-9-1827-2016, 2016.Smith, P.: Soil carbon sequestration and biochar as negative emission
technologies, Glob. Change Biol., 22, 1315–1324, 10.1111/gcb.13178,
2016.Smith, P., Davis, S. J., Creutzig, F., Fuss, S., Minx, J., Gabrielle, B.,
Kato, E., Jackson, R. B., Cowie, A., Kriegler, E., van Vuuren, D. P., Rogelj,
J., Ciais, P., Milne, J., Canadell, J. G., McCollum, D., Peters, G., Andrew,
R., Krey, V., Shrestha, G., Friedlingstein, P., Gasser, T., Grübler, A.,
Heidug, W. K., Jonas, M., Jones, C. D., Kraxner, F., Littleton, E., Lowe, J.,
Moreira, J. R., Nakicenovic, N., Obersteiner, M., Patwardhan, A., Rogner, M.,
Rubin, E., Sharifi, A., Torvanger, A., Yamagata, Y., Edmonds, J., and
Yongsung, C.: Biophysical and economic limits to negative CO2 emissions,
Nat. Clim. Chang., 6, 42–50, 10.1038/nclimate2870, 2015.Sonntag, S., Pongratz, J., Reick, C. H., and Schmidt, H.: Reforestation in a
high-CO2 world-Higher mitigation potential than expected, lower adaptation
potential than hoped for, Geophys. Res. Lett., 1–8,
10.1002/2016GL068824, 2016.Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498,
10.1175/BAMS-D-11-00094.1, 2012.
The Royal Society: Geoengineering the climate, 2009.Tokarska, K. B. and Zickfeld, K.: The effectiveness of net negative carbon
dioxide emissions in reversing anthropogenic climate change, Environ. Res.
Lett., 10, 94013, 10.1088/1748-9326/10/9/094013, 2015.UNFCCC: Paris Agreement of the 21st session of the Conference of Parties on
climate change, 2016.Vaughan, N. E. and Gough, C.: Expert assessment concludes negative emissions
scenarios may not deliver, Environ. Res. Lett., 11, 95003,
10.1088/1748-9326/11/9/095003, 2016.Vaughan, N. E. and Lenton, T. M.: A review of climate geoengineering
proposals, Climatic Change, 109, 745–790, 10.1007/s10584-011-0027-7,
2011.Vichi, M., Navarra, A., and Fogli, P. G.: Adjustment of the natural ocean
carbon cycle to negative emission rates, Climatic Change, 118, 105–118,
10.1007/s10584-012-0677-0, 2013.
Walker, J. C. G., Hays, P. B., and Kasting, J. F.: A negative feedback
mechanism for the long-term stabilization of Earth's surface temperature, J.
Geophys. Res., 86, 9776, 10.1029/JC086iC10p09776, 1981.Wang, X., Heald, C. L., Ridley, D. A., Schwarz, J. P., Spackman, J. R.,
Perring, A. E., Coe, H., Liu, D., and Clarke, A. D.: Exploiting simultaneous
observational constraints on mass and absorption to estimate the global
direct radiative forcing of black carbon and brown carbon, Atmos. Chem.
Phys., 14, 10989–11010, 10.5194/acp-14-10989-2014, 2014.Wang, Y. P., Law, R. M., and Pak, B.: A global model of carbon, nitrogen and
phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7,
2261–2282, 10.5194/bg-7-2261-2010, 2010.Wolf-Gladrow, D. A., Zeebe, R. E., Klaas, C., Körtzinger, A., and
Dickson, A. G.: Total alkalinity: The explicit conservative expression and
its application to biogeochemical processes, Mar. Chem., 106, 287–300,
10.1016/j.marchem.2007.01.006, 2007.Wu, P., Ridley, J., Pardaens, A., Levine, R., and Lowe, J.: The reversibility
of CO2 induced climate change, Clim. Dynam., 45, 745–754,
10.1007/s00382-014-2302-6, 2014.Zhang, Q., Wang, Y. P., Matear, R. J., Pitman, A. J., and Dai, Y. J.:
Nitrogen and phosphorous limitations significantly reduce future allowable
CO2 emissions, Geophys. Res. Lett., 41, 632–637,
10.1002/2013GL058352, 2014.Zickfeld, K., Eby, M., Weaver, A. J., Alexander, K., Crespin, E., Edwards,
N. R., Eliseev, A. V., Feulner, G., Fichefet, T., Forest, C. E.,
Friedlingstein, P., Goosse, H., Holden, P. B., Joos, F., Kawamiya, M.,
Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov, I. I., Monier, E.,
Olsen, S. M., Pedersen, J. O. P., Perrette, M., Philippon-Berthier, G. G.,
Ridgwell, A., Schlosser, A., Schneider Von Deimling, T., Shaffer, G.,
Sokolov, A., Spahni, R., Steinacher, M., Tachiiri, K., Tokos, K. S.,
Yoshimori, M., Zeng, N., and Zhao, F.: Long-Term Climate Change Commitment
and Reversibility: An EMIC Intercomparison, J. Climate, 26, 5782–5809,
10.1175/jcli-d-12-00584.1, 2013.Zickfeld, K., MacDougall, A. H., and Matthews, H. D.: On the proportionality
between global temperature change and cumulative CO2 emissions during
periods of net negative CO2 emissions, Environ. Res. Lett., 11, 55006,
10.1088/1748-9326/11/5/055006, 2016.