The University of Victoria Earth System Climate Model (UVic ESCM) of
intermediate complexity has been a useful tool in recent assessments of
long-term climate changes, including both paleo-climate modelling and
uncertainty assessments of future warming. Since the last official release
of the UVic ESCM 2.9 and the two official updates during the last decade,
considerable model development has taken place among multiple research
groups. The new version 2.10 of the University of Victoria Earth System
Climate Model presented here will be part of the sixth phase
of the Coupled Model Intercomparison Project (CMIP6). More precisely it will
be used in the intercomparison of Earth system models of intermediate
complexity (EMIC), such as the C4MIP, the Carbon Dioxide Removal and Zero
Emissions Commitment model intercomparison projects (CDR-MIP and ZECMIP,
respectively). It now brings together and combines multiple model
developments and new components that have come about since the last
official release of the model. The main additions to the base model are
(i) an improved biogeochemistry module for the ocean, (ii) a vertically resolved
soil model including dynamic hydrology and soil carbon processes, and (iii) a
representation of permafrost carbon. To set the foundation of its use, we
here describe the UVic ESCM 2.10 and evaluate results from transient
historical simulations against observational data. We find that the UVic
ESCM 2.10 is capable of reproducing changes in historical temperature and
carbon fluxes well. The spatial distribution of many ocean tracers,
including temperature, salinity, phosphate and nitrate, also agree well with
observed tracer profiles. The good performance in the ocean tracers is
connected to an improved representation of ocean physical properties. For
the moment, the main biases that remain are a vegetation carbon density that
is too high in the tropics, a higher than observed change in the ocean heat
content (OHC) and an oxygen utilization in the Southern Ocean that is too low.
All of these biases will be addressed in the next updates to the model.
Introduction
The University of Victoria Earth System Climate Model (UVic ESCM) of
intermediate complexity has been a useful tool in recent assessments of long-term climate changes including paleo-climate modelling (e.g. Alexander
et al., 2015; Bagniewski et al., 2017; Handiani et al., 2012; Meissner et
al., 2003; Menviel et al., 2014), carbon cycle dynamics (e.g. Matthews
et al., 2009b; Matthews and Caldeira, 2008; Montenegro et al., 2007;
Schmittner et al., 2008; Tokarska and Zickfeld, 2015; Zickfeld et al., 2009,
2011, 2016) and climate change uncertainty assessments (e.g. Ehlert et al.,
2018; Leduc
et al., 2015; MacDougall et al., 2015, 2017; MacDougall and Friedlingstein, 2015;
Matthews et al., 2009a; Mengis et al., 2018, 2019; Rennermalm et al., 2006;
Taucher and Oschlies, 2011). The UVic ESCM has been
instrumental in establishing the irreversibility of CO2-induced climate
change after the cessation of CO2 emissions (Matthews et al., 2008; Eby et al., 2009) and the proportional relationship between global warming and
cumulative CO2 emissions (Matthews et al., 2009; Zickeld et al., 2009). As an Earth system model of intermediate complexity, the UVic ESCM has a
comparably low computational cost (4.6–11.5 h per 100 years on a simple
desktop computer, depending on the computational power of the machine)
while still providing a comprehensive carbon cycle model with a fully
represented ocean physics. It is therefore a well-suited tool to, for example,
perform large perturbed parameter ensembles to constrain process level
uncertainties (e.g. MacDougall and Knutti, 2016; Mengis et al., 2018).
Such experiments are still not yet feasible in a state-of-the-art Earth
system model (ESM). Thanks to its representation of many important
components of the carbon cycle and the physical climate and its ability to
simulate dynamic interactions between them, the UVic ESCM is a more
comprehensive tool for uncertainty assessment compared to the simple climate
models such as the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC).
Since the last official release of the UVic ESCM 2.9, and the two official
updates during the last decade (Eby et al., 2009; Zickfeld et al., 2011), there are representations for a new marine ecosystem
model (Keller et al., 2012) and higher vertically resolved soil dynamics (Avis
et al., 2011) and permafrost carbon (MacDougall et al., 2012; MacDougall and
Knutti, 2016).
The marine ecosystems and biological processes play an important, but often
less understood, role in global biogeochemical cycles. They affect the
climate primarily through the “carbonate” and “soft tissue” pumps (i.e.
the “biological” pump) (Longhurst
and Harrison, 1989; Volk and Hoffert, 1985). The biological pump has been
estimated to export between 5 and 20 Gt C yr-1 out of the surface layer (Henson
et al., 2011; Honjo et al., 2008; Laws et al., 2000). However, as indicated
by the large range of estimates, there is great uncertainty in our
understanding of the magnitude of carbon export (Henson et al., 2011), its
sensitivity to environmental change (Löptien and Dietze, 2019) and thus its
effect on the Earth's climate. Above that, marine ecosystems also play a
large role in the cycling of nitrogen, phosphorus and oxygen. In surface
waters, nitrogen and phosphorus constitute major nutrients that are consumed
by, and drive, primary production (PP) and thus are linked back to the
carbon cycle.
In the recent special report on global warming of 1.5 ∘C from the
Intergovernmental Panel on Climate Change (IPCC), one of the key uncertainties for
the assessment of the remaining global carbon budget was the impact from
unrepresented Earth system feedbacks. On the decadal to centennial
timescales, this specifically refers to the permafrost carbon feedback (Lowe
and Bernie, 2018). Quantifying the strength and timing of this permafrost
carbon cycle feedback to climate change has been a goal of Earth system
modelling in recent years (e.g.
Burke et al., 2012; Koven et al., 2011, 2013; MacDougall et al., 2012;
Schaefer et al., 2011; Schneider Von Deimling et al., 2012; Zhuang et al.,
2006).
For version 2.10 of the University of Victoria Earth System Climate Model,
we combined version 2.9 with the new marine ecosystem model component as
published in Keller et al. (2012), as well as the soil dynamics and permafrost
carbon component as published by Avis et al. (2011) and MacDougall and
Knutti (2016). For the sixth phase of the Coupled Model Intercomparison
Project (CMIP6) simulations, the merging of these two components will allow
a more comprehensive representation of the carbon cycle in the UVic ESCM
while incorporating the model developments that have taken place in the
context of the UVic ESCM. In addition to the structural changes, we also
changed the spin-up protocol to follow CMIP6 protocols and applied the newly
available CMIP6 forcing.
The objective of the new model development is to have a more realistic
representation of carbon and heat fluxes in the UVic ESCM 2.10 that is in agreement
with the available observational data and with current process understanding and that can be used within the context of the next round of model intercomparison
projects for models of intermediate complexity. To set the foundation of its
use, we will in the following describe the UVic ESCM 2.10 (Sect. 2.1.) and the
newly formatted historical CMIP6 forcing that has been and will be used
(Sect. 2.2.), explicitly describe changes that have been implemented in the
UVic ESCM with respect to the previously published versions (Sect. 2.3.),
and then evaluate results from transient historical simulations against
observational data (Sect. 3.).
MethodsDescription of the University of Victoria Earth System Climate Model version 2.10
The UVic ESCM is a model of intermediate complexity (Weaver et al., 2001).
All model components have a common horizontal resolution of 3.6∘
longitude and 1.8∘ latitude, and the oceanic component has a
vertical resolution of 19 levels, with vertical thickness varying between 50 m near the surface to 500 m in the deep ocean. The Modular Ocean Model
version 2 (MOM2) (Pacanowski, 1995) describes the ocean physics; it is
coupled to a thermodynamic-dynamic sea ice model (Bitz et al., 2001) with
elastic visco-plastic rheology (Hunke and Dukowicz, 1997). The atmosphere is
represented by a two-dimensional atmospheric energy moisture balance model
(Fanning and Weaver, 1996). Wind velocities are prescribed as monthly climatological wind fields from NCAR/NCEP reanalysis data (Eby et al., 2013). They are used to calculate the advection of atmospheric heat and moisture as well as the air–sea–ice fluxes of surface momentum, heat, and water fluxes. In transient
simulations, wind anomalies, which are determined from surface pressure
anomalies with respect to pre-industrial surface air temperature, are added
to the prescribed wind fields (Weaver et al., 2001).
Schematic of the University of Victoria Earth System Climate Model
version 2.10 (UVic ESCM 2.10).
In addition, the terrestrial component represents vegetation dynamics
including five different plant functional types (Meissner et al., 2003).
Sediment processes are represented using an oxic-only calcium-carbonate
model (Archer, 1996). Terrestrial weathering is diagnosed from the net
sediment flux during spin-up and held fixed at the equilibrium
pre-industrial value for transient simulations (Meissner et al., 2012). The
new version 2.10 of the University of Victoria Earth System Climate Model
(UVic ESCM) presented here brings together and combines multiple model
developments and new components that have come about since the last
official release of the model in the CMIP5 context. In the following, the
novel model components are described in detail.
Marine biogeochemical model
The ocean biogeochemistry model as published by Keller et al. (2012) is novel compared to the 2009 version of the model. It now includes
equations describing phytoplankton light limitation and zooplankton grazing,
a more realistic zooplankton growth and grazing model, and formulations for
an iron limitation scheme to constrain phytoplankton growth. In this context,
the ocean's mixing scheme was changed from a Bryan–Lewis profile to a scheme
for the computation of tidally induced diapycnal mixing over rough
topography (Simmons et al., 2004) (see ocean diffusivity profiles in Fig. S3). In addition, the air to sea gas parameterization was updated following
the ocean carbon-cycle model intercomparison project updates for these
numbers (Wanninkhof, 2014), which impacts the carbon exchange between the
atmospheric and marine components. Furthermore, we now apply the
stoichiometry from Paulmier et al. (2009) to consistently account for the
effects of denitrification and nitrogen fixation on alkalinity and oxygen.
Soil model
The terrestrial component has also been updated relative to the latest
official release of the UVic ESCM. It now includes a representation of soil
freeze–thaw processes resolved in 14 subsurface layers of which the
thicknesses exponentially increase with depth: the surface layer having a
thickness of 0.1 m, the bottom layer a thickness of 104.4 m and the total
thickness of the subsurface layers being 250 m. The top eight layers (to a
depth of 10 m) are soil layers; below this are bedrock layers having the
thermal characteristics of granitic rock. Moisture undergoes free drainage
from the base of the soil layers, and the bedrock layers are hydrologically
inactive (Avis et al., 2011). In addition, the soil module includes a
multi-layer representation of soil carbon (MacDougall et al., 2012). Organic
carbon from the litter flux is allocated to soil layers as a decreasing
function of depth and is only added to soil layers with a temperature above
1 ∘C. If all layers are below this temperature threshold, the
litter flux is added to the top layer of soil. Soil respiration remains a
function of temperature and moisture (Meissner et al., 2003) but is now
implemented in each layer individually. Respiration ceases if the soil layer
is below 0 ∘C. Soil carbon is present in the top six layers
of the soil column down to a depth of 3.35 m.
Permafrost model
A representation of permafrost carbon has also been added to the model.
Permafrost carbon is prognostically generated within the model using a
diffusion-based scheme meant to approximate the process of cryoturbation
(MacDougall and Knutti, 2016), which is to say a freeze–thaw generated mechanical
mixing process that causes subduction of organic carbon rich soils from the
surface into deeper soil layers in permafrost-affected soils. In model
grid cells with perennially frozen soil layers, soil carbon is diffused
proportional to the effective carbon concentration of each soil layer.
Effective carbon concentration is carbon concentration divided by porosity
and a saturation factor (MacDougall and Knutti, 2016). Carbon that is
diffused into perennially frozen soil is reclassified as permafrost carbon
and is given different properties from regular soil carbon. Permafrost
carbon decays with its own constant decay rate and is subject to an
“available fraction” which determines the fraction of permafrost carbon
that is available to decay. The available fraction slowly increases if
permafrost carbon becomes thawed and decreases if permafrost carbon decays.
Using this scheme, the model can represent the large fraction of permafrost
carbon that is in the passive soil carbon pool while still allowing the
passive pool to eventually decay (MacDougall and Knutti, 2016).
Description of the CMIP6 forcing for the UVic ESCM
Anthropogenic forcing from greenhouse gases (GHGs), stratospheric and
tropospheric ozone, aerosols, and stratospheric water vapour from methane
oxidation is considered. Natural forcing includes solar and volcanic. All
data used in the creation of this dataset can be accessed from input4MIP from
the Earth System Grid Federation (ESGF) unless otherwise specified. In the
following, we will briefly describe how the input data for our simulations
with the University of Victoria Earth System Climate Model (UVic ESCM) were
created.
In the standard CMIP6 configuration, the UVic ESCM is forced with CO2 concentration data (ppm) (Meinshausen et al., 2017) and then calculates
the radiative forcing internally. These equations were updated to represent
the newest findings from Etminan et al. (2016). In contrast to that,
radiative forcing for non-CO2 GHGs was calculated
externally and summed up to be used as an additional model input using
concentration data of 43 GHGs (Meinshausen et al., 2017). We use updated
radiative forcing formulations for CO2, CH4 and N2O following
the findings of Etminan et al. (2016). Radiative forcing of other GHGs was
calculated using the formulations in Table 8.A.1 from the IPCC's Fifth Assessment Report (IPCC AR5; Shindell
et al., 2013). Meinshausen et al. (2017) introduced three options for
calculating radiative forcing from GHG concentrations. For this study, we
chose to use the option with which one uses specific calculations for all
available 43 GHGs rather than treating some groups of GHGs in a similar
manner.
The radiative forcing of stratospheric water vapour from methane oxidation
was calculated following the suggestion from Smith et al. (2018) by
multiplying CH4 effective radiative forcing by 12 %. To calculate
radiative forcing of tropospheric ozone, FO3tr, the equations from Smith et al. (2018) were used:
FO3tr=βCH4CCH4-CCH4,pi+βNOx(ENOx-ENOx,pi)+βCOECO-ECO,pi+βNMVOCENMVOC-ENMVOC,pi+f(T)
and
f(T)=min0,0.032×ext-1.35×T-0.032,
where β are the forcing efficiencies, CCH4 are methane
concentrations, EX are emissions of the respective species (NOx –
nitrate aerosols, CO – carbon monoxide, NMVOC – non-methane volatile organic compounds), EX,pi are the respective pre-industrial constants for the specific species, and T is temperature in Kelvin. Note that f(T) was not included in our calculations because the forcing is not
calculated dynamically. Concentrations and emissions data were obtained from
input4MIPs from the Earth System Grid Federation. Pre-industrial values were
taken from Table 4 from Smith et al. (2018). Again following Smith et al. (2018), radiative forcing of stratospheric ozone, FO3st, can be
calculated from GHG concentration data using
FO3st=a(bs)c
with
s=rCFC11∑i∈ODSnCliCirirCFC11+45nBriCirirCFC11,
where a=-1.46×10-5, b=2.05×10-3, and c=1.03 are curve
fitting parameters and rCFC11 is the fractional release values for
trichlorofluoromethane. Equivalent stratospheric chlorine of all ozone
depleting substances (ODSs) is represented by Eq. (4) as a function of ODS
concentrations. The ri are fractional release values for each ODS as
defined by Daniel and Velders (2011). There are no data provided for the ODS Halon 1202, which is accordingly not included in the calculation.
Three-dimensional aerosol optical depth (AOD) input for the UVic ESCM was
created using a UVic ESCM grid and the scripts and data provided by Stevens
et al. (2017), which describe nine plumes globally that are scaled with time to
produce monthly sulfate aerosol optical depth forcing for the years
1850–2018 (for comparison see Fig. S1). The resulting AOD caused a forcing
that was too strong in the historical period. Therefore, an option was
implemented into the UVic ESCM which allows the user to scale the aerosol
forcing from AOD data to fit it to current values. For transient
simulations, the scaling factor was set to 0.7, which gives a globally
average forcing of -1.04 W m-2 in 2014, consistent with the IPCC AR5
range estimate of between -1.9 and -0.1 W m-2 (Boucher et al., 2013; Myhre et al., 2013) and the newest updates of this forcing of -1.04±0.23 W m-2 from Smith et al. (2020).
Anthropogenic land-use changes (LUCs) in the UVic ESCM are prescribed from
standardized CMIP6 land-use forcing (Ma et al.,
2020) that has been re-gridded onto the UVic grid. These gridded land-use
data products (LUH2), which contain information on multiple types of crop
and grazing lands, were adapted for use with UVic by aggregating the crop
lands and grazing lands into a single “crop” type, which can represent any
of five crop functional types, and a single “grazing” variable, which
represents both pasture and rangelands. This forcing is used by the model to
determine the fraction of each grid cell that is crop or grazing land, with
those fractions of each terrestrial grid cell then assigned to C3 and C4
grasses and excluded from the vegetation competition routine of the Top-down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) vegetation model. CO2 emissions from LUC affect the model runs so that when
forest or other vegetation is cleared for crop lands, range lands or
pasture, 50 % of the carbon stored in trees is released directly into
the atmosphere, and the remaining 50 % is placed into the short-lived carbon
pool.
Historical volcanic radiative forcing data are provided by Schmidt et al. (2018). Following CMIP6 spin-up forcing recommendations (Eyring et al.,
2016), volcanic forcing is applied as an anomaly relative to the 1850 to 2014
period in the UVic ESCM.
Solar constant data for 1850 to 2300 were accessed from input4MIPs (Matthes
et al., 2017). The available monthly data were annually averaged. Following
CMIP6 spin-up forcing recommendations (Eyring et al., 2016), spin-up values
were set to the mean of 1850–1873, which is equal to 1360.7471 W m-2.
A comparison of radiative forcing used for the UVic ESCM for Coupled
Model Intercomparison Projects 5 and 6 (CMIP5 and CMIP6, respectively) and
the data for the historical period as given by the IMAGE model (Meinshausen
et al., 2011) is shown in Fig. S2. Even though the model has been fine tuned
to reproduce the recent observational period while following the CMIP6
forcing data and protocols, the model is not limited to CMIP6 context
applications.
Description of the CMIP6 forcing for the UVic ESCM
The model was spun up with boundary conditions as described in the CMIP6
protocol by Eyring et al. (2016) for over 10 000 years, in which the
weathering flux was dynamically simulated and diagnosed. For all transient
and diagnostic simulations, the weathering flux was then set as a constant to the
value at the end of the spin-up of 8703 kg C s-1. To diagnose the
transient climate response (TCR), equilibrium climate sensitivity (ECS), the
ocean heat uptake efficiency (κ4x) and the transient
climate response to cumulative emissions (TCREs), as given in Table 1, we ran
1000 year simulations starting with a 1 % yr-1 increase in CO2
concentrations until a doubling (2xCO2) and quadrupling (4xCO2) were reached
after which the concentration was kept constant. Before switching from
CO2-concentration-driven simulations to CO2-emissions-driven
simulations, a 1500-year drift simulation was run. Finally, the historical
simulation is forced with fossil CO2 emissions, dynamically diagnosed
land-use change emissions, non-CO2 GHG forcing, sulfate aerosol
forcing, volcanic anomalies forcing and solar forcing.
Fine tuning of the UVic ESCM 2.10
We tested version 2.10 of the UVic ESCM with the main incentive to improve
its skill in simulating carbon fluxes, historical temperature trajectories
and ocean tracers. While evaluating the model with available observational
data, specific additional changes and updates were applied with respect to
the UVic ESCM versions 2.9-02 (Eby et al., 2009) and 2.9-CE (Keller et al.,
2014).
After merging the two model versions, the UVic ESCM's simulated historical
cumulative land-use change emissions were close to zero since its
pre-industrial vegetation closely resembled the pattern of plant functional
types of today. In order to get a good representation of deforested biomass,
we updated the vegetation parameterization to ensure that diagnosed
historical land-use change carbon emissions agree with observational
estimates from Le Quéré et al. (2018). During this process there was
a trade-off between getting the right amount of LUC emissions and a good
representation of present-day broadleaf trees in tropical areas. In the end,
the representation of LUC emissions had the higher priority to be able to
simulate emissions-driven simulations. To slightly mitigate the high
broadleaf tree density, we then decreased the terrestrial CO2
fertilization by 30 % following Mengis et al. (2018) by adjusting the
atmospheric CO2 concentration that is used by the terrestrial model
component. This was done to reduce the overestimation of broadleaf tree
vegetation especially in tropical areas which, in the real world, are limited
by phosphorus (Camenzind et al., 2018). The broadleaf tree representation
and the terrestrial carbon flux were improved by the scaling of the CO2
fertilization strength (see Sect. 3.1. and 3.2.); the terrestrial carbon
fluxes are now in better agreement with the Global Carbon Budget 2018 by
Le Quéré et al. (2018).
(a) Global mean air temperature change for the UVic ESCM 2.10
relative to 1850–1900 (red line) in comparison with the average observed
warming using the filled-in HadCRUT4-CW dataset from Haustein et al. (2017)
(grey line) and the IPCC's special report on 1.5 ∘C GSAT temperature change
for 2006–2015 (light grey cross). (b) Atmospheric CO2 concentrations in
the UVic ESCM 2.10 (red line) in comparison with the Keeling curve from the
Mauna Loa observatory (Keeling et al., 2005; grey line). (c) Zonal means of
temperature change of the HadCRUT median near-surface temperature anomaly
(grey line) (Morice et al., 2012) in comparison to the UVic ESCM 2.10. All
temperature changes are for a 30-year mean around 1995 with respect to the
1961–1990 period (in K). (d) The global carbon budget for the UVic ESCM 2.10
partitioned into fossil fuel carbon, land-use carbon emissions, and
atmosphere, land, and ocean sinks, compared to cumulative carbon fluxes
between 1850 and 2005 and 1850 and 2015 from the Global Carbon Project 2018
(grey lines) (Le Quéré et al., 2018).
The new model version equilibrated with a rather low oceanic overturning
strength; we therefore increased the ocean background vertical diffusivity
from the previous value of 0.15 cm2 s-1 in Keller et al. (2014) to
0.25 cm2 s-1 to increase ocean overturning (see Figs. S3 and S4).
This caused ocean diffusivity to slightly increase in depths between 0 and
3500 m relative to the previous model version (Fig. S3) but to follow the
tidal mixing profile very closely for greater depths. Global diffusivity
increased by about 4 %. This change enabled us to reach a very similar
ocean overturning as found for the UVic ESCM 2.9-02, which uses the
Bryan–Lewis mixing scheme (Figs. S3 and S4). This stronger overturning then
in turn also improved ocean physical properties (see Sect. 3.3 and
Supplement), as well as the global mean temperature and warming trends.
However, it also causes the ocean heat content (OHC) anomaly for the upper 700 m to amount to 23.9×1022 J, which is an overestimation of the observed 700 m
OHC anomaly of 16.7±1.6×1022 J (Levitus et al., 2012) (Table 1). This seems to be a general feature of Earth system models of intermediate complexity (EMICs) (Eby et al., 2013), but it
might still be problematic. An overestimation in the change in the ocean heat
content anomaly would, for example, result in a similar overestimation of
thermosteric sea level change. Another possible impact of the overestimated
ocean heat uptake can be the estimates of the Zero Emissions Commitment,
which is directly linked to the state of thermal equilibration of the Earth
system (Ehlert and Zickfeld, 2017; MacDougall et al., 2020). So the fact
that EMICs in general, but the UVic ESCM 2.10 in particular, here
overestimates the OHC anomaly trend has to be kept in mind if the model were to be used for experiments concerning this metric.
Evaluation of model components
In this section, we evaluate the performance of the different components of
the UVic ESCM version 2.10 based on observations.
Key global mean metrics of the UVic ESCM 2.10 compared to relevant
observations or model intercomparison projects. The ΔT20th
century is the change in surface air temperature over the 20th century from
the historical “all” forcing experiment. TCR2xCO2,
TCR_4xCO2 and ECS4xCO2 are the
changes in global average model surface air temperature from the decades
centered at years 70, 140 and 995, respectively, from the idealized 1 %
increase to 4xCO2 experiment. The ocean heat uptake efficiency,
κ4x, is calculated from the global average heat flux
divided by TCR_4xCO2 for the decade centered at year
140 from the same idealized experiment. Note that ECS4xCO2
was calculated from the decade centered at year 995 from the idealized 1 % increase to 2xCO2 experiment.
UVic ESCM 2.10Comparison data ValuesCitationΔT20th century– Global0.77 ∘C 0.75±0.21∘C 0.78 (0.38–1.15) ∘CHaustein et al. (2017) EMIC range – Eby et al. (2013)– Ocean – Land0.74 ∘C 0.82 ∘CTCR2xCO21.79 ∘C1.8 (0.8–2.5) ∘C 1.8±0.6∘CEMIC range – Eby et al. (2013) CMIP5 range – IPCC AR5 WG1TCR_4xCO2 4.28 ∘C4.0 (2.1–5.4) ∘CEMIC range – Eby et al. (2013)ECS2xCO23.39 ∘C3.0 (1.9–4.0) ∘C 3.2±1.3∘CEMIC range – Eby et al. (2013) CMIP5 range – IPCC AR5 WG1ECS4xCO26.47 ∘C5.6 (3.5–8.0) ∘CEMIC range – Eby et al. (2013)κ4x1.05 W m-2∘C-10.8 (0.5–1.2)W m-2∘C-1EMIC range – Eby et al. (2013)TCRE 1.70 K (1000 Pg C)-10.8–2.5 K (1000 Pg C)-1IPCC AR5 Summary for Policy Makers (SPM)NH sea ice area (Sep) 2005–2015: 3.4 million km25.5 (3–10) million km2CMIP5 – Stroeve et al. (2012)-0.24 million km2decade-1-1.07 to -0.73million km2 decade-1 (1979–2012)IPCC AR5, Chap. 4SH sea ice area (Feb) 2005–2015: 1.3 million km21979–2010: 3.1 million km2IPCC AR5, Chap. 4-0.25 million km2decade-10.13 to 0.2 millionkm2 decade-1Parkinson and Cavalieri (2012)Precipitation– Global – Ocean1060 mm 1166 mm– Land814 mm818 mmHulme et al. (1998)dPrecip.– Global – Ocean-0.07 mm decade-1 0.15 mm decade-1– Land-0.58 mm decade-1-1.17 mm decade-1(-4.2–1.2) mm decade-1 (-7–2) mm decade-1CMIP5 – Kumar et al. (2013) Obs. – IPCC AR4NH permafrost area 16.6 million km218.7 million km2Brown et al. (1997) Tarnocai et al. (2009)Overturning– AMOC17.9 Sv17.6±3.1 Sv 18.7±4.8 SvLumpkin and Speer (2007) Rayner et al. (2011)– AABW-8.9 Sv-5.6±3.0 SvOcean surface pH 2005: 8.07∼ 8.1IPCC AR5, Fig. 6.28Ocean heatcontent anomaly0–700 m24.6×1022 J16.7±1.6×1022 JLevitus et al. (2012)0–2000 m35.8×1022 J24.0±1.9×1022 J
Global carbon cycle fluxes for the year 2005 (in Pg C yr-1) (– flux)
or cumulated fluxes between 1850 and 2005 (in Pg C) (– cum) from the UVic ESCM
2.10 compared to data-based estimates from the Global Carbon Project 2018
and the IPCC AR5 Chap. 6. Note the observational estimates of the carbon
stocks are calculated from 1750 to 2005.
UVic 2.10Comparison data ValuesCitationFossil fuel– cum – flux332 8.2320±157.8±0.4Le Quéré et al. (2018)Land-use change– cum – flux165 1.6185±701.3±0.7Le Quéré et al. (2018)Change in atmos. C– cum – flux202 4.9200±54.0±0.02Le Quéré et al. (2018)Land carbon sink– cum – flux177 1.8160±452.7±0.7Le Quéré et al. (2018)Land gross146 Pg C yr-1123±8 Pg C yr-1Beer et al. (2010)primary productionCiais et al. (2013)Ocean carbon sink– cum – flux115 2.7125±202.1±0.5Le Quéré et al. (2018)Ocean net70 Pg C yr-144–67 Pg C yr-1Behrenfeld et al. (2005)primary productionWestberry et al. (2008)NH permafrost carbon 497∼ 500Hugelius et al. (2014)Permafrost-affected soil carbon 10081035±150Hugelius et al. (2014)Global key metrics – temperature, carbon cycle, climate sensitivity and radiation balance
The emissions-driven, transient historical climate simulation of the UVic
ESCM version 2.10 forced with CMIP6 data reproduces well the historical
temperature trend in the 20th century of 0.75±0.21∘C as derived from the Global Warming Index (Haustein et al., 2017) (Table 1;
Fig. 2). Starting from the year 2000, the simulated global mean temperature
increases at a higher rate than previously, but the total temperature change
since pre-industrial times remains within the uncertainty range of the estimate in
the latest IPCC special report on 1.5 ∘C (Fig. 1; light grey
cross) (Rogelj et al., 2018). This steep temperature increase over the last
20 years of simulations amounts to a rate of temperature change of
0.27 ∘C decade-1, which is higher than the best estimate from the
infilled HadCRUT4-CW dataset of 0.17 ∘C decade-1 (uncertainty range
of 0.13–0.33 ∘C decade-1) (Haustein et al., 2017).
The simulated transient climate response (TCR) for a doubling and
quadrupling of atmospheric CO2 concentration is 1.79 and
4.28 ∘C, respectively, and therefore well within the reported
ranges from the EMIC comparison study by Eby et al. (2013). The main
differences between model versions are the updated CO2 forcing
formulation that was adopted from Etminan et al. (2016). For an atmospheric
CO2 concentration of 1120 ppm (i.e. 4 times pre-industrial CO2),
the new formulation gives a forcing of 8.08 W m-2 compared to the
previous formulation implemented in the UVic ESCM that gave a forcing of
7.42 W m-2. In the same way, there is good agreement with the EMIC
multi-model mean and the diagnosed values for the equilibrium climate
sensitivity for a 2 times and 4 times increase in atmospheric CO2 concentrations
with temperature increases of 3.39 and 6.47 ∘C,
respectively. Since the ocean heat uptake efficiency is assessed at year 140
of the TCR4x simulation, it is, like the TCR4x, on the higher end with 1.05 W m-2∘C-1 but still within the EMIC range. In the same
way, the transient climate response to cumulative emissions (TCRE) agrees
well with previous model versions, with 1.70 ∘C (1000 Pg C)-1, and it remains within the likely range reported by the IPCC AR5
(Table 1).
Overall, the global carbon-cycle fluxes of the UVic ESCM 2.10 are within the
uncertainty ranges of the Global Carbon Project (Le Quéré et al.,
2018; GCP18) (Table 2; Fig. 2). The CO2 concentrations as simulated in
the emissions-driven simulation follow the Keeling curve closely, but there
is a slightly higher increase between 1960 and 2010 in the simulation with
an increase of 77 ppm compared to the observations of 73 ppm. The change
in atmospheric carbon between 1850 and 2005 is, however, within the
uncertainty estimate of the GCP18 (Table 2). The land-use change emissions,
which are generated dynamically in the model by changes in agriculturally
used areas, reach a cumulative level of 165 Pg C between 1850 and 2005 and
are hence well within the uncertainty range of the GCP18 estimate of 185±70 Pg C (Table 2). Both the cumulative ocean sink with 115 Pg C and
the land sink with 177 Pg C in the period between 1850 and 2005 are within
the uncertainty range of the GCP18 (Table 2). While the land sink is
slightly higher than the best estimates, the ocean sink is at the lower end
of the given range.
Global radiation balance of the UVic ESCM 2.10 in comparison with
Wild et al. (2013). Unitless albedo values and radiation fluxes (in
W m-2) are shown.
UVic 2.10Wild et al., 2013 2000–2010Observations 2001–2010CMIP5 range 1985–2004TOA solar down341340 (340,341)(338.9,341.6)TOA solar up104100 (96,100)(96.3, 107.8)Planetary albedo0.3050.2940.300TOA solar net237240(233.8, 244.7)TOA thermal up235239 (236,242)(232.4, 243.4)Solar absorbed atmos.6979 (74,91)(69.7, 79.1)Surface solar down203185 (179,189)(181.9, 197.4)Atmospheric albedo0.2270.2500.255Surface solar up3524 (22,26)(20.9, 31.5)Surface albedo0.1710.1300.131Surface solar net168161 (154,166)(159.6, 170.1)Surface thermal net-51-55(-65.2, -49.4)Surface latent heat7685 (80,90)(78.8, 92.9)Surface sensible heat3120 (15,25)(14.5, 27.7)
TOA – top of the atmosphere.
Seasonality of surface air temperature as differences between
December–January–February and June–July–August means for the Climate Research Unit global
1961–1990 mean monthly surface temperature climatology (Jones et al., 1999)
and the UVic ESCM for the period of 2000–2005.
The simulated top of the atmosphere (TOA) short-wave and long-wave radiation
of the UVic ESCM for the year 2005 lies well within the range of the CMIP5
models as reported by Wild et al. (2013) and agrees reasonably well with the
observed estimates for both the solar and the thermal radiation fluxes
(Table 3; Fig. S5). The same is true for the simulated net surface thermal
flux, which is -51 W m-2 and therefore at the lower end of the CMIP5
range (Table 3). Now following the solar radiation through the energy
moisture balance model, however, we find that the simulated atmospheric
albedo of 0.227 is comparatively low to the observed value of 0.250. This
causes the rather low simulated absorption of solar radiation by the
atmosphere of 69 W m-2, for which the observed estimate from Wild et al. (2013) is 79 W m-2. Thanks to a rather high simulated surface albedo
(0.171 compared to 0.130 from observations), the resulting absorbed solar
radiation at the surface is still high, but, in contrast to the atmospheric
absorption, it is within the CMIP5 range. This results in a global mean surface net
radiation of 117 W m-2 which is rather high compared to the observed
best estimate of 106 W m-2. This is the radiative energy available at
the surface to be redistributed amongst the non-radiative surface energy
balance components. Accordingly, the simulated sensible heat flux in the
UVic ESCM of 31 W m-2 is also too high compared to the CMIP5 range of
14.5 to 27.7 W m-2. Finally, the latent heat flux calculated from
simulated evaporation of 76 W m-2 is on the very low end of
observational and CMIP5 estimates, which is likely linked to the high
transpiration sensitivity of plants in the UVic ESCM (Mengis et al., 2015).
September (top row) and February (bottom row) sea ice
concentrations from passive microwave observations (Meier et al., 2013) and
the UVic ESCM 2.10 for the Northern Hemisphere and Southern Hemisphere for the period
of 2003–2013 (in %).
Spatially resolved atmospheric and land surface metrics
The simulated polar amplification of the UVic ESCM 2.10 compares well to the
HadCRUT near-surface temperature anomaly data for all latitudes except for the
Southern Ocean south of 40∘ S (Fig. 2). Here the UVic ESCM 2.10
shows more of a warming trend than what is observed. Previous studies have
shown that this warming is connected to the representation (or the lack
thereof) of poleward-intensified winds (Fyfe et al., 2007). This warming
trend was already evident in previous versions of the UVic, as well as in
other EMICs (see Fig. S6 and Fig. 4 in Eby et al., 2013). While the pattern of the seasonal cycle
concerning surface air temperature agrees well with the Climate Research Unit (CRU) global 1961–1990
mean monthly surface temperature climatology (Fig. 3), the magnitude,
especially in the Northern Hemisphere land areas, is substantially lower by
up to 25 ∘C, which is also reflected in the latitudinal means.
The simulated Northern Hemisphere summer sea ice extent with 3.4 million km-2 is at the lower end of the CMIP5 estimates and considerably
smaller than the observed sea ice concentration (Table 1; Fig. 4). This
lower extent seems to be mainly due to a lack of simulated summer sea ice
concentrations between 15 % and 60 %, whereas higher concentrations show
good agreement with the observed pattern (Fig. 4). The southward extension
of the winter sea ice concentration in the UVic ESCM is considerably smaller
than the observations from passive microwave satellite missions. Concerning
Northern Hemisphere (NH) summer sea ice trends, the UVic ESCM shows lower trends of -0.24 million km-2 decade-1 during the last 30 years, compared to what is observed
(-1.07 to -0.73 million km-2 decade-1) (IPCC AR5, Chap. 4; Ciais et al., 2013). The summer sea ice extent in the Southern Hemisphere of
1.3 million km-2 is also smaller than the observed 3.1 million km-2
and, in contrast to the observed increasing trends in sea ice, shows a decline
of -0.25 million km2 decade-1 (Table 1). While this is consistent
with the simulated warming trend in the Southern Hemisphere surface air
temperature, this is still a bias in the model.
Mean precipitation flux for the period 1979–2013 (in mm d-1) from Obs4MIP (Adler et al., 2003) (a; grey line in c) and the
UVic ESCM 2.10 (b; red line in c), and zonally averaged values as a
function of latitude (c).
Air–sea carbon flux for the year 2005 (in mol C m-2 yr-1) from the revised dataset from Takahashi et al. (2009) (a; grey line in c) and the UVic ESCM 2.10 (b; red line in c). Zonally
averaged values as a function of latitude (c).
Vegetation carbon density for the 1960–2000 period (in kg C m-2) from the revised CDIAC NDP-017 dataset (Olson et al., 2001)
(a; grey line in c) and the UVic ESCM 2.10 (b; red line in c). Zonally averaged values as a function of latitude (c).
Soil organic carbon content in permafrost-affected soils for the
1980–2000 period in the top 3 m of soil (in kg C m-2) from the
dataset by Hugelius et al. (2014) (a) and for the UVic ESCM 2.10 (b). Zonally
averaged values as a function of latitude (c).
Observed depth of permafrost for the region of northern Canada
(a) (data source: Smith and Burgess, 2002; figure source: Avis, 2012).
The colour bar has been restricted to 250 m depth to aid in comparison
despite the fact that many locations are deeper (Avis, 2012). Simulated mean
permafrost depth for 1966–1990 of the UVic ESCM 2.10 (b).
Observed global mean terrestrial precipitation between 1961 and 1990 amounts to
818 mm (Hulme et al., 1998). The adjusted CO2 fertilization strength in
the UVic ESCM 2.10 results in global mean terrestrial precipitation of 814 mm for the same period (Table 1), bringing it close to the observed amount.
Concerning terrestrial precipitation trends, the UVic ESCM 2.10 shows a
negative trend in terrestrial precipitation of -0.58 mm decade-1 for
the period between 1930 and 2004 (Table 1). This is in agreement with the
range of terrestrial precipitation trends of -4.2–1.2 mm decade-1 given by Kumar et al. (2013). The terrestrial precipitation trend of -1.17 mm decade-1 also agrees well with the observed terrestrial precipitation
changes for the recent historical period (1951–2005) of -7 to +2 mm decade-1, with error bars ranging from 3 to 5 mm decade-1 (IPCC AR4) (Table 1).
The simulated pattern of annual mean precipitation flux for the last 30
years generally agrees well with the observed pattern (Fig. 5). Similar to
the seasonal temperature maps, the UVic ESCM slightly underestimates the
most extreme amplitudes of annual mean precipitation located in the tropical
areas. The latitudinal mean values agree well in magnitude, but the tropical
rain bands are extending too far north and south.
The simulated air–sea carbon flux for 2000 to 2010 agrees with observations
from Takahashi et al. (2009) (Fig. 6). Oceanic carbon uptake takes place at
high latitudes, and carbon is mainly released in the tropical Pacific. In the
Southern Ocean, observations show slightly positive values (i.e. carbon being
released to the atmosphere) which are not reproduced by the UVic ESCM 2.10.
This is also evident in the latitudinal means, in which the UVic ESCM 2.10 generally shows good agreement with the observations but simulates ocean carbon uptake south of 50∘ S, where the observations show low uptake or even a small carbon release.
The UVic ESCM overestimates vegetation carbon density in tropical
rainforest regions, such as in South America and central Africa, when
compared to the revised estimates of Olson (1983, 1985, 2001) (Fig. 7). More
recent biomass studies have challenged Olson's estimates for some regions of
the world, but Olson (1983, 1985) still provides the only globally consistent
estimate of global carbon stored in vegetation. This positive bias in the
UVic ESCM 2.10 in the tropics is due to an overestimation of broadleaf
trees, which is the plant functional type with the highest carbon density in
the UVic ESCM (see Fig. S7). This overestimation of broadleaf trees leads to
a small overestimation of global mean gross primary production in 2005 on
land, 146 Pg C yr-1, compared to the observation-based estimate of 123±8 Pg C yr-1 using eddy covariance flux data and various
diagnostic models (Beer et al., 2010) (Table 2). In contrast, the simulated
vegetation coverage of carbon densities of 2–5 kg C m-2 is lower than
observations especially in central Asia and at higher northern latitudes.
This, however, does not imply that the dominant plant functional types,
namely C3/C4 grasses, are underrepresented in this area. In the UVic ESCM
2.10, the representation of C3/C4 grasses, as well as needleleaf trees, in
high northern latitudes improved compared to earlier versions (see Fig. S7)
thanks to the more complex soil module and the corresponding vegetation
tuning. In summary, the UVic ESCM overestimates broadleaf tree cover in the
tropics but improved the representation of the vegetation cover at
latitudes north of 20∘ N compared to previous model versions.
Taylor diagram (Taylor, 2001) of multiple global UVic ESCM 2.10
fields (dots) and the UVic ESCM 2.9 fields (×) with respect to re-gridded
observations from the World Ocean Atlas 2018 (Locarnini et al., 2018; Zweng
et al., 2019; Garcia et al., 2018a, b), Global Ocean Data Analysis Project (GLODAP) and GLODAP
mapped climatologies v2.2016b (Key
et al., 2004; Lauvset et al., 2016), NASA-GSFC precipitation (Adler et al.,
2003), air–sea gas fluxes from Takahashi et al. (2009), and vegetation carbon
data from the CDIAC NDP-017 dataset (Olson et al., 2001). All datasets are
normalized by the standard deviation of the observations. A perfect model
with zero root mean square deviation, a correlation coefficient of 1 and a normalized standard
deviation of 1 would plot at (1,0).
Simulated soil carbon densities at high northern latitudes compare
reasonably well with the map of permafrost soil carbon based on observations
by Hugelius et al. (2014) (Fig. 8). While there are regional biases
especially in eastern Canada, the simulated carbon densities in the
permafrost areas do have the correct order of magnitude. The total global
permafrost carbon of 497 Pg C and the total soil carbon in the permafrost
region of 1009 Pg C agree well with the reported ∼ 500 Pg C and
1035±150 Pg C, respectively (Hugelius et al., 2014). The simulated
permafrost area is limited to about 60∘ N and does not extend as
far south as what is observed.
Global and basin-wide averaged vertical profiles of multiple UVic
ESCM 2.10 metrics (red lines) compared to observations from the World Ocean
Atlas 2018 (Locarnini et al., 2018; Zweng et al., 2019; Garcia et al.,
2018a, b) and GLODAP and GLODAP mapped climatologies v2.2016b (Key et al., 2004; Lauvset et al., 2016), including standard errors
(solid and dashed black lines, respectively) and their respective global
misfit (last row) for the period of 1980–2010. Note that for salinity we
excluded the fresh water masses observed in the Arctic Ocean (i.e. all
values north of 70∘ N for both datasets).
Smith and Burgess (2002) provide a dataset of permafrost depth observations
for Canada based on temperature readings, which is a compilation of borehole
data across Canada ranging in observational dates from between 1966 and 1990.
Each borehole is a single observed value; this compares the simulation to a
snapshot in time rather than a temporal average. Permafrost depth in the
observational dataset was determined based on the bottom boundary identified
by the temperature gradient to be below 0 ∘C. Permafrost depth
distribution in North America simulated by the UVic ESCM broadly agrees with
the observed distribution (Fig. 9). The UVic ESCM 2.10 simulates permafrost
thicknesses of up to 250 m all around the Arctic circle. Recall that the
depth of the UVic ESCM is limited to 250 m and that the vertical resolution
is coarser at deeper soil layers. As already seen for the soil organic
carbon content, the simulated permafrost areas do not extend as far south as
what is observed. However, for the purpose of this comparison, the scale for
observed permafrost depths was limited to 250 m, whereas actually many
observations show deeper permafrost thicknesses.
Ocean metrics – physical and biogeochemical
In the following section, we will compare simulated ocean metrics with
observations from the World Ocean Atlas 2018 (WOA18) (Locarnini et al.,
2018; Zweng et al., 2019; Garcia et al., 2018a, b) and
the Global Ocean Data Analysis Project (GLODAP) and the new mapped
climatologies version 2 (Key et al., 2004; Lauvset et al., 2016) for the
period of 1980 to 2010.
Ocean section of ΔC14 (in ‰) for the Atlantic Ocean
including the Arctic Ocean (left column), the Pacific Ocean (middle left
column), the Indian Ocean (middle right column) and the global average (right
column) compared to observations (Key et al., 2004). From top to bottom, what is shown are the published UVic ESCM version 2.9 by Eby et al. (2013) spun up and
forced with CMIP6 forcing, the UVic ESCM version 2.10, both as a mean of the
period 1980–2010, and the observed ocean sections.
The Taylor diagram for eight different ocean metrics illustrates that the
UVic ESCM 2.10 improves ocean ΔC14 and slightly improves ocean
temperature, salinity, and nitrate and phosphate distributions (dots in Fig. 10) relative to the UVic ESCM 2.9 (crosses in Fig. 10) given the same
forcing. In contrast, mainly ocean alkalinity but also dissolved inorganic
carbon and oxygen show either a larger deviation or lower correlation
compared to observations than the previous model version. Generally, the
model demonstrates skill in simulating these ocean properties with
correlation coefficients higher than 0.9 for all but the salinity and
alkalinity fields and a root mean square deviation (rmsd) of below 50 % of
the global standard deviation of the observations – again with the exception
of salinity and alkalinity. In the following, we will discuss these features
in more detail.
Ocean section of NO3 (in µmol kg-1) for the
Atlantic Ocean including the Arctic Ocean (left column), the Pacific Ocean
(middle left column), the Indian Ocean (middle right column) and the global
average (right column) compared to the World Ocean Atlas 2018 (Garcia et al.,
2019). From top to bottom, what is shown are the published UVic ESCM version 2.9 by
Eby et al. (2013) spun up and forced with CMIP6 forcing, the UVic ESCM
version 2.10, both as a mean of the period 1980–2010, and the observed ocean
sections.
Maps of sea surface salinity (in practical salinity units; psu) for the published
UVic ESCM version 2.9 (Eby et al., 2013) spun up and forced with CMIP6
forcing, for the UVic ESCM 2.10, and for the World Ocean Atlas 2018 (Zweng
et al., 2019).
Ocean section of apparent oxygen utilization (AOU) (in
µmol kg-1) for the Atlantic Ocean including the Arctic Ocean (left
column), the Pacific Ocean (middle left column), the Indian Ocean (middle
right column) and the global average (right column) compared to the World Ocean
Atlas 2018 (Garcia et al., 2019). From top to bottom, what is shown are the published
UVic ESCM version 2.9 by Eby et al. (2013) spun up and forced with CMIP6
forcing, the UVic ESCM version 2.10, both as a mean of the period 1980–2010,
and the observed ocean sections.
The vertical profiles of simulated temperature (temp), phosphate (PO4),
nitrate (NO3), ΔC14, dissolved inorganic carbon (dic) and alkalinity (alk)
agree well in magnitude and shape with the observed profiles for all ocean
basins and the global ocean (Fig. 11). The general good agreement for these
ocean metrics is also seen in comparisons with vertical ocean sections and
ocean surface maps of these simulated fields with observations (see
Supplement). The only noteworthy biases are ΔC14 that is too low in the
central Indian Ocean basin, indicating an overturning rate that is too low in this
ocean basin (see Figs. 11 and 12), and small biases in simulated nitrate
showing values in the Arctic Ocean that are too high compared to observations and values in the Indian Ocean that are too
low (see Figs. 11 and 13 and Supplement).
Maps of apparent oxygen utilization in approx. 300 m depth (i.e.
the depth of oxygen minimum zones, OMZs; in µmol kg-1) for
the published UVic ESCM version 2.9-02 (Eby et al., 2013) spun up and forced
with CMIP6 forcing, for the UVic ESCM 2.10, and for the World Ocean Atlas
2018 (Garcia et al., 2019).
To compare ocean salinity profiles (Fig. 11 second column), we removed values
in the high northern latitudes, north of 70∘ N, for all regarded
datasets. This substantially improved the comparison for the Atlantic Ocean
(which in the partitioning includes the Arctic Ocean; see Supplement) since the UVic ESCM 2.10 does not reproduce well the recent
freshening trend associated with sea ice loss and seasonal melt in the
Arctic Ocean. The maps of sea surface salinity clearly show this freshening
trend (Fig. 14), which also extends to the Pacific Ocean and is hence
evident in the vertical profile (Fig. 11). Furthermore, the UVic ESCM 2.10
does not reproduce the salinity properties of Antarctic Intermediate Water
(AAIW) well, which can be seen by the lack or insufficient representation of
the local minimum in about 900 m depth for the global and Atlantic profile
(see Supplement for latitudinal average sections). Note that
the global mean temperature misfit shows similar patterns (Fig. 11). The
bias in AAIW salinity in the UVic ESCM 2.10 is caused by surface
waters that are too salty extending southward into the Southern Ocean regions, in which the
water is subducted.
The apparent oxygen utilization (AOU) shows lower values (∼ 15 % lower) in the deep ocean but otherwise reproduces the shape
including the local maximum around 1000 m depth well (Figs. 11 and 15). The
main bias in AOU is found in the Southern Ocean, where the UVic ESCM 2.9
simulated values that were too high and the UVic ESCM 2.10 now simulates oxygen
utilization values that are too low. This is especially true for the Atlantic and Indian oceans.
These biases in AOU are probably linked to biases in the ocean
biogeochemistry in the Southern Ocean. Comparing the simulated oxygen
minimum zones (OMZs) of the UVic ESCM 2.9 and UVic ESCM 2.10 to observed
OMZs, there is an improved representation of the asymmetry of the Pacific
OMZ in the newer model version, as well as a reduced bias in the Indian
Ocean (Fig. 16).
Summary, conclusion and outlook
In order to obtain a new version of the University of Victoria Earth System
Climate Model (UVic ESCM) that is to be part of the comparison of Earth
system models of intermediate complexity (EMICs) in the sixth phase of the
Coupled Model Intercomparison Project (CMIP6), we have merged previous
versions of the UVic ESCM to bring together the ongoing model development of
the last decades. In this paper, we evaluated the model's performance with
regard to a realistic representation of carbon and heat fluxes, as well as
ocean tracers, in the UVic ESCM 2.10 in agreement with the available
observational data and with current process understanding.
We find that the UVic ESCM 2.10 is capable of reproducing changes in
historical temperature and carbon fluxes. There is a higher warming trend in
the Southern Hemisphere south of 40∘ S compare to observations, which causes a
bias in Southern Hemisphere (SH) sea ice trends. The simulated seasonal cycle of global mean
temperature agrees well with the observed pattern but has a lower amplitude
especially in the high northern latitudes. The air to sea fluxes of the UVic
ESCM agree well with the observed pattern. The newly applied CO2
forcing formulation has increased the model's climate sensitivity. Land
carbon stocks concerning permafrost and vegetation carbon are within
observational estimates even though the spatial distribution of permafrost-affected soil carbon and vegetation carbon densities show regional biases.
The top of the atmosphere radiation balance of the UVic ESCM is well within
the observed ranges, but the internal heat fluxes show biases. The simulated
precipitation pattern shows good agreement with observations but is
regionally too spread out especially in the tropics and, as expected, does not
reach the most extreme values. Terrestrial total precipitation and
precipitation trends agree well with observations. Many ocean properties and
tracers show good agreement with observations. This is mainly caused by a
good representation of the general circulation, although problems remain,
mainly the Southern Ocean oxygen utilization being too low and the salinity bias in the AAIW.
These model data deviations, especially for the ocean tracers, have not yet
been fully addressed (note the misfits have been reduced relative to the
previous model version forced with new forcing) since we are already
planning for the next update of the UVic ESCM, which will incorporate more
comprehensive biogeochemical modules that will require the re-tuning of the
oceans biogeochemical parameters as well. Model developments that have not
been incorporated in the model version described here, like carbon-nitrogen
feedbacks on land (Wania et
al., 2012), explicit representation of calcifiers in the ocean
(Kvale et al., 2015), the dynamic
phosphorus cycle in the ocean (Niemeyer et
al., 2017) and others, will in the following be implemented and tested
within this new model version.
Code and data availability
The model code and data shown in the figures and used for this paper are available at
https://hdl.handle.net/20.500.12085/c565622a-9655-42bc-840c-c20e7dfd0861 (last access: 31 August 2020, GEOMAR OPeNDAP Service, 2020) and
will also be made available on the official UVic ESCM webpage
(http://terra.seos.uvic.ca/model/, last access: 31 August 2020), including all necessary documentation
and data, upon final publication of the paper.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-13-4183-2020-supplement.
Author contributions
All authors decided on the content of the new model version; DPK merged the model code; DPK, NW, KZ, and NM compiled the CMIP6 forcing input; NM tuned it in consultation with AO, ME, AM, KM, AS, HDM, and KZ. NM and AJM ran the simulations for this publication, and NM wrote the paper with contributions from all authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to acknowledge Claude-Michel Nzotungicimpaye for his helpful discussions about soil hydrology and carbon context; Karin Kvale, Christopher Somes, and Wolfgang Koeve for their help looking at ocean alkalinity; and Heiner Dietze for his help with setting up the Fortran compiler.
Financial support
Nadine Mengis was supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant (NSERC) awarded to Kirsten Zickfeld and the Helmholtz Initiative for Climate Adaptation and Mitigation (HI-CAM). The Helmholtz Climate Initiative (HI-CAM) is funded by the Helmholtz Association's Initiative and Networking Fund. Alex MacIsaac received support from the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant awarded to Kirsten Zickfeld. Katrin J. Meissner received funding from the Australian Research Council (grant nos. DP180100048 and DP180102357). Andrew MacDougall was supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant program.
The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Steven Phipps and reviewed by two anonymous referees.
ReferencesAdler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P. P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The version-2 global precipitation climatology
project (GPCP) monthly precipitation analysis (1979–present), J.
Hydrometeorol., 4, 1147–1167, 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.Archer, D.: A data-driven model of the global calcite lysocline, Global
Biogeochem. Cy., 10, 511–526, 10.1029/96GB01521, 1996.Avis, C. A., Weaver, A. J., and Meissner, K. J.: Reduction in areal extent of
high-latitude wetlands in response to permafrost thaw, Nat. Geosci., 4,
444–448, 10.1038/ngeo1160, 2011.
Avis, C. A.: Simulating the Present-Day and Future Distribution of
Permafrost in the UVic Earth System Climate Model, PhD thesis, University of Victoria, 2012.Bagniewski, W., Meissner, K. J., and Menviel, L.: Exploring the oxygen
isotope fingerprint of Dansgaard-Oeschger variability and Heinrich events,
Quat. Sci. Rev., 159, 1–14, 10.1016/j.quascirev.2017.01.007, 2017.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais,
N., Rödenbeck, C., Altaf, M. A., Baldocchi, D., Bonan, G. B., Bondeau,
A., Cescatti, A., Lasslop, G., Lomas, A. L. M., Luyssaert, S., Margolis, H.,
Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C.,
Woodward, F. I., and Papale, D.: Terrestrial Gross Carbon Dioxide Uptake:
Global Distribution and Covariation with Climate, Science,
329, 834–839, 2010.Behrenfeld, M. J., Boss, E., Siegel, D. A., and Shea, D. M.: Carbon-based
ocean productivity and phytoplankton physiology from space, Global
Biogeochem. Cy., 19, GB1006, 10.1029/2004GB002299, 2005.Bitz, C. M., Holland, M. M., Weaver, A. J., and Eby, M.: Simulating the
ice-thickness distribution in a coupled, J. Geophys. Res., 106,
2441–2463, 10.1029/1999JC000113, 2001.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen,
V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K.,
Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and Aerosols, in: 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, UK,
New York, NY, USA, 2013.Burke, E. J., Hartley, I. P., and Jones, C. D.: Uncertainties in the global temperature change caused by carbon release from permafrost thawing, The Cryosphere, 6, 1063–1076, 10.5194/tc-6-1063-2012, 2012.Camenzind, T., Hättenschwiler, S., Treseder, K. K., Lehmann, A., and
Rillig, M. C.: Nutrient limitation of soil microbial processes in tropical
forests, Ecol. Monogr., 88, 4–21, 10.1002/ecm.1279, 2018.Cavalieri, D. J. and Parkinson, C. L.: Arctic sea ice variability andtrends, 1979–2010, The Cryosphere, 6, 881–889, 10.5194/tc-6-881-2012, 2012.Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C.,
Quéré, C. Le, Myneni, R. B., Piao, S., and Thornton, P.: The physical
science basis. Contribution of working group I to the fifth assessment
report of the intergovernmental panel on climate change, Intergovernmental Panel on Climate Change,
465–570, 10.1017/CBO9781107415324.015, 2013.
Daniel, J. and Velders, G.: A focus on information and options for
policymakers, in: Scientific Assessment of Ozone Depletion, edited by:
Ennis, C. A., World Meteorological Organization, Geneva,
Switzerland, p. 516, 2011.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. Clim.,
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.Ehlert, D., Zickfeld, K., Eby, M., and Gillett, N.: The effect of variations in
ocean mixing on the proportionality between temperature change and
cumulative CO2 emissions, J. Climate, 30, 2921–2935, 2017.Ehlert, D. and Zickfeld, K.: What determines the warming commitment after
cessation of CO2 emissions?, Environ. Res. Lett., 12, 015002, 10.1088/1748-9326/aa564a, 2017.
Etminan, M., Myhre, G., Highwood, E. J., and Shine, K. P.: Radiative forcing
of carbon dioxide, methane, and nitrous oxide: A significant revision of the
methane radiative forcing, Geophys. Res. Lett., 43, 12614–12623, 2016.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.
Fanning, A. F. and Weaver, A. J.: An atmospheric energy-moisture balance
model: climatology, interpentadal climate change, and coupling to an ocean
general circulation model, J. Geophys. Res., 101, 111–115, 1996.Fyke, J. G., Weaver, A. J., Pollard, D., Eby, M., Carter, L., and Mackintosh, A.: A new coupled ice sheet/climate model: description and sensitivity to model physics under Eemian, Last Glacial Maximum, late Holocene and modern climate conditions, Geosci. Model Dev., 4, 117–136, 10.5194/gmd-4-117-2011, 2011.
Garcia, H. E., Weathers, K. W., Paver, C. R., Smolyar, I., Boyer, T. P.,
Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D.,
and Reagan, J. R.: WORLD OCEAN ATLAS 2018 Volume 3: Dissolved Oxygen,
Apparent Oxygen Utilization, and Dissolved Oxygen Saturation, NOAA Atlas
NESDIS 83, 1, 38 pp., 2019a.Garcia, H. E., Weathers, K. W., Paver, C. R., Smolyar, I., Boyer, T. P.,
Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D.,
and Reagan, J. R.: WORLD OCEAN ATLAS 2018 Volume 4: Dissolved Inorganic
Nutrients (phosphate, nitrate and nitrate+nitrite, silicate), NOAA Atlas
NESDIS 84, 2019b.GEOMAR OPeNDAP Service: Catalog of Gridded Data, available at:
https://hdl.handle.net/20.500.12085/c565622a-9655-42bc-840c-c20e7dfd0861, last access: 31 August 2020.Handiani, D., Paul, A., and Dupont, L.: Climate and vegetation changes around the Atlantic Ocean resulting from changes in the meridional overturning circulation during deglaciation, Clim. Past Discuss., 8, 2819–2852, 10.5194/cpd-8-2819-2012, 2012.Haustein, K., Allen, M. R., Forster, P. M., Otto, F. E. L., Mitchell, D. M.,
Matthews, H. D., and Frame, D. J.: A real-time global warming index, Sci.
Rep., 7, 15417, 10.1038/s41598-017-14828-5, 2017.Henson, S. A., Sanders, R., Madsen, E., Morris, P. J., Le Moigne, F. and
Quartly, G. D.: A reduced estimate of the strength of the ocean's biological
carbon pump, Geophys. Res. Lett., 38, 10–14, 10.1029/2011GL046735,
2011.Honjo, S., Manganini, S. J., Krishfield, R. A., and Francois, R.: Particulate
organic carbon fluxes to the ocean interior and factors controlling the
biological pump: A synthesis of global sediment trap programs since 1983,
Prog. Oceanogr., 76, 217–285, 10.1016/j.pocean.2007.11.003, 2008.Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J. W., Schuur, E. A. G., Ping, C.-L., Schirrmeister, L., Grosse, G., Michaelson, G. J., Koven, C. D., O'Donnell, J. A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., and Kuhry, P.: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps, Biogeosciences, 11, 6573–6593, 10.5194/bg-11-6573-2014, 2014.Hulme, M., Osborn, T. J., and Johns, T. C.: Precipitation sensitivity to
global warming: Comparison of observations with Had CM2 simulations,
Geophys. Res. Lett., 25, 3379–3382, 10.1029/98GL02562, 1998.
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea
ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997.Jones, P. D., New, M., Parker, D. E., Martin, S., and Rigor, I. G.: Surface
air temperature and its changes over the past 150 years, Rev. Geophys.,
37, 173–199, 10.1029/1999RG900002, 1999.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.Key, R. M., Kozyr, A., Sabine, C. L., Lee, K., Wanninkhof, R., Bullister, J.
L., Feely, R. A., Millero, F. J., Mordy, C., and Peng, T. H.: A global ocean
carbon climatology: Results from Global Data Analysis Project (GLODAP),
Global Biogeochem. Cy., 18, 1–23, 10.1029/2004GB002247, 2004.Koven, C. D., Ringeval, B., Friedlingstein, P., Ciais, P., Cadule, P.,
Khvorostyanov, D., Krinner, G., and Tarnocai, C.: Permafrost carbon-climate
feedbacks accelerate global warming, Proc. Natl. Acad. Sci. USA,
108, 14769–14774, 10.1073/pnas.1103910108, 2011.
Koven, C. D., Riley, W. J., and Stern, A.: Analysis of permafrost thermal
dynamics and response to climate change in the CMIP5 Earth System Models, J.
Clim., 26, 1877–1900, 2013.Kumar, S., Merwade, V., Kinter, J. L., and Niyogi, D.: Evaluation of
temperature and precipitation trends and long-term persistence in CMIP5
twentieth-century climate simulations, J. Clim., 26, 4168–4185,
10.1175/JCLI-D-12-00259.1, 2013.Kvale, K. F., Meissner, K. J., Keller, D. P., Eby, M., and Schmittner, A.:
Explicit Planktic Calcifiers in the University of Victoria Earth System
Climate Model, Version 2.9, Atmos.-Ocean, 53, 332–350,
10.1080/07055900.2015.1049112, 2015.Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the 1∘× 1∘ GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, 10.5194/essd-8-325-2016, 2016.Laws, E. A., Falkowski, P. G., Smith, W. O., Ducklow, H., and McCarthy, J.
J.: Temperature effects on export production in the open ocean, Global
Biogeochem. Cy., 14, 1231–1246, 10.1029/1999GB001229, 2000.Leduc, M., Matthews, H. D., and De Elía, R.: Quantifying the limits of a
linear temperature response to cumulative CO2 emissions, J. Clim., 28,
9955–9968, 10.1175/JCLI-D-14-00500.1, 2015.Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Hauck, J., Pongratz, J., Pickers, P. A., Korsbakken, J. I., Peters, G. P., Canadell, J. G., Arneth, A., Arora, V. K., Barbero, L., Bastos, A., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Doney, S. C., Gkritzalis, T., Goll, D. S., Harris, I., Haverd, V., Hoffman, F. M., Hoppema, M., Houghton, R. A., Hurtt, G., Ilyina, T., Jain, A. K., Johannessen, T., Jones, C. D., Kato, E., Keeling, R. F., Goldewijk, K. K., Landschützer, P., Lefèvre, N., Lienert, S., Liu, Z., Lombardozzi, D., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S., Neill, C., Olsen, A., Ono, T., Patra, P., Peregon, A., Peters, W., Peylin, P., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rocher, M., Rödenbeck, C., Schuster, U., Schwinger, J., Séférian, R., Skjelvan, I., Steinhoff, T., Sutton, A., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F. N., van der Laan-Luijkx, I. T., van der Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., Wright, R., Zaehle, S., and Zheng, B.: Global Carbon Budget 2018, Earth Syst. Sci. Data, 10, 2141–2194, 10.5194/essd-10-2141-2018, 2018.Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E.,
Locarnini, R. A., Mishonov, A. V., Reagan, J. R., Seidov, D., Yarosh, E. S.,
and Zweng, M. M.: World ocean heat content and thermosteric sea level change
(0-2000 m), 1955–2010, Geophys. Res. Lett., 39, 1–5,
10.1029/2012GL051106, 2012.
Locarnini, R. A., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M., Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K., Paver, C. R., and Smolyar, I.: World Ocean Atlas 2018, vol. 1: Temperature, edited by: Mishonov, A., NOAA Atlas NESDIS 81, 52 pp., 2018.Löptien, U. and Dietze, H.: Reciprocal bias compensation and ensuing uncertainties in model-based climate projections: pelagic biogeochemistry versus ocean mixing, Biogeosciences, 16, 1865–1881, 10.5194/bg-16-1865-2019, 2019.Longhurst, A. R. and Glen Harrison, W.: The biological pump: Profiles of
plankton production and consumption in the upper ocean, Prog. Oceanogr.,
22, 47–123, 10.1016/0079-6611(89)90010-4, 1989.Lumpkin, R. and Speer, K.: Global ocean meridional overturning, J. Phys.
Oceanogr., 37, 2550–2562, 10.1175/JPO3130.1, 2007.Ma, L., Hurtt, G. C., Chini, L. P., Sahajpal, R., Pongratz, J., Frolking, S., Stehfest, E., Klein Goldewijk, K., O'Leary, D., and Doelman, J. C.: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 13, 3203–3220, 10.5194/gmd-13-3203-2020, 2020.MacDougall, A. H., Frölicher, T. L., Jones, C. D., Rogelj, J., Matthews, H. D., Zickfeld, K., Arora, V. K., Barrett, N. J., Brovkin, V., Burger, F. A., Eby, M., Eliseev, A. V., Hajima, T., Holden, P. B., Jeltsch-Thömmes, A., Koven, C., Mengis, N., Menviel, L., Michou, M., Mokhov, I. I., Oka, A., Schwinger, J., Séférian, R., Shaffer, G., Sokolov, A., Tachiiri, K., Tjiputra, J., Wiltshire, A., and Ziehn, T.: Is there warming in the pipeline? A multi-model analysis of the Zero Emissions Commitment from CO2, Biogeosciences, 17, 2987–3016, 10.5194/bg-17-2987-2020, 2020.MacDougall, A. H. and Friedlingstein, P.: The Origin and Limits of the Near
Proportionality between Climate Warming and Cumulative CO2 Emissions, J.
Clim., 28, 4217–4230, 10.1175/JCLI-D-14-00036.1, 2015.MacDougall, A. H. and Knutti, R.: Projecting the release of carbon from permafrost soils using a perturbed parameter ensemble modelling approach, Biogeosciences, 13, 2123–2136, 10.5194/bg-13-2123-2016, 2016.MacDougall, A. H., Swart, N. C., and Knutti, R.: The Uncertainty in the
Transient Climate Response to Cumulative CO2 Emissions Arising from the
Uncertainty in Physical Climate Parameters, J. Clim., 30, 813–827,
10.1175/JCLI-D-16-0205.1, 2017.
MacDougall, A. H., Avis, C. A., and Weaver, A. J.: Significant contribution
to climate warming from the permafrost carbon feedback, Nat. Geosci., 5,
719–721, 2012.Matthes, K., Funke, B., Kruschke, T., and Wahl, S.:
input4MIPs.SOLARIS-HEPPA.solar.CMIP.SOLARIS-HEPPA-3-2, Earth System Grid Federation,
10.22033/ESGF/input4MIPs.1122, 2017.Matthews, H. D. and Caldeira, K.: Stabilizing climate requires near-zero
emissions, Geophys. Res. Lett., 35, L04705, 10.1029/2007GL032388, 2008.Matthews, H. D., Cao, L., and Caldeira, K.: Sensitivity of ocean
acidification to geoengineered climate stabilization, Geophys. Res. Lett.,
36, L10706, 10.1029/2009GL037488, 2009a.Matthews, H. D., Gillett, N. P., Stott, P. A., and Zickfeld, K.: The
proportionality of global warming to cumulative carbon emissions, Nature,
459, 829–832, 10.1038/nature08047, 2009b.MacDougall, A. H., Zickfeld, K., Knutti, R., and Matthews, H. D.: Sensitivity of carbon budgets to permafrost carbon feedbacks and non-CO2 forcings, Environ. Res. Lett., 10, 125003, 10.1088/1748-9326/10/12/125003, 2015.Meinshausen, M., Smith, S. J., Calvin, K. V., Daniel, J. S., Kainuma, M. L.
T., Lamarque, J., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K.,
Thomson, A. M., Velders, G. J. M., and van Vuuren, D. P.: The RCP greenhouse
gas concentrations and their extensions from 1765 to 2300, Clim. Change,
109, 213–241, 10.1007/s10584-011-0156-z, 2011.Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, 10.5194/gmd-10-2057-2017, 2017.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, 10.1007/s00382-003-0352-2,
2003.Meissner, K. J., McNeil, B. I., Eby, M., and Wiebe, E. C.: The importance of
the terrestrial weathering feedback for multi-millennial coral reef habitat
recovery, Global Biogeochem. Cy., 26, 1–20, 10.1029/2011GB004098,
2012.Mengis, N., Keller, D. P., Eby, M., and Oschlies, A.: Uncertainty in the
response of transpiration to CO2 and implications for climate change,
Environ. Res. Lett., 10, 094001, 10.1088/1748-9326/10/9/094001, 2015.
Mengis, N., Keller, D. P., Rickels, W., Quaas, M., and Oschlies, A.: Climate
Engineering-induced changes in correlations between Earth system
variables-Implications for appropriate indicator selection, Clim. Change, 153, 305–322, 2019.Mengis, N., Partanen, A. I., Jalbert, J., and Matthews, H. D.: 1.5 ∘C carbon budget dependent on carbon cycle uncertainty and future
non-CO2 forcing, Sci. Rep., 8, 5831, 10.1038/s41598-018-24241-1,
2018.Menviel, L., England, M. H., Meissner, K. J., Mouchet, A., and Yu, J.:
Atlantic-Pacific seesaw and its role in outgassing CO2 during Heinrich
events, Paleoceanography, 29, 58–70, 10.1002/2013PA002542, 2014.Montenegro, A., Brovkin, V., Eby, M., Archer, D., and Weaver, A. J.: Long
term fate of anthropogenic carbon, Geophys. Res. Lett., 34, L19707,
10.1029/2007GL030905, 2007.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, D08101, 10.1029/2011JD017187, 2012.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J.,
Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T. and Zhang, H.: Anthropogenic and Natural
Radiative Forcing, in: 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, UK,
New York, NY, USA, 2013.Niemeyer, D., Kemena, T. P., Meissner, K. J., and Oschlies, A.: A model study of warming-induced phosphorus–oxygen feedbacks in open-ocean oxygen minimum zones on millennial timescales, Earth Syst. Dynam., 8, 357–367, 10.5194/esd-8-357-2017, 2017.
Olson, J. S., Watts, J. A. and Allison, L. J.: Carbon in live vegetation of major world ecosystems, Oak Ridge National Laboratory, ORNL-5862, Oak Ridge TN, 1983.
Pacanowski, R. C.: MOM 2 Documentation, users guide and reference manual,
GFDL Ocean Group Technical Report 3, Geophys, Fluid Dyn. Lab., Princet.
Univ. Princeton, NJ, 1995.Paulmier, A., Kriest, I., and Oschlies, A.: Stoichiometries of remineralisation and denitrification in global biogeochemical ocean models, Biogeosciences, 6, 923–935, 10.5194/bg-6-923-2009, 2009.Rayner, D., Hirschi, J. J.-M., Kanzow, T., Johns, W. E., Wright, P. G.,
Frajka-Williams, E., Bryden, H. L., Meinen, C. S., Baringer, M. O.,
Marotzke, J., Beal, L. M., and Cunningham, S. A.: Monitoring the Atlantic
meridional overturning circulation, Deep-Sea Res. Pt. II, 58, 1744–1753, 10.1016/j.dsr2.2010.10.056, 2011.Rennermalm, A. K., Wood, E. F., Déry, S. J., Weaver, A. J., and Eby, M.:
Sensitivity of the thermohaline circulation to Arctic Ocean runoff, Geophys.
Res. Lett., 33, L12703, 10.1029/2006GL026124, 2006.Rogelj, J., Shindell, D., Jiang, K., Fifita, S., Forster, P., Ginzburg, V., Handa, C., Kheshgi, H., Kobayashi, S., Kriegler, E., Mundaca, L., Séférian, R., and Vilariño, M. V.: Mitigation Pathways Compatible with 1.5 ∘C in the Context of Sustainable Development, in: Global Warming of 1.5 ∘C, An IPCC Special Report on the impacts of global warming of 1.5 ∘C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, edited by: Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., and Waterfield, T., in Press, 2018.Schaefer, K., Zhang, T., Bruhwiler, L., and Barrett, A. P.: Amount and timing
of permafrost carbon release in response to climate warming, Tellus B, 63, 165–180,
10.1111/j.1600-0889.2011.00527.x, 2011.Schmidt, A., Mills, M. J., Ghan, S., Gregory, J. M., Allan, R. P., Andrews,
T., Bardeen, C. G., Conley, A., Forster, P. M., Gettelman, A., Portmann, R.
W., Solomon, S., and Toon, O. B.: Volcanic Radiative Forcing From 1979 to
2015, J. Geophys. Res.-Atmos., 123, 12491–12508,
10.1029/2018JD028776, 2018.Schmittner, A., Oschlies, A., Matthews, H. D., and Galbraith, E. D.: Future
changes in climate, ocean circulation, ecosystems, and biogeochemical
cycling simulated for a business-as-usual CO2 emission scenario until year
4000 AD, Global Biogeochem. Cy., 22, GB1013, 10.1029/2007GB002953,
2008.Schneider von Deimling, T., Meinshausen, M., Levermann, A., Huber, V., Frieler, K., Lawrence, D. M., and Brovkin, V.: Estimating the near-surface permafrost-carbon feedback on global warming, Biogeosciences, 9, 649–665, 10.5194/bg-9-649-2012, 2012.Shindell, D. T., Lamarque, J.-F., Schulz, M., Flanner, M., Jiao, C., Chin, M., Young, P. J., Lee, Y. H., Rotstayn, L., Mahowald, N., Milly, G., Faluvegi, G., Balkanski, Y., Collins, W. J., Conley, A. J., Dalsoren, S., Easter, R., Ghan, S., Horowitz, L., Liu, X., Myhre, G., Nagashima, T., Naik, V., Rumbold, S. T., Skeie, R., Sudo, K., Szopa, S., Takemura, T., Voulgarakis, A., Yoon, J.-H., and Lo, F.: Radiative forcing in the ACCMIP historical and future climate simulations, Atmos. Chem. Phys., 13, 2939–2974, 10.5194/acp-13-2939-2013, 2013.Simmons, H. L., Jayne, S. R., St. Laurent, L. C., and Weaver, A. J.: Tidally
driven mixing in a numerical model of the ocean general circulation, Ocean
Model., 6, 245–263, 10.1016/S1463-5003(03)00011-8, 2004.Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., and Regayre, L. A.: FAIR v1.3: a simple emissions-based impulse response and carbon cycle model, Geosci. Model Dev., 11, 2273–2297, 10.5194/gmd-11-2273-2018, 2018.Smith, S. and Burgess, M.: Ground temperature database for northern
Canada, Geological Survey of Canada, Open File 3954, 2000, 28 pp., 10.4095/211804, 2000.Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433–452, 10.5194/gmd-10-433-2017, 2017.Stroeve, J. C., Kattsov, V., Barrett, A. P., Serreze, M. C., Pavlova, T.,
Holland, M. M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5,
CMIP3 and observations, Geophys. Res. Lett., 39, L16502,
10.1029/2012GL052676, 2012.Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson,
A., Bakker, D. C. E., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishii,
M., Midorikawa, T., Nojiri, Y., Körtzinger, A., Steinhoff, T., Hoppema,
M., Olafsson, J., Arnarson, T. S., Tilbrook, B., Johannessen, T., Olsen, A.,
Bellerby, R., Wong, C. S., Delille, B., Bates, N. R., and de Baar, H. J. W.:
Climatological mean and decadal change in surface ocean pCO2, and net
sea-air CO2 flux over the global oceans, Deep-Sea Res. Pt. II, 56, 554–577, 10.1016/j.dsr2.2008.12.009, 2009.Tarnocai, C., Canadell, J. G., Schuur, E. A. G., Kuhry, P., Mazhitova, G.,
and Zimov, S. A.: Soil organic carbon pools in the northern circumpolar
permafrost region, Global Biogeochem. Cy., 23, GB2023,
10.1029/2008GB003327, 2009.Taucher, J. and Oschlies, A.: Can we predict the direction of marine primary
production change under global warming?, Geophys. Res. Lett., 38, L02603,
10.1029/2010GL045934, 2011.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, 2001.Tokarska, K. B. and Zickfeld, K.: The effectiveness of net negative carbon
dioxide emissions in reversing anthropogenic climate change, Environ. Res.
Lett., 10, 094013, 10.1088/1748-9326/10/9/094013, 2015.Vaughan, D. and Comiso, J. C.: Observations: Cryosphere, in: Climate Change
2013: The Physical Science Basis, Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Encycl. Earth Sci. Ser., 1030–1032,
10.1007/978-1-4020-4411-3_53, 2013.Volk, T. and Hoffert, M. I.: Ocean carbon pumps: Analysis of relative
strangths and efficiencies in ocean-driven atmospheric CO2 changes, Carbon
Cycle Atmos. CO2 Nat. Var. Archean to Present, 32, 99–110, 1985.Wania, R., Meissner, K. J., Eby, M., Arora, V. K., Ross, I., and Weaver, A. J.: Carbon-nitrogen feedbacks in the UVic ESCM, Geosci. Model Dev., 5, 1137–1160, 10.5194/gmd-5-1137-2012, 2012Wanninkhof, R.: Relationship between wind speed and gas exchange over the
ocean revisited, Limnol. Oceanogr., 12, 351–362,
10.4319/lom.2014.12.351, 2014.Weaver, A. J., Eby, M., Wiebe, E. C., Bitz, C. M., Duffy, P. B., Ewen, T.
L., Fanning, A. F., Holland, M. M., MacFadyen, A., Matthews, H. D.,
Meissner, K. J., Saenko, O., Schmittner, A., Wang, H., and Yoshimori, M.: The
UVic earth system climate model: Model description, climatology, and
applications to past, present and future climates, Atmos.-Ocean, 39,
361–428, 10.1080/07055900.2001.9649686, 2001.Westberry, T., Behrenfeld, M. J., Siegel, D. A., and Boss, E.: Carbon-based
primary productivity modeling with vertically resolved photoacclimation,
Global Biogeochem. Cy., 22, GB2024, 10.1029/2007GB003078, 2008.Wild, M., Folini, D., Schär, C., Loeb, N., Dutton, E. G., and
König-Langlo, G.: The global energy balance from a surface perspective,
Clim. Dynam., 40, 3107–3134, 10.1007/s00382-012-1569-8, 2013.Zhuang, Q., Melillo, J. M., Sarofim, M. C., Kicklighter, D. W., McGuire, A.
D., Felzer, B. S., Sokolov, A., Prinn, R. G., Steudler, P. A., and Hu, S.:
CO2 and CH4 exchanges between land ecosystems and the atmosphere in northern
high latitudes over the 21st century, Geophys. Res. Lett., 33, 2–6,
10.1029/2006GL026972, 2006.Zickfeld, K., Eby, M., Matthews, H. D., and Weaver, A. J.: Setting cumulative
emissions targets to reduce the risk of dangerous climate change, P.
Natl. Acad. Sci. USA, 106, 16129–16134,
10.1073/pnas.0805800106, 2009.Zickfeld, K., Eby, M., Matthews, H. D., Schmittner, A., and Weaver, A. J.:
Nonlinearity of Carbon Cycle Feedbacks, J. Clim., 24, 4255–4275,
10.1175/2011JCLI3898.1, 2011.
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, 055006, 10.1088/1748-9326/11/5/055006, 2016.
Zweng, M., Reagan, J. R., Seidov, D., Boyer, T. P., Locarnini, R. A.,
Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K. W., Paver, C.
R., and Smolyar, I. V.: World Ocean Atlas 2018, Volume 2: Salinity,
edited by: Mishonov, A., NOAA Atlas NESDIS 82, 50 pp., 2019.