ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus GmbHGöttingen, Germany10.5194/esd-6-411-2015Climate and carbon cycle dynamics in a CESM simulation from 850 to 2100 CELehnerF.lehner@climate.unibe.chJoosF.https://orcid.org/0000-0002-9483-6030RaibleC. C.MignotJ.BornA.KellerK. M.StockerT. F.Climate and Environmental Physics, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandLOCEAN Laboratory, Sorbonne Universités, Paris, Francenow at: National Center for Atmospheric Research, Boulder, USAF. Lehner (lehner@climate.unibe.ch)10July20156241143428January201526February201518May201529May2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://esd.copernicus.org/articles/6/411/2015/esd-6-411-2015.htmlThe full text article is available as a PDF file from https://esd.copernicus.org/articles/6/411/2015/esd-6-411-2015.pdf
Under the protocols of phase 3 of the Paleoclimate Modelling
Intercomparison Project, a
number of simulations were produced that provide a range of potential climate
evolutions from the last millennium to the end of the current century. Here,
we present the first simulation with the Community Earth System Model (CESM),
which includes an interactive carbon cycle, that covers the last millennium.
The simulation is continued to the end of the twenty-first century. Besides
state-of-the-art forcing reconstructions, we apply a modified reconstruction
of total solar irradiance to shed light on the issue of forcing uncertainty
in the context of the last millennium. Nevertheless, we find that structural
uncertainties between different models can still dominate over forcing
uncertainty for quantities such as hemispheric temperatures or the land and
ocean carbon cycle response. Compared to other model simulations, we find
forced decadal-scale variability to occur mainly after volcanic eruptions,
while during other periods internal variability masks potentially forced
signals and calls for larger ensembles in paleoclimate modeling studies. At
the same time, we were not able to attribute millennial temperature trends to
orbital forcing, as has been suggested recently. The climate–carbon-cycle
sensitivity in CESM during the last millennium is estimated to be between 1.0
and 2.1 ppm ∘C-1. However, the dependence of this sensitivity
on the exact time period and scale illustrates the prevailing challenge of
deriving robust constraints on this quantity from paleoclimate proxies. In
particular, the response of the land carbon cycle to volcanic forcing shows
fundamental differences between different models. In CESM the tropical land
dictates the response to volcanoes, with a distinct behavior for large and
moderate eruptions. Under anthropogenic emissions, global land and ocean
carbon uptake rates emerge from the envelope of interannual natural
variability by about year 1947 and 1877, respectively, as simulated for the
last millennium.
Introduction
The last about 1000 years constitute the best opportunity
previous to the instrumental period to study the transient interaction of
external forcing and internal variability in climate, atmospheric CO2,
and the carbon cycle on interannual to multi-decadal timescales. In fact, the
instrumental record is often too short to draw strong conclusions on
multi-decadal variability. The relatively stable climate together with the
abundance of high-resolution climate proxy and ice core data makes the last
millennium an interesting target and test bed for modeling studies. However,
the large and sometimes controversial body of literature on the magnitude and
impact of solar and volcanic forcing on interannual to multi-decadal climate
variability illustrates the challenges inherent in establishing a robust
understanding of a period that is characterized by a small signal-to-noise
ratio in many quantities and for which uncertainties in the external forcing
remain see, e.g.,. In
addition, a process-based quantitative explanation of the reconstructed
preindustrial variability in atmospheric CO2 and carbon fluxes is
largely missing.
Compared to glacial–interglacial climate change, the last millennium
experienced little climate variability, yet there is evidence for distinct
climate states during that period e.g.,.
Within the last millennium, the Medieval Climate Anomaly (MCA,
∼ AD 950–1250) and the Little Ice Age (LIA, ∼ AD 1400–1700)
are two key periods of documented regional or global temperature excursions
suggested to be driven by a combination of variations in solar irradiance and
volcanic activity see, e.g.,
. Despite great efforts in
reconstructing e.g., and simulating
e.g., the transition
from the MCA to the LIA, substantial uncertainties remain with respect to the
mechanisms at play. Recent studies point towards solar insolation playing a
minor role with regard to climate over the last millennium
, while regional feedback processes in
response to volcanic eruptions and solar variability need to be considered to
explain decadal-scale climate variability
e.g.,. At high northern
latitudes, the importance of millennial-scale orbital forcing is another
debated issue e.g.,.
Further, the last millennium offers the possibility to study the natural
variability in the carbon cycle and its response to external forcing. Models
with a carbon cycle module are extensively tested against present-day
observations and widely used for emission-driven future projections
see, e.g.,. However, there are only few
last-millennium simulations including a carbon cycle see,
e.g.,. The
sensitivity of the carbon cycle to climate has been shown to be mostly
positive, i.e., with warming, additional CO2 is released to the
atmosphere . However, the magnitude of this feedback
remains poorly constrained by observations and models
e.g.. In
particular, determining the role of land in past and future carbon cycle
variability and trends is still challenging. In both idealized
and scenario-guided multi-model studies
, land constitutes the largest relative uncertainty
in terms of intermediate- to long-term carbon uptake.
As for physical climate quantities, explosive volcanic eruptions constitute
an important forcing for the carbon cycle. The sensitivity of the carbon
cycle to such eruptions has been investigated by ,
, , and
using different Earth system models. For this short-lived forcing, the land
response appears to be the driver of most post-eruption carbon cycle changes,
with a range of magnitudes and time horizons associated with the different
models. Further, pointed out that the magnitude of
the carbon cycle response to volcanoes depends critically on the climate
state during the eruption.
The third Paleoclimate Modelling Intercomparison Project
PMIP3; and fifth Coupled Model Intercomparison
Project CMIP5; represent joint efforts, in which
different modeling groups perform identical experiments, allowing for a
systematic comparison of the models e.g.,. Here we
contribute to the existing set of simulations an integration from
850–2100 CE with the Community Earth System Model, including a carbon cycle
module. The aims of this study are (i) to detect coherent large-scale
features of forced variability in temperature and carbon cycle quantities, in
particular in response to volcanic eruptions, (ii) to investigate the
relative role of forcing uncertainty and model structural uncertainty, and
(iii) to provide an estimate of preindustrial variability and the time of
emergence from it under anthropogenic climate change. The setup chosen here
is unique in a number of ways and tailored to address the abovementioned
aims: first, the carbon cycle is fully interactive with the other model
components with the exception of the radiation code, which is fed by
reconstructed CO2. This allows us to study the isolated effect of
climate on the carbon cycle, while guaranteeing an external forcing
consistent with existing reconstructions. Second, the orbital parameters are
held constant to study their importance relative to simulations with
transient orbital parameters. Third, the solar forcing incorporated into the
simulation has a larger amplitude than the majority of PMIP3 simulations and
hence enables us to investigate whether the results are sensitive to this
amplitude.
This paper is structured as follows: a description of the model and
experimental setup is presented in Sect. 2. In Sects. 3 and 4 we address the
general simulated climate and carbon cycle evolution and investigate forced
and unforced variability in the simulated climate by comparing models to
reconstructions and models to models. Section 5 focuses on the response of
the climate and carbon cycle to volcanic forcing. Section 6 deals with
estimating the climate–carbon-cycle sensitivity in the Community
Earth System Model (CESM). A discussion and
conclusions follow in Sect. 7.
Data and methodsModel description
The Community Earth System Model CESM; is a
fully coupled state-of-the-art Earth system model developed by the National
Center for Atmospheric Research (NCAR) and was released in 2010. In terms of
physics, CESM relies on the fourth version of the Community Climate System
Model CCSM4;. Additionally, a carbon cycle module is
included in CESM's atmosphere, land, and ocean components. The CESM version
used here is release 1.0.1 in the 1∘ version and includes
components for the atmosphere, land, ocean, and sea ice, all coupled by
a flux coupler.
The atmospheric component of CESM 1.0.1 is the Community Atmosphere Model
version 4 CAM4;, which has a finite volume core with a
uniform horizontal resolution of 1.25∘× 0.9∘ at
26 vertical levels. The land component is the Community Land Model version 4
CLM4;, which operates on the same horizontal grid
as CAM4 and includes a prognostic carbon–nitrogen cycle that calculates
vegetation, litter, soil carbon, vegetation phenology, and nitrogen states.
Further, it includes a prognostic fire module, which is governed by
near-surface soil moisture conditions and fuel availability.
The ocean component is the Parallel Ocean Program version 2
POP2; with an nominal 1∘
horizontal resolution and 60 depth levels. The horizontal resolution varies
and is higher around Greenland, to where the North Pole is displaced, as well
as around the equator. Embedded in POP2 is the Biogeochemical Elemental Cycle
model BEC; that builds on a
nutrient–phytoplankton–zooplankton–detritus food web model and distinguishes
three phytoplankton functional types . Carbon export and
remineralization are parameterized according to .
Alkalinity, pH, partial pressure of CO2, concentrations of
bicarbonate, and carbonate ions are diagnosed from prognostic dissolved
inorganic carbon, alkalinity, and temperature- and salinity-dependent
equilibrium coefficients. Biogenic calcification is implemented as being
proportional to a fraction of small-phytoplankton production, which is
temperature-dependent. An exponential curve is prescribed to simulate the
dissolution of sinking CaCO3. There is no
dependence of calcification–dissolution rates on saturation state. Organic
material reaching the ocean floor is remineralized instantaneously, i.e., no
sediment module is included. River discharge from CLM4 does not carry
dissolved tracers but nitrogen deposition to the ocean surface has been
prescribed. The sea ice component is the Community Ice Code version 4 (CICE4)
from the Los Alamos National Laboratories , including
elastic viscous plastic dynamics, energy-conserving thermodynamics, and a
subgrid-scale ice thickness distribution. It operates on the same horizontal
resolution as POP2.
List of simulations conducted for this study. See text for details
regarding the forcing. TSI: total solar irradiance; GHGs: greenhouse
gases; ECO2: anthropogenic CO2 emissions from fossil
fuel burning and cement production; LULUC; land use and land use
change.
Control simulation (CTRL)Transient simulation (CESM)Forcing850 CE (500 years)850–2099 CETSI1360.228 W m-2adjusted and VolcanicnoneGHGsCO2 (279.3 ppm)CH4 (674.5 ppb)N2O (266.9 ppb)ECO2none and Aerosol1850f CE from Orbital1990 CE after 1990 CE after LULUC850 CE from and Experimental setup
Table provides an overview of the simulations conducted for
this study. First, a 500-year control simulation with perpetual 850 CE
forcing (hereafter CTRL) was branched off from an 1850 CE control
simulation with CCSM4 . However, restart files for the land
component were taken from an 850 CE control simulation, kindly provided by
the NCAR, in which the land use maps by were applied.
This procedure has the advantage that the slowly reacting soil and ecosystem
carbon stocks are closer to 850 CE conditions than in the 1850 CE control
simulation. A transient simulation covering the period 850–2099 CE was then
branched off from year 258 of CTRL. Despite the shortness of CTRL leading up to
the start of the transient simulation, most quantities of the surface
climate, such as air temperature, sea ice, or upper-ocean temperature, can be
considered reasonably equilibrated at the start of the transient simulation,
as the forcing levels due to total solar irradiance (TSI) and most greenhouse
gases are similar between 1850 and 850 CE . However,
weak trends in CTRL are still detectable in slowly reacting quantities, such
as deep-ocean temperature (below 2000 m;
∼-0.04 ∘C 100 yr -1), Atlantic meridional overturning
circulation (∼-0.22 Sv 100 yr -1), Antarctic Circumpolar
Current (∼ 0.70 Sv 100 yr -1), dissolved inorganic carbon in
the ocean (∼-0.01 % 100 yr -1), or soil carbon storage
(∼ 0.2 % C 100 yr -1). The Antarctic Bottom Water
formation rate shows no drift.
Forcings used in the last millennium simulation with CESM. (a) TSI
in comparison with the different TSI reconstructions proposed by PMIP3.
(b) Volcanic forcing as total volcanic aerosol mass.
(c) Radiative forcing RF, calculated according
to from the greenhouse gases CO2, CH4, and N2O.
(d) Major changes in land cover (as fraction of global land area).
See text for details.
The applied transient forcing largely follows the PMIP3 protocols
and the Coupled Model Intercomparison Project 5
CMIP5;, consisting of TSI, greenhouse gases (GHGs),
volcanic and anthropogenic aerosols, and land use changes
(Fig. ). Here, the TSI reconstruction by
TSIVS10 is used, to which a synthetic
11-year solar cycle is added . In light of the recently
enlarged envelope of reconstructed TSI amplitude , we
scale TSI by a factor of 2.2635 to have an amplitude of 0.2 % between
present day (1961–1990 CE) and the late Maunder Minimum (1675–1704 CE),
which is about twice as large as the 0.1 % used in most PMIP3 simulations:
TSI=2.2635⋅(TSIVS10-TSIVS10‾)+TSIVS10‾.
Figure a shows that the TSI used here lies in between the
large-amplitude reconstruction by and the bulk of
small-amplitude reconstructions of the original PMIP3 protocol
. Note that a recent detection and attribution study
indicates that small-amplitude TSI reconstructions agree better with
temperature reconstructions over the last millennium than large-amplitude
reconstructions , in agreement with
. For the twenty-first century, the last three solar
cycles of the data set are repeated continuously. The insolation due to
Earth's orbital configuration is calculated according to
with the orbital parameters held constant at 1990 CE values.
The volcanic forcing follows from 850 to 2001 CE. It provides
estimates of the stratospheric sulfate aerosol loadings from volcanic
eruptions as a function of latitude, altitude, and month and is implemented
in CESM as a fixed single-size distribution in the three layers in the lower
stratosphere . Post-2001 CE volcanic forcing remains zero.
Land use and land use changes (LULUC) are based on from
850 to 1500 CE, when this data set is splined into , a synthesis data set that extends into
the future. The two data sets do not join smoothly but exhibit a small
stepwise change in the distribution of crop land and pasture at the year
1500 CE. Up until about 1850 CE global anthropogenic LULUC are small;
however, they can be significant regionally . Approaching
the industrial era, LULUC accelerate, dominated by the expansion of crop land
and pasture. Here, only net changes in land use area are considered. The
impact of shifting cultivation and wood harvest on carbon emissions from land
use is neglected; these processes are estimated to have contributed around
30 % to the total carbon emissions from land use
.
The temporal evolution of long-lived greenhouse gases (GHGs; CO2,
CH4, and N2O) is prescribed based on estimates from high-resolution
Antarctic ice cores that are joined with measurements in the mid-twentieth
century and references therein. While the carbon
cycle module of CESM interactively calculates the CO2 concentration
originating in land use changes, fossil-fuel emissions
post-1750 CE, following, and carbon-cycle–climate
feedbacks, it is radiatively inactive. Instead, the ice core and measured
data are prescribed in the radiative code, keeping the physical model as
close to reality as possible. As a result, the impact of the interactive
coupling of the carbon cycle module is minor for simulated climate and
limited to changes in surface conditions due to changing vegetation. For the
extension of the simulation post-2005 CE the Representative Concentration
Pathway 8.5 (RCP 8.5) is used, representing the unmitigated
“business-as-usual” emission scenario, corresponding to a forcing of
approximately 8.5 W m-2 in the year 2100 .
Aerosols such as sulfate, black and organic carbon, dust, and sea salt are
implemented as non-time-varying up to 1850 CE, perpetually inducing the
spatial distributions of the 1850 CE control simulation during this time
. Post-1850 CE, the time-varying aerosol data sets
provided by are used, although CESM only
includes a representation of direct aerosol effects. Similarly, nitrogen
(NHx and NOy) input is held constant until it starts to be
time-varying from 1850 CE onwards, also following
. Iron fluxes from sediments are held fixed
.
Other model simulations
In addition to comparing CESM results to output from current Model
Intercomparison Projects, we also compare them to results from a similar
simulation with CCSM4 and IPSL-CM5A-LR (Institut Pierre
Simon Laplace–climate model 5A–low resolution) , two simulations without interactive carbon
cycle. The solar and volcanic forcing reconstructions applied to CCSM4 and
IPSL-CM5A-LR are identical to ours with the exception of the scaling of TSI
that we applied to CESM. The goal here is to investigate the question of
whether different solar forcing amplitudes applied to the same physical model
(CESM vs. CCSM4) have a larger effect than applying the same solar forcing to
two different physical models (IPSL-CM5A-LR vs. CCSM4).
Selected forcing details of simulations used in comparisons with
CESM. TSI: total solar irradiance; LULUC: land use and land use
change.
CESMCCSM4IPSL-CM5A-LRMPI-ESMForcingTSIadjusted and and and VolcanicOrbital1990 CE, transient; transient; transient; LULUCnon-transientand and
Further, we compare CESM to MPI-ESM (Max-Planck-Institut Earth System Model)
ECHAM5/MPIOM;, a model that
includes an interactive carbon cycle, to assess the robustness of the
simulated climate and carbon cycle variations in response to external
forcing. MPI-ESM uses as TSI forcing and
as volcanic forcing. These differ from the CESM
forcing in amplitude much more than in timing and therefore allow for a
comparison of the forced response. All simulations, except for ours, apply
transient orbital forcing and are summarized in Table . If
not using the full ensemble of MPI-ESM, we focus on the member “mil0021”.
Another difference in terms of experimental setup between CESM and MPI-ESM is
that MPI-ESM was run with a fully interactive carbon cycle, i.e., the
prognostic CO2 interacts with the radiation and through that again
influences climate, while in our setup this is a one-directional interaction
only. Further, MPI-ESM is more coarsely resolved than CESM in both ocean and
atmosphere and applies the A1B scenario for the twenty-first century
, which corresponds roughly to the current intermediate
scenario as compared to the high-scenario RCP8.5 used in CESM.
(a) Northern Hemisphere and (b) Southern Hemisphere temperature
anomalies in model simulations and reconstructions. The anomalies are with
reference to 1500–1899 CE (left panels) and 1850–1899 CE (right panels).
Gray shading in (a) indicates the reconstruction overlap
; in (b), it indicates the reconstruction by
. The 5–95 % range of the simulations from the third
Paleoclimate Modelling Intercomparison Project (PMIP3) and the fifth Coupled
Model Intercomparison Project (CMIP5; applying the RCP8.5) are given in green
and red shading, respectively. Note that MPI-ESM applies the A1B scenario
, which has a weaker forcing than RCP8.5. Hemispheric means
from observations are shown as thick black line . All
time series have been smoothed by a local regression filter which suppresses
variability higher than 30 years. The Medieval Climate Anomaly (MCA) and the
Little Ice Age (LIA) are indicated as defined in .
(c) Evolution of atmospheric CO2 in CESM (black), MPI-ESM
(grey; ensemble range), from ice cores (red), from measurements (orange), and
from RCP8.5 used to force the radiative code in CESM (magenta). The small
inset in the middle panel shows the observed annual cycle at Mauna Loa,
Hawaii, and a 2∘× 2∘ average over Hawaii from CESM,
both derived from the period 1958–2012.
(a) Mean June–August (JJA) Arctic (> 60∘ N land)
solar insolation in CCSM4 with time-varying orbital parameters and CESM with
fixed orbital parameters. (b) Arctic JJA temperature difference
between CCSM4 and CESM. The least-squares linear trend of this temperature
difference is given in red. (c) Arctic JJA temperature anomalies
(from their 850 to 1850 AD mean) versus solar insolation as 100- and 200-year
averages (10 and 5 circles, respectively) from CCSM4 and CESM (red and blue,
respectively). The least-squares linear trend for each cloud of 100- and
200-year averages is given in the respective color. The shading envelops the
range of temperature versus solar insolation for each cloud of means.
General climate and carbon cycle evolutionTemperature
The simulated annual mean Northern Hemisphere (NH) surface air temperature
(SAT) follows the general evolution of proxy reconstructions: a warm Medieval
Climate Anomaly (MCA, ∼ 950–1250 CE), a transition into the colder
Little Ice Age (LIA, ∼ 1400–1700 CE), followed by the
anthropogenically driven warming of the nineteenth and twentieth centuries
(Fig. ). The NH MCA-to-LIA cooling amounts to
0.26 ± 0.18 ∘C taking the time periods defined above,
which are as in, placing it in the lower half of reconstructed
amplitudes that range from about 0.1 to 0.7 ∘C .
The subsequent warming between 1851–1880 CE and 1981–2010 CE amounts to 1.23 ± 0.15 ∘C,
while observations report only 0.71 ± 0.13 ∘C
. This overestimation by CESM takes place almost
entirely after 1960 and arises largely from missing negative forcing from the
indirect aerosol effect, which is not implemented in CAM4
. The late twentieth century being the warmest period in
the NH in the past millennium is consistent with reconstructions
e.g.,.
In CESM, the inception of the NH LIA occurs in concert with decreasing TSI
and a sequence of strong volcanic eruptions during the thirteenth century.
Reconstructions differ substantially in this matter and start to cool as
early as 1100 CE or as late as 1400 CE. Further, new regional multi-proxy
reconstructions of temperature provide no support for a hemispherical or
globally synchronous MCA or LIA but show a clear tendency towards colder
temperatures and exceptionally cold decades over most continents in the
second half of the millennium .
The last millennium simulation with CCSM4 shows a largely coherent behavior
with CESM in terms of amplitude and decadal variability in NH SAT
(850–1850 CE correlation of 5-year filtered annual means r=0.88,
p<0.001). The difference in NH SAT due to the different TSI amplitudes in
CESM and CCSM4 scales roughly with the regression slope of NH SAT vs. TSI of
both CESM and CCSM4 (∼0.13 ∘C per W m-2), although
internal variability can easily mask this effect at times. For example, the
Maunder Minimum (1675–1704 CE), the 30-year period with the lowest TSI
values and – when using TSI scaling – with the largest
difference between CESM and CCSM4 (1.5 W m-2), is only
0.14 ∘C cooler than in CCSM4 and not 0.20 ∘C as expected
from the regression.
The NH temperature evolution of additional PMIP3 and CMIP5 simulations shows
that the multi-model range is within the range of the reconstructions and encompasses
the instrument-based observations (Fig. ).
Disagreement between models and reconstructions exists in particular on the
magnitude of response to the eruptions at 1258 CE and around 1350 CE. The
1258 CE eruption is the largest volcanic event recorded for the last
millennium, and its climatic impact was likely enhanced through the
cumulative effect of three smaller eruptions following shortly after
. However, the pronounced cooling
that is simulated by the models for this cluster of eruptions is largely
absent in temperature reconstructions. Conversely, around 1350 CE
temperature reconstructions show a decadal-scale cooling presumably due to
volcanoes that is absent in the models, as the reconstructed volcanic forcing
shows only two relatively small eruptions around that time. Part of this
incoherent picture may arise from the unknown aerosol size distribution
, the geographic location of past volcanic eruptions
, and
differences in reconstruction methods. As many proxy reconstructions of
temperature rely heavily on tree ring data, it is worth noting that the
dendrochronology community is currently debating whether the trees' response
to volcanic eruptions resembles the true magnitude of the eruption
.
Disagreement among the models exists on the relative amplitude of the MCA,
where most models show colder conditions than CESM and CCSM4. Remarkably, the
simulation by IPSL-CM5A-LR applied the same TSI and volcanic forcing as
CCSM4, yet it comes to lie at the lower end of the PMIP3 model range during
the MCA. In other words, the way in which models respond to variations in TSI and
other forcings can still make a larger difference in the simulated amplitude
than the scaling of TSI by a factor of 2, which in turn complicates a proper
detection and attribution of solar forcing during the last millennium
. Further disagreement among the models
exists on the response to volcanic eruptions, where CESM and CCSM4 are among
the more sensitive models an oversensitivity of CCSM4 to volcanoes
based on twentieth-century simulations was reported by. Turning
to the century-scale change over the industrial era, CESM and CCSM4 are at the upper end of the CMIP5 range and show an overestimation of the observed
warming.
The simulated mean SAT of the Southern Hemisphere (SH) generally shows a
similar evolution as in the NH, with the signature of the MCA and LIA
superimposed on a weak millennial cooling trend. Models and reconstructions
disagree to a larger extent in the SH than in the NH, in particular regarding
cold excursions due to large volcanic eruptions, which are largely absent in
the reconstructions. Similar results have been reported in a recent study on
interhemispheric temperature variations that finds much less phasing of the
two hemispheres in reconstructions than in models, potentially related to
underestimated internal variability on the SH in models
. A lingering question of climate modeling in general is
whether models are too global in their response to external forcing. That is,
they might show too little regional variability that is independent of the
global mean response during a forced period. However, the uncertainties in
the early period of the reconstructions make it impossible to robustly answer this
question. Similar to the NH, the industrial warming in the SH from 1851–1880
to 1981–2010 CE (0.53 ± 0.07 ∘C) is overestimated by CESM
(0.71 ± 0.13 ∘C).
The differential warming between the hemispheres in CESM is among the
smallest among CMIP5 models (not shown). This is mainly due to the
underestimated deep-water formation in the Southern Ocean, leading to a
comparably strong warming of the SH and likely an underestimation of the
oceanic uptake of anthropogenic carbon . With a transient
climate response of 1.73 ∘C and an equilibrium climate sensitivity
of 3.20 ∘C , CESM lies in the middle of recent
estimates of 1.0 to 2.5 ∘C and 1.5 to 4.5 ∘C .
Orbital forcing
To detect and attribute the influence of orbital forcing on SAT trends during
the last millennium, we compare our simulation with fixed orbital parameters
to the CCSM4 simulation with time-varying orbital parameters
(Fig. ). While both models experience a negative long-term trend
in global TSI until about 1850 CE (Fig. ), the difference
arising from the different orbital setup can be best seen in Arctic summer
land insolation (Fig. ). Hence, Arctic summer land SAT has been
proposed as a quantity that, on timescales of centuries to millennia, may be
affected by orbital forcing . However, we find no detectable difference between the
two simulations in the trend of Arctic summer land SAT (Fig. b).
In fact, the Arctic multi-decadal to centennial summer land SAT anomalies in
CESM span a very similar range as in CCSM4, despite CESM not accounting for
time-varying orbital parameters: Fig. c shows non-overlapping 100-
and 200-year mean SAT anomalies plotted against the corresponding mean solar
insolation. The results from CCSM4 suggest a clear relationship between of
the two quantities; however, the results of CESM show that nearly identical
SAT anomalies are possible without orbital forcing. In other words, while we
detect a long-term cooling trend in Arctic summer SAT in both CESM and CCSM4,
we fail to attribute this trend to orbital forcing alone, as suggested by
. This is confirmed in new simulations with decomposed
forcing, again comparing simulations with fixed and time-varying orbital
parameters (B. Otto-Bliesner, personal communication, 2014).
Cumulative carbon emissions in Pg C by different components over different
time periods in CESM. Positive (negative) values indicate emission
to (uptake from) the atmosphere.
The prognostic carbon cycle module in CESM allows us to study the response of
the carbon cycle to transient external forcing. The land biosphere is a
carbon sink during most of the first half of the last millennium, but becomes
a source as anthropogenic land cover changes start to have a large-scale
impact on the carbon cycle (Table ). The ocean is a carbon
source at the beginning and becomes a sink in the second half of the last
millennium. The residual of these fluxes represents changes in the
atmospheric reservoir of carbon, illustrated in Fig. c by the
prognostic CO2 concentration. The amplitude of the simulated
concentration does not resemble the one reconstructed from ice cores (i.e.,
imposed on the radiative code of CESM); in particular, the prominent CO2
drop in the seventeenth century is not captured by CESM. This raises the
question of whether the sensitivity of the carbon cycle to external forcing
is too weak in CESM, whether the imposed land use changes are too modest
, whether major changes in ocean circulation
are not captured by models , and whether the ice core
records are affected by uncertainties due to in situ production of CO2. Ensemble simulations with MPI-ESM also do not reproduce
the reconstructed amplitudes or the drop . Further,
Earth system models of intermediate complexity or vegetation models driven by
GCM (general circulation model) output do not reproduce the
uptake of carbon by either ocean or land needed to explain the reconstructed
amplitudes .
Annual mean net carbon flux from the atmosphere to (a) land
and (b) ocean. Green bars given the full and 10–90 % range from
the preindustrial part of the simulation. Observational estimates are from
.
The rise in atmospheric CO2 due to fossil-fuel combustion is in good
agreement with ice cores until about the 1940s. After that, a growing offset
exists, leading to an overestimation of about 20 ppm by 2005 in CESM,
qualitatively similar to the CMIP5 multi-model mean .
From the observational estimates one can diagnose that the discrepancy arises
primarily from overestimated carbon release from land
Fig. a; see also. From
1750 to 2011 CE the cumulative total land release (including LULUC) is
83 Pg C compared to 30 ± 45 Pg C from observational
estimates;, while the cumulative net land uptake is 95 Pg C
160 ± 90 Pg C;. The ocean cumulative uptake of 151 Pg C compares more favorably
to current estimates of 155 ± 30 Pg C . Note,
however, that given the overestimation of atmospheric CO2, one would
expect a higher ocean uptake. This bias originates largely in an
underestimation of the uptake in the Southern Ocean . Along
with this goes an underestimated seasonal cycle in CESM, originating from too
weak a growing season net flux in CLM4 . MPI-ESM, on
the other hand, underestimates atmospheric CO2 due to weak emissions
from LULUC .
The twenty-first century sees substantial emissions from fossil-fuel burning
under RCP8.5 (Fig. c). In addition, LULUC is associated with a
positive flux into the atmosphere, particularly until around 2050 CE
(Table ). After accounting for LULUC (which constitutes a
carbon loss for land) the net land sink increases to about
7 Pg C yr-1 at the end of the twenty-first century
(Fig. a). The rate of ocean uptake, on the other hand, peaks
around 2070 at about 5 Pg C yr-1, despite the fact that atmospheric CO2
continues to rise (Fig. c). This decoupling of the trends in
atmospheric CO2 growth and ocean uptake flux is linked to
nonlinearities in the carbon chemistry . The change in
dissolved inorganic carbon per unit change in the partial pressure of
CO2 decreases with increasing CO2, and thus so does the uptake capacity of
the ocean. Additionally, differences in the ventilation timescales of the
upper and the deep ocean likely play a role. While the surface ocean and the
thermocline exchanges carbon on annual-to-multi-decadal timescales with the
atmosphere, it takes a century to ventilate the deep ocean, as evidenced by
chlorofluorocarbon and radiocarbon data
. CESM has a documented low bias in
Southern Ocean ventilation due to too shallow mixed layer depths,
contributing to the underestimated carbon uptake of the ocean
.
The prognostic atmospheric CO2 increases to 1156 ppm by 2100 CE. This
would imply a forcing of 7.6 W m-2 from CO2 relative to 850 CE,
significantly more than the approximately 6.5 W m-2 that are imposed
by the radiative code calculated according tosee also
Fig. c. This propagation of the twentieth-century
bias is consistent with the CMIP5 multi-model mean
and has motivated attempts to reduce such biases by using observational
constraints for ocean ventilation , the tropical land
carbon storage sensitivity to temperature variations
, and the oceanic and terrestrial
carbon fluxes . CESM with CLM4, however, shows very
little sensitivity to tropical land carbon, in part due to the inclusion of
an interactive nitrogen cycle, which – through enhanced photosynthetic
uptake due to nitrogen fertilization – tends to counteract accelerated soil
decomposition from warming . Together with
the underestimated oceanic uptake, this leads to the roughly 20 % larger
airborne fraction in CESM as compared to what is actually prescribed as
atmospheric concentration in the radiative code according to the RCP8.5.
Figure places the current and projected changes within the
context of preindustrial variability. Estimated interannual variability prior
to 1750 CE is ±0.94 Pg C yr-1 (1 standard deviation) for the net
atmosphere–land and ±0.42 Pg C yr-1 for the net
atmosphere–ocean flux. The much larger interannual variability in land than
ocean flux is consistent with independent estimates and results from other
models e.g.,. Large volcanic eruptions, as they have
occurred in the last millennium, cause anomalously high uptake rates that,
for a short period of time, are on par with current uptake rates
(Fig. a and b, full range). We estimate when the
anthropogenically forced, global mean land and ocean uptake fluxes exceeds
the envelope of preindustrial natural variability
. As a threshold criteria, it is required that
the decadally smoothed uptake fluxes are larger than the upper bound of
2 standard deviations of the annual fluxes prior to 1750 CE. In that case, the simulated global mean land and ocean uptake fluxes have
emerged from natural interannual variability by 1947 CE and by 1877 CE,
respectively. The prognostic atmospheric CO2 concentration emerges
already in 1755 CE, while the simulated global mean temperature does not
emerge until 1966 CE.
Five-year filtered zonal mean anomalies of surface air temperature (SAT),
relative to
the 850–1849 CE mean from (a) CESM and (b) MPI-ESM.
(c) 100-year running-window correlation of zonal mean SAT from CESM
and MPI-ESM. A 0.75 Tukey window has been applied to the data before
correlation to weaken sharp transitions. Stippling indicates significance
(5 % level), taking into account autocorrelation estimated from the entire
time period. Panel (d): as (c) but for the correlation of
CESM with CCSM4. Panel (e): as (d) but for global mean SAT.
Small inset on top shows volcanic and solar forcing of CESM and MPI-ESM.
Volcanic forcing of CESM scaled to have the same radiative forcing as MPI-ESM
for Pinatubo in 1991 CE. Solar forcing relative to 1850 CE.
Model–model coherence
A classical approach to assess the robustness of model results is to rely on
the multi-model mean response to a given forcing . However,
as there are only very few last millennium simulations with comprehensive
Earth system models to date, this approach is not feasible for investigating the
decadal-scale climate–carbon-cycle responses to external forcing in the
period before 1850 CE. Instead, we estimate periods of forced variability
with a 100-year running-window correlation of CESM and MPI-ESM, indicating
phasing of the two models. The time series are anomalies from their 850–1849
mean and are smoothed with a 5-year local regression filter before
calculating the correlation. Thereby, we focus on the preindustrial period,
as the twentieth and twenty-first centuries are dominated by anthropogenic
trends, which are nontrivial to remove for a proper correlation analysis. In
addition, regression analysis is used.
Temperature
Figure a and b show anomalies of zonal mean annual SAT from
CESM and MPI-ESM. In both models the northern high latitudes show the
strongest trend, from positive anomalies during the MCA to negative anomalies
during the LIA. This is consistent with the current understanding of polar
amplification during either warm or cold phases
. The twentieth and twenty-first centuries then
see the strong anthropogenic warming, although this occurs earlier in CESM
due to missing negative forcings from indirect aerosol effects
(Sect. ). Superimposed on the preindustrial long-term negative
trend are volcanic cooling events. In CESM many of these are global and are
able to considerably cool the SH extra-tropics around 60∘ S, while
in MPI-ESM the SH extra-tropics are only weakly affected. These differences
are likely related to the Southern Ocean heat uptake rates in the two models
arising from under- and overestimation of Southern Ocean mixed layer
depths in CESM and MPI-ESM, respectively;.
This is evident also in the delayed warming at these latitudes in the
twenty-first century in MPI-ESM as compared to CESM. The consistent SH high-latitude positive anomalies before the thirteenth century, on the other hand,
appear to be related to a positive phase of the Southern Annular Mode (SAM)
in both models (not shown), a behavior common to most PMIP3 models. Note,
however, that a recent reconstruction of the SAM finds the models to lack
amplitude in their simulated variability, challenging the models'
capabilities to represent SAM .
Regression of total solar irradiance (TSI) on surface air temperature
(SAT) for the period 850–1850 CE in (a) CESM and
(b) MPI-ESM. Time series at each grid point have been 5-year-filtered. Only significant regression coefficients at the 5 % level are
shown. The small panel shows zonal means.
The phasing on interannual to decadal scales between the two models is
largely restricted to periods of volcanic activity and, within those, mainly to
land-dominated latitudes (except Antarctica, which shows no forced
variability on these timescales; Fig. c). Despite the
largest absolute temperature anomalies occurring in the Arctic, the
correlations are highest in the subtropics, due to the smaller interannual
variability there. Periods of centennial trends, such as the MCA or the
Arctic cooling during the Maunder Minimum around 1700 CE, do not show up in
the correlation analysis that focuses on 100-year windows, suggesting
that multi-decadal low-frequency forcing, such as centennial TSI trends, or
internal feedback mechanisms are responsible for the missing correlation. A
regression analysis between the 5-year filtered annual TSI and SAT at each
grid point (different filter lengths of up to 50 years have been tested as
well without the results changing) reveals a clear link between the two quantities
at high latitudes. In CESM this link seems to be driven primarily by a
displacement of the sea ice edge (Arctic) and Southern Ocean heat uptake
(Fig. a). As the sea ice response has not been detected in an
earlier model version their Fig. 4, it warrants the
question of whether the regression of SAT on TSI might be biased by imprints of
volcanoes , even when the time series are filtered,
especially in a model like CESM that has a very strong volcanic imprint.
Forthcoming simulations with solar-only forcing will be able to answer that
question. MPI-ESM, on the other hand, shows a similar polar amplification
signal from solar forcing but is not as clearly linked to sea ice
(Fig. b). MPI-ESM also displays a stronger land–ocean contrast than
CESM see also.
Five-year-filtered zonal mean anomalies of horizontally averaged ocean temperature,
relative to 850–1849 CE from (a) CESM and (b) MPI-ESM.
Panel (c): 100-year running-window correlation of zonal mean SAT from CESM
and MPI-ESM. A 0.75 Tukey window has been applied to the data before
correlation to weaken sharp transitions. Stippling indicates significance at
the 5 % level, taking into account autocorrelation estimated from the
entire time period. Panel (d): 100-year running-window correlation of the
Atlantic meridional overturning circulation (AMOC) in CESM and MPI-ESM.
In addition to the comparison with MPI-ESM, Fig. d shows
results from the correlation analysis between CESM and CCSM4, two simulations
that in terms of physics differ only in their applied TSI amplitude and
orbital parameters. Not unexpectedly, there are generally more robust signals
of forced variability as compared to CESM vs. MPI-ESM
(Fig. c), very likely due to the identical physical model
components in CESM and CCSM4. Similarly, global mean SAT shows generally
stronger phasing between CESM and CCSM4 (Fig. e). However,
the latitudinal and temporal pattern of the CESM vs. CCSM4 analysis agrees
well with the one arising from CESM vs. MPI-ESM (Fig. c;
with exception of the much stronger phasing in CESM and CCSM4 during the
volcanic eruptions in the 1450s) and suggests that the physical mechanism behind
periods of phasing is robust across the two models.
Five-year filtered zonal mean anomalies of horizontally integrated dissolved
inorganic carbon (DIC), relative to 850–1849 CE, from (a) CESM and (b) MPI-ESM.
Panel (c): 100-year running-window correlation of zonal mean SAT from CESM and
MPI-ESM. A 0.75 Tukey window has been applied to the data before correlation
to weaken sharp transitions. Stippling indicates significance at the 5 %
level, taking into account autocorrelation estimated from the entire time
period.
Applied to ocean temperature, the above approach enables us to investigate
the penetration depth of a forced signal seen at the surface
(Fig. ). Indeed, most of the surface signals also show
up as significant correlations down to depths of about 150–200 m, and their timing again suggests volcanic forcing as the origin. Reduced heat loss
from the tropical equatorial Pacific
together with reduced heat uptake at high latitudes are responsible for ocean cooling
after volcanoes (not shown).
The Atlantic Meridional Overturning Circulations (AMOC) in the CESM and
MPI-ESM shows no significant correlation; however, the highest correlation
occurs during the thirteenth century and coincides with a phasing of the
upper-ocean temperatures due to strong volcanic forcing
(Fig. d). The correlation between CESM and CCSM4 at
that time is even higher and points to a significant imprint of the volcanic
forcing on ocean circulation
. However, during the
remainder of the millennium, no phasing of the AMOC is found.
Carbon cycle
We apply the same correlation analysis to zonally integrated land and ocean
carbon fluxes from the two models to detect forced variability in the carbon
cycle. Compared to SAT hardly any phasing can be found between the models in
atmosphere-to-land carbon fluxes (not shown), which is due to its large
interannual variability and to distinctly different responses to external
forcing in the two models, as will be illustrated in Sect. .
Similarly, there is little model phasing in net atmosphere-to-ocean carbon
fluxes (not shown). Results become somewhat clearer when considering globally
integrated upper-ocean dissolved inorganic carbon (DIC;
Fig. ). There appear to exist spurious trends in
CESM, likely related to model drift. We repeated the analysis, but with the
CESM output detrended in each grid cell by subtracting the CTRL over the
corresponding period 850–1372 CE. Due to the shortness of CTRL, we cannot
apply this method to the whole simulation. However, these tests showed that the
correlation between the two simulations is largely insensitive to the drift in
CESM. In Fig. c there are periods of coherent carbon
drawdown coinciding with volcanic eruptions around 1450 and 1815 CE in
response to temperature-driven solubility changes. Interestingly, MPI-ESM
shows a distinct behavior for the strong eruption of 1258 CE, with a
prolonged ocean carbon loss after a weak initial uptake. CESM shows a
stronger and more sustained carbon uptake, leading to no correlation between
the two models for this eruption. The reasons for this discrepancy are
discussed in Sect. .
Superposed epoch analysis of the strongest three (top3) and
subsequent strongest seven eruptions (top10) of the period 850–1850 CE in (a–e) CESM and
(f–j) MPI-ESM for (a, f) global mean surface air temperature, (b, g) global
mean precipitation, (c, h) atmospheric carbon given in Pg C on the left
y axis and in ppm CO2 on the right y axis, (d, i) ocean carbon, and
(e, j) land carbon. Time series are deseasonalized and calculated as anomalies
to the mean of the preceding 5 years. The shading shows the 10–90 % range.
Generally, the largest changes in upper-ocean carbon storage occur in
response to volcanoes and take place in the tropical Pacific
, with other significant changes occurring in the North
and South Pacific, the subtropical Atlantic, and the Arctic
(Sect. ). Within the tropical oceans, the models show different
characteristics: CESM shows a larger variability in DIC than MPI-ESM and,
when influenced by anthropogenic emissions in the twentieth and twenty-first
centuries, takes up a larger portion of the total ocean carbon uptake than in
MPI-ESM (not shown). In MPI-ESM, the Southern Ocean shows stronger
variability and larger carbon uptake in the twenty-first century,
illustrating the different behavior of the two models in terms of ocean
carbon cycle variability and trend magnitude, closely related to the
different mixed layer depths in the Southern Ocean region.
Volcanic forcing
To further isolate the response of the climate system and carbon cycle to
volcanic eruptions, a superposed epoch analysis is applied to both
simulations. Thereby, composite time series for the strongest three (top3)
and subsequent strongest seven eruptions (top10), according
to optical depth
anomaly, over the period 850–1850 CE are calculated for the CESM and
MPI-ESM (Fig. ). The time series are calculated as
deseasonalized monthly anomalies from the 5 years preceding an eruption.
The physical parameters' global mean surface air temperature and global mean
precipitation decrease in both models after volcanic eruptions, although the
response of CESM is stronger by roughly a factor of 2–2.5 (Fig. a,
b, f, g). Consequently, CESM temperature and precipitation take longer
(∼ 15 years) to relax back to pre-eruption values than MPI-ESM
(∼ 9 years).
The atmospheric carbon inventory, on the other hand, shows a remarkably
different response in the two models. In CESM the atmosphere initially looses
about 2–3 Pg C, irrespectively of the eruption strength, with the minimum
occurring after about 1–2 years. In the top10 case, values return to normal
after about 16 years, while in the top3 case, they tend to return already
after about 6 years and overshoot. This overshoot is not straightforward
to understand and did not seem to occur in earlier versions of the model
. In MPI-ESM the response is a priori
more straightforward and slower: in the top10 case the atmosphere looses
about 2.5 Pg C, reaches a minimum after 2–4 years, and returns to
pre-eruption values after 10–16 years. The top3 case reaches its minimum
(-6 Pg C) a bit faster, but then takes about 20 years to return to
pre-eruption values .
Partitioning these atmospheric carbon changes into land and ocean changes
indicates that land is primarily responsible for the differing response
behavior of the two models, confirming the findings in the previous section.
While in both models, land drives the atmospheric change by taking up carbon
initially, it is released back to the atmosphere within about 3 years in CESM
but retained in land areas for at least 15 years in MPI-ESM
and up to 50 years for the 1258 CE eruption;. In
the top3 case of CESM the land starts to even loose carbon after about
5 years, causing the overshoot seen in the atmospheric carbon.
Superposed epoch analysis of the strongest three (top3) and
subsequent strongest seven eruptions (top10) for tropical land (25∘ S to 25∘ N)
in CESM during the period 850–1850 CE. Land carbon inventory changes split up in
(a) vegetation, (b) dead biomass (litter and wooden debris), and (c) soil.
Furthermore, changes in (d) solar radiation, (e) net primary production (NPP), and
(f) loss of carbon through fire. Time series are deseasonalized and calculated
as anomalies from the mean of the preceding 5 years. The shading shows the
10–90 % range.
A closer look at CESM reveals a distinct response to the top3 and the top10
volcanoes. The response to top3 must be understood as the interaction of a
number of processes: the initial global cooling triggers a La Niña-like
response and a corresponding cloud and precipitation reduction that is
particularly pronounced over tropical land, where large changes in
carbon storage also occur (see Fig. a–c for the spatial pattern).
Figure and the following analysis therefore focuses on
tropical land. Direct solar radiation decreases, and indirect radiation
increases, with a net decrease (Fig. d). These
unfavorable conditions cause a reduction in net primary productivity and a
strong decrease in vegetation (-8 Pg C; Fig. a and
e). At the same time, decomposition of dead biomass becomes less efficient
due to reduced temperature similar to, e.g., the case in.
Despite the simultaneous decrease in net primary production this results in a
buildup of dead biomass of about 5 Pg C (Fig. b). Due
to the dry conditions and availability of dead biomass, there is increased
fire activity, leading to increased carbon loss from land. However, the fire
cannot get rid of the large amount of dead biomass immediately
(Fig. f). While vegetation decrease and dead biomass
buildup balance each other, the soil takes up about 2 Pg C
(Fig. c), stores it for at least 16 years, and is
therefore responsible for the initial net land uptake seen in
Fig. e (see also the left section of Fig. c). After about 2 years, tropical precipitation increases again and puts a halt to the decrease
in vegetation (Figs. a and b right).
The vegetation does not recover fully for about another 20 years. The dead
biomass, on the other hand, gets decomposed entirely within about 15 years
and therefore turns land into a carbon source, causing the overshoot in
CO2. In the top10 case, the precipitation and radiation response is
about half of the top3 case and so is the vegetation decrease. Consequently,
vegetation recovers more quickly. The decomposition of dead biomass, however, takes
about the same amount of time as in the top3 case as the decomposition rates
are similar for both cases. Hence, land acts as a more sustainable carbon
sink in the top10 case. In MPI-ESM it is the soil as well which acts as the main
land carbon storage pool, while the vegetation decrease is significantly less
than in CESM , leading to the different response
behavior of the two land models, particularly striking in the top3 case. Note
that there are subtle regional differences between CESM and the earlier
version of the carbon-cycle-enabled NCAR model CSM1.4 carbon
: tropical Africa sees a reduction in land carbon in
CESM, related to a persistent increase in cloud cover and precipitation after
volcanoes, while CSM1.4 carbon saw a decrease in precipitation and an
increase in land carbon.
Composites of top10 post-volcanic eruption years as anomalies from the
preceding 5 years, averaged over (left) the first 2 years starting with the
year of the eruption and (right) the following 3 years. (a) Surface air
temperature, (b) precipitation, (c) total land carbon, (d) dissolved inorganic
carbon (DIC) integrated over the top 200 m. Shading or stippling indicates
significance at the 5 % level. Note that for land carbon in an individual
grid cell hardly any significant changes are detected due to the large
interannual variability.
The ocean, on the other hand, shows a qualitatively similar response in CESM
and MPI-ESM with an uptake of carbon and a gradual relaxation back to
pre-eruption values over 20 or more years. In CESM the radiative cooling
leads to increased uptake in the Western Pacific, while in the Eastern
Pacific, cooling is less as this region is more controlled by upwelling
rather than direct radiative forcing, as suggested by
(Fig. d). Two or more years after the volcano, a La Niña-like
pattern settles in both surface temperature as well as carbon uptake. Some
model differences exist; e.g., in the top3 case of MPI-ESM, the ocean starts
to release carbon, compensating for the persistent positive anomaly in the land
inventory imposed on the ocean via atmospheric CO2
concentration;, a feature not present in CESM, in which
the land does not store the anomalous carbon for as long. In CESM the tropical
oceans appear to be more sensitive to volcanic forcing than to TSI
variations. The equatorial Pacific shows the strongest response in DIC to
volcanoes (Fig. d), while the response to TSI variations of
comparable radiative forcing is up to 1 order of magnitude weaker and
confined to higher latitudes (not shown). Overall, it seems, therefore, that the
response of the land vegetation governs the overall different responses in
the two models.
Global mean changes in response to Pinatubo. (a) Global mean surface
air temperature and (b) atmospheric carbon, as (left y axis) Pg C and (right y axis) ppm CO2 equivalent, both deseasonalized and linearly
detrended over 30 years centered on June 1991; temperature observations were
corrected for El Niño–Southern Oscillation and other dynamical components
; CO2 observations were corrected for El Niño–Southern
Oscillation and anthropogenic emissions .
In an attempt to validate the two models, one is restrained to the
well-observed eruption of Pinatubo in 1991 CE, as the CO2 records from
ice cores do not adequately resolve short-term variations induced by
volcanoes over the last millennium. Figure shows the global
temperature and atmospheric carbon response to Pinatubo as extracted from
observations, CESM, and the three-member ensemble of MPI-ESM. Note that the
effects of El Niño–Southern Oscillation and anthropogenic emissions have
been removed from the CO2 observations to obtain a tentative estimate of
the actual CO2 response to the Pinatubo eruption
. The initial cooling of about - 0.5 ∘C and
the relaxation back to initial temperatures around 1998 CE is captured well
by both models. The MPI-ESM ensemble, however, shows a large and
consistent variation around 1995 CE, seemingly related to a phasing of El Niño–Southern Oscillation (ENSO) variability
in response to the eruption see also. Further, the
magnitude of atmospheric carbon response matches better in CESM, although the
overshoot of the observation-based estimate is not captured. CESM's response
also falls within the range of the earlier model version
. It remains unclear whether this mismatch
reflects a model deficiency or is due to uncertainties arising from removing
the ENSO signal from the CO2 observations. However, the mechanisms
described above that lead to an atmospheric CO2 overshoot for large
eruptions in CESM offer an opportunity to resolve this
discrepancy. Further, the precipitation response (and therewith the cloud
and surface shortwave response) to volcanic eruptions is not well
constrained due to the small number of observed eruptions
. Biases in the representation of these processes can
influence a model's carbon cycle response.
Climate–carbon-cycle sensitivity
Due to the absence of large anthropogenic disturbances of the carbon cycle,
the last millennium represents a test bed to estimate the
climate–carbon-cycle sensitivity, expressed as ppm ∘C-1, and
can thus potentially help to constrain this quantity
e.g.,.
Note, however, that there exist important differences between studies in how
this sensitivity is calculated and what it implies. Studies using
observations and fully coupled simulations
estimate the sensitivity from the ratio of changes in CO2 to changes in
temperature. This quantity incorporates feedbacks into the
response as the initial
sensitivity of the carbon cycle to climate change modifies itself via the
climate change that arises from the changed carbon cycle. This is distinct
from the climate–carbon-cycle feedback sensitivity γ, which uses
idealized simulations with atmospheric CO2 held constant, while the
climate varies naturally to isolate the feedback parameter
. The sensitivity that can be derived from our CESM
transient simulation is subtly different again in that the carbon cycle will
respond to changes in climate and this response will feed back on the carbon
stocks through increased or decreased atmospheric CO2 concentrations,
yet these changes in CO2 are not allowed to feed back on the climate.
Such a sensitivity is expected to be lower than γ, which we can derive
from CTRL.
Here, we estimate the climate–carbon-cycle sensitivity for CESM as follows.
We focus on the period before significant LULUC (850–1500 CE) and apply
different low-pass filters of 20 to 120 years, taking 5-year increments, to
the time series of NH SAT and global CO2. The filtering aims at
minimizing the influence of short-lived forcings such as volcanic eruptions
that have a relatively direct impact on temperature and CO2 (as seen
above) and thus may hinder the detection of a low-frequency influence of
temperature on CO2. For each filter length, we determine the highest lag
correlation of the two time series, considering lags of up to 100 years. Due
to the design of our simulation, we expect NH SAT to lead CO2, which is
confirmed by all lag correlations indicating positive lags for NH SAT (peak
of lag correlation at 80.5 ± 3.4 years). We regress the lagged time
series and find a median estimate of 1.3 ppm ∘C-1 with a range
from 1.0 to 1.8 ppm ∘C-1, depending on the filter length.
About -1 ppm ∘C-1 is explained by the land carbon cycle,
while the ocean shows smaller sensitivities of about
-0.4 ppm ∘C-1. Note that we use NH SAT in order to be
comparable with existing studies . Using
global SAT instead of NH SAT can influence the sensitivity estimate,
especially for the forced simulation: including the
vast ocean area of the SH tends to dampen temperature variability induced by
volcanoes and TSI variations. With temperature variability dampened, the
sensitivity increases to 1.7 ppm ∘C-1 (1.4–2.1).
Temporal dependence of the climate–carbon-cycle sensitivity
in CESM. Normalized probability density functions (PDFs) of
climate–carbon-cycle sensitivity for 200-year windows overlapping by 50 years (color-filled)
for the full period 850–1500 CE (black solid) and for the CTRL (black
dashed). The spread of each PDF arises from the range of low-pass filters
applied (20 to 120 years).
This estimate is barely within the reconstruction-constrained range of
1.7–21.4 ppm ∘C-1 and suggests a
comparably low sensitivity of the carbon cycle in CESM. This low sensitivity
is in agreement with, e.g., . Note that
found different sensitivities for the early and late part of the last
millennium with the mean for the period 1050–1549 CE being
4.3 ppm ∘C-1. Indeed, a strong temporal dependence of the
climate–carbon-cycle sensitivity is also found in CESM when looking at
individual 200-year windows (Fig. ). The period 1300–1500 CE
even shows negative sensitivity, which seems to be related to the different
timescales with which SAT and CO2 relax back to the pre-eruption
conditions after perturbations from large volcanic eruptions
(Fig. a and c): atmospheric CO2 decreases after having overshot, while SAT increases after the initial cooling, leading to a
negative correlation of the two quantities.
This illustrates the time-variant character of the climate–carbon-cycle
sensitivity, which substantially complicates any attempt to constrain it by
last millennium data and warrants caution when making inferences from past to
future sensitivities. Besides, and
found the sensitivity to vary greatly in a coupled model with the timescale
and magnitude of volcanic forcing considered. This issue is further
highlighted by the larger sensitivity derived for idealized
+1 % CO2 year-1 simulations with CESM
(11.9 ppm ∘C-1), for which a dependence on the background
state, the scenario, and even the method is reported
. Further, it is worth stressing that such
sensitivity estimates cannot be extrapolated easily across timescales, as
different processes might be at play .
Applying the identical analysis to CTRL reveals other timescales of
climate–carbon-cycle feedback, suggesting maximum lags of less than 10 years
and a sensitivity of 2.3 (1.4–2.9) ppm ∘C-1. Using global SAT
instead of NH SAT has no discernible effect (2.3 ppm ∘C-1), as
the CTRL does not see volcanoes or TSI variations. A later peak in the lag
correlation of NH SAT and CO2 is found at 73.3 ± 1.1 years in
CTRL, i.e., close to where the forced simulation shows its highest lag
correlation, but these lag correlations are much weaker (r∼ 0.4
compared to r∼ 0.7 in the forced simulation). This is generally
consistent with the finding by that a forced simulation
exhibits increased power on lower frequencies compared to a control
simulation.
Discussion and conclusions
This study presents a simulation from 850 to 2100 CE with the fully coupled
CESM, including the carbon cycle, and provides an overview of the imprint of
external forcing on different climate and carbon cycle diagnostics in the
simulation. For comparison we draw on a number of PMIP3 simulations,
particularly simulations with CCSM4 and MPI-ESM. The evolution of
NH SAT during the preindustrial era in CESM is in reasonable agreement with
both reconstructions and other models, albeit the uncertainties in
reconstructions and forcing still being considerable. Compared to more
reliable data in the twentieth century, the anthropogenic warming in CESM is
overestimated due to a lack of negative forcing from indirect aerosol
effects. In the SH, CESM and most other models do not capture the evolution
of the mean SAT as well. The discrepancies could be explained by
(i) significant model biases in SH and also interhemispheric SAT variability
, (ii) spectral biases in proxies used in the
reconstructions , (iii) uncertainties in the external
forcing , or (iv) natural internal variability
. Unfortunately, these potential explanations are neither
exclusive nor independent. Arguments for model bias come from the fact that
reconstructed interhemispheric SAT variability lies outside the models' range
over 40 % of the time ; but these arguments are
weakened by the uncertainty in external forcing. We show here that
implementing the same TSI forcing in two different models results in a larger
difference in simulated SAT than implementing two different TSI forcings in
the same model. Hence, model structural uncertainty remains an issue in
determining the role of external forcing over the last millennium.
Albeit beyond the scope of this study, detecting structural and spatial
dependencies such as those illustrated here offers an opportunity to reconcile the
discrepancies (e.g., regarding SH volcanic signals) between reconstructions
and simulations, which might originate from sampling bias, model
deficiencies, a combination of these, or the fact that reality may be the one
realization that, by chance, is not encompassed by a multi-model ensemble
.
Further, we compare simulations with and without orbital forcing and were not able to
attribute northern high-latitude SAT trends over the last millennium to
orbital forcing. This hampers, if not challenges, the validation of recent
findings based on proxy archives that claim a distinct low-frequency orbital
component in millennial trends . Instead,
the decreasing trend in annual TSI – as opposed to seasonal and regional
insolation – together with local feedbacks is able to account for a similar
magnitude of trend.
When forced with emissions from LULUC, TSI variations, and volcanic eruptions
over the last millennium, both CESM and MPI-ESM do not reproduce atmospheric
CO2 variability, as suggested by ice cores. Notably, the large drop of
CO2 in the seventeenth century is not reproduced, similar to the case in earlier
studies .
hypothesized that the unique, globally synchronous cooling during the LIA
(which might be related to ocean dynamics) can serve as an explanation for
this drop. While both CESM and MPI-ESM show a global cooling during the LIA,
they develop no apparent phasing of ocean dynamics or carbon uptake and do
not show any marked CO2 reduction around that time, leaving this issue
unresolved. The strong volcanic forcing during the thirteenth century, on the
other hand, is able to synchronize the AMOC on decadal scales, confirming
similar results from the Bergen Climate Model and IPSL-CM5A-LR
. With anthropogenic emissions, land
and ocean carbon uptake rates emerge from the envelope of natural variability
as simulated for the last millennium by about 1947 and 1877 CE,
respectively. Atmospheric CO2 and global temperature emerge by 1755 and
1966 CE, suggesting that changes in carbon-cycle-related variables would be
easier to detect than temperature, given sufficient observational data
.
We find forced decadal-scale variability in CESM and MPI-ESM in response to
major volcanic eruptions in both SAT and upper-ocean temperature, while the
response in carbon cycle quantities is less coherent among models see
also. Outside volcanically active periods, large parts of
the decadal-scale variations cannot be attributed to external forcing,
suggesting that internal variability masks external forcing influence. Note,
however, that recent work suggests that small volcanic eruptions, which are
typically not well-resolved in reconstructions of volcanic activity, exert a
significant cumulative effect on global temperature and climate
.
Volcanoes trigger a coherent global response in SAT and precipitation that is
qualitatively in line with earlier studies on the volcanic influence on
climate and the carbon cycle
e.g.,.
However, the carbon cycle response, in particular on land, shows fundamental
model differences in terms of perturbation amplitude and persistence after
volcanic eruptions. These differences arise from a differing land vegetation
responses in the two models. The extent to which such structural
uncertainties matter is illustrated by the large spread in the airborne
fraction of CO2 between these two (and other) models in the twenty-first
century see also. In particular, known biases
in CESM's carbon uptake in response to anthropogenic emissions in the
twentieth and twenty-first centuries lead to a 20 % overestimation of the
atmospheric CO2 concentration and the corresponding prognostic radiative
forcing as compared to the prescribed RCP8.5 at year 2100 CE.
The climate–carbon-cycle sensitivity of CESM as estimated from the
anthropogenically unperturbed first part of the last millennium is between
1.0 and 2.1 ppm ∘C-1, depending on the filtering and
the exact time period considered. Generally, the sensitivity of the carbon
cycle to temperature variations in CESM is comparably small
and reveals a strong component of unforced natural
variability. In a transient last-millennium simulation with small temperature
variations, the proper detection of a lead–lag relation between temperature
and the carbon cycle is complicated by the superposition of perturbations and
responses. In addition to the classic climate–carbon-cycle sensitivity
experiments e.g.,, it is therefore desirable to conduct
step-function-like sensitivity experiments in order to isolate the response
of the carbon cycle to a particular external forcing .
Despite the challenges that paleoclimate modeling faces, a number of lessons
regarding forcing and structural uncertainties can be learned from these
experiments. In order to better understand the role of internal versus
externally forced variability – which remains particularly critical for a
period of relatively weak external forcing, such as the last millennium –
larger simulation ensembles as well as ensembles with decomposed forcing
should become standard in paleoclimate modeling. Since these are
computationally expensive simulations, this calls for an informed discussion
on the optimal usage of computing resources, to which studies like the one
here can contribute valuable information. At the same time, uncertainties in
forcings and reconstructions need to be further reduced to be able to better
validate models in the past with the goal of constraining their future
response. Key targets for such constraints are the sensitivity of temperature
to solar and volcanic forcing and the climate–carbon-cycle sensitivity.
Acknowledgements
We gratefully acknowledge Axel Timmermann, Bette Otto-Bliesner, Peter
Lawrence, and Rosie Fisher for valuable discussions as well as four anonymous
reviewers for very helpful comments. We are grateful to the NCAR in Boulder,
USA, for providing the code of the CESM, to the World Climate Research
Programme's Working Group on Coupled Modelling, which is responsible for
CMIP, and to the climate modeling groups for producing and making available their
model output. This study is supported by the Swiss National Science
Foundation (grant no. 200020 147174), and the European Commission through Seventh
Framework Program (FP7) projects CARBOCHANGE (grant no. 264879) and
Past4Future (grant no. 243908). J. Mignot has benefited from the support of
the French Agence Nationale de la Recherche (HAMOC: ANR 13-BLAN-06-0003). The
simulations for this study were performed on a CRAY XT5 and XE6 at the Swiss
National Supercomputing Centre (CSCS) in Lugano. Edited by: G. Bala
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