Holocene Special Issue
Comparisons of atmosphere–ocean
simulations of greenhouse gas-induced
climate change for pre-industrial and
hypothetical ‘no-anthropogenic’ radiative
forcing, relative to present day
The Holocene
21(5) 793­–801
© The Author(s) 2011
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DOI: 10.1177/0959683611400200
hol.sagepub.com
J.E. Kutzbach,1 S.J.Vavrus,1 W.F. Ruddiman2 and
G. Philippon-Berthier1
Abstract
We compare climate simulations for Present-Day (PD), Pre-Industrial (PI) time, and a hypothetical (inferred) state termed No-Anthropogenic (NA)
based upon the low greenhouse gas (GHG) levels of the late stages of previous interglacials that are comparable in time (orbital configuration) to the
present interglacial. We use a fully coupled dynamical atmosphere–ocean model, the CCSM3. We find a consistent trend toward colder climate (lower
surface temperature, more snow and sea-ice cover, lower ocean temperature, and modified ocean circulation) as the net change in GHG radiative forcing
trends more negative from PD to PI to NA. The climatic response of these variables becomes larger relative to the changed GHG forcing for each step
toward a colder climate state (PD to PI to NA). This amplification is significantly enhanced using the dynamical atmosphere–ocean model compared with
our previous results with an atmosphere–slab ocean model, a result that conforms to earlier idealized GHG forcing experiments. However, in our case
this amplification is not an idealized result, but instead helps frame important questions concerning aspects of Holocene climate change. This enhanced
amplification effect leads to an increase in our estimate of the climate’s response to inferred early anthropogenic CO2 increases (NA to PI) relative to the
response to industrial-era CO2 increases (PI to PD). Although observations of the climate for the hypothetical NA (inferred from observations of previous
interglacials) and for PI have significant uncertainties, our new results using CCSM3 are in better agreement with these observations than our previous
results from an atmospheric model coupled to a static slab ocean. The results support more strongly inferences by Ruddiman concerning indirect effects
of ocean solubility/sea-ice/deep ocean ventilation feedbacks that may have contributed to a further increase in late-Holocene atmospheric CO2 beyond
that caused by early anthropogenic emissions alone.
Keywords
anthropogenic change, climate models, climate sensitivity, early agriculture, greenhouse warming, past climate, pre-industrial climate
Introduction
Great attention is being given to possible changes of climate in
the next 50–100 years in response to continuing increases in the
concentration of greenhouse gases (GHGs) (Intergovernmental
Panel on Climate Change (IPCC), 2007). There is also interest in
establishing how much the GHG increases of the industrial-era
have already influenced the climate of the past 100–200 years
(IPCC, 2007; Meehl et al., 2004). This topic of past changes has
led to simulations of the climate of the pre-industrial (PI) period
with a variety of models (Otto-Bliesner et al., 2006a; Sedlacek
and Mysak, 2008a, b; Vavrus et al., 2008). PI climate simulations
employ reduced net radiative forcing based on estimates of GHG
concentrations prior to the time of their rapid increase. The simulated PI climate, with lowered GHGs, is typically 1–1.5K colder
than present-day (PD) climate simulations. This simulated difference is larger than the estimated observed difference (0.7–1.2K
colder), where the substantial range, 0.5K, reflects both measurement and sampling uncertainties especially for early periods
extending back to 1850 (IPCC, 2007; Jones and Mann, 2004;
Jones et al., 1999). (Our choice of 1850 for the time of PI, the time
when rapid increase in industrial-era GHGs commenced, is
described in the Appendix.)
Interest in explaining the cause of GHG changes and estimating their role in climate change now extends back to the past several thousand years. Ruddiman (Ruddiman, 2003, 2008;
Ruddiman et al., 2011, this issue) has established that the trends in
GHGs in the Holocene differ significantly from the trends in
GHGs in similar stages of previous interglacials. Whereas CO2
and CH4 trend downward in varying amounts following Northern
1
University of Wisconsin-Madison, USA
University of Virginia, USA
2
Received 23 March 2010; revised manuscript accepted 11 November 2010
Corresponding author:
J.E. Kutzbach, University of Wisconsin-Madison, Center for Climatic
Research, Nelson Institute for Environmental Studies, 1225 W. Dayton
Street, Madison WI 53706, USA.
Email: [email protected]
794
Hemisphere (NH) summer insolation maxima in previous interglacials, the downward trend in CO2 and CH4 stops around 7000–
5000 years ago in this interglacial (the Holocene) and thereafter
the trend is upward. The estimated difference in GHG levels
between the late stages of previous interglacials (roughly comparable in orbital forcing to the present) and the GHG levels of the
present day is termed a state of ‘No-Anthropogenic’ (NA) GHG
forcing. In response to the question: Why do GHGs trend upward
in the current interglacial but trend downward in similar stages of
previous interglacials?, Ruddiman (2003, 2008) proposed that
early agriculture and animal husbandry was the explanation, and
developed estimates of the likely magnitude of these anomalous
emissions in the period prior to the industrial revolution – see
Kaplan et al. (2010, this issue); Yu (2011, this issue), Fuller et al.
(2011, this issue), and Ruddiman et al. (2011, this issue) for the
most recent estimates. Alternatively, if human activities are not
the cause of this atypical upward trend in GHGs in the late
Holocene, then an alternative natural explanation (or process) is
required to explain both the upward trends of this interglacial and
the absence of such trends in previous interglacials (Ruddiman,
2008; Ruddiman et al., 2011, this issue).
Our previous modeling studies using reduced GHG forcing
examined the climatic response to the GHG trends observed in earlier interglacials for various model configurations including atmosphere–static slab ocean (Ruddiman et al., 2005), atmosphere–static
slab ocean with interactive terrestrial vegetation (Vavrus et al.,
2008), and dynamical atmosphere–ocean (Kutzbach et al., 2010).
The experiments with interactive terrestrial vegetation exhibit a
positive feedback in high latitudes related to snow cover/vegetation
interaction that enhances the cooling simulated with atmosphere–
ocean models.
We have not yet included two other potentially important biogeophysical factors in our simulations. The biogeophysical
impact of changes in land cover associated with human activities
(such as the albedo increase from deforestation associated with
the spread of agriculture) has not been included because there has
been ongoing work to estimate the potentially large changes in
area of land-use per capita among early agriculturalists compared
with present day – this larger-than-present agricultural ‘footprint’
of earlier times could influence the climatic impact of early agriculture even although the population was relatively small (Kaplan
et al., 2009, 2010 (this issue); Ruddiman and Ellis, 2009). We
intend to include these new estimates of the agriculture ‘footprint’
in future simulations. One recent modeling study simulated the
combined biogeophysical and biochemical (CO2) climatic effects
of anthropogenic land cover change (ALCC) in the past millennium (Pongratz et al., 2010). They found that the higher surface
albedo caused by the ALCC change produced a slight global cooling (−0.03K) whereas the CO2 rise of about 18–20 ppm led to a
strong warming (0.18K); regionally, such as in Europe, China,
and eastern North America, the biogeophysical change alone produced a more pronounced cooling – but the combination of the
increased surface albedo and the CO2 rise caused a net warming
everywhere. Finally, we have not yet experimented with models
with interactive ocean biogeochemistry, a topic mentioned briefly
in the Discussion and conclusions.
This paper addresses only three items within this large overall
topic: (1) the characteristics of the climate simulated by coupled
dynamical atmosphere–ocean models for lowered levels of GHG
forcing associated with PI and NA (relative to PD); (2) the magnitude of the climatic response caused by the decreased GHG
The Holocene 21(5)
forcing from PD to PI relative to the corresponding response from
PI to NA; and (3) some implications of these results for oceanrelated CO2 feedbacks and for comparisons of simulations and
observations.
Simulations, models, and
boundary conditions
We compare selected results from two publications describing
three climate simulations: Present Day (PD), the time around
1850 (PI), and the state of ‘No Anthropogenic’ forcing (NA). The
NA simulation (Kutzbach et al., 2010 – where NA was called
NOANTHRO), the PI simulation (Otto-Bliesner et al., 2006a)
and the PD (modern control) simulation are described in greater
detail in the original publications. The comparison of these three
simulations will allow us to partition the relative climatic response
to changed GHG forcing (from NA to PI, and from PI to PD). This
partitioning will permit new insights concerning the climatic
response to GHG forcing for two climate states (NA and PI) that
are colder than PD. All three simulations (PD, PI, NA) are made
with the NCAR Community Climate System Model version 3
(CCSM3), a fully coupled dynamical atmosphere–ocean model
(Collins et al., 2006a); the resolution of the atmospheric model is
T42. The land cover model employs prescribed present-day vegetation (Dickinson et al., 2006).
The CCSM3 has biases that are relevant to studies of the
response of Northern Hemisphere temperature/snow to reduced
radiative forcing (Kutzbach et al., 2010; Vavrus et al., 2008). In
middle to subpolar latitudes, summer temperatures are biased
1–2K cold and winter temperatures are biased 0–1K warm (zonal
averages). The summer cold bias is large in some areas, for example, as much as 2 to 6K over parts of northeastern Siberia and
northwestern North America. However, with regard to snow
cover, these seasonal surface temperature biases compensate at
least partially such that the simulated extent of residual snow
cover during summer is fairly realistic. Nevertheless, cold-biased
control climates can influence the sensitivity of snow cover
changes to reduced insolation (orbital) forcing as demonstrated
by Vettoretti and Peltier (2003a, b). On the other hand, a model
intercomparison of six coupled atmosphere–ocean models for the
last glacial maximum (LGM) showed that the response of CCSM3
to LGM forcing changes (ice sheet, GHG, and orbital changes)
was similar to that of the other five models for large-scale measures such as global average and Northern Hemisphere average
surface temperature (Braconnot et al., 2007). We will return to
this topic of model bias in the section ‘Results’.
The concentrations of five GHGs for the three simulations
(PD, PI, NA) are listed in Table 1 along with the associated net
change in GHG radiative forcing relative to PD: −2.05 W/m2 for
PI and −3.06 W/m2 for NA. The GHG values for CO2 and CH4 for
the hypothetical (inferred) state NA are based on the levels found
at the late stages of previous interglacials that are comparable in
time (orbital configuration) with the present interglacial. For CO2,
this value was initially estimated to be 240–245 ppm based on
examination of three previous interglacials (Ruddiman, 2003).
We picked the lower value (240 ppm) for our modeling studies,
begun in 2004, and have continued to use that value for consistency over a suite of sensitivity experiments with various model
configurations of increasing components and complexity. Estimates of GHG gases for this hypothetical state NA are now based
on seven previous interglacials, rather than three, and now yield
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Kutzbach et al.
long-term mean value). Because of the relatively small presentday orbital eccentricity and near average axial tilt, the present-day
Northern Hemisphere summer insolation minimum is not as
extreme as in the comparable stages of previous interglacials (the
combined effects of these two orbital forcing factors are illustrated for the present interglacial and seven previous interglacials
using caloric summer half year insolation in Kutzbach et al.,
2010). Reduced GHG forcing (values lower than PI) has been
shown to augment the effects of low summer insolation – due to
orbital (astronomical) forcing – on summer temperature and snow
cover (Vettoretti and Peltier, 2004); this combination of low
Northern Hemisphere summer insolation forcing and reduced
GHG forcing is present in both the PI and NA simulations.
For reasons noted in the Introduction, we use the same prescribed vegetation in all three experiments.
Table 1. Greenhouse gas concentrations in PD, PI, and NA
simulations, and the associated change in net radiative forcing
relative to PD (the control). Refer to the Appendix for discussion of
additional changes in radiative forcing in PI
Greenhouse gases
PD
PI
NA
CO2 (ppm)
CH4 (ppb)
N2O (ppb)
CFC11 (ppt)
CFC12 (ppt)
Radiative forcing change
relative to PD (W/m2)
355
1714
311
30
50
–
280
760
270
0
0
−2.05
240
450
270
0
0
−3.06
an NA estimate of ~250 ppm for CO2 (Ruddiman et al., 2011, this
issue). The NA value for CO2 will be adjusted upward to 245 ppm
in our future simulations for NA. However, a sensitivity experiment by Vavrus et al. (2008) found that using 250 ppm rather than
240 ppm for CO2 (with no changes in other GHGs) produced only
minor differences in the simulated NA climate. The PI simulation
by Otto-Bliesner et al. (2006a) included other small changes in
forcing but the net effect of these changes is assumed to be small
(see Appendix). The length of the simulations, the simulation procedures, and the averaging intervals for each experiment are summarized in the Appendix.
We use modern (PD) orbital parameters in all three simulations. (The orbital changes and associated insolation differences
between PD and PI are trivially small.) The use of modern (PD)
orbital parameters is consistent with the assumption that the
hypothetical climate state NA has GHG levels appropriate to the
late stages of previous interglacials – i.e. times with a Northern
Hemisphere summer insolation minimum (aphelion in northern
summer). The present-day Northern Hemisphere summer insolation minimum (aphelion in northern summer) is not as extreme as
previous minima because the orbital eccentricity is small –
Ruddiman et al. (2011, this issue) and Kutzbach et al (2010). Low
obliquity (small axial tilt) has also been shown to contribute to
cooler summers and lower annual-average temperature in high
latitude (Phillipps and Held, 1994, Vettoretti and Peltier, 2004);
the present-day obliquity is not at a minimum (but instead near its
Results
We focus on four indicators of the simulated climate changes
associated with lowered GHG concentrations: global average and
polar temperature, snow cover, sea-ice cover, and several ocean
variables (Table 2 and Figures 1 and 2). Changes in these variables for PI and NA, relative to PD, are given in absolute terms or
percents. The standard deviations (or coefficients of variation) for
these variables for PD are listed in Table 2. The differences
between the experiments are large relative to these indicators of
variability within PD (and PI and NA, not shown).
Temperature and snow cover
The global annual average surface temperature for PD is 14.7°C.
With the lowered GHG concentrations, the temperature is lowered
to 13.5°C (PI) and 12.0°C (NA). Relative to these changes in global temperature, the annual temperature changes over polar and
subpolar lands are larger (Table 2). With lowered temperatures, the
annual-average area of NH snow cover increases by 13% (PI) and
29% (NA), and the area of year-round snow cover increases by
71% (PI) and 129% (NA). The regions of year-round snow cover
are limited mainly to Greenland and Ellesmere Island in PD but they
expand to northern Baffin Island, and parts of Alaska, eastern Siberia, and the Eurasian Arctic in PI; the area of year-round snow cover
increases further in each of these four regions in NA (Figure 1). The
Table 2. Changes in selected global and regional climate variables, annual average, for NA-PD, NA-PI, and PI-PD. Percentage changes are
calculated relative to PD. Arctic and Antarctic are defined as the area poleward of 60 degrees. Control (PD) interannual standard deviations
(for temperature, K) and coefficients of variation (for areas, % – the ratio of the interannual standard deviation to the mean, in percent) are
small relative to the differences between experiments. Global ocean temperature standard deviations (not shown) are very much smaller than
the global surface temperature standard deviations
Climate variables
2
Net longwave radiative forcing (W/m )
Global surface temperature (K)
Arctic surface temperature (K)
Antarctic surface temperature (K)
N. H. land snow area (%)
N. H. permanent snow area (%)
N. H. sea ice area (%)
S. H. sea ice area (%)
Global ocean temperature (K)
Global ocean temperature between 0 and 1km (K)
Global ocean temperature between 1km and the bottom (K)
NA-PD
NA-PI
PI-PD
−3.06
−2.74
−7.16
−4.08
29.1
129.0
48.8
44.0
−1.25
−1.39
−1.19
−1.01
−1.55
−4.07
−2.77
16.5
58.3
28.2
32.3
−0.88
−0.79
−0.90
−2.05
−1.19
−3.09
−1.31
12.6
70.7
20.6
11.7
−0.37
−0.60
−0.29
PD: s, CV
0.07
0.36
0.42
2.5
14.0
1.2
3.3
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Figure 2. Annual average meridional overturning circulation
(MOC) in Sv as a function of latitude and depth for PD, PI, and NA
Figure 1. Months of snow cover for PD, PI, and NA.Year-round
snow cover is shown in white. See text for discussion of fractional
area of snow cover within each white grid square
129% increase in area (NA) indicates that the total area of yearround snow extends over more than twice the corresponding area
of Greenland/Ellesmere Island in PD. The areas of increased
year-round snow cover correspond well to areas with expanded
mountain glaciers during the ‘Little Ice Age’ (Kutzbach et al.,
2010), an indication that the simulations produce increased yearround snow cover in regions that have seen glacial expansion in
the past (see also Mysak, 2008). The area of snow cover in these
simulations is less than the area of the white grid squares in Figure
1 because CCSM3 includes an algorithm that translates average
snow depth for a grid square into ‘fractional snow cover’. This
algorithm is based on the idea that if a grid square has varying
small-scale topography and surface characteristics, then it is likely
to be only partially snow-covered if the average snow depth of the
square is small. In Figure 1, we show the entire grid square as
‘white’ if it has permanent year-round snow of sufficient depth so
that at least 5% of the area of the square would be snow covered.
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As mentioned in the section ‘Simuations, models, and boundary
conditions’, the modern control climate of CCSM3 (PD) has
biases in summer and winter land surface temperature that could
influence its seasonal snow climatology, although its annual average and summertime snow cover climatology agrees reasonably
well with observations (Kutzbach et al., 2010; Vavrus et al., 2008).
Nevertheless, this cold bias in summer land temperature in PD
could have influenced the sensitivity of the model to lowered GHG
forcing. Indeed, two regions of large cold bias in PD, northwestern
North America and eastern Siberia, are regions of significantly
increased snow cover in PI and NA (Figure 1).
Sea-ice cover, ocean temperature, and ocean
circulation
Annual average sea-ice cover increases in both hemispheres: in
the NH by 21% (PI) and 49% (NA), and in the SH by 12% (PI)
and 44% (NA). The global and vertically averaged ocean temperature changes by −0.37K (PI) and −1.25K (NA), both relative
to PD. While the vertical and latitudinal distribution of the ocean
temperature change is not uniform, the cooling occurs at all
depths and all latitudes (Table 2, and see latitude–depth ocean
temperature change, Kutzbach et al., 2010: figure 6).
The lowered ocean temperature and expanded sea ice cover
(Table 2) are linked to changes in latitude–depth distribution of
ocean salinity in PI and NA (not shown, but see Kutzbach et al.,
2010; Otto-Bliesner et al., 2006b). The expanded area and thickness of sea ice enhances the salt flux to the ocean in the ice formation regions, particularly in the region surrounding the Antarctic
continent. The colder and more saline surface water sinks to the
deep ocean and flows northward at depth. In contrast, and except
for these sea ice formation zones of higher surface salinity, the
upper ocean is generally less saline (less evaporation from the
tropical and subtropical oceans, melting of sea-ice in ice export
regions, and, in the Northern Hemisphere, reduced northward
transport) (Kutzbach et al., 2010; Sedlacek and Mysak, 2008a, b).
These changes in water flow are apparent in changes of the
meridional overturning circulation (MOC) for the three simulations: PD, PI, and NA (Figure 2). The narrow Antarctic Cell (area
shown in blue centered near 70S) intensifies and elongates (−4.7
Sv in PD, −5.9 Sv in PI, −6.6 Sv in NA) as the cold, saline water
sinks, and the large Antarctic Bottom Water (AABW) cell (area
shown in blue centered at a depth of 3–4 km) intensifies (−16.9 Sv
in PD, −21.2 Sv in PI, −24.7 Sv in NA) as this water flows north
at depth. In contrast with these two major changes, the Deacon Cell
(area shown in red and orange centered near 45S) weakens only
slightly, but shifts slightly equatorward and retracts at depth. The
North Atlantic cell intensifies slightly in PI and weakens in NA
(relative to PD). (The North Atlantic cell, averaged for the Atlantic
basin only, is not shown; however a somewhat similar response in
the North Atlantic was reported by Otto-Bliesner et al., 2006b,
where the North Atlantic overturning increased slightly in PI but
decreased significantly at the LGM.) These changes in the North
Atlantic in our simulations are reflected to some extent in the
changes in global average MOC in the NH (Figure 2) (areas shown
in yellow and green north of 30N). Changes in the global average
MOC from PD to NA are in the same direction as those reported
by Stouffer and Manabe (2003) for simulations with CO2 forcing
of 300 ppm and 150 ppm, respectively.
The northward transport of heat by the ocean decreases
slightly in PI and decreases by about 10% in NA (relative to PD)
especially in the NH (not shown). This decrease may contribute to
NH sea ice expansion (Table 2).
Carbon dioxide feedbacks
The changes in temperature, sea ice, and the MOC for NA (relative
to PD), summarized above, may in turn cause further changes in
the atmospheric concentration of CO2 (Kutzbach et al., 2010).
First, the lowering of ocean temperature by 1.25K (Table 2) (NA,
compared with PD) increases the solubility of CO2 in the ocean and
would be associated with a reduction in atmospheric concentration
of CO2 of ~12.5 ppm (using the lower-end conversion factor of 10
ppm CO2 per 1K described in Martin et al., 2005). The ocean cooling of 0.88K between PI and NA (Table 2) is, correspondingly,
equivalent to a CO2 reduction of ~9 ppm. This change, 9 ppm, is
almost twice our previous estimate (5 ppm, Kutzbach et al., 2010)
and is explained as follows. The change of 9 ppm is based upon the
large simulated change in global ocean temperature from NA to PI
(0.88K), whereas our previous estimate of a 5 ppm change was
based upon attributing only 0.38 of the global ocean temperature
change to the period from NA to PI (~0.5K). The small fraction,
0.38, was the fractional change in global slab ocean temperature
from NA to PI that we had calculated using the CAM3+SO model;
i.e. the different results stem from the different GHG sensitivity of
the slab ocean model compared with the full ocean model (section
‘Comparison of size of climatic responses of PI and NA, relative
to PD’). Second, the increased sea-ice cover and the reduced
upward motion from the deep ocean near 60S (Figure 3, Table 2)
could have reduced the ventilation of CO2 from the deep ocean to
the atmosphere – as discussed in the Introduction.
An increase in global mean salinity could decrease ocean solubility of CO2. However the mean salinity changes in PI (~0.005%)
and NA (~0.01%) were extremely small because this version of
CCSM3 largely omitted the important contribution to salinity
change from snow buildup on land by capping snow depth
increase at 1 m water equivalent (and in any case the simulation
was relatively short for allowing substantial snow/ice buildup on
land – see Appendix). However, even for the last glacial maximum when the observed increase in salinity was several percent
(Adkins et al., 2002) the salinity-related effect on CO2 solubility
due to greatly enlarged continental ice sheets was considerably
smaller in magnitude (and of opposite sign) compared with the
effect of the much colder glacial age ocean (Sigman and Boyle,
2000). Therefore any salinity-related change in CO2 solubility in
PI or NA would be extremely small relative to the solubility effect
of the simulated change in ocean temperature described above.
We return to further discussion of CO2 feedbacks in the
Discussion and conclusions.
Comparison of size of climatic
responses of PI and NA, relative
to PD
We compare the relative forcing and relative response of the
industrial era (PI-PD) and the early anthropogenic era (NA-PI)
(Figure 3). The net radiative forcing associated with GHG changes
of the industrial era (PI-PD) is −2.05 W/m2, whereas the net forcing of the early anthropogenic era (NA-PI) is −1.01 W/m2, i.e. the
early anthropogenic GHG forcing change represents 33% of the
total change (NA-PD) (Figure 3). In contrast to the 33% change in
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Figure 3. Decomposition of the total change (NA-PD) into two components: (NA-PI), in blue, and PI-PD, in pink – shown in percent, for: net
GHG radiative forcing (W/m2), global surface temperature (K), NH area of permanent snow cover (ru), SH area of sea ice (ru), and global
ocean temperature (K). The numbers within the bars show the magnitude of the change (same as Table 2). The units for snow cover and sea
ice are relative units (ru)
GHG forcing (NA-PI), the partitioning of the climatic response
(Figure 3) is considerably larger than 33%: the response of global
surface temperature is 57%; the response of NH permanent snow
cover area is 45%; the response of SH sea-ice area is 73%; and the
response of global ocean temperature is 70%.
This result, an amplification of the climate response relative to
the GHG forcing for colder climate states, i.e. NA-PI compared
with PI-PD, (Figure 3) is considerably larger for CCSM3 than for
our previous experiment with an atmosphere–static slab ocean
model (Vavrus et.al., 2008). The atmosphere–slab ocean model consisted of the Community Atmospheric Model (CAM3) – the same
dynamical atmospheric model contained in CCSM3 – but it was
coupled to a slab ocean (here called SO – a variable depth mixedlayer ocean model, Collins et al., 2006b) – the coupled model is here
called CAM3+SO. As an example of the larger response obtained
with CCSM3, the response of global surface temperature (NA-PI)
is 54% of the total response (NA-PD) using CCSM3, whereas the
corresponding value was 38% using CAM3+SO.
Our conclusion from these comparisons with both CCSM3
and CAM3+SO is that increments of changed GHG forcing in
colder climate states (NA-PI) produce a proportionately larger
climate response than increments of changed GHG forcing
in warmer climate states (PI-PD), and that this effect is greater for
the dynamical atmosphere–ocean model than for the atmosphere–
slab ocean model. A similar non-linear response of climate to a
broad range of GHG forcing is reported by Manabe and Bryan
(1985). Using CO2 forcing of 150, 212, 300, 600, 1200, and 2400
ppm in a coupled atmosphere–ocean model for an idealized landocean planet, Manabe and Bryan (1985) found a more pronounced
rate of increase of surface temperature for values of CO2 moving
from 150 to 212 ppm, and from 212 to 300 ppm, than for
subsequent increases in CO2. They attributed this proportionately
larger climate response to GHG forcing at colder climate states
largely to temperature-albedo feedbacks. They also reported that
when they repeated a similar set of experiments with an atmosphere–slab ocean model, the corresponding proportional changes
were smaller, thus helping to confirm their conclusion that sea ice
albedo feedback processes in the fully coupled dynamical atmosphere–ocean model were enhanced, relative to the slab ocean, by
changes in the meridional overturning circulation and in ocean
heat transport.
Discussion and conclusions
We compared previously published climate simulations for
Present-Day (PD), Pre-Industrial (PI) times, and a hypothetical
(inferred) state termed ‘No Anthropogenic’ forcing (NA) based
upon the GHG levels of the late stages of previous interglacials
relative to the present interglacial. These simulations used a fully
coupled dynamical atmosphere–ocean model, the CCSM3, an
improvement in model design compared with our previous examination of these three climate states using an atmospheric model
coupled to a static slab ocean (Vavrus et al., 2008). We note the
following points:
(1) By comparing the PI simulation (Otto-Bliesner et al., 2006a)
and the NA simulation (Kutzbach et al., 2010) with the control, PD, we find a consistent direction of climatic response as
the net radiative forcing associated with decreased concentrations of GHGs becomes increasingly negative. The response
includes lower surface temperature, more snow cover, more
sea-ice cover, a colder ocean, and changes in ocean overturning as the forcing changes from PD to PI to NA.
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(2) The climatic response to decreased GHG forcing is relatively larger for the colder climate states (NA-PI, compared
with PI–PD). This result confirms the sense of our earlier
results using CAM3+SO (Vavrus et al., 2008), but the nonlinearity of this response is significantly larger for CCSM3
than for CAM3+SO. This result agrees with the results of
idealized experiments by Manabe and Bryan (1985) – see
section ‘Comparison of size of climatic responses of PI and
NA, relative to PD’. However, rather than being idealized
experiments, as was the case in Manabe and Bryan (1985),
in our case this non-linearity is now linked to specific climatic states (PI and NA) and therefore helps frame important questions about Holocene climate trends.
(3) The relatively larger response of climate to changes in GHG
forcing for colder climate states has the effect of enhancing
the climate’s response to the inferred early anthropogenic CO2
increases (NA to PI) relative to the industrial-era increases
(PI to PD) (Figure 3). The CCSM3 simulated a larger global ocean temperature change between NA and PI than we
had previously calculated using the fractional amplification
effect obtained with CAM3+SO, a temperature change of
0.88K (Table 2) rather than ~0.5K (Kutzbach et al., 2010).
This larger warming of 0.88K (from NA to PI) matches very
closely the temperature increase inferred from the observed
late-Holocene trend in marine benthic δ18O, relative to the
trend in previous interglacials (Lisiecki and Raymo, 2005) –
an increase of 0.84K (Kutzbach et al., 2010). Thus there is
now close agreement in the amount of global ocean warming
simulated by CCSM3 (0.88K) and estimated from the observations (0.84K). (This temperature estimate based on the late
Holocene δ18O trend obviously has some error component
because the marine observations may not be representative
of the entire ocean – however, the observations included data
from 47 ocean cores and the Holocene δ18O trend is larger
than the standard deviation of the trends of previous interglacials (see Kutzbach et al., 2010: figure 4).) A related result
of this enhanced amplification effect at the coldest climate
state is that the CCSM3-simulated global surface temperature increase since the onset of the industrial era (from PI to
PD), 1.2K, is smaller than the CAM3+SO-simulated increase,
1.7K, and therefore closer to observational estimates (Jones
and Mann, 2004) – although the accuracy of the estimate from
observations is also uncertain as noted in the Introduction.
(4) The potential relative contributions of direct and indirect
effects to the pre-industrial CO2 increase are different than
reported in our previous studies – and have changed over
time. Ruddiman (2003) estimated that CO2 increased from
240 ppm (NA) to 280 ppm (PI), an increase of ~40 ppm,
because of anthropogenic factors directly related to early
agriculture. Estimates of much smaller direct effects of early
agriculture, 5 ppm or less, were published by Joos et al.
(2004), Elsig et al. (2009), and others (see Ruddiman et al.,
2011, this issue). Ruddiman (2007) likewise noted that the
direct effect may have been considerably smaller than 40
ppm (his initial estimate), and proposed that substantial CO2
positive feedback from an anomalously warm Holocene
ocean might be required to satisfy the full carbon budget
required to support a 40 ppm rise in CO2.
We briefly summarize current observation-based estimates
of the direct effect and the CCSM3-based estimate of the
indirect (climate feedback) effect. The direct effect of early
anthropogenic activities may now, according to recent
studies, be considerably larger than some of the previous
estimates that had been as low as 10 ppm or even lower.
Indeed, the direct effect may have increased CO2 by as much
as 21–22 ppm based upon studies reported in this volume.
Kaplan et al. (2010, this issue) estimates 310 GtC emissions
from pre-industrial land clearance that equate to a CO2
increase of ~22 ppm. Another estimate (300 GtC emissions
and a CO2 increase of 21 ppm) comes from a mass-balance
calculation that takes into account the need for a very large
release of anthropogenic terrestrial carbon to balance a similarly large burial of terrestrial carbon in boreal peats (Ruddiman et al., 2011, this issue; Yu, 2011, this issue).
The indirect (positive feedback) from decreased ocean
solubility may have increased CO2 by about 9 ppm (sections
‘Results’ and ‘Comparison of size of climatic responses of
PI and NA, relative to PD’). This value of 9 ppm is larger
than the value we had estimated previously (5 ppm) because
the simulated warming of the global ocean in CCSM3 from
NA to PI (0.88K) (Table 2) is larger than the corresponding value we had estimated previously (0.5K) based upon the
fractional partitioning of the slab ocean temperature response
from NA to PI (0.38) – the fraction we had calculated from
the CAM3+SO simulation – see sections ‘Results’ and ‘Comparison of size of climatice responses of PI and NA, relative
to PD’. As noted in (3), above, this larger temperature change
(simulated) agrees closely with estimates of temperature
change from marine oxygen isotope observations.
Adding the direct contribution to CO2 increase from early
deforestation (perhaps up to 21–22 ppm) and the contribution from ocean-solubility feedback (up to ~ 9 ppm), the
early anthropogenic-related total increase in CO2 (direct, plus
the indirect solubility feedback) could be as much as 30–31
ppm – i.e. an amount closer to the value of 40 ppm originally
proposed by Ruddiman (2003), and even closer if the revised
value for the CO2 difference between NA and PI is in the
range 35–40 ppm, a slightly lower range that seems likely now
that additional CO2 observations from early interglacials are
included in the analysis (see section ‘Simulations, models, and
boundary conditions’, and Ruddiman et al., 2011, this issue).
The CCSM3 does not include a biogeochemical submodel,
but Southern Ocean processes that have been claimed to
affect atmospheric CO2 (such as changes in southern sea ice
and southern ocean upwelling – simulated in both NA and PI)
are likely to have added more positive CO2 feedback. In particular, decreased southern sea ice and enhanced wind-driven
upwelling in the region of the Southern Ocean westerlies would
increase ocean ventilation and add to the atmospheric CO2
concentration (Kutzbach et al., 2010). This positive feedback
mechanism is supported by inferences of enhanced upwelling
as diagnosed from Southern Ocean sediments during the most
recent deglaciation and associated CO2 rise (Anderson et al.,
2009). Only a climate model with interactive oceanic biogeochemical cycling can address this topic quantitatively. Finally,
as described in the Introduction, this simulation did not include
terrestrial vegetation processes or prescribed changes in land
use associated with early agriculture.
(5) There are several important caveats to be highlighted: (i)
simulations by other models, and by models of higher resolution, will be needed to confirm or modify these results
from CCSM3; (ii) the two simulations (PI and NA) used different computational procedures for calculating the equilibrium response to the changed forcing, and the PI simulation
included additional changes in forcing that could not be calculated exactly (see Appendix); future simulations should avoid
these differences – an option not possible here because of
computer-time limitations; (iii) quantitative assessment of the
impacts of these different climate states on feedbacks affecting
atmospheric CO2 levels must await simulations with climate
models that include biogeochemical feedbacks; (iv) observations show some agreement with the results for PI and NA, but
more observations will be very useful; and (v) the CCSM3 has
800
a significant summertime cold bias that may affect model sensitivity to snow-related feedbacks with lowered GHG forcing
– this underscores the need for simulations with other models and improved versions of CCSM (Kutzbach et al., 2010;
Vavrus et al., 2008). For example, the newly released (2010)
version of CCSM, named CESM1, has a smaller summertime
temperature cold bias than CCSM3.
Appendix
Notes on radiative forcing, simulation procedures, and
the calendar time associated with Pre-Industrial (PI)
Radiative forcing. The caption of Table 1 mentions that the PI
simulation of Otto-Bliesner et al. (2006a) changed not only the
five GHGs listed in Table 1, but other variables: namely, the concentrations of ozone, sulphate aerosols, and carbonaceous aerosols, and the solar constant and the orbital year (see table 1 of
Otto-Bliesner et al. and discussion therein for details). Small
decreases in ozone, sulphate aerosols, and the solar constant (relative to PD) acted to enhance the net negative forcing, whereas the
increase in carbonaceous aerosols acted to diminish the net negative forcing. The slight change in orbital forcing had near zero
effect. Unfortunately, the exact net effect of these additional prescribed changes in radiative forcing for PI is not available (OttoBliesner, personal communication, 2010). However, we have
concluded that the overall net effect of these additional changes of
radiative forcing was small. We base this conclusion on a comparison of results from two PI experiments using CAM3+SO (this
atmosphere–slab ocean model is described briefly in section
‘Comparison of size of climatic responses of PI and NA, relative
to PD’). Vavrus et al. (2008) found, using CAM3+SO, that the
change in global average temperature was −1.71K in response to
the reduced GHG forcing of −2.05 W/m2 (Table 1). Otto-Bliesner
found, also using CAM3+SO, that the change in global average
temperature was −1.73K in response to the reduced GHG forcing
of −2.05 W/m2 plus the net effect of the other adjustments to radiative forcing described above (Otto-Bliesner, personal communication, 2010). Based upon the almost identical change in global
mean temperature for both PI experiments (Vavrus et al. and OttoBliesner et al.) using the same model (CAM3+SO), we will
assume that the net radiative forcing changes in the two PI experiments were very similar; i.e. that the additional changes in radiative forcing made by Otto-Bliesner et al. (their table 1 and
discussion) were largely offsetting and had relatively little effect
on the total net forcing. We therefore conclude (sections ‘Results’
and ‘Comparison of size of climatice responses of PI and NA,
relative to PD’) that we can compare results of PI with NA, assuming that the net change in radiative forcing is well represented by
the forcing associated with the five GHGs listed in our Table 1.
Applying this conclusion based upon the results with CAM3+SO
to the results with CCSM3, is supported by the finding that the
sensitivity of global average temperature to doubling of CO2 is
nearly identical for CAM3+SO and CCSM3 (Kiehl et al., 2006).
Averaging intervals, initialization, and length of runs. The previously published experiments by Kutzbach et al. and Otto-Bliesner
et al., conducted independently, had chosen somewhat different
periods within NCAR’s long 1000-year CCSM3 control simulation
to define a present-day average (PD); here we use years 981–1000
for the PD control. In both studies, CCSM3 was run additional years
after introducing the changes in net radiative forcing (Table 1) in
The Holocene 21(5)
order to establish new equilibriums for PI and NA. The model
atmosphere, land surface, and upper ocean typically reach new
equilibriums quickly, but the deep ocean and the slowly changing
sea-ice require more time. Otto-Bliesner et al. ran CCSM3 an additional 400 years for the PI experiment (starting from year 700 of the
long control run) after introducing the changed radiative forcing
associated with PI; the PI experiment is the average of years 350–
399 of the additional 400 year run. The total length of the simulation was 1100 years (700 plus 400). Kutzbach et al. initialized the
CCSM3 ocean for the NA experiment (starting from year 1000 of
the long control run) using the larger reduction in net radiative forcing
(Table 1), but they first adjusted the initial ocean temperatures to be
colder than at the end of the PI simulation (Otto-Bliesner et al.);
this initialization caused the model to reach quasi-equilibrium after
only 100 years of additional simulation. The model was then run an
additional 33 years; the NA experiment is the average of years
1114–1133. The total length of simulation was 1133 years (1000
plus 133) – (Kutzbach et al.). We mention the similarity in length
of simulations for PI and NA (1100 years and 1133 years, respectively) because the temperature of the deep ocean in CCSM3
exhibits a small downward ‘drift’ of about −0.04K per century, so
at least this ‘drift’ component of the deep ocean temperature change
is similar in all experiments. In sections ‘Results’ and ‘Comparison
of size of climatic responses of PI and NA, relative to PD’, we
compare the responses of global ocean temperature (including the
deep ocean) for PI and NA. Because these responses of global
ocean temperature are relatively large compared to the small rate of
‘drift’ of the deep ocean temperature, we assume that any differences in deep ocean temperature between PI and NA are caused
primarily by the changed radiative forcing. We cannot estimate any
additional effect on our results caused by our method of initializing
NA with ocean temperatures colder than those at the end of the PI
simulation, but we note that all simulations had reached quasiequilibrium – except that the deep ocean in all three simulations
maintained approximately the same small drift of deep ocean temperature. The averaging intervals for PD, PI and NA are all relatively short (20, 50 and 20 years, respectively). However, the
interannual variability within these intervals was small (see Table
2). Because CCSM3 exhibits considerable decade-scale variability,
longer averaging intervals on the order of a century would have
been desirable but would have required longer simulations, particularly for NA. (Brandefelt and Otto-Bliesner, 2009, provide illustrations of long time series showing considerable decade-scale
variability from a CCSM3 simulation of the LGM.) We have
focused our analysis on large spatial averages and on annual averages that are least likely to be affected by the relatively short averaging periods; the only exception is the mapping of months of
snow cover (Figure 1) which might be expected to change somewhat depending upon the exact choice of averaging interval.
Calendar time associated with the Pre-Industrial era. Various calendar dates have been associated with the start of the Industrial
Era. A number of versions of coal-fired steam engines were in
development by 1750, and James Watts’ improved engine was
introduced in 1775. While these dates may influence a choice of ~
1750 for the start of the industrial revolution, the evidence suggests that the ‘revolution’ took some decades to gather full
momentum – hence our choice of 1850 for dating the revolution as
fully underway. Several kinds of observations and emissions estimates support this date of 1850. The ice core records from Greenland and Antarctica indicate that CO2 concentrations exceeded
280 ppm (the canonical PI level) at times during the Medieval
Kutzbach et al.
period and climbed consistently above the highest pre-industrial
levels only around 1850 (IPCC, 2007; Ruddiman, 2007, 2008).
Sulphate aerosol concentrations in ice cores exhibit a detectable
rise around 1800 but this upward trend strengthens considerably
by 1850 (IPCC, 2007: figure 6.15 of Chapter 6 of Fourth Assessment Report, Working Group 1). Estimates of human-related carbon emissions (millions of metric tons of C per year) are 3 in
1750, 8 in 1800, 55 in 1850, and about 7000 in 2000 (Andres et al.,
2000); i.e. carbon emissions before 1850 were very small.
Acknowledgements
This work has been supported by National Science Foundation grants
ATM-0902982 to the University of Virginia and ATM-0602270 and
ATM-0902802 to the University of Wisconsin. Computational support for this research was provided by NCAR’s Climate Simulation
Laboratory, which is supported by the National Science Foundation.
We thank Bette Otto-Bliesner, NCAR, for permitting us to use the
pre-industrial simulation described herein in our analysis, and for
facilitating access to the history tapes. We thank the anonymous
reviewers for their criticisms, comments, and suggestions that have
helped us modify, elaborate and clarify our findings.
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