There remain substantial uncertainties in future
projections of Arctic climate change. There is a potential to constrain
these uncertainties using a combination of paleoclimate simulations and
proxy data, but such a constraint must be accompanied by physical
understanding on the connection between past and future simulations. Here,
we examine the relevance of an Arctic warming mechanism in the mid-Holocene (MH) to the
future with emphasis on process understanding. We conducted a
surface energy balance analysis on 10 atmosphere and ocean general
circulation models under the MH and future Representative Concentration Pathway (RCP) 4.5 scenario forcings. It is
found that many of the dominant processes that amplify Arctic warming over
the ocean from late autumn to early winter are common between the two
periods, despite the difference in the source of the forcing (insolation vs.
greenhouse gases). The positive albedo feedback in summer results in an
increase in oceanic heat release in the colder season when the atmospheric
stratification is strong, and an increased greenhouse effect from clouds
helps amplify the warming during the season with small insolation. The
seasonal progress was elucidated by the decomposition of the factors
associated with sea surface temperature, ice concentration, and ice surface
temperature changes. We also quantified the contribution of individual
components to the inter-model variance in the surface temperature changes.
The downward clear-sky longwave radiation is one of major contributors to
the model spread throughout the year. Other controlling terms for the model
spread vary with the season, but they are similar between the MH and the
future in each season. This result suggests that the MH Arctic change may
not be analogous to the future in some seasons when the temperature response
differs, but it is still useful to constrain the model spread in the future
Arctic projection. The cross-model correlation suggests that the feedbacks
in preceding seasons should not be overlooked when determining constraints,
particularly summer sea ice cover for the constraint of autumn–winter
surface temperature response.
Introduction
The magnitude of climate change has been shown to be larger at high
latitudes with paleoclimate evidence (Masson-Delmotte et al., 2006, 2013) and climate model equilibrium simulations
(Manabe and Wetherald, 1975; Stouffer and Manabe, 1999). The Arctic is
currently experiencing a more rapid warming than the rest of the world
(Screen and Simmonds, 2010; Serreze and Barry, 2011), and this Arctic
amplification is expected to continue at least until the end of this century
(Collins et al., 2013; Laîné et al., 2016). A much slower rate of
warming occurs in the Southern Ocean primarily due to oceanic processes
(Armour et al., 2016), although it is possible that stratospheric
ozone change and cloud feedback play additional roles (Marshall et al., 2014; Yoshimori et al., 2017). A substantial part of the uncertainty in the
future Arctic warming projections is attributed to the differences among
numerical models (Hodson et al., 2013). In addition, the
projected range of future Arctic warming within each Representative Concentration Pathway (RCP) scenario is much
larger than that for the global mean. For example, the 90 % confidence
interval for the annual mean surface air temperature (SAT) change from the
late 20th century to the late 21st century for the Arctic mean
(67.5–90∘ N) is estimated as 1.6–6.9 ∘C, while that
for the global mean is 1.1–2.6 ∘C under the RCP4.5 scenario
(Collins et al., 2013).
It is often assumed that the study of paleoclimate, particularly of warm
periods, is useful for understanding future climate change projections. It
is, however, non-trivial to demonstrate the relation between these two
different periods. Earlier studies discussed whether past climate can be
used as an analogue for the future and refuted the use of past warm periods
as an analogue (Crowley, 1990; Mitchell 1990). A relatively large number
of studies have been conducted on the link between the past, including the
Last Glacial Maximum (LGM), and the future in the context of climate
sensitivity based on processes and statistical correlation (Crucifix,
2006; Hargreaves and Annan, 2009; Hargreaves et al., 2007, 2012; Yoshimori et al., 2009, 2011). More recently, broader
applications of the relation between paleo and future climate were
summarized by Schmidt et al. (2014), who demonstrated the potential to
constrain uncertainties using both paleoclimate simulations and proxy data.
Indeed, they found a weak statistical inter-model correlation between the
sea ice changes in the mid-Holocene (MH) and in future projections (RCP8.5
scenario) relative to the modern period. Such an “emergent constraint”
provides a powerful tool to directly reduce the range of uncertainty,
provided that the necessary paleoenvironmental information is available. We
note that Hargreaves and Annan (2009) also found statistically
significant correlations between the northern mid-to-high latitude
temperature for the MH and an elevated CO2 scenario (2×CO2). The mechanism behind these emergent relations, however, remains
unclear.
The purpose of the current study is to investigate commonalities and
differences in the Arctic warming mechanisms in the past (MH) and future,
and to discuss the relevance of Arctic warming in the MH for understanding
future warming based on physical processes. We aim to obtain insight into
the feasibility of constraining uncertainty in future climate change
projections using paleoclimate data. It is not, however, the purpose of the
current study to derive a specific emergent constraint. The MH was chosen
because proxy records suggest this period had a warmer Arctic state relative
to the pre-industrial period, and multi-model simulation data are available
from the Coupled Model Intercomparison Project (CMIP5) data archive
(ESGF, 2019).
The data, models, and experiments are briefly explained in the next section.
Analysis methods for diagnosing factors contributing to the surface
temperature change in each model and to the inter-model differences are
described in Sect. 3. Results are presented in Sect. 4, followed by
discussion and conclusion in Sects. 5 and 6, respectively.
Climate models, experiments, and proxy data
The main analysis in the current study relies on the multi-model simulation
data available from the CMIP5 data archive. The pre-industrial control (approximately 1850 CE), historical (approximately 1850–2005 CE), and RCP4.5 scenario
(2006–2100 CE) simulations were designed and coordinated by the CMIP5
project (Taylor et al., 2012). The MH simulation was designed and
coordinated by the Paleoclimate Modelling Intercomparison Project (PMIP3)
(Braconnot et al., 2012) and later endorsed and archived as part of
CMIP5. The MH aims to simulate the climate of approximately 6000 years ago,
and the PMIP3 forcing differs only in the Earth's orbital configuration
(obliquity, seasonal timing of precession, and eccentricity; Table 1)
compared to the pre-industrial simulations. The difference between the MH and
pre-industrial (PI) simulations (hereafter ΔMH) and the difference
between the RCP4.5 and historical (HIST) simulations (hereafter ΔRCP4.5) are compared throughout the paper. For the MH and PI simulations,
we use monthly climatological data averaged over periods longer than a
century, which were already available. The climatological data are
constructed from monthly time series if these data are unavailable from the
CMIP5 dataset (Table S1 in the Supplement). The 20-year averages for 1980–1999 are used from
the HIST simulations and those for 2080–2099 are used from the RCP4.5
simulations, so that ΔRCP4.5 represents the climate change for the
entire 21st century, as in Laîné et al. (2016). We use
10 models that produced data for all four experiments (Table 2), and we
analyze one simulation run (r1i1p1) for each model and each experiment.
Prior to the analysis, all model output data are interpolated onto the T42
Gaussian grid (nominally 2.8∘×2.8∘) as in
Laîné et al. (2016). A common land mask is constructed in
such a way that a grid point is judged as ocean if more than 50 % of
models (that have fractional land cover data) indicate the grid point as
ocean. The same procedure is used for the ocean mask, and consequently a
small number of grid points are classified as neither ocean nor land.
Orbital configurations for the PI and MH experiments. The PI and MH
values here represent the values for the years 1850 CE and 6000 years
before 1950 CE, respectively, taken from the PMIP3 web page
(PMIP3, 2019). They originate from Berger (1978).
Parameters for PI may vary slightly with the model.
EccentricityObliquity (∘)Longitude ofperihelion from thevernal equinox – 180 (∘)PI0.01676423.459100.33MH0.01868224.1050.87
The simulated ΔMH is compared with temperature reconstructions based
on proxy data. Sundqvist et al. (2010) compiled such a dataset
primarily based on pollen and chironomids records. The oxygen isotope ratio
from ice cores and borehole temperature are also used for the Greenland
temperature. Another dataset is compiled by Bartlein et al. (2011) based
on pollen records. We use the extended dataset of Bartlein et al. (2011)
for the annual mean, which includes additional data from
Schmittner et al. (2011) and
Shakun et al. (2012) as in Harrison et al. (2014) and is available from the PMIP3 web site (PMIP3, 2019). The model ensemble mean data are further
interpolated onto 2∘×2∘ grids for comparison
with Bartlein et al. (2011).
Models used in the current study and the annual, global, and Arctic
(north of 60∘ N) mean surface air temperature changes (∘C).
ModelΔMH ΔRCP4.5 GlobalArcticGlobalArcticbcc-csm1-1-0.130.871.744.27CCSM4-0.220.011.833.89CNRM-CM50.181.422.075.02CSIRO-Mk3-6-00.020.432.373.06FGOALS-g2-0.75-0.481.433.57FGOALS-s2-0.160.461.662.34GISS-E2-R-0.100.771.342.45IPSL-CM5A-LR-0.130.252.374.84MIROC-ESM-0.25-0.272.586.00MRI-CGCM3-0.020.811.703.84Mean-0.160.431.913.93Analysis methodSurface energy balance and partial temperature changes
Processes contributing to the surface temperature difference between two
experiments are evaluated based on the surface energy balance equation. The
basic formulation follows Lu and Cai (2010). The surface energy
balance equation for a reference climate is given by
1-αS+F-R-H-L-Q=0,
where S=Sclr+Scld and F=Fclr+Fcld are the downward shortwave
(SW) and longwave (LW) radiation at the surface, respectively, with the
superscripts, “clr” and “cld” denoting the clear-sky and cloud
(total-sky – clear-sky) radiative effects, respectively. The upward LW
radiation is given by the Stefan–Boltzmann law, R=σTs4, where σ is the Stefan–Boltzmann constant and Ts is
the surface temperature. The surface emissivity is assumed to be 1. H
and L are the net upward sensible and latent heat fluxes, respectively,
and Q represents the net downward surface energy flux including the latent
heat consumed by snow/ice melting. In the ocean, Q is stored locally or
transported. For the difference (Δ) between the two experiments, Eq. (1) becomes
4σTs3ΔTs=-ΔαS-ΔαΔS+1-αΔSclr+1-αΔScld+ΔFclr+ΔFcld-ΔH-ΔL-ΔQ≡∑jΔRj,
where ΔRj represents the individual energy terms.
The Stefan–Boltzmann law implies that a larger surface warming (ΔTs) is required to balance the same amount of energy flux anomaly
(ΔR) by emitting LW radiation at a colder background temperature
(Ts). Laîné et al. (2016) called this effect the
“surface warming sensitivity”, whose importance for the Arctic
amplification has been pointed out in multiple studies (Laîné et al., 2009, 2016; Ohmura 1984, 2012; Pithan and
Mauritsen, 2014). The warming sensitivity and other energy flux terms may be
converted to the same temperature scale (partial surface temperature
changes) by
ΔTs=∂Ts∂R‾∑jΔRj′+∂Ts∂R′∑jΔRj‾+∂Ts∂R′∑jΔRj′,
where overbars and dashes represent the global mean and deviations from the
global mean (local anomaly), respectively, and
∂Ts∂R=14σTs3.
Equation (3) enables the quantification of the effect of a colder winter
Arctic requiring more warming to balance the anomalous surface energy flux
on the same partial temperature change scale as other components. The left
side of Eq. (3) is the simulated surface temperature change. The first,
second, and third terms on the right side of Eq. (3) represent local
feedbacks evaluated with the global mean warming sensitivity, global mean
feedbacks with the local warming sensitivity, and local feedbacks with the
local warming sensitivity, respectively. Note that previous studies used the
tropical mean in place of the global mean (Laîné et al., 2016; Pithan and Mauritsen, 2014). In Sect. 4.3 and 4.5, each component of
the first term is evaluated separately, and the second and third terms are
evaluated collectively as the “S-B” effect and “synergy” effect,
respectively (Table 3). Accordingly, the surface temperature change
formulated by Eqs. (2) and (3) can be written in a more explicit form as
ΔTs=∂Ts∂R‾-(ΔαS)′-(ΔαΔS)′+1-αΔSclr′+1-αΔScld′+ΔFclr′+ΔFcld′-(ΔH)′-(ΔL)′-(ΔQ)′+∂Ts∂R′∑jΔRj‾+∂Ts∂R′∑jΔRj′≡alb+alb∗clr_sw+clr_sw+cld_sw+clr_lw+cld_lw+sens+evap+surface+S-B+synergy.
Here, α is computed from the ratio of upward to downward SW
radiations at the surface; S, F, H, and L are taken directly from
the model output; and Q is computed as a residual of surface heat fluxes
(net radiation, sensible heat, and latent heat fluxes). ∑jΔRj is computed by summing changes in all surface energy flux
terms after they are either averaged globally for overbars or the global average is
subtracted for dashes. We use the average of Ts from the paired
experiments (PI and MH, or HIST and RCP4.5) to calculate ∂R/∂Ts. Although using the average of two experiments
or a single experiment for this term has little impact on the results of the
current study, we found that the average provided better agreement between
the two sides of Eq. (5) for larger perturbations such as a quadrupling of
the CO2 experiment. The diagnosis is made for each grid point and each
month. All models are used in this analysis. We note that direct comparisons
between different forcing simulations are possible as there is no change in
the land–sea mask among the simulations.
A list of the energy flux terms used in Figs. 5 and 12. Row no. 1
represents the strength of the global mean feedback calculated with local
warming sensitivity. Row nos. 2–10 represent the strength of local
feedback calculated with global mean warming sensitivity.
No.SymbolPhysical meaning1S-Bnonlinearity of Stefan–Boltzmann law2albsurface albedo change3alb*clr_swnonlinear effect of surface albedo and clear-sky shortwave radiation changes4clr_swclear-sky shortwave radiation change5clr_lwclear-sky longwave radiation change6cld_swshortwave cloud radiative effect7cld_lwlongwave cloud radiative effect8evapsurface latent heat flux via evaporation9senssurface sensible heat flux10surfacenet surface energy flux including latent heat for snow/ice melting and heat exchange with the subsurface11synergysynergy term for local feedbacks and local warming sensitivityInterpretation of surface temperature change at partially ice-covered
ocean grid points
The surface temperature archived in the CMIP5 dataset represents the
grid-mean “skin” temperature. At the fractionally ice-covered ocean grid
points, this variable is a mixture of the sea surface temperature (SST) and
ice surface temperature. We assume that the surface temperature Ts at
each grid point is reconstructed by
Ts=1-ATo+ATi,
where To and Ti are the SST and ice surface temperature,
respectively, and A is the ice concentration. The factors contributing to
the surface temperature difference for the paired experiments are then
diagnosed by
ΔTs=1-AΔTo+AΔTi+Ti-ToΔA.
The first and second terms on the right side represent the effect of SST and
ice surface temperature changes, respectively. The last term on the right
side represents the effect of the ice concentration change, which is
weighted by the surface temperature difference between ice and water: the
reduction of sea ice cover (ΔA<0) and the exposure of the
warmer ocean surface to the atmosphere (Ti-To<0) lead to an increase
in the grid-mean surface temperature (ΔTs). In the current
analysis, To, Ti, and A are obtained from the average of paired
experiments. We use To in place of Ti for ice-free ocean grids.
Only five models (bcc-csm-1, CCSM4, CNRM-CM5, IPSL-CM5A-LR, and MRI-CGCM3)
are used for this analysis due to the availability of the required
variables, and the consistency of the analysis is verified by agreement
between the left and right sides of Eq. (7). The diagnosis is made for each
grid point and each month.
Factors responsible for the model spread
The fractional contribution of individual partial surface temperature
changes (or feedbacks in other words) to the inter-model spread of the
simulated surface temperature change is given by
Vj=∑k=1nΔTj,k-ΔT‾jΔTk-ΔT‾σ2n-1×100[%],
where Vj is the fractional contribution and ΔT is the surface
temperature change (the subscript “s” in ΔTs is omitted here).
The subscripts j and k denote indices for feedbacks (jth feedback) and
models (kth model out of n models), respectively. The overbars denote
the average over the models. σ is the inter-model
standard deviation of the total surface temperature change. The numerator
represents the product of the model spread for each feedback and the model
spread for the total feedback, while the denominator represents the ensemble
variance of the total feedback. Here, the key points are that (1) Vj
accounts for 100 % of the surface temperature change when summed over the
feedbacks; (2) positive Vj means that the jth feedback amplifies the
model spread, while negative Vj means that it suppresses the model
spread. We note that the same formula was used in Yoshimori et al. (2011)
and the references therein. The statistical significance of the fractional
contribution is tested using the Monte Carlo method by randomly shuffling
the model index (k) 105 times. The null hypothesis is that the
Vj values neither amplify nor suppress the model spread. When the original
Vj is outside the range of the 5-95th percentiles of Vj resulting
from the shuffling, it is considered significant. The diagnosis is made
separately for ocean and land averages in the Arctic region (north of
60∘ N). All models are used for this analysis.
Multi-model mean (all 10 models listed in Table 2) annual mean
surface air temperature response (∘C): (a)ΔMH and (b)ΔRCP4.5.
ResultsSimulated surface air temperature response
Figure 1 shows the ensemble mean of the annual mean SAT response for ΔMH and ΔRCP4.5. In both cases, the warming in the polar regions is
larger than for the rest of the world, particularly in the Arctic. The
Arctic-mean response is 0.4 and 3.9 ∘C for ΔMH and ΔRCP4.5, respectively, whereas the global mean response is
-0.2 and 1.9 ∘C for ΔMH and ΔRCP4.5, respectively (see Table 2 for individual models). This feature
reflects the so-called Arctic warming amplification in ΔRCP4.5. The
warming at high latitudes and cooling at low latitudes in ΔMH are
consistent with the annual mean insolation anomaly caused by the obliquity
difference. From this figure, it is unclear whether the Arctic warming in
ΔMH is due to forcing and/or feedbacks.
Seasonal progress of the zonal mean effective radiative forcing,
effective radiative
forcing (ERF) (a, b; W m-2), and surface air temperature change (c, d,
∘C): (a, c)ΔMH and (b, d)ΔRCP4.5.
The ERF for ΔRCP4.5 is drawn using the data from
Yoshimori et al. (2018), and it is computed in the current study
for ΔMH. Both ERFs are constructed with a single model, MIROC4m-AGCM
(Yoshimori et al., 2018). The surface air temperature changes are the means
of all 10 models listed in Table 2.
Figure 2a and b show the seasonal progress of the effective radiative
forcing (ERF) for ΔMH and ΔRCP4.5, respectively. The ERF is
the top-of-the-atmosphere (TOA) radiation change induced by the forcing
constituents and is computed here using the atmospheric GCM (MIROC4m) of
Yoshimori et al. (2018), with prescribed climatological SST and
sea ice distribution. The ERF for ΔMH was computed by applying the
PI and MH insolation to the atmospheric GCM (AGCM) separately, with other boundary conditions
held fixed. The TOA net radiation in the MH was averaged for 20 years after
a 10-year spin-up and the difference from the PI was taken as ΔMH
ERF. The ERF for ΔRCP4.5 was drawn using the data from
Yoshimori et al. (2018) in which the time-varying historical and
RCP4.5 forcings were applied continuously to the AGCM, with other boundary
conditions held fixed. The three-ensemble-member mean of the differences between
the 2080–2099 and 1980–1999 averages was taken as ΔRCP4.5 ERF.
While this so-called Hansen-style method (Flato et al., 2013; Hansen et al., 2005) is one of the standard procedures for calculating future scenario
forcing, e.g., ΔRCP4.5, it is uncommon in paleoclimate applications.
With this method, the ERF includes both rapid stratospheric and tropospheric
adjustments as well as the land surface response to the instantaneous
radiative forcing. Although the land surface response should not be
considered as a forcing, we present the ERF to facilitate a consistent
comparison between different perturbation experiments. As a supplementary
reference, another measure of radiative forcing evaluated by ΔS1-αp is presented for ΔMH in Fig. S1. Here,
ΔS is the insolation anomaly and αp is the pre-industrial
planetary albedo. The ΔMH forcing patterns in both Figs. 2a and
S1 in the Supplement are qualitatively similar to the familiar insolation anomaly ΔS
(e.g., Hewitt and Mitchell, 1996; Ohgaito and Abe-Ouchi, 2007): an increase
and a decrease in summer and autumn, respectively, in the Northern
Hemisphere, and an increase and a decrease in spring and summer,
respectively, in the Southern Hemisphere. For the Arctic average
(> 60∘ N), the peak positive ERF of about 19.9 W m-2 occurs in July and the peak negative ERF of about -4.8 W m-2
occurs in September. The ΔRCP4.5 ERF is, in contrast, spatially and
seasonally more homogeneous with an annual mean of about 3.0 W m-2
for the Arctic region. Figure 2c and d show the ensemble mean of the
seasonal progress of SAT changes for ΔMH and ΔRCP4.5,
respectively. A common and striking feature is that the maximum Arctic
warming occurs in autumn (though the magnitude differs substantially) when
the ERF is negative or weakly positive. This result suggests that feedbacks
play an important role in shaping the seasonality of the Arctic warming for
both ΔMH and ΔRCP4.5. This interpretation is in line with
Zhang et al. (2010) for ΔMH and Laîné et al. (2016) for ΔRCP4.5.
Seasonal progress of the surface air temperature change
(∘C) in the Arctic (north of 60∘ N): (a)ΔMH
land; (b)ΔMH ocean; (c)ΔRCP4.5 land; and (d)ΔRCP4.5 ocean. Thick black lines show the multi-model mean. Note that the
range of vertical axis is different for ΔMH (a, b) and ΔRCP4.5 (c, d).
Figure 3 shows SAT changes over the land and ocean for individual models.
The seasonality of the SAT change over land is distinct between ΔMH
and ΔRCP4.5, but there are some similarities over the ocean: the
warming is modest in summer and largest in autumn. Significantly, the model
spread over the ocean is also larger in autumn than in summer. The maximum
land warming in summer for ΔMH corresponds to the maximum local
insolation anomaly, and it thus may appear that the SAT warming over land is
not related to the SAT warming over the ocean. However, there are strong
cross-model correlations at the 5 % statistical significance level
(Student's two-tailed t test) between the Arctic land and ocean for the
October–November–December (OND) mean as well as for the annual mean (0.95
for OND and 0.94 for the annual mean). The statistically significant
cross-model correlations at the 5 % level also exist for ΔRCP4.5
(0.92 for OND and 0.89 for the annual mean). In addition, the inter-model
variance of the Arctic-mean SAT anomaly is larger over the ocean than over
land. Although the available surface temperature proxy data for the
mid-Holocene Arctic are more abundant on land than over the ocean
(Bartlein et al., 2011; Sundqvist et al., 2010), it is useful to focus our
analysis on the oceanic region, which has a larger response, and to explore
which processes are responsible for the model difference there. We note that
there is no statistically significant correlation at the 5 % significance
level between ΔMH and ΔRCP4.5 for either the OND or annual
means (for both the ocean and land in the Arctic).
Surface air temperature anomaly (∘C) for ΔMH
from the simulations (shading) and reconstruction (solid circles): (a)
annual mean; (b) July; and (c) January. The reconstruction data are taken
from Sundqvist et al. (2010). The mean of all 10 models listed
in Table 2 was used.
Comparison with proxy data
Figure 4 shows the ensemble mean of the simulated ΔMH annual mean,
July, and January SAT anomalies superimposed with the reconstructed SAT
anomaly at proxy sites taken from Sundqvist et al. (2010). We
note that a detailed comparison with earlier PMIP1 and PMIP2 simulations was
given by Zhang et al. (2010). There is substantial disagreement
between the model and the reconstruction: the warming indicated by the
reconstruction is not captured by the model mean in January as well as in
the annual mean. The discrepancies are on the order of a few degrees.
Although better agreement is seen in July, the simulated warming is
overestimated at some North American sites. O'Ishi and Abe-Ouchi (2011) reported that the model–data discrepancy improved substantially when
the interaction between the MH climate change and vegetation distribution
change is included in one model although the improvement is somewhat limited
in other models (Zhang et al., 2010). Unfortunately, none of the
models analyzed in the current study include this dynamic vegetation
feedback. Comparisons of the model ensemble mean with Bartlein et al. (2011) for the ΔMH annual mean, warmest month, and coldest month are
shown in Fig. S2. We note that a more comprehensive comparison with PMIP2
and PMIP3 simulations was presented in Harrison et al. (2014). Again, the model–data discrepancy is large, although the qualitative
tendencies of the warming in parts of Scandinavia appear in both. While
these limitations need to be kept in mind, they do not reduce the
significance of the following results on the understanding of the Arctic
warming process. As stated in the introduction, it is not the purpose here
to derive a specific emergent constraint using these proxy data, as such a
study requires a rigorous statistical approach in parallel to the mechanism
understanding and appropriate proxy searches, and is beyond the scope of
this article.
Simulated and diagnosed surface temperature changes (∘C)
for the ocean (north of 60∘ N): (a)ΔMH and (b)ΔRCP4.5. The solid black polygonal lines denote simulated changes and dashed blue polygonal lines denote the sum of the diagnosed partial changes; the
two lines are superimposed. The graphs represent the means of all 10 models
listed in Table 2. See Table 3 for the interpretation of each component.
Partial temperature changes
Figure 5 shows the contribution of individual energy flux components to the
surface temperature change (partial Ts changes) over the ocean in the Arctic
diagnosed by the feedback analysis described in Sect. 3.1. As expected, the
simulated Ts changes (solid black polygonal lines) are reproduced by
the sum of the individual contributions (dashed blue polygonal lines),
indicating that the decomposition is useful.
In spring (March–April–May), the total surface temperature change is
negative for the case of ΔMH, whereas it is positive for ΔRCP4.5. Therefore, there is no analogy in the response between the two
cases. While the synergy effect of local Arctic feedbacks and local warming
sensitivity (synergy) slightly contributes to the warming in both cases, the
contributions from the downward clear-sky LW radiation components
(clr_lw) have opposite signs between the two cases. The
albedo feedback (alb) exhibits a relatively large warming effect for ΔRCP4.5, accompanied by cooling due to the surface effect in late spring
(net surface heat flux component, or equivalently ocean heat storage and
dynamics components). On the other hand, the surface effect is positive for
ΔMH, and is accompanied by anomalous turbulent heat fluxes from the
ocean to the atmosphere (evap and sens).
In summer (June–July–August), the total surface temperature change is
positive but small for both ΔMH and ΔRCP4.5. The albedo
feedback is distinctly positive for both cases. An even larger clear-sky SW
radiation component (clr_sw) contributes to the additional
warming for the case of ΔMH, which is largely driven by the
astronomical forcing but it plays little role in ΔRCP4.5. The
increased SW radiation reaching the sea surface through the albedo feedback
and/or increased seasonal insolation is counteracted by the increased net
surface heat flux component, implying that the extra energy is likely stored
in the form of ocean heat content. The net result is a small surface warming
in summer. It is a common feature of both ΔMH and ΔRCP4.5
that the SW cloud radiative effect (cld_sw) weakens warming
by the albedo feedback. This canceling role of clouds in the warm season is
consistent with previous studies using future climate projections (Crook
et al., 2011; Laîné et al., 2016; Lu and Cai 2009). In both cases,
the downward clear-sky LW radiation component plays a substantial role in
warming the surface (except for ΔMH in June).
From September to January, the total surface temperature change is larger
than in other seasons for both ΔMH and ΔRCP4.5. From
September to November, the clear-sky SW radiation component associated with
the astronomical forcing contributes to the surface cooling for ΔMH,
which is absent for ΔRCP4.5. From October to January for both
ΔMH and ΔRCP4.5, the positive surface effect is counteracted
by the negative surface turbulent flux components, indicating that the heat
is released from the ocean to the atmosphere in the form of latent and
sensible heat fluxes. It is, however, unclear how the heat release to the
atmosphere leads to the surface warming (or, more precisely, grid-mean skin
temperature rise). This point is discussed in the next subsection in detail.
It is a common feature of both ΔMH and ΔRCP4.5 that the LW
cloud radiative effect (cld_lw) helps warming by the surface
effect. This amplifying role of clouds in the cold season is consistent with
previous studies using future climate projections (Laîné
et al., 2016; Yoshimori et al., 2014). The general increase of cloud cover in
autumn to winter for both ΔMH and ΔRCP4.5 is consistent with
the enhanced greenhouse effect of clouds (Fig. 6a and c).
Seasonal progress of the total cloud fraction change (%) over
the ocean (north of 60∘ N): (a)ΔMH ensemble mean; (b)ΔMH ensemble standard deviation; (c)ΔRCP4.5 ensemble mean;
and (d)ΔRCP4.5 ensemble standard deviation. All 10 models listed in
Table 2 are used.
Throughout the year, the downward clear-sky LW radiation component exhibits
a large contribution and follows the shape of the seasonal progress of the
total response for ΔRCP4.5. This component is, however, not large in
winter (and June) for ΔMH. This term includes the effect of air
temperature and specific humidity changes (and also the radiative forcing of
greenhouse gases for the case of ΔRCP4.5), and is qualitatively
consistent with changes in both variables (Figs. 7a, c, 8a,
c). Obtaining a clear physical interpretation of its role in the surface
temperature change is difficult because the primary component of clear-sky
LW radiation is emitted from the atmospheric layer near the surface
(Ohmura, 2001) where the temperature is tightly coupled with the
surface, thus obscuring the causality. Nevertheless, the importance of this
component has been reported in previous studies (Pithan and Mauritsen,
2014; Sejas and Cai, 2016). The positive local feedbacks in the cold season
with a larger local warming sensitivity make the synergy term an important
contributor to the total response for both ΔMH and ΔRCP4.5,
as found by Laîné et al. (2016) in future climate
projections. For completeness, the same analysis for the land surface
temperature is shown in Fig. S3.
Same as in Fig. 6 but for the air temperature change (∘C) (north of 60∘ N). All 10 models listed in Table 2 are used.
(Note that the figure appears blocky compared to Fig. 6 due to the use of a
different interpolation scheme in the plotting software which was chosen to
avoid a technical issue for pressure coordinate, but it is irrelevant to the
data.)
Same as in Fig. 6 but for the specific humidity change (g kg-1) (north of 60∘ N). All 10 models listed in Table 2 are
used. (Note that the figure appears blocky compared to Fig. 6 due to the use
of a different interpolation scheme in the plotting software which was
chosen to avoid a technical issue for pressure coordinate, but it is
irrelevant to the data.)
Interpretation of surface temperature change in partially ice-covered
ocean grids
Figure 9 shows the surface temperature change (left side of Eq. 7, ΔTs) and the individual contributions of surface conditions (the
individual terms on the right side of Eq. 7). The surface air temperature
change (ΔTa) is also plotted for reference. The seasonal progress
of ΔTa closely follows that of ΔTs, suggesting the
importance of understanding the grid-mean surface temperature change. The
surface and surface air temperature changes have maximum values of 3.3 and
2.9 ∘C, respectively, in October for ΔMH. They have
maximum values of 10.2 and 9.3 ∘C in November for ΔRCP4.5. The figure indicates that the large increase in grid-mean surface
temperature during winter is largely due to the ice surface temperature
increase when the SST anomaly decreases seasonally through oceanic heat
release after its peak value (Fig. 10a and c). The contribution from ice
temperature change has a maximum value of 2.2 ∘C in October for
ΔMH and 6.8 ∘C in November for ΔRCP4.5. The
contribution from SST change has a maximum value of 0.7 ∘C for
ΔMH and 2.0 ∘C for Δ RCP4.5, both in August. The
magnitude of the SST anomaly effect on the grid-mean surface temperature
change is small, as the SST change itself is small because SST is fixed at
the melting point where sea ice is present and due to the large heat
capacity of sea water. The reduction of sea ice cover makes an important
contribution to the grid-mean surface temperature increase during autumn.
Its peak contribution does not, however, coincide with the timing of the
maximum ice concentration anomaly (ΔA; Fig. 11a and c), as the
effect is weighted by the surface temperature difference between the sea ice
and ocean (Ti-To). The interpretation of the results of the feedback
analysis in the previous section is that the oceanic heat release in the
cold season represented by the positive net surface heat flux term in Fig. 5
contributes to the surface air temperature rise and subsequent ice (and
grid-mean) surface temperature rise. This diagnosis is simple but reveals a
chain of processes whose temporal links are less clear from the conventional
analysis on surface energy balance alone.
Contribution of the individual components to the surface
temperature change (∘C) over the ocean (north of 60∘ N): (a)ΔMH and (b)ΔRCP4.5. The surface temperature change
is decomposed into the components of the SST change ((1-A)ΔTo), sea ice concentration change ((Ti-To)ΔA), and sea ice surface temperature change (AΔTi).
Simulated surface temperature (ΔTs) and surface air temperature
changes (ΔTa) are also plotted for reference. Only five models
(bcc-csm-1, CCSM4, CNRM-CM5, IPSL-CM5A-LR, and MRI-CGCM3) are used.
Same as in Fig. 6 but for the upper ocean temperature change
(∘C) (north of 60∘ N). The nine models except for FGOALS-g2
listed in Table 2 are used.
Factors for the inter-model difference in surface temperature changes
Figure 12 shows the fractional contribution of the partial surface
temperature changes to the model spread in the total surface temperature
changes. The average is taken for ocean areas in the Arctic, and positive or
negative values indicate factors increasing or reducing the model
differences, respectively. In the following, individual components whose
contributions are either small or inconsistent between the ΔMH and
ΔRCP4.5 cases are not discussed, after considering the statistical
significance.
Same as in Fig. 6 but for the sea ice concentration (%) (north
of 60∘ N). All 10 models listed in Table 2 are used.
In spring (Fig. 12a), large contributions to the model spread are made by
the albedo feedback (alb) and the downward clear-sky LW radiation component
(clr_lw) for both ΔMH and ΔRCP4.5. Each of
these factors contributes to more than 50 % of the model spread. LW cloud
feedback (cld_lw) and the synergy effect of local Arctic
feedbacks and local surface warming sensitivity (synergy) also contribute to
the model spread but to a lesser degree. In contrast, the turbulent heat
flux components (evap and sens) as well as the cloud SW radiation component
(cld_sw) tend to suppress the model spread.
Fractional contribution of individual processes to the model
spread in the simulated surface temperature change (%) over the ocean
(north of 60∘ N) for ΔMH and ΔRCP4.5: (a) spring
(March–April–May); (b) summer (June–July–August); (c) autumn
(September–October–November); and (d) winter (December–January–February)
means. The sum of the bar graphs in the same color for each plot adds up to
100 %. The hatching indicates the contribution is statistically
significant at the 10 % level. All 10 models listed in Table 2 are used.
See Table 3 for the interpretation of each component. Note that the vertical
scale for panel (b) is 3-fold larger than those for the other plots.
In summer (Fig. 12b), the albedo feedback (alb) exhibits by far the largest
(more than 170 %) contribution to the model spread for both ΔMH
and ΔRCP4.5. Note that the vertical scale in Fig. 12b is enlarged
3-fold compared to other plots. As in spring, the downward clear-sky LW
radiation component also contributes to more than 50 % of the model
spread. The surface effect (net surface heat flux component, or equivalently
ocean heat storage and dynamics components) substantially suppresses the
model spread for ΔMH, but it is insignificant for ΔRCP4.5.
In autumn and winter (Fig. 12c and d), the downward clear-sky LW
radiation component, LW cloud feedback, and surface effect contribute to the
model spread, whereas the turbulent heat flux components tend to suppress it
for both ΔMH and ΔRCP4.5. As the oceanic heat content is
reduced in these seasons through latent and sensible heat fluxes, it is
understandable that these two terms have the opposite sign to the surface
effect, similar to how the albedo feedback and surface effect have opposite
signs in summer. The surface effect contributes to more than 40 % of the
model spread in autumn and more than 50 % in winter for both ΔMH
and ΔRCP4.5. In contrast to spring and summer, the contribution by
the albedo feedback is small in autumn and winter.
The downward clear-sky LW radiation consistently exhibits a large positive
contribution (more than 50 %) in all seasons for both ΔMH and
ΔRCP4.5. The clear-sky LW radiation is often dominant for ΔMH and ΔRCP4.5 even in spring when the ensemble mean shows surface
cooling in ΔMH and warming in ΔRCP4.5. It is also one of
major contributors to the model spread even in winter when there is little
contribution from the clear-sky LW radiation to the ensemble mean response
of ΔMH. The large contribution of this term to the model spread is
somewhat expected because this radiative flux largely reflects the surface
air temperature, which is thermally coupled with the surface temperature as
shown in the previous section. This term, however, also includes the effect
of water vapor and lapse-rate changes, whose quantitative contributions are
not evaluated separately here. The model variances of the air temperature
change are concentrated near the surface in non-summer seasons (Fig. 7b and
d), and those of the specific humidity change are large in non-spring
seasons (Fig. 8b and d). The relative contribution of air temperature and
water vapor to the clear-sky LW radiation may thus vary with the season.
It is also important to point out that the LW cloud feedback contributes
positively to the model spread in almost all seasons for both ΔMH
and ΔRCP4.5. While the inter-model variability in cloud cover peaks
in summer for ΔMH and late autumn for ΔRCP4.5 (Fig. 6b and
d), the result suggests that the correct representation of LW cloud
feedback is important throughout the year. It is important to
recognize that the cloud response is not, however, independent of other
feedbacks such as sea ice cover, water vapor, lapse rate, large-scale
condensation, and convection (e.g., Abe et al., 2016; Yoshimori et al., 2017). It is also important to notice that the synergy term contributes
positively to the model spread. As the surface warming sensitivity depends
on the background temperature, this result may suggest that the differences
in the reference surface temperature, i.e., model bias, have the potential to
reduce the simulated model spread. Taken together, attention needs to be
paid to the models' representation of surface albedo, turbulent heat fluxes
(and thus the atmospheric stratification including inversion), clouds, and
temperature bias to reduce the differences in the models' response.
These results suggest that the processes responsible for the model spread
may depend on the season. While the albedo feedback shows only a small
contribution to the autumn–winter model spread, this result does not mean
that the summer albedo feedback is irrelevant to the model spread in
autumn–winter, however. As the reduction of sea ice cover is considered to
enhance the oceanic heat uptake through the enhanced albedo feedback, and
the reduction of sea ice cover is also considered to enhance the oceanic
heat release through the reduced thermal insulating effect, a chain of
processes is expected. The model variances of the sea ice concentration
change are large from late summer to early autumn with peaks in
September–October for both ΔMH and ΔRCP4.5 (Fig. 11b and
d), and the model variances of the ocean heat content change are also
large in late summer to early autumn, although the peaks occur slightly
earlier (Fig. 10b and d). These results are not sufficient to prove the
existence of inter-seasonal linkage, but they are consistent with its
existence. We calculate cross-model correlations between the summer albedo
feedback and October–November–December (OND) feedbacks. The correlations of
the summer albedo feedback are 0.72 (ΔMH) and 0.60 (ΔRCP4.5)
with the OND surface effect, 0.66 (ΔMH) and 0.69 (ΔRCP4.5)
with the OND LW cloud feedback, and 0.85 (ΔMH) and 0.87 (ΔRCP4.5) with the OND surface temperature response (i.e., sum of all
feedbacks). These values are statistically significant at the 5 % level
according to a Student's two-tailed t test. The significant correlations
with the surface effect and with the cloud greenhouse effect are consistent
with the chain of processes discussed in Sect. 4.4 and in previous studies
(e.g., Abe et al., 2016). Therefore, the model spread in the
OND surface temperature response is closely related to the summer sea ice
distribution, indicating that feedbacks in preceding seasons should not be
overlooked. The recent sensitivity experiment with a single model by
Park et al. (2018) demonstrates the dominant influence of
sea ice albedo feedback on the MH Arctic winter and annual mean warmings.
For completeness, the same analysis for the land surface temperature is
shown in Fig. S4.
Discussions
While the ensemble mean surface temperature response over the ocean in the Arctic
shows a consistent warming trend from summer to autumn for both ΔMH
and ΔRCP4.5, the temperature anomaly in spring is neutral or
negative for ΔMH and positive for ΔRCP4.5. Although the
source of the peak negative anomaly occurring in April for ΔMH is
unclear without dedicated numerical experiments, the zonal mean patterns of
ERF and surface air temperature change in Fig. 2 suggest that it may
originate from a negative insolation anomaly at lower latitudes. This
interpretation is consistent with the downward clear-sky LW radiation
contributing to the surface cooling. In addition, the peak mid-tropospheric
cooling in spring and warming in summer for ΔMH in Fig. 7a are
suggestive of remote influence through atmospheric heat transport. The
significant remote influence on the Arctic temperature change has been
suggested by previous studies in the context of future climate change
(Stuecker et al., 2018; Yoshimori et al., 2017). The opposite signs in the
total surface temperature change and also in the partial temperature change
by downward clear-sky LW radiation between ΔMH and ΔRCP4.5
do not suggest a strong similarity between MH and future Arctic response in
spring. While the ensemble mean surface temperature response over the ocean in the Arctic shows relatively small warming in summer for both ΔMH and
ΔRCP4.5, they are the downward clear-sky SW radiation for ΔMH and albedo feedback for ΔRCP4.5 that dominate in the partial
temperature changes. Nevertheless, the increased absorption of SW radiation
by the ocean and increased reflection of SW radiation by clouds occur for
both ΔMH and ΔRCP4.5, suggesting that the relevant processes
are controlling the Arctic response in summer. The positive partial
temperature changes by the surface effect, cloud greenhouse effect, and
synergy effect are common in ΔMH and ΔRCP4.5 in autumn.
Together with the concurrent largest warming, it is suggested that the MH
Arctic warming in this season is strongly relevant to the future Arctic
warming. While the contribution from downward clear-sky LW radiation to the
partial temperature change is large throughout the year for ΔRCP4.5,
it plays a role only in some months for ΔMH. As the near-surface air
temperature is thermally coupled to the surface temperature as shown in Fig. 9, it was thought that the partial temperature change by downward LW
radiation behaves similarly to the total surface temperature change. In the
ΔMH, however, the contribution by this component is small in winter.
As this term consists of vertically uniform temperature change, lapse-rate
change, and water vapor change, the different behavior does not immediately
mean that the mean tropospheric temperature response is decoupled from the
surface. Nevertheless, it is possible that the different behavior is caused
by the remote influence from lower latitudes where insolation is reduced for
ΔMH. In any case, this difference may weaken the similarity in the
surface temperature response between ΔMH and ΔRCP4.5.
As expected from the magnitude of the influence, the processes found to be
important for the warming trend from summer to autumn in ΔMH and
ΔRCP4.5 are also primarily responsible for the model spread in these
seasons. What is interesting is that the processes contributing to the model
spread in other seasons are relatively similar between ΔMH and
ΔRCP4.5 even when the ensemble mean surface temperature response is
very different. The most notable example is spring when cooling occurs in
ΔMH and warming occurs in ΔRCP4.5. Such a discordance can
occur because the feedback with the largest magnitude is not necessarily the
feedback with the most uncertainty. In the global mean radiative feedback
analogy, for example, Planck and water vapor feedbacks have large magnitude
but the response to the smaller SW cloud feedback is thought to contain the
most uncertainty. In spring, the albedo feedback and downward clear-sky LW
radiation are the major contributors to the model spread. As discussed in
the above, the temperature response in ΔMH is not highly similar to
the future Arctic response in this season. Nevertheless, the model spread
occurs through similar feedback processes. This result suggests that if the
models are constrained by ΔMH proxy reconstruction in this season,
there is a potential that the constraint may affect the future Arctic
projection in the same season even though the response is not alike. In this
sense, ΔMH Arctic change is useful for constraining future Arctic
projection in all seasons. However, the confirmation of this statement
requires a rigorous statistical analysis.
In the current analysis, the target variable of interest is surface
temperature change, and an emphasis was made on atmospheric feedbacks.
Previous studies reported that many important feedbacks also reside in the
interaction of sea ice and ocean (Goosse et al., 2018). For example, sea ice grows faster when it is thin and this feedback
works to counter warming. While sea ice related terms such as albedo
feedback (a function of ice cover among others) and heat release from the
ocean (a function of ice thickness among others) are diagnosed, the ice
thickness feedback itself was not quantified in the current study. Such a
diagnosis would require an energy budget analysis for sea ice and probably
for the mixed-layer ocean as well, and it is worth further investigation in
the future.
Recently, Hu et al. (2017) argued that “the global warming
projection spread is inherited from the diversity in the control climate
state”. They also pointed out a possibility that the diversity of feedbacks
can arise from the same control climate state which may be constructed from
the compensation of different processes. We add to these points that there
may be a systematic bias or uncertainty due to common missing feedbacks in
many climate models that do not appear as the model spread. The paleoclimate
has the potential to provide a constraint for the future projections in the
second and third cases, beyond the emergent constraint. Related to this
discussion, there remains an outstanding issue to be explored.
O'Ishi and Abe-Ouchi (2011) showed that the vegetation change in
response to climate change in both the mid-Holocene and elevated CO2
experiments amplifies the Arctic warming. In particular, the expansion of
boreal forest in place of tundra lowers the surface albedo through earlier
snow melting and leads to the amplification of continental warming in spring
and subsequent maritime warming in winter. None of the models analyzed in
the current study include the effect of climate–vegetation interaction.
Therefore, the conclusion of the current study needs to be verified by
models with a dynamic vegetation component.
The current study focuses on the mid-Holocene partly because multi-model
simulations for this period are easily accessible through the CMIP5 data
archive, and the compiled reconstruction dataset is also available. There
are, however, other periods that appear to exhibit larger Arctic warming
such as the last interglacial (MIS5e), MIS11, and mid-Pliocene (Berger et al., 2016; Dutton et al., 2015; Lunt et al., 2013). These warm periods surely
would be useful for expanding the analysis conducted in this study. While
the energy balance feedback analysis has been applied to the MH, LGM, and
mid-Pliocene (Braconnot and Kageyama, 2015; Hill et al., 2014), which are
very useful for understanding past climate change, a study focusing on the
relevance to the future is encouraged. It should be straightforward to
expand the current study to other periods once the multi-model simulations
are easily accessible. In addition, the current analysis does not separate
the downward LW radiation in the Arctic region into local and remote
origins, and thus provides only a local feedback perspective. As the change
in orbital configurations redistributes the insolation latitudinally, a
significant change in the meridional heat transport is expected. The change
in the meridional heat transport by both the atmosphere and ocean in
response to the wider variety of orbital configurations is worth further
investigation in the future. Furthermore, expanding the current study to
cases with more general astronomical forcing (e.g., only considering the
effect of the obliquity change or precession change), and to consider the
implications for the mechanism for glacial–interglacial cycles (e.g.,
Abe-Ouchi et al., 2013) may also be valuable.
Conclusions
The relevance of Arctic warming mechanisms in the MH to the future under the
RCP4.5 scenario was investigated. The emphasis was placed on the surface
temperature change over the ocean where peak warming occurs nearly in the
same season for both periods and the model spread is large. Although the
insolation in the Arctic region decreases in autumn for the MH relative to
the modern period, the largest MH Arctic warming occurs in autumn. Although
the elevated CO2 radiative forcing is rather uniform globally and
seasonally, the largest future Arctic warming also occurs almost in the same
season as for the MH. Within the limited range of processes investigated,
the current study suggests that the dominant processes causing the Arctic
warming trend from summer to autumn in the MH and in the future are common:
positive albedo feedback in summer (though partially counteracted by the
sunshade effect from clouds), the consequent increase in heat release from
the ocean to the atmosphere in the colder season when the atmospheric
stratification is strong, and an increased greenhouse effect from clouds
during the season with small insolation. A chain in the seasonal progress
was elucidated by a decomposition into factors associated with SST, ice
concentration, and ice surface temperature changes, whose temporal links are
less clear from the conventional surface energy balance analysis alone. In
addition, the synergy effect of local Arctic feedbacks and local warming
sensitivity contributes to the enhanced warming during the cold season for
both cases. There are some differences, however. The contribution from the
downward clear-sky SW radiation is large positive in summer and negative in
autumn for the MH, but it plays only a minor role in the future.
Furthermore, the large contribution from the downward clear-sky LW radiation
occurs throughout the year for the future projections, but it is only
distinct in April–May and July–October for the MH.
The downward clear-sky LW radiation is one of the major contributors to the
model spread for surface temperature changes throughout the year. Although
whether this term originates from remote sources or local feedbacks is
unclear from the current analysis, the importance of this term is common for
the model spread in the MH and the future simulations. The processes found
to be important for the warming trend from summer to autumn (albedo
feedback, surface effect, cloud greenhouse effect, and synergy effect) are
also found to be primarily responsible for the model spread in these
seasons. The dominant feedbacks for the model spread depend on the
season – albedo feedback for spring and summer, and surface effect for
autumn and winter – although the importance of the inter-seasonal linkage of
feedbacks is not excluded. Cloud feedbacks are less important for the model
spread in summer and a small contribution from downward clear-sky SW
radiation is found throughout the year.
The fact that MH ocean warming in the Arctic is moderate in all seasons except for
late autumn to early winter and the model spread is large in the cold season
underlines the importance of model validation with proxy reconstruction in
the cold season. However, the factors contributing to the model spread are
also common between the MH and the future in other seasons, including
spring, when opposite signs of temperature response occur. This result
suggests that the MH Arctic change may not be directly relevant to the
future in some seasons but it is still useful to constrain the future Arctic
projection. In this sense, the seasonal evolution of surface temperature
response in the MH Arctic is a useful variable. In practice, however, the
available constraint would be limited to the cold season when the
temperature response over the ocean is well correlated with that over land
across models. The significant correlation found between the summer albedo
feedback and autumn–winter temperature response across models suggests that
feedbacks in preceding seasons should not be overlooked and the sea ice
cover may be another useful constraint.
The relevance between past and future climate arises not only from a common
forcing to the climate system but also from the feedbacks inherent in the
climate system. While basic physical principles do not change with time, it
is not trivial that the dominating processes for the climate variations are
the same for different climate forcing and response. Therefore, more effort
should be made in seeking possible analogues in the dominant physical
processes between the past and future climate, rather than in the past
forcing. The following points are highlighted from the current study.
Many of the dominant processes that amplify Arctic warming over the ocean
from late autumn to early winter are common between the two periods, despite
the difference in the source of the forcing (insolation vs. greenhouse
gases).
A chain of processes responsible for the warming trend from summer to autumn
can be elucidated by the decomposition to factors associated with SST, ice
concentration, and ice surface temperature changes.
The downward clear-sky longwave radiation is one of major contributors to
the model spread throughout the year. Other controlling terms vary with the
season, but they are similar between the MH and the future in each season.
The MH Arctic change may not be analogous to the future in some seasons when
the temperature response differs, but it is still useful to constrain the
model spread in the future Arctic projection.
The significant cross-model correlation found between the summer albedo
feedback and autumn–winter surface temperature response in both forcing
cases suggests that the feedbacks in preceding seasons, particularly sea ice
cover, should not be overlooked when determining constraints.
Data availability
The PI, MH, HIST, and RCP4.5 simulation data can be downloaded from the ESGF
server (https://esgf-node.llnl.gov/search/cmip5/, last access:
12 March 2019) (ESGF, 2019) as piControl, midHolocene, historical, and
rcp45. Temperature reconstructions from proxy data used in Fig. 4 are taken
from Table 1a of Sundqvist et al. (2010). Temperature
reconstructions from proxy data used in Fig. S2 can be downloaded from the
PMIP3 web site (https://pmip3.lsce.ipsl.fr/, last access: 12
March 2019) (PMIP3, 2019). ERF data calculated with MIROC4m-AGCM are
available from the corresponding author upon request. Computer codes used
for the analysis for Figs. 5, 9, and 12 were written in Fortran and they are
also available by request except for a random number generator (ran3) taken
from Press et al. (1992).
The supplement related to this article is available online at: https://doi.org/10.5194/cp-15-1375-2019-supplement.
Author contributions
This study was developed based on parts of MS's bachelor's and master's theses
at Hokkaido University. MY designed the analysis. MS conducted the initial
analysis, which was completed by MY. MY prepared the manuscript with
contributions from MS. Both authors contributed to the interpretation of the
results.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We are thankful to Francois Massonnet and two anonymous reviewers for their
useful suggestions which helped us to improve the manuscript substantially.
The method for diagnosing the surface temperature change described in Sect. 3b originates from discussions with Alexandra Laîné in previous
works. This study also benefitted from discussions with Ayako Abe-Ouchi. We acknowledge the World Climate Research Programme's Working
Group on Coupled Modelling, which is responsible for CMIP, and we thank the
climate modeling groups (listed in Table 2 of this paper) for producing and
making available their model output. For CMIP, the US Department of
Energy's Program for Climate Model Diagnosis and Intercomparison provides
coordinating support and led the development of the software infrastructure,
in partnership with the Global Organization for Earth System Science
Portals. We thank PMIP for coordinating the experiment and preparing the
dataset. We also thank the developers of the freely available software, NCO,
CDO, and NCL. The calculation of the radiative forcing with MIROC4m-AGCM was
carried out using the JAMSTEC Earth Simulator 3, and the support from the
MIROC model development team is appreciated.
Financial support
This research has been supported by the Japan Society for the Promotion of Science (KAKENHI grant no. JP17H06104) and the Ministry of Education, Culture, Sports, Science and Technology (Arctic Challenge for Sustainability (ArCS) project).
Review statement
This paper was edited by Qiuzhen Yin and reviewed by Francois Massonnet and two anonymous referees.
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