Introduction
Improving our knowledge of the last millennium (LM) climate variability is
key for a better understanding of the mechanisms that determine the Earth
system response to natural (solar, volcanic and orbital) and anthropogenic
(land use and atmospheric composition changes) external forcings
. Instrumental records represent the most adequate
alternative to study past climate variations. However, they only provide
coverage since the mid-19th century e.g.,. Therefore, to understand the nature of climate variability operating
on longer temporal scales, LM reconstructions from a variety of proxy data
e.g., tree-rings, corals, preserved pollen, ice cores,
etc.; and simulations using general climate models (GCMs) are
generally employed.
The current available LM temperature reconstructions agree, depicting a
general pattern of temperature evolution going from a relatively warmer
period at the beginning of the LM (MCA; Medieval Climate Anomaly) to a colder
period from about 1450 to ca. 1850 (LIA; Little Ice Age), which is interrupted by the
industrial warming in the 19th century . Despite this
general agreement, there is still a large range of uncertainty that stems
from different sources, including different reconstruction methods, various
calibration and verification processes, spatial and temporal coverage, the
different proxy locations (land, land and ocean, etc.) and the alternative
statistical methods employed e.g.,. For
instance, the range of uncertainty during the MCA regarding reconstructing Northern
Hemisphere (NH) temperature is about 0.6 ∘C. Furthermore,
estimate that the cooling during the Maunder Minimum
(1645–1745) relative to present is about 0.7 ∘C, while
report a colder LIA (1.4 ∘C) which is similar to
findings by .
Addressing the range of uncertainties in reconstructing past temperature
changes is relevant not only for assessing our understanding of past
temperature changes and the confidence we have on available estimates
, but also for model–data comparison exercises
e.g., and for constraining the range of estimates of the
system response to changes in the external forcing, i.e., the sensitivity of
the climate system .
Borehole temperature inversion is a well-established reconstruction technique
and leans on two main assumptions: first, surface air temperatures (SAT) are
expected to be closely coupled to ground surface temperatures (GST); second,
variations in SAT propagate downward through the subsurface
via conduction . As a result, a
thermal signature of the surface temperature is imprinted in the subsurface.
This reconstruction technique is limited to recovering only the low-frequency
information (decadal, and longer timescales) since the soil acts as a
low-pass filter and progressively filters out the higher-frequency variations
with depth. Oscillations with periods on the order of days only penetrate
∼50 cm deep , seasonal cycles are solely observed
close to the surface (<10 m depth), decadal variations propagate within
the upper 50 m and multi-centennial changes within the LM are observed in
the upper 500 m below the surface .
As with every type of reconstruction method, the borehole technique is also
subject to diverse sources of uncertainty. One of them is that the
assumption of a conductive heat transfer may not always be substantiated,
as non-conductive processes such as advection and convection may influence
or even dominate the subsurface thermal regime in areas
with important groundwater flows or geothermal activity. Nevertheless, a
number of studies suggest that the impacts of such processes can be reduced
with the appropriate treatment of the affected borehole temperature logs
e.g
Additionally, the most important contributions to uncertainty arise from
changes in surface processes that affect the SAT–GST coupling. Snow cover is
especially important since it insulates the ground surface from the cold
winter air causing large differences between the soil and air temperatures
e.g.,. The impact
of this effect on borehole theory has caused considerable discussion.
argued that borehole-based reconstructions may be
substantially biased by this seasonal influence of snow cover. Hence, under
changing snow cover conditions GST may be not completely representative of
SAT variations. argued against this based on the assumption
that SAT and GST coupling is strong at longer than seasonal timescales;
therefore, snow biases would only influence the high-frequency oscillations.
Additionally, the long-term SAT–GST coupling has also been supported by
observations and modeling assessments
. Besides snow cover,
other land surface and soil properties such as soil water content, vegetation
and land use land cover (LULC) changes also have the potential to impact
SAT–GST coupling. For instance, deforestation, afforestation and other
land cover changes modify the land-surface properties including albedo,
roughness and evapotranspiration altering the energy transfer between the
atmosphere and the ground . In a recent study,
found that deforestation tends to warm the ground surface
mainly by reducing the transport of heat away from the surface. They also found
that such continuous vegetation changes would result in long-term surface
temperature anomalies; thus, deforestation should be considered as a
possible source of bias for temperature reconstructions from subsurface
temperatures.
One way of addressing the uncertainties of paleoclimate reconstruction methods is
by using climate model simulations as a surrogate reality in which pseudoproxy
records of varying complexity are created and reconstruction methods are
replicated in pseudoproxy experiments (PPEs) mimicking real-world cases
. The robustness of using the borehole method for
reconstructing past SAT variations has been tested in PPEs
e.g.,.
investigated the relationship between simulated SAT and
GST from interannual to centennial timescales in a forced climate simulation
of the LM (1000–1900 common era; CE) using the ECHO-G GCM .
They found that in spite of the seasonal and longer-term variability of snow
cover, the coupling was stable for decadal and longer timescales, meaning that GST
should be a good proxy for long-term SAT variations.
extended this analysis by implementing the borehole method in the simplified
reality of the same model. They simulated underground temperature
perturbation profiles using a heat-conduction forward model driven by
simulated GST. Then, they applied an inversion approach to reconstruct ground
surface temperature histories from the simulated profiles. Their results
supported the overall performance of the borehole methodology.
employed a similar approach to
in order to retrieve past GST variations from GCMs: both studies applied an inversion method.
They simulated global synthetic temperature profiles using a
one-dimensional conductive model driven by simulated GST as an upper boundary
condition from the CMIP5/PMIP3 Coupled Model Intercomparison Project
phase 5 / Paleoclimate Modeling Intercomparison Project phase 3; LM
simulations. Unlike the GCMs used in the early works that do not incorporate
some external forcings, the PMIP3/CMIP5 simulations include a larger
representation of LM forcings than pre-PMIP3 experiments incorporating a variety of land-surface model components. Their results
have reinforced the reliability of recovering past global surface temperature
variations from subsurface temperature measurements using current
state-of-the-art GCMs.
The previous analyses support estimations of global/hemispheric past
temperature obtained from borehole temperature inversions. First, they
support the overall performance of the methodology in retrieving past GST
histories from borehole profiles. Second, the use of up-to-date forcing
representations in CMIP5 model ensembles also ensures that long-term
alterations of surface properties like those induced by LULC changes, the
effect of anthropogenic aerosols cooling and the potential long-term snow
cover feedbacks induced by both forcings, do not seem to bias inversion
results at global/hemispheric scales. In spite of these positive results, the
analysis of last generation model experiments, including the complete set of
agreed CMIP5 forcings in the so-called “all-forcing”
experiments, does not allow for insights into the individual effect of each of the
forcings and the related feedbacks, at either global/hemispherical or
continental/regional scales. This would be desirable in order to quantify the
effect of the individual forcings on SAT–GST coupling and to obtain estimates
of its particular temporal evolution which would allow for the disentanglement of its
contribution from that of other forcings. Therefore, the present work
considers all-forcing and single-forcing types of experiments in order to
address how state-of-the-art climate models simulate SAT–GST coupling from
global to regional scales and to evaluate the potential influence of the
external forcings on the SAT–GST relationship. This will also provide
information about where and when a decoupling of SAT–GST may exist, with
implications for borehole inversion practices at different spatiotemporal
scales. For this purpose, we use the Community Earth System Model–Last
Millennium Ensemble CESM-LME;, which is the
largest existing ensemble of LM simulations with a single model to date and has not
been used in previous assessments of this kind. The CESM-LME includes all-
and single-forcing experiments that jointly or separately consider the
transient evolution of solar variability, volcanic activity, orbital changes,
greenhouse gases (GHGs), anthropogenic aerosols and LULC (see details in
Sect. )
Using all-forcing simulations (ALL-F hereafter) allows for the evaluation of the
SAT–GST relationship throughout the LM with a realistic representation of
real-world conditions. Additionally, the single-forcing experiments are
suitable for identifying the specific role that each forcing might play and its
“fingerprint” on the presence of biases in the SAT–GST
coupling. Most external natural (solar and volcanic variability) and
anthropogenic (GHGs, LULC and aerosols) forcings have the potential to
indirectly affect SAT–GST thermodynamics through snow cover feedbacks
. Moreover, LULC changes can also have a direct influence
on SAT–GST. Over the LM the Earth′s land cover has been
substantially modified by the replacement of natural ecosystems with
agricultural land, especially since the industrial period
. Such land use changes have the potential to alter the surface
energy balance by modifying energy fluxes and moisture budgets
leading to direct impacts on the atmosphere and
ground–surface temperature relationship . Furthermore,
vegetation changes may additionally lead to indirect non-linear effects that
are relevant in the borehole context. The most important of these is the fact that
deforestation at high latitudes leads to an increase in snow cover; this is due to the fact that low
vegetation accumulates continuous snow cover more readily than forests in
early winter favoring its permanence longer in spring .
The local-, regional- and large-scale implications of these interactions on
long-term climate variability and specifically on the validity of the
borehole method assumptions have not been explored so far.
The first part of this paper (Sect. ) describes the
CESM-LME simulations and forcings used in the experiment. Subsequently,
Sect. describes the methodological approach employed for the
analysis of the coupling between SAT and GST during the LM.
Section presents the main results in the analysis of the global
covariance of SAT, GST and soil temperature (ST) at different model depths
throughout the LM. This analysis includes the global SAT–GST long-term
coupling at annual and seasonal timescales. The latter helps identify the
impacts of seasonality on the SAT–GST coupling. In addition, the spatial
distribution of the covariance structure, as well as the SAT–GST offset, are
illustrated. This allows for the detection of possible failures in the
air–ground temperature coupling at regional and local scales. The spatial
analysis is extended to investigate the heat transfer within the shallow
subsurface by comparing GST relative to deep model layers.
Section specifically addresses the long-term trend of the
SAT–GST relationship in order to evaluate whether this association
experiences variation with time during the simulated LM. Finally,
Sect. provides a discussion about the implications of
decoupling processes at different temporal and spatial scales for the
borehole temperature reconstruction method.
Results
CESM-LME supports the assumption that SAT is tightly coupled with GST at
global scales and longer than multi-decadal scales. Figure a
illustrates the stability of the coupling at global scales during the LM
using the ALL-F2 ensemble member as an example. Results are comparable for
other ALL-F ensemble members. The LM evolution of global continental SAT,
GST, STL8 and STL15 anomalies relative to the
1850–2005 mean are shown. For STL15 non-filtered model output is
represented and evidences the low-pass filter influence of the heat
conduction below the surface, whereas for SAT, GST and STL8 31-year
running mean low-pass filter outputs are shown. Subsurface temperature
anomalies closely track SAT anomalies with relative small differences between
them; this indicates that air and soil temperature are coupled above
multi-decadal timescales. The correlation coefficients for the 31-year
filtered series in Table indicate high correlation
(p<0.05) for the soil layers close to the surface that diminishes slightly
with depth, as expected, due to the phase shift of the signal.
Table also indicates that the correlation considering the
high-frequency variations (yearly; left column) is only high at the levels
close to the surface, whereas at the deepest layer the correlation is low
since the high-frequency variations are progressively filtered out and phase
shifted as depth increases. Hence, the assumption of conductive heat transfer
within the subsurface is realistically represented in the CESM-LME
simulations.
Despite the strong coupling between air and subsurface temperatures at global
scales, the existence of a relatively small offset between SAT and GST that
grows backwards in time is evident for the annual (Fig. a)
averages. This indicates a slight long-term decoupling between SAT and GST.
Previous works have argued that the nature of this offset arises from the
changes in snow cover and its influence insulating the ground from very cold
air temperatures e.g.,. In order to explore the
influence of processes such as this on the global SAT–GST relationship, this analysis
is extended to consider the boreal winter and boreal summer seasons (DJF and JJA
hereafter) independently (Fig. c, e). The offset between SAT
and GST observed in the annual plot is only apparent in the DJF season.
Indeed, the differences are larger during this season than in the annual
data, whereas the JJA evolution of SAT and GST anomalies is virtually identical. In
addition, even if SAT and GST are highly correlated and significant for both
seasons, Table suggest a slightly lower correlation for DJF
than for JJA. This feature and the largest offset occurring in DJF suggest an
important role for snow cover. Figure b, d, f further
illustrate the strong relation between the SAT–GST offset and snow cover by
displaying the 31-year low-pass filter outputs of the LM evolution of global
snow cover and SAT–GST differences. Note that in DJF, due to its
influence on annual averages, the decrease (increase) in snow cover leads to
a decrease (increase) in the SAT–GST offset. This is due to the insulating
effect of snow that keeps GST close to zero while SAT can reach large
negative values. Thus, an increase in snow cover leads to larger negative
SAT–GST differences. For JJA on the contrary, an in-phase relationship is
found at all timescales. Long-term trends change in both snow cover and in
SAT–GST after the end of the 19th century. During the boreal summer increases
in snow also enhance SAT–GST differences due to the insulation of the ground
from the warmer summer SAT, whilst the opposite is noted for snow cover decreases.
This effect dominates the global average over that of the JJA austral winter
during which SAT–GST and snow cover changes experience an anti-phase
relationship as described above. Therefore, the NH influences anti-phase
covariability of snow cover and SAT–GST during DJF (detrended correlations,
r=-0.52; p<0.05) and annual (r=-0.67; p<0.05) and in-phase
covariability during JJA (r=0.62; p<0.05).
Beyond the anti- and in-phase covariability at multi-decadal to centennial
timescales, changes in the longer-term relationship between SAT and GST can
play an important role in decoupling with implications for the borehole
theory. At global scales, the long-term offset is relatively small, as shown
in Fig. , and therefore has very limited implications.
Nevertheless, it is interesting to asses the consistency and relative
magnitude of changes in snow cover and SAT–GST. Two-phase regression in
annual and seasonal SAT–GST (Fig. b, d, f) shows consistent
date of change during the 18th century. All changes are towards smaller
SAT–GST differences and are significant for annual and DJF (p<0.05). For snow
cover trends, using the same dates of change as for SAT–GST, indicate
anti-phase (in-phase) relationships for the annual and DJF (JJA) periods as in the
multi-decadal timescales described above. However, changes in snow cover
trends during the last centuries are small and can hardly be invoked to
solely account for the comparatively larger SAT–GST trend changes. This calls
for the consideration of other possible mechanisms and a more spatial perspective.
(a) Annual, (c) DJF and (e) JJA spatial
distribution of SAT minus GST differences during the 850–2005 CE period.
(b) Annual, (d) DJF and (f) JJA spatial
distributions of the correlation coefficients between SAT and GST for the same period.
Black dots indicate that 80 % of the members within the ALL-F ensemble
deliver statistical significance (p<0.05).
The spatial variability of the relationship between SAT and GST gives further
insights into the role of different processes on the SAT–GST coupling.
Figure shows the spatial distribution of the differences
between mean LM SAT and GST as well as the correlation coefficients for the
annual, DJF and JJA averages in the ALL-F ensemble. In the case of the
annual temperatures (Fig. a), GST is generally warmer than
SAT with the differences being low over most of the globe (less than 2 ∘C)
except in the NH mid- and high-latitude areas where the differences are
higher (up to 15 ∘C). The correlation maps (Fig. b)
provide a similar pattern with high and significant values over most of the globe
(>0.8 in regions located between 45∘ N and 90∘ S) and
lower correlations over NH mid- and high-latitudes, especially over eastern
Siberia. Similar behavior is seen in the DJF season although it is more
pronounced. During these months, GST is much warmer than SAT, reaching
differences up to 30 ∘C in the northernmost parts of North America
and Eurasia (Fig. c). Differences are smaller at mid- and
low-latitudes. The influence of the ocean over coastal areas providing larger
SAT relative to GST is noticeable. Similarly, the correlation is lower over
the northern snow covered areas (Fig. d), while over the rest
of the globe it remains high. In contrast, Fig. e, f show
that during JJA, when the snow cover is scarce, the SAT–GST coupling is
strong globally with temperature differences lower than 2 ∘C and
high correlation coefficients (>0.9). Consequently, the role of snow
cover in decoupling SAT and GST is highlighted. Positive correlation values
are low over borderline areas where snow cover is more variable, which
produces variability in the SAT–GST offset and thereby alters the
covariance structure. Close to these areas in central Asia the high negative
correlation within the Tibetan Plateau is noteworthy (see discussion below).
Evolution of DJF and JJA SAT, GST, STL6,
STL8, STL10, STL12 and STL15
and the percentage of snow cover during the 1900–2005 CE period for a
grid point from the ALL-F2 simulation (northeast of Russia;
60∘ N, 45∘ E) where snow cover is characteristic during the
cold season. Dashed lines indicate 0 ∘C.
Figure illustrates the SAT–GST decoupling due to the snow
cover at the local scale for a particular grid point as an example. The
grid point is located over a region with considerable snow cover during the
boreal cold season (northeastern Russia). The DJF and JJA evolutions of SAT,
GST and ST at different depths and the snow cover for the last 105 years from
the ALL-F2 simulation are shown. Note that in DJF snow covers
100 % of the grid cell during almost the whole period. Thus, the soil is
insulated and the difference between SAT and GST is ∼-15 ∘C
on average. The temperature of the deeper layers is presented in order to
illustrate the amplitude attenuation and phase shift with the depth of the
temperature signal. Note that during DJF, GST is only slightly below
0 ∘C, and the agreement of its variations with those of SAT is only
noticeable in the largest changes of both, while STL6 and deeper STs are
above 0 ∘C. In JJA, SAT and GST are very similar and their low
frequency variability propagates to deeper levels, all above 0 ∘C.
Some aspects of the SAT–GST spatial distribution deserve further attention.
One of the most noteworthy of these is the fact that over the Tibetan Plateau region
temperature differences between SAT and GST are as large as in the mid- and
high-NH latitudes for both annual and DJF periods (Fig. a, c).
However, SAT and GST are negatively correlated for annual and DJF seasonal
resolution data (Fig. b, d), with the Tibetan Plateau being the only region of the
globe where this occurs. Nevertheless, the correlation for JJA is positive and
high (Fig. e). The nature of this opposite phase arises from
discontinuous snow cover over this region and the very low air temperatures
during DJF. Usually, the snow cover insulates the soil from the colder air,
avoiding the heat exchange with the atmosphere (as shown in
Fig. ). Nevertheless, discontinuous snow cover only partially insulates the
soil, leading to this particular SAT–GST interaction.
Figure displays this behavior for a grid point located
over the Tibetan Plateau. SAT, GST, ST at different depths, snow cover and
surface sensible heat flux (SHFLX) are shown. In DJF during periods of low
snow cover the fraction of surface exposed to the atmosphere allows for
energy exchange from the warmer soil to the colder air. Conversely, when
snow cover is high, the large fraction of insulated soil reduces almost
completely the heat transfer from the soil to the atmosphere. Therefore, with
lower (higher) fractions of snow cover, higher (lower) heat transfer takes
place with GST decreasing (increasing) and SAT increasing (decreasing).
Indeed, there is a high negative correlation (-0.84; p<0.05) between the
snow cover fraction and SHFLX which is the main way that energy dissipates
within this region since latent heat fluxes (LHFLX) are negligible.
Higher/lower albedo due to variations in snow cover fraction also contribute
to the negative SAT–GST correlation over this region (not shown). Horizontal
model resolution does not seem to be an issue since a higher resolution
version of the model CCSM4; produces a similar behavior
which other models of similar resolution do not show . During JJA
in comparison, snow cover is negligible and SAT–GST coupling is consequently strong.
Note that GST and all ST are above zero and GST is higher than SAT as it is
warmed by radiative gain and the transfer of heat to the atmosphere, hence the
positive correlation (0.5; p<0.05) between SAT–GST and SHFLX.
(a) DJF and JJA evolution of SAT, GST, STL6,
STL8, STL10, STL12 and STL15
for a grid point from the ALL-F2 simulation (Tibetan Plateau region;
31∘ N, 90∘ E) during the 1900–2005 CE period. Dashed
lines indicate 0 ∘C. (b) Percentage of snow cover and
surface sensible heat flux (SHFLX).
The spatial SAT–GST differences during JJA (Fig. e) depict
other relevant aspects that have an influence on the SAT–GST relationship at
relatively short timescales. SAT is generally colder than GST globally.
However, for JJA, there are large areas inland, mainly located in the
southeastern US, some parts of central and eastern Europe and
eastern Asia, with warmer SAT relative to GST. Variations in LHFLX from DJF
to JJA drive this effect. Figure shows that the areas
where LHFLX increases in JJA relative to DJF are related to the same areas
where SAT is higher than GST in JJA (Fig. e). Therefore,
there is a direct relation between the increase in evapotranspiration in JJA
and the ground temperature response at these locations. The time series in
Fig. display this behavior for a grid point located over
southeastern China. During JJA the surplus of energy due to higher solar
radiation reaching the surface is mostly dissipated as latent heat leading to
a net heat loss at the ground surface. Note the anticorrelation between
the surface soil moisture and LHFLX (-0.46; p<0.05) during JJA as well as the
large SHFLX values that occur when soil moisture is at its lowest and
evapotranspiration is limited. Therefore, the high rate of LHFLX contributes
to cool the surface and GST tends to be lower relative to SAT. Soil water
content also exhibits large changes during JJA consistent with the large
evapotranspiration (Fig. a) and provides a source of
moisture that contributes to temperate SAT and cool GST. Large variations in
evapotranspiration from DJF to JJA are also present at midlatitudes of the
SH summer continents (America, Africa and Australia), although only a very
limited impact on Fig. e is perceived, especially over the
western coast. Over these regions, high incoming energy impinging the surface
during SH summer (not shown) supports high rates of latent heat fluxes.
However, as soil water becomes a limiting factor, more energy is dissipated
as sensible heat and the ground surface is warmed. Therefore, GST experiences
higher temperature than SAT on average. Figure also shows
high evapotranspiration over the tropical rainforest in America and Africa,
both in DJF and JJA, which does not translate to positive SAT–GST differences.
Over the rainforest, the energy fluxes at the surface do not vary
significantly from DJF to JJA since incoming radiation is relatively constant
throughout the year, and is transferred to evaporation and evapotranspiration
within the canopy, while soils are well watered by precipitation to support
the large amounts of evapotranspiration. This situation leads to a small
range of variation in both SAT and GST with very small differences between
them and higher GST relative to SAT.
(a) DJF and JJA average surface latent heat fluxes (LHFLX)
over the 850–2005 CE period. (b) DJF and JJA time evolution of
SAT, GST, STL6, STL8, STL10,
STL12 and STL15 during the 1900–2005 CE period for
a grid point from the ALL-F2 simulation (southeastern China,
31∘ N, 111∘ E). (c) Same for SHFLX, LHFLX and
water content in the top 10 cm of soil (W-10 cm).
Figure a, c, e show similar higher SAT values relative to GST
at some coastal areas in both JJA and DJF. During DJF the effect is mainly
present in the NH midlatitudes, such as in most European coastal areas,
both the east and the west coasts of North America, and in Japan and
the east coast of China. During JJA, in comparison, this
behavior is mostly seen in the SH midlatitudes, such as southern South
America, South Africa and southern Australia. Interestingly, higher SAT
relative to GST is also evident in some coastal areas over tropical regions; this applies for both
JJA and in DJF, and is mainly observed over southeastern Asia, the southern areas of the Indian
subcontinent, the Gulf of Guinea and some areas of South and Central America.
In the CESM-LME the atmospheric grid box of the coastal areas is partitioned
into land and ocean fractions. For the areas with sea ice formation, an
additional sea ice fraction is considered . This
configuration of the coastal grid points leads to a partition of the energy
fluxes at the surface into those of the land fraction and those of the ocean
fraction. During the cold season, partitioning such as this determines the higher SAT
warming relative to GST, as the relatively low net radiation that impinges
the surface at mid- and high-latitudes limits the ground surface heating as
well as the energy fluxes out of the land surface fraction. In contrast, the
energy fluxes from the ocean surface to the air above are large, primarily as a
result of the temperature difference between the water and the comparatively
colder air above. The dissipation of energy from the ocean fraction to the
atmosphere warms the air, so the net effect is higher SAT relative to GST
in the winter season. Over the tropical coasts that exhibit the same
behavior, the energy fluxes out of the ocean fraction of each grid point also
contribute to the higher warming of the air relative to the ground surface.
Nevertheless, at these locations high rates of evapotranspiration
all year long also play an important role as they generate evaporative
cooling of the ground surface, as seen in the example described in
Fig. .
(a) Annual, (c) DJF and (e) JJA spatial
distribution of GST minus STL8 differences during the
850-2005 CE period. (b) Annual, (d) DJF and (f) JJA
spatial distribution of the correlation coefficients between GST and
STL8 for the same period. Black dots indicate that 80 % of
the members within the ALL-F ensemble deliver statistical significance
(p<0.05).
The different examples used to illustrate the most important processes that
may influence the air and soil temperature relationship at short timescales
also depict relevant information about the propagation with depth of the
annual cycle. For instance, at the grid point located over southeast
China in Fig. , both in DJF and JJA, the temperature offset
between contiguous levels is noticeable with a gradient of about
5 ∘C in the first meter of the ground and of about 10 ∘C
down to the lowest level. Comparable pictures with some differences in the
magnitude of gradients can be seen in the previous figures.
Therefore, it is interesting to also understand the propagation of
temperature below the surface. GCMs simulate purely conductive regimes, and
the temperature variations that propagate to deeper soil layers are
established at or near the ground surface . Thus it is
important to asses the propagation of the temperature signal within the
shallow subsurface. This issue is addressed by analyzing the relationship
between GST and STL8.
Figure a provides a spatial view of the temperature
differences between GST and STL8 for annual, DJF and JJA periods. The
correlation is also shown in the right panels. SAT–GST differences, for DJF
and JJA show the yearly cycle of temperature with negative (positive) SAT–GST
for the NH (SH) in DJF and vice-versa for JJA, illustrating the conductive
regime within the shallow subsurface. The annual temperature differences are
low and the correlation is high almost globally as it is the balance between
the respective patterns in JJA and DJF. However, the northernmost part of the
globe exhibits larger temperature differences (between 4 and 5 ∘C)
and lower correlation coefficients. The DJF and JJA patterns show that the
annual offset and correlations for this part of the globe are mostly the
result of the larger weight of those in JJA given that, during these months,
the temperature differences for a latitudinal band at ca. 60–70∘ N
are as large as 15 ∘C and the correlation coefficients are close to
zero. describe a similar behavior in some of the GCMs used in
their analysis, detected over areas where frozen ground persists during JJA.
Indeed, the nature of the large departure in the temperature response at the
shallow subsurface at these locations arises from non-conductive processes
related to latent heat release/uptake of freezing and thawing of the water
content above a depth of 1 m that may account for the subsurface heat transfer
.
To illustrate this mechanism, Fig. shows the temperature
evolution of SAT, GST and ST at different depths as well as the soil ice
content (SIC) in the upper soil layers for a grid point located in the north
of Canada. Over these areas the SIC in the upper 1 m of soil increases
(decreases) during the cold (warm) season. During JJA, SAT and GST
increase/decrease at the same rate since no ice is present at the ground
surface so it is warmed by radiative gain and heat is transferred to the
atmosphere. However, for deeper soil layers, the energy available is employed
to melt the SIC (note the lower SIC in L6 during JJA relative to DJF), and
latent heat is required so that these layers do not experience a temperature
increase like the shallowest layers. Therefore the temperature at L8 (∼1 m
depth) is kept below/near 0 ∘C during the warm season due to the
zero-curtain effect , while GST is centered around
12 ∘C, leading to differences of ca. 15 ∘C between GST and
STL8 and a low correlation coefficient (0.28 for this
grid point) during JJA. In turn, during DJF, SAT sits around
-35 ∘C and the frozen ground experiences skin temperatures of
about -8 ∘C and of about -4 ∘C at a depth of 1 m.
Consequently, the temperature offset between GST and STL8 is
largest in JJA. As a result, there are some non-conductive processes
associated with permanent frozen soils in the shallow subsurface that are
included in the CLM4 parameterization and that play an
important role in heat transport. The higher SIC in L8 during JJA
relative to DJF (Fig. ) arises from the fact that freezing of
the active layer begins in late autumn and, due to the release of the latent
heat of fusion, the freezing front is inhibited and only
reaches L8 in spring when SIC at this layer peaks. Then, as the thawing front
penetrates downward in JJA, ice in the shallowest soil is melted, but it does
not reach L8 until autumn when SIC at this layer is lowest. Therefore, due to
the thawing–freezing processes, seasonal changes at the upper and deeper
subsurface levels are phase-shifted.
(a) DJF and JJA evolution of SAT, GST, STL6,
STL8, STL10, STL12 and STL15
during the 1900–2005 CE period for a grid point from the ALL-F2
simulation located in the north of Canada (65∘ N,1 30∘ W),
an area with permanent frozen soils. Dashed lines indicate 0 ∘C.
(b) Soil ice mass content (SIC) in the L1, L6 and L8 soil
layers.
SAT–GST long-term changes
The mechanisms that have been described have an impact on the coupling
between SAT and GST at short timescales but they do not affect the long-term
SAT–GST association if they are stationary as its influence would be
constant at long timescales. However, if such mechanisms experience
variations with time, the SAT–GST relationship would also change over time.
Thus, the thermal signature imprinted in the subsurface would not be
representative of the long-term SAT variations .
Figure b illustrates the existence of a constant offset
between SAT and GST within the preindustrial period that changes during the
industrial period indicating variation in the long-term SAT–GST
relationship. This may be relevant in the interpretation of borehole climate
reconstructions because it may induce a long-term decoupling between SAT and
GST in the CESM-LME. At a global scale, the changes in the long-term SAT–GST
offset have an impact of about 0.05 ∘C (Fig. a, b);
thus, they do not seem to be very relevant at these scales. However, the
impact could be larger for other GCMs with higher climate sensitivity or a
different representation of surface processes that may contribute to decouple
GST from SAT (e.g., snow cover). Similarly, within the CESM-LME simulations,
impacts on decoupling may be important at regional or local scales.
To examine the spatial distribution of the long-term SAT and GST evolution
during industrial times, we evaluate the linear trends of both temperatures
independently during this period at every land model grid point. Besides the
ALL-F ensemble, we also considered the anthropogenic single-forcing
ensembles (Fig. ), bearing in mind their potential
influence on the processes that modulate the relationship between SAT and
GST, such as variations in snow cover, soil moisture and albedo,
among others. The results in this section are shown considering information
from all members of the ensembles (see details in Table ). For
specific examples, one of the members will be used as indicated accordingly
in figure captions.
Figure a, b describe a predominant warming for both SAT
and GST in the ALL-F ensemble with the largest values distributed over
northwest North America, north and central Eurasia, northeast Africa and
southern South America. Interestingly, there are also regions showing
negative trends like southeastern China, the north of the Black and Caspian
sea regions, Pakistan, some relatively small central and southern areas of
Africa and Brazil. The warming trend pattern can be explained to a large
extent if the 1850–2005 trends are calculated on the basis of the GHG-only
ensemble (Fig. c, d) which is consistent with the global
warming pattern due to the influence of GHGs . Indeed, if
only the contribution of GHGs is considered, the warming would be higher and
globally distributed. Figure also indicates that the
cooling in the ALL-F ensemble is mainly driven by the contribution of the
LULC and OZ/AER external forcings. For instance, the cooling trends over the
Baltic Sea and the north of the Black and Caspian seas that dominate the SAT
and GST cooling trends during the industrial period in the ALL-F ensemble
are the result of the influence of LULC changes (Fig. e,
f) with additional contributions of OZ/AER (Fig. g, h). In
addition, the negative trends of both SAT and GST over some areas of Africa,
as well as over the northeast of Brazil, are also detectable in the LULC-only
ensemble (Fig. e, f). Similarly, the OZ/AER-only ensemble
also contributes to the cooling over Brazil, and the strong negative trends
observed in southeast China in the ALL-F ensemble are clearly identifiable
in this ensemble (Fig. g, h).
Spatial distribution of the linear trends in the industrial period
for the ALL-F (a, b), GHG-only (c, d), LULC-only
(e, f) and OZ/AER-only (g, h) ensembles for SAT (a, c, e, g) and GST (b, d, f, h). Trends are indicated in
Celsius century-1. Dots indicate that
80 % of the ensemble members agree in delivering significant trends at a
grid point in the case of the ALL-F ensemble. For the GHG-only and
LULC-only ensembles, dots indicate that at least two of the three ensemble
members agree in delivering significant trends for a grid point, whereas for
the OZ/AER-only dots indicate that both of the ensemble members deliver
significant trends. Note the different scale for
OZ/AER-only.
Although the general pattern of cooling/warming during industrial times is
broadly similar for SAT and GST, with a spatial pattern correlation of 0.60,
0.64, 0.38 and 0.53 in the ALL-F, GHG-only, LULC-only and OZ/AER-only
ensembles, respectively, it differs substantially in some regions. Note that
there are considerable differences in the amplitude of warming trends in SAT
and GST over Fennoscandia as well as at the northernmost part of North
America in the ALL-F ensemble (Fig. a, b). Similarly,
over some areas of central and eastern Europe, SAT and GST industrial trends
have different sign. There are also considerable differences in the amplitude
of the cooling in SAT and GST over northeastern Brazil. Such dissimilar
behaviors of SAT and GST during the industrial period are connected to
variations in the energy fluxes at the surface in response to changes in the
land surface characteristics due to the influence of the external forcings
during this period.
For a more detailed analysis of the SAT and GST long-term relationship, a
two-phase regression model (Sect. ) was applied at every land
model grid point to the SAT–GST differences in the ALL-F, GHG-only and
LULC-only ensembles (Fig. ). This allows for the analysis
of long-term changes in the coupling without assuming any a priori
condition on their time of occurrence and also to separately analyze the
contribution of the different forcing factors and the temporal consistency
among them. In the case of the OZ/AER-only ensemble, since this set of
simulations spans from 1850 to 2005 CE, linear trends are only shown for the
industrial period (Fig. ). Figure a
shows the year of change for the three ensembles; changes significant for
80 % of the ensemble members are shown exclusively in
Fig. b. The three ensembles show dates of change that span
the whole millennium. However, significant changes only occur during the last
centuries. Two-phase regression allows for identification of the fact that times of change
in most regions take place prior to 1850, during the 18th and even the
17th century (e.g., India) in the ALL-F. Trends before the change are not
significant in any of the ensembles (not shown). Trends after the change are
shown in Fig. c; significant areas in at least 80 % of
the ensemble members are exclusively shown in Fig. d. Note
that large positive and negative trends in Fig. c coincide
with the significant dates of change occurring during the last centuries of
the LM.
Spatial distribution of the two-phase regression results for SAT
minus GST in the ALL-F2, LULC1 and GHG1 ensembles as examples:
(a) dates (years) of change; and (c) trends after the
year of change. (b) and (d) are the same as (a) and (c) but only for
areas where at least 80 % of the ensemble members show significant
changes. Trends before the year of change (not shown) are not significant in
any of the ensembles. Significance (p<0.05) is obtained based on an F test
(t test) for year of change (trends) following .
Significance also accounts for autocorrelation . Note that the
spatial window has been modified to enhance visualization of land areas.
In general, annual SAT minus GST yields negative values (as shown in
Fig. a) with the exception of the coastal areas as explained
in Sect. . Thus, for continental areas (SAT–GST < 0),
positive (negative) trends indicate that differences tend to get smaller
(larger) in absolute values, whilst the opposite is true for the limited coastal areas
where SAT–GST differences are positive. Figure b allows for
visualization of this behavior. Note the positive trend after the change when
the difference between SAT and GST anomalies becomes smaller with time.
Regionally, several circumstances account for impacting SAT–GST long-term
coupling. On the one hand, decreasing SAT–GST differences over land may
emerge from two conditions. Firstly, when there is a higher warming rate of
SAT relative to GST as depicted in Fig. a, b over the
northernmost part of North America, Fennoscandia, northeast Russia and some
areas of central Eurasia. Secondly, when there is a cooling of both SAT and
GST but the latter decreases at a higher pace as described in
Fig. a, b for the northeastern Brazilian region and some
areas of Africa. These two scenarios are represented in
Fig. c for the ALL-F, with positive trends over these
regions after the change. On the other hand, the increase in the SAT–GST
difference either arises from the effect of rising GST in the presence of
stable/decreasing SAT or due to the higher warming rate of GST relative to
SAT. The former case is displayed in Fig. a, b for
central and eastern European areas as well as the eastern US, whereas the latter
is found over the Indian subcontinent and southeastern Asia. Note that both
cases are represented in Fig. c (ALL-F) with negative
trends.
Trends from the GHG-only and the LULC-only ensembles help with understanding the
relative contributions to the long-term variations seen in the ALL-F
simulations. For instance, the GHG-only ensemble shows similar positive
trends to the ALL-F (Fig. c) over northern North America,
Fennoscandia, northeast Russia and central Eurasia, although with a much larger
magnitude and geographical extension. Correspondingly, negative trends after
the change in the LULC-only ensemble are comparable to those in the ALL-F
for central and eastern Europe, the eastern US, the Indian
subcontinent and southeastern Asia. Additionally, the positive values over
Brazil, as well as over central and southern Africa in the ALL-F, are also
depicted in the LULC-only ensemble. Most of these changes are robust in 80 % of
the ensemble members (Fig. d).
Interestingly, the two-phase regression analysis does not expose any
variation in the SAT–GST long-term relationship over southeastern China, where
the linear trends during the industrial period show a relatively strong
decrease in both SAT and GST in the ALL-F and the OZ/AER-only ensembles
(Fig. a, b, g, h). Furthermore, the linear trend of
SAT–GST differences during industrial times for the OZ/AER-only simulations
(Fig. ) does not exhibit any SAT–GST decoupling over this
region either. This suggests that the dominant effect of OZ/AER forcing on
the SAT and GST responses over this region is not affecting their long-term
coupling. Nonetheless, Fig. illustrates some interesting
aspects of the SAT–GST relationship in the OZ/AER-only ensemble, such as the
negative contribution to the SAT–GST trends over North America, northern
Europe, the Tibetan Plateau and central Asia. Additionally, the positive
trends over northern Siberia are also notable, as well as the positive values
over some relatively small areas of central and eastern Africa, the coast of
Angola and eastern Brazil. Although the bulk of these SAT–GST responses
depicted in Fig. do not translate to SAT–GST long-term
decoupling in the ALL-F ensemble, they play an important role in either
counteracting the influence of other external forcings or contributing to
decoupling-related processes over some regions.
The following paragraphs aim at providing an insight into the relative
contribution of the individual forcings and the associated physical
mechanisms to the variations of the long-term SAT–GST association detected in
Figs. and .
Spatial distribution of the linear trends for SAT minus GST in the
OZ/AER-only ensemble. Trends are indicated in Celsius century-1. Dots
indicate agreement in both of the ensemble members.
In the cases of the long-term variations due to the LULC influence, changes
in vegetation cover alter the radiative fluxes and water cycling at the
surface due to the modification of the physical properties such as albedo,
roughness and evapotranspiration . Figure
gives an example of how long-term changes in the energy fluxes at the surface
due to LULC changes do impact the SAT–GST coupling at long timescales. It
shows the 31-year low-pass filter outputs of SAT, GST, reflected shortwave
radiation (RSW) and SHFLX evolution for a characteristic grid point over the
Great Lakes region (US) where a warming of GST relative to SAT is simulated
in CESM-LME during the industrial period. Results are shown for one of the
members of the ALL-F, LULC-only, GHG-only and OZ/AER-only ensembles. Around
1800 CE SAT tends to decrease whereas GST tends to increase in both the
ALL-F and LULC-only simulations, while GHG-only and OZ/AER-only simulations
do not display the same behavior that produces larger differences between SAT
and GST represented by negative trends in Fig. c. At the
same time, RSW and SHFLX exhibit large long-term variations in the ALL-F
and LULC-only simulations. Therefore, this modification of the long-term
SAT–GST relationship is clearly a response to LULC changes. Such variations
in the surface energy fluxes over this region are likely a response of
vegetation replacement from forested areas to grassland or croplands.
Forested landscapes dissipate SHFLX more efficiently to the atmosphere due to
a higher surface roughness than open fields . In addition,
lower vegetation types have higher reflectivity than forests. All of the
previously listed factors contribute to SAT decreases over these regions, especially in DJF.
Furthermore, deforestation at mid- and high-latitudes tends to positively
feedback with increases in snow cover . These types of
changes in LULC contribute to increase albedo, which is reinforced by changes
in snow cover at these latitudes. Additionally, higher DJF snow cover tends
to increase the insulation of the soil from the cold overlying air. The
combination of these mechanisms lead to the observed temperature response of SAT and
GST. This particular LULC process is important for corrupting the SAT–GST
coupling at timescales relevant for the borehole theory (centennial) since
the thermal signature recorded in GST during the industrial period would not
be representative of the past long-term SAT variations in regions where this
effect is dominant.
For the areas of central and eastern Europe, where a GST warming relative to
SAT is also observed in Fig. c, the mechanisms are similar
to those described in Fig. , because these areas were also
subject to an intense transformation from forested areas to cropland prior to
the beginning of the industrial period according to the LULC forcings
considered in the CESM-LME .
LM evolution of SAT, GST, reflected shortwave radiation (RSW) and
surface sensible heat flux (SHFLX) for a grid point located at
40∘ N, 82∘ W in the south of the Great Lakes, US, in the
ALL-F2, LULC1, GHG1 and OZ/AER1 simulations. The left
axes correspond to SAT and GST, while the right axes correspond to the energy fluxes at
the surface. Note that for the energy fluxes the anomalies with respect to
850–2005 CE are shown, whereas for temperature absolute values are
presented. All series are 31-year moving average filter outputs. For SAT and
GST the result of the two-phase regression model is displayed with thin solid
lines. Note the change in timing on the x axis after
1700 CE.
Changes in vegetation cover are also important for the long-term SAT and GST
temperature differential response over tropical regions, although the driving
mechanisms are different from those at mid- and high-latitudes
and they deserve to be considered. In the northeast of
Brazil, both SAT and GST have negative trends in both the ALL-F and the
LULC-only ensembles during the industrial period (Fig. a,
b, e, f). However, the decrease of GST is much larger than that of SAT as
represented in Fig. c with positive trends after the change
in both ensembles.
The same as in Fig. but for a grid point located at
12∘ S, 40∘ W in northeast Brazil. LHFLX is also
represented instead of SHFLX.
Figure shows the temporal evolution of SAT, GST, RSW and LHFLX
for a grid point located in northeast Brazil. At the end of the 18th
century, GST drops sharply whereas SAT slightly decreases. Similarly, RSW and
LHFLX simultaneously experience significant changes as a result of the
modification of the surface characteristics. Such changes are present solely
in the All-F and LULC-only simulations, whereas the GHG-only and OZ/AER-only
simulations show no differences in their evolution. The changes observed at this location
in the energy fluxes likely correspond to transitions from open lands to a
forested area (reforestation or afforestation) leading to lower albedo and
higher evapotranspiration rates as is shown in Fig. . This
situation leads to an apparent long-term cooling of GST relative to SAT at
this location. The temperature response over this area is influenced by
different mechanisms. First, the conversion from lower-type to higher-type
vegetation reduces the solar radiation that impinges on the surface and GST
decreases due to a radiative effect. Second, forested lands usually have lower
albedo and thus absorb more shortwave radiation . This
surplus of energy is balanced by the increase in transpiration;
consequently, GST also decreases by a non-radiative process. The latter is
especially important in humid climates such as the one in
this example. According to these results, there is a net heat loss at the ground
surface with a higher decrease in GST relative to the overlying air. The
SAT–GST coupling becomes strong again after the new
vegetation cover reaches a stable state by the mid 20th century. For the
African areas, where there is also a cooling of GST relative to SAT
(Fig. c in the ALL-F and LULC-only ensembles), the
mechanisms are comparable to those described for Fig. .
The same as in Fig. but for a grid point located at
2.5∘ N, 33∘ E over Uganda. Note that the incoming shortwave
radiation at the surface (SSW) is represented instead of RSW and both LHFLX
and SHFLX are shown.
Over some of these tropical regions, there is also a contribution from the
OZ/AER forcing to the SAT–GST response. Note that Fig.
shows positive trends over Uganda, the coast of Angola and over eastern
Brazil, which is also noticeable in Fig. c. In these regions, the
incoming solar radiation is reduced due to the effect of aerosols, which are
an important element for cloud formation and contribute to a higher
reflectivity of solar radiation . The description of the
specific processes related to aerosol–cloud interaction goes beyond the scope
of this study. Therefore, only the influence on the energy balance at the
surface is addressed. As lower shortwave radiation impinges the surface, the
energy gain decreases and the ground surface heating is consequently lower. The
reduction in the energy gain at the surface is compensated for by a lower
dissipation via sensible heat, whereas the fluxes of latent heat remain
relatively constant or even increases in some areas due to higher moisture
as a result of increased precipitation. This mechanism is illustrated in
Fig. for a grid point located in the Uganda region. Note
in the ALL-F simulation, the reduction in incoming shortwave radiation
(SSW) after 1900 CE leads to a decrease in SHFLX. Interestingly, for
this location LHFLX experiences an increase at the same time as a result of
increased precipitation (not shown); thus, this provides a source of moisture for
evapotranspiration. This situation leads to a higher decrease in GST relative
to SAT due to a net loss of energy at the ground surface. Similar SAT and GST
responses are observed in the OZ/AER-only and LULC-only simulations, whereas
in the GHG-only SAT and GST increase.
The same as in Fig. but for two different regions:
northeast Brazil between 1–11∘ S and 47–35∘ W
(a) and southeast China between 22–32∘ N and
103–122∘ E (b). For northeast Brazil RSW is
represented whereas for southeast China SSW is shown instead.
LHFLX and SHFLX are shown in both cases.
The SAT–GST decoupling processes described above for individual grid points
are also important at larger spatial scales.
Figure a shows an extension of the mechanism
depicted in Fig. including a larger area over the northeast of
Brazil (between 1–11∘ S and 47–35∘ W). The negative trend
since the 18th century is less accentuated for SAT than for GST (-0.14 and
-0.53 ∘C century-1 respectively in the All-F simulation and
-0.07 and -0.33 in the LULC-only ensemble) indicating a strong contribution of
past LULC changes. The same air–subsurface temperature response occurs in
other tropical and subtropical areas such as the east of Africa.
The regional analysis is extended to southeastern China in order
to illustrate additional information about the influence of different
external forcings on the SAT–GST relationship. As previously discussed, the
negative trends for both SAT and GST over this region during the industrial
period represented in the ALL-F and OZ/AER-only ensembles
(Fig. a, b, g, h) do not entail a corruption of the
SAT–GST long-term coupling. Figure b allows for
insight into this particular effect and the role of the OZ/AER forcing on the
air and soil temperature responses over this region during industrial times.
The negative long-term trend within the industrial period is only seen in the
ALL-F and the OZ/AER-only simulations. Similarly, in both of these simulations, there is
a reduction in the RSW as well as in both the LHFLX and SHFLX. Conversely,
the SAT and GST evolution during industrial times in the GHG-only and
LULC-only simulations does not follow the same path depicted in the ALL-F,
which highlights the dominant influence of OZ/AER forcing. In this case, the
variations of the energy fluxes at the surface depend on the reduction of the
incoming shortwave radiation as a response of the anthropogenic aerosol–cloud
interaction rather than by modifications of the land surface properties.
Therefore, the decrease in the energy that impinges the surface is balanced
by a decrease in both the sensible and latent heat fluxes. Hence, the
air–soil interactions are not significantly altered and the SAT–GST
relationship remains stable.
(a) LM evolution of the global SAT–GST offset for the
ALL-F2, LULC1, GHG1 and OZ/AER1 simulations. Straight
lines indicate the long-term trend during the LM from a two-phase regression
analysis except for the OZ/AER1 results that indicate a linear trend in the
industrial period. The bottom panel illustrates the LM evolution of global snow
cover percentage for the same ensemble members. Straight lines indicate the
long-term trend within the 1750–2005 period except in the case of OZ/AER
that only covers the 1850–2005 period. The period from 1750–2005 was
selected in order to match the time span when the SAT–GST offset experiences
the variation in the ALL-F2 simulation. All series are 31-year moving
average filter outputs. Note the change in timing on the x axis after
1700 CE.
Although the majority of the important long-term variations in the SAT–GST
relationship at regional and local scales observed in the ALL-F ensemble
(Fig. c) are induced by LULC changes, there are some
regions in which the GHG forcing is the main driver for long-term SAT–GST
decoupling. For instance, the positive trends after the change over
Fennoscandia, northeast Russia and the north of North America observed in the
ALL-F ensemble can be explained to a large extent by the influence of
GHG-only ensemble inasmuch as a broadly similar picture is portrayed over
these regions in both ensembles (Fig. c). In the case of
the GHG-only ensemble, the strong warming of SAT relative to GST over these
regions is driven by the increasing air temperature during industrial times
due to the positive radiative forcing of GHGs in the presence of a
considerable long-term reduction in simulated snow cover.
showed that such a scenario would lead to a higher exposure of soil to cold
winter air; therefore, the soil would partially record colder temperatures,
which had previously been prevented by the snow cover insulating effect. In the ALL-F
ensemble, this effect is damped as additional
forcings that keep the snow cover relatively constant during industrial
times are considered. For instance, the contribution of the OZ/AER forcing is particularly
important for counteracting the effect of GHGs as it leads to colder
climate conditions due to its negative radiative forcing. Note the strong
negative trends in Fig. over North America, northern
Europe and the Tibetan Plateau that partially balance the effect of the GHGs
over these regions. Nonetheless, in the ALL-F there is still an overall SAT
warming relative to GST since the relatively stable snow cover is insulating
the soil from a warmer SAT reducing the overall offset between them
. Additionally, other processes can play some local roles
in SAT–GST changes, such as CO2 fertilization generating
increases in leaf area index (e.g., Amazon rainforest), which leads to higher
evapotranspiration and GST cooling relative to SAT.
Figure gives further insights into the interactions
between anthropogenic forcings, their influence on global snow cover and
consequently on the long-term SAT–GST relationship in the CESM-LME. The LM
evolution of global SAT minus GST (top) and the annual global snow cover
(bottom) for the ALL-F, LULC-only, GHG-only and OZ/AER-only simulations are
shown for one member of each ensemble. Similar results are obtained if other
members are selected. On the one hand, when the GHG-only ensemble is
considered the global SAT–GST offset experiences a sharp long-term decrease
in absolute value at the start of the industrial period as well as a strong
long-term reduction in global snow cover. In fact, the correlation between
changes in the SAT–GST offset and snow cover in the GHG-only ensemble member
is high -0.93 (p<0.05). On the other hand, the overall effect of the
LULC-only ensemble is a relatively small increase in the global snow cover
mainly due to deforestation at mid and high latitudes as well as due to the
negative radiative forcing of LULC as Earth's albedo increases
; this leads to a small increase in the global SAT–GST
offset. In the same way, the OZ/AER-only ensemble shows an increase in global
snow cover while the SAT–GST offset in industrial times exhibits a relatively
slight increase. The interaction between different external forcings in the
ALL-F ensemble leads to a relatively stable snow cover during the
industrial period since the sharp decrease in snow, induced by the GHG
forcing, is partially compensated for by the counteracting effect of the LULC and
OZ/AER forcings. Additional forcings such as volcanic eruptions may also contribute to
counteracting GHG effects at multi-decadal timescales (not shown).
Consequently, in the presence of a warmer climate, there is a difference in
the warming rate of SAT and GST in industrial times at a global scale in the
ALL-F ensemble member (0.25 and 0.18 ∘C century-1,
respectively). This scenario leads to the net effect of a long-term decrease in
the SAT–GST differences starting around 1800 CE as discussed in
Fig. and as is also evident in Fig. .
Conclusions
This work evaluates the stationarity of
the coupling between SAT and GST temperatures as simulated by the CESM in an
ensemble of experiments spanning the LM. The initial motivation for this work
is rooted on previous literature that addresses the realism of the borehole hypothesis for climate
reconstruction, namely, that SAT and GST vary synchronously and that
reconstructing past GST changes from borehole temperature profiles is a good
proxy for past SAT variations. The use of the CESM-LME allows for the
analysis of the influence of forcing changes on the SAT–GST covariability, both
individually and as a group, by considering the different all-forcing and
single-forcing ensembles. Additionally, having several experiment ensemble
members for each given forcing type allows for the disentanglement of the effects of
internal variability from those of the forcing response. Ultimately, the
coupling between SAT and GST is assessed at a global and also at regional/local
scales. This assessment requires the consideration of different mechanisms that
contribute to SAT–GST variability within different climate types. In doing
this, a variety of factors and conditions that contribute to the surface energy
balance via different mechanisms are provided.
The CESM-LME shows that at global scale the SAT–GST coupling is strong above
multi-decadal timescales since GST tracks SAT throughout the LM, as found in
previous studies. However, in spite of the strong coupling, the CESM-LME also
reflects that the SAT–GST relationship has not remained constant throughout the
whole LM at these spatiotemporal scales. Hence, the nature of such variation
is evaluated.
Globally, snow cover is the most important agent in modulating the connection
between SAT and GST. Therefore the variation of the SAT–GST relationship
described by the CESM-LME simulations should, in principle, be driven by
variations in global snow cover. Nevertheless, the simulated snow cover
remains relatively stable at the time when SAT–GST coupling varies; thus, this
change cannot be solely explained by the influence of the snow cover. With
this in mind, we explored, in some detail, different processes that may
influence the SAT–GST relationship at different spatiotemporal scales.
Firstly, we address processes acting at seasonal timescales that were
identified from a spatial analysis of the SAT–GST differences and
correlations. Secondly, the long-term evolution of the SAT–GST relationship
is evaluated in the ALL-F, LULC-only, GHG-only and OZ/AER-only ensembles.
Several processes over different regions relevant during either DJF or JJA play
an important role in impacting the SAT–GST coupling, such as
snow cover over mid- and high-latitudes, discontinuous snow cover over the
Tibetan Plateau region and seasonal variations in the energy fluxes at the
surface. Although these processes are important for disrupting the SAT–GST
relationship at seasonal scales, they have no implications on the long-term
coupling if they are stationary. Nonetheless, if they experience variations
with time the SAT–GST long-term relation may be impacted.
As discussed in Sect. some of the anthropogenic external
forcings have the potential to impose long-term variations on processes that
regulate the relationship between SAT and GST. Among them, LULC changes
are the most important of these forcings as they modify the energy fluxes at the
ground–air interface, and consequently corrupt the SAT–GST coupling locally
and regionally at various timescales. One example is the response to the
deforestation processes triggered by the expansion of agriculture mainly
during the industrial period at mid- and high-latitudes, where SAT and GST
long-term coupling is impacted due to the variations in the albedo, surface
roughness and hydrology. Similar decoupling processes related to LULC changes
are found over different regions around the globe, such as those described in
Sect. over northeastern Brazil, and over some areas in Africa and
the Indian subcontinent. All of these examples are driven by the long-term modifications of
the energy fluxes at the surface, either from increased evapotranspiration,
reduced energy dissipation via sensible heat or other factors. Besides these kinds
of decoupling processes induced by individual forcings, the interactions of a
variety of mechanisms and feedbacks from different external forcings can also
exert an influence on the long-term SAT–GST relationship at different spatial
scales. For instance, the effect of GHGs leads to a reduction of the snow
cover during industrial times that is counterbalanced by the opposite effect
of both LULC and OZ/AER forcings. As a consequence, the snow cover remains
relatively stable over some regions during the industrial period in the
presence of a warmer climate. This situation leads to a difference in the SAT–GST
long-term evolution during the industrial period, since the snow
cover insulates the soil from a warmer SAT. This effect is present over
the NH high-latitudes of North America, Fennoscandia and northeastern Russia.
Indeed, at a global scale, the combination of steady snow cover under warmer
climate conditions is the dominant effect for explaining the variations in
the long-term SAT–GST relationship.
Our findings indicate that the assumption of a strong relationship between
SAT and GST may be impacted from local to regional scales by different
mechanisms especially by the influence of LULC changes due to the
modification of the energy balance at the surface. Therefore, the
interpretation of temperature reconstructions from borehole measurements at
these spatial scales must consider LULC changes as a source of possible bias.
The effects of additional external forcings may also exert some influence on
processes such as variations in the snow cover, hydrology and other land
surface properties, which may in turn feedback on the SAT–GST long-term
coupling. At a global scale, the influence of such local and regional
decoupling processes is only ca. 0.05 ∘C; hence, the SAT–GST coupling
at this spatial scale is supported by the CESM-LME.
The analysis using different GCMs may yield different levels of impacts.
Overall, some consistency in the impacts among the available simulations of
the LM with different GCMs can be expected. This is due to the fact that the external forcings
considered in the PMIP3/CMIP5 LM simulations are similar
and should have a similar contribution to the extent that the impacts on the
SAT–GST long-term relationship are mainly driven by the external forcings.
Nevertheless, not all model simulations consider exactly the same set of
forcings. Some consider land use land cover changes and aerosols, and other
do not (Masson-Delmotte et al., 2013). In addition, different model climate
sensitivities and differences in the representation of surface processes
(e.g., snow cover or soil moisture) may also contribute to produce different
responses. Even if the role of external forcings should be dominant in a
long-term context, addressing this issues from a multi-model ensemble
approach would help with understanding the uncertainties associated with all the
factors of variation described above. The CMIP6/PMIP4
offers an opportunity to explore better sensitivity to different models,
as larger ensembles of the LM with a more systematic sampling of forcings
and processes would be expected.