Introduction
The signature of climate variability on orbital timescales (i.e. mid-Holocene, 6 kyr ago, and Last Glacial Maximum, 21 kyr ago) as derived
from proxy archives serves as a test bed for a critical evaluation of general
circulation models (GCMs; see, e.g., Braconnot et al., 2007, 2012; Hargreaves et
al., 2013; Lohmann et al., 2013; Harrison et al., 2014). In this context,
terrestrial vegetation dynamics have been identified as a crucial part of
physical land surface characteristics within climate models in order to
assess climate variability (Kutzbach et al., 1996; Texier et al., 1997,
2000; Claussen 1997; Claussen and Gayler, 1997; Braconnot et al., 1999,
2007). Subsequently, land surface schemes with interactive vegetation have
been implemented into GCMs (e.g. Foley et al., 1998, 2000). In addition to
vegetation dynamics, however, various studies also highlight the impact of
physical soil characteristics on climate, as it represents another component
of the land surface (Kutzbach et al., 1996; Doherty et al., 2000; Levis et
al., 2004; Knorr and Schnitzler, 2006; Shellito and Sloan, 2006; Wanner et
al., 2008; Brovkin et al., 2009; Micheels et al., 2009; Knorr et al., 2011;
Krapp and Jungclaus, 2011; Vamborg et al., 2011; Lohmann et al., 2015). So
far, model studies designed to examine soil dynamics have mainly focused on the
sensitivity of individual physical soil characteristics and have therefore
primarily addressed the monocausal impact of land albedo (Bonfils et al.,
2001; Levis et al., 2004; Knorr and Schnitzler, 2006; Schurgers et al.,
2007; Vamborg et al., 2011), soil texture, and maximum water-holding field
capacity (Wang, 1999; Levis et al., 2004) on climate. Furthermore, Jiang (2008)
investigated the role of physical soil characteristics (soil colour,
soil texture) for the Last Glacial Maximum (LGM) by applying an atmosphere
GCM, which was asynchronously coupled to a terrestrial biosphere model.
In this procedure soil characteristics in a grid cell were iteratively
adapted if the dominant plant functional type had changed. The integrated
vegetation–soil feedback reinforced glacial cooling to some extent (Jiang,
2008). Further progress is shown in Vamborg et al. (2011), in which the
authors implemented a dynamic background albedo scheme within a comprehensive
fully coupled GCM with vegetation dynamics (earth system model, ESM).
In this model, the model additionally accounts for land surface albedo changes
based on the calculation of carbon fluxes between terrestrial carbon pools
(vegetation, litter and phytomass, and soil).
So far, various studies have focused on the climate effect of physical soil
parameter changes. However, the combined impact and feedback of soil albedo,
texture, and total water-holding field capacity within the fully coupled
atmosphere–vegetation–ocean system remains to be addressed. The current
inability of comprehensive state-of-the-art ESMs to compute the dynamics of
physical soil characteristics reveals a potential failure to represent paleoclimate dynamics and quantifying paleoclimate sensitivity.
Here, we investigate the impact of dynamic soils on the climate of the
mid-Holocene and the Last Glacial Maximum by means of the comprehensive ESM
with vegetation dynamics, COSMOS (Community of Earth System Models; Jungclaus
et al., 2006; Raddatz et al., 2007). We develop a simple soil scheme which
incorporates the computation of soil albedo, soil texture, and total water-holding field capacity and couple this asynchronously to the ESM. The
coupled version of COSMOS allows us to examine the potential soil feedback
in the atmosphere–vegetation–ocean system.
The model approach utilizes boundary conditions of the preindustrial period, the
mid-Holocene, and the Last Glacial Maximum (LGM). The simulations of the
extended model set-up are further compared to the respective standard
time-slice studies (Wei and Lohmann, 2012; Zhang et al., 2013) to show that
dynamic soils drive significant positive feedbacks in the coupled climate
system for past climate states.
Methods
We use the ESM configuration presented by Jungclaus et al. (2006) and
describe the design of the soil scheme and the asynchronous coupling
procedure. The experimental design includes a short
description of the applied model boundary conditions that are representative
of the mid-Holocene and LGM (Wei and Lohmann, 2012; Zhang et al., 2013).
Furthermore, a forcing factor separation method (Stein and Alpert, 1993;
Zhang et al., 2015) has been applied to isolate the potential soil feedback
in the climate system.
Model description
In the current ESM set-up, three model components (atmosphere, sea-ice–ocean,
land surface) are coupled to each other within the COSMOS model suite. The
atmospheric component of COSMOS is ECHAM5 (Roeckner et al., 2003), with a
spectral resolution of T31L19 (3.75 × 3.75∘ horizontal
grid-space resolution and 19 vertical layers), which enables a direct
comparison with previous studies of the mid-Holocene and LGM (Wei and
Lohmann, 2012; Zhang et al., 2013). The ocean model MPIOM uses an orthogonal
curvilinear grid (3∘ × 1.5∘ lateral resolution)
with a higher resolution at the grid poles (Greenland, Antarctica) and 40
unevenly spaced vertical layers (Marsland et al., 2003). The transfer of
fluxes and momentum between atmosphere and ocean is handled by the coupler
OASIS3 without any flux corrections (Jungclaus et al., 2006). The modular
land surface scheme JSBACH (Raddatz et al., 2007) with vegetation dynamics
(Brovkin et al., 2009) is embedded in the ECHAM5 atmosphere model. JSBACH is
based on the semiempirical terrestrial ecosystem model BETHY, which
incorporates an energy and water balance, photosynthesis, phenology, and a
carbon balance compartment (Knorr, 2000). JSBACH employs a subgrid-scale
tiling approach in which the actual cover fraction of each plant functional
type (PFT) is associated with a tile within each grid cell. In our study,
JSBACH uses the standard model configuration of eight PFTs. The land surface
energy balance considers soil albedo, leaf area index of the actual PFT, the
albedo of stems, snow fraction on the ground and on the canopy, and the
masking effect of snow under the canopy. Further background information on COSMOS and its components can be found in Jungclaus et al. (2006). So far,
COSMOS has been applied to various palaeo set-ups such as the Holocene (Wei et al.,
2012; Wei and Lohmann, 2012; Varma et al., 2012; Lohmann et al., 2013), Last
Glacial Maximum and Marine Isotope Stage 3 (Gong et al., 2013; Zhang et al., 2013, 2014), last interglacial (Lunt et al., 2013), Pliocene (Stepanek and
Lohmann, 2012), and Miocene (Knorr et al., 2011; Knorr and Lohmann, 2014).
Design of the soil scheme
The design of the soil scheme is based on the concept that an organism
actively regulates and transforms its abiotic environment (Lovelock, 1979).
We therefore specify soil types that correspond to PFTs. Applying this
concept, the soil scheme diagnoses soil types by the composition of computed
PFTs for each grid cell. The computed PFTs are derived from the land surface
model JSBACH (Fig. 1; see Sect. 2.4). This procedure does not explicitly
capture all pedogenic factors (e.g. parent rock; Targulian and Krasilnikov, 2007); however, some may be indirectly considered by
vegetation demands (e.g. plant water accessibility, viable temperature
limits). The aim of the soil type classification is to provide constraints
of physical characteristics (total water-holding field capacity, albedo, and
texture) for the land surface scheme of COSMOS and therefore does not refer to
the pedologic nomenclature.
Flowchart of the asynchronous coupling procedure between the earth
system model (ESM) and the soil scheme (COSMOS_soil). (PI: preindustrial
period; HOL: mid-Holocene.)
First, the soil scheme takes the mean output of 100 model years, provided by
COSMOS, to calculate the physical soil properties (soil albedo in the
spectrum of visible and near-infrared light, soil texture, total water-holding field capacity of soils). Thereafter, the model integrates 600 years
in total, including an asynchronous coupling time step of 200 years between
COSMOS and the soil scheme. Thus, for a model integration period of
600 years, the soil scheme iterates three times in total. The asynchronous coupling is
defined by using the previous 100-model-year mean of COSMOS as input for
the soil scheme (Fig. 1). Within this study, the coupled version of the ESM
with the soil scheme is called COSMOS_soil. At the end of the
model simulation, the final 100 years of model integration, including the
physical soil characteristics of the third iteration are used for
analysis.
Deriving physical soil characteristics
The physical soil characteristics of JSBACH comprise snow-free soil albedo
(α) in the visible (αvis, 0.3–0.7 µm) and near-infrared (αnir, 0.7–3 µm) light spectrum, total water-holding field capacity of soil (hcws), and soil data flags (soil texture)
from the United Nations Food and Agriculture Organization (FAO) soil
classification (Hagemann et al., 1999; Hagemann, 2002; Rechid et al., 2009). The original
data set of the snow-free soil albedo is derived from modified satellite
measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS;
Rechid et al., 2009). The FAO soil data flags (refer to Gildea and Moore in
Henderson-Sellers et al., 1986) represent a general grain size
classification ranging from sand to clay. The assessment of the total water-holding field capacity of soils (hcws) reveals considerable
uncertainties and is estimated by a data set of the parameter hpwp
(permanent wilting point) and hava (maximum amount of water that plants
may extract from the soil before they start to wilt), which is based on
optimised rooting depths (Hagemann et al., 1999).
To derive physical soil characteristics that agree with vegetation changes,
we use calculated PFTs from the final 100-year mean of a preindustrial model output (Wei et al., 2012; Fig. 2). Physical soil characteristics are
specified for all dominant PFTs (defined by a cover fraction > 50 %
of the grid cell) by global mean calculation (Table 1). Additionally,
we define a soil type that represents global desert areas (defined by the
cover fraction that lacks vegetation cover for > 50 model years)
and a subgroup specifying the Arabian Peninsula–Sahara desert region
(defined by < 50 % vegetation cover within a zonal belt of 13–35.26∘ N). The desert subgroup is mandatory when
considering extremes of hcws and soil albedo values of bare soils (Fig. 2, yellow
crosses). Further, we subdivide C3 grass into “warm-tolerance” (defined by
the mean annual temperature, MAT, > 0 ∘C) and “cold-tolerance grass” (MAT < 0 ∘C). In total, the adjusted
classification consists of 10 soil types (Fig. 2, Table 1). The FAO soil
data flags constitute a coarse-soil texture classification, ranging from 1 (sand) to 5 (clay), and are rather insensitive to the
specified soil types (Table 1). Though calculated by the soil scheme, we do
not further consider this variable in our study.
Physical soil properties (abscissa) used in the dynamical vegetation
module JSBACH associated with plant functional types along latitudes. Albedo
values are derived from modified satellite measurements of the Moderate
Resolution Imaging Spectroradiometer (MODIS; Rechid et al., 2009), and total
water-holding field capacity of soils are taken from Hagemann et al. (1999).
Specified soil types and associated physical soil characteristics. (VIS: visible spectrum; NIR: near-infrared spectrum.)
Soil types based on calculated PFTs
Physical soil characteristics (soln)
hcws, total water-holding
soil texture, FAO soil
αvis, soil albedo VIS
αnir, soil albedo NIR
field capacity of soil (metres)
data flags
(0.3–0.7 µm)
(0.7–3 µm)
Tropical broadleaved evergreen forest
0.77
loam and clay
0.12
0.20
Tropical deciduous broadleaved forest
0.73
loam
0.18
0.34
Temper. or boreal evergreen forest
0.50
loam
0.08
0.10
Temper. or boreal deciduous forest
0.58
loam
0.09
0.11
Raingreen shrubs
0.68
loam
0.12
0.17
C3 perennial grass
C3 perennial grass > 0 ∘C MAT
0.77
loam
0.12
0.21
C3 perennial grass or cold shrubs (tundra) < 0 ∘C MAT
0.23
loam
0.08
0.13
C4 perennial grass
1.07
loam
0.14
0.26
Desert fraction
global desert fraction 0–100 %
0.17
loam
0.20
0.37
Sahara–Arabian Peninsula desert fraction > 50 % (13–35.26∘ N)
0.11
loam
0.25
0.49
Soil scheme
The soil scheme computes grid-cell-specific soil characteristics soln (n
refers to hcws, fao, αs,vis, αs,nir) according to
the soil classification described in Sect. 2.3. The soil characteristic
soln is calculated by a soil index. The soil index includes the cover
fraction of the soil types (fi), which are generally derived by
PFT-specific cover fractions (Table 1). The soil index weights the soil
characteristic of a specific soil type (fisoli) by the cumulative
cover fraction of all soil types ∑i=110fi per
grid cell:
soln=∑i=110fisoli∑i=110fi.
If ∑i=110fi does not occupy the full grid cell
space, the residual is interpreted as desert (see Sect. 2.3, Table 1). The
weighted soil characteristics are summed to a grid cell average of soil
characteristics and thereafter transferred to the atmospheric component of
COSMOS.
Experimental design
We investigate the soil feedback for three different time slices: the preindustrial period (PI_ctl; Wei et al., 2012), the mid-Holocene
(HOL_ctl, 6 kyr before present; Wei and Lohmann, 2012), and
the LGM (LGM_ctl; ∼ 21 kyr before present,
Zhang et al., 2013). All time-slice model simulations are performed by the
standard COSMOS configuration. The model simulations utilise background
conditions and forcing parameters described by the PMIP3 protocol
(Paleoclimate Modelling Intercomparison Project; Braconnot et al., 2012) and
are run until they reach equilibrium.
The mid-Holocene set-up (HOL_ctl) accounts for changes in
orbital parameters and minor modifications in greenhouse gases as compared
to the preindustrial period (Wei et al., 2012). Adjustments of LGM boundary
conditions include orbital parameter settings, greenhouse gases, sea-level
and ice sheet changes, and an initial salinity change in the glacial ocean by
+1 ‰ (Zhang et al., 2013). The length of the
simulations PI_ctl, HOL_ctl, and
LGM_ctl amounts to at least 3000 years for each model run (Wei
et al., 2012; Zhang et al., 2013). The model simulations, including dynamic
soils, refer to the respective time-slice model simulation
(PI_ctl, HOL_ctl, LGM_ctl) and
continue the integration for another 600 years in total. All model output
shows mean climate conditions for the final 100 years of the simulations.
Analysis of the soil feedback to the climate system
The respective soil feedback to the climate of the mid-Holocene and the LGM
is analysed by using a factor separation method for identifying synergies in
numerical models (Stein and Alpert, 1993; Lunt et al., 2012; Zhang et al.,
2015). The method calculates the effect between applied changes to a
reference state with respect to an unperturbed reference state
(f0,0), e.g. the calculated single effect of changing two forcing
factors f1,0 and f0,1 separately, as well as the combination
of both factors f1,1. Thereafter, the synergetic effect f^1,1
between both factors can be calculated (Stein and Alpert, 1993). To
isolate the synergetic effect f^1,1 between the two factors, the anomaly term of their linear combinations is
calculated:
f^1,1=f1,1-f1,0-f0,1+f0,0.
Assuming a linear (climate) system between two factors, the combination of
both factors is identical to the sum of each single factor; thus, the synergy
term equals 0. Deviations from 0 indicate nonlinearities (climate
feedbacks) in the system.
By means of the factor separation method, the model simulations are
factorised representing the background climate (fclimate,0) and dynamic
soils (f0,soil). Thereafter, we disentangle the joint effect of climate
and dynamic soils (fclimate,soil), which we call soil feedback. The soil
feedback is indicated by f^climate,soil:
f^climate,soil=fclimate,soil-fclimate,0-f0,soil+f0,0.
The factors f0,0 and f0,soil refer to the preindustrial model run
without (PI_ctl) and with dynamic soils (PI_sol), respectively.
The defined background climate of the model run
fclimate,0 represents several changes in the boundary conditions with
respect to the preindustrial period that are applied for each time slice
(mid-Holocene, LGM; see Sect. 2.5), and the “dynamic soils” factor
f0,soil refers to model runs, which have been performed including the
interactive soil scheme (COSMOS_soil). The combination of
both factors is given by fclimate,soil.
The forcing of the mid-Holocene model run is mainly driven by insolation
anomalies as compared to the preindustrial period; therefore, the soil feedback is
largely attributed to changes in orbital forcing. However, the multitude of
changes in the LGM boundary conditions that is representative of the LGM
background climate (see Sect. 2.5) does not allow the identification of a
monocausal relationship. The calculated soil feedback of the respective
model studies is presented in Sects. 3.1 and 3.2.
Results
This section gives a brief presentation on the climate anomalies, which are
introduced by coupling of the soil scheme and the ESM for the preindustrial period. In
the next step, the simulated mid-Holocene and LGM climate that
are derived from the extended model set-up including dynamic soils
(COSMOS_soil) are described, and, thereafter the soil feedback to the
respective climate state is shown.
Preindustrial simulation (PI_sol – PI_ctl)
The preindustrial mean surface air temperature (SAT), precipitation, and
evaporation anomalies between COSMOS_soil and COSMOS (PI_sol – PI_ctl)
show the effect of coupling the soil scheme to the ESM (Fig. 3). In general,
the global mean SAT increases by 0.27 ∘C. High northern latitude
SATs locally increase by about 1 ∘C at the Canadian
Arctic Archipelago, and Baffin Bay is affected by reduced sea-ice cover as
a consequence of changes in the soil characteristics. Regional warming in
remote areas like the Southern Ocean is sensitive to sea-ice changes as a
function of anomalous wind fields and ocean current shifts.
Differences in 100-year annual mean of the preindustrial climate
state with dynamic soils and fixed soil characteristics
(PI_sol – PI_ctl) included for (a) surface air
temperature in degrees Celsius, (b) total precipitation in
millimetres per day, (c) evaporation in kilograms per square metre per day, (d) forest
fraction, (e) grass fraction, and (f) desert fraction. Hatched areas identify
non-significant values, calculated by means of a Student t test with 0.05 levels of
significance.
In parallel with boreal sea-ice retreat, the soil scheme particularly
favours the expansion of forests in the high northern latitudes
(60–90∘ N). The Arabian Peninsula and Sahara region exhibit
relatively strong temperature anomalies caused by the threshold
parameterization (see Sect. 2.3) and the absence of vegetation covering
the ground. Precipitation and evaporation anomalies in the low latitudes are
typically affected by anomalous changes in the inner tropical convergence
zone (Fig. 3b, c). On a global scale, the total water-holding field capacity
of soils increases (+0.06 m) with respect to PI_ctl (0.63 m), but
the global soil water budget is slightly reduced (-0.008 m). In
PI_sol the simulated forest cover slightly increases in high
latitudes and decreases in subtropical regions where it is replaced by grass
cover (Fig. 3d, e). In the subtropical areas
(30∘ S–30∘ N), grass cover increases at the expense of forest cover, likely due to a
reduction in the total water-holding field capacity.
Surface air temperature (∘C) anomalies of a 100-year
annual mean climate state with respect to the preindustrial period for (a) mid-Holocene
(HOL_sol – PI_sol), (b) mid-Holocene soil
feedback (f^HOL,soil), (c) Last Glacial Maximum
(LGM_sol – PI_sol), (d) Last Glacial Maximum
soil feedback (f^LGM,soil). A nonlinear scaling is used for the
colour bar displayed in panel (c). Hatched areas identify non-significant
values, calculated by means of a Student t test with 0.05 levels of significance.
As Fig. 4 but for total precipitation (mm day-1).
Part of the warming anomaly between PI_sol and
PI_ctl relates to the nature of the equilibrium soil scheme of
COSMOS_soil, calculating physical soil characteristics under
steady-state conditions. Therefore, soil characteristics in a final state
may not reflect the same status of present soil characteristics. For a more
detailed discussion, the reader is referred to the beginning of Sect. 4.
As Fig. 4 but for evaporation (kilograms per square metre per day).
Mid-Holocene climate (HOL_sol – PI_sol)
The application of Holocene boundary conditions to COSMOS_soil
(HOL_sol) leads to a global surface air temperature
(SAT) warming of 0.34 ∘C with respect to PI_sol.
The anomalous SAT warming of the standard Holocene model simulation
(HOL_ctl – PI_ctl: +0.1 ∘C)
undergoes a substantial temperature rise by including the soil feedback
(f^HOL,soil=+0.24 ∘C). High northern latitudes
exhibit largest amplitudes, i.e. the Barents Sea is affected by sea-ice
retreats (Fig. 4). Hatched areas shown in Fig. 4 and the subsequent Figs. 5
and 6 mark anomalies in regions with no significant climate change
(significance level: 0.05). A general feature of the mid-Holocene climate
pattern is given by negative temperature anomalies, accompanied by
significant precipitation (Fig. 5) and evaporation anomalies (Fig. 6), which
arise especially in regions that are sensitive to the African and Asian
monsoon. In principle, the mid-Holocene land surface is basically
characterised by higher forest (+2.3 %) and grass abundance (+1.2 %)
at the expense of desert fraction (-3.9 %). More specifically, the forest
cover expands in northern Canada, northern Siberia and at the Sahara–Sahel
transition; this expansion is accompanied by anomalous warming (Fig. 4) and increasing
precipitation (Fig. 5), respectively, or a combination of these two factors.
Simultaneously, decreased forest fraction is simulated in regions exhibiting
negative precipitation anomalies with respect to PI_sol, as
can be exemplarily seen in the South America tropical rainforests (Fig. 7).
Vegetation differences in 100-year mean of the mid-Holocene and
preindustrial climate state with dynamic soils (HOL_sol – PI_sol) and enclosed soil feedback
(f^HOL,soil) included for (a) forest fraction and (b) soil impact on
forest fraction, (c) grass fraction and (d) soil impact on grass fraction,
(e) desert fraction and (f) soil impact on desert fraction.
Changes in water-holding field capacities (metres) in soils and zonally
averaged land surface albedo and soil albedo for (a) mid-Holocene
(HOL_sol – PI_sol) and (b) Last Glacial
Maximum (LGM_sol – PI_sol).
Figure 8 shows the simulation of soil albedo (α) and total water-holding field capacity (hcws) under different climate background
conditions of the mid-Holocene and LGM. For the mid-Holocene, the global
mean of hcws increases by 2.38 cm, which is most pronounced in
northern Africa at the Sahara–Sahel transition. As a result, the global soil moisture
budget increases by 1.85 cm as compared to PI_sol (Fig. 9); this increase is mainly attributed to the soil feedback (+68 %). A change in the
physical soil characteristics (soil feedback) enhances the atmospheric
hydrological transport from the ocean towards the continent
(+1.29 kg m-2 yr-1), which in turn contributes to elevated land
surface runoff and drainage (+3.35 kg m-2 yr-1).
Globally, the land surface albedo decreases by 0.011, with maximum
temperature anomalies at the transition zone of the Sahara–Sahel region.
This is accompanied by changes in soil characteristics and a northward
migration of vegetation towards the Sahara desert (Fig. 4). The
high northern latitudes, in particular, undergo a “darkening” of the land surface due to
the integrated effect of lowered soil albedo (Fig. 8), albedo effects caused
by the expansion of forest cover (Fig. 7), and regional snow depth changes
(given in millimetres of water equivalent) in the continental realm of the Barents
Sea (Fig. 10). The replacement of C3 grass by forest in these locations
increases vegetation canopy and governs associated changes in soil
characteristics: a higher abundance of boreal evergreen forests in the high
northern latitudes leads to a decreased soil albedo and to an increase in
the total water-holding field capacity relative to soils that are associated with C3 grasses.
Soil wetness (metres) anomalies of a 100-year annual mean climate state
with respect to the preindustrial period for (a) mid-Holocene (HOL_sol –
PI_sol) and (b) Last Glacial Maximum (LGM_ sol – PI_sol) and
changes in land surface albedo for (c) mid-Holocene (HOL_sol –
PI_sol) and (d) Last Glacial Maximum (LGM_sol – PI_sol).
As Fig. 4 but for snow depth (water equivalent in millimetres).
As Fig. 7 but for the Last Glacial Maximum.
Application of a zero-dimensional energy balance model (EBM) on
model studies of the preindustrial period (including dynamic soils, PI_sol),
mid-Holocene (HOL) and Last Glacial Maximum (LGM). Panel (a) Global
EBM and surface radiation diagnostics that are used for (b) low latitudes
(30∘ S–30∘ N) and (c) high northern latitudes
(30–90∘ N). Investigations of the EBM and surface radiation
diagnostics refer to model results with (blank bars) and without (hatched
bars) dynamic soils with respect to the preindustrial period (PI_ctl). The
effect of planetary albedo and effective longwave emissivity is expressed in
terms of temperature changes.
LGM climate (LGM_sol – PI_sol)
The LGM climate, as calculated in COSMOS_soil (LGM_sol), reveals an overall
global SAT cooling of 7.03 ∘C. The impact of the implemented soil
scheme (f^LGM,soil) yields an additional cooling of
1.07 ∘C with maximum anomalies in the high northern latitudes and
localised cooling anomalies in the low-latitude desert regions (Fig. 4). Like
the overall temperature decrease, the water vapour capacity in the atmosphere
– as a function of temperature that is given by the Clausius–Clapeyron
relation – is strongly reduced, which therefore dampens precipitation and
evaporation fluxes in a glacial climate (Fig. 5, Fig. 6). Total mean
precipitation decreases by 0.42 mm day-1 with pronounced amplitudes at
the inner tropical convergence zone near the equator and at latitudes
> 40∘. The equatorward-expanding sea-ice cover reduces
precipitation in the high latitudes and governs increased rainout at the
sea-ice edge (Fig. 5). Precipitation and snow depth decrease especially in
the western part of northern Siberia, whereas regional cooling governs
increased snow depth in eastern Siberia and close to the Fennoscandian ice
sheet (Fig. 10). The global forest cover decrease (-14.9 %) is
compensated for by the expansion of grass (+10 %) and desert area
(+12.7 %). Given that subaerial shelf areas developed due to the
sea-level drop, forest (+6 %) and grass (+7 %) can evolve there.
Major retreats in boreal forest cover arise in Eurasia in a zonal belt
between 50–65∘ N and in proximity to the Scandinavian and
Laurentide ice sheets (Fig. 11). Cold desert areas as well as snow depth
increase in the high northern latitudes (Fig. 10). Interestingly, forest
cover increases slightly along ∼ 30∘ S most likely as a
response to greater precipitation there. Tropical forest cover is drastically
reduced by 59 % compared to PI_sol. In most areas grasses replace
forests, and deserts (e.g. in the Arabian Peninsula, Sahara–Sahel
borderline, and northern Siberia) expand at the expense of grasses.
The computed change in physical soil characteristics shows a general
decrease in hcws (-8.3 cm; Fig. 8b) as a response to climate and
vegetation change. However, this effect is mainly compensated for (global mean
hcws, including subaerial shelf regions: -0.4 cm) by considering
additional soil processes in subaerial regions, as an effect of the sea
level drop (Fig. 8b). The strongest decline in total water-holding field
capacities of soils is localised at the southern borderline of the Eurasian
and Laurentide ice sheets and the Sahara desert. This responds to a decrease
in surface temperatures and precipitation, which is in line with a reduction
in the forest fraction and a parallel expansion of the desert area (Fig. 11a, e).
Comparing Figs. 8b and 9b, the total water-holding field capacity of
soils constitutes a direct soil wetness control. Thereby, elevated soil
wetness along the ∼ 30∘ S latitudinal band (Fig. 9b)
is not directly linked to hcws but rather reflects a change in
atmospheric circulation that is associated with increased precipitation,
evaporation, and forest cover (Figs. 5c, 6c, 11a).
The global change in soil albedo (Fig. 8b) yields a remarkable brightening
of the land surface, with pronounced amplifications in the high latitudes
(Fig. 9d). This generally accompanies desert areas expanding at the expense
of grass and forest cover (Fig. 11) and thus gives a rise to the overall
shortwave reflectivity of the land surface (Fig. 9d). Especially the
Siberian region provides maximum changes in soil and land albedo (and
hcws) that are associated with a southward shift of the
taiga–tundra transition. Amplified by changes in the physical soil
characteristics at the transition zone, the latitudinal shift is well
represented by a general retreat of Siberian forests and an additional
southward shift of the boreal treeline (Fig. 11b). North of the boreal
treeline (> 60∘ N), the incorporated soil feedback
promotes an expansion of the polar desert (Fig. 11f) and an increase in the
snow cover (Fig. 10d) that parallels a demise of grass cover there (Fig. 11d).
The integrated feedback effect of boreal soil changes that interacts
with vegetation snow cover and sea ice is reflected by additional regional
surface air temperature cooling of more than 3 ∘C (Fig 4d).
Energy balance model
To separate the effect of radiative components of the isolated soil feedback
on the respective climate, we additionally present results of a
zero-dimensional energy balance model. The radiative balance of the soil
feedback is quantified by applying a zero-dimensional energy balance model
(0-dim EBM) for a grey atmosphere (Sellers, 1969):
(1-α)S/4=εσT4.
The 0-dim EBM comprises the total irradiance (S= 1367 Wm-2), the
Stefan–Boltzmann constant (σ= 5.67 × 10-8 Wm-2 K-4),
the planetary albedo α, and the effective
longwave emissivity ε. The planetary albedo is calculated by
the ratio of incoming and outgoing shortwave radiation at the top of the
atmosphere, and the effective longwave emissivity is given by the fraction
of outgoing longwave radiation at the top of the atmosphere and the
surface – all radiative fluxes are provided by the globally and regionally
averaged model output. Thereafter, the radiative equilibrium temperature T is
derived by the 0-dim EBM.
For the preindustrial period (PI_ctl) the 0-dim EBM constitutes a
planetary albedo of 0.32 and an effective longwave emissivity of 0.584,
which equals a radiative equilibrium temperature of 289.4 K, in line with previous EBM considerations
(Heinemann et al., 2009). To compare the
radiative contributions in the prevailing model studies, α and
ε are expressed in terms of temperature anomalies with respect
to the preindustrial period (Fig. 12). Motivated by the 0-dim EBM, we apply radiation
diagnostics at the surface on a regional scale comprising the tropics
(30∘ S–30∘ N) and mid-to-high northern latitudes
(30–90∘ N).
For the mid-Holocene and LGM (without dynamic soils), the 0-dim EBM
calculates a similar warming (+0.1 ∘C) and cooling
(-5.4 ∘C), respectively, as for the global SAT changes in
the model simulations (HOL_ctl – PI_ctl:
+0.1 ∘C; LGM_ctl – PI_ctl:
-5.96 ∘C). Both the planetary albedo and effective longwave
emissivity contribute to an amplification of the original temperature
anomaly, taking dynamic soils into account. The anomaly of
COSMOS_soil and COSMOS with respect to the preindustrial period
(PI_sol – PI_ctl: +0.1 ∘C) shows
the error of procedure by including the soil scheme in the model set-up
(see Sect. 3.1). This anomaly accounts for the offset between model runs
with and without dynamic soils and should be considered in the comparison of
the two model versions. As a reference, the regional and global deviations
regarding the error of procedure (PIsol) are displayed as well
(Fig. 12). Nevertheless, we state that anomalies between model studies with
(COSMOS_soil) and without the interactive dynamic soil
component (COSMOS) consistently reveal stronger soil effects as compared to
the error of procedure (PIsol).
Interestingly, based on the 0-dim EBM analysis the Holocene temperature rise
without dynamic soils (+0.1 ∘C) is solely attributed to a
reduction in the effective longwave emissivity, corroborated by an increase
in the water vapour content in the atmosphere (+0.28 kg m-2).
Dynamic soils in the mid-Holocene simulation additionally amplify
the Holocene warming by changes in both α (+0.14 ∘C)
and ε (+0.19 ∘C). The EBM, which is applied to low
latitudes (30∘ S–30∘ N) of the Holocene, calculates
minor temperature reductions (HOL_ctl – PI_ctl: -0.1 ∘C).
Moderate cooling in the low latitudes is largely
associated with orbital forcing during the mid-Holocene, by insolation
redistribution from low to the high latitudes (Berger and Loutre, 1991;
Laepple and Lohmann, 2009). Instead, including dynamic soils in the climate
system, the EBM calculates a change in temperature that overcompensates for the initial cooling of the standard model set-up (HOL_sol –
PI_ctl: +0.3 ∘C, Fig. 12b). In the mid-to-high
northern latitudes (30–90∘ N), the surface radiation
diagnostics exerts amplified temperature changes throughout all of the model
studies as compared to the global climate signature (Fig. 12c). The boreal
temperature change (30–90∘ N) is further corroborated
by dynamic soils reinforcing the trend of the original signal in
HOL_ctl and LGM_ctl by changes in α
(HOL: +27 %; LGM: +21 %) and ε (HOL: +51 %; LGM:
+9 %).
Generally, changes in the planetary albedo of the model are caused by the
integrated albedo changes in cloud cover, snow, sea ice, vegetation, and the effect of dynamic soils shown. Interestingly, additional boreal planetary
albedo changes (Fig. 12c) that are associated with the soil feedback are
relatively weak for the mid-Holocene but much stronger for the Last Glacial
Maximum. This fact may be best explained by the limited changes in the
vegetation canopy for the Holocene (Fig. 7) as compared to the combined
effect of vegetation reductions and the exposition of relatively high albedo
soils during the LGM (Fig. 11).
A change in the effective longwave emissivity is fundamentally affected by
changes in radiatively active gas concentrations, water vapour and clouds in
the atmosphere. Especially the dynamic changes in the total water-holding
field capacity of soils affect net evaporation and precipitation fluxes
between the atmosphere and the land surface, hence controlling part of the
water vapour content in the atmosphere. In turn, additional temperature
anomalies that are related to soil albedo changes feed back to the total
capacity of water vapour in the atmosphere. However, the present set of
model studies does not allow a quantification of the separated feedbacks
between the atmosphere and the total water-holding field capacity of soils
and soil albedo. For the LGM temperature, the effective longwave emissivity
change that is induced by dynamic soils is weaker than compared to the
effect of soil albedo changes. A likely explanation for the dominance of the
albedo effect is given by additional regional soil formation in subaerial
shelf seas that dampens the overall reduction in total water-holding field
capacities of soils (Fig. 8b). Instead, the additional mid-Holocene
temperature rise that is related to a reduced effective longwave emissivity
as a consequence of soil processes is stronger than the temperature effect
which is associated with planetary albedo changes. Especially in the low
latitudes, dynamic soils may even reverse a conservative regional cooling
trend towards a warming signal via radiative effects of increased
atmospheric water vapour as shown by changes in the effective longwave
emissivity (Fig. 12b).
Discussion
The design of the soil scheme, similar to equilibrium terrestrial vegetation
models (e.g. Prentice et al., 1992), does not account for pedogenesis (soil
evolution) and rather reflects the potential soil feedback to climate. As a
consequence, the soil scheme may overestimate the soil evolution in regions such North America (not shown) in which present soil formation still
lags behind as a consequence of Northern Hemisphere ice sheet retreat during the
last deglaciation 19–7 kyr ago (Fairbanks et al., 1992; Harden et al.,
1992). As defined by the nature of equilibrium models, the soil scheme
calculates a specific soil type which is expected under steady-state
climate conditions. The global SAT warming of PI_sol with respect to the
static soil properties of PI_ctl may be related to the
modelled steady state of the soil properties. Especially in the
north-eastern part of North America, prescribed soil properties
(PI_ctl) as derived by the MODIS satellite measurement
product (Rechid et al., 2009) are characterised by relatively high soil
albedos and low total water-holding field capacities (Hagemann et al.,
1999). This is typical for soils at an early stage of development.
Harden et al. (1992) show that over the past 18 kyr, the age and development of exposed
soils in North America have been directly controlled by the Laurentide ice sheet
retreat. Such time-dependent lagging processes are not captured by the
present equilibrium soil scheme; therefore, anomalous SAT warming in northern
North America reflects the temperature response of more developed soil
types. Nevertheless, as shown for the mid-Holocene model runs, the soil
scheme is not sensitive at locations which have been covered by the
Laurentide ice sheet (Fig. 4b).
Given the timescales of various pedogenic processes, specific soil processes
can be categorised as rapid (101-2 years, e.g. litter formation),
medium-rate (103-4 years, e.g. mollic, umbric humification), and slow
(105-6 years, e.g. ferrallitisation, saprolitisation) (Targulian and Krasilnikov, 2007). Zones in which soils are
associated with a low climatic potential of pedogenesis (e.g. in cold and
hot deserts) reveal rapid pedogenic processes and are considered
to be young soils. More developed soils can be found in humid boreal and
temperate regions, whereas the humid tropics with the most developed soils
have the highest climate potential of pedogenesis and are characterised
by slow specific pedogenic processes (Targulian and Krasilnikov, 2007).
So far, the scheme does not account for the potential evolution of soil
types, and therefore any transient effects, including lag effects, are
currently not considered in the simulated climate–soil feedback. Future
transient GCM studies may utilise more sophisticated soil schemes, which
should have the aim of deriving time-dependent variables in terms of nonlinear progressive and regressive
soil development, acting on different timescales (Johnson et al., 1990;
Hoosbeek and Bryant, 1992).
Dynamic soil feedback to the mid-Holocene climate
Compared to the preindustrial period, the mid-Holocene climate is
predominantly forced by seasonal insolation changes, with insolation
governing warming in high latitudes and cooling in the low latitudes. So far,
most of the ESMs cannot reconcile the amplitude of regional temperature
changes as suggested by the geological data, raising the question of lacking
or impeded feedbacks in present state-of-the-art ESMs (O'ishi and Abe-Ouchi,
2011; Hargreaves et al., 2013; Lohmann et al., 2013). When including the
dynamics of physical soil characteristics, our results indicate a warmer
mid-Holocene climate than shown in most previous model studies with dynamic
vegetation (Gallimore et al., 2005; Braconnot et al., 2007; Otto et al.,
2009a, b). Our mid-Holocene study with incorporated dynamic soils exhibits a
global SAT rise of 0.34 ∘C, similar to the study of O'ishi and
Abe-Ouchi (2011) showing a global SAT anomaly of +0.36 ∘C by
applying a general climate model with slab–ocean–atmosphere–vegetation
dynamics (dynamic soils not included). O'ishi and Abe-Ouchi (2011) find a
mid-Holocene warming in their model which is mainly attributed to vegetation
changes (+0.23 ∘C). However, our model simulations (with
vegetation and without dynamic soils) result in a moderate SAT rise (HOL_ctl
– PI_ctl: +0.10 ∘C), whereas the synergy analysis suggests a
relatively strong soil feedback (+0.24 ∘C) that leads to most of
the mid-Holocene warming (+71 % of the overall temperature change).
Further, the EBM analysis suggests that mid-Holocene warming (HOL_ctl; Wei
et al., 2012) is likely attributed to a decrease in the effective longwave
emissivity (Fig. 12) as a result of increased water vapour in the atmosphere
(+0.28 kg m-2). However, the additional consideration of dynamic
soils in the model can significantly corroborate the global temperature
response by changes in both the planetary albedo and the effective longwave
emissivity (Fig. 12).
Sundqvist et al. (2010a, b) reconstruct the mid-Holocene climate north of
60∘ N as being 2 ∘C warmer than the preindustrial climate,
with seasonal variations of +1 ∘C in summer and +1.7 ∘C
in winter. They conclude that the year-round warming in the high northern
latitudes is mainly attributed to warming during spring and/or autumn. We
find that north of 60∘ N, polar amplification leads to +1.15 and
+1.83 ∘C of warming without and with dynamic soils, respectively.
The highest seasonal warming peaks occur in winter (+2.37 ∘C) and
autumn (+2.35 ∘C), which can be attributed to changes in
insolation; however, these changes are much stronger in magnitude than other
studies have estimated (e.g. Sundqvist et al., 2010a, b). Due to a lack of
spatial sampling heterogeneity in proxy sample availability (a majority of
the data are found in terrestrial European climate archives; Otto et al.,
2009a, b), the multiproxy approach of Sundqvist et al. (2010a, b) may be
biased (Sundqvist et al., 2010a, b; Otto et al., 2009a, b). Nevertheless,
Holocene winter warming in high latitudes seems to be essential for the
northward migration of trees and the expansion of temperate deciduous forests
in Europe by reducing the limiting effect of the temperature of the coldest
month (Prentice et al., 2000).
Climate reconstructions of the mid-Holocene that encompass the high northern
latitudes indicate an anomalous spring warming (Sundqvist et al., 2010a, b),
though insolation during this season was reduced. Otto et al. (2009a, b)
address this discrepancy by means of an atmosphere–vegetation model study,
in which they test the sensitivity of increased forest canopy masking snow cover. The
atmosphere–vegetation feedback reveals a spring warming of 0.34 ∘C
together with a forest cover expansion of 13 %, which helps to reduce part
of the model–proxy data mismatch (Otto et al., 2009a, b). Despite lower
spring insolation, HOL_sol warms up by about 0.98 ∘C,
strongly amplified by the soil feedback (+0.72 ∘C).
HOL_sol exhibits an increase in forest cover (+11%) in
high latitudes (60–90∘ N) but does not show strong forest cover
changes (+0.7 %) in midlatitudes (35–60∘ N), as also indicated by
the pollen record (Prentice et al., 2000). Midlatitude temperature
anomalies during spring are close to PI_sol (-0.07 ∘C), compensated for by the soil feedback (+0.4 ∘C) and reducing the
effect of low insolation forcing during spring.
In principle, physical soil characteristics of the mid-Holocene climate
provide year-round elevated soil moisture, as a first-order effect of
increased total water-holding field capacities of soils, and lower soil
albedo as compared to the preindustrial period. However, depending on the seasonal
variation, both soil characteristics may respond differently: for example, the local insolation increase at the transition from boreal winter
to spring triggers increased snowmelt due to the presence of relatively
dark soils in the high northern latitudes (60–90∘ N), which
accounts for an amplified spring warming. The model simulations show that the effect of darker soils, in particular, increases spring
snowmelt (+0.15 mm day-1) in the high northern latitudes
(60–90∘ N) during the Holocene as compared to the standard model study without
dynamic soils (+0.05 mm day-1).
In addition to this, the general effect of increased total water-holding field
capacity of soils in the midlatitudes (35–60∘ N) develops in parallel with
increased water vapour in the atmosphere as a result of higher soil moisture
throughout the year. Nevertheless, especially for the comparison of the
mid-Holocene and preindustrial model simulations with dynamic soils
(HOL_sol – PI_sol), atmospheric water vapour
shows a lesser decrease during spring (-0.07 kg m-2) and a
stronger summer increase (+3.04 kg m-2) compared to
the spring (-0.44 kg m-2) and summer response (+2.22 kg m-2)
of the standard model simulations
(HOL_ctl – PI_ctl). This response reflects
the interaction between higher availability of soil moisture, evaporation
processes, and anomalies in seasonal insolation changes in the mid-Holocene
as compared to the preindustrial period. For the mid-Holocene, elevated soil moisture
provides additional evaporation fluxes, which in turn increases water vapour
in the atmosphere. This process counterbalances lower local spring
insolation as compared to the preindustrial period by reducing the effective longwave
emissivity (see EBM analysis, Sect. 3.4).
Though spring insolation is reduced compared to the preindustrial period, the soil
and vegetation feedback (Wohlfahrt et al., 2004) potentially provide a
fundamental mechanism to explain this anomalous spring warming (Sundqvist et
al., 2010a, b; Otto et al., 2009a, b).
Dynamic soil feedback to the Last Glacial Maximum climate
In comparison to the coupled GCM studies of PMIP2, the overall SAT cooling in
this study (-7.03 ∘C) exceeds the
cooling range of previous studies (-3 to -6 ∘C; Braconnot et
al., 2007, 2012) and proxy-based cooling estimates (-4 ∘C; Annan
and Hargreaves, 2013). As previously shown (Schneider von Deimling et al.,
2006a, b), most model studies do not take additional negative radiative
forcings into account (e.g. dust content; Jansen et al., 2007), which may
amplify glacial cooling. By applying an ensemble of simulations with varying
forcing parameters, performed by a model of intermediate complexity,
Schneider von Deimling et al. (2006a, b) constrain LGM cooling estimates to
5.8 ± 1.4 ∘C. They infer that atmospheric dust forcing and
vegetation dynamics support additional cooling of 1.0 to 1.7 ∘C.
Here, the synergy term ensures that the soil feedback in our model yields an
additional cooling of 1.07 ∘C, revealing the capability of dynamic
soils to shift LGM temperature constraints further towards cold extremes.
Thereby, additional cooling in response to the soil feedback is governed by
changes in planetary albedo and effective longwave emissivity, as shown by
the 0-dim EBM analysis. Braconnot et al. (2012) suggest that part of the
spread of cooling among the PMIP2 models arises from changes in surface
albedo parameterisations, highlighting the importance of key physical land
surface characteristics. Jiang (2008) utilised an atmosphere GCM
asynchronously coupled to the BIOME3 terrestrial vegetation equilibrium model
and changed soil characteristics iteratively, an approach similar to ours.
However, this approach considers a change in soil characteristics only if the
grid-cell-related dominant vegetation type has changed. Instead, our soil
scheme calculates physical soil characteristics by assessing all plant
functional types. Applying LGM boundary conditions, the study of Jiang (2008)
shows some additional SAT cooling (-0.05 ∘C) and polar
amplification (-0.42 ∘C) as calculated over the ice-free
continental area (60–90∘ N). The additional cooling presented in Jiang (2008) confirms the trend of the positive soil feedback of our findings, but its
magnitude does not reflect it. Such
discrepancies may be attributed to different climate sensitivities of the
models, diverging set-ups of glacial boundary conditions, lacking feedbacks
in the atmosphere GCM, or methodological differences in defining soil
characteristics.
Using a model of intermediate complexity, Jahn et al. (2005) quantified the
single effects of lowering atmospheric CO2, ice sheets, and vegetation
dynamics for the LGM time period. They constrained additional global mean
cooling to 0.6 ∘C with a pronounced response in the high
latitudes, which is induced by the consideration of vegetation dynamics. The
amplified polar cooling, as seen in their model, can be partly inferred from
a southward shift of the boreal treeline, i.e. the taiga–tundra transition,
which accounts for 2 ∘C of cooling over Eurasia. More specifically,
the southward migration of the taiga–tundra transition reinforces an initial
cooling by replacing comparably low-albedo forests with high-albedo grass
cover. Especially during the spring season, the retreat of forest cover exposes
the snow-covered ground that results in a late start of the snowmelt as a
consequence of the radiative effects (e.g. Jahn et al., 2005; Brovkin et
al., 2003). The climate response of the taiga–tundra feedback largely agrees
with pollen-based biome reconstructions for the LGM; this requires strong
amplified high-latitude cooling in order to infer an extensive equatorward
regression of the boreal treeline to ∼ 55∘ N (e.g.
Prentice et al., 2000; Bigelow et al., 2003; Kaplan et al., 2003). Referring
to the combination of vegetation and soil dynamics within our model
simulations, an overall decline in forest cover is shown in the mid-to-high
northern latitudes as a response of amplified cooling (Fig. 11a). The soil
feedback analysis suggests the interplay between soil processes and the
taiga–tundra feedback that results in an additional southward retreat of the
boreal forest cover from ∼ 61.2 to
∼ 57.5∘ N (Fig. 11b). Moreover, the temperature
response to changes in physical soil characteristics north of the
taiga–tundra transition also reinforces the replacement of cold deserts at
the expense of grass cover (Fig. 11d, f). As suggested by the EBM analysis,
the dominant effect of soil processes, cooling the mid-to-high latitudes, is
the result of (soil) albedo changes. However, reduced atmospheric water vapour
that is associated with changes in the total water-holding field capacity of
soils is also important (see EBM analysis, Sect. 3.4). The additive
interaction of climate feedbacks, e.g. vegetation dynamics, with dynamic
soil changes in the high northern latitudes provides part of an explanation
for an increased amplification of the temperature response (more than
-3 ∘C). This is stronger as compared to the effect of vegetation
dynamics without soil dynamics (-2 ∘C), shown by Jahn
et al. (2005). The integrative and mutual effect of, in particular, sea-ice, snow,
vegetation, and soil changes may provide sufficient cooling in the high
northern latitudes to explain the extensive southward regression of the
boreal treeline that has been reconstructed for the LGM (Prentice et al.,
2000; Bigelow et al., 2003).
Conclusions
This study highlights the importance of soil feedbacks within climate models
in order to capture the sensitivity of past climates as suggested by the
proxy record. We developed a soil scheme simplified by the assumption that
vegetation actively transforms and regulates the abiotic environment
(Lovelock, 1979). Therefore, the physical soil characteristics are tightly
linked to bioclimatic factors like vegetation and thus climate change. The
soil scheme is asynchronously coupled to a comprehensive climate model with
vegetation dynamics. The scheme is tested for time slices of the Last
Glacial Maximum and mid-Holocene. We find that dynamic soils reinforce the
original climate signal for climates warmer and colder than the preindustrial period
with a significant amplification in the mid-to-high northern latitudes. This
could – at least partly – reconcile the overly weak response in climate models
to external forcing during the mid-Holocene (Braconnot et al., 2012;
Hargreaves et al., 2013; Lohmann et al., 2013).
Various studies provide ample evidence that terrestrial vegetation dynamics
positively feed back to climate changes. As shown herein, the consideration
of dynamic soils on climate change undergoes the same trend of direction.
Claussen et al. (2006) show that only the fully integrated interaction of
atmosphere, ocean, and vegetation dynamics provides the strongest amplitude
of climate variation by precessional forcing. In addition to and by analogy
with vegetation dynamics (Claussen, 2009), we have shown that the soil feedback
may also reinforce the climate response during the late Quaternary.
For the pre-Quaternary, climate reconstructions reveal particularly strong
warming in the polar regions (e.g. Jenkyns et al., 2004; Moran et al., 2006;
Huber and Caballero, 2011). The simulation of this polar amplification is a
key challenge for current model approaches (e.g. Knorr et al., 2011; Krapp
and Jungclaus, 2011; Fedorov et al., 2013; Salzmann et al., 2013). To provide
a potential solution for the model–data discrepancy, numerous studies
provoke processes for polar amplification, dealing with energy balance and
heat transport changes (e.g. Sloan et al., 1995; Otto-Bliesner and Upchurch,
1997; Sloan and Pollard, 1998). Alternatively, we suggest that the positive
climate feedback due to soil dynamics offers a testable alternative
contribution towards understanding polar amplification of greenhouse climate
states in the geological past.