We have developed a
new module to calculate soil organic carbon (SOC) accumulation in perennially
frozen ground in the land surface model JSBACH. Running this offline version
of MPI-ESM we have modelled long-term permafrost carbon accumulation and
release from the Last Glacial Maximum (LGM) to the pre-industrial (PI) age.
Our simulated near-surface PI permafrost extent of
16.9
Compared to a control simulation without describing the transport of SOC into perennially
frozen ground, the implementation of our newly developed module for simulating permafrost
SOC accumulation leads to a doubling of simulated LGM permafrost SOC storage (amounting
to a total of
The amount of carbon stored in the atmosphere, in the ocean, and on land has varied
strongly between glacial and modern times (Ciais et al., 2012). Ice-core records suggest
a large increase of about 100 ppm in atmospheric
Large amounts of SOC have accumulated in soils of the northern permafrost regions as a
consequence of the low heterotrophic respiration in the surface soil layer that thaws
during the short summer period (active layer) and permanent sub-zero temperatures in
permafrost. Vertical soil mixing through consecutive freeze-thaw cycles (cryoturbation)
is specific to permafrost soils and further favours high SOC accumulation, but is
generally not accounted for in current ESMs (Koven et al., 2009, 2015; Beer, 2016). The
large carbon storage capacity of high-latitude soils is underlined by observational
evidence which points to a total SOC stored in the permafrost region of
Given the sensitivity of permafrost soils to climate change, manifested in
permafrost aggregation or degradation, which decreases or increases
A focus of many paleo modelling studies is the LGM climate, as it was distinctively different to the PI climate and therefore is considered an ideal test case for model evaluation, e.g. within the framework of the Paleoclimate Modelling Intercomparison Project (PMIP; Braconnot et al., 2012; Kageyama et al., 2017). With respect to permafrost dynamics, PMIP models have been used to analyse permafrost extent under LGM and PI climate conditions (Saito et al., 2013), but not with respect to changes in permafrost soil carbon storage. Willeit et al. (2015) have run the ESM CLIMBER-2 over the last glacial cycle to explore the interaction of permafrost and ice-sheet evolution. Using the ORCHIDEE land surface model, Zhu et al. (2016) simulated glacial SOC storage in Yedoma deposits showing a rather large potential of these thick ice- and organic-rich sediments to affect the global carbon cycle. Having analysed a permafrost loess–paleosol sequence, Zech et al. (2011) inferred that more SOC was sequestered during glacials than during interglacials.
Permafrost carbon dynamics are only recently being implemented into ESMs. Due to this, and the computational costs of high-resolution glacial–interglacial model runs, we know of no previous full-complexity ESM experiments (in contrast to Earth system Model of Intermediate Complexity, EMIC, studies) that quantify the dynamic role of circum-Arctic permafrost carbon during deglacial warming.
In this study, we investigate permafrost soil carbon dynamics from the LGM to PI using a process-based land surface model. In particular, we want to analyse the amount of SOC which was stored under LGM climate conditions, and the dynamics of this SOC store under deglacial warming into the Holocene – in response to receding ice-sheets, broad-scale shifts in permafrost regime, and increases in vegetation productivity and soil respiration.
For simulating soil carbon dynamics from the LGM to PI climate, we have used
a standalone (offline) configuration of the MPI-ESM land surface model JSBACH
(Reick et al., 2013; Brovkin et al., 2013; Schneck et al., 2013) in coarse
T31GR30 resolution (
In this study we focus on long-term (millennial scale) dynamics of near-surface SOC
stocks, and therefore only consider carbon accumulation in permafrost soils to a depth of
3 m. Consequences of accounting for deeper SOC deposits are briefly discussed in
Sect. 2.5. For many JSBACH permafrost grid cells, bedrock depths are shallower than 3 m
and therefore limit the maximum depth of SOC accumulation in our model. Here, we use a
global dataset of soil depths compiled by Carvalhais (2014) (see Fig. A8). Vegetation
cover is assumed to respond to changing glacial–Holocene climatic conditions and is
dynamically calculated throughout the simulation. Pre-industrial land use changes in high
latitudes are negligible (Hurtt et al., 2011), and therefore we do not account for
human-induced modifications of vegetation cover. We prescribe glacial ice sheet coverage
and
Here we describe key aspects of the physical soil representation in JSBACH, while a detailed model description can be found in Ekici et al. (2014). In our study we use a set-up with 11 vertical soil layers of increasing vertical thicknesses reaching a lower boundary at 40 m. Surface air temperature is used as an upper boundary forcing for calculating soil temperatures during the snow-free season. When snow is present, a 5-layer snow scheme is applied for forcing soil temperatures. An organic layer of constant thickness is assumed to cover the soil top. The bottom boundary condition is given by a zero heat flux assumption. Heat transfer into the soil is calculated for each soil layer by using a one-dimensional heat-conduction equation accounting for phase change of soil water.
Extended soil carbon pool structure in JSBACH accounting for active layer and permafrost carbon (modified from Goll et al., 2014). In YASSO, soil organic matter (SOM) is separated into groups of different chemical compounds (A, W, E: labile pools), an intermediate pool (N), and a slow pool (H). Carbon gain results from litter input, carbon loss from heterotrophic respiration (HR) in active layer pools. Changes in maximum seasonal thaw depth induce a transfer of carbon (blue arrows) from non-active to active pools (warming), and vice versa (cooling).
Soil carbon dynamics within JSBACH are simulated by YASSO (Liski et al., 2005). We
describe key characteristics of this soil carbon model, while a detailed description of
its implementation into JSBACH can be found elsewhere (Thum et al., 2011; Goll et al.,
2015). YASSO calculates the decomposition of soil organic matter (SOM) considering four
different lability classes based on a chemical compound separation of litter into
acid-soluble (A), water-soluble (W), ethanol-soluble (E), and non-soluble (N) fractions
with the pools replicated for above and below ground. In addition to these four pools,
there is a slow pool that receives a fraction of the decomposition products of the more
labile pools (Fig. 1). These pools are replicated for green and woody litter on each
vegetation tile. Decomposition rates of each carbon pool were inferred from litter bag
experiments and soil carbon measurements and range from turnover times of up to a few
years (labile pools) to multi-centennial for the slow pool. Furthermore, litter input
into the soil is separately considered for leaf and woody components (not shown in
Fig. 1), assuming decreasing decomposition rates for increasing size of woody litter. The
dependence of decomposition on temperature is described by an optimum curve, combined
with a scaling factor
A main limitation for modelling the transfer of carbon between the active layer and the underlying permafrost body is the zero-dimensional structure of YASSO within JSBACH which does not allow for calculating vertical SOC profiles. Yet permafrost-affected grounds can store SOC in depths of several metres in the soil and reveal rather pronounced vertical gradients of typically decreasing carbon concentrations with depth within the active layer (Harden et al., 2012). Therefore we have developed a soil carbon build-up model which describes carbon gain through litter input, carbon loss through heterotrophic respiration, and vertical carbon transport through cryoturbation and sedimentation. Based on this model, we infer the needed information of vertical SOC distributions (see Appendix). We then use this new model to determine SOC concentrations at the level of the ALD, which determine the amount of carbon transferred between perennially frozen (permafrost) and seasonally thawed (active layer) pools. For this purpose we added an additional non-active pool into JSBACH for each YASSO soil carbon pool which describes carbon of the same chemical compound class, but which is not subject to seasonal thaw and remains perennially frozen (Fig. 1).
For each grid cell and each SOM class
The transfer of carbon into permafrost strongly depends on the thickness of the active layer: with increasing thickness, the share of labile soil organic material, which gets incorporated into permafrost, decreases as the distance of carbon transport to the permafrost boundary gets longer and therefore allows for more time for microbial decomposition. Our approach enables us to capture this key characteristic of soil carbon accumulation in permafrost soils, i.e. it describes the fractionation of SOM of differing lability with depth. As a consequence, shares of more labile SOC are increasing when organic material accumulates in colder climate conditions under shallower ALDs (Fig. A11).
The full dynamics of deglacial permafrost SOC accumulation and release are determined by a multitude of factors, ranging from past climatic boundary conditions and associated permafrost evolution to process-based descriptions of SOC formation.
Here we discuss structural model aspects and model limitations with regard to these factors which can explain part of the model data discrepancies which we discuss in Sect. 4.
The T31 resolution of JSBACH used in this study has allowed us to run a set of model experiments from the LGM to the PI. Yet the coarse resolution does not allow for simulating permafrost which covers only a fraction of the landscape (such as isolated and sporadic permafrost). We therefore underestimate the total area of ground subject to perennially frozen conditions.
We also do not account for effects of excess ice, which affects the temporal thaw dynamics and soil moisture conditions upon thaw in ice-rich grounds (Lee et al., 2014).
We do not account for water-logged soils – an environment which allows for the formation of peatlands and which requires specific model descriptions of SOC build-up (Kleinen et al., 2012). As a peat module was not included in our version of MPI-ESM, we do not describe carbon storage following deglacial peat dynamics (Yu et al., 2010). In this study, we focus on typical carbon profiles in mineral soils which decline with depth (in line with large-scale observational evidence (Harden et al., 2012). We acknowledge that we therefore underestimate total carbon storage by not capturing high carbon accumulation in saturated organic-rich soils (see discussion in Sect. 4.1.4).
We do not account for the evolution of syngenetic permafrost deposits through sustained accumulation of new material on the top of the active layer. High sedimentation rates result in deep soil deposits, rich in organic matter, as found in Yedoma soils or river deltas, which can store hundreds petagrams of SOC down to some tens of metres (Hugelius et al., 2014; Strauss et al., 2017). Using a multi-box model of permafrost carbon inventories, Schneider von Deimling et al. (2015) have shown that large amounts of perennially frozen carbon in depths below 3 m can get mobilized on a century timescale if abrupt thaw through thermokarst lake formation is accounted for. A consideration of the accumulation and release dynamics of these deep deposits under deglacial warming in JSBACH would require a process-based description of Yedoma formation and of abrupt thaw processes which is beyond the scope of this study.
Our model of describing soil carbon dynamics (YASSO) is based on the assumption that soil carbon decomposition can be inferred based on a chemical compound separation of litter. Model parameters are fitted based on annual litter-bag experiments. Therefore, uncertainties in long-term soil carbon dynamics (centennial to millennial timescale) can be large. Other factors, including soil texture and mineralogy, can strongly affect decomposition timescales. The stabilization of SOM due to its interaction with mineral compounds strongly reduces SOM decomposability (Schädel et al., 2014; Xu et al., 2016), but this particular process is not considered in YASSO.
Probably of more importance in the process-based SOM transport model is the fact that we did not explicitly account for soil moisture effects on SOC build-up, which, for example, can result in profiles of increasing SOC with depth (Zimov et al., 2006) due to slowing down respiration with soil moisture approaching saturation (see also discussion in Appendix A7.1).
We further focus on SOM decomposition under aerobic conditions and do not
model
We do not model soil genesis but prescribe stationary soil depths according to Carvalhais et al. (2014) by assuming a balance between deglacial soil erosion and formation rates. In Appendix A4.3 we discuss implications of this assumption on modelled SOC build-up. We account for the fact that many soils have only formed after the LGM by assuming that SOC build-up was prevented for grid cells when covered by LGM ice sheets.
Protection of warm permafrost through insulation effects by organic surface layers have likely varied between the glacial and Holocene climate but are not captured in our modelling study. A more elaborate scheme of organic layer treatment is subject to current JSBACH model development, coupling organic layer thickness to litter carbon amounts.
Our approach of accounting for vertical SOC gradients (Sect. 7.7) is based on using a process-based model of SOM transport assuming equilibrium conditions. Transient deviations from the equilibrium profiles cannot be captured and would be most pronounced for the faster cycling SOC pools, which reveal pronounced vertical gradients (Fig. A11). In this study we focus on long-term carbon dynamics along the deglacial warming, and therefore do not capture full SOC dynamics resulting from short-term climate changes on decadal to centennial timescales.
For spinning-up soil carbon pools we start our simulations at 28 ka (kiloyears before
present) from zero soil carbon concentrations
and run JSBACH for 7000 years under a stationary climate forcing representative for LGM
conditions. The chosen spin-up time allows for the slow soil carbon pools to come close
to equilibrium (see Fig. A13). As cold glacial climate conditions have prevailed for tens
of thousands of years before LGM, we assume that soils had enough time to accumulate soil
carbon into permafrost under pre-LGM conditions, leading to approximately depth-constant
SOC profiles between the permafrost table and our considered lower soil boundary (see
Fig. A11 and discussion in Appendix A7.1). With a focus on near-surface permafrost in
this study, this lower boundary
Further, we assume that SOM accumulation is prevented for sites covered for millennia under LGM ice sheets.
During the LGM spin-up phase we calculate
After 7000 years of model spin-up (when we diagnose permafrost SOC), we activate our
scheme of transient SOC transfer between seasonally thawed and perennially frozen SOC
pools and calculate
Performed transient and time-slice model experiments with MPI-ESM.
Unless otherwise stated, we discuss simulated model output by referring to
our transient model experiment from LGM to PI with standard model parameters
(experiment L2P). The model requires 16.43 s per model year on 108 nodes of
our high-performance machine, giving a total computation time requirement of
0.5 node-h yr
Organic layers at the top of permafrost soils cover only a small fraction of the soil
profile, but exert a large effect on subsoil temperatures by their insulating effect
(Porada et al., 2016; O'Donnell et al., 2009). Given a tendency to overestimate active
layer depths (ALDs) in JSBACH compared to observations (see Sect. 4.1.2), we have set up
a sensitivity experiment in which we have lowered the standard value of thermal
conductivity of the surface organic layer (0.25 W m
Vertical mixing of SOC in permafrost soils through cryoturbation is a
well-studied process, but as the timescale involved is rather large, the
magnitude of cryoturbation mixing rates is hard to constrain by observations
and is subject to large uncertainty (Koven et al., 2009). We
investigate consequences of doubling our assumed standard cryoturbation rate
of 10 cm
Decomposition timescale parameters in YASSO are inferred from a large set of
litter-bag experiments. However, the slow pool decomposition timescale is
less constrained. We therefore perform an additional experiment in which we
increase the standard reference slow pool turnover time of 625 years
(assumed for 0
SOC accumulation critically depends on simulated litter input. In our standard simulation we simulate a rather low net primary productivity (NPP) in permafrost regions under the harsh climatic conditions at 20 ka (see discussion in Sect. 4.1.3). We therefore have performed an additional experiment in which we have doubled litter input to YASSO soil pools to investigate SOC accumulation under a more productive LGM vegetation compared to our standard parameter setting.
A time-slice experiment was run, in which MPI-ESM was run without permafrost physics in T31GR30 resolution in a fully coupled atmosphere–ocean setting (see Sect. 7.2). Stationary PI boundary conditions were defined following the CMIP5 protocol.
A time-slice experiment for stationary LGM boundary conditions following the PMIP3 protocol was run (Braconnot et al., 2011), with LGM land-sea and ice sheet masks, as well as greenhouse gases and orbit modified to LGM conditions (see Sect. 7.3). The model was run without permafrost physics in a fully coupled atmosphere–ocean setting in T31GR30 resolution.
Ice sheet coverage and permafrost extent at PI and LGM simulated
with JSBACH. Mustard-coloured areas illustrate grid cells of simulated
near-surface permafrost with active layers above 3 m (PF
We first show simulated spatial patterns of physical and biogeochemical drivers for SOC build-up, along with simulated SOC distributions in permafrost regions under LGM and PI climatic conditions. We then discuss transient SOC dynamics from the LGM at 20 ka to the Holocene at 0 ka. Finally, we discuss the robustness of our findings with regard to uncertainty in specific model parameter choices.
Under PI climate conditions we model a Northern Hemisphere (NH) permafrost extent of
20.3 million km
Under the cold climatic conditions prevailing at LGM, JSBACH simulates an additional
3.7 million km
Another part of the discrepancy might result from precipitation biases leading to snow depth biases under glacial conditions. Overestimates in simulated snow depth can easily translate into too excessive snow insulation and therefore result in unrealistic high soil temperature (Stieglitz et al., 2003; Zhang, 2005; Lawrence and Slater, 2010; Slater and Lawrence, 2013; Langer et al., 2013).
Simulating too little LGM permafrost coverage underlines the challenge of modelling warm permafrost occurrence in non-continuous permafrost regions in which smaller-scale variations in snow thickness, vegetation cover, and topography play an increasingly dominant role on the soil thermal state.
Despite a limited simulated decrease in permafrost extent (20 %), pronounced spatial changes in permafrost coverage from the LGM to the PI climate are evident (Fig. 2). As a consequence of the cold climatic conditions prevailing at the LGM, permafrost extent has spread further south in most regions. At the same time, large areas of the NH, especially in North America, have been covered by thick ice sheets, thus limiting the maximum area for permafrost to establish in NH grounds.
Active layer depths (ALDs) in near-surface permafrost soils at PI and LGM simulated by JSBACH. Light bluish-coloured areas show prescribed ice sheet coverage. Data shown represent 100-year time averages.
Figure 3 shows simulated ALDs for PI and glacial conditions. Compared to the PI experiment performed with the CMIP-5 MPI-ESM model version, a slight warm bias in simulated mean surface air temperatures is evident in most grid cells of North America for MPI-ESM1.2T31 (Fig. A2 in the Appendix). As a consequence of this warm bias, ALDs in Alaska are biased high for most grid cells (Fig. A9) in our model version when compared to CALM observations (Brown et al., 2000). Given rather poor data coverage of monitored ALDs, especially in Asia, a model–data comparison should be seen with caution as it is questionable to what extent the site-level data are representative for scales simulated by JSBACH. Nevertheless, we here compare large-scale simulated ALDs to local-scale observational estimates based on the CALM database. Model data mismatches are less pronounced in Asia, and generally the model experiment of an increased organic layer insulating effect (experiment L2P_ALD, see Sect. 4.3) suggests improved agreement with the data. The simulated ALDs further suggest a tendency towards too warm soil temperatures in southerly permafrost regions (see Fig. A9). This is in line with underestimating LGM southward spread of permafrost in JSBACH compared to reconstructions.
Vegetation productivity (NPP, summed over all vegetation types) in near-surface permafrost regions for PI and LGM simulated by JSBACH. Light bluish-coloured areas show prescribed ice sheet coverage. Data shown represent 100-year time averages.
The amount of SOC stored in the ground crucially depends on vegetation litter input.
Figure 4 shows high-latitude vegetation productivity under LGM and PI climate conditions.
We infer highest vegetation productivity (with NPP larger than
250 gC m
The low bias in vegetation productivity proved especially critical for simulating glacial
permafrost SOC storage (see next section), as low levels of glacial temperatures,
precipitation, and
Seasonally thawed SOC in the active layer (
Simulated SOC storage in the active layer (SOC
Field data of mineral horizons in loamy permafrost-affected soils of Kolyma
lowlands have organic carbon contents of 1 %–3 %, with possible peaks up to
7 %, likely due to cryoturbation (Mergelov and Targulian, 2011).
These soils contain 7–25 kgC m
In contrast to observational datasets, such as the NCSCD (Northern Circumpolar Soil Carbon Database), we represent SOM quantity and its degree of decomposition by our
simulation approach (see Sect. 7.7.1). Weighted over the permafrost domain, we model a
SOC composition in the seasonally thawed soil surface of roughly equal shares of the slow
(H) pool (
Simulated SOC storage under LGM and PI conditions in near-surface permafrost soils for the standard parameter setting (L2P), reduced thermal conductivity of the organic layer (L2P_ALD), increased vertical soil mixing (L2P_VMR), increased slow pool lifetime (L2P_HDT), doubled litter input (L2P_LIT), and a control run without transfer of SOC to permafrost (L2P_CTR). Storages are expressed as totals, as the ratio between active layer and permafrost carbon, and normalized by the near-surface permafrost area (PFA).
JSBACH simulates a total SOC storage in near-surface permafrost soils of 168 PgC under
PI climate conditions, of which about a third is stored in permafrost (see also Fig. 7
and Table 2). When considering the full area of simulated permafrost ground (i.e. also
considering grid cells with active layers below 3 m depth), a total of 194 PgC is
stored in permafrost soils. This is significantly lower than a recent empirically based
reconstruction of LGM SOC stocks which includes ca. 800 PgC in mineral soils, but for a
larger permafrost area and assuming that lower glacial
Data-based estimates of pan-Arctic SOC storage in permafrost regions suggest a total of
1042 PgC (NCSCDv2.2; Hugelius et al., 2013). This amount also comprises SOC
contributions from soils within the permafrost domain that are not underlain by
permafrost. When focusing on continuous and discontinuous permafrost and constraining the
NCSCD data to grid cells with permafrost coverages larger than 50 %, an estimated
575 PgC is stored in northern Gelisols. As we do not model organic soils (
As discussed above, part of the low bias in simulated SOC storage can be explained by low
litter input into the soils due to an underestimate in vegetation productivity. Simulated
vegetation productivity is especially low under the glacial climate as a consequence of
low levels of glacial temperatures, precipitation, and
Another contribution to differences in modelled and observed SOC storage is likely to come from SOM decomposition timescale assumptions. In Sect. 4.3 we discuss an increase in simulated SOC accumulation due to increasing the residence time of the slow pool. These results underline that accounting for processes of long-term SOM stabilization (e.g. through the interaction with mineral compounds) would further increase simulated long-term SOC storages and should be considered an important aspect for further model development. A further aspect with regard to an improved representation of soil decomposition in permafrost will come from accounting for the full vertical soil temperature profile instead of tuning decomposition parameters to surface climatology only (see Sect. 2.4).
Despite a slightly larger simulated permafrost extent under LGM conditions, total LGM SOC storage of 147 PgC is lower than PI storage due to a reduced vegetation productivity under harsh glacial climate conditions (see Fig. 4). In contrast, using the land surface model ORDCHIDEE-MICT (Zhu et al., 2016) infers much larger LGM SOC storage in permafrost regions of about 1220 PgC (without accounting for fast sedimentation in Yedoma regions). This large discrepancy results from of a factor of 2 lower LGM permafrost extent and much lower glacial vegetation productivity simulated by JSBACH compared to ORCHIDEE-MICT.
The dynamics of total SOC storage in permafrost are determined by the interplay of changes in permafrost extent and active layer thickness, as well as by changes in soil carbon net fluxes determined by litter input and losses mainly due to heterotrophic respiration. These factors are analysed in detail in the following sections.
Deglacial evolution of simulated permafrost extent and prescribed ice sheet area from LGM to PI. Permafrost extent (brown lines) is shown separately for total (dashed line) and near-surface (solid line) Northern Hemisphere (NH) permafrost, subdivided into North America (NA, line with triangles), and Eurasia (EA, line with circles). Data shown represent 100-year time averages.
Deglacial changes in permafrost extent are strongly shaped by the retreat of northern hemisphere ice sheets. Especially the decline in the Laurentide Ice Sheet has exposed large areas of soil in North America to cold air temperatures, which led to a build-up of permafrost in these regions. As a consequence, JSBACH simulates the maximum in permafrost extent not during the LGM with maximum ice-sheet coverage, but around 13 ka due to an increase in permafrost extent in North America (Fig. 6). Consequently, deglacial warming results in a strong decline in total permafrost extent towards the beginning of the Holocene at 10 ka, while changes in permafrost extent over the Holocene period are less pronounced.
Deglacial evolution of seasonally thawed and perennially frozen SOC in near-surface permafrost from LGM to PI. Total SOC (black line) is composed of seasonally thawed SOC (green lines) and perennially frozen SOC (blue lines). Contributions from North America (lines with triangles) and Eurasia (lines with circles) are shown separately. Data shown represent 100-year time averages and were summed over near-surface permafrost grid cells (accounting for temporally evolving permafrost extents).
Deglacial climate dynamics have also affected permafrost carbon storage by increases in
vegetation productivity through higher
Pronounced changes in permafrost SOC become evident after 17 ka, showing
alternating phases of total permafrost carbon release and storage. Over the full
deglacial period from the LGM to PI, we model a net accumulation of 29 PgC from SOC in
near-surface permafrost – in line with a recent empirically based reconstruction of
higher SOC accumulation in mineral soils of the permafrost domain under present-day
conditions as compared to the LGM (Lindgren et al., 2018). Changes in the pools of
seasonally thawed and perennially frozen carbon are more pronounced. Total organic carbon
storage in the active layer (SOC
With regard to continental-scale deglacial SOC evolution, the temporal dynamics of
permafrost coverage and SOC storage turn out to be only weakly linked (see Fig. A10).
Under climate warming, the direct loss of permafrost extent lowers the total amount of
SOC stored in active layer and permafrost grounds. At the same time, this SOC loss is
compensated by an increased litter input to permafrost soils due to higher vegetation
productivity. For instance, the strong warming at 13 to 10 ka results in a
pronounced reduction in North American and Eurasian permafrost extents (Fig. A10) and
increased soil respiration, but related SOC losses are more than outweighed by concurrent
increases in NPP, which finally result in increased total SOC
In contrast to Crichton et al. (2016), we infer a slight increase in permafrost SOC
storage between 17.5 and 15 ka instead of a large release of thawed permafrost
carbon. Part of the discrepancy in simulated permafrost carbon dynamics can be explained
by modelling rather different trajectories of deglacial permafrost extents. We also do
not capture abrupt permafrost carbon release as suggested by Köhler et al. (2014)
during the onset of the Bølling-Allerød. The authors hypothesize that the source of
old
Using an ESM of intermediate complexity, Ganopolski and Brovkin (2017) have recently
analysed the contribution of terrestrial and ocean processes to the glacial–interglacial
We have tested the robustness of our simulations with regard to model parameter choices which affect ALDs, vertical SOC profiles, and decomposition timescale. Further, we have run an additional experiment in which we have doubled litter input to the YASSO soil carbon module to compensate for low biases in simulated vegetation productivity. Table 2 shows how the individual sensitivity experiments affect simulated SOC storages under LGM and PI conditions.
When decreasing the organic surface layer conductivity by a factor of 2 (experiment
L2P_ALD), we model a slight increase in permafrost extent (by 13.6 % for PI, by
6.0 % for LGM). Yet the dominant effect on simulated permafrost soils is manifested
in shallower ALDs (see Fig. A9), thus shifting the weight between active layer and
permafrost carbon towards a larger carbon store in permafrost layers (Table 2). This
increase in the fraction of permafrost carbon favours SOC accumulation by reducing
heterotrophic respiration losses. Under LGM conditions, this SOC gain is compensated by
simulating very shallow ALDs in many grid cells, which results in lower vegetation
productivity in L2P_ALD compared to L2P as a consequence of modified soil moisture and
soil water availability. Therefore, simulated total LGM storage is comparable in both
experiments (Table 2). In contrast, SOC
We have further investigated how a doubling of the cryoturbation rate in the process-based model of SOC accumulation is affecting vertical SOC distributions, and therefore simulated SOC transport between active layer and permafrost carbon pools. We infer a slight increase in the fraction of permafrost SOC as a consequence of the faster SOM transport through the active layer, but the overall effect on simulated SOC storages is rather small (Table 2). Of larger impact is uncertainty in the assumed decomposition time of the slow pool. After increasing the slow pool turnover time in YASSO from 625 years (L2P) to 1000 years (L2P_HDT), we infer an increase in total SOC storage of 31.8 % (LGM) and 22.0 % (PI). An increase in simulated SOC storage would also result if YASSO soil decomposition parameters were scaled by soil instead of surface air temperatures (see discussion in Sect. 2.4). The extent to which SOC storage will increase is uncertain and an improved description of temperature sensitivity of decomposition is subject to current JSBACH model development. Finally, we have run an additional experiment with doubling of the soil litter input to YASSO to compensate for our inferred low bias in vegetation productivity. Simulated SOC stores in L2P_LIT almost double and amount to 288 PgC (LGM) and 320 PgC (PI), reducing the mismatch to observational data (see discussion in Sect. 4.1.4).
Using a new land surface model offline version of JSBACH, we have simulated long-term
permafrost carbon dynamics from the Last Glacial Maximum (LGM) to pre-industrial (PI)
climate, driven by climate forcing fields generated from MPI-ESM (version 1.2 in T31GR30
resolution). Focusing on continuous and discontinuous permafrost extent, we simulate a
near-surface permafrost extent (i.e. permafrost in the upper 3 m of the soil) of
16.9
The implementation of our newly developed model to calculate SOC accumulation in JSBACH in perennially frozen ground has strongly increased total simulated SOC storage at high latitudes: we model a LGM SOC storage of 72 PgC in seasonally thawed soil layers comprising all grid cells with permafrost in the upper 3 m. When additionally accounting for SOC accumulation in perennially frozen soil layers, which prevents permafrost organic matter from decomposition, we infer a total SOC storage of 147 PgC – doubling the amount of simulated LGM SOC in a control experiment with identical permafrost physics but without modelling carbon transport to permafrost layers.
Simulated deglacial warming triggers pronounced changes in regional permafrost extent and
ALDs. In parallel, litter input into the soils increases through higher vegetation
productivity, while soil respiration increases due to warming temperatures. As a
consequence of combined deglacial changes in physical and biogeochemical driving factors
we infer an increase in total permafrost SOC storage towards the Holocene (168 PgC at
PI), with largest changes seen in the individual contributions of permafrost and active
layer carbon. Our modelled PI SOC storage is low compared to observations of total carbon
stored in soils of the permafrost region (
Rather than a steady increase in carbon release from the LGM to PI as a consequence of deglacial permafrost degradation, our results show alternating phases of permafrost carbon release and accumulation, which illustrates the highly dynamic nature of this part of the global soil carbon pool. The temporal evolution of active layer SOC proved to be strongly linked to changes in NPP in permafrost regions, rather than to changes in permafrost extent, which can be explained by pronounced time lags between establishment of new permafrost after ice sheet retreat and onset of intense vegetation productivity. Our simulations show a long-term shallowing trend of ALDs towards reaching the PI climate, which results in a sustained but slow transfer of active layer SOC to perennially frozen pools after 10 ka.
Over the full deglacial period from the LGM to the PI climate, we model a net accumulation of 21 PgC in near-surface permafrost soils (i.e. an increase by 14 % above LGM SOC). The full extent to which carbon accumulation and release as a consequence of deglacial permafrost degradation has likely affected past variations in atmospheric glacial–interglacial greenhouse gas levels critically depends on the realism of simulated glacial vegetation productivity and permafrost thermal state, which are both the subject for future model improvements.
Primary data and scripts used in the analysis and other supplementary information that may be useful in reproducing this work are archived by the Max Planck Institute for Meteorology and can be obtained by contacting publications@mpimet.mpg.de.
We have performed experiments with a standalone configuration of the MPI-ESM land surface model JSBACH, driven with climate forcings derived from coupled climate time-slice model experiments in coarse T31 resolution performed under pre-industrial and glacial conditions with MPI-ESM1.2 (as described in Mauritsen et al., 2018; with differences to the base version described in Mikolajewicz et al., 2018). As the availability of MPI-ESM1.2 experiments is limited to these two time slices, we follow an anomaly approach for modelling deglacial climate dynamics and used climatic fields from a transient glacial cycle experiment with the intermediate-complexity ESM CLIMBER2 (Ganopolski et al., 2010).
For the study presented here, we use the MPI-ESM1.2 pre-industrial climate experiment (described in Sect. 7.2) as the basis of the climate forcings. Climate forcings for earlier times were derived by applying monthly anomalies to the pre-industrial climate, with absolute anomalies used for surface air temperature fields and relative anomalies for precipitation, humidity, radiation, and wind speed. The anomaly applied to the MPI-ESM PI climate is derived as a linear interpolation between MPI-ESM LGM and CLIMBER2 anomalies, depending on the distance of CLIMBER2 global mean temperature to the LGM state. The weight of the MPI-ESM anomaly in this interpolation is shown in Fig. A1.
This procedure of determining the climatic anomalies ensures that near-LGM conditions are derived from the high-resolution MPI-ESM climatic fields, while climate during the deglaciation and the Holocene is derived from the lower resolution but spatiotemporally consistent CLIMBER2 fields.
To derive the climate forcings for the standalone JSBACH model we performed an experiment
with MPI-ESM in version 1.2 in resolution T31GR30 (MPI-ESM1.2T31, corresponding to
Transient weighting of MPI-ESM anomaly (LGM-PI). At LGM, the anomaly is fully described by MPI-ESM. CLIMBER2 anomaly weight is 1 – MPI-ESM anomaly.
Difference in simulated annual mean surface air temperature by MPI-ESM1.2T31_PI compared to CMIP5 PI experiment.
PI winter surface air temperatures (DJF), summer surface air temperature (JJA), and annual precipitation simulated by MPI-ESM1.2T31_PI.
Simulated and observed high-latitude GPP for Eurasia and North America. Model results are for pre-industrial conditions. Observations are from Jung et al. (2011) and scaled to 85 % to account for lower pre-industrial GPP (Ciais et al., 2013). Regional zonal averaging was only performed for grid cells containing near-surface permafrost.
In the version we are using here, the global mean temperature is 286.78 K, nearly identical to the global mean temperature of 286.66 K of the CMIP5 model in T63 resolution. However, the spatial distribution of temperature is modified, as shown in Fig. A2. Annual mean temperatures over northern North America are warmer than the CMIP5 reference, while temperatures over Eurasia are cooler. This spatial pattern is also affecting simulated pre-industrial vegetation productivity (Fig. A4) which shows higher GPP in North America and lower GPP in Eurasia compared to observational evidence based on up-scaled flux tower measurements (Jung et al., 2011).
As shown in Fig. A3, PI winter (DJF) temperatures reach
Difference in
The LGM climate experiment is set up following the PMIP3 protocol (Braconnot et al.,
2011), with LGM land-sea and ice sheet masks, as well as greenhouse gases and orbit
modified to LGM conditions. The global mean surface air temperature is 282.94, 3.84 K
colder than for PI (which is at the lower end of PMIP3 model results; Schmidt et al.,
2014). The differences in annual mean temperatures, shown in Fig. A5, are largest over
the Laurentide Ice Sheet, where cooling is up to 30
Precipitation changes, shown in Fig. A5, show a general drying trend, with
pronounced differences east of the Fennoscandian ice sheet where
precipitation reduces by 200–400 mm yr
Ice sheet extent is prescribed from a transient glacial cycle experiment performed with CLIMBER2 and the ice sheet model SICOPOLIS (Ganopolski et al., 2010). As SICOPOLIS ice sheet extent for LGM is slightly larger than the ice sheet extent used in the MPI-ESM LGM experiment, we limit ice sheet extent to the MPI-ESM LGM ice sheet mask, shown in Fig. A6.
The transient total ice sheet area, shown in Fig. A6, is maximal at 20 ka
(20.3
Atmospheric
Prescribed soil depths after Carvalhais et al. (2014).
Atmospheric
We prescribed stationary soil depths in JSBACH based on a global soil map compiled by Carvalhais et al. (2014).
Bluish grid cells (Fig. A8) indicate regions in which LGM permafrost SOC accumulation in the upper 3 m is constrained by soil depth. Using Eq. (2), we estimated the consequence of neglecting the limitation in SOC build-up through soil depth. Assuming a lower boundary of 3 m for all near-surface permafrost grid cells, our simulated LGM permafrost carbon pool would be 33 % (or 26 PgC) larger. By using constant soil depths, we implicitly assume that soil accumulation and erosion rates in non-ice-covered grid cells were in equilibrium over the model simulation time horizon. For ice-covered grid cells we assume a full removal of soil through the ice movement.
Depth to bedrock is also a poorly constrained variable for concurrent soil C stock estimates. Jackson et al. (2017) showed that applying different products of soil depth led to differences in global soil C stocks of up to 800 PgC in the upper 3 m, with most of the differences occurring in high-latitude soils. Future model developments should analyse whether alternative soil depth data products (e.g. Pelletier et al., 2016 or Hengl et al., 2017) might better capture soil depths in permafrost regions, possibly supporting less shallow soils and therefore larger LGM SOC storage. During the transient deglacial warming phase, permafrost SOC build-up is prevented in our model when the simulated ALD falls below soil depth, which is the case for 17 % of permafrost grid cells under PI climate conditions. These grid cells accumulate 41 PgC in the active layer, but they do not allow for additional SOC accumulation in permafrost.
Latitudinal dependency of active layer depths (ALDs) inferred from JSBACH simulations (dots) and CALM observations (triangles). Blue dots represent the standard model experiment (L2P) and green dots the sensitivity run with increased thermal insulation of the organic surface layer (L2P_ALD).
Deglacial evolution of seasonally thawed
To compare simulated active layer depths with data, we focused on CALM observations (Brown et al., 2000). Figure A9 shows this comparison for different latitudinal bands from north to south.
Permafrost soils reveal typical profiles of depth-declining soil organic carbon concentrations (SOCC) in the active layer (Harden et al., 2012). To capture this key characteristic, which strongly affects the amount of carbon transferred between seasonally thawed and perennially frozen carbon pools, we have developed a process-based model which allows for the calculation of vertical SOCC profiles dependent on factors such as ALDs, lability of the organic matter, and vertical soil mixing rates. In this section we describe the physics of this model and its implementation into JSBACH.
Soil carbon build-up is modelled by assuming that the carbon flux balance in
each soil layer is determined by litter input, by a transport of carbon
through diffusion and advection within the soil column, and by loss of
carbon through heterotrophic respiration within the active layer (Braakhekke et al., 2014):
Simulated SOCC profiles inferred from the process-based model of vertical SOM
dynamics. The coloured curves represent different SOM lability classes according to
YASSO, separated into fast (A, W, E), intermediate (N), and slowly decomposing (H)
compounds. The black line shows aggregated total SOC. The horizontal dashed lines
indicate ALD, and determine
To determine SOCC
Once transported into permafrost layers, SOC is protected from microbial decomposition
and establishes a depth-constant SOCC profile. The decline of SOCC in soil layers below
about 2 m is an expression of our chosen simulation time of 20 kyr in combination with
the slow sedimentation rate of 10 cm kyr
With increasing SOM lability, the SOC profiles get more pronounced with highest concentrations in the upper layers and lowest concentrations in the lower part of the soil (Fig. A11). With increasing thickness of the active layer, less SOC gets incorporated into permafrost. This decrease is a consequence of a longer transport distance to the permafrost table, and therefore more time for conditions favourable to decomposition.
The implemented transport scheme does not fully capture vertical SOC distributions as inferred from observations (e.g. an increase with depth in SOC in the uppermost Turbel soil profile; Harden et al., 2012). But the scheme allows for capturing the general tendency of decreasing SOC contents with depth, especially the lower SOCC at the permafrost table as compared to mean SOCC in the active layer (which determines the SOC transfer between permafrost and active layer carbon in our model, see next section).
The lability-dependent decline in SOCC leads to a stronger fractionation of SOM into slow- and fast-cycling SOC, resulting in a higher share of more labile SOC under cold climate conditions as compared to more moderate climate conditions; For example, the share of labile SOC getting incorporated into perennially frozen ground is negligible with the slow pool representing the largest contribution to permafrost SOC build-up (Fig. A11, upper panel), in contrast to much higher shares of labile components (Fig. A11, lower panel).
For calculating the transfer of SOC between perennially frozen and seasonally thawed
pools in JSBACH, the SOC concentration
Based on the process-based model, we determine, for each lability class, the ratio of
SOCC at the permafrost table
Dependency of vertical soil carbon concentration at the permafrost table on
active layer depth (ALD) as simulated by the process-based SOM transport model. The ratio
of equilibrium SOCC at the ALD
Figure A12 illustrates the lability-dependent vertical decline in SOCC and shows that for active layer thicknesses larger than 2 m the SOC transfer into permafrost in the process-based model is strongly dominated by the slow pool (green lines).
Simulation set-up for deglacial model runs. Shown is the spin-up and fully transient phase of active layer SOC pools for all SOM lability classes (A, W, E, N, H) from the experiment initialization at 28 to 0 ka. The vertical dashed line at 21 ka illustrates the end of the SOC spin-up phase, the vertical dashed line at 20 ka illustrates the end of the stationary LGM climate forcing. Individual SOC contributions were summed over all permafrost grid cells.
TSvD developed the process-based SOM transport model; implemented the model into JSBACH; and designed, performed, and analysed the model simulations. TK generated deglacial climate forcing data, supported the model setup and development, and extensively discussed model simulation results. GH provided soil carbon data and helped in model designing. CK supported the discussion of carbon lability aspects, and CB contributed to the development of the physical permafrost scheme. VB supported the development of the study design, the model development, and the interpretation of model results. TSvD wrote the manuscript, with contributions from all authors. All authors contributed to the interpretation of simulation results presented in this study.
The authors declare that they have no conflict of interest.
Thomas Schneider von Deimling acknowledges support from BMBF projects CARBOPERM (grant 03G0836C), KOPF (grant 03F0764C), and PERMARISK (grant 01LN1709A). Gustaf Hugelius acknowledges the EU-JPI COUP consortium and a Marie Curie Skłodowska and Swedish Research Council International Career Grant (INCA; no. 330-2014-6417). We thank Nuno Carvalhais for providing a compilation of soil depth data and Andrey Ganopolski for the provision of glacial–interglaical simulation data which we used to generate forcing anomalies. Philipp de Vrese has reviewed the manuscript and contributed helpful feedback for improving the paper. Many thanks also to Veronika Gayler, Rainer Schnuur, and Thomas Raddatz for discussing JSBACH model aspects. The article processing charges for this open-access publication were covered by the Max Planck Society. Edited by: Ran Feng Reviewed by: two anonymous referees