Few Earth system models adequately represent the unique permafrost soil
biogeochemistry and its respective processes; this significantly contributes
to uncertainty in estimating their responses, and that of the planet at
large, to warming. Likewise, the riverine component of what is known as the
“boundless carbon cycle” is seldom recognised in Earth system modelling.
The hydrological mobilisation of organic material from a
High-latitude permafrost soils contain large stores of frozen, often ancient,
and relatively reactive carbon up to depths of over 30 m. Soil warming caused
by contemporary anthropogenic climate change can be expected to destabilise
these stores (Schuur et al.,
2015) via microbial or hydrological mobilisation following spring–summer
thaw and riverine discharge
(Vonk et al., 2015a)
as the boundary between discontinuous and continuous permafrost migrates
poleward and toward the continental interior over time. The high-latitude
soil carbon reservoir may amount to
The fact that, to our knowledge, no existing land surface models are able to adequately simultaneously represent this unique high-latitude permafrost soil environment, the transformation of soil organic carbon (SOC) to its eroded particulate and DOC forms and their subsequent lateral transport, as well as the response of all these to warming, entails significant additional uncertainty in projecting global-scale biogeochemical responses to human-induced environmental change.
Fundamental to these efforts is the ability to predict the medium under which carbon transformation will occur – in the soil, streams, rivers, or sea – and under what metabolising conditions, since these will determine the process mix that will ultimately enable either terrestrial redeposition and retention, ocean transfer, or the atmospheric release of permafrost-derived organic carbon. In the permafrost context, this implies being able to accurately represent (i) the source, reactivity, and transformation of released organic matter; and (ii) the dynamic response of hydrological processes to warming, since water phase determines carbon, heat, and soil moisture availability for metabolisation and lateral transport.
For this purpose, we take a specific version of the terrestrial component of
the Institut Pierre Simon Laplace (IPSL) global Earth system model (ESM)
ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), one that
is specifically coded for, calibrated with, and evaluated on high-latitude
phenomena and permafrost processes, called ORCHIDEE-MICT (where MICT stands
for aMeliorated Interactions between Carbon and Temperature;
Guimberteau et al., 2018). This code
is then adapted to include DOC production in the soil (ORCHIDEE-SOM;
Camino-Serrano et al., 2018), the “priming”
of SOC
(ORCHIDEE-PRIM; Guenet
et al., 2016, 2018), and the riverine transport of DOC and
The resulting model, dubbed ORCHIDEE MICT-LEAK, hereafter referred to as
MICT-L for brevity, is therefore able to represent (a) permafrost soil and
snow physics, thermodynamics to a depth of 38 m, and dynamic soil hydrology to
a depth of 2 m; (b) improved representation of biotic stress response to
cold, heat, and moisture at high latitudes; (c) explicit representation of
the active layer and frozen soil hydrologic barriers, as well as the buildup of soil carbon
stocks via the primary production and vertical translocation (turbation) of SOC
and DOC; (d) DOC leaching from tree canopies, atmospheric deposition, litter
and soil organic matter, its adsorption–desorption to and from soil particles,
its transport and transformation to dissolved
Cartoon diagram illustrating the landscape-scale emergent
phenomena observed in high-latitude river systems that are captured by the
processes represented in this model. Here, the terrestrial area is shown in
vertically ascending order as subsoil, discontinuous permafrost, continuous
permafrost, and the maritime boundary. Note that tributaries in the figure
may be represented in the model by either the “fast” or “stream” pool,
depending on their size. Representative soil types, their distributions, and
carbon concentrations are shown for the two permafrost zones, as are the
different dynamics occurring on “flat” (left) and “sloping” land (right)
arising from their permafrost designation. Carbon exports from one subsystem
to another are shown in red. The relative strength of the same processes
occurring in each permafrost band are indicated by relative arrow size. Note
that the high
To our knowledge very few attempts have been made at the global scale to model DOC production and lateral transfer from the permafrost region that explicitly accounts for such a broad range of high-latitude-specific processes, which in turn allows us to match and evaluate simulation outputs with specific observed processes, enhancing our ability to interpret the output from theses models and improve our understanding of the processes represented. The only other attempt at doing so is a pan-Arctic modelling study by Kicklighter et al. (2013), which is based on a relatively simplified scheme for soil, water, and biology. The following segment briefly overviews the dynamics, emergent properties, and overall significance across scales of permafrost region river basins.
Permafrost has a profound impact on Arctic river hydrology. In permafrost
regions, a permanently frozen soil layer acts as a “cap” on groundwater
flow (see the permafrost barrier on the right-hand side of Fig. 1). This implies
that (i) near-surface runoff becomes by far the dominant flow path draining
permafrost watersheds (Ye et al., 2009), as
shown in Fig. 1d; (ii) the seasonal amplitude of river discharge, expressed
by the ratio of maximum to minimum discharge (
Rapid melting of snow and soil or river ice during spring freshet (May–June) drives intense seasonal discharge, with peaks often 2 orders of magnitude (e.g. Van Vliet et al., 2012) above baseflow rates (Fig. 1d). These events are the cause of four largely synchronous processes: (i) biogenic matter is rapidly transported from elevated headwater catchments (Fig. 1, right-hand side) (McClelland et al., 2016). (ii) Plant material at the soil surface is intensely leached, with subsequent mobilisation and transformation of this dissolved matter via inland waters (Fig. 1d, b, j); during spring freshet, riverine DOC concentrations increase and bulk annual marine DOC exports are dominated by the terrestrial DOC flux to the rivers that occurs at this time (Holmes et al., 2012). Indeed, DOC concentrations during the thawing season tend to be greater than or equal to those in the Amazon, particularly in the flatter Eurasian rivers (Holmes et al., 2012; McClelland et al., 2012), and DOC concentrations are affected at watershed scale by parent material, ground ice content (O'Donnell et al., 2016), and active layer depth (Suzuki et al., 2006). (iii) Sudden inundation of the floodplain regions in spring and early summer (Fig. 1h; Smith and Pavelsky, 2008) further spurs the lateral flux of both particulate and dissolved matter in the process and its redeposition (Zubrzycki et al., 2013) or atmospheric evasion (Fig. 1j, m). (iv) Snowmelt-induced soil water saturation favours the growth of moss and sedge-based ecosystems (e.g. Selvam et al., 2017; Tarnocai et al., 2009; Yu, 2011), as well as the retention of their organic matter (OM), i.e. peat formation, which is not shown in Fig. 1 as this is not represented in this model version but is generated in a separate branch of ORCHIDEE (Qiu et al., 2018).
Midsummer river low flow and a deeper active layer allow for the
hydrological intrusion and leaching of older soil horizons (e.g. the top
part of Pleistocene-era Yedoma soils), and their subsequent dissolved
transport (e.g. Wickland et al., 2018). These
sometimes-ancient low-molecular-weight carbon compounds appear to be
preferentially and rapidly metabolised by microbes in headwater streams
(Fig. 1j), which may constitute a significant fraction of aggregate summer
Average annual discharge in Eurasian Arctic rivers increased by at least 7 % between 1936 and 1999 (Peterson et al., 2002), driven by increasing temperatures and runoff (Berezovskaya et al., 2005), the subsequent interplay of increasing annual precipitation, decreasing snow depth and snow water equivalent (SWE) mass (Kunkel et al., 2016; Mudryk et al., 2015), and greater evapotranspiration (Suzuki et al., 2018; Zhang et al., 2009). Although net discharge trend rates over North America were negative over the period 1964–2003, since 2003 they have been positive on average (Déry et al., 2016). These dynamic and largely increasing hydrologic flux trends point towards temperature- and precipitation-driven changes in the soil column, in which increased soil water–snow thaw and microbial activity (Graham et al., 2012; MacKelprang et al., 2011; Schuur et al., 2009) converge to raise soil leaching and DOC export rates to the river basin and beyond (e.g. Vonk et al., 2015b). Further, microbial activity generates its own heat, which incubation experiments have shown may be sufficient to significantly warm the soil further (Hollesen et al., 2015) in a positive feedback.
Arctic region fire events are also on the rise and likely to increase with temperature and severity over time (Ponomarev et al., 2016). The initial burning of biomass is accompanied by active layer deepening, priming of deeper soil horizons (De Baets et al., 2016), and a significant loading of pyrogenic DOC in Arctic watersheds, up to half of which is rapidly metabolised (Myers-Pigg et al., 2015).
In these contexts, the implications of (polar-amplified) warmer temperatures leading to active layer deepening towards the future (transition from continuous to discontinuous permafrost, as shown in the upper and lower segments of Fig. 1) are clear and unique: the potentially sizable aquatic mobilisation and microbial metabolisation (Xue, 2017) of dissolved and eroded OM, deeper hydrological flow paths, an increase in total carbon, and water mass and heat transfer to the aquatic network and, ultimately, the Arctic Ocean and atmosphere (Fig. 1i).
The advantage of having a terrestrial model that can be coupled to a marine component of an overarching global climate model (GCM) is in this case the representation of a consistent transboundary scheme such that output from one model is integrated as input to another. This is particularly important given the context in which these terrestrial outflows occur.
Because of its small size, a uniquely large and shallow continental shelf, the global climatological significance of its seasonal sea ice (Rhein et al., 2013) and its rapid decline (Findlay et al., 2015), the Arctic Ocean has been described as a giant estuary (McClelland et al., 2012) acting as a funnel for the transport, processing, and sedimentation of terrestrial OM. Because of its small surface area and shallow seas (Jakobsson, 2002), the Arctic Ocean holds relatively little volume and is consequently sensitive to inputs of freshwater, heat, alkalinity, and nutrients that flush out from terrestrial sources, particularly at discharge peak.
High suspended particle loads in river water as they approach the mouth (Heim et al., 2014) cause lower light availability and water albedo and hence higher temperatures (Bauch et al., 2013; Janout et al., 2016), which can affect the nearshore sea ice extent, particularly in spring (Steele and Ermold, 2015). Volumes of riverine freshwater and total energy flux (Lammers et al., 2007) are expected to increase with warmer temperatures, along with an earlier discharge peak (Van Vliet et al., 2012, 2013). In doing so, freshwaters may in the future trigger an earlier onset of ice retreat (Stroeve et al., 2014; Whitefield et al., 2015) via a feedback between freshwater albedo, ice melt, and seawater albedo amplified by intermediary state variables such as water vapour and cloudiness (Serreze and Barry, 2011).
Both terrestrially exported and older shelf carbon in the Arctic Ocean face
considerable disruption
(McGuire
et al., 2009; Schuur et al., 2015) from the combined effects of increased
freshwater, heat, sediment, nutrient, and organic carbon flows from rapidly
warming Arctic river watersheds, as well as those from melting sea ice,
warmer marine water temperatures, and geothermal heat sources
(Janout et al., 2016;
Shakhova et al., 2015). Because ORCHIDEE is a subcomponent of the
overarching IPSL ESM, there is scope for coupling riverine outputs of water,
DOC,
The Methods section summarises the model structure and associated rationale for each of the model sub-branches or routines relevant to this study and follows with the setup and rationale for the simulations carried out as validation exercises.
This section overviews the processes represented in the model being described in this paper, which is referred to as ORCHIDEE MICT-LEAK, hereafter referred to MICT-L for brevity. MICT-L is at its heart a merge of two distinct models: the high-latitude land surface component of the IPSL Earth system model ORCHIDEE MICT and the DOC production and transport branch of ORCHIDEE's default or “trunk” version (Krinner et al., 2005), ORCHILEAK. The original merger of these two code sets was between ORCHILEAK and ORCHIDEE-MICT, which are described in Camino-Serrano et al. (2018), Lauerwald et al. (2017), and Guimberteau et al. (2018).
However, numerous improvements in code performance and process additions postdating these publications have been included in this code. Furthermore, novel processes included in neither of these two core models are added to MICT-L, such as the diffusion of DOC through the soil column to represent its turbation and preferential stabilisation at depth in the soil, as described in Sect. 2.11.
In terms of code architecture, the resulting model is substantially different from both of its parents owing to the fact that the two models were developed on the basis of ORCHIDEE trunk revisions 2728 and 3976 for ORCHILEAK and MICT, respectively, which have a temporal model development distance of over 2 years and subsequently evolved in their own directions. These foundational differences, which mostly affect the formulation of soil, carbon, and hydrology schemes, mean that different aspects of each are necessarily forced into the subsequent code. Where these differences were considered scientific or code improvements, they were included in the resulting scheme. Despite architectural novelties introduced, MICT-L carries with it a marriage of much the same schemes detailed exhaustively in Guimberteau et al. (2018) and Lauerwald et al. (2017). As such, the following model description details only new elements of the model, those that are critical to the production and transport of DOC from permafrost regions, and parameterisations specific to this study (Fig. 2).
Carbon and water flux map for core DOC elements in model structure
relating to DOC transport and transformation.
MICT-L is based largely on ORCHIDEE-MICT, into which the DOC production,
transport, and transformation processes developed in the ORCHILEAK model
version and tested so far only for the Amazon have been transplanted,
allowing these same processes to be generated in high-latitude regions
with permafrost soils and a river flow regime dominated by snowmelt. The
description that ensues roughly follows the order of the carbon and water
flow chain depicted in Fig. 2b. At the heart of the scheme is the vegetative
production of carbon, which occurs along a spectrum of 13 plant functional
types (PFTs) that differ from one another in terms of plant physiological
and phenological uptake and release parameters
(Krinner et al., 2005). Together, these determine
grid-scale net primary production. At the northern high latitudes,
boreal trees (PFTs 7–9) and
Biomass generation, consisting of foliage, roots, aboveground and belowground sapwood and heartwood, carbon reserves, and fruit pools in the model, results in the transfer of these carbon stores to two downstream litter pools: structural and metabolic litter (Fig. 2b). This distinction, defined by lignin concentration of each biomass pool (Krinner et al., 2005), separates the relatively reactive litter fraction such as leafy matter from its less-reactive, recalcitrant counterpart (woody, “structural” material), with the consequence that the turnover time of the latter is roughly fourfold that of the former. These two litter pools are further subdivided into aboveground and belowground pools, with the latter explicitly discretised over the first 2 m of the soil column, a feature first introduced to the ORCHIDEE model by Camino-Serrano et al. (2014, 2018). This marks a significant departure from the original litter formulation in ORCHIDEE-MICT, in which the vertical distribution of litter influx to the soil carbon pool follows a prescribed root profile for each PFT. This change now allows for the production of DOC from litter explicitly at a given soil depth in permafrost soils.
The vast majority of DOC produced by the model is generated initially from
the litter pools via decomposition such that half of all of the decomposed
litter is returned to the atmosphere as
The approximate ratio of relative residence times for the three SOC pools in
our model (active : slow : passive) is
In MICT-L, DOC generation also occurs in the form of wet and dry atmospheric
deposition and canopy exudation, collectively attributed to the throughfall,
i.e. the amount of precipitation reaching the ground. Wet atmospheric
deposition originates from organic compounds dispersed in atmospheric
moisture, which become deposited within rainfall and are assumed here to
maintain a constant concentration. This concentration we take from the
average of reported rainfall DOC concentrations in the empirical literature
measured at sites
We take the average total observation-based throughfall DOC flux rate per
square metre of forest from the aforementioned literature bundle (15.7 gC m
All DOC pools, leached from the decomposition of either litter and SOC or as throughfall inputs, reside at this point in discrete layers within the soil column but are now also available for vertical advection and diffusion, as well as lateral export from the soil column as a carbon tracer, via soil drainage and runoff.
The export of DOC from the soil to rivers occurs through surface runoff, soil-bottom drainage, or flooding events (see Sect. 2.8, “Representation of floodplain hydrology and their DOC budget”). Runoff is activated when the maximum water infiltration rate of the specific soil has been exceeded, meaning that water arrives at the soil surface faster than it can enter, forcing it to be transported laterally across the surface. DOC is drawn up into this runoff water flux from the first five layers of the soil column, which correspond to a cumulative source depth of 4.5 cm.
Drainage of DOC occurs first as its advection between the discrete soil layers and its subsequent export from the 11th layer, which represents the bottom of the first 2 m of the soil column, from which export is calculated as a proportion of the DOC concentration at this layer. Below this, soil moisture and DOC concentrations are no longer explicitly calculated, except in the case that they are cryoturbated below this up to a depth of 3 m. DOC drainage is proportional to but not a constant multiplier of the water drainage rate for two reasons. First, as water percolates through the soil column, it carries DOC along from one layer to another through the entirety of the soil column, but this percolation is blocked when the soil is entirely frozen; i.e. it is assumed that all soil pores are filled with ice, which blocks percolation. This implies that DOC transport is not just determined by what enters from the top but also by the belowground production from litter, the sorption and desorption to and from particulate soil organic carbon in the soil column, DOC mineralisation within the soil column, and water vertical transport entraining DOC between the non-frozen soil layers using the hydraulic conductivity calculated by the model as a function of soil texture, soil carbon, and time-dependent soil moisture (Guimberteau et al., 2018).
Secondly, in order to account for preferential flow paths in the soil created by the subsoil actions of flora and fauna, as well as for the existence of non-homogenous soil textures at depth that act as aquitards, DOC infiltration must account for the fact that area-aggregated soils drain more slowly, increasing the residence time of DOC in the soil. Thus, a reduction factor which reduces the vertical advection of DOC in soil solution by 80 % compared to the advection is applied to represent a slowdown in DOC percolation through the soil and increase its residence time there.
In MICT-L, as in ORCHILEAK, a “poor-soil” module reads off from a map
giving fractional coverage of land underlain by Podzols and Arenosols at the
0.5
By regulating both decomposition and soil moisture flux, the poor-soil
criterion effectively serves a similar if not equal function to a soil
“tile” for DOC infiltration in the soil column (inset box of Fig. 1)
because soil tiles (forest, grassland–tundra–cropland, and bare soil) are
determinants of soil hydrology, which affects moisture-limited decomposition.
Here however, the poor-soil criterion is applied uniformly across the three
soil tiles of each grid cell. This modulation in MICT-L is of significance
for the Arctic region, given that large fractions of the discontinuous
permafrost region are underlain by Podzols, particularly in Eurasia. For the
Arctic as a whole, Podzols cover
Thus, for all the soil layers in the first 2 m, DOC stocks are controlled by production from litter and SOC decay, as well as their advection, diffusion, consumption by DOC mineralisation, and buffering by adsorption and desorption processes.
The routing scheme in ORCHIDEE, first described in detail in Ngo-Duc et al. (2007) and presented after some version iterations in Guimberteau et al. (2012), is the module which, when activated, represents the transport of water collected by the runoff and drainage simulated by the model along the prescribed river network in a given watershed. In doing so, its purpose is to coarsely represent the hydrologic coupling between precipitation inputs to the model and subsequent terrestrial runoff and drainage (or evaporation) calculated by it, on the one hand, and the eventual discharge of freshwater to the marine domain on the other. In other words, the routing scheme simulates the transport of water by rivers and streams by connecting rainfall and continental river discharge with the land surface.
To do so, the routing scheme first inputs a map of global watersheds at the
0.5
The water residence time in each reservoir depends on the nature of the
reservoir (increasing residence time in the following order: stream
Waterborne, terrestrially derived DOC and dissolved
The river-routing calculations, which occur at a daily time step, are then
aggregated to 1 d for the lateral transfer of water,
In this framework, the fast and slow residence times of the water pools in the routing scheme determine the time that water and DOC remain in overland and groundwater flow before entering the river network. Note that while we do not explicitly simulate headwaters as they exist in a geographically determinant way in the real world, we do simulate what happens to the water before it flows into a waterbody large enough to be represented in the routing scheme by the water pool called “stream”, representing a real-world river of stream order 4 or higher. The fast reservoir is thus the runoff water flow that is destined to enter the stream water reservoir and implicitly represents headwater streams of Strahler order 1 to 3 by filling the spatial and temporal niche between overland runoff and the river stem. The dynamics of headwater hydrological and DOC dynamics (Sect. 2.10) are of potentially great significance with respect to carbon processing, as headwater catchments have been shown to be “hotspots” of carbon metabolisation and outgassing in Arctic rivers, despite their relatively small areal fraction (Denfeld et al., 2013; Drake et al., 2015; Mann et al., 2015; Suzuki et al., 2006; Venkiteswaran et al., 2014; Vonk et al., 2013, 2015a, b). Thus, in what follows in this study, we refer to what in the code are called the fast and stream pools, which represent the small streams and large stream or river pools, respectively, using the terms stream and river to denote these from here on.
Furthermore, the differentiated representation of water pools and mean grid cell slope, combined with the dynamic active layer simulated for continuous versus discontinuous permafrost, is important for reproducing the phenomena observed by Kutscher et al. (2017) and Zhang et al. (2017) for sloping land, as shown on the right-hand side of Fig. 1. In discontinuous permafrost and permafrost-free regions, these phenomena encompass landscape processes (sub-grid in the model) through which water flow is able to re-infiltrate the soil column and leach more refractory DOC deeper in the soil column, leading to a more refractory signal in the drainage waters. In contrast, in continuous permafrost regions, the shallow active layer will inhibit the downward re-infiltration flux of water and encourage leaching at the more organic-rich and labile surface soil layer, resulting in a more labile DOC signal from the drainage in these areas (Fig. 1). In addition, places with higher elevation and slope in these regions tend to experience extreme cold, leading to lower net primary production (NPP) and therefore DOC leaching. The re-infiltration processes mentioned are thought to be accentuated in areas with higher topographic relief (Jasechko et al., 2016), which is why they are represented on sloping areas in Fig. 1.
The third terrestrial DOC export pathway in MICT-L is through flooding of
floodplains, a transient period that occurs when stream water is forced by
high discharge rates over the riverbanks and flows onto a flat floodplain
area of the grid cell that the river crosses, thus inundating the soil. Such
a floodplain area is represented as a fraction of a grid cell with the
maximum extent of inundation, termed the “potential flooded area” being
predefined from a forcing file
(Tootchi et al.,
2019). Here, the DOC pools that are already being produced in these
inundated areas from litter and SOC decomposition in the first five layers of
the soil column are directly absorbed by the overlying floodwaters. These
floodwaters may then either process the DOC directly, via oxidisation to
MICT-L includes the floodplain hydrology part of the routing scheme
(d'Orgeval et
al., 2008; Guimberteau et al., 2012), as well as additions and improvements
described in Lauerwald et al. (2017). The spatial areas that are available
for potential flooding are predefined by an input map originally based on
the map of Prigent et al. (2007).
However, for this study, we used an alternative map of the “regularly
flooded areas” derived from the method described in Tootchi et al. (2019),
which in this study uses an improved input potential flooding area forcing
file specific to the Lena basin that combines three high-resolution surface
water and inundation datasets derived from satellite imagery: GIEMS-D15
(Fluet-Chouinard et al., 2015),
which results from the downscaling of the map of
Prigent et al. (2007) at 15 arcsec
(ca 500 m at the Equator); ESA-CCI land cover (at 300 m
With this improved forcing, river discharge becomes available to flood a
specific predefined floodplain grid fraction, creating a temporary
floodplain hydrologic reservoir, the magnitude of which is defined by the excess
of discharge at that point over a threshold value given by the median
simulated water storage in each grid cell over a 30-year period.
The maximum extent of within-grid flooding is given by another threshold,
the calculated height of floodwaters beyond which it is assumed that the
entire grid is inundated. This height, which was previously fixed at 2 m, is now
determined by the 90th percentile of all floodwater height levels
calculated per grid cell from the total water storage of that grid cell over a
reference simulation period for the Lena basin using the same methodology
introduced by Lauerwald et al. (2017). The residence time of water on the
floodplains (
The routing of water and DOC through the river network ultimately leads to their
export from the terrestrial system at the river mouth (Fig. 1), which for
high-latitude rivers is almost always a sub-delta of the greater
“estuary” described by McClelland et al. (2012) that drains into the Arctic
Ocean. Otherwise, the only other loss pathway for carbon export once in the
river network is through its decomposition to
Soil
With our water temperature estimate, both
The soil carbon module is discretised into a 32-layer scheme totalling 38 m of depth, which it shares with the soil thermodynamics to calculate temperature through the entire column. An aboveground snow module (Wang et al., 2013) is discretised into three layers of differing thickness, heat conductance, and density, which collectively act as a thermodynamically insulating intermediary between the soil and atmosphere (Fig. 2a). Inputs to the three soil carbon pools are resolved only for the top 2 m of the soil, where litter and DOC are exchanged with SOC in decomposition and adsorption–desorption processes. The decomposition of SOC pools, calculated in each soil layer, is dependent on soil temperature, moisture, and texture (Koven et al., 2009; Zhu et al., 2016), while the vertical transfer of SOC is enabled by the representation of cryoturbation (downward movement of matter due to repeated freeze–thaw) in permafrost regions and bioturbation (by soil organisms) in non-permafrost regions in terms of a diffusive flux.
Cryoturbation, given a diffusive mixing rate (Diff) of 0.001 m
MICT-L also incorporates a scheme for the priming of organic matter
decomposition, a process in which the relative stability of SOC is impacted
by the intrusion of or contact with SOC of greater reactivity, resulting in
enhanced rates of decomposition. This was first introduced by
Guenet et al. (2016) and updated
in Guenet et al. (2018). This process has
shown itself to be of potentially large significance for SOC stocks and
their respiration in high-latitude regions based on empirical in situ and soil
incubation studies
(De
Baets et al., 2016; Walz et al., 2017; Wild et al., 2014, 2016; Zhang et
al., 2017), as well as modelling exercises (Guenet et al., 2018). Here,
priming of a given soil pool is represented through the decomposition of
soil carbon (dSOC
The variable FOC (fresh organic carbon) is an umbrella term used for specifying all of the carbon pools which together constitute carbon that is considered potential priming donor material – i.e. more labile – to a given receptor carbon pool. Thus, for the slow soil carbon pool, FOC incorporates the active soil carbon pool plus the aboveground and belowground structural and metabolic litter pools because these pools are donors to the slow pool and are considered to accelerate its turnover through priming. Importantly, previous studies with priming in ORCHIDEE employed this scheme on a version which resolves neither the vertical discretisation of the soil column nor the explicit vertical diffusion processes presented here. This is potentially significant, since the vertical diffusion of relatively reactive matter may strongly impact (accelerate) the decomposition of low-reactivity matter in the deeper non-frozen horizons of high-latitude soils, while the explicit discretisation of the soil column is a significant improvement in terms of the accuracy of process representation within the column itself.
Anther carbon-relevant scheme included in MICT-L is a prognostic fire
routine (SPITFIRE) calibrated for the trunk version of ORCHIDEE
(Yue et al., 2016), which is
available in our code but not activated in the simulations conducted here.
As a result, we do not simulate the
A module introduced in the last version of ORCHIDEE-MICT (Guimberteau et al., 2018), in which the soil thermal transfer, porosity, and moisture are strongly affected by SOC concentration, is deactivated here because it is inconsistent with the new DOC scheme. Specifically, while carbon is conserved in both the MICT and MICT-L soil schemes, MICT-L introduces a new reservoir into which part of the total organic carbon in the soil – the DOC – must now go. This then lowers the SOC concentration being read by this thermix module, causing significant model artefacts in soil thermodynamics and hydrology in early exploratory simulations. Ensuring the compatibility of this routine with the DOC scheme will be a focal point of future developments in MICT-L. Other processes being developed for ORCHIDEE-MICT, including high-latitude peat formation (Qiu et al., 2018), methane production, and microbial heat-generating processes that are being optimised and calibrated, are further pending additions to this particular branch of the ORCHIDEE-MICT series.
Data type, name, and sources of data files used to drive the model in the study simulations.
The soil carbon spin-up component of ORCHIDEE, which is available to both its trunk and MICT branches, was omitted from this first version of MICT-L owing to the code burden required for ensuring compatibility with the soil carbon scheme in MICT-L. However, because we are simulating high-latitude permafrost regions, having a realistic soil carbon pool at the outset of the simulations is necessary if we are to untangle the dynamics of SOC and DOC with a changing environment. Because the soil carbon spin-up in ORCHIDEE-MICT is normally run over more than 10 000 years (Guimberteau et al., 2108) and because running MICT-L for this simulation period in its normal, non-spin-up simulation mode would impose an unreasonable burden on computing resources, here we directly force the soil carbon output from a MICT spin-up directly into the restart file of a MICT-L simulation.
A 20 000-year spin-up loop over 1961–1990 (these years were chosen to mimic coarsely warmer mid-Holocene climate) forced by GSWP-3 climatology, the configuration of which derives directly from that used in Guimberteau et al. (2018), was thus used to replace the three soil carbon pool values from a 1-year MICT-L simulation to set their initial values. A conversion of this soil carbon from volumetric to areal units was applied owing to different read–write standards in ORCHILEAK versus ORCHIDEE-MICT. This artificially imposed, MICT-derived SOC stock would then have to be exposed to MICT-L code, the large differences of which in terms of soil carbon module architecture compared to MICT would drive a search for new equilibrium soil carbon stocks.
Due to the long residence times of the passive SOC pool, reaching full
equilibrium requires a simulation length on the order of 20 000 years
– again an overburden. As we are interested primarily in DOC in this study,
which derives mostly from the active and slow SOC pools, the model was run
until these two pools reached a quasi-steady-state equilibria (Part 2
Supplement, Fig. S1). This was done by looping over the same 30-year cycle
(1901–1930) of climate forcing data from GSWP-3 during the pre-industrial
period (Table 1) and the first year (1901) of a prescribed vegetation map
(ESA CCI Land Cover Map; Bontemps et al., 2013) – to
ensure that the equilibrium of DOC, dissolved
Flow diagram illustrating the stepwise stages required to implement the model's soil carbon stock prior to conducting transient historical simulations.
This first part of a two-part study has described a new branch of the high-latitude version of the ORCHIDEE-MICT land surface model, in which the
production, transport, and transformation of DOC and dissolved
The source code for ORCHIDEE MICT-LEAK revision 5459 is available via
Primary data and scripts used in the analysis and other supplementary information that may be useful in reproducing the author's work can be obtained by contacting the corresponding author.
This software is governed by the CeCILL licence under French law and abiding
by the rules of distribution of free software. You can use, modify, and/or
redistribute the software under the terms of the CeCILL licence as
circulated by CEA, CNRS, and INRIA at the following URL:
SPKB coded this model version, conducted the simulations, and wrote the main body of the paper. RL gave consistent input to the coding process and made numerous code improvements and bug fixes. BG advised on the inclusion of priming processes in the model and advised on the study design and model configuration; DZ gave input on the modelled soil carbon processes and model configuration. MG, AT, and AD contributed to improvements in hydrological representation and floodplain forcing data. PC oversaw all developments leading to the publication of this study. All authors contributed to suggestions regarding the final content of the study.
The authors declare that there is no conflict of interest.
Simon P. K. Bowring acknowledges funding from the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 643052, “C-CASCADES” programme. Simon P. K. Bowring received a PhD grant. Matthieu Guimberteau acknowledges funding from the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P. Ronny Lauerwald acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 703813 for the Marie Skłodowska-Curie European Individual Fellowship “C-Leak”.
This research has been supported by the Marie Skłodowska-Curie ESR grant (C-CASCADES (grant no. 643052)), the Marie Skłodowska-Curie European Individual Fellowship (C-Leak (grant no. 703813)), and the European Research Council Synergy grant (IMBALANCE-P grant no. (ERC-2013-SyG-610028)).
This paper was edited by Hisashi Sato and reviewed by Hisashi Sato and two anonymous referees.