The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
Abstract. Climate change and increased fire are eroding the resilience of boreal forests. This is problematic because boreal vegetation and the cold soils underneath store approximately 30 % of all terrestrial carbon. Society urgently needs projections of where, when, and why boreal forests are likely to change. Permafrost (i.e., subsurface material that remains frozen for at least two consecutive years) and the thick soil-surface organic layers (SOLs) that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and SOL module that operates at fine spatial (1 ha) and temporal (daily) resolutions. The module mechanistically simulates daily changes in depth to permafrost, annual SOL accumulation, and their complex effects on boreal forest structure and functions. We coupled the module to an established forest landscape model, iLand, and benchmarked the model in interior Alaska at spatial scales of stands (1 ha) to landscapes (61,000 ha) and over temporal scales of days to centuries. The coupled model could generate intra- and inter-annual patterns of snow accumulation and active layer depth (portion of soil column that thaws throughout the year) consistent with independent observations in 17 instrumented forest stands. The model was also skilled at representing the distribution of near-surface permafrost presence in a topographically complex landscape. We simulated 34.6 % of forested area in the landscape as underlain by permafrost; a close match to the estimated 33.4 % from the benchmarking product. We further determined that the model could accurately simulate moss biomass, SOL accumulation, fire activity, tree-species composition, and stand structure at the landscape scale. Modular and flexible representations of key biophysical processes that underpin 21st-century ecological change are an essential next step in vegetation simulation to reduce uncertainty in future projections and to support innovative environmental decision making. We show that coupling a new permafrost and SOL module to an existing forest landscape model increases the model’s utility for projecting forest futures at high latitudes. Process-based models that represent relevant dynamics will catalyze opportunities to address previously intractable questions about boreal forest resilience, biogeochemical cycling, and feedbacks to regional and global climate.
Winslow D. Hansen et al.
Winslow D. Hansen et al.
Inputs and outputs for iLand simulations https://doi.org/10.25390/caryinstitute.21339090
Model code and software
iLand source code https://doi.org/10.25390/caryinstitute.21339090
Winslow D. Hansen et al.
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Hansen et al. present a computationally efficient permafrost and soil organic layer module and its coupling to an established forest landscape model. The new module can be used to simulate the annual soil-surface organic layer accumulation and the interannual and seasonal patterns of snow accumulation and active layer depth. Coupled with iLand, the model is used to simulate moss biomass, fire activity, forest composition, stand structure, the soil-surface organic layer accumulation, and the permafrost distribution in a complex landscape in interior Alaska. The computational efficiency of the new module offers great opportunities for the simulation of large spatial extents – also demonstrated here.
The manuscript in its current form is well-written and well-structured but requires improvements. It will then be an exciting addition to a growing body of work concerned with the complex interactions between boreal forests and permafrost. As such, some of the model results fit well with observations (e.g. the permafrost distribution). Still, it is unclearly described where exactly these observations were made and how the data was selected from the previous studies cited. The authors should include the most important data and study site descriptions rather than pointing the readers at former studies. To me, some of the figures seem to be suggesting relationships that aren’t necessarily statistically proven. Adding to this, the time periods for the comparison between observed and simulated data are different between different values (e.g. annual fire probability is compared for the model output years 201-300, and near-surface permafrost for the years 271-300). The reason for this should be discussed.
The modelling approach is reasonable and I don’t see any major issues with the new modules. Nevertheless, the modelled active layer thaw and freezing are delayed compared to observations, and the snow cover height does not fit the observations very well (from what is shown). This raises the question if the modelling results regarding the snow phenology are meaningful. While this can be explained by the fact that the snow parameterization is simple and does not include canopy interception, snow compaction or redistribution, the snow depth is one of the key factors impacting the thermal and hydrological permafrost regime. Similarly, shading and the reduction of wind underneath the forest canopy have been found to severely impact the thermal and hydrological regime of the ground, dampening the below-ground temperatures and changing the available plant water. I understand, that this is not the main focus here but should be mentioned and “used” to explain the found differences between model simulations and observations.
It would additionally be interesting to show the differences and especially the improvements that the new module adds to the overall model performance in terms of stand structure and tree species composition. This could be done by showing an additional map.
l.34: Here it should be noted that while air temperatures are rising (warming) another important aspect are changes in the precipitation patterns (leading to droughts -> increasing fire/pest risks)
l.56: This is the case for Alaska/Canada but not in Eastern Siberia. Specify the geographical focus here already.
l.58: Also, hydrology (strongly interlinked with topography).
l.59: Furthermore, boreal forests are important in protecting permafrost (shading, snow cover interception, lowering of turbulent heat fluxes, litter layer):
l.60/61: active-layer vs. active layer
l.70: It’s unclear to me what’s meant here in regard to Kruse et al. 2022
l.73: This is true but there are many newer models available:
l.98: Is this the density of falling snow or the snowpack below the canopy? What about the representation of transient internal snow properties (such as e.g. snow compaction)?
l.124: Air temperature at what height? Above the canopy? There are no canopy density-specific parameterizations/factors for shading of the ground, precipitation interception, or changes in the turbulent surface fluxes, correct? While these are very important factors for permafrost conditions under boreal forest canopies, this is not the focus here. I would, nevertheless, suggest discussing this in more detail in the conclusions section (and possibly restructuring it into a discussion and a short conclusion section).
l.147: Why 0.7?
l.149: Where is the SLA value for moss from?
l.172: I wasn’t able to find this value in the given reference.
l.176: full full
l.180: I’d propose mentioning the reference here.
l.236: Where were these measurements conducted? I’d suggest describing the validation studies in more detail here rather than having the readers go through all the individual studies cited.
l.246: This is unclear: How many are located in mid-elevations, and what is the bias in low- or high-elevation locations?
l.340: The model struggles to simulate the timing of thawing and freezing, 15 days is a rather long period compared to the overall short unfrozen season. I suppose this is mostly due to the o
l.398: While this is one of the major advances of the presented model, I’d suggest adding this to the main discussion and omit from adding an extra subchapter for one sentence only.
l.390: This conclusion reads more like a discussion section, and could maybe be split up into a more extensive discussion and a short conclusion.
l.406: Also, because there is no mechanistic snow parameterization included.
Figure 1: Would you need any references here for the vectorized trees or is this a modified photograph?
Figure 2: The shown relationships (solid lines) between simulated and observed values are misleading. The simulated snow depth seems to be overestimated on average. Instead of the solid lines, it would be interesting to see actual correlation statistics here.
Figure 3: Here, only one of seven forest stands is shown which leaves the reader wondering whether this one shows the best fit out of the seven validation sites.
Figure 4: Is the dark grey area the “sampled landscapes”?