SUMMARY AND
CONCLUSIONS
The
commonly applied spin-up method for land surface model (LSM)
initialization was assessed, under different climate conditions for the
spin-up year and using various initial soil moisture states, at a
sporadic and a discontinuous permafrost site in the Liard River Basin in
Canada. The single-year spin-up technique for 2000 cycles was evaluated
to initialize and simulate permafrost dynamics using 1D MESH/CLASS model
simulations. The study highlighted that employing a deep soil
configuration in LSMs requires adequate attention to the initial states
as these play a central role in the rate of spin-up stabilization and
the fidelity of subsequent simulation. The initial water storage and its
partitioning into liquid and frozen contents can affect the system
hydraulic and thermal memories, and consequently, the capability of
initializing permafrost in LSMs/ESMs. Previous studies focused on
identifying appropriate initial soil saturation for model
initialization, which is shown in this study to be insufficient due to
the interplay between soil liquid and frozen contents on the quality of
spin-up. For instance, five different partitioning conditions of a fully
saturated soil column required different spin-up effort to attain stable
state-variables. Our analysis shows that model spin-ups based on a
back-to-back repetition of 200-1000 cycles could be appropriate for
initializing soil temperature and water content profiles under different
climate conditions, moisture conditions, and model configurations. Such
a conclusion can be extendable to other LSMs/ESMs, given the immense
computational resources needed for large-scale applications.
Further, initializing the soil column with near field capacity
conditions (25% saturation: 25% liquid + 0% Ice, or as 18.75% liquid
+ 6.25% Ice) required minimal spin-up effort to form permafrost under
different climates. Similarly, the wet climate spin-up year led to the
shortest spin-up to initialize permafrost in the deep soil column. On
the other hand, utilizing only the annual totals/averages while
identifying the initial year’s climate condition could be insufficient
and leads to non-representative transient conditions. The selection of
the initial year’s climate is challenging as the interplay between the
external forcing (i.e. precipitation and air temperature)
dominantly control permafrost initialization behaviour in LSM.
Considering additional statistical measures is advisable, especially
those measuring the seasonal patterns (monthly/seasonal statistics) or
using more comprehensive measures such as the coefficient of variation
along with the annual totals/averages.
Further, it is suggested to avoid
any peculiarities around the beginning and the end of the spin-up year
to ensure successful initialization of permafrost. The
large variations observed in the
initialization experiments necessitate assessing the associated impact
of the uncertain initial conditions on the simulation.
We analyzed the effect of initialization uncertainty on various soil
states at the end of spin-up. The portion of the soil column between the
permeable depth (SDEP) and the organic depth (ODEP) showed a high range
of variability for frozen water content and soil temperature to
uncertainties of model initialization for both setups. Below SDEP,
temperature profiles showed a decaying sensitivity to the initial
condition perturbation, with no impact at the bottom of the soil column.
The magnitude of variability for soil temperature was 4-5°C for the
permeable part of the soil column, and 0.4 m3m-3 for the frozen water content down to SDEP. Layers
at the ODEP and SDEP interfaces showed significant oscillations in soil
liquid and frozen contents due to the abrupt change in soil properties,
which requires further modelling efforts to improve the smoothness of
transition, reducing numerical issues and enhancing the realism of
natural systems’ representation. Further, the initial climate condition
has a dominant role in the simulated soil temperature and liquid
moisture content. In contrast, the initial water content (and its
partitioning into liquid and frozen) had a stronger influence on the
formed ice than the initial climate condition.
The assessment also incorporated different aspects that describe
permafrost dynamics on annual basis, noting that previous studies on
permafrost simulation in LSMs considered limited features of permafrost
in their assessments. We selected two performance metrics, the bias in
simulated active layer thickness (ɛALT) and the root mean square error
(RMSE) of temperature envelopes, to examine the impact of different
spinning conditions on the simulation quality. ɛALT showed high
dependency on soil-texture, and land-cover parameterizations, with
systematic errors in the range of ±1 m observed at the two sites. Also,
RMSEs of maximum and minimum annual temperature envelopes (Tmax and
Tmin) varied by ~1.5 °C and ~0.75°C at
the two sites, noting that the two sites yielded poorer RMSE of Tmin
compared to Tmax. The mean annual ground temperature at the permafrost
table (MAGTp) showed a stronger response to the driving climate over
initial soil storage components, ranging between 2-3 °C annually at the
two sites. Examining the temporal evolution of freezing/thawing cycles
highlighted the high variability of the date of maximum thaw (ALT-DOY),
shifting by up to four weeks between August and October. The depth of
the zero-annual temperature amplitude (DZAA) and the depth to the base
of permafrost (BP) exhibited similar responses to the initialization’s
uncertainty, as the results indicate considerable variability to the
initial soil moisture, with a minor impact of the initial climate
condition, having a magnitude of variability of three- to four-fold
among all the designed experiments.
Notably, modelers employ different initialization techniques to generate
self-consistent model states, which are assumed sufficient for the
subsequent simulation once it attains quasi-equilibrium. The main
assumption at the start of model initialization is the presence of a
quasi-equilibrium with the external forcing. However, the atmospheric
climate has been transient over the last millennium (Mann et al. ,
1999) and is in strong disequilibrium with the ‘transient’ ground
thermal regime at decadal-to-millennial scales (Zhang et al. ,
2008b). In the current study, we followed the same conventional approach
of assuming an equilibrium state at the end of the successful spinning.
However, the study showed that there are several self-consistent states,
generated under different initial conditions, which would yield
divergent simulations of permafrost. This outcome raises the fundamental
issue of attempting to initialize models to a steady state while the
real system is transient, which yet has no simple resolution.
To conclude, our study accentuated
the importance of LSM initialization for permafrost-related analysis,
which could alter state-variable stabilization and, therefore, the
simulation itself. The work assessed the propagation of initialization
uncertainty on different aspects characterizing permafrost dynamics and
underscored the huge variability in permafrost simulation. In terms of
simulation quality, the two setups were able to produce Tmax envelopes
and ALTs in reasonable agreement with observation, which is not the case
for Tmin envelopes that were colder than observed. The relatively poor
simulated cold soil envelope (Tmin) suggests inadequate surface
insulation that could be attributed to the quality of snow simulation,
which can be addressed through integrating a multi-layer snow scheme
(e.g. JULES: Burke et al. , 2013), a complex canopies
module (e.g. CLASS-CTEM: Melton et al. , 2019), and/or
representing the lateral migration of heat/moisture fluxes (e.g.Noah-MP: Aas et al. , 2019). Therefore, further development is
needed in MESH/CLASS to elevate the realism of permafrost simulations,
and consequently, the hydrologic and climate simulations. Future work
can be directed towards generalizing our analysis outcomes to other
observational sites in other permafrost regions/classes, and extension
to different regional and global models with varying complexity levels
in large-scale applications. Lastly, assessing the influence of LSM
parameters on simulated permafrost through a comprehensive sensitivity
analysis is recommended in light of the large impact of initial
conditions on LSM permafrost simulation.