A thick top layer of organic matter is a dominant feature in boreal forests and can impact land–atmosphere interactions. In this study, the multi-parameterization version of the Noah land surface model (Noah-MP) was used to investigate the impact of incorporating a forest-floor organic soil layer on the simulated surface energy and water cycle components at the BERMS Old Aspen site (OAS) field station in central Saskatchewan, Canada. Compared to a simulation without an organic soil parameterization (CTL), the Noah-MP simulation with an organic soil (OGN) improved Noah-MP-simulated soil temperature profiles and soil moisture at 40–100 cm, especially the phase and amplitude (Seasonal cycle) of soil temperature below 10 cm. OGN also enhanced the simulation of sensible and latent heat fluxes in spring, especially in wet years, which is mostly related to the timing of spring soil thaw and warming. Simulated top-layer soil moisture is better in OGN than that in CTL. The effects of including an organic soil layer on soil temperature are not uniform throughout the soil depth and are more prominent in summer. For drought years, the OGN simulation substantially modified the partitioning of water between direct soil evaporation and vegetation transpiration. For wet years, the OGN-simulated latent heat fluxes are similar to CTL except for the spring season when OGN produced less evaporation, which was closer to observations. Including organic soil produced more subsurface runoff and resulted in much higher runoff throughout the freezing periods in wet years.
Land surface processes play an important role in the climate system by controlling land–atmosphere exchanges of momentum, energy, and mass (water, carbon dioxide, and aerosols). Therefore, it is critical to correctly represent these processes in land surface models (LSMs) that are used in weather prediction and climate models (e.g., Dickinson et al., 1986; Sellers et al., 1996; Chen and Dudhia, 2001; Dai et al., 2003; Oleson et al., 2008; Niu et al., 2011). Niu et al. (2011) and Yang et al. (2011) developed the Noah LSM with multi-parameterization options (Noah-MP) and evaluated its simulated seasonal and annual cycles of snow, hydrology, and vegetation in different regions. Noah-MP has been implemented in the community Weather Research and Forecasting (WRF) model (Barlage et al., 2015), which is widely used as a numerical weather prediction and regional climate model for dynamical downscaling in many regions worldwide (Chotamonsak et al., 2012). The performance of Noah-MP was previously evaluated using in situ and satellite data (Niu et al., 2011; Yang et al., 2011; Cai et al., 2014; Pilotto et al., 2015; Chen et al., 2014). Those evaluation results showed significant improvements in modeling runoff, snow, surface heat fluxes, soil moisture, and surface skin temperature compared to the Noah LSM (Chen et al., 1996; Ek et al., 2003). Recently, Chen et al. (2014) compared Noah-MP to Noah and four other LSMs regarding the simulation of snow and surface heat fluxes at a forested site in the Colorado headwaters region, and found a generally good performance of Noah-MP. However, it is challenging to parameterize the cascading effects of snow albedo and below-canopy turbulence and radiation transfer in forested regions as pointed out by Clark et al. (2015) and Zheng et al. (2015).
The Canadian boreal region contains one-third of the world's boreal forest,
approximately 6 million km
Boreal forest soils often have a relatively thick upper organic horizon. The thickness of the organic horizon directly affects the soil thermal regime and soil hydrological processes. Compared with mineral soil, the thermal and hydraulic properties of the organic soil are significantly different. Dingman (1994) found that the mineral soil porosity ranges from 0.4 to 0.6, while the porosity of organic soil is seldom less than 0.8 (Radforth and Brawner, 1977). The hydraulic conductivity of organic soil horizons can be very high due to the high porosity (Boelter, 1968). Less suction is observed for a given volumetric water content in organic soils than in mineral soils, except when it reaches saturation. The thermal properties of the soil are also affected by the underground hydrology. Organic soil horizons also have relatively low thermal conductivity, relatively high heat capacity, and a relatively high fraction of plant-available water. Prior studies illustrated the importance of parameterizing organic soil horizons in LSMs for simulating soil temperature and moisture (e.g., Letts et al., 2000; Beringer et al., 2001; Mölders and Romanovsky, 2006; Nicolsky et al., 2007; Lawrence and Slater, 2008).
The current Noah-MP model does not include a parameterization for organic soil horizons. It is thus critical to evaluate the effects of incorporating organic matter in surface energy and water budgets in order to enhance the global applicability of the WRF Noah-MP coupled modeling system. Here we conduct a detailed examination of the performance of the Noah-MP model in a Canadian boreal forest site. The main objective of this research is to enhance the modeling of vertical heterogeneity (such as organic matter) in soil structures and to understand its impacts on the simulated seasonal and annual cycle of soil moisture and surface heat fluxes. We recognize that Noah-MP has weaknesses in existing subprocess parameterizations; however the goal of this study is to explore the impact of incorporating organic soil in surface energy and water budgets, rather than comprehensively addressing errors in existing Noah-MP parameterization schemes. In this paper, we present the BERMS observation site in central Saskatchewan (Sect. 2) and our methodology for conducting 12-year Noah-MP simulations with and without the organic soil layer for that boreal forest site (Sect. 3). Section 4 discusses the simulations of the diurnal and annual cycles of the surface energy and hydrological components, in dry and wet periods. Summary and conclusions are given in Sect. 5.
The Old Aspen site (OAS, 53.7
The location of the study site (Old Aspen flux tower).
Air temperature and humidity were measured at 36 m above ground level using a Vaisala model HMP35cf or HMP45cf temperature/humidity sensor (Vaisala Oyj, Helsinki, Finland) in a 12-plate Gill radiation shield (R.M. Young model 41002-2, Traverse City, MI, USA). Wind speed was measured using a propeller anemometer (R.M. Young model 01503-, Traverse City, MI, USA) located at 38 m above ground level. Atmospheric pressure was measured using a barometer (Setra model SBP270, distributed by Campbell Scientific Inc., Logan, UT, USA). Soil temperature was measured using thermocouples in two profiles at depths of 2, 5, 10, 20, 50, and 100 cm. The two upper measurements were in the forest-floor LFH. Soil volumetric water content was measured using TDR probes (Moisture Point Type B, Gabel Corp., Victoria, Canada) with measurements at depths of 0–15, 15–30, 30–60, 60–90, and 90–120 cm. Three of the eight probes that were the most free of data gaps were used in this analysis. The TDR probes were located in a low-lying area of the site that was partially flooded after 2004, resulting in high volumetric water content (VWC) values that may not be characteristic of the flux footprint. VWC is also measured at 2.5 and 7.5 cm depth in the forest-floor LFH layer, using two profiles of soil moisture reflect meters (model CS615, Campbell Scientific Inc., Logan, UT, USA), inserted horizontally at a location that did not flood.
Eddy-covariance measurements of the sensible and latent heat flux densities were made at 39 m above the ground from a twin scaffold tower. Details of the eddy-covariance systems are given in Barr et al. (2006). Data gaps were filled using a standard procedure (Amiro et al., 2006).
The net radiation flux density, Rn, was calculated from component
measurements of incoming and outgoing shortwave and long-wave radiation, made
using paired Kipp and Zonen (Delft, the Netherlands) model CM11 pyranometers
and paired Eppley Laboratory (Newport, RI, USA) model PIR pyrgeometers. The
upward-facing radiometers were mounted atop the scaffold flux tower in
ventilated housings to minimize dew and frost on the sensor domes. The net
radiometer and the downward-facing radiometers were mounted on a horizontal
boom that extended 4 m to the south of the flux tower,
Noah-MP parameterization options used in this study.
Noah-MP is a new-generation of LSM, which was developed to improve the performance of the Noah LSM (Chen et al., 1996; Chen and Dudhia, 2001). It is coupled to the WRF community weather and regional climate model (Barlage et al., 2015), and also available as a stand-alone 1-D model (Noah-MP v1.1). Noah-MP simulates several biophysical and hydrological processes that control fluxes between the surface and the atmosphere. These processes include surface energy exchange, radiation interactions with the vegetation canopy and the soil, hydrological processes within the canopy and the soil, a multilayer snowpack with freeze–thaw, groundwater dynamics, stomatal conductance, and photosynthesis and ecosystem respiration. The major components include a one-layer canopy, three-layer snow, and four-layer soil. Noah-MP provides a multi-parameterization framework that allows using the model with different combinations of alternative process schemes for individual processes (Niu et al., 2011). Alternative submodules for 12 physical processes can provide more than 5000 different combinations. Soil water fluxes are calculated by the Richards equation using a Campbell/Clapp–Hornberger parameterization of the hydraulic functions (Clapp and Hornberger, 1978).
We use an offline stand-alone 1-D mode (Noah-MP) with four soil layers: 0–10, 10–40, 40–100, and 100–200 cm. The selected Noah-MP physics options used in this study are similar to Barlage et al. (2015), Gao et al. (2015) and Chen et al. (2014) and are list in Table 1. In the default configuration of Noah-MP, the entire vertical soil profile was treated as one mineral ground texture only, and no organic soil matter is included.
Soil parameters used in Noah-MP for mineral soil texture classes (sandy clay loam) and organic soil (Hemic Peat).
The soil parameters are as follows:
The OAS research site has an organic LFH (forest floor) soil horizon,
8–10 cm deep. This study evaluates the impact of adding an organic soil
horizon in the Noah-MP model using a similar approach to Lawrence and
Slater (2008), which parameterizes soil thermal and hydrologic properties in
terms of carbon density in each soil layer. Soil carbon or organic fraction
for each layer is determined as
The 30 min meteorological observations, including air temperature, specific
humidity, wind speed, pressure, precipitation, downward solar, and long-wave
radiation, at 36 m height from OAS were used as atmospheric forcing data to
drive Noah-MP in an offline 1-D mode. Figure 2 shows the annual mean
temperature (1.5
Monthly air temperature above the canopy and precipitation at BERMS SK-OAS site.
Outputs from the Noah-MP simulations were evaluated against observations,
using the root mean squared error (RMSE), square of the correlation
coefficient (
The LSM spin-up is broadly defined as an adjustment process as the model
approaches its equilibrium following the initial anomalies in soil moisture
content or after some abnormal environmental forcing (Yang et al., 1995).
Without spin-up, the model results may exhibit drift as model states try to
approach their equilibrium values. To initialize LSMs properly, the spin-up
time required for LSMs to reach the equilibrium stage needs to be examined
first (Chen and Mitchell, 1999; Cosgrove et al., 2003). In this study, model
runs for the year 1998 were performed repeatedly until all the soil-state
variables reached the equilibrium state, defined as when the difference
between two consecutive 1-year simulations becomes less than 0.1 % for
the annual means (Cai et al., 2014; Yang et al., 1995). Yang et al. (1995)
discussed the spin-up processes by comparing results from 22 LSMs for grass
and forest sites, and showed a wide range of spin-up timescales (from 1 to
20 years), depending on the model, state variable, and vegetation type.
Cosgrove et al. (2003) used four NLDAS-1 LSMs to discuss the spin-up time at
six subregions covering North America, and showed that all models reached
equilibrium between 1 and 3 years for all six subregions. In this study, we
found that it requires 9 years for deep-soil moisture (100–200 cm layer) in
Noah-MP to reach its equilibrium, 8 years for latent heat flux and
evapotranspiration, but only 3 years for the surface soil moisture (Fig. 3).
Cosgrove et al. (2003) and Chen et al. (1999) indicated that it takes a long
time to reach equilibrium, especially in the deep soil layers and sparse
vegetation, because the evaporation was limited by slow water diffusion
timescales between the surface and deep soil layers. When using the
groundwater component of Noah-MP, it might take at least 250 years to spin up
the water table depth in arid regions (Niu et al., 2007). Cai et al. (2014)
found that water table depth requires less than 10 years to spin up in a wet
region, but more than 72 years for a dry region. For this boreal forest site
where the water table depth is shallower (less than 2.5 m), it takes
Averaged spin-up time (in years) for individual variables.
We defined the simulation without incorporation of organic soil as the control experiment (CTL), and the simulation with the organic soil incorporated as the organic layer experiment (OGN). We first evaluated the CTL- and OGN-simulated soil temperature and moisture at the OAS site in relation to observations for the period of 1998–2009.
As shown in Fig. 4, the effects of including a 10 cm organic topsoil layer
on simulated soil temperature are not uniform both throughout the soil depth
and during the year. Figure 4a shows that the CTL and OGN simulations produced
nearly identical top-layer temperatures which are in agreement with the
observations except for a low bias in the winter period, especially during
drought years 2002–2003. However, for deep layers (10–100 cm), soil
temperature from the OGN is lower (higher) than the CTL simulation during
summer (winter), especially for the drought years 2002–2003, leading to a
good agreement between OGN and observations for second- and third-layer soil
temperature (Fig. 4b, c). Lawrence and Slater (2008) indicated that strong
cooling in summer is due to the modulation of early and midsummer soil heat
flux, while higher soil temperature in fall and winter is due to less
efficient cooling of organic soils. The soil thawing period in spring is
significantly affected by the OGN parameterization since the thermal
conductivity of the organic horizon is much lower than that of the mineral
soil (
Observed and Noah-MP-simulated monthly soil temperature for BERMS
SK-OAS site at a depth of
In winter, with the presence of soil ice, the thermal heat conductivity in
OGN (
For the topsoil layer, the OGN parameterization increases the liquid soil water content in summer as water fills the larger pore space of organic soil, though the liquid soil water content in winter did not change much, due to the contrasting water retention characteristics of organic and mineral soil (Koven et al., 2009; Rinke et al., 2008; Lawrence and Slater, 2008). Higher porosity in OGN leads to an increase in total soil water content, while the lower topsoil temperature (Fig. 4a) in OGN enhances the ice content. Note that the observed soil water content during wet years may be higher than the site truth because the sensors were located in a low spot that is prone to flooding. This site got flooded in 2004 and the ground water has not dried since then; so the soil was oversaturated during the period of 2004–2008. In the second soil layer, the observed soil water content was incorrect after the site got flooded (2004–2008). With more precipitation during the wet period, the real soil water content should have a relatively high value. Since the OGN increases the soil water content, it should be closer to the true observation. From Fig. 5, it can be seen that the OGN improved the liquid water simulation in non-frozen periods. The soil moisture data are not reliable when the soil is frozen and are therefore not very useful during the winter. In late spring when snow starts melting, both CTL and OGN simulate the same topsoil temperature (Fig. 4). It is clear that the soil liquid water content is mainly controlled by precipitation, soil hydraulic conductivity, and runoff. The high porosity of organic soil in the topsoil layer helps to retain more snowmelt water and hence increases the topsoil layer liquid water content. For the deep soil layers, the soil liquid water content is highly influenced by the soil temperature. Liquid soil water content increases during soil-ice thawing period. The higher deep soil layer liquid water content in OGN is mainly because the soil hydraulic conductivity is higher for organic soil than mineral soil, so liquid water in the first layer can be transported downward quickly into the deeper layers. Although the organic soil layer is only added to the first two layers in this study, it still can affect the deep layer due to the infiltration characteristics of the topsoil.
Observed and Noah-MP-simulated monthly soil moisture for BERMS
SK-OAS site at a depth of
The water retention characteristics of the organic soil horizon favor both higher water retention and reduced evaporation. The thermal conductivity is lower compared with that of the mineral soil, which then prevents the deeper soil from warming up rapidly after the snowmelt season. The lower thermal conductivity of the top organic soil affects the annual cycle of the ground heat flux. In summer, the top layer is warmer than the deep layers; the ground heat flux then transfers heat downward. Because air temperature is lower than land surface temperature, heat is transferred upward from soil to the land surface; the low thermal conductivity of the organic soil can prevent the soil from cooling. On the other hand, snowfall in winter may form a snow layer that will insulate the soil and make the simulations less sensitive to thermal conductivity. This may be the reason why the OGN-simulated winter soil temperature is higher compared to CTL simulations. With the organic soil layer on the top, the reduction of surface layer saturation levels in wintertime (Fig. 5) reduces the heat loss through evaporation. The winter soil temperature then becomes significantly higher compared with the CTL experiment. On the contrary, the higher soil water content in the topsoil layer during summertime (Fig. 5) increases the heat loss through evaporation; the summer soil temperature then becomes significantly lower compared with the CTL experiment.
Averaged statistical indices for CTL- and OGN-simulated SH and LH
compared with the observations for each year (daytime, 08:00–16:00 local time
(LT)) (
Simulated differences in top-layer soil temperature and liquid soil water content lead to the differences in simulated surface energy fluxes. Figure 6 shows that the CTL run captures the observed monthly mean daytime sensible heat and latent heat flux reasonably well. However, SH is underestimated in spring and overestimated in summer. Accordingly, LH is overestimated in spring and underestimated in summer during most of the time period except for drought years 2002–2003 where LH is slightly overestimated. Generally, the OGN simulations show similar characteristics to the CTL, with improved correlation coefficients between observations and simulations: increasing from 0.88 (CTL) to 0.92 (OGN) for SH and from 0.94 (CTL) to 0.96 (OGN) for LH (Fig. 7). Overall, both CTL and OGN perform well in winter when snow is present and fluxes are small. During the spring snowmelt season, the OGN results are much better than the CTL (Figs. 6 and 7).
Observed and the Noah-MP-simulated (CTL and OGN) daytime
monthly average sensible and latent heat flux above the canopy. Error bars
represent the average and deviations [(RN
Scatter plots of the daytime monthly averaged
The OGN simulations also improved the underestimation of SH in spring in CTL, but they still overestimate summer SH. The reason for high bias in summer SH will be further discussed in Sect. 4.4. SH and especially LH show improvement in OGN compared to CTL, which is related to timing of soil thaw and warming in spring. CTL thaws the soil too early, causing a premature rise in LH in spring (April–May) and an associated underestimation of spring SH. The spring (April–May) fluxes are much improved in the OGN parameterization. However, both OGN and CTL retain a serious positive bias in SH from June to September, especially for wet years. The reduction of surface layer saturation levels in OGN led to lower soil evaporation and associated reductions in the total latent heat flux, and the reduction of LH is accompanied by a rise in SH (Fig. 6).
Comparison of the seasonal averaged diurnal cycle of the sensible and latent heat fluxes at OAS site for drought years.
The quality of nighttime flux-tower data is questionable (Chen et al., 2015),
especially for OAS located in a boreal forest. Therefore, we focused our
analysis on daytime observation data. In general, the OGN parameterization
improved the simulation of daily daytime LH in terms of both RMSE and IOA,
and increased IOA for SH (Table 3). Nevertheless, compared with CTL, OGN
increased the bias in SH slightly by
Comparison of the seasonal averaged diurnal cycle of the sensible and latent heat fluxes at OAS site for wet years.
For the 12-year simulation period, the study site experienced a prolonged drought that began in July 2001 and extended throughout 2002 and 2003. We choose year 2002 and 2003 to represent typical drought years, and year 2005 and 2006 to represent typical wet years (Fig. 2), to examine the effect of the organic soil under different climate conditions. For drought years 2002–2003, OGN increased daytime SH especially in spring, and slightly decreased SH at nighttime (Fig. 8a, b, c, and d). LH is well simulated in both OGN and CTL (Fig. 8e, f, g, and h), with slightly increased daytime LH in OGN. OGN overestimates daytime SH compared with observations, while CTL underestimates daytime SH for spring (Fig. 8a). Both OGN and CTL overestimate SH for summer, autumn, and winter (Fig. 8b, c, d).
For wet years (Fig. 9), OGN produces higher daytime SH than CTL in general. For spring, OGN-simulated SH agrees with the observation better than CTL, but it is similar to or slightly worse than CTL for other seasons. Simulated LH for both OGN and CTL agree with observations well, with an improvement by OGN in spring, because the snowmelt process dominates during spring months. For other seasons, the OGN results are close to CTL.
It is clear from Figs. 4, 8, and 9 that in both CTL and OGN, summer sensible
heat fluxes are overestimated for wet and dry years. We hypothesized that
such high bias in summer sensible heat flux is partly attributed to energy
imbalance in observations. We then calculated the energy balance residual
term: Rnet
Annual cycle of selected surface energy and hydrologic cycle
fields for drought years. The black line is the observation. Note that
In the previous section, it is clear that the incorporation of the top organic layer helps improve the simulation of the diurnal cycle of the surface energy and hydrologic components in spring season. In the following, we focus on a detailed analysis of the annual cycle of the surface energy and hydrology variables for dry (Fig. 10) vs. wet years (Fig. 11). Between June and September as shown in Fig. 10h, the upper two soil layers were unfrozen. The topsoil is wetter in OGN for both dry and wet years compared with CTL because organic soil can retain more water. As discussed in Sect. 4.2, for the deep soil layers, the liquid water content is influenced by the soil temperature and the movements of the soil liquid water content between soil layers. Since the soil hydraulic conductivity is higher for OGN than mineral soil, the water moves faster into deep soil layers than CTL; therefore the OGN simulates higher soil liquid water content in deep layers. OGN has a major impact on the daily cycle of soil temperature. Consistent with discussions in Sect. 4.2, the soil temperature below 10 cm simulated by OGN is lower in summer and higher in winter than that of the CTL simulation, and the OGN simulation shows less bias than the CTL simulation (Fig. 4). In the OGN simulation, the water moves faster into deep layers than in the CTL simulation, leading to more infiltrated water in the deep soil and hence a higher base flow. Consequently, the total runoff is increased. Due to the high soil porosity of the organic soil, OGN simulation shows higher soil-ice fraction at the topsoil layer during the freezing periods. The higher water capacity and higher soil-ice fraction of the organic soil then reduce liquid water content/soil moisture, leading to less evaporation (i.e., latent heat flux) during spring freezing periods, and a compensating increase of the sensible heat flux.
Annual cycle of selected surface energy and hydrologic cycle
fields for wet years. The black line is the observation. Note that
By adding an organic soil layer, the soil-ice content becomes higher due to higher porosity. For dry years, the impact of the organic soil on surface and subsurface runoff is not significant (Fig. 10e, f). The increase in the summer latent heat flux and sensible heat flux are compensated by a decrease in soil heat flux, leading to a significant decrease in summer soil temperature. In winter, the latent and sensible heat fluxes are not modified by the organic soil, but increased soil heat flux leads to an increased soil temperature in winter. The most prominent change by including the organic soil layer is the partition between vegetation transpiration and direct ground evaporation (Fig. 12a and b), where the OGN simulation slightly increased ground surface evaporation and vegetation transpiration.
Water budgets: blue lines are accumulated surface runoff (mm),
blue dots are accumulated underground runoff (mm), red lines are accumulated
evaporation of intercepted water (mm), red dots are accumulated ground
surface evaporation (mm), red dash lines are accumulated transpiration (mm),
green lines are snow water equivalent changes (mm), purple lines are soil
water content changes in the soil column (mm);
For wet years (Fig. 11), the impact of the organic soil on surface and subsurface runoff becomes more significant, especially for subsurface runoff. The organic soil decreases the surface runoff during the summer season, and increases the subsurface runoff during the freezing periods, while it decreases the subsurface runoff during summer season. Because of the higher surface layer soil-ice content, the increase of subsurface flow should be due to the production of a wetter soil profile by OGN. The sensible heat flux also increases significantly in spring, with an associated reduction in latent heat flux and soil heat flux. The summer soil temperature also decreases but to a lesser degree than that in dry years, because the soil heat flux decreases less compared with dry years. Unlike dry years, there is a significant runoff change in wet years, and the ground evaporation is also decreased (Fig. 12c and d). OGN produces more soil-ice content and higher soil porosity, and leads to higher soil water content than CTL simulations as the higher ice content severely restricts movement of water out of the soil column. In the wet season, by adding an organic topsoil layer, the soil water increases due to the infiltration of the soil water into the deep soil. This then leads to an increase in the subsurface runoff. As a consequence, the volumetric liquid water becomes higher in summer for OGN compared with CTL simulation.
In this study, the Noah-MP LSM was applied at the BERMS Old Aspen site to investigate the impact of incorporating a realistic organic soil horizon on simulated surface energy and water cycle components. This site has about an 8–10 cm deep organic forest-floor soil horizon, typical of boreal deciduous broadleaf forests. When including, for the first time, an organic soil parameterization within the Noah-MP model, simulated sensible heat flux and latent heat flux are improved in spring, especially in wet years, which is mostly related to the timing of spring soil thaw and warming. However, in summer the model overestimated sensible heat fluxes. Such high bias in summer sensible heat flux is largely attributed to surface-energy imbalance in observations, especially in dry years. Due to lower thermal conductivity, the OGN-simulated soil temperature was decreased during summer and slightly increased during winter compared with the CTL simulation, and the OGN-simulated soil temperature (10–100 cm) was more consistent with observations and with previous studies (Lawrence and Slater, 2008). Simulated top-layer soil moisture is better in OGN than in CTL in summer but worse in winter.
Additionally, due to higher porosity of the organic soil, the OGN simulation was able to retain more soil water content in summer. However, the effects of including an organic soil layer on soil temperature are not uniform throughout the soil depth and year, and those effects are more prominent in summer and in deep soils.
For drought years, the OGN simulation substantially modified the partition between direct soil evaporation and vegetation transpiration. When water is limited in drought years, the OGN simulation slightly increased the direct soil evaporation and produced higher summer total evapotranspiration. Increased latent heat flux and sensible heat flux in summer in OGN are compensated by decreased soil heat flux, leading to reduced soil temperature in summer. For wet years, the OGN-simulated latent heat fluxes are similar to CTL, except for the spring season where OGN produced less evaporation. In addition, the impact of the organic soil on subsurface runoff is substantial with much higher runoff in freezing periods and lower runoff in summer season.
This preliminary study explored the effects of incorporating organic soil parameterization in Noah-MP on the surface energy and water cycles for one flux site in a boreal forest area. Given the important role of boreal forests in the regional climate system through reducing winter albedo and also acting as a carbon sink and water source to the atmosphere, further work is needed to evaluate the Noah-MP with organic soil parameterization at regional scales. We plan to evaluate the performance of the offline Noah-MP model and Noah-MP coupled with WRF for a broad boreal forest region including Alberta and Saskatchewan.
The code for incorporation of an organic soil layer in the Noah-MP land
surface model is available upon request from Liang Chen at the University of
Saskatchewan (liang.chen@usask.ca). The FLUXNET data are publicly available
from the ORNL DAAC (Distributed Active Archive Center) at
The author Liang Chen acknowledges support from the National Basic Research Program (grant no. 2012CB956203) and the National Natural Science Foundation of China (grant no. 41305062). The authors Liang Chen, Yanping Li, and Alan Barr gratefully acknowledge the support from Global Institute of Water Security at University of Saskatchewan. Fei Chen, Michael Barlage, and Bingcheng Wan appreciate the support from the Water System Program at the National Center for Atmospheric Research (NCAR), and NOAA MAPP-CTB grant (NA14OAR4310186). NCAR is sponsored by the National Science Foundation. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.Edited by: L. Zhang Reviewed by: two anonymous referees