The incorporation of an organic soil layer in the Noah-MP Land Surface Model and its evaluation over a Boreal Aspen Forest

The incorporation of an organic soil layer in the Noah-MP Land Surface Model and its evaluation over a Boreal Aspen Forest L. Chen, Y. Li, F. Chen, A. Barr, M. Barlage, and B. Wan Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK, Canada Key Laboratory of Regional Climate Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China National Center for Atmospheric Research, Boulder, Colorado, USA Environment Canada, National Hydrology Research Center, Saskatoon, SK, Canada


Introduction
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).The Noah LSM with multi-parameterization op-Figures

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Full tions (Noah-MP) is a new-generation community land model, with multiple options for many land-atmosphere interaction processes representing the seasonal and annual cycles of snow, hydrology, and vegetation (Niu et al., 2011).Noah-MP has been implemented in the community Weather Research and Forecasting (WRF) model (Barlage et al., 2015), the most widely used numerical weather prediction and regional climate model in the world.The performance of Noah-MP was previously evaluated using insitu and satellite data (Niu et al., 2011;Yang et al., 2011;Cai et al., 2014).Those evaluation results showed, compared to the legacy Noah LSM (Chen et al., 1996;Ek et al., 2003), significant improvements in modeling runoff, snow, surface heat fluxes, soil moisture, and land skin temperature.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.
Despite continuous evaluation and improvements, Noah-MP has not been evaluated in boreal forest regions.The Canadian boreal region contains one third of the world's boreal forest, approximately 6 million km 2 (Bryant et al., 1997).The boreal forests have complex interactions with the atmosphere and significant impact on regional and global climate (Bonan, 1991;Bonan et al., 1992;Thomas and Rowntree, 1992;Viterbo and Betts, 1999;Ciais et al., 1995).Several field experiments were conducted to better understand and model these interactions, including BOREAS (Boreal Ecosystem Atmosphere Study) and BERMS (Boreal Ecosystem Research and Monitoring Sites).Numerous studies evaluated LSMs using the BOREAS and BERMS data (Bonan et al., 1997).Levine and Knox (1997) developed a frozen soil temperature (FroST) model to simulate soil moisture and heat flux and used BOREAS northern and southern study areas to calibrate the model.They found that soil temperature was underestimated and large model biases existed when snow was present.Bonan et al. (1997)  model reasonably simulated the diurnal cycle of the fluxes.Bartlett et al. (2002) used the BOREAS Old Jack Pine (OJP) site to assess two different versions of CLASS, the Canadian Land Scheme (2.7 and 3.0) and found that both versions underestimated the snow depth and soil temperature values, especially the old version CLASS 2.7.Boreal forest soils often have a relatively thick upper organic horizon.The thickness of the organic horizon directly affects the soil thermal regime and indirectly affects 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 et al., 1977).The hydraulic conductivity of organic soil horizons can be very high due to the high porosity (Boelter, 1968).Less suction is observed for 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, 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 (Letts et al., 2000;Beringer et al., 2001;Molders and Romanovsky, 2006;Nicolsky et al., 2007;Lawrence and Slater, 2008, etc.).Nevertheless, 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 on surface energy and water budgets in order to enhance the global applicability of the WRF-Noah-MP coupled modeling system.Here we conducted 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.In this paper, we present the BERMS observation site in central Saskatchewan (Sect.2) used in this study, and our methodology of conducting 12-year Noah-MP simulations with and without organic soil layer for that boreal forest site (Sect.3).Section 4 dis-Figures

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Full cusses the simulations of the diurnal and annual cycles of the thermal and hydrological components, in dry and wet period separately.Summary and conclusions are given in Sect. 5.
2 Field site and observations

BERMS site descriptions
The Old Aspen Site (OAS, 53.7 • mN, 106.2 • W, altitude 601 m) is located in mature deciduous broadleaf forest at the southern edge of the Canadian boreal forest in Prince Albert National Park, Saskatchewan, Canada (Fig. 1).The forest canopy consists of a 22-m trembling aspen overstory (Populus tremuloides) with ∼ 10 % balsam poplar (Populus balsamifera.)and a 2 m hazelnut understory (Corylus cornuta) with sparse alder (Alnus crispa).The fully-leafed values of the leaf area index varied among years from 2.0 to 2.9 for the aspen overstory and 1.5 to 2.8 for the hazelnut understory (Barr et al., 2004) verse City, MI, USA).Windspeed 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, which were 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 VWC values that may not be characteristic of the flus footprint.θ 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 eddycovariance systems are given in Barr et al. (2006).Data gaps were filled using a standard procedure.
The net radiation flux density Rn was calculated from component measurements of incoming and outgoing shortwave and longwave 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, ∼ 10 m above the forest canopy.Details of the minor terms in the surface energy balance; including soil heat flux and biomass heat storage flux are given in Barr et al. (2006).During the warm season when all components of the surface energy balance were resolved, the sum of the eddy-covariance sensible and latent heat fluxes Introduction

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Full underestimated the surface available energy (net radiation minus surface storage) by ∼ 15 % (Barr et al., 2006).

Meteorology forcing data
The 30 min meteorological observations at 36 m height from OAS were used as atmospheric forcing data to drive Noah-MP in an uncoupled 1-D mode, including air temperature, specific humidity, wind speed, pressure, precipitation, downward solar, and longwave radiation.Figure 2 shows the annual mean temperature (1.5 • C) and total precipitation (406 mm) at this site during the study period (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009).The most significant climatic features during the study period are a prolonged drought that began in July 2001 and extended throughout 2003, and an extended wet period from 2004-2007.

The Noah-MP Model
Noah-MP is a new-generation of LSM, developed to improve major weaknesses 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 multi-layer snowpack, freeze-thaw, groundwater dynamics, stomatal conductance, and photosynthesis and ecosystem respiration.The major components include a 1-layer canopy, 3-layer snow, and 4-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 sub-modules for Figures

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Full 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).In the original version of Noah-MP, the vertical soil profile was treated as one mineral ground texture only.

Thermal and hydraulic parameterizations for organic soil
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.The original Noah-MP soil parameterization included mineral soil horizons only.
We added an organic soil horizon 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 where f sc,i is the carbon fraction of the each layer, ρ sc,i is the soil carbon density, and ρ sc,max is the maximum possible value (peat density of 130 kg m −3 , Farouki, 1981).The soil properties for each layer are specified as a weighted combination of organic and mineral soil properties.
where P m is the value for mineral soil, P o is the value for organic soil, and P is the weighted average quantity.In this study, we assume that the top layer of the soil is made up of 100 % organic matter, consistent with the 8-10 cm LFH horizon at OAS.The remaining soil layers were assumed that made up of 100 % mineral soil.We have also conducted sensitive test for some key parameters, like Saturated hydraulic conductivity, porosity, Suction, Clapp and Hornberger parameter, following the work of Letts Introduction

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Full  (2000).The sensitivity test showed that the soil moisture is not very sensible to these parameters change, so we decided to use Lawrence and Slater (2008), Letts et al. (2000) recommended values instead, which are close enough to explain the observations (see Table 1).

Evaluation of model performance
Outputs from the Noah-MP simulations were evaluated against observations, using the Root Mean Squared Error (RMSE), square of the correlation coefficient (R 2 ), and Index of Agreement (IOA) (Zhang et al., 2013).The IOA is calculated as where M i and O i are simulated and observed values of the same variable, respectively, and O is the mean of the observed values.IOA ranges from 0 (no agreement) to 1 (perfect match).

Noah-MP model Spin-up
The LSM spin-up is broadly defined as an adjustment processes 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 can be unstable and 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 Introduction

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Full 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.The criterion that defines the equilibrium is when the difference between two consecutive one-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 year 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 selected six sub-regions covering North America, and showed that all models reached equilibrium between one to three years for all six sub-regions.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 (Fig. 3), 8 years for latent heat flux and evapotranspiration, but only 3 years for the surface soil moisture.It takes a long time period to reach equilibrium especially in the deep soil layers.In OAS, the freezing/thawing is a relatively slow process, the "long-term" memory of the initial condition comparing to surface variables, so the freeze-thawing process makes the spin up slower.So we set 10 years for the spin-up time for all the experiments discussed here.

Sensitivity test of Noah-MP physics options
There are more than 5000 different combinations of physics parameterization schemes for the 12 sub-land processes in Noah-MP.We only selected the following critical processes to which our preliminary test results are sensitive: (1) CRS: canopy stomatal resistance, (2) BTR: soil moisture factor for stomatal resistance, (3) RUN: Runoff/soil lower boundary, (4) SFC: surface layer drag coefficient calculation, (5) FRZ: supercooled liquid water, (6) INF: soil permeability, and (7) RAD: radiative transfer.The options used in this study are listed in Table 2, with all possible combinations (1152) investigated.The impact of each parameterization option was investigated using two statistical indices and IOA.Table 3 shows the effect of each critical process, averaged 29274 Introduction

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Full over all possible combinations of the other six processes (Gayler et al., 2014).The options that produced negative mean IOA values are ignored.For latent heat and sensible heat flux simulations, the scheme combinations generating good skill scores are the following: CRS (1), BTR (1), RUN (2), SFC (2), FRZ (1), INF (2), and RAD (3).They are then treated as the most appropriate combinations for our study site (see Table 3).
The order of the categories based on the IOA scores from the highest to the lowest is SFC > FRZ > CRS > BTR > RUN > RAD > INF.

Evaluation results
We defined the combination of the parameterization schemes that produced the best performance in our simulation without incorporating organic soil as the "control experiment" (CTL); the simulation that used the same parameterization option combination but with the organic soil incorporated as the "organic layer experiment" (OGN).
We first evaluated the simulated CTL sensible and latent heat fluxes at the OAS site in relation to observations for the period of 1998-2009.Figure 4 show that the CTL run captures the observed monthly mean sensible heat and latent heat flux reasonably well.However, SH is underestimated in spring and slightly overestimated in summer.We then evaluated the Noah-MP model simulation with the organic-soil parameterization (Fig. 5).The OGN simulation shows similar characteristics to the CTL, with improved correlation coefficients between observations and simulations: increasing from 0.82 (CTL) to 0.89 (OGN) for SH and from 0. Figure 6 shows good agreement between the observed and simulated monthly soil temperature for the OAS site.However, the effects of including an organic soil layer at the top (0-10 cm) on soil temperature are not uniform throughout the soil depth and year.The CTL and OGN simulations produced nearly identical shallow soil temperature.However, the OGN simulation substantially decreased deep soil temperatures (10-100 cm) in summer and slightly increased deep-layer soil temperature in winter, especially for the drought years 2002-2003, causing much improvement with the observations.The soil thawing period in spring is significantly affected by the OGN parameterization, the thermal conductivity of the organic horizon is much lower than that of the mineral soil, which delays the warming of the deep soil layers after snowmelt.In winter, the organic soil layer insulates the soil and results in relatively warmer wintertime soil temperatures for OGN compared with CTL.The difference is most pronounced in drought years (2002 and 2003) (Fig. 6) when the thinner snowpack provides less insulation.The less precipitation/snow leads to a stronger evaporation, and thus a reduction of heat conductivity.With an organic soil horizon, the lower soil water content in the topsoil layer (Fig. 7) reduces the heat loss through evaporation; the winter soil temperature then becomes significantly higher compared with CTL.This result is consistent with recent studies that show a simulated increase in winter soil temperature of up to 5 • C in boreal regions when including an organic layer (Koven et al., 2009;Rinke et al., 2008;Lawrence and Slater, 2008) in LSMs.
The inclusion of an organic soil horizon also affects the hydrologic cycle components such as soil water content, runoff, and evaporation (Fig. 7).For the top layer, the OGN parameterization increases the shallow soil water content in summer but decreases the liquid soil water content in winter, due to the contrasting water retention characteristics of organic and mineral soil.Note that the observed soil water content during wet years may be higher than the site means 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 that 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 Figures

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Full got flooded (2004)(2005)(2006)(2007)(2008).With more precipitation for this wet period, the real soil water content should have a relatively high value.Since the OGN increases the soil water content, it should be more close to the true observation.From Fig. 7, it can be seen that the OGN improved the liquid water simulation in frozen-free periods, as the soil moisture data are not so unreliable when the soil is frozen and are therefore not very useful during the winter.So it is hard to evaluate which experiment is better in winter.
In late spring when snow starts melting, both CTL and OGN simulate the same topsoil temperature (Fig. 6), with temperature increases to above the freezing point.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 moisture increases during soil ice thawing period.The higher deep soil layer liquid water content in OGN than in CTL is mainly because that the soil hydraulic conductivity is higher for organic soil than mineral soil, so liquid water at firstlayer can transport downward quickly into the deep layers.That is probably the reason although the organic soil layer is only added to the topsoil in this study, but it still can affect the deep layer due to the infiltration characteristics of the topsoil.Simulated summer evaporation from the ground is smaller for OGN than CTL (Fig. 12).The water retention characteristics of the organic soil horizon favor both higher water retention and reduced evaporation, related to the high air-filled porosity of the organic soil at field capacity.The higher hydraulic conductivity of the organic soil allows the precipitation to be quickly transported downward from the topsoil layer to deep layers, leading to a relatively dry surface layer and less soil evaporation.The thermal conductivity is lower compared with that of the mineral soil, which then prevents deeper soil to warm up rapidly after 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 of the lower thermal conductivity of the organic soil, the total soil heat gained in Figures OGN is less than CTL.In winter, the organic soil layer insulates the deep soil from the cold atmosphere, resulting in a relatively higher wintertime soil temperature for OGN compared with the CTL simulation without organic layer.Because air temperature is lower than land surface temperature so heat is transferred upward from soil to the land surface, the lower thermal conductivity of the organic soil can prevent the soil to cool.
On the other hand, the snowfall in winter may form a snow layer on the top of the soil, then the thermal conductivity of both organic and mineral soil become very close.This is maybe the reason why the OGN simulated soil temperature is only slightly increased in winter compared to CTL simulations.The OGN-CTL difference is strongest for the drought years 2001, 2002 and 2003.During these years, less precipitation/snow leads to stronger evaporation.With the organic soil layer on the top, the lower soil water content in the topsoil layer (Fig. 7) reduces the heat loss through evaporation; the winter soil temperature then becomes significantly higher compared with CTL experiment.

Impact of organic soil on diurnal cycle of surface energy and hydrology
In general, the OGN parameterization improved the simulation of daily daytime SH and LH in terms of both RMSE and IOA (Table 4), although the improvement was not apparent in the mean daily cycle.Compared with CTL, OGN increased IOA for both SH and LH, increased the bias in SH slightly by ∼ 6 %, and decreased the bias in LH by 14 %, (Table 4).
For the 12-year simulation period, the study site experienced a prolonged drought, (Fig. 8c, d).OGN has a major impact on the daily cycle of soil temperature.Consistent with discussions in Sect.4.3, 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 has less bias than the CTL simulation.In OGN simulation, the soil hydraulic conductivity of the top soil layer is higher than CTL, the water moves faster into deep layers than in CTL simulation, the runoff then increases.Due to the high soil porosity of the organic soil, OGN simulation shows higher soil-ice fraction at the top soil 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, these further lead to less evaporation (i.e., latent heat flux) during freezing periods (Spring), and a compensating increase of the sensible heat flux.
For wet years (Fig. 9), OGN produces in general higher daytime SH than CTL.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 a significant improvement by OGN in spring.
Note that the OGN simulation also improves surface heat fluxes significantly in drought years, because the snowmelt process dominates during spring months.For other seasons, the OGN results are fairly close to CTL.

Impact of an organic soil horizon on annual cycle of surface energy and hydrology
In the last 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) versus "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.4.3, for the deep soil layers, the liquid water content is influenced by the soil temperature and the 29279 Introduction

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Full 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 simulate higher soil liquid water content in deep layers.The total soil column liquid water content keeps increasing before the soil temperature reaches above the freezing point (Figs. 10g,11g), which is because the deep soil temperature is usually higher than the top soil so ice get melts earlier in deep layer.
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 sub-surface 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 adding organic soil layer is the partition between vegetation transpiration and direct ground evaporation (Fig. 12a and     b) where the OGN simulation decreased ground surface evaporation.
For wet years (Fig. 11), the impact of the organic soil on surface and sub-surface runoff becomes more significant, especially for sub-surface runoff.The organic soil increases the surface runoff during the spring snowmelt season, and increases the sub-surface runoff throughout the year, because of the higher surface layer soil ice content and higher porosity.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 in a less degree than that in dry years, because the soil heat flux deceases 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 lead to higher deep-soil-layer water content than CTL simulations.In wet season, by adding an organic topsoil layer, the total column soil water increases due to the infiltration of the soil water into the deep soil.This then leads to an increase in the sub-surface Introduction

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Full runoff.As the consequence, the volumetric liquid water becomes higher in summer for OGN compared with CTL simulation.

Summary and conclusions
In this study, the Noah-MP LSM was applied at the BERMS Old Aspen site to investigate the impact of adding a realistic organic soil horizon on simulated surface energy and water cycle components.This site has an about 8-10 cm deep organic forestfloor soil horizon, typical of boreal deciduous broadleaf forests.The selection of different parameterization schemes for each process within the current Noah-MP model significantly affected the simulation results.It is found that, based on the IOA scores from the highest to the lowest, the importance of physics parameterization scheme is When including, for the first time, an organic-soil parameterization within the Noah-MP model, the verification results against site show significantly improved performance of the model in simulated surface energy fluxes.For instance, the simulated SH in spring were much improved, while in later summer the model overestimated those fluxes.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 simulations were more consistent with observations and with previous studies (Lawrence and Slater, 2008).Also, due to higher porosity of the organic soil, the OGN simulation was able to retain more soil water content in summer.
However, in winter, the OGN experiment produced less liquid soil-water content due to the lower temperature and higher porosity.Since most of the soil moisture is stored in soil ice, the liquid water content is reduced.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.years, the OGN simulation significantly reduced the direct soil evaporation but still 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 spring season where OGN produced less evaporation.The incorporation of an organic layer at the top of the soil helps improve the nighttime sensible heat flux for all seasons.In addition, the impact of the organic soil on sub-surface runoff is substantive with much higher runoff throughout the season.This preliminary study explored the effects of incorporating organic soil parameterization in Noah-MP on surface energy and water cycle for one flux site in boreal forest area.Given important role of boreal forests in the regional climate system by reducing winter albedo and also acting as a carbon sink and water source to the atmosphere and differences in forest structures and soils in the general boreal ecosystem, 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.Introduction

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Full   The soil parameters are λ s is the thermal conductivity of soil solids, λ sat is the unfrozen saturated thermal conductivity, λ dry is the dry soil thermal conductivity, c s is the soil solid heat capacity, θ sat is the saturated volumetric water content (porosity), κ sat is the saturate hydraulic conductivity, ψ sat is the saturated matric potential, and b is the Clapp and Hornberger parameter.Figures

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Full  Full  Full Discussion Paper | Discussion Paper | Discussion Paper | examined NCAR LSM1 with flux-tower measurements from the BOREAS, and found that the Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . The forest regenerated after a natural fire in 1919 and had a 1998 stand density of ∼ 830 stems ha −1 .The soil is an Orthic Gray Luvisol with an 8-10 cm deep surface organic Litter layer, Fermentation layer and Humus (LFH) horizon overlying a loam to sandy clay loam mineral soil.30 % of the fine roots are in the LFH horizon and 60 % are in the upper 20 cm of mineral soil.The water table lies from 1 to 5 m below the ground surface, varying spatially in the hummocky terrain and varying in time in response to variations in precipitation.A small depression near the tower had ponded water at the surface during the wet period from 2005 to 2010.Mean annual air temperature and precipitation at the nearest long-term weather station is 0.4 • C and 467 mm, respectively (Waskesiu Lake, 53 • 55 N, 106 • 04 W, altitude 532 m, 1971-2000 climatic normal).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, Tra-Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | et al.
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 93 (CTL) to 0.97 (OGN) for LH.The OGN simulations also resolve the underestimation of SH in spring, but the overestimation in summer still remains.SH and LH show improvement of OGN over CTL, 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 and 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-September, especially for wet years.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | beginning in July 2001 and extending throughout 2002 and 2003.We then 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 and 2003), OGN increased daytime SH especially in spring, and slightly decreased SH at nighttime (Fig. 8).LH is well simulated by both OGN and CTL (Fig. 8), with OGN reducing daytime LH slightly.OGN overestimates daytime SH compared with observations, while CTL underestimates daytime SH for spring and summer (Fig. 8a, b) and slightly overestimates SH for autumn and winter 29278 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | For drought years, the OGN simulation substantially modified the partition between direct soil evaporation and vegetation transpiration.When water is limited in drought Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Thomas, G. and Rowntree, P. R.: The Boreal Forests and Climate.Q.J. Roy.Meteorol.Soc., 118, 469-497, doi:10.1002/qj.49711850505,1992.Viterbo, P. and Betts, A. K.: Impact on ECMWF forecasts of changes to the albedo of the boreal forests in the presence of snow, J. Geophys.Res., 104, 27803-27810, doi:10.1029/1998JD200076,1999.Yang, Z. L., Dickinson, R. E., Henderson-Sellers, A., and Pitman, A. J.: Preliminary study of spin-up processes in land surface models with the first stage data of project for intercomparison of land surface parameterization schemes phase 1 (a), J. Geophys.Res.-Atmos., 100, 16553-16578, 1995.Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., and Tewari, M.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys.Res.-Atmos., 116, D12110, doi:10.1029/2010JD015140,2011.Zhang, G., Zhou, G., Chen, F., Barlage, M., and Xue, L.: A trial to improve surface heat exchange simulation through sensitivity experiments over a desert steppe site, J. Discussion Paper | Discussion Paper | Discussion Paper | /soil lower boundary = 1 : TOPMODEL with ground water(Niu et al., 2007)   = 2 : TOPMODEL with equilibrium water table(Niu et al., 2005) = 3 : original surface and subsurface runoff (free drainage) = 4 : BATS surface and subsurface runoff (free drainage) SFC: Surface layer drag Coefficient calculation = 1 : Monin-Obukhov = 2 : original Noah = 3 : MYJ consistent = 4 : YSU consistent FRZ: Supercooled liquid water = 1 : no iteration (Niu and Yang, 2006) = 2 : Koren's iteration INF: Soil permeability = 1 : linear effects, more permeable (Niu and Yang, 2006) = 2 : nonlinear effects, less permeable (old) RAD: Radiative transfer = 1 : modified two-stream = 2 : two-stream applied to grid-cell = 3 : two-stream applied to vegetated fraction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 8 .Figure 9 .Figure 10 .Figure 11 .Figure 12 .
Figure 8.Comparison of the seasonal averaged diurnal cycle of the sensible and latent heat fluxes at OAS site for drought years.

Table 1 .
Soil parameters used in Noah-MP for mineral soil texture classes (SANDY CLAY LOAM) and organic soil.

Table 3 .
Averaged statistical indices for SH and LH to the observations (R 2 : correlation coefficient square; RMSE: root mean square error; IOA: index of agreement).

Table 4 .
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)] (R 2 : correlation coefficient square; RMSE: root mean square error; IOA: index of agreement).