1 1 Effects of soot-induced snow albedo change on snowpack and 2 hydrological cycle in western U.S. based on WRF chemistry and 3 regional climate simulations 4 5 6 7 8 9 10 11 Yun Qian, William I. Gustafson Jr., L. Ruby Leung, Steven J. Ghan 12 13 Atmospheric Science and Global Change Division 14 Pacific Northwest National Laboratory, Richland, WA 15 16 17 18 19 20 21 22 23 (To be submitted to J. Geophysical Research, 2008) 24 Corresponding author: Yun Qian, Pacific Northwest National Laboratory, Richland, WA 25 E-mail: [email protected]26
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1
Effects of soot-induced snow albedo change on snowpack and 2
hydrological cycle in western U.S. based on WRF chemistry and 3
regional climate simulations 4
5
6
7
8
9
10
11
Yun Qian, William I. Gustafson Jr., L. Ruby Leung, Steven J. Ghan 12
13
Atmospheric Science and Global Change Division 14
Pacific Northwest National Laboratory, Richland, WA 15
16
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23
(To be submitted to J. Geophysical Research, 2008) 24
Corresponding author: Yun Qian, Pacific Northwest National Laboratory, Richland, WA 25
where SALP is a tuning parameter; RSNOW = SNEQV/SNUP; SNEQV is the snow depth in SWE; 8
and SNUP is a threshold snow depth in SWE that implies 100 percent snow cover. Hence, 9
surface albedo (ALBEDO) is calculated based on a simple weighting of the background albedo 10
(ALB) and the maximum albedo over deep snow (SNOALB) based on the fractional snow free 11
and snow cover area within a model grid cell. 12
13
To limit the values of surface albedo when snow is present, the Noah LSM inputs the 14
measured maximum albedo over deep snow [Robinson and Kukla, 1985, referred to as RK]. 15
Figure 6a shows the spatial distribution of the maximum snow albedo (SNOALB) used in the 16
default configuration of WRF. The RK dataset has a spatial resolution of 1o by 1o and is 17
produced from 109 measured scenes from the winter of 1978–1979. In RK, scene brightness was 18
converted to surface albedo by linear interpolation between the brightest tundra (albedo of 0.8) 19
and the darkest snow-covered forest (albedo of 0.18). Barlage et al. [2005] updated the RK 20
dataset by using over four years of MODIS products at 0.05º spatial resolution to produce a new 21
high resolution maximum albedo dataset of snow-covered land. The default SNOALB shown in 22
Figure 6a is what was used for the control simulation described below. 23
22
1
Figure 6 2
3
3.4 Results of control simulation 4
Precipitation 5
Figure 7 (a & b) shows the observed and simulated seasonal mean precipitation averaged 6
between December 1993 and November 1998. During the cold season, the observed precipitation 7
at 1/8o resolution shows distinct spatial distributions that resemble the complex orography. The 8
two precipitation bands along the West Coast correspond to orographic precipitation associated 9
with the coastal range and the Cascades and the Sierra Nevada that are further inland. East of 10
these mountains, precipitation decreases sharply in the basins and the intermountain zone, before 11
it increases slightly again over the Rockies. 12
13
Figure 7 14
15
The simulated cold season precipitation clearly has a spatial pattern similar to the 16
observations. The two precipitation bands near the coast are very distinguishable, with 17
precipitation decreasing in the valleys between the two elevated regions. In addition, 18
precipitation on the east side of the Cascades and the Sierra Nevada is significantly less. 19
20
Although there is a one-to-one correspondence between most areas of maximum 21
precipitation in the observations and the control simulation, the simulated orographic 22
precipitation along the windward slopes of the Cascades and Sierra Nevada is too strong. This is 23
consistent with results based on real time weather forecasts for the Pacific Northwest [e.g., Colle 24
23
et al. 2000; Westrick and Mass 2001] and MM5 regional climate simulation [Leung and Qian 1
2003] that show an increasing amount of area averaged precipitation as spatial resolution 2
increases. 3
4
During spring, the precipitation pattern is similar but with a smaller magnitude than in 5
winter. Again, WRF overpredicts the orographic precipitation along the Cascades and Sierra 6
Nevada as well as the Rockies (not shown). Figure 8 shows the monthly time series of 7
precipitation from December 1993 to November 1998 averaged over CRB and SSJ. WRF 8
captures the seasonal cycle and inter-annual variability of precipitation very well over the two 9
basins. However, it overpredicts precipitation during winter and spring by 25-35% over the two 10
basins. This behavior is similar to the MM5 model that tends to have higher large-scale moisture 11
convergence during the cold season when driven by global reanalysis at high spatial resolution 12
[Leung et al., 2003]. 13
14
Figure 8 15
16
Temperature 17
Figure 7 (c & d) shows the seasonal mean surface air temperature averaged for December, 18
January and February (DJF) based on observation and WRF control simulation. The spatial 19
distribution of large-scale features is very consistent between the two. The mesoscale details of 20
temperature are also captured over the mountains in the WRF simulation at relatively high spatial 21
resolution. The magnitude of simulated temperature during the DJF and MAM seasons (not 22
shown) is very close to the observed, with the exception of a small cold bias over the Rockies. 23
As shown in Figure 8 the basin-averaged temperature is slightly underpredicted during winter 24
24
and spring, but the cold bias is generally less than 1oC over CRB and SSJ basin. Surface air 1
temperature is simulated very well during summer and fall. 2
3
Snow water 4
Figure 9 (a, b, c & d) shows the simulated and observed distribution of DJF mean snow 5
depth in terms of snow water equivalent. We present three observational datasets of SWE to 6
provide an estimate of uncertainty in the observed quantity. Because of the improved simulation 7
of surface temperature gained through the modified snow emissivity in the model, the phase of 8
the precipitation is more accurate than with a default WRF configuration. This results in 9
improved snow accumulation and melting processes at the surface and the SWE is generally well 10
simulated. The simulation reproduces many of the features of the observed snow distribution, 11
with heavy snow cover over mountains (e.g. Cascades and the Sierra Ranges, and Rockies) and 12
light snow in the valleys (e.g. central valley) or basins (e.g. CRB). Based on the SNOTEL station 13
data, the CMC data likely have a negative bias over both high and low elevation areas. Since the 14
NOHRSC data combined station measurements of snow water in its product, the magnitude of 15
SWE from NOHRSC is very close to the SNOTEL data where measurements are available. 16
17
Figure 9 18
19
Figure 9 (e & f) shows the simulated and observed spatial distribution of mean SWE in 20
MAM. Compared to DJF, the snow cover area has significantly decreased in spring due to 21
snowmelt at the lower elevations. However, the SWE is much higher in MAM than in DJF over 22
the mountains (e.g. the Cascades, Sierra Nevada, and Rockies). During MAM, the model 23
25
captured the maximum SWE over the Sierra Nevada range and northern Rockies but 1
underpredicted SWE over the Cascades and southern Rockies. Since the surface temperature is 2
well simulated and precipitation is overpredicted in the Cascades and southern Rockies, the 3
overpredicted liquid rain on snow may have increased the melt process and decreased the SWE 4
during spring. 5
6
Runoff 7
The spatial distribution of modeled runoff generally agrees with the GRDC runoff in 8
winter (not shown). Both the modeled and observed data show higher runoff in wet regions such 9
as the high elevations, which is at least partially related to the overestimated orographic 10
precipitation. Further inland, runoff rapidly decreases coincident with decreasing precipitation in 11
the rain shadows. The main contribution to total runoff during winter is surface runoff generated 12
by liquid rain. The discrepancy between the modeled and observed runoff may also result from 13
the inconsistency of time periods used for averaging. GRDC runoff is estimated through longer 14
past discharge records, while the modeled runoff is an average over the years 1993-1998. 15
16
During spring both precipitation and snowmelt contribute to runoff. Generally, the spatial 17
distribution of large-scale features is very consistent between the observations and simulation 18
except for an overprediction of runoff resulting from the wet bias over the Cascades and Sierra 19
Nevada ranges (see Figure 10). The larger runoff over the hilly coastal areas overlaps with the 20
maximum precipitation over the central Rockies and maximum snowpack in the Cascades/Sierra 21
Nevada ranges (see Figure 9). 22
23
26
Figure 10 1
2
4. Simulation of soot-induced snow albedo change 3
To determine the impact of soot induced changes to snow albedo, a second WRF-RCM 4
simulation was done using the same settings as the control simulation except that the snow 5
albedo was modified based on soot deposition determined from the WRF-Chem simulation. This 6
was done by modifying the map of maximum snow albedo (Figure 6) used by the Noah LSM, 7
which was described in Section 3.3. The albedo perturbation, A′ , is subtracted from SNOALB. 8
We compared the results of these two 5-year long WRF regional climate simulations with and 9
without soot-induced snow albedo perturbations, to investigate the effects of soot on the surface 10
energy and water budget in the western U.S. The following analyses focus on the changes of 11
variables such as surface solar radiation, temperature, precipitation, evaporation, soil moisture, 12
snowpack, and runoff. These variables are the most relevant to the hydrological cycle and water 13
resources in the western U.S. 14
15
Surface solar radiation 16
Figure 11a shows the change in net surface shortwave radiation flux (NSW) for March 17
between the control and soot-sensitivity simulations. The change of NSW is spatially correlated 18
with the change of surface albedo (Figure 11d). The largest changes are over the central Rockies 19
and southern Alberta, where NSW increases 4-10 W m-2 in late winter and 8-14 W m-2 in early 20
spring. As seen in Figure 12, the NSW increases in winter and spring, and the maximum change 21
occurs in March. The NSW in March increases 6.4 W m-2 over the Central Rockies (CR) and 2.1 22
W m-2 over the Sierra Nevada (SN), respectively (see Table 2). The change of NSW is caused 23
27
mainly by the perturbed surface albedo rather than changes to the downward solar radiation flux. 1
As shown in Table 2 the surface NSW increases 4.8% in March over CR and 1.4% over SN, 2
respectively. However, downward solar radiation at the surface only changes -0.5% over CR and 3
-0.2% over SN, respectively. It can be seen from Figure 12 that the albedo change is similar 4
between December and March over both river basins; however, NSW increases significantly 5
during this period and reaches a peak in March because of increased downward solar radiation 6
resulting from the increased solar height angle. 7
8
Figure 11 9
10
Surface air and skin temperature 11
Surface skin (air) temperature increases by 0.3 - 2.0oC (0.2 - 1.5oC) over the majority of 12
the snow covered areas in the western U.S. during late winter to early spring. The temporal and 13
spatial distributions of temperature changes, mainly driven by the change of surface absorbed 14
solar radiation, which is correlated with the NSW change. Similar to the NSW changes, the 15
warming period is from November to April and the maximum change occurs in March. Figure 11 16
(b&c) shows the spatial distribution of changes for surface skin and air temperature, respectively, 17
averaged for March. Regional averaged increases of surface skin (air) temperature are 0.7oC 18
(0.6oC) over CR and 0.2oC (0.1oC) over SN, respectively. The increase in surface skin 19
temperature is 20-50% higher than that of surface air temperature since the incoming energy is 20
partitioned between both sensible and latent heat, as well as melting the snow and warming the 21
sub-surface soil layers. 22
23
Figure 12 24
28
1
Snowpack 2
As a result of soot-induced reduction in NSW and surface warming, there are significant 3
reductions in snowpack between December and May. Figure 13 shows the spatial distribution of 4
SWE change from January to April between the control and soot-perturbed simulations. The 5
SWE decreases 10-50 mm over mountain areas in the western US during late winter to early 6
spring. Maximum reductions of SWE are over CR, SN, and the mountainous areas of western 7
Canada. The precipitation difference between the two simulations is small (not shown), less than 8
0.2% over the two river basins (Table 2). However, the warmer temperature as a result of soot 9
perturbations lead to more precipitation comes in the form of rain rather than snow, while total 10
precipitation amount has no change, resulting in less snow accumulation during the peak snow 11
season. Meanwhile, warmer surface temperatures speed up snowmelt during spring. Driven by 12
reduced snow accumulation in the winter and increased snowmelt in spring, SWE reduction 13
reaches a maximum in March over most of the snow-covered areas except for SN, where the 14
SWE reduction reaches a maximum in April. The mean SWE in March is reduced by 8.8 mm (-15
6.2%) over the CRB basin, 10.4 mm (-11.7%) over CR, 2.7 mm (-3.2%) over the SSJ basin, and 16
8.1 mm (-3.1%) over SN, respectively. Comparing the monthly change of SWE over the two 17
river basins (Figure 12), the SWE reduction lasts longer in SSJ than in CRB because SWE was 18
overpredicted in SSJ and the incoming solar radiation is higher at the lower latitudes, both of 19
which allow changes in NSW, ALB, T2/TS, and SWE to extend into the summer. 20
21
Figure 13 22
23
29
Runoff 1
Figure 14 shows the spatial distribution of runoff from February to May. It can be seen 2
that the runoff increases during late winter because in the soot-perturbed simulation more 3
precipitation comes in the form of rain rather than snow. Figure 14a shows that runoff increases 4
by 0.1-1.0 mm per day in February over the majority of the snow covered areas. Runoff 5
increases in February by 11.0% averaged over CR and by 1.6% averaged over SN, respectively. 6
However, runoff decreases by 0.1-0.7 mm per day in May over major snow covered areas as 7
shown in Figure 14d. Runoff decreases in May by 4.4% averaged over CR and by 1.3% averaged 8
over SN, respectively. Since snow accumulation is less during winter (see Figure 13), runoff 9
from snowmelt is reduced in spring. 10
11
Figure 14 12
13
The spatial distribution of runoff changes shows an interesting mixed pattern in March 14
and April. Driven by the higher rain to snow ratio in the soot-perturbed simulation, the runoff 15
increases in March over about half of the areas. Over the other half less snowmelt from the 16
reduced snowpack leads to runoff decreases for March (Figure 14b). The resulting net mean 17
runoff change is very small (less than 0.6%) in March averaged over the CRB or SSJ basins (see 18
Table 2). Generally, the areas with increased runoff correspond to regions with higher elevation 19
(See Figure 1b) and larger SWE (see Figure 9), whereas the areas with decreased runoff 20
correspond to regions with lower elevation and smaller SWE where snow begins to melt earlier 21
and faster. Runoff changes still show a mixed spatial pattern in April but the ratio of decreases to 22
30
increases grows to 70%; the runoff increases only over a small portion of mountain areas such as 1
SN range and eastern CR ranges. 2
3
Surface water budget 4
To illustrate changes in the seasonal cycle of water partitioning, Figure 15 shows the 5
basin mean monthly changes of precipitation, total runoff, evapotranspiration, soil moisture, and 6
snowpack accumulation for the surface water budget over CRB and SSJ. In both basins, 7
precipitation changes are small throughout the seasonal cycle. This suggests that changes in other 8
components of the water budget are mostly driven by changes in snowmelt or accumulation 9
rather than precipitation amount. As a result of warming, there are significant reductions in 10
snowpack between November and April (Figure 12), which are reflected in reduced snow 11
accumulation between November and February (peak in February) and less snow melt between 12
March and May (peak in April) in Figure 15. Note that the snow accumulation rate (SP) is 13
denoted positive and snowmelt is negative. In snow-dominated basins such as CRB, inter-annual 14
variability of runoff is mainly controlled by the precipitation amount, which is not significantly 15
changed in this study. However, on a monthly basis the runoff is affected by the changes not 16
only in precipitation but also in snow accumulation or melt. As shown in Figure 15, runoff 17
increases during January and March. This is because the higher surface temperature in the soot-18
perturbed simulation causes more precipitation to come in the form of rain rather than snow. By 19
contributing directly to runoff or by causing snowmelt, a higher percentage of rainfall versus 20
snowfall during the cold season increases runoff. As less snow accumulates during winter, runoff 21
as a result of snowmelt between April and June decreases. Soil moisture shows an abrupt change, 22
in Figure 15, from February to March. During winter, increased rainfall and runoff enhanced soil 23
31
moisture. In March and April, soil moisture accumulation is reduced because of reduced 1
snowmelt and increased evapotranspiration caused by warmer temperatures. 2
3
Figure 15 4
5
Changes in the water budget over SSJ are generally similar to changes over CRB, except 6
for a smaller magnitude and delayed timing in changes of snowmelt and runoff. Warmer 7
temperatures in the soot-perturbed simulation also cause snowpack reductions in SSJ. 8
Additionally, snowmelt decreases occur later in the year than for CRB and last a month longer. 9
As a result of snowmelt change, the runoff decreases between May and August rather than 10
between April and June in CRB basin. The change of soil moisture accumulation is small during 11
most months in SSJ. 12
13
Feedbacks between albedo, solar radiation, temperature, and snowpack 14
The soot-induced decrease of maximum snow albedo causes the land surface to absorb 15
more solar radiation, increasing the skin and air temperatures. As a result of the perturbed 16
radiation and warming, snow depth and fraction decrease during winter and spring, further 17
bringing down the albedo over areas with at least some snow cover. This is a positive feedback 18
process initialized by soot deposition on snow [see Figure 16]. 19
20
Figure 11d shows the spatial distribution of mean albedo change in March, which is the 21
month with maximum albedo change. It can be seen that the albdeo decreased 0.02-0.12 over 22
snow covered areas of the Central Rockies. Numerically in the Noah LSM, the simulated albedo 23
32
reduction in the soot-perturbation simulation is caused by two processes: 1) the reduction in 1
maximum albedo (SNOALB) due to the soot perturbations, and 2) the reduction of snowpack 2
from the warmer skin temperature. Comparing the SNOALB and mean albedo changes shown in 3
Table 2, we can find that the SNOALB decreased 5.43% but albedo decreased 9.07% over CR, 4
which indicates that only 60% of the albedo reduction is directly caused by the SNOALB change. 5
Snowpack reduction accounts for the remaining 40%. In the SN region the decrease of mean 6
albedo is only slightly larger than the decrease of SNOALB, which implies that the major 7
contributor to albdeo change over SN was SNOALB instead of the snowpack reduction. In the 8
SN region, snowpack is deep and confined to the high terrain because of the sharp topographic 9
gradients, and the model overpredicted precipitation as well as snowpack. In deep snow, the 10
change in snow fractional coverage due to warming is small. Therefore, the change in ALB is 11
dominated by the change in SNOALB, and the percentage changes of both are smaller in SN and 12
the SSJ basin because the high terrain only accounts for a small fraction of the basin area defined 13
in this study. 14
15
Figure 16 16
17
5. Conclusion and discussion 18
The 2007 IPCC report listed the radiative forcing induced by “soot on snow” as a major 19
anthropogenic forcing affecting climate change between 1750 and 2005. However, it is still 20
uncertain how the soot-induced snow albedo perturbation affects regional snowpack and the 21
hydrological cycle in mountain areas. In this paper we simulate the deposition of soot aerosol on 22
snow using a coupled regional aerosol/chemistry model, WRF-Chem, to estimate the soot-23
33
induced snow albedo perturbation. This is then used to modify the maximum snow albedo for 1
sensitivity tests using WRF as a regional climate model. Investigation of the WRF regional 2
climate simulations focuses primarily on the impact of the soot-induced snow albedo 3
perturbation on snowpack and the hydrological cycle in the western United States. 4
5
The WRF-Chem simulated large spatial variability in BC concentrations that reflect the 6
localized emissions, as well as the influence of the complex terrain and the resulting 7
meteorological processes that affect chemical reactions and transport of pollutants. The 8
simulated BC concentrations are generally higher than the observed values derived from the 9
IMPROVE networks. The climate simulations have been compared against observations for 10
surface air temperature, precipitation, runoff, and snow water. The WRF-RCM model captured 11
the seasonal cycle and inter-annual variability of precipitation very well. The spatial pattern of 12
precipitation is also reasonably simulated during the cold season except for an overestimate of 13
the orographic precipitation along the Cascades and Sierra Nevada mountains. The surface air 14
temperature is spatially consistent between the observations and simulation and the bias is less 15
than 1oC over the CRB and SSJ basins. Through modification of the snow emissivity, surface 16
temperatures were substantially improved in the model, resulting in an accurate reproduction of 17
the phase of precipitation and the snow accumulation and melt processes at the surface. This 18
results in SWE being well simulated in WRF-RCM. The simulation reproduces many of the 19
features of the observed snowpack distribution. The runoff is also evaluated against observation 20
and the spatial distribution of the modeled runoff generally agrees with that of the GRDC runoff 21
in winter and spring. 22
23
34
The WRF-RCM simulations show that soot-induced snow albedo perturbations 1
significantly change the regional climate and water budget in western US. NSW, which is very 2
well spatially correlated with the change of surface albedo, increases 2-14 W m-2 from late 3
winter to early spring over the central Rockies and southern Alberta, which drives a surface skin 4
(air) temperature increase of 0.3 - 2.0oC (0.2 - 1.5oC) over the majority of the snow covered areas 5
in the western U.S. The SWE decreases 10-50 mm over the mountains during late winter to early 6
spring, which is reflected in reduced snow accumulation in winter and less snowmelt in spring. 7
The maximum change occurs in March for albedo, NSW, temperature, and snowpack. Our 8
simulations show that the precipitation difference is negligible between the perturbed and control 9
simulations. In a warmer perturbed climate more precipitation comes in the form of rain rather 10
than snow, which results in less snow accumulation but more runoff during the snowy winter 11
period. The runoff decreased in late spring, driven by the reduced snowmelt from the reduced 12
snowpack. Therefore this indirect forcing of soot has accelerated snowmelt and altered stream 13
flows, including a trend toward earlier melt dates in the western United States. 14
15
In summary, soot-induced albedo reduction causes the snow to absorb more solar 16
radiation, thus heating the snow/land surface and warming the surface air more than if there were 17
no soot. As a result of this warming, snow depth and fraction reduce during winter and spring, 18
which further reduce the albedo for areas fully or partially covered by snow. This is a positive 19
feedback process initialized by the soot deposition on snow. Our simulations indicate that over 20
the central Rockies only 60% of the albedo reduction is directly caused by the change in 21
maximum snow albedo induced by the soot in snow. Snowpack reduction accounts for the 22
remaining 40% of the surface albedo change. 23
35
1
The same feedback process in Figure 16 can also be initialized by increases in 2
greenhouse gases, which lead to skin/air warming and the subsequent changes in the feedback 3
loop. Hence, the suite of changes described in Section 5 are very similar to what have been 4
discussed by studies that investigated changes in mountain hydrology due to greenhouse 5
warming (e.g., Leung et al. [2004]). But while greenhouse forcings are more uniformly 6
distributed spatially (though the response in snowpack is still highly regional), soot forcing on 7
snow is more regional and depends on the co-existence of snowpack and soot. Therefore, larger 8
uncertainties may be expected in estimating both the forcing and response related to soot-9
induced snow albedo effects simply because of the inherent spatial scale of processes involved. 10
Additionally, other uncertainties are introduced by the methodology used in this study, which we 11
plan to address in future studies. 12
13
(a) WRF-Chem simulation and emission data. Because of the extreme computational cost, the 14
WRF-Chem simulation is run only for one year. The interannual variability of concentration and 15
deposition of soot therefore is not accounted for in this study. We used the EPA NEI99 emission 16
dataset in the WRF-Chem simulations and no adjustments were made to bring the 1999 values 17
closer to the 2003 values and no seasonal cycle was included. Also, the NEI99 estimates do not 18
include forest fire or biogenic emissions. However, most of the fires occur during summer and 19
fall, after the snow has melted, so the impact on snowpack may be not significant. 20
21
Since WRF-Chem is a regional model, assumptions must be made regarding trace gases 22
and particulates entering the model boundaries. Fixed profiles were used as inflow boundary 23
36
conditions, so the model does not reproduce the impact of trans-Pacific transport. The net effect 1
of these assumptions is that the deposition in these simulations should be considered a minimum 2
estimate. Yet, our simulated BC concentrations are noticeably higher than the observed values. 3
This suggests a need for more analysis and development to understand and reduce model biases. 4
The WRF-Chem simulation, however, provided representative magnitude and spatial distribution 5
of BC for investigating soot affects on snow in a mountainous region. 6
7
(b) Estimation for snow albedo perturbation. In the current Noah LSM, the snow albedo is 8
calculated based on snow fraction, which is parameterized according to simulated snow depth, 9
and the maximum snow albedo (SNOALB), which is based on the measured snow albedo over 10
deep snow. The SNOALB input has included spatial variability, albeit at a coarser resolution 11
than the model resolution, but not included temporal variation, which is related to other possible 12
effects of snow properties (e.g. grain size) on snow albedo and emissivity. Flanner and Zender 13
[2006] suggested several positive feedbacks amplifying the first-order warming effect of BC in 14
snow. In this study we estimated soot induced-snow albedo reduction based on very limited 15
measurements and an empirical relationship from Jacobson [2004]. We plan to incorporate an 16
interactive aerosol-snow surface radiative model [e.g. Flanner and Zender, 2006] in the land 17
surface model in WRF. Sensitivity experiments using the present model framework (e.g. by 18
halving or doubling the current snow albedo perturbation) could also provide a range for the 19
uncertainties of soot induced snow albedo effects. 20
21
(c) WRF-RCM simulations. The control and sensitivity WRF-RCM simulations are only run 22
for five years due to limited resources. It would be better to extend the simulations to better 23
37
represent the interannual variations of mountain hydroclimate and the impacts of soot. More 1
importantly, although the spatial pattern and seasonal cycle of precipitation are simulated 2
reasonably well by WRF-RCM during the cold season, an overestimation of the orographic 3
precipitation is also apparent along the Cascades and Sierra Nevada as well as CRB and SSJ 4
basins. These errors result in biases in simulating snowpack and runoff. The overpredictions of 5
both precipitation (hence snowpack) and BC concentrations have likely biased our results in the 6
same direction toward larger impacts of soot-induced snow effects. Lastly, WRF-Chem and 7
WRF-RCM are run separately (offline) so interactions between the concentration and deposition 8
of soot and climate that would modify the soot-induced snow albedo perturbation are not 9
addressed. 10
11
12
13
Acknowledgments. This research is supported by a Pacific Northwest National Laboratory 14
(PNNL) Laboratory Directed Research and Development (LDRD) project. PNNL is operated for 15
the U.S. DOE by Battelle Memorial Institute under Contract DE-AC06-76RLO1830. This 16
research used resources of the National Center for Computational Sciences at Oak Ridge 17
National Laboratory, which is supported by the Office of Science of the U.S. Department of 18
Energy under Contract No. DE-AC05-00OR22725. 19
20
38
1
2
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Figure Captions: 1 2
1. Model domain and subregions (a), elevation (b) and daily Black Carbon emission (c) for 3
particle diameter <10µm (unit: mg m-2 day-1). 4
2. Spatial distribution of WRF-Chem simulated and IMPROVE observed BC concentration 5
(particle diameter < 10µm) at near surface for (a) MAM, (b) JJA, (c) SON and (d) DJF. 6
3. Monthly averaged BC concentrations for the IMPROVE stations within the domain 7
along with the coincident WRF-Chem grid point averages. The minimum and maximum 8
value is given by the narrow bar and 25th percentile and 75th percentile is given by the 9
wider bar. 10
4. Accumulated BC deposition (dry + wet) for particles less than 10µm in diameter for the 11
period of (a) DJF and (b) MAM. Depositions units are µg m-2. Areas in WRF-Chem with 12
seasonal mean snow cover greater than 1 cm in depth are overlaid with white hatching. 13
5. DJF averaged (a) BC-SNOW mixing ratio (ng/g) and (b) corresponding snow albedo 14
perturbation. 15
6. The spatial distribution of (a) maximum snow albedo (SNOALB) used by the standard 16
WRF Noah Land Surface Model (LSM) and (b) mean albedo simulated for March in the 17
WRF-RCM control simulation. 18
7. Spatial distribution of simulated (left) and observed (right) precipitation in mm day-1 (top) 19
and surface air temperature in oC (bottom) for DJF. 20
8. Monthly mean time series of simulated and observed precipitation and surface air 21
temperature for CRB and SSJ basin. 22
9. Spatial distribution of simulated (a) and three observed (b:CMC; c:NOHRSC; d: 23
SNOTEL) snow water equivalent datasets (SWE, unit: mm) for DJF and simulated (e) 24
and CMC (f) SWE for MAM. 25
10. Spatial distribution of simulated (a) and observed (b) runoff (GRDC) for MAM, unit: mm 26
day-1. 27
11. The spatial distribution of change in March for (a) net shortwave radiation flux at the 28
surface (NSW, W m-2), (b) surface (2 meter) air temperature (oC), (c) skin temperature 29
(oC), and (d) surface albedo. 30
44
12. Changes in monthly mean Snow Water Equivalent (SWE, unit: 10mm), albedo (*0.1), 1
surface Net Shortwave radiation flux (NSW, *5 W m-2), 2-meter air temperature (oC) and 2
skin temperature (oC) averaged over the CRB and SSJ basin. 3
13. The spatial distribution of change in mean Snow Water Equivalent (SWE, mm) from 4
January to April. 5
14. The spatial distribution of change in runoff (mm/day) from February to May. 6
15. Changes in monthly mean surface water budget averaged over the CRB and SSJ basin. 7
Shown in the figure are changes in precipitation (P), snowpack accumulation rate (SP), 8
runoff (R), soil moisture accumulation rate (SM), and evapotranspiration (ET) in mm/day. 9
16. Diagram for feedbacks between albedo, solar radiation, temperature, and snowpack.10
45
1
Table 1. Comparison of measured and simulated soot mixing ratio in snow and corresponding snow albedo perturbation Measured, Cascade, Washington 22-59 ng/g (melting or wet snow) Grenfell et al., 1981. Measured, New Mexico, West Texas (32N, 106W)
4.9 – 15.9 ug/L Chylek et al., 1987 Measured, Hurricane Hill (48N, 123.5W)
10.1-18.5 (ng/g) Clarke and Noone, 1985 GCM Simulated, NH Land
20-60 ng/g (1.5-14%) Hansen and Nazarenko, 2004 GCM Simulated, western US
10-46 ng/g Flanner et al., 2007 GCM Simulated, New Mexico, West Texas, Washington
14-32 ng/g Jacobson, 2004. GCM Simulated, Global average
5 ng/g 0.4% (1.0%, NH) Jacobson, 2004. This study, Western US
10-120 ng/g (1 – 10%)
Table 2 Mean changes in March in maximum snow albedo (maxsnoalb), surface albedo (albedo), surface downward solar radiation flux (downsw, W/m2) and net solar radiation flux (nsw, W/m2), surface air temperature (t2, C) and skin temperature (ts, C), precipitation 9mm/day), evaporation (mm/day), snow water equivalent (swe, mm), soil water content (mm), and runoff (mm/day) averaged for CRB and SSJ basins as well as CR and SN regions (see Figure 6). Change in percentage is in bracket. CRB SSJ CR SN