Examining the relationship between albedo and cold air pool strength during the Persistent Cold Air Pool Study Christopher S. Foster John Horel Dave BowlingSebastian Hoch

1. Introduction The Persistent Cold Air Pool Study (PCAPS “addressed the need for modern observations capable of resolving the hierarchy of scales affecting persistent cold air pools (CAPs).” The two month long (1 December 2010 to 7 Feb 2011) field campaign in the Salt Lake Valley (SLV) provides a uniquely diverse data set ripe for analysis. One area of ongoing research is the relationship between land surface, albedo, and CAP strength/longevity. Table 1. An overview of the 9 intensive observation periods (IOP) that took place during PCAPS. A CAP is assumed to exist during each.

1. Introduction Cont. The increased albedo of fresh snow cover causes more solar radiation to be reflected by the surface, rather than absorbed/reemitted, and thus air near the surface cannot warm as much as it would otherwise. The presence of snow also enhances the nocturnal transfer of heat away from the surface. These are major reasons why persistent (multiday) cold air pools can exist. Figure 1. Time-height diagram of potential temperature from 1 December 2010 to 31 January Cool colors represent lower potential temperatures relative to warm.

2. Prior Research Neemann et al. (2015) found that snow cover increases stability by increasing the surface albedo, which reduces absorbed solar insolation and lowers near-surface temperatures. WRF numerical simulations showed that a lack of snow in the Uinta Basin increased average CAP temperatures by as much as 8 o C. Figure 2. Vertical profiles of potential temperature at Roosevelt, UT for 1:00 MST 4 February 2013 (left) and 1 1:00 MST 5 February 2013 (right). The dashed line is observations, the green line is a WRF run with snow in the Uinta Basin with cloud ice sedimentation on, the blue is a run with snow but sedimentation off, and the red line has no snow or sedimentation.

3. Data 7 Integrated Surface Observing Systems (ISFS) with observations every 5 minutes. 2 Integrated Sounding Systems (ISS) were used in conjunction with soundings launched during IOPs to create near surface time-height diagrams (Lareau and Horel 2015) of relevant variables. Potential temperature and pressure from these integrated time heights were used to calculate the valley heat deficit (Q). Figure 3. Terrain (m) map of the Salt Lake Valley with ISFS and ISS station locations overlaid.

4. Methodology In order to determine if a connection between albedo and cold air pool strength (valley heat deficit) existed in the Salt Lake Valley during PCAPs, both have to be calculated using the following equations: Once calculated, the temporal resolution of albedo (λ) had to be reduced in order to compare it to valley heat deficit (Q). This was done by averaging the two observations on either side of the hour (resulting in hourly data). Then a variety of statistical approaches were used to compare these variables, including exploratory data analysis, regression, and EOF analysis.

4. Methodology Cont. λ and Q only considered between 17 Z and 22 Z when being compared. Incoming and net solar radiation depend on the time of day and corresponding sun angle. Only when incoming solar radiation is above ~50 W/m 2 is the data considered valid. Any NaN’s were replaced by zeros and not considered in statistical calculations. NaN’s resulted from division by 0 (when incoming solar equaled 0, such as at night or during periods with cloud cover. Figure 4. Time series of raw albedo data.

5. Results Melting period characterized by gradually decreasing λ. Snow in benches (ISFS 5), no snow on valley floor (ISFS 2). IOP 5, characterized by consistent λ and elevated Q. Figure 5. Panel 1: Hourly albedo (λ) plotted as a function of time between 17 Z and 22 Z for entire PCAPs period. Panel 2: Hourly valley heat deficit plotted as a function of time for entire PCAPs period.  IOP 5   Inconsistent  Snowpack  Melting  Period

5. Bulk Statistics Table 2. Every column pertains to averages calculated during the stated IOP, besides the first column, which is the average of every non-IOP observation. Every row pertains to a different variable averaged over the time period defined by the rows, where Q is the valley heat deficit in J/m 2, ISFS1 Alb is the albedo at ISFS station 1, etc, and Mean Alb is the average of the albedo at all stations (bottom row) and average albedo at each station (right-most column). Strongest IOPs (Q) tend to occur when albedo is high and consistent throughout valley (IOP 5) and (IOP 1). Lowest average albedo observed at urban site (ISFS 2). Highest average albedo observed on east slope (ISFS 5). Playa (ISFS 1) tends to be highest during strong IOPs. NON-IOPIOP 01IOP 02IOP 03IOP 04IOP 05IOP 06IOP 07IOP 08IOP 09Mean Alb Q ISFS1 Alb ISFS2 Alb ISFS3 Alb ISFS4 Alb ISFS5 Alb ISFS6 Alb ISFS7 Alb Mean Alb

5. Correlation Plots Figure 6. Scatter plot of daily valley-wide averaged albedo versus daily average valley heat deficit. Figure 7. Scatter plot of IOP valley-wide averaged albedo versus valley heat deficit. Some of the highest values of Q are observed at the highest albedos. Scatter around 0.2 could be IOPs that occurred with an inconsistent snow pack in the valley. Strong IOPS (1 and 5) had high albedos.

5. Covariance Figure 8. Scatter matrix of hourly albedo for entire PCAPs period. Labels along the top represent the x-axis data of the plots under them. Labels along the left represent the y- axis data of the plots to the right of them. Histograms along the diagonal show binned albedos from the stations shown in the upper labels.

5. Covariance Cont. Figure 9. Correlation matrix of mean daily albedo at all 7 ISFS stations plotted as a function of the two stations being compared. Table 3. NCAR integrated surface flux system (ISFS) station metadata overview. Stations with similar characteristics tend to have higher correlation coefficients than those that do not. ISFS 2 vs 5 is low (~0.4) ISFS 2 vs 4 is high (~0.75) All correlations are positive, which will allow for a more confident interpretation of the first principle component of the following empirical orthogonal function analysis.

5. EOF Analysis and Overview Figure 10. Loading of the first EOF of detrended, standardized albedo at the 7 ISFS stations. Figure 11. Bar chart of correlation coefficients comparing the albedo measurements as outlined on the horizontal axis to valley heat deficit (Q). ISFS 1 through 7 correspond to the correlation of Q to hourly daily mean albedo at the stations, daily mean corresponds to the daily valley-wide mean of all 7 stations, and PC 1 corresponds to the first principle component. Loading of first PC shows clear differences between ISFS station locations. PC 1, 65% of variance.

5. Results Cont. Figures 6 and 7 show the variability in snowpack during the PCAPs period and each IOP. Figure 8 shows that clear connections exist between the albedo observed at different locations through the SLV. –The second row of scatter plots show that ISFS 2 (urban) tends to exist at lower albedos than the rest in the SLV until a consistent snowpack is present. Figure 10 suggests that albedo throughout the SLV varies in unison (increase/decrease at the same time) since all loading is positive and that some stations, (ISFS 2, 3, and 5), do not respond as much as the others. Figue 11 shows that overall, no significant connection exists between cold air pool strength and albedo (all correlations are less than 0.5).

6. Summary Strongest cold air pools occurred when highest albedos were observed. The sample size is not large enough to draw statistically significant conclusions about the relationship. Siting of solar radiation sensors makes its difficult to discern difference between locations. Considering the limitations of measuring solar radiation at a single location (it is not necessarily representative of its surroundings), the differences that appear in the EOF analysis are most likely a result of each station’s general location and the snowfall it received. Models do tend to respond significantly to the existence of a snowpack and varying land use type, which supports the findings related to albedo variations during PCAPs.

7. References Lareau, N. P., E. Crossman, C. D. Whiteman, J. Horel, S. Hoch, W. Brown, and T. Horst, 2013: The Persistent Cold-Air Pool Study. Bull. Amer. Meteor. Soc., 94, Lareau, N. P. and J. Horel, 2014: Dynamically Induced Displacements of a Persistent Cold-Air Pool. Boundary-Layer Meteorology, 154, Neemann, E. M., E. Crosman, J. Horel, and L. Avey,2014: Simulations of a cold-air pool associated with elevated wintertime ozone in the Uintah Basin, Utah. ACPD, 14, Silcox, G. D., K. Kelly, E. Crosman, C. D. Whiteman, B. Allen, 2012: Wintertime PM2.5 concentrations during persistent, multi-day cold-air pools in a mountain valley. Atmospheric Environment, 46, Whiteman, C. D., X. Bian, S. Zhong, 1999: Wintertime Evolution of the Temperature inversion in the Colorado Plateau Basin. J. Appl. Meteor., 38,