The Simulated Response of Diurnal Mountain Winds to Regionally Enhanced Warming Caused by the Snow Albedo Feedback

Theodore W. Letcher University at Albany, State University of New York, Albany, New York

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Justin R. Minder University at Albany, State University of New York, Albany, New York

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Abstract

The snow albedo feedback (SAF) is an important climate feature of mountain regions with transient snow cover. In these regions, where patterns of snow cover are largely determined by the underlying terrain, the SAF is highly variable in space and time. Under climate warming, these variations may affect the development of diurnal mountain winds either by altering the thermal contrast between high and low elevations or by increasing boundary layer mixing. In this study, high-resolution regional climate modeling experiments are used to investigate and characterize how the SAF modulates changes in diurnal wind systems in the Rocky Mountains of Colorado and Utah during the spring when SAF strength is at a maximum. Two separate 7-yr pseudo–global warming climate change experiments with differing model configurations are examined. An evaluation of the control simulations against a mesoscale network of observations reveals that the models perform reasonably well at simulating diurnal mountain winds within this region. In the experiment with a strong SAF, there is a clear increase in the strength of daytime upslope flow under climate warming, which leads to increased convergence and cloudiness near the snow margin. Additionally, there is a decrease in the strength of nighttime downslope flows. In the simulation with a weaker SAF, the results are generally similar but less pronounced. In both experiments, an altered thermal contrast, rather than increased boundary layer mixing, appears to be the primary mechanism driving changes in diurnal mountain wind systems in this region.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author address: Theodore Letcher, Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, 1400 Washington Ave., Albany, NY 12222. E-mail: tletcher@albany.edu

Abstract

The snow albedo feedback (SAF) is an important climate feature of mountain regions with transient snow cover. In these regions, where patterns of snow cover are largely determined by the underlying terrain, the SAF is highly variable in space and time. Under climate warming, these variations may affect the development of diurnal mountain winds either by altering the thermal contrast between high and low elevations or by increasing boundary layer mixing. In this study, high-resolution regional climate modeling experiments are used to investigate and characterize how the SAF modulates changes in diurnal wind systems in the Rocky Mountains of Colorado and Utah during the spring when SAF strength is at a maximum. Two separate 7-yr pseudo–global warming climate change experiments with differing model configurations are examined. An evaluation of the control simulations against a mesoscale network of observations reveals that the models perform reasonably well at simulating diurnal mountain winds within this region. In the experiment with a strong SAF, there is a clear increase in the strength of daytime upslope flow under climate warming, which leads to increased convergence and cloudiness near the snow margin. Additionally, there is a decrease in the strength of nighttime downslope flows. In the simulation with a weaker SAF, the results are generally similar but less pronounced. In both experiments, an altered thermal contrast, rather than increased boundary layer mixing, appears to be the primary mechanism driving changes in diurnal mountain wind systems in this region.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author address: Theodore Letcher, Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, 1400 Washington Ave., Albany, NY 12222. E-mail: tletcher@albany.edu

1. Introduction

Thermally generated diurnal circulations that develop along gradients in topography are a key aspect of the local climate in mountain regions worldwide. These diurnal mountain wind systems play an important role in determining climatological temperature and precipitation patterns (e.g., Barker Schaaf et al. 1988; Mahrt 2006), pollutant lofting and transport (e.g., Lu and Turco 1994; Wolfe et al. 2001; Reddy and Pfister 2016), and wildfire management strategies (e.g., Millán et al. 1998). They are often characterized as twice-daily reversals in wind direction that occur in response to differential heating of elevated terrain relative to the free atmosphere at the same altitude (Zardi and Whiteman 2013). Winds blow upslope during the day when the sun heats the mountain slope such that it is warmer than the surrounding atmosphere. Conversely, winds blow downslope overnight as this thermal contrast reverses as a result of surface cooling. These wind regimes exist across a broad spectrum of spatial scales ranging from the meso-γ scale (2–20 km) to the meso-α scale (200–2000 km) and are most prevalently observed during the warm season under synoptically weak conditions: that is, clear days with weak synoptic flow (e.g., Zardi and Whiteman 2013).

Little attention has been paid to how these local circulations may change in response to climate change. One pathway through which a warmed climate may modify these circulations is via the snow albedo feedback (SAF). In a warming climate, the SAF locally enhances the warming in regions where snow cover is diminished owing to a local decrease in the surface albedo (e.g., Randall et al. 1994). In mountainous regions, where snow cover patterns are determined largely by the underlying topography, the SAF generates large mesoscale variability in the temperature response to a large-scale climate forcing (e.g., Salathé et al. 2008; Letcher and Minder 2015; Rupp et al. 2016). This mesoscale enhancement of warming may modify the diurnal mountain winds in two main ways: 1) by differentially warming high and low elevations, thereby modulating the thermal contrast between the mountain slope and the adjacent free troposphere, which generates and maintains diurnal mountain wind systems, and 2) by increasing the downward mixing of momentum to the surface via a deeper convective boundary layer over regions of enhanced warming (e.g., Banta and Cotton 1981; Neemann et al. 2015). Alternatively, a deeper convective boundary layer may facilitate stronger mountain breeze circulations by reducing the stability of the lower atmosphere (e.g., Kirshbaum 2013; Wang and Kirshbaum 2015).

Previous work indicates that snow cover variations can drive diurnal mesoscale circulations in the absence of significant terrain (e.g., Cramer 1988; Johnson et al. 1984; Taylor et al. 1998). These “snow breeze” circulations form in response to a thermal contrast between the cold snow and the adjacent warmer bare ground. In mountain regions, where springtime snow cover is largely determined by elevation, snow-breeze circulations and diurnal mountain winds can interact. Segal et al. (1991) used idealized modeling experiments and showed that the presence of snow cover at high elevations counteracts the diurnal upslope flow, forcing convergence and rising motion near the snow edge. A more recent study by Mott et al. (2015) showed, using three-dimensional large-eddy simulations, that the evolution of diurnal mountain winds was highly influenced by the fractional amount of snow cover on the mountain. In particular, they showed that downslope winds can persist throughout the day over snow-covered slopes and that a snow cover fraction of 65% was sufficient to fully suppress diurnal upslope winds under quiescent synoptic flow.

Here we focus on the mountain regions of the western United States where diurnal mountain winds are intrinsic to the warm season climatology (Bossert and Cotton 1994; Stewart et al. 2002; Brewer and Mass 2014). On local scales (10–50 km), the confluence of various mountain breezes help determine focal points for boundary layer cloud formation and, if the atmosphere is conditionally unstable, convective initiation (e.g., Barker Schaaf et al. 1988; Weckwerth et al. 2014).

The SAF is also expected to play a major role in modulating the future climate of the western United States (e.g., Fyfe and Flato 1999; Salathé et al. 2008; Rangwala et al. 2012; Letcher and Minder 2015). The SAF is particularly active during the spring ablation season when snow cover is most sensitive to warming (Salathé et al. 2008; Letcher and Minder 2015).

In this study, we investigate the interaction between the springtime SAF and diurnal mountain winds over a subregion of the Rocky Mountains (shown in Figs. 1a,b) using output from high-resolution regional climate model (RCM) simulations. Specifically, we seek to interpret how the SAF modulates the strength and character of these wind systems under climate change. In section 2, the model configuration and experimental design are presented. In section 3, we evaluate the model against a mesoscale network of surface observations. In section 4, we show how the SAF modulates changes in diurnal mountain winds and discuss the dynamics of these interactions. In section 5, we discuss the implications of these findings and potential avenues for future research.

Fig. 1.
Fig. 1.

Study region. (a) WRF regional topography (km) and mesonet stations included in the analysis: Price Carbon County Airport (KPUC), Yellowstone Drainage (YLSU1), Five Mile (FIVU1), Bryce Canyon (KBEC), Grand Junction (KGJT), Meeker Airport (KEEF), Cortez-Montezuma Airport (KCEZ), Nucla (NUCC2), Porcupine Creek (PCP2), Taylor Park (TAPC2), Gunnison/Crested Butte (KGUC), Telluride Airport (KTEX), Fort Collins/Loveland (KFNL), Limon (KLIC), Pueblo (KPUB), and La Junta (KLHX). Stars and text indicate stations used for wind direction comparisons. (b) Light-shaded region with black outline shows the large domain; the red outline indicates the Uintah region. (c) WRF land surface types merged into five broadly representative categories. (d) Detailed geography of the Uintah region. Topography is shaded; color scale is the same as in (a). Contours added to select elevations.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

2. Data and methods

a. Model description

We analyze output from two separate RCM experiments conducted using the Weather Research and Forecasting (WRF) Model (Skamarock et al. 2008). One experiment was run over a relatively small domain centered over the Colorado Rockies. We refer to this simulation as “Headwaters” (Rasmussen et al. 2014). The second experiment was run over a much larger domain and encompasses the entire continental United States. We refer to this simulation as “CONUS” (Liu et al. 2016). In both experiments, we limit our analysis to the regions outlined in Fig. 1b. Both the Headwaters and CONUS simulations spanned the time frame from October 2000 to October 2008. Both experiments were run with a convection-permitting horizontal grid spacing of 4 km. At this resolution, many of the regional-scale diurnal circulations and mesoscale features of interest should be well resolved. Though of equal horizontal resolution, the Headwaters and the CONUS simulations have important differences as summarized in Table 1. These differences, including differences in physics parameterizations and computational domain, preclude a controlled isolation of the cause of differences in results between the two simulations; however, such a comparison is not the main focus of this study. Rather, the inclusion of both experiments provides a limited test of the robustness of our results and broadly elucidates how the interaction between the SAF and diurnal wind systems may vary with model set up and climate forcing.

Table 1.

WRF Headwaters vs CONUS setup.

Table 1.

For both Headwaters and CONUS, a pseudo–global warming (PGW; e.g., Schär et al. 1996; Rasmussen et al. 2011) experiment was used to investigate the regional response to a large-scale climate forcing. In the PGW experiment, two simulations are compared: a control simulation, forced with reanalysis lateral boundary conditions, and a PGW simulation, forced by the same reanalysis boundary conditions with an added idealized large-scale climate perturbation. The advantage of this approach is that it provides a thermodynamic climate forcing while generally preserving large-scale circulation patterns, essentially separating the effects of large-scale thermodynamic change from effects related to changes in midlatitude circulation.

Different climate perturbations were used for the Headwaters and the CONUS experiments. The climate perturbation applied to the Headwaters simulation was the CCSM3 ensemble 10-yr mean difference between 2045 and 2055 and 1995 and 2005, forced with the Special Report on Emissions Scenarios (SRES) A2 emissions scenario (Nakicenovic and Swart 2000). The climate perturbation applied to the CONUS simulation was the CMIP5 ensemble 30-yr mean difference between 2070 and 2100 and 1975 and 2005, forced with the RCP8.5 climate scenario (Riahi et al. 2011). The mean end-of-century RCP warming applied to the boundaries of the CONUS simulation is approximately 2.7 K warmer than the midcentury SRES warming applied to the Headwaters simulation; therefore, it is expected that the overall climate warming in CONUS will be greater. In both cases, spatially variable, monthly mean perturbations of temperature, relative humidity, geopotential height, and winds were linearly interpolated in time to match the reanalysis forcing and added to the boundary conditions. Additionally, the surface skin and soil temperatures are initialized with a temperature perturbation equal to the temperature difference at the lowest model level. In our analysis, the first year of output is discarded to allow the land surface model (LSM) to adjust to the perturbed climate. We utilize the full 3D model output from the Headwaters simulations; however, we are limited to two-dimensional surface output from the CONUS simulations owing to data storage constraints.

Another important difference between the Headwaters and the CONUS simulations is that Headwaters simulations were coupled to the Noah LSM, whereas the CONUS simulations were coupled to the more sophisticated Noah LSM with multiparameterization options (Noah-MP). Key advances in Noah-MP most relevant to this study include a multilayer snow model, a more sophisticated treatment for fractional snow cover, canopy precipitation interception, and subgrid partitioning of the surface energy budget into ground and canopy components. These advances substantially improve the representation of snow cover, albedo, and temperature in regions of complex terrain over the standard Noah LSM (Wrzesien et al. 2015; Chen et al. 2014; Minder et al. 2016).

The analysis is limited to days classified as “synoptically weak” during April and May in order to focus on the interaction between the SAF and diurnal wind systems, as overlap between these two phenomena is most likely under these conditions. To isolate synoptically weak days from the dataset, a slightly modified criteria from Stewart et al. (2002) is used: days are excluded from the analysis if the WRF-simulated daily domain-average downwelling shortwave (SW) radiation at the surface is less than 80% of the clear-sky surface SW radiation or if the daily domain average wind speed at 650 hPa is greater than 7.5 m s−1. This shrinks the dataset to 75 days in April and 113 days in May, which amounts to 45% of the days within the full 7-yr dataset. Adjusting both the wind and solar filter criteria 15% in either direction does not substantially affect the results.

After filtering the dataset to only retain synoptically weak days, monthly mean diurnal cycles are calculated for the control and PGW simulations. Bulk statistical analysis is performed over the large domain outlined in black in Fig. 1b. This region includes all of the significant mountain ranges of Colorado and Utah. Here vegetation classification is broadly elevation dependent, with open grass and shrub lands at low elevations, forest canopy on the intermediate mountain slopes between 2.5 and 3.25 km, and grasslands above the tree line on the higher mountain peaks (Fig. 1c). A more focused analysis is performed over the Uintah Mountains to gain a more detailed understanding of the dynamical mechanisms linking the SAF to diurnal circulations (red outline in Fig. 1b). The detailed geography of this region is shown in Fig. 1d. We chose this region because of the relatively simple terrain configuration, which allows for a cleaner investigation of the mesoscale dynamics affected by the SAF.

b. Observational evaluation

A substantial amount of work has been performed to validate the Headwaters and CONUS simulations against precipitation and snow observations (Rasmussen et al. 2014; Minder et al. 2016; Liu et al. 2016). In general, the Headwaters simulation performs well at simulating snow water equivalent (SWE) during the fall and midwinter seasons; however, simulated SWE is too low during the spring (Barlage et al. 2010; Rasmussen et al. 2014). The CONUS simulation has an SWE performance comparable to Headwaters (Rasmussen et al. 2015). Figure 2 compares April and May fractional snow cover between the Headwaters and CONUS control simulations. While both simulations show similar spatial patterns of snow cover, the Headwaters simulation is clearly snowier than CONUS with both a greater snow extent and greater snow cover fraction. Minder et al. (2016) compared these two simulations against MODIS satellite data and concluded that the Headwaters simulation performs better than CONUS in representing the areal extent of snow cover during the spring; however, it overpredicts the fractional snow cover of snowy grid cells by up to 50%. When subject to PGW forcing, these differences in snow cover between the Headwaters and CONUS control simulations lead to a stronger SAF and more locally enhanced warming in Headwaters relative to CONUS (Minder et al. 2016). Presumably, these differences also affect the SAF’s subsequent impacts on diurnal wind systems.

Fig. 2.
Fig. 2.

The 7-yr April and May mean control for the Headwaters and CONUS simulations. Black contour shows 3000-m elevation.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

Of additional relevance to this study is the models’ ability to properly simulate the diurnal variations in surface temperature and winds. To assess model performance in this regard, hourly model output is compared to hourly meteorological observations from the Mesowest network (Horel et al. 2002). In this comparison, only sites from the NOAA Automated Surface Observing System (ASOS) and the Remote Automatic Weather Station (RAWS) networks that span the 7-yr time frame of the model simulations are included (sites shown in Fig. 1a). While ASOS stations are highly standardized and quality controlled, they are sparsely distributed throughout the study domain. ASOS records temperature and humidity at a height of 2 m and wind at a site-dependent height between approximately 8.5 and 10.0 m. RAWS are less standardized than ASOS, but provide additional observational coverage, particularly in the remote mountains. RAWS records temperature and humidity at a site-dependent height between 1.5 and 2.5 m and wind at 6.0 m. These differences in measurement height between the two networks are one likely source of bias. In addition to differences in measurement height, there are substantial siting differences between the two networks that may also be a source of bias, as influences of proximity terrain and vegetation on surface weather conditions will vary between sites. For example, ASOS sites are typically located at an airport whereas RAWS are often located in more remote locations such as small forest clearings.

A simple quality control mechanism is applied to remove clearly erroneous observations from the dataset. If a measured temperature is outside the range of ±50°C or if the humidity is reported as >100%, then all of the data for the hourly observation are removed. In total, this quality control check removes less than 1% of the data. The model is evaluated against the observations by simply comparing the model grid point nearest to the station. While this method may not be the most robust way to validate the model against observations (e.g., Jiménez et al. 2010), it is sufficient for the broad comparison here.

3. Model evaluation

Figure 3 shows the diurnal cycle for 2-m temperature , 2-m dewpoint temperature , and 10-m wind speed for the models and the observations averaged over all stations within the large domain for synoptically weak days. The timing of the diurnal cycle of is well represented. However, the Headwaters simulation has a daytime cold bias of about 1.5 K. This bias is generally confined to high elevations (Figs. 4a,b) and causes an underestimate in the amplitude of the diurnal cycle of of about 2.2 K in the Headwaters simulation (Fig. 4c). This result is consistent with the results from Chen et al. (2014), who found that, when coupled to Noah, WRF-modeled was often too cold over snow-covered regions owing to an overestimate in surface albedo and a poor forest canopy representation. In CONUS, the diurnal cycle of is underestimated by approximately 1.5 K. In contrast to Headwaters, this underestimate is due to a moderate warm bias in the daily minimum 2-m temperature , which appears to be uniformly distributed throughout the domain (Figs. 4e,f). In both models, daily mean compares very well to the observations (Fig. 3). The timing of the diurnal cycle of appears to be more poorly represented; however, the significance of this difference is questionable, since the diurnal cycle is weak in observations and small compared to station-to-station and day-to-day variability.

Fig. 3.
Fig. 3.

Values of (top) , (middle) , and (bottom) averaged over the large domain for April–May synoptically weak days. Time is reported in MST. The green fill indicates the spatial and temporal variability of the observed data (10th and 90th percentiles).

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

Fig. 4.
Fig. 4.

Average bias between the Headwaters and CONUS control simulation and the mesonet data for April–May synoptically weak days. Results are shown for (a),(d) daily max 2-m temperature ; (b),(e) ; and (c),(f) diurnal amplitude .

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

While both simulations are able to broadly reproduce the amplitude and timing of the diurnal cycle of , the simulated winds are stronger than the observed winds by, on average, 0.9 and 0.4 m s−1 in Headwaters and CONUS, respectively. It is unclear if this wind speed bias is associated with a deficiency in the model or a nonideal observational comparison. For example, the WRF 10-m wind diagnostic is compared directly with the station winds, which, as mentioned above, have variable measurement heights. This mismatch could lead to a substantial apparent bias, especially at RAWS where winds are measured at 6.0 m. Additional network-dependent apparent bias could originate from differences in exposure associated with dissimilar siting preferences between the two networks. An observational comparison that includes only the ASOS sites nearly eliminates the wind speed bias (not shown); however, it cannot be concluded from this comparison alone that observational discrepancies between RAWS and ASOS are the source of the bias. However, because the magnitude of the wind speed is of secondary importance to the representation of the diurnal cycle, we feel that a more advanced comparison is beyond the scope of this paper.

Maps comparing the modeled versus observed average diurnal amplitude (defined as daily max − min) of and are shown in Fig. 5. The diurnal amplitude of is largest over low-elevation valleys and plains without forest canopy and smallest at high elevations with snow cover. The relatively low diurnal variability of at high elevations is likely due to both the limiting influence of snow cover on the daytime high temperature and differences in surface energy exchange between the middle-elevation forests and low-elevation grasslands. For instance, evapotranspiration is likely greater over forested areas than over grasslands, which reduces the diurnal cycle of . Additionally, the heat capacity of a forest canopy is higher than that of a grassland, so the canopy may act as a buffer to energy exchange between the surface and the atmosphere, damping the diurnal cycle of in the forest. In both the Headwaters and CONUS simulations, the diurnal amplitude of generally matches the terrain with the largest amplitude found over unforested valleys and plains.

Fig. 5.
Fig. 5.

Amplitude of the diurnal cycle for April–May of (top) temperature and (bottom) wind on synoptically weak days. Markers indicate the observed diurnal cycle. ASOS sites are indicated as circles; RAWS sites are shown as squares.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

In general, these spatial structures are similar between Headwaters and CONUS, and in both models these structures are significantly correlated with the observations (coefficient of determination = 0.58 for Headwaters and = 0.61 for CONUS). For example, the observations show similar elevation-dependent structures in diurnal amplitudes of both temperature and wind speed. However, there are important differences between the two simulations. The amplitude of the diurnal cycle is stronger at low elevations (e.g., the Great Plains) and weaker over the mountains in Headwaters than it is in CONUS. However, the diurnal cycle of is substantially weaker in CONUS over forested grid cells. This diminished amplitude better matches from the observations and is likely due to the better canopy representation in CONUS.

To evaluate model performance with respect to wind direction, the diurnal cycle of wind direction is shown for 16 sites (indicated in Fig. 1a) spanning the domain from west to east (Fig. 6). Both the Headwaters and the CONUS simulations perform remarkably well in reproducing the diurnal reversals in wind direction at nearly all sites. In the Uintah basin and along the southeastern Wasatch Mountains (KPUC, KBEC, FIVU1, and YLSU1) both models simulate the observed abrupt shift from downslope to upslope flow beginning at approximately 0600 mountain standard time (MST). The flow then slowly transitions to westerly throughout the course of the day, likely reflecting downward mixing of westerly winds aloft. While the wind direction at YLSU1 is not well represented by the models, its modeled diurnal cycle is still indicative of a downslope to upslope transition, similar to that of the observed. On the western slope of the Colorado Rockies (KEEO, KGTJ, KCEZ, and NUCC2) there is a clear transition of downslope (easterly) flow overnight to upslope (westerly) flow during the day. The sites on the Great Plains east of the Rockies (KFNL, KLIC, KLHX, and KPUB) are characterized by a similar, but more muted, upslope–downslope pattern. The diurnal cycles of the sites located in the mountains of central Colorado (PCP2, TAPC2, KTEX, and KGUC) are more varied but still well represented by the models (except PCP2), indicating that smaller-scale diurnal circulations are also well resolved in addition to the broader mountain–plain-type circulations on the flanks of the larger mountain chains. Interestingly, the Headwaters and CONUS simulations generally agree very well despite more substantial differences in both surface temperature and wind speed.

Fig. 6.
Fig. 6.

Diurnal cycle of meteorological wind direction for April–May synoptically weak days. (from left to right) Sites are grouped geographically from west to east. The network that each site belongs to (RAWS or ASOS) is denoted in parentheses.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

These findings are largely similar to those from Brewer and Mass (2014), who performed an analogous evaluation and analysis of diurnal mountain winds for the Olympic and Cascade Mountains in the northwest United States. In their study, as in ours, it was found that WRF generally captures the observed regional-scale diurnal variations in temperature and wind. Furthermore, the spatial structures in the diurnal variability of and found in each study are broadly similar, including the relatively large (small) diurnal variability of at low (high) elevations.

Overall, both Headwaters and CONUS simulations adequately represent the diurnal cycle of temperature and wind under weak synoptic forcing. In particular, they are both able to capture the daily wind direction reversals associated with diurnal wind systems. We believe this favorable performance justifies using these models to investigate how these wind regimes are modulated by the SAF under climate change.

4. Diurnal mountain wind response to climate change

a. Mesoscale structure of diurnal mountain winds in the control simulations

For this analysis, we focus on the Uintah region (Fig. 1d). Figure 7 shows the 7-yr April and May composite diurnal cycle of modeled 10-m wind vectors for synoptically weak days over the Uintah region from the Headwaters control simulation as well as monthly mean snow cover fraction. The 10-m winds from CONUS only show minor qualitative differences from Headwaters and are not shown.

Fig. 7.
Fig. 7.

The diurnal cycle of synoptically weak mean 10-m wind vectors from the Headwaters simulation for the Uintah region in (top) April and (bottom) May. (right) Monthly mean fsn from the control simulation. Values of are masked where < 2.0 m s−1. (top left) Note the scale vector.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

During April, the mean diurnal wind system for the Uintah region progresses as follows: overnight (2100–0300 MST), nearly every mountain slope exhibits weak to moderate downslope flow, with little or no wind present in the larger basins. At 0900 MST, the winds are at their weakest, as they undergo a transition from nocturnal downslope flow to weak upslope flow in response to the solar heating. At 1500 MST, the surface winds are generally weak and from the west, likely reflecting the downward mixing of westerly synoptic flow to the surface. Downslope flow persists on the western and northern Uintah Mountains that is not aligned with the synoptic-scale westerly flow and likely reflects a snow-breeze circulation caused by substantial mountain snow cover.

During May, the nocturnal phase (2100–0300 MST) of the diurnal wind cycle is very similar to that of April, with broad downslope flow across the entire region. There are more substantial differences during the day. At 0900 MST, there is weak upslope flow on the southern Uintah and the Tavaput Mountains that was largely absent in April, reflecting thermal forcing more favorable to upslope flow. At 1500 MST, the winds are nearly uniform throughout the domain from the west indicating that downward momentum mixing dominates over thermally forced upslope flow at this time. Additionally, the downslope flow that prevailed on the western Uintah Mountains in April is no longer present, as the snow-breeze forcing is diminished. In both months, the general lack of coherent and persistent daytime upslope flow during the afternoon may be due to the focus on the springtime, rather than the summertime when insolation is greater and the surface albedo is lower.

b. Sensitivity to climate change

To summarize regional differences in SAF-enhanced warming and snow loss between Headwaters and CONUS, maps of 7-yr April and May average change in 2-m temperature and surface albedo change (PGW − control) are plotted for the Headwaters and CONUS experiments (Fig. 8). There are several important distinctions between the two experiments. While both simulations show patterns of enhanced warming that are significantly correlated (p < 0.05) with patterns of ( = 0.78 for Headwaters and = 0.47 for CONUS) indicating a strong influence of the SAF, the stronger overall warming in CONUS is due to larger external forcing, which overshadows some of the SAF-enhanced warming (note the different color scales used in Fig. 8). Differences in are less prominent; however, is larger and more widespread at middle and lower elevations in Headwaters, particularly during April. The relatively modest albedo decrease in CONUS in April is largely due to more limited snow extent and subpixel fractional snow cover in the control simulation (e.g., Fig. 2). This limits the potential reduction of snow cover and albedo for a given amount of warming and, thus, reduces the regional strength of the SAF (Minder et al. 2016).

Fig. 8.
Fig. 8.

April and May 7-yr mean and for the Headwaters and CONUS simulations. Black contour shows 3000-m elevation. Note the different color scales used for Headwaters and CONUS .

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

Changes in diurnal wind systems are characterized by plotting the change (PGW − control) of the 10-m wind vectors overlain on maps of surface warming for April and May. Figures 9 and 10 show synoptically weak composite plotted throughout the diurnal cycle for the Uintah subregion from the Headwaters and CONUS experiments, respectively. Here, the spatial patterns and diurnal variability of are largely controlled by the SAF in both the Headwaters and CONUS simulations.

Fig. 9.
Fig. 9.

The diurnal cycle of mean (shading) and (vectors) (PGW − control) from the Headwaters simulation for the Uintah region. Black contours indicate terrain. Plots show synoptically weak composite monthly means for (top) April and (bottom) May. The is masked where < 0.3 m s−1. (left) Note the scale vectors.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

Fig. 10.
Fig. 10.

As in Fig. 9, but for the CONUS simulation. Note the different color scale for .

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

In the Headwaters simulation, warming follows the snow margin as it retreats from low to middle elevations in April and toward high elevations in May (Fig. 9). However, the highest elevations (>3 km) do not experience strong warming as this elevation range is still snow covered in May under the perturbed climate. In general, spatial patterns of are well correlated with gradients in surface warming, indicating a wind response to spatially heterogeneous warming generated by the SAF. The magnitude of this response is generally in the range of 0.5–2 m s−1. During April, this thermal forcing leads to a mixed response where both upslope and downslope anomalies occur at different locations. For example, during the day, there is enhanced upslope flow on the southern slopes of the Uintah Mountains in response to warming along the mountain slopes and enhanced downslope north of the Uintah Mountains in response to an area of very strong warming in the Green River basin. For the most part, the wind response is limited to the daytime when warming is maximized; however, the Green River basin warming drives a wind response that persists throughout the night as well as the day. In May, there is a more uniform enhancement of upslope flow that appears to converge near the summits of the Uintah and Wasatch Mountains.

In the CONUS simulation, there are substantially different spatial patterns and magnitudes of and (Fig. 10). In general, spatial variability of warming is weaker compared to that of Headwaters, indicative of a weaker SAF consistent with smaller values resulting in smaller albedo changes. Warming is also shifted to higher elevations relative to Headwaters in both months consistent with reduced control climate snow extent and greater PGW forcing magnitude in CONUS. In particular, CONUS lacks the strong warming in the Green River basin owing largely to a lack of snow cover in this basin in the control simulation (e.g., Fig. 2). Additionally, warming patterns are more persistent overnight in CONUS, possibly owing to the higher effective heat capacity of the explicit canopy in Noah-MP.

Changes in are also weaker in CONUS, likely because of the diminished spatial variability of . However, in similarity to Headwaters, is largest along gradients in surface warming, particularly at 1500 and 2100 MST, suggesting an overall similar, though less pronounced, dynamic response to patterns of SAF-enhanced warming.

c. Physical mechanisms

To investigate the relative roles of changes in the thermal contrast and downward momentum mixing in causing changes in diurnal wind systems, we focus on the Headwaters simulation where the dynamic changes are clearer. We expect the mechanisms to be the same in the two experiments and that differences in the responses are mostly due to different PGW forcing and SAF strength.

We compare changes in the surface temperature gradient and the surface horizontal pressure gradient force as indices of thermal forcing (Fig. 11). In general, the spatial structures of and closely match those of and . This supports the hypothesis that the near-surface winds are changing in response to changes in the surface pressure gradient caused by gradients in SAF-enhanced warming. The results from CONUS reveal a similar spatial correlation between and , though the overall changes are weaker (not shown).

Fig. 11.
Fig. 11.

Values of (color shading) from the Headwaters simulation for the Uintah region with (vectors). Plots show synoptically weak composite monthly means for (top) April and (bottom) May.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

To determine the role of enhanced boundary layer mixing, we compare differences in the WRF diagnostic planetary boundary layer height (PBLH) and top of PBL wind vectors from the control simulation over the Uintah region (Fig. 12). In general there is a strong correlation between and the afternoon with an increase in PBLH up to 700 m above regions with strong surface warming, an indication of more vigorous boundary layer mixing where the SAF is active. However, if increased downward momentum mixing were a dominant mechanism driving changes in the surface winds, one would expect more uniform directionality in consistent with the synoptic flow. Comparing with at top of PBL shows that this is not the case; rather, is maximized on gradients of and oriented toward the strongest warming, suggesting momentum mixing is secondary to thermal forcing. It is important to note that this analysis does not rule out the possibility that increases in boundary layer depth are important in driving changes in mountain-breeze strength by decreasing the atmospheric stability (e.g., Kirshbaum 2013).

Fig. 12.
Fig. 12.

Values of from the Headwaters simulation for the Uintah region. The thick black vectors show the control wind near the top of the boundary layer. Light gray vectors show . Plots show synoptically weak composite monthly means for (top) April and (bottom) May.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

d. Bulk analysis

In this section, we seek to quantify the bulk relationship between and the patterns of surface warming independently of terrain aspect and wind direction. To accomplish this, the 10-m wind vectors are projected onto the terrain gradient to determine the upslope component of the wind:
e1
where h is the terrain height. To quantitatively summarize changes in upslope flow as they relate to thermal forcing, is compared to the thermal contrast between the mountain slopes and the lowlands, which is quantified by the following index:
e2
where angle brackets denote the spatial mean. The 2200-m threshold was chosen as it roughly separates the mountains from the surrounding lowlands in both the Uintah and the large domains. Focusing on the relationship between and helps relate the wind response to thermal forcing across different regions, seasons, and simulations in an objective manner.

Figure 13 shows scatterplots of and for the Uintah region and the large domain for both Headwaters and CONUS. Each point represents a 6-hourly (i.e., 0000–0600, 0600–1200, 1200–1800, and 1800–0000 MST) monthly average for a given year. There is a significant positive relationship between and in both Headwaters and CONUS. This positive relationship indicates that as high elevations warm more than low elevations, upslope flow increases in proportion to the warming contrast. This relationship does not hold for the Uintah region in the Headwaters simulation during April, where there is little correlation owing to the very strong warming in the Green River basin. This entire basin is below 2200-m elevation, and strong warming localized in this region skews to such an extent that the relationship between and breaks down in the regional average. In May, when the warming in the Green River basin is diminished, there is a similar relationship as in the other three figure panels. Importantly, this relationship is independent of time of day, indicating that the daytime and overnight branches of mountain wind systems are affected similarly by the thermal contrast, and that the muted response in overnight is due to diminished spatial variability in .

Fig. 13.
Fig. 13.

Change in compared to change in . Each marker represents a monthly mean for a given year and month. Marker types and colors denote different months. Crosses indicate nighttime averages (0000–0600 and 0600–1200 MST). Shown is (left) Headwaters vs (right) CONUS and (top) large domain vs (bottom) Uintah region.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

We summarize the spatial and temporal structures of the data by averaging the synoptically weak results as a function of time and elevation. This allows for a detailed investigation of the diurnal cycle of and its relationship to other relevant fields over a broad range of elevations. Figure 14 shows from the control simulation the PGW − control differences in near-surface divergence and vertically integrated cloud liquid water , and averaged over the Uintah region for Headwaters and CONUS. To assess the statistical significance of these changes, a Monte Carlo resampling technique is applied by randomly subsampling the synoptically weak control data 200 times into two separate groups and differencing the mean of each group. Significance is attained where the absolute PGW − control difference is greater than the 95th percentile of the control differences.

Fig. 14.
Fig. 14.

Uintah region: (from top to bottom) from control (color shaded), (color shaded), (color shaded) and (g m−3; blue contours), and (color shaded). All variables are 7-yr synoptically weak monthly averages as a function of elevation and time of day (MST). The black lines show mean [(top) only] and scaled by the upper x axis. In CONUS, is a weighted combination of snow cover on the ground and on the canopy. Stippling indicates statistical significance at the 95th percentile for and .

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

In the Headwaters experiment, the diurnal cycle of follows a transition from downslope flow (blue shading) overnight to the upslope flow (red) during the day. At high elevations, the daytime upslope flow is partially or completely suppressed by abundant snow cover. In the warmed climate, in both April and May, there are significant increases in the daytime upslope flow focused near the largest changes in and . At elevations where is increased, convergence is increased as well. This increase is collocated with increases in CLW, suggesting that enhanced convergence is associated with increased vertical motion and cloud formation as well. The overlap between and is generally limited to the daytime (e.g., there is no increase in CLW between 1800 and 2100 MST in May, despite significantly increased convergence). This is likely because boundary layer cloud formation is most sensitive to minor changes in near-surface convergence during the day when the boundary layer is most unstable.

In CONUS, the effect of the SAF on diurnal winds is less pronounced. In the control, upslope flow is present over a greater elevation range than in Headwaters because of lower at the high elevations. In the warmed climate, snow loss and SAF-enhanced warming are reduced and shifted to higher elevations relative to Headwaters. For instance, April snow loss in CONUS is at roughly the same elevation as snow loss is in May for Headwaters. Additionally, in both April and May, the diurnal cycle of warming is generally shifted later in the day by approximately 2 h compared to Headwaters. Accordingly, changes in the upslope flow are also weaker, shifted to higher elevations, and maximized later in the diurnal cycle. In April, the structures of , , and bear some resemblance to those seen in the Headwaters; however, nowhere do they exceed the significance threshold. These results are consistent with a weaker dynamic response in CONUS due to a weaker SAF.

In May, the only statistically significant structures of and occur overnight. This increase is consistent with persistent warming and snow loss above 2800 m, suggesting that these changes are in response to the SAF. During the day, changes in , while not significant, are less similar to those seen in Headwaters. In particular, the anomalous downslope flow collocated with local maxima of snow loss and warming at 3000 m is opposite the expected result. A comparison of Figs. 10 and 12 reveals that above the 3000-m elevation contour at both 0900 and 1500 MST is generally aligned with the synoptic winds from the control simulation suggesting that downward momentum mixing may play a larger role than thermal forcing in this region at these times. This is supported by the abrupt appearance of the significant upslope flow anomaly at sunset as the boundary layer shoals during this time, leaving thermal forcing as the dominant driver of changes in .

This analysis is extended to the large domain to explore the generality of these results (Fig. 15). In Headwaters, the Uintah results generalize well to the large domain, which shows significant increases in upslope flow coincident with the SAF-enhanced warming and enhanced convergence and CLW near the new snow margin. In CONUS, the large domain results show both similarities and differences with those of the Uintah region. One similarity is the presence of broad upslope anomalies during the overnight hours in both months, though they are only statistically significant in May. In contrast, there is no increase in upslope flow during the daytime. Again, the CONUS results differ from the Headwaters results, suggesting a high sensitivity of these results to experimental design and model configuration. It is likely that many of the complicating factors that influenced the results over the Uintah region are present for the large domain as well.

Fig. 15.
Fig. 15.

As in Fig. 14, but for the large domain.

Citation: Journal of the Atmospheric Sciences 74, 1; 10.1175/JAS-D-16-0158.1

5. Discussion

While both simulations show robust changes in diurnal winds caused by the SAF, the details of these changes appear to be highly sensitive to experimental design and model configuration. While the dynamic response to the warming is similar in both experiments analyzed in this study, the specific results are quite different owing in part to differences in strength, spatial structure, and diurnal timing of SAF-enhanced warming. These factors appear to be influenced primarily by the differing LSMs and large-scale climate forcings used in each model.

In Minder et al. (2016), it was shown that the SAF is stronger in Headwaters owing primarily to differences in and snow extent in the control simulations. The stronger SAF in Headwaters leads to stronger changes in mountain wind systems in Headwaters. In addition to LSM choice, these results may also be sensitive to other factors such as PBL scheme and model resolution. A broader comparison across numerous RCM simulations may help further clarify robust dynamic responses to the SAF.

The strong warming in the Uintah and Green River basins is much greater than it is in other locations with similar changes in snow cover, suggesting that these basins are especially sensitive to the SAF, possibly through modification of cold air pool formation and evolution. Furthermore, this warming is notably absent in CONUS, suggesting that interactions between the SAF and cold pools may be a significant source of uncertainty over complex terrain. This interaction may favor enhanced warming in large basins as the snow loss increases surface warming and decreases the near-surface stability, leading to weaker and shorter-lived cold pools (e.g., Neemann et al. 2015). Alternatively, SAF-enhanced warming on nearby mountain slopes may be advected into these basins overnight via nocturnal katabatic winds and become trapped for an extended period of time if the daytime boundary layer does not become deep enough to loft the warming above the surrounding mountain peaks into the free troposphere. More focused modeling studies are needed to study these potential interactions.

A recent study by Giorgi et al. (2016) shows an increase in RCM-simulated mountain-top convective precipitation over the European Alps. While they argue this increase is largely due to an increase in atmospheric instability and soil moisture feedbacks, an increase in convective precipitation is consistent with the increased convergence and cloudiness presented in this study. However, we exclude an analysis of precipitation changes, as precipitation, and in particular convective precipitation, by nature is a highly stochastic process influenced by a wide array of factors, such as the large-scale thermodynamic environment, synoptic- and mesoscale forcing, and antecedent surface properties (e.g., soil moisture). In PGW experiments like those analyzed in this paper, all of these properties are altered simultaneously as part of the experimental design, and linking changes in convective precipitation to any single mechanism is not straightforward. More idealized experimentation is required to achieve this goal.

6. Conclusions

In this study, we analyzed a pair of regional climate model pseudo–global warming experiments to study the connection between springtime SAF and diurnal mountain winds within the Rocky Mountains of the United States. Our key findings are as follows:

  • A model evaluation against mesonet observations revealed that both the Headwaters and CONUS simulations are able to simulate the diurnal cycle of temperature, moisture, and wind reasonably well within the study region. In particular, both models simulate the daily reversals and wind direction associated with mesoscale terrain diurnal mountain winds. However, there is a notable cold bias in the daytime high temperature present in the Headwaters simulation at snow-covered locations because of an overestimate in surface albedo and a warm bias in the overnight low temperature in CONUS. These biases lead to a general underestimate in the amplitude of the diurnal cycle of temperature in both simulations.

  • In a warmed climate, the SAF enhances the regional variability of warming, which leads to enhanced upslope flow during the day and weakened downslope flow overnight in both Headwaters and CONUS. These changes are spatially well correlated with gradients in and largest during the afternoon when SAF-enhanced warming is maximized, suggesting a robust link between the SAF and diurnal mountain wind systems in the Uintah region.

  • The main dynamic cause of SAF-induced changes in the diurnal wind systems is an enhanced thermal contrast between high and low elevations, which generates an anomalous surface pressure gradient force that influences the evolution of the diurnal mountain winds. Consistent with this mechanism, bulk analysis revealed a strong relationship between changes in the thermal contrast and changes in upslope flow in both Headwaters and CONUS. Increased downward momentum mixing associated with increases in the depth of the boundary layer above enhanced surface warming are a secondary factor.

  • The dynamic response to the SAF is stronger in the Headwaters than in the CONUS simulation, consistent with a weaker SAF in the latter. Furthermore, in CONUS the SAF was shifted to higher elevations where the surface winds were more susceptible to increased momentum mixing. In Headwaters, time- and elevation-dependent structures in warming, upslope flow, and surface convergence are strongly correlated in both April and May. In CONUS, these relationships are less pronounced than in Headwaters, apparently because of factors such as an overall weaker SAF, differences in the diurnal timing and geographic extent of SAF-enhanced warming, and differences in the large-scale forcing. These results suggest that the dynamic response to the SAF is sensitive to experiment configuration.

The recent advent of high-resolution RCM simulations has enabled this and other recent studies to examine detailed interactions between large-scale climate forcing, land surface processes, and mesoscale wind systems over complex terrain. Much work remains to further characterize such interactions and the physical processes involved, to quantify model skill at representing them, and to understand their sensitivity to model configuration.

Acknowledgments

Support for this work was provided by the NSF Grant AGS-1349990. The NCAR Water Systems Program funded by the NSF supported the generation of the RCM simulations analyzed here. We thank Roy Rasmussen, Kyoko Ikeda, Changhai Liu, and Michael Barlage for providing assistance in accessing the RCM output and details of the model configuration. High-performance computing support was provided by NCAR’s Computational and Information Systems Lab, sponsored by the NSF. We are grateful to the anonymous reviewers for helping improve this manuscript by providing their insightful comments and suggestions.

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  • Fig. 1.

    Study region. (a) WRF regional topography (km) and mesonet stations included in the analysis: Price Carbon County Airport (KPUC), Yellowstone Drainage (YLSU1), Five Mile (FIVU1), Bryce Canyon (KBEC), Grand Junction (KGJT), Meeker Airport (KEEF), Cortez-Montezuma Airport (KCEZ), Nucla (NUCC2), Porcupine Creek (PCP2), Taylor Park (TAPC2), Gunnison/Crested Butte (KGUC), Telluride Airport (KTEX), Fort Collins/Loveland (KFNL), Limon (KLIC), Pueblo (KPUB), and La Junta (KLHX). Stars and text indicate stations used for wind direction comparisons. (b) Light-shaded region with black outline shows the large domain; the red outline indicates the Uintah region. (c) WRF land surface types merged into five broadly representative categories. (d) Detailed geography of the Uintah region. Topography is shaded; color scale is the same as in (a). Contours added to select elevations.

  • Fig. 2.

    The 7-yr April and May mean control for the Headwaters and CONUS simulations. Black contour shows 3000-m elevation.

  • Fig. 3.

    Values of (top) , (middle) , and (bottom) averaged over the large domain for April–May synoptically weak days. Time is reported in MST. The green fill indicates the spatial and temporal variability of the observed data (10th and 90th percentiles).

  • Fig. 4.

    Average bias between the Headwaters and CONUS control simulation and the mesonet data for April–May synoptically weak days. Results are shown for (a),(d) daily max 2-m temperature ; (b),(e) ; and (c),(f) diurnal amplitude .

  • Fig. 5.

    Amplitude of the diurnal cycle for April–May of (top) temperature and (bottom) wind on synoptically weak days. Markers indicate the observed diurnal cycle. ASOS sites are indicated as circles; RAWS sites are shown as squares.

  • Fig. 6.

    Diurnal cycle of meteorological wind direction for April–May synoptically weak days. (from left to right) Sites are grouped geographically from west to east. The network that each site belongs to (RAWS or ASOS) is denoted in parentheses.

  • Fig. 7.

    The diurnal cycle of synoptically weak mean 10-m wind vectors from the Headwaters simulation for the Uintah region in (top) April and (bottom) May. (right) Monthly mean fsn from the control simulation. Values of are masked where < 2.0 m s−1. (top left) Note the scale vector.

  • Fig. 8.

    April and May 7-yr mean and for the Headwaters and CONUS simulations. Black contour shows 3000-m elevation. Note the different color scales used for Headwaters and CONUS .

  • Fig. 9.

    The diurnal cycle of mean (shading) and (vectors) (PGW − control) from the Headwaters simulation for the Uintah region. Black contours indicate terrain. Plots show synoptically weak composite monthly means for (top) April and (bottom) May. The is masked where < 0.3 m s−1. (left) Note the scale vectors.

  • Fig. 10.

    As in Fig. 9, but for the CONUS simulation. Note the different color scale for .

  • Fig. 11.

    Values of (color shading) from the Headwaters simulation for the Uintah region with (vectors). Plots show synoptically weak composite monthly means for (top) April and (bottom) May.

  • Fig. 12.

    Values of from the Headwaters simulation for the Uintah region. The thick black vectors show the control wind near the top of the boundary layer. Light gray vectors show . Plots show synoptically weak composite monthly means for (top) April and (bottom) May.

  • Fig. 13.

    Change in compared to change in . Each marker represents a monthly mean for a given year and month. Marker types and colors denote different months. Crosses indicate nighttime averages (0000–0600 and 0600–1200 MST). Shown is (left) Headwaters vs (right) CONUS and (top) large domain vs (bottom) Uintah region.

  • Fig. 14.

    Uintah region: (from top to bottom) from control (color shaded), (color shaded), (color shaded) and (g m−3; blue contours), and (color shaded). All variables are 7-yr synoptically weak monthly averages as a function of elevation and time of day (MST). The black lines show mean [(top) only] and scaled by the upper x axis. In CONUS, is a weighted combination of snow cover on the ground and on the canopy. Stippling indicates statistical significance at the 95th percentile for and .

  • Fig. 15.

    As in Fig. 14, but for the large domain.

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