Mapping Temperature across Complex Terrain

Transcription

1 Mapping Temperature across Complex Terrain Jessica Lundquist 1, Nick Pepin 2, Phil Mote 3 1 Assistant Professor, Civil and Environmental Engineering, University of Washington 2 Lecturer, Department of Geography, University of Portsmouth 3 Washington State Climatologist, Climate Impacts Group, U. Washington

2 Talk outline World overview: temperatures at higher elevations Implications for snowmelt and the rain-snow line Temperature inversions Mapping cold-air pools Temperature Toolbox webpage

3 Trends from 1084 stations ranging in elevation from m across the globe (Pepin and Lundquist, in press, and Pepin and Seidel, 2005) 1 1 Temperature trend ( o C per decade) mean Tropical Stations elevation Extratropical Stations -1 elevation

4 What accounts for huge scatter? Measurements are sparse at higher elevations. Measurements may be unrepresentative of surrounding topography.. This can have huge implications for modeling ecology, snowmelt, and the rain-snow line.

5 Streamflow simulations depend on knowing high elevation temperatures. 80 Streamflow (CMS) OBS SIM winter Month spring Snohomish River in Western Washington, courtesy of Alan Hamlet

6 Streamflow simulations depend on knowing high elevation temperatures. Streamflow (CMS) OBS SIM winter Month spring Modeled mountain temp is too cold = too much snow falls = underestimates winter rain runoff & overestimates spring snowmelt

7 Area contributing to runoff (through rain or melting snow) depends on how temperature decreases with altitude Depends on lapse rate: elevation Average decrease = 6.5 C per km But could range from 3 to 9.8 C per km 0 C +15 C Sea level

8 A biased valley temperature sensor could also misrepresent the elevation where snow melts. elevation 0 C +15 C Sea level

9 Temperature inversions and coldair pools are common in mountain valleys, which is where many temperature sensors are located.

10 Inversions occur during high pressure, when large-scale winds are weak, and local topography controls mountain weather. alpine sites valley sites From Lundquist and Cayan, 2007 Inversions are common in valleys near Mt. Rainier, making the standard lapse rate often wrong.

11 Fortunately, we know how mountain winds and cold air pools work and can map them with a DEM (Example, Loch Vale, Rocky Mountain National Park, Colorado) 1) Density-driven drainage 2) Pressure-gradient-driven drainage

12 1) At night, longwave radiation cools air adjacent to the surface. 2) Cold air is denser than warm air, and flows down hill and down valley. 3) Therefore, cold air can collect in flat valley bottoms and local depressions.

13 Loch Vale, Rocky Mtn NP ibutton Location

14 Primary Mode of Variability is cold-air pooling: Explains 72% of the Variance Positive Weight Negative Weight

15 Mapping likely cold-air pools using a digital elevation map (DEM). Flat slope Local depression Concave Lundquist et al (submitted to JGR) Important factors identified in forestry literature.

16 This method identifies flat valley bottoms (white), but only one of two identified is cold-air pool. Andrew s Meadow = flat bottom without cold-air pooling Loch Vale = correctly identified cold-air pool

17 From mountain meteorology literature, Whiteman, 1990; McKee and O Neal 1989 Topographic Amplification Factor Represents how much more a valley cools at night compared to a flat plain Energy lost to space = Volume x ΔT (TAF) H W

18 Whiteman, 1990; McKee and O Neal 1989 Topographic Amplification Factor Represents how much more a valley cools at night compared to a flat plain Smaller enclosed volume = cools more = bigger TAF H W

19 Whiteman, 1990; McKee and O Neal 1989 Topographic Amplification Factor Areas that cool more have local higher pressure. Winds flow from high to low pressure. Places where TAF decreases downvalley drain, and where TAF increases, cold air pools W

20 TAF decreases through Andrew s Meadow, so it drains.

21 Main Loch Vale: TAF increases, cold air pools.

22 Applied mapping algorithm to study areas in the Rocky Mountains, Pyrenees, and Sierra Nevada, and can predict areas of CAP with over 80% accuracy. Lundquist et al. 2008, submitted to JGR

23 Using CAP-mapping to interpolate station temperature data results in an average of 1 o C improvement over standard interpolation techniques.

24 Find: Links to all the papers discussed here. Directions for deploying temperature sensors in trees. Code for CAP-mapping algorithm and empirical orthogonal function (EOF) based method of identifying modes of temperature variability. Valley Fog, Guipuzcoa, Basque Country, Spain from wallpaperme.com

25 Conclusions 1) Mountains poorly sampled: samples may not represent surrounding topography 2) Temperature patterns strongly influenced by large-scale weather patterns and by local topography 3) GIS-based mapping can help improve how we model temperature variations across complex terrain

26 The Temperature Sensors: Dallas Semiconductor Maxim ibutton DS-1922L mm diameter mm thickness - temperature range: -35 C to +85 C - records temperature at user-defined rate: bit readings (0.5 C resolution) or bit readings ( C resolution) at intervals ranging from: 1s to 273hr bytes for application info - 64 bytes for calibration data 0.5 C resolution+ Sample once per hour= 11 months of data

27 Tuolumne i-buttons Different radiation shielding from trees

28 The Pacific Northwest has taller trees than most of the Rockies or the Sierra Jeremy Littel

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30 Biology Professor Janneke Hille Ris Lambers

31 Eset Alemu

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34 Rocky Mountain Field Study: Caitlin Rochford CIRES Innovative Research Fellowship Dave Clow (Colorado USGS), Mark Losleben (Colorado Mountain Research Station(MRS)), Kurt Chowanski (MRS), Todd Ackerman(MRS), Jen Kelley, Hollings Scholarship Program

35 Acknowledgements Yosemite Field Study: Brian Huggett (NPS), Dan Cayan (SIO), Mike Dettinger (USGS), Heidi Roop (Mt. Holyoke), Jim Roche (NPS), Frank Gehrke (CA DWR), Kelly Redmond (WRCC), Canon Research Fellowship, NSF RoadNET program, CIRES Postdoctoral Fellowship