1. Mapping the Thermal Climate of the HJ Andrews Experimental Forest, Oregon Jonathan Smith Dept. of Geosciences Oregon State University PHOTOS COURTESY OF AL LEVNO

3. Project goal To create mean monthly maximum and minimum temperature maps of the HJ Andrews… … taking into account as many environmental factors as possible affecting microclimates in forested, mountainous terrain - Cloudiness - Topography - Vegetation … and their effects on radiation regimes

4. Why are temperature maps of the HJ Andrews needed? The HJ Andrews is part of the Long-Term Ecological Research (LTER) network and a major forest research site Spatial temperature distributions are needed for research and decision- making in studies involving: - vegetation characteristics- animal habitats - insect distributions- forest management - hydrology (snow patterns, water balance)

5. Why now? A complete 30-year dataset now available (1971-2000) Computer software now available to quantify environmental effects on microclimates (cloudiness,topography, vegetation cover) Opportunity to use a temperature spatial interpolation model well- suited to HJ Andrews geography Current GIS capabilities allowed us to spatially represent temperature distributions at high resolution Badly needed

6. Large-scale influences on HJ Andrews temperature Proximity to Pacific Ocean Latitude Position relative to crest of Cascades

7. Small-scale influences on HJ Andrews temperatures TOPOGRAPHY elevation:determines temperature, precipitation regimes slope/aspect:determine radiation regime on a surface tree canopy:attenuates incoming shortwave radiation blocks amount of visible sky Highly variable topography and canopy cover at the HJ Andrews lead to complex microclimates! valleys/ridges:induce cold air drainage  inversions  thermal belts block amount of visible sky (lower sky view factors) VEGETATION

8. Project is based on the assumption that: At a given elevation, radiation is a major determinant of temperature regimes TMAX: incoming direct and diffuse shortwave solar radiation TMIN: outgoing longwave terrestrial radiation in forested, mountainous terrain, radiation regimes are determined by cloudiness, topography, and tree canopy depict cloudiness and topographic effects of radiation on temperatures depict temperatures across the HJ Andrews with minimal canopy influence … therefore the resulting maps:

9. Why minimize canopy influence? To approximate standard siting conditions common in other station networks (NWS) To provide a universal ‘starting point’ for studies using these data as input To provide appropriate input for the many types of resource models that assume temperature data collected from forest clearings To provide stable datasets: vegetation changes from year to year, and if canopy is used to estimate temperatures, results will be temporally unstable

10. Open thermisters shielded above with PVC Digital data loggers MET sites: relatively open, (sometimes) flat terrain thermister towers (1.5, 2.5, 3.5, 4.5m), many other sensors Other sites:highly variable canopies, slopes, aspects air temperature sensor (~1.5m), other sensors Climate station instrumentation and siting

11. Initial adjustments to datasets Any site not having at least 3 years of data eliminated (62-19=43) Monthly means computed from daily TMAX, TMIN Data flagged in any way during initial processing discarded 1.5m sensors from MET sites used when possible 71819101

12. 1Each short-term site paired with highest correlated long-term site (month-by-month) 2Using long-term site data, calculated the difference between the mean TMAX and TMIN for the full period of record and the mean TMAX and TMIN for the short-term site’s period of record 3Applied the above differences as overall adjustments to the short-term datasets To eliminate warm/cold temporal biases in dataset, short-term sites were adjusted to the full 30-year period: Temporal adjustments to datasets 43 sites:13 with at least 22.5 years (75%) of data (long-term) 30 with less than 22.5 years (75%) of data (short-term)

13. Multiplied monthly radiation grids by cloud factors to get total daily radiation over area for each month Determined monthly cloud factors using UPLMET radiation data 1 – [UPLMET observed rad’n / IPW clear-sky rad’n] Used Image Processing Workbench (IPW) to create clear-sky radiation grids, using 50-meter DEM Radiation adjustments to datasets STEP 1: Determine monthly topo/cloud-sensitive radiation at each temperature site JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 0.49 0.45 0.40 0.35 0.34 0.26 0.19 0.17 0.19 0.34 0.47 0.48

14. Explicitly accounted for cloudiness on topographic shading by adjusting ratio of direct to diffuse radiation: - Used Bristow & Campbell’s (1985) equation relating direct and diffuse proportions of solar radiation to transmissivity - Necessary to produce ‘sharp’ radiation regimes on clear days (  direct,  diffuse), ‘flat’ radiation regimes on cloudy days (  direct,  diffuse) STEP 1: (continued) Picked out appropriate grid pixels to get estimated radiation at each temperature site ‘SHARP’ REGIME‘FLAT’ REGIME

15. At each site, separated out proportion of radiation blocked by topography only: Used HemiView to calculate proportions of direct and diffuse radiation blocked by canopy and topography Took fisheye photos at each site STEP 2: Determine monthly topo/cloud/canopy-sensitive radiation at each temperature site At each site, determined proportion of radiation blocked by canopy only: Reduced each sites’ topo/cloud-sensitive radiation by its proportion of radiation blocked by canopy only 1 - IPW topo/cloud-sensitive rad’n = IPW proportion of rad’n blocked IPW flat, open cloud-sensitive rad’n by topography only HemiView proportion blocked _ IPW proportion blocked = proportion of rad’n blocked by canopy and topography by topography by canopy only RS10UPLMETOPEN

16. For TMAX: 1. Selected site pairs for comparison (7) 2. Plotted differences in monthly topo/cloud/canopy-sensitive radiation against differences in monthly TMAX between site pairs, compute regression functions STEP 3: Calculate regression functions to adjust temperatures to flat, open siting conditions JAN: y = 1.17x(R² = 0.91) FEB: y = 0.73x(R² = 0.96) MAR: y = 0.45x(R² = 0.96) APR: y = 0.33x(R² = 0.99) MAY: y = 0.24x(R² = 0.82) JUN: y = 0.22x(R² = 0.84) JUL: y = 0.20x(R² = 0.74) AUG: y = 0.25x(R² = 0.82) SEP: y = 0.34x(R² = 0.87) OCT: y = 0.52x(R² = 0.91) NOV: y = 0.76x(R² = 0.92) DEC: y = 1.41x(R² = 0.95) (y = difference in TMAX, x = difference in radiation) 3.Applied regression functions to each sites’ monthly TMAX to adjust temperatures ‘into the open’ (removing topo, vegetation effects)

17. For TMIN: 1. Selected site pairs for comparison (14) 2. Plotted differences in sky view factors against differences in monthly TMIN, and compute regression functions: JAN: y = -1.00x(R² = 0.49) FEB: y = -1.18x(R² = 0.33) MAR: y = -1.36x(R² = 0.58) APR: y = -1.01x(R² = 0.31) MAY: y = -1.51x(R² = 0.50) JUN: y = -1.80x(R² = 0.55) JUL: y = -3.41x(R² = 0.81) AUG: y = -3.46x(R² = 0.83) SEP: y = -3.25x(R² = 0.75) OCT: y = -2.07x(R² = 0.66) NOV: y = -1.56x(R² = 0.63) DEC: y = -1.14x(R² = 0.47) (y = difference in TMIN, x = difference in sky view factors) 3.Applied regression functions to each sites’ monthly TMIN to adjust temperatures ‘into the open’ (removing topo, vegetation effects)

18. TMAX regression slopes varied throughout the year because of monthly variations in solar radiation: TMIN regression slopes varied throughout the year because of monthly variations in cloudiness:

19. ‘Parameter-elevation Regressions on Independent Slopes Model’ (PRISM) used to spatially interpolate TMAX, TMIN Uses a combination of geographic and statistical methods Elevation-based interpolator, using linear temperature-elevation regression functions, DEM, and point (station) data Accounts for elevation, inversion layers (2-layer atmosphere model) Spatially interpolating temperatures across the HJ Andrews * Results of different adjustment procedures removed effects of everything but elevation – PRISM’s strength is the elevation-temperature relationship

20. Station selection criteria: - no stream sites - no sites with obviously biased temperatures - no sites with questionable locations

21. PRISM temperature-elevation plots showed inversions throughout the year TMAX-Elevation Plot for JanuaryTMAX-Elevation Plot for July TMIN-Elevation Plot for JanuaryTMIN-Elevation Plot for July

22. For TMAX: 1. (flat, open radiation grid – topographically-sensitive radiation grid) = radiation difference grid 2. For each pixel in radiation difference grid, applied TMAX/rad’n regression function to get proper TMAX adjustment value 3. Added TMAX adjustment value to original PRISM TMAX grid Applying topographic radiation/sky view factor effects to PRISM temperature grids For TMIN: 1. [flat, open grid (SVF=1) – topographically-sensitive SVF grid] = SVF difference grid 2. For each pixel in SVF difference grid, applied TMIN/SVF regression function to get proper TMIN adjustment value 3. Added TMIN adjustment value to original PRISM TMIN grid

23. a) PRISM September TMAX map showing no radiation effects b) IPW September radiation map c) PRISM September TMAX map showing radiation effects d) Difference between a) and c)

24. a) PRISM September TMIN map showing no SVF effects b) IPW SVF map c) PRISM September TMIN map showing SVF effects d) Difference between a) and c)

27. Study quantified effects of cloudiness, topography, and vegetation on radiation/temperature regimes to spatially interpolate temperatures across the HJ Andrews Temperatures were modeled to minimize effects of vegetation Final temperature maps most sensitive to effects of elevation and topographic position (year-round valley inversions) TMAX was sensitive to shortwave radiation variations, especially in winter (due to low solar radiation load) TMIN was sensitive to sky view factor variations, especially during clear summer months (fewer clouds enhance radiative nighttime cooling) Study inventoried historical HJ Andrews temperature datasets, documented climate station sites (radiation regimes, canopy types, improved site locations) Summary and conclusions

28. More research into the nature of cold-air drainage in the HJ Andrews More sites in under-represented areas Quantification of stream effects on temperatures needed Would be useful to test TMAX/radiation, TMIN/sky view factor functions elsewhere Additional climate station pairs at similar elevations with different canopy types would help fine-tune regression functions Recommendations for future work

29. Some vegetation modification around climate stations seems justified to bring them up to NWS-standards Development of historical database of vegetation changes at each site Possible to create canopy-sensitive temperature maps by reintroducing vegetation effects with remotely-sensed canopy coverages