Evaluating Global Performance of MTCLIM (and other algorithms)

1. Evaluating Global Performance of MTCLIM (and other algorithms) Ted Bohn & Ben Livneh UW Hydro Seminar August 3, 2011

2. Motivation Large-scale hydro/ecological models need accurate radiation & humidity inputs • Reanalysis products aren’t generally available in near-real-time or at resolution we desire • Most met stations record only Daily P, Wind, Tmax, Tmin • Fortunately algorithms exist to convert Daily Tmax/Tmin to Humidity, SW, and LW

3. Forcing Algorithms and VIC • VIC uses MTCLIM algorithm to get daily SW and VP (and cloudiness) • from University of Montana (also used in UM’s BIOME-BGC model) • Original version (in VIC) is 4.2 • Version 4.3 released in 2001 – not in VIC • Should we upgrade VIC’s MTCLIM to 4.3? • MTCLIM SW depends on local slope, aspect, horizon angles • Large-scale models like VIC don’t have a good way of representing these over large grid cells • VIC sets these to 0 • Is this biasing our results?

4. Forcing Algorithms and VIC • VIC uses TVA algorithm to get LW • Depends on T, cloudiness, and VP • Cloudiness and VP come from MTCLIM • Diurnal cycles: • VIC also uses spline to interpolate between Tmin and Tmax for hourly T • Accuracy? • Other hourly variables (SW, VP, LW) derived from daily quantities and hourly T

5. Not Fully Tested • Original MTCLIM algorithms were only tested against observations in continental US • (Kimball et al 1997; Thornton and Running 1999) • Shi et al (2010) evaluated MTCLIM SW on monthly basis for pan-Arctic • MTCLIM 4.3 contains updates: • SW correction for snow albedo effect • VP correction for better performance in humid climates • These updates were only partially tested in Austrian alps (Thornton et al 2000) • Performance of 4.2 and 4.3 SW and VP, and resulting TVA LW, not fully explored across full range of global climates

6. Opportunity to Test • BSRN network • Hourly radiation, humidity, and temperature observations • Global coverage • Stations range up to 18 years of data

7. Questions • How do the original MTCLIM algorithms perform vs. BSRN observations across the full range of global climates? • What effects do the MTCLIM 4.3 updates have on results, across the globe? • How does using 0 for slope, horizon affect MTCLIM 4.2 and 4.3? • How does the TVA LW algorithm perform globally (esp. when linked to MTCLIM)? • How does VIC’s spline interpolation to hourly perform, globally?

8. Methods: MTCLIM SW SW before any atm. absorption Total daily clear-sky trans. (effect of optical mass) Total daily cloud trans. SW at ground SW at ground SW at ground • Rpot = sum of direct and diffuse components • Direct depends on local slope, aspect • Diffuse depends on local horizon • VIC sets slope, aspect, and horizon to 0… Clear-sky trans. As f’n of solar angle Hourly Rpot, Sunrise to sunset Humidity Effect NOTE: we need VP observations to estimate SW (Thornton and Running, 1999)

9. Methods: MTCLIM SW Daily T Range (DTR) Cloud Trans. 30-Day Average Daily T Range (DTR) Tfmax has large daily variability, and influences both SW and LW (and VP indirectly)

10. Methods: MTCLIM SW • 4.3 SWE correction (Thornton et al, 2000) • Account for extra reflections of SW off snow pack • Effect consists of a flat-ground component plus reflections off hill slopes (which depend on local horizon angle) • Essentially proportional to SWE up to 300mm • MTCLIM uses degree-day snow model to compute daily SWE • Tested in Austrian Alps but not globally

11. Methods: MTCLIM VP First approximation: dewpoint temperature Tdew = Tmin Kimball et al. (1997): • where • ΔT = daily temperature range Effective annual precip from 90-day window centered around current day Daylength (seconds) Water density Potential evap from Priestly-Taylor (1972) Ground flux assumed 0 Net SW assuming albedo of 0.2 = 1.26 Tetens (1930) Finally, compute VP as saturation vapor pressure at T = Tdew

12. Methods: MTCLIM VP • Recall that SW depends on VP estimate (through Ttmax eqn). But VP depends on SW estimate (through Priestly-Taylor) – need to iterate • Iteration: • Assume Tdew = Tmin, constant over day • Compute VP from Tdew, compute Ttmax and SW • Use SW to compute more sophisticated VP • Update SW from updated VP • 4.3 VP correction (Thornton et al, 2000): • for stations with annual Epot/P ratio < 2.5, don’t iterate

13. Methods: TVA where (TVA, 1972) Cloud fraction, either from observations or from MTCLIM (MTCLIM) Tfmax from MTCLIM SW estimate MTCLIM VP estimate

14. Methods: Summary • SW depends primarily on daily T range • SW also depends on local topography • VP depends on Tmin and Epot/Prcp ratio • SW and VP depend on each other as well • LW depends primarily on T4 • LW also depends on daily T range and VP

15. Methods: Hourly • Air Temperature: • Assume Tmin occurs at sunrise, Tmax occurs in mid-afternoon • Interpolate to hourly T via spline • Vapor Pressure: • Assume constant over entire day • Vapor Pressure Deficit: • = svp(Tair(hour)) – VP(day) • SW: • Compute hourly solar angle, scale daily total between sunrise and sunset by MTCLIM daily SW • LW: • Apply TVA algorithm using Tair(hour), VP(day), Tskc(day)

16. Methods: BSRN • Station selection – record length >= 5 y and met variables available within 20 km

17. Methods: BSRN • BSRN doesn’t record precip; some stns don’t record T or VP either • Took prcp and whatever other vars were needed from the nearest GSOD met station • Filled gaps by repeating last good value (or 0 in case of prcp)

18. Methods: Simulations • Ran VIC/MTCLIM at hourly time step • Gap-filled days nulled out of VIC results • Aggregated to daily, monthly, computed monthly averages

20. Results: SW, 4.2 • Strong negative bias for monthly average DTR < 6 C

21. Results: SW, 4.2 • Almost all low DTR cases occur at maritime sites (within 5 km of ocean) Maritime Continental

22. Can be traced to bias in Tfmax (cloud effect) • Appears that ocean’s moderating influence causes lower DTR even on clear days • MTCLIM is fooled into thinking it’s cloudy All Maritime Continental

23. Maritime sites showed up as outliers in the original Thornton and Running (1999) paper Optimal B higher than curve → simulated B (and Tfmax) will be too low T & R thought that seasonality of precip had something to do with it. Note: some maritime sites (Eugene, Portland) had optimal B lower than curve → simulated B (and Tfmax) will be too high

24. Maritime sites are a large portion of our data set • SW biases may affect the other variables • To allow us to use data from these sites, applied a simple linear bias correction for DTR < 5.7 C • Now, SW data are relatively unbiased globally • Does this have much effect on VP or LW? • Turns out, not really – wait to see maritime VP and LW plots… (bias-corrected) Note: we don’t claim that this is a fix to the MTCLIM algorithm; we are only doing this to clean up the data

25. MTCLIM 4.3 SW snow correction • Select only days when MTCLIM snow model believed snow was present All Maritime Continental

26. MTCLIM 4.2 VP, and 4.3 VP correction • 4.2 VP relatively unbiased • 4.3 VP tends to make things worse • Individual months from each station may be more humid or arid than the station’s annual average • Would monthly aridity criterion help? Probably not… • Aridity = Epot/Pannual • lnpp = ln(aridity) for the given month • For aridity = 2.5 (threshold), lnpp = 0.9 All Arid (annual aridity > 2.5) Humid (annual aridity < 2.5 aridity aridity

27. MTCLIM 4.2 VP, and 4.3 VP correction • Maritime stations introduce weird trend… • Bias-correcting SW didn’t have much effect on this…

28. TVA LW Continental, arid • Using MTCLIM 4.2, we see big trend in bias • Unbiased for monthly average Tair around 10 C All Continental Continental, humid

29. TVA LW • Is surface Tair really the correct temperature for estimating cloud-base LW emissions? • Cloud-base T depends on cloud-base height • Depends on planetary boundary layer (PBL) height • PBL height depends on T, humidity • Also depends on storm activity • Cloud tops are much higher/colder in tropics than elsewhere • Could be that we should be lapsing Tair to T at the cloud-base height

30. Diurnal Cycle

31. Conclusions • MTCLIM SW does poorly near coasts • Bias correction dependent solely on DTR may be possible • Arctic coastal areas don’t have this problem in winter, when sea ice reduces oceanic temperature influence • MTCLIM 4.3’s SW snow correction is OK • MTCLIM VP had large scatter but small bias overall • MTCLIM 4.3’s VP correction tended to hurt more than help • Apply to monthly instead of annual criterion? • TVA LW bias has strong dependence on Tair • Relatively unbiased for Tair near 10 C • Diurnal Cycle: T good, SW good, VP and LW need work…