Evaluating Global Performance of
MTCLIM (and other algorithms)
Ted Bohn & Ben Livneh
UW Hydro Seminar
August 3, 2011
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
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?
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
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
Opportunity to Test
• BSRN network
– Hourly radiation, humidity, and temperature
observations
– Global coverage
– Stations range up to 18 years of data
Questions
1. How do the original MTCLIM algorithms
perform vs. BSRN observations across the full
range of global climates?
2. What effects do the MTCLIM 4.3 updates have
on results, across the globe?
3. How does using 0 for slope, horizon affect
MTCLIM 4.2 and 4.3?
4. How does the TVA LW algorithm perform
globally (esp. when linked to MTCLIM)?
5. How does VIC’s spline interpolation to hourly
perform, globally?
Methods: MTCLIM SW
SW at
ground
SW before any
atm. absorption
Total daily clear-sky trans.
(effect of optical mass)
Total daily
cloud trans.
Rgh  Rpot  Tt ,max  T f ,max
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…
Hourly Rpot,
Sunrise to sunset
Tt ,max
Clear-sky trans.
As f’n of solar angle
 ss
 Pz / P0 m
   R pot ,s   0,nadir ,dry
s sr
(Thornton and Running, 1999)
Humidity Effect

R pot ,s   VP

s  sr

ss
NOTE: we need VP
observations to
estimate SW
Methods: MTCLIM SW
Daily T
Range (DTR)
Cloud
Trans.

Tf ,max  1.0  0.9 exp  B  T C

30-Day Average
Daily T Range (DTR)

B  bo  b1 exp  b2  T

Tfmax has large daily variability, and influences
both SW and LW (and VP indirectly)
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
Methods: MTCLIM VP
First approximation: dewpoint temperature Tdew = Tmin
Kimball et al. (1997):



Tdew  Tmin  0.127 1.1211.003 1.444EF  12.312EF 2  32.766EF 3  0.0006T

where
ΔT = daily temperature range


EF  E p,day  w t day Pann,eff
Water
density
Effective annual precip from 90-day
window centered around current day
Daylength
(seconds)
Potential evap from Priestly-Taylor (1972)
E p,day       Rn  G 
= 1.26
Net SW assuming
albedo of 0.2
Ground flux
assumed 0
Finally, compute VP as saturation vapor
pressure at T = Tdew
Tetens (1930)
 17.27  T 
es  0.6108exp

 237.3  T 
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:
1. Assume Tdew = Tmin, constant over day
2. Compute VP from Tdew, compute Ttmax and SW
3. Use SW to compute more sophisticated VP
4. 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
Methods: TVA
LW  KEaT
4
where
2
K  1  0.17t skc
(TVA, 1972)
Cloud fraction, either from
observations or from MTCLIM
2
t skc
 1  T f ,max  0.65
(MTCLIM)
Tfmax from MTCLIM SW estimate
Ea  0.740 0.0049 VP
MTCLIM VP estimate
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
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)
Methods: BSRN
• Station selection – record length >= 5 y
and met variables available within 20 km
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)
Methods:
Simulations
• Ran VIC/MTCLIM
at hourly time
step
• Gap-filled days
nulled out of VIC
results
• Aggregated to
daily, monthly,
computed
monthly averages
Results: SW, 4.2
• Strong
negative bias
for monthly
average DTR
<6C
Results: SW, 4.2
• Almost all low DTR cases occur at
maritime sites (within 5 km of ocean)
Maritime
Continental
• 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
• 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
• 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
MTCLIM 4.3 SW snow correction
• Select only days when MTCLIM snow
model believed snow was present
All
Maritime
Continental
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…
All
Arid (annual aridity > 2.5)
aridity
aridity
•Aridity = Epot/Pannual
•lnpp = ln(aridity) for the
given month
•For aridity = 2.5
(threshold), lnpp = 0.9
Humid (annual aridity < 2.5
MTCLIM 4.2 VP, and 4.3 VP
correction
• Maritime stations introduce weird trend…
• Bias-correcting SW didn’t have much
effect on this…
TVA LW
• Using MTCLIM 4.2, we
see big trend in bias
Continental, arid
– Unbiased for monthly
average Tair around 10 C
All
Continental
Continental, humid
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
Diurnal Cycle
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…