Download Observed and Simulated HIRS Diurnal Cycles

The Diurnal Cycle in HIRS
Brightness Temperatures
I.A. MacKenzie1, A.V. Lindfors1,2,
S.F.B. Tett1 and Lei Shi3
1The University of Edinburgh, UK
2The Finnish Meteorological Institute,
Finland
3NOAA’s National Climatic Data
Center, USA
Motivation & Outline
• Big Question: What does HIRS tell us about climate change?
– Temperature & Water Vapour channels.
– Expectation – greater warming with height & constant RH.
• NOAA operational satellites have drifted in LECT
– So unless correct for this (or trust models diurnal cycle) will alias in
diurnal cycle to multi-decadal signal.
• Derive satellite based estimate of HIRS diurnal cycle based on
uneven sampling
• Compute same from models.
• Compare –
– Models any good?
– Is uneven sampling adequate?
• Papers: Lindfors et al, 2011 JAOT & MacKenzie et al, 2012, J.
Climate
Local ascending equator crossing
(LECT) times for the HIRS satellites.
NOAA platforms
have changes in
LECT which, if
uncorrected, would
alias diurnal cycle
into climate records.
To correct need to
know what the
diurnal cycle is.
20022007
Earlier work used
model simulations
but 2002-2007 has
4 satellites
operating at
different LECT (1417)
Approach
Fit to the brightness temperatures as a function of local
time leading two diurnal harmonics and mean.
Harmonics have amplitude and phase information.
Do this from data for all available data for the period
2002-2007.
Aim being to generate a climatological dataset.
 (t  t1)
2 (t  t 2)
Tb  a 0  a1 cos
 a 2 cos
12
12
(1)
Tb - brightness temperature; a0 - mean/base Tb; a1, a2 - 24 and 12
hourly amplitudes; t1, t2 - phases
Data
• Use corrected data from NOAA – all data corrected to
NOAA-12 using SNO’s (polar regions)
• To get high temperatures also correct (for monthly
average) equatorial ocean surface region within 20S20N and 160W-100W where diurnal variation, confirmed
by climate model simulations, is at minimum.
• Corrections are temperature dependant.
• Only use near-nadir observations
• Clear sky
• Require at least 10 observations in each quarter of the
day
• Work with all data gridded to 2.5 x 2.5.
Example fit for July in the Sahara
Channel 8 fit
Uncertainty estimates
• Use the standard deviations from the fit for each satellite
and ascending/decending node.
• Then Monte-carlo with no diurnal cycle and repeat
calculations.
• Uncertainties at the 95% level for a1 and a2 vary by
position (reflecting different data coverage) but are 0.2K
over the tropical and sub-tropics and increase to 1-2K at
high latitudes.
• To reduce uncertainties do analysis on zonal values
(land and sea separately) grouping together the same
local times in the fits.
• 95% is between 0 and 0.2K.
Zonal fits
Uncert
NOAA-14 drift
• NOAA-14 shows the most drift in LECT over a
decade (or so)
• So ask how much would the diurnal cycle
contribute to changes?
• Expected climate change signal is 0.20.3K/decade. For water vapour would expect
essentially no change if RH constant holds.
• From change in LECT and estimated diurnal
cycle can compute max global-mean difference
for each channel.
Drift over NOAA-14
Errors
(from
MonteCarlo) are
negligible.
Expected
impact
large
compared
to expected
climate
change
signal over
a decade.
Even over
the oceans.
Simulating HIRS Diurnal Cycle
• Model sampling – same orbits and only data
when HIRS instrument functioning. Clear sky
when model cloud less than 40%.
• Two models. HadGEM-2A (state-of-the-art) and
HadAM3 (last-generation). Driven with HadISST
SST and sea-ice. No diurnal SST cycle – so
would not expect diurnal cycle over oceans.
• RTTOV (v9.3) to compute brightness
temperatures. Channels 1-12. NOAA-12 SRF for
comparison with observations
• CO2 increased linearly from 371 ppm to 384
ppm.
Simulated and Observed Diurnal
Tb Range
Shown is
globalaverage max
– min for each
channel.
Sea
Land
Amplitude falls
approximately
exponentially
with temp
channel. Rises
in Strat. WV
range falls
with height.
Trop temp channels (land) in good agreement with obs. Sea weaker in models –
surface diurnal cycle. Stratosphere is very model dependant. Problems with chan 12.
Simulated Ratios
a1 represented well
in HIRS vs 1 hourly.
a2 adequate – 1030% too large.
Some issues with
water vapour
channels
For model-data
comparison
suggests need to
compare HIRS
sampled model with
observations.
Conclusions
• HIRS sampling provides reasonable estimate of “true”
sampling.
– Observational product ok though some differences in 2nd
harmonic
• Qualitative agreement between simulated and observed
Clear Sky Tb.
– Diurnal cycle largely controlled by large scale (surface)
processes.
• Quantitative differences though.
– Model corrections for tropospheric temperatures OK.
– Poor in stratosphere – ozone??
– And for key parameter: Upper Trop. Water Vapour (ch12).
Models do not appear to be reliable enough to use to correct.
• Really would like observations of diurnal cycle for
(A)TOVS instruments. Can we use Metop-A at end-oflifetime to do this?