AIRS Cloud Fraction Trends from a PDF-based Approach ......PATMOS-x Satellite Cloud Records, Journal...
Transcript of AIRS Cloud Fraction Trends from a PDF-based Approach ......PATMOS-x Satellite Cloud Records, Journal...
AIRS Cloud Fraction Trends from a PDF-basedApproach Compared to PATMOS
AIRS Virtual Science Team Meeting
L. Larrabee Strow1,2, Andy Tangborn 2, and Howard Motteler2
May 12, 2019
1UMBC Physics Dept.
2UMBC JCET
Motivation
• Climate trending with hyperspectral IR will require
• Frequent reprocessing
• Simple algorithms
• Ability to switch instruments transparently
• Using less $
• UMBC Goal: Gridded T/H2O/O3 etc. Anomaly Products• Requires "transpose" of data (a big deal)
• Quick access to L1c/CHIRP for all times for each grid point• We were ready to do this, but our new RAID caught COVID-19 in lat Feb.
• So, we transposed a few channels instead
• Which, we were going to use to break up our gridded radiances in
"mostly clear", "less clear", and "very cloudy" for separate processing.
• PDFs were made for a 64 x 120 lat/lon grid (equal area) spanning 17years every 16-days (rough metric for cloud amount)
• OISST was matched to ocean obs
• (Hope to match ERA-I or (ERA5/MERRA2) to these in the future
• Here we use these PDFs to produce statistical measurements of Cloud
Fraction in 16-day increments
Cloud fraction is a simple parameter for comparisons between
observations and climate models
1
Validate versus Patmosx
Patmosx
• Heidinger A. et. al., The Pathfinder Atmospheres–Extended
AVHRR Climate Dataset, V 95 BAMS, 2014
• Starts in 1983
• AVHRR based, very high spatial resolution
• Many cloud products, not just cloud fraction
• Fairly heavily used, long-term NOAA support
Patmosx has been used for climate model studies, more below.
Validation versus AIRS L3 and/or MODIS could follow. . .
2
PDF-based Cloud Fraction using AIRS
• Need surface temperature: for now OISST (big lien, ocean only
for now)• Compute cloud forcing per observation for a window channel
• Forcing = BTobs - BTcalc clear. Using 1231 cm−1 channel.
• BTcalc clear not done yet. Approximate with 2-channel regression
that converts Tsurf to BTcalc clear for the 1231 cm−1 channel
(introduced by H. Aumann).
• Minimal accuracy needed for BTcalc,surf, but do require some
stability
• Use PDF bins = -140:2:70 K (Quite coarse for now.)
• Compute PDFs of cloud forcing for every 16-day time period
for 64 x 120 lat/lon grid
• This produces small files that are quickly processed• Cloud Fraction (CF)
• CF =∑α
-140K PDFcloud forcing
• α is a "clear threshold" to separate clear from cloudy
• Generally use -(5-10)K for α. 3
Sample Cloud Forcing PDF
Grid point in Atlantic Ocean south of northern Africa: at (-5,0)°
lat/lon, (1.8,3.0)°
4
Mean Cloud Fraction: AIRS versus Patmosx
Threshold = 5K Threshold = 10K
5
Cloud Fraction Variability
AIRS versus Patmosx Standard Deviation (over time)
6
Mean Statistical Differences
0 0.2 0.4 0.6 0.8 1
Patmos CF
0
0.2
0.4
0.6
0.8
1
AIR
S C
F
Cloud Fraction
• Correlation Coefficient: 0.98
• Mean (Patmos - AIRS): 0.37% ± 2.5% (std)
• Mean (Patmos - AIRS): ±60° lat= 0.03% ± 3%(std)
Cloud Trends
• Mean (Patmos - AIRS): -0.05 ±0.12 %/yr(std)
• Mean (Patmos - AIRS): ±60° lat = = -0.066±0.015 %/yr (std)
Estimated AIRS trend uncertainty due to SST trend errors: ~0.06%/yr 2σ 7
Cloud Fraction Trends (%/year)
• Many similarities, but note Equatorial Atlantic
• Note SAO issue for Patmosx
• Note scale is cloud fraction in %
8
Statistical Trend Uncertainties: 2σ values
9
AIRS Cloud Fraction Trends above Noise
• Trends in gray regions less than confidence interval
• Note 90% label on LHS should be 68%, not 90%
10
Do Trends Vary with Season?
More similar with season than not.
11
Previous Cloud Fraction Studies
Climate Model Comparisons
• Norris, J. et. al., Empirical Removal of Artifacts from the ISCCP and
PATMOS-x Satellite Cloud Records, Journal of Atmospheric and
Oceanic Technology, V 32, 2015
• Norris J. et. al., Evidence for climate change in the satellite cloud
record, Nature, Nautre, V. 536, 2016
• Trenberth, K. et. al., Global warming due to increasing absorbed
solar radiation, GRL, V 36, 2009
Issues
• Climate literature indicates that accurate cloud fraction observations
are difficults to find
• Norris used patterns of cloud fraction, not absolute trends!
Cloud Fraction Datasets
• ISSCP• PATMOSx pretty much succeeded ISSCP
• Heidinger A. et. al., The Pathfinder Atmospheres–Extended AVHRR
Climate Dataset, V 95 BAMS, 2014
• Starts in 1983, AVHRR
• MODIS/VIIRS: not examined, but there appear to be MODSIS to VIIRS
difficulties (maybe not with gridded like here??)
• AIRS Level 3 (need to examine!)
12
Comparison of AIRS vs Patmosx Means
• PATMOS transitions from NOAA Series to METOP1 during this time period.
Cloud Fraction Anomalies
2004 2006 2008 2010 2012 2014 2016 2018-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Clo
ud F
rac A
nom
aly
(%
)
Patmosx
AIRS
Cloud Fraction Trends vs Latitude
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2
% Change in CF / Year
-80
-60
-40
-20
0
20
40
60
80
La
titu
de
AIRS
Patmos
• AIRS product very simple, more stable and insensitive to SAO
• Transition to CrIS/CHIRP should be simple (maybe none)13
Norris: 25-Year CF Trends (1983-2008)
4 A U G U S T 2 0 1 6 | V O L 5 3 6 | N A T U R E | 7 3
LETTER RESEARCH
albedo and the 1985–1989 ERBS albedo. Every observational record exhibits a decline in cloud amount or albedo at mid-latitudes in both hemispheres that is nearly always statistically significant. The ocean-only MAC-LWP dataset also reports less liquid water path around 40° N and 40° S (Extended Data Fig. 1b). Previous research found evi-dence for tropical expansion in recent decades19. Reduced cloudiness around 40° N and 40° S is consistent with a poleward expansion of
the subtropical dry zone cloud minimum and poleward retreat of the storm-track cloud maximum.
Figure 2d displays trends in zonal mean total cloud amount dur-ing the period 1983–2009 from the ALL simulations. Most individual simulations exhibit reduced cloud amount in the mid-latitudes of both hemispheres, and the ensemble mean trends are statistically significant (P < 0.05 two-sided). Furthermore, the majority of simulations repro-duce the observed increase in cloud amount and albedo occurring in the northern tropics. The spatial correlation between observed and simulated zonal cloud trends is highly significant (Table 1 and Extended Data Fig. 3).
Since the correction procedures applied to the satellite datasets removed any real global mean change that might be present, for maxi-mum comparability we subtracted the 60° S–60° N average change in total cloud amount from the model output before creating Figs 1c and d, and 2d. Without this adjustment, the ALL ensemble mean cloud amount averaged over 60° S–60° N decreases by 0.13% over 25 years. Although highly statistically significant (P < 0.0001 two-sided), the modelled reduction in 60° S–60° N average cloud amount during the period 1983–2009 is far smaller than what is detectable by our obser-vational systems. Extended Data Fig. 4a and b shows ALL cloud trends without the subtraction of the 60° S–60° N average change. They exhibit patterns similar to those seen in Figs 1c and 2d.
ISC
CP
–PA
TMO
S-x
cloud trend (percentage am
ount per 25-year period)
CER
ES–ER
BS
albedo change (percentage albedo
per 25-year period)
CM
IP5 A
LL cloud trend (percentage am
ount per 25-year period)
Majority of observations and CMIP5 simulations agree
3.0 1.5 0.5 0.0 –0.5 –1.5 –3.0
1.8 0.9 0.3 0.0 –0.3 –0.9 –1.8
0.6 0.3 0.1 0.0 –0.1 –0.3 –0.6
a
b
c
d
Positive
Negative
Figure 1 | Change in observed and simulated cloud amount and albedo between the 1980s and 2000s. a, Trend in average of PATMOS-x and ISCCP total cloud amount 1983–2009. b, Change in albedo from January 1985–December 1989 (ERBS) to July 2002–June 2014 (CERES). c, Trend in ensemble mean total cloud amount 1983–2009 from CMIP5 historical simulations with all radiative forcings (ALL). d, Locations where majority of observations and majority of simulations show increases (blue) or decreases (orange). Black dots indicate agreement among all three satellite records on sign of change in a and b and trend statistical significance (P < 0.05 two-sided) in c. All trends and changes are relative to the 60° S–60° N mean change.
Table 1 | Correlation between observed and modelled cloud trend patterns
Forcing type
Spatial pattern ALL GHG AA OZ NAT
Grid box total cloud amount
0.39 (0.0001) [0.003]
0.21 (0.05) [0.08]
0.00 0.00 0.26 (0.03) [0.04]
Zonal mean total cloud amount
0.80 (0.002) [0.009]
0.62 (0.008) [0.06]
−0.35 0.27 0.69 (0.03) [0.03]
Zonal mean cloud amount in the 50–180 hPa and 180–320 hPa intervals
0.76 (0.003) [0.03]
0.73 (0.004) [0.04]
−0.62 0.73 (0.003) [0.04]
Parentheses and square brackets indicate one-sided P values obtained from the preindustrial simulations shown in Extended Data Fig. 3 and from formal significance tests, respectively.
ISC
CP
clo
ud tr
end
(per
cent
age
amou
nt
per 2
5-ye
ar p
erio
d) 2.0 100
1.0 85
0.0 70
–1.0 55
–2.0 40
PA
TMO
S-x
clo
ud tr
end
(per
cent
age
amou
nt
per 2
5-ye
ar p
erio
d) 2.0 100
1.0 85
0.0 70
–1.0 55
–2.0 40
CER
ES –
ER
BS
al
bedo
cha
nge
(per
cent
age
albe
dope
r 25-
year
per
iod) 1.2 50
0.6 40
30 0.0
–0.6 20
–1.2 10
CM
IP5
ALL
clo
ud tr
end
(per
cent
age
amou
nt
per 2
5-ye
ar p
erio
d)
Clim
atol
ogy
(per
cent
age
amou
nt)
Clim
atol
ogy
(per
cent
age
amou
nt)
Clim
atol
ogy
(per
cent
age
amou
nt)
Clim
atol
ogy
(per
cent
age
albe
do)
0.8 100
0.4 85
0.0 70
–0.4 55
–0.8 40 60° S 40° S 20° S Eq 20° N 40° N 60° N
a
b
c
d
Figure 2 | Zonal mean change in observed and simulated cloud amount and albedo between the 1980s and the 2000s. a, Trend in ISCCP total cloud amount 1983–2009. b, Trend in PATMOS-x total cloud amount 1983–2009. c, Change in albedo from January 1985–December 1989 (ERBS) to July 2002–June 2014 (CERES). d, Trend in ensemble mean total cloud amount 1983–2009 from CMIP5 historical simulations with all radiative forcings (ALL). Zonal mean climatology is dotted, linear trend or change is solid, circles indicate trend statistical significance (P < 0.05 two-sided), and bars indicate the interquartile range of individual simulations. All trends and changes are relative to the 60° S–60° N mean change.
© 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
• Norris only showing trend versus ±60° mean value!
• Don’t expect 1983 - 2008 trends toagree with AIRS, but "reasonable"
-60 -40 -20 Eq 20 40 60
Latitude
-4
-2
0
2
% O
ve
r 2
5-Y
ea
rs
AIRS
Patmosx
• AIRS and Patmosx trends in theabove graph are 17 years(2002-2019)
• AIRS trends appear more realistic
• High Patmosx value near -20 islikely Southern Atlantic Anomalyproblems
14
Summary
• Extremely simple cloud fraction algorithm competitive or
better than Patmosx (one channel)
• Magnitudes as expected, in ballpark of 30-year
• Amenable to rigorous error analysis
• Simple to compute allowing easy reprocessing• Many ways to extend:
• Roughly assign to high/middle/low clouds although mixed
scenes will confuse this metric
• Use more optically thick channels for middle versus high cloud
differentiation: clear RTA calcs are very accurate
• Use alternative data for cloud classification
• Use multiple window channels for thin cirrus classification
• Very difficult to produce if radiances are not binned by grid
point (transpose)
• Next: add land and use BTcalc intead of Tsurf with regression
for forcing15