MPI Metorolo gy comparing ISCCP and GEWEX products Madison, July 2006 Stefan Kinne Max Planck Institute for Meteorology Hamburg, Germany Ehrhard Raschke University of Hamburg Hamburg, Germany
Jan 08, 2016
MPIMetorology
comparing
ISCCP and GEWEX products
Madison, July 2006
Stefan Kinne Max Planck Institute for Meteorology Hamburg, Germany
Ehrhard RaschkeUniversity of HamburgHamburg, Germany
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overview
available long-term global data-sets for radiative fluxes at the Top of Atmosphere (ToA) at the surface (sur)
concept on investigating consistency
assessments of solar flux comparisons
assessments of infrared flux comparisons
recommendations
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Earth’s radiation budget
how accurate defined is the radiation budget of our climate system?
know your clouds … size-distribution (z) cover (z)
know ancillary data … surface + s-processes anthop. influences
…on regional and seasonal scales
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2 long-term data-setsdescribe radiation budgets at ToA and surface
ISCCP GOAL: extract data on cloud field characteristics from
operational meteorological satellite sensors years: 1983-2004, res: 250km (spatial) , 3hr (temp) processedC at NASA-GISS (Rossow, Zhang)
GEWEX-SRB GOAL: determine radiation budgets at the surface years: 1983-2004, res: 100km (spatial) , daily (temp) processed at NASA-Langley (Stackhouse) clouds properties are ‘based‘ on the ISCCP climatology !
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task at hand
two bb-flux data sets for same time-period based on the same cloud data
we should expect similar (if not the same) data
let’s test that stratify data into zonal bands of monthly means display differences (always ISCCP minus GEWEX) interpret differences and highlight issues
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regional temporal choices
75-90N (1.7%)
60-75N (5.0%) 30-60N (18.3%)
0-30N (25.0%)
0-30S (25.0%)
30-60N (18.3%)
60-75N (5.0%)
75-90N (1.7%)
use monthly averages
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solar fluxes
solar ToA the ‘solar’ driver solar surface solar atm.
transmittance solar / surface surface albedo solar / ToA planetary albedo
typical plot: timeseries of monthly averagesdiff.colors for diff.latitude zones
ISCCP -GEWEXdeviation
Time (starting in 1983)
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ISCCP – GEWEX sol toa DECEMBER 2005WHY DEVIATIONS ?
simplified treatment ofGEWEX solar insolationat low sun-elevations
for the record: largerdeviations are goneIn new GEWEX data
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conclusion # 1
un-necessary deviation for ‘solar driver’
low sun, avg (lat, t)
also an issue in global modeling IPCC-4AR
use consistent routines for ToA insolation !
agree on orbit and So
implement properly!spat/temp integration
-2
-1.5
-1
-0.5
0
0.5
1
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
CCS-1368
CCs-1368
CNR-1370
DNM-1368
GFD-1366
GI-1366
GIS-1367
IAP-1380
MPI-1367
MRI-1365
NCA-1367
PCM-1369
PCMOD-1366
-4,22 -6,84
Global annual insolation anomalies (1980 to 1999)
solar insolation of IPCC models
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ISCCP – GEWEX sol sur
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ISCCP – GEWEX sol sur
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at surface: differences among data-sets are larger !high lat. peaks are out phase to ToA peaks
TOA
a cloud issue !
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conclusion # 2
‘sol ToA’ differences are lost at ‘sol surface’ and ‘sol surface’ differences are larger (!)
differences in atmospheric properties dominate larger differences (season dep.) at higher latitudes
most probable explanation diff. in cloud-cover / cloud opt.depth (for data-sets)
assessment: cloud cover / optical depth differ ! ‘cloud’ differences have a seasonal dependence GEWEX cloud (opt. depth/cover) impact is stronger
especially during polar summers (particularly in SH) (… yet weaker during mid-latitude summer in SH)
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ISCCP – GEWEX sol / sur
largest differences during NH mid-lat winters- at high latitudes (not shown) even worse !
a snow issue !
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conclusion # 3
solar surface albedo in models differs differences have a seasonal dependence sign of diff. varies between high and low latitudes largest differences are linked to snow (alb. / cover)
GEWEX has smaller solar surface albedos at higher latitudes
especially in seasons, when snow can be expected … yet larger solar surface albedos in the tropics
assessment on solar surface albedo: accuracy and consistency of ancillary (non-cloud data) data matters !
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ISCCP - GEWEX sol / toa
text
a combination of all previous biases
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conclusion # 4
diff. in plantetary albedo display combined effect solar insolation biases solar surface albedo atmospheric properties (especially those of clouds)
potential for offsetting errors
planetary albedo at ToA differences surface albedo diff. at mid/ high lat. are modulated
as expected by cloud impact based on solar transm. - except for tropics: GEWEX clouds less reflective!
assessment: cloud microphysics differ
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infrared
IR surface [emission] surf. temp effect IR surface (low) cloud effect IR at ToA [OLR] (high) cloud
effect
ISCCP -GEWEXdeviation
typical plot: timeseries of monthly averagesdiff.colors for diff.latitude zones
Time (starting in 1983)
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ISCCP – GEWEX ir sur
text
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ISCCP – GEWEX ir sur
textcan this trendbe detected at- ir sur ?- ir toa ?
‘false’ trend due to the use ofincorrect surface temperature data for ISCCP in the tropics
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ISCCP – GEWEX ir sur
there NO: atm. effects (clouds) dominate
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ISCCP-GEWEX ir toa
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NO: atm. effects (clouds) dominate
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ISCCP-GEWEX ir toa/sur
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toa
sur
lower GEWEX opt.depth/coverhigher GEWEX opt.depth/cover
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conclusion # 5
atmospheric properties are main IR modulators
surface emission differences vs OLR differences usually consistent with cloud (opt.depth/cover) bias … though not always !
cloud boundary temperatures matter atm. temp. profile or altitude placement of cloud?
assessment: cloud altitude placement differs
other important ancillary data: surface temperature / atm. temperature profile
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conclusions
ISCCP and GEWEX radiations products often disagree on cloud and ancillary data
significant difference for cloud properties surprise, given the same cloud data-source
larger disagreements at high-latitudes potential offsets can dilute severity of problem
careful validation to quality data are needed ground-based network (BSRN) ? use synergy of advanced space sensors (A-train)
collaboration of data/analyzing groups needed
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recommendations
develop a reference algorithm for ToA solar insolation Earth’s orbital data, solar constant, low sun elevation issue
re-evaluate cloud properties and ancillary data (T, snow) compare to in-situ and ground-based quality data identify systematic diff. on regional / seasonal scales
treat cloud and ancillary data in a consistent manner implementation ( … to suit model / data-set resolution)
document your steps ! supply complete and detailed explanations on assumptions
and methods – including a brief summary to allow a hasty user to understand major characteristics and error sources.
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extras
solar downward surface flux ‘trend’ solar transmission ratio and ‘trend’ solar planetary ‘trend’ / ‘trend’ differences infrared surf emission ‘trend’ / ‘trend’ differences infrared outgoing ir flux ‘trend’ differences all-sky vs. clear-sky: the cloud effect
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ISCCP – GEWEX sol sur
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MAY 2006
high latitudes only
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ISCCP sol sur
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MAY 2006
lower latitudes
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GEWEX sol sur
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lower latitudes
MAY 2006
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ISCCP/GEWEX sol (sur/toa)
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ISCCP/GEWEX sol (sur/toa)
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GEWEX sol (sur /toa)
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ISCCP – GEWEX sol / toa
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lower latitudes
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ISCCP – GEWEX sol / toa
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high latitudes
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ISCCP sol / toa
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GEWEX sol / toa
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ISCCP – GEWEX ir sur
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ISCCP ir sur
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ISCCP-GEWEX ir toa
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lower latitudes
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ISCCP – GEWEX ir toa
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high latitudes
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ISCCP-GEWEX cld effect solsur
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ISCCP cloud effect sol / toa
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ISCCP cloud effect ir toa
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ISCCP 91-95 sol+ir toa
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Raschke et al., Int.J. Clim. 2005
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ISCCP 91-95 sol+ir atm
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Raschke et al., Int.J. Clim. 2005