Climate Change: Moonshine, Millions of Models,

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Climate Change Moonshine, Millions of Models,
Billions of DataNew Ways to Sort Fact from
Fiction

• Bruce Wielicki
• March 21, 2007
• University of Miami Lecture

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0.21 Wm-2
Shows consistent calibration stability at lt 0.3
Wm-2 per decade (95 conf) Unfortunately only
works for tropical mean ocean (nband vs bband
issues) Regional trends differ by 2 to -5
Wm-2/decade SeaWiFS vs CERES
Loeb et al. 2007 J. Climate
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Using CERES to Determine Length of Climate Data
Record Needed to Constrain Cloud Feedback
Half of Anthrop Forcing of 0.6 Wm-2 /decade

• Given climate variability, 15 to 20 years is
  required to first detect climate trends at cloud
  feedback level with 90 confidence,
• and 18 to 25 years to constrain to /- 25 in
  climate sensitivity

Loeb et al. 2007 J. Climate
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Annual Mean Global SW TOA Flux Anomaly (Earthshine
versus CERES 2000 to 2004)
Earthshine data implies large change of 6 Wm-2
in global reflected SW flux is the Earth's
albedo changing? (Palle et al., Science, 2004)
CERES shows an order of magnitude
less variability than Earthshine
Earthshine approach is incapable of capturing
changes in global albedo at climate accuracy.
Loeb et al. 2007 GRL
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CERES Shortwave TOA Reflected Flux Changes Ties
to Changing Cloud Fraction
Tropics drive global albedo variations global
is in phase with tropics and 1/2 the magnitude
Cloud fraction variations are the cause (not
optical depth)
Unscrambling climate signal cause and effect
requires complete parameter set at climate
accuracy, e.g. for forcing/response energetics
radiation, aerosol, cloud, land, snow/ice,
temperature, humidity, precipitation
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Early Cloud Feedback Signals in the Arctic from
CERES dataSeiji Kato and the CERES Science Team
Mean Cloud Fraction at Barrow AK
Trends derived from Terra and Aqua Data over the
Arctic
Terra
Aqua
Linear Fit to Terra
Missing days

• Snow/sea ice fraction changed at a rate of
• 0.064 per decade (significant at an 80
• confidence level)
• Cloud fraction changed at a rate of 0.047
• per decade (significant at an 80 confidence
• level)
• Albedo change is insignificant at an 80
• confidence level.

• CERES Derived from MODIS
• radiances by the CERES cloud algorithm.
• Radar Derived from a ARM cloud radar.
• Lasers Derived from a micro-pulse lidar and
• a Vaisala ceilometer
• Error bars and dashed lines indicate max.
• and min. during 4 years.

From Kato, S., N. G. Loeb, P. Minnis, J. A.
Francis, T. P. Charlock, D. A. Rutan, E. E.
Clothiaux, S. Sun-Mack, 2006 Seasonal and
Interannual Variations of Top-of-Atmosphere
Irradiance and Cloud Cover over Polar Regions
Derived from the CERES Data Set, Geophys. Res.
Lett., 33, L19804, doi10.1029/2006GL026685.
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Ocean Heat Content and Net RadiationA case study
in the need for independent observations
analysis
Ocean Cooling? Lyman et al., Science 2006 Net
Radiation no Altimeter Sea Level no GRACE Ice
Sheet no
1992 to 2003 data from Wong et al. J. Climate 2006

• Possible Causes of 2004/5 Drop in Ocean Heat
  Storage
• transition from XBT to ARGO ocean in-situ data
  and sampling?
• cooling upper 750m of the ocean, but larger
  heating deeper?
• unmeasured heating under sea ice?
• The answer warm bias in XBTs (dominate
  pre-2002) cold bias in ARGO (dominate post 2002)
  no cooling in 2004/5 when bias is corrected.
  mystery solved.

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"Global Dimming" is it real? What about new
CERES fusion satellite surface fluxes?
ARM/BSRN/CMDL/Surfrad Surface Radiation Sites
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Surface SW Flux Validation NoiseSpatial mismatch
of surface point to satellite area
Error decreases as simple 1/sqrt(N) random noise
but takes 20 sites for 1 year to reach 1 Wm-2
10,000 samples.
(Wielicki, AMS Radiation Conference, 2006)
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CERES Surface Fluxes vs Surface
Sites Interannual Anomalies Consistent at 0.2
or 0.3 Wm-2
Global satellite sampling of radiation fields
remains key regional variability (climate noise)
is very large 10 times the global forcing of 0.6
Wm-2/decade even averaging 40 disperse surface
sites. Result from GEWEX Radiative Flux
Assessment (in progress)
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How well can we pull climate records from
meteorological satellite data like ISCCP from
geostationary?
Geo calibration sampling errors dominate
inter-annual signals
Uncertainty in Geo trends are a factor of 10
larger than climate goal can we learn how to
improve past data sets?
Loeb et al., 2007 J. Climate
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Trend in All-sky Downward SW flux at the Surface
(2000-2004) ISCCP vs CERES
CERES (SRBAVG_GEO)
ISCCP minus CERES

• ISCCP trends show systematic regional patterns
  that coincide with the area of coverage by the
  individual GEO instruments.
• Artifacts in the GEO data are removed in CERES
  processing by a normalization procedure that
  corrects for GEO calibration, narrow-to-broadband,
  and radiance-to-flux coversion errors, so that
  fluxes from each GEO instrument are consistent
  with CERES.

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Stainforth et al., 2005, Nature
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Neural Net StructureClimate OSSEs
Difference in neural net performance with and
without observation errors Isolates effect of
observation error on constraining climate
uncertainty
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Climate OSSE's Perturbed Physics Ensembles

• Early Conclusions
• - Uses 2500 climateprediction.net mixed layer
  CO2, 2 x CO2 runs
• - Trains neural net on 5 of 6 million climate
  run "pairs", tests rest
• - Use of base climate state model differences
  can predict to 0.4C (1?) the doubled CO2
  sensitivity differences over range of 2 to 12C.
• - Of 33 global mean climate variables, most
  information is in 11 (radiative fluxes, cloud
  cover, precipitation, snowfall, latent heat)
• - Use of base state climate metrics is highly
  nonlinear linear regression factor of 2.5 lower
  accuracy.
• - Use of base state climate metrics from
  climateprediction.net simulations fails to
  predict accurately for IPCC mixed layer runs.
• - Use of climate change metrics (e.g. decadal
  change) are much more accurate than base state,
  are more linear, and show modest loss of accuracy
  when applied to IPCC runs.
• - Adding observation errors seriously degrades
  accuracy of neural net predictions if
  observational error exceeds 25 of climate change
• - Writing up for BAMS, next steps are coupled
  ocean/atmosphere models and more complete climate
  metric tests.