Transcript Climate Science Briefing for Kathie L. Olsen NASA Chief

Climate Change:
Moonshine, Millions of Models, & Billions of Data
New Ways to Sort Fact from Fiction
Bruce Wielicki
March 21, 2007
University of Miami Lecture
0.21 Wm-2
Shows consistent calibration stability at < 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
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
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
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
Early Cloud Feedback Signals in the Arctic from CERES data
Seiji 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
•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.
•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.
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, doi:10.1029/2006GL026685.
Ocean Heat Content and Net Radiation
A 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.
"Global Dimming": is it real?
What about new CERES fusion satellite surface fluxes?
ARM/BSRN/CMDL/Surfrad Surface Radiation Sites
Surface SW Flux Validation Noise
Spatial 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)
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)
How well can we pull climate records from meteorological
satellite data like ISCCP from geostationary?
Geo calibration &
sampling errors
dominate interannual 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
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.
Stainforth et al.,
2005, Nature
Neural Net Structure
Climate OSSEs
Input Variables
Planet “I” - Planet “J”
base state CO2 climate
TOA SW Flux
TOA LW Flux
Total Cloud Fraction
Conv. Cloud Fraction
Total Precipitation
Large Scale Snowfall
Large Scale Rainfall
Surface Latent Ht Flux
Surface Net SW Flux
Surface Net LW Flux
Surface Net Radiation
Output Variables
Planet “I” - Planet “J”
2xCO2 minus 1xCO2
Add
Observation
Error
Bias, 
Bias, 
Bias, 
Bias, 
Bias, 
Bias, 
Bias, 
Bias, 
Bias, 
Bias, 
Bias, 
Neural
Network
Surface Temperature
Summer U.S. Precip
Sea Level
etc...
Difference in neural net performance with and without observation errors
Isolates effect of observation error on constraining climate uncertainty
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.