An intercomparison of reanalysis and climate model simulations using atmospheric angular ...

37 Pages·2013·5.3 MB·English

An intercomparison of reanalysis and climate model
simulations using atmospheric angular momentum
Huei-Ping Huang
Arizona State University

The material in this talk is from Danny Paek's PhD thesis (2013)
Paek and Huang (2012, J. Climate, 2012 JGR, 2013 J. Climate)
Dramatic increase in the amount of climate data in
public depositories in the last 5 years
Atmospheric Reanalysis:
NCEP R-1/R-2 and ERA-40 only until 2008
At least 8 reanalysis datasets are available in 2013
Many ocean reanalysis projects are also ongoing
Climate model simulation:
CMIP3: 23 models, 12 TB output in public archive
CMIP5: ~ 30 models, 3,000 TB output will be in public archive
(CLIVAR Exchange 2012)
Climate research relies heavily on reanalysis & climate model
simulations; Important to check the quality of these datasets
Reanalysis
~ 10 atmospheric reanalysis datasets being created or
under development
Some unique approaches:
• 20th Century Reanalysis: Uses surface obs only but
covers a long period (late 19th century - present)
• NCEP CFSR: Uses a coupled model for data assimilation
What differences would these approaches make?
What new information do we gain by having multiple reanalysis
datasets that were derived from the same raw obs?
Assessing the uncertainty in reanalysis is also important for
the validation of climate models. In that practice, we usually
use reanalysis as the "truth".
First, we "stretch" raw observations different ways to make
different reanalysis datasets

Reanalysis #1
Reanalysis #2, etc
Raw
observation
The reanalysis is then used to validate climate models
Ensemble of climate model
Ensemble of reanalyses simulations
Raw
observation
Spread of reanalyses
Spread of climate models
Model bias
A straightforward "intercomparison" study
• Atmospheric reanalysis (8 datasets)
20th century trend
Decadal-to-interdecadal variability
Interannual variability
• CMIP3 (23 models) & CMIP5 (28 models) climate model
simulations
20th and 21st century trend
Decadal-to-interdecadal variability
Key climate indices to use:
Atmospheric angular momentum + Length-of-day (LOD)
-- zonal wind
-- surface pressure
Tropical SST
Global atmospheric angular momentum
M = M + M
R W
(total) (relative) (mass, "omega")
M reflects the strength and location (latitude) of
R
principal zonal jets
M reflects the meridional distribution of atmospheric mass
W
(surface pressure)
R3 pS 2 2
M = ∫∫ ∫ u cos2 d d d p
R
g
0 0 −2
4 2 2
R 
∫ ∫ 3
M = p cos  d d 
W
g S
0 −2
Additional indices & datasets
Length-of-day (LOD) from geodetic measurements
COMB2007 Earth Rotation dataset
Sea surface temperature (SST)
Kaplan, HadISST, NCEP (Reynolds)
Intercomparison of reanalysis:
decadal-to-interdecadal variability and trend
Global relative angular momentum
60-month running mean

20CR has a negative bias against other reanalyses