Boulder Philharmonic & Boulder Ballet Magazines 2015/2016 Season Media Kit
Tropical Biases in Next-Generation Climate Models Stefan Tulich 1 and George Kiladis 2 1 CIRES,...
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Transcript of Tropical Biases in Next-Generation Climate Models Stefan Tulich 1 and George Kiladis 2 1 CIRES,...
Tropical Biases in Next-Generation Climate Models
Stefan Tulich1 and George Kiladis2
1CIRES, University of Colorado, Boulder CO, USA
2NOAA ESRL, Boulder CO, USA
Coupled climate models are known to suffer from biases
Kim et al. (2008)
“High” = 1-2 deg. horizontal grid spacing (5 models)
Coupled climate models are known to suffer from biases
Kim et al. (2008)
“High” = 1-2 deg. horizontal grid spacing (5 models)
Uncoupled models too
12 CMIP3 AGCMs run in AMIP mode
Uncoupled models too
12 AGCMs run in AMIP mode (CMIP3 Archive)
Tropical variability also poorly simulated
Lin et al. (2004)
Focus of this talk
Mean climate and variability of 3 “next-generation” climate models:
1)WRF tropical channel model (36-km, 45S-45N, Kain-Fritsch, AMIP-type run: 2000-2005)
2)GFDL AM2.1 (0.5-deg, global, relaxed AS, SST = climatological ann. cycle, courtesy of G. Lau)
3)SP-CAM (2.5-deg, global, 2D CRM, AMIP-type run, courtesy of M. Khairoutdinov)
Annual Rainfall Bias
SPCAM
WRF
AM2.1
Annual Rainfall Bias
SPCAM
WRF
AM2.1
Annual Rainfall Bias
SPCAM
WRF
AM2.1
Annual Rainfall Bias
SPCAM
WRF
AM2.1
Annual Rainfall Bias
SPCAM
WRF
AM2.1
Annual Rainfall Bias
SPCAM
WRF
AM2.1
Annual Rainfall Bias
SPCAM
WRF
AM2.1
Basic Question
• How do these mean rainfall biases (e.g., too much rain off the equator/too little rain near the equator) relate to the space-time variability of rainfall?
Rainfall Spectra (Symmetric)
TRMM
WRF AM2.1
SPCAM
Rainfall Spectra (Symmetric)
TRMM
WRF AM2.1
SPCAM
Rainfall Spectra (Symmetric)
TRMM
WRF AM2.1
SPCAM
Rainfall Spectra (Symmetric)
TRMM
WRF AM2.1
SPCAM
Rainfall Spectra (Symmetric)
TRMM
WRF AM2.1
SPCAM
Rainfall Spectra (Antisym.)
TRMM
WRF AM2.1
SPCAM
Rainfall Spectra (Antisym.)
TRMM
WRF AM2.1
SPCAM
Summary thus far:
• Kelvin waves generally too weak or non-existent; n = 0 EIG wave non-existent
• Westward-moving waves better represented, but generally too active, especially in a relative sense:
Westward Power (PW) /Eastward Power (PE)
Ratio of westward- to eastward-moving variance (k =1-25)
SPCAM
WRF
AM2.1
10 5 3.3 2.5
Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day)
TRMM SPCAM
WRF AM2.1
Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day)
TRMM SPCAM
WRF AM2.1
Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day)
TRMM SPCAM
WRF AM2.1
Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day)
TRMM SPCAM
WRF AM2.1
Maps of westward- minus eastward-moving rainfall variance (k=1-25, 2-96 day)
TRMM SPCAM
WRF AM2.1
Summary
• Results of this study indicate a linkage between biases in simulated time-mean rainfall and its variability:
– Too much rain at off-eq. latitudes
– Too strong coupling between convection and westward-propagating (rotational) wave disturbances
Open questions
• Is the westward bias in convective wave activity a consequence or a cause of time-mean biases in rainfall?
• What determines the degree of coupling between convection and rotational (Rossby) vs. divergent (Kelvin, n = EIG wave modes)?
Proposal
• A test case for measuring the strength of coupling between convection and rotational vs. divergent circulation anomalies
• Basic idea: perturb a large-domain model by imposing 3D rotational vs. divergent circulations anomalies (i.e., a step beyond SCM approaches) – Parameterized vs. explicit– Coarse vs. fine resolution