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