uncertainties in the climate mean of reanalyses, observations, and the gfdl climate model

Thomas Reichler and Junsu Kim

Univ. of Utah, Salt Lake City, USA

Supported by the Center for High Performance Computing, Univ. of Utah

3rd WCRP International Conference on Reanalysis, Tokyo, Japan, 28 th January – 1st February 2008

Motivation

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Multi-variate model performance index

wor

sebe

tter

avg.

Some models outperform NCEP/NCAR reanalysis

Error

Models

Questions1. Why are NCEP/NCAR reanalyses not better

than freely evolving coupled model?

2. How do the other reanalyses do?

3. Can the reanalyses be improved?

4. How large are the observational uncertainties?

3

DataObservationsMany global datasets; often multiple

data for same quantity

Reanalyses NCEP/NCAR NNR

NCEP/DOE NDR

ERA40 ERA

JRA25 JRA

Model GFDL CM2.1 GFD

Base period ’79-’99 (in most cases)4

Climate Quantities

5

“Physics” (18)

“Dynamics” (13)

ERA-40 as reference

(2000-2005) (1985-1989) (1984-1999)

RSUT: ObservationsAnnual mean outgoing shortwave radiation TOA

2000-

<10 Wm-2 RMS error amongst different observations:= Observational uncertainty

Mean of different observations:= Best observational estimate

RSUT: Reanalysis vs. ObservationsRMS

24

22

19

15

12

Wm-2

Observational uncertainty (<10 Wm-2) is smaller than reanalysis error (20 Wm-2) Observational uncertainty is acceptable

Reanalyses and model show similar error patterns, independent of observations Errors are real Common biases due to similar physics?

What about other quantities?

RSUT Summary

Common Reanalyses Biases

Break-down by

• product NNR, NDR, ERA, JRA, GFD

• quantity physics - dynamics

• region NH, TR, SH

• season DJF, MAM, JJA, SON

• observation 1-5

Normalized RMS error:

Error Analysis

22

n nn

n n

r oNRMS w

“Physics”

UPPER

PH

YS

ICS

DY

NA

MIC

S

SURFACE CLOUDS / RADIATION

E

3 2 2 3 5 5 2 2 1 2 3 4 4 4 3 3 4 4

small largeNRMS-error

Validated against multi-observational

mean

OBS

MOD

REA

• Large uncertainties for surface fluxes• Largest errors for “clouds and radiation”• Model sometimes as realistic as reanalyses

“Dynamics”UPPER

PH

YS

ICS

DY

NA

MIC

SSURFACE CLOUDS / RADIATION

E

GLANN

small largeNRMS-error

Validated against ERA-40

MOD

REA

Lack of global observations Smaller differences than “physics” Model clearly not as close Except: meridional wind (MMC, VA) and

specific humidity (HUS)

Cumulative ErrorsPhysics•Largest errors over SH and during spring and summer•Model does quite well

Dynamics•JRA closest to ERA, NNR most different•Large model errors, in particular Tropics

Physics Dynamics

NNR

NDR

JRA

ERA

GFD

Conclusion1. Why do NNR not better

than models?

2. What about other reanalyses?

3. Room for improvement?

4. Observational uncertainties?

• Mostly “physics” quantities• Tuning, model physics,

forcings, data assimilation

• Common biases• Overall, ERA closest to

observations

• Yes, see 1.

• Large uncertainties in surface fluxes

Thanks

Global RMS errorsOBS ERA GFD

PR 0.8 1.6 1.3 mm/day

CLT 10 10 14 %

RLUT 6 12 11 Wm-2

RSUT 10 20 17 Wm-2

RLDS 13 10 12 Wm-2

RLUS 14 10 13 Wm-2

RSDS 17 28 20 Wm-2

RSUS 14 9 9 Wm-2

PSL 0.8 0.6 2.0 hPa

TAS 0.1 1.1 2.0 K

TAUU/V 1.6 1-2 1-2 10-2 Nm-2

HFLS 20 13 20 Wm-2

HFSS 7 3.5 7 Wm-2

Observational Uncertainties

uncertainity

error

Uncertainty Ratio:

How tolerable are errors?

<<1 tolerable