Title: Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models Richard Allan Environmental Systems Science Centre, University of Reading Thanks to: Tony Slingo and Mark Ringer
1Using Satellite Observations and Reanalyses to
Evaluate Climate and Weather ModelsRichard
AllanEnvironmental Systems Science Centre,
University of ReadingThanks to Tony Slingo
and Mark Ringer
2INTRODUCTION
- Evaluation of Weather and Climate Prediction
Models (some examples) - Climate prediction uncertainty dependent on
feedback processes - What time/space-scales are important for climate
change - Feedbacks generally operating on shorter
time-scales - but diagnosis of feedbacks may only be possible
on longer time-scales
3OVERVIEW OF TALK
- 1) Evaluating simulated radiation budget
- dynamical regimes, climate model, reanalysis
- 2) Clear-sky radiation and sampling
- 3) Interannual Variability
- Water vapour, cloud radiative effect, reanalyses?
- 4) Geostationary Earth Radiation Budget
- GERB, Met Office NWP model, surface radiation
41) Evaluating model simulations of top of
atmosphere radiation budget
- Important for the radiative/convective balance of
model - Valuable diagnostic of model clouds, water
vapour, etc
5OLR (Wm-2) (colours) Omega, hPa/day
(contours) April 1998
Model
Obs
Model - Obs
6Ggg
Omega hPaday
SST (K)
Ringer Allan (2004) Tellus A
7- Climate models must simulate adequately the
properties of cloud within each dynamic regime
and how they respond to warming - See also, e.g.
- Bony et al. (2003) Clim. Dyn
- Williams et al. (2003) Clim. Dyn.
- Tselioudis and Jakob (2002) JGR
- Chen et al. (2002) Science
82) Clear-sky radiation
- Longwave cooling important for determining
subtropical subsidence - Clear-sky OLR important diagnostic for water
vapour and temperature - Difficulties in observing clear-sky radiation
- Monthly mean clear-sky radiation over convective
regions - Satellite will sample highly anomalous situations
9Using ERA-40 Daily data to illustrate clear-sky
sampling bias of CERES data
10Model-obs differences Clear-sky Sampling
Type II HadAM3-OBS Type-I
DT6.7 DOLRc
11DOLRc (Wm-2)
12Using ERA40 clear-sky OLR to evaluate dynamical
regimes
ERA40-CERES similar ERA40 lt CERES
ERA40 minus CERES clear-sky OLR (January-August
1998)
Allan Ringer 2003, GRL
13- Need to account for clear-sky sampling
differences between satellite and models - Reanalyses offer one alternative
- Especially important where clear-sky situations
are rare - e.g. monthly mean clear-sky OLR differences of
about 15 Wm-2 for tropical convective regimes
143) Interannual variability in water vapour and
clouds
- How do clouds and water vapour respond to global
warming? - Interannual variability one example of range of
tests of climate models - e.g. paleo, century, decadal, ENSO, seasonal,
diurnal, etc - Water vapour variation
- Boundary layer, free tropospheric RH, reanalyses?
- Decadal changes in cloud radiative effect
15Evaluation of HadAM3 Climate Model
- AMIP-type 1979-1998 experiments
- Explicitly simulate 6.7 mm radiance in HadAM3
- Modified satellite-like clear-sky diagnostics
16 Interannual variability of Column Water vapour
(Allan et al. 2003, QJRMS, p.3371)
SST
CWV
1980 1985
1990 1995
See also Soden (2000) J.Clim 13
17CWV Sensitivity to SST
- dCWV/dTs 3.5 kgm-2 K-1 for HadAM3 and
Satellite Microwave Observations (SMMR, SSM/I)
over tropical oceans - Corresponds to 9K-1 in agreement with Wentz
Schabel (2000) who analysed observed trends - But what about moisture away from the marine
Boundary Layer?
18Can we use reanalyses?
Allan et al. 2004, JGR, accepted
Reanalyses are currently unsuitable for detection
of subtle trends associated with water vapour
feedbacks BUT Climatology from ERA40 is good.
Variability from 24 hr forecast from ERA40 is
much better than above.
19Clear-sky OLRInterannual monthly anomalies
tropical oceansHadAM3 vs ERBS, ScaRaB and CERES
ga1-(OLRc/sTs4)
1980 1985 1990 1995
(Allan et al. 2003, QJRMS, p.3371)
20dOLRc/dTs2 Wm-2 K-1 doesnt indicate consistent
water vapour feedback?
HadAM3
GFDL
HadAM3
GFDL
dTa(p)/dTs dq(p)/dTs
Allan et al. 2002, JGR, 107(D17), 4329.
21Sensitivity of OLRc to UTH
22Interannual monthly anomalies of 6.7 micron
radiance HadAM3 vs HIRS (tropical oceans)
(Allan et al. 2003, QJRMS, p.3371)
Small changes in T_6.7 (or RH) in model and obs
(dUTH/dTs 0 ?)
23(additional forcings)
(Allan et al. 2003, QJRMS, p.3371)
24Small changes in RH but apparently larger changes
in tropical cloudiness? (Wielicki et al, 2002)
25Altitude and orbit corrections (40S-40N)
Clear LW
LW
SW
Following Wielicki et al.
(2002) Allan Slingo (2002)
26- Water vapour changes in models and satellite data
consistent with constant RH - Variability in cloud radiative effect in models
appears underestimated compared to ERB data even
after recent corrections - Reanalysis are at present unsuitable for looking
at subtle changes and trends in water vapour and
cloud
274) Comparisons between Geostationary Earth
Radiation Budget (GERB) data and Met Office NWP
model (SINERGEE)
- Similar spatiotemporal sampling
- model time step GERB time 15-20 minutes
- Spatial resolution 60 km
- Near real time comparisons
- http//www.nerc-essc.ac.uk/rpa/GERB/gerb.html
28OLR
SINERGEE comparisonof Met Office NWP Model with
GERB data
GERB
Model
Example comparison 31st March 2004, 12h00
Albedo
29 Combining GERB and BSRN radiation data
30CONCLUSIONS
- Radiation budget as function of dynamical
regimes evaluate cloud radiative effect in
models - Need to account for different clear-sky sampling
between models and data - Interannual variability
- Decadal variations of RH small in models and data
- Variations in cloud radiative effect appear to be
underestimated by models - Comparisons of GERB with NWP model shorter
timescales closer to details of parametrizations