A 'model-to-radiance' comparison of simulated brightness temperatures and radiances
from the Hadley Centre Global Environmental Model 2 (HadGEM2-A) with
longwave measurements from the High Resolution Infrared Radiation Sounder/4
(HIRS/4) and the Infrared Atmospheric Sounding Interfermeter (IASI) onboard the
MetOp-A satellite is presented for all-sky and clear-sky global means. The fast Radiative
Transfer model for TOVS 10 (RTTOV-10) is applied to HadGEM2 output
to simulate observational-equivalent data. The results are compared with corresponding
broadband analyses. A method is developed to extend hyperspectral IASI
radiances to cover the whole outgoing terrestrial spectrum, in order to identify any
compensating biases, and explore wavebands in the unobserved Far Infrared (FIR)
region.
For the all-sky HIRS analysis, the model overestimates brightness temperatures in
the atmospheric window region with the greatest biases over areas associated with
deep convective cloud. In contrast to many global climate models, much smaller
clear-sky biases are found indicating that model clouds are the dominating source of
error. Simulated values in upper atmospheric CO2 channels approximate observations
better as a result of compensating cold biases at the poles and warm biases at
lower latitudes, due to a poor representation of the Brewer Dobson circulation in the
38 level 'low-top' configuration of the model. Simulated all and clear-sky outgoing
longwave radiation evaluated against the Clouds and the Earth's Radiant Energy
System (CERES) and HIRS OLR products reveal good agreement, in part due to
cancellation of positive and negative biases. Through physical arguments relating
to the spectral energy balance within a cloud, it is suggested that broadband agreement
could be the result of a balance between positive window biases and unseen
negative biases originating from the water vapour rotational band in the FIR (not
sampled by HIRS). Simple sensitivity tests show that dramatically altering existing
cloud properties has little effect on the prominent window biases, however raising
clouds a maximum of 5 atmospheric levels minimises the error in cloud contaminated
channels, due to the introduction of spatially compensating errors. Sensitivities to
the way ice clouds are parameterised in RTTOV-10 display a range of up to 2.5 K
in window channels but absolute biases still exceed 3 K for all choices.
Because of the lack of satellite based FIR observations due to a technological gap in
the spectral region, an algorithm is created to 'fill in' the available data. Correlations
between selected IASI channels and simulated unobserved wavelengths in the
far infrared are used to estimate radiances between 25.25 - 644.75 cm-1 at 0.5 cm-1
intervals. The same method is used in the 2760 - 3000 cm-1 region. The spectrum
is validated by comparing the Integrated Nadir Longwave Radiance (INLR) product
(spanning the whole 25.25 - 3000 cm-1 range) with the corresponding broadband
measurements from the Clouds and the Earth's Radiant Energy System (CERES)
instrument on the Terra and Aqua satellites at simultaneous nadir overpasses, revealing
mean differences of 0.3 Wm-2sr-1 (0.5% relative difference) lower for IASI relative
to CERES and significantly lower biases in nighttime only scenes. Averaged global
data over a single month produces mean differences of about 1 Wm-2sr-1 in both the
all and the clear-sky (1.2% relative difference). The new high resolution spectrum
is presented for global mean clear and total skies where the far infrared is shown
to contribute 44% and 47% to the total OLR respectively, which is consistent with
previous estimates. In terms of spectral cloud radiative forcing, the FIR contributes
19% and in some subtropical instances appears to be negative, results that would go
un-observed with a traditional broadband analysis.
The equivalent complete IASI OLR model product is simulated from GCM data using
RTTOV-10. The same process of applying predictors to the satellite measurements is
applied to the model simulated radiances, with appropriate modifications, to produce
a directly comparable model product. Annual mean all-sky radiances are still greatly
overestimated at all wavenumbers with a total radiance bias of 4.52 Wm-2 across the
whole range. Compensating negative biases outside of the HIRS coverage that were
hypothesised are absent, with the far infrared contributing to the overall bias rather
than cancelling it. Equivalent clear-sky biases are much lower overall at 0.39 Wm-2,
in part due to spectral and spatial cancellation of errors. A flux-to-flux comparison
is enabled by estimating the spatial distribution of anisotropic factors, using collated
HIRS OLR fluxes and IASI OLR radiances, which yields global mean model fluxes
in excess of 12 Wm-2 higher than observations in the all-sky. The difference between
this and the fluxes calculated using the climate model's broadband radiation code
(Edward-Slingo) are around 10 Wm-2 which is outside the range of uncertainty in
the method used to estimate the flux. However, it is discussed that tuning of the
climate model's broadband code to known flux values is a required practice to ensure
global energy budgets balance but can produce inaccurate parameterised variables.
An equivalent analysis adjusting the ice cloud parametrisation to reflect the radiances
that have the biggest differences to the original configuration selected showed a bias
reduction of 4.5 Wm-2, which is still not enough to completely explain its size,
suggesting the existence of residual cloud problems. Finally, it is suggested that the
way forward in separating and constraining cloud errors, in both radiative transfer
codes, is a rigorous process of testing them with observation cloud properties and
reanalysis data as inputs
