NOTES AND CORRESPONDENCE Initial Tendencies of Cloud ...

15 FEBRUARY 2008 NOTES AND CORRESPONDENCE 833

NOTES AND CORRESPONDENCE

Initial Tendencies of Cloud Regimes in the Met Office Unified Model

K. D. WILLIAMS

Met Office Hadley Centre, Exeter, United Kingdom

M. E. BROOKS

Met Office, Exeter, United Kingdom

(Manuscript received 16 February 2007, in final form 30 May 2007)

ABSTRACT

The Met Office unified forecast–climate model is used to compare the properties of simulated climato-
logical cloud regimes with those produced in short-range forecasts initialized from operational analyses. The
regimes are defined as principal clusters of joint cloud-top pressure–optical depth histograms. In general,
the cloud regime properties are found to be similar at all forecast times, including the climatological mean.
This suggests that weaknesses in the representation of fast local processes are responsible for errors in the
simulation of the cloud regimes. The increased horizontal resolution of the model used for numerical
weather prediction generally has little impact on the cloud regimes, although the simulation of tropical
shallow cumulus is improved, while the relative frequency of tropical deep convection and cirrus compare
less favorably with observations. Analysis of the initial temperature tendency profiles for each cloud regime
indicates that some of the initial temperature tendency, which leads to a systematic bias in the model
climatology, is associated with a particular cloud regime.

1. Introduction Recently, several groups have investigated running
climate models in “weather forecast mode” (i.e., initial-
Differences in the radiative response from clouds ac- izing from an analysis produced by a data assimilation
count for much of the variation in climate sensitivity system) to diagnose the cause of systematic errors
between global general circulation models (GCMs) (Phillips et al. 2004; Klein et al. 2006; Strachan 2007).
used for climate change projection (e.g., Cess et al. Meanwhile, Rodwell and Palmer (2007, hereafter
1990; Senior and Mitchell 1993; Webb et al. 2006; Soden RP07) analyze initial tendencies in state variables
and Held 2006; Ringer et al. 2006). Accurate simulation (e.g., Klinker and Sardeshmukh 1992) over the first
of cloud is also crucial for numerical weather prediction few hours of a weather forecast. They suggest that these
(NWP) models due to the direct effect on key forecast initial tendencies may be used to form a metric for the
products, such as surface temperature, precipitation, representation of fast processes in a climate model.
and visibility, and also the large impact that diabatic The Met Office has the unique asset of a unified
processes exert on the evolution of the forecast (Liou weather forecast–climate model (UM; Cullen 1993);
and Zheng 1984; Forbes and Clarke 2003). Since indi- that is, a GCM with the same physical parameteri-
vidual cloud processes act on relatively short time zations of the atmosphere is used for operational
scales, it is appropriate to consider cloud errors in cli- weather forecasts and climate change projection. This
mate models on NWP time scales and address the cloud enables the climate model to be initialized from an
simulation in both sets of models in a consistent frame- analysis that has been produced by a data assimilation
work. system developed around the same physical model.
This “seamless forecasting” approach is recognized by
Corresponding author address: Keith Williams, Met Office Had- the World Climate Research Program (WCRP) as be-
ley Centre, FitzRoy Road, Exeter EX1 3PB, United Kingdom. ing important to the continued development of GCMs
E-mail: [email protected] (WCRP 2005).

DOI: 10.1175/2007JCLI1900.1

834 JOURNAL OF CLIMATE VOLUME 21

Using the concept of “regimes” in the evaluation of tal resolution; the NWP model is run at N216 (0.833° ϫ
GCMs can be useful for both NWP and climate models 0.556°). Other differences are mostly related to the
since they provide a summary of model performance resolution change (e.g., diffusion settings, the time scale
across a range of synoptic situations and tend to be for the dissipation of convective available potential en-
broadly aligned with some of the large-scale atmo- ergy (CAPE) by the convection scheme, etc.).
spheric processes. Jakob and Tselioudis (2003) use a
clustering technique on International Satellite Cloud Twenty-four simulations of both the N96 and N216
Climatology Project (ISCCP) joint cloud-top pressure– models have been run from analyses between 1 Novem-
optical depth histograms to objectively identify cloud ber 2005 and 31 October 2006. Each forecast is run for
regimes. Williams and Tselioudis (2007, hereafter 5 days. ISCCP simulator diagnostics, which aim to emu-
WT07) apply the technique to ISCCP simulator output late the ISCCP observational data (Klein and Jakob
from an ensemble of climate GCMs and demonstrate 1999; Webb et al. 2001; http://gcss-dime.giss.nasa.gov/
that a reduction in the range of climate sensitivity simulator.html), and top-of-atmosphere (TOA) fluxes
amongst the GCMs might be achieved if the simula- are saved at the end of the first time step and every 3 h
tions of the mean present-day regime characteristics thereafter (which is equal to the radiation time step) for
were closer to those observed. In this study, one of the the first day and then as daily means for days 2–5. Al-
models analyzed by WT07 (the Met Office UM) is used though 24 forecasts do not provide a large ensemble,
to investigate the simulation of cloud regimes in the results were found to be very similar when only half
weather forecast mode. The primary aims are to com- of the forecasts are analyzed. The 24 analyses are
pare the cloud regimes in the model climatology and in evenly distributed through the year so that results can
a short-range forecast, to investigate whether the in- be compared with the HadGAM1 climatology and are
creased resolution of the operational forecast model distributed 6 hourly through the diurnal cycle (i.e., six
improves the simulation, and to investigate whether ini- forecasts starting 0000, 6000, 1200, and 1800 UTC), so
tial tendencies in the model’s state variables may be that when the forecasts are combined, all regions have
attributed to regime-specific errors. some sunlit points for each forecast validation time.
This is necessary as ISCCP simulator diagnostics are
only available at sunlit points.

2. Model, observations, and methodology b. Observational data

a. Model simulations The cloud regimes produced from the UM are com-
pared with ISCCP observational data (Rossow and
The current climate version of the Met Office UM is Schiffer 1999). The ISCCP D1 product is used, which
known as the Hadley Centre Global Atmospheric contains cloud amount in six optical depth (␶) and
Model version 1 (HadGAM1; Martin et al. 2006). The seven cloud-top pressure (CTP) categories on a 2.5°
standard climate resolution for this model is N96 grid (i.e., the dataset is formed of a ␶–CTP histogram
(1.875° ϫ 1.25°). For this study, the model has been run for each grid point). TOA fluxes from the ISCCP FD
from a series of operational analyses generated using product (Zhang et al. 2004) and the S4G product from
the Met Office’s four-dimensional variational assimila- the Earth Radiation Budget Experiment (ERBE; Bark-
tion system (Rawlins et al. 2007). These analyses are strom et al. 1990) are used for observations of cloud
produced at operational resolution; hence, the initial radiative forcing (CRF; e.g., Cess et al. 1990).
conditions are regridded onto the coarser N96 grid at
the start of the first time step. The only difference be- c. Generation of cloud regimes
tween the model used for these short-range forecasts
and the standard HadGAM1 atmosphere model is that The cloud regimes are obtained following Jakob and
interactive aerosols used in HadGAM1 have been re- Tselioudis (2003). The KMEANS clustering algorithm
placed by aerosol climatologies. This alteration was (Anderberg 1973) is applied to the ISCCP observa-
necessary as the operational NWP model does not in- tional data and ISCCP simulator output from HadGAM1
clude interactive aerosols (due to their expense); hence, forced with observed sea surface temperatures. In both
their initial conditions are not available in the analyses. cases daily mean data for the period March 1985–
The NWP model has also been rerun from the same set February 1990 are used (as this is the period when
of analyses using the configuration that was operational ISCCP and ERBE overlap), and the HadGAM1 data
from January to December 2005. The unified nature of are regridded onto the ISCCP observational grid prior
the model means that the primary difference between to clustering. As noted by WT07, very similar clusters
the NWP and climate models used here is the horizon-

15 FEBRUARY 2008 NOTES AND CORRESPONDENCE 835

are obtained when using 3-hourly or daily mean data. evidence of shallow cumulus in the tropical cloud re-
The relative frequency of occurrence (RFO) and mean gime with a low total cloud cover [which has been as-
shortwave CRF (SCRF) and longwave CRF (LCRF) sociated with the observed shallow cumulus regime on
are calculated for each cluster. The method of WT07 is the basis of its geographical location and meteorologi-
used to identify the principal cloud regimes in the trop- cal conditions (not shown)]. The regimes obtained from
ics (20°N–20°S) and the ice-free extratropics separately. the N216 model are very similar to the N96 version, but
Ice- and snow-covered regions are not considered here there is some tropical shallow cumulus present. Since
since the observational data are considered less reliable the ISCCP dataset is formed from averages over satel-
there. lite pixels in which there may be several cumulus clouds
with clear-sky between, the observations will have a
For each forecast validation time, data from the 24 bias to higher cloud cover and lower optical depth.
simulations are pooled and both the N96 and N216 Based on an estimate of this effect by WT07, the N216
models are regridded onto the ISCCP observational shallow cumulus simulation may be considered to com-
grid. The forecast model data are then projected onto pare well with ISCCP.
the clusters obtained from the HadGAM1 climatology
(i.e., the data are categorized according to the HadGAM1 WT07 show that cloud feedback under climate
cluster centroid to which they are closest) and the prin- change is sensitive to the mean present-day RFO and
cipal cloud regimes are calculated, together with their CRF of the regimes. The evolution of these regime
RFO, SCRF, and LCRF. Since 3-hourly diagnostics are characteristics through the forecast, together with the
used from T ϩ 0 to T ϩ 24 h, the SCRF is normalized climatological values from observations and HadGAM1,
by the local insolation (labeled nSCRF) to enable com- is shown in Fig. 3. The regime mean RFO, nSCRF, and
parison with the daily mean SCRF at later forecast LCRF at T ϩ 108 in the N96 forecasts are very similar
times. to the HadGAM1 climatology, suggesting that only
comparatively short simulations are required to analyze
Each forecast validation time has also been clustered cloud regime biases and subsequently test improve-
independently to produce its own centroids, and the ments. This is consistent with the work of Strachan
N96 and N216 output clustered on their respective (2007) who found that systematic biases in the same
grids. On average, over several repetitions of the clus- GCM across the Indo-Pacific region spin up within the
tering, very similar clusters are produced using the dif- first few days of model integration. The main exception
ferent methods; however, due to the random initial is the stratocumulus regime for which the nSCRF is
seeding of the cluster centroids, the number of data stronger (more negative) in the N96 forecast than the
points at each validation time is not sufficient to ensure HadGAM1 climatology (Fig. 3). This is due to the fore-
reliable reproduction of the clusters when they are gen- cast using an aerosol climatology with fixed droplet
erated independently. concentrations, whereas HadGAM1 includes interac-
tive aerosol concentrations and interactive indirect ef-
3. Evolution of cloud regime errors fects of sulfate aerosols on clouds (Jones et al. 1994).

WT07 identify five observed principal cloud regimes The mean regime characteristics are generally similar
in the tropics and five in the ice-free extratropics (Figs. in the N216 and N96 models, although there is a slight
1, 2). Each regime has been given a name based on the improvement in the higher-resolution model for several
principal morphological cloud types that are expected of the regimes. However, the RFO of tropical cirrus is
to be present; however, as noted by WT07, a cloud type considerably higher at the expense of deep convection
cannot be uniquely identified by ␶, CTP, and total cloud in the N216 model, which compares less favorably with
cover alone. the ISCCP climatology. This may be a result of deep
convection being triggered in more intense, localized
The UM at N96 resolution produces a good simula- events in the higher-resolution model; hence, less of the
tion of many of the regimes. The main error in the tropics is covered by deep convective cloud at a par-
model is the lack of midlevel cloud regimes. Investiga- ticular time and is replaced by thin cirrus resulting from
tion by WT07 and Jakob et al. (2005) of the geographi- the detrained moisture. Although the CAPE time scale
cal location and meteorological characteristics of the differs between the two resolutions, a sensitivity experi-
regime suggests this mainly reflects a lack of tropical ment has indicated that this alone is not responsible for
and extratropical cumulus congestus cloud in the the difference in the RFO of these regimes.
model, although there may also be contributions from a
lack of midlevel cloud in decaying weather systems and Over the first few time steps of the model forecast,
a poor simulation of instances where there is thin high there is an initial adjustment of the regime character-
cloud overlaying low cloud. In addition, there is little

836 JOURNAL OF CLIMATE VOLUME 21

FIG. 1. Mean CTP–␶ regime histograms for the principal cloud regimes over the tropics (20°N–20°S). Shading indicates the cloud
amount (%) in each CTP–␶ category. (top) Observed climatology by ISCCP (note ISCCP does not observe cloud in the thinnest optical
depth category, ␶ Ͻ 0.3); (middle) mean over the 5-day forecast from the N96 model; and (bottom) mean over the 5-day forecast from
the N216 model. The model does not simulate the midlevel convection regime.

istics for the N96 model and to a lesser extent in the a dynamical response, which reduces the amount of
N216 model, due to the change in resolution from the deep convective cloud during the forecast. Since the
analyses. (During the period of study, the resolution of meteorological conditions should be well constrained
the operational global forecast model was changed by the data assimilation system, the lack of any notable
from N216 to N320; hence, the later analyses had to be trend in the cloud characteristics for the other regimes
regridded for the N216 model used here, accounting for suggests that the errors in these characteristics are a
the small initial adjustment in Fig. 3.) Thereafter, there result of weaknesses in the representation of local pro-
appears to be little trend in the regime characteristics cesses. This implies that, for example, the nSCRF of
over the duration of the forecast. The exception is for stratocumulus being too strong is due to problems in
the tropical deep convection and cirrus regimes for the cloud and/or boundary layer schemes or local reso-
which there is a slow evolution of the RFO over the lution, rather than being due to a dynamical response to
first few days of the forecast. This slow change suggests errors in other regimes.

15 FEBRUARY 2008 NOTES AND CORRESPONDENCE 837

FIG. 2. As in Fig. 1 but for the ice-free extratropics. The model does not simulate the midlevel cloud regime.

4. Initial tendencies within cloud regimes perature tendency profile (i.e., mean tendency for the
whole region) is less than 1 K dayϪ1 at all levels. This
RP07 suggest that mean initial tendencies in the indicates that the model is in reasonable balance (com-
model state variables over the first few hours of a fore- pared to some models run by RP07). The total ten-
cast are indicative of errors in the model physics, which dency profile most closely follows the tendency profile
cause the spread in climate sensitivity amongst GCMs. for shallow cumulus as this regime has the largest RFO.
Since WT07 show that differences in the simulation of However, in the extratropics, there is an initial upper-
cloud regimes contribute to the spread in climate sen- tropospheric cooling and a warming in the mid- and
sitivity, it might be expected that initial tendencies in lower troposphere. These tendencies in the total profile
the state variables are related to particular cloud re- appear to be primarily associated with the cirrus re-
gimes. gime. (The cirrus regime is mainly present toward
lower latitudes of the extratropics, hence the high
The method of RP07 has been followed to calculate tropopause in the profile.) Examination of the regime
the mean temperature tendency profile over the first 6 (Fig. 2) reveals that the simulated cloud is too thick and
h of the N96 forecast for each cloud regime (Fig. 4). high (or too thick for the satellite simulator to see cloud
Over both the tropics and extratropics, the total tem-

838 JOURNAL OF CLIMATE VOLUME 21

FIG. 3. Evolution of regime characteristics through the model forecast. N96 is shown solid; N216 is shown dashed.
Observed climatology from ISCCP is shown with an asterisk; from ERBE is shown with a diamond; HadGAM1
climatology is shown with a triangle. Note that the ISCCP tropical stratocumulus and deep convective RFO are
identical.

below). This is consistent with the simulated LCRF for cal processes means it is no longer possible to associate
the regime being stronger than observed (Fig. 3). The this bias with the cirrus regime. This example highlights
data assimilation system should ensure that the tem- the value of analyzing initial tendencies in the context
perature profile is initially close to that observed; how- of cloud regimes for identifying which areas of model
ever, the extratropical cirrus appears to be too thick physics may be responsible for the total initial tenden-
from the start of the forecast. This results in the ten- cies and climatological systematic biases.
dency to enhance the cooling from the upper part of the
cloud and reduce the cooling below (e.g., Liou 1986). Over the tropics, the initial temperature tendency in
most of the regimes is small; however, there is a warm-
This extratropical upper-tropospheric cool and moist ing in the stratocumulus regime with a maximum at 925
bias (not shown) is present as a systematic error in the hPa. This is consistent with the tropical stratocumulus
HadGAM1 climatology (Martin et al. 2006). However, nSCRF being considerably stronger than observed (Fig.
as the climate simulation evolves, the effect of dynami- 3). It is likely that the longwave cooling of the cloud is

15 FEBRUARY 2008 NOTES AND CORRESPONDENCE 839

FIG. 4. Initial temperature tendency profiles for each regime and the total for the whole region over the first
6 h of the N96 forecast.

near saturated, so the excessive optical thickness of the logical conditions are constrained by the data assimila-
cloud, which leads to it being brighter than observed, tion system, this suggests the cloud regime errors are
also results in a warming tendency within the cloud. In associated with weaknesses in the representation of lo-
the total tendency for the tropics, this warming in the cal physical processes. However, the UM provides a
stratocumulus regime partly offsets a small cooling ten- reasonably good simulation of cloud regimes in com-
dency from the shallow cumulus regime. parison with other models (WT07), so it would be in-
teresting to repeat the analysis with other GCMs to see
5. Conclusions whether this result is applicable generally.

This study illustrates the benefit of having a unified Some of the total initial tendency, which leads to a
forecast–climate model with its own data assimilation systematic bias, has been shown to be associated with,
system. It has been demonstrated that climatological and at least in part driven by, errors in the cloud re-
errors in cloud regimes can be identified in short-range gimes. In the UM, extratropical cirrus is found to be too
forecast simulations. This implies that addressing these thick, which leads to a cool, moist bias in the upper
regime biases will improve both NWP and climate troposphere. Analysis of initial tendencies of state vari-
simulations and that future model improvements tar- ables in cloud regimes is a useful extension to the work
geting these errors can be tested in short forecast runs. of RP07 since it provides information on which regimes
Generally, the simulated regimes are similar at N96 and are associated with the initial tendencies and hence may
N216, although the increased resolution does improve assist with identifying areas of the model physics which
the simulation of tropical shallow cumulus. However, are in error. Since the variation in cloud response is
the RFO of tropical deep convection and cirrus com- believed to contribute to much of the range in climate
pares less well with ISCCP at N216. sensitivity, the association between the initial tempera-
ture tendency and particular cloud regimes supports the
Apart from a shift from tropical deep convection to method of RP07. However, WT07 show that errors in
cirrus during the forecast, the cloud regime errors are the RFO of the regimes provide a large contribution to
very similar at the beginning of the model forecast as the range of climate sensitivity and it might be difficult
they are in the model climatology. Since the meteoro- to deduce this particular error from the initial tendency

840 JOURNAL OF CLIMATE VOLUME 21

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Acknowledgments. This work was funded under the Martin, G. M., M. A. Ringer, V. D. Pope, A. Jones, C. Dearden,
U.K. Government Meteorological Research Programme. and T. J. Hinton, 2006: The physical properties of the atmo-
We thank Alejandro Bodas-Salcedo, William Ingram, sphere in the new Hadley Centre Global Environmental
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on drafts of the paper. ISCCP and ERBE data were climatology. J. Climate, 19, 1274–1301.
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mospheric Sciences Data Center. in general circulation models: Climate simulation meets
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