GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus GmbHGöttingen, Germany10.5194/gmd-8-1509-2015The Met Office Global Coupled model 2.0 (GC2) configurationWilliamsK. D.keith.williams@metoffice.gov.ukHarrisC. M.Bodas-SalcedoA.CampJ.ComerR. E.CopseyD.https://orcid.org/0000-0002-9706-9033FeredayD.GrahamT.HillR.HintonT.HyderP.InesonS.MasatoG.MiltonS. F.RobertsM. J.RowellD. P.SanchezC.ShellyA.SinhaB.WaltersD. N.https://orcid.org/0000-0003-0383-7378WestA.https://orcid.org/0000-0002-9818-6848WoollingsT.XavierP. K.Met Office, Exeter, UKUniversity of Reading, Reading, UKNational Oceanography Centre, Southampton, UKAtmospheric, Oceanic and Planetary Physics, Oxford, UKK. D. Williams (keith.williams@metoffice.gov.uk)21May201521May201588551509152418November201427January201524April20154May2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/8/1509/2015/gmd-8-1509-2015.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/8/1509/2015/gmd-8-1509-2015.pdf
The latest coupled configuration of the Met Office Unified Model
(Global Coupled configuration 2, GC2) is presented. This paper
documents the model components which make up the configuration
(although the scientific description of these components is detailed
elsewhere) and provides a description of the coupling between the
components. The performance of GC2 in terms of its systematic errors
is assessed using a variety of diagnostic techniques. The
configuration is intended to be used by the Met Office and
collaborating institutes across a range of timescales, with the
seasonal forecast system (GloSea5) and climate projection system
(HadGEM) being the initial users. In this paper GC2 is compared
against the model currently used operationally in those two systems.
Overall GC2 is shown to be an improvement on the configurations used
currently, particularly in terms of modes of variability
(e.g. mid-latitude and tropical cyclone intensities, the
Madden–Julian Oscillation and El Niño Southern
Oscillation). A number of outstanding errors are identified with the
most significant being a considerable warm bias over the Southern
Ocean and a dry precipitation bias in the Indian and West African
summer monsoons. Research to address these is ongoing.
Introduction
The Met Office produces forecasts across a range of timescales from numerical
weather predictions (NWP) for days ahead or less, through
monthly–seasonal–decadal forecasts, to climate change projections. For over
20 years, the framework of the Met Office Unified Model (MetUM,
) has been used to produce models which between them span
these timescales. Over the last few years, the development of the science
within the MetUM has been made more seamless across timescales than ever
before, with numerous benefits including greater scientific robustness of the
model, improved ability to investigate model biases and a more efficient use
of resources . Model development now progresses on an
approximately annual timescale with a new configuration of the coupled
atmosphere–land–ocean–sea-ice model (and components, e.g. atmosphere–land
for short-range NWP) being released each year for use across timescales by
the Met Office and its collaborators .
The latest configuration of the coupled model, released in March 2014, is
known as Global Coupled model 2.0 (GC2). This is comprised of
component configurations Global Atmosphere 6.0 (GA6.0), Global Land
6.0 (GL6.0), Global Ocean 5.0 (GO5.0) and Global Sea Ice 6.0
(GSI6.0). GA6.0 and GL6.0 are fully documented by ,
whilst GO5.0 is described by and GSI6.0 by
. In this paper we provide a technical description of
the coupling between the components and then present the coupled model
performance in terms of systematic errors through a range of
diagnostic techniques. We do not discuss predictions/projections from
GC2 as these will be presented elsewhere
(e.g. ). Currently, coupled models are used in Met
Office systems on monthly and longer timescales, hence most of the
results presented here will be for the seasonal forecasting system
(referred to as GloSea5-GC2) and the climate model (referred to as
HadGEM3-GC2). In each case, comparisons will be made against the
current “operational” configuration which is GloSea5-GA3 for
seasonal and HadGEM2-AO for climate
. It should be noted that, unlike the new GC2
configuration which is identical in the different systems, these two
control configurations from which we are upgrading differ
significantly. It is envisaged that future coupled configurations
will also start to be used on shorter NWP timescales, hence a few
results are also included from these timescales.
The “physical” model presented here does not include Earth system
components such as interactive vegetation or ocean bio-geochemistry
(note, our definition of the physical model does include interactive
aerosols). Due to the additional resource required to build an Earth
system model (ESM), the intention is that Earth system components will
be built on top of a subset of the annual physical model releases to
form an ESM every 6 years or so. GC2 will not be a configuration to
which Earth system components will be added, although it is envisaged
that the next coupled model release (GC3) will be developed into an
ESM.
In the next section we provide details of the coupling and experiments
subsequently presented. In Sect. the climatological
biases of the model are discussed, whilst systematic errors in
mid-latitude variability are presented in Sect. , and
tropical variability in Sect. . We summarize in
Sect. .
Coupled model details
The GC2 configuration is defined by the combination of the component
model scientific configurations (GA6.0, GL6.0, GO5.0, GSI6.0) and
associated choices about the way these model components are coupled
together. The component models are fully documented in the model
description sections of , and
, whilst the technical details of the coupling are
described below.
Relative to GloSea5-GA3, GC2 has a significant revision to the
atmosphere dynamical core and a number of parametrization
revisions. HadGEM2-AO predates GloSea5-GA3, so relative to HadGEM2-AO,
GC2 has additional changes including a new ocean model, new sea-ice
model, new cloud scheme, and considerable revisions to all of the
existing parametrization schemes.
The vertical resolution is set by the component definitions, being 85
levels in the atmosphere (with a top at 85 km), four soil levels,
75 levels in the ocean (with a 1 m top level) and five sea-ice
thickness categories. The ocean resolution is 0.25∘ on
a tri-polar grid. The GA6 science can be run over a wide range of
horizontal resolutions on a regular latitude–longitude grid with no
explicit changes to model parametrizations, however results presented
here all use a horizontal resolution of N216 (60 km in
mid-latitudes). The atmosphere and ocean horizontal and vertical
resolutions presented here are an increase on HadGEM2-AO (which uses
an N96 (135 km) L38 atmosphere and 1∘ L40 ocean) but
the same as GloSea5-GA3.
Description of coupling
The atmosphere (UM) and land surface (JULES, the Joint UK Land
Environment Simulator; ) models run on the same
grid and as part of the same model executable so can be considered to
be “tightly coupled”, passing data where
necessary by subroutine arguments or shared data arrays. Similarly
the ocean (NEMO (Nucleus for European Modelling of the Ocean);
) and sea-ice (CICE, ) models are
compiled into a single executable and are “tightly
coupled” on the same grid (with the caveat
that CICE uses an Arakawa “B grid”
placement of velocities in contrast to the “C grid” in NEMO).
Any relevant details of the UM-JULES and NEMO-CICE coupling are
largely covered by and
respectively, so here the focus is on the coupling of GA6.0/GL6.0 with
GO5.0/GSI6.0 using the OASIS3 coupler . As
already mentioned, although the atmosphere (plus land surface) science
can be run over a wide range of horizontal resolutions, this is not
true for the ocean (plus sea-ice) configuration which is fixed at
0.25∘ (using the ORCA025 tri-polar grid; ).
This means that GC2 coupled configurations are limited to those using
an ORCA025 ocean. At present no resolution-dependent choices have
been made in the details of the atmosphere–ocean coupling although
this will not necessarily be true in all future GC configurations.
The coupled model infrastructure remains essentially unchanged from
that described by . The atmosphere and ocean
models run concurrently with OASIS3 (now at version 3.0) handling the
exchange and interpolation of model fields between the two
executables. OASIS restart dumps are not used and so all relevant
fields to initialize the component models at start-up are stored in
their restart dumps. Given that OASIS fulfils a technical and
(relatively) simple interpolation task it might be envisaged that the
same coupled scientific configuration could be reproduced using an
alternative coupler. This may theoretically be true but currently
details of the way models are sequenced, along with interpolation
options available, mean that OASIS3 (although not necessarily the
specific code version) is considered to be part of the definition of
GC2.
The momentum, freshwater and heat fluxes passed from the atmosphere
via OASIS to the ocean are largely as described for “HadGEM3-AO
r1.1” in . To ensure energy conservation, the
coupling part of the NEMO name-list is set to ensure that in most
cases there are separate coupling fields received in NEMO as relevant
to ocean (solar and non-solar heat fluxes; evaporation) or sea-ice
(top and bottom conductive heat fluxes as calculated in the JULES land
surface model; sublimation). These fields are converted to mean
values over atmosphere grid boxes before being conservatively
interpolated by OASIS, and once received by NEMO are applied to the
ocean or sea-ice component as appropriate. Where necessary, CICE can
pass any excess heat or freshwater fluxes back to NEMO – this may be
required if the interpolation of coupling fields produces sea-ice
fluxes in ocean grid boxes without sea-ice, or if the sea-ice melts
either between coupling exchanges or within a CICE time step. The
wind stress components provided from the atmosphere model are
currently mean values which are assumed to apply equivalently to ocean
and sea-ice.
There are a number of minor changes since the configuration described
by . Firstly the coupling period is now 3 h for
most GC2 simulations to allow the diurnal cycle to be better resolved
in both atmosphere and ocean boundary layers (the NWP simulations use
hourly coupling – this is something we intend to unify across
timescales in future configurations and will aid in reducing the
inherent lag in ocean forcing fields as a result of running atmosphere
and ocean components concurrently). To ensure conservation, coupling
fields passed from atmosphere to ocean are still time-averages but now
over a 3 h (1 h for NWP) rather than a 24 h period. In addition,
a constant field representing iceberg calving is now added to the
run-off field within the atmosphere model before passing to OASIS.
There has also been a change to the solar radiation field passed from
the atmosphere to allow the use of the RGB (red–green–blue)
penetrative radiation scheme in GO5.0.
Coupling fields (sea surface temperature, surface velocities, ice
fraction, ice and snow thickness) passed from the ocean to the
atmosphere are instantaneous fields, but again at the new coupling
frequency. Consistent with the treatment of momentum fluxes described
above, the surface velocities passed to the atmosphere model are
simply mean ocean and sea-ice values, weighted according to ice
fraction.
described some of the choices made for the
interpolation schemes for atmosphere to ocean and vice versa. These
were made based on detailed assessment of regridding between the N96
atmosphere grid and the ORCA1 ocean grid and have not been re-examined
for the higher-resolution ORCA025 grid (although N216-ORCA025 is
a comparable resolution combination to N96-ORCA1, so similar
conclusions are expected to be valid). Hence, with the exception of
vector fields which all use bi-linear interpolation, atmosphere-to-ocean fields are regridded using first-order conservative
interpolation (to avoid undershoots and overshoots for fields which
must be positive everywhere) whereas second-order conservative
interpolation is used for ocean-to-atmosphere fields.
For long climate integrations, energy and freshwater budgets are
clearly critical and so conservation of both heat and freshwater
across the coupler has been checked in the GC2 configurations and
found to be accurate to within around 10-4Wm-2
(equivalent top of the atmosphere flux) and 10-5Sv
respectively. These numbers are smaller than the internal
conservation errors of some of the individual model components and are
therefore not viewed as significant.
Although OASIS3 has the capability of generating interpolation weights
at run-time, we continue to calculate these weights off-line using
SCRIP . This is much more efficient, traceable
and also allows some minor adjustments to be made where weights are
otherwise calculated incorrectly due to complications caused by the
north fold of the tri-polar ocean grid. The method for coupling the
ocean component with the UM atmosphere is such that the ocean grid
determines the coastline (with land fractions in all grid boxes as
either 0 or 1) but the atmosphere model then uses “coastal tiling”
allowing the grid box land fractions around the coast to take a value
between 0 and 1 (calculated by interpolating the ocean land–sea mask
onto the atmosphere grid). A consequence of the way the atmosphere
deals with ocean information on these fractional land grid boxes is
that when ocean fields are regridded to the atmosphere the OASIS3
“FRACAREA” option is used (rather than the standard “DESTAREA”).
Equivalently when checking conservation for atmosphere to ocean
fluxes, the atmosphere fields on coastal points must be multiplied by
the land fraction.
The technical details of model set-up are dependent on the machine
architecture being used, but typically when running with several
hundred processors for both atmosphere and ocean components (e.g. on
the IBM Power7 machine), the “pseudo-parallel” capability of OASIS3
is used such that the various coupling fields are typically
distributed between eight OASIS3 processes in order to reduce elapsed
time for coupling exchanges. This has been shown to provide
satisfactory performance without the coupling being a significant
overhead on model run time. Given that atmosphere and ocean in GC2
run concurrently it is necessary though to ensure that the model is
well “load-balanced” to minimize time when processors are standing
idle. On 36 nodes of the Met Office IBM Power7 machine, HadGEM3-GC2 at
N216-ORCA025 achieves 1.87 simulated years per wall clock day. Of the
36 nodes, 17 (544 processors) are used by the atmosphere, 18.75 (600
processors) by the ocean and the remaining 8 processors by OASIS.
Experimental design
Results from three types of coupled model experiment are presented in the
following sections: (1) a long present-day climate simulation (CLIM),
(2) seasonal hindcasts (SEAS), (3) NWP hindcasts (NWP).
CLIM is a 100-year free-running simulation with forcings set to use values
from the year 2000 (this is the same as experiment 2 in the Coupled Model
Intercomparison Project 3, CMIP3). Where appropriate (e.g. for aerosol
emissions), these forcings vary through the annual cycle. The ocean is
initialized from EN3 climatology . The
top-of-atmosphere radiative imbalance in a parallel atmosphere-only
simulation is 0.8 Wm-2, consistent with using present-day
forcings, hence a small drift due to the net energy flux would be expected.
Average results from the final 50 years of the simulation are shown
unless otherwise stated. For those variability diagnostics using high
temporal resolution (e.g. daily) data, the final 20 years of the
simulation are used. The largest global-mean ocean temperature drift over the
100-year simulation occurs at a depth of 563 m with a rate of
0.11 K decade-1. Over the final 50 years this reduces to
0.08 K decade-1. Below 1000 m the average drift is less than
0.02 K decade-1 at all depths.
Results presented from SEAS are a mean of seasonal hindcasts for the years
1996–2009, each of 140 days in length. Within each year, there are three DJF
hindcasts initialized on 25 October, 1 November, 9 November and three JJA
hindcasts initialized on 25 April, 1 May, 9 May and each start date has
a three-member initial condition ensemble, resulting in 120 hindcasts being
averaged for each of DJF and JJA. The ocean and sea-ice are initialized from
Met Office Ocean Forecast analyses, the atmosphere from ECMWF analyses and
soil moisture from a climatology of the land surface model used within GC2
forced with ECMWF analyses. More details on the initialization can be found
in .
The NWP experiment comprises 15-day hindcasts, run daily at
12:00 UTC for the period 2–14
December 2011. The atmosphere and land surface are initialized from Met
Office NWP analyses, and ocean from Met Office Ocean Forecast analyses. Both
NWP and SEAS use prescribed aerosol concentrations from a HadGEM2-AO AMIP
(Atmosphere Model Intercomparison Project) simulation, but with direct and
indirect effects being calculated interactively as for CLIM
.
GC2 mean biases
HadGEM2-AO is characterized by a cold SST bias over much of world,
especially in the North Atlantic, with a slight warm bias over the
Southern Ocean and Southern Hemisphere stratocumulus regions
(Fig. ). The change to the new NEMO ocean model and
higher ocean resolution has resulted in GC2 SSTs being generally
warmer, which is beneficial over most regions, but detrimental over
the Southern Ocean.
Mean SST bias (K) against EN3 for the CLIM experiment for
HadGEM2-AO and HadGEM3-GC2.
A considerable amount of work is ongoing to investigate the Southern
Ocean warm bias in the Met Office model
(e.g. ). To first order, the surface flux
biases are similar in AMIP simulations parallel to HadGEM2-AO and
HadGEM3-GC2 (Fig. a), both having a large downwards
surface flux bias over the Southern Ocean which is only slightly worse
in HadGEM3-GC2. However, the coupled SST (and upper ocean heat
content) biases are much larger in HadGEM3-GC2 than HadGEM2-AO. This
appears to be related to changes to both the lateral and vertical
ocean heat transports associated with the change in ocean model and
ocean resolution. The HadGEM3-GC2 errors also include a contribution
associated with too shallow Southern Ocean summer mixed layers.
A detailed analyses of this problem is currently underway which will
be documented separately, although it is believed that the primary
problem is the atmospheric heat flux biases
(e.g. ; ;
). In GC2 both excess downward SW flux
and too little upward heat transport from turbulent fluxes are thought
to contribute to the net heat flux bias and these are the focus of our
efforts to improve future configurations.
(a) Zonal mean net downward surface flux bias in
AMIP simulations. The observed surface flux is a developmental
version of the University of Reading surface flux product which
combines satellite-based radiative fluxes
with re-analysis estimates of atmospheric column energy storage and
horizontal divergences . (b) Zonal mean SST
bias against EN3 for years 5–15 of the coupled model CLIM
experiment (before any biases from the deep ocean influence the
SSTs).
The warm bias over the Southern Ocean can be seen early in NWP
simulations for the austral summer (Fig. ). Hindcasts
for the austral winter with an earlier configuration did not show such
a rapid warming (not shown), providing further evidence that fast
atmosphere processes are contributing, and that biases in the SW flux
are likely to be significant.
Mean day 3 and day 15 SST bias (K) against analyses in the
NWP experiment.
Mean 1.5 m temperature over the North Atlantic
(10–50∘ W, 40–60∘ N) for the SEAS hindcasts
(coloured). ERA-I is shown in black.
The increase in SSTs in HadGEM3-GC2 is particularly notable over the
North Atlantic to the south of Greenland where, in common with many
climate models, HadGEM2-AO has a very large cold bias
(Fig. ). Here, the higher horizontal resolution of
the ocean model leads to a significantly improved Gulf stream
extension, accurately reproducing the northward turn around
Newfoundland. have shown the importance of
this SST improvement for European climate variability. On seasonal
timescales, these relatively small SST biases in the North Atlantic
have been further improved between GloSea5-GA3 and GloSea5-GC2 by
introducing aerosol indirect effects from the aerosol climatological
concentrations, consistent with climate model simulations, rather than
using fixed droplet concentrations for land and sea. As a result, the
JJA hindcasts in particular have improved to now match the observed
seasonal cycle very well (Fig. ).
(a) DJF zonal mean temperature (K) from the
HadGEM3-GC2 CLIM experiment, (b) HadGEM3-GC2 minus
HadGEM2-AO, (c) HadGEM2-AO minus ERA-I, (d)
HadGEM3-GC2 minus ERA-I.
The cold SST bias in HadGEM2-AO impacted the atmosphere with DJF
biases of over 6 K in the boundary layer at high latitudes,
but also biases of 2 K extending through much of the
troposphere in the Northern Hemisphere (Fig. ). The
improved SSTs in HadGEM3-GC2 mean that these biases have reduced,
although the troposphere remains slightly cool. An exception is over
the Southern Ocean where the warm bias has increased. HadGEM2-AO has
a warm stratosphere with a 5 K temperature bias in the tropics
at 70 hPa. This warm bias is of concern when developing ESMs
with interactive chemistry as these processes are particularly
sensitive to the stratospheric temperature and humidity. The
stratospheric specific humidity is largely determined by the cold
point temperature , hence minimizing this bias is
particularly important. It can be seen that this tropical tropopause
warm bias has been considerably improved in HadGEM3-GC2 through
a combination of many parametrization changes, changes to the dynamics
and increased vertical resolution.
(a) JJA 1.5 m temperature (K) from the
HadGEM3-GC2 CLIM experiment, (b) HadGEM3-GC2 minus
HadGEM2-AO, (c) HadGEM2-AO minus CRUTEM3 observations
, (d) HadGEM3-GC2 minus CRUTEM3
observations.
In common with many climate models (e.g. ;
), HadGEM2-AO develops a warm bias over mid-latitude
continents in summer (Fig. ). This bias is over
6 K in atmosphere-only simulations, but is mitigated in
coupled simulations through the cold Northern Hemisphere SST bias in
HadGEM2-AO. The summer warm bias is reduced in HadGEM3-GC2 through
developments to the land surface and
parametrization improvements (including increasing the frequency of
radiation calls from 3 hourly to hourly). Changes elsewhere are
generally small, although a cold bias is now starting to develop at
high latitudes, consistent with the troposphere remaining a little
cold. Overall, the area-weighted root mean square (RMS) error for the
field is reduced from 2.02 to 1.55.
The accurate simulation of sea-ice extent and thickness in the
present-day climate is of importance for the estimation of climate
sensitivity , projections of when the Arctic will
be ice-free in summer, and seasonal forecasts of sea-ice extent
(e.g. to inform the use of Arctic routes by shipping). For GC2, the
sea-ice parameters in the model were tuned within the range of
observational uncertainty , and the sea-ice
simulation generally does a reasonable job of simulating the annual
cycles of Arctic sea-ice extent and volume (Fig. ).
The Arctic sea-ice volume simulation is most accurate at N216
resolution, where throughout the year the ice is around 20 %
thicker than at N96, on a spatial average. The warm SST bias over the
Southern Ocean results in there being far too little Antarctic
sea-ice, which further exacerbates the bias.
Annual cycle of Arctic sea-ice (a) extent,
(b) volume. Colours show HadGEM3-GC2 and HadGEM2-AO CLIM
experiment and the black line is HadISST observations
(a), PIOMAS (Pan-Arctic Ice–Ocean
Modeling and Assimilation System) analyses (b). Grey lines show 20 % intervals around the
observations.
The mean value of the Atlantic meridional overturning circulation
(MOC) at 26∘ N in HadGEM3-GC2 (over years 11–50) is
16.4 Sv. This is close to the observed value from the RAPID
array, particularly since a downward trend has been observed since
2004 (average observed value 2004–2012 is 17.5 Sv;
), and is an improvement over HadGEM2-AO in which
the MOC was around 15.1 Sv. However, the depth of the North
Atlantic Deep Water return flow remains too shallow. This is a common
bias in z-level models which tend not to simulate overflows well as
there is excess entrainment. The maximum ocean heat transport remains
similar to HadGEM2-AO, being below 1 PW and therefore low compared with
observational estimates.
Turning to precipitation, the general structure of the mean biases
remains similar to HadGEM2-AO, with a southward displaced
Inter-Tropical Convergence Zone (ITCZ) over the Atlantic and Indian
oceans (Fig. ). This is consistent with the asymmetry
in the SST bias , and results in lack of
precipitation in the summer monsoon systems over West Africa and
India. However, there are other processes contributing to reduced
precipitation over West Africa and India since a dry bias exists here
in AMIP simulations whereas the southward displacement of the ITCZ
over the oceans does not. Unlike a number of CMIP5 models
, there is no pronounced split ITCZ in either
HadGEM2-AO or HadGEM3-GC2, although a significant wet bias exists on
the north side of the warm pool, Pacific ITCZ and South Pacific
Convergence Zone (SPCZ). Whilst the geographical pattern is similar,
mid-latitude precipitation biases are slightly reduced in HadGEM3-GC2,
particularly the dry bias over the northern North Atlantic. The RMS
error is slightly increased (from 1.68 to 1.76), primarily due to an
increased mean bias over the tropical West Pacific and East Indian Ocean.
(a) JJA mean precipitation rate
(mmday-1) from the HadGEM3-GC2 CLIM experiment,
(b) HadGEM3-GC2 minus HadGEM2-AO, (c) HadGEM2-AO
minus GPCP observations, (d) HadGEM3-GC2 minus GPCP
observations.
The accurate simulation of clouds is a particular strength of
HadGEM2-AO (e.g. ; ;
). GC2 includes a new prognostic cloud scheme
(PC2, ) which gives similar or even slightly
improved cloud properties (amount, height and albedo) for optically
thicker clouds, including a good simulation of cloud in marine
stratocumulus regions which are of particular importance for climate
sensitivity (e.g. ) (not shown). The main
difference in the cloud simulation between the two models is for
optically thin cirrus. The scheme used in HadGEM2-AO
had an implicit coupling between cloud fraction and optical depth,
preventing high fractional coverage of very thin cirrus. This coupling
does not exist in PC2 and consequently HadGEM3-GC2 has almost double
the amount of cirrus, much of which is sub-visual. CALIPSO
(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations)
is a space-borne cloud lidar and is particularly suited to detecting
optically thin “sub-visual” cirrus, in addition to thicker cirrus
which can be detected with passive instruments
(; ). A comparison of the
models with CALIPSO using the CFMIP (Cloud Feedback Model
Intercomparison Project) Observational Simulator Package (COSP)
(; ) suggest that
the true amount of cirrus should be between what is simulated by the
two models (Fig. ). HadGEM2-AO also has a bias for the
tropical cirrus to be too low in altitude. The altitude of the peak
amount of cirrus has been improved slightly in HadGEM3-GC2, but still
remains lower than observed.
Tropical-mean (20∘ N–20∘ S) vertical
profile of cloud frequency in atmosphere-only (AMIP) simulations of
HadGEM2-AO and HadGEM3-GC2 using the CALIPSO simulator from
COSP. The observed profile from CALIPSO is shown in black.
Mid-latitude variability
One of the most recent changes in the development of GC2 was the
inclusion of a significant revision to the atmosphere dynamical core
– ENDGame (Even Newer Dynamics for Global Atmospheric Modelling of
the Environment, ). discuss
how one of the main aims of this change was to improve the accuracy of
the semi-Lagrangian dynamics, so less implicit smoothing is used, with
the effect of increasing synoptic variability. A weakening of synoptic
variability, measured through the intensity of tracked extra-tropical
cyclones, has been shown to be a general problem in many NWP models
and climate models in CMIP5
. illustrate the
improvement in cyclone intensities in GA6 on NWP timescales, and this
carries through to the GC2 CLIM experiment (Fig. ). In
HadGEM2-AO there is a general negative bias in cyclone intensity (as
measured by 850 hPa relative vorticity) in the storm tracks in
both hemispheres, especially on the equatorward side of the storm
tracks. This bias is largely eliminated in HadGEM3-GC2, although there
remains a slight deficit in intensity at the end of the Atlantic storm
track and the eastern hemisphere of the Southern Ocean storm track.
Bias in winter tracked cyclone intensity, as measured by
850 hPa relative vorticity, relative to ERA-I. Tracking is
on the final 20 years of the respective model simulations
and for the period 1988–2008 for ERA-I using TRACK
. Contours show ERA-I values of relative
vorticity at 10-5s-1 and the colours indicate the
bias on the same scale (contours at 10-5s-1
intervals) for the CLIM experiment.
Normalized frequency distribution of DJF jet latitude defined
as the maximum 850 hPa wind over the North Atlantic
60∘ W–0∘ E (following )
for ERA-I 1979–2012 and the final 20 years of HadGEM3-GC2
CLIM experiment.
The winter jet over the North Atlantic follows a tri-modal structure
(Fig. ; ). These jet positions have
been shown to have some correspondence to primary blocking locations
with the southernmost jet position being associated with Greenland
blocking, the central position with no blocking and the northernmost
position being correlated with European blocking (although some
studies suggest European blocking can exist as a separate regime)
(; ;
). GC2 reproduces the tri-modal structure well
although there is a slight tendency for the jet to occupy the
northernmost position too frequently (Fig. ). At other
times of the year, the jet is more uni-modal, which GC2 captures,
although again there tends to be a slight northward displacement (not
shown). These results are robust in that a very similar structure is
seen in different atmosphere resolutions, including when the model is
run at the lower atmospheric resolution of N96.
The tendency to have a more favoured northward position of the jet is
in contrast to most CMIP3 and CMIP5 models which either do not capture
the tri-modal structure at all, or tend to have the southern jet
location simulated too frequently (;
). indicate that
a North Atlantic cold bias, resulting from a weak and displaced North
Atlantic Drift, can result in the simulated jet favouring the southern
position. The improved SSTs in HadGEM3-GC2 relative to other models
may account for the more frequent simulation of the northern
location. Despite the good distribution of jet latitudes, there is
still around a 25 % deficit in European blocking in HadGEM3-GC2
(defined using a 2-D “wave-breaking” index based on
; not shown), which is again insensitive to
horizontal resolution and is the subject of ongoing research.
Spatial correlation of the NAO pattern (the leading empirical
orthogonal function of the winter mean sea-level pressure fields)
and interannual variability in the CLIM experiment compared with NCEP (National Centers for
Environmental Prediction) re-analyses . NCEP
re-analyses are used here rather than ERA-I (ECMWF Interim Re-analyses;
) which are used throughout the rest
of the paper since a longer record is needed to estimate the
observed variability.
The North Atlantic Oscillation (NAO) is a leading mode of variability
affecting Europe. Seasonal forecasts of the winter NAO are now
starting to show skill , hence an accurate
simulation of the NAO in a proposed replacement model is
important. Table illustrates a further improvement in
the pattern correlation and slight improvement in variability of the
winter NAO in GC2 relative to the model currently used for seasonal
forecasts. Some studies have suggested a link between the
Quasi-Biennial Oscillation (QBO) and the winter NAO with a more
positive NAO during westerly QBO events (;
). Figure shows a composite of
westerly minus easterly QBO events for the Arctic stratospheric vortex
and pressure at mean sea-level (PMSL) in the GC2 coupled control
simulation. It can be seen that this relationship does exist in GC2,
albeit somewhat weaker than observed and with some displacement of the
PMSL pattern in the Euro-Atlantic sector. The simulation of this
teleconnection is noteworthy given the potential implications for
seasonal predictability, and is a relationship which is not present in
all CMIP5 models.
DJF composite mean 50 hPa geopotential height and
PMSL for westerly minus easterly QBO events (mean 50 hPa
equatorial wind, using a threshold of ±5ms-1) in
HadGEM3-GC2 CLIM experiment (a, b) and a combined data set
of ERA40 (1957–1978) and ERA-I (1979–2013) (c, d). White
contours show significance at the 90 % level using a two-sided
t-test.
Tropical variability
A significant improvement in GA6 relative to earlier configurations is
in the simulation of tropical cyclones . Both the
change to the ENDGame dynamical core and convection parametrization
changes have contributed, resulting in more intense tropical cyclones
and improved tracks. At N216 resolution (around 100 km in the
tropics), GC2 is now able to simulate tropical cyclone central
pressures which would be expected from category 3 storms
(Fig. ). However, the 10 m wind speed
associated with these systems remains below category 1, suggesting
that the storms are too large. It might be expected that increased
horizontal resolution would improve the pressure–wind-speed
relationship, however even increasing the resolution to N1024 (around
20 km in the tropics) in an atmosphere-only simulation only
partially improves the bias (Fig. )
.
Scatter plot of 10 m wind speed vs. central PMSL for
tropical
cyclones in HadGEM2-AO and HadGEM3-GC2 CLIM simulations. Also shown are
GA6 atmosphere-only (AMIP) simulations at N216, N512 and N1024. Hurricane
database (HURDAT, ) is in
black.
Seasonal forecasts of landfalling Atlantic hurricanes are an emerging
product which relies on the accurate tropical cyclone track
distributions in the basin. Figure shows the track
densities from the SEAS experiment for the existing seasonal forecast
system and GC2. In the Atlantic basin, GloSea5-GC2 has more storms
than its predecessor (GloSea5-GA3) and has a better distribution of
tracks with more early recurvature into the central North Atlantic,
more reaching the Caribbean and a broad peak making landfall on the US
coast. The West Pacific has slightly too many storms and there is
a lack of a clear break in storms in the central Pacific.
Seasonal hindcast (SEAS experiment) tropical cyclone track
densities obtained using TRACK for JJA 1996–2009. (a)
GloSea5-GA3, (b) GloSea5-GC2, (c) ERA-I,
(d) observations (North Atlantic and eastern Pacific from
HURDAT; western North Pacific and North Indian Ocean from the US
Navy's Joint Typhoon Warning Centre (JTWC) best-track
.
There have been a large number of changes to the convection parametrization
between HadGEM2-AO and HadGEM3-GC2. Out of these, increases to the
entrainment and detrainment rates have been primarily responsible for an
improved simulation of the Madden–Julian Oscillation (MJO), although the
amplitude of the systems remains significantly weaker than observed. Model
performance is measured using simplified metrics proposed by the
international MJO Task Force (which is under the WMO Working Group on
Numerical Experimentation, WGNE) . One simple measure
of the MJO is based on the space–time power spectrum of equatorial rainfall.
The ratio of eastward to westward power (E/W ratio)
at MJO time and space scales (zonal wavenumbers 1–3 and periods of
30–60 days) reveals the prominence of the eastward propagating
intraseasonal variability relative to its westward counterpart and is
a useful indicator of how prominent the MJO is relative to the background
variability . On this metric, HadGEM3-GC2 has improved
relative to HadGEM2-AO, although it remains below the observational range
(Table ). Another measure of MJO fidelity is
Rmax, proposed by , which is the maximum
correlation between the two time series obtained by projecting model outgoing
long-wave radiation (OLR) anomalies onto the leading pair of empirical
orthogonal functions (EOFs) of observed OLR that capture the MJO. The MJO is
deemed well simulated if the correlation between the two leading principal
components (PCs) is strong at a lead time of about 10–15 days, thereby
demonstrating coherent eastward propagation with appropriate spatiotemporal
structure. Again, HadGEM3-GC2 performs better than HadGEM2-AO
(Table ).
E/W ratio in spectral power following
and Rmax following
for the CLIM experiment. GPCP (Global Precipitation
Climatology Project; ) and
TRMM (Tropical Rainfall Measuring Mission;
) data are used for the observational values.
Previous studies have suggested that atmosphere–ocean coupling is
important for the propagation of the MJO
. Relatively clear skies ahead of the MJO
result in higher SSTs which encourage propagation of the MJO which in
turn cools the SSTs due to the high cloud amounts and precipitation as
the system passes. This is seen in the NWP experiment using
a configuration similar to GC2 . The coupled model
maintains the observed lag of about 5 days of the outgoing long-wave
radiation (OLR) anomaly behind the maximum SSTs in the coupled
simulation, whereas the convection moves over the maximum SSTs within
the first few days in parallel atmosphere-only simulations and then
remains static since the SSTs are not being updated with the cooling
effect from the cloud. This is one example of why it is desirable to
move to coupled weather forecast models for even relatively short-range predictions. Similar coupled model feedbacks might be expected
to impact forecast tropical cyclone intensities
.
(a) Annual mean Pacific SST averaged over
5∘ N–5∘ S for the CLIM experiment. Black lines
show HadISST. (b)
Annual mean Pacific wind stress averaged over
5∘ N–5∘ S for the CLIM experiment. Black lines
show ERA-I (dashed) and Southampton Oceanography Centre climatology
(solid) (c) El Niño SST composite from
HadGEM3-GC2 CLIM experiment. (d) El Niño SST composite
from HadISST observations. Composites are of events with a Niño
3.4 SST anomaly >0.8 K.
A reliable simulation of the El Niño Southern Oscillation (ENSO) is
important for seasonal prediction and climate projections alike since it
forms a leading mode of global variability and the major source of seasonal
predictability. For many years, Met Office climate models have suffered from
excess equatorial easterly wind stress. Improvement in this was a focus of
HadGEM2-AO development and it has been further improved in GC2 with
a contribution from a number of the science changes, most notably a change to
the gravity wave drag scheme which reduces the coupling between the low-level
flow-blocking drag and gravity wave drag following
(Fig. b). As a result of the improved windstress, improved MJO
(which can be the source of westerly wind bursts – e.g.
) and higher horizontal resolution of the ocean,
ENSO is well simulated in HadGEM3-GC2 with a good spatial pattern
(Fig. c and d). When assessed against a range of metrics
(Table ) we see that variability in SST agrees well
with observations in the central East Pacific although somewhat weaker
than observed near the dateline. A power spectrum analysis shows that the
frequency lies within the observed range (3 to 7 years), with no dominant
short (e.g. 2 year) or longer period peaks. The model seasonality is good,
with maximum (minimum) variability in boreal winter (spring). The standard
deviation of precipitation in the central Pacific gives a measure of model
capability for regional climate impacts and although slightly underestimated,
is good in comparison with other climate models which tend to underestimate
this quantity. Overall, HadGEM3-CG2 compares favourably with a range of CMIP5
models . The main observed ENSO teleconnections to
remote precipitation anomalies (S. America, Sahel, India, E. Africa, etc.)
are also present in the model (not shown).
Metrics for ENSO assessment. M1 and M2 are standard
deviation of monthly SST anomaly for regions Niño3
(90–150∘ W, 5∘ N–5∘ S)
and Niño4 (160∘ E–150∘ W,
5∘ N–5∘ S) (K), M3 is the ratio of power in the
3–7 year range relative to 0 to 10 years for monthly Niño3 SST
anomaly (%), M4 is a seasonality metric defined as the ratio of
November to January and March to May standard deviation of
Niño3 SST anomaly , M5 is the standard
deviation of precipitation anomaly for Niño4
(mm day-1). CLIM is the final 50 years of experiment CLIM,
CLIM2 is the final 100 years of a 150-year experiment equivalent to CLIM differing only in the initial conditions. SST observations are HadISST (1901–2000) and precipitation is GPCP (1979–2013).
Africa is a region where accurate predictions and projections of
rainfall are particularly important for those living there and model
simulations have generally been poor
. Teleconnections from remote SST
anomalies are primarily responsible for the large interannual
variability of seasonal mean rainfall over many areas of the
continent. investigated the ability of CMIP models
to accurately represent teleconnections from remote SST anomalies to
African rainfall. Figure is a reproduction of
Fig. 10 from but with HadGEM3-GC2 added. It shows that
HadGEM3-GC2 has the joint highest proportion of the teleconnections
accurately simulated compared with CMIP3 and CMIP5 models previously
analysed, supporting the use of GC2 for seasonal predictions and
climate change projections over the region.
Proportion of teleconnections to Africa in each of the five
skill categories (details in ). Green: at
least reasonable model skill; yellow: marginal skill; pale brown:
moderate and significant difference between model and observed
teleconnection strength, dark brown and red: poor or very poor
skill; and white: SST-rainfall associations of little practical
interest. CMIP3 and CMIP5 models, together with HadGEM3-GC2 CLIM
experiment, are ranked by the number of teleconnections that do not
differ significantly from those observed at the 10 % level.
Summary
In this paper we have presented the performance of the GC2
configuration of the Met Office Unified Model in terms of its
systematic errors. The focus has been on seasonal and climate
timescales since these are the timescales on which the model is to be
used operationally for predictions/projections, and GC2 has been
compared with models currently used in those systems. The results
presented here should be considered alongside the atmosphere-only
results in and ocean/sea-ice results in
and .
Overall, GC2 provides a significant improvement in mean bias and
variability over the coupled configurations currently used, with
temperature biases in most regions, simulation of atmospheric regimes
over the North Atlantic, simulation of tropical cyclones and ENSO
being particularly notable. However, there are a number of systematic
errors requiring further work, the highest priority being the Southern
Ocean warm SST bias and low levels of rainfall over India and West
Africa during the summer monsoons. Consequently, caution is required
when considering predictions/projections from GC2 in these regions.
Climate change simulations using HadGEM3-GC2 are already in progress and will
be reported by , whilst GloSea5-GC2 is being used
operationally for Met Office seasonal forecasts since 3 February 2015.
Consistent with the annual development discussed in the Introduction,
work is already underway to further develop GC2 to form GC3. It is
envisaged that GC3 will subsequently have Earth system components
built on top of it to form UKESM1 (United Kingdom Earth System Model
version 1), which will be the UK's submission to CMIP6. Hence, the
assessment presented here provides an initial picture of the model
performance which will help inform the next stages of UKESM1
development.
Code availability
The MetUM is available for use under licence. A number of research
organizations and national meteorological services use the MetUM in
collaboration with the Met Office to undertake basic atmospheric
process research, produce forecasts, develop the MetUM code and build
and evaluate Earth system models. For further information on how to
apply for a licence see
http://www.metoffice.gov.uk/research/collaboration/um-collaboration. Version
8.5 of the source code is used in this paper.
JULES is available under licence free of charge. For further
information on how to gain permission to use JULES for research
purposes see https://jules.jchmr.org/software-and-documentation.
The model code for NEMO v3.4 is available from the NEMO website
(www.nemo-ocean.eu). On registering, individuals can access the
code using the open source subversion software
(http://subversion.apache.org/). The revision number of the base
NEMO code used for this paper is 3424.
The model code for CICE is freely available from the United States Los
Alamos National Laboratory
(http://oceans11.lanl.gov/trac/CICE/wiki/SourceCode), again
using subversion. The revision number for the version used for this
paper is 430.
A number of branches are applied to the above codes. Please contact
the authors for more information on these branches and how to obtain
them.
Acknowledgements
This work was primarily supported by the Joint DECC/Defra Met Office
Hadley Centre Climate Programme (GA01101). Part of the work was
undertaken with National Capability funding from NERC for ocean
modelling. We thank R. Allan and C. Liu for providing the surface
flux data set used in Fig. .Edited by: D. Lunt
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