Free-running and nudged versions of a Met Office chemistry–climate model are evaluated and used to investigate the impact of dynamics versus transport and chemistry within the model on the simulated evolution of stratospheric ozone. Metrics of the dynamical processes relevant for simulating stratospheric ozone are calculated, and the free-running model is found to outperform the previous model version in 10 of the 14 metrics. In particular, large biases in stratospheric transport and tropical tropopause temperature, which existed in the previous model version, are substantially reduced, making the current model more suitable for the simulation of stratospheric ozone. The spatial structure of the ozone hole, the area of polar stratospheric clouds, and the increased ozone concentrations in the Northern Hemisphere winter stratosphere following sudden stratospheric warmings, were all found to be sensitive to the accuracy of the dynamics and were better simulated in the nudged model than in the free-running model. Whilst nudging can, in general, provide a useful tool for removing the influence of dynamical biases from the evolution of chemical fields, this study shows that issues can remain in the climatology of nudged models. Significant biases in stratospheric vertical velocities, age of air, water vapour, and total column ozone still exist in the Met Office nudged model. Further, these can lead to biases in the downward flux of ozone into the troposphere.
Previous studies have identified numerous couplings between ozone, greenhouse
gases, tropospheric ozone precursors and stratospheric ozone-depleting
substances, and climate change. Increased carbon dioxide and near-surface
ozone levels, for example, can impact vegetation and the strength of the land
carbon sink
As a result, coupled chemistry–climate models have evolved to encompass both
stratospheric and tropospheric chemistry coupled to state-of-the-art
atmosphere–ocean climate models, in order for such couplings to be studied
and fully understood. Chemistry–climate models are also used to provide
policy-relevant information, such as the assessment of strategies for
mitigating and adapting to a changing climate with changing atmospheric
composition (
Nudging the dynamics of chemistry–climate model simulations towards
observations is a technique used both to look at the impact of specific
physical processes on atmospheric composition, and/or to remove the influence
of unrealistic model climatology from the evolution of chemical fields. Case
studies covering just the length of a single observational campaign and
simulations covering long-term trends over the historical period are both
ways in which the use of nudged chemistry–climate models can enhance our
understanding of the evolution of the chemical composition of the atmosphere.
For example,
In the present study, the stratospheric dynamics, transport, and simulated total column ozone (TCO) in free-running and nudged versions of the Met Office chemistry–climate model, HadGEM3-ES, are evaluated. The nudged simulations here make it possible to determine the ways in which biases in the model dynamical fields affect the accuracy of simulated TCO, and thereby help attribute the remaining biases in TCO to other components of the model (i.e. the transport and chemistry schemes).
This study is set out as follows. Section 2 describes the model setup and the simulations evaluated here. Section 3 presents the results and is split into sections focusing on model metrics and the dynamics and TCO of the tropics and extratropics. Conclusions and discussion are given in Sect. 4.
The Met Office model configuration used in this study is the
chemistry–climate model HadGEM3-ES. The underlying atmosphere model is the
Global Atmosphere 4.0 (GA4.0) configuration of HadGEM3
This configuration represents a significant improvement in the physical model
since the Met Office's contribution
This atmosphere-only or coupled atmosphere–ocean model HadGEM3 is, in turn,
coupled to the gas-phase chemistry component of the United Kingdom Chemistry
and Aerosol (UKCA) model
The results shown in this paper come from HadGEM3-ES simulations set up to
follow the Chemistry–Climate Model Initiative (CCMI) reference simulations
The coupled (REF-C2) simulation is spun up to 1960 conditions as follows. A
400-year spin-up of the coupled atmosphere–ocean model to a perpetual
pre-industrial state is followed by a transient spin-up of the coupled
model, without interactive chemistry, to 1950 conditions. Chemistry is then
included, and a 10-year spin-up to 1960 conditions is performed, as
recommended by
Alongside the free-running atmosphere-only historical simulations (REF-C1),
simulations in which temperature and horizontal wind fields are nudged
Model simulations.
Details of these simulations are summarized in Table 1. Free-running simulations are run over the period 1960–2010 (REF-C1) and 1960–2100 (REF-C2), and nudged simulations are run over the period 1980–2010 (using initial conditions taken from REF-C1). As such, we analyse the period 1980–2010 in this study.
Metrics.
Metrics of dynamical fields and processes (see Table 2). Bold italic font indicates metrics which are not directly constrained in the nudged simulations. Column numbers are printed above each column, and the model simulation is printed below each column. For details of model simulations, see Table 1 (where “24smth” corresponds to “24 h, smoothed”, etc.).
Metrics for evaluating the processes in chemistry–climate models relevant for
the simulation of stratospheric ozone were developed as part of the CCMVal-2
project
Following the method of
It is interesting to note that the UMUKCA-METO values for some of these
metrics show a significant degradation compared to those given in
the reanalysis dataset used here as the benchmark is ERA-Interim as opposed to ERA-40 and the analysis here is over the period 1980–2010 as opposed to 1980–2000 as used in CCMVal-2.
In particular, using a different period can substantially alter the values of
some metrics. For example, the PW_sh diagnostic considers the variability in
the heat flux and polar vortex temperatures in the Southern Hemisphere
high-latitude winter. The sudden warming observed in 2002 (the only Southern Hemisphere sudden warming on record) significantly increases the overall
variability in both these quantities. The semi-annual oscillation (measured
by the SAO metric) increases in amplitude for the years 2000–2010, such that
its mean amplitude for the period 1980–2000 is 15 m s
Since reanalysis datasets and the period analysed will continue to be
updated, there are issues with referring back to the values of metrics in
previous reports using information from multiple reanalyses datasets as the metric
“observations” and ensuring that the period analysed is of sufficient length to reduce the impact of interannual
variability,
where the “interannual variability” in this case is the interannual
standard deviation of the observations, as noted above in Eq. (1). Of
course, if possible, recalculating metrics from older simulations and
reports, using identical benchmark datasets and time periods for consistency,
would allow for the cleanest comparison to the latest simulations. In any
case, metrics continue to provide an invaluable and concise indication of
current model performance, indicating diagnostics where models are performing
well and those where improvement is required.
Comparing column 1 with columns 2 and 3 of Fig. 1, the free-running version
of HadGEM3-ES is shown to perform better than UMUKCA-METO in 10 of the 14
metrics (with umx_sh and SAO significantly better in UMUKCA-METO, and up_70
and PW_sh better in UMUKCA-METO but not significantly so). Further, as noted
above, the SAO metric is particularly sensitive to the period analysed, so
the differences in this metric between UMUKCA-METO and the CCMI simulations
cannot be considered reliable (i.e. robust across different periods). Thus,
apart from the strength of the Southern Hemisphere polar night jet, the
dynamics of HadGEM3-ES show improvements over (or no difference to) the
version of HadGEM used for CCMVal-2
As denoted in Fig. 1 and Table 2, the metrics are divided into those that
measure the mean climate of model simulations and those that measure their
variability. This division follows that in
The nudged simulations that use the discontinuity corrected ERA-Interim
dataset (
The nudged simulations perform very well (
The grading of the QBO metric below 0.8 for the nudged simulations is
somewhat more surprising. Although the QBO is internally generated in the
free-running REF-C1 and REF-C2 simulations, the QBO metric depends only on
zonal wind which is directly nudged in the REF-C1SD simulations. In
fact, the nudged model accurately simulates the quasi-biennial oscillation in
the zonal-mean winds at 20 hPa used in this metric, matching the reanalysis
winds closely, except not quite reaching the peak values of the oscillation
and thus underestimating the amplitude of the relevant Fourier harmonics by
4 % (not shown). However, since the power-spectrum approach inherent in
this metric does not give a measure of uncertainty, this is calculated
differently
Figure 1 shows that, whilst there are small differences between the nudged simulations with 24 and 48 h relaxation timescales, there are (with the exception of the SAO and heat flux metrics) no significant differences between the simulations using smoothed and unsmoothed datasets. From this point on, we will just consider the simulations using the smoothed dataset, with a particular focus on the 24 h relaxation timescale integration (REF-C1SD-24 h, smoothed).
Despite the issues caused by changing the reanalysis dataset and analysing
over a different period, it is worth noting that, if a “direct” comparison
is made, then values for the free-running CCMI simulations (REF-C1 and
REF-C2) are above the CCMVal-2 multi-model mean
Figure 2 shows climatologies of the annual mean zonal-mean temperature and
zonal wind in the REF-C1 simulation and biases in this simulation relative
to ERA-Interim. A cold bias in the troposphere and a warm bias at the
tropical tropopause, which have existed in all the Met Office HadGEM models
Biases in the climatological seasonal cycle of the REF-C1
simulation, relative to ERA-Interim, for zonal-mean
Figure 3 considers the seasonal cycle in temperature at 50 hPa (relevant to
polar stratospheric cloud formation during winter and spring) and zonal wind
at 10 hPa (a measure of polar vortex variability). Figure 3a shows that
there are biases in the 50 hPa temperature in both the Northern Hemisphere and Southern
Hemisphere high latitudes. The seasonal cycle in temperature is too weak in
both hemispheres, but this signal is more pronounced in the Southern Hemisphere,
with up to a 4 K warm bias seen in August. In both hemispheres,
a warm bias of 1–2 K is seen in polar spring. In the nudged version of the
model, temperature biases are largely removed, with biases at 50 hPa ranging
from
Polar vortex variability for the
Figure 3b shows that the winter polar vortex (at 10 hPa) in both hemispheres
is biased weak relative to the ERA-Interim reanalysis, consistent with the
warm biases in the polar vortex shown in Fig. 3a. The weak bias is most
significant in the Southern Hemisphere winter, with a negative bias of up to
6 m s
A detailed look at the strength and variability of the zonal-mean wind at
10 hPa in both hemispheres (Fig. 4) demonstrates that this is well simulated
in the northern high latitudes in all seasons, with the free-running models
showing a small negative bias and slightly too much interannual variability
in October and November. However, the vortex strength and variability in
Southern Hemisphere winter and early spring are too weak in the free-running
models. Despite this, the time of the vortex breakup, determined as the time
when the zonal wind transitions from eastward to westward, is shown to be
very accurately simulated in both hemispheres. Since the polar vortex acts as
a barrier to transport, this vortex breakup allows transport of ozone into
and out of the polar region, impacting springtime TCO in the high latitudes.
Accurate simulation of the vortex breakup time is also important since the
dynamical impact of the Southern Hemisphere extratropical stratosphere on the
troposphere is shown to be greatest during the time of the vortex breakup
Polar vortex final warming times, as defined by the final transition
from eastward to westward of the zonal-mean zonal wind at 60
Figure 5 shows this polar vortex breakup time at all altitudes for both
hemispheres. This is accurately simulated in all simulations. The largest
bias is seen in the Northern Hemisphere lower stratosphere for REF-C2 where
the vortex breakup is around 10 days late, although even this is well within
the 95 % confidence interval for vortex breakup times calculated using
ERA-Interim
Tropical (20
Of course, another important factor in determining the simulated
heterogeneous ozone depletion is the area of the PSCs. In this study, the size of the area in which temperature at 50 hPa
falls below 195 K is used as a proxy for the PSC area. Figure 6a shows that
the average October daily PSC area in the Southern Hemisphere high latitudes
is too low in the free-running model, consistent with the warm biases in the
Southern Hemisphere high-latitude temperatures at 50 hPa shown in Fig. 3a.
The average daily October PSC area across all years (1980–2010), in units of
10
Tropical tape recorder signal,
Zonal-mean annual mean climatologies in residual vertical velocity
for
Traditionally, the Met Office climate model has suffered from a warm bias in
the tropical tropopause region MERRA is used
in Fig. 7b as it is shown in
Accurate water vapour concentrations are very important for correctly
simulating chemical species in the stratosphere, including ozone. Water
vapour, although not constrained in the nudged model, is strongly influenced
by the cold-point temperature at the tropical tropopause. The annual cycle in
cold-point temperature causes an equivalent annual cycle in water vapour
concentrations entering the stratosphere in the tropics, and the upward
transport of water vapour in the tropics gives rise to the so-called
“tape recorder” signal, shown in Fig. 8. Due to an 8 K warm bias in
tropical tropopause temperature in the UMUKCA-METO CCMVal-2 simulation
Whilst temperatures and horizontal winds are forced close to the ERA-Interim
reanalysis in the nudged model, vertical winds are notoriously difficult to
simulate accurately and are therefore not nudged. Figure 9 demonstrates that,
as shown in Fig. 1, nudging temperature and horizontal wind fields does
not imply that the simulated vertical wind field will also be close
to the reanalysis
Stratospheric age of air (1990–2010) in the
Total column ozone (TCO):
Although the HadGEM3-ES simulations do capture the double-peaked nature of
the 70 hPa residual vertical velocity in the tropics (Fig. 10a), like other
models the peaks are too hemispherically symmetric
An alternative diagnostic of the strength of stratospheric transport is the
so-called age of air (Fig. 11). The mean age of stratospheric air
Figure 12 shows time series of TCO as simulated in the free-running and
nudged models, compared to the Total Ozone Mapping Spectrometer (TOMS)
satellite data
Figure 13a shows the global net annual mean stratosphere–troposphere exchange
(STE) of ozone (i.e. the net mass flux of ozone across the tropopause; see
caption of Fig. 13 for details). Consistent with Fig. 10b, which showed the
tropical mass upwelling from the troposphere to the stratosphere to be biased
weak, the STE ozone flux in the model simulations is found to be too low as
compared to ERA-Interim. Currently, the best estimate of STE ozone flux
inferred from observations is 550
Stratosphere–troposphere exchange of ozone for
Climatological TCO during October in the Southern Hemisphere for
The same as in Fig. 14, but for climatological TCO in Northern Hemisphere March.
Anomalies, averaged over the 30 days following a stratospheric
sudden warming, in
The change in TCO in the high latitudes, during the period 1980–2010, is
similar in all simulations (Fig. 12c, d) and agrees well with the TOMS
observations. However, TCO that is too high is indicative of an ozone hole
that is too small in area. Further, we have seen 50 hPa temperatures biased
high in the free-running model (Fig. 3a), PSC areas biased too low (Fig. 6),
and negative biases in the Southern Hemisphere polar vortex strength
(Fig. 4b). Figure 14 shows TCO over the South Pole in October, averaged over
the years 1997–2002, as compared against the 220 DU contour from the TOMS
satellite data averaged over the same 6 years. Southern Hemisphere
high-latitude TCO is biased high, by around 40 DU), in all versions of the
model (Fig. 12d). Figure 3-11c from Chap. 3 of
Northern high-latitude zonal-mean TCO is very well simulated (Fig. 12c). In
terms of azonal ozone structure, conclusions for the Northern Hemisphere
(Fig. 15) are the same as for the Southern Hemisphere. The amplitudes of the
two ozone maxima simulated around 120
A further way in which dynamics influence ozone concentrations is through the
enhanced poleward transport that follows sudden stratospheric warmings
The simulated interannual variability in tropical TCO (Fig. 12b), in both
free-running and nudged simulations, agrees well with the observations.
However, all simulations show a
This study analyses the historical period (1980–2010) of free-running and
nudged simulations using HadGEM3-ES, the Met Office chemistry–climate model
as configured for inclusion in the Chemistry–Climate Model Initiative. In the
nudged model configuration, the relaxation timescale of the applied nudging
was found to be important
Metrics of dynamical processes relevant for the simulation of stratospheric
ozone were calculated for all model configurations. These were compared
against the metrics as recalculated over the period 1980–2010 for the
previous model configuration, UMUKCA-METO, used in CCMVal-2
Particularly significant improvements to the free-running model are that
HadGEM3-ES no longer suffers from the large positive bias in stratospheric
age of air or large warm bias in tropical tropopause temperature that were
present in UMUKCA-METO
Metrics are split into those assessing mean climate and those assessing
variability. The mean climate was found to be well simulated in both
free-running and nudged versions of HadGEM3-ES with the notable exception of
stratospheric transport, as diagnosed by the upwelling mass flux in the
tropics. Vertical velocities are very noisy in reanalysis data
Comparison of the free-running model climatology to that of the nudged
version shows that accurately simulated dynamics, specifically temperature
and horizontal wind fields, do play a role in the spatial structure of the
ozone hole. This structure is correct in both hemispheres in the nudged
model. However, the high ozone biases that exist in the tropics and southern
high latitudes of the free-running model persist also in the nudged model,
and these are therefore not solely attributable to biases in the dynamical
fields. Thus, despite the fact that the area of Southern Hemisphere polar
stratospheric clouds is correctly simulated in the nudged model, the ozone
hole area, defined as the area over which TCO drops to below 220 DU, is too
small in both free-running and nudged models
Tropical TCO is improved in the nudged simulations over that seen in the free-running model, but is still biased high relative to observations, with these biases occurring in the tropical tropopause region. It is worth noting that both water vapour and TCO are not perfect in the nudged simulation, and significant biases in the simulated transport and chemistry still exist in this model.
The fact that tropical upwelling and the stratospheric meridional circulation are found difficult to constrain and, indeed, are found to be worse in the nudged simulations than in the free-running simulations, means that ozone fluxes, in particular from the stratosphere to the troposphere, are not well constrained in the nudged model either, with obvious implications for the simulated extratropical tropospheric ozone budget. Again, this issue is not unique to HadGEM3-ES – even the ERA-Interim reanalysis shows ozone fluxes from the stratosphere to the troposphere with only around half the value inferred from observations.
In summary, biases in transport and ozone remain in the nudged simulations, demonstrating that these biases are not solely due to the model dynamics. Nevertheless, HadGEM3-ES is found to have good climatology and variability in basic meteorological fields, and a realistic simulation of stratospheric ozone loss. HadGEM3-ES represents a significant improvement over its predecessor, UMUKCA-METO.
Due to intellectual property right restrictions, we
cannot provide either the source code or documentation papers for the Unified Model (UM). The
Met Office Unified Model is available for use under licence. A number of
research organizations and national meteorological services use the UM in
collaboration with the Met Office to undertake basic atmospheric process
research, produce forecasts, develop the UM code and build and evaluate Earth
system models. For further information on how to apply for a licence, see
Steven C. Hardiman wrote Sects. 1, 3, and 4 of the paper and produced the figures. Fiona M. O'Connor wrote Sect. 2 of the paper. Steven C. Hardiman, Neal Butchart, and Fiona M. O'Connor contributed to running model integrations and to discussion on the structure and content of the paper. Steven T. Rumbold processed the chemistry and aerosol emissions datasets used in model integrations.
The work of Steven C. Hardiman, Neal Butchart, Fiona M. O'Connor, and Steven T. Rumbold was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). Steven C. Hardiman and Neal Butchart were also supported by the European Commission's 7th Framework Programme, under grant agreement no. 603557, StratoClim project. Fiona M. O'Connor also acknowledges additional funding received from the Horizon 2020 European Union's Framework Programme for Research and Innovation “Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO)” project under grant agreement no. 641816. The authors would like to acknowledge Jeff Knight for processing the sea surface temperature and sea ice datasets used in the model integrations. ERA-Interim data used in this study were provided by ECMWF. Met Office CCMVal-2 data used in this study are stored at the British Atmospheric Data Centre (BADC). We acknowledge use of the MONSooN high-performance computing system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the Natural Environment Research Council. Edited by: J. Williams Reviewed by: two anonymous referees