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Journal of Advances in Modeling Earth Systems
Volume 16, Issue 8 e2023MS004135
Research Article
Open Access
THE HADGEM3-GC3.1 CONTRIBUTION TO THE CMIP6 DETECTION AND ATTRIBUTION MODEL
INTERCOMPARISON PROJECT
Gareth S. Jones,
Corresponding Author
Gareth S. Jones
* gareth.s.jones@metoffice.gov.uk
* orcid.org/0000-0002-8335-2794
Met Office Hadley Centre, Exeter, UK
Correspondence to:
G. S. Jones,
gareth.s.jones@metoffice.gov.uk
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Martin B. Andrews,
Martin B. Andrews
* orcid.org/0000-0003-3145-2264
Met Office Hadley Centre, Exeter, UK
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Timothy Andrews,
Timothy Andrews
* orcid.org/0000-0002-8248-8753
Met Office Hadley Centre, Exeter, UK
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Ed Blockley,
Ed Blockley
* orcid.org/0000-0002-0489-4238
Met Office Hadley Centre, Exeter, UK
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Andrew Ciavarella,
Andrew Ciavarella
* orcid.org/0000-0001-8581-422X
Met Office Hadley Centre, Exeter, UK
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Nikos Christidis,
Nikos Christidis
Met Office Hadley Centre, Exeter, UK
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Daniel F. Cotterill,
Daniel F. Cotterill
Met Office Hadley Centre, Exeter, UK
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Fraser C. Lott,
Fraser C. Lott
* orcid.org/0000-0001-5184-4156
Met Office Hadley Centre, Exeter, UK
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Jeff Ridley,
Jeff Ridley
* orcid.org/0000-0002-2612-9924
Met Office Hadley Centre, Exeter, UK
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Peter A. Stott,
Peter A. Stott
Met Office Hadley Centre, Exeter, UK
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Gareth S. Jones,
Corresponding Author
Gareth S. Jones
* gareth.s.jones@metoffice.gov.uk
* orcid.org/0000-0002-8335-2794
Met Office Hadley Centre, Exeter, UK
Correspondence to:
G. S. Jones,
gareth.s.jones@metoffice.gov.uk
Search for more papers by this author
Martin B. Andrews,
Martin B. Andrews
* orcid.org/0000-0003-3145-2264
Met Office Hadley Centre, Exeter, UK
Search for more papers by this author
Timothy Andrews,
Timothy Andrews
* orcid.org/0000-0002-8248-8753
Met Office Hadley Centre, Exeter, UK
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Ed Blockley,
Ed Blockley
* orcid.org/0000-0002-0489-4238
Met Office Hadley Centre, Exeter, UK
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Andrew Ciavarella,
Andrew Ciavarella
* orcid.org/0000-0001-8581-422X
Met Office Hadley Centre, Exeter, UK
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Nikos Christidis,
Nikos Christidis
Met Office Hadley Centre, Exeter, UK
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Daniel F. Cotterill,
Daniel F. Cotterill
Met Office Hadley Centre, Exeter, UK
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Fraser C. Lott,
Fraser C. Lott
* orcid.org/0000-0001-5184-4156
Met Office Hadley Centre, Exeter, UK
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Jeff Ridley,
Jeff Ridley
* orcid.org/0000-0002-2612-9924
Met Office Hadley Centre, Exeter, UK
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Peter A. Stott,
Peter A. Stott
Met Office Hadley Centre, Exeter, UK
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First published: 03 August 2024
https://doi.org/10.1029/2023MS004135
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* 2 Model Configuration and Experimental Set up
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ABSTRACT
The UK contribution to the Detection and Attribution Model Intercomparison
Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison
Project (CMIP6), is described. The lower atmosphere and ocean resolution
configuration of the latest Hadley Centre global environmental model,
HadGEM3-GC3.1, is used to create simulations driven either with historical
changes in anthropogenic well-mixed greenhouse gases, anthropogenic aerosols, or
natural climate factors. Global mean near-surface air temperatures from the
HadGEM3-GC31-LL simulations are consistent with CMIP6 model ensembles for the
equivalent experiments. While the HadGEM3-GC31-LL simulations with anthropogenic
and natural forcing factors capture the overall observed warming, the lack of
marked simulated warming until the 1990s is diagnosed as due to aerosol cooling
mostly offsetting the well-mixed greenhouse gas warming until then. The model
has unusual temperature variability over the Southern Ocean related to
occasional deep convection bringing heat to the surface. This is most prominent
in the model's aerosol only simulations, which have the curious feature of
warming in the high southern latitudes, while the rest of the globe cools, a
behavior not seen in other CMIP6 models. This has implications for studies that
assume model responses, from different climate drivers, can be linearly
combined. While DAMIP was predominantly designed for detection and attribution
studies, the experiments are also very valuable for understanding how different
climate drivers influence a model, and thus for interpretating the responses of
combined anthropogenic and natural driven simulations. We recommend institutions
provide model simulations for the high priority DAMIP experiments.
KEY POINTS
* The UK's contribution to the Detection and Attribution Model Intercomparison
project (DAMIP) is described
* The climate model's global temperature response to different anthropogenic
and natural drivers is examined and compared to other models
* Southern Ocean temperature variability is unusual and sensitive to climate
driver
PLAIN LANGUAGE SUMMARY
We describe the UK submission to the Detection and Attribution Model
Intercomparison Project (DAMIP), using the HadGEM3-GC3.1 climate model. The
model's near-surface temperature responses to different human and natural
climate drivers are compared with other climate models and observed temperature
changes. The experiments help to understand the evolution of the model's
simulated historical global temperatures. One of the more interesting model
features is the variability in the Southern Ocean which manifests itself as
occasional surface warming due to deep ocean heat coming to the surface. This
behavior, which occurs more often in simulations that cool than in simulations
that warm, appears to be unusual compared to other models. The investigation of
this model behavior demonstrates that DAMIP model experiments are not just
useful for climate change detection and attribution, but also for understanding
how a model responds to different climate drivers. Climate model participation
in DAMIP is encouraged.
1 INTRODUCTION
The Climate Model Intercomparison Project (CMIP) has, for the last 20 years,
been a cornerstone of climate research, providing a framework for modeling
centers around the world to share data. The project has been used by researchers
to investigate climate processes, understand what causes past changes, and make
projections of future changes. Being able to compare simulations from a variety
of climate models allows an exploration of uncertainties in the modeling of
climate. The resulting studies have made substantial contributions to
assessments made by the Intergovernmental Panel on Climate Change, IPCC, from
IPCC (2001) to IPCC (2021). The sixth phase, CMIP6 (Eyring et al., 2016), is the
most ambitious phase of the Climate Model Intercomparison Project to date,
comprising a framework of core experiments to enable a consistent assessment of
models within each CMIP phase, and across future CMIP phases, and additional
groupings of experiments, or MIPs (Model Intercomparison Projects), aligned with
specific research interests.
The Met Office Hadley Centre has contributed model data to each of the CMIP
phases, beginning with data from HadCM2 (Johns et al., 1997) submitted to CMIP1
(Meehl et al., 1997), then data from a further 6 models submitted to CMIP2
(Meehl et al., 2000), CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor
et al., 2012)—there was no formal CMIP4 (Stouffer et al., 2017). The data from
these models have been used in many climate attribution studies (e.g., Tett
et al. (1999); Allen et al. (2000); Stott and Kettleborough (2002); Christidis
et al. (2012); Jones et al. (2013)) and assessments (Bindoff et al., 2013;
Eyring et al., 2021; Hegerl et al., 2007). For the latest phase of the Climate
Model Intercomparison project, CMIP6, the UK (represented by the Met Office and
the Natural Environment Research Council) has submitted data from several model
configurations (Sellar et al., 2020; Senior et al., 2020), with the same
physical model at their core, the Hadley Centre Global Environmental Model
3—Global Coupled configuration 3.1, HadGEM3-GC3.1 (Williams et al., 2018).
The Detection and Attribution Model Intercomparison Project, DAMIP (Gillett
et al., 2016), formally brings together several different experiments for use in
detection and attribution, and other studies. Model simulations with differing
histories of changes in climate drivers or forcing factors have been used in
detection studies to attribute past observed climate changes to specific factors
(Hegerl & Zwiers, 2011), which have contributed to IPCC climate attribution
assessments (Bindoff et al., 2013; Eyring et al., 2021; Hegerl et al., 2007;
Mitchell et al., 2001). The sharing of model data for detection and attribution
had been done in an ad-hoc manner until some specific experiments were first
formally introduced in CMIP5 (Taylor et al., 2012), and then in CMIP6 with DAMIP
formally describing a range of single forcing experiments. Two of the CMIP6 core
experiments are of particular relevance to attribution analyses, the piControl
and historical experiments. The piControl experiment is a model simulation with
constant 1850 forcing factors, representing pre-industrial conditions, and is
often used to characterize climate internal variability. The historical model
experiment comprises an ensemble of simulations, initialized from different
points in the model's piControl simulation, driven by changes in anthropogenic
and natural forcing factors over the 1850 to 2014 period (Eyring et al., 2016).
To complement these experiments DAMIP has three tiers of experiments with
differing priorities. The first tier, with highest priority is made up of
experiments for the 1850–2020 period with historical variations in well-mixed
greenhouse gases only, anthropogenic aerosols only, and natural influences only.
The other lower priority tiers in the DAMIP proposal requested simulations
driven by other combinations of historical forcing factors and for extending the
other experiments to 2100 (Gillett et al., 2016).
In this paper we will describe the UK submission to the DAMIP, including the
choice of model configuration to use, what forcing data sets were required and
how they were implemented, and what experiments were produced. We will compare
the simulated near-surface temperatures produced from the model's experiments
with the other CMIP6 submissions to DAMIP, as well as to historic observed
surface temperatures. How previously reported radiative forcings for the model
compare with other CMIP6 models are also examined. The paper is laid out as
follows; Section 2 describes the model configuration choice and set up, and
experiments, Section 3 describes the CMIP6 models and observational data and how
they are processed, the results are given in Section 4 and the conclusions are
in Section 5.
2 MODEL CONFIGURATION AND EXPERIMENTAL SET UP
This section describes the chosen model configuration, the model experiments and
their forcing factor implementation.
2.1 CONFIGURATION CHOICE
The Hadley Centre Global Environmental Model 3—Global Coupled configuration 3.1,
HadGEM3-GC3.1 (Williams et al., 2018), developed at the Met Office, is the
physical core of the UK's model submissions to CMIP6 (Sellar et al., 2020;
Senior et al., 2020). The different configurations of the models reflect an
increase in complexity over previous model generations and improvements in how
physical processes are modeled.
Of the several model configurations available we chose the HadGEM3-GC31-LL model
for submission to DAMIP. The model has N96 atmosphere resolution (1.875°
longitude, 1.25° latitude) and 1° ocean resolution (Kuhlbrodt et al., 2018). The
other configurations either have higher atmosphere and ocean resolution, for
example, HadGEM3-GC31-MM (Andrews et al., 2020), or have additional atmospheric
and oceanic chemistry, biochemistry, land vegetation, and advanced aerosol
modules, UKESM1-0-LL (Sellar et al., 2019). The higher resolution configurations
use more computer resources (Menary et al., 2018) and the configurations with
additional modules make setting up DAMIP experiments more complex (Jones
et al., 2011). Thus HadGEM3-GC31-LL was chosen to enable a reasonable number of
initial condition ensemble members to be completed for the requested tier one
experiments, whilst minimizing the technical challenges. The climate responses
for the different configurations are similar (Andrews et al., 2020; Sellar
et al., 2019), with similar climate sensitivities (Senior et al., 2020), but
there are some significant differences such as in sea ice concentrations
(Andrews et al., 2020). Details of the aerosol scheme used in HadGEM3-GC31-LL,
that uses prescribed emissions of sulfur dioxide, black carbon, organic carbon
from burning of fossil fuels and organic material), and sea-salt, are described
in Mulcahy et al. (2020).
2.2 MODEL EXPERIMENTS
The HadGEM3-GC31-LL 500 years long piControl, and the model's four initial
condition historical simulation ensemble, are comprehensively described in
Menary et al. (2018) and Andrews et al. (2020) respectively. An extra historical
simulation was run to expand on the originally submitted four initial condition
ensemble members (Andrews et al., 2020). For the fifth ensemble member, initial
conditions from the piControl in year 2030 were chosen, 60 years after the
fourth initial condition date, suggested by an examination of the location in
phase space of the Interdecadal Pacific Oscillation and the Atlantic
Multidecadal Oscillation indices. This approach was used in Andrews
et al. (2020) to select the first four initial conditions (dates 1850, 1885,
1930 and 1970). The piControl was extended from 500 to 2000 years in length
(Ridley et al., 2022), with the aim to have more data to better characterize the
internal variability of the model, for future attribution studies.
The single HadGEM3-GC31-LL ssp245 simulation, originally submitted to CMIP6 as
part of Scenario Model Intercomparison Project (ScenarioMIP, O’Neill
et al. (2016)), was expanded to five ensemble members to enable all five
historical ensemble simulations to be extended to 2020 and beyond. Within the
forcing data sets, the transition from historical to ssp245 had been harmonized
(O’Neill et al., 2016), and any remaining discontinuities were very small
relative to trends over the 2014–2015 transition period (e.g., Lurton
et al. (2020)). From hereon we will use the name “historicalssp245” to refer to
our concatenation of the data from the historical and ssp245 simulations.
Simulations for the three tier 1 DAMIP experiments were run for 1850–2020,
hist-GHG (historical variations in well-mixed greenhouse gases), hist-aer
(historical variations in anthropogenic aerosols) and hist-nat (historical
variations in natural influences). Five initial condition ensemble members were
run for each, using the same initial conditions as for the historical ensemble.
It had been shown that low signal-to-noise ratios from natural only model
simulations can limit their usefulness in some detection studies (Jones
et al., 2016), so a further five ensemble members of hist-nat were run, using
initial conditions from piControl with dates 50 years apart (dates 2080, 2130,
2180, 2230 and 2280).
A single tier-2 DAMIP experiment was run, ssp245-GHG, which used well-mixed
greenhouse gas (WMGHG) concentrations from the SSP2-4.5 scenario (O’Neill
et al., 2016) to extend the first hist-GHG ensemble member to year 2100 (Gillett
et al., 2016). Only five other models, to date, have had data submitted to CMIP6
for this experiment. The ssp245-GHG experiment, together with the other DAMIP
experiments, is intended for use in so called ASK (Allen, Stott and
Kettleborough) analyses (Allen et al., 2000; Kettleborough et al., 2007; Stott &
Kettleborough, 2002), which use attribution results to constrain the magnitude
of future greenhouse gas warming. To concentrate on the analysis of the tier 1
DAMIP experiments, the ssp245-GHG HadGEM3-GC31-LL simulation is not described
here.
2.3 FORCING FACTOR IMPLEMENTATION
The anthropogenic and natural forcing factors, and how they were applied to
HadGEM3-GC31-LL for the historical experiment, are fully described in Sellar
et al. (2020) and Andrews et al. (2020). The anthropogenic forcing factors
comprise changes in atmospheric concentrations of well-mixed greenhouse gases
(WMGHGs: CO2, CH4, N2O and CFC/HCFCs), aerosols (sulfate, black carbon and
organic carbon from burning of fossil fuels and organic material), ozone, and
land cover changes. The natural forcing factors comprise solar irradiance
changes and changes in stratospheric aerosol concentrations due to explosive
volcanic eruptions, both also affecting ozone concentrations.
The forcing factors required for each of the DAMIP experiments are described in
Gillett et al. (2016). Selections of the forcing factors from the historical
experiment are used for 1850–2014 (Eyring et al., 2016), then from the ssp245
experiment for 2015–2020 (O’Neill et al., 2016). For hist-GHG all forcing
factors are set to piControl settings, except for the WMGHGs, which evolve as in
the historical and ssp245 experiments. For hist-aer only anthropogenic aerosol
and their precursor species, are varied. In both hist-aer and hist-GHG
experiments the tropospheric and stratospheric ozone concentrations are set to
the same as in the piControl. For hist-nat solar irradiance and stratospheric
volcanic aerosols are varied, as in the historical and ssp245 experiments. The
tropospheric and stratospheric ozone concentrations from solar and volcanic
influences were also prescribed in the hist-nat simulations, with the ozone
concentration data set constructed as described in Sellar et al. (2020) (Section
3.5), but using ozone concentration source data intended for hist-nat
(Hegglin, 2019; Plummer et al., 2018).
All the HadGEM3-GC31-LL DAMIP experiments incorporated the same ozone remapping
scheme (Hardiman et al., 2019), which corrects a mismatch between the prescribed
ozone concentrations and the location of the simulation's tropopause in
HadGEM3-GC3.1 (Andrews et al., 2020; Sellar et al., 2020). This ozone remapping
was used in the historical and ssp245 simulations, but not in the piControl
simulation. To indicate the differences in ozone remapping, the “variant_label”
(Taylor et al., 2018) within the ESGF (Petrie et al., 2021), has the forcing
index set to 1 for the piControl (“f1”) and 3 for simulations using ozone
remapping (“f3”) (Sellar et al., 2020). The impact of applying the ozone
remapping on the piControl has been shown to have a very small influence on
surface climate (Hardiman et al., 2019).
3 DATA
This section describes the data used to compare the HadGEM3-GC31-LL simulations
with other CMIP6 models and observations. Because of the prevalence of the use
of temperatures in detection and attribution studies (Eyring et al., 2021), not
least due to the availability of quality controlled temperature data sets
covering the last 150 years or so (Jones, 2016), the analysis focus is on
simulated near-surface air temperatures and observed near-surface temperatures,
with the effective radiative forcing also examined to provide some context of
the causes of the simulated temperature changes.
3.1 CMIP6
Suites of simulations from multiple models are often considered “ensembles of
opportunity” (Allen & Stainforth, 2002), as they don't fully sample the entire
range of modeling uncertainties (Hegerl & Zwiers, 2011). Nonetheless, such
ensembles of simulations from a variety of models have proved to be invaluable
in many studies (e.g., references in Hegerl et al., 2007; Bindoff et al., 2013;
Eyring et al., 2021). At time of writing, 45 institutions had submitted data
from 122 climate models to the CMIP6 data redistribution system, ESGF (Petrie
et al., 2021). Data from 67 models were submitted for the historical experiment,
and data from 47 models were submitted for all three piControl, historical and
ssp245 experiments. Of these only 14 models also have data in CMIP6 for all of
the tier 1 DAMIP experiments.
To allow a consistent approach, we apply some basic quality criteria for which
models and simulations to analyze. We only examine models that had data
submitted to CMIP6 for the piControl and historical experiments, and
ScenarioMIP's ssp245 experiment. We only use models that have a piControl that
is at least 500 years in length, following the CMIP6 core requirement (Eyring
et al., 2016). For the DAMIP experiments, we only use simulations that cover the
1850–2020 period, as in the experimental design of Gillett et al. (2016). We
assume that all the models incorporated the same well-mixed greenhouse gas
concentrations, ozone concentrations, aerosol emissions, land cover changes, and
solar and volcanic forcing factors, applied as described in the CMIP6 and DAMIP
designs (Eyring et al., 2016; Gillett et al., 2016). Models were excluded if
there were any obvious discrepancies. The Supporting Information S1 contains a
list of the models that do not pass these criteria.
Many CMIP6 models have different physics versions and different forcing set ups,
as reflected by differing “p” and “f” settings in the simulations
“variant_label” (Taylor et al., 2018). Following Jones et al. (2013), only one
physics/forcing version from each model is used.
Table S1, in Supporting Information S1, lists the models used, the
“variant_labels” (“p” and “f” settings), and the number of initial condition
ensemble for each model/experiment. The number of models with acceptable
piControl and historicalssp245 experiments is 31, with the historicalssp245
experiment having 252 initial condition ensemble members in total. The number of
acceptable DAMIP models is 12, with hist-GHG, hist-aer and hist-nat experiments
having 76, 73, and 125 ensemble members respectively. Basic details of the CMIP6
models, such as modules used, resolution and their institutions, can be found in
Table AII.5 in Gutiérrez and Tréguier (2021), and their equilibrium climate
sensitivities (ECS) and transient climate responses (TCR) in Meehl et al. (2020)
and Table 7.SM.5 in Forster et al. (2021).
The Radiative Forcing Model Intercomparison Project, RFMIP (Pincus
et al., 2016), is complementary to DAMIP, in that several of its experiments can
be used to deduce the radiative forcings in the tier 1 DAMIP experiments.
Radiative forcing is a useful index for estimating the radiative impact on a
climate system due to a change in a climate driver, or forcing factor
(IPCC, 2001). Diagnosing radiative forcings has been used to understand a
model's climate response to different forcings (e.g., Tett et al. (2002) and
Hansen et al. (2005)). How radiative forcing has been defined has changed
somewhat over time (Ramaswamy et al., 2019), with effective radiative forcing
(ERF) becoming the current standard concept to measure the change of energy into
a climate system, due to different drivers (Hansen et al., 2005; Smith
et al., 2020). The RFMIP experiments of most relevance to DAMIP use atmosphere
model components coupled to fixed sea surface temperatures and sea ice for the
1850–2100 period. Each experiment has differing forcing factors imposed
corresponding to those in the historical, hist-GHG, hist-aer and hist-nat
experiments; that is, piClim-histall, piClim-histghg, piClim-histaer, and
piClim-histnat experiments respectively. Differences in the top-of-atmosphere
radiative fluxes between each forced experiment and an experiment with no
changes in climate drivers (piClim-control) are used to deduce the time varying
effective radiative forcings, ERFs (Andrews et al., 2019; Pincus et al., 2016),
for anthropogenic and natural, well-mixed greenhouse gases, aerosols, and
natural factors.
Only nine models (Table S2 in Supporting Information S1) have some or all of
RFMIP's experiments needed to diagnose the time varying ERF for the forced
experiments. Not all the models have data reaching 2020, so to retain as much of
the limited availability of models to begin with as possible, the period
1850–2014 is used. RFMIP and the Aerosol Chemistry Model Intercomparison Project
(AerChemMIP) also have “time-slice” experiments to diagnose models' ERF from
different factors for the year 2014 (Collins et al., 2017; Pincus et al., 2016).
While referred to, these “time-slice” experiments are not directly used in this
study.
3.2 OBSERVATIONS
For comparing model simulated temperatures with observed temperatures, the
HadCRUT5 near-surface temperature data set is used (Morice et al. (2021):
HadCRUT.5.0.1.0). The HadCRUT5 data set is a blend of the land near-surface air
temperature data set CRUTEM5 (Osborn et al. (2021): CRUTEM.5.0.1.0), with the
sea surface temperature data set HadSST4 (Kennedy et al. (2019):
HadSST.4.0.1.0). The data is provided as monthly anomalies relative to the
1961–1990 mean, on a 5° × 5° grid from 1850 to the present day. There are two
configurations of the data set, a “HadCRUT5 noninfilled data set” and a
“HadCRUT5 analysis.” The “HadCRUT5 noninfilled data set” (“HadCRUT5” from
hereon) only has data within a grid-box where measurements have been made. The
“HadCRUT5 analysis” (“HadCRUT5analysis” from hereon), on the other hand, is more
spatially complete, with a statistical method extrapolating available
measurements into data sparse regions.
It has been shown many times that when comparing model simulations to
observations it is best to make sure spatial coverages are the same when making
global or regional means of diagnostics (e.g., Tett et al., 1997), to not
introduce unnecessary biases or uncertainties. An observational data set that
infilled missing data areas would appear at first glance to be the most
appropriate configuration to compare with model simulations. However, infilling
methods can damp down the observational data sets spatial and temporal
variability (Jones, 2016), and in regions with very few measurements much of the
infilled data are effectively deduced from what happens at the edges of the
regions. When comparing with models, the simulated data will not have the same
statistical infilling methods applied to them which can make any interpretation
of similarities and differences more complicated (Jones, 2020). To minimize
these issues, we use the noninfilled version of the data set, HadCRUT5, to
compare with the simulated data, following previous approaches (e.g., Jones
et al. (2013)). For completeness the analyses are repeated using the infilled
HadCRUT5analysis data set and presented in the Supporting Information S2.
HadCRUT5 and HadCRUT5analysis have sophisticated error models which are used to
estimate observational data set uncertainties (Morice et al., 2021). HadCRUT5
has a 200 member ensemble that samples bias correction uncertainties. Separate
variances for the independent sampling and measurement errors, and covariance
fields for partially correlated sampling and measurement errors are also
provided. It is non-trivial to include the measurement and sampling
uncertainties into the ensemble (Jones & Kennedy, 2017), but they are expected
to only increase the variance of the uncertainties on the trend of global means
over the last 100 or so years by approximately 2% (Jones & Kennedy, 2017). Here
we use the HadCRUT5 bias correction uncertainty ensemble to calculate
uncertainties around its mean. In contrast the infilled data set,
HadCRUT5analysis, has a 200 member ensemble which includes sampling the bias,
measurement and sampling uncertainties, and uncertainties in the infilling
method. Analyses using the HadCRUT5analysis ensemble and its mean is given in
the Supporting Information S1. Observational coverage uncertainty is not
considered here, as the model data is sampled at the same locations as the
observational data sets. Uncertainty due to climate internal variability is not
part of the observational error models (Jones & Kennedy, 2017), so is not
included here, although they should be considered in more formal
model-observation comparisons (Santer et al., 2008), attribution studies (Hegerl
& Zwiers, 2011), and model performance assessments (Bodas-Salcedo et al., 2023).
The land near-surface air temperature data set, CRUTEM5 (Osborn et al., 2021),
is used for comparison with the simulated land air temperatures. CRUTEM5 does
not have an ensemble of data sets sampling uncertainties, so a land mask is
applied to HadCRUT5 to create an estimated bias correction ensemble. For a
sensitivity analysis we use the fuller coverage data set HadCRUT5analysis with
the land mask applied (HadCRUT5analysis [land]), as there is no infilled version
of the CRUTEM5 data set available (Osborn et al., 2021). The same land fraction
data set, used in the blending construction of the HadCRUT5 data sets (Morice
et al., 2021), is used as the land mask, where data grid-boxes are masked out
where the land fraction is less than 25%.
Gridded monthly anomalies are calculated relative to 1961–1990 (Morice
et al., 2021). For the time series analysis, presented in Section 4, annual
means for each grid-box are calculated requiring at least 2 months to be
non-missing. Global and hemispheric means are calculated with grid-box area
weighting. CRUTEM5 is processed in the same way, bar the calculation of global
means where there is both grid-box area and land area fraction weighting (using
the same land fraction data set used in the blending construction of the
HadCRUT5 data sets (Morice et al., 2021). For the zonal change analysis, because
of the sensitivity to the amount of missing data across some latitudes, annual
means for each grid-box are calculated requiring at least 8 months to be
non-missing (following Jones et al., 2013).
For the time series analyses of spatial area means, anomalies are calculated
with respect to the reference baseline 1880–1919, as used in the description of
the historical simulations of the model (Andrews et al., 2020), its previous
generations (Johns et al., 1997; Mitchell et al., 1995; Stott et al., 2000; Tett
et al., 2002) and in comparisons of CMIP5 simulations with observed temperature
changes (Bindoff et al., 2013; Jones et al., 2013), to show temperature
evolutions from around 1900. Other reference baselines are commonly used, for
instance the latest IPCC assessment used 1850–1900 and 1995–2014 as baselines
(Chen et al., 2021), which will give different emphases of when there are
similarities and/or differences between different simulated and observational
data sets.
3.3 MODEL DIAGNOSTICS
For simulated temperatures, the CMIP6 near-surface air temperature diagnostic,
TAS, is used, corresponding to air temperatures at 2 m (the data request is
described in Juckes et al., 2020). Comparing simulated historical variations in
near-surface air temperatures to observations, which are generally blended sea
surface temperatures and land air temperatures (Section 3.2), may introduce
small biases (Cross chapter box 2.3 in Gulev et al., 2021). Accounting for this
is challenging when there are differences in how sea surface temperatures are
diagnosed across models and between models and observations, and with sea ice
coverages varying across models, experiments and observations (Jones, 2020). We
use simulated near-surface air temperatures, as any biases are likely very small
compared to other uncertainties and impacts from methodological choices
(Jones, 2020). All model data are projected onto the same 5° × 5°
longitude-latitude grid, using grid-box area weighting. To calculate global mean
land temperatures, each model's land area fraction (SFTLF) is used to weight the
model's near-surface air temperatures (TAS), masking out where land area
fraction is less than 25%. This is because near-surface air temperatures only
over land are not a CMIP6 diagnostic and to minimize the impact of differences
in land coverage across models, such as absence and presence of islands. For the
full coverage data, annual means are simply calculated from the monthly means.
To compare with the observational data sets the modeled monthly data are masked
by the relevant observational data set's monthly spatial coverage, then
processed as for the observational data sets to create annual global,
hemispheric and latitudinal zonal means. Spatial mean time series anomalies are
calculated relative to their 1880–1919 mean (Jones et al., 2013).
For the calculation of effective radiative forcing (Sections 3.1 and 4.6)
top-of-atmosphere shortwave and long wave radiative fluxes (CMIP6 diagnostics
RSDT, RSUT and RLUT) from the relevant RFMIP experiments are used (Pincus
et al., 2016). The methodology to calculate each model's ERF follows that
described in Andrews et al. (2019).
For an examination of HadGEM3-GC31-LL's climate variability over the Southern
Ocean (Section 4.5) the diagnostics “sea ice area fraction” and “ocean mixed
layer thickness defined by sigma t” (CMIP6 diagnostics SICONC and MLOTST) are
used to measure sea ice area and mixed layer depth. The diagnostics, on the
model's tripolar ocean grid (Sellar et al., 2020), are nearest-neighbor
interpolated onto a 1° × 1° regular grid.
4 RESULTS
In this section, the statistics of global, hemispheric, and latitudinal zonal
means are presented as follows. For the calculation of multi-model statistics
for an experiment, each simulation from a given model is weighted by the inverse
of the number of ensemble members for that model multiplied by the inverse of
the number of models. Thus each model is given equal weight regardless of the
number of ensemble members a model may have (Jones et al., 2013). Then ordered
statistics of the weighted multi-model ensemble, and the observational ensemble,
are used to calculate the median, 5%–95% ranges and 25%–75% ranges. In the text
changes and trends are given as the “median (5%–95%),” or for a single model's
ensemble as “mean ± 1.6449 × sample standard deviation.”
4.1 TRENDS AND VARIABILITY OF PICONTROL
A model's piControl is useful to assess the characteristics of the model's
unforced internal variability. Trends in piControl climate have also been used
to assess any climate drift due to the model being out of radiative balance (Sen
Gupta et al., 2013), and often studies will remove estimates of this drift from
climate diagnostics from forced simulations (e.g., Andrews et al., 2020).
However the long term trend of a piControl's climate will have contributions not
only from climate drift, but also from internal variability, so can only be used
as a guide to the magnitude of any drift present (Sen Gupta et al., 2013). Here
we follow Hegerl et al. (2007) and Jones et al. (2013) by choosing not to remove
an estimate of drift from the examined near-surface air temperatures, to avoid
adding uncertainty from the piControl's internal variability, and not using a
model if its long term piControl trend has a magnitude greater than
0.2 K/century.
The HadGEM3-GC31-LL piControl near-surface temperatures have a global mean
linear trend over the first 500 years of 0.03 K/100 years (Menary et al., 2018),
and a global annual mean standard deviation of 0.12 K, with the 10 year means
having a standard deviation of 0.07 K (Figure S1 in Supporting Information S1).
The simulation's variability is similar to what was seen for previous Met Office
Hadley Centre models (cf. Figure S1 in Jones et al. (2013)). None of the 31
CMIP6 models used in this study have piControl trends greater in magnitude than
0.05 K/100 years (Figure S1 in Supporting Information S1). The trends are
similar to the CMIP6 piControl trends for years 100–400 assessed by Parsons
et al. (2020) for surface (“skin”) temperature (Table S1 in Supporting
Information S1 therein). Thus none of the CMIP6 models are excluded from the
analysis due to having too large piControl long term trend (Hegerl et al., 2007;
Jones et al., 2013). The CMIP6 piControls have annual variability ranging
between 0.08 and 0.22 K, and decadal variability from 0.05 to 0.2 K (estimated
from standard deviations of annual means and 10 years means, Figure S1 in
Supporting Information S1). The range of the CMIP6 model piControl trends is
smaller than what has been seen in CMIP3 and CMI5 piControl simulations. Some of
the models have larger variability than seen in their equivalent models in CMIP3
and CMIP5 (cf. Figure S1 in Jones et al., 2013). This is consistent with what
Parsons et al. (2020) found when comparing the interdecadal internal variability
for CMIP3, CMIP5 and CMIP6 models.
As described earlier, the HadGEM3-GC31-LL piControl was extended from 500 to
2000 years in length. Around the year 500 the simulation's near-surface
temperatures went through what appeared to be a step change, and its variability
increased (Ridley et al., 2022). Whilst the overall trend for the entire
2000 year period is still low (0.02 K/100 years), the interannual standard
deviation increased to 0.21 K. Investigations found that the model had a large
amount of heat come to the surface from the deep ocean near Antarctica, which
had built up over time due to the model's small positive top-of-atmosphere
radiative imbalance, which reduced the amount of Southern Ocean sea ice and
increased global mean near-surface air temperatures to a new more variable
pseudo-equilibrium. Ridley et al. (2022) suggested that the model's “spin-up”
(Eyring et al., 2016) may not have been long enough. Noteworthy variability in
the Southern Ocean in the HadGEM3 and UKESM models had been reported previously
(Andrews et al., 2019, 2020; Menary et al., 2018; Sellar et al., 2019).
The change in variability and apparent step change in HadGEM3-GC31-LL's
piControl surface temperatures raises questions about how models are spun up,
how close to radiative balance they are, and what parts of the piControl to use
for measuring a model's internal variability (Ridley et al., 2022). Considering
whether a similar large change in oceanic variability could happen in reality is
challenging given that the underlying heat that built up in the deep ocean of
the HadGEM3-GC31-LL piControl and driving its instabilities, is considered to be
an artifact in most models (Kuhlbrodt et al., 2023).
4.2 NEAR-SURFACE AIR TEMPERATURE CHANGES IN HISTORICAL AND DAMIP EXPERIMENTS
Figure 1 shows the global annual mean near-surface air temperatures (TAS) for
HadGEM3-GC31-LL's individual ensemble members, for historicalssp245 and the
three DAMIP experiments, hist-GHG, hist-aer and hist-nat. Also shown are the
temperature distributions of the equivalent CMIP6 simulations (not including
HadGEM3-GC31-LL), and the global annual mean of HadCRUT5. All the model
simulations have the same spatial coverage imposed as HadCRUT5's coverage before
the global means were calculated. The HadGEM3-GC31-LL historicalssp245
simulations lie within the 5%–95% range of CMIP6 for almost the entire period
(Figure 1a). As described in Andrews et al. (2020), the model shows little
warming until the 1990s, with the only notable changes from short periods of
cooling following major explosive volcanic eruptions, then a rapid warming up to
2020. The model is cooler than HadCRUT5 between the 1960s and 2000s, but with
more rapid warming after about 1990.
Figure 1
Open in figure viewerPowerPoint
Global annual mean near-surface temperatures for (a) historicalssp245,
(b) hist-GHG, (c) hist-aer, and (d) hist-nat experiments. HadGEM3-GC31-LL's
individual ensemble members shown as red lines. The distribution of CMIP6
simulations—excluding HadGEM3-GC31-LL—shown as blue shading, representing the
minimum to maximum, 5%–95% and 25%–75% ranges. HadCRUT5 shown as a black line,
with vertical bars representing the 5%–95% range as estimated from its bias
correction ensemble. Note that uncertainty due to internal variability (Jones
et al., 2013), is not included in the observational uncertainty range. All
simulated data have the same spatial coverage as HadCRUT5. Temperatures shown
with respect to 1880–1919 period. See text for further details.
Following Jones (2020) we calculate the difference between the end of the
nineteenth century and beginning of the 21st century to estimate the overall
changes in global mean temperatures. For HadGEM3-GC31-LL's historicalssp245
simulations the change between 1880–1919 and 2001–2020 is 0.93 ± 0.28 K. This is
consistent with the CMIP6 change of 0.90 (0.64–1.42) K, and overlaps the
HadCRUT5 change of 0.97 (0.91–1.02) K (Figure 2b). The spread of the
historicalssp245 temperature changes between 1880–1919 and 2001–2020 (Figure 2b)
is larger for HadGEM3-GC31-LL than any of the other CMIP6 models (not shown),
although many of the models have too small number of ensemble members to fully
assess.
Figure 2
Open in figure viewerPowerPoint
Changes in global mean near-surface temperatures between 1880–1919 and
2001–2020. (a) simulations with full spatial coverage, (b) HadCRUT5, and
simulations with same spatial coverage as HadCRUT5. HadGEM3-GC31-LL's individual
ensemble members shown as crosses (+), and the CMIP6 distribution—excluding
HadGEM3-GC31-LL—shown as red box-whisker plots. HadCRUT5 change between the two
periods shown in panel b as a black box-whisker plot deduced from its bias
correction ensemble. Note that uncertainty due to internal variability (Jones
et al., 2013), is not included in the observational uncertainty range. The
box-whisker plots represent the median, minimum to maximum, 5%–95% and 25%–75%
ranges. See text for details.
The majority of the CMIP6 hist-GHG simulations warm faster than HadCRUT5
(Figures 1b and 2b) with HadGEM3-GC31-LL being at the upper end of the other
CMIP6 models distribution. This is in line with HadGEM3-GC31-LL having a large
TCR, and largest ECS, relative to the other CMIP6 models providing hist-GHG
simulations (Meehl et al., 2020; Table 7. SM.5 in Forster et al., 2021).
The CMIP6 hist-aer simulations all show cooling (Figures 1c and 2b) as expected
(e.g., Gillett et al., 2021). The HadGEM3-GC31-LL hist-aer simulations have
particularly strong cooling with a change between 1880–1919 and 2001–2020 of
−0.78 ± 0.31 K. Only CanESM5 and NorESM2-LM models have similar magnitude
cooling over the period (not shown), consistent with HadGEM3-GC31-LL and CanESM5
having large TCR and ECS (Meehl et al., 2020). Although NorESM2-LM, with its
lower ECS, suggests that the radiative forcing differences across the models can
be a substantial influence on the spread of responses (Section 4.6).
The global annual mean temperature responses of HadGEM3-GC31-LL's hist-nat
simulations are consistent with that from the other CMIP6 models (Figures 1d and
2b). The largest explosive volcanic eruptions in the simulation, Krakatoa in
1883 and Mt. Pinatubo in 1991, cause cooling of about 0.3 K in the simulations,
but with smaller cooling in HadCRUT5. Visual inspection of the HadGEM3-GC31-LL's
hist-nat ensemble mean (not shown) suggests the global mean temperature response
to the solar cycle in the total solar irradiance is about 0.03 K in amplitude,
consistent with what was seen for CMIP5 natural forced simulations (Jones
et al., 2013).
The hist-aer cooling in HadGEM3-GC31-LL is almost the same magnitude as the
hist-GHG warming up to the 1980s. While it is not possible to assess the ozone
and land cover contributions with the DAMIP experiments run with
HadGEM3-GC31-LL, the lack of warming of the global mean near-surface air
temperatures in the model's historical experiment until 1990 can be explained by
the cooling from aerosols almost completely offsetting the warming from WMGHGs.
The spread of HadGEM3-GC31-LL hist-GHG simulations temperature change between
1880–1919 and 2001–2020 is similar to the spread in hist-nat (Figure 2), with
both having smaller spreads than the spread in temperature changes for
historicalssp245 and hist-aer. A F-test of the full global coverage temperature
changes (Figure 2a) suggests that the spread in HadGEM3-GC31-LL's
historicalssp245 and hist-aer ensembles are both significantly larger (at 2.5%
level) than the spreads of either of the hist-GHG or hist-nat ensembles, and of
the spread of changes in the sections of the piControl (not shown) that are
parallel to the ensemble members (Section 2.2). The spread of temperature
changes for the HadGEM3-GC31-LL's historicalssp245 and hist-aer ensembles are
not significantly different, as are the hist-GHG and hist-nat ensemble spreads.
The spread in temperature change of the HadGEM3-GC31-LL hist-aer ensemble is
particularly striking, and is larger than the ensemble spreads of any of the
other models with hist-aer simulations. This suggests that the spread in
temperature change for historicalssp245 for HadGEM3-GC31-LL is mostly associated
with the anthropogenic aerosol forcing. Of the five other DAMIP models with
greater than three ensemble members for the experiments (Table S1 in Supporting
Information S1), four (CNRM-CM6-1, CanESM5, GISS-E2-1-G and IPSL-CM6A-LR) have
wider spreads in their hist-aer ensemble than their hist-GHG ensemble, but none
are significant at the 2.5% level according to a F-test.
The change in temperatures, between 1880–1919 and 2001–2020, for the
HadGEM3-GC31-LL and the CMIP6 historicalssp245 and hist-GHG simulations are
slightly larger when all their spatial data are included (Figure 2a), than when
they are masked by the observational coverage of the noninfilled HadCRUT5
(Figure 2b). This is mostly due to high latitude warming (Section 4.5) in those
simulations being masked out when constrained to the observational HadCRUT5
coverage (Jones et al., 2013). The change in temperatures when the simulations
have their data coverage limited to the infilled HadCRUT5analysis coverage are
close to the full coverage results (Figures S2, S3 and S4 in Supporting
Information S1).
4.3 LAND NEAR-SURFACE AIR TEMPERATURES
Figure 3 shows global annual mean land near-surface air temperatures, where the
spatial coverage has been limited to that of CRUTEM5. Equivalent figures for the
simulations with full coverage (Figure S5 in Supporting Information S1) and
coverage limited to that of HadCRUT5analysis [land] (Figure S6) are in
Supporting Information S1. Generally the evolution of HadGEM3-GC31-LL’ s land
temperatures are similar to the global temperatures (Figure 1), but with larger
magnitude changes. The change in land near-surface temperature between 1880–1919
and 2001–2020 is 1.20 ± 0.42 K for HadGEM3-GC31-LL's historicalssp245
simulations, consistent with the CMIP6 warming of 1.16 (0.77–1.88) K, and
similar to the CRUTEM5 warming of 1.47 (1.30–1.63) K (Figure 4b). Again, with
full spatial coverage the models show slightly larger magnitude changes for the
historicalssp245 and hist-GHG experiments (Figure 4a and Figure S4b in
Supporting Information S1), than for the simulations with coverage matching
CRUTEM5's.
Figure 3
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Global annual mean land near-surface temperatures for (a) historicalssp245,
(b) hist-GHG, (c) hist-aer, and (d) hist-nat experiments. As Figure 1, but for
simulated near-surface land temperatures, and CRUTEM5. All simulated data have
the same spatial coverage as CRUTEM5. See text for details.
Figure 4
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Changes in global mean land near-surface temperatures between 1880–1919 and
2001–2020. (a) HadGEM3-GC31-LL and CMIP6 simulations with full land coverage,
(b) CRUTEM5, and HadGEM3-GC31-LL and CMIP6 simulations with same spatial
coverage as CRUTEM5. All other details as in Figure 2.
Perhaps the most notable feature of the HadGEM3-GC31-LL simulations is the
cooling of land near-surface air temperatures in the historicalssp245
simulations over the end of the nineteenth century, as highlighted by Andrews
et al. (2020). Figure 5b shows the linear trend of temperatures over the
1850–1879 period, just before the influence of the Krakatoa eruption in 1883.
For this relatively short period, a linear trend is more informative than a
difference between two periods. For the historicalssp245 experiment,
HadGEM3-GC31-LL cools by −0.17 ± 0.16 K/decade. The CMIP6 simulations have a
trend of −0.03 (−0.18 to 0.15) K/decade, with only one model showing a
significant cooling, MIROC-ES2L −0.14 ± 0.12 K/decade. CRUTEM5 changes little in
this period, with a trend of −0.01 (−0.04 to 0.02) K/decade. For the hist-aer
experiment, HadGEM3-GC31-LL shows a strong cooling trend of
−0.27 ± 0.20 K/decade, with the other CMIP6 models showing no significant change
−0.08 (−0.27 to 0.03) K/decade. Individually most of the models show
insignificant cooling in hist-aer, with only ACCESS-CM2 and MRI-ESM2-0 with
significant cooling but not with the magnitude seen by HadGEM3-GC31-LL.
Figure 5
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Trends (K/decade) in global mean land near-surface temperature over the
1850–1879 period. (a) HadGEM3-GC31-LL and CMIP6 simulations with full land
coverage, (b) CRUTEM5, and HadGEM3-GC31-LL and CMIP6 simulations with same
spatial coverage as CRUTEM5. All other details as in Figure 2.
When the simulations have full coverage over land (Figure 5a, and Figure S5 in
Supporting Information S1) the magnitude of the 1850–1879 cooling in
HadGEM3-GC31-LL's historicalssp245 and hist-aer are substantially reduced. The
trends when using the HadCRUT5analysis [land] coverage (Figure S4c in Supporting
Information S1) have magnitudes somewhere between the full coverage and the
CRUTEM5 masked coverage trends. This supports the argument by Andrews
et al. (2020) that HadGEM3-GC31-LL's land temperature response, when masked with
CRUTEM5's coverage, is characterizing a regional change rather than a global
change. HadCRUT5's coverage in 1850–1879 is mostly sea based with only Eastern
North America and Western Europe having substantial land based observations
(e.g., 1877 shown in Figure 3 in Morice et al., 2021). These regions also have
the strongest growths in anthropogenic SO2 and other aerosol emissions in the
CMIP6 historical experiment during the nineteenth century (Figure 3 in Hoesly
et al., 2018 and Figure S2 in Dittus et al., 2020) which likely led to
disproportionately larger cooling over the regions than over the rest of the
globe in HadGEM3-GC31-LL's historicalssp245 and hist-aer experiments. The wide
range of the CMIP6 trends indicate that there are substantial uncertainties from
internal variability and differing forcing responses across the models. Together
with uncertainties in the aerosol emission data sets not being accounted for
(Hoesly et al., 2018), this makes further interpretation of apparent differences
between models and between models and observed changes for short periods in the
nineteenth century challenging.
4.4 INTERHEMISPHERIC TEMPERATURE CONTRAST
The interhemispheric temperature contrast, the difference between the Northern
and Southern hemisphere (NH-SH) temperatures, has been used as a climate index
to help distinguish the influence of the different forcing factors in detection
and attribution studies (Friedman et al., 2013; Karoly & Braganza, 2001; Stott
et al., 2006). Figure 6a shows that the hemispheric temperature difference for
HadGEM3-GC31-LL's historicalssp245 simulations is generally within the CMIP6's
5%–95% range, and has a similar evolution to that of HadCRUT5. Full coverage and
masked by HadCRUT5analysis versions of the figures can be found in the
Supporting Information S1 (Figures S7, S8 and S9). During the 1960s–1990s the
Northern Hemisphere is cooling relative to the Southern Hemisphere, then
subsequently shows rapid warming. A previous comparison of CMIP3 and CMIP5's
historical simulations with the observed interhemispheric temperature contrast
(Friedman et al., 2013) found that the mean of the simulations did not capture
an observed decrease in the 1960s. Figure 6a suggests that the central 50% of
the CMIP6 distribution shows a stronger decrease than Friedman et al. (2013)
found for CMIP3 and CMIP5, and that HadGEM3-GC31-LL has a similar magnitude
decrease as HadCRUT5. However, CMIP6 has a wide spread after the 1940s, showing
that some models have quite different hemispheric contrast responses. The change
between 1880–1919 and 2001–2020 (Figure S9b in Supporting Information S1) is
0.37 (0.32–0.41) K for HadCRUT5, 0.42 ± 0.28 K and 0.35 (0.09–0.67) K for
HadGEM3-GC31-LL's and CMIP6's historicalssp245 simulations, respectively.
Figure 6
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Global annual mean hemispheric near-surface air temperature differences for
(a) historicalssp245, (b) hist-GHG, (c) hist-aer, and (d) hist-nat experiments.
As Figure 1, but for difference between Northern and Southern Hemisphere (NH-SH)
temperatures for HadGEM3-GC31-LL and CMIP6 simulated near-surface temperatures,
and HadCRUT5. All simulated data have the same spatial coverage as HadCRUT5. See
text for details.
The HadGEM3-GC31-LL and CMIP6 hist-GHG interhemispheric temperature contrast
increases gradually over time (Figure 6b), caused by the larger land mass in the
Northern Hemisphere warming faster than the larger ocean areas in the Southern
Hemisphere. The hist-nat model responses are relatively stable, only punctuated
occasionally with slightly more cooling in the Northern Hemisphere than the
Southern Hemisphere from explosive volcanic eruptions. HadGEM3-GC31-LL's
hist-aer response has much stronger cooling over the northern hemisphere than in
the southern hemisphere, with a large spread across the five ensemble members
(this will be examined in more detail below). The change in HadGEM3-GC31-LL's
hist-aer interhemispheric temperature contrast between 1880–1919 and 2001–2020
is −0.86 ± 0.38 K, which is large in magnitude compared to the rest of CMIP6,
−0.30 (−0.80 to −0.05) K (Figure S9b in Supporting Information S1). The
hemispheric asymmetry of the aerosol radiative forcing, driving the hist-aer
interhemispheric temperature contrast, is also associated with a late twentieth
century strengthening of the Atlantic Meridional Overturning Circulation (AMOC)
in the historicalssp245 and hist-aer simulations (Menary et al., 2020),
influencing north-south heat transport.
4.5 LATITUDE ZONAL TEMPERATURE CHANGES
Maps can be useful to see spatial patterns of change and variability, but can be
hard to interpret when comparing data from individual simulations with other
model ensembles and observed changes at the same time. While latitudinal zonal
plots show limited spatial information compared to maps, they can enable
simultaneous comparisons between different data sets (Jones et al., 2013 uses
both approaches). Figure 7 shows the latitudinal zonal change in near-surface
temperatures between 1880–1919 and 2001–2020 for the model simulations only,
with no spatial coverage limitations. The high northern latitudes show a greater
warming for the CMIP6 historicalssp245 and hist-GHG simulations, and greater
cooling for hist-aer simulations than over the other latitudes. The CMIP6
simulations also show a large spread across the multi-model ensemble over the
high northern latitudes for all four experiments. The hist-aer experiment has a
particularly large spread, not just due to differences between models. For
instance the CNRM-CM6-1 and IPSL-CM6A-LR models have initial condition ensembles
of simulations that almost span the entire range of the CMIP6 hist-aer, a
reflection of the large internal variability present in those models (Parsons
et al., 2020). The limited number of ensemble members produced by some of the
models makes drawing further conclusions about differences between the CMIP6
models difficult.
Figure 7
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Latitudinal zonal mean change in near-surface temperatures between 1880–1919 and
2001–2020 for (a) historicalssp245, (b) hist-GHG, (c) hist-aer, and (d) hist-nat
experiments. HadGEM3-GC31-LL's individual ensemble members are shown as colored
lines, with the extra ensemble members of hist-nat shown as dashed lines. The
distribution of CMIP6 simulations, excluding HadGEM3-GC31-LL, are shown as gray
shading, representing the minimum to maximum, 5%–95% and 25%–75% ranges. All
simulated data have full spatial coverage. See text for further details.
HadGEM3-GC31-LL's historicalssp245 simulations follow the central 50% of the
CMIP6 distribution for most of the latitudes (Figure 7a), while HadGEM3-GC31-LL
is generally warmer for hist-GHG and cooler for hist-aer relative to most of the
CMIP6 models (Figures 7b and 7c). HadGEM3-GC31's variability across the 70–60°S
latitude range appears to be unusual relative to the rest of CMIP6. For
historicalssp245 two of HadGEM3-GC31-LL's ensemble members are close to the
maximum CMIP6 warming range. HadGEM3-GC31-LL's hist-GHG and hist-nat simulations
also have ensemble members with some warming in that latitude zone, near or
above the upper range of CMIP6. Most striking is the strong warming in four of
the five HadGEM3-GC31-LL hist-aer simulations across 70–60°S, of between 0.6 and
1.8 K.
The large warming across 70–60°S in two of the HadGEM3-GC31-LL historicalssp245
simulations, has been associated with sea ice area reduction around Antarctica
(Andrews et al., 2019) and larger increases in 0–700 m deep ocean heat content
trends than in the other ensemble members, linked with differing Southern Ocean
deep water formation rates (Andrews et al., 2020). UKESM1-0-LL's piControl was
also found to have strong near-surface variability on long time scales across
the Southern Ocean, again linked to intermittent deep ocean overturning (Sellar
et al., 2020). As described in Section 4.1, large variability in Southern Ocean
temperatures and sea ice coverage has been recorded in HadGEM3-GC31-LL's
piControl simulation when it was extended beyond its initial 500 years long
length (Ridley et al., 2022).
Variability in Southern Ocean temperatures and sea ice coverage in many climate
models has been linked to complex interactions between stratification, salinity,
wind and bathymetry (Beadling et al., 2020; de Lavergne et al., 2014; Heuzé
et al., 2020; Mohrmann et al., 2021; Pedro et al., 2016). The mechanism involves
a preconditioning through build up heat in the mid and deep subpolar gyres and a
trigger associated with wind or sea ice anomalies. The heat build-up can come
from the Atlantic Meridional Overturning Circulation (AMOC) lower cell (Martin
et al., 2013) or from the tropics (Ridley et al., 2022). The triggers of wind
(Campbell et al., 2019) and sea ice formation (Heuzé et al., 2015) anomalies
might change as the southern jet moves poleward with warming. It is essentially
the weak stratification in the polar gyres that can lead to ocean deep
convection, bringing heat to the surface, temporarily melting sea ice and
increasing air temperatures. Studies have shown that such Southern Ocean deep
convection events can have impacts on regional and global climate in models (de
Lavergne et al., 2014; Pedro et al., 2016), including CMIP6 models (e.g., Dunne
et al., 2020). In global warming scenarios, ocean stratification is expected to
increase eventually with rising temperatures and freshening, thus reducing deep
convection (Chen et al., 2023; de Lavergne et al., 2014; Heuzé et al., 2020;
Ridley et al., 2022).
To examine in a little more detail the variability over the Southern Ocean in
the HadGEM3-GC31-LL simulations, Figure 8 shows the annual mean global and
75–60°S mean near-surface air temperatures, southern hemisphere sea ice
coverage, and average mixed-layer depth for the Ross and Weddell seas—the
Southern Ocean regions with notable deep convection in HadGEM3-GC31-LL (Chen
et al., 2023; Mohrmann et al., 2021; Ridley et al., 2022). As shown in
Figure 7a, two of the historicalssp245 ensemble members have more warming over
75–60°S than the other ensemble members after about 1990 (Figure 8e). This is
coincident with decreases in sea ice coverage (Andrews et al., 2019) and
substantial increases in mixed-layer depth (mostly over the Weddell sea),
indicating deep convection (Figures 8i–8m). In contrast the hist-GHG simulations
have few deep convection events in the Ross and Weddell seas (Figure 8n). The
hist-aer simulations show many deep convection events in all the ensemble
members with four out of the five showing large events at the end of the
twentieth century (Figure 8o), corresponding to decreases in sea ice coverage
(Figure 8k) and warming over the Southern Ocean (Figures 7c and 8g). The
hist-nat simulations show some variability in mixed-layer depth in the Ross and
Weddell seas (Figure 8p), but not at the same magnitude as for historicalssp245
or hist-aer experiments.
Figure 8
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Global annual mean near-surface air temperatures (a, b, c, d), zonal mean
near-surface air temperatures over Southern Ocean, 75–60°S (e, f, g, h),
Southern Hemisphere sea ice area, 106 km2 (i, j, k, l), and the annual mean
mixed layer depth for the Ross (170°E–140°W, 75°S–62°S) and Weddell (30°W–20°E,
75°S–62°S) seas (m, n, o, p), for the HadGEM3-GC31-LL experiments (columns). The
initial condition ensemble members shown as colored lines, as in Figure 7.
The historicalssp245 simulations eventually follow the behavior of the hist-GHG
simulations in the 21st century, and have reductions in the frequency and
magnitude of deep convection events (Figure S14 in Supporting Information S1),
consistent with an increase in ocean stratification (de Lavergne et al., 2014;
Heuzé et al., 2020). This also leads to a reduction in the spread of global, and
Southern Ocean temperatures across the ensemble after 2030. A similar behavior
is seen in the UKESM1-0-LL historicalssp245 ensemble (not shown). While it was
noted how the historical simulations were initialized from the HadGEM3-GC31-LL
piControl may have influenced the possibility of the deep convection events
(Andrews et al., 2020), there are few large deep convection events in the
piControl until the extraordinary event after 500 years of the control (Figure
S14 in Supporting Information S1).
The strong warming across the Southern Ocean in most of HadGEM3-GC31-LL's
hist-aer simulations, but not in its hist-GHG simulations, suggests that a
cooler global climate increases the chance of triggering the deep water
convection across the Southern Ocean in the model. This contrasting behavior has
also been noted in HadGEM3-GC31-LL experiments with idealized changes in carbon
dioxide concentrations (Fredriksen et al., 2024). A simulation that has carbon
dioxide concentrations halved has a climate response with much larger variations
in global temperatures and Southern Hemisphere sea ice concentrations, than
simulations with increasing carbon dioxide concentrations (Figure S15 in
Supporting Information S1). The global cooling simulation produces very large
deep convection events in the Southern Oceans, while the increasing CO2
experiments lack deep convection events (Figure S15d in Supporting
Information S1). Examination of the other CMIP6 models suggest UKESM-1-0-LL and
GFDL-ESM4 (Dunne et al., 2020) have Southern Ocean warming behavior related to
deep convection in some of their historicalssp245 simulations. Other models
share the same ocean and sea ice modules as HadGEM3-GC31-LL and UKESM1-0-LL
(Table AII.5 in Gutiérrez and Tréguier, 2021), but none of them appear to have
the same variability in near-surface temperatures across the Southern Ocean. The
limited number of CMIP6 models with hist-aer experiments prevents judging
whether this Southern Ocean mechanism, associated with aerosol forcing, is more
widespread across CMIP6.
The coincident frequency of deep convection, and Southern Ocean warming events
at the end of the twentieth century, in many of the HadGEM3-GC31-LL hist-aer and
HadGEM3-GC31-LL and UKESM1-0-LL historicalssp245 simulations, suggest a common
mechanism that is not simply due to global temperatures. As the historicalssp245
simulations are warming by 2000, changes in wind, ocean circulation and/or
freshening due to the aerosol forcing are possible drivers of the weakening of
the ocean stratification (Beadling et al., 2020). A role for the AMOC, which
increases in magnitude with aerosol forcing in CMIP6 models (Menary
et al., 2020), is another possible factor. More research will be needed to fully
understand the mechanisms of the Southern Ocean variability response to
different forcing factors, in HadGEM3 and other models.
Comparing the HadGEM3-GC31-LL and CMIP6 changes in zonal temperatures with
observations is difficult, due to the limited observations south of 60°S,
especially before the 1950s. Figure 9 shows zonal trends between 1880–1919 and
2001–2020 (panel a) and for the more recent change between 1960–1979 and
2001–2020 (panel b) for the historicalssp245 experiment only and for HadCRUT5,
where the simulations have the same coverage as HadCRUT5. Further alternative
figures, including comparisons with HadCRUT5analysis, can be found in the SI
(Figures S10–S13 in Supporting Information S1). The slightly larger spread in
CMIP6 simulated temperature changes between 1880–1919 and 2001–2020 at high
latitudes, than seen when full spatial coverage is used (Figure 7), is due to
the limited coverage of HadCRUT5 in the earlier period introducing uncertainty
(Figure 9a). Again HadGEM3-GC31-LL's historicalssp245 simulations are largely
within the CMIP6 5%–95% range, with little differences with HadCRUT5's changes
in temperature across the latitudes. For the changes between 1960–1979 and
2001–2022 (Figure 9b) HadGEM3-GC31-LL is nearer the upper 95% limit of CMIP6 and
only slightly warmer than HadCRUT5 for some of the latitudes, consistent with
model's slightly warmer trend after the 1960s (Figure 1).
Figure 9
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Latitudinal zonal mean change in near-surface temperatures between (a) 1880–1919
and 2001–2020 and (b) 1960–1979 and 2001–2020, for HadGEM3-GC31-LL (red lines)
and CMIP6 (blue shading) historicalssp245 simulations, and HadCRUT5 (black line
and 5%–95% uncertainty bars). Simulated data have same spatial coverage as
HadCRUT5. All other details as in Figure 7. See text for further details.
4.6 EFFECTIVE RADIATIVE FORCING
Andrews et al. (2019), presented diagnosed global annual mean effective
radiative forcing (ERF) estimates for HadGEM3-GC31-LL, based on RFMIP
experiments (Section 3.1). The model configuration is the same as used for the
DAMIP experiments, including the same ozone remapping scheme (Hardiman
et al., 2019). Figure 10 compares these time varying ERF estimates for
HadGEM3-GC31-LL, for the 1850–2014 period, with estimates from eight other RFMIP
models (Table S2 in Supporting Information S1). The anthropogenic and natural
ERF for all nine models (Figure 10a) do not show much change, apart from
occasional punctuations of negative ERF from explosive volcanic eruptions, until
the 1970s when there are rapid increases. This is due to the aerosol negative
ERF (Figure 10c) offsetting much of the WMGHG positive ERF (Figure 10b) until
the second half of the twentieth century, when WMGHGs dominate (Andrews
et al., 2019). While HadGEM3-GC31-LL lies within the spread of the other
available models, it at the lower limit of the aerosol forcing range
(Figure 10c) and at the upper limit of the well-mixed greenhouse gas forcing
range (Figure 10b). This is consistent with the near-surface air temperature
responses for HadGEM3-GC31-LL and the other models (Figures 1b and 1c). In
contrast HadGEM3-GC31-LL is at the lower limit of the range of anthropogenic and
natural ERF for the other models (Figure 10a), but is not at the edge of the
historicalssp245 temperature distribution, except at the end of the twentieth
century (Figure 1a).
Figure 10
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Global annual mean effective radiative forcing (ERF), calculated as differences
in net top-of-atmosphere radiative fluxes between the relevant RFMIP experiment
and piClim-control experiment (Andrews et al., 2019). (a) Anthropogenic and
natural (piClim-histall), (b) Well-mixed greenhouse gases (piClim-histghg),
(c) Aerosols (piClim-histaer) and (d) Natural (piClim-histnat). For
HadGEM3-GC31-LL (black) and CMIP6 (gray) models (Table S2 in Supporting
Information S1).
The recommendation for the production of the CMIP6 piControl simulations says
that “background volcanic aerosol should be specified that results in radiative
forcing matching, as closely as possible, that experienced, on average, during
the historical simulation” (Eyring et al., 2016). This implementation causes
simulations with natural forcing factors present to have small positive values
of ERF in 1850, due to low levels of volcanic activity then causing less
stratospheric aerosol to be present than in the piControl. For HadGEM3-GC31-LL
this implementation causes the 1850 net anthropogenic and natural ERF to be
about +0.2 Wm-2 (Andrews et al., 2019). All previous Met Office Hadley Centre
model historical simulations, back to HadCM3 (Stott et al., 2000), imposed a
background stratospheric aerosol in their piControls. The HadCM3 anthropogenic
and natural simulations of the historic period had an estimated radiative
forcing of approximately +0.25 Wm-2 in 1860 (Tett et al., 2002). A number of the
CMIP6 models that provided the RFMIP experiments, however, do not show natural
forcing starting with small positive values in 1850 (Figure 10d), possibly
indicating that they did not implement a background stratospheric aerosol as
recommended by Eyring et al. (2016) in their piControl simulation.
RFMIP and AerChemMIP have alternative “time-slice” ERF experiments (Section 3)
which have been used to diagnose ERFs for the year 2014 for 17 CMIP6 models
(Smith et al., 2020). Those ERFs were found to be 2.89 ± 0.31 Wm-2 (mean and
±1.6449 × standard deviation) for well mixed greenhouse gases −1.01 ± 0.37 Wm-2
for aerosols, and 2.00 ± 0.38 Wm-2 for net anthropogenic climate drivers (in
lieu of estimates from anthropogenic + natural “time-slice” ERF experiments).
Those values are largely consistent with the ERF estimates calculated here
(Figure 10).
Figure 11 shows the latitudinal zonal mean ERF for the mean of the 2000–2014
period, for HadGEM3-GC31-LL and the other eight models that produced the
relevant RFMIP experiments. The anthropogenic and natural ERF latitudinal
structure of the models (Figure 11a) has been noted previously in other models
(Andrews, 2014; Hansen et al., 2005) and in CMIP6 (Smith et al., 2020). Much of
the asymmetry between the hemispheres can be explained by the generally
symmetric positive ERF from well-mixed greenhouse gases (Figure 11b) being
partially offset by the aerosol ERF, which is more negative in the northern
hemisphere (Figure 11c). Ozone and land use change forcing factors (there are no
appropriate time-varying RFMIP experiments to examine here) also contribute to
the net anthropogenic latitudinal ERF structure (Smith et al., 2020).
Figure 11
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Zonal latitudinal mean effective radiative forcing for 2000–2014, calculated as
differences in net top-of-atmosphere radiative fluxes between the relevant RFMIP
experiment and piClim-control experiment (Andrews et al., 2019).
(a) Anthropogenic and natural (piClim-histall), (b) Well-mixed greenhouse gases
(piClim-histghg), (c) Aerosols (piClim-histaer), and (d) Natural
(piClim-histnat). For HadGEM3-GC31-LL (black) and CMIP6 (gray) models (Table S2
in Supporting Information S1).
The relative diversity of the models' aerosol ERF across latitudes is likely due
to the differences in how the radiative forcings are partitioned between the
different species of aerosols, and the climate responses to them (Shindell
et al., 2015). Over mid and tropical latitudes, HadGEM3-GC31-LL is near the
bottom of the range of models for anthropogenic and natural (Figure 11a), and
for aerosols (Figure 11c). This is particularly prominent around 30–60°N for
anthropogenic and natural forcing factors where, while most of the models have
low positive ERF, HadGEM3-GC31-LL and NorESM2-LM have occasionally negative ERF.
This can be understood to be mainly due to those two models having the strongest
negative forcing for aerosols in that latitudinal band (Figure 11c) overwhelming
the WMGHG positive forcing. While the latitudinal and spatial temperature
response is not always expected to be highly spatially correlated with a climate
driver (Shindell et al., 2015), the strong cooling of temperatures in mid to
high northern latitudes in HadGEM3-GC31-LL hist-aer simulations (Figure 7c) is
consistent with the models strong aerosol forcing.
The apparent disconnect between the temperature response (Figure 7c) and ERF
(Figure 11c) across the Southern Ocean for HadGEM3-GC31-LL's hist-aer experiment
is consistent with the temperature response being due to changes in internal
variability indirectly influenced by the aerosol forcing factors (Section 4.5).
5 DISCUSSION AND CONCLUSIONS
We have described the UK's contribution to the Detection and Attribution Model
Intercomparison Project (DAMIP). We have shown how, from several different
configurations, we chose to use HadGEM3-GC31-LL to enable sufficient simulations
to be completed for the required DAMIP experiments. The experimental set up for
the model was described, and an analysis produced that compares HadGEM3-GC31-LL
simulated near-surface air temperatures with other CMIP6 DAMIP simulations and
observed temperatures.
The evolution of global mean near-surface temperatures of the experiments are
largely within the 90% ranges of the other CMIP6 models, with the exception of
the hist-aer experiment where HadGEM3-GC31-LL simulated more cooling than almost
all of the other models. The cooling of land temperatures at the end of the
nineteenth century in the historical experiment (Andrews et al., 2020) is also
seen in hist-aer, confirming that the HadGEM3-GC31-LL's historical response is
to aerosol cooling, emphasized by the data coverage sampling North America and
Western Europe. We investigated HadGEM3-GC31-LL's near-surface air temperature
variability over the Southern Ocean, that manifests as an occasional warming in
some simulations, which is unusual compared to the other CMIP6 models. This
warming is most apparent in HadGEM3-GC31-LL's hist-aer simulations, supporting
the view that the upwelling of heat from the deep ocean to the Southern Ocean
surface (Andrews et al., 2019, 2020; Ridley et al., 2022; Sellar et al., 2020)
occurs more often in the model's simulations that are globally cooling than
those that are warming.
The non-linear behavior of temperatures over the Southern Ocean has potential
implications for studies and frameworks that assume that the mean of an initial
condition simulation ensemble is a reasonable estimate of the forced response in
a model. The apparent non symmetric response over the Southern Ocean to
greenhouse gas and aerosol forcing factors also raises issues for the use of
these experiments in studies that assume responses are linearly additive, such
as in detection and attribution (Hegerl & Zwiers, 2011). Experiments, such as an
historical experiment where anthropogenic aerosols have been kept at
pre-industrial levels (hist-piAer), as defined in Collins et al. (2017), could
be useful to investigate this issue. Understanding whether the behavior seen in
HadGEM3-GC31-LL is more widespread across CMIP6 is hindered by the limited
number of models providing DAMIP and related experiments.
The Large Ensemble Single Forced Model Intercomparison Project (LESFMIP; Smith
et al., 2022) has recently been proposed, with a design for models to each
produce large numbers of initial condition members, for a wide range of
historically forced experiments. A large ensemble of HadGEM3-GC31-LL simulations
is currently being run by the Met Office Hadley Centre's decadal forecasting
team, following the DAMIP experimental design. Analysis of these simulations
will be useful in decadal climate forecasting, where trying to understand a
model's responses to different forcing factors to a high precision is required.
While a plurality of climate models is preferable in detection and attribution
studies (Jones et al., 2016), to try and sample as wide a range as possible of
model and forcing uncertainties and so minimize the limitations of an “ensemble
of opportunity” (Allen & Stainforth, 2002), models participation in LESFMIP will
help the forced and internal variability characteristics of each model to be
better understood.
Observations of historical changes in climate have not been used in the past
development of Met Office Hadley Centre models, so when model simulations of
historical forcing changes are compared to observed temperature changes, any
similarities can add confidence to the ability of the model's simulations to be
informative (Stott et al., 2000). While it has been stated that institutions are
not systematically tuning models to observed historic temperature changes (Smith
et al., 2020), some institutions are openly tuning the model in various ways to
the observational record (Hourdin et al., 2017), although it is debatable how
successful such tuning exercises have been (Bock et al., 2020). For
HadGEM3-GC3.1 and UKESM1-0, the observed historical temperature record was not
used formally in the development of models (Kuhlbrodt et al., 2018; Senior
et al., 2020). However, for HadGEM3-GC3.1 there was a model acceptance criteria
based on the net present day radiative forcing being positive and not
substantially different to previous model generations (Senior et al., 2020;
Williams et al., 2018), which led to aerosol scheme improvements that reduced
the magnitude of the net aerosol radiative forcing at the end of the twentieth
century (Mulcahy et al., 2018). Historical simulations were run with pre-CMIP6
forcing data sets to informally assess the model's historical performance, and
historical simulations were also run before an ozone remapping scheme was
implemented (Dittus et al., 2020) which reduced the model's climate sensitivity
(Hardiman et al. (2019); Section 3). The historical temperature record will
become more formally included in future model development cycles in the UK. A
version of UKESM (UKESM1-1) has been developed explicitly to account for a
perceived cooling bias in UKESM1-0's historical global temperature evolution
(Mulcahy et al., 2023). Techniques have been adapted to assess the model's
temperature evolution for the purpose of improving the match of future model's
historical simulations with observed temperatures (Bodas-Salcedo et al., 2023).
How these developments are communicated, and how model and observational
comparisons are interrelated when the latter are involved in the development of
the former, will be an increasing challenge in the future (Hourdin et al., 2017;
Rodhe et al., 2000).
In this study we have shown that DAMIP experiments are important for
interpretating and understanding a model's responses to different forcing
factors. The studies that have used data from CMIP6 models taking part in DAMIP,
including HadGEM3-GC31-LL, are diverse; from analyses of mean temperatures
(Gillett et al., 2021), diurnal temperature range (Lu et al., 2022), total
precipitable water (Douville et al., 2022), and extremes in European rainfall
(Christidis et al., 2021), to analyses of soil moisture (Qiao et al., 2021),
snow cover (Paik & Min, 2020), and tropical cyclone frequency (Cao
et al., 2021), and in the assessment of climate sensitivity and feedbacks (Dong
et al., 2021). We hope that HadGEM3-GC31-LL's contribution to DAMIP has been
useful to those and other analyses and that more modeling centers will see the
utility of taking part in DAMIP in future.
ACKNOWLEDGMENTS
We wish to thank the editor, and the reviewers for their insightful and useful
comments. We acknowledge the World Climate Research Programme, which, through
its Working Group on Coupled Modeling, coordinated and promoted CMIP6. We thank
the climate modeling groups for producing and making available their model
output, the Earth System Grid Federation (ESGF) for archiving the data and
providing access, and the multiple funding agencies who support CMIP6 and ESGF.
We thank the many people involved in the development and support of HadGEM3 and
in the dissemination of its data. We also thank the observers, scientists and
teams involved in the construction of the near-surface temperature data sets. We
would like to thank Mark McCarthy for comments and Ben Booth, Mark Ringer, Harry
Mutton, Doug Smith, Leon Hermanson, Alejandro Bodas-Salcedo and many other
colleagues for useful discussions about HadGEM3. All the authors were supported
by the Met Office Hadley Centre Climate Programme funded by DSIT.
OPEN RESEARCH
DATA AVAILABILITY STATEMENT
All the CMIP6 model data, including HadGEM3-GC31-LL data, were downloaded from
https://esgf-node.llnl.gov/projects/cmip6/. Details of model data used,
including data set revision numbers, are provided in the Supporting
Information S1 (Text S2). The HadCRUT5 (HadCRUT.5.0.1.0) and CRUTEM5
(CRUTEM.5.0.1.0) near-surface temperature data sets were downloaded from
https://www.metoffice.gov.uk/hadobs/hadcrut5 and
https://www.metoffice.gov.uk/hadobs/crutem5 on 26/4/2022. The observational land
fraction data set, was downloaded from
https://podaac.jpl.nasa.gov/dataset/UKMO-L4HRfnd-GLOB-OSTIA on 16/5/2022. The
software used in this study is available at https://zenodo.org/records/13120909.
SUPPORTING INFORMATION
Filename Description 2023MS004135-sup-0001-Supporting Information
SI-S01.pdf968.5 KB Supporting Information S1 2023MS004135-sup-0002-Supporting
Information SI-S02.xlsx45.5 KB Supporting Information S2
Please note: The publisher is not responsible for the content or functionality
of any supporting information supplied by the authors. Any queries (other than
missing content) should be directed to the corresponding author for the article.
REFERENCES
* Allen, M. R., & Stainforth, D. A. (2002). Towards objective probabilistic
climate forecasting. Nature, 419(6903), 228.
https://doi.org/10.1038/nature01092a
10.1038/nature01092a
CASADSPubMedGoogle Scholar
* Allen, M. R., Stott, P. A., Mitchell, J. F. B., Schnur, R., & Delworth, T. L.
(2000). Quantifying the uncertainty in forecasts of anthropogenic climate
change. Nature, 407(6804), 617–620. https://doi.org/10.1038/35036559
10.1038/35036559
CASADSPubMedWeb of Science®Google Scholar
* Andrews, M. B., Ridley, J. K., Wood, R. A., Andrews, T., Blockley, E. W.,
Booth, B., et al. (2020). Historical simulations with HadGEM3-GC3.1 for
CMIP6. Journal of Advances in Modeling Earth Systems, 12(6), e2019MS001995.
https://doi.org/10.1029/2019MS001995
10.1029/2019MS001995
ADSWeb of Science®Google Scholar
* Andrews, T. (2014). Using an AGCM to diagnose historical effective radiative
forcing and mechanisms of recent decadal climate change. Journal of Climate,
27(3), 1193–1209. https://doi.org/10.1175/jcli-d-13-00336.1
10.1175/JCLI-D-13-00336.1
ADSWeb of Science®Google Scholar
* Andrews, T., Andrews, M. B., Bodas-Salcedo, A., Jones, G. S., Kuhlbrodt, T.,
Manners, J., et al. (2019). Forcings, feedbacks, and climate sensitivity in
HadGEM3-GC3.1 and UKESM1. Journal of Advances in Modeling Earth Systems,
11(12), 4377–4394. https://doi.org/10.1029/2019MS001866
10.1029/2019MS001866
ADSWeb of Science®Google Scholar
* Beadling, R. L., Russell, J. L., Stouffer, R. J., Mazloff, M., Talley, L. D.,
Goodman, P. J., et al. (2020). Representation of Southern Ocean properties
across coupled model intercomparison project generations: CMIP3 to CMIP6.
Journal of Climate, 33(15), 6555–6581.
https://doi.org/10.1175/JCLI-D-19-0970.1
10.1175/JCLI-D-19-0970.1
ADSWeb of Science®Google Scholar
* Bindoff, N. L., Stott, P. A., AchutaRao, K. M., Allen, M. R., Gillett, N.,
Gutzler, D., et al. (2013). Detection and attribution of climate change: From
global to regional. In T. F. Stocker, et al. (Eds.), Climate change 2013: The
Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change (pp.
867–952). Cambridge University Press.
Google Scholar
* Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., et
al. (2020). Quantifying progress across different CMIP phases with the
ESMValTool. Journal of Geophysical Research-Atmospheres, 125(21),
e2019JD032321. https://doi.org/10.1029/2019JD032321
10.1029/2019JD032321
ADSWeb of Science®Google Scholar
* Bodas-Salcedo, A., Gregory, J. M., Sexton, D. M. H., & Morice, C. P. (2023).
Assessment of large-scale indices of surface temperature during the
historical period in the CMIP6 ensemble. Journal of Climate, 36(7),
2055–2072. https://doi.org/10.1175/JCLI-D-22-0398.1
10.1175/JCLI-D-22-0398.1
ADSGoogle Scholar
* Campbell, E. C., Wilson, E. A., Moore, G. W. K., Riser, S. C., Brayton, C.
E., Mazloff, M. R., & Talley, L. D. (2019). Antarctic offshore polynyas
linked to southern hemisphere climate anomalies. Nature, 570(7761), 319–325.
https://doi.org/10.1038/s41586-019-1294-0
10.1038/s41586-019-1294-0
CASADSPubMedWeb of Science®Google Scholar
* Cao, J., Zhao, H., Wang, B., & Wu, L. (2021). Hemisphere-asymmetric tropical
cyclones response to anthropogenic aerosol forcing. Nature Communications,
12(1), 6787. https://doi.org/10.1038/s41467-021-27030-z
10.1038/s41467-021-27030-z
CASADSPubMedGoogle Scholar
* Chen, D., Rojas, M., Samset, B., Cobb, K., Diongue Niang, A., Edwards, P., et
al. (2021). Framing, context, and methods. In climate change. In V.
Masson-Delmotte, et al. (Eds.), Climate change 2021: The Physical Science
Basis. Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change (pp. 147–286). Cambridge University
Press.
Google Scholar
* Chen, J.-J., Swart, N. C., Beadling, R., Cheng, X., Hattermann, T., Jüling,
A., et al. (2023). Reduced deep convection and bottom water formation due to
Antarctic meltwater in a multi-model ensemble. Geophysical Research Letters,
50(24), e2023GL106492. https://doi.org/10.1029/2023GL106492
10.1029/2023GL106492
ADSWeb of Science®Google Scholar
* Christidis, N., McCarthy, M., Cotterill, D., & Stott, P. A. (2021).
Record-breaking daily rainfall in the United Kingdom and the role of
anthropogenic forcings. Atmospheric Science Letters, 22(7), e1033.
https://doi.org/10.1002/asl.1033
10.1002/asl.1033
Web of Science®Google Scholar
* Christidis, N., Stott, P. A., Zwiers, F. W., Shiogama, H., & Nozawa, T.
(2012). The contribution of anthropogenic forcings to regional changes in
temperature during the last decade. Climate Dynamics, 39(6), 1259–1274.
https://doi.org/10.1007/s00382-011-1184-0
10.1007/s00382-011-1184-0
ADSWeb of Science®Google Scholar
* Collins, W. J., Lamarque, J. F., Schulz, M., Boucher, O., Eyring, V.,
Hegglin, M. I., et al. (2017). AerChemMIP: Quantifying the effects of
chemistry and aerosols in CMIP6. Geoscientific Model Development, 10(2),
585–607. https://doi.org/10.5194/gmd-10-585-2017
10.5194/gmd-10-585-2017
CASADSWeb of Science®Google Scholar
* de Lavergne, C., Palter, J. B., Galbraith, E. D., Bernardello, R., & Marinov,
I. (2014). Cessation of deep convection in the open Southern Ocean under
anthropogenic climate change. Nature Climate Change, 4, 278–282.
https://doi.org/10.1038/nclimate2132
10.1038/nclimate2132
Web of Science®Google Scholar
* Dittus, A. J., Hawkins, E., Wilcox, L. J., Sutton, R. T., Smith, C. J.,
Andrews, M. B., & Forster, P. M. (2020). Sensitivity of historical climate
simulations to uncertain aerosol forcing. Geophysical Research Letters,
47(13), e2019GL085806. https://doi.org/10.1029/2019GL085806
10.1029/2019GL085806
ADSWeb of Science®Google Scholar
* Dong, Y., Armour, K. C., Proistosescu, C., Andrews, T., Battisti, D. S.,
Forster, P. M., et al. (2021). Biased estimates of equilibrium climate
sensitivity and transient climate response derived from historical CMIP6
simulations. Geophysical Research Letters, 48(24), e2021GL095778.
https://doi.org/10.1029/2021GL095778
10.1029/2021GL095778
ADSWeb of Science®Google Scholar
* Douville, H., Qasmi, S., Ribes, A., & Bock, O. (2022). Global warming at
near-constant tropospheric relative humidity is supported by observations.
Communications Earth & Environment, 3(1), 237.
https://doi.org/10.1038/s43247-022-00561-z
10.1038/s43247-022-00561-z
ADSGoogle Scholar
* Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John,
J. G., et al. (2020). The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1):
Overall coupled model description and simulation characteristics. Journal of
Advances in Modeling Earth Systems, 12(11), e2019MS002015.
https://doi.org/10.1029/2019MS002015
10.1029/2019MS002015
ADSWeb of Science®Google Scholar
* Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R.
J., & Taylor, K. E. (2016). Overview of the coupled model intercomparison
project phase 6 (CMIP6) experimental design and organization. Geoscientific
Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
10.5194/gmd-9-1937-2016
ADSWeb of Science®Google Scholar
* Eyring, V., Gillett, N. P., Achuta Rao, K. M., Barimalala, R., Barreiro
Parrillo, M., Bellouin, N., et al. (2021). Human influence on the climate
system. In V. Masson-Delmotte, et al. (Eds.), Climate Change 2021: The
Physical Science Basis. Contribution of Working Group I to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change (pp.
423–552). Cambridge University Press.
Google Scholar
* Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame,
D., et al. (2021). The Earth’s energy budget, climate feedbacks, and climate
sensitivity. In V. Masson-Delmotte, et al. (Eds.), Climate change 2021: The
Physical Science Basis. Contribution of Working Group I to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change (pp.
923–1054). Cambridge University Press.
Google Scholar
* Fredriksen, H.-B., Eiselt, K.-U., & Good, P. (2024). Testing linearity and
comparing linear response models for global surface temperatures. ESS Open
Archive. https://doi.org/10.22541/essoar.170559496.63972629/v1
10.22541/essoar.170559496.63972629/v1
Google Scholar
* Friedman, A. R., Hwang, Y. T., Chiang, J. C. H., & Frierson, D. M. W. (2013).
Interhemispheric temperature asymmetry over the twentieth century and in
future projections. Journal of Climate, 26(15), 5419–5433.
https://doi.org/10.1175/jcli-d-12-00525.1
10.1175/JCLI-D-12-00525.1
ADSWeb of Science®Google Scholar
* Gillett, N. P., Kirchmeier-Young, M., Ribes, A., Shiogama, H., Hegerl, G. C.,
Knutti, R., et al. (2021). Constraining human contributions to observed
warming since the pre-industrial period. Nature Climate Change, 11(3),
207–212. https://doi.org/10.1038/s41558-020-00965-9
10.1038/s41558-020-00965-9
ADSWeb of Science®Google Scholar
* Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K.,
et al. (2016). The detection and attribution model intercomparison project
(DAMIP v1.0) contribution to CMIP6. Geoscientific Model Development, 9(10),
3685–3697. https://doi.org/10.5194/gmd-9-3685-2016
10.5194/gmd-9-3685-2016
ADSWeb of Science®Google Scholar
* Gulev, S., Thorne, P., Ahn, J., Dentener, F., Domingues, C., Gerland, S.,
et al. (2021). Changing state of the climate system. In V. Masson-Delmotte,
et al. (Eds.), Climate Change 2021: The Physical Science Basis. Contribution
of Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change (pp. 287–422). Cambridge University Press.
Google Scholar
* Gutiérrez, J. M., & Tréguier, A.-M. (2021). Annex II: Models. In V.
Masson-Delmotte, et al. (Eds.), Climate Change 2021: The Physical Science
Basis. Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change (pp. 2087–2138). Cambridge
University Press.
Google Scholar
* Hansen, J., Sato, M., Ruedy, R., Nazarenko, L., Lacis, A., Schmidt, G. A., et
al. (2005). Efficacy of climate forcings. Journal of Geophysical Research,
110(D18), D18104. https://doi.org/10.1029/2005JD005776
10.1029/2005JD005776
ADSWeb of Science®Google Scholar
* Hardiman, S. C., Andrews, M. B., Andrews, T., Bushell, A. C., Dunstone, N.
J., Dyson, H., et al. (2019). The impact of prescribed ozone in climate
projections run with HadGEM3-GC3.1. Journal of Advances in Modeling Earth
Systems, 11(11), 3443–3453. https://doi.org/10.1029/2019MS001714
10.1029/2019MS001714
ADSWeb of Science®Google Scholar
* Hegerl, G., & Zwiers, F. (2011). Use of models in detection and attribution
of climate change. Wiley Interdisciplinary Reviews: Climate Change, 2(4),
570–591. https://doi.org/10.1002/wcc.121
10.1002/wcc.121
Web of Science®Google Scholar
* Hegerl, G. C., Zwiers, F. W., Braconnot, P., Gillett, N. P., Luo, Y., Marengo
Orsini, J. A., et al. (2007). Understanding and attributing climate change.
In S. Solomon, et al. (Eds.), Climate Change 2007: The Physical Science
Basis. Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change (pp. 663–745). Cambridge University
Press.
Web of Science®Google Scholar
* Hegglin, M. (2019). Forcing databases in support of CMIP6.
https://blogs.reading.ac.uk/ccmi/forcing-databases-in-support-of-cmip6/
Google Scholar
* Heuzé, C., Mohrmann, M., Andersson, E., & Crafoord, E. (2020). Global decline
of deep water formation with increasing atmospheric CO2. Down to Earth.
https://doi.org/10.31223/X56K6D
10.31223/X56K6D
Google Scholar
* Heuzé, C., Ridley, J. K., Calvert, D., Stevens, D. P., & Heywood, K. J.
(2015). Increasing vertical mixing to reduce southern ocean deep convection
in NEMO3.4. Geoscientific Model Development, 8(10), 3119–3130.
https://doi.org/10.5194/gmd-8-3119-2015
10.5194/gmd-8-3119-2015
ADSWeb of Science®Google Scholar
* Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G.,
Pitkanen, T., et al. (2018). Historical (1750-2014) anthropogenic emissions
of reactive gases and aerosols from the Community Emissions Data System
(CEDS). Geoscientific Model Development, 11(1), 369–408.
https://doi.org/10.5194/gmd-11-369-2018
10.5194/gmd-11-369-2018
CASADSWeb of Science®Google Scholar
* Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J. C., Balaji, V., Duan,
Q., et al. (2017). The art and science of climate model tuning. Bulletin of
the American Meteorological Society, 98(3), 589–602.
https://doi.org/10.1175/BAMS-D-15-00135.1
10.1175/BAMS-D-15-00135.1
ADSWeb of Science®Google Scholar
* IPCC. (2001). In J. T. Houghton, et al. (Eds.), Climate Change 2001: The
Scientific Basis. Contribution of Working Group 1 to the Third Assessment
Report of the Intergovernmental Panel on Climate Change (p. 881pp). Cambridge
University Press.
Google Scholar
* IPCC. (2021). Climate change 2021: The physical science basis. Contribution
of working group I to the sixth assessment report of the intergovernmental
panel on climate change. In V. Masson-Delmotte, et al. (Eds.). Cambridge
University Press.2391.
Google Scholar
* Johns, T. C., Carnell, R. E., Crossley, J. F., Gregory, J. M., Mitchell, J.
F. B., Senior, C. A., et al. (1997). The second Hadley Centre coupled
ocean-atmosphere GCM: Model description, spinup and validation. Climate
Dynamics, 13(2), 103–134. https://doi.org/10.1007/s003820050155
10.1007/s003820050155
ADSWeb of Science®Google Scholar
* Jones, C. D., Hughes, J. K., Bellouin, N., Hardiman, S. C., Jones, G. S.,
Knight, J., et al. (2011). The HadGEM2-ES implementation of CMIP5 centennial
simulations. Geoscientific Model Development, 4(3), 543–570.
https://doi.org/10.5194/gmd-4-543-2011
10.5194/gmd-4-543-2011
ADSWeb of Science®Google Scholar
* Jones, G. S. (2020). “Apples and oranges”: On comparing near-surface
temperatures from climate models with observations. Quarterly Journal of the
Royal Meteorological Society, 146(733), 3747–3771.
https://doi.org/10.1002/qj.3871
10.1002/qj.3871
ADSWeb of Science®Google Scholar
* Jones, G. S., & Kennedy, J. J. (2017). Sensitivity of attribution of
anthropogenic near-surface warming to observational uncertainty. Journal of
Climate, 30(12), 4677–4691. https://doi.org/10.1175/jcli-d-16-0628.1
10.1175/JCLI-D-16-0628.1
ADSGoogle Scholar
* Jones, G. S., Stott, P. A., & Christidis, N. (2013). Attribution of observed
historical near-surface temperature variations to anthropogenic and natural
causes using CMIP5 simulations. Journal of Geophysical Research, 118(10),
4001–4024. https://doi.org/10.1002/jgrd.50239
10.1002/jgrd.50239
ADSWeb of Science®Google Scholar
* Jones, G. S., Stott, P. A., & Mitchell, J. F. B. (2016). Uncertainties in the
attribution of greenhouse gas warming and implications for climate
prediction. Journal of Geophysical Research, 121(12), 6969–6992.
https://doi.org/10.1002/2015JD024337
10.1002/2015JD024337
CASADSWeb of Science®Google Scholar
* Jones, P. D. (2016). The reliability of global and hemispheric surface
temperature records. Advances in Atmospheric Sciences, 33(3), 269–282.
https://doi.org/10.1007/s00376-015-5194-4
10.1007/s00376-015-5194-4
ADSWeb of Science®Google Scholar
* Juckes, M., Taylor, K. E., Durack, P. J., Lawrence, B., Mizielinski, M. S.,
Pamment, A., et al. (2020). The CMIP6 data request (DREQ, version 01.00.31).
Geoscientific Model Development, 13(1), 201–224.
https://doi.org/10.5194/gmd-13-201-2020
10.5194/gmd-13-201-2020
CASADSGoogle Scholar
* Karoly, D. J., & Braganza, K. (2001). Identifying global climate change using
simple indices. Geophysical Research Letters, 28(11), 2205–2208.
https://doi.org/10.1029/2000gl011925
10.1029/2000GL011925
ADSWeb of Science®Google Scholar
* Kennedy, J. J., Rayner, N. A., Atkinson, C. P., & Killick, R. E. (2019). An
ensemble data set of sea surface temperature change from 1850: The Met Office
Hadley Centre HadSST.4.0.0.0 Data Set. Journal of Geophysical Research -
Atmospheres, 124(14), 7719–7763. https://doi.org/10.1029/2018JD029867
10.1029/2018JD029867
ADSWeb of Science®Google Scholar
* Kettleborough, J. A., Booth, B. B. B., Stott, P. A., & Allen, M. R. (2007).
Estimates of uncertainty in predictions of global mean surface temperature.
Journal of Climate, 20(5), 843–855. https://doi.org/10.1175/jcli4012.1
10.1175/JCLI4012.1
ADSWeb of Science®Google Scholar
* Kuhlbrodt, T., Jones, C. G., Sellar, A., Storkey, D., Blockley, E., Stringer,
M., et al. (2018). The low-resolution version of HadGEM3 GC3.1: Development
and evaluation for global climate. Journal of Advances in Modeling Earth
Systems, 10(11), 2865–2888. https://doi.org/10.1029/2018MS001370
10.1029/2018MS001370
ADSPubMedWeb of Science®Google Scholar
* Kuhlbrodt, T., Voldoire, A., Palmer, M. D., Geoffroy, O., & Killick, R.
(2023). Historical ocean heat uptake in two pairs of CMIP6 models: Global and
regional perspectives. Journal of Climate, 36(7), 2183–2203.
https://doi.org/10.1175/jcli-d-22-0468.1
10.1175/JCLI-D-22-0468.1
ADSGoogle Scholar
* Lu, C., Sun, Y., & Zhang, X. (2022). Anthropogenic influence on the diurnal
temperature range since 1901. Journal of Climate, 35(22), 3583–3598.
https://doi.org/10.1175/JCLI-D-21-0928.1
10.1175/JCLI-D-21-0928.1
ADSGoogle Scholar
* Lurton, T., Balkanski, Y., Bastrikov, V., Bekki, S., Bopp, L., Braconnot, P.,
et al. (2020). Implementation of the CMIP6 forcing data in the IPSL-CM6A-LR
model. Journal of Advances in Modeling Earth Systems, 12(4), e2019MS001940.
https://doi.org/10.1029/2019MS001940
10.1029/2019MS001940
ADSWeb of Science®Google Scholar
* Martin, T., Park, W., & Latif, M. (2013). Multi-centennial variability
controlled by Southern Ocean convection in the Kiel climate model. Climate
Dynamics, 40(7–8), 2005–2022. https://doi.org/10.1007/s00382-012-1586-7
10.1007/s00382-012-1586-7
ADSWeb of Science®Google Scholar
* Meehl, G. A., Boer, G. J., Covey, C., Latif, M., & Stouffer, R. J. (1997).
Intercomparison makes for a better climate model. Eos, 78(41), 445–446.
https://doi.org/10.1029/97eo00276
10.1029/97EO00276
ADSGoogle Scholar
* Meehl, G. A., Boer, G. J., Covey, C., Latif, M., & Stouffer, R. J. (2000).
The coupled model intercomparison project (CMIP). Bulletin of the American
Meterological Society, 81(2), 313–318.
https://doi.org/10.1175/1520-0477(2000)081<0313:tcmipc>2.3.co;2
10.1175/1520-0477(2000)081<0313:TCMIPC>2.3.CO;2
ADSGoogle Scholar
* Meehl, G. A., Covey, C., Delworth, T., Latif, B., McAvaney, M., Mitchell, J.
F. B., et al. (2007). The WCRP CMIP3 multi-model dataset: A new era in
climate change research. Bulletin of the American Meteorological Society,
88(9), 1383–1394. https://doi.org/10.1175/bams-88-9-1383
10.1175/BAMS-88-9-1383
ADSWeb of Science®Google Scholar
* Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J. F.,
Stouffer, R. J., et al. (2020). Context for interpreting equilibrium climate
sensitivity and transient climate response from the CMIP6 Earth System
Models. Science Advances, 6(26), eaba1981.
https://doi.org/10.1126/sciadv.aba1981
10.1126/sciadv.aba1981
ADSPubMedWeb of Science®Google Scholar
* Menary, M. B., Kuhlbrodt, T., Ridley, J., Andrews, M. B., Dimdore-Miles, O.
B., Deshayes, J., et al. (2018). Preindustrial control simulations with
HadGEM3-GC3.1 for CMIP6. Journal of Advances in Modeling Earth Systems,
10(12), 3049–3075. https://doi.org/10.1029/2018MS001495
10.1029/2018MS001495
ADSWeb of Science®Google Scholar
* Menary, M. B., Robson, J., Allan, R. P., Booth, B. B. B., Cassou, C.,
Gastineau, G., et al. (2020). Aerosol-forced AMOC changes in CMIP6 historical
simulations. Geophysical Research Letters, 47(14), e2020GL088166.
https://doi.org/10.1029/2020GL088166
10.1029/2020GL088166
ADSWeb of Science®Google Scholar
* Mitchell, J. F. B., Johns, T. C., Gregory, J. M., & Tett, S. F. B. (1995).
Climate response to increasing levels of greenhouse gases and sulfate
aerosols. Nature, 376(6540), 501–504. https://doi.org/10.1038/376501a0
10.1038/376501a0
CASADSWeb of Science®Google Scholar
* Mitchell, J. F. B., Karoly, D. J., Hegerl, G. C., Zwiers, F. W., Allen, M.
R., & Marengo, J. (2001). Detection of climate change and attribution of
causes. In J. T. Houghton, et al. (Eds.), Climate Change 2001: The Scientific
Basis. Contribution of Working Group I to the Third Assessment Report of the
Intergovernmental Panel on Climate Change (pp. 695–738). Cambridge University
Press.
Web of Science®Google Scholar
* Mohrmann, M., Heuzé, C., & Swart, S. (2021). Southern Ocean polynyas in CMIP6
models. The Cryosphere, 15(9), 4281–4313.
https://doi.org/10.5194/tc-15-4281-2021
10.5194/tc-15-4281-2021
ADSWeb of Science®Google Scholar
* Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E.,
Killick, R. E., et al. (2021). An updated assessment of near-surface
temperature change from 1850: The HadCRUT5 data set. Journal of Geophysical
Research - Atmospheres, 126(3), e2019JD032361.
https://doi.org/10.1029/2019JD032361
10.1029/2019JD032361
ADSWeb of Science®Google Scholar
* Mulcahy, J. P., Johnson, C., Jones, C. G., Povey, A. C., Scott, C. E.,
Sellar, A., et al. (2020). Description and evaluation of aerosol in UKESM1
and HadGEM3-GC3.1 CMIP6 historical simulations. Geoscientific Model
Development, 13(12), 6383–6423. https://doi.org/10.5194/gmd-13-6383-2020
10.5194/gmd-13-6383-2020
CASADSGoogle Scholar
* Mulcahy, J. P., Jones, C., Sellar, A., Johnson, B., Boutle, I. A., Jones, A.,
et al. (2018). Improved aerosol processes and effective radiative forcing in
HadGEM3 and UKESM1. Journal of Advances in Modeling Earth Systems, 10(11),
2786–2805. https://doi.org/10.1029/2018MS001464
10.1029/2018MS001464
ADSWeb of Science®Google Scholar
* Mulcahy, J. P., Jones, C. G., Rumbold, S. T., Kuhlbrodt, T., Dittus, A. J.,
Blockley, E. W., et al. (2023). UKESM1.1: Development and evaluation of an
updated configuration of the UK Earth system model. Geoscientific Model
Development, 16(6), 1569–1600. https://doi.org/10.5194/gmd-2022-113
10.5194/gmd-16-1569-2023
CASADSGoogle Scholar
* O’Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein,
P., Hurtt, G., et al. (2016). The scenario model intercomparison project
(ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), 3461–3482.
https://doi.org/10.5194/gmd-9-3461-2016
10.5194/gmd-9-3461-2016
ADSWeb of Science®Google Scholar
* Osborn, T. J., Jones, P. D., Lister, D. H., Morice, C. P., Simpson, I. R.,
Winn, J. P., et al. (2021). Land surface air temperature variations across
the globe updated to 2019: The CRUTEM5 data set. Journal of Geophysical
Research - Atmospheres, 126(2), e2019JD032352.
https://doi.org/10.1029/2019JD032352
10.1029/2019JD032352
ADSWeb of Science®Google Scholar
* Paik, S., & Min, S.-K. (2020). Quantifying the anthropogenic greenhouse gas
contribution to the observed spring snow-cover decline using the CMIP6
multimodel ensemble. Journal of Climate, 33(21), 9261–9269.
https://doi.org/10.1175/JCLI-D-20-0002.1
10.1175/JCLI-D-20-0002.1
ADSGoogle Scholar
* Parsons, L. A., Brennan, M. K., Wills, R. C. J., & Proistosescu, C. (2020).
Magnitudes and spatial patterns of interdecadal temperature variability in
CMIP6. Geophysical Research Letters, 47(7), e2019GL086588.
https://doi.org/10.1029/2019GL086588
10.1029/2019GL086588
ADSWeb of Science®Google Scholar
* Pedro, J. B., Martin, T., Steig, E. J., Jochum, M., Park, W., & Rasmussen, S.
O. (2016). Southern Ocean deep convection as a driver of Antarctic warming
events. Geophysical Research Letters, 43(5), 2192–2199.
https://doi.org/10.1002/2016GL067861
10.1002/2016GL067861
ADSWeb of Science®Google Scholar
* Petrie, R., Denvil, S., Ames, S., Levavasseur, G., Fiore, S., Allen, C., et
al. (2021). Coordinating an operational data distribution network for CMIP6
data. Geoscientific Model Development, 14(1), 629–644.
https://doi.org/10.5194/gmd-14-629-2021
10.5194/gmd-14-629-2021
ADSWeb of Science®Google Scholar
* Pincus, R., Forster, P. M., & Stevens, B. (2016). The radiative forcing model
intercomparison project (RFMIP): Experimental protocol for CMIP6.
Geoscientific Model Development, 9(9), 3447–3460.
https://doi.org/10.5194/gmd-9-3447-2016
10.5194/gmd-9-3447-2016
ADSWeb of Science®Google Scholar
* Plummer, D., Kinnison, D., & Hegglin, M. (2018).
input4mips.cmip6.damip.cccma.ccmi-hist-nat-1-1. version: 20180525 [Dataset].
Earth System Grid Federation. https://doi.org/10.22033/ESGF/input4MIPs.2301
10.22033/ESGF/input4MIPs.2301
Google Scholar
* Qiao, L., Zuo, Z., Xiao, D., & Bu, L. (2021). Detection, attribution, and
future response of global soil moisture in summer. Frontiers in Earth
Science, 9, 745185. https://doi.org/10.3389/feart.2021.745185
10.3389/feart.2021.745185
Web of Science®Google Scholar
* Ramaswamy, V., Collins, W., Haywood, J., Lean, J., Mahowald, N., Myhre, G.,
et al. (2019). Radiative forcing of climate: The historical evolution of the
radiative forcing concept, the forcing agents and their quantification, and
applications. Meteorological Monographs, 59, 14.1–14.101.
https://doi.org/10.1175/amsmonographs-d-19-0001.1
10.1175/AMSMONOGRAPHS-D-19-0001.1
Google Scholar
* Ridley, J. K., Blockley, E. W., & Jones, G. S. (2022). A change in climate
state during a pre-industrial simulation of the CMIP6 model HadGEM3 driven by
deep ocean drift. Geophysical Research Letters, 49(6), e2021GL097171.
https://doi.org/10.1029/2021GL097171
10.1029/2021GL097171
ADSWeb of Science®Google Scholar
* Rodhe, H., Charlson, R. J., & Anderson, T. L. (2000). Avoiding circular logic
in climate modeling. Climatic Change, 44(4), 419–422.
https://doi.org/10.1023/a:1005536902789
10.1023/A:1005536902789
Google Scholar
* Santer, B. D., Thorne, P. W., Haimberger, L., Taylor, K. E., Wigley, T. M.
L., Lanzante, J. R., et al. (2008). Consistency of modelled and observed
temperature trends in the tropical troposphere. International Journal of
Climatology, 28(13), 1703–1722. https://doi.org/10.1002/joc.1756
10.1002/joc.1756
ADSWeb of Science®Google Scholar
* Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire,
A., et al. (2019). UKESM1: Description and evaluation of the UK Earth system
model. Journal of Advances in Modeling Earth Systems, 11(12), 4513–4558.
https://doi.org/10.1029/2019MS001739
10.1029/2019MS001739
ADSWeb of Science®Google Scholar
* Sellar, A. A., Walton, J., Jones, C. G., Wood, R., Abraham, N. L.,
Andrejczuk, M., et al. (2020). Implementation of U.K. Earth system models for
CMIP6. Journal of Advances in Modeling Earth Systems, 12(4), e2019MS001946.
https://doi.org/10.1029/2019MS001946
10.1029/2019MS001946
ADSWeb of Science®Google Scholar
* Sen Gupta, A., Jourdain, N. C., Brown, J. N., & Monselesan, D. (2013).
Climate drift in CMIP5 models. Journal of Climate, 26(21), 8597–8615.
https://doi.org/10.1175/JCLI-D-12-00521.1
10.1175/JCLI-D-12-00521.1
Web of Science®Google Scholar
* Senior, C. A., Jones, C. G., Wood, R. A., Sellar, A., Belcher, S.,
Klein-Tank, A., et al. (2020). U.K. Community Earth system modeling for
CMIP6. Journal of Advances in Modeling Earth Systems, 12(9), e2019MS002004.
https://doi.org/10.1029/2019MS002004
10.1029/2019MS002004
ADSPubMedWeb of Science®Google Scholar
* Shindell, D. T., Faluvegi, G., Rotstayn, L., & Milly, G. (2015). Spatial
patterns of radiative forcing and surface temperature response. Journal of
Geophysical Research, 120(11), 5385–5403.
https://doi.org/10.1002/2014jd022752
10.1002/2014JD022752
ADSWeb of Science®Google Scholar
* Smith, C. J., Kramer, R. J., Myhre, G., Alterskjær, K., Collins, W., Sima,
A., et al. (2020). Effective radiative forcing and adjustments in CMIP6
models. Atmospheric Chemistry and Physics, 20(16), 9591–9618.
https://doi.org/10.5194/acp-20-9591-2020
10.5194/acp-20-9591-2020
CASADSWeb of Science®Google Scholar
* Smith, D. M., Gillett, N. P., Simpson, I. R., Athanasiadis, P. J., Baehr, J.,
Bethke, I., et al. (2022). Attribution of multi-annual to decadal changes in
the climate system: The large ensemble single forcing model intercomparison
project (LESFMIP). Frontiers in Climate, 4, 955414.
https://doi.org/10.3389/fclim.2022.955414
10.3389/fclim.2022.955414
Google Scholar
* Stott, P. A., & Kettleborough, J. A. (2002). Origins and estimates of
uncertainty in predictions of twenty-first century temperature rise. Nature,
416(6882), 723–726. https://doi.org/10.1038/416723a
10.1038/416723a
CASADSPubMedWeb of Science®Google Scholar
* Stott, P. A., Mitchell, J. F. B., Allen, M. R., Delworth, T. L., Gregory, J.
M., Meehl, G. A., & Santer, B. D. (2006). Observational constraints on past
attributable warming and predictions of future global warming. Journal of
Climate, 19(13), 3055–3069. https://doi.org/10.1175/jcli3802.1
10.1175/JCLI3802.1
ADSWeb of Science®Google Scholar
* Stott, P. A., Tett, S. F. B., Jones, G. S., Allen, M. R., Mitchell, J. F. B.,
& Jenkins, G. J. (2000). External control of 20th century temperature by
natural and anthropogenic forcing. Science, 290(5499), 2133–2137.
https://doi.org/10.1126/science.290.5499.2133
10.1126/science.290.5499.2133
CASADSPubMedWeb of Science®Google Scholar
* Stouffer, R. J., Eyring, V., Meehl, G. A., Bony, S., Senior, C., Stevens, B.,
& Taylor, K. E. (2017). CMIP5 scientific gaps and recommendations for CMIP6.
Bulletin of the American Meteorological Society, 98(1), 95–105.
https://doi.org/10.1175/BAMS-D-15-00013.1
10.1175/BAMS-D-15-00013.1
ADSWeb of Science®Google Scholar
* Taylor, K. E., Juckes, M., Balaji, V., Cinquini, L., Denvil, S., Durack, P.
J., et al. (2018). CMIP6 global attributes, DRS, filenames, directory
structure, and CV’s 10 September 2018 (v6.2.7).
https://docs.google.com/document/d/1h0r8RZr_f3-8egBMMh7aqLwy3snpD6_MrDz1q8n5XUk
Google Scholar
* Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An overview of CMIP5
and the experiment design. Bulletin of the American Meteorological Society,
93(4), 485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
10.1175/BAMS-D-11-00094.1
ADSWeb of Science®Google Scholar
* Tett, S. F. B., Johns, T. C., & Mitchell, J. F. B. (1997). Global and
regional variability in a coupled AOGCM. Climate Dynamics, 13(5), 303–323.
https://doi.org/10.1007/s003820050168
10.1007/s003820050168
ADSGoogle Scholar
* Tett, S. F. B., Jones, G. S., Stott, P. A., Hill, D. C., Mitchell, J. F. B.,
Allen, M. R., et al. (2002). Estimation of natural and anthropogenic
contributions to 20th Century temperature change. Journal of Geophysical
Research, 107(D16), ACL10-1–ACL10-24. https://doi.org/10.1029/2000JD000028
10.1029/2000JD000028
Google Scholar
* Tett, S. F. B., Stott, P. A., Allen, M. R., Ingram, W. J., & Mitchell, J. F.
B. (1999). Causes of twentieth-century temperature change near the Earth’s
surface. Nature, 399(6736), 569–572. https://doi.org/10.1038/21164
10.1038/21164
CASADSWeb of Science®Google Scholar
* Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D.,
Comer, R., et al. (2018). The Met Office global coupled model 3.0 and 3.1
(GC3.0 and GC3.1) configurations. Journal of Advances in Modeling Earth
Systems, 10(2), 357–380. https://doi.org/10.1002/2017MS001115
10.1002/2017MS001115
ADSWeb of Science®Google Scholar
REFERENCES FROM THE SUPPORTING INFORMATION
* Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A.
K., Edwards, J., et al. (2020). The community Earth system model version 2
(CESM2). Journal of Advances in Modeling Earth Systems, 12(2), e2019MS001916.
https://doi.org/10.1029/2019MS001916
10.1029/2019MS001916
ADSWeb of Science®Google Scholar
* Matthes, K., Funke, B., Andersson, M. E., Barnard, L., Beer, J., Charbonneau,
P., et al. (2017). Solar forcing for CMIP6 (v3.2). Geoscientific Model
Development, 10(6), 2247–2302. https://doi.org/10.5194/gmd-10-2247-2017
10.5194/gmd-10-2247-2017
CASADSWeb of Science®Google Scholar
* Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S.,
Chadwick, R., et al. (2017). The cloud feedback model intercomparison project
(CFMIP) contribution to CMIP6. Geoscientific Model Development, 10(1),
359–384. https://doi.org/10.5194/gmd-10-359-2017
10.5194/gmd-10-359-2017
CASADSWeb of Science®Google Scholar
Volume16, Issue8
August 2024
e2023MS004135
* FIGURES
* REFERENCES
* RELATED
* INFORMATION
RECOMMENDED
* Preindustrial Control Simulations With HadGEM3‐GC3.1 for CMIP6
Matthew B. Menary, Till Kuhlbrodt, Jeff Ridley, Martin B. Andrews, Oscar B.
Dimdore-Miles, Julie Deshayes, Rosie Eade, Lesley Gray, Sarah
Ineson, Juliette Mignot, Christopher D. Roberts, Jon Robson, Richard A.
Wood, Prince Xavier,
Journal of Advances in Modeling Earth Systems
* The Response of the Northern Hemisphere Storm Tracks and Jet Streams to
Climate Change in the CMIP3, CMIP5, and CMIP6 Climate Models
B. J. Harvey, P. Cook, L. C. Shaffrey, R. Schiemann,
Journal of Geophysical Research: Atmospheres
* Regional Climate Sensitivity of Climate Extremes in CMIP6 Versus CMIP5
Multimodel Ensembles
Sonia I. Seneviratne, Mathias Hauser,
Earth's Future
* Arctic Sea Ice in CMIP6
Dirk Notz, SIMIP Community,
Geophysical Research Letters
* Forcings, Feedbacks, and Climate Sensitivity in HadGEM3‐GC3.1 and UKESM1
Timothy Andrews, Martin B. Andrews, Alejandro Bodas-Salcedo, Gareth S.
Jones, Till Kuhlbrodt, James Manners, Matthew B. Menary, Jeff Ridley, Mark A.
Ringer, Alistair A. Sellar, Catherine A. Senior, Yongming Tang,
Journal of Advances in Modeling Earth Systems
METRICS
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* Check for updates
RESEARCH FUNDING
* Met Office Hadley Centre Climate Programme funded by DSIT
* Met Office
* Department for Science, Innovation and Technology
KEYWORDS
* climate models
* CMIP6
* DAMIP
* detection and attribution
PUBLICATION HISTORY
* Issue Online: 03 August 2024
* Version of Record online: 03 August 2024
* Manuscript accepted: 04 July 2024
* Manuscript revised: 16 April 2024
* Manuscript received: 20 November 2023
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