GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-11-1009-2018Overview of experiment design and comparison of models participating in
phase 1 of the SPARC Quasi-Biennial Oscillation initiative (QBOi)ButchartNealneal.butchart@metoffice.gov.ukAnsteyJames A.https://orcid.org/0000-0001-6366-8647HamiltonKevinhttps://orcid.org/0000-0002-3560-8097OspreyScotthttps://orcid.org/0000-0002-8751-1211McLandressCharlesBushellAndrew C.https://orcid.org/0000-0001-5683-4387KawataniYoshiohttps://orcid.org/0000-0003-3260-8830KimYoung-Hahttps://orcid.org/0000-0003-4014-073XLottFrancoisScinoccaJohnStockdaleTimothy N.AndrewsMartinBellpratOmarBraesickePeterCagnazzoChiaraChenChih-ChiehChunHye-YeongDobryninMikhailhttps://orcid.org/0000-0003-3533-3529GarciaRolando R.https://orcid.org/0000-0002-6963-4592Garcia-SerranoJavierGrayLesley J.HoltLauraKerzenmacherTobiasNaoeHiroakiPohlmannHolgerRichterJadwiga H.ScaifeAdam A.SchenzingerVerenaServaFedericohttps://orcid.org/0000-0002-7118-0817VersickStefanWatanabeShingoYoshidaKoheihttps://orcid.org/0000-0002-2422-5584YukimotoSeijihttps://orcid.org/0000-0002-0415-1661Met Office Hadley Centre (MOHC), Exeter, UKCanadian Centre for Climate Modelling and Analysis (CCCma),
Victoria, CanadaInternational Pacific Research Center (IPRC), Honolulu, USANational Centre for Atmospheric Science (NCAS),
University of Oxford, Oxford, UKUniversity of Toronto, Toronto, CanadaMet Office, Exeter, UKJapan Agency for Marine-Earth Science and Technology (JAMSTEC),
Yokohama, JapanEwha Womans University, Seoul, South KoreaLaboratoire de Météorologie Dynamique (LMD), Paris, FranceEuropean Centre for Medium-Range Weather Forecasts (ECMWF),
Reading, UKBarcelona Supercomputing Center (BSC), Barcelona, SpainKarlsruher Institut für Technologie (KIT), Karlsruhe, GermanyIstituto di Scienze Dell'Atmosfera e del Clima (ISAC-CNR), Rome, ItalyNational Center for Atmospheric Research (NCAR), Boulder, USAYonsei University, Seoul, South KoreaUniversität Hamburg, Hamburg, GermanyNorthWest Research Associates (NWRA), Boulder, USAMeteorological Research Institute (MRI), Tsukuba, JapanMax-Planck-Institut für Meteorologie (MPI), Hamburg, GermanyUniversity of Exeter, Exeter, UKUniversität Wien, Vienna, AustriaUniversità degli Studi di Napoli “Parthenope”, Naples, ItalyNeal Butchart (neal.butchart@metoffice.gov.uk)16March2018113100910326August201726October201722January201829January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://gmd.copernicus.org/articles/11/1009/2018/gmd-11-1009-2018.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/11/1009/2018/gmd-11-1009-2018.pdf
The Stratosphere–troposphere Processes And their Role in Climate (SPARC)
Quasi-Biennial Oscillation initiative (QBOi) aims to improve the fidelity of
tropical stratospheric variability in general circulation and Earth system
models by conducting coordinated numerical experiments and analysis. In the
equatorial stratosphere, the QBO is the most conspicuous mode of variability.
Five coordinated experiments have therefore been designed to (i) evaluate and
compare the verisimilitude of modelled QBOs under present-day conditions,
(ii) identify robustness (or alternatively the spread and uncertainty) in the
simulated QBO response to commonly imposed changes in model climate forcings
(e.g. a doubling of CO2 amounts), and (iii) examine model dependence of
QBO predictability. This paper documents these experiments and the
recommended output diagnostics. The rationale behind the experimental design
and choice of diagnostics is presented. To facilitate scientific
interpretation of the results in other planned QBOi studies, consistent
descriptions of the models performing each experiment set are given, with
those aspects particularly relevant for simulating the QBO tabulated for easy
comparison.
Introduction
Over the last decade or so, there has been a move toward global climate,
Earth system, and weather forecasting models having properly resolved
stratospheres and elevated upper boundaries. In some cases
e.g. these boundaries are above 100 km and thus
nominally located in space (as defined by the Fédération
Aéronautique Internationale). Despite this, tropical stratospheric
variability, in particular the Quasi-Biennial Oscillation (QBO), has
generally been rather poorly represented in models used in
recent international assessments of stratospheric ozone depletion
. Likewise only a handful of the models central to the
last international assessment of climate change simulated
tropical variability approaching a realistic QBO (see
Fig. ). Even with the latest generation of models the
representation of the QBO remains problematic in many cases
. For instance, several of the state-of-the-art
chemistry–climate models participating in the concurrent Chemistry–Climate
Model Initiative (CCMI) prescribe a QBO in order to “improve” the accuracy
of their simulations . Consequently the World Climate
Research Programme (WCRP) Stratosphere–troposphere Processes And their Role
in Climate (SPARC) core project has promoted a new QBO initiative (QBOi) to
improve the simulation of tropical stratospheric variability in general
circulation models and Earth system models (GCMs and ESMs). While QBOi is
focused on modelling studies, it is also closely aligned with other SPARC
activities including the SPARC Reanalysis Intercomparison Project
S-RIP; providing supporting analysis of observations
and reanalyses and the SPARC gravity waves activity
that is studying an important driver of the QBO.
A 10-year (1990–1999) time series of monthly and zonal-mean zonal
wind at the Equator from 100 to 10 hPa for 47 models that uploaded data to
the Coupled Model Intercomparison Project Phase 5 (CMIP5) data repository.
Only five models (CMCC-CMS, HadGEM2-CC, MIROC-ESM, MIROC-ESM-CHEM, and
MPI-ESM-MR) spontaneously produce the iconic QBO behaviour of alternating
descending layers of eastward and westward winds such as indicated in the
upper left-hand panel for the ERA-Interim reanalysis . Equatorial
stratospheric winds in the CMIP5 version of CESM1(WACCM) are strongly relaxed
(“nudged”) toward observations, which is why it shows a close resemblance
to ERA-Interim in this figure. (Note that the version of WACCM participating
in QBOi, described in Sect. , is a different version of
this model.)
Unlike the Coupled Model Intercomparison Project Phase 6
CMIP6;, and to a lesser extent CCMI, the design of
experiments for QBOi is not governed by the huge and rather diverse
requirements from policy makers and scientists that have presented such a
massive cultural and organizational challenge to the modelling community
. Instead, QBOi has adopted a less onerous approach for
experimental set-up using stand-alone experiments
(Sect. ) specifically focused on improving the
representation of the QBO in GCMs and addressing scientific questions related
to advancing the understanding of the QBO (Sect. ).
This is an essential prerequisite to improving the representation in models
of important QBO influences , such as the modulation of the
transport of aerosols and chemical constituents into and within the
stratosphere e.g. or the dynamical teleconnections to
the extra-tropics , and their subsequent surface climate and
weather impacts. These aspects are expected to be included more prominently
in the next phase of QBOi. The purpose of this paper is to describe the
experiments to be used in phase 1 of QBOi and provide supporting
documentation for other publications analysing and interpreting the output
from the experiments. To help promote the widest possible participation in the
experiments and thereby maximize the size of the multi-model ensembles, the
design of the experiments has involved input from the community throughout
. The scientific rationale for the experiments
also evolved through community discussion and is presented
in the next section.
An important part of the multi-model analysis and interpretation of the
experiments is the availability of a consistent set of relevant diagnostics
from each model. For this QBOi follows best practices and, where possible,
variable and file naming conventions of CMIP5 and CCMI (see the Supplement). In
particular the recommended output diagnostics are adapted from those
requested by the Dynamics and Variability Model Intercomparison Project
DynVarMIP;. These will allow the zonal-mean zonal
momentum budgets to be examined in detail in the transformed Eulerian mean
(TEM) framework e.g.pp. 127–130 including
contributions from parameterized (sub-grid-scale) gravity waves. Other
requested diagnostics are aimed at characterizing the sources, propagation,
and filtering (i.e. breaking) of both resolved and unresolved waves in the
participating models, particularly in the equatorial region. Precise
specification of the requested diagnostics can be found in
Sect. . To facilitate the comparison of these
diagnostics among participating models, salient model features that are
important for capturing QBO-like behaviour are described and tabulated in
Sect. , with some emphasis in particular on the
non-orographic gravity wave drag (GWD) parameterizations used by almost all
of the QBOi models. Closing remarks including future plans follow in
Sect. .
Scientific rationale
A crucial test of our understanding and ability to model the QBO
occurred around the beginning of 2016 when the QBO cycle was
unexpectedly disrupted for the first time since its discovery
in the late 1950s .
The well-established QBO paradigm, originating from the 1960s, of
alternate eastward and westward momentum deposition from vertically
propagating equatorial waves could not account
for this disruption . Despite the fact that the QBO
is normally highly predictable the
disruption was completely missed by seasonal forecasts, and this
failure illustrates the difficulty models have in capturing the
complex phenomenology of the QBO and its full range of variability.
Similar disruptions have only very rarely been seen in multi-decadal
simulations and from just a few models with QBO-like oscillations
e.g.. It is possible that the models may be
over-tuned to ensure that they capture the mean behaviour of
selected metrics (e.g. mean period and amplitude) of the present-day
QBO. Furthermore, the disruption itself raises the possibility that
the real QBO is less robust than previously thought, although it
has since returned to its usual cycling as predicted.
With the advent of non-orographic GWD parameterizations and/or the
use of increased vertical resolution in the stratosphere, a growing
number of global models have been able to reproduce QBO-like
variability in the equatorial stratosphere
e.g..
However, common deficiencies exist in all current simulations,
notably with QBO winds often being unrealistically weak in the
lowermost stratosphere and having unrealistically small
cycle-to-cycle variability e.g..
The simulated QBOs can also be quite “fragile”, which means
sensitive to many different aspects of model formulation depending
on the model. For example, the QBO in the Canadian Middle Atmosphere
Model (AGCM3-CMAM) is sensitive to the balance of resolved and
parameterized wave forcing , while in different versions
of the Met Office Unified Model (MetUM) the QBO is sensitive to the
specification of stratospheric ozone
and/or the parameterized gravity waves .
Sensitivity to vertical resolution has been reported by numerous
studies, for example by for the middle atmosphere
version of the ECHAM5 (MAECHAM5) model and by for
the NASA Goddard Institute for Space Studies (GISS) climate model.
In addition identified a sensitivity to the choice of
dynamical core. Other key questions concerning simulation of the QBO
regard its possible synchronization with other modes of variability,
such as the annual cycle e.g. and
El Niño–Southern Oscillation e.g.,
the QBO's predictability e.g.,
and finally the robustness of the QBO response to climate
change e.g..
Phase 1 of QBOi focuses on reducing these uncertainties in simulated
QBOs by conducting coordinated experiments that will allow for more
rigorous intercomparison of models than is otherwise possible from
individual studies. The aim is to address the ability of GCMs to
capture the QBO in the present climate, to predict its behaviour
under climate-change forcings, and to predict its evolution when
initialized with observations (i.e. hindcasts).
Experiments
and briefly describe a set of
five QBO experiments which are designed to be simple and accessible
to a wide range of modelling groups. The motivation and specific
goals for each of these experiments is presented below with the
technical specifications given in Appendix .
The aim is for modelling groups to perform all five experiments, and
even if this is not possible, it is important that the same model
version is used for the subset of experiments that are conducted;
i.e. there should be no tuning of free parameters between experiments.
Use of the same model version for the different experiments is
crucial for learning the most from this study. The model version used
should be that which the group considered gave the “best”
representation of the QBO under present-day conditions (e.g.
in Experiment 1 or similar preparatory simulations). Of course there
are situations when two different versions of a model might be used
to perform the experiment set, such as when high- and low-resolution
versions or alternative non-orographic GWD parameterizations are
available. In these situations the results would then be treated for
the purpose of the QBOi multi-model analysis as if they were obtained
from two separate models (although interpretation of results will need
to be aware of and test for sensitivity to the possible dominance
of the results by one particular family of models). All experiments are
for AGCMs apart from an option to perform Experiment 5 with a coupled
ocean, which is denoted as Experiment 5a (see below).
Experiment list and goalsPresent-day climate
The first two experiments are designed with the goal of identifying
and distinguishing the properties of and mechanisms underlying the
variety of model simulations of the QBO in present-day conditions:
Experiment 1 (“AMIP”) involves specified observed inter-annually varying sea
surface temperatures (SSTs), sea ice, and external forcings for 1 January 1979
to 28 February 2009 (one- to three-member ensemble); and
Experiment 2 (present-day time slice) is identical to Experiment 1 except
employing repeated annual cycle SSTs, sea ice, and external forcings (100
years or ensemble of 3 × 30 years).
The main differences between these two experiments are expected to arise from
the differences between their specified SSTs. Figure
compares the variability in the tropics (5∘ N–5∘ S) of the
prescribed SSTs for Experiments 1 and 2. Averaged over all longitudes the
differences are relatively small (Fig. a),
although regionally there are large differences, for instance due to the
effects of the El Niño–Southern Oscillation (Fig. b).
Comparison of monthly mean sea surface temperature (SST) anomalies
(red and blue) from the 30-year mean (1979–2008) of the CMIP5 AMIP SSTs used
in Experiment 1 with the mean annual cycle (black curve) for the same period.
(a) Average for all longitudes between 5∘ N and
5∘ S. (b) Average for the Niño 3.4 region
(120∘–170∘ W, 5∘ N–5∘ S).
These experiments will allow for an evaluation of the accuracy of modelled QBOs
under present-day climate conditions by employing the diagnostics and metrics
discussed in Sect. . The impact of inter-annually
varying forcing (e.g. Fig. ) on the model QBO will be
assessed through a comparison of the two experiments. Experiment 2 also
provides the control for the climate projection in Experiments 3 and 4.
Climate projections
Two further experiments are designed to subject the modelled QBOs
(i.e. the QBO simulated by the present-day experiments) to an
external forcing similar to that typically applied for climate
projections:
Experiment 3 (2×CO2 time slice) is identical to Experiment 2, but
with a change in CO2 concentration and specified SSTs appropriate for a
2×CO2 world (100 years or ensemble of 3 × 30 years); and
Experiment 4 (4×CO2 time slice) is identical to Experiment 2, but
with a change in CO2 concentration and specified SSTs appropriate for a
4×CO2 world (100 years or ensemble of 3 × 30 years).
These experiments will allow the response (i.e. 2×CO2 –
1×CO2 and 4×CO2 – 1×CO2) of the QBO, its
forcing mechanisms, and its impact and influence to be evaluated using the same
diagnostics and metrics used in the analysis of Experiments 1 and 2. Key
questions that will be addressed are the following.
What is the spread and uncertainty of the forced model response?
Do different models cluster in any particular way?
Can a connection or correlation be made between QBOs that exhibit similar
values of metrics and diagnostics under present-day climate forcing and the
behaviour of the QBO in these same models under future climate forcing?
The motivation is to investigate what aspects of modelled QBOs determine the
spread, or uncertainty, of the QBO response to CO2 forcing. These
aspects are considered high priority by QBOi in order to reduce uncertainty
in future projections. These experiments also will provide context for the
uncertainty in climate change projections of QBO behaviour among the
state-of-the-art GCMs being used in CMIP6. Furthermore, the possibility was
noted in Sect. that some models may be over-tuned to
ensure that they capture the behaviour of the present-day QBO. If so, then a
large multi-model spread in the forced response may indicate that such tuning
constitutes, in effect, an “overfitting” of models to present-day
conditions.
QBO hindcasts
The goal of the final experiment is to evaluate and compare the
predictive skill of modelled QBOs in a retrospective hindcast context,
quantify this predictive capability in multiple models, and study the
model processes driving the evolution of the QBO.
Experiment 5 (hindcasts) involves a set of initialized QBO hindcasts of 9–12
months using the observed SSTs and forcings specified as in Experiment 1.
Specified start dates are 1 May and 1 November for the years 1993–2007
(i.e. 15 years, 30 start dates) with initial atmospheric conditions obtained
from reanalyses (at least three-member ensemble).
Because of the prescribed SSTs these are not true prediction
experiments; nonetheless they provide an important test of how well
models can predict the evolution of the QBO from specified initial
conditions that reasonably sample the full range of QBO phases,
despite some clustering of the 1 May initial profiles
(Fig. ). Key questions that will be addressed are
the following.
How does prediction skill vary among models, and to what extent
and for how long are models able to predict the QBO evolution
correctly at different vertical levels and different phases of the QBO?
How does the forecast skill relate to the behaviour of the QBO in
Experiment 1? Are realistic QBO simulations in a multi-decadal
simulation well correlated with skillful long-term deterministic
predictions?
Do the models that cluster and/or do well in the prediction experiments
cluster in the CO2 forcing experiments?
One aim is to investigate which aspects of modelled QBOs determine
the quality of QBO prediction and therefore where development needs
to be focused for model improvement. The hindcast framework can also
be helpful for directly assessing model changes, possibly driving
improvements in free-running models. A further motivation for these
experiments is to investigate the possibility of using the hindcast
results to narrow the range of plausible models for climate change
experiments.
Zonal-mean and daily mean zonal wind (m s-1) profiles at the
Equator for 1 May and 1 November for the 15 years 1993–2007 from the
ERA-Interim reanalyses . The two profiles shown in coloured
lines (May 1993 and November 2005, taken as representative of eastward and
westward QBO phases in the lower stratosphere, respectively) are those used
in offline comparison of the gravity wave drag parameterizations presented in
Sect. .
It is recognized that some groups may already have completed for the
period 1993–2007 operational seasonal hindcasts using a coupled
ocean–atmosphere model, and therefore for the QBOi multi-model
analysis an acceptable alternative (or addition) to Experiment 5 is
Experiment 5a (hindcasts), which involves a set of initialized QBO hindcasts of 9–12
months identical to Experiment 5 apart from replacing the specified SSTs with
a coupled ocean model appropriately initialized (at least three-member ensemble).
Full comparison with the other models providing Experiment 5
output will nonetheless depend on most of the diagnostics discussed
in Sect. 5 being available from those groups providing
Experiment 5a output.
Process studies
A secondary purpose of Experiment 5 is to investigate and evaluate
differences in wave dissipation and momentum deposition to
understand the processes driving the QBO in each model and separate the
contributions from resolved and unresolved waves
e.g.. Due to the initialization of
the hindcasts, each model will have essentially the same initial basic
state, and its evolution immediately after the start of the forecast
will allow the properties of wave dissipation and momentum deposition
to be compared and contrasted between different models given a
near-identical basic state. Specifying the same observed SST in all
models (rather than allowing each model to predict its own SST
evolution) facilitates the comparison as it eliminates any differences
resulting from the evolving ocean. Short periods of additional high-frequency diagnostics are requested to maximize the benefits of the
multi-model comparison.
Diagnostics
The diagnostics requested by QBOi draw on those requested by other major
multi-model intercomparison projects, in particular DynVarMIP
, though they have been specifically tailored through
community discussion for the analysis of the QBO in Experiments 1–5.
The requested diagnostics are described in this section; additional
technical information on how they should be formatted and uploaded to
the shared QBOi repository is available in the Supplement.
Spatial and temporal resolution
For ease of comparison among models most output variables are requested on a
standard set of 30 pressure levels: 1000, 925, 850, 700, 600, 500, 400, 300,
250, 200, 175, 150, 120, 100, 85, 70, 60, 50, 40, 30, 20, 15, 10, 7, 5, 3, 2,
1.5, 1.0, and 0.4 hPa. These are adapted from the extended levels set
requested by DynVarMIP for CMIP6 e.g. to obtain a
vertical resolution in the upper tropical troposphere and lower stratosphere
(i.e. between 200 and 40 hPa) of 1.0 to 1.5 km. There are two exceptions,
however.
Data to be used for calculating equatorial wave spectra
(6-hourly instantaneous fields) should be provided at a vertical
resolution equivalent to the model resolution (i.e. with
the same number of levels in the specified altitude range) to ensure
accurate calculation of QBO wave forcing e.g.;
see below for further details.
To reduce data volume, daily mean three-dimensional (3-D) variables are requested
for only the eight pressure levels used by CMIP5: 1000, 850, 700, 500, 250, 100,
50, and 10 hPa. These data will be used mainly to examine the QBO influence
on other regions of the atmosphere (e.g. on the North Atlantic Oscillation, NAO) and higher vertical resolution is not considered necessary.
Horizontal resolution should be the same as the model but if data volume
is an issue then a reduced grid is acceptable, provided the reduction
method is documented.
To examine the daily mean and monthly mean QBO zonal mean momentum budget,
terms making up the TEM zonal momentum equation
e.g.pp. 127–130 are requested following the recipe
given by Appendix A3, but also see their corrigendum
. In particular note the importance of calculating the
individual terms from 6-hourly or higher-frequency data (e.g. every time
step) and the need for sufficient vertical resolution (e.g. the standard
pressure levels listed above) for accurate estimates of the vertical
derivatives. Furthermore to examine the wavenumber–frequency spectra of the
equatorial waves e.g. instantaneous values
of 3-D winds and temperature are requested every 6 h on model levels or on
pressure levels at roughly equivalent vertical resolution to the model levels
but, to reduce data volumes, only for levels between 100 and 0.4 hPa and for
latitudes between 15∘ N and 15∘ S. For ease of analysis,
pressure levels at model-level resolution are preferred over actual model
levels.
An additional benefit of requesting these 6-hourly data is that they can
provide a check on the requested TEM budget terms
(Table ), albeit only for tropical latitudes. Calculating
the budget terms from the 3-D 6-hourly wind and temperature fields
(Table ) in a self-consistent way across all models
removes the possibility that some of the inter-model differences in the
requested TEM diagnostics (Table ) are due to differences
in how the calculation of these terms was performed by different modelling
groups.
Climate and variability. Monthly and daily means, with 2-D
indicating a longitude–latitude–time (XYT) field and 3-D indicating a
longitude–latitude–pressure–time (XYPT) field. XY is typically the model's
horizontal output grid and P is the standard 30-level set of diagnostic
pressure levels described in Sect. : 1000,
925, 850, 700, 600, 500, 400, 300, 250, 200, 175, 150, 120, 100, 85, 70, 60,
50, 40, 30, 20, 15, 10, 7, 5, 3, 2, 1.5, 1.0, and 0.4 hPa.
NameLong name (units)Dimensionpslsea level pressure (Pa)2-Dprcconvective precipitation flux (kg s-1 m-2)2-Dprtotal precipitation flux (kg s-1m-2)2-Dtasnear-surface air temperature (K)2-Duaseastward near-surface wind (m s-1)2-Dvasnorthward near-surface wind (m s-1)2-Dtaair temperature (K)3-D*uaeastward wind (m s-1)3-D*zggeopotential height (m)3-D*
* For daily 3-D variables P is reduced to eight pressure
levels:
1000, 850, 700, 500, 250, 100, 50, 10 hPa.
Dynamics. (a) Monthly mean and daily mean fields and contributions
to zonal-mean zonal momentum equation (YPT). (b) Monthly mean tendencies and
fluxes from parameterized gravity waves (XYPT). (c) Daily mean sources for
orographic and non-orographic gravity waves (XYT). P is the standard
30-level set of diagnostic pressure levels described in
Sect. (also Table
caption).
(a) Monthly mean and daily mean zonal mean fields – YPT NameLong name (units)Dimensionuaeastward wind (m s-1)2-Dtaair temperature (K)2-Dzggeopotential height (m)2-Dvstarresidual northward wind (m s-1)2-Dwstarresidual upward wind (m s-1)2-Dfynorthward EP flux (N m-1)2-Dfzupward EP flux (N m-1)2-Dutenddivfu tendency by EP flux divergence (m s-2)2-Dutendu tendency (m s-2)2-Dutendogwu tendency by orographic gravity waves (m s-2)2-Dutendnogwu tendency by non-orographic gravity waves (m s-2)2-Dpsistarresidual stream function (kg s-1)2-D(b) Monthly mean gravity wave tendencies and fluxes – XYPT utendogwu tendency by orographic gravity waves (m s-2)3-Dutendnogwu tendency by non-orographic gravity waves (m s-2)3-Dvtendogwv tendency by orographic gravity waves (m s-2)3-Dvtendnogwv tendency by non-orographic gravity waves (m s-2)3-Dtaunogeeastward wind stress of non-orographic gravity waves (Pa)3-Dtaunogssouthward wind stress of non-orographic gravity waves (Pa)3-Dtaunogwwestward wind stress of non-orographic gravity waves (Pa)3-Dtaunognnorthward wind stress of non-orographic gravity waves (Pa)3-D(c) Daily mean gravity wave sources – XYT tauogusurface eastward wind stress by orographic gravity waves (Pa)2-Dtauogvsurface northward wind stress by orographic gravity waves (Pa)2-Dtaunoge*launch eastward wind stress of non-orographic gravity waves (Pa)3-Dtaunogs*launch southward wind stress of non-orographic gravity waves (Pa)3-Dtaunogw*launch westward wind stress of non-orographic gravity waves (Pa)3-Dtaunogn*launch northward wind stress of non-orographic gravity waves (Pa)3-D
* Only if non-isotropic and/or non-stationary at
launch level (e.g. coupled to convection or fronts).
Output period
Monthly mean output is requested for the full duration of all experiments and
all ensemble members. Likewise for Experiment 5 daily mean output is
requested for the full duration of each ensemble member. On the other hand,
for Experiments 1–4 daily mean output is only requested for the first
30 years and/or the first ensemble member.
High-frequency (6-hourly) diagnostics for calculating equatorial
wave spectra are requested for the following periods and ensemble
members for each experiment:
Experiment 1, 1997–2002 (note that this period encompasses positive,
negative,
and neutral El Niño–Southern Oscillation (ENSO) phases) of first
ensemble member;
Experiments 2-4, years 1–4 of first ensemble member; and
Experiment 5, first 3 months of all ensemble members.
Requested output variables
Similarly to DynVarMIP , the requested variables are
separated into three categories: standard variables
(Table ) for diagnosing the climate and variability in
the models, dynamical variables (Table ) for analysing
momentum transport and budgets, and thermodynamic quantities
(Table ). In addition a fourth category of
variables (Table ) will enable the equatorial wave
spectra e.g. to be compared among the
models.
Participating models
Thermodynamics. Monthly mean and daily mean zonal mean fields (YPT).
P is the standard 30-level set of diagnostic pressure levels described in
Sect. (also
Table caption).
Monthly mean and daily mean zonal mean fields – YPT NameLong name (units)Dimensionhusspecific humidity (kg kg-1)2-Dzmtntdiabatic heating rate (K s-1)2-Dtntlwlongwave heating rate (K s-1)2-Dtntswshortwave heating rate (K s-1)2-Do3*mole fraction of ozone in air (mole mole-1)2-D
* Only if model has prognostic ozone.
(a) Vertical profiles of vertical grid spacing, Δz
(km), for models participating in QBOi. Log-pressure altitude on the model
levels is calculated by assuming a surface pressure of 1013.25 hPa and a fixed
scale height of 7 km. The grey horizontal lines denote the set of
30 QBOi diagnostic pressure levels (see
Sect. ), while the grey vertical profile (left end of the grey horizontal lines) indicates the Δz of
the diagnostics. (b) Same as in (a), but zoomed in to the
altitude range most relevant for the QBO. Thin horizontal lines in
(b) indicate the locations of the model levels.
All the experiments for phase 1 of QBOi have been designed for
atmosphere-only GCMs. From the experiment descriptions in
Sect. it is also clear that for an AGCM to
participate in these experiments it must be configured with a number of
essential characteristics (e.g. land–ocean contrast, annual cycle, and a
radiation scheme that can accommodate changes in CO2 amounts). Apart
from this QBOi does not impose any restrictions on the representation in
participating models of any physical process or, indeed, chemical process for
those models with interactive ozone. Of course, participating models are
expected to properly resolve the stratosphere with an average vertical
resolution of the order of 2 km or less between 100 and 1 hPa and an upper
boundary somewhere above that cf. high- and low-top results
in. However, it is not strictly necessary for a model to display
QBO-like variability in the equatorial stratosphere as additional insight can
be gained by comparing models with and without this property. Models with
QBO-like variability but without a properly resolved stratosphere (e.g. with
upper boundary below 1 hPa) are also considered since, again, this
potentially provides guidance on the level of stratospheric detail that is
required in order to reproduce a QBO.
There are 17 models or model versions participating in phase 1 of QBOi (i.e.
data from 17 models have been uploaded or are planned for upload to the shared
QBOi repository; see the Supplement for details of this repository). These models
are listed in Table along with the institutes and
investigators using the models and their contact information. The model names
given refer to the names used in the repository, while the information given
in Tables – refers
specifically to the configuration and parameter settings used by each model
when producing the uploaded data. More comprehensive descriptions of the
individual models can be found in the references given in the last column of
Table .
Equatorial wave spectra. The 6-hourly instantaneous 3-D (XYPT)
equatorial field (15∘ N to 15∘ S) output for selected
sub-periods of each experiment (see Sect. ). Here P is not the standard set of pressure levels used in
Tables –. Rather, as
described in Sect. , it indicates a set of
pressure levels with equivalent vertical resolution to the model levels,
covering the altitude range 100 to 0.4 hPa. Alternatively the data can be
provided on actual model levels, although in this case the data required for
conversion between model and pressure levels must also be provided.
The 6-hourly equatorial fields – XYPT NameLong name (units)Dimensiontaair temperature (K)3-Duaeastward wind (m s-1)3-Dvanorthward wind (m s-1)3-Dwavertical wind (m s-1)3-D
For spectral models the horizontal resolution is given in terms of
triangular truncation of spectral coefficients, from which a grid
spacing can be estimated as described in the
Fig. caption. For example,
T63 ∼2.8∘×2.8∘,
T159 ∼1.125∘×1.125∘, and
T255 ∼0.7∘×0.7∘,
corresponding roughly to grid lengths of 310, 130, and 80 km,
respectively. Upper boundary altitude is given in terms of pressure
and log-pressure altitude as described in the
Fig. caption.
EUL: Eulerian, EUL/ST: Eulerian/spectral transform, FV: finite volume,
SE: spectral element, SL/SI: semi-Lagrangian/semi-implicit.
1 CESM1(WACCM5-110L) momentum equations also
include fourth-order divergence damping .
2 Second order in uppermost levels.
3.
4 Increasing with height with a more powerful mesospheric sponge being
added above 1 hPa.
5 The vertical turbulent fluxes
are related to the gradient of the respective variable
chap. 5.
6 All the UM family include some implicit damping from the SL/SI
advection though this is much reduced in UMGA7, UMGA7gws, and UMGC2
.
7 A short-tail formulation was used in the lower stratosphere to
reduce vertical diffusion compared to that given in for
the standard IFS configuration.
8 Using the level-2 closure scheme of .
9 Rayleigh friction was applied at five levels above 5 hPa and the
strength of the fourth-order horizontal diffusion was successively
doubled in this layer .
10 Weak Rayleigh damping applied only to vertical velocity.
Non-orographic gravity waves, convection, and ozone
chemistry.
Schemes marked a are non-orographic GWD
parameterizations based on a wave-spectrum approach, while in schemes
marked b the wave spectrum is treated as a collection of
monochromatic waves.
For the models using the scheme (HadGEM2-A, HadGEM2-AC,
UMGA7, UMGA7gws, and UMGC2), the use of the scheme to generate a QBO
is described in . For IFS43r1, the use of the
scheme to generate a QBO is described in .
For MPI-ESM-MR, the use of the scheme is
described in . The abbreviation in square brackets for
each scheme (second column; [WM], [H], or [L])
denotes the type of dissipation used in the scheme as labelled in
Figure . “Fixed” in column 3 refers to sources of
parameterized gravity waves that are not linked to any other model
physical variable (see footnote 1,
Sect. ).
Note, however, that “fixed” includes sources that vary in time and/or
space in a prescribed way and stochastically (e.g. as is done
in the ECHAM5sh model).
Vertical resolution, Δz, vs. horizontal resolution of models
participating in QBOi. Since Δz can vary with altitude, as shown in
Fig. , here Δz is shown for the three layers
10–15, 15–20, and 20–25 km spanning the tropical upper troposphere and
lower stratosphere, with the size of the markers scaled by the average
density in each layer. See column 3 of Table for
the total number of vertical levels in each model. The horizontal grid spacing is
estimated by calculating the average of the zonal and meridional grid
spacings, (Δλ+Δϕ)/2, and converting this to a value
in kilometres at the Equator. For spectral models with triangular truncation we
assume Δλ=Δϕ=23180∘/(T+1)
as an estimate of the transform grid resolution, where T is the truncation
wavenumber as given in column 2 of Table .
It should be noted that common model development history can lead to a lack
of full independence among models. For example, 60LCAM5 and
CESM1(WACCM5-110L) have developed from the NCAR Community Atmosphere Model
(CAM); HadGEM2-A, HadGEM2-AC, UMGA7, UMGA7gws, and UMGC2 have developed out
the Met Office Unified Model (UM); EC-EARTH3.1 and IFS have their origins in
the ECMWF Integrated Forecasting System (IFS); MIROC-AGCM and MIROC-ESM
belong to the family of MIROC models; and ECHAM5sh, EMAC, and MPI-ESM-MR all
originate from the MPI ECHAM line of model development.
Tables – indicate that
some model components are shared by different models. The extent to which
shared development history affects model independence can be difficult to
assess and varies among models e.g.. Apart from
describing those aspects of model formulation that are expected to be
relevant to the QBO
(Tables –), detailed
consideration of model independence is outside the scope of this paper.
However, note that out of the 17 QBOi models, there are two pairs of models
that are identical in all respects but one: HadGEM2-A and UMGA7 used fixed
sources for their non-orographic gravity wave parameterizations, while their
counterparts HadGEM2-AC and UMGA7gws, respectively, use parameterized gravity
wave sources; this distinction is described in more detail below.
Properties of the models
(Tables –) that are of
particular relevance for simulating a QBO are the following.
Vertical domain and resolution. A high upper boundary is potentially
important depending on how much influence the semi-annual oscillation has on
the timing of the start of each new descending QBO cycle. Likewise vertical
resolution is important both for accurately simulating vertically propagating
equatorial waves and for representing the wave dissipation and descending
sharp shear zones that are a characteristic feature of the QBO.
Figure (see also columns 3 and 4 of
Table ) shows the different vertical resolutions
used by the QBOi participating models (although note that a small number of
models share common vertical grids), along with the vertical resolution of
the set of 30 diagnostic pressure levels described in
Sect. .
Horizontal resolution. This is likely to have a significant impact on the
development and evolution of wave sources in the tropical troposphere, which
are important for forcing the QBO. Horizontal resolution may also affect the
propagation and breaking of large-scale Rossby waves propagating from the
extra-tropics, which are now known to affect the QBO
e.g.. Figure (see also column 2
of Table ) shows the horizontal resolution of each
model and how the differences in horizontal resolution compare to the
differences in stratospheric vertical resolution.
Time step. The increasing use of inherently stable advection schemes such as
semi-implicit semi-Lagrangian methods allows for longer time steps than are
possible, for example, with a more traditional Eulerian advection. While this can
lead to significant savings in computing requirements, particularly at higher
spatial resolution, an adverse effect is the filtering or damping of high-frequency equatorial waves e.g. that can potentially
make a significant contribution to the QBO momentum budget. See column 5 of
Table for the different dynamical time steps used
by the participating models.
Dynamical core. In a set of idealized experiments
demonstrated that spontaneous generation of QBO-like behaviour in general
circulation models was sensitive to the dynamical core chosen. This involved
both the choice of numerical advection scheme (including any associated
numerical diffusion) and the dissipation mechanisms. As well as impacting
wave generation, propagation, and dissipation mechanisms, the choices can
impact the simulation of the Brewer–Dobson circulation
, in particular its tropical upwelling component which
opposes the descending QBO cycles in the standard paradigm .
The different advection schemes used by the QBOi models are given in column 2
of Table . In the equatorial stratosphere most of the
unresolved mechanical (e.g. GWD; see below) and thermal (e.g. radiative
heating and cooling) dissipation in these models is from complex physical
parameterizations though many of the models also include some explicit
diffusion and a “sponge layer” to prevent spurious reflections from
the upper boundary. A condensed summary of the diffusion and sponge layer
information for the QBOi models is also given in Table .
For more details see the references given in column 6 of
Table .
Parameterized sub-grid-scale waves (non-orographic gravity waves). A very
significant development in models that has led to increased success in
simulating QBO-like variability has been the introduction of non-orographic
GWD parameterizations. Early schemes focused on parameterizing the (vertical)
propagation and dissipation of sub-grid-scale waves from spatially and
temporally fixed sources, while more recent developments have also included
parameterized sources e.g.. Broadly speaking there have been two approaches to parameterizing
the propagation and dissipation. The first, followed by and , aims to represent a broad spectrum of
unresolved gravity waves generated by a variety of sources, while the
alternative method is to represent the wave spectrum by a finite number or
collection of monochromatic waves such as described by or
. All models or model versions participating in QBOi, with
the exception of MIROC-AGCM-LL, include at least one parameterization of
non-orographic GWD, with the superscripts a or
b in the second column of
Table indicating, respectively, whether the spectrum
or collection of monochromatic waves method is used. A comparison of how the
different schemes attenuate parameterized eastward and westward momentum
fluxes of non-orographic gravity waves propagating upward through typical
wind profiles with opposite phases of the QBO is shown in
Fig. and described in detail in
Sect. .
Five of the 17 models (60LCAM5, CESM1(WACCM5-110L) HadGEM2-AC, LMDz6, and
UMGA7gws) have extended their non-orographic GWD parameterizations to include
parameterized gravity wave sources
A
“source parameterization” denotes a gravity wave source that is coupled
with other physical fields in the model, such as precipitation or deep
convective heating, and therefore varies temporally and spatially. In
contrast, “fixed” gravity wave sources are not coupled to other physical
fields. Fixed sources are often constant in time, although this category
could also include sources that have a prescribed temporal variation (e.g.
seasonal cycle) or are stochastic.
. References giving details of these
extended parameterizations are listed in column 3 of
Table . In most cases this has simply involved
replacing an ersatz “fixed” source with one that is more physically based,
although for the LMDz6 model the previously used Hines scheme was replaced
with a new GWD parameterization . There are two pairs of
models that are identical except for their gravity wave source being
fixed or parameterized: UMGA7–UMGA7gws and HadGEM2-A–HadGEM-AC. Hence it will be
possible to assess the impact that these model developments have on the simulation
of the QBO and how it responds to changes in climate forcings, at least for a
small subset of the participating models.
Convection. An important source of equatorial waves in the models is
convection and its associated diabatic heating.
Gravity wave source parameterizations also typically couple the
generation of parameterized GWD to parameters obtained from the convection
schemes such as the precipitation e.g.. The different
convection schemes used by the participating models are listed in column 4 of
Table for easy comparison.
Ozone. Although differences in ozone climatologies can potentially impact
simulated QBOs e.g., precise specifications for the
ozone forcing were not included in the experiment descriptions
(Sect. ; Appendix ) to
allow for the inclusion of models with prognostic ozone and also to keep the
experiment specifications as simple as possible. Therefore for those models
without ozone chemistry (see column 5 of Table )
there are some variations among the ozone climatologies that have been
prescribed. Figure illustrates these variations in the
tropics for the ozone climatologies used in the QBOi experiments, except for
MPI-ESM-MR and UMGC2 which provided existing hindcast results for Experiment 5a. Any sensitivity of the simulated QBOs to these variations in the ozone
climatology is, however, not considered critical for phase 1 of the QBOi
analysis, as each model was tuned to give its “best” QBO with its
prescribed ozone. On the other hand, it is important for the analysis that
for a particular model the same ozone was used across all the
experiments performed with that model (see Sect. ).
Sensitivity of the QBO to ozone is expected to be considered in the next
phases of QBOi (see Sect. ).
Offline comparison of non-orographic gravity wave drag schemes used in the participating models
(a) Vertical profiles in the tropics of the climatological
ozone concentration prescribed in QBOi experiments for models that do not
include ozone chemistry (as indicated in Table ) and
excluding MPI-ESM-MR and UMGC2, which uploaded existing hindcast data for
Experiment 5a. Each vertical profile is an average over the
5∘ S–5∘ N latitude band, zonal mean, and annual mean. (b)
As (a), but showing differences from a reference profile so that
inter-model variations are more clearly visible. The reference profile is the
1988–2007 climatology of the SPARC ozone referred to in
Appendix (item A1). The period 1988–2007 is the
same as that recommended for Experiment 2 for the SST and sea ice
climatologies (Appendix , item A2).
As noted above, non-orographic GWD parameterizations have been important for
the generation of a QBO in many climate models. Only MIROC_AGCM-LL does not
use parameterized GWD for QBOi (see Table ). The
non-orographic GWD parameterization schemes used in all the other QBOi
models, except for 60LCAM5 and CESM1(WACCM5-110L), are compared by performing
offline calculations for prescribed equatorial wind and temperature profiles
(see Appendix for details and why it was not
possible to include results for 60LCAM5 and CESM1(WACCM5-110L), which use the
same parameterization based on ). The 1 May 1993 and
1 November 2005 start dates for Experiment 5
(Fig. ) are used since they have oppositely
phased QBOs. Three experiments are performed. The first two use a prescribed
amount of momentum flux (MF) at a launch height
For the models with
parameterized gravity wave sources, this “launch height” is instead a
reference height at which the offline scheme are tuned to have the specified
properties; see Appendix for further details.
of
100 hPa, namely 1 and 10 mPa. Inter-model differences in GWD in these two
experiments arise solely from differences in the phase speed spectrum at the
launch height and the non-linear dissipation mechanism inherent in the schemes
(e.g. Hines' Doppler spreading or Warner and McIntyre's imposed saturated
spectrum). The purpose of the 10 mPa experiment is to see how linearly the
MF (and GWD) scales with the MF at 100 hPa in comparison with the 1 mPa
experiment. The third experiment uses the models' own launch heights and
amplitudes; hence this experiment most closely matches the set-up used in the
QBOi simulations. For all three experiments the GWD is computed at each
longitude and the results are zonally averaged.
Vertical profiles of zonal mean non-orographic GWD computed using
the parameterization schemes used by the different models. The offline
calculations are performed using ERA-Interim equatorial zonal and meridional
winds and temperatures for 1 May 1993 (a, b, c) and 1 November 2005 (d, e, f). The
middle panels show results for the case in which the momentum flux is set to
1 mPa at 100 hPa (≈16 km). The right panels show results for the
case in which the models' own launch amplitudes and launch heights are used.
Note that the results in the right-hand panel for MRI-ESM2 and UMGA7gws have
been multiplied by 0.1 and 0.6, respectively, and the GWD profiles plotted
using dotted lines (see Appendix ). The labels in
parentheses to the right of the model names denote the type of GWD scheme:
“F” or “P” for fixed or parameterized sources;
“H” for Hines, “WM” for Warner–McIntyre, or “L” for
for the type of dissipation used. Note that “WM” here
includes both the and schemes
(Table ), which are both implementations
of the framework for gravity wave parameterization.
The insets show the parameters of Gaussian fits,
Aexp[-((z-B)/C)2], to the zonal mean GWD profiles. The peaks
of the Gaussians (A, m s-1 per day, horizontal axes) and their
heights (B, km, vertical axes) are denoted by the filled circles.
The e-folding widths of the Gaussians (C, km) are given by the
vertical bars.
See text and Appendix for more details.
Vertical profiles of zonal mean GWD for the 1 mPa experiment are
shown in Fig. b and e. Results
for the 10 mPa experiment (not shown) are quite similar to
the 1 mPa results but are larger by a factor of 10, confirming
that to a good first approximation the GWD at these heights scales
linearly with the MF at 100 hPa. This is perhaps not too surprising
given that critical level absorption by the background winds, as
opposed to non-linear dissipation resulting from the exponential
growth with height of the gravity wave amplitudes, is the primary
cause of the momentum flux deposition in these highly sheared wind
profiles. The results of the third experiment are shown in Fig. 7c and f. Compared to the 1 mPa results, these show much more
inter-model spread. Since the source specifications used in this
experiment are the ones that produce each model's best QBO, the larger
inter-model spread in the third experiment is a reflection of model-dependent biases in, for instance,
the mean winds and temperatures and
resolved waves that must be overcome by tuning the gravity wave
sources. The GWD profiles between 20 and 40 km are approximately
Gaussian in form and can be simplified by fitting the zonal mean GWD to
a function of the form Aexp[-((z-B)/C)2]. The three fit parameters
are shown in the insets in Fig. 7b–c and e–f. The increase in
inter-model spread of the maximum GWD (fit parameter A) in the
experiment using the models' launch amplitudes and heights is more
readily seen. As observed (not simulated) precipitation is used in the
offline calculations for two of the models using parameterized gravity
wave sources (LMDz6 and UMGA7gws), the results in Fig. c and f may not accurately reflect what the models
themselves would
produce. Hence the parameterized-source and fixed-source results are not entirely comparable. A case in point is the
rather large difference in the peak GWD in the UMGA7 (fixed source)
and UMGA7gws (parameterized source) results; for this reason the
UMGa7gws results have been scaled to fit on the plot. Also note in
the 1 mPa experiment that the GWD peaks are wider in the vertical and
weaker for the models that use Hines than for the others. This is
consistent with the vertical smoothing of the momentum fluxes that is
conventionally applied in the Hines scheme before the GWD is computed.
The differences in the 1 mPa Hines results are a consequence of the
different amount of smoothing used by the different models; if the
smoothing is removed from the offline calculation, the 1 mPa Hines
results for the different models are identical.
In summary, the offline comparison shows that most of the
inter-model differences in the parameterized GWD in the
equatorial stratosphere arise from the differences in their
launch height and launch amplitude, not from differences in the
wave dissipation mechanism and the shape of the assumed launch spectrum.
Closing remarks and future plans
The QBO is arguably the most conspicuous and regular mode of variability
observed anywhere in the atmosphere that is not directly related to either
the annual or diurnal cycles. At a fundamental level and for current
conditions, it can be considered to be purely an atmospheric dynamical mode
of variability, despite possible external influences from variability in the
oceans, the solar cycle, or changes in atmospheric composition. Therefore the
primary goals of phase 1 of QBOi are achievable using atmosphere-only global
models that are computationally relatively inexpensive to run. To date
(January 2018) output from 17 models and/or model versions (Table 5) has been
uploaded or is planned for uploading to the shared database.
The goals of phase 1 of QBOi are to
compare, for present-day conditions, the accuracy of the morphology
of the simulated QBOs across models and relate this to differences
between models in the representation of the forcing mechanisms (e.g.
terms contributing to the zonal-mean zonal momentum equation) and
other model properties such as the resolution and sources of waves;
compare how the morphology of the simulated QBOs and QBO forcing
mechanisms respond to climate change (i.e. a doubling and quadrupling
of CO2 amounts) and identify which aspects of these responses are
robust; and
compare QBO predictive skill between models and its dependence on
the QBO's initialized phase, the underlying state of the atmosphere,
and/or properties of the individual models (e.g. why was there an
absence of skill in predicting the disruption of the QBO in 2016?).
Phase 1 of QBOi therefore addresses the challenges associated with
modelling, predicting the evolution of, and projecting long-term
changes in the QBO. Results from planned studies are expected to provide
information
on requirements for future model development leading to more accurate
representations of the QBO and its variability in the individual
models and across the multi-model ensemble. Benefits, however, are
likely to extend well beyond this and range from potential enhancements
in skill in seasonal to decadal predictions resulting from concomitant
improvements in QBO extra-tropical dynamical teleconnections, to better
capabilities for assessing the consequences of geoengineering proposals
involving the injection of aerosol into the equatorial stratosphere
where its redistribution away from the tropics is likely to be
significantly influenced by the QBO.
Beyond phase 1, QBOi is expected to focus more on QBO extra-tropical
dynamical teleconnections and couplings to other aspects of the
climate system. In this respect QBOi again differs from
multi-model activities like CMIP and CCMI that are largely policy-driven
and hence place considerable emphasis on continually updating
projections using the latest generation of models.
Instead the developing consensus in the QBOi community, which has
emerged primarily from the September 2016 QBO workshop
seefor a workshop summary, is to build on the
experiments described in this paper, though of course results from
phase 1 studies are expected to feed through into improving the
representation of the QBO in the next generation of models.
Some new coordinated studies that have been proposed for future
endorsement by QBOi include
increasing the ensemble size of Experiment 1 (“AMIP”) to examine
the robustness across models of possible synchronization between ENSO
events and the QBO e.g. and other ENSO
influences on the QBO e.g.;
extending Experiment 2 (present-day time slice) to increase the sample
size to examine QBO teleconnection robustness in an idealized framework
in which there is no other externally forced variability, apart from the
annual and diurnal cycles;
repeating Experiment 2 (present-day time slice) with idealized perpetual El
Niño–La Niña SST anomalies to examine the interaction of ENSO and QBO
teleconnections;
empirically separating the effects of stratospheric and tropospheric climate
change on the QBO by modifying Experiments 3 and 4 (future time slice) such
that the increases in CO2 amount (∼ forcings stratospheric climate
change only) and SSTs (∼ forcing tropospheric climate change only) are
applied separately;
extending Experiment 5–5a (retrospective hindcasts) to examine the
2016 QBO disruption and its predictability; and
examining the impact of ozone on the QBO either through prescribed
ozone perturbations or through ozone feedbacks for models that
can rerun with and without ozone chemistry.
The above list is by no means exhaustive and other possible
extensions of the research plans for QBOi include more idealized
studies comparing simulations using only “dynamical cores”
e.g. or perhaps simulations in which the QBO is
artificially removed (e.g. by turning off the non-orographic
GWD parameterization in the tropics). However, in line with current
QBOi practices, details of any new coordinated studies will again be
formulated through community discussion at forthcoming QBOi workshops
and will depend on the outcomes of the phase 1 studies.
For information on the code availability for the
individual models considered in this paper see the appropriate references
given in Table . Details of the QBOi data repository and
how to access it are provided in the Supplement.
Experiment 1 is based on the CMIP5 experiment 3.3 alternatively referred to as the
“Atmospheric Model Intercomparison Project (AMIP)” experiment
. It is a one- to three-member ensemble of 30-year simulations using
observed SSTs and sea ice amounts from 1 January 1979 to 28 February 2009.
These can be obtained from
http://www-pcmdi.llnl.gov/projects/amip/AMIP2EXPDSN/BCS/amipbc_dwnld.php.
The corresponding external forcings for the CMIP5 AMIP experiment (e.g.
radiative trace gas concentrations, aerosol distributions, solar irradiance,
and appropriate forcings from explosive volcanoes) can be found here:
http://cmip-pcmdi.llnl.gov/cmip5/forcing.html#amip apart from ozone
which, for high-top models, can be obtained from :
10.5281/zenodo.1035142.
These ozone data
are identical to those which previously could be obtained from
https://groups.physics.ox.ac.uk/climate/osprey/QBOi_O3/.
Initial conditions are not prescribed and it is left to individual
groups to use whatever is appropriate for their model and to include
any spin-up if this is considered necessary.
Experiment 2 – 1×CO2
Experiment 2 is similar to Experiment 1 but with a repeated annual
cycle for the SSTs and sea ice amounts plus all the other forcings
(i.e. there is no inter-annual variability or any secular changes
in the forcings). It can either be a one- to three-member ensemble of 30-year
simulations or preferably a single 100-year (or longer)
simulation. The long single integration has the additional potential
of providing information on very low-frequency variations.
Ideally the external annual cycle forcings should be 30-year climatologies
based on Experiment 1, although as these are generally not readily available,
a suitable alternative is to apply annually repeating forcings based on the
2002 CMIP5 forcings. The year 2002 is well removed from any explosive
volcanic eruptions and the ENSO and Pacific Decadal Oscillation (PDO) are
both in their neutral phases and hence conditions in this year can be
considered as a useful proxy for the multi-year mean for most quantities.
However, 2002 ozone amounts are likely to be strongly perturbed because of the
Southern Hemisphere sudden stratospheric warning e.g.
and for ozone a 2-D climatological field representative of the 1990s is
preferable. For SSTs and sea ice amounts CMIP5 1988–2007 climatologies are
available from
http://www-pcmdi.llnl.gov/projects/amip/AMIP2EXPDSN/BCS/amipbc_dwnld.php.
As Experiment 2 is the control for Experiments 3 and 4 (2×CO2
and 4×CO2, respectively) the average CO2 amount for 2002
should be used as the baseline 1×CO2 amount.
Although the use of different length climatologies for different
forcings is not ideal and does not provide direct comparison to
the 30-year period of Experiment 1, the observed dependence of the
QBO on a changing climate through this period appears to be negligible.
Thus for QBOi the benefits of the simpler experimental set-up is
considered to far outweigh any possible disadvantages. Nonetheless it
important to emphasize that the same idealized set of climatologies
and forcings are to be used throughout Experiments 2–4, apart
from the changes to the CO2 amounts and SSTs described below.
As with Experiment 1, atmospheric initial conditions are not prescribed.
Experiments 3 – 2×CO2, and
4 – 4×CO2
Experiments 3 and 4 are the same as Experiment 2 but for
2×CO2 and 4×CO2 climates, respectively. Again
these can either be a one- to three-member ensemble of 30-year simulations or
preferably a single 100-year simulation after allowing for a
suitable spin-up to the new climate (without a coupled ocean this is expected
to be fairly rapid though for the 4×CO2 experiment this can be of
the order or 5 years). Compared to the amount specified for Experiment 2 the
CO2 concentration should either be doubled (Experiment 3) or quadrupled
(Experiment 4) with a corresponding idealized adjustment made to the SSTs of
a spatially uniform perturbation of +2 K for 2×CO2 and +4 K
for 4×CO2. Sea ice amounts should be kept the same as in
Experiment 2.
All other forcings in these two experiments should be exactly
the same as in Experiment 2 including the amounts of all radiatively
active greenhouse gases other than CO2. If ozone is prescribed
(i.e. if the model does not have interactive chemistry) then this too
should be exactly the same as in Experiment 2. Alternatively if the
model does have interactive chemistry then the source gases and/or
emissions should be kept exactly the same as in Experiment 2. This
idealized set-up for Experiments 3 and 4 is appropriate as these are
sensitivity experiments and not attempts to predict specific periods
in the future.
As with Experiment 2 atmospheric initial conditions are not prescribed,
but note the need to allow for spin-up to the new climates.
Experiment 5 – QBO hindcasts
These are atmosphere-only experiments initialized from reanalysis data and
providing multiple ensembles of short integrations from a relatively
large set of start dates sampling different phases of the QBO. The
prescribed start dates (i.e. atmospheric initial conditions) are
1 May and 1 November for the years 1993–2007 (i.e. 15
years with a total 30 start dates). The duration of each hindcast
should be at least 6 months but preferably 9–12 months.
As with Experiment 1 the boundary conditions and external forcings should be
the same as those specified for the CMIP5 AMIP experiment .
CMIP5 inter-annually varying sea ice and SSTs can be obtained from
http://www-pcmdi.llnl.gov/projects/amip/AMIP2EXPDSN/BCS/amipbc_dwnld.php,
while the CMIP5 external forcings for radiative trace gas concentrations,
aerosols, solar, explosive volcanoes, etc., can be obtained from
http://cmip-pcmdi.llnl.gov/cmip5/forcing.html#amip.
Ozone forcing
datasets appropriate for use in high-top models are available from
: https://doi.org/10.5281/zenodo.1035142.
Initial data
for the hindcasts should be taken from the ERA-Interim reanalysis
, which can be downloaded from
http://apps.ecmwf.int/datasets.
Registration is required; if downloading many start dates from this
site, it may be easier to use the “batch access” method described on
the site, although interactive download of each date is also possible.
Data are available on either standard pressure levels or original model
levels and in either grib or netCDF formats. The ensemble is expected
to be generated by perturbing the initial conditions by a small anomaly,
which requires no more than changing the bit pattern of the simulation. For
some models this is possible through stochastic physics; however, each
group should use an ensemble generation method that is most appropriate
to their model and that is most readily available to them.
Experiment 5a – QBO forecasts
This experiment is as Experiment 5, but using a coupled ocean–atmosphere
model and predicting the SST instead of specifying observed values. External
forcings should also be fixed at the initial start time so as not to use
future information. This is then a true forecast experiment for the QBO and
can be compared with the results of Experiment 5. Some groups may have already
performed these hindcasts as part of their operational seasonal
forecasts but note that for QBOi purposes it is important that the majority
of the diagnostics discussed in Sect. are available
for a full comparison to Experiment 5 results.
This appendix provides details about the offline GWD calculations shown in
Fig. . The background equatorial winds and temperatures are
from a single day (daily mean) of ERA-Interim data on a 1∘ longitude
grid and on pressure levels at the ECMWF model-level resolution.
For models that use “fixed” gravity wave sources (e.g. AGCM3-CMAM),
the calculations are straightforward and simply involve computing the
GWD above the launch height. Since these models all use a horizontally
isotropic gravity wave source, the MF in a single azimuth is set to
either 1 or 10 mPa for the first two experiments. All fixed-source
calculations are done using offline versions of the ,
, and non-orographic GWD
schemes using each model's parameter settings. Results for the third
offline experiment, in which the models' own source amplitudes (i.e.
momentum flux for Scinocca, root mean square (RMS) winds for Hines)
and launch heights are used, are validated by comparing to results
from QBOi Experiment 5 for models that provided daily mean GWD. With
the exception of one model, the agreement is reasonably good, which is
all that can be expected given that the resolution of the models
differs from that used in the offline calculations. For MRI-ESM2 the
offline results for the third experiment are 10 times larger than
the Experiment 5 results and have been scaled in
Fig. c and f. The reason for this large discrepancy is
unknown. For models that tie their non-orographic gravity wave sources
to parameterized processes in the troposphere (referred to in the
Fig. caption as parameterized sources), the
calculations are more involved.
For the models that were able to perform the offline calculations for
parameterized-source schemes (LMDz6, UMGA7gws, and HadGEM2-AC) the procedure
was as follows. For LMDz6, daily precipitation observations were used to
generate an ensemble of monochromatic waves. The background winds and
temperatures are held fixed in time using either the 1 May or 1 November
data. A similar procedure is used for the other two models, except that the
launch momentum fluxes in HadGEM2-AC are obtained by sampling from the
Experiment 1 result for the month since the source parameterization in
HadGEM2-AC requires convective heating profiles not provided by observations.
As momentum flux is not prescribed for these models, tuning the gravity
wave parameters is required to achieve the desired MF at 100 hPa for the
first two experiments such that (|MFeast|+|MFwest|)/2= 1 or 10 mPa at 100 hPa. Due to time
constraints, the NCAR group, which also ties its GWD scheme to convection in
the 60LCAM5 and CESM1(WACCM5-110L) models, was unable to participate in this
comparison.
The Supplement related to this article is available online at https://doi.org/10.5194/gmd-11-1009-2018-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
The design of the experiments described here grew out of community
discussions at the first QBOi workshop in March 2015 in Victoria, Canada.
Funding for the workshop from the UK Natural Environment Research Council
(NE/M005828/1), the World Climate Research Programme (WCRP),
Stratosphere–troposphere Processes And their Role in Climate (SPARC)
activity,
and the Canadian Centre for Climate Modelling and Analysis is gratefully
acknowledged. We further acknowledge the scientific guidance of the WCRP for
helping motivate this work, coordinated under the framework of the SPARC QBO
initiative (QBOi) led by James Anstey, Neal Butchart, Kevin Hamilton, and Scott Osprey. The Centre for Environmental Data
Analysis (CEDA) have very kindly offered to host the QBOi data archive. Neal Butchart and Adam Scaife were supported by the Joint UK BEIS/Defra Met Office Hadley Centre
Climate Programme (GA01101). Scott Osprey and Lesley Gray were supported by NERC projects NE/M005828/1
and NE/P006779/1. Shingo Watanabe and Yoshio Kawatani used the Earth simulator for QBOi simulations and
were supported by the SOUSEI programme, MEXT Japan, and the Japan Science and
Technology Agency (JST) as part of the Belmont Forum. Yoshio Kawatani was supported by
Grant-in-Aid for Scientific Research B (26287117), joint international
research (15KK0178) from the Japan Society for the Promotion of Science, and
the Environment Research and Technology Development Fund (2-1503) of the
Ministry of the Environment, Japan. Francois Lott and Scott Osprey were supported by the
ANR/JPI-Climate/Belmont Forum project GOTHAM (ANR-15-JCLI-0004-01). Federico Serva was
supported by the European Commission under grant
StratoClim-603557-FP7-ENV.2013.6.1-2, with computing resources for the
ECHAM5sh simulations provided by an ECMWF special project. Young-Ha Kim was supported
by the Basic Science Research Program through the National Research Foundation of
Korea funded by the Ministry of Science, ICT & Future Planning
(NRF-2015R1C1A1A02036449). Holger Pohlmann was supported by the German Federal Ministry
for Education and Research (BMBF) project MiKlip (FKZ 01LP1519A) and thanks
Elisa Manzini for providing additional information on the MPI model.
BSC
contribution is supported by the Spanish MINECO-funded DANAE project
(CGL2015-68342-R) and Red Española de Supercomputación (RES project
AECT-2017-3-0015).Edited by: Paul Ullrich
Reviewed by: three anonymous referees
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