Introduction
Changes in the precipitation and atmospheric circulation of the regional
monsoons are of great scientific and societal importance owing to their
impacts on more than two-thirds of the world's population. Prediction of
changes to monsoon rainfall in the coming decades is of great societal
concern and vital for infrastructure planning, water resource management,
and sustainable agricultural and economic development, often in less
developed regions.
Global monsoon domain and its local components, indicating by the
differences of 850 hPa wind and precipitation between the June–July–August
and December–January–February mean, modified from Wang and Ding (2008).
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The dominant monsoon systems defined by precipitation characteristics include
the Asian, Australian, northern and southern African, the North American and
the South American monsoons (Wang, 1994; Wang and Ding, 2008; Fig. 1). Each
system generally has its own unique characteristics in terms of the
evolution, variability and impacts due to its indigenous land–sea
configuration and the particular atmosphere–ocean–land interaction involved.
At the same time, the regional monsoons have the fundamental
driving factors of temperature and pressure gradients in common, and they are bounded
by the global divergent circulation necessitated by mass conservation as they
evolve through the season (Trenberth et al., 2000). The global monsoon
represents the dominant mode of the annual variation of precipitation and
circulation in the global tropics and subtropics (Wang and Ding, 2008) and as
such, the global monsoon is a defining feature of Earth's climate. On the
annual timescale, the global monsoon is a planetary scale circulation system
with a seasonal reversal of the three-dimensional monsoon circulation that is
accompanied by the migration of the monsoon rainfall zones. However, it
remains debatable to what extent and at which timescales the global monsoon
– defined as the regional monsoons acting together – can be viewed as a
major mode of climate variability (Wang et al., 2014). To facilitate the
discussion, we use “global monsoon” to regard all the monsoon domains as a
whole and a single phenomenon to highlight the integrated role of monsoons in
global hydrological cycle, whereas we use “global monsoons” to highlight
the regional features of different monsoon domains around the globe.
To what extent can internal feedback processes in the climate system drive
the interannual variations of global monsoon precipitation? Wang et
al. (2012) showed that from one monsoon year (defined as May to the next
April) to the next, most continental monsoon regions, separated by vast areas
of arid trade winds and deserts, vary in a cohesive manner driven by the El
Niño–Southern Oscillation (ENSO). On decadal timescales, numerous
studies have investigated the linkage between regional monsoons and other
major modes of climate variability. For instance, the Australian summer
monsoon was linked to the Interdecadal Pacific Oscillation (IPO; Power et
al., 1999); the Indian summer monsoon precipitation
has a correlation with the North Atlantic Oscillation (NAO) (Goswami et al.,
2006) and the IPO (Meehl and Hu, 2006); the East Asian summer monsoon is
related to the Atlantic Multidecadal Oscillation (AMO; Enfield et al., 2001;
Lu et al., 2006) and the Pacific Decadal Oscillation (PDO; Mantua and Hare,
2002; Li et al., 2010; Qian and Zhou, 2014; Zhou et al., 2013); the
variability of the west African and North American monsoons is related to the
AMO (Sutton and Hodson, 2005; Zhang and Delworth, 2006; Gaetani and Mohino,
2013); and the African monsoon system is sensitive to inter-hemispheric sea surface temperature (SST)
variability in the Atlantic (Folland et al., 1986; Hoerling et al., 2006).
Many decadal and interdecadal variations of regional monsoons have been
identified, with differing periodicity and phase change points (Yim et al.,
2014; Chen and Zhou, 2014; Lin et al., 2014). While these concepts can be
collated and simplified by considering processes controlling the position of
the zonal mean intertropical convergence zone (ITCZ) (Schneider et al., 2014), a coherent global structure and
the underlying causes of global monsoon interdecadal variability have yet to
be widely studied.
The combination of changes in monsoon area and rainfall intensity has led to
an overall weakening trend of global land monsoon rainfall since the 1950s
(Wang and Ding, 2006; Zhou et al., 2008a). This decreasing tendency is
dominated by the African and South Asian monsoons, as shown by the
significant decreasing tendencies of both rainfall intensity and monsoon
coverage area (Zhou et al., 2008b). Beginning in the 1980s, however, the
Northern Hemisphere global monsoon precipitation has had an upward trend
(Wang et al., 2012). These studies of the trends in relatively short
precipitation records have not been able to confirm whether these trends are
part of longer-period fluctuations. Recently, Wang et al. (2013) studied
coherent interdecadal variations of the Northern Hemisphere summer monsoon
(NHSM) by using the NHSM circulation index (defined by the vertical shear of
zonal winds between 850 and 200 hPa averaged in 0–20∘ N, 120∘ W–120∘ E). The NHSM circulation
index is highly correlated with the NHSM rainfall intensity over the modern
record (r=0.85 for 1979–2011). They demonstrated that the NHSM circulation
has experienced large-amplitude multidecadal fluctuations since 1871,
primarily attributed to a mega-ENSO (a leading mode of
interannual-to-interdecadal variation of global sea surface temperature) and
the AMO. Only about one-third of the recent increasing trend in the NHSM
rainfall since 1979, when measured across the whole northern hemisphere, has
been attributed to anthropogenic warming.
How forcing agents, including both of the anthropogenic and natural, impact
global monsoons changes is another important but tough question. Dynamical
and thermodynamical changes of monsoon rainfall could cancel each other to
some extent under greenhouse gases (GHGs) forcing (Cherchi et al., 2011; Endo
and Kitoh, 2014; Chen and Zhou, 2015). However, the relative contributions of
these two processes to observed global monsoon rainfall changes due to
anthropogenic GHG forcing are still unknown. The interaction of aerosol
forcing with monsoon dynamics may alter the redistribution of energy in the
atmosphere and at the Earth's surface, thereby changing the monsoon-related
water cycle and climate (Lau et al., 2008). Aerosols may reduce surface solar
insolation, thus weakening the land–ocean thermal contrast and modifying the
formation and development of monsoons. Many mechanisms have been proposed in
the past 2 decades regarding the impact of aerosols on monsoon circulation
and precipitation. These mechanisms are complicated by the feedbacks with
large-scale moist environmental dynamics, so large uncertainties still remain
(Qian and Giorgi, 1999; Menon et al., 2002; Qian et al., 2006, 2009, 2011,
2015). The aerosol–monsoon interaction has attracted rapidly increasing
interest in the global climate modeling community. The relative importance of
aerosol forcing and global warming to observed trends of monsoon rainfall,
for example the decreasing of Indian rainfall in the recent decades, also
needs to be clarified (Bollassina et al., 2011; Annamalai et al., 2013; He et
al., 2016). Understanding the mechanisms of precipitation changes in the
global monsoon system and identifying the roles of natural and anthropogenic
forcing agents have been central topics of the monsoon research community
(Cook and Seager, 2013; Liu et al., 2013; Song et al., 2014; Polson et al.,
2014; Guo et al., 2015).
While all monsoons are associated with large-scale cross equatorial
overturning circulations, major differences in the characteristics of the
regional monsoons arise because of the different orography and underlying
surface as well as the external forcing. This is most apparent for the Asian
region, due to the Tibetan–Iranian Plateau, Himalayan mountains and strong
anthropogenic forcing from aerosol emissions and land-use change. The
highlands may act as a physical barrier that isolates the heat and moisture
south of the Himalaya and a high-level heat source (pump) that directly
drives the monsoon circulation through meridional thermal contrast (Yeh et
al., 1957; Flohn, 1957; Yeh and Wu, 1998; Yanai and Wu, 2006). However, the
relative role of the two effects deserve more discussion (Boos and Kuang,
2010, 2013; Wu et al., 2012; Qiu, 2013).
Climate models are useful tools in climate variability and climate change
studies. However, the performance of current state-of-the-art climate models
is very poor and needs to be greatly improved over the monsoon domains (Cook
et al., 2012; Kitoh et al., 2013; Wang et al., 2005; Zhou et al., 2009a;
Sperber et al., 2013; Song and Zhou, 2014a, b). As one of the endorsed model inter-comparison projects (MIPs)
in the sixth phase of the Coupled Model Inter-comparison Project (CMIP6)
(Eyring et al., 2016), the Global Monsoons Model Inter-comparison Project
(hereafter GMMIP) aims to improve our understanding of physical processes in
global monsoon systems by performing multi-model inter-comparisons,
ultimately to work towards better simulations of the mean state, interannual
variability and long-term changes of the global monsoons. The contributions
of internal variability (IPO and AMO) and external anthropogenic forcing to
the historical evolution of global monsoons in the 20th and 21st century will
also be addressed.
GMMIP aims to answer four primary scientific questions:
What are the relative contributions of internal processes and external
forcing that are driving the historical evolution of monsoons over the late
19th through early 21st centuries?
To what extent and how does atmosphere–ocean interaction contribute to
the interannual variability and reproducibility of monsoons?
How can high-resolution and associated improved model dynamics and
physics help to reliably simulate monsoon precipitation and its variability
and change?
What is the effect of the orography of the Himalaya–Tibetan Plateau on
the development and maintenance of the Asian monsoon? Similarly, what is the
impact of orography elsewhere on other regional monsoons?
By focusing on addressing these four questions, we expect to deepen our
understanding of the capability of models to reproduce the monsoon mean
state and its natural variability as well as the forced response to natural
and anthropogenic forcing, which ultimately will help to reduce model
uncertainty and improve the credibility of models in projecting future
changes in the monsoon. The coordinated experiments will also help advance
our physical understanding and prediction of monsoon changes.
Due to the uncertainties in physical parameterizations in current models,
particularly in convection schemes (Chen et al., 2010), the best way to
address the above questions is through a multi-model framework in order
capture the range of possible responses to forcing. The multi-model database
to be produced for CMIP6 (Eyring et al., 2016), in conjunction with the
GMMIP experiments will provide an opportunity for advancement of monsoon
modeling and understanding. GMMIP will also contribute to the Grand
Challenges of the World Climate Research Programme (WCRP) and address them
in the following way:
Water availability:
the water resources in global monsoon domains are greatly affected by the
anomalous activities of monsoons. The summer monsoons produce more than
80 % of the annual rainfall in some areas, e.g., in India, Africa and
Australia, and the percentage is more than 60 % averaged across all global
monsoon regions (Fig. 2). Understanding the mechanisms of monsoon
variability on interannual and longer timescales as posed by GMMIP will
lead to improvement of monsoon prediction and projection and provide useful
information for policymakers in water availability-related decision-making.
Climate extremes:
extreme events such as mega-droughts and flooding are frequent occurrences
in monsoon domains. GMMIP will allow the impact of changing lower boundary
forcing on the statistics of extreme events to be examined in a consistent
manner.
Clouds, circulation and climate sensitivity:
a reasonable simulation of monsoon circulation is a prerequisite for a
successful simulation of monsoon precipitation (e.g., Sperber et al., 2013).
At the same time, tropical precipitation is strongly dependent on
convection, with monsoon precipitation biases very sensitive to convective
parameterizations and therefore clouds. These parameterizations also lead to
large uncertainties in climate sensitivity (e.g., Stainforth et al., 2005).
By comparing the performance of climate models with relatively high and low
resolutions, and model simulations with and without air–sea interaction
processes, GMMIP will attempt to link monsoon precipitation simulation with
the fidelity of the large-scale circulation and the latest remote sensing
estimates of clouds.
Climatological percentage of summertime rainfall amount (JJAS in the
Northern Hemisphere and DJFM in the Southern Hemisphere) in annual
accumulation. Monsoon region is circled by red curves. GPCP data are used and
the time covers 1979–2014.
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Experimental protocol
Based on the priority level of proposed scientific questions, the main
experiments of GMMIP, which are summarized in Table 2, are divided into
tier-1, tier-2, and tier-3 of decreasing priority (Fig. 3). In order to diagnose internal variability, at least three members integrated
from different initial conditions are required for tier-1 and tier-2
experiments. Pending the availability of computer resources at
GMMIP-committed climate-modeling centers, realizations with more than three members are encouraged.
Experiment list of GMMIP.
EXP name
Integration time
Short description and purpose of the EXP design
Model type
Tier-1
amip-hist
1870–2014
Extended AMIP run that covers 1870–2014. All natural and anthropogenic historical forcings as used in CMIP6 historical simulation will be included. AGCM resolution as CMIP6 historical simulation. The HadISST data will be used. Minimum number of integrations is 3, more realizations are encouraged.
AGCM
Tier-2
hist-resIPO
1870–2014
Pacemaker historical run that includes all forcing as used in CMIP6 historical simulation, and the observational historical SST is restored in the tropical lobe of the IPO domain (20∘ S–20∘ N, 175∘ E–75∘ W); to understand the forcing of IPO-related tropical SST to global monsoon changes. How to restore the SST refers to Appendix A. Models resolutions as CMIP6 historical simulation. The HadISST data will be used. Minimum number of integrations is 3, more realizations are encouraged.
coupled general circulation model (CGCM) with SST restored to the model climatology plus ob- servational historical anomaly in the tropical lobe of IPO domain
hist-resAMO
1870–2014
Pacemaker historical run that includes all forcing as used in CMIP6 historical simulation, and the observational historical SST is restored in the AMO domain (0–70∘ N, 70–0∘ W); to understand the forcing of AMO-related SST to global monsoon changes. How to restore the SST refers to Appendix A. Models resolutions as CMIP6 historical simulation. The HadISST data will be used. Minimum number of integrations is 3, more realizations are encouraged.
CGCM with SST restored to the model climatology plus observational historical anomaly in the AMO domain
Tier-3
amip-TIP
1979–2014
The topography of the TIP is modified by setting surface elevations to 500 m; to understand the combined thermal and mechanical forcing of the TIP. Same model as DECK. Minimum number of integrations is 1. The topography above 500 m is set to 500 m in a polygon region. Coordinates of the polygon corners are as follows: longitude (from west to east), 25, 40, 50, 70, 90 and 180∘ E; latitude (from south to north), 5, 15, 20, 25, 35, 45 and 75∘ N. The reason to remove all the topography above 500 m over the Asian continent is to avoid any artificial forcings from the topography gradient when suddenly cut off at a certain height, and we also suppose the circulation response to the difference of topography between 0 and 500 m can be neglected in climate models with resolutions from 100 to 200 km. This experiment is also close to the no topography settings such as setting the topography to zero over whole Asian continent as far as possible.
AGCM
amip-TIP-nosh
1979–2014
Surface sensible heat released at the elevation above 500 m over the TIP is not allowed to heat the atmosphere; to compare of impact of removing thermal effects. Same model as DECK. Minimum number of integrations is 1. The sensible heating is removed on the topography where is above 500 m as in the same polygon region in amip-TIP; in these experiment, we have to artificially cut off the sensible heating region with a specific criterion. One practical method is set vertical temperature diffusion term to zero in the atmospheric thermodynamic equation at the bottom boundary layer (see Appendix B). There are obvious concerns over the energy conservation here, but because the suppression of heating is only in a fairly small limited area, one expects the energy balance to be compensated elsewhere.
AGCM
Continued.
EXP name
Integration time
Short description and purpose of the EXP design
Model type
Tier-3
amip-hld
1979–2014
The topography of the East African Highlands in Africa and Arabian Peninsula, Sierra Madre in N. America and Andes in S. America is modified by setting surface elevations to a certain height (500 m) in separate experiments. Same model as DECK. Minimum number of integrations is 1. See descriptions of amip-TIP for technical details and regions as outlined in Fig. 5. The East African Highlands is in a polygon region. Coordinates of the polygon is as follows: longitude (from west to east), 27 and 52∘ E; latitude (from south to north), 17∘ S, 20 and 25, 35∘ N. Sierra Madre domain is 120–90∘ W, 15–30∘ N. Andes domain is 90–60∘ W, 40∘ S–10∘ N.
AGCM
Tier-1: extended AMIP experiment
The tier-1 experiments are extended Atmospheric Model Intercomparison Project
(AMIP) runs from 1870 to 2014. This is the entry card for GMMIP.
All external forcings (solar, aerosol, GHGs, etc.) should be derived from
those used in the historical simulation of the CMIP6 fully coupled model.
This will allow for a direct comparison of the historical simulation and
extended AMIP run, to determine the importance of SST variability to long-
and short-term trends in the monsoon circulations and the associated
precipitation. The boundary conditions for sea-surface temperature and sea
ice are derived from a merged version of the Hadley Centre Sea Ice and Sea
Surface Temperature (HadISST) and Optimum Interpolation Sea Surface
Temperature (OISST) data sets (Hurrell et al., 2008), which can be downloaded
from the PCMDI
website.
Tier-2: decadal mode relaxation experiments
The tier-2 experiments are initialized from the “historical” run year 1870 and
integrated up to year 2014 with historical forcings. Additionally, the
variation in the tropical Pacific and North Atlantic SST are restored to the
observation in the “hist-resIPO” and “hist-resAMO” runs, respectively.
The tier-2 “hist-resIPO” (historical anthropogenic forcing plus restoring
IPO SST) run is a pacemaker-coupled historical climate simulation that
includes all forcings as in the CMIP6 historical experiment, but with SST
restored to the model climatology plus observed historical anomaly in the
tropical lobe of the IPO (Power et al.,
1999; Folland et al., 2002) domain (20∘ S–20∘ N,
175∘ E–75∘ W). This relaxation is applied with
weight = 1 in the inner box (15∘ S–15∘ N, 180–80∘ W) and linearly reduced to zero in the buffer zone (zonal and
meridional ranges are both 5∘) from the inner to outer box (Fig. 4a). There are several restoring methods to realize such “pacemaker”
simulations (see the Appendix A). To ensure stability during integration, we
recommend nudging to the specified SST described above with a 10-day timescale (see the Appendix A for technical details).
The restoring regions for tier-2 experiments
(a) hist-resIPO and (b) hist-resAMO.
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Similarly, the tier-2 “hist-resAMO” (historical anthropogenic forcing plus
restoring AMO SST) run is a pacemaker-coupled historical climate simulation
that includes all forcings but with the SST restored to the model
climatology plus observational historical anomaly in the AMO (Enfield et al., 2001; Trenberth and Shea,
2006) domain (0–70∘ N, 70–0∘ W).
The restoration is fully applied in the inner box (5–65∘ N, 65–5∘ W), and linearly reduced to
zero in the buffer zone (zonal and meridional ranges are both 5∘)
from the inner to outer box (Fig. 4b).
Tier-3: orographic perturbation experiments
The tier-3 experiments is generally the same as the “amip” run in the CMIP6
DECK covering 1979–2014 except that some key topographies or the air–land
sensible heat flux are modified. The aim of the “orographic perturbation”
is to understand quantitatively the regional response to the orographic
perturbation from both the thermal and dynamical aspects. The results will be
very helpful to understand the topography effect on the atmosphere and
associated physical processes locally and quantitatively, such as the
distribution, intensity and frequency changes in the precipitation over wide
monsoon regions. In the tier-3 “amip-TIP” run (viz. no Tibetan–Iranian
Plateau) run, following Boos and Kuang (2010,
2013) and Wu et al. (2007, 2012), the topography of the Tibetan–Iranian
Plateau (hereafter TIP; see Table 2 for detailed descriptions) in the model
is modified by leveling off the TIP to 500 m, with other surface properties
unchanged (Asia region in Fig. 5 and details seen in Appendix B).
Other settings of the integration are the same as the standard DECK AMIP run.
This experiment represents perturbations to both thermal and mechanical
forcing of the TIP with respect to the standard DECK AMIP run. In an ensemble
of experiments comprising the tier-3 “amip-hld” run (viz. no HighLanDs)
group, the topography of the East African Highlands in Africa (after Slingo
et al., 2005), Sierra Madre in North America and the Andes in South
America is modified by setting surface elevations to 500 m in those
respective regions (Fig. 5).
The sensible heat over the elevated topography is regarded as the main
driver of the behavior of the low level atmosphere and possibly also the
upper troposphere and lower stratosphere (Wu et al., 2016). To examine the
importance of elevated heating in monsoon from perspective of multi-model
comparison, in the tier-3 “amip-TIP-nosh”run (viz. Tibetan–Iranian
Plateau – no sensible heating), the surface sensible heat flux at elevations
above 500 m over the TIP is not allowed to heat the atmosphere; i.e., the
vertical temperature diffusion term in the atmospheric thermodynamic
equation at the bottom boundary layer is set to zero (Wu et al., 2012;
details seen in Appendix B). The atmospheric component will not see the
surface upward sensible heat flux (zero), whereas the land component is as
usual. Other settings of the integration are the same as the standard DECK
AMIP run. The differences between the standard DECK AMIP run and the
amip-TIP-nosh are considered to represent the removal of TIP thermal forcing
only and thus the circulation pattern of amip-TIP-nosh reflects the impacts
of mechanical forcing.
Experiment outputs
The recommended output variables are listed in Appendix C.
The orography regions specified for the tier-3 experiments for the
Asia region (comprising the Tibetan–Iranian Plateau and Himalayas), the East
African Highlands (adapted from Slingo et al., 2005), the Andes and
Sierra Madre. Within each marked region, orography would be capped at 500 m
height. Orographic data derived from a ∼ 30 km resolution (N512)
boundary field of the Met Office HadGEM3 model.
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Connection with DECK, historical simulation and endorsed MIPs
The tier-1 experiment of GMMIP, i.e., the extended AMIP, uses the same
resolution as in the DECK (Eyring et al., 2016). The amip-hist specifies
external forcings that are consistent with those from the same model's CMIP6
historical simulation over the 1870–2014 period. To comprehensively
investigate the proposed GMMIP scientific questions, such as the impact of
high resolution and roles of different forcing agents, the output from other
related MIPs will be used in the diagnostic analysis of GMMIP as described
below.
DECK and historical simulation
The pre-industrial control simulations from each modeling group's DECK
experiments will be used to study the relation between global monsoon and
IPO/AMO at decadal timescale. Comparing the control simulation (constant
forcing) with the GMMIP tier-2 decadal mode relaxation experiments in which
all historical forcings are added will then allow us to find which parts of
apparent decadal variations in the monsoons are caused by underlying SST,
and which are more forced by externally driven sources, such as volcanic
emissions. The CMIP6 historical simulations will also be used to examine the
response of the global monsoon to external forcings such as anthropogenic
GHG and aerosol emissions. The results of CMIP6 historical simulation will
be compared with those of hist-resIPO and hist-resAMO in tier-2 to identify
the relative contributions of external forcing and apparently internal modes
of variability (IPO/AMO).
DAMIP (Detection and Attribution MIP)
Several DAMIP experiments are useful to GMMIP. The histALL (enlarged
ensemble size of historical all-forcing runs in DECK), histNAT (historical
natural forcings-only run), histGHG (historical well-mixed GHG-only run),
and histAER (historical anthropogenic-aerosols-only run) experiments of
DAMIP will be used in the analysis of changes in global monsoons dating back
to 1870.
Analyzing combinations of the histALL, histNAT and histGHG ensembles will
allow us to understand the observed evolution of global monsoon
precipitation and circulation changes since 1870 in the context of
contributions from GHG, the other anthropogenic factors and natural forcings.
The contributions of these external forcings to global monsoon changes will
be compared to those from modes of internal variability such as the IPO and
AMO.
HighResMIP (High-Resolution MIP)
The tier-1 experiments of HighResMIP, which consist of AMIP runs with a
minimum horizontal resolution of 25–50 km, will be used to compare with
standard resolution control configurations and examine the added benefit, if
any, of high-resolution models in reproducing both the mean state and
year-to-year variability of global monsoons. It should be noted that the
boundary conditions (both of SST and sea ice) used to build the AMIP
experiments of HighResMIP is a new data set with daily time frequency
(Haarsma et al., 2016), which may make a differences when compared with
standard AMIP forced by monthly data sets.
The tier-2 experiments of HighResMIP, which are coupled runs consisting of
pairs of both historic runs and control runs using fixed 1950s forcing
including anthropogenic GHG concentrations and aerosol forcing, will be used
in the analysis of climatology and variability of global monsoons, which
aims to understand the role of air–sea interaction in modulating the
simulation skill of the monsoon mean state and year-to-year variability. The
anthropogenic aerosols are required to be prescribed in HighResMIP
experiments following a standard method in CMIP6 DECK (Haarsma et al.,
2016), rather than interactive aerosol processes embedded in atmosphere
general circulation models (AGCMs). Different ways to deal with aerosols
could lead to different aerosol distributions as well as aerosol forcings,
which should be taken in consideration when comparing with GMMIP
experiments.
VolMIP (Volcanic forcing MIP)
The tier-1 experiment of the short set of VolMIP simulations is designed to
create a large ensemble of short-term simulations of the 1991 Pinatubo
eruption, using the same volcanic forcing recommended for the CMIP6
historical simulation. It will be used in comparison with observations to
understand the global monsoon response to injection of stratospheric
aerosols over the tropics and to study impact mechanisms on global monsoon
precipitation and circulation changes. Via its ensemble design, VolMIP can
address the substantial uncertainty associated with the effects of volcanism
during the historical period.
DCPP (Decadal Climate Prediction Project)
The outputs of DCPP near-term climate prediction experiments will be used to
assess the skill of global monsoons in initialized decadal climate
prediction. The C-component of DCPP is similar to the tier-2 experiment of
GMMIP but focuses on a shorter time period starting from 1950 (Boer et al.,
2016). The outputs will be used to add to the ensemble size of pacemaker
experiments from GMMIP tier-2 during the 1950–2014 period.
CORDEX (international Coordinated Regional Downscaling
Experiment)
In the core framework of CORDEX phase 2 (CORDEX2 hereafter), a core set of
regional climate models (RCMs) downscales a core set of global climate models (GCMs) over all or most
CORDEX domains at 10–20 km resolutions (Gutowski et al., 2016). The
comparisons of CORDEX2 historical climate downscaling with the driving GCMs
historical simulations, will give insight into the importance of model
resolution and the added value of RCMs in the simulation of climatology and
variability of global monsoon, especially the global land monsoon. A
comparison of CORDEX2 evaluation framework experiments forced with daily mean
SST to HighResMIP tier-1 runs over global monsoon domains will provide
information on the similarities and differences of the added values derived
respectively from high-resolution global models and regional climate models.
Analysis plan
The analysis plan will focus on the scientific objectives of GMMIP. We list
the key scientific questions that we hope that the community will be able to
answer following the implementation of GMMIP below.
Understanding the changes of global monsoons since the 1870s
We will examine whether decadal and multi-decadal variability of local
monsoon systems and coherent changes of the global monsoon can be reproduced
in the amip-hist experiment. First, the skill of reproducing interannual
and interdecadal changes in the regional monsoons will be compared with
long-term observed records in local monsoon regions, such as using the
All-India Rainfall index from 1870 (Parthasarathy et al., 1994) and the
global land precipitation from 1901 from Climatic Research Units (CRU; Harris et al., 2014; Zhang and Zhou,
2011). The simulated monsoon circulation can be compared with two
twentieth-century reanalyses from the National Oceanic and Atmospheric Administration
(20CR) and European Centre for Medium-Range Weather Forecasts (ERA20C), which are also derived from AGCM simulations driven by
observational SST, where surface pressure (marine wind additionally used in
ERA20C) records are assimilated (Compo et al., 2011; Poli et al., 2016).
Second, the interannual variability of the monsoon systems has experienced
dramatic interdecadal variations during past 60 years (e.g., since the 1950s
to present; Wang and Ding 2006). The amip-hist results will be used to
explore whether similar modulations occurred during the past 150 years, and
what mechanisms are responsible for them.
Third, the contributions of apparently internal variability modes (IPO and
AMO) to global monsoon variability and the role of air–sea interaction will
be evaluated based on the hist-resIPO and hist-resAMO experiments of tier-2.
Combined with CMIP6 DECK and DAMIP experiments, the roles of external
forcing (GHG, aerosol, solar, etc.) and internal variability can be
quantified. The impact of tropical volcanic eruptions on the global monsoons
can be explored specifically by analyzing VolMIP. Current state-of-the-art
climate models still show bias in the simulation of monsoons (Sperber et al.,
2013). We acknowledge that attention should be paid to the model bias in the
analysis of model outputs, although multi-model ensemble/intercomparison
approach is a useful way to reduce the uncertainty related to model bias.
Effect of air–sea interaction on interannual variability of
precipitation in the global monsoons
Previous studies have noted that AGCM simulations with specified SST
generally have low skill in simulating the interannual variation of the
summer precipitation over global monsoon domains, especially the East
Asian–western North Pacific summer monsoon domain (Wang et al., 2005). It is
noted that in the real world the precipitation is negatively correlated with
underlying SST in the western North Pacific monsoon domain, which is not
reproduced by the AMIP runs (Wang et al., 2005). The deficiency of the AMIP
simulations can be partially attributed to the exclusion of air–sea
interactions (Song and Zhou, 2014b). Comparison between the tier-1 and
tier-2 experiments of GMMIP can provide information about how the air–sea
interactions influence the monsoon simulations on the interannual and
interdecadal timescales in different monsoon domains. However, mean state
tropical SST biases prevalent in coupled models are also known to affect the
accurate connection of monsoon interannual variability with teleconnected
drivers such as ENSO (Turner et al., 2005).
Measuring improvement in the global monsoons with high-resolution
modeling
Monsoon rainbands such as the Mei-yu–Baiu–Changma front usually have a
maximum width of about 200 km (Zhou et al., 2009b). Climate models with low
or moderate resolution are generally unable to realistically reproduce
mesoscale cloud clusters embedded in the rainbands, thus partly leading to
biases in the mean state, variability of monsoon precipitation and the
northward propagation of these rainbands. We will examine the performance of
high-resolution models in reproducing both the mean state and year-to-year
variability of global monsoons. High-resolution rain-gauge observations and
satellite precipitation products will be used to evaluate model performance.
Effects of large orographic terrain on the regional/global
monsoons
The influence of the large-scale orography on the Asian summer monsoon
includes both mechanical and thermal forcing. Various mechanisms have been
suggested concerning the topographic effects; however, an overarching
paradigm delineating the dominant factors determining these effects and the
strength of impacts needs further study. We will analyze the tier-3
experiments to provide a benchmark of current model behavior in simulating
the impact on the monsoon of the TIP (as well as
surrounding regions of significant orography; see Table 2 for detailed
descriptions) so as to stimulate further research on the thermodynamic and
dynamic influence of the TIP on the monsoon. In particular the relative
contributions of thermal and orographic mechanical forcing by the TIP on the
Asian monsoon will be addressed. We will extend the studies from the TIP to
other highlands including highlands in Africa, North America and South
America.
Aerosol–monsoon interaction
While aerosol–cloud interaction (ACI) effects are partially incorporated in
GCMs with various levels of complexity, the aerosol–radiation interaction
(ARI) effect, which is believed to have more explicit impact on land–sea
thermal contrast by reducing the surface solar insolation, is fully
incorporated in most of the CMIP6 models. To investigate the aerosol impacts on
monsoon climate including both local forcing and remote forcing effects, we
will examine the responses of climate models to natural (solar variability
and volcanic aerosols) and anthropogenic (GHGs and aerosols) forcings based
on DECK and DAMIP experiments. In particular, we will quantify and compare
the separate climatic response of natural vs. anthropogenic forcing, as well
as
aerosol vs. GHG forcing, over the global monsoon area (e.g., Song et al.,
2014). We will analyze how different forcings influence the general
circulation and precipitation characteristics, such as extreme events, shift
of precipitation spectrum, and diurnal cycle.