Introduction
Water vapour is one of the most important components of the atmosphere,
affecting global climate and weather patterns (Held and Soden, 2000). Among
current studies of the hydrological cycle, the identification of moisture
sources to the atmosphere is an important topic, because a better
understanding of these sources will benefit long-term forecasting, disaster
prevention, and allocation of water resources (Bosilovich and Schubert,
2002).
Source apportionment methods have been developed to identify atmospheric
moisture source regions. These methods generally can be divided into three
types, namely analytical models, isotopes, and numerical (Lagrangian and
Eulerian) atmospheric water tracers (AWTs) (Gimeno et al., 2012). In
addition, sensitivity experiments in numerical simulations, such as shutting
down water vapour flux at the lateral boundaries or surface evaporation (Chow
et al., 2008), are an approach to study the contributions of moisture from
diverse regions. Analytical models, widely used in earlier studies (Brubaker
et al., 1993; Burde and Zangvil, 2001; Eltahir and Bras, 1996; Savenije,
1995; Trenberth, 1999), are generally based on various simplifying
assumptions such as a well-mixed atmosphere. The stable isotopes of water,
HDO and H218O, can be used to investigate the water cycle. However,
water isotope data reflect a series of processes that occur simultaneously,
which makes it difficult to interpret isotope results for the water cycle
(Numaguti, 1999; Sodemann and Zubler, 2010). The Lagrangian method has become
a popular way to analyse the transport of moisture and moisture sources of
precipitation (Dirmeyer and Brubaker, 1999; Gustafsson et al., 2010; Sodemann
et al., 2008; Stohl and James, 2004; Stohl et al., 2008). However, Gimeno et
al. (2012) pointed out that the treatments of water vapour transport and
changes of atmospheric water vapour in the Lagrangian method are not based on
detailed physical equations. Sodemann and Zubler (2010) pointed out that a
strong bias exists in Lagrangian precipitation estimates because all cloud
processes are neglected. Sensitivity experiments generally contain
non-linearities, which may lead to changes in the dynamic and thermodynamic
structures of meteorological fields, suggesting that their results cannot be
used to directly diagnose moisture sources. In contrast, the Eulerian AWT
method has been developed based on detailed physical parameterisations in
atmospheric models, enabling a direct and exact tracking of the behaviour of
atmospheric water substances (Numaguti, 1999; Bosilovich, 2002).
The Eulerian AWT method was firstly developed by Joussaume et al. (1986) and
Koster et al. (1986) for global circulation models (GCMs). Later, this AWT
method was applied to diagnose regional water sources in GCMs. For example,
Numaguti (1999) identified the moisture sources of Eurasian precipitation,
and Bosilovich and Schubert (2002) diagnosed the moisture sources of
precipitation over North America and India. Bosilovich et al. (2003) studied
water sources of the large-scale North American monsoon, Bosilovich (2002)
investigated the vertical distribution of water vapour tracers over North
America, and Sodemann et al. (2009) used this method to study sources of
water vapour leading to a flood event in central
Europe using a mesoscale model.
Finally, Knoche and Kunstmann (2013) incorporated the AWT method into a
fifth-generation mesoscale model to study the transport of atmospheric
moisture in western Africa.
In summer, the Asian summer monsoon (ASM) brings large amounts of water
vapour to the East Asian (EA) continent, leading to a wet season and abundant
precipitation. Simmonds et al. (1999) pointed out that the dominant moisture
transport pathways during summer can be divided into three branches, namely
(i) south-westerly flow associated with the Indian summer monsoon,
(ii) southerly or south-easterly flow associated with the south-eastern Asian
monsoon, and (iii) the mid-latitude Westerlies. Correspondingly, these
pathways transport moisture from (i) the Bay of Bengal (BOB) and the Arabian
Sea (AS), (ii) the South China Sea (SCS) and the north-western Pacific (NWP),
and (iii) the mid-latitude regions. Simmonds et al. (1999) and Xu et
al. (2008) pointed out that the BOB to SCS are the main source regions for
rainfall over south-east China. Using the Lagrangian FLEXible
PARTicle (FLEXPART) dispersion
model (Stohl and James, 2004), Drumond et al. (2011) discovered that the
inland regions of China receive moisture mostly from western Asia, whereas
the East China Sea (ECS) and SCS are the main source regions for rainfall in
China's eastern and south-eastern coastal areas, and the AS and BOB are the
main source regions for southern and central China from April to September.
With the FLEXPART model, Baker et al. (2015) demonstrated that the Indian
Ocean is the primary source of moisture for East Asian summer monsoon (EASM)
rainfall. Using the same model, Chen et al. (2013) suggested that the ECS,
the SCS, the Indian peninsula and BOB, and the AS were the four major
moisture source regions for summer water vapour over the Yangtze River valley
(YRV) during 2004–2009. Chow et al. (2008) suggested that water vapour
supplied by the Indian summer monsoon contributed about 50 % to early
summer precipitation over China in 1998, and inferred that the SCS may act as
a pathway for water vapour transport affected by the Indian and Southeast
Asian summer monsoon. However, recently Wei et al. (2012), using a Lagrangian
model, showed that the major moisture transport pathways to the YRV are over
land and not over the ocean. Therefore, the dominant source regions of
moisture for summer rainfall over EA are still uncertain.
Baker et al. (2015) pointed out that the water vapour transport mechanisms
for precipitation over China during the ASM are still unquantified. Previous
studies have pointed out that analytical models need simplifying assumptions,
isotope data reflect more than just the water cycle, the Lagrangian methods
lack cloud processes, and sensitivity experiments contain non-linearities,
limiting diagnostic studies of moisture sources. On the other hand, the
Eulerian AWT method does not have these shortcomings and is an accurate way
to quantitatively determine water sources (Bosilovich, 2002). Therefore, in
this study, we aim at incorporating an Eulerian AWT approach into an advanced
global atmosphere model – the Community Atmosphere Model version 5.1
(CAM5.1) (Neale et al., 2012). Using this method, we address the following
questions. (1) What moisture source regions are most important for
precipitation and water vapour amounts over EA, including the YRV and southern
China (SCN)? (2) What is the role of the SCS for precipitation and water
vapour amount over EA during the EASM: a dominant source region or just a
pathway for water vapour transport from other source regions?
In this study, detailed descriptions of physical parameterisation schemes and
means of implementing the AWT mechanisms in CAM5.1 are given in Sect. 2.
Simulation results, including evaluation and discussion, are presented in
Sect. 3. Finally, summary and concluding remarks are presented in Sect. 4.
Model and methods
The CAM5.1, released by the US National Center for Atmospheric Research, is
the atmospheric component of the Community Earth System Model
(Neale et al., 2012). Compared to
CAM4, CAM5.1 contains a range of improvements in the representation of
physical processes such as moist turbulence, shallow convection, stratiform
microphysics, cloud macrophysics schemes, and others (Neale et al., 2012).
The horizontal resolution used in this study is 1.9∘ in latitude and
2.5∘ in longitude. The vertical range is from the surface to
approximately 4 hPa (≈ 40 km).
Moisture source regions: the regions are denoted as (1) Bay of
Bengal: BOB; (2) Arabian Sea: AS; (3) South China Sea: SCS; (4) north-western
Pacific: NWP; (5) northern Indian Ocean: NIO; (6) southern Indian Ocean: SIO;
(7) southern Pacific: SP; (8) north-eastern Pacific: NEP; (9) southern Atlantic
Ocean: SAO; (10) northern Atlantic Ocean: NAO; (11) Arctic Ocean: ARC;
(12) North America: NAM; (13) South America: SAM; (14) Africa: AF; (15) Australia:
AUS; (16) Antarctic: ANC; (17) Southeast Asia: SEA; (18) Tibet Plateau: TP;
(19) Indo-China Peninsula: ICP; (20) India: IND; (21) Europe: EUP;
(22) northern
Asia: NA; (23) north-eastern Asia: NEA; (24) Yangtze River valley: YRV; (25) southern China: SCN.
In this study, the chemistry mechanism of CAM5.1 is taken from MOZART-4
(Emmons et al., 2010), in which water vapour is invariant, which means that
it is unnecessary to consider changes in water vapour during chemical
processes. The basic simulations setup, including emissions and upper and
lower boundary conditions, is identical to that of the specified dynamics
simulations of CAM5 in Lamarque et al. (2012). In this study, the wet removal
scheme in Horowitz et al. (2003) is adopted. The temporal evolution of the
mass mixing ratios (MMRs) of different water substances (water vapour, cloud
droplets, and ice) is determined by deep convection, shallow convection,
cloud macrophysics, cloud microphysics, advection, and vertical diffusion. To
diagnose the dominant moisture source regions of atmospheric water over EA,
the global surface is divided into 25 source regions as shown in Fig. 1. Most
regions are defined based on the locations of continents and oceans. Due to
the focus on moisture sources over EA in this study, EA and its adjacent
regions are further divided to provide more detail. Within source region k,
the surface flux of the tagged water vapour tracer Ek is equal to the
surface evaporation flux of water vapour E; otherwise, Ek=0. As in the
treatment described in Knoche and Kunstmann (2013) and Bosilovich and
Schubert (2002), water is “tagged” when it evaporates at its source region
and is no longer tagged when it precipitates from the atmosphere to the
Earth's surface via atmospheric processes. When previously tagged
precipitation reevaporates from the surface, it is regarded as newly tagged
water (Knoche and Kunstmann, 2013), which then belongs to the region from
where it reevaporates.
The MMRs of water vapour, cloud droplets, and ice at a particular level are
defined as qv, ql, and qi, respectively. The
corresponding MMRs of tagged water substances from source region k are
qv,tgk, ql,tgk, and qi,tgk. We assume
that all the tagged water substances from the source regions have the
identical physical properties and are well-mixed. All these tagged water
substances are passive, which means that they are entirely separate from the
original water substances in CAM5.1 and have no impact on dynamical and
thermal fields. Numaguti (1999) suggested that the lifetime of atmospheric
water vapour is about 10 days. In this study, the simulation
begins on 1 January 1997, and
the initial MMRs of tagged substances are set to zero. To attain stable
initial concentrations of tagged water substances, the simulation experiment
takes a year to spin up. We then investigate the 10-year-averaged results for
1998 to 2007. In the following, we describe the treatment of tagged AWTs in
CAM5.1's physical parameterisations.
Deep convection
In CAM5.1, deep convection is parameterised using the approach described in
Zhang and McFarlane (1995), but with modifications following Richter and
Rasch (2008) and Raymond and Blyth (1986, 1992). For the temporal evolution
of qv,tgk, it is calculated in the same way as that of
qv, but the relevant variables of tagged water vapour are
substituted for the corresponding variables of original water vapour,
expressed as
∂qv,tgk∂tdp=ϵtgk-ctgk-1ρ∂∂zMu,dpqv,u,tgk+Md,dpqv,d,tgk-Mc,dpqv,tgk,
where Mc,dp is the net vertical mass flux, Mu,dp is
the upward mass flux, and Md,dp is the downward mass flux in the
deep convection. ϵtgk and ctgk are the
large-scale-mean evaporation and condensation rates of tagged water vapour,
respectively. Here, qv,u,tgk and qv,d,tgk are the
MMR of tagged water vapour in the updraft and that in the downdraft,
respectively. The ratio between the MMR of tagged water vapour and the
corresponding sum is used to calculate the condensation rate
ctgk:
ctgk=qv,tgk∑k=1nqv,tgkc,
where c is the condensation of original water vapour. In this study,
n=25, which is the total number of defined source regions (Fig. 1). In this
scheme, the tagged cloud water in the updraft, the detrainment of tagged
cloud water, rain production rate, and the evaporation rate of tagged rain in
the downdraft are calculated in the same manner as that for the corresponding
quantities for original water. However, the relevant variables of tagged
water vapour are substituted for the corresponding variables of original
water vapour. Detailed formulas for relevant quantities for original water in
the updraft and downdraft are presented in Sect. 3 of Zhang and
McFarlane (1995). The evaporation of convection precipitation is also
considered in this parameterisation. The evaporation rate ∂qvk∂tdp_evap at
level m is associated with the deep convection precipitation flux Qmdp at the top interface of this level (Sundqvist,
1998), expressed as
∂qvk∂tdp_evap=ke(1-RHm)Qmdp,
where RHm is the relative humidity at level m and the
coefficient ke=2×10-6 (kg m-2 s-1)-1/2 s-1. The individual
evaporation rate of tagged convection precipitation from source region k is
calculated as
∂qv,tgk∂tdp_evap=ke1-RHmQm,tgkdp∑k=1nQm,tgkdp,if∑k=1nQm,tgkdp≠0,0,if∑k=1nQm,tgkdp=0.
In general, the evaporation rate of convection precipitation is very small
compared to the tendency of water vapour in the deep convection (Neale et
al., 2012). For the temporal evolution of ql,tgk and
qi,tgk in the deep convection parameterisation, both are treated
in the same subroutine as ql and qi.
Shallow convection
The shallow convection scheme in CAM5.1 is taken from Park and
Bretherton (2009). Similar to the MMR of the total water qt, the
MMR of the tagged total water qt,tgk is also assumed to be a
conserved quantity in non-precipitating moist adiabatic processes. In this
scheme, the diagnostic equations for the shallow convective mass flux
Mu,sh and the MMR of the updraft total water qt,u
(Bretherton et al., 2004) are expressed as
∂Mu,sh∂z=Etr-Dtr
and
∂∂zqt,uMu,sh=Etrq‾t-Dtrqt,u+∂qt∂zMu,sh,
where Etr is the entrainment rate, Dtr is the detrainment
rate, and q‾t is the MMR of the mean environmental total
water. The fractional entrainment and detrainment rates are denoted as
ε and δ, then
Etr=εMu,sh,Dtr=δMu,sh.
Finally, attaining the updraft dilution equations
∂Mu,sh∂z=Mu,shε-δ,∂qt,u∂z=εq‾t-qt,u+∂qt∂z.
Similarly, the updraft dilution equation for the tagged total water is
expressed as
∂qt,u,tgk∂z=εq‾t,tgk-qt,u,tgk+∂qt,tgk∂z.
Equation (A5) of Bretherton et al. (2004) is used to calculate
qt,u, as well as qt,u,tgk, in the shallow convection.
In this scheme, because the detrainment of cloud water and ice (Dql and Dqi) is assumed to be
proportional to the total water detrainment and the detrained air is assumed
to be a representative of cumulus updraft (Park and Bretherton, 2009), we use
the ratio of tagged total water in the updraft qt,u,tgk and the
corresponding sum to distribute the detrainment of tagged cloud water and ice
Dql,tgkandDqi,tgk:
Dql,tgk=qt,u,tgk∑k=1nqt,u,tgk×Dql,Dqi,tgk=qt,u,tgk∑k=1nqt,u,tgk×D(qi).
This ratio is also applied to the calculations of in-cumulus tagged
condensates and the production rates of tagged rain/snow by cumulus expulsion
of condensates to the environment. Tagged condensate tendencies for
compensating subsidence or upwelling, the tagged condensate tendencies due to
detrained cloud water and ice without precipitation contribution, and the
updraft/penetrative entrainment mass flux of tagged total water are
calculated using the same equations for the original water-related quantities
in this scheme. Similar to the calculation of the tendency of water vapour,
the tendency of tagged water vapour is computed as the difference between the
tendency of tagged total water and the tendencies of tagged condensates in
non-precipitating processes within the shallow convection scheme. The shallow
convection scheme relates precipitation evaporation rate ∂qv∂tsh_evap to shallow
convection precipitation flux Qsh, similar to the deep convection
scheme of CAM5.1. Therefore, we use an assumed expression similar to Eq. (4)
to calculate the tagged precipitation evaporation rate at a level m:
∂qv,tgk∂tsh_evap=ke1-RHmQm,tgksh∑k=1nQm,tgksh,if∑k=1nQm,tgksh≠00,if∑k=1nQm,tgksh=0,
where Qm,tgksh is the tagged precipitation
flux at the top interface of level m.
Cloud macrophysics
Park et al. (2014) provided a detailed description of CAM5.1's cloud
macrophysics, in which cloud fractions, horizontal and vertical overlapping
structures of clouds, and net condensation rates of water vapour into cloud
droplets and ice are computed. Since the tendencies of water substances
caused by cumulus convection have been calculated in deep and shallow
convection schemes, we focus on the treatment of the tagged stratus fraction
and net condensation rates of tagged water vapour in stratus clouds in this
section.
The separate liquid stratus fraction al,st is a unique function
of grid-mean relative humidity (RH) over water, u‾l≡q‾v/q‾s,w, where
q‾v is the grid-mean water-vapour-specific humidity and
q‾s,w is the grid-mean saturation-specific humidity over
water, which is shown in Eq. (3) of Park et al. (2014). Then the single-phase
(no separate liquid and ice phases) liquid stratus fraction is
Al,st=1-Acual,st.
Here Acu is the total cumulus fraction.
We allocate the tagged liquid stratus fraction Al,st,tgk, which
depends on the ratio of grid-mean tagged water-vapour-specific humidity
q‾v,tgk and the corresponding sum, expressed as
Al,st,tgk=q‾v,tgk∑k=1nq‾v,tgkAl,st.
The tagged grid-mean liquid stratus condensate
q‾l,a,tgk is calculated in the same way as the
grid-mean liquid stratus condensate q‾l,a, but
Al,st,tgk is substituted for Al,st:
q‾l,a,tgk=Al,st,tgk×ql,st.
Here, ql,st is the in-stratus liquid water content. Similar to al,st, the ice
stratus fraction ai,st is a function of the grid-mean total ice RH
over ice, v‾i≡(q‾v+q‾i)/q‾s,i,
where q‾i is the grid-mean ice-specific humidity and
q‾s,i is the grid-mean saturation-specific humidity over
ice, as shown in Eq. (4) of Park et al. (2014). Similar to Al,st,
the single-phase ice stratus fraction is calculated as
Ai,st=1-Acuai,st.
As in the treatment of Al,st,tgk, the tagged ice stratus
fraction Ai,st,tgk is computed based on the ratio of grid-mean-total
tagged ice-specific humidity
(q‾v,tgk+q‾i,tgk) and the
corresponding sum:
Ai,st,tgk=(q‾v,tgk+q‾i,tgk)∑k=1n(q‾v,tgk+q‾i,tgk)Ai,st.
The tagged grid-mean ice stratus condensate
q‾i,a,tgk is calculated in the same way as the
grid-mean ice stratus condensate q‾i,a:
q‾i,a,tgk=Ai,st,tgk×qi,st.
Here, qi,st is the in-stratus ice water content. Using the same formula as for the
calculation of the grid-mean ambient water-vapour-specific humidity, the
tagged grid-mean ambient water-vapour-specific humidity
q‾v,a,tgk is computed as follows:
q‾v,a,tgk=q‾v,tgk+q‾l,tgk+q‾i,tgk-q‾l,a,tgk-q‾i,a,tgk.
In CAM5.1, Park et al. (2014) defined the grid-mean net condensation rate of
water vapour into liquid stratus condensate Q‾l as the time
change of q‾l,a minus the external forcing (all
processes except stratus macrophysics, including stratus microphysics,
moisture turbulence, advection, and convection) of cloud droplets
F‾l:
Q‾l=q‾˙l,a-F‾l=Al,stq˙l,st+αql,stA˙l,st-F‾l,
where q‾˙l,a, q˙l,st, and
A˙l,st are the time tendency of q‾l,a,
ql,st, and Al,st, respectively, during
Δt=1800 s. In CAM5.1, α=0.1 is the ratio of newly formed
or dissipated stratus to the preexisting ql,st. Similarly, the
tagged grid-mean net condensation rate Q‾l,tgk is
calculated as
Q‾l,tgk=q‾˙l,a,tgk-F‾l,tgk=Al,st,tgkq˙l,st+αql,stRA˙l,st+Al,stR˙-F‾l,tgk,andR=q‾v,tgk∑k=1nq‾v,tgk.
Here, R˙ is the tendency of R during Δt, and
F‾l,tgk is the changes of tagged cloud droplets in processes
such as microphysics, moisture turbulence, advection, and deep and shallow
convections.
Cloud microphysics
The CAM5.1 model uses the double-moment cloud microphysical scheme described
in Morrison and Gettelman (2008) and a modified treatment of ice
supersaturation and ice nucleation from Gettelman et al. (2010). In addition,
CAM5.1's stratus microphysics is formulated using a single-phase stratus
fraction Ast, which is assumed as the maximum overlap between
Al,st and Ai,st (Park et al., 2014). In this study,
the same assumption is applied to each tagged single-phase stratus fraction
Ast,tgk. The microphysical processes in CAM5.1 include
condensation/deposition, evaporation/sublimation, autoconversion of cloud
droplets and ice to form rain and snow, accretion of cloud droplets and ice
by rain or by snow, heterogeneous freezing, homogeneous freezing, melting,
sedimentation, activation of cloud droplets, and primary ice nucleation.
Detailed formulations for these microphysical processes are described in
Morrison and Gettelman (2008).
Condensation/deposition and evaporation/sublimation of cloud water and
ice
In CAM5.1, the net grid-mean
evaporation/condensation rate of
cloud water and ice (condensation minus evaporation) Q is calculated
following Zhang et al. (2003). In this microphysics scheme, the total
grid-scale condensation rates of tagged ice and tagged cloud water, as well
as the total grid-scale evaporation rates of tagged cloud water and tagged
ice, are calculated using the same formulas but the tagged variables are
substituted for the corresponding original variables:
∂qi,tgk∂tcond=minAst,tgkA,Ast,tgkQ+ql,tgkΔt,Q>0,∂ql,tgk∂tcond=maxAst,tgkQ-∂qi,tgk∂tcond, 0,Q>0,∂ql,tgk∂tevap=maxAst,tgk,-ql,tgkΔt,<0,∂qi,tgk∂tevap=maxAst,tgkQ-∂ql,tgk∂tevap,-qi,tgkΔt,Q<0,
where A is the in-cloud deposition rate of water vapour onto cloud ice (see
Eq. 21 of Morrison and Gettelman, 2008).
Conversion of cloud water to rain and conversion of cloud ice to
snow
The grid-mean autoconversion and accretion rates of water cloud in CAM5.1 are
expressed in Eqs. (27) and (28) of Morrison and Gettelman (2008). The two
rates can be regarded as a term
multiplied by Ast.
Therefore, the grid-mean autoconversion and accretion rates of tagged water
cloud can be calculated in the same formula but Ast,tgk is
substituted for Ast:
∂ql,tgk∂tauto=Ast,tgkAst∂ql∂tauto=-∂qr,tgk∂tauto,∂ql,tgk∂taccr=Ast,tgkAst∂ql∂taccr=-∂qr,tgk∂taccw,
where qr,tgk is the MMR of tagged stratiform rain.
Similarly, the grid-mean autoconversion rate of ice to form snow can be seen
as a term multiplied by Ast (see Eq. 29 of Morrison and
Gettelman, 2008), as well as the accretion of ice following Lin et
al. (1983). Thus, the autoconversion and accretion rates of tagged ice to
form snow are expressed as
∂qi,tgk∂tauto=Ast,tgkAst∂qi∂tauto=-∂qs,tgk∂tauto,∂qi,tgk∂taccs=Ast,tgkAst∂qi∂taccs=-∂qs,tgk∂tacci,
where qs,tgk is the MMR of tagged stratiform snow.
Other collection processes
The accretion of cloud water by snow ∂ql∂taccs=-∂qs∂taccw is attained by the continuous
collection equation, whose collection efficiency is a function of the Stokes
number following Thompson et al. (2004). Similar to the calculation of
∂ql∂tauto,
∂ql∂taccs can be
regarded as a term multiplied by Al,st. Thus, ∂ql,tgk∂taccs is
computed using the same equation but by multiplying it by
Al,st,tgk instead of Al,st:
∂ql,tgk∂taccs=Al,st,tgkAl,st∂ql∂taccs=-∂qs,tgk∂taccw.
The collection of rain by snow ∂qr∂tcoll=-∂qs∂tcoll can also be regarded as a term multiplied by
Ast. Therefore, ∂qr,tgk∂tcoll is computed using the same formula but by multiplying
it by Ast,tgk instead of Ast:
∂qr,tgk∂tcoll=Ast,tgkAst∂qr∂tcoll=-∂qs,tgk∂tcoll.
Freezing of cloud water and rain
The heterogeneous freezing of cloud water and rain is considered in CAM5.1
(Reisner et al., 1998; Morrison and Pinto, 2005). The heterogeneous freezing
of tagged cloud water is computed using the same formula as that of original
cloud water, but by multiplying with Al,st,tgk instead of
Al,st:
∂ql,tgk∂thet=Al,st,tgkAl,st∂ql∂thet.
Similarly, the heterogeneous freezing of tagged rain is computed using the
same formula as that of original rain, but by multiplying it by
Ast,tgk instead of Ast:
∂qr,tgk∂thet=Ast,tgkAst∂qr∂thet.
The homogeneous freezing of tagged cloud droplets and tagged rain are
computed using the same equations as those of the original cloud droplets
and rain, but ql,tgk and Sr,tot,tgk (the vertical-integrated
tagged rain source/sink term) are substituted for the original quantities:
∂ql,tgk∂thom=∂ql∂thomqlΔtql,tgkΔt=-∂qi,tgk∂thom∂qr,tgk∂thom=∂qr∂thomSr,totSr,tot,tgk=-∂qs,tgk∂thom.
Melting of cloud ice and snow
Similar to the calculations of the homogeneous freezing of cloud water and
rain, the melting of tagged ice and tagged snow are computed using the same
equations as those of the original ice and snow, but qi,tgk and
Ss,tot,tgk (the vertical-integrated tagged snow source/sink term)
are substituted for the original quantities:
∂qi,tgk∂tmelt=∂qi∂tmeltqiΔtqi,tgkΔt=-∂ql,tgk∂tmelt∂qs,tgk∂tmelt=∂qs∂tmeltSs,totSs,tot,tgk=-∂qr,tgk∂tmelt.
Evaporation/sublimation of precipitation
For the calculations of the evaporation of tagged rain and the sublimation of
tagged snow, both are calculated using the same formula as original
quantities but Ast,tgk is substituted for Ast:
∂qr,tgk∂tevap=Ast,tgkAst∂qr∂tevap,∂qs,tgk∂tevap=Ast,tgkAst∂qs∂tevap.
Sedimentation of cloud water and ice
The time tendencies ∂ql∂tsedand∂qi∂tsed of cloud water and ice for sedimentation, as well as
those of tagged cloud water and tagged ice ∂ql,tgk∂tsedand∂qi,tgk∂tsed,
are calculated with a simple forward
differencing scheme in the vertical dimension (Morrison and Gettelman, 2008).
In CAM5.1, the sedimentation of cloud water and ice can lead to evaporation
or sublimation when the cloud fraction at the level above is larger than the
cloud fraction at the given level and the evaporation or condensation rate is
assumed to be proportional to the difference in cloud fraction between the
levels. This assumption is also applied to calculate the evaporation of
tagged cloud water or sublimation of tagged ice, when the tagged cloud
fraction at the level above is larger than the tagged cloud fraction at the
given level.
The diagnosis of precipitation
The grid-scale time tendency of the MMR of precipitation qp in
CAM5.1's
microphysics is expressed as
∂qp∂t=1ρ∂(Vqρqp)∂z+Sq,
where z is height, Vq is the mass-weighted terminal fall speeds (see
Eq. 18 of Morrison and Gettelman, 2008), and Sq is the grid-mean
source/sink terms for qp:
Sq=∂qp∂tauto+∂qp∂taccw+∂qp∂tacci+∂qp∂thet+∂qp∂thom+∂qp∂tmelt+∂qp∂tevap+∂qp∂tcoll.
For the diagnostic treatments of tagged rain and tagged snow, the qp in
Eqs. (40) and (41) is replaced by qr,tgk and qs,tgk,
respectively.
Advection
The finite volume dynamical core is chosen in this study due to its
excellent properties for tracer transport (Rasch et al., 2006). The CAM5.1
model can be driven by offline meteorological fields (Lamarque et al., 2012)
following the procedure initially developed for the Model of Atmospheric
Transport and Chemistry (MARCH) (Rasch et al., 1997). This procedure allows
for more accurate comparisons between measurements of atmospheric
composition and CAM5.1's output (Lamarque et al., 2012). In this study, the
external meteorological fields are obtained from Modern Era
Retrospective analysis for Research and Applications (MERRA) data sets
(Rienecker et al., 2011), whose horizontal resolution is identical to
CAM5.1's and time resolution is 6 h. In the simulation procedure, the zonal
and meridional wind components, air temperature, surface pressure, surface
temperature, surface geopotential, surface stress, and sensible and latent
heat fluxes are read from the MERRA data sets to drive CAM5.1 (Lamarque et
al., 2012). To prevent jumps, all input fields are linearly interpolated at
time steps between the reading times. Later, these fields are used to drive
the CAM5.1's parameterisations to generate the necessary variables and
calculate subgrid-scale transport and the hydrological cycle (Lamarque et
al., 2012). Temporal evolutions of qv,tgk, ql,tgk, and
qi,tgk in the advective process are treated in the same manner
as other constituents without any modification.
Vertical diffusion
CAM5.1's moist turbulence scheme is taken from the scheme presented by
Bretherton and Park (2009), which calculates the vertical transport of heat,
moisture, horizontal momentum, and tracers by symmetric turbulence. The
vertical diffusion of tagged water substances is treated by the procedure in
the same way as other constituents without any modification.
Adjustment
Ideally, the differences between the MMRs of water substances and the summed
MMRs of all corresponding tagged water substances should be zero. However,
there are exceptional differences in a few grid points (see Fig. S6 in the Supplement).
Figures S1–S5 show comparisons between the tendencies
of the original water substances and the sum of the tendencies of the tagged
water substances for the relevant physical processes described in Sect. 2.1
through 2.6. Although differences are small for most grid points, some
abnormal values still appear randomly. For tagged water vapour, evident
biases mainly occur in deep convection, cloud processes (cloud macrophysics
and microphysics), and advection in the tropics; for tagged cloud droplets,
the apparent biases generally occur in cloud processes; for tagged cloud
ice, the main differences occur in cloud processes, advection, and vertical
diffusion. Non-linearities in the calculations of the tendencies of water
substances in the physical schemes cause these differences. A bias occurred
in one physical parameterisation can affect the calculations of the
tendencies of tagged water substances in other parameterisations, since
there are interactions among various physical and dynamical processes in
CAM5.1. Eventually, clear differences between the summed MMRs of tagged
water substances and the MMRs of original water substances may occur, as
shown in Fig. S6. To reduce these accumulated biases in the relevant
physical schemes, additional criteria are applied to the relevant quantities
of the tagged water substances:
Comparisons between (left) GPCP data and (right) CAM5.1
precipitation simulations during (top) winter and (bottom) summer (10-year
averages for 1998–2007).
If the positive or negative sign of the tendency of a tagged water
substance is identical to the sign of the tendency of the original water
substance, the absolute value of the tendency of the tagged water substance
should not be larger than that of the original water substance. If their
signs are different, the tendency of the tagged water substance is set to
zero. This adjustment can be expressed as
∂qtgk∂t=min∂qtgk∂t,∂q∂t,if∂qtgk∂t≥0and∂q∂t≥0max∂qtgk∂t,∂q∂t,if∂qtgk∂t≤0and∂q∂t<00,if∂qtgk∂t<0and∂q∂t≥0or∂qtgk∂t>0and∂q∂t<0,
where ∂qtgk∂t and ∂q∂t represent the tendency of the tagged water substances and the
tendency of the corresponding original water substance in a given physical
process, respectively.
After the adjustment in Eq. (42) has been applied, the sum of the tendencies of
all tagged water substances should be equal to the tendency of the
corresponding original water substance in each scheme. This adjustment can be
described as follows:
∂qtgk∂t=Rq∂qtgk∂t,if∑k=1n∂qtgk∂t≠0,hereRq=∂q∂t∑k=1n∂qtgk∂t1n∂q∂t,if∑k=1n∂qtgk∂t=0.
Results and discussion
Model assessment
Numaguti (1999) pointed out that the results of the tagged AWTs method suffer
from the bias of the model used. Therefore, we first estimate the
precipitation in the specified dynamics simulation of CAM5.1, which is
compared to the Global Precipitation Climatology Project (GPCP) version 2.2
combined precipitation data set (Huffman and Bolvin, 2011), as shown in
Fig. 2. In winter (December, January, and February), high-precipitation zones are located in the tropics
of the Southern Hemisphere and in the mid-latitude areas of the NWP.
Precipitation is generally less than 3 mm day-1 over most parts of
Eurasia. In summer (June, July, and August), there is heavy precipitation
over the southern and south-eastern parts of Eurasia and over central Africa.
Although CAM5.1 generally shows a bias towards relatively high precipitation
in the tropics of the summer hemisphere, the precipitation pattern and amount
over Eurasia and its adjacent areas is captured well by CAM5.1. In addition,
the water vapour data from the Atmospheric Infrared Sounder (AIRS) and wind
field data from National Centers for Environmental Prediction (NCEP) are used
to assess the CAM5.1's results, as shown in Fig. S7. Overall, the water
vapour and horizontal wind fields can be well simulated by CAM5.1.
Distribution of the relative contribution to precipitation from all
land source regions defined in Fig. 1 (colours; unit: ratio of tagged
precipitation over total precipitation) and the vertically integrated total
tropospheric water vapour flux (arrow streamlines; unit: kg m-1 s-1)
during (a) winter and (b) summer.
Distribution of CAM5.1's 10-year-averaged surface evaporation flux
(unit: mg m-2 s-1) in (a) winter and (b) summer between 1998 and
2007.
Terrestrial and oceanic contributions to precipitation over
Eurasia
Figure 3 shows the spatial distribution of the relative contribution of
evaporation from all land source regions to precipitation (colours). In
winter, evaporation from land source regions generally contributes
∼ 30–60 % to the precipitation over Eurasia. The largest
contribution (∼ 80 %) is located in central China. In summer,
≥ 60 % of precipitation over most parts of Eurasia is supplied by
evaporation from land, especially for the inland region where ≥ 80 % of precipitation originates from the land surface. However, the
contribution of evaporation from land to summer precipitation over IND, Indo-China Peninsula (ICP),
and eastern China is generally less than 50 %, due to moisture transport by
the Indian summer monsoon and EASM. Overall, the contribution of evaporation
from land to precipitation over Eurasia is smaller in winter and larger in
summer, which is consistent with the variation of evaporation from the land
surface over Eurasia in winter and summer as shown in Fig. 4. The pattern of
precipitation contributed by land evaporation is similar to that shown in
Numaguti (1999). Our result is close to that of Numaguti (1999) for summer
but the contribution of land evaporation to precipitation is evidently larger
for winter.
The distributions of the relative contributions of evaporation from the northern Atlantic Ocean (NAO),
the extended northern Indian Ocean (includes NIO, BOB, and AS), and the extended
north-western Pacific (includes NWP and SCS), which are three important moisture
source regions, are shown in Fig. 5. In winter, ∼ 10–60 % of the
precipitation over the northern part of Eurasia originates from the NAO, with
a westward or north-westward increasing gradient in the relative contribution.
The extended northern Indian Ocean supplies moisture for ∼ 10–30 % of
the precipitation over northern Africa and southern Asia. The extended
north-western
Pacific only provides moisture for 10–30 % of the precipitation over the
southern and eastern coastal regions of Asia. In summer, evaporation from the
NAO only affects precipitation over Europe, with a contribution of 10–30%
to total precipitation. Precipitation areas influenced by the extended
northern
Indian Ocean extend to EA, while areas impacted by the extended north-western
Pacific retreat eastward.
The arrow streamlines in Fig. 3 show the total tropospheric water vapour flux
in winter and summer. There is a westward component of water vapour flux over
the tropics of both the extended northern Indian Ocean and the extended
north-western Pacific in the Northern Hemisphere in winter. In summer, there is a
very large north-westward water vapour flux over the NIO, turning
north-eastward over the BOB and AS. Over the extended north-western Pacific, there
is a northward component of water vapour flux at 30–60∘ N and a
westward flux in the tropics between 120 and 180∘ E. In addition,
Fig. 4 shows strong surface evaporation over the NWP and NAO in winter,
whereas
evaporation is weaker in summer. In contrast, evaporation over the NIO is
larger in summer and smaller in winter. These results help to explain the
variations in the contributions of the NAO, extended northern Indian Ocean, and
extended north-western Pacific to precipitation in winter and summer as shown in
Fig. 5.
Distributions of the ratios of precipitation (unit: ratio of tagged
precipitation over total precipitation) supplied from the NAO (slate blue),
the extended northern Indian Ocean (NIO + BOB + AS, pink), and the extended
north-western Pacific (NWP + SCS, orange) during (a) winter and (b) summer.
Contour interval is 0.1.
(a) Monthly averaged evaporative contributions of 25 defined source
regions to the precipitation over the YRV. (b) Same as (a), but for the
relative contribution to precipitation. (c) Monthly averaged evaporative
contributions of 25 defined source regions to the tropospheric total water
vapour amount over the YRV. (d) Same as (c), but for the relative
contribution to water vapour. Stacked column colours correspond to source
region colours in Fig. 1.
The overall contributions from these three oceanic regions are generally less
than those in Numaguti (1999). The resolution of the climate model used in
Numaguti (1999) is ∼ 5.6∘, both in latitudinal and longitudinal
direction. The different model resolutions are a probable reason for the
different quantitative contributions in our study and that of
Numaguti (1999). In addition, CAM5.1 is driven by MERRA data, so its surface
evaporation flux is approximate to that of MERRA. MERRA land evaporation is
larger over southern and eastern Asia and northern Europe compared to other global
estimates (Jiménez et al., 2011), and Bosilovich et al. (2011) suggested
that MERRA ocean evaporation is lower compared to other reanalyses but is
much closer to observation. Therefore, the bias in MERRA surface evaporation
may lead to the higher land contribution and lower oceanic contribution to
precipitation.
Same as Fig. 6, but for the contributions and relative
contributions of 25 source regions to precipitation and tropospheric total
water vapour amount over SCN.
Atmospheric moisture source attribution of precipitation and water
vapour over the YRV
Figure 6a and b show the time series of evaporative contribution of each
source region to precipitation over the YRV. The contributions of evaporation
to precipitation from the BOB and AS are lower during autumn–winter and
higher during spring–summer with relative contributions of ≤ 3.9 %.
Chow et al. (2008) (see their Fig. 20a) also found that evaporation from the
AS had little impact on precipitation over China. Figures S10–S13 show the
distributions of 25 tagged water vapour tracers and 25 tagged precipitations
over Eurasia and surrounding areas in winter and summer. Figures S10a and
S12a show that evaporation from the BOB contributes to water vapour and
precipitation over the extended northern Indian Ocean in winter, corresponding
to the direction of water flux shown in Fig. 3a. The centre of
BOB-contributed precipitation (15 mg m-2 s-1) is located in the
south of the TP in summer (Fig. S13a). In addition, the BOB supplies moisture
to areas around the north-eastern BOB in summer (Fig. S11a). The contribution
of the SCS to precipitation is also very small (≤ 3.4 %), which
supports the view of Chow et al. (2008), who suggested that the SCS may serve
as a pathway for water vapour transport from the south-westerly flow of the
Indian summer monsoon and the easterly flow of the north-western Pacific
subtropical high. A detailed discussion of this issue is presented in
Sect. 3.5. The NWP serves as the dominant oceanic source region for
precipitation over the YRV during the whole year except during June and July.
The relative contribution is ∼ 8.1–10.6 % in June and July and
15.8–24.6 % in other months. As shown in Fig. 3, there is strong
westward water vapour flux over 20–45∘ N for the NWP and
south-westward water vapour flux over the tropics of the NWP. However, there
is no evident moisture transports from the NWP to EA in the long-term-mean
water vapour flux. Following Eq. (S1), the water vapour flux is divided into
the stationary and transient components, as shown in Figs. S8–S9. The
transient component of the meridional flux brings some of the moisture from
south over most of the NWP and the north of the SCS (Fig. S8c), and the
transient component of the zonal flux leads to westwards water vapour
transport over 20–30∘ N for the NWP (Fig. S9c). Both the transient
components indicate that the synoptic disturbances can bring moisture
originating from the NWP to the southern and eastern coastal regions of Asia
during winter. Evaporation from the NIO shows a clear contribution to
precipitation during May to October. In particular, the NIO is the dominant
oceanic source region in June and July, with a contribution of
∼ 30 %. This is in agreement with the result of a Lagrangian
diagnostic method described in Baker et al. (2015) and the results of
sensitivity experiments in Chow et al. (2008). However, in other months, the
contribution of the NIO is very small. The contributions from evaporation
from the BOB, AS, and NIO are in phase with the EASM, which was also reported
by Baker et al. (2015). The ICP is an important terrestrial source region for
the YRV precipitation, supplying moisture to ∼ 9.9 % of the annual
precipitation. The relative contribution of the ICP from April to September
is close to the result of Wei et al. (2012). The contribution of evaporation
from the YRV to its precipitation can be regarded as the local recycling
ratio, which is lower (4.5–7.4 %) in summer and higher (9.2–13.4 %)
in other seasons. In general, the contribution of evaporation from SCN is
comparable to the local contribution of the YRV. The relative contribution
from the NEA is higher in autumn–winter and lower in spring–summer, which
may be associated with the shift of the EA monsoon. Though the individual
contributions of evaporation from the YRV or SCN are smaller than those from
the NIO in summer, their combined contributions exceed 10 %. This implies
that evaporation from these two regions is important for precipitation over
China. This is contrary to the view expressed in Simmonds et al. (1999) and
Qian et al. (2004), but consistent with Wei et al. (2012). Figure 6c and d
show a time series of evaporative contribution from each source region to the
tropospheric water vapour amount over the YRV. The overall relative
contribution from each source region to the total water vapour amount is
similar to the corresponding relative contribution to precipitation shown in
Fig. 6a and b.
(a) Monthly averaged evolution of evaporative contribution of 25
defined source regions to the tropospheric total water vapour amount over the
SCS. (b) Same as (a), but for the relative contribution of water vapour.
Stacked column colours correspond to source region colours in Fig. 1.
Atmospheric moisture source attribution of precipitation and water
vapour over SCN
Figure 7a and b show the contribution of each source region to precipitation
over SCN. The NIO is the dominant source region in summer, while the NWP
dominates precipitation over SCN during other seasons, which is similar to
the situation over the YRV. The contribution from the NIO is 28.4–37.8 %
in summer. The contribution from the NWP is 8.7–17.2 % in summer and
∼ 15.3–37.2 % during other seasons. During spring and summer,
∼ 2–4.4 % of precipitation is supplied from the BOB, with smaller
contributions during other seasons. The contribution from the AS is similar
to that of the BOB. In summer, only 2.7–3.7 % of precipitation
originates from the SCS, but the area contributes ∼ 6.7–7 % to the
precipitation in early spring (March–April). Similar to precipitation over
the YRV, the dominant terrestrial source region for SCN is the ICP, which
contributes ∼ 9.8 % to the precipitation. In addition,
∼ 5.6 % of summer precipitation originates from SEA. Compared to
precipitation over the YRV, the contribution from the TP is smaller. In
addition, the contribution from the YRV is small in summer. The local
recycling ratio or percentage contribution of evaporation from SCN is
generally 4.3–7.2 % during May to September, but larger than 9.3 %
during the remaining months. As shown in Fig. 7d, the overall relative
contribution of each source region to the water vapour amount is similar to
each region's contribution to precipitation over SCN.
Atmospheric moisture source attribution of water vapour over the
SCS
Simmonds et al. (1999) and Lau et al. (2002) suggested that interannual
variation of summer precipitation over China is associated with water vapour
transport over the SCS. However, Chow et al. (2008) suggested that the SCS
may act as a water vapour transport pathway where the south-westerly stream of
the Indian summer monsoon and the easterly stream of the south-eastern Asian
monsoon meet. Previous studies have conducted sensitivity experiments or
analysed the water vapour budget to indirectly determine moisture sources for
the SCS. In contrast, our AWT method can directly quantify the contribution
of each source region to the water vapour amount over the SCS, which is shown
in Fig. 8. The local contribution of the SCS is small
(∼ 4.7–5.5 %) in summer, and the mean contribution in other months
is ∼ 6.8 %. The contribution of the NIO shows clear seasonal
variations: the contribution is high during May to October, but very small
during the other months. Similar to the results for water vapour over the YRV
and SCN, the NIO is the dominant source region from June to September, with a
contribution of 22.7–31 %. During this period, the contribution of the
NWP is 14.1–21.2 %. However, the NWP dominates the water vapour over the
SCS in the remaining months, with contributions of 25.7–51.3 %. In
addition, the SP and north-eastern Pacific (NEP) are also important oceanic source regions, with
combined annual contributions of ∼ 11–16.6 %. The most important
terrestrial moisture source region is the SEA, whose contribution is larger
(13.8–16.2 %) in summer and smaller (∼ 5.3 %) in winter.
During late autumn to winter, about 5.3–6.3 % of water vapour is
supplied from NEA, but its contribution is very small in other seasons. The
other land source regions contribute relatively little to the water vapour
amount over the SCS.
From the SCS to SCN and further to the YRV (from south to north), surface
evaporation from the SCS generally represents a small (≤ 5.5 %)
contribution to the water vapour amount over the three target areas in
summer. In contrast, much more water vapour is supplied by evaporation from
the NWP and NIO. This confirms the inference proposed by Chow et al. (2008)
that the SCS is a water vapour transport pathway where moisture from the NIO
and NWP meet in summer.
Conclusions
In this study, an Eulerian tagged AWT method was implemented in CAM5.1, which
provides the capacity to separately trace the behaviour of atmospheric water
substances originating from various moisture source regions and to quantify
their contributions to atmospheric water over an arbitrary region.
Numaguti (1999) pointed out that the weakness of the tagged AWT method is
that its results suffer from the performance of the model in reproducing the
hydrological cycle. However, a comparison between GPCP and CAM5.1
precipitation shows that CAM5.1 has the capability to represent total
precipitation. CAM5.1 also can reproduce water vapour and large-scale
circulation reasonably, as compared to AIRS and NCEP data. Using this method,
we investigated the contribution of evaporation from land, as well as the
contributions from the North Atlantic Ocean, extended northern Indian Ocean, and
extended north-western Pacific to precipitation over Eurasia. Our results are
similar to those of Numaguti (1999), except that our results indicate a
larger contribution from terrestrial source regions, while the three oceanic
regions show smaller contributions. Different model resolutions and a bias in
MERRA surface evaporation are probable causes for the differences between our
results and those of Numaguti (1999).
We then investigated the contribution of various source regions to
precipitation and water vapour amounts over the YRV and SCN. Our results
suggest that the dominant oceanic moisture source region during summer is the
NIO (20.5–30.3 % of precipitation over the YRV; 28.4–37.8 % of
precipitation over SCN), consistent with Baker et al. (2015) and Chow et
al. (2008), whereas during other seasons, the NWP is the dominant source region
(15.8–24.6 % of precipitation over the YRV; 15.3–37.1 % of
precipitation over SCN), with smaller contributions from the BOB, AS, and
SCS. The ICP is an important terrestrial source region, with a mean annual
contribution of ∼ 10 %. For precipitation over the YRV, the
combined contribution of evaporation from the YRV and SCN is non-negligible
(exceeding 10 %), consistent with Wei et al. (2012). For precipitation
over SCN, the local recycling ratio is generally 4.3–7.2 % during May to
September, and reaches 9.4–18.7 % in other months. The contribution from
the YRV is very small in summer. The overall relative contribution of each
source region to the water vapour amount is similar to the corresponding
contribution to precipitation over the YRV and SCN.
An analysis of water vapour amount over the SCS shows that the NIO is the
dominant source region (22.7–31 % of water vapour) during June to
September, while the NWP dominates (25.7–51.3 % of water vapour) in the
remaining months. In contrast, the local contribution of the SCS is smaller
(∼ 4.7–5.5 %) in summer. In addition, the SP, NEP, and SEA are
also important source regions. Evaporation over the SCS represents a small
contribution to water vapour amounts over the SCS, SCN, and the YRV in
summer, implying that the SCS acts as a water vapour transport pathway rather
than a dominant source region, which confirms the inference of Chow et
al. (2008).
At present, the tagged AWT method has only been applied to a few GCMs and
regional models, and has generally focused on identifying the moisture
distribution over a few regions such as North America (Bosilovich and
Schubert, 2002; Bosilovich et al., 2003). We expect that the AWT method will
be applied to additional models and used to identify moisture sources over
more climate regions, which will improve our understanding of atmospheric
moisture transport.