Clim Dyn (2013) 41:341–365
DOI 10.1007/s00382-013-1662-7
Influence of convective parameterization on the systematic errors
of Climate Forecast System (CFS) model over the Indian monsoon
region from an extended range forecast perspective
S. Pattnaik • S. Abhilash • S. De • A. K. Sahai
R. Phani • B. N. Goswami
•
Received: 30 March 2012 / Accepted: 2 January 2013 / Published online: 12 January 2013
Springer-Verlag Berlin Heidelberg 2013
Abstract This study investigates the influence of Simplified Arakawa Schubert (SAS) and Relax Arakawa Schubert
(RAS) cumulus parameterization schemes on coupled Climate Forecast System version.1 (CFS-1, T62L64) retrospective forecasts over Indian monsoon region from an extended
range forecast perspective. The forecast data sets comprise
45 days of model integrations based on 31 different initial
conditions at pentad intervals starting from 1 May to 28
September for the years 2001 to 2007. It is found that mean
climatological features of Indian summer monsoon months
(JJAS) are reasonably simulated by both the versions (i.e. SAS
and RAS) of the model; however strong cross equatorial flow
and excess stratiform rainfall are noted in RAS compared to
SAS. Both the versions of the model overestimated apparent
heat source and moisture sink compared to NCEP/NCAR
reanalysis. The prognosis evaluation of daily forecast climatology reveals robust systematic warming (moistening) in
RAS and cooling (drying) biases in SAS particularly at the
middle and upper troposphere of the model respectively.
Using error energy/variance and root mean square error
methodology it is also established that major contribution to
the model total error is coming from the systematic component of the model error. It is also found that the forecast error
growth of temperature in RAS is less than that of SAS;
however, the scenario is reversed for moisture errors, although
the difference of moisture errors between these two forecasts
is not very large compared to that of temperature errors.
Broadly, it is found that both the versions of the model are
underestimating (overestimating) the rainfall area and amount
over the Indian land region (and neighborhood oceanic
region). The rainfall forecast results at pentad interval
exhibited that, SAS and RAS have good prediction skills over
the Indian monsoon core zone and Arabian Sea. There is less
excess rainfall particularly over oceanic region in RAS up to
30 days of forecast duration compared to SAS. It is also
evident that systematic errors in the coverage area of excess
rainfall over the eastern foothills of the Himalayas remains
unchanged irrespective of cumulus parameterization and
initial conditions. It is revealed that due to stronger moisture
transport in RAS there is a robust amplification of moist static
energy facilitating intense convective instability within the
model and boosting the moisture supply from surface to the
upper levels through convergence. Concurrently, moisture
detrainment from cloud to environment at multiple levels
from the spectrum of clouds in the RAS, leads to a large
accumulation of moisture in the middle and upper troposphere
of the model. This abundant moisture leads to large scale
condensational heating through a simple cloud microphysics
scheme. This intense upper level heating contributes to the
warm bias and considerably increases in stratiform rainfall in
RAS compared to SAS. In a nutshell, concerted and sustained
support of moisture supply from the bottom as well as from
the top in RAS is the crucial factor for having a warm temperature bias in RAS.
Keywords
S. Pattnaik (&)
School of Earth, Ocean and Climate Sciences, Indian Institute
of Technology Bhubaneswar, Bhubaneswar, India
e-mail: [email protected]
S. Abhilash S. De A. K. Sahai R. Phani B. N. Goswami
Indian Institute of Tropical Meteorology (I.I.T.M), Pune, India
Cumulus parameterization
1 Introduction
Identifying and correcting systematic forecast errors and
biases in the weather and climate models are major
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components of the model developmental activities in
leading research and operational organizations. As a part of
the ‘‘National Monsoon Mission (NMM)’’ program (http://
dod.nic.in/monsoon_mission.pdf), the National Centers for
Environmental Prediction (NCEP) Climate Forecast System version 1 (CFS-1T62L64, Saha et al. 2006) fully
coupled model is adapted by the Indian Institute of Tropical Meteorology (IITM), Pune, India and other similar
agencies across India with a primary objective to improve
summer monsoon rainfall forecasts on intraseasonal and
seasonal time scales over the Indian region. With this larger goal in mind, we attempt to characterize and evaluate
systematic forecast biases of the CFS-1 coupled model
over the Indian monsoon region from an extended range
time scale perspective (up to 45 days forecast). This lead
time is a prerequisite particularly for the farming community of the country to prepare, plan and implement their
seasonal agricultural activities which has a significant
implication on India’s Gross Domestic Product (GDP) and
economy.
There are many studies by leading operational centers
across the world characterizing forecast systematic errors
of their respective operational models. In this paper a few
important findings are discussed. Kamga et al. (2000)
analyzed 120-h forecast results for summer 1995 obtained
from the ECMWF model (T213L31) and found that the
model has a strong tendency to overestimate the lower
troposphere warming over Africa’s Sahel region and mid
tropospheric cooling over the tropical Southern Hemisphere. They also identified that during extreme events,
convection and boundary layer parameterization schemes
are the major contributors to the total error of the model.
Jung and Rodwell (2005) evaluated systematic errors in the
ECMWF model from short range to extended range forecast time scale perspectives. He noted biases in the model
such as the development of a large anticyclonic bias over
the central North Pacific, underestimation of the kinetic
energy of transient eddies, and underestimation of synoptic
systems at higher latitudes and underestimation of atmospheric blockings in medium and extended range forecast
time scales. Sun et al. (2010) showed that systematic errors
such as the lack of stratocumulus clouds over southeast
Pacific region in the Global Forecasting System (GFS)
were corrected by modifying the low level inversion and
vertical mixing representations of the model. Moorthi
(1997) highlighted some of the important systematic errors
such as increase of easterlies in the lower troposphere and
decrease over the middle and upper troposphere, weak
Hadley circulation, relative humidity dry bias tendencies at
the lower levels in the NCEP operational Medium Range
Forecast (MRF) model. Goswami et al. (2006) emphasized
that one of the major roadblocks for improvement in
dynamical seasonal and intraseasonal predictability of the
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south Asian summer monsoon is the existence of large
systematic biases in the models. Similar important studies
emphasizing systematic error issues as the major factors
responsible in the model for poor predictability of the
seasonal mean monsoon are also carried out by Brankovic
and Palmer (2000); Drbohlav and Krishnamurthy (2010)
and others.
The skill of monsoon simulation and prediction by the
CFS has been discussed in a number of previous studies
(Liang et al. 2009; Achuthavarier and Krishnamurthy 2010;
Gao et al. 2011; Rai and Krishnamurthy 2011). Huang et al.
(2007) showed that the CFS model has a tendency to
overestimate the warming of sea surface temperature (SST)
in the southeastern tropical Atlantic Ocean. It is also suggested that due to excessive radiative forcing the model
was not able to generate low level cloud cover over these
regions. Yang et al. (2008) found that the CFS model
simulated a weaker monsoon circulation due to a cold bias
at the surface over the Asian continent. Indeed, an
improvement in the land surface model in the CFS reduces
the cold bias and enhances simulation of the Asian summer
monsoon (Yang et al. 2011).
It has long been recognized and well documented that
cumulus parameterization is one of the important contributors to the model forecast uncertainty and has profound
impact on the model rainfall prediction skills (Krishnamurti
2005; Arakawa 2004; Tiedtke 1993; Pan and Wu 1995).
Hence thorough evaluation of the model convective
parameterization scheme is essential. Yang et al. (1999)
concluded that precise characterization of temperature and
moisture fields in the sub-cloud layers and mid-troposphere
are critical for accurate representation of cumulus cloud
effects in the model. The role of cumulus parameterization
schemes on the Indian summer monsoon (ISM) rainfall has
been studied using global and regional models by many
authors. Through model intercomparison projects, Palmer
and Anderson (1994) found that systematic errors in simulating the monsoon circulation are large in many models
and convective parameterization is one of the major contributors to these errors. Zhang (1994) suggested that mass
flux-based cumulus parameterization schemes provide
more realistic monsoon dynamical characteristics and
rainfall distribution compared to the moist convective
adjustment schemes in an atmospheric general circulation
model (GCM). Slingo et al. (1988) noted that the Asian
summer monsoon and onset dates are most sensitive to the
radiation and convective parameterization schemes in the
European Center for Medium Range Weather Forecasts
(ECMWF) general circulation model. Eitzen and Randall
(1999) showed that prognostic stratiform cloud parameterization and increasing convective adjustment time in the
model leads to improvement in simulation of precipitation
and upper level wind patterns in the GCM.
Climate Forecast System
In addition, there are studies showing the robust sensitivity of cumulus parameterization schemes on the ISM in
short, medium and seasonal time scales (Pattanaik and
Satyan 2000, Das et al. 2002, Mukhopadhyay et al. 2010).
All these studies suggest strong influence of convective
parameterization on rainfall and other benchmark dynamical features of the ISM; however, these studies are carried
out using stand-alone atmospheric models or are forced
with analyzed (observed) sea surface temperature as
boundary forcing to the model (i.e. Tier II). In Tier II type
integrations, there is a standalone atmospheric model and
sea surface temperature (SST) is prescribed from other
sources. It has no embedded prognostic coupled ocean
model component. Also, these models were integrated
from a limited number of initial conditions. As we know,
improving extended range forecast skills of the model is an
enormously challenging task for the community (Morgan
et al. 2007) because skill is simultaneously influenced by
uncertainties arising from model initial conditions,
parameterization of physical processes and boundary
forcings. Therefore, this study provides a unique opportunity to examine systematic biases of the fully coupled
dynamical model (CFS-1). The model is integrated from
several initial conditions using different convection
schemes during the summer monsoon months i.e. June,
July, August and September (JJAS) for a reasonable retrospective forecast data set in order to have a comprehensive assessment of model systematic biases from an
extended range forecast time scale. In addition, findings of
this study will support one of the important objectives of
the NMM programme in identifying important deficiencies
within the CFS model, so that necessary and feasible corrections can be incorporated to minimize systematic error
growth and improve model forecast skills over the Indian
region.
A brief description of the CFS-1 coupled model and a
short note highlighting the major differences in convective
parameterization schemes used in this study are presented
in Sect. 2; description of retrospective forecast experiments
and data sets are illustrated in Sect. 3; detailed discussion
of results are presented in Sect. 4, discussion on the factors
responsible for manifestations of these errors in the model
are presented in Sect. 5; results are summarized in Sect. 6
of the manuscript.
2 Description of the coupled model (CFS-1)
and cumulus parameterization schemes
The NCEP fully coupled CFS-1 model used in this study is
comprised of the GFS atmospheric model at T62
(*210 km) resolution with 64 vertical sigma levels and
the Geophysical Fluid Dynamical Laboratory (GFDL)’s
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Modular Ocean Model Version 3 (MOM3) as ocean model.
Heat and momentum flux exchange processes between the
atmospheric and ocean components of the model take place
on a daily basis but without any flux adjustment or correction. Both atmospheric and ocean initial states for the
model are provided to IITM by NCEP under the NMM
programme. The comprehensive information about the
model with detailed illustration is presented in Saha et al.
(2006).
Here we will highlight some of the major differences
that exist between Simplified Arakawa Schubert (SAS) and
Relax Arakawa Schubert (RAS) cumulus parameterization
as this will be more relevant to the current study. Both SAS
and RAS follow a mass flux approach to parameterize
cumulus clouds and both are based on the ArakawaSchubert Scheme (Arakawa and Schubert 1974). One
fundamental difference between SAS (Pan and Wu 1995)
and RAS (Moorthi and Suarez 1992) is the type of cloud
model used in each scheme. An ensemble of clouds with
different cloud tops exists in RAS, whereas a single tallest
cloud type representation is used in SAS. The detrainment
in SAS happens only at the top whereas for RAS it can
occur at various levels of the cloud spectrum. Besides,
saturated downdrafts have been introduced in SAS by Grell
(1993), which is not incorporated in this version of RAS. A
quasi equilibrium mechanism is implemented in the case of
RAS in a gradual relax manner compared to being mandatorily achieved in SAS at each time step once the scheme
is triggered. More information on differences between
these two schemes can be found in Das et al. (2002) and
Park et al. (2010).
3 Retrospective forecasts experiments and data sets
The CFS-1 (T62L64) model is integrated in an extended
range time scale up to 45 days forecast lead time from 31
initial conditions starting from 1 May to 28 September at
intervals of 5 days (i.e. 1 May, 6 May, 11 May …. 28 Sept)
for 7 years (i.e. 2001–2007). The coupled model is integrated twice for each initial condition, once with SAS and
once with RAS cumulus parameterization. These two
simulations have identical model configurations and initial
conditions except cumulus parameterizations. Model output is stored daily (24 hourly).
The NCEP/NCAR reanalysis-1 (Kalnay et al. 1996), and
the ERA interim analysis (Dee et al. 2011; Uppala et al.
2005) at 2.5 deg 9 2.5 deg resolution are used as analysis
data sets (hereafter ANA and ERAI). The Global Precipitation Climatology Project (GPCP, Adler et al. 2003)
1 deg 9 1 deg (interpolated to 2.5 deg 9 2.5 deg to fit
model resolution) and Tropical Rainfall Measuring Mission
(TRMM 3A12 at 25 km, Kummerow et al. 2001) data sets
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are used for comparison of total observed and stratiform
precipitation. Both these data sets are interpolated to the
resolution of the CFS output at 2.5 9 2.5. A detailed
variability study about partitioning TRMM precipitation
into startiform and convective components has discussed in
Yang and Smith (2008).
4 Results and discussions
4.1 Climatology
Each monsoon month’s (i.e. JJAS) climatology from CFS-1
model is created from the ensemble mean forecasts
obtained from four different initial conditions of the previous month. For example, in order to create June month’s
climatology, the forecasts valid for June 1–30th based on
31 May, 26 May, 21 May and 16 May initial conditions are
considered. Hence total four ensemble members are used
for each month in each year for 7 years period (i.e.
2001–2007) in order to create the climatology of June from
the CFS-1 model. Similarly, climatologies for other months
such as July, August, and September are created and from
the mean of all these months JJAS climatology is obtained.
The differences in mean spatial structure of wind direction
and speed at 850 hPa and total rainfall between respective
JJAS retrospective forecast climatology (RAS/SAS) and
observation/analysis (GPCP/ANA) are shown in Figs. 1a–c
and 2a–c. The forecasts obtained from both SAS and RAS
simulations are able to replicate the low level cross equatorial flow (Somali jet); however, the zonal component of
the flow and the wind strength are found to be stronger in
case of RAS ([6 ms-1) as compared to that of ANA and
SAS (Fig. 1a–c). It is also found that the extent of low level
westerlies and their strength from the Bay of Bengal (BoB)
into the mainland is stronger in RAS compared to SAS.
Difference plots of total rainfall (Fig. 2a–c) suggest that
both RAS and SAS simulations are overestimating
(underestimating) precipitation over the ocean (Indian
landmass) compared to GPCP observations. It is noted that
the quantitative magnitude of rainfall deficit is same in
both these simulations over the Indian land region. However, it appears RAS forecast has less rain deficit area
compared to that of the SAS. Fig. 2c suggests that SAS has
overestimated rainfall (up to 4 mm day-1) over the west
coast of India and the Andaman sea region compared to
RAS. And RAS has overestimated rainfall (up to
4 mm day-1) over the central, eastern India and BoB
compared to SAS. Figure 2a, b also indicates that the
center of maximum precipitation over west coast of India is
distinctly different for both these simulations. It is evident
from these results that model has inherent systematic biases
to produce excess precipitation over the north eastern
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Fig. 1 JJAS 850 hPa (2001–2007) wind difference a SAS–ANA,
b RAS–ANA, and c RAS–SAS. Shaded represent wind speed (ms-1)
region of India (i.e. foothills of Himalayas) which is not
being corrected in these two simulations. This is mainly
attributed to the coarser resolution of the model. Because
of that, it is unable to resolve the orographic effects
accurately and produced excessive orographic induced
precipitation.
It is now well recognized that stratiform rainfall has a
profound impact on dynamical modulations of various
monsoon processes over the Indian region (Chattopadhyay
et al. 2009; Choudhury and Krishnan 2011; Li et al. 2009).
The JJAS climatology of stratiform component of the total
precipitation simulated from these two versions of CFS-1
model are presented in Fig. 3a–c. The mean difference
plots clearly suggest that RAS (SAS) is overestimating
(underestimating) stratiform rain throughout the region
compared to TRMM. It is also evident that the contribution
of stratiform rain to the excess rainfall over the north
eastern foot hills of Himalayas is higher in RAS compared
to SAS. Explanations for this suppressed and excess
stratiform rainfall in RAS and SAS are provided in Sect. 5
of this paper.
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Fig. 2 JJAS (2001–2007) total rainfall (mm day-1) difference
a SAS–GPCP, b RAS–GPCP, and c RAS–SAS
It is well known that apparent heat source and moisture
sink are the major thermodynamical factors modulating
ISM circulations. Following Yanai et al. (1973) and Xavier
et al. (2007), JJAS climatological mean vertically integrated (900 hPa * 400 hPa) apparent heat source Q1 and
moisture sink Q2 are computed (Wm-2 units) for SAS,
RAS and ANA and presented in Figs. 4a–c and 5a–c
respectively.
Q1 ¼ R þ SH þ LP;
ð1Þ
Q2 ¼ LðP EÞ;
ð2Þ
where R is the radiative heating rate, SH is sensible heat
flux, LE is evaporative moistening flux, L is latent heat of
condensation and P is the precipitation rate. These results
show strong positive values of both Q1 and Q2 over the
oceanic regions (BoB in particular) for all three data sets
suggests contribution from latent heating mainly associated
with deep convection over these regions. However, over the
northern India, contrasting character of heating are noted
for all three data sets. Quantitatively model forecasts have
Fig. 3 JJAS (2001–2007) stratiform rainfall (mm day-1) a SAS,
b RAS, and c TRMM
stronger magnitudes of heating/cooling compared to ANA.
Large positive values of Q1 are indicating the dominant
radiative heating compared to sensible and latent heating
and large negative values of Q2 suggesting evaporation is
higher than the precipitation over these regions. We also
note that the center of maxima in the spatial distribution of
Q2 over the head bay region in RAS data sets has a close
resembles with ANA. However, RAS has overestimated Q2
over the north east region of India compared to ANA and
SAS. The Q2 values are being overestimated in both SAS
and RAS simulations over the west coast region compared
to ANA and SAS has higher values than RAS. In general,
over the northern India regions positive values of Q1
accompanied by negative values of Q2 which are quantitatively much higher than the ANA. This suggest that over
this region the radiative heating and evaporative drying
processes are dominant (overestimated) for both these
models. The point to note that these discrepancies in the
heat source and moisture sink terms have significant impact
on the model’s ability to forecast monsoon active and break
cycles over the Indian region (Wong et al. 2011).
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Fig. 4 JJAS (2001–2007) vertically integrated (900–400 hPa) apparent heat source Q1 (W m-2) a SAS, b RAS, and c ANA
Fig. 5 JJAS (2001–2007) vertically integrated (900–400 hPa) apparent moisture sink Q2 (W m-2) a SAS, b RAS, and c ANA
4.2 Evaluation of model systematic error
irrespective of forecasts based on different initial conditions i.e. 1 May, 5 June and 5 July. The robust increase in
warm bias in RAS is conspicuous particularly during
forecast days valid for monsoon onset period based on 1
May initial condition. We have also seen that RAS has
stronger warm bias in temperature compared to that of SAS
for same set of initial conditions (figure not shown). On the
contrary, SAS (Fig. 6b, d, f) simulations has prominently
dominated by cold bias (1.8 K) compared to ANA
throughout the model forecast duration (up to 45 days) over
the Indian region, though the magnitude of cold bias shows
gradual decreasing pattern as the forecast length increases
from day 1 to day 45 for all three initial conditions.
Keeping these results in view, we have also analyzed the
impact of temperature biases on relative humidity (RH)
fields of the model from daily forecast climatology data
sets. The Fig. 7a–f is same as Fig. 6a–f except for RH. It is
prominently showing that the moisture availability in RAS
(Fig. 7a, c, e) has substantially increased ([14 %) compared to ANA and SAS (Fig. 7b, d, f), particularly in
middle and upper troposphere (in resemblance with temperature biases) in the respective model. It is also found that
zonal wind flow over Arabian seas has been improved in
RAS forecasts (Figures not shown).
After examining mean biases in the climatological spatial
structures of some of the basic model parameters, now
systematic error in the model are prognostically evaluated
and discussed in an extended range forecast time scale.
Figure 6a–f shows difference plots of the time height daily
climatology mean of 7 years (i.e. 2001–2007) over the ISM
region (i.e. 10S–40N; 50E–110E) of temperature (deg K)
between two cumulus simulations (SAS and RAS) and
ANA. The total forecast length is up to 45 days based on
the initial conditions of 1 May (Fig. 6a, b), 5 June (Fig. 6c, d)
and 5 July (Fig. 6e, f). We have shown these three initial
conditions in three separate months because there will be
less similarity in the initial states of the model and these
forecasts (up to 45 days lead time) will cover main monsoon rainfall months i.e. June, July and August. In Fig. 6a–f
positive and negative values are shaded and contoured
respectively. These results clearly suggest, though initially
both SAS and RAS models have cold biases in the temperature field compared to ANA, after 15 days of model
integration RAS simulations has strong warm bias ([1.2 K)
at middle and upper levels (600 hPa and above) compared
to ANA (Fig. 6a, c, e). This feature is consistently evident
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Fig. 6 Daily climatology mean (2001–2007) difference time–pressure (hPa) plots for temperature (K) for forecast days 1–45 averaged
over 10S–40N, 50E–110E based on different initial conditions (IC).
1 May IC a RAS–ANA, b SAS–ANA, 5 June IC, c RAS–ANA,
d SAS–ANA, 5 July IC, e RAS–ANA, f SAS–ANA
Bearing in mind the different biases among reanalysis
data sets (Annamalai et al. 1999) and to bring more confidence into model systematic biases results, comparisons
are also made between both versions of the CFS-1 forecasts
(i.e. SAS and RAS) and ERA interim reanalysis (ERAI)
data sets (Fig. 8a–f). Figure 8a–f is same as Fig. 6a–f
except the comparisons are made with ERAI. We note that
the similar pattern of strong warm bias ([1.2 K) and cold
bias ([2.5 K) in the middle and upper troposphere temperature of RAS and SAS forecasts compared to ERAI. In
addition, it is noted that RAS simulation too have consistent cold bias in temperature though small in magnitudes
(0.6 degK) particularly at the lower levels compared to
ERAI. This feature is absent when compared to ANA (i.e.
NCEP-NCAR reanalysis). It appears that the lower levels
of ERAI analysis are slightly warmer than ANA over the
region. It is also evident that during early stages (day 1–day
10) of the model integration both SAS and RAS have
slightly stronger colder biases (2.4 and 1.5 degK) compared to ERAI compared to ANA. However, the middle
and upper level warming pattern beyond 15 days in RAS
and cooling pattern spread throughout the troposphere for
SAS for the entire forecast integration period (45 days) are
qualitatively in agreement with each other when comparisons are made with two benchmark reanalysis data sets (i.e.
ERAI and ANA). As far as relative humidity is concerned,
the comparison plots (Fig. 9a–f) suggest that RAS forecasts are moister than ERAI and SAS. However, the
magnitude of availability of moisture is substantially lower
(*6 %) in these simulations compared to ERAI than
compared to ANA for all forecasts based on three different
initial conditions. It is also noted that at the lower levels
both RAS and SAS are drier compared to ERAI and this
dryness is not seen in these forecasts when compared to
ANA (Fig. 7a–f). However, results do indicate that SAS
forecasts are dry both at the lower as well as that the upper
troposphere for all forecasts and throughout the entire
forecast duration (45 days) based on 1 May, 5 June and 5
July initial conditions. The magnitudes of dryness are also
higher (*6 %) in SAS compared to ERAI than ANA.
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Fig. 7 Daily climatology mean (2001–2007) difference time–pressure (hPa) plots for relative humidity (%) for forecast days 1–45
averaged over 10S–40N, 50E–110E based on different initial
conditions (IC). 1 May IC a RAS–ANA, b SAS–ANA, 5 June IC,
c RAS–ANA, d SAS–ANA, 5 July IC, e RAS–ANA, f SAS–ANA
Broadly, the characteristics patterns of systematic errors in
relative humidity and temperature fields of the SAS and
RAS version of the CFS-1 model are found to be similar
when compared to ANA and ERAI. However, there are
differences in their (i.e. SAS and RAS) respective magnitudes. Therefore, these results are qualitatively consistent
with the systematic biases of the model with a higher
degree of certainty.
The daily climatology of total heat forcing (i.e. Q1–Q2)
averaged over 10S–40N and 50E–110E region for three
initial conditions 1 May, 5 June and 5 July are shown in
Fig. 10a–c. Vertical integrals of Q1, Q2 (i.e. Eqs. 1 and 2)
and the total heat forcing (i.e. Q1–Q2) can be presented as
follows (Yanai et al. 1973; Xavier et al. 2007);
compared to SAS throughout the forecast duration for all
three initial conditions. These results indicates that apart
from latent heat contribution from precipitation, the
cumulus parameterization schemes in respective models are
robustly impacting other physical components of the model
and supplementing the warm (i.e. in RAS) and cold (i.e. in
SAS) bias in their forecasts. The key point to mention that,
although significant impact has been noted in the thermodynamical fields of the model due to the changes in cumulus
parameterizations, the impact on dynamical wind flow
fields over the Indian region is minimal. Further analysis
has been carried out and results are discussed in the following sections to support the aforementioned results.
Q1 Q2 ¼ hRi þ SH þ LE
ð3Þ
Figure 10a–c suggest that after precipitation component
cancels out (LP) in Eqs. (1) and (2), in general the combined
forcing of radiative heating, sensible heating and evaporative moistening (i.e. net total heating) is stronger in RAS
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4.2.1 Segregation, distribution and growth of model
systematic error
In this section systematic error for SAS and RAS forecast
data sets of wind field are examined in energy/variance
form (Boer 1984; De and Chakraborty 2004) whereas the
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Fig. 8 Daily climatology mean (2001–2007) difference time–pressure (hPa) plots for temperature (K) for forecast days 1–45 averaged
over 10S–40N, 50E–110E based on different initial conditions (IC).
1 May IC a RAS–ERAI, b SAS–ERAI, 5 June IC, c RAS–ERAI,
d SAS–ERAI, 5 July IC, e RAS–ERAI, f SAS–ERAI
same for temperature and relative humidity are represented
as root mean square error (RMSE). The total forecast error
are evaluated by taking the difference between the 45-day
run of CFS-1 model (SAS/RAS) using 31 initial conditions
starting from 1st May to 28th September at 5 days interval
over a period of 7 years (i.e. 2001–2007), referred in Sect.
3 and the corresponding daily reanalysis (ANA) data. Here
the 45-days run are treated as forecast data. Hence, the total
error may be written as
The parameter ‘X’ in Eqs. (4) and (5) represents the wind
field (V), temperature (T) and relative humidity (RH). Now,
the systematic error of wind field in energy/variance form
may be written as
Xe ¼ Xf Xa
ð4Þ
where, Xf is the model forecast data, Xa be the
corresponding reanalysis (ANA) and Xe is the total error.
Now the total error is partitioned into its systematic (Xes )
(time mean) and non-systematic (Xer ) (time transient) part
as
Xe ¼ Xes þ Xer
ð5Þ
1
Kes ¼ Ves Ves
2
ð6Þ
where the over-bar represents time average. It is a mean
square error generated from the deficiencies in the model
formulations and inadequate representation of different
physical processes in the model (Boer 1993). Here, the time
average has been taken for all the years 2001–2007, for
45 days forecast run and for all 31 initial conditions. The
systematic errors in temperature and relative humidity are
calculated as the RMSE averaging over the 45 days forecast
run of all the years and for all initial conditions (Heckley
1985; Kanamitsu 1985). The error analysis in winds, temperature and RH are carried out using these equations.
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Fig. 9 Daily climatology mean (2001–2007) difference time–pressure (hPa) plots for relative humidity (%) for forecast days 1–45
averaged over 10S–40N, 50E–110E based on different initial
conditions (IC). 1 May IC a RAS–ERAI, b SAS–ERAI, 5 June IC,
c RAS–ERAI, d SAS–ERAI, 5 July IC, e RAS–ERAI, f SAS–ERAI
Figure 11 depicts the spatial distribution of the total and
systematic error of 850 hPa wind (Fig.11a–d), 300 hPa
temperature (Fig. 11e–h) and 400 hPa RH (Fig. 11i–l) over
India and adjoining oceanic region for two cumulus
schemes SAS and RAS of CFS model. These levels are
selected because they are where the maximum error is
found in the CFS runs. The contour shading colors are
same for wind field and relative humidity. The total and
systematic errors of temperature for SAS are presented in
Fig. 11e, f. The total error and systematic error components
of temperature for RAS are presented in Fig. 11g, h with
different color shading than Fig. 11e, f because of different
magnitudes. The objective of these figures is to show the
influence of the cumulus parameterization on the dynamical, temperature and RH parameters in terms of total and
systematic biases of CFS-1 model. The results clearly
suggest that the magnitude and the geographical distribution of total and systematic errors are nearly similar with
the total error showing marginally larger magnitude for
thermodynamical parameters in the CFS-1 model with
different cumulus parameterization schemes (i.e. SAS and
RAS) (comparing Fig.11 e, f; g, h; i, j; k, l). This implies
that the errors are mainly attributed to the systematic error.
There are also negligible changes in error characteristics of
wind fields due to the changes in convection schemes
(Fig. 11a–d) suggesting that dynamics has no significant
impact on these systematic errors. On the contrary, major
changes in the total and systematic errors for SAS and RAS
are observed in model thermodynamical parameters (i.e.
temperature and RH fields) at 300 and 400 hPa, respectively (Fig. 11e–l). In particular the magnitudes of temperature errors show higher values in SAS compare to
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Fig. 10 Q1–Q2 Daily time
series (Wm-2) for forecast days
averaged over 10S–40N,
50–110E based on different
initial conditions (IC). a 1 May,
b 5 June and c 5 July
RAS, however the values of error in RH are less in SAS
compare to RAS. Therefore, error growth and distribution
pattern at 300 hPa temperature and 400 hPa RH in forecast
data sets (SAS/RAS) based on two initial conditions (i.e. 1
May and 5 June) are exclusively examined and discussed in
the following section.
In Fig. 12a–d the spatial distribution of errors in temperature at 300 hPa and relative humidity at 400 hPa over
the India and adjoining oceanic regions for SAS (1st column) and RAS (2nd Column) forecasts based on 1 May
initial condition are presented. The third column of both
figures (Fig. 12e–f) shows the daily error growth averaged
over India and adjoining region (10S–30N, 50E–110E) for
45 days of model integration starting from 1 May. Units
are deg K and % for temperature and RH respectively.
These results illustrate that the systematic errors in
temperature forecast are greater in SAS than RAS
(Fig. 12a–c). In particular, over the north-west India and
northern Arabian sea SAS exhibit more error than RAS for
both the forecast with initial condition of 1 May. As far as
400 hPa RH (Fig. 12b–d) is concerned, the spatial distribution of errors show that the larger values of error covering more area in RAS compared to SAS for this initial
condition except over 15N–20N, 60E region, where SAS
has shown more error than RAS. Time series of temperature error (Fig. 12e) indicates that RAS shows considerably
less error compared to SAS throughout the forecast duration for this initial condition. However, the time series of
error in RH (Fig. 12f) suggests more error growth in RAS
than that of SAS, though the magnitudes of differences
between these two errors are reduced in RH compared to
that of temperature. We have also examined forecast based
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Fig. 11 Spatial distribution of wind total error and systematic errors
of SAS (a, b) and of RAS (c, d), temperature total error and
systematic errors in total wind energy of SAS (e, f) and of RAS
(g, h) and relative humidity total error and systematic errors of SAS
(i, j) and of RAS (k, l) over India and adjoining oceanic region for the
two cumulus schemes SAS and RAS of CFS model. The error in wind
is expressed as m2s-2 whereas temperature and RH are in K and %
respectively. The contour colors are same for SAS and RAS in wind
and RH but different for the same in temperature field
on 5 June and 5 July initial conditions and found similar
pattern of error characteristics (figures not shown).
forecast based on 1 May initial condition (Fig. 13a, b)
shows that the deficit pattern over Indian land regions are
very similar for SAS and RAS, however, magnitudes and
the area of deficit is greater in SAS ([4 mm day-1) over
BoB and coastal region compared to RAS. It is interesting
to note that forecast based on 5 June initial condition
(Fig. 14c–d) rainfall deficit area and magnitude are significantly higher over Indian landmass in SAS
([6 mm day-1) compared to RAS ([3 mm day-1) and the
area of excess rainfall ([6 mm day-1) for RAS simulation
is less compared to SAS over the west coast of India and
Arabian Sea. The spatial structure of rainfall for 5 July
initial condition (Fig. 14e, f) indicates that the magnitudes
of difference between simulations and observations have
been increased. The RAS has less area of deficit over
4.3 Precipitation
Figures 13a–f and 14a–f show the 15 days mean differences in the spatial distribution of daily forecast climatology of precipitation for 7 years (i.e. 2001–2007) between
two model forecasts (i.e. SAS and RAS) and observed
(GPCP) data sets over the Indian region. The forecasts are
based on 1 May (1st row), 5 June (2nd row) and 5 July (3rd
row) initial conditions respectively. The mean differences
are segregated for rainfall forecast valid for each 15 days.
Figures 13a–f and 14a–f show the differences valid for
01–15 days and 16–30 days respectively. First 15 days of
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Fig. 12 Spatial distribution of
systematic errors of temperature
at 300 hPa and relative
humidity at 400 hPa for SAS
(a, b) and for RAS (c, d). Time
series of error growth in
temperature (e) and relative
humidity (f) for forecast days
(up to 45 days) based on 1 May
initial condition over the region
for both SAS and RAS. The
units for temperature are deg K
and relative humidity is in %
Indian land compared to SAS. In general both SAS and
RAS have similar kind of overestimating pattern over
Arabian Sea and the west coast region, however magnitudes of errors in RAS are smaller than SAS over these
regions. It is also found that RAS is overestimating rainfall
([3 mm day-1) over BoB compared to SAS especially for
the forecast based on 5 June initial condition. The excess
rainfall area over the foothills of Himalayas (northeastern
India) has the similar structure in both RAS and SAS,
nevertheless area and magnitude of excess rainfall area is
marginally less in RAS than SAS. It is also evident that
excess rainfall area over Arabian ocean and the west coast
of India is smaller in RAS.
Figure 14a, b show errors in 16–30 days total rainfall
forecast based on 1 May initial condition are significantly
less for RAS compared to SAS especially over the oceanic
regions (i.e. Arabian Sea and BoB). However, rainfall
deficit area over the land is higher in RAS compared to
SAS, although the magnitude of maximum deficit remains
same (2 mm day-1) in both the forecasts. It is interesting
to note that over the oceans there is a substantial increase in
magnitude of rainfall (overestimation) in both the forecasts
based on 5 June initial condition (Fig. 14c, d). The spread
of overestimation tendency remains same for both SAS and
RAS over the oceans, however, over the BoB the magnitudes of overestimation are reduced for SAS compared to
RAS. Over the land regions both the forecasts are
underestimating the rainfall, although the area coverage of
precipitation deficit is less in RAS compared to SAS.
Analyzing Fig. 14e, f (5 July initial condition) it is found
that there is a substantial increase in area coverage and
magnitude over the land for SAS ([5 mm day-1) compared to RAS (3 mm day-1). The overestimation pattern
over the eastern foothills of Himalayas reappears in both
the forecasts with less area of excess rainfall in RAS
compared to SAS. The excess rainfall area particularly over
the oceanic region is considerably higher in SAS
([9 mm day-1) compared to RAS.
Besides examining spatial distribution structure of
rainfall, the skills of SAS and RAS forecasts over the
monsoon core zone (18–28N, 73–82E) up to four pentad
lead time (20 days) for mean 5 days forecast valid from 01
June to 24 September based on initial condition starting
from 16 May to 23 September are shown in Fig. 15a–d. For
example, 01 June forecast corresponding June 01–05 mean
rainfall forecast and 24 September corresponds to September 24–28 mean rainfall. In general it is found that over
the monsoon core zone both the forecasts are underestimating rainfall in all the pentads lead time (up to 40 days)
based on different initial conditions. However, over the
Arabian sea region (5–20N, 58–73E), both the forecasts are
over predicting rainfall compared to observations for
majority of pentads (Figures not shown). Similar kind of
analysis is also carried out up to 8 pentads over the
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Fig. 13 Difference of rainfall
forecasts climatology
(2001–2007) valid for
01–15 days based on initial
conditions a, b 1 May, c, d 5
June and e, f 5 July. Left column
shows SAS–GPCP and right
column is form RAS–GPCP.
Units are in mm day-1
monsoon core zone, Arabian Sea and BoB. As far as precipitation correlation is concerned, over Indian monsoon
core zone SAS has marginally higher skill than RAS for
most of the pentad forecasts (Fig. 16a). However, over the
Arabian Sea, RAS skills are higher than SAS for most
pentads up to 40 days (Fig. 16b). These skills are statistical
significant at 95 % confidence level. The similar skills are
also computed over BoB, however skills for both the
forecasts are very low and inconsistent (Figures are not
shown).
5 Factors for systematic errors
To address systematic biases in temperature and moisture
parameters of SAS and RAS versions of the CFS-1 model,
we examine the moist static energy (MSE), the magnitude
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of horizontal moisture transport fields of the respective
models for these three initial conditions (i.e. 1 May, 5 June
and 5 July) over Indian Monsoon Region [10S–40N, 50–
110E]. The MSE for a certain level of the atmosphere (h) is
defined by,
h ¼ Cp T þ Lq þ gz;
ð7Þ
where T is the temperature (in K), q the specific humidity
(in kg kg-1) and z the height of the atmospheric layer
(in m). Cp is the specific heat at constant pressure
(J K-1 kg-1), L latent heat of evaporation (J kg-1) and g
acceleration due to gravity (ms-2). The magnitude of
horizontal moisture transport (MMT) is computed by sum
of the square root of the zonal (uq) and meridional (vq)
components of moisture transport.
MMT ¼ ðUq þ VqÞ0:5 ;
ð8Þ
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Fig. 14 Difference of rainfall
forecasts climatology
(2001–2007) valid for
16–30 days based on initial
conditions a, b 1 May, c, d 5
June and e, f 5 July. Left column
shows SAS–GPCP and right
column is form RAS–GPCP.
Units are in mm day-1
Here u, v and q are zonal, meridional components of the
horizontal wind and specific humidity respectively.
The difference in MSE (positive is shaded, negative is
dashed) and moisture transport (positives are solid lines,
negatives are dotted contours) between RAS and ANA and
SAS and ANA are presented in Fig. 17a–f. It is evident that
RAS forecasts have much higher (Fig. 17a, c, e) and SAS
forecasts have moderate increase in moisture transport
compare to ANA. The quantitative amount moisture
incursion is higher for both forecasts based on 1 May initial
condition because of the onset phase of the monsoon. It is
also noted that for SAS forecasts based on 5 June and 5
July initial conditions (Fig. 17d, f), the magnitudes of the
transport of moisture are marginally higher than that of
ANA, in contrast RAS forecasts based on same initial
conditions (Fig. 17c, e) have sustained similar pattern of
higher magnitudes of moisture incursion compare to ANA.
In all these forecasts the vertical intrusion of moisture
transport are extended up to 400 hPa in RAS and this
feature in SAS is confined to 700 hPa. This distinct pattern
of enhanced moisture transport in RAS forecasts facilitate
large manifestation of MSE in the middle to upper troposphere (up to 400hpa) and their magnitudes are much
higher ([350 kJ kg-1) than the ANA except forecasts
based on 5 July initial condition where magnitudes are
greater than 250 kJ kg-1. However, forecasts obtained
from SAS irrespective of different initial conditions have
modest increase in MSE (250–300 kJ kg-1) and mostly
confined to lower troposphere (i.e. 900–700 hPa) compare
to ANA. It is also interesting to note that, at the surface
MSE values for both SAS and RAS are less compare to
ANA, however SAS forecasts have higher negative biases
([300 kJ kg-1) compare to RAS.
Figure 18a–c is same as Fig. 17, except it shows the
differences between RAS and SAS. It is evident that RAS
have stronger magnitudes and vertical extent of moisture
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Fig. 15 Area averaged rainfall
(mm) 5 day mean rainfall over
Indian monsoon core zone
(18–28 N, 73–82E) valid from 1
June to 24 September
(2001–2007) with lead time of
Pentad 1 (a), Pentad 2 (b),
Pentad 3 (c) and Pentad 4 (d) for
SAS, RAS and GPCP
transport than SAS throughout the forecast duration. There
is a distinct higher accumulation of MSE ([150 kJ kg-1) in
RAS from surface to upper troposphere (Fig. 18b, c) except
forecasts obtained from 1 May initial condition (Fig. 18a),
where there are marginal lower MSE (\60 kJ kg-1) in the
lower troposphere for couple of days forecasts compare to
SAS. It is also interesting to note that during few initial days
of forecast (up to day 5) there are reduction in MSE in RAS
compare to SAS though restricted to the only few lower
levels (\90 kJ kg-1) for all initial conditions. From these
results it is evident that enhanced incursion of moisture due
to stronger monsoon low level westerlies (i.e. cross equatorial flow, Fig. 1a–c) is facilitating substantial increase in
MSE throughout the troposphere in RAS compare to SAS.
In addition, the extent of moisture transport and MSE
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accumulations are coherent and consistent with each other
in both the models.
All the results discussed hereafter compare SAS and
RAS forecasts. Figure 19a–f show the time and pressure
level evolution of diabatic heating (Q1) and moisture
convergence averaged over the 10S–40N and 50–110E
region for SAS (Fig. 19a, c, e) and RAS (Fig. 19b, d, f)
forecasts from three initial conditions respectively. There
are distinct differences in the heating pattern between SAS
and RAS forecasts are seen in the middle and upper troposphere. All forecasts from RAS have a stronger middle
and upper level heating features ([1.6 K day-1) compare
to SAS. There are lower level heating patterns in both the
models and this warming patterns is attributed to the
increase in sensible heat flux due to warmer surface and
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Fig. 16 Correlation coefficient
of pentads (P1–P8) lead time
area averaged rainfall (mm)
over a Indian monsoon core
zone (18–28N, 73–82E) and
b Arabian Sea (5–20N, 58–73E)
valid for 1 June to 24 September
(2001–2007) for SAS, RAS.
Correlation coefficient with
respect to GPCP data
cooler air temperature (figure not shown). However, the
stronger heating patterns in the middle and upper troposphere of RAS forecasts with maxima in the upper troposphere are attributed to the release of latent heat due to
large scale condensation. We also note that for SAS forecasts based on 5 July initial condition has a cooling
structure (0.4 K day-1). In addition, we have plotted
moisture convergence (blue colour solid lines, mm day-1).
The result suggests that though magnitudes of moisture
convergence are not drastically different. However, the
vertical extents of lower level moisture convergences are
higher in RAS forecasts compare to SAS. This lower level
moisture supply is one of the important factors for sustaining convection and heating in the RAS model.
Figure 20a–c presents temporal evolution of differences
in vertically integrated moisture convergence and total
atmospheric column precipitable water between RAS and
SAS forecasts for same three initial conditions. These
results suggest that the higher warming in RAS compare to
SAS occurs in those forecast days where there is a stronger
moisture convergence. Once the moisture convergence
is reduced in RAS corresponding strength of warming
pattern also reduced. However, irrespective of moisture
convergence magnitude in RAS, its total precipitable water
content in the atmospheric column always remains higher
than SAS (0.8–3.8 kg m-2) throughout the forecast duration for all initial conditions. Although its magnitudes of
variation has a strong dependency on the strength of
moisture convergence. These results also indicate that there
is a large-scale accumulation of moisture in the middle and
upper troposphere of the RAS model which sustains its
moisture availability even when the strength of moisture
convergence is weak (Fig. 20b, c). The primary reason
attributed for this is, once the convection triggers in RAS
the detrainments of moisture from cloud to environment
occurs at several levels from a spectrum of clouds
embedded in the RAS cumulus parameterization. This
leads to large spread of moisture in the middle and upper
troposphere and this is absent in SAS. This systematic
detrainment of moisture from the convective clouds is
mainly facilitating moisture accumulation at the middle
and upper level of the model, which in turn strengthens the
large scale condensational warming in the model (i.e.
RAS).
In Fig. 21a–c the differences of area averaged temporal
evolution of stratiform and convective components of the
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Fig. 17 Daily climatology mean (2001–2007) difference time–
pressure (hPa) plots for moist static energy (KJ kg-1) in shaded
(positive) and dashed (negative) with interval 50, magnitude of
moisture transport (kg m-1 s-1) in solid contours (positive) and
dotted (negative) at 0.4 interval for forecast days 1–45 averaged over
10S–40N, 50–110E based on different initial conditions (IC). 1 May
IC a RAS–ANA, b SAS–ANA, 5 June IC, c RAS–ANA, d SAS–
ANA, 5 July IC, e RAS–ANA, f SAS–ANA. No subterranean
unidentified values are gone in computation
rainfall, total rainfall and vertically integrated diabatic
heating between RAS and SAS for same three initial
conditions are presented. In general, these results suggest
that the total rainfall (red lines) amounts in SAS are higher
than that of RAS. However, when the stratiform and convective components of the rainfall examined separately,
distinct differences in their respective patterns are observed
between these two models. It is noted that as forecast days
increase irrespective of initial conditions there is an
increase in stratiform rainfall amount in RAS compare to
SAS throughout the forecast duration. However, it is also
interesting to point out that convective component of the
rainfall is much higher in SAS compared to RAS and
grows very rapidly as the forecast lead time increases.
These results clearly suggest that stratiform and convective
rainfalls are dominant in RAS and SAS respectively. It is
also seen that the temporal evolution of vertically integrated diabatic heating in RAS is stronger than SAS
throughout the forecast duration and has an excellent correspondence with the increase in stratiform rainfall of RAS
irrespective of initial conditions. These results supports the
argument that the abundance availability of moisture in
RAS particularly at the upper levels leading to the
enhancement of stratiform heating and rainfall.
In general, all these results demonstrate that because of
enhanced moisture transport through lower level westerlies
in the RAS, there is a vigorous amplification in the moist
static energy throughout its troposphere, particularly in the
middle and upper levels. This creates conducive condition
to facilitate more moisture convergence (Neelin and Held
1987; Srinivasan and smith 1996). In addition to that, the
detrainment of moisture at multiple levels from the cloud
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Fig. 18 Daily climatology
mean (2001–2007) difference
time–pressure (hPa) plots
(RAS–SAS) for moist static
energy (KJ kg-1) in shaded
(positive) and dashed (negative)
with interval 50, magnitude of
moisture transport (kg m s-1) in
solid contours (positive) and
dotted (negative) at 0.4 interval
for forecast days 1–45 averaged
over 10S–40N, 50–110E based
on different initial conditions
(IC). a 1 May IC, b 5 June IC,
c 5 July IC
to environment in different cloud types of the RAS
parameterization leading to large scale accumulation of
moisture in the middle and upper levels. This abundance of
moisture availability further enhances the formation
of condensate through simple cloud microphysical parameterization (Zhao and Carr 1997) and directly facilitates
intense grid scale condensational heating at the higher
levels and produces more stratiform rain in the model. This
upper level availability of moisture further augments and
sustains higher magnitudes of the moist static energy and
diabatic heating in the RAS and leads to the robust manifestation of warm biases in the model. This warming pattern in RAS is primarily due to the large scale condensation
facilitating increase in grid scale rainfall and decrease in
convective rainfall.
In case of SAS, besides less moisture transport factor,
the key issue in the parameterization scheme is the cloud
mass fluxes detrainment happen entirely and only from the
top of a single deep cloud at each model time step. It is
found that due to higher convective available potential
energy (CAPE) in SAS than RAS (figure not shown)
throughout the forecast duration leads to reduction in the
level of free convection (lower cloud bases) in SAS and
producing more convective rain in the model. However,
because of the drier upper level and lower cloud tops
(based on moist static energy difference between cloud and
environment) the sustained moisture supply is absent at the
higher levels leading to less stratiform clouds (rainfall) in
the model compared to RAS. In both the schemes, convective adjustments are strongly modulated by magnitudes
of moist static energy of the model. It is revealed that, in
the case of RAS the supply and sustainability of moisture is
happening both from the lower level as well as at the upper
levels leading to enhanced moist static energy and heating.
Whereas in case of SAS the upper levels are dry and has
moderate moist static energy mostly confined to the lower
levels. It is also seen that moist static energy is too weak in
the boundary layer of SAS compare to RAS and ANA.
6 Summary
This study is an effort not only to evaluate and characterize
the CFS-1 (T64L62) model systematic biases over Indian
monsoon region employing two different convective
parameterization schemes (i.e. SAS and RAS) but also to
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Fig. 19 Daily climatology
mean (2001–2007) time–
pressure (hPa) Q1 diabatic
heating (K day-1) and moisture
convergence (mm day-1) of
SAS (a, c, e) and RAS (b, d, f).
Shaded (positive), dash
contours (negative, 0.1 interval)
for Q1, moisture convergence in
blue solid contours (0.02
interval). These plots are for
forecast days 1–45 averaged
over 10S–40N, 50–110E based
on different initial conditions
(IC) a, b 1 May IC c, d 5 June
IC and e, f 5 July IC of SAS and
RAS, respectively
elucidate mechanisms responsible for these errors in
respective models. In general, ensemble climatological
mean results imply that forecasts from two different
cumulus schemes are able to replicate the mean characteristics of the low level cross equatorial flow over Indian
monsoon region for JJAS months; however the strength of
the flow is stronger in RAS compare to ANA and SAS
(figures not shown). The simulated seasonal rainfall over the
neighborhood oceanic regions are overestimated in both
SAS and RAS compare to GPCP (observed). Broadly, the
excessive rainfall bias over the eastern foothills of Himalayas remain unchanged in both forecasts, suggesting higher
resolution model simulations are necessary to address the
issue of orography and its influence on precipitation. The
differences in rainfall between SAS and RAS suggest that
SAS (RAS) is predicting higher rainfall over Arabian Sea
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(Central India) although qualitatively, they are the same.
The JJAS seasonal mean stratiform component of the total
rainfall of RAS is much higher in magnitude with larger
spatial coverage over the ISM domain compare to SAS and
TRMM. The spatial distribution of seasonal mean climatology (i.e. JJAS) of apparent heat sources field indicate that
both SAS and RAS are overestimating their magnitudes as
well as areal coverage when compared with ANA. Over the
oceanic regions RAS is able to capture the zone of maximum heating better compare to SAS. Similarly for apparent
moisture sink, both SAS and RAS are overestimating its
magnitude; however zonal patterns of moistening over BoB
are better captured in RAS than in SAS when both are
compared with ANA. Broadly, the spatial distributions of
maximum heating (moistening) areas in these two simulations have reasonable resemblance with ANA.
Climate Forecast System
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Fig. 20 Daily time series
differences (RAS–SAS) of
vertically integrated moisture
convergence (mcn, mm day-1)
and total atmospheric column
precipitable water (ppw,
kg m-2) for forecast days 1–45
averaged over 10S–40N,
50–110E based on different
initial conditions (IC). a 1 May,
b 5 June and c 5 July
The major impact from cumulus parameterization on
critical thermodynamical parameters of the model such as
temperature and relative humidity are established by analyzing 7 years daily forecast climatology data sets (up to
45 days lead time) covering monsoon rainfall months from
three different initial conditions namely 1 May, 5 June and
5 July. The profound warming pattern at the middle and
upper troposphere is distinctly noted in RAS after
15–20 days of model integrations for all initial conditions
(i.e. 1 May, 5 June and 5 July) compare to ANA and SAS.
For SAS, though the model troposphere has a slow
warming tendency as forecast progresses, however the
magnitude of warming is not robust. Therefore, when
compare with ANA strong cooling bias appears throughout
its troposphere for the entire forecast duration (i.e. up to
45 days) for all the three initial conditions. The warming
trend in the model is being well reciprocated by the
moisture holding capacity of the respective models. It is
found that the middle and higher troposphere of RAS
simulations have significantly higher availability of moisture (relative humidity) compare to SAS and ANA. These
strong warming (moistening) biases have been revalidated
against ERAI data sets to support the robustness of the
prevailing model systematic errors. Comparison with ERAI
qualitatively supports the findings that, a robust systematic
warming (moistening) bias exist in the RAS version of
CFS-1 model, particularly at the middle and upper troposphere irrespective of different initial states of the model.
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Fig. 21 Daily climatology mean (2001–2007) time series difference
(RAS–SAS) plots vertical integrated \Q1[ diabatic heating (Wm-2)
scaled 9 102, startiform rain (mm day-1), convective rain
(mm day-1), total rain (mm day-1) for forecast days 1–45 averaged
over 10S–40N, 50–110E based on different initial conditions (IC) a 1
May IC, b 5 June IC and c 5 July IC
The segregation of systematic error from total error of
dynamical and thermodynamical parameters using all the
forecasts from 31 initial conditions of all 7 years (i.e.
2001–2007) for both SAS and RAS are examined using
error energy/variance and RMSE techniques. It is found
that the major contribution to the total error is coming from
the systematic component of the model error. The results
also suggest that temperature forecasts in RAS having less
error compare with SAS, however for relativity humidity it
is vice versa, although the difference of magnitudes in
moisture errors are not as large as compared to that of
temperature. Moreover, the error analysis results suggest
that the impact of cumulus parameterization changes on
error characteristics of the wind fields in both forecasts are
not as profound as seen in the thermodynamical fields.
The skills of SAS and RAS forecasts are examined from
spatial as well as temporal perspectives over the ISM region
to access the impact of cumulus convection on respective
precipitation predictions. The spatial characteristics of the
predicted rainfall for these two simulations are examined
over the Indian monsoon region from 0 to 15 and 16 to
30 days for three different initial conditions (i.e. 1 May, 5
June and 5 July). It is noted that, the differences in rainfall
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characteristics are apparent between these two simulations.
In general it is found that the extent of rainfall deficit area
over land regions is higher in SAS compared to RAS. Over
the ocean, particularly over the Arabian Sea and the west
coast of India, rainfall forecast errors in RAS are reduced
compared to SAS for all three initial conditions up to 30-days
lead time. The pentad mean predicted rainfall skills up to
40 days lead time from all three initial conditions of 7 years
(i.e. 2001–2007) is carried out over three important geographical regions relevant to Indian summer monsoon processes i.e. Indian monsoon core zone, Arabian Sea and BoB.
In general it is found that over the monsoon core region SAS
prediction skills are better than RAS, however over the
Arabian Sea RAS has higher skills than SAS for all initial
conditions and there is no consistency in skills for both these
simulations over BoB region. It is to mention that although
SAS forecasts have strong cooling biases, nevertheless, it has
higher skills over the Indian monsoon core region.
In order to elucidate the possible mechanisms for these
biases in the extended range forecasts of the CFS-1 model
over Indian region, a number of important parameters are
examined for both SAS and RAS. It is found that, due to
strong lower level cross equatorial flow there is an
enhanced moisture transport with higher vertical extent of
moisture intrusion in the RAS compare to SAS. This
condition leads to a robust increase in moist static energy in
RAS dominating its middle and upper tropospheric levels
by facilitating more convectively unstable condition and
moisture convergence in the model. Both the schemes (i.e.
SAS and RAS) use a modified version of the Arakawa and
Schubert (1974) parameterization and have higher sensitivity to vertical distribution of moist static energy which is
a crucial factor for instability in the model. As it is seen
that, RAS has large accumulation of moist static energy it
promotes more moisture convergence in the model compare to SAS. In addition, it is also found that due to
inherent detrainments of moisture from clouds to environment at multiple levels from spectrum of clouds
parameterized in RAS, there is a continuous supply and
large accumulation of moisture in the middle and upper
troposphere of the model. This abundant moisture availability in RAS facilitated the production of more condensate through simple cloud microphysics scheme. This in
turn plays a principal role in manifestation of strong latent
heating due to large scale condensation in the upper troposphere and significantly enhancing the stratiform rainfall
in RAS compared to SAS.
In the case of SAS it is evident that the moisture
transport is weaker than RAS, leading to more modest
generation of moist static energy mostly confined only to
the lower tropospheric levels of the model. The poor
moisture availability at upper level of SAS is primarily
because it considers single deepest cloud for detrainment.
Climate Forecast System
The detrainment happens only and entirely from this cloud
top contrary to the spectrum of clouds with multiple level
moisture detrainments in case of RAS. This is the principal
factor for which there is a deprivation of moisture in the
SAS particularly at the middle and upper levels contrast to
RAS. This also suggest that even when the moisture convergence is higher and the level of free convection is lower
in SAS compare to RAS, still it is unable to generate and
sustained penetrative convective clouds to higher heights
with elevated cloud tops (as seen in the case of RAS).
Therefore the heating with convective signatures in SAS is
mostly restricted to the lower levels. It also seen that CAPE
and the contributions of convective rainfall are higher in
SAS over the Indian region. Due to the simultaneous
moisture supply both from the lower as well as at the upper
level in RAS and higher sensitivity of parameterization
scheme to moisture changes are the dominant factors for
the manifestation of robust warming in the model which is
not seen in SAS. Here, the key point to convey that
although using RAS parameterization in CFS-1 model’s the
cooling bias in the troposphere has been corrected; however, its rainfall forecast skills over the Indian landmass
(i.e. Monsoon core zone) are consistently lower than SAS.
Therefore, more forecast experiments and analysis are
needed before arriving at a concrete conclusion that one
parameterization is superior/inferior to the other.
Here we would like to underscore the point that the
coarser resolution of the CFS-1 model (T62L64), is not
adequate enough to address the issue of mesoscale organization of convection which is one of most challenging
problems for convective parameterization as of now. In
addition, we believe at a higher resolution, the CFS model
can better resolve orographic effects and improve prediction skill of orographic induced precipitation. As previous
studies indicated (Yang et al. 2008; Krishnamurti et al.
2006 and references therein; Sperber et al. 1994) that using
higher resolution version of the coupled CFS model (T126
or higher) might further enhance the monsoon simulation
and prediction skills of the dynamical couple model.
The major findings of this study suggest that, systematic
errors in temperature and humidity has direct impact not
only on the fundamental parameters of the convective and
radiative parameterization schemes of the model, but also
indirectly affect the individual/ensemble cloud characteristics such as vertical extent of the cloud (height), cloud
base, cloud fraction and their rain bearing capacity. These
all impact the overall model forecast skill.
Finally, we would like to emphasize that the implications
of warming (moistening) and cooling (drying) biases in the
middle and upper troposphere have substantial influence
(direct/indirect) on the model’s cumulus cloud effects,
prediction capability of low frequency intra seasonal
oscillations (ISOs) leading to active/break cycles, high
363
frequency synoptic events and other key dynamical/thermodynamical processes of the monsoon circulations eventually affecting its prediction skills. We would also like to
highlight that the cloud microphysical scheme which plays
a crucial role and act in tandem with cumulus parameterization scheme in formation of stratiform rainfall and upper
level heating in the model also needs to be further investigated at a higher resolution to understand the complex
intricacies of clouds (i.e. mesoscale organization, interactions between stratiform and convective clouds). These in
turn can be affected by impacts of the grid-scale microphysics scheme on radiative transfer in the model atmosphere. In addition, the land surface scheme and Planetary
Boundary Layer (PBL) schemes can both affect PBL stability and impact triggering of convection in the model.
In nutshell, these biases can impact model’s ability to
predict not only the overall rainfall pattern, heavy rainfall
episodes and diurnal cycle but also its location, frequency
and intensity of the rainfall. Therefore, further analysis and
experiments will be carried out on important components
of the both implicit and explicit parameterization schemes
in the operational version of CFS model (i.e. CFSV2) and
other state of the art mesoscale models at a very high
resolution to further enhance our understanding of these
systematic biases. Eventually, feasible and suitable modifications in the specific parameterization modules will be
incorporated to minimize systematic biases and to augment
model’s prediction skills over Indian monsoon region from
an extended range timescale perspective.
Acknowledgments We are thankful to The Director, I.I.T.M and
High Performance Computing (HPC) facility at I.I.T.M for computation and storage support for carrying out the study. We thank
NASA, NCEP-NCAR, ECMWF, GPCP for providing free access to
their respective data sets. The Grid Analysis and Display System
(GrADS) software has been used for visualization and plotting purpose, therefore our sincere thanks to the GrADs team. Our sincere
gratitude and obligation to the Ministry of Earth Sciences (MoES),
Government Of India for conceiving National Monsoon Mission
(NMM) project jointly with National Oceanic Atmospheric Administration (NOAA), USA and its sustained support and encouragement
to I.I.T.M for taking up the challenging research initiatives to improve
Indian monsoon rainfall prediction using state of the art coupled
ocean atmospheric dynamical models. We are also grateful to our
esteem reviewers for their valuable comments and suggestions which
help us to enhance the quality of the manuscript. We are also grateful
to Head, School of Earth, Ocean and Climate Sciences, Indian
Institute of Technology Bhubaneswar for his encouragement and
support to carry out this work.
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