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This is a repository copy of Investigation of the snow-monsoon relationship in a warming atmosphere using Hadley Centre climate model.
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Panda, SK, Dash, SK, Bhaskaran, B et al. (1 more author) (2016) Investigation of the snow-monsoon relationship in a warming atmosphere using Hadley Centre climate model. Global and Planetary Change, 147. pp. 125-136. ISSN 0921-8181
https://doi.org/10.1016/j.gloplacha.2016.10.013
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1
Investigation of the Snow-Monsoon Relationship in a Warming 1 Atmosphere Using Hadley Centre Climate Model 2
3
4
5
6 S. K. Pandaa, S. K. Dasha, B. Bhaskaranb,*, and K. C. Pattnayaka 7
8 aCentre for Atmospheric Sciences 9
Indian Institute of Technology Delhi 10 Hauz Khas, New Delhi -110 016, India 11
12 13
bMet Office Hadley Centre 14
FitzRoy Road, Exeter, EX1 3PB, UK 15
16 * Current Affiliation: 17
Fujitsu Laboratories of Europe 18 Hayes Park Central, Hayes 19
Middlesex, UK 20 21
22 23 24 25 26 27 28 29
Corresponding Address 30 31
Dr. Subrat Kumar Panda 32 Centre for Atmospheric Sciences 33
Indian Institute of Technology Delhi 34 Hauz Khas, New Delhi -110 016, India 35
Tel: +91 11 2659 6023 36 Fax: +91 2659 1386 37
E-mail: [email protected] 38 39
2
1
Abstract 2 3 Several studies based on observed data and models show that there is an 4
inverse relationship between the strength of the Indian summer monsoon and the 5
extent/depth of Eurasian snow in the preceding season. Perturbed Physics Ensemble 6
(PPE) simulations of HadCM3 have been used in this study to re-examine the snow-7
monsoon relationship in the longer time scale. The PPE monthly precipitation values 8
during June, July, August and September (JJAS) have been compared with the 9
corresponding values of Climatic Research Unit (CRU) for the period 1961-1990. The 10
PPEs which simulated the Indian Summer monsoon reasonably well have been used for 11
examining snow-monsoon relationship. Atmospheric fields such as wind, geopotential 12
height, velocity potential and stream function from the PPE simulations have been 13
examined in detail. Results show that because of the west Eurasian snow depth anomalies, 14
the mid-latitude circulation undergoes significant changes, which in turn lead to 15
weak/strong monsoon circulation during deficient/excess Indian Summer Monsoon 16
Rainfall (ISMR) respectively. The first Empirical Orthogonal Function (EOF1) of winter 17
snow depth for the period 1961-1900 over the whole of Eurasia explains 13% variability. 18
Thus the significant correlation patterns are consistent with the most dominant EOF of 19
snow depth, in which the first mode describes a dipole type structure as observed. The 20
study confirms that snow depth in the western part of Eurasia (200E-650E and 450N-21
650N) has negative correlation with the ISMR. 22
23
Keywords: Indian Summer Monsoon Rainfall (ISMR), Perturbed Physics Ensemble 24
(PPE), Empirical Orthogonal Function (EOF1) 25
26
3
1. Introduction 1
The summer monsoon rainfall over the Indian sub-continent is found to be 2
more significant in terms of its space and time variability. The Indian summer 3
monsoon (also known as southwest monsoon) contributes about 80% of the annual 4
rainfall over most parts of the country during the four months from June to 5
September, while Tamil Nadu receives maximum rainfall during winter monsoon. 6
Indian summer monsoon is part of a seasonally reversing wind system characterized 7
by wet summers and dry winters. The wind during summer monsoon season are 8
associated with a large scale vorticity at 850 hPa and the Low Level westerly Jet 9
(LLJ) over the Arabian Sea and an anticyclone (the Tibetan anticyclone) at the 10
upper level (200 hPa) with the monsoon easterly jet. The economical progress of the 11
country is mostly dependent on the behavior and changes in summer monsoon 12
precipitation. Dash et al. (2012) examined the characteristics of extreme 13
precipitation and temperature events during the Indian summer monsoon months 14
with 9-member ensembles over the period 1982 to 2009 using RegCM3. Their study 15
shows that, the simulated extreme weather conditions like very wet days, extremely 16
wet days, warm days and warm nights are more often as compared to those in India 17
Meteorological Department (IMD) observed values in Central India. Ashfaq et al. 18
(2009) simulated the dynamical features of the summer monsoon with 25 km 19
resolution over South Asia region and found that enhanced greenhouse forcing 20
resulted in the overall suppression of summer monsoon precipitation, delay in onset 21
and an increase in the occurrence of monsoon break periods. 22
23
4
Long back Blanford (1884) suggested that the varying extent and thickness of 1
continental snow cover exerts an influence on the land surface’s thermal characteristics 2
and in turn, influences the onset of the Asian summer monsoon. Because continental 3
snow cover in the Asian region persists for a long time and snow-monsoon relationship is 4
observed to be inverse, it has remained as one of the important parameters in empirical 5
long range prediction of monsoon. Studies of Hahn and Shukla (1976), Kripalani and 6
Kulkarni (1999), Bamzai and Shukla (1999), Fasullo (2004) based on observed data 7
emphasized on the inverse relationship between the Indian Summer Monsoon Rainfall 8
(ISMR) and the Eurasian snow cover/depth in the preceding season. Kripalani et al. 9
(2003) further reiterated that the summer monsoon over India and south Asia are 10
primarily governed by large-scale atmospheric features like ENSO and Eurasian winter 11
snow cover. 12
Barnett et al. (1989), Yasunari et al. (1991) and Vernekar et al. (1995) used a 13
number of General Circulation Models (GCM) to examine and analyses the relationship 14
between snow and monsoon. Their results clearly indicate that a positive snow anomaly 15
over Eurasia in winter/spring does affect the monsoon circulation in the following 16
summer in terms of weaker monsoon. Sankar-Rao et al. (1996) using data from National 17
Environmental Satellite Data and Information Service (NESDIS) of the National Oceanic 18
and Atmospheric Administration (NOAA) for the period 1967 to 1992 concluded that 19
following the winters of more snow, stationary perturbations with higher pressures over 20
central Asia north of India are produced in the lower atmosphere and the following Asian 21
summer monsoon is weaker. Simultaneously, in the upper-atmosphere, lower anomalous 22
pressure occurs during summer which weakens the upper level monsoon high. 23
5
Dash et al. (2004, 2005) tried to show the impact of Eurasian snow depth 1
anomalies on the Indian summer monsoon rainfall anomaly through the mid-latitude 2
circulations. They used Soviet snow depth data and Indian rainfall data for the period 3
1951-1994 and showed that in winter through spring undergo significant changes in the 4
upper and lower level winds, geopotential height, velocity potential and stream function 5
fields. Such changes in the large-scale circulation pattern may be interpreted as 6
precursors to weak/strong monsoon circulation and deficient/excess ISMR. Dash et al. 7
(2006a) confirmed the inverse snow-ISMR relationship by using the actual observed 8
values of snow depth of Historical Soviet Daily Snow Depth (HSDSD) version II data set 9
in the spectral GCM of IITD. The Regional Climate Model version 3 (RegCM3) has also 10
been integrated from 1st April to 30th September in four years (Dash et al. 2006b, Shekhar 11
and Dash 2005) starting from 1993. Monthly snow depth data based on NIMBUS-7 12
SMMR satellite are used for conducting sensitivity experiments with RegCM3. Their 13
simulations reveal that excess Tibetan snow in April decreases the rainfall over India in 14
the following monsoon season. Their simulations also show that lower level westerlies 15
and upper level easterlies are weakened in the snow experiment than in the control 16
experiment. 17
From the global warming point of view relation between Eurasian snow and ISMR is 18
an important issue, some studies in the recent past show changes in the characteristics of 19
summer monsoon rainfall in the warming atmosphere. The robust inverse relationship 20
between Eurasian snow and ISMR is essential to be examined in the warming atmosphere. 21
PPE simulations of Hadley Centre, UK Met Office have provided data over a large 22
number of years and hence the objective of this study is to use those model scenarios to 23
6
examine above. CRU rainfall data have been used for comparison. The PPEs which give 1
better results in terms of ISMR have been selected for the study of snow monsoon 2
relationship in a warming scenario. Finally, the characteristics of upper and lower-level 3
mean monsoon circulations and midlatitude circulations during extreme snow depths 4
years over Eurasia will also be examined in detail. The description of model details and 5
selection of PPEs has been described in Section 2. Section 3 discusses the results, while 6
summary and conclusions are given in the last Section 4. 7
2. Description of the Model and Simulations 8
2.1. Model Details 9
The PPE simulations are made mainly on an ensemble of variants of the slab 10
version of HadCM3. HadCM3 is the third generation Hadley Centre model of UK Met 11
Office. It is a coupled atmosphere-ocean GCM developed at the Hadley Centre and 12
described by Gordon et al. (2000). The atmospheric component of the model has 19 13
levels with a horizontal resolution of 2.5 degrees of latitude by 3.75 degrees of longitude, 14
which produces a global grid of 96 x 73 grid cells. ). The governing equations are solved 15
using a semi explicit finite difference scheme at 30 minute interval. More details of the 16
formulation and climate of the atmosphere component of the model can be found in Pope 17
et al. (2000). The oceanic component of the model has 20 levels with a horizontal 18
resolution of 1.25 degrees of latitude by 1.25 degrees of longitude. 19
20
2.2. Simulation Details and Description of PPEs 21
Perturbed Physics Ensemble (PPE) is mainly based on an ensemble of variants of 22
the slab version of the HadCM3, mainly where variations are generated by changing the 23
7
values of the input parameters those control physical processes. It provides a new 1
approach in which modeling uncertainties are sampled systematically by perturbing 2
known but uncertain parameters. As per Stainforth et al. (2005) PPE identifies a set of 3
model variants which samples the settings of multiple uncertain parameters according to 4
prior distributions estimated by experts based on their knowledge of the relevant physical 5
processes. The PPE method represents an attempt to develop a systematic method of 6
sampling modeling uncertainties within a single model framework. The most thorough 7
way to investigate this uncertainty is to run a massive ensemble experiment in which each 8
relevant parameter combination is investigated. Its main advantage is that it allows 9
greater control over the design of experiments to sample process uncertainties. 10
A recent study by Murphy et al. (2007) shows that the probabilistic predictions of 11
future climate change is based on a set of ensemble simulations of equilibrium and time 12
dependent changes, carried out by perturbing poorly constraints parameters controlling 13
key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere 14
global climate model These experiments allow quantification of the effects of earth 15
system modeling uncertainties and internal climate variability of feedbacks likely to exert 16
a significant influence on twenty-first century climate at regional scales. Parameters were 17
perturbed in the atmosphere and sea-ice components in order to generate the ensemble 18
and sample the uncertainties in climate feedback processes. 19
PPEs were performed with CO2 levels set at pre-industrial and double pre-20
industrial levels. In Murphy et al. (2004), each of the 53 ensemble members was 21
generated by perturbing atmosphere, sea ice or land-surface parameters away from their 22
standard value or by switching on and off particular options within a parameterisation 23
8
scheme. Using the 53 experiments with single parameter perturbations the effects of 1
multiple parameter perturbations to the model could be inferred from a linear 2
combination of the output from those runs. From those output, they picked 128 versions 3
by splitting the resultant distribution of climate sensitivity into 64 equally probable bins 4
and picking 20 model versions from each bin with the best Climate Prediction Index 5
(CPI) score. Taking this 1280 subset of the four million, and starting from the model 6
version with the best CPI score, then they picked the model that was furthest away from 7
that version as measured by a non-dimensional measure of distance in model parameter 8
space. The 128 model versions were run in slab configuration. They also picked 16 9
versions to run contemporaneously in fully coupled configuration by taking the parameter 10
settings with the best predicted CPI score in the subset of eight model versions from four 11
adjacent climate sensitivity bins. Again, the aim was to sampling a wide range of 12
(predicted) sensitivities and parameter values from the 128 member slab model ensemble. 13
A new 17-member PPE of HadCM3 transient simulations (denoted by PPE_A1B) 14
has recently been produced, in order to provide user-relevant predictions of twenty-first 15
century climate change. These are driven from 1860 to 2000 by historical time series of 16
well-mixed GHGs, ozone, anthropogenic sulphur emissions and reconstructions of 17
variations in solar activity and volcanic aerosol and from 2000 to 2100 by future GHG 18
concentrations and sulphur emissions from the SRES A1B scenario (Nakicenovic & 19
Swart 2000). One ensemble member uses standard parameter settings, with 16 perturbed 20
members chosen from the parameter combinations used by Webb et al. (2006). These 21
were selected by choosing the member with the best simulation of present-day climate, 22
followed by 15 further members designed to maximize a non-dimensional measure of the 23
9
average distance between members in terms of both climate sensitivity and model 1
parameter values. 2
3
3. Results 4
3.1. Simulated Indian Summer Monsoon in PPEs 5
Indian summer monsoon circulation along with the accompanied rainfall is one of the 6
most important atmospheric phenomena which needs to be tested by a GCM. Hence, in this 7
study some of the important meteorological variables such as precipitation, wind at both 8
850 hPa & 200hPa and surface temperature from the 17 PPE simulations are used for the 9
period 1961 to 1990 in order to examine the main characteristics on Indian summer 10
monsoon. For this study, Climatic Research Unit (CRU) rainfall data from 1961 to 1990 11
has been used for the validation of model. 12
13
The time series of ISMR averaged for the period 1860-2098 over all the Indian 14
land points have been depicted in Figure 1 for all the PPEs. The thick line in the middle 15
indicates the mean of all the PPEs. In this figure, the summation of mean and standard 16
deviation are depicted above the thick line and the difference between mean and standard 17
deviation are depicted below the thick line. The figure indicates increase in ISMR after 18
around 2000. Mean, Standard Deviation (SD) and Coefficient of Variation (CV) of 19
summer monsoon rainfall are shown in Figure 2a, b and c respectively during the period 20
1961-1990. Figure 2a shows that rainfall is more over the west coast, north east and Bay 21
of Bengal. Mean PPEs (Figure 2a) simulated rainfall spatial distribution depicts that JJAS 22
mean rainfall are about 10-12 mm/day over the Northeast India, 8-12 mm/day over the 23
East Coast, 8-10 mm/day over the West Coast and 6-8 mm/day over the Northwest India. 24
10
Thus the mean PPEs is able to capture the major rainfall regions over India. Figure 2b 1
show that the standard deviations of summer monsoon rainfall simulated by mean PPEs 2
are 3.5 mm/day, 2.5 mm/day and 1.5 mm/day over the North east, East coast and West 3
Coast of India respectively. Figure 2c explains CV over North east, East coast and West 4
Coast of India are 35%, 25% and 20% respectively. PPE monthly precipitation values 5
during June, July, August and September (JJAS) have been compared with the 6
corresponding values from CRU for the period 1961-1990. First the mean PPE JJAS 7
precipitation have been regridded with respect to those of CRU and then applied an 8
Indian mask in the PPE simulations and CRU. The differences between each regridded 9
PPE and CRU are depicted in Figure 3. The PPEs such as 0, 2, 4, 7, 8, 13, 14 and 15 10
has overestimated the rainfall over east coast and north east part of India by 11
2-3 mm/day compared with the CRU rainfall. These PPEs underestimates the 12
simulated rainfall over the plains of Central India and Rajasthan by 2-3 13
mm/day. Another group of PPEs such as 1, 3, 5, 6, 9, 10, 11, 12 and 16 has 14
overestimated the rainfall over southern Peninsular by 2-3 mm/day. These 15
groups of PPEs are not able capture the rainfall over the central India. These 16
PPEs underestimates 1-3 mm/day rainfall over the northern and north east of 17
India. The Root Mean Square Errors (RMSE), pattern correlations and standard 18
deviations between PPE and CRU rainfall have been examined to select which PPEs 19
result are better representation of observed ISMR. Figure 4 shows that there are two 20
groups of PPEs out of which one gives better result in terms of high pattern correlation 21
and less RMSE. The group consists of 0, 2, 4, 7, 8, 13, 14 and 15 members is selected for 22
snow-monsoon study. 23
11
1
2
3.2. Snow Monsoon Relationship in PPEs 3
Based on the above results, the first groups are used for examining snow-4
monsoon relationship. When entire period of 1860 to 2098 is considered. Figure 5 shows 5
the ensemble mean of December, January and February (DJF) Eurasian snow-depth 6
and ISMR standardised anomalies are inversely related. Spatial patterns of correlation 7
coefficients depicted in Figure 6a show two dominant modes. Thus DJF snow depth over 8
western Eurasia has negative correlation while those over eastern Eurasia have positive 9
correlation with the subsequent ISMR. EOFs of winter (DJF) snow depth for the period 10
1961–1990 have also been computed for the western part of Eurasia. It is found that 11
EOF-1 over the western Eurasia explains about 13% of variability as shown in Figure 6b. 12
Hence the significant correlation patterns are consistent with the most dominant EOF of 13
snow depth, in which the first mode describes a dipole type structure. 14
15
3.3. Categorization of Snow and Monsoon Years: 16
Using the monthly snow depth data for the years 1861–2008, DJF mean values for 17
each year in west Eurasia are computed along with the mean of the series and the 18
standard deviations. The snow depth is expressed as a standardized snow-depth anomaly 19
by dividing the variations of each year from its normal value by the standard deviation. 20
The standardized snow-depth anomaly thus calculated over west Eurasia for the period 21
1861–2008 is shown by solid line with open circle in Figure 7. The years with snow-22
depth anomaly between ±0.5 standard deviations are considered as normal snow years. 23
Similarly, the years with snow-depth anomaly above +0.5 standard deviation are counted 24
12
as high snow years and those with less than –0.5 standard deviation snow depth anomaly 1
are identified as low snow years. In order to increase the sample size, the classification 2
based on ±0.5 standard deviation is adopted here. The ISMR anomaly for each year has 3
also been computed and plotted using the solid line with closed circle in Figure 7. 4
Following the criterion of IMD, the years having ISMR anomaly more than +1 standard 5
deviation (above 90% of its long-term average value) are termed as excess monsoon 6
years and those less than –1 standard deviation are considered deficient monsoon years. 7
The years with an ISMR anomaly between –1 and +1 standard deviations are classified as 8
normal monsoon years. We have selected the years 1888, 1913, 1959 and 1976 are high 9
snow years and 1867, 1923, 1964 and 1989 are low snow years. 10
11
3.4. Characteristics of Circulations in the extreme snow-monsoon years: 12
Bamzai and Shukla (1999) emphasized that the inverse snow-monsoon 13
relationship holds especially in those years when snow is anomalously high or low for 14
both the winter as well as the consecutive spring season. If there is heavy snow cover in 15
winter it is likely that it will affect the snow cover in spring. Heavy snow cover in 16
midwinter usually does not easily melt because of low-level solar insolation. If there is 17
already snow on the ground, the precipitation in February, March and in April is likely to 18
be in the form of snow (because of the surface temperature close to 0°C) rather than rain. 19
Such a process could explain the results of Bamzai and Shukla (1999). Hence, to examine 20
the evolution of the mean monsoon fields a season in advance in response to the snow 21
depth over west Eurasia, the two groups of extreme snow and ISMR years are selected. 22
The difference in the circulation characteristics in high snow (1888, 1913, 1959 and 23
13
1976) and low snow (1867, 1923, 1964 and 1989) years are studied in detail by analyzing 1
the composite difference fields of wind, geopotential height, stream function and velocity 2
potential in the mean of JJAS separately. 3
4
3.4.1 Winds 5
The wind differences (m/s) between the composites of high and low snow years at 6
850 hPa and 200hPa are shown in Figure 8 for the monsoon season. Figure 8a indicates 7
that a well-organized anomalous cyclonic circulation over central Eurasia. There is an 8
anomalous westerly to the south of Caspian Sea, which implies that the westerly is 9
stronger in the high snow depth case than in the low snow depth case. There is indication 10
of an anomalous anticyclonic circulation over the north Arabian sea and west India 11
centred at about 20°N and 75°E which contributes to the anomalous easterlies over the 12
Arabian Sea during JJAS. This implies that Asian summer monsoon lower-level 13
westerlies are weaker in response to high Eurasian snow depth than in low snow depth. 14
Most of India affected by the anomalous anticyclonic circulation during monsoon season 15
at the lower level (Figure 8a) results in a weak monsoon circulation in high snow 16
compared to low snow years. From the examination of the upper level (200 hPa) (Figure 17
8b) wind difference fields in JJAS, a well-developed anomalous anticyclonic circulation 18
is found over southeast Asia in high snow years compared to low snow years. An 19
anomalous anticyclonic circulation is shown over central Eurasia and an anomalous weak 20
anticyclonic circulation is simulated over Bay of Bengal and south east Asia. It seems 21
that such anomalous features suppress the development of upper tropospheric 22
anticyclones and hence the monsoon circulations over India. These results indicate that 23
Asian summer monsoon easterlies are weaker in response to high snow depth than in 24
14
snow depths are less. The anomalous winds at the upper level correspond to weaker 1
easterlies in the deficient monsoon years in response to high snow compared to the excess 2
rain years. It also shows that during the year of high snow, the easterlies started 3
weakening to give rise to weak monsoon upper level easterlies during JJAS. Also in 4
JJAS, anomalous westerlies are found over the South China Sea. Such anomalous 5
westerlies during high snow years arrest the north northwest movement of the Tibetan 6
anticyclone from its winter position over the equator to its summer position over Tibet. 7
Such anomalous features suppress the development of upper tropospheric anticyclones 8
and hence the monsoonal circulation over India. 9
10
3.4.2 Geo-potential Height 11
The geo-potential height difference (m) between composites of high and low 12
snow years field in JJAS are shown in Figures 9a at the lower troposphere and in Figure 13
9b at the upper troposphere. The negative difference fields over the western Eurasia in 14
Figure 5.12a can be attributed to the higher snow. From, Figure 9a it is seen that the 15
monsoon heat low over central south Asia is weaker (positive values) in high snow years 16
compared to low snow years. This positive geo-potential difference field indicates an 17
adverse pressure gradient anomaly in winter which persists through spring up to the 18
monsoon season. The geo-potential difference field at 200 hPa in Figure 9b indicates a 19
weaker upper level high (negative values) over west Eurasia and east Asia in JJAS. Such 20
lower anomalous pressure at the upper level covering the entire region from the middle 21
latitudes in Asia to India was reported by Sankar-Rao et al. (1996). 22
23
24
15
1
3.4.3 Velocity Potential 2
It is well known that there is a strong upper level divergent centre associated with 3
the Asian monsoon. The correlations between the monsoon rainfall and 200-hPa velocity 4
potential, a good surrogate for the "Walker circulation," are strong in the period 1958-80 5
(Kumar et al., 1999). The differential heating leads to the growth of available potential 6
energy which is next passed on to the divergent motions via the covariance of vertical 7
velocity and temperature. The final link in this scenario is the transfer of energy from the 8
divergent to the rotational part of the motion field that describes the monsoon. These are 9
largely described by and ぬ interaction (where is a stream function and ぬ is a velocity 10
potential) Krishnamurti et al. (1998). 11
The lower and upper level seasonal tropospheric velocity potential difference 12
between composites of high and low snow years in JJAS are shown in Figures 10a and b. 13
It is well known that there is a strong upper level divergent center (Krishnamurti, 1972) 14
associated with the Asian monsoon. At 850 hPa (Figure 10a) the negative values over 15
Indian subcontinent are prominent which gives a weaker convergence of monsoon. The 16
positive difference fields over the Indian subcontinent and the negative difference fields 17
to the east in Figure 10b indicate that the upper level divergence center was weaker over 18
India in high snow years compared to low snow. This divergent circulation changed in 19
such a way that the intensity of easterlies over the monsoon regions of southern Asia and 20
Africa was weak in high snow years. In their study Chen and van Loon (1987) have 21
inferred that the anomalous divergent circulation during years of weak tropical easterly 22
jet reduces the generation of kinetic energy on the upstream side of the jet and the 23
16
destruction of kinetic energy on the downstream side of the jet. From this figure, it may 1
be noticed that there is a dipole structure in the form of divergence and convergence 2
centers over Indian subcontinent and east of India. Analysis of velocity potential 3
difference fields both at the upper and lower levels yields that the upper level 4
divergence/convergence centers have their corresponding convergence/divergence 5
centers in the lower level. 6
7
3.4.4. Stream Function 8
Large-scale divergence and convergence are analyzed with velocity potential, and 9
non-divergence component of wind is monitored with stream function. The stream 10
function differences (m2/s) between composites of high and low snow years at the lower 11
and upper level troposphere are shown in Figures 11(a) and (b). The formation of 12
negative stream function in the upper troposphere (200hPa), the negative is stronger over 13
north India region and as well as over west Eurasia. Based on this analysis it may be 14
inferred that weakening of the upper level circulation over India in the high snow years in 15
JJAS. 16
17
4. Conclusions 18
The PPE simulations of Hadley Centre, UK Met Office have been used to examine the 19
relationship between winter snow depth anomalies over Eurasia and ISMR anomalies. By 20
and large the model simulates the ISMR satisfactorily. The snow depth in the western 21
part of Eurasia is found to have negative correlation with ISMR. EOF1 of winter snow 22
depth for the period 1961-1990 over the whole of Eurasia explains 13% variability. 23
Hence the significant correlation patterns are consistent with the most dominant EOF of 24
17
snow depth, in which the first mode describes a dipole type structure. Atmospheric fields 1
such as wind, geopotential height, velocity potential and stream function from the 2
simulations are examined in detail to study the influence of Eurasian snow on the 3
monsoon circulation. Wind fields show that in the years of high snow, upper level 4
easterlies weakens and gives rise to weak monsoon. During the monsoon time anomalous 5
westerlies are found over the South China Sea. Such anomalous westerlies during high 6
snow years arrest the north northwest movement of the Tibetan anticyclone from its 7
winter position over the equator to its summer position over Tibet. Such anomalous 8
features suppress the development of upper tropospheric anticyclones and hence the 9
monsoonal circulation over India. The geopotential height fields at lower levels show that 10
the monsoon heat low over central south Asia is weaker in the high snow years compared 11
to the low snow years. The geo-potential difference indicates weaker upper level high 12
over west Eurasia and India. Finally the study concludes that even in the warming 13
atmosphere the inverse relationship between the depth of Eurasian and ISMR holds good. 14
15
Acknowledgements 16
This paper is the outcome of UK-India collaborative research. Dr. S. K. Panda is thankful 17
UKMO and British Council, New Delhi for supporting his visit to UKMO. The authors 18
are thankful to Dr. Saroj Kanta Mishra for his valuable suggestion and help for giving 19
some input to the text. 20
21
18
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17
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27
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29
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31
21
Figure Captions 1 2 Fig. 1. Time series of simulated ISMR averaged over the Indian land for the period 1860-2098. 3
4
Fig. 2. Simulated ISMR for the period 1961-1990: (a) mean, (b) Standard deviation, and (c) 5
Coefficient of variation. 6
7
Fig. 3. Difference in ISMR between the individual PPE and CRU for the period 1961-1990. 8
9
Fig. 4. (a) The Root Mean Square Error (RMSE), and (b) pattern correlation of 17 PPE. (c) 10
RMSE versus pattern correlation, and (d) RMSE versus standard deviation between 17 PPE and 11
CRU precipitations for the period 1961-1990. 12
13
Fig. 5. Standardized DJF snow depth anomalies over western Eurasia (open circles) and ISMR 14
anomalies (closed circles) during 1861–2098. 15
16
Fig. 6. (a) Correlation coefficients between DJF Eurasian snow depth anomalies with the 17
following ISMR anomalies during 1961-1990 and (b) EOF-1(13%) of DJF snow depth during 18
1961-1990. 19
20 Fig. 7. Standardized DJF snow depth anomalies over western Eurasia (open circles) and ISMR 21
anomalies (closed circles) during 1861–2008. 22
23
Fig. 8. Geopotential height differences (m) between composites of high and low snow years at (a) 24
850 hPa level and (b) 200 hPa. 25
26
Fig. 9. Geopotential height differences (m) between composites of high and low snow years at (a) 27
850 hPa level and (b) 200 hPa. 28
29
Fig. 10. Velocity potential (ぬ) differences (m2/s) between composites of high and low snow years 30
at (a) 850 hPa and (b) 200 hPa. 31
32
Fig. 11. Stream function differences (m2/s) between composites of high and low snow years at (a) 33
850 hPa and (b) 200 hPa. 34
35
22
Figures 1 2 3
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 Figure 1: Time series of simulated summer monsoon rainfall (mm/day) averaged over 55
the Indian land for the period 1860-2098. 56
0.00
2.00
4.00
6.00
8.00
10.00
12.00
1860
1874
1888
1902
1916
1930
1944
1958
1972
1986
2000
2014
2028
2042
2056
2070
2084
2098
Years
Rai
nfa
ll (m
m/d
ay)
PPE0 PPE1 PPE2
PPE3 PPE4 PPE5
PPE6 PPE7 PPE8
PPE9 PPE10 PPE11
PPE12 PPE13 PPE14
PPE15 PPE16 MEAN PPE
MEAN PPE+STDV 1.0 MEAN PPE-STDV 1.0
23
1
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
40
(c)
(b)
(a)
Figure 2: Simulated Indian summer monsoon rainfall for the period 1961-1990: (a) mean, (b)
Standard deviation, and (c) Coefficient of variation.
24
1
2
3
4
5
6
7
8
PPE0-CRU PPE1-CRU PPE2-CRU PPE3-CRU
PPE4-CRU PPE5-CRU PPE6-CRU PPE7-CRU
PPE8-CRU PPE9-CRU PPE10-CRU PPE11-CRU
PPE12-CRU PPE13-CRU PPE14-CRU PPE15-CRU
PPE16-CRU MEAN PPE-CRU
Figure 3: Difference in Indian summer monsoon rainfall between the individual PPE
and CRU for the period 1961-1990.
25
1 2
3
4
5
6
Ro
ot
Mea
n S
qu
are
Err
or
(RM
SE
) 6
5
4
3
2
0 5 10 15 20
1.0
8.0
0.6
0.4
0.2
0 5 10 15 20
Pat
tern
Co
rrel
atio
n (
PC
)
PPE
PPE
Root Mean Square Error (RMSE) 2 4
2 4 Root Mean Square Error (RMSE)
1.0
8.0
0.6
0.4
1.0
8.0
0.6
0.4
Pat
tern
Co
rrel
atio
n (
PC
) S
tan
dar
d D
evia
tio
n
(a)
(b)
(c)
(d)
Figure 4: (a) The Root Mean Square Error (RMSE), and (b) pattern correlation of 17 PPE. (c) RMSE versus pattern correlation, and (d) RMSE versus standard deviation between 17 PPE and CRU precipitations for the period 1961-1990.
26
1
2
3
4
5
6
7
1880 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080
Figure 5: Standardized DJF snow depth anomalies over western Eurasia (open circles) and
ISMR anomalies (closed circles) during 1861–2098.
27
1
Figure 6: (a) Correlation coefficients between DJF Eurasian snow depth anomalies with the following ISMR anomalies during 1961-1990 and (b) EOF-1(13%) of DJF snow depth during 1961-1990.
28
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
19
20
21
22
23
24
25
26
3.0
2.5
2.0
1.5
1.0
0.5
0
-0.5
-1.0
-1.5
-2.0
-2.5
-3.0 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
Figure 7: Standardized DJF snow depth anomalies over western Eurasia (open circles) and
ISMR anomalies (closed circles) during 1861–2008.
29
1 2
(b)
(a)
Figure 8: Wind differences (m/s) between the composites of high and low snow years at (a)
850 hPa level and (b) 200 hPa.
30
1 2
(b)
(a)
Figure 9: Geopotential height differences (m) between composites of high and low
snow years at (a) 850 hPa level and (b) 200 hPa.
31
1 2
(b)
(a)
Figure 10: Velocity potential (ぬ) differences (m2/s) between composites of high and low
snow years at (a) 850 hPa and (b) 200 hPa.
32
1 2 3
(b)
(a)
Figure 11: Stream function differences (m2/s) between composites of high and low snow
years at (a) 850 hPa and (b) 200 hPa.