regional climate model simulations of the 2009 indian summer...

Indian Journal of Radio & Space Physics Vol 41, August 2012, pp 488-500

Regional climate model simulations of the 2009 Indian summer monsoon

Jyoti Bhate1,$,*, C K Unnikrishnan1 & M Rajeevan2

1National Atmospheric Research Laboratory, Gadanki, P O, Chittoor District Andhra Pradesh 517 502, India 2Ministry of Earth Sciences, Prithvi Bhavan, Lodi Road, New Delhi 110 003, India

$E-mail: [email protected], [email protected]

Received 19 March 2012; accepted 28 August 2012

Using a regional climate model, the 2009 Indian summer monsoon circulation and rainfall are simulated using observed sea surface temperatures as boundary conditions. For this purpose, the Weather Research and Forecasting (WRF) model (V 3.2) was used as a regional climate model. The model simulations were made for the period 1 May to 30 September with 1 May initial conditions with two domains of 45 km and 15 km and 51 vertical levels. The large scale monsoon circulation and rainfall patterns simulated by the model were examined along with the diurnal and intra-seasonal variations during the 2009 monsoon season using the TRMM 3G68 rainfall data. The results showed a wet bias in the model rainfall simulations over the Indian region with more rainfall in the model compared to the TRMM rainfall observations. This wet bias was attributed to stronger low level monsoon flow in the model simulations over the Indian region. Even though, the model simulations of atmospheric humidity are reasonably accurate, the positive bias in the low level vorticity contributed to the wet bias of rainfall over the Indian region. Model simulations showed errors in characterizing the diurnal variation of monsoon rainfall over the Indian region, especially in the observed phase angle (time of rainfall peak). The model simulations could not capture the observed early morning rainfall peak along the foothills of Himalayas caused by katabatic wind flow over the hills. Model simulations of amplitude of diurnal variation are, however, comparable with the observed amplitude derived from TRMM satellite data. Model simulations also showed encouraging results in simulating the intra-seasonal rainfall variations over the central Indian region and the monsoon onset phase over the Kerala coast.

Keywords: Rainfall simulation, Regional climate model, Sea surface temperature

PACS Nos: 92.60.jf; 92.40.eg

1 Introduction

The Indian monsoon is one of the most dominant tropical circulation systems in the general circulation of

the atmosphere. The country receives more than 80% of the annual rainfall during a short span of four months (June to September) of the southwest monsoon

season. Variability in the onset, withdrawal and quantum of rainfall during the monsoon season has profound impacts on water resources, power generation,

agriculture, economics and ecosystems in the country.

The increasing human activities due to industrial

revolution have led to unprecedented changes in the composition of the earth’s atmosphere and thus the earth’s delicate climate system. There is now

unequivocal evidence that the earth’s surface has warmed during the past 100 years, which is mainly attributed to the anthropogenic activity. Changes in

many components in the climate system, like precipitation, snow cover, sea ice, extreme weather events, etc. also have been observed. These changes,

however, showed significant regional variations.

The only way to understand the impact of global warming on the Indian monsoon and to assess future monsoon climate is to use climate models based on carefully constructed scenarios of emission of greenhouse gases. Previous studies1-5 examined the possible impact of the global warming on Indian summer monsoon using the output of different climate models. However, there are uncertainties in the regional climate projections due to the inaccuracies of the global climate models. The confidence in regional climate projections will depend on how well the models are able to simulate the 20th century monsoon climate6. Moreover, global climate models provide useful climate change projections at continental scale of several thousand kilometers but their projections at the regional scale, i.e. several hundred kilometers and less are considered unreliable7.

Regional climate models (RCM) have shown promising performance as one of the downscaling techniques in reproducing the regional detail in surface climate characteristics as forced by regional details such as topography, lakes, coastline and land

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use distribution7,8. Giorgi et al.9 have proposed more

systematic and wider application of RCMs to adequately assess their performance and uncertainties in producing the regional climate information. Simulating regional climate poses difficulties such as capturing effects of forcing and circulations at the planetary, regional and local scales along with teleconnection effects of regional anomalies. These processes are characterized by a range of temporal variability scales and can be highly nonlinear. Regional climate models may constitute an appropriate alternative solution, since their dynamical and physical packages are able to disaggregate climate data at very high resolutions. They are, however, highly sensitive to a large set of parameters, including the size and location of the domain, lateral boundary conditions, horizontal and vertical resolutions. Both atmospheric and surface physics are also likely to affect their simulated fields.

A number of attempts have been made in the past to demonstrate the capability of regional models embedded in a GCM in simulating the Indian summer monsoon climatology10-15. The general conclusion of all these studies is that the regional models are able to show an improvement in the spatio-temporal distribution of monsoon rainfall which is attributed to increased resolution of these models.

In this study, simulation of 2009 Indian summer monsoon season using a regional climate model has been done and features like large scale monsoon circulation and rainfall, diurnal variation and intra-seasonal variations of rainfall within the 2009 monsoon season have been examined.

2 Data and Methodology For the present study, the Advanced Research

WRF (ARW) version 3.2 model has been used as a regional climate model. The model is used with two nested domains with horizontal resolutions of 45 and 15 km, respectively. There are 51 sigma levels with model top at 10 hPa. The model parent domain covers the large scale Indian monsoon region (40°-120°E, 10°S - 40°N) and the inner domain mainly covers the land mass, Arabian sea and the Bay of Bengal (65°-100°E, 5°-35°N). The two domains used in the model simulations are shown in Fig. 1.

The initial and boundary conditions for the model simulations are derived from the final analysis (FNL) data of 1° × 1° degree resolution prepared at the National Centers of Environmental Prediction (NCEP). The lateral boundary conditions are updated

every six hours. At lower boundary conditions, the observed sea surface temperature data were used. For this purpose, the daily, high-resolution, real-time, global, sea surface temperature (SST) analysis16 that has been developed at the NCEP/Marine Modeling and Analysis Branch (MMAB) was used. The daily SST product is produced on a half-degree (latitude, longitude) grid, with a two-dimensional variational interpolation analysis of the most recent 24-hours buoy and ship data, satellite-retrieved (NOAA-17 AVHRR) SST data. The model was initialized on 1 May 2009, initial conditions and the model was integrated from 1 May to 15 October 2009.The first one month simulation has been considered for model spin up and therefore, not used for the present analysis. One month spin-up period is sufficient for the dynamical equilibrium between the lateral forcings and the internal physical dynamics of the model.

The physical parameterization schemes used in the model are the microphysics scheme of Lin et al.

17, Monin-Obukhov similarity scheme18 for surface layer, Yonsei university scheme for PBL19, RRTM scheme for long wave20 and Dudhia scheme21 for short wave in this experiment. For surface physics, the unified Noah land surface model21 has been used, which consists of one canopy layer and four soil layers with the thicknesses of 10, 30, 60 and 100 cm from the top to down and which employ Reynolds number based approach for the determination of the ratio between the roughness lengths for momentum and heat transport. Also, Betts Miller Janjic scheme22,23 has been used for convective parameterization. The model simulation results were archived at every hour to study the diurnal variation of rainfall during the 2009 June-September season.

Fig. 1 — Two domains used for the model simulations [outer domain has resolution of 45 km and inner domain has resolution of 15 km]

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To examine the seasonal and diurnal variation of rainfall, the hourly rainfall data derived from satellite data has been used. The data set used in this study is TRMM 3G68 version 6 (http://trmmopen.gsfc.nasa.gov/pub). The data consists of essentially instantaneous precipitation rates derived from TMI, from PR, and from the combination of both instruments averaged over the 0.5° × 0.5° (latitude × longitude) boxes between 38°S and 38°N. Compared to the TRMM 3B42 data, TRMM 3G68 covers only a small region at a time. However, the TRMM instruments are believed to provide the most reliable precipitation estimate for the tropics from space. It consists of rainfall estimates, number of total pixels, rainy pixels and percentage of convective rainfall from TMI, PR and TMI-PR combined algorithm gridded at 0.5° × 0.5° resolutions for every minute. Hourly data were obtained by applying a 4-h running mean that reduces the spatial variability in the sampling.

A harmonic analysis following Sen Roy & Balling24 is carried out for the 2009 monsoon season to examine the diurnal variation using the amplitude, phase and variance.

)r(cosAPP r

2/N

1r

r φθ −+= ∑=

where, P, is the estimated rainfall amount;θ, derived as 2πX/N with X as the hour and N is number of observations (=24);ϕ, the phase angle of the

harmonic curve (time of maximum precipitation); P , the average hourly frequency over N observations; and r, frequency of number of times the harmonic curve is repeated in 24 h.

The phase angle can be reinterpreted as the hour of maximum frequency. Two important derived parameters in the harmonic analysis are the standardized

amplitude defined as Ar/2 P , and the portion of variance explained by the rth harmonic computed as A2

r/2σ2; here, σ, is the standard deviation of the

hourly values. The rth standardized amplitude when multiplied by 2 and added to 1 gives the ratio of the value at the hour of maximum to the average in all 24 h for the rth harmonic. Since there are 24 hourly intervals in a day, the harmonic analysis can, at best, provide amplitudes and phase angles of 12 harmonics. The first harmonic is the diurnal cycle of rainfall, while the second harmonic is the semidiurnal cycle. The 12th harmonic has a cycle of 2 hours. In this work, intensity values of the hourly rainfall have been considered to compute the harmonics present in the TRMM 3G68 and the model-predicted rainfall.

3 Results and Discussions

3.1 Mean monsoon circulation and rainfall

Figure 2 shows the circulation features at 850 hPa during the 2009 summer monsoon from the model simulations and ECMWF interim ERA data. The observations show a strong cross equatorial flow across the western Indian Ocean into the Arabian sea with a low level jet stream. Maximum wind speed is observed at around 12°N and it decreases towards north and changes into monsoon easterlies over the monsoon trough region. Figure 2(b) shows the 850 hPa wind circulation simulated by the RCM. The model simulates much stronger monsoon westerlies, especially over the south Bay of Bengal. Over the central India, model simulated easterlies are weaker than the observations. Due to stronger westerlies, the model simulation of low level vorticity is higher than the observations as shown in Fig. 3. The model simulates large positive vorticity over the north Bay of Bengal, compared to the observations. Another area of maximum vorticity is seen over the southeast equatorial Indian Ocean.

Figure 4 shows the spatial distribution of relative humidity at 850 hPa during the 2009 monsoon season from the interim ERA data and model simulations. The observed pattern shows maximum relative humidity along the west coast of India and northeast India and the adjoining Myanmar. The model simulates the spatial distribution reasonably well with maxima along the west coast of India and Myanmar over the northeast India. The model simulates less relative humidity compared to the observations. Model also simulates less relative humidity over the equatorial Indian Ocean as seen in the observations.

The spatial pattern of rainfall simulated by the RCM and observed is shown in Fig. 5. The observed rainfall pattern derived from the TRMM observations is shown in Fig. 5(a). The observations suggest rainfall maxima along the west coast, northeastern Bay of Bengal adjoining Myanmar coast. Another area of maximum rainfall is observed over the southeast equatorial Indian Ocean. The simulated spatial distribution of rainfall during the 2009 monsoon season is shown in Fig. 5(b). The RCM simulates much higher monsoon rainfall over the Indian region. The rainfall maxima along the west coast, Bay of Bengal and south equatorial Indian Ocean are overestimated in the model simulations. Over central India also, the model simulates more rainfall compared to the observations. This large wet


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bias in the model simulations could be due to higher vorticity in the lower troposphere simulated by the RCM (Fig. 3). The model simulates the spatial distribution of specific humidity reasonably well as shown in Fig. 4. Therefore, the model bias in the rainfall distribution could be associated with the positive biases in the model simulated dynamical features like circulation and vorticity.

3.2 Diurnal and intra-seasonal variations

Diurnal cycle is one of the most important modes in the Tropical convective systems. It is a manifestation of the response of the atmosphere-ocean-land system to solar radiation. The diurnal cycle has been a subject of research for several years. However, the lack of observational datasets with high temporal and spatial resolution has not led to some general conclusions.

Fig. 2 — Mean wind flow at 850 hPa during June - September 2009 in: (a) observations, ERA Interim analysis and (b) Regional climate model simulations [wind speed is shown in shading in m s-1]


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Some studies25,26 used very limited years of satellite or station based rainfall data to examine the diurnal variation. Many researchers24,30 examined the diurnal variation of summer monsoon rainfall over south Asia. The most comprehensive study of the diurnal cycle over the tropics was done by Kikuchi & Wang29. Using different satellite data products, they

made an attempt to provide a unified view of the diurnal variation of the global tropical precipitation. Using the TRMM 3B42 data set, Sahany et al.

30 systematically analyzed the statistical characteristics of the diurnal scale signature of rainfall over the Indian region. Their study revealed that over the Gangetic plains, the peak octet is around 1430 hrs LT,

Fig. 3 — Seasonal (June to September) mean vorticity at 850 hPa in: (a) observations, ERA interim analysis and (b) model simulations

Fig. 4 — Seasonal (June to September) mean relative humidity at 850 hPa in: (a) observations, ERA interim analysis and (b) model simulations


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a few hours earlier compared to the typical early evening maxima over land. Basu26 used hourly precipitation data derived from satellites to analyze the diurnal variations of rainfall during the summer monsoon season of 2004.

Figure 6 shows the 3-hourly TRMM rainfall distribution over the Indian monsoon region during the 2009 monsoon season. The most significant features of diurnal variation are observed over central

India, along the foothills of Himalayas, Bay of Bengal and the west coast of India. Over central India, there is a significant diurnal variation of rainfall with maximum rainfall during the evening/early night. This is associated with the diurnal cycle of surface heating due to solar radiation and resultant convection. Along the foothills of the Himalayas, maximum rainfall is observed during the early morning. During the afternoon/evening, convection is suppressed over the

Fig. 5 — Seasonal (June to September) mean rainfall (mm per day) in: (a) TRMM 3G68 and (b) model simulations


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region. Over the Bay of Bengal, peak rainfall is observed in the morning hours. It is interesting to note that the maximum rainfall propagates towards south Bay of Bengal during the day. Along the west coast of India, peak rainfall is observed during the early morning hours.

Thus, there are significant differences in the diurnal variation of observed monsoon rainfall over the Indian region. Romartschke & Houze31 examined the diurnal variation of convection over the Himalayas. They have noted an early morning peak over the

foothills of the Himalayas. They suggested that convergence and upslope flow due to daytime heating over elevated terrain is responsible for triggering convection during the afternoon in the western Himalayan region. At night, cooling over the high terrain leads to an opposite pattern with divergence over the mountains, convergence in the plains and down slope winds. This downslope wind converges with the moist monsoon flow in lower elevations which leads to triggering of convection first over the foothills and later over the plains.

Fig. 6(a)—3-hourly averaged seasonal mean rainfall (mm per 3h) in TRMM 3G68


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Model simulates rainfall over central India throughout the day with a peak during the evening. In observations, very little rain is observed during night and early morning of the day. However, model simulates rainfall throughout the day. This could be a reason for the positive bias in the model rainfall over the region (Fig. 5). Over the north Bay of Bengal, model simulates peak rainfall during the morning hours of the day. It also shows southward shift of rainfall during the later time of the day. Similarly, the model simulates rainfall throughout the day along the

west coast of India. There is hardly any diurnal variation of rainfall in the model along the west coast of India. Model also does not simulate the diurnal variation of monsoon rainfall over the foothills of the Himalayas. The model simulates peak rainfall in the afternoon over the region, which is possibly caused by surface heating due to solar radiation.

Figures (7 and 8) show the results of the harmonic analysis of the observed and model simulated rainfall. The amplitude of the diurnal cycle from the TRMM observations and model simulations is

Fig. 6(b) — 3-hourly averaged seasonal mean rainfall (mm per 3 h) in model simulations


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shown in Fig. 7. In the TRMM observations, maximum amplitude of the diurnal cycle is observed over the north Bay of Bengal, central India, foothills of the Himalayas and along the west coast of India. Model simulations also show maximum amplitude over these regions, except over the foothills of Himalayas. Thus, the RCM has shown good fidelity to simulate the amplitude of the diurnal cycle of monsoon rainfall during the 2009 monsoon season.

Figure 8 shows the phase angle (maximum amplitude) of diurnal variation. Along the east coast of India, an early morning peak is observed, which is

reasonably well predicted by the model, especially over the eastern part of south peninsula. This maximum, in precipitation in the early hours, occurs as the synoptic-scale westerly wind is weak over the region and the diurnal cycle is dominated by the land-sea breeze. Over northeast India and adjoining Myamnar also, an early morning rainfall peak is observed. Over central India, the observations suggest rainfall peak in the evening/early night, which is well captured by the model. However, the early morning peak over the foothills of Himalayas is not well simulated by the model. Over this region, the model

Fig. 7 — Amplitude (mm per day) of the diurnal cycle in: (a) TRMM 3G68 observations and (b) model simulations

Fig. 8 — Phase angle (time of peak rainfall) of the diurnal cycle in: (a) TRMM 3G68 observations and (b) model simulations


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simulates maximum rainfall in the evening. Sahany et al.

25 showed southward propagation of convection and rainfall over Bay of Bengal in the diurnal cycle. They found that over north Bay of Bengal, peak is observed in the morning 0530-0830 hrs LT. Over central India, the peak is observed around 1130 hrs LT and over south Bay of Bengal around 1430 hrs LT. They showed this kind of southward propagation using nine years of observations of TRMM. In this study, such a southward propagation in the model simulations is observed.

The Indian monsoon is characterized by convectively coupled monsoon ISO’s that manifests in the form of active and breaks phases32,33. The overall mean monsoon precipitation distribution significantly depends on the manifestation of ISO’s in a season34. Global observations have revealed that the active and break monsoon over the Indian subcontinent, are manifestation of a superposition of 10-20 day and 30-60 day oscillations. During the break spells, the monsoon trough is located close to the foothills of the Himalayas, which lead to striking decrease of rainfall over most of the country but increases along the Himalayas and parts of northeast India and southeastern parts of Peninsula. During the active

(break) spells, intensification (suppression) of deep cloud activity takes place over the Indian sub-continent. During the active monsoon conditions, the northern Arabian Sea and northern Bay of Bengal play a major role by the way of increased evaporation rates and water vapour and convection.

Figure 9(a) shows the daily rainfall variations over southeast Arabian Sea off the Kerala coast from 1 May to 30 September, averaged over the area, 7.5-12.5°N, 72-77°E. In 2009, the monsoon onset over Kerala occurred on 4 June. The model simulations show a sharp rise in monsoon rainfall around 4 June suggesting the monsoon onset over Kerala. Model also simulated the subsequent intra-seasonal rainfall variations off the Kerala coast reasonably well, except the sharp rise during the end of the monsoon season. Figure 9(b) shows the intra-seasonal rainfall variations averaged over the central India (18-25°N, 72-88°E). Actual rainfall observations suggest a sharp rise during the fourth week of June, which is associated with the delayed monsoon progress during the season. Rainfall activity over central India was suppressed during the end of July and the beginning of August, which was associated with the monsoon break conditions. However, the regional climate

Fig. 9(a) — Daily rainfall (mm per day) averaged over the Kerala coast using TRMM 3G68 observations and model simulations from 1 May to 30 September 2009


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model did not simulate the reduction/suppression of rainfall over central India during the break period. It showed a reduction of rainfall during the fourth week of July, however, the suppressed monsoon conditions were not sustained during the remaining days in the model simulations. Another lull rainfall activity was observed during the first half of September, which was well simulated by the model. In fact, the delayed monsoon in June and two extended breaks in July/August and September caused the 2009 monsoon to be a major drought year.

4 Conclusions In this study, a regional climate model was used to

simulate the 2009 Indian summer monsoon circulation and rainfall. The 2009 monsoon was a drought year due to many reasons like the presence of El Nino and mid-latitude intrusion of dry winds, etc. The 2009 monsoon was simulated with 1 May initial conditions and the observed daily sea surface temperature data were used as boundary conditions. The regional climate model simulation shows a wet bias in rainfall. Model simulates more rainfall over the Indian monsoon region, especially over central India and north Bay of Bengal, compared to the observations. This wet bias is attributed to the stronger monsoon flow in the model simulation and

the associated positive bias in low level positive vorticity over the Indian region. Even though, model simulates moisture distribution reasonably well, the positive bias in low level vorticity (caused due to stronger monsoon westerly flow) might have contributed to the wet bias in monsoon rainfall.

Model simulations show errors in characterizing the diurnal variation of monsoon rainfall over the Indian region, especially in the observed phase angle (time of rainfall peak). In the TRMM 3G68 data, maximum rainfall is observed over the central Indian region during evening/early morning. Along the foothills of the Himalayas, maximum rainfall is observed during the early morning hours due to the convergence of down-slope winds and monsoon westerlies. Over the Bay of Bengal, a morning rainfall peak is observed. Model predictions showed errors in predicting the timing of peak rainfall. The early morning rainfall peak observed over the foothills of the Himalayas is not well simulated by the regional climate model. The regional climate model showed some errors in the simulations of intra-seasonal variations of monsoon rainfall. Model could not simulate the suppressed rainfall activity associated with the break conditions prevailed over central India during the end of the second extended break spell occurred in the first week of September.

Fig. 9(b) — Daily rainfall (mm per day) averaged over central India using TRMM 3G68 observations and model simulations from 1 May to 30 September 2009


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More detailed studies are required to examine the sensitivity of model simulations of the mean monsoon circulation and the diurnal cycle on the model physics. Diurnal cycle is the key test for checking the global and regional model skill. It has been found that there has been limited success in representing the diurnal cycle of rainfall. Chakraborty35 examined the ECMWF forecasts and found errors in the model in predicting the characteristics of diurnal variation of monsoon rainfall. The results suggest that over land, the model overestimates precipitation during the morning hours and underestimates it during the afternoon to evening hours. Over the oceans, the model overestimates precipitation during the evening to morning hours. The general inference of the results is that the ECMWF model missed the phase of the diurnal cycle of precipitation. This analysis of medium range forecasts suggests that the error in the diurnal cycle is at a minimum during the first 24 hour forecasts. The error increases during the subsequent forecast hours and remains almost constant up to the 96-hour forecasts.

The analysis of Basu26 also revealed many errors in the NCMRWF medium range weather forecast model in characterizing the diurnal variation of monsoon rainfall over India. His analysis showed that the hour of maximum precipitation is earlier compared to the observations, which suggest that early release of convective instability and precipitation in the model compared to the observations. The late night /early morning maxima along the foothills of the Himalayas due to mesoscale wind circulations are significantly reduced in the model, possible due to the coarse horizontal resolution of the model that weakens the strength of the katabatic winds in the forecasts.

It is also worthwhile to examine whether the model predictions of diurnal variations improve if convection in the model is treated explicitly instead of using some convective parameterization scheme.

Acknowledgements The authors thank Prof A Jayaraman, Director

NARL for his kind support and encouragement. They also thank Dr P Mukhopadhyay and Dr Sourav Taraphdar from IITM, Pune for their valuable guidance and discussions. The authors would like to acknowledge Dr Amit Kesarkar for providing support and guidance for running WRF on HPC facility at NARL. Finally, they thank the anonymous referees for their important suggestions to improve the quality of this paper.

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