Science of the Total Environment 476–477 (2014) 79–89

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AIRS retrieved CO2 and its association with climatic parameters overIndia during 2004–2011

K. Ravi Kumar, J.V. Revadekar, Yogesh K. Tiwari ⁎Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Pune, India

H I G H L I G H T S

• Association of AIRS CO2 with climate parameters over India• CO2 show positive correlation with surface temperature• Vertical/horizontal winds contribute towards CO2 transport• Vegetation and monsoonal rainfall show inverse relationship with CO2

⁎ Corresponding author. Tel.: +91 20 25904539; fax: +E-mail addresses: yktiwari@gmail.com, yktiwari@trop

0048-9697/$ – see front matter © 2014 Elsevier B.V. All rihttp://dx.doi.org/10.1016/j.scitotenv.2013.12.118

a b s t r a c t

a r t i c l e i n f o

Article history:Received 21 May 2013Received in revised form 4 December 2013Accepted 29 December 2013Available online 21 January 2014

Keywords:AIRS CO2

Indian sub-continentRainfallTemperatureVegetationWinds

Atmospheric Infrared Sounder (AIRS) retrievedmid-tropospheric Carbon Dioxide (CO2) have been used to studythe variability and its association with the climatic parameters over India during 2004 to 2011. The study alsoaims in understanding transport of CO2 from surface to mid-troposphere over India. The annual cycle of mid-tropospheric CO2 shows gradual increase in concentration from January till the month of May at the rate~0.6 ppm/month. It decreases continuously in summer monsoon (JJAS) at the same rate during which strongwesterlies persists over the region. A slight increase is seen during winter monsoon (DJF). Being a greenhousegas, annual cycle of CO2 show good resemblance with annual cycle of surface air temperature with correlationcoefficient (CC) of +0.8. Annual cycle of vertical velocity indicate inverse pattern compared to annual cycle ofCO2. High values of mid-tropospheric CO2 correspond to upward wind, while low values of mid-troposphericCO2 correspond to downward wind. In addition to vertical motion, zonal winds are also contributing towardsthe transport of CO2 from surface to mid-troposphere. Vegetation as it absorbs CO2 at surface level, show inverseannual cycle to that of annual cycle of CO2 (CC-0.64). Seasonal variation of rainfall-CO2 shows similarities withseasonal variation of NDVI-CO2. However, the use of long period data sets for CO2 at the surface and at themid-troposphere will be an advantage to confirm these results.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

Atmospheric Carbon Dioxide (CO2), an important greenhouse gas inthe atmosphere, is increasing globally at a rate of approximately2 ppm yr−1 (global average value during year 2000–2010) mainlyas a consequence of fossil fuel combustion and human activities. Inaddition to the trends, atmospheric CO2 also exhibits year-to-yearstrong seasonal cycles. Amplitudes of variations in CO2 seasonal cycleare closely related to carbon exchange with the biosphere (Bacastowet al., 1985; Buermann et al., 2007; Cleveland et al., 1983; Keelinget al., 1996; Pearman and Hyson, 1980, 1981). Atmospheric CO2 alsodemonstrates intraseasonal and interannual variabilities (Bacastow,1976; Dargaville et al., 2000; Dettinger and Ghil, 1998; Enting, 1987;

91 20 2586 5142.met.res.in (Y.K. Tiwari).

ghts reserved.

Feely et al., 1987; Jiang et al., 2010; Keeling and Revelle, 1985; Keelinget al., 1995).

Recent advances in carbon cycle science have heightened the needfor space-based global observations of CO2 (Kort et al., 2011; McKainet al., 2012; Schneising et al., 2012). Recently columnmean CO2 concen-trations (XCO2) are retrieved from SCanning Imaging AbsorptionspectroMeter for Atmospheric CartograpHY (SCIAMACHY), AtmosphericInfrared Sounder (AIRS) and Greenhouse gases Observing SATellite(GOSAT) instruments (Jiang et al., 2010; Schneising et al., 2011;Yoshida et al., 2011). Theoretical analyses have shown that satellitemeasurements combined with inverse modelling can significantlyreduce surface flux uncertainties provided that the satellite data areaccurate and precise enough (Chevallier et al., 2007; Houweling et al.,2004; Hungershoefer et al., 2010; Miller et al., 2007; Rayner andO'Brien, 2001).

India is the largest growing economies in south Asia,which is rankedthird in greenhouse gas emissions by fossil fuel burning in the world. A

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significant (57%) increase in the country's CO2 emissions since theearly 1990s is associated with rapid economic growth (Boden et al.,2010). The localised increase in GHG emissions in India, and theirheterogeneous distribution in the atmosphere, highlights the needfor more CO2 monitoring stations at various locations in the country(Bhattacharya et al., 2009; Indira et al., 2011; Patra et al., 2011; Tiwariet al., 2011). However, there has been little monitoring of atmosphericCO2 concentration over India to date. In collaboration with theCommonwealth Scientific and Industrial Research Organization(CSIRO), Physical Research Laboratory (PRL) and National Instituteof Oceanography (NIO), Goa, established air sampling station at CapeRama, India, (CRI; 73.9°E,15.1°N) in 1993, to monitor the concentrationsof CO2 and other trace gases. The station operated for 10 years i.e. till2002 (Francey et al., 2003) and again resumed in 2009. Several observa-tional networks are established over the globe in recent time for betterunderstanding of the carbon cycle. However, the limited spatial coverageof the current surface observation system is not sufficient to obtain re-gional andglobal distributionof CO2 concentrations. Satellite observationswith better spatial coverage can provide a stable, long timeseries, overwide regions, as well as ground to aerial three dimensional informationregarding atmospheric composition (Zhang et al., 2007). Currently theAtmospheric Infrared Sounder (AIRS) instrument, orbiting on Aquasatellite, is only available mid-tropospheric CO2 concentration datasince 2003. (Chahine et al., 2005, 2008; Engelen and McNally,2005; Maddy et al., 2008).

In view of the above, an attempt has been made to study the spatialdistribution of AIRS retrieved mid-tropospheric CO2 and its associationwith the surface meteorological parameters over India. Also, year toyear variability in mid-tropospheric CO2 has been studied in relationto surface meteorological parameters over Central Indian region.

2. Data and methodology

2.1. CO2 retrieval

The Atmospheric Infrared Sounder (AIRS) is the first in a newgeneration of high-spectral resolution infrared sounder instrumentsflown aboard Aqua research mission. AIRS is mounted on the sun-synchronous, near-polar orbiting NASA Aqua satellite with equatorlocal ascending crossing time of 13:38 local solar time, it is a cross-track scanning grating spectrometer covering the 3.74 μm to 15.4 μmspectral range with 2378 channels having a nominal spectral resolvingpower, λ/δλ, of 1200. Data from AIRS and its companion instrument,the Advanced Microwave Sounding Unit (AMSU), are combinedto eliminate the effects of clouds. AIRS was launched into a 705-km-altitude polar orbit on the EOS Aqua aircraft on 4 May 2002, andhas an expected on-orbit lifetime of 7 years. The instrument field ofview is 1.1°, corresponding to a nadir footprint of 13.5 km on thesurface. The scan angles vary from 49°S to 49 N° (Aumann et al.,2003). The satellite crosses the equator at approximately 1:30 am and1:30 pm, resulting in global coverage twice a day. Most clouds areopaque at the radiance frequencies used in the CO2 estimation.McNally and Watts (2003) developed a cloud detection scheme thatidentifies which channels are not affected by clouds which helps innot to lose all information about the atmospheric profile in the presenceof clouds. Within the European Centre For Medium-range WeatherForecasts (ECMWF) data assimilation system, this scheme identifiesand removes those AIRS channels that are affected by clouds, andkeeps only those channels which are cloud free. CO2 estimates wereonly used when all 18 AIRS CO2 channels were not affected by clouds.Chahine et al. (2005) found that the range 690–725 cm−1 is best forselecting the main channel set to retrieve the mid-troposphere CO2

mixing ratio. The mixing ratios of mid-tropospheric CO2 are retrievedusing the Vanish Partial Derivative Method (VPD) (Chahine et al.,2005, 2008). The weighting function of AIRS mid tropospheric CO2

channels peaks between 500 hPa to 300 hPa. AIRS mid-tropospheric

CO2 is retrieved globally in the middle troposphere day and nightunder clear and cloudy conditions. These data sets are available fromSeptember 2002 to February 2012. AIRS CO2 retrievals have beenregridded to 2° × 4° (latitude by longitude) resolution. Validationby comparison to in situ aircraft measurements and retrievals byland-based upward looking Fourier Transform Interferometersdemonstrates that AIRS CO2 is accurate to 1–2 ppm between latitudes30°S and 80°N (Chahine et al., 2005, 2008).

2.2. NDVI data

TheNDVI landdata set used in this study are taken fromMODIS/AQUA(Huete et al., 2002) at 1°x1° (latitude by longitude) horizontal andmonthly temporal resolution for the period of 2004–2011 respectively.Data are available via the internet at NASA website. The NDVI issatellite-derived surface greenness values (Bounoua et al., 2000),Moderate-resolution Imaging Spectrometer (MODIS) onboardNASA/AQUAearth observing system.MODIS is referred to as a compositeapproach to the existing NOAA/AVHRR derived NDVI which is extendedmy MODIS data to provide longer-term record. The reflectance ismeasured from channel 1 (visible: 0.58–0.68 m) and channel 2 (nearinfrared: 0.725–1.0 m) are used to calculate the index. The NDVI valueis defined as the ratio of the difference to the total reflectance.

NDVI ¼ channel 2–channel 1ð Þ= channel 2þ channel 1ð Þ

Green leaves commonly have larger reflectances in the near infraredthan in the visible range. Clouds, water, and snow have larger reflec-tance in the visible than in the near infrared range, so that negativevalues of vegetation index corresponds to snow or ice cover, whereasthe difference in reflectance is almost zero for bare soils such as deserts.So as a result, NDVI values can range from−1.0 to 1.0 but typical rangesare from 0.1 to 0.7, with higher values associated with greater densityand greenness of plant canopies.More details of theprocessingmethodsused in generating the data set can be found in James andKalluri (1994).Because our objective is to investigate the large-scale features in climateand NDVI, we used the coarser monthly data sets to suppress the noiseusually existing in the high spatial resolution data. The seasonal meanwas averaged for April and May at each grid. Grids having insufficientdata (nv18) or low mean NDVI (0.05) were removed from the dataset. All-India monthly time-series of NDVI are constructed by averagingland-point grid values of NDVI for corresponding month.

2.3. Meteorological parameters used

To understand the relationship between the mid tropospheric CO2

with the climatic parameters, daily mean rainfall data obtained fromTropical Rainfall Measuring Mission (TRMM) are used over Indianregion. This data is available at NASA website. Monthly means havebeen computed for further analysis.

NCEP Reanalysis Derivedmonthly surface air temperature and zonalcomponent of wind provided by the NOAA/OAR/ESRL PSD, Boulder,Colorado, USA, from their NOAA website have also been used to under-stand association between CO2 and climatic parameters (Kalnay et al.,1996). The above data is used in the present study for the period from2004 to 2011. Monthly data for vertical velocity are obtained from theNCEP/NCAR reanalysis data site at the surface level.

3. Results and discussions

3.1. General characteristics

3.1.1. Seasonal spatial patterns

3.1.1.1. Mid-tropospheric CO2. From a climatic point of view, India is atropical country dominated by the monsoons with the annual march

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of the climate being punctuated by the winter during December,January, February (DJF); spring during March, April, May (MAM);summer monsoon during June, July, August, September (JJAS) andnortheast monsoon during October, November (ON). Most parts ofIndia receive a major proportion of their annual rainfall during thesummer (JJAS) monsoon season. Extreme departures from normalrainfall, such as large-scale droughts and floods, seriously affectagricultural output and regional economies (Kumar et al., 1999). TheIndian winter (DJF) contributes almost one third of the annualprecipitation over the western Himalayas (Dimri, 2013). Yadav (2012)found that the internnual variability of Indian northeast monsoon(ON) rainfall has increased in the recent decades. The south-easternparts of India and Sri Lanka receive substantial rainfall from the north-east monsoon. Mid-tropospheric CO2 concentration also show signifi-cant seasonal variation in CO2 over Indian region. Average seasonalspatial distribution ofmid-tropospheric CO2 duringwinter (DJF), spring(MAM), summer monsoon (JJAS) and northeast monsoon (ON) basedon the data for the period from 2004–2011 are presented in Fig. 1a.The clear spatial variation is observed in all the seasons. Higher valuesof CO2 concentrations are seen over the northern parts of the countrycompared to the southern part. Since the winter, CO2 concentrationincreases all over the land. The highest values are seen over large partof the country during the spring (MAM). This is the hot season of thecountry during which strong convective activities occur over the regionwhichmay be leading to vertical transport of CO2. Further low values ofCO2 are seen in the summer monsoon season during which strongwesterlies prevail over the Indian region. Northwest India and parts ofthe southern peninsula show higher concentration compared to rest

Fig. 1. a. Seasonal spatial distribution of mid-tropospheric CO2 (ppm) duringwinter (DJF), sprin(1) surface air temperature(°C) during winter (DJF), spring (MAM), monsoon (JJAS) and northewinter (DJF), spring (MAM), monsoon (JJAS) and northeast-monsoon (ON)(bottom). c. Seasonmonsoon (JJAS) and northeast-monsoon (ON)(top), (2) Correlation between vertical velocit(ON)(bottom). d. Seasonal spatial distribution of zonal component of wind (m/sec) at surface l(ON). Shades represents the zonal component of wind(U-Wind). e. Annual cycle of mid tropoCentral India, super-imposed with the surface meteorological parameters, (1) surface Air temp

of the country. With the onset of the monsoons vegetation startsgrowing, causes to absorb the considerable amount of CO2. Vegetationplays a major role in the lowering CO2 concentrations during thesummer monsoon season with availability of water due to rainfallactivity. CO2 values substantially increases in the northeast monsoonseason over northern parts of the country. But over the north-easternand southern parts of the country, where winter monsoon is active,CO2 concentration decreases. Thus, it appears that variability in mid-troposphere CO2 may be due to horizontal and vertical transport andalso due its absorption of CO2 at surface level due to vegetation.

3.1.1.2. Surface air temperature. The seasonal distribution of surface airtemperature during the period 2004–2011 is presented inFig. 1b(top). The high values of temperature over the country are seenduring spring months (MAM). During summer monsoon season (JJAS)due to large rainfall activities over the region temperature decreasesover the most parts of the country. However, high values persist overnorth-western parts of the country where rainfall activities are low.Temperatures during winter (DJF) and northeast-monsoon (ON), tem-peratures are lower than the temperatures during summer monsoon.The lowest values are seen during thewinter season (DJF). The high var-iability in temperature in terms of standard deviation (SD) is observedduringwintermonsoon and springmonths (Fig. 5). Though the temper-ature patterns are dominated by seasonal cycle associated with Sun'smigration, the variability in temperature may have association withvariability in CO2 being a greenhouse gas.

To examine this seasonal, correlation coefficients are computed be-tween CO2 and surface air temperature in Fig. 1b(bottom). Positive

g (MAM), monsoon (JJAS) and northeast-monsoon (ON). b. Seasonal spatial distribution ofast-monsoon (ON)(top), (2) Correlation between surface air temperature and CO2 duringal spatial distribution of (1) vertical velocity (pa/sec) during winter (DJF), spring (MAM),y and CO2 during winter (DJF), spring (MAM), monsoon (JJAS) and northeast-monsoonevel during winter (DJF), spring (MAM), summer monsoon (JJAS) and northeast monsoonspheric CO2 in terms of monthly means based on the data for the period 2004–2011 over(top), (2) Vertical Velocity (middle) and (3) Zonal component of wind (bottom).

Fig. 1 (continued).

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Fig. 1b (continued).

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Fig. 1c (continued).

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Fig. 2. a. Interannual variability in mid tropospheric CO2 (ppm) and surface air tempera-ture (degree C) for the period 2004 to 2011 based on the monthly means over CentralIndian region. b. Interannual variability ofmid tropospheric CO2 (ppm) and vertical veloc-ity (pa/sec). c. Interannual variability of mid tropospheric CO2 (ppm) and zonal compo-nent of wind (m/sec).

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correlation is seen over most parts of the India during all season exceptDJF. High magnitude positive correlations are seen during ON. This maybe due to northeast monsoon sets over the region duringwhich surfacewinds are coming from land areas may be causing CO2 transport overthe region.

3.1.1.3. Vertical velocity. The seasonal spatial distribution of verticalvelocity for the period of 2004–2011 is shown in Fig. 1c(top). The figureclearly indicates strong upwardmotion during spring (MAM)which is ahot season due to convective activities over most parts of Indian land.During summer monsoon (JJAS) season also it persists over manyparts of the country. However, during the winter monsoon season(DJF) most parts of the country show downwardmotion. But the south-ern tip of the country continues upward motion. This strong verticalmotion during hot spring may cause vertical transport of the CO2 upto the mid troposphere levels as seen in the Fig. 1a. Fig. 1c(bottom)show spatial distribution of seasonal correlation coefficients betweenvertical velocity and CO2 inmid-troposphere. This also show similar fea-tures indicating upward wind cause vertical transport of CO2 from sur-face to mid-troposphere. Wide-spread relationship is seen during JJASin which summer monsoon sets over country. DuringMAM also associ-ated with strong convective activities transport of CO2 is seen. Strongvertical transport of CO2 is observed during spring and summer mon-soon seasons compared to the other seasons.

In addition to this, there may exist a horizontal transport of CO2 atsurface level as strong westerlies (easterlies) prevail over the countryduring summer (winter) monsoon season.

3.1.1.4. Zonal wind. The spatial distributions of zonal wind at surfacelevel in all four seasons have been depicted for in Fig. 1d. As the seasonsare dominating over the country, zonal wind also shows strong season-ality over the region. Associated with the heat low over north-westernpart of the country, westerlies set over the region. The strongwesterliesare seen over large part of the country during the summer monsoonseason (JJAS). These are basically rain bringing system which bringsthe moisture from the Arabian Sea to the Indian continent. In additionto this, these strong westerlies may cause horizontal transport of CO2

from one region to another during withdrawal phase of summermonsoon and onset of winter monsoon season (DJF), the strongeasterlies set over the southern parts. Associated with this seasonalreversal of winds also, there may exist the horizontal transport of CO2

along with easterlies.

3.1.2. Annual cycleTo understand the seasonal variability in mid-tropospheric CO2 and

its associationwith surfacemeteorological parameters, the annual cycleof CO2 based on the monthly mean values are superimposed with theannual cycle of temperature, vertical velocity and zonal wind overCentral Indian region (Fig. 1e). The top panel of the figure show theannual cycle mid tropospheric CO2 superimposed with annual cycletemperature, which clearly show similarity in the variation of theirmonthly means. Since the month of January, CO2 concentrationsincreases by 0.6 ppm/month till the month of May which is the endspring season. CO2 attains maxima in the month of May, further itdecreases at the same rate with onset of the summer monsoon andattains its minimum value in the month of August. Thereafter slightincrease is seen till the end of the year. This clearly indicates the season-al variability of the climate of the region and its association with themid-tropospheric CO2.

Tiwari et al. (2011) have studied surface CO2 observations over CapeRama India. During rainy summer monsoon season, there is an increasein vegetation over the region which causes to decrease in CO2 concen-tration at surface level from May to August. Its reflection is seen atmid-tropospheric level also. So the first minima in CO2 occur at theend of summer monsoon. The concentration further increases till theend of the year.

CO2 being a greenhouse gas, the positive relationship between CO2

and temperature is obvious (Humlum et al., 2012). Therefore, an annualcycle of surface air temperature has strong resemblance with annualcycle of mid tropospheric CO2. Associated with the peak CO2 values inthe month of May, the highest surface air temperature is also seen inthe May. Temperature increases by ~2.2 °C/month since the month of

Fig. 3. a. Monthly Correlation Coefficients (CC) between CO2 and surface air temperature.b. Monthly Correlation Coefficients (CC) between CO2 and vertical velocity. c. MonthlyCorrelation Coefficients (CC) between CO2 and zonal wind. Fig. 4. a. Interannual variability of mid tropospheric CO2 (ppm) and NDVI. b. Interannual

variability of mid tropospheric CO2 (ppm) and rainfall (mm). c. Annual cycle of mid tropo-spheric CO2 in terms ofmonthlymean, super-imposedwith (1)NDVI(top) and (2) Rainfallin mm(bottom).

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January and it reaches its maxima in the month of May. May onwardstemperature decreases with the same rate till the onset of the summermonsoon till the month of August. Slight increase in temperature isseen up to October, which further decreases sharply till the month ofDecember. Basic character of the annual cycle of surface temperatureis modified by the monsoon due to rainfall activity. The summer mon-soon is active over most parts of the Indian region for three months(July to the middle of September); the highest temperatures aregenerally experienced in the month of May, further monsoon sets inover the country (Revadekar and Patil, 2011). The positive relationshipis seen between CO2 and temperature (CC 0.8).

The middle panel of the Fig. 1e shows the annual cycle of CO2

superimposed with annual cycle of vertical velocity. Inverse relation-ship is seen between annual cycle of CO2 and annual cycle of verticalvelocity. The negative (positive) values of vertical velocity indicate theupward (downward) motion of the air. Thus the inverse relationshipindicate the upward winds causes vertical transport of CO2 from thesurface to the upper troposphere and downward winds causes todecrease in CO2 concentration at mid-troposphere. In the figure nega-tive values (i.e. upward motion) are seen during the months of spring

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and monsoon seasons especially due to the large convective activityover the country due to hot condition. This convective activity furtherhelps for the vertical transport of the CO2 from surface level to themid troposphere. During the end of the monsoon season in the monthof the September, the low values of CO2 have been observed due tothe weakening of monsoon which dampens the transport mechanism.

In addition to vertical motion of the air, zonal winds also can lead tohorizontal transport of CO2. During summer monsoon season (JJAS)westerlies are dominated over country and during northeast monsoonseason (ON) easterlies prevail over southernparts of the country. There-fore annual cycle of mid-tropospheric CO2 is examined along with theannual cycle of zonal winds also in bottom panel of Fig. 1e. The highermagnitude westerlies are seen during the monsoon season whichblows from oceanic regions to land. During winterseason (DJF) windsare mainly easterlies but comparatively calm and they blow fromcontinent to Indian land. Similarity in pattern of annual cycle is seenduring winter; however during summer monsoon strong westerliespersist but CO2 decreases. Thus there is a combine impact of zonal andvertical winds in transport of CO2.

3.1.3. Interannual variabilityThe Fig. 2a present detrended timeseries of mid-tropospheric CO2

and surface temperature based on monthly means over central Indianregion. These timeseries exhibits both the intraseasonal aswell as inter-annual variability. The clear resemblance has been observed betweenthe variability in CO2 and surface temperature. The high (low) value oftemperature corresponds with the high (low) values of the CO2. Thevariability of the mid-tropospheric CO2 computed in terms of thestandard deviation (SD) show high variability during summermonsoonseason, This may be due variability in summer monsoon activity overthe country which causes variability in horizontal and vertical transportof CO2. This may also be due to variability of the vegetation and rainfall.During northeast monsoon season also, slight higher variability is seen.This may be due to variability in vegetation associated with the wateravailability in these months. But in the surface temperature the highvariability is seen in the spring season, and low variability is observedduring the summermonsoonmonths. Higher anomalies in temperatureduring summer monsoon of 2004 and 2009 are associated withdroughts over the country during the years. No flood occurs duringthe study period, however during 2005, 2006 and 2007 monsoonrainfall was normal but slight above long-term mean. Normal rainfallbut below the long-term mean observed during 2008. During weakand drymonsoon, as the currents are weakmid-tropospheric CO2 showlower magnitudes of anomalies. This may be due to weak transport ofCO2 from surface to upper level.

The Fig. 2b explains the year to year variability of both CO2 andvertical velocity. This figure also explains intra seasonal and interannualvariability over the study region. Inverse relationship is observedbetween the two variables indicating upwardmotion causes to increasein CO2 concentration at mid-tropospheric level and downward motioncauses to decrease in concentration. During the strong summermonsoon years the high magnitude of the vertical velocities has beenobserved which also corresponds to high values of the CO2. This couldbe due to the more convection leads to the strong vertical transport ofCO2. The low magnitude of vertical velocity observed during the weaksummer monsoon years corresponds to the low values of the CO2

compared to the strong monsoon years. High variability of verticalvelocities in terms of standard deviation has been observed in themonth of May to September. This may be due to the high convectionactivities leads to the upward motions. Less variability is observedduring winter seasons.

The Fig. 2c explains the year to year variability of CO2 and zonalwind. This figure exhibits the intraseasonal and inter-annual variabilityin both the parameters. The maximum values of the zonal windsobserved during the monsoons months while the maximum values ofthe CO2 is observed in the month of may and then starts decreasing

when the starting of the vegetation. And minimum of zonal winds isobserved in northeast monsoon months and as well as in wintermonsoon months. The high variability of zonal winds is seen duringthe monsoons months. The low variability is observed in the monthsof northeast monsoon season.

3.2. Correlation analysis

Fig. 3a shows the monthly correlation between mid troposphericCO2 and temperature. The positive correlations are seen during the allmonsoonmonths except Julywhich is an activemonth of summermon-soon season. Positive correlation is also seen in the months of FebruaryandMarch. It indicates increase in temperaturewith increase in the CO2.April is a transition month of the season it shows negative relationship.Negative correlations are seen duringwinter monsoonmonths, Decem-ber and January. Fig. 3b shows the monthly correlation between CO2

and vertical velocity. Negative correlations are seen during most of themonths. This indicates that upward winds are responsible for transportof CO2 from surface level tomid-troposphere. Fig. 3c shows themonthlycorrelation between CO2 and zonal wind. The negative correlations areseen during most of the months, especially during summer and wintermonsoon. Horizontal as well as vertical transport that is also shown inFig. 3b causes the positive correlation between CO2 and zonal wind.

3.3. Role of vegetation and rainfall

3.3.1. Annual cycleUsing data over Cape Rama India, Tiwari et al. (2013) studied

variability and growth rate of atmospheric CO2. Their results indicatenegative relationship between CO2 concentration and vegetation overthe Cape Rama. In this section, an attempt is made to study relationshipbetween mid-tropospheric CO2 and vegetation & rainfall over CentralIndia to understand role of vegetation and rainfall in variability ofmid-tropospheric CO2. Top panel of Fig. 4c shows the annual cycle ofCO2 and NDVI. The annual cycle have been prepared based on the midtropopsheric CO2 and NDVI data over Central Indian region during theperiod from 2004 to 2011. Climatological means have been computedfor both CO2 and NDVI for each month. Fig. 4c shows inverse patternin the annual cycles for Central India region also. Since the month ofJanuary, NDVI decreases by 0.03 per month till the end of spring,which is a hot season of the country. NDVI having the low values andits minima are observed in themonth of Maywhich is exactly coincidesto the highest value of mid-tropospheric CO2. NDVI further startsincreasing with the onset of summer monsoon season over the countryin the month of June. The increase in NDVI continues till the end of theseason at the rate 0.07 per month. With the start of the northeast mon-soon (October–November) NDVI decreases by ~0.05 per month mainlybecause there is no source of water for the further growing of NDVI.Opposite pattern is seen in annual cycle of CO2. The bottom panel ofFig. 4c shows the annual cycles of both CO2, and rainfall over CentralIndia. The climatological mean values of the rainfall are calculatedfrom TRMM daily data for each month. India receives high rainfallduring summer monsoon months from June to September and duringthe October which active month of northeast monsoon or wintermonsoon over India. As the rainfall controls the growth of vegetation,patterns of annual cycles of vegetation and rainfall show resemblance.High values of rainfall correspond to low values of CO2, as the rainfallactivity favours vegetation growth. However there appears slight lagbetween rainfall activity and vegetation growth.

Initially during the month of June vegetation starts increasing withthe availability of water and as vegetation increases then the amountof CO2 concentrations starts decreasing due to photosynthesis processwhich will taken up by the plants. Vegetation is further sustained withinitial soil moisture gained by the main rainy season. Therefore thereexists negative relationship between summer monsoon rainfall (JJAS)precipitation and monthly values of CO2 concentration during the

Fig. 5. Standard deviation of mid tropospheric CO2 and climate variables. (a) CO2 (ppm), (b) surface air temperature (°C), (c) Vertical Velocity (pa/sec), (d) ZonalWind (m/s), (e) Rainfall(mm) and (f) NDVI.

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season as well as CO2 in subsequent months (Tiwari et al., 2013). Thecorrelation coefficient between mid-tropospheric CO2 and vegetationshow negative relationship with correlation coefficient of−0.64.

3.3.2. Interannual variability of mid-tropospheric CO2 with vegetation andrainfall

Fig. 4a shows timeseries of CO2 and NDVI based on the monthlyvalues. It shows intraseasonal as well as the inter annual variabilityover central India. In this timeseries there is a clear opposite relationshipobserved in all years as expected from the above sections. The highvalues of vegetation relate to the low values of CO2. Due to the photo-synthesis process which causes the high vegetation period it absorbsthe CO2 content from the atmosphere leads to the decreases of theCO2 in the atmosphere. During the strong summer monsoons months,there is more vegetation due to the availability of water causes inmuch lower values of CO2 in the atmosphere. During theweak summermonsoon seasons low vegetation appear compared to the strongsummer monsoon years causes slightly higher values of the CO2 in theatmosphere when compared to the strong monsoon years. Highvariability of NDVI is observed in the months of northeast monsoonseason as well as in winter season while low variability is seen in themonths of monsoon months.

Fig. 4b explains the year to year variability of CO2 and rainfall overcentral India. The CO2 start decrease with the onset of the monsoonscauses to the increase in the vegetation, which leads to the decrease inthe mid-tropospheric CO2.

4. Summary and conclusions

In this paper, spatial distribution of mid tropospheric CO2 andsurface meteorological parameters in different seasons is presented.Also the variation in CO2 is examined in association with the variationsurface meteorological parameters over the Central Indian region.High CO2 have been observed during the spring (MAM) which is the

hottest season of the country. Low CO2 has been observed during thesummer monsoon season except northwest part of the country andsome parts of southern India. Spatial pattern of surface air temperatureshow high value during s spring (MAM) and continues till summermonsoonmonths (JJAS). Low values observed during thewinter season(DJF). Spatial pattern of zonal winds (south westerlies) are strongduring monsoon season. Flow from Arabian Sea to the western coastand adjacent continental region may be the cause of the horizontaltransport of marine CO2 to the continental region. In the spatial patternof vertical velocity, the negative values are observed during spring andmonsoons seasons over most parts of the country. This indicates theupward motion during spring and monsoon months.

Annual cycle of CO2 and surface air temperature over central Indianregion show high resemblance which attains maxima (minima) in themonth of May (August). Variation in CO2 and vertical velocity showsan inverse relationship. Negative values of vertical velocity indicateupward motion of winds which eventually carries surface CO2 to themid troposphere. Strong convection in the tropics leads upward verticalvelocity and so upward motion of the winds leads to vertical transportof the CO2. Minima in NDVI are observed in the month of May whichcorresponds to the maxima in CO2. NDVI starts increasing with theonset of summer monsoon (JJAS) over the country in the month ofJune till the end of the season. The mid-tropospheric CO2 shows adecrease in concentration with the onset of summer monsoon till theend of the season. The highest (lowest) values of NDVI are associatedwith the low (high) values of mid tropospheric CO2. Maximum rainfallover continental India occurs during June to September, whereasOctober onwards, winter monsoon proceeds and covers southeastIndia. The variation in NDVI depends on the water availability annualcycle of NDVI and rainfall shows similar characteristics especially duringsummer and winter monsoon season.

In addition to this, intra-seasonal and inter-annual variability of CO2

and other surface meteorological parameters also studied. There is apositive correlation between observed CO2 and surface temperature

89K.R. Kumar et al. / Science of the Total Environment 476–477 (2014) 79–89

(CC 0.8). High variability in mid tropospheric CO2, vegetation and rain-fall in terms of standard deviation is seen during summer monsoonmonths. The high variability in vertical velocities in terms of standarddeviation is observed during the end of summer and spring months(Fig. 5). Thismay be due to high outgoing longwave radiation associatedwith convection during this period. NDVI show negative correlationwith CO2 mainly due to vegetation growth during monsoon monthsand dry during winter months. Vegetation and rainfall plays a key rolein controlling CO2 in the atmosphere. Surface CO2 observations overIndia are strongly sought and which eventually will play a lead role inunderstanding variability of climate parameters and CO2 transportover this region.

Acknowledgement

Authors are thankful to the Director IITM for encouragement andsupport to carry out this research work. Also we are grateful to NASAfor providing the AIRS CO2 data, NDVI data (http://eosdata.gsfc.nasa.gov/) and TRMM rainfall data (ftp://disc3.nascom.nasa.gov/data/s4pa/TRMM_L3/TRMM_3B42_daily/). We also thank NCEP/NCAR for makingavailable reanalysis meteorological data sets (http://www.cdc.noaa.gov/,http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.pressure.html).

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