Lokesh Kumar Sahu
email: lokesh@prl.res.in

Varun Sheel

Mizuo Kajino

Philippe Nédélec

P Nedelec

Variability in tropospheric carbon monoxide over an urban site in Southeast Asia

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Carbon monoxide (CO) is mainly emitted from incomplete combustion of fossil fuel (FF) and biomass burning (BB), while oxidation of methane (CH 4 ) and volatile organic compounds (VOCs) can significantly contribute to the atmospheric budget of CO [START_REF] Jaffe | Impact of Asian emissions on the remote North Pacific atmosphere: interpretation of CO data from Shemya, Guam, Midway and Mauna Loa[END_REF][START_REF] Holloway | Global distribution of carbon monoxide[END_REF]. These sources also emit other gaseous and particulate species which play important roles in the chemistry and climate of the earth'sa t m o s p h e r e [START_REF] Duncan | Interannual and seasonal variability of biomass burning emissions constrained by satellite observations[END_REF]Streets et al., 2003a, b). Typically, the emissions of CO from vehicular traffic and industrial activity are year-round and do not show significant seasonality. On the other hand, emissions of CO from BB-related sources such as forest fire, savanna fire, and agricultural waste burning exhibit strong seasonality [START_REF] Jaffe | Impact of Asian emissions on the remote North Pacific atmosphere: interpretation of CO data from Shemya, Guam, Midway and Mauna Loa[END_REF]. In terms of causes, locations, and timing the emissions of CO from BB sources are different from anthropogenic or FF-related sources.

Reaction with the hydroxyl (OH) radical is the primary sink of CO in the atmosphere. In the troposphere, the reaction between CO and OH represents 90e95% of the CO sink, and about 75% removal of OH [START_REF] Novelli | Distributions and recent changes of carbon monoxide in the lower troposphere[END_REF]references therein). In the downwind of polluted urban or open BB sources, the photochemistry of CO can lead to the net production of O 3 [START_REF] Fishman | Correlative nature of ozone and carbon monoxide in the troposphere: implications for the tropospheric ozone budget[END_REF]. The transport of CO emitted from different sources and regions in Asia has been observed to impact the remote regions of troposphere [START_REF] Lelieveld | The Indian Ocean experiment: widespread air pollution from south and Southeast Asia[END_REF][START_REF] Cooper | A case study of transpacific warm conveyor belt transport: influence of merging airstreams on trace gas import to North America[END_REF]Sahu et al., 2006). The mixing ratio of CO has been used as a tracer for the long-range transport due to reasonably long photochemical lifetime of 1e2 months in tropical troposphere (e.g., [START_REF] Jaffe | Impact of Asian emissions on the remote North Pacific atmosphere: interpretation of CO data from Shemya, Guam, Midway and Mauna Loa[END_REF][START_REF] Allen | Observations of middle atmosphere CO from the UARS ISAMS during the early northern winter 1991/92[END_REF][START_REF] Shindell | Multimodel simulations of carbon monoxide: comparison with observations and projected near-future changes[END_REF]. CO is a pollutant and exposure to high levels can impact the human health [START_REF] Maynard | Carbon monoxide[END_REF]Sahu and Lal, 2006).

Anthropogenic emissions of CO and various other pollutants in Asia are increasing due to rapid industrialization and urban development (Streets et al., 2003a;[START_REF] Ohara | An Asian emission inventory of anthropogenic emission sources for the period 1980e2020[END_REF]). The regional inventories of CO and various other gaseous pollutants over Asia have been reported in the literature [START_REF] Olivier | Applications of EDGAR: Emission Database for Global Atmospheric Research[END_REF]Streets et al., 2003a;[START_REF] Heald | Biomass burning emission inventory with daily resolution: application to aircraft observations of Asian outflow[END_REF]. [START_REF] Van Der Werf | Interannual variability in global biomass burning emissions from 1997 to 2004[END_REF] have reported that the global emissions of CO from BB sources could be about 15% higher compared to those emitted from FF sources. The Asian emissions of CO were estimated to be w279 Tg in the year 2000 of which almost 50% originated from the BB sources, especially from slash-and-burn agriculture and deforestation (Streets et al., 2003b). Emissions of CO in SeSE Asia from BB sources exhibit strong seasonality with a primary peak during Februarye April and secondary peak during AugusteOctober (Streets et al., 2003b). In SE Asia, including Indonesia and Malaysia, emissions of CO from BB were estimated to be w118 Tg y r À1 (Streets et al., 2003b).

The spatial and temporal variations of CO have been characterized based on the measurements using different techniques such as remote sensing from the space, ground based remote sensing, and in-situ sampling. [START_REF] Emmons | Data composites of airborne observations of tropospheric ozone and its precursors[END_REF] have compiled the global tropospheric data of CO obtained from a number of aircraft campaigns during 1983e1999. However, the aircraft observations in Asia are negligibly small compared to those in North America and Europe. Most of the studies of CO in Asia have relied on data from space borne techniques such as the Measurement of Air Pollution from Space (MAPS) (web page http://www.nasa.gov/centers/ langley/news/factsheets/MAPS.html), Scanning Imaging Absorption Spectro Meter for Atmospheric ChartographY (SCIAMACHY) (http://www.sciamachy.org/), Atmospheric Infrared Sounder (AIRS) (http://www-airs.jpl.nasa.gov/), Tropospheric Emission Spectrometer (TES) (http://tes.jpl.nasa.gov/), Measurements Of Pollution In The Troposphere (MOPITT) (http://www.atmosp.physics.utoronto. ca/MOPITT/home.html).

The environmental and climatic impacts of BB in SE Asia have been well recognized based on the observational studies [START_REF] Thompson | Tropical tropospheric ozone and biomass burning[END_REF]. The present manuscript is based on the analysis of tropospheric profiles of CO measured over Bangkok using the MOZAIC data during 2005e2006. The objective of this study is to investigate the causes of variability in tropospheric CO using the meteorological, fire count, and back trajectory data. We have also presented a brief comparison of CO from the MOZAIC and simulations by MOZART. A comparison between model and observations will be an opportunity to evaluate the simulations in the tropical region.

MOZAIC program and MOZART model

The MOZAIC program was started in 1993 by the European scientists, aircraft manufacturers, and airlines supported by the European Union (EU) [START_REF] Marenco | Measurement of ozone and water vapor by Airbus in-service aircraft: the MOZAIC airborne program, an overview[END_REF]. The MOZAIC flights are sponsored by Air France, Austrian Airlines, Lufthansa, and Sabena/ Air France. This project has been operational since August 1994 for the automatic measurements on board Airbus A340's on scheduled flights. In the second phase (1997e2000), the measurements of O 3 and H 2 O were continued while instrumentations for CO and nitrogen oxides (NO y ) were developed for on board measurements. In the third phase (2001e2004), the aircrafts were equipped for automated in-situ measurements of CO.

An improved version of Thermo Environmental Instruments (Model 48CTL) based on the Gas Filter Correlation (GFC) technique was used for the measurements of CO. The absorption of IR radiation at a wavelength of 4.67 micron was used for the detection of CO. The measurement precision of AE5 ppbv (signal noise), AE5% (calibration) and minimum detection limit of 10 ppbv were achieved for a 30 s integration time. Detailed description of measurement technique, instrument validation and quality testing were reported in [START_REF] Nedelec | An improved infrared carbon monoxide analyser for routine measurements aboard commercial Airbus aircraft: technical validation and first scientific results of the MOZAIC III programme[END_REF].

The flight observations taken during both take-off and landing of the aircraft provide the vertical profile data over the airports. In this study, we have used about 143 profiles of CO measured over Bangkok during year 2005e2006. The histogram of the number of MOZAIC profiles in different months is shown in Fig. 1.

MOZART is a 3-D global chemical transport model of atmospheric composition designed to simulate both chemical and transport processes in the troposphere. MOZART-2 deals with 63 different chemical constituents employing 133 gas phase, 2 heterophase, and 33 photolytic reactions [START_REF] Horowitz | A global simulation of tropospheric ozone and related tracers: description and evaluation of MOZART, version 2[END_REF][START_REF] Sheel | Comparison of satellite observed tropospheric NO 2 over India with model simulations[END_REF]. The horizontal resolution used in this run is 2.8 Â 2.8 (equivalent to T42) translating to 128 longitude and 64 latitude grid points. Model can provide the simulations at 28 vertical sigma levels from surface to w40 km of altitude. The emission data of most species were taken from the Precursors of Ozone and their Effect on the Troposphere (POET). The POET inventory is based on the European projects of the Global Emission Inventory Activity (GEIA) and the Emissions Database for Global Atmospheric Research (EDGAR) [START_REF] Olivier | Present and Future Surface Emissions of Atmospheric Compounds[END_REF][START_REF] Olivier | Recent trends in global greenhouse gas emissions: regional trends 1970e2000 and spatial distribution of key sources in 2000[END_REF]. The POET inventory gridded for 1 Â 1 includes anthropogenic, fire, and natural emission data. The fire and natural emissions are monthly data, while anthropogenic emissions are annual. Anthropogenic emissions of CO and NO x were replaced by the emission inventory for Asia (Regional Emission inventory in ASia (REAS) version 1.1) available at 0.5 Â 0.5 grid. In this study, the meteorological fields taken from the National Centers for Environmental Prediction (NCEP) reanalysis is used for the MOZART simulations [START_REF] Sheel | Comparison of satellite observed tropospheric NO 2 over India with model simulations[END_REF]. In our institute (Physical Research Laboratory), we run the model on the 3 Terraflop HPC Linux cluster with 20 nodes. A typical one year run takes about 12 h of time.

The modifications of MOZART-4 over MOZART-2 include detailed anthropogenic hydrocarbon chemistry, improved scheme for determination of albedo, inclusion of tropospheric aerosols and online calculations of photolysis rates, dry deposition, H 2 O concentration and biogenic emissions [START_REF] Horowitz | A global simulation of tropospheric ozone and related tracers: description and evaluation of MOZART, version 2[END_REF]. MOZART-4 simulates comprehensive tropospheric chemistry with 85 gas phase species, 12 bulk aerosol species, 39 photolysis and 157 gas phase reactions [START_REF] Emmons | Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)[END_REF]. The model simulated at a resolution of 2.8 Â 2.8 with 28 sigma pressure levels from the surface to w2 mb using the NCEP's Global Forecast System (GFS) meteorological fields have been used in the present work. The emission inventories for major anthropogenic sources, i.e. FF combustion and biofuel burning used in the present simulations are taken from the POET for year 2000 [START_REF] Granier | Present and Future Surface Emissions of Atmospheric Compounds[END_REF]. These anthropogenic emissions have been updated over Asia for year 2006 using REAS inventory developed by [START_REF] Ohara | An Asian emission inventory of anthropogenic emission sources for the period 1980e2020[END_REF]. The monthly averaged BB emissions for the given year of simulation are taken from the Global Fire Emission Database, version 2 (GFED-v2) (van der [START_REF] Van Der Werf | Interannual variability in global biomass burning emissions from 1997 to 2004[END_REF].

Site and emission sources in Bangkok

Bangkok city is located in the southwest (SW) region of Thailand on the eastern bank of the Chao Phraya river near the Gulf of Thailand. Bangkok is the largest city in Thailand and one of the economically leading cities in SE Asia. The city is heavily populated with about 15% of the entire population of Thailand lives or works there. The population of greater Bangkok is about 10 million but the number of people officially registered is about 6 million. Bangkok and five surrounding provinces namely Samut Prakarn, Nonthaburi, Pathumthani, Nakorn Pathom, and Samut Sakorn are known as the Bangkok Metropolitan Region (BMR). The international airport (Suvarnabhumi, 13.72 N, 100.48 E) is located at about 33 km southeast (SE) from the main city center (see Fig. 2). According to the estimates there are more than 5.4 million vehicles running in the roads of Bangkok city (DLT, 2008). Vehicular traffic is a major source of air pollution in Bangkok. While other emission sources such as the Don Muang airport and small industrial estates are located at 30e40 km northwest (NW) from the Suvarnabhumi airport. The traffic in Bangkok consists of motorcycle (gasoline, 40% of total), three-wheeler or "tuktuk" (liquefied petroleum gas (LPG), 0.2%), car (gasoline/LPG/compressed natural gas (CNG), 37%), taxi (LPG/CNG/gasoline, 1.6%), van/pickup (diesel/CNG, 18%), bus (diesel/ CNG, 0.6%), and truck (diesel/CNG, 1.8%) (DLT, 2010). Despite the increasing trend in total number of vehicles, the roadside measurements of CO in Bangkok show a steady reduction from 1992 to 2005 (BMA, 2009). The emissions from biomass burning and agricultural waste burning in the surrounding regions of Bangkok are also important source of various gaseous and particulate pollutants [START_REF] Chuersuwan | Levels and major sources of PM 2.5 and PM 10 in Bangkok Metropolitan Region[END_REF]. Over 90% of rice straw is burned during the months of November to April, but the activities are particularly extensive during FebruaryeMarch [START_REF] Tipayarom | Effects from open rice straw burning emission on air quality in the Bangkok metropolitan region[END_REF]. Among the SE Asian countries, Thailand has the highest number of registered vehicles, while the amount of fuel and biomass burned was estimated to be second after Indonesia (Streets et al., 2003a, b).

Seasonality in general circulation and meteorological parameters

The three main seasons are wet (MayeOctober), winter (NovembereFebruary) and local summer (MarcheApril) in Thailand [START_REF] Pochanart | Carbon monoxide, regionalscale transport, and biomass burning in tropical continental Southeast Asia: observations in rural Thailand[END_REF][START_REF] Sahu | Seasonal and diurnal variations of black carbon and organic carbon aerosols in Bangkok[END_REF]. The period from winter to summer is also known as the dry season. In most of Asia, the seasonal shift in the large-range transport is controlled by the progression of the Inter-Tropical Convergence Zone (ITCZ) [START_REF] Asnani | Climatology of the tropics[END_REF]. The ITCZ is a region of lower pressure where winds from the northern and southern hemispheres come together. The wet season prevails due to the SW wind flow from the Indian Ocean and the winter season due to the northeast (NE) and northwest (NW) winds from the continents. The wet season is associated with the northward progression of the ITCZ which brings cleaner oceanic air from the southern Indian Ocean. In the winter season, the long-range transport of continental air mass mainly from the East Asia takes place due to the southward progression of the ITCZ. The observation site is influenced by the mixed air masses (marine and continental) during the summer season as the city is located near the Gulf of Thailand. In addition to the seasonal reversal in the ITCZ, the large scale phenomena like El Niño have been known to impact SE Asia. The coupled phenomena of El Niño and Southern Oscillation (ENSO) originate from cyclic warming and cooling of the surface oceans of the central and eastern Pacific. Several countries in SE Asia have been affected by the ENSO having the most severe impact in Indonesia.

The boxewhisker plots in Fig. 3 show the monthly variations of surface level wind speed, RH, surface pressure, temperature, and total rainfall from January 2005 to December 2006. We have used the meteorological data recorded at Don Muang Airport (DMK) in Bangkok (http://www.wunderground.com). Based on the daily data, the monthly mean temperature varies between a minimum of 27 C in the month of December and maximum of 32 C in the month of March. The temperature tends to increase from March to April (or till mid-May) therefore; this period is known as the summer season. The daily wind speed data shows large variability in Bangkok. During both the years, the monthly wind speed data shows slightly higher range (1.0e2.0 m s À1 ) in the wet season, while calm weather (<1.0 m s À1 ) prevailed during the winter months. The monthly median of sea level pressure varies in the ranges of 1004e1012 hPa and 1006e1012 hPa during the year 2005 and 2006, respectively with the lowest during June and highest in the month of December. The monthly RH varied in the ranges of 70e81% and 62e79% during the 2005 and 2006, respectively. It is worth to mention that the levels of RH during OctobereDecember of 2006 were w10% lower compared to the values during same period of 2005. During both the years, the rainfalls during November to April were lowest and accounted for about less than 20% of annual rainfall therefore, this period is also known as the dry season. The annual rainfalls were 1685 mm and 1623 mm at Bangkok during the 2005 and 2006, respectively.

Results

Vertical profiles of CO

The MOZAIC profiles of CO over Bangkok for the three different seasons are shown in Fig. 4. In detail, we have plotted all the data points (150 m height interval) along with their average, 5th percentile, and 95th percentile for each season. In wet season, the average mixing ratio of CO decreases from w250 ppbv near the surface to 100 ppbv at 2 km of altitude. In the middle troposphere (2e6 km), the mixing ratio of CO was lowest with a nearly constant value of about 85 ppbv. In the upper troposphere (6e12 km), the mixing ratio of CO showed some enhancements and CO increased slightly with the altitude. In winter season, the variation in CO was largest throughout the troposphere. In this season, the average mixing ratio of CO decreased from w370 ppbv near the surface to 130 ppbv at 2 km of altitude. In this season, the mixing ratio of CO was lowest with a nearly constant value of about 105 ppbv in the middle troposphere. Similar to the features seen in the wet season, the mixing ratio of CO showed some enhancements in the middle and upper troposphere. In summer season, the mixing ratio of CO varied considerably in the lower troposphere (0e2 km) but showed little variation in the free troposphere. In this season, the mixing ratio of CO decreases from w340 ppbv near the surface to 115 ppbv at 2 km of the altitude. In the free troposphere, the average profile of CO does not show any trend with altitude and remains constant at a level of w 95 ppbv in the summer.

The separation between the two bounding profiles (5th and 95th percentiles) shows altitude dependence indicating the different ranges of vertical variability in CO over Bangkok. In each season, the mixing ratio of CO showed highest variability in the lower troposphere and lowest in the middle troposphere. In the upper troposphere, the variability in the mixing ratio of CO was moderate but increased with altitude particularly in the wet and winter seasons. The seasonal averages of 5the95th percentiles of CO mixing ratio in the different regions of troposphere over Bangkok are presented in Table 1.

Based on the definition presented in several studies [START_REF] Sahu | Anthropogenic aerosols observed in Asian continental outflow at Jeju Island, Korea, in spring 2005[END_REF][START_REF] Sahu | Seasonal and diurnal variations of black carbon and organic carbon aerosols in Bangkok[END_REF], the profile of 5th percentile can be assumed as background profile of CO in the troposphere. It is to be noticed, however, that the definition of the "background" is not very strict and may vary from study to study. In the lower troposphere, the background level of CO decreased with an increase in altitude. In the free troposphere, the background profiles remained almost constant with average values of 77 AE 5 ppbv, 90 AE 8 ppbv, 80 AE 5 ppbv in the wet, winter and summer seasons, respectively.

The average profiles of CO from MOZAIC observations and MOZART simulations over Bangkok for the three different seasons are shown in Fig. 5. The MOZART-2 simulation tends to b ac Fig. 4. MOZAIC profiles of CO using 150 m height interval data and their average, 5th percentile, and 95th percentile over Bangkok during different seasons of year 2005e2006.

Table 1

The averages of 5the95th percentiles of CO mixing ratio in the lower, middle and upper troposphere in three different seasons over Bangkok. underestimate the observations in all seasons. The comparison between the model and observation showed significant disagreement in the lower troposphere but gets better in the free troposphere. In comparison to MOZAIC data the MOZART-2 simulations were lower by 48%, 16%, and 30% in the wet, winter, and summer seasons, respectively in the lower troposphere. In the free troposphere, the differences were within 15%, 27%, and 15% in the wet, winter, and summer seasons, respectively. The MOZART-4 simulations in the lower troposphere also underestimated the observation by 23% in the wet season. On the other hand, MOZART-4 overestimated slightly by 5% and 13% during winter and summer seasons, respectively. In the free troposphere, MOZART-4 consistently overestimated the MOZAIC observations by 18%, 8%, and 32%, during the wet, winter, and summer seasons, respectively. In the summer seasons, MOZART-4 shows a positive bias of 7e16 ppbv in the CO total column amount over SE Asia indicating high CO emissions from biomass burning [START_REF] Emmons | Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)[END_REF]. A similar over-estimations of CO by MOZART-4 were observed in the free troposphere over Indonesia in OctobereNovember, 2006 [START_REF] Srivastava | Study of tropospheric CO and O 3 enhancement episode over Indonesia during Autumn 2006 using the Model for Ozone and Related chemical Tracers (MOZART-4)[END_REF].

In this discussion we have compared the average profiles, but for the CO mixing ratios near to surface both MOZART-2 and MOZART-4 tend to underestimate the MOZAIC observations. For the profiles very near to the surface, both the simulations show rapid decline of CO with altitude compared to MOZAIC observations particularly in the wet season. In summary, the MOZART-4 simulation provided better representation of CO observed over Bangkok. The significant disagreement between observation and model in the lower troposphere could partly be attributed to rather coarse resolution of MOZART. The lack of proper representation of emission and local dynamical processes over Bangkok can also be important factors. The mixing ratio of CO in Bangkok shows significant diurnal Fig. 6. Monthly mixing ratio of CO using MOZAIC data in the different regions of the troposphere. The averages of MOZART-2, and MOZART-4 simulations are also plotted for the comparison.

variation but profiles were measured at specific time of day [START_REF] Sahu | Seasonal and diurnal variations of black carbon and organic carbon aerosols in Bangkok[END_REF][START_REF] Nedelec | An improved infrared carbon monoxide analyser for routine measurements aboard commercial Airbus aircraft: technical validation and first scientific results of the MOZAIC III programme[END_REF]. In the lower troposphere, the range of 1s variance in CO mixing ratios might also be due to the diurnal variability of CO in Bangkok. In this region, the large negative bias between MOZART and MOZAIC in the wet season is due to much higher production of OH. In the free troposphere, the difference in MOZART-2 and MOZART-4 could be due to less OH formation in MOZART-4 and hence relatively less CO is lost through oxidation. In fact, it has been noticed that MOZART-4 simulated OH is somewhat lower than the climatology for the tropical regions [START_REF] Emmons | Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4)[END_REF].

Seasonal characteristics of CO

The box-whisker plots representing monthly statistics of CO observed in the different regions of the troposphere (0e2 km, 2e 4 km, 4e6 km, 6e8 km, 8e10 km, and 10e12 km) over Bangkok during 2005e2006 are shown in Fig. 6. The observed mixing ratio of CO exhibits clear seasonal and inter-annual variability. The mixing ratio of CO was lowest in wet season and high in winter season. The seasonal variation was strongest in the lower troposphere, while the strength of seasonality decreased with the increasing altitude. In the lower troposphere, the mixing ratio of CO shows large variation during each month as can be seen from the levels of different percentiles. In the lower and middle troposphere, the monthly mixing ratios of CO show minima in the month of July and maxima in December during composite years of 2005e2006. In the lower troposphere, the mixing ratios of CO were 142 AE 50 ppbv and 210 AE 60 ppbv in September month of years 2005 and 2006, respectively. In the upper troposphere, the minima occurred in the month of July but maxima were observed in the October month of 2006. The observed monthly ranges of CO were 128e378 ppbv, 80e 160 ppbv, 80e128 ppbv, 85e142 ppbv, 87e141 ppbv, and 88e 146 ppbv at 0e2 km, 2e4 km, 4e6 km, 6e8 km, 8e10 km, and 10e12 km of altitudes, respectively.

In Fig. 6, the monthly averages of MOZART-2 and MOZART-4 simulations are also plotted for the comparison. The monthly simulations tend to capture the seasonality in CO observed in the different regions of the troposphere. Both MOZART-2 and MOZART-4 underestimate the near surface (0e2 km) observations, however, the agreement with MOZART-4 appears to be better. Other chemical transport models have underestimated CO in urban PBL (especially those in Asia) by as much as 39% [START_REF] Chen | Regional CO pollution and export in China simulated by the high-resolution nested-grid GEOS-Chem model[END_REF] due to the coarse resolution. In the free troposphere, most of the observed data fall between the two simulations representing the underestimation and overestimation by MOZART-2 and MOZART-4, respectively. In the upper troposphere (6e12 km of altitudes), both the observed and simulated monthly mixing ratios of CO during SeptembereNovember of year 2006 were higher by 15e30 ppbv compared to the same period of year 2005. However, depending on the regions of the troposphere, the ratios of CO MOZART-2 /CO MO- ZAIC and CO MOZART-4 /CO MOZAIC varied in the ranges of 0.6e0.9 and 0.8e1.3, respectively.

The contributions of CO over Bangkok from the anthropogenic sources in China and BB in SeSE Asia using MOZART-4 simulations are estimated for the year 2006 (see Fig. 7). The mixing ratios of CO over Bangkok from both these sources showed clear seasonality. In the lower troposphere, the contribution from the anthropogenic sources in China in winter is much higher compared to those calculated for the other seasons. In the upper troposphere, however except for the wet season, the MOZART-4 simulations reveal significant contribution of CO from anthropogenic sources in China. The impact of BB activities in SeSE Asia in winter season was confined mostly in the lower troposphere. The BB emission in SeSE Asia provides major contribution to CO in the free troposphere from September to December, mainly associated with the forest fires in Indonesian and Malaysian islands.

Discussion

Biomass burning emissions and long-range transport

Emissions of CO from global BB sources show strong seasonal and inter-annual variations (van der [START_REF] Van Der Werf | Interannual variability in global biomass burning emissions from 1997 to 2004[END_REF]. In this study, we have used the fire count data detected by the Along Track Scanning Radiometer (ATSR). Details of the ATSR methodologies can be found elsewhere [START_REF] Buongiorno | ERS-2 monitors exceptional fire event[END_REF][START_REF] Arino | The ERS-2 ATSR-2 World Fire Atlas and the ERS-2 ATSR-2 World Burnt Surface Atlas Projects[END_REF]. The seasonal maps of fire counts detected over SeSE Asia during the years 2005 and 2006 are shown in Fig. 8. In the wet season, however which is dry season in the southern hemisphere regions of Asia, the fire count data shows major BB activities only in the southern regions of SeSE Asia. The fires in the forested regions make the highest contribution among the other BB sources in Indonesia. The BB activities in Indonesian islands during year 2006 were significantly high compared to those during year 2005. Both the MOZAIC observations and MOZART simulations of CO over Bangkok during late wet season show the impact of extensive BB in Indonesia during year 2006.

In the winter season, the major activities of BB shift to the northern hemisphere regions of SE Asia most prominently in Thailand and Burma. In addition to BB in SeSE Asia in winter, many hot spots can be located in the far upwind over the southeast of China. The BB in Indonesia remained active till Novembere December of each year. In this season (winter), the BB activities in Thailand during year 2006 were significantly low compared to those during year 2005. The fire count maps during summer of both the years exhibit wide spread activities of BB in the SeSE Asia mostly over northern India, Pakistan, Burma, Laos and northern Thailand. Also in this season, the density of hot spots detected in Thailand during the summer of year 2006 was lesser than those during the year 2005. The monthly fire count data detected over different countries in Se SE Asia during year 2005 and 2006 are plotted in Fig. 9. In all countries of northern hemisphere of SeSE Asia the BB emissions are significant during FebruaryeMay when relatively dry weather prevails. The highest BB in Thailand was during the months of FebruaryeMarch. In India, the BB occurs from February to May but the month of April experiences the highest activities. From Mid of June to October the activities shift toward SE Asia when activities are only due to the fires in Indonesian and Malaysian islands. In summary, the analysis of fire count data reveals systematic seasonal and inter-annual variations in BB emissions over SeSE Asia. However, the roles of CO emitted from BB and anthropogenic sources in different regions of SeSE Asia on the vertical distribution of CO over Bangkok can be ascertained by incorporating the seasonality in the long-range transport.

Back trajectory models have been widely used to track the transport of air parcels originated from different source regions. In this study, the isentropic back trajectories were calculated using the Japanese 25-year Reanalysis data (JRA-25, 6 h, 1.25 Â 1.25; [START_REF] Onogi | The JRA-25 reanalysis[END_REF] with the same advection algorithm as [START_REF] Draxler | Description of the HYSPLIT_4Modeling System[END_REF]. The trajectories were calculated for a total run time of 168 h (7 days) at 1 km, 5 km and 10 km of altitudes with a time step of 5 min. For both the year of 2005 and 2006, the origins and pathways of trajectories arriving over Bangkok (at 1 km of altitude) show very clear and systematic seasonality (Fig. 10). We have not shown the trajectories at 5 and 10 km, however, we have briefly discussed the seasonality in trajectories also at these two altitudes.

In the wet season, the site is influenced by the cleaner air from the southern Indian Ocean due to the long-range transport from the SW direction. In the lower troposphere, the ambient pressure along the trajectories suggests that the air masses were confined within the marine boundary layer (MBL) of the Indian Ocean before they arrived over Bangkok. In addition to the negligible presence of BB sources in the wet season, the flow of cleaner marine air could be the major cause for the low CO observed in the wet season. The trajectories at 5 km of altitude originated over the Arabian Sea and Bay of Bengal and did not pass through major source regions. On the other hand, the trajectories arriving at 10 km of altitude were originated over the East China Sea and had passed over the southeast regions of China before entering in SE Asia. In the absence of BB the anthropogenic activities in northern hemisphere regions of SE Asia can be dominant source of CO. Additionally, the transport of CO from both anthropogenic and BB sources in southeast of China could be the cause of enhanced CO in the upper troposphere during the wet season.

In winter the back trajectories show the impact of long-range transport of continental air from the NE direction in the lower troposphere. The trajectories at 1 km of altitude were traced to have originated in the free troposphere over the eastern continental regions of China where activities of BB were significant. In the middle troposphere, the trajectories (at 5 km of altitude) show the predominance of air mass originated over the Pacific Ocean, however some trajectories originated or passed over the Indo-Gangetic Plain (IGP). The mid-tropospheric level of CO in winter was higher compared those observed in other seasons. In winter, relatively higher levels of CO in the middle troposphere could be due to the transport of polluted air from IGP. In the upper troposphere, the back trajectories were originated over Pacific Ocean and passed through the fire active regions of Indonesia islands. Therefore, the enhancements in upper tropospheric CO particularly during October of year 2006 could be attributed to the extensive BB in Indonesia. Supporting the impact of extensive BB in Indonesia in the upper tropospheric CO over Bangkok, the Microwave Limb Sounder (MLS) data averaged over 100 Ee125 E of longitudes show widespread enhancements in the mixing ratios of CO at 215 hpa (w11 km) between 20 S and 20 N of latitudes [START_REF] Zhang | Impacts of 2006 Indonesian fires and dynamics on tropical upper tropospheric carbon monoxide and ozone[END_REF]. Similarly, very high CO concentrations in the middle to upper troposphere (400e200 hPa) were also observed by the Atmospheric Chemistry Fourier Transform Spectrometer (ACEFTS) and by the MOPITT instrument [START_REF] Yurganov | Global AIRS and MOPITT CO measurements: validation, comparison, and links to biomass burning variations and carbon cycle[END_REF] over the same region during Indonesian fires in the year 2006.

In the summer season, the trajectories were originated over the Pacific Ocean and arrived over Bangkok from the SE and SW directions in the lower troposphere. The activities of BB were most extensive in many parts of SE Asia during this season. However unexpectedly, the mixing ratio of CO throughout the troposphere was lower compared to those measured during the winter season. The analyses of fire count data and back trajectories suggest that the impact of strong emissions in the summer season were subdued by the dilution due to mixing with cleaner and from Pacific and Indian Oceans. In other words, the wind flow from the SE/SW directions in the lower troposphere diluted the impact of local emissions significantly. In the upper troposphere, the trajectories were traced to have originated over the Indian Ocean hence the enhancements in CO were not significant compared to those observed in two other seasons over Bangkok.

Impact of local wind on seasonality of CO in lower troposphere

Aside from local emissions, levels of CO are influenced by meteorology, coastal and urban effects and trans-boundary transport in the lower troposphere. However, to estimate the contributions of different factors require detailed studies using regional scale models. Nonetheless, variations in meteorological parameters were observed to play important role in the distribution of primary pollutants in many urban areas. For instance, the local wind can significantly influence the mixing ratios of various pollutants. [START_REF] Sahu | Seasonal and diurnal variations of black carbon and organic carbon aerosols in Bangkok[END_REF] have shown that the surface concentrations of black carbon (BC) and organic carbon (OC) aerosols in Bangkok show strong dependencies with the wind speed.

In this study, we investigated the dependence of CO mixing ratio on wind speed in the lower troposphere (<2 km). The polar plots of wind parameters, color-coded with the mixing ratio of CO, for the three different seasons are shown in Fig. 11. As shown in Fig. 11(a), the observations were mostly influenced by the strong winds (0e 20 m s À1 ) from the SW direction in the wet season. This flow of cleaner marine air could have caused significant dilution of CO emitted from Bangkok city. However occasionally in this season, the higher levels of CO were observed during the episodes of stagnant air mass originated from the N-NE directions. In the winter season, rather weak winds from the NWeNE and NEeSE sectors dominated in the lower troposphere over Bangkok (see Fig. 11(b)). The influence of dilution was less pronounced as the mixing ratio of CO remained high under stagnant conditions (0e 6ms À1 ) of continental air from the NWeNE sectors in winter. In the summer season, the winds from S-SW directions transport cleaner air from the Gulf Thailand and Indian Ocean and dilute the mixing ratio of CO significantly (see Fig. 11(c)). Like other seasons, some enhancements in CO were observed under stagnant weather conditions in the summer season. Statistics (average AE standard deviation) of CO and meteorological parameters in different wind sectors in the lower troposphere over Bangkok are presented in Table 2. During the N-NE wind flow, the mixing ratio of CO was highest of 247 AE 132 ppbv and wind speed was recorded to be lowest (3.5 AE 1.7 m s À1 ). In the N-NE sector, the mixing ratio of CO was lowest of 124 AE 48 ppbv which coincided with the strong wind flow of 7.3 AE 2.7 m s À1 .

The dependencies of CO on the magnitude of wind speed for different seasons are shown in Fig. 12. The CO-wind relation varies with the season and magnitude of the wind (regimes), overall the mixing ratio of CO decreases with the increasing wind speed. In the lower wind speed regime (<5ms À1 ), CO decreases sharply with increasing wind speed in the wet and summer seasons. During these two seasons, the mixing of air from Indian and Pacific Oceans having low levels of CO resulted in sharp decrease CO over Bangkok. In the winter season, the mixing ratio of CO tends to increase with the magnitude of wind in the low wind regime. In the winter season, the prevailing long-range transport brings continental pollutants with elevated background of CO from NE and NW directions. In the higher wind flow regimes (>5ms À1 ), the mixing ratios of CO were lowest and also show weaker dependence with the wind speed.

Although not shown in this study, the regression analysis of average CO versus wind speed (averaged for a bin of 1.0 m s À1 ) data shows very good linear fits with correlation coefficient (r 2 ) values of 0.88, 0.89 and 0.95 during the wet, winter and summer seasons, respectively. The slopes of DCO/Dwind speed were À3.0 ppbv m À1 s À1 , À10 ppbv m À1 s À1 and À6 ppbv m À1 s À1 during the wet, winter and summers seasons, respectively. The mixing ratio of CO does not exhibit significant relation with wind speed in the middle and upper troposphere over Bangkok.

Summary and conclusions

Study of tropospheric mixing ratio of CO over Bangkok, Thailand has been presented based on the MOZAIC aircraft profile data measured during the year 2005 and 2006. We have investigated the causes of seasonality in the vertical distribution of CO by analyzing the back trajectory, ATSR fire count and meteorological data. We have also used MOZART simulations for the comparison with the observations. In Thailand, the three different seasons viz. wet, winter, and summer are linked to the variations in meteorological parameters and monsoonal transport. The meteorological parameters such as wind speed, RH, and precipitation showed lows and highs during the winter and wet seasons, respectively.

The observations in the lower troposphere were influenced by the flow of cleaner air due to prevailing SW monsoon in the wet season, while NE flow brings continental pollutants during the winter. The mixed air originated from different regions of SE Asia and cleaner air from surrounding marine regions prevailed in the summer season. The trajectories arriving at 5 km of altitude did not pass through major source regions except IGP in the winter. The back trajectories at 10 km were originated over Pacific Ocean and passed through the fire active regions of Indonesia islands during winter season. In the free troposphere, the background mixing ratios of CO were estimated to be w77 ppbv, 90 ppbv, 80 ppbv in the wet, winter and summer seasons, respectively.

In the wet season, the major activities of BB were located in the southern hemisphere regions of SeSE Asia. In the winter season, the major activities of BB shift to the northern hemisphere regions of SE Asia and many hot spots were also located in the far upwind over the southeast China. The activities of BB during summer were extensive and wide spread in the SeSE Asia. T h el a r g es c a l ep h e n o m e n al i k eE lN i ñ oc a ni m p a c tt h eB B emissions in SE Asia year as 2005 was a normal year but year 2006 was an El Niño year. The mixing ratio of CO was highest in the lower atmosphere and lowest in the middle troposphere during all the seasons. The average mixing ratios of CO near the surface were w250 ppbv, 370 ppbv and 340 ppbv during the wet, winter and summer seasons, respectively. In the free troposphere, the profiles remains almost constant with average values of 77 AE 5 ppbv, 90 AE 8 ppbv, 80 AE 5 ppbv in the wet, winter and summer seasons, respectively. In the upper troposphere, the mixing ratio of CO shows significant enhancements particularly during wet and winter seasons.

The observed monthly mixing ratio of CO exhibits clear seasonal and inter-annual variability in the different regions of the troposphere. In the lower and middle troposphere, CO shows minima in July and maxima in December. In the upper troposphere, the minima occur in the month of July but maxima were observed in the October month of 2006. In the lower troposphere, the mixing ratio of CO was w70 ppbv higher in September 2006 compared to same month year 2005. In the upper troposphere, the monthly mixing ratios of CO during SeptembereNovember of year 2006 were higher by 15e30 ppbv compared to the same period of year 2005. The enhancements in tropospheric CO during the wet season of year 2006 can be attributed to the extensive forest fire in Indonesia.

In the lower troposphere, both the MOZART simulations underestimate the observations but the agreement with MOZART-4 appears to be better. In the free troposphere, the observations lie between the two simulations representing the underestimation and overestimation by MOZART-2 and MOZART-4, respectively. Depending on the altitude, the ratios of CO MOZART-2 /CO MOZAIC and CO MOZART-4 /CO MOZAIC vary in the ranges of 0.6e0.9 and 0.8e1.3, respectively. The mixing ratio of CO depends on the magnitude of local wind speed in the lower troposphere but no clear dependencies were seen in the free troposphere. The relationship between CO and wind speed varies with the season and magnitude of the wind. Overall the mixing ratio of CO decreases with the increasing wind speed.

Fig. 1 .

 1 Fig. 1. The monthly histogram of number of MOZAIC profiles measured over Bangkok during year 2005 and 2006.

Fig. 2 .

 2 Fig. 2. Left figure shows the map of Thailand and surrounding nations and right figure is about the road networks and location of the international airport in Bangkok (taken from wikimapia web, http://wikimapia.org).

Fig. 3 .

 3 Fig.3. The monthly variations of meteorological parameters in Bangkok during the year 2005 and 2006. In the boxewhisker plots the horizontal solid lines represent 10th, 25th, 50th, 75th, and 90th percentiles, filled circles are 5/95th percentiles, and dashed lines give the mean obtained using daily data.

AltitudeFig. 5 .

 5 Fig. 5. The average (AEstandard deviation) profiles of CO mixing ratio obtained from the MOZAIC observations, MOZART-2, and MOZART-4 simulations over Bangkok during different seasons of year 2005e2006.

Fig. 7 .

 7 Fig. 7. Monthly contributions of CO estimated using the MOZART-4 simulations over Bangkok from BB in SeSE Asia and anthropogenic sources in China.

Fig. 8 .

 8 Fig. 8. Maps of ATSR fire count data detected over south and Southeast Asia (SeSE Asia) during different seasons of year 2005 and 2006.

Fig. 9 .

 9 Fig. 9. The monthly variation of fire count data detected over different countries in SeSE Asia during year 2005e2006.

Fig. 11 .

 11 Fig. 11. Polar plots of wind parameters (wind speed (m s À1 ) and wind direction (deg.)) over Bangkok during different seasons of year 2005e2006.

Fig. 12 .

 12 Fig.12. The scatter and boxewhisker plots show the dependencies of the mixing ratio of CO on wind speed during the wet, winter and summer seasons.

Table 2

 2 Statistics (average AE standard deviation) of CO and meteorological parameters in different wind sectors in the lower troposphere (<2 km of altitudes) over Bangkok.

	Wind	Wind speed	Temperature	Relative	CO
	sector	(m s À1 )	( C)	humidity (%)	(ppbv)
	N-NE	3.5 AE 1.7	22.4 AE 6	7 1 AE 19	247 AE 132
	E-NE	4.8 AE 2.3	20.1 AE 5	7 8 AE 14	190 AE 75
	E-SE	5.4 AE 2.8	20.1 AE 5	8 3 AE 12	165 AE 58
	SeSE	4.5 AE 2.3	23.2 AE 4	7 4 AE 15	187 AE 72
	S-SW	5.7 AE 2.7	22.4 AE 5	7 8 AE 12	157 AE 61
	W-SW	7.3 AE 2.7	20.0 AE 4	8 2 AE 10	124 AE 48
	W-NW	7.8 AE 4.3	20.1 AE 5	7 9 AE 12	125 AE 70
	N-NW	4.2 AE 2.1	22.2 AE 5	7 4 AE 16	234 AE 127

Acknowledgments

The authors acknowledge for their strong support the European Commission, Airbus, CNRS-France, FZJ-Germany and the airlines (Lufthansa, Air France, Austrian and former Sabena who carry free of charge the MOZAIC instrumentation since 1994). The ATSR World Fire Atlas data has been taken from Ionia products of European Space Agency.


Daniela Cianelli

Frédéric Diaz

Yann Leredde

Patrick Marsaleix

Francois Carlotti

Particle exchange and residence times in the North Western Mediterranean

Keywords: 92.10.Sx -Coastal, estuarine, and near shore processes

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

-Introduction

In coastal areas mesoscale hydrodynamic structures (10 s to 100 s of km) are crucial for basin-scale particle transport. Investigating the relationships between particle distribution and mesoscale structures provides a rationale to estimate pollutant dispersion and oil spills fate [1,2], as well as to understand the influence of such physical structures on the dynamics of local ecosystems [3,4]. The north-western Mediterranean Sea and especially c Società Italiana di Fisica 139 the Gulf of Lions (GoL) represent an interesting site to study the influence of water mass circulation and estuarine inputs on particle transport. A strong mesoscale circulation associated with the North Mediterranean Current (NMC) penetrates the GoL from the northeastern side splitting into two branches: a southern one following the continental slope, and a northern one occasionally entering the shelf zone following a northwestern direction [5,6]. The modelling efforts carried out during the last 15 years over this area have been mainly focused on the fresh-water plume dynamics (e.g. [7][8][9]), on the mesoscale circulation and distribution of water masses (e.g., [10,6]) and on the interactions of coastal currents with a shallow shelf [11,12]. The GoL has also relevant biological implications as it is a major anchovy spawning area in the northwestern Mediterranean Sea [13], owing to its relative high primary production over the year [14].

The main goals of the present work are to understand how the hydrodynamic features of the GoL may determine the development of retention/dispersive areas and to provide a preliminary estimate of particle transport during the winter period (January-February). To this aim we investigate particle drift in relation to physical forcing variations by means of a 3D hydrodynamic model (Symphonie) of the GoL [7,9] coupled with a Lagrangian module. The Lagrangian approach represents a suitable tool by which to study transport processes over an entire basin [15][16][17] and to simulate complex and interactive processes acting at different scales [18,19]. Here we present a first step considering passive drifters for which the transport processes are determined uniquely by the velocity fields of the model circulation. By estimating the residence times we then characterize the exchange processes on the shelf zone.

-Materials and methods

The Symphonie hydrodynamic model is basically a three-dimensional primitive equation model of the coastal ocean. The three components of the velocity field, surface elevation, temperature and salinity are computed on a C Arakawa grid with horizontal mesh of 3 km; the vertical resolution consists of a generalized sigma coordinate system with 25 levels [10]. Turbulence closure is achieved through a prognostic equation for the turbulent kinetic energy and a diagnostic equation for the mixing and dissipation length scales [20]. A leap-frog scheme is used for the time stepping; the computation costs are limited thanks to a time splitting technique which permits computing separately the vertical shear of the current and its depth-averaged component with appropriate time steps [START_REF] Blumberg | inA description of a three-dimensional coastal ocean circulation model[END_REF]. On the basis of the surface boundary conditions, the momentum flux equals the wind stress, whereas the heat flux results from the atmospheric and radiative fluxes. The solar flux penetrates the water column with a decreasingly exponential law and the salinity flux takes into account the balance between evaporation and precipitation. Open boundary conditions are the same used in Estournel et al. [10], i.e. a radiation condition (e.g. [START_REF] Oey | [END_REF]). The external forcings are of three types: river discharges, atmospheric fluxes and large-scale inputs at lateral open boundaries. The real fresh-water inputs from the most important rivers (Rhône, Hérault, etc.) are updated at high frequency (from 3 to 24 hours); the atmospheric forcings are provided by the 3-hours averaged outputs of the weather-forecast model Aladin (Météo-France). The restoring terms of the open boundary scheme allow forcing the model with the features of the general circulation at the regional scale; this latter, clearly identified as the NMC, is given by the regional-scale model MOM (e.g. [23]).

To compute the trajectories in the three-dimensional flow field of the Symphonie model a Lagrangian particle routine has been developed. The Lagrangian simulations are based on the output velocity fields from Symphonie with a temporal resolution of 3 h. We consider purely passive floaters, and their simulated walks are computed assuming that the velocity is constant over the sampling interval. Given the properly Eulerian velocity fields, the Lagrangian particles are advected with the flow. At each point, the velocity field is computed by linearly interpolating the nearest grid points along the three spatial dimensions. Once the particle position is known, additional local variables-such as temperature, salinity, density and turbulent kinetic energy-are interpolated using the values computed by Symphonie on the neighbouring grid points.

Following [24] and [17] the transport properties on the shelf area are evaluated computing the total particle concentration C(t) and the residence time T . C(t) is the ratio between N (t), which is the number of particles inside the region at time t, and the total number of particles initially released in the same region (N 0 ). The difference between N 0 and N (t) gives the number of particles already exited from the domain N out (t). The residence time T is computed as

T = lim t→∞ T * (t), (1)
T * (t)=t N (t) N 0 + Nout i=1 t out (i) N 0 , (2)
where T * represents the average time taken by the drifters to exit from the release area for the first time and t out (i) is the exit time of each particle that has already left.

-Results

Figure 1a shows the modelled domain, comprised between longitude 2 • 58 ′ and 7 • 38 ′ E and latitude 40 • 50 ′ and 43 • 50 ′ N and the corresponding bathymetry. To classify the different zones of the GoL as aggregative or dispersive, we divided our research area into four sectors: sector A corresponds to the shelf zone approximately delimited by the isobath of 200 m; sector B marks the boundary between the offshore and coastal zones; sectors C bounds the area where the predominant physical structure is the return branch of the NMC cyclonic flow. Sector D is a narrow strip representing the external boundary of the modelled domain; owing to the boundary conditions set in the Symphonie model [10], we consider all particles entering sector D as leaving the domain.

Basing on preliminary tests providing the main direction of particle transport, the single release coordinates in the x, y and z reference frame have been fixed deterministically for both sectors A and B, in the area between Cap Creus and Saint-Tropez (fig. 2a). The initial distribution in sector A covers (almost homogeneously) the continental shelf up to the 200 m bathymetric line. In sector B the initial coordinates have been set basing on a regular lattice overlapping with both a limit zone between the shelf and offshore dynamics (from 5 • 12 ′ to 5 • 25 ′ E), which is the most interesting area owing to the intrusion of the NMC in the GoL (see the velocity fields in fig. 1b). Particle concentration in sector A is much higher than in sector B because our experimental design was aimed at understanding if this sector A could be considered an aggregation area or not. The simulations of a 600 particles cluster, released at time step t =0(t 0 ) in the layer 0-25 m, have been run between January 1st and February 25th 1999, time span for which the Symphonie model has been validated using field data from the oceanographic cruise Moogli 3 [23].

At the initial state, particles are scattered along the GoL near shore in sector A and within a large area comprised between the coast and 42 • 50 ′ N and between 5 • 12 ′ and 2a). At t 0 the presence of NMC along the continental slope and its intrusion on the eastern side of the shelf is relevant, the horizontal velocities in the central part of the continental shelf are much weaker (less than 10 cm s -1 ) than those observed for the cyclonic circulation, which reaches values greater than 60 cm s -1 (fig. 1b). Consequently, this zone of the shelf appears relatively cut off from the rest of the studied area. One month later (fig. 2b and3a), most of the particles initially spread from sector A predominantly remain over the continental shelf; a certain aggregation may be observed in the East of the Rhône River delta (ca. 43 • 30 ′ N-5 • 00 ′ E), where the fresh- and GRc, respectively) mark the separation of the NMC in two branches separated by a zone of weak currents. At the end of the simulation (February 25th, fig. 2c and3b), part of the particles initially launched in sector A remains on the continental shelf. During the second half of February the coexistence of seasonal north to northwesterly winds, blowing with velocities greater than 10 knots, generate strong upwellings along the Languedoc-Roussillon coast [10] inducing an absence of particles in the littoral zone of the GoL and a particle aggregation towards the central part of the shelf. The remaining stock drifts from sector A principally towards the offshore sectors B and C. Particles launched from the western side of sector A firstly move northeastward towards the central part of the shelf, with a speed of order of 30 cm s -1 , advected by the eastern counter-current observed by Petrenko [5], and then depict an anticyclonic eddy, as measured at the same location and with similar spatial scales under conditions of Mistral and Tramontane northerlies winds [10]. On the contrary, the particles launched from sector B are exclusively observed offshore in sectors B and C. During winter, offshore currents are typically oriented to the southwest thus the particles found in the southern part of sector B cover the whole span of the regional cyclonic circulation by drifting firstly south-westward within the main branch of the NMC, then north-eastward within the return current. Their path mark the deep water formation area observed during the Moogli 3 cruise [23] due to the intense cooling and evaporation induced by the northerlies winds blowing during February.

6 • 55 ′ E in sector B (fig.
Figure 4 shows the vertical distribution of the sample previously considered on February 25th for sectors A, B and C. The number of particles in sector A (fig. 4a) decrease from the surface down to ca. 280 m. This zone is a typical shallow-water area, thus the majority of particles are concentrated in the surface layer (0-100 m). The intrusion of the NMC on the eastern side of the GoL and its spreading from the slope up to the 100 m isobath [5] is here clearly visible. In sector B (fig. 4b) particles reach a maximum depth of ∼ 460 m, with a number of particles in the layer 0-250 m much greater than in the other ones. The mean depth (∼ 160 m) of the particles reflects the vertical size of this mixing layer that almost exactly matches the observed core of the NMC during Moogli 3 (see table 3 in [5]) in this area. The weak stratification, the winds from the north and the occurrence of dense water formation enhance the vertical dispersion of particles in the mixed layer and only few particles are found deeper in the water column. Moving offshore in sector C (fig. 4c), the number of particles decreases from the surface to a maximum depth around 410 m; as generally observed in the other sectors, a large proportion of particles is found in a surface layer reaching ca. 200 m. This latter distribution probably results from distinct processes occurring in this sector, such as the Mediterranean Deep Water formation (e.g. [START_REF] Madec | i nThermohaline-driven deep water formation in the Northwestern Mediterranean Sea,e d i t e db[END_REF]) and the presence of a return cyclonic branch of the NMC flowing north-eastward.

The bar diagram in fig. 5 shows the exchanges between the continental shelf and the offshore sectors of the GoL. The plot points out the time evolution of the tracers concentration in the release area, as well as their redistribution in the neighbouring sectors as time goes by. The particle concentrations for sectors A and B (C A and C B , respectively) are computed as described in sect. 2.A tt 0 , when 436 particles are spread over sector A (fig. 5a), the particle concentration in this sector C A = 1 exponentially decreasing when the particles start to leave the origin area. After three weeks the majority of the particles remains confined to the continental shelf and only a minimum amount is transported to the offshore areas (C A =0.63), whilst at the end of simulation the highest concentration of particles is found in sector B. A different repartition is observed for the group of particles (164) initially released in sector B (fig. 5b). Throughout the simulation period, they mainly remain in the release sector (on Feb. 6th, C B ∼ = 0.43) and only few of them drift towards the offshore sector C. On the whole, by the end of the simulation only few particles exit from the modelled domain reaching sector D. It is worth noting that, in about 60 days, no particle starting from sector B reaches the shelf zone.

A proxy of the aggregative or dispersive character of the shelf area (sector A) is obtained calculating the residence time T for two crucial shelf zones: the upwelling one along the Languedoc-Roussillon coast and the Rhône river delta (A 1 and A 2 , respectively). Since a particle, once left its release area, may eventually re-enter it, we define as in [17] a buffer zone corresponding to the area comprised between the limit of sector A and the 800 m isobath (see fig. 1a). When a particle remains in the buffer, it is considered as still on the shelf and the relative residence times is computed; on the contrary, if the particle goes beyond the buffer it is assumed to be definitely leaving the shelf area. In the upwelling zone, 150 particles have been released and the estimated residence time is T * A1 ∼ = 54 days; almost the same value is found on the Rhône delta area, where the residence time T * A2 is about 52 days. These T * correspond to the initiation of the asymptotic behaviour of theorethical residence time curves; more detailed investigation should be carried on longer time series, for which no model validations with in situ data are yet available.

Calculated T * values should therefore be considered as an underestimate indicating that the two shelf areas are able to retain the particles approximately two months on average.

-Conclusions

The coupling between a Lagrangian approach and the 3D circulation model allows us to preliminary analyse the distribution of passively drifted particles induced by the winter hydrodynamic structures and meteorological forcings in the GoL. By reconstructing the single 3D Lagrangian trajectories we evaluate the contribution of the realistic hydrodynamics in the gain and loss of particles through the shelf and the offshore zone of the GoL during the winter period.

Starting at the beginning of January from an initial distribution evenly covering the whole coastal area of the GoL and a wide offshore zone eastern of the GoL, we obtain at the end of February two major distribution areas separated by the NMC. The coastal area shows a dispersive character: by the end of the simulation, less than one third of the particles originated from sector A remains on the shelf (fig. 2), while a relevant number is transported towards sectors B and C. The second distribution area is identified in the central offshore zone, between sector C and the south of sector B. This area can be properly defined as both a retention area, because particles entering the NMC cyclonic gyre are confined in it, and a transition area, since NMC sustains particle drift towards the western margin of the basin. In our simulations, almost the 50% of the initial shelf population is found in sector A after one month from the release, showing that the local hydrodynamic structures only initially work as a potential retention system for the shelf population. On the other hand, during the same period sector B favours particle transport mainly owing to the presence of the main branch of the NMC, particles trapped in it being quickly drifted to the offshore zone. On the whole, a large part of the particles released in sector B are drifted by an east to west transport, developing their trajectories in the central part of the cyclonic gyre; only few of them finish their paths nearby the release point.

The estimate of residence times of shelf particles is strictly linked to the marked shelf-offshore transport directions. Particles entering the offshore branch of the NMC current are not able to re-enter the shelf, and after ∼ 55 days only 20% remains in the release area. Drifters launched from the western side of the GoL are captured by the strong eastern counter-currents favoured by the numerous intrusions of the NMC on the shelf and on its western side [5]. Before the end of January, the weak surface currents in correspondence of the Rhône River aggregate the particles around the river mouth. After this date, the fresh-water inputs rapidly increase (above 3000 m 3 s -1 ) until the end of February, directing the drifters towards the western area of sector A. At the same time, the development of sub-mesoscale eddies favours the retention of particles in front of the Roussillon coast, while the occurrence of northwesterly winds inducing coastal upwellings (e.g. [START_REF] Millot | [END_REF]) drives the cluster towards the central part of the shelf.

The present analysis represents the first attempt to characterise particle transport in the GoL. The results are in agreement with a previous study (e.g. [10]) showing that the hydrodynamic circulation in the GoL is mainly affected by the direction and strength of wind as well as by river discharge. The evidence that none of the particles initially released offshore enters the shelf area hints at the presence of a physical barrier hampering such transport. On these grounds it is evident that, at least in winter conditions, the seasonal features of the wind-induced shelf circulation, the fresh-water inputs and the NMC (flowing deep (250-300 m) and fast (70 cm s -1 ) close to the coast) favour a shelf-offshore and an east-to-west particle exchange, whilst an offshore-shelf transport is inhibited. These outcomes provide new elements to understand the interactions between the local hydrodynamics and the ecosystems dynamic of the GoL and they also highlight which are the zones meriting special focus in coastal zone management program.

Fig. 1 .

 1 Fig. 1. -a) The modelled domain divided into four sectors and the corresponding bathymetry (interval isolines 50 m); b) the cyclonic structure of the large-scale circulation and the northwestern intrusion of the NMC in the GoL visible in the horizontal velocity field (at 20 m depth) on January 1st.

Fig. 2 .

 2 Fig. 2. -The evolution of particle distribution released from sectors A (•)andB(•)ata)initial time step; b) after one month from the release; c) after two months from the launch. For graphical purposes only a subset of 300 particles is shown.

Fig. 3 .

 3 Fig. 3. -The horizontal velocity field at 20 m depth: a) after one month from the release; b) after two months from the launch.

Fig. 4 .

 4 Fig. 4. -Histograms of vertical distribution at the end of simulation for sectors A, B and C.

Fig. 5 .

 5 Fig. 5. -Time evolution of particle concentration in the release area and particle redistribution in the different sectors: a) particles initially released from sector A; b) particles initialliy released from sector B.

The authors acknowledge Prof. E. Zambianchi for helpful discussions and the modellers team of the Pôle d'Océanographie Côtière at the Laboratoire d'Aérologie (Toulouse, France). Comments from an anonymous referee improved an earlier version of the manuscript. This work has been performed in the framework of the "French Programme National d'Environnement côtier".