A study of the impact of synoptic weather conditions and water vapor on aerosol-cloud relationships over major urban clusters of China

Introduction Conclusions References


Introduction
Aerosols are known to impact the formation, optical properties, and life cycle of clouds (e.g. Ramanathan et al., 2001;Lohmann and Feichter, 2005;Tao et al., 2012;Boucher et al., 2013), either by increasing the cloud droplet number concentration and simultaneously decreasing the droplet size with a fixed water content, known as the first indirect effect (Twomey, 1974), or by suppressing precipitation formation, enhancing at 20 the same time the cloud cover and cloud lifetime, known as the second indirect effect (Albrect, 1989). In addition, by scattering or absorbing solar and terrestrial radiation (direct effect), aerosols affect temperature on the Earth's surface also perturbing the vertical temperature structure (Haywood and Boucher, 2000;Menon et al., 2002). So, it is important to understand and quantify the microphysical impact of both natural and Introduction anthropogenic aerosols on clouds, in order to understand and predict climate change (Anderson et al., 2003;Forest et al., 2002;Knutti et al., 2002). Urban clusters constitute a major political and economic issue in China. Increased numbers of cities of different sizes and intensive urbanization are prominent features in these regions, which extend over hundreds of kilometers. These city clusters are 5 among the most dynamic and rapidly growing regions of China. Several such clusters have emerged in the past two decades and are still evolving. The city clusters studied here, namely the ones in the Yangtze River Delta (YRD), the Pearl River Delta (PRD), and the Beijing-Tianjin-Hebei area (BTH), are the among the most rapidly growing, characterized by a spectacular population growth the last 20 years (Fig. 1). These re-10 gions, with aerosol loads some times higher than the global average ( Fig. 1), constitute extensive spatial sources of large quantities of aerosols as a result of human activities (industry, construction, traffic, etc.) and biomass burning, while occasionally transport of mineral dust from China's deserts adds to the aerosol burden of these regions (Zhao and Li, 2007;Jin and Shepherd, 2008). Moreover, the three regions exhibit significant 15 climatic differences, driven also by the Asian monsoon, and hence they are suitable for the investigation of aerosol-cloud relations under different meteorological conditions. The aim of this study is to study the influence of synoptic weather conditions and atmospheric water vapor amounts on AOD-CC relationships, while at the same time obtain some insight on possible impacts on local climate that might result over the 20 extended urbanization clusters of China due to aerosols. Towards this aim, we use 10 consecutive years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) of AOD, CC, clear sky water vapor (WV), and Cloud Top Pressure (CTP) satellite data from MODIS TERRA and AQUA in conjunction with sea level pressure (SLP) from NCEP/NCAR reanalysis data.
2 Data and methods 25 The 3 major urban clusters of China have been selected so as to be representative of 3 different climatic regions. China can be divided into five climate regions (Song et  Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2011), namely the temperate monsoon, the subtropical monsoon, the tropical monsoon, the temperate continental and the plateau/mountain climate region. These are mainly influenced by the Asian monsoon systems and the Tibetan Plateau (Domrös and Peng, 1988;Ye and Gao, 1979;Ding and Murakami, 1994). In particular, the Asian monsoon system has a major effect on the rainy seasons across the country. It starts 5 with the pre-monsoonal rain period over South China in early April and lasts from May until August. The summer monsoon rain belt propagates northward to the Yangtze river basin in June and finally to northern China in July. In August, when the monsoon period ends, the rain belt moves back to southern China. Due to the migration of the monsoon across China, the length of the rain season differs between southern and northern 10 China (Song et al., 2011). In particular, the BTH urban cluster is a temperate monsoon climate region, while, the YRD urban cluster is a subtropical monsoon climate region, and the PRD urban cluster is a tropical monsoon climate region (Fig. 1). The BTH domain (35. 5-40.5 • N, 113.5-120.5 • E) is an area with rapid industrial and economic development, reflected also at the high AOD levels (more than 4 times the global av-15 erage) over the region (Fig. 1). The YRD domain (28. 5-33.5 • N, 117.5-123.5 • E), is an area with significant black carbon (Streets et al., 2001;Bond et al., 2004) and sulfate (Lu et al., 2010) emissions. Finally, the PRD domain (21. 5-24.5 • N, 111.5-115.5 • E) is an area within the Inter-Tropical Convergence Zone (ITCZ) migration belt, with high anthropogenic aerosol emissions (Streets et al., 2003(Streets et al., , 2008Lei et al., 2011). Over the 20 3 regions of interest and within the study period, only weak overall upward trends have been reported (Guo et al., 2011). Aerosol and cloud parameters from the MODIS instrument aboard the TERRA and AQUA satellites (collection 5.1, level-3, 1 • × 1 • daily data) for the period 2003-2013 are used in this study. In particular, to investigate aerosol-cloud interactions, we use 25 aerosol optical depth at 550 nm (AOD 550 or just AOD), CC, WV for clear conditions (Remer et al., 2005;King et al., 2003) and CTP from both satellites. Aerosol index (AI), defined as the product of the AOD and Ångström exponent, is a good proxy to quantify cloud condensation nuclei and has been applied in many previous ACI studies Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (e.g. Costantino and Bréon, 2010 (off-coast Namibia and Angola), 2013 (over the SE Atlantic Ocean)). However, in the present study the use of AI would not be appropriate, because our study is conducted over land areas. This has to do with the use of the Ångström exponent in the derivation of AI. Namely, the Ångström exponent is not reliable over land areas. We quote a personal communication with L. Remer, NASA GSFC:

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"Ångström over land is not reliable and we recommend strongly not to use it". Hence, AOD is used in our study. Additionally, to examine the aerosol-cloud interactions under different meteorological conditions, such as low and high pressure systems, we used daily Sea Level Pressure (SLP) data from the NCEP/NCAR Reanalysis for the same period. The original 2.5 • × 2.5 • NCEP/NCAR SLP data were regridded using bi-linear 10 interpolation in order to match the MODIS 1 • × 1 • level-3 dataset.
Considering that meteorological conditions may have an impact on satellite derived aerosol-cloud relationships, the AOD and CC observations were classified into three different SLP condition classes (less than 1008 hPa for low pressure systems, between 1008 and 1017 hPa, and finally greater than 1017 hPa for high pressure systems) and 15 also according to atmospheric WV quantities.

Results and discussion
To gain an insight into the levels, trends, interannual variability and seasonal variation of AOD and CC over the study regions, we first examined the timeseries of AOD, CC, CTP and WV from MODIS TERRA and AQUA satellites over 5 grid points where cities 20 of the 3 major urban clusters under study are located, for the period 2003-2013 (Figs. S1 and S2 in the Supplement). The results from both satellites are similar, with the highest values of AOD, CC and WV occurring during the summer months, while CTP is higher during winter and lower during summer over all 5 cities (i.e. Cloud Top Height hereafter denoted as CTH also peaks in summer). The majority of the AOD values 25 over the BTH urban cluster are between 0.3-1.4, while over YRD they are between 0.5-1.3 and between 0.5-1 over the PRD urban cluster. BTH, with an average AOD 550 ACPD 15,[14007][14008][14009][14010][14011][14012][14013][14014][14015][14016][14017][14018][14019][14020][14021][14022][14023][14024][14025][14026]2015 Impact of weather conditions and water vapor on aerosol-cloud relationships  [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013] experiences somewhat heavier aerosol loading than the other 2 regions with average AODs of 0.646 ± 0.18 (YRD) and 0.590 ± 0.16 (PRD). Further, as we move from north to south, CC and WV increases while CTP variability also increases. Additionally, in the variables we will use in this study, no large trends are apparent during the study period (Figs. S1 and S2 in the 5 Supplement).
To investigate the influence of synoptic meteorological conditions on the AOD-CC relationship ( Fig. S3 in the Supplement), and to exclude artifacts on the AOD-CC relationship resulting from synoptically induced co-variance (Mauger and Norris, 2007;Loeb and Schuster, 2008;Quaas et al., 2010;Gryspeerdt et al., 2014), the MODIS data were 10 classified into 3 SLP classes (from NCAR/NCEP data, see above) and examined the AOD-CC-WV relationship at the low and high SLP classes. The SLP < 1008 hPa class is representative of the core of low pressure systems and hence of atmospheric circulation typical of these systems (e.g. ascending motions of air). The SLP > 1017 hPa class is representative of the core of high pressure systems and hence of atmospheric 15 circulation typical of these systems (e.g. descending motions of air). Furthermore, the low and high SLP systems are completely different in terms of horizontal transport patterns. The 1008 hPa < SLP < 1017 hPa class is less clearly defined in terms of atmospheric conditions, since it might contain meteorological conditions typical of the periphery of low pressure systems or typical of the periphery of high pressure systems 20 (e.g. troughs, ridges etc.), and hence it is omitted from the discussion.  15,[14007][14008][14009][14010][14011][14012][14013][14014][14015][14016][14017][14018][14019][14020][14021][14022][14023][14024][14025][14026]2015 Impact of weather conditions and water vapor on aerosol-cloud relationships  (Figs. 2a, b, e, f, 3a, b, e, f and 4a, b, e, f). This is due to the fact that the majority of available AOD-CC retrieval pairs for the low SLP class occurs during summer, when also the majority of available AOD-CC retrieval pairs for WV > 3 cm occurs (Fig. not shown). Additionally, as low SLP synoptic systems are associated with up-5 drafts, the occurrence of these systems in summer, when land and sea temperatures and hence also evaporation are higher, more WV can be transported up in the atmosphere. Other authors have also noted the correlation of AOD with WV. For example, Alam et al. (2010), report positive AOD-WV correlation over Pakistan due to their common seasonal patterns. On the other hand, Balakrishnaiah et al. (2012), report positive 10 AOD-WV correlation over India but negative over some Indian Ocean regions. It is evident that WV has a strong impact on CC, perhaps even stronger that the AOD impact on CC (Figs. 2-4a, b, e and f). In fact, over PRD the impact of AOD on CC for constant WV seems negligible (Fig. 4a, b, e and f). In the other two regions, BTH and YRD, CC might increase by up to 0.1 at most as AOD increases from 0.2 to 1 under constant WV, 15 while CC might increase by up to 0.4 for WV increases from 1 to 8 cm under constant AOD. For detailed statistics of the AOD-CC and AOD-CTP relationships please refer to Tables S1 to S5 of the Supplement. Also, for the response of CC to AOD in terms of seasonality, given the strong seasonal variability in aerosol and cloud shown in Figs. S1 and S2, please refer to Tables S1 and S2 and Fig. S3 of the Supplement.

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Hence, studies of AOD-CC relationships based on satellite data that do not take into account WV, might greatly overestimate the AOD impact on CC in regions where AOD and WV have similar seasonal variations, while they might greatly underestimate the AOD impact on CC in regions where AOD and WV have opposite seasonal variations. This result is in agreement with recent results from other authors that noted the large 25 possible impact of different meteorological variables on the AOD-CC relationships (e.g. Mauger and Norris, 2007;Quaas et al., 2010;Koren et al., 2010;Engstrom and Ekman, 2010;Chand et al., 2012;Grandey et al., 2013). Most importantly, it is in agreement with recent reports that gave qualitative indications that water vapor (Ten Hoeve et al., 2011) ACPD 15,[14007][14008][14009][14010][14011][14012][14013][14014][14015][14016][14017][14018][14019][14020][14021][14022][14023][14024][14025][14026]2015 Impact of weather conditions and water vapor on aerosol-cloud relationships  (Loeb and Schuster, 2008;Koren et al., 2010;Grandey et al., 2013) might have a strong influence on AOD-CC relationships. We also note, that despite the remarks made above, AOD does have an impact on CC even if synoptic and WV variability are accounted for (Figs. 2-4a, b, e and f), although this impact in our study regions is much smaller than the one that would have been estimated ignoring synoptic 5 and WV variability. In fact, in the three areas of study, where AOD and WV have similar seasonal variations, if the water vapor effect is taken into account the slopes of the CC/AOD relationship for AOD > 0.2 might be reduced up to 90 %. We suggest that these results should be taken into consideration in future studies trying to explain the weekly cycles of cloud cover and other meteorological parameters (e.g. temperature, 10 solar radiation, precipitation, etc.) observed in some regions of the planet through the human working cycle and the indirect effects of aerosols (e.g. Georgoulias et al., 2015). In all SLP and urban cluster cases, there is no apparent systematic increase of AOD with WV, and it does not appear that increased WV is systematically associated with large increases in AOD (Fig. 5). This indicates that there is no large systematic AOD 15 retrieval bias due to aerosol swelling at increased WV. Further, it is apparent that the largest part of the differences in the AOD-CC slope between low and high SLP synoptic conditions is due to the differences in WV between these conditions (see Fig. 5 and also compare parts b and f of Figs. 2-4).
CTP is a cloud parameter that can be used as a proxy for cloud vertical development.

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Hence a number of recent studies investigated its role in AOD-CC interactions over the region of Eastern Asia (e.g. Kumar, 2013;Alam et al., 2014;Tang et al., 2014;Wang et al., 2014) and globally (e.g. Gryspeerdt et al., 2014). CTP variations over the study areas were not dominantly driven by AOD, irrespective of pressure system and WV bins (Figs. 2-4c Temperature (CTT) long-term changes to changes in aerosol load without accounting for this WV effect (e.g. Devasthale et al., 2005) might lead to wrong quantifications. Although outside the scope of this study, we mention briefly that CTP from AQUA is lower than TERRA over all regions and under all pressure systems which is possibly due to the fact that clouds are more well-developed in the afternoon (AQUA overpass) 5 than in the morning (TERRA overpass).

Conclusions
In this work, we used a decade (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) of aerosol and cloud parameters from the MODIS instrument, to investigate the aerosol-cloud interactions over 3 major urban clusters of China, representative of 3 different climatic regions. We investigated the AOD-CC relationship under different synoptic conditions using SLP data, and under different clear sky WV contents. Over all urban clusters, and for all SLP regimes, CC is found to increase with AOD, thus pointing out that the CC dependence on AOD is not driven by synoptic co-variability. It is found that at AOD > 0.2 the AOD impact on CC at low SLP conditions is about two times higher than its impact at high SLP 15 conditions. Further, at its largest part this difference is due to WV differences between low and high SLP conditions rather than arising from differences in horizontal transport patterns. Hereupon, we stratified the data into 3 SLP bins to examine AOD-CC-WV relationships under different pressure systems. In most cases, WV is found to be constant with increasing AOD loading, while there is a positive relationship between cloud 20 cover and water vapor for fixed AOD. Moreover, the AOD-CC relationship is positive under all pressure conditions. In general, WV has a strong impact on CC and thus, studies of aerosol-cloud interactions based on satellite data that do not account for this parameter, may result in erroneous quantitative and qualitative results. Namely, studies of AOD-CC relation- 25 ships based on satellite data, might greatly overestimate the AOD impact on CC in regions where AOD and WV have similar seasonal variations, while they might greatly aerosol climatic impact and we need to improve our understanding of this interference. In addition, Cloud Top Pressure (CTP) at low SLP regimes is found to decrease more with AOD over the PRD and much less so over the YRD urban cluster, while there is no significant impact by AOD over BTH. On the other hand, at high SLP regimes, AOD does not seem to impact significantly CTP. Finally, over the BTH and YRD urban 10 clusters, CTP is found to increase considerably with increasing WV only at low SLP synoptic regimes. Similar to the case of AOD-CC relations, these results suggest that studies trying to relate CTP, CTH and CTT changes with changes in aerosol load, should account for this WV effect.
It is also found that there is no large systematic AOD retrieval bias due to aerosol