Geosphere-Biosphere Programme Research Articles

Top-of-the-atmosphere reflected shortwave radiative fluxes from GOES-R

Abstract. Under the GOES-R activity, new algorithms are being developed at the National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) to derive surface and top-of-the-atmosphere (TOA) shortwave (SW) radiative fluxes from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R. This paper describes a support effort in the development and evaluation of the ABI instrument capabilities to derive such fluxes. Specifically, scene-dependent narrow-to-broadband (NTB) transformations are developed to facilitate the use of observations from ABI at the TOA. Simulations of NTB transformations have been performed with MODTRAN 4.3 using an updated selection of atmospheric profiles and implemented with the final ABI specifications. These are combined with angular distribution models (ADMs), which are a synergy of ADMs from the Clouds and the Earth's Radiant Energy System (CERES) and from simulations. Surface conditions at the scale of the ABI products as needed to compute the TOA radiative fluxes come from the International Geosphere–Biosphere Programme (IGBP). Land classifications at 1/6∘ resolution for 18 surface types are converted to the ABI 2 km grid over the contiguous United States (CONUS) and subsequently re-grouped to 12 IGBP types to match the classification of the CERES ADMs. In the simulations, default information on aerosols and clouds is based on that used in MODTRAN. Comparison of derived fluxes at the TOA is made with those from CERES, and the level of agreement for both clear and cloudy conditions is documented. Possible reasons for differences are discussed. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).

Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions

Land surface temperature (LST) is a vital parameter associated with the land–atmosphere interface. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product can provide precise LST with high time resolution, and is widely applied in various remote sensing temperature research. However, due to its inability to penetrate the cloud and fog, its quality is not able to meet the requirements of actual research. Hence, obtaining continuous and cloudless MODIS LST datasets remains challenging for researchers. The critical point is to reconstruct missing pixels. To compare the performance of different methods, first, three kinds of methods were used to reconstruct the missing pixels, namely, temporal, spatial, and spatiotemporal methods. The predicted values using these methods were validated by the automatic weather system data (AWS) in the Heihe river basin of China. The results demonstrated that, compared with other methods, linear temporal interpolation using Aqua data had the best performance in MODIS LST reconstruction in the Heihe river basin, with an RMSE of 7.13 K and an R2 of 0.82, and the NSE and PBias were 0.78 and −0.76%, respectively. Furthermore, the interpolation method was improved using adaptive windows and robust regression. First, the international Geosphere–Biosphere Program (IGBP) classification was employed to distinguish the different land surface types. Then, the invalid LST values were reconstructed using adjacent days’ effective LST values combined with a robust regression. Finally, a mean filter was applied to eliminate outliers. The overall results combined with ERA5 data were validated by AWS, with an RMSE of 6.96 K and an R2 of 0.79 and the NSE and PBias were 0.77 and −0.20%, respectively. The validation demonstrated that the scheme proposed in this paper is able to accurately reconstruct the missing values and improve the accuracy of the interpolation method to a certain extent when reconstructing MODIS LST.

Vegetation Expansion on the Tibetan Plateau and Its Relationship with Climate Change

The natural shift in land cover from non-vegetated to vegetated land is termed as vegetation expansion, which has substantial impacts on regional climate conditions and land surface energy balance. Barrens dominate the northwestern Tibetan Plateau, where vegetation is predicted to expand northwestward with the ongoing climate warming. However, rare studies have confirmed such a forecast with large-scale vegetation monitoring. In this study, we used a landcover dataset, classified according to the International Geosphere–Biosphere Program criteria, to examine previous model-based predictions and the role of climate on the expansion rate across the plateau. Our results showed that shrublands, open forests, grasslands, and water bodies expanded while evergreen and deciduous broadleaf forests, croplands and barrens shrank during the period 2001–2018. Vegetation expanded by 33,566 km2 accounting for about 1.3% of the total area of this plateau and the land cover shifting from barrens to grasslands was the primary way of vegetation expansion. Spatially, the vegetation expanded northwestward to lands with colder, drier, and more radiation in the climate. Increasing precipitation positively correlated with the vegetation expansion rate for the arid and semi-arid northwest Tibetan Plateau and warming contributed to the vegetation expanding in the semi-humid southeast Tibetan Plateau. Our results verified the predictions of models and highlighted the “greening” on barrens in recent years.

Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

The spatial local accuracy of land cover datasets over the Qiangtang Plateau, High Asia

We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.

Impacts of Land-Use Data on the Simulation of Surface Air Temperature in Northwest China

This study examines the impacts of land-use data on the simulation of surface air temperature in Northwest China by the Weather Research and Forecasting (WRF) model. International Geosphere–Biosphere Program (IGBP) landuse data with 500-m spatial resolution are generated from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products. These data are used to replace the default U.S. Geological Survey (USGS) land-use data in the WRF model. Based on the data recorded by national basic meteorological observing stations in Northwest China, results are compared and evaluated. It is found that replacing the default USGS land-use data in the WRF model with the IGBP data improves the ability of the model to simulate surface air temperature in Northwest China in July and December 2015. Errors in the simulated daytime surface air temperature are reduced, while the results vary between seasons. There is some variation in the degree and range of impacts of land-use data on surface air temperature among seasons. Using the IGBP data, the simulated daytime surface air temperature in July 2015 improves at a relatively small number of stations, but to a relatively large degree; whereas the simulation of daytime surface air temperature in December 2015 improves at almost all stations, but only to a relatively small degree (within 1°C). Mitigation of daytime surface air temperature overestimation in July 2015 is influenced mainly by the change in ground heat flux. The modification of underestimated temperature comes mainly from the improvement of simulated net radiation in December 2015.

Dependence of Simulation Biases at AHI Surface-Sensitive Channels on Land Surface Emissivity over China

AbstractThis study compares the simulation biases of Advanced Himawari Imager (AHI) brightness temperature to observations made at night over China through the use of three land surface emissivity (LSE) datasets. The University of Wisconsin–Madison High Spectral Resolution Emissivity dataset, the Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer and Moderate Resolution Imaging Spectroradiometer Emissivity database over Land High Spectral Resolution Emissivity dataset, and the International Geosphere–Biosphere Programme (IGBP) infrared LSE module, as well as land skin temperature observations from the National Basic Meteorological Observing stations in China are used as inputs to the Community Radiative Transfer Model. The results suggest that the standard deviations of AHI observations minus background simulations (OMBs) are largely consistent for the three LSE datasets. Also, negative biases of the OMBs of brightness temperature uniformly occur for each of the three datasets. There are no significant differences in OMB biases estimated with the three LSE datasets over cropland and forest surface types for all five AHI surface-sensitive channels. Over the grassland surface type, significant differences (~0.8 K) are found at the 10.4-, 11.2-, and 12.4-μm channels if using the IGBP dataset. Over nonvegetated surface types (e.g., sandy land, gobi, and bare rock), the lack of a monthly variation in IGBP LSE introduces large negative biases for the 3.9- and 8.6-μm channels, which are greater than those from the two other LSE datasets. Thus, improvements in simulating AHI infrared surface-sensitive channels can be made when using spatially and temporally varying LSE estimates.

A drought indicator reflecting ecosystem responses to water availability: The Normalized Ecosystem Drought Index

Drought, one of the most destructive natural disasters is projected by numerous studies to become more severe and widespread under climate change. These water limitations will have profound effects on terrestrial systems across the globe. Yet, most of the existing drought monitoring indices are based on drought stress derived from environmental conditions rather than ecosystem responses. Here, we propose using a new approach, the Normalized Ecosystem Drought Index (NEDI), coupled with modified Variable Interval Time Averaging (VITA) method, to quantify drought severity according to ecosystem transitional patterns with water availability. The method is inspired by Sprengel’s and Liebig’s Law of the Minimum for plant nutrition. Eddy covariance measurements from 60 AmeriFlux sites that cross 8 International Geosphere–Biosphere Programme (IGBP) vegetation types were used to validate the use of NEDI coupled to VITA. The results show that NEDI can reasonably depict both drought stress posed by the environment and drought responses presented by various ecosystems. Water availability becomes a dominant limiting factor for ecosystem evapotranspiration when NEDI falls below zero, and normalized evapotranspiration strength generally decreases with decreasing NEDI under this regime. The widely used self-calibrating Palmer Drought Severity Index (sc-PDSI) and Standardized Precipitation Index (SPI) have difficulty capturing ecosystem responses to water availability, although they can reasonably represent drought conditions detected in the environment. The normalization feature employed in NEDI makes it feasible to compare drought severity over different regions, seasons and vegetation types. The new drought index also provides a valuable tool for irrigation and water distribution management practices which may enhance water conservation efforts as drought conditions become more prevalent.

Urban growth dynamics of an Indian metropolitan using CA Markov and Logistic Regression

The inescapable phenomenon of growth due to urbanization is witnessed by urban centres. The rate of growth of an urban area can be attributed to a number of factors that play a pivotal role in depicting the land use dynamics. The need to identify, quantify and analyse the drivers of growth is essential to understand the phenomenon of urban growth in a fast growing agglomeration like Lucknow, capital of the most populous state, Uttar Pradesh in India. In this study the urban growth within the planning area was analysed for the year 1993, 2003 and 2013 using certain bio-physical and proximity factors affecting the growth pattern of the city. Factors maps were generated for the various years and the growth was predicted for the year 2023 using integrated Logistic Regression based CA-Markov analysis embedded in the LULC Dynamics Modelling Platform v1.0 developed under the ISRO Geosphere Biosphere Programme at IIRS, ISRO, Dehradun. The predictions show that the city is expected to grow manifolds to 441.2 sq. km in 2023 from mere 53.6 sq. km in 1993. Results show that the model was successful in depicting the infill growth but could not completely predict the expansion phenomenon. The results indicate that integration of remote sensing, GIS and growth models provide important information related to the process of urban expansion useful for planners preparing vision documents for cities.

Usability Study to Assess the IGBP Land Cover Classification for Singapore

Our research focuses on assessing the usability of the International Geosphere Biosphere Programme (IGBP) classification scheme provided in the MODIS MCD12Q1-1 dataset for assessing the land cover of the city-state, Singapore. We conducted a user study with responses from 33 users by providing them with Google Earth images from different parts of Singapore, asking survey-takers to classify these images according to their understanding by the IGBP definitions provided. We also conducted interviews with experts from major governmental agencies working with satellite imagery, which highlighted the need for a detailed land classification for Singapore. In addition to the qualitative analysis of the IGBP land classification scheme, we carried out a validation of the MCD12Q1-1 remote sensing product against SPOT-5 imagery for our study area. The user study revealed that survey-takers were able to correctly classify urban areas, as well as densely forested areas. Misclassifications between Cropland and Mixed Forest classes were highest and were attributed by users to the broad terminology of the IGBP of the two land cover class definitions. For the accuracy assessment, we obtained validation points using weighted and unweighted stratified sampling. The overall classification accuracy for all 17 IGBP land classes is 62%. Upon selecting only the four most occurring IGBP land classes in Singapore, the classification accuracy improved to 71%. Validation of the MCD12Q1-1 against ground truth for Singapore revealed less-common land classes that may be of importance in a global context but are sources of error when the same product is applied at a smaller scale. Combining the user study with the accuracy assessment gives a comprehensive overview of the challenges associated with using global-level land cover data to derive localized land cover information specifically for smaller land masses like Singapore.

An evaluation of multiple land-cover data sets to estimate cropland area in West Africa

ABSTRACTWest Africa is one of the fastest growing regions of the world and depends heavily on rain-fed agriculture for its food production. This study evaluates 12 freely available land-cover and land-use (LCLU) data sets at the ecoregion, country, and pixel levels in West Africa to estimate croplands. The selected data sets are primarily derived using remote-sensing data, representing different time periods and using various classification schemes. The result shows a very high variability of the estimated cropland at all levels. Despite this variability, data sets having a finer spatial resolution and representing a similar time period – specifically data from the International Institute for Applied Systems Analysis-International Food Policy Research Institute (IIASA−IFPRI), Global Land Cover−Share (GLC−Share), Moderate Resolution Imaging Spectroradiometer–University of Maryland (MODIS−UMD), Global Cropland Extent, Moderate Resolution Imaging Spectroradiometer−International Geosphere Biosphere Programme (MODIS−IGBP), and GlobCover version 2.3 (GlobCover V23) – estimated comparable cropland areas at the ecoregion and country levels. The countrywide cropland area, obtained from the selected data sets, when compared with the sum of arable land and permanent crop area obtained from the Food and Agriculture Organization (FAO), showed a high coefficient of determination (R2 > 0.95) for IIASA–IFPRI and GLC−Share. At the pixel level, at the original resolution, the newer data sets have a comparable user’s accuracy (UA>53%) and producer’s accuracy (PA>46%), except for the Global Cropland Extent data. Overall, two data sets – IIASA−IFPRI and GLC−Share – performed better in the region to estimate the cropland area at all levels.

Land-use and land-cover change in Western Ghats of India.

The Western Ghats (WG) of India, one of the hottest biodiversity hotspots in the world, has witnessed major land-use and land-cover (LULC) change in recent times. The present research was aimed at studying the patterns of LULC change in WG during 1985-1995-2005, understanding the major drivers that caused such change, and projecting the future (2025) spatial distribution of forest using coupled logistic regression and Markov model. The International Geosphere Biosphere Program (IGBP) classification scheme was mainly followed in LULC characterization and change analysis. The single-step Markov model was used to project the forest demand. The spatial allocation of such forest demand was based on the predicted probabilities derived through logistic regression model. The R statistical package was used to set the allocation rules. The projection model was selected based on Akaike information criterion (AIC) and area under receiver operating characteristic (ROC) curve. The actual and projected areas of forest in 2005 were compared before making projection for 2025. It was observed that forest degradation has reduced from 1985-1995 to 1995-2005. The study obtained important insights about the drivers and their impacts on LULC simulations. To the best of our knowledge, this is the first attempt where projection of future state of forest in entire WG is made based on decadal LULC and socio-economic datasets at the Taluka (sub-district) level.

Evapotranspiration dynamics over a temperate meadow ecosystem in eastern Inner Mongolia, China

Ecosystem evapotranspiration links surface energy and water balance, which is very important to the forming and evolution of regional climate. To understand the evapotranspiration dynamic over the temperate meadow in Inner Mongolia grassland, a long-term continuous measurement of water vapour flux was conducted using eddy covariance technique from 2008 to 2013. The results showed that the seasonal variation of daily evapotranspiration displayed a unimodal pattern with maximum value of 6.45 mm day−1. The mean value of annual evapotranspiration (ET) was 650 mm with 72 % occurring during the growing season from May to September. The annual evapotranspiration was larger than the annual precipitation (P), while less than the annual evaporation (E). The ET/P reached up to 1.91, while the ET/E was only 0.60. The evapotranspiration was not limited by precipitation due to additional water supply from surrounding dunes. The daily evapotranspiration was mainly driven by atmospheric moisture demand in the growing season with high Priestley–Taylor parameter, averaged 1.04. The daily evapotranspiration presented positive correlation with net radiation, and the correlation was affected by water vapour pressure deficit. The net radiation and water vapour pressure deficit controlled the evapotranspiration process together. The study site had the largest annual evapotranspiration and ET/P compared with the other ecosystems along the transection of Northeast China Transect, International Geosphere Biosphere Programme. The harvest activity could increase the albedo and then decrease the available energy of the surface, eventually reducing the monthly evapotranspiration as much as 33.98 % in September.

International Geosphere–Biosphere Programme and Earth system science: Three decades of co-evolution

The maturing of Earth system science as a discipline has underpinned the development of concepts such as the Anthropocene and planetary boundaries. The International Geosphere–Biosphere Programme’s (IGBP) scientific and institutional history is deeply intertwined with the development of the concept of the Earth as a system as well as the discipline of Earth system science. Here we frame the broader programme of IGBP through its core projects and programme-level activities and illustrate this co-evolution. We identify and discuss three phases in the programme’s history. In its first phase beginning in 1986, IGBP focused on building international networks and global databases that were key to understanding Earth system component processes. In the early 2000s IGBP’s first major synthesis and associated activities promoted a more integrated view of the Earth system informed by greater emphasis on interdisciplinarity. Human actions were seen as an integral part of the Earth system and the concept of the Anthropocene came to the fore. In recent years IGBP has increased focus on sustainability and multifaceted engagement with policy processes. IGBP closed at the end of 2015 after three decades of coordinating international research on global change. The programme’s longevity points to its capacity to adapt its scientific and institutional structures to changing scientific and societal realities. Its history may offer lessons for the emerging Future Earth initiative as it seeks to rally international collaborative research around sustainability and solutions.

Land–Ocean Interactions in the Coastal Zone: Past, present & future

Abstract The Land–ocean Interactions in the Coastal Zone (LOICZ) project was established in 1993 as a core project of the International Geosphere–Biosphere Programme (IGBP) to provide the science knowledge to answer “How will changes in land use, sea level and climate alter coastal systems, and what are the wider consequences?” In its first phase of operation (1993–2003) LOICZ began a fundamental investigation focused on biophysical dimensions, including seminal assessments of coastal seas as net sources or sinks of atmospheric CO2, river discharge to the oceans, and biogeochemical modelling. In the second generation of LOICZ (2004–2014), increased attention was paid to the human dimensions of the coast, involving the inclusion of cross-cutting themes such as coastal governance, social-ecological systems, ecological economics and activities around capacity building and the promotion of early career scientists. This paper provides a synthesis of this work and looks forward to the future challenges for the project. With the transition to Future Earth, there is a paradigm shift emerging. The new vision is to support transformation to a sustainable and resilient future for society and nature on the coast, by facilitating innovative, integrated and solutions-oriented science. Realising this vision takes LOICZ into a third generation: to be at the forefront of co-designing, co-producing and co-implementing knowledge for coastal resilience and sustainability. LOICZ as Future Earth Coasts will continue to address ‘hotspots’ of coastal vulnerability, focusing on themes of dynamic coasts, human development and the coast, and pathways to global coastal sustainability and constraints thereof.

Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The “woodland” LAI/FPAR is the worst, with a spatial similarity (O) of 58.17% due to the misclassification between “woodland” and “others”. The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.

Surface ocean-lower atmosphere study: Scientific synthesis and contribution to Earth system science

The domain of the surface ocean and lower atmosphere is a complex, highly dynamic component of the Earth system. Better understanding of the physics and biogeochemistry of the air–sea interface and the processes that control the exchange of mass and energy across that boundary define the scope of the Surface Ocean-Lower Atmosphere Study (SOLAS) project. The scientific questions driving SOLAS research, as laid out in the SOLAS Science Plan and Implementation Strategy for the period 2004–2014, are highly challenging, inherently multidisciplinary and broad. During that decade, SOLAS has significantly advanced our knowledge. Discoveries related to the physics of exchange, global trace gas budgets and atmospheric chemistry, the CLAW hypothesis (named after its authors, Charlson, Lovelock, Andreae and Warren), and the influence of nutrients and ocean productivity on important biogeochemical cycles, have substantially changed our views of how the Earth system works and revealed knowledge gaps in our understanding. As such SOLAS has been instrumental in contributing to the International Geosphere–Biosphere Programme (IGBP) mission of identification and assessment of risks posed to society and ecosystems by major changes in the Earth’s biological, chemical and physical cycles and processes during the Anthropocene epoch. SOLAS is a bottom-up organization, whose scientific priorities evolve in response to scientific developments and community needs, which has led to the launch of a new 10-year phase. SOLAS (2015–2025) will focus on five core science themes that will provide a scientific basis for understanding and projecting future environmental change and for developing tools to inform societal decision-making.

ARIES, Nainital:A Strategically Important Location for Climate Change Studies in the Central Gangetic Himalayan Region

ARIES, acronym for Aryabhatta Research Institute of Observational Sciences, located in the Central Gangetic Himalayan (CGH) region is emerging as one of the unique sites for climate change studies. The long-term, in situ, precise measurements of aerosols and trace gases obtained from this region provide valuable inputs for climate studies. Atmospheric scientists from ARIES are actively involved in nearsurface measurements for meteorology, aerosols and trace gases as well as vertical profiling. The Institute is also providing the observational infrastructure and research support to three major projects of the Indian Space Research Organization, Geosphere Biosphere Programme, which basically deals with the measurement of aerosols, trace gases and boundary-layer experiments. The upcoming stratosphere-troposphere radar and high-power micro-pulse lidar observational facilities will be utilized for the continuous vertical profiling of winds, aerosol and cloud properties at a very fine resolution in time and space. Apart from this, atmospheric scientists of ARIES also have active national and international research collaborations. The important results obtained from these research activities are highlighted and upcoming major observational facilities in the field of atmospheric sciences are discussed. They clearly demonstrate the importance of the unique geographical location of ARIES for climate change studies in the CGH region. These measurements and routine meteorological observations provide the necessary atmospheric corrections to the astrophysical observations taken using optical telescopes located at the site.

Winter time chemical characteristics of aerosols over the Bay of Bengal: continental influence.

As part of the Integrated Campaign for Aerosols, gases and Radiation Budget (ICARB) conducted under the Geosphere Biosphere Programme of Indian Space Research Organisation, ship-based aerosol sampling was carried out over the marine environment of Bay of Bengal (BoB) during the northern winter months of December 2008 to January 2009. About 101 aerosol samples were collected, covering the region from 3.4° to 21° N latitude and 76° to 98° E longitude-the largest area covered-including the south east (SE) BoB for the first time. These samples were subjected to gravimetric and chemical analysis and the total aerosol loading as well the mass concentration of the ionic species namely F(-), Cl(-), Br(-), NO2 (-), NO3 (-), PO4 (2-), SO4 (2-), NH4 (+), etc. and the metallic species, Na, Mg, Ca, K, Al, Fe, Mn, Zn, and Pb were estimated for each sample. Based on the spatial distribution of individual chemical species, the air flow pattern, and airmass back trajectory analysis, the source characteristics of aerosols for different regions of BoB were identified. Significant level of continental pollution was noticed over BoB during winter. While transport of pollution from Indo-Gangetic Plain (IGP) contributed to aerosols over north BoB, those over SE BoB were influenced by SE Asia. A quantitative study on the wind-induced production of sea salt aerosols and a case study on the species dependent effect of rainfall are also presented in this paper.

Regional initiatives for interlinking global coastal scientific research projects

LOICZ (Land Ocean Interactions in the Coastal Zone) (see http://www.loicz.org/) is a core research project of International Geosphere Biosphere Program (IGBP) (and previously also the International Human Dimensions Program (IHDP) involving scientists from across the globe investigating biogeochemical as well as social, economic and governance related coastal zone research. Since 2003, its focus on human dimensions allows better informing the scientific community, policymakers, managers and other stakeholders on the relevance of global environmental change in the coastal zone. LOICZ has six existing regional nodes: in South Asia (Chennai, India), Southeast Asia (Singapore), East Asia (Yantai, China), Latin America (Rio de Janeiro) and North America (Louisiana, USA). The four upcoming nodes include Arctic (Canada), West Africa (Nigeria), Caribbean (Trinidad & Tobago) and China (Taiwan). Each regional node coordinates and promotes global change research at the regional and local level as well as facilitates links and exchanges between international, national and local science and policy.