NASA Earth Exchange Research Articles

Advantage of NEX-GDDP over CMIP5 and CORDEX Data: Indian Summer Monsoon

Recently released NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data (surface air temperature and precipitation) are evaluated using the observations from the India Meteorological Department (IMD) and compared with CMIP5 and CORDEX data to show its advantage over the Indian subcontinent for the summer monsoon season. The multi-model mean of 21 NEX-GDDP models, 28 CMIP5 models, and 10 CORDEX models are analyzed for the period of 1975–2005. In general and over most parts of the subcontinent, the NEX-GDDP is found to be quite satisfactory and surpasses the CMIP5 and CORDEX data. The NEX-GDDP data captures the spatial patterns of seasonal mean temperatures and precipitation with highest accuracy (pattern correlation of ~0.8) and least errors (root mean square error of ~ 4.25 °C and ~2.48 mm day−1); the inter-annual variations in precipitation are closer to the observations (r = 0.66 and standard deviation = 0.36 mm day−1); bias in the annual cycle reduces; simulation of extremes is more realistic with less inter-model differences. Remarkable improvements in this data suggest its potential application for future projections and climate change impact studies at the regional scale.

Impact Assessment of Future Climate Change on Streamflows Upstream of Khanpur Dam, Pakistan using Soil and Water Assessment Tool

The study aims to evaluate the long-term changes in meteorological parameters and to quantify their impacts on water resources of the Haro River watershed located on the upstream side of Khanpur Dam in Pakistan. The climate data was obtained from the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) for MIROC-ESM model under two Representative Concentration Pathway (RCP) scenarios. The model data was bias corrected and the performance of the bias correction was assessed statistically. Soil and Water Assessment Tool was used for the hydrological simulation of watershed followed by model calibration using Sequential Uncertainty Fitting version-2. The study is useful for devising strategies for future management of Khanpur Dam. The study indicated that in the future, at Murree station (P-1), the maximum temperature, minimum temperature and precipitation were anticipated to increase from 3.1 °C (RCP 4.5) to 4.0 °C (RCP 8.5), 3.2 °C (RCP 4.5) to 4.3 °C (RCP 8.5) and 8.6% to 13.5% respectively, in comparison to the baseline period. Similarly, at Islamabad station (P-2), the maximum temperature, minimum temperature and precipitation were projected to increase from 3.3 °C (RCP 4.5) to 4.1 °C (RCP 8.5), 3.3 °C (RCP 4.5) to 4.2 °C (RCP 8.5) and 14.0% to 21.2% respectively compared to baseline period. The streamflows at Haro River basin were expected to rise from 8.7 m3/s to 9.3 m3/s.

High‐resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States

High‐resolution gridded climate data products are crucial to research and practical applications in climatology, hydrology, ecology, agriculture, and public health. Previous works to produce multiple data sets were limited by the availability of input data as well as computational techniques. With advances in machine learning and the availability of several daily satellite data sets providing unprecedented information at 1 km or higher spatial resolutions, it is now possible to improve upon earlier data sets in terms of representing spatial variability. We developed the NEX (NASA Earth Exchange) Gridded Daily Meteorology (NEX‐GDM) model, which can estimate the spatial pattern of regional surface climate variables by aggregating several dozen two‐dimensional data sets and ground weather station data. NEX‐GDM does not require physical assumptions and can easily extend spatially and temporally. NEX‐GDM employs the random forest algorithm for estimation, which allows us to find the best estimate from the spatially continuous data sets. We used the NEX‐GDM model to produce historical 1‐km daily spatial data for the conterminous United States from 1979 to 2017, including precipitation, minimum temperature, maximum temperature, dew point temperature, wind speed, and solar radiation. In this study, NEX‐GDM ingested a total of 30 spatial variables from 13 different data sets, including satellite, reanalysis, radar, and topography data. Generally, the spatial patterns of precipitation and temperature produced were similar to previous data sets with the exception of mountain regions in the western United States. The analyses for each spatially continuous data set show that satellite and reanalysis led to better estimates and that the incorporation of satellite data allowed NEX‐GDM to capture the spatial patterns associated with urban heat island effects. The NEX‐GDM data is available to the community through the NEX data portal.

Observed, simulated and projected extreme climate indices over Pakistan in changing climate

This article explores the observed, simulated and projected extreme climate indices from daily observed dataset for several meteorological stations as well as NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. The NEX-GDDP dataset is an extension of the General Circulation Model (GCM) runs, performed under the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Two greenhouse gas emission scenarios have been considered, known as Representative Concentration Pathways, i.e., RCP4.5 and RCP8.5. The core climate indices, proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used for investigation of climatic extremes over different provinces of Pakistan. The results show that summer days have significantly increased in the Gilgit Baltistan and Azad Jammu Kashmir (GB-AJK) region by 24 and 18 days, respectively, triggering the frequency of Glacial Lake Outburst Flood (GLOF) events in the region. Moreover, the projected climate shows an increase of 6 days in the frequency of summer days in the GB-AJK region in the next 30 years. The frequency of heavy precipitation is significantly increasing in the Khyber Pakhtunkhwa (KP) province, which may augment the flood hazard in the region. Future projection shows an increase of 1.3 °C in the mean maximum temperature in the KP province by the year 2045. An average increase of 1.9 °C in the mean minimum temperature has been observed in the Punjab province over the past 54 years (1960-2013), whereas there is a projected increase of 1.4 °C in the mean minimum temperature in the next 30 years. Such an increasing trend in the mean minimum temperature may cause implications for food and agriculture sectors in the Punjab province. The results show a significantly increasing trend in the mean maximum temperature and summer days in the Sindh province, which may enhance the frequency and intensity of heat waves in the region. There is a projected increase of 20 days in summer days in the Sindh province. Similar rising trend in the temperature has been observed in the Balochistan province, which may ultimately enhance the dust and sand storms in this region.

Assessment of future changes in Southeast Asian precipitation using the NASA Earth Exchange Global Daily Downscaled Projections data set

Extreme precipitation and associated flooding cause severe damage to society and the environment. Future climate projections suggest an intensification of precipitation extremes in many regions. However, there is an increasing need for climate change impact assessment at higher spatial resolution, particularly for regions with complex geography such as Southeast Asia (SEA). In this study, we analysed the NASA Earth Exchange 0.25° resolution daily precipitation projections from an ensemble of 20 climate models under two emission scenarios RCP4.5 and RCP8.5. The variability in future precipitation projections is analysed and quantified for six geographical subregions, two climatological regions (wet and dry), and the low‐elevation coastal zones in SEA. Various aspects of precipitation structure are studied using indices that characterize precipitation amount, number of heavy precipitation days, extreme precipitation amount, and maximum daily precipitation at annual and seasonal scales. The results show substantial increases in mean and extreme precipitation in many parts of SEA by the end of the 21st century under both emission scenarios, thus increasing the region's vulnerability to precipitation‐driven hazards. The projected centennial increase in total annual precipitation relative to the baseline period of 1970–1999 when averaged over all land grid cells is about 15% under RCP8.5 scenario, with larger values (∼20%) over mainland SEA and Philippines and smaller values (∼6%) in Java island. The projected changes in extreme precipitation are stronger compared to the total annual precipitation under both emission scenarios. The New Guinea and Java regions show the largest and smallest increases in annual maximum daily precipitation, with ensemble mean values of 30 and 17%, respectively, under RCP8.5 scenario. The results also reveal large inter‐model spread in projected changes, particularly during boreal winter and summer months.

Future Climate Change Projections of the Kabul River Basin Using a Multi-model Ensemble of High-Resolution Statistically Downscaled Data

In this study, we examined the future climatic changes in the Kabul River basin located in the Hindu Kush Mountain ranges of Pakistan and Afghanistan. We used the latest data set of statistically downscaled CMIP5 Global Climate Models (GCMs), i.e., NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The data set delivers valuable local scale, high-resolution climate change information for past and future periods (1950–2100) on a daily basis, which is very suitable for exploring future changes in mean and extremes of both temperature and precipitation. Multi-model ensemble derived from NEX-GDDP data effectively produces observed spatial patterns and magnitude of both temperature and precipitation that otherwise cannot be captured with coarse resolution GCMs. For the historical period (1975–2005), NEX-GDDP presented an improved seasonal cycle climatology and correlation coefficient with the observed data set. Future projections using multi-model ensembles indicate a consistent rise in mean temperature over the entire Kabul River Basin, relative to the baseline under RCP4.5 and RCP8.5 emission scenarios. Although the increase in temperature is not uniform across the domain, upper reaches of the basin show annual and seasonal warming of approximately 6.8 °C by the end of the twenty first century under the RCP8.5 scenario. These changes are significant at a 95% confidence level. The rise in summer and winter temperatures may negatively affect the snow accumulation during winter and has the potential to accelerate glacier melting during summers. Projections of future precipitation under both scenarios show an overall decrease in mean precipitation, particularly under the RCP8.5 scenario.

Centennial Heat Wave Projections Over Pakistan Using Ensemble NEX GDDP Data Set

Heat waves are one of the extreme weather events affecting as many people as with other climatic hazards such as droughts and floods. Due to rise in global temperatures, the occurrences of heat waves are likely to exacerbate in many regions in future. The projections for future heat waves over Pakistan are analyzed in this study. The heat waves calculation has been done using the latest NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data set which composed of 21 Global Climate Models (GCMs) statistically downscaled at 0.25° × 0.25° spatial resolution. The heat waves analysis is done with ensemble modelling approach and using two definitions of heat waves: the Heat waves Duration Index (HWDI) and fixed method approach. Two climate change scenarios; RCP4.5 and RCP8.5 are used for investigating future heat wave projections. The historical heat waves are calculated from 1976–2000 and future projected changes are analyzed in three 25 year time spans: 2025–2049, 2050–2074 and 2075–2099. Two metrics heat wave events and heat wave days are separately shown. The results indicate an increase in both number of heat wave events and heat wave days in Pakistan. The most vulnerable areas for future heat waves are northern areas, plains of Sindh and Punjab, the central and western parts of Baluchistan, and all regions of Khyber Pakhtunkhwa (KPK). The increased number of heat waves in northern areas of the country could result in rapid snow melting and can cause flooding downstream. The agricultural regions of the country would be highly vulnerable to increasing number of heat waves.

Development of Landsat-based annual US forest disturbance history maps (1986–2010) in support of the North American Carbon Program (NACP)

In Phase III of the North American Forest Dynamics (NAFD) study an automatic workflow has been developed for evaluating forest disturbance history using Landsat observations. It has four major components: an automated approach for image selection and preprocessing, the vegetation change tracker (VCT) forest disturbance analysis, postprocessing, and validation. This approach has been applied to the conterminous US (CONUS) to produce a comprehensive analysis of US forest disturbance history using the NASA Earth Exchange (NEX) cloud computing system. The resultant NAFD-NEX product includes 25 annual forest disturbance maps for 1986–2010 and two time-integrated maps to provide spatial-temporal synoptic view of disturbances over this time period. These maps were derived based on 24,000+ scenes selected from 350,000+ available Landsat images at 30-m resolution, and were validated using a visual assessment of Landsat time-series images in combination with high-resolution and other ancillary data sources over samples selected using a probability based sampling method. The validation revealed no major biases in the NAFD-NEX maps for disturbance events that resulted in at least 20% canopy cover loss. The average user's and producer's accuracies for the disturbance class were 53.6% and 53.3%, respectively, with the individual year's user's accuracy varying from 42.8% to 73.6% and producer's accuracy from 39.0% to 84.8% over the 25-year period. The NAFD-NEX disturbance maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNLDAAC/1290.

Historical simulations and climate change projections over India by NCAR CCSM4: CMIP5 vs. NEX-GDDP

A new bias-corrected statistically downscaled product, namely, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), has recently been developed by NASA to help the scientific community in climate change impact studies at local to regional scale. In this work, the product is validated over India and its added value as compared to its CMIP5 counterpart for the NCAR CCSM4 model is analyzed, followed by climate change projections under the RCP8.5 global warming scenario using the two datasets for the variables daily maximum 2-m air temperature (Tmax), daily minimum 2-m air temperature (Tmin), and rainfall. It is found that, overall, the CCSM4-NEX-GDDP significantly reduces many of the biases in CCSM4-CMIP5 for the historical simulations; however, some biases such as the significant overestimation in the frequency of occurrence in the lower tail of the Tmax and Tmin still remain. In regard to rainfall, an important value addition in CCSM4-NEX-GDDP is the alleviation of the significant underestimation of rainfall extremes found in CCSM4-CMIP5. The projected Tmax from CCSM4-NEX-GDDP are in general higher than that projected by CCSM4-CMIP5, suggesting that the risks of heat waves and very hot days could be higher than that projected by the latter. CCSM4-NEX-GDDP projects the frequency of occurrence of the upper extreme values of historical Tmax to increase by a factor of 100 towards the end of century (as opposed to a factor of 10 increase projected by CCSM4-CMIP5). In regard to rainfall, both CCSM4-CMIP5 and CCSM4-NEX-GDDP project an increase in annual rainfall over India under the RCP8.5 global warming scenario progressively from the near term through the far term. However, CCSM4-NEX-GDDP consistently projects a higher magnitude of increase and over a larger area as compared to that projected by CCSM4-CMIP5. Projected daily rainfall distributions from CCSM4-CMIP5 and CCSM4-NEX-GDDP suggest the occurrence of events that have no historical precedents. Worth noting is that the extreme daily rainfall values projected by CCSM4-NEX-GDDP are two to three times larger than that projected by CCSM4-CMIP5.

Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing

Land cover (LC) refers to the physical and biological cover present over the Earth’s surface in terms of the natural environment such as vegetation, water, bare soil, etc. Most LC features occur at finer spatial scales compared to the resolution of primary remote sensing satellites. Therefore, observed data are a mixture of spectral signatures of two or more LC features resulting in mixed pixels. One solution to the mixed pixel problem is the use of subpixel learning algorithms to disintegrate the pixel spectrum into its constituent spectra. Despite the popularity and existing research conducted on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of several subpixel learning algorithms based on least squares, sparse regression, signal–subspace and geometrical methods. Analysis of the results obtained through computer-simulated and Landsat data indicated that fully constrained least squares (FCLS) outperformed the other techniques. Further, FCLS was used to unmix global Web-Enabled Landsat Data to obtain abundances of substrate (S), vegetation (V) and dark object (D) classes. Due to the sheer nature of data and computational needs, we leveraged the NASA Earth Exchange (NEX) high-performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into four classes, namely forest, farmland, water and urban areas (in conjunction with nighttime lights data) over California, USA using a random forest classifier. Validation of these LC maps with the National Land Cover Database 2011 products and North American Forest Dynamics static forest map shows a 6% improvement in unmixing-based classification relative to per-pixel classification. As such, abundance maps continue to offer a useful alternative to high-spatial-resolution classified maps for forest inventory analysis, multi-class mapping, multi-temporal trend analysis, etc.

Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set

AbstractRecently, a new high-resolution daily downscaled data-set derived from 21 CMIP5 model simulations has been released by NASA, called ‘NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP). In this study, the performance of this data-set in simulating precipitation extremes and long-term climate changes across China are evaluated and compared with CMIP5 GCMs. The results indicate that NEX-GDDP can successfully reproduce the spatial patterns of precipitation extremes over China, showing results that are much closer to observations than the GCMs, with increased Pearson correlation coefficients and decreased model relative error for most models. Furthermore, NEX-GDDP shows that precipitation extremes are projected to occur more frequently, with increased intensity, across China in the future. Especially at regional to local scales, more information for the projection of future changes in precipitation extremes can be obtained from this high-resolution data-set. Most importantly, the uncer...

Characterization of future drought conditions in the Lower Mekong River Basin

This study evaluates future changes to drought characteristics in the Lower Mekong River Basin using climate model projections. The Lower Mekong Basin (LMB), covering Thailand, Cambodia, Laos and Vietnam, is vulnerable to increasing droughts. Univariate analysis was employed in this study to compare drought characteristics associated with different return periods for the historical period 1964–2005 and future scenarios (RCP 4.5 2016–2057, RCP 4.5 2058–2099, RCP 8.5 2016–2057 and RCP 8.5 2058–2099). Because a single drought event is defined by several correlated characteristics, drought risk assessment by a multivariate analysis was deemed appropriate, and a multivariate analysis of droughts was conducted using copula functions to investigate the differences in the trivariate joint occurrence probabilities of the historical period and future scenarios. The Standardized Precipitation Index (SPI) was selected as the drought index because of its ability to detect and compare metrological droughts across time and space scales. Historical precipitation data from 1964 to 2005 and future precipitation projections from 2016 to 2099 for 15 global circulation models (GCMs) obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset were employed. In all future scenarios, the Lower LMB and 3S subbasins were expected to experience more severe and intense droughts. The multivariate drought risk assessment revealed an increase in drought risks in the LMB. However, the Chi-Mun subbasin may experience an alleviation of future drought characteristics. Because the basin was expected to experience an increase in average monthly precipitation in most months, the variability in magnitude suggested that the LMB region requires adaptation strategies to address future drought occurrences.

From Open Data to Open Analyses—New Opportunities for Environmental Applications?

In this study, we explore the potential of open and accessible data in combination with interactive cloud-processing capabilities for applications in environmental monitoring and policies. During the last few years, the amount of open Earth observation and open national data has increased substantially. In parallel, access to analysis capabilities for such data has improved. The search and extraction of data from larger Earth observations archives and the processing of larger amounts of data have hitherto been an obstacle for many potential users. With the availability of new cloud solutions such as Google Earth Engine, NASA Earth Exchange, or ESA Cloud Toolbox, accessing and processing of large datasets have become easier for a wider range of users. In this communication, we briefly summarize these recent trends and illustrate their potential by four application showcases from terrestrial and aquatic environmental monitoring. We accessed and processed data from US and European Earth observation satellite archives with Google Earth Engine. As a complement, we also used open Swedish national data and open source desktop tools. We hope that our positive user experiences can encourage other environmental data users to further explore the new opportunities for easy access to open data and cloud-based processing capabilities.

Projection of China’s near- and long-term climate in a new high-resolution daily downscaled dataset NEX-GDDP

The projection of China’s near- and long-term future climate is revisited with a new-generation statistically downscaled dataset, NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections). This dataset presents a high-resolution seamless climate projection from 1950 to 2100 by combining observations and GCM results, and remarkably improves CMIP5 hindcasts and projections from large scale to regional-to-local scales with an unchanged long-term trend. Three aspects are significantly improved: (1) the climatology in the past as compared against the observations; (2) more reliable near- and long-term projections, with a modified range of absolute value and reduced inter-model spread as compared to CMIP5 GCMs; and (3) much added value at regional-to-local scales compared to GCM outputs. NEX-GDDP has great potential to become a widely-used high-resolution dataset and a benchmark of modern climate change for diverse earth science communities.

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

ABSTRACTUsing the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.

A community-oriented workflow reuse and recommendation technique

NASA Earth Exchange (NEX) is a collaborative compute platform aiming to improve the availability of Earth science data, models, analysis tools and scientific results through a centralised environment that fosters knowledge sharing, collaboration, innovation and direct access to compute resources. One of the main objectives of NEX is to help Earth scientists leverage and reuse various data processing software modules developed by their peers, in order to quickly run value-added executable experiments (workflows). Toward this goal, this paper reports our efforts of leveraging social network analysis to intelligently extract hidden information from data processing workflows. By modelling Earth science workflow modules as social entities and their dependencies as social relationships, this research opens up new vistas for applying social science to facilitate software reuse and distributed workflow development. As a proof of concept, a prototyping system has been developed as a plug-in to the NEX workflow design and management system (VisTrails) to aid Earth scientists in discovering and reusing workflow modules and extending them to solve more complex science problems.

Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011

Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.

NASA EARTH EXCHANGE: NEXT GENERATION EARTH SCIENCE COLLABORATIVE

Abstract. The NASA Earth Exchange (NEX) is a collaboration platform for the Earth science community creating new ways for scientific interaction and knowledge sharing. NEX combines state-of-the-art supercomputing, Earth system modeling, workflow management, NASA remote sensing data feeds, and a social networking platform to deliver a complete work environment in which users can explore and analyze large datasets, run modeling codes, collaborate on new or existing projects, and quickly share results among the Earth science communities.The work environment provides NEX members with community supported modeling, analysis and visualization software in conjunction with datasets that are common to the Earth systems science domain. By providing data, software, and large-scale computing power together in a flexible framework, NEX reduces the need for duplicated efforts in downloading data, developing pre-processing software tools, and expanding local compute infrastructures–while accelerating fundamental research, development of new applications, and reducing project costs. The social networking platform provides a forum for NEX members to efficiently share datasets, results, algorithms, codes, and expertise with other members. Since all members''work environments reside on the collaborative platform, sharing may be done without the transfer of large volumes of data or the porting of complex codes– making NEX an ideal platform for building upon and exchanging research, and fostering innovation.

Collaborative Supercomputing for Global Change Science

There is increasing pressure on the science community not only to understand how recent and projected changes in climate will affect Earth's global environment and the natural resources on which society depends but also to design solutions to mitigate or cope with the likely impacts. Responding to this multidimensional challenge requires new tools and research frameworks that assist scientists in collaborating to rapidly investigate complex interdisciplinary science questions of critical societal importance. One such collaborative research framework, within the NASA Earth sciences program, is the NASA Earth Exchange (NEX). NEX combines state‐of‐the‐art supercomputing, Earth system modeling, remote sensing data from NASA and other agencies, and a scientific social networking platform to deliver a complete work environment. In this platform, users can explore and analyze large Earth science data sets, run modeling codes, collaborate on new or existing projects, and share results within or among communities (see Figure S1 in the online supplement to this Eos issue (http://www.agu.org/eos_elec)).