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NASA NTRSDate not supplied

Earth Observation Data Provenance for Future Climate Research - Requirements and Challenges

Observations and measurements of the Earth’s environment have been collected from space since the 1960’s. Flight Projects, airborne and field campaigns have developed and operated multi-year missions with global observing instruments, and Principle Investigator Science Teams have developed algorithms, calibrated, and derived a wide variety of Earth system environmental parameters. These data are archived and distributed for research purposes by NASA’s ESDIS Project and Distributed Active Archive Centers (DAACs). They are expected to be an important basis for Earth science and climate change research extending well beyond their observation times, the Principle Investigators/Science Teams research projects and life of the Flight Projects.

John Moses↗
NASA NTRS1987

The role of earth radiation budget studies in climate and general circulation research

The use of earth radiation budget (ERB) data for climate and general circulation research is studied. ERB measurements obtained in the 1960's and 1970's have provided data on planetary brightness, planetary global energy balances, the greenhouse effect, solar insolation, meridional heat transport by oceans and atmospheres, regional forcing, climate feedback processes, and the computation of albedo values in low latitudes. The role of clouds in governing climate, in influencing the general circulation, and in determining the sensitivity of climate to external perturbations needs to be researched; a procedure for analyzing the ERB data, which will address these problems, is described. The approach involves estimating the clear-sky fluxes from the high spatial resolution scanner measurement and defining a cloud radiative forcing; the global average of the sum of the solar and long-wave cloud forcing yields the net radiative effect of clouds on the climate.

Ramanathan, V.↗
NASA NTRS2010

Preliminary Estimation of Black Carbon Deposition from Nepal Climate Observatory-Pyramid Data and Its Possible Impact on Snow Albedo Changes Over Himalayan Glaciers During the Pre-Monsoon Season

The possible minimal range of reduction in snow surface albedo due to dry deposition of black carbon (BC) in the pre-monsoon period (March-May) was estimated as a lower bound together with the estimation of its accuracy, based on atmospheric observations at the Nepal Climate Observatory-Pyramid (NCO-P) sited at 5079 m a.s.l. in the Himalayan region. We estimated a total BC deposition rate of 2.89 g m-2 day-1 providing a total deposition of 266 micrograms/ square m for March-May at the site, based on a calculation with a minimal deposition velocity of 1.0 10(exp -4) m/s with atmospheric data of equivalent BC concentration. Main BC size at NCO-P site was determined as 103.1-669.8 nm by correlation analysis between equivalent BC concentration and particulate size distribution in the atmosphere. We also estimated BC deposition from the size distribution data and found that 8.7% of the estimated dry deposition corresponds to the estimated BC deposition from equivalent BC concentration data. If all the BC is deposited uniformly on the top 2-cm pure snow, the corresponding BC concentration is 26.0-68.2 microgram/kg assuming snow density variations of 195-512 kg/ cubic m of Yala Glacier close to NCO-P site. Such a concentration of BC in snow could result in 2.0-5.2% albedo reductions. From a simple numerical calculations and if assuming these albedo reductions continue throughout the year, this would lead to a runoff increases of 70-204 mm of water drainage equivalent of 11.6-33.9% of the annual discharge of a typical Tibetan glacier. Our estimates of BC concentration in snow surface for pre-monsoon season can be considered comparable to those at similar altitude in the Himalayan region, where glaciers and perpetual snow region starts in the vicinity of NCO-P. Our estimates from only BC are likely to represent a lower bound for snow albedo reductions, since a fixed slower deposition velocity was used and atmospheric wind and turbulence effects, snow aging, dust deposition, and snow albedo feedbacks were not considered. This study represents the first investigation about BC deposition on snow from atmospheric aerosol data in Himalayas and related albedo effect is especially the first track at the southern slope of Himalayas.

Yasunari, T. J.↗
NASA NTRS2011

Validation of the MODIS "Clear-Sky" Surface Temperature of the Greenland Ice Sheet

Surface temperatures on the Greenland Ice Sheet have been studied on the ground, using automatic weather station (AWS) data from the Greenland-Climate Network (GC-Net), and from analysis of satellite sensor data. Using Advanced Very High Frequency Radiometer (AVHRR) weekly surface temperature maps, warming of the surface of the Greenland Ice Sheet has been documented from 1981 to present. We extend and refine this record using higher-resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) data from March 2000 to the present. To permit changes to be observed over time, we are developing a well-characterized monthly climate-data record (CDR) of the "clear-sky" surface temperature of the Greenland Ice Sheet using data from both the Terra and Aqua satellites. We use the MODIS ice-surface temperature (IST) algorithm. Validation of the CDR consists of several facets: 1) comparisons between the Terra and Aqua IST maps; 2) comparisons between ISTs and in-situ measurements; 3) comparisons between ISTs and AWS data; and 4) comparisons of ISTs with surface temperatures derived from other satellite instruments such as the Thermal Emission and Reflection Radiometer. In this work, we focus on 1) and 2) above. Surface temperatures on the Greenland Ice Sheet have been studied on the ground, using automatic weather station (AWS) data from the Greenland-Climate Network (GC-Net), and from analysis of satellite sensor data. Using Advanced Very High Frequency Radiometer (AVHRR) weekly surface temperature maps, warming of the surface of the Greenland Ice Sheet has been documented from 1981 to present. We extend and refine this record using higher-resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) data from March 2000 to the present. To permit changes to be observed over time, we are developing a well-characterized monthly climate-data record (CDR) of the "clear-sky" surface temperature of the Greenland Ice Sheet using data from both the Terra and Aqua satellites. We use the MODIS ice-surface temperature (IST) algorithm. Validation of the CDR consists of several facets: 1) comparisons between the Terra and Aqua IST maps; 2) comparisons between ISTs and in-situ measurements; 3) comparisons between ISTs and AWS data; and 4) comparisons of ISTs with surface temperatures derived from other satellite instruments such as the Thermal Emission and Reflection Radiometer. In this work, we focus on 1) and 2) above. First we provide comparisons between Terra and Aqua swath-based ISTs at approximately 14:00 Local Solar Time, reprojected to 12.5 km polar stereographic cells. Results show good correspondence when Terra and Aqua data were acquired within 2 hrs of each other. For example, for a cell centered over Summit Camp (72.58 N, 38.5 W), the average agreement between Terra and Aqua ISTs is 0.74 K (February 2003), 0.47 K (April 2003), 0.7 K (August 2003) and 0.96 K (October 2003) with the Terra ISTs being generally lower than the Aqua ISTs. More precise comparisons will be calculated using pixel data at the swath level, and correspondence between Terra and Aqua IST is expected to be closer. (Because of cloud cover and other considerations, only a few common cloud-free swaths are typically available for each month for comparison.) Additionally, previous work comparing land-surface temperatures (LSTs) from the standard MODIS LST product and in-situ surface-temperature data at Summit Camp on the Greenland Ice Sheet show that Terra MODIS LSTs are about 3 K lower than in-situ temperatures at Summit Camp, during the winter of 2008-09. This work will be repeated using both Terra and Aqua IST pixel data (in place of LST data). In conclusion, we demonstrate that the uncertainties in the CDR will be well characterized as we work through the various facets of its validation.

Hall, Dorothy K.↗
NASA NTRS1991

Positive water vapour feedback in climate models confirmed by satellite data

It has recently been suggested that GCMs used to evaluate climate change overestimate the greenhouse effect due to increased concentrations of trace gases in the atmosphere. Here, new satellite-generated water vapor data are used to compare summer and winter moisture values in regions of the middle and upper troposphere that have previously been difficult to observe with confidence. It is found that, as the hemispheres warm, increased convection leads to increased water vapor above 500 mbar in approximate quantitative agreement with results from current climate models. The same conclusion is reached by comparing the tropical western and eastern Pacific regions. Thus, water vapor feedback is not overestimated in models and should amplify the climate response to increased trace-gas concentrations.

Rind, D.↗
NASA NTRS1978

The earth radiation budget satellite system for climate research

The mission implications of providing earth radiation budget data for climate studies have been thoroughly studied. The results of these studies indicate the need for a multisensor, multisatellite system consisting of high and midinclination orbits. To meet this need, NASA and NOAA are planning a joint Earth Radiation Budget Satellite System (ERBSS) composed of instruments on two of NOAA's near-polar Sun-synchronous TIROS-N/NOAA A through G series of operational satellites and on an NASA midinclination satellite of the Applications Explorer Mission (AEM) type referred to as ERBS-A/AEM. This paper describes the scientific objectives of ERBSS, the associated data analysis methods, mission analysis (sampling), and instrument definition.

Woerner, C. V.↗
DOE OSTI2016

Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Finally, data from models and observations should be put on the same spatial and temporal scales before comparison.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Quasi-periodic climatic changes on Mars and earth

Evidence of climatic changes on Mars and the earth due to geologic and astronomical variations is discussed. Finely striped ice-free bands in the Martian polar caps have been taken to indicate that long term variations in the orbit and axial tilt of Mars have precipitated these features at the rate of a mm/yr. Photogrammetric and photometric methods have contributed to measurements of the composition and depth of the Martian caps (14-46 m), and observations of higher solar energy absorption in the northern ice cap implies greater dust deposition in that region than on the south cap; however, the transport mechanisms are not well understood. Comparisons of earth and Martian climatic variations data are made, noting a lack of information on the age intervals of marine and nonmarine sediments on the earth. The possibilities of using quantitative data other than layer thickness to constrain climate models are discussed, and the slope or albedo of layers, or the spacing of polar undulations are suggested.

Cutts, J. A.↗
NASA NTRS2016

Impact of Spatial Sampling on Continuity of MODIS-VIIRS Land Surface Reflectance Products: A Simulation Approach

With the increasing need to construct long-term climate-quality data records to understand, monitor, and predict climate variability and change, it is vital to continue systematic satellite measurements along with the development of new technology for more quantitative and accurate observations. The Suomi National Polar-orbiting Partnership mission provides continuity in monitoring the Earths surface and its atmosphere in a similar fashion as the heritage MODIS instruments onboard the National Aeronautics and Space Administrations Terra and Aqua satellites. In this paper, we aim at quantifying the consistency of Aqua MODIS and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Reflectance (LSR) and NDVI products as related to their inherent spatial sampling characteristics. To avoid interferences from sources of measurement and/or processing errors other than spatial sampling, including calibration, atmospheric correction, and the effects of the bidirectional reflectance distribution function, the MODIS and VIIRSLSR products were simulated using the Landsat-8s Operational Land Imager (OLI) LSR products. The simulations were performed using the instruments point spread functions on a daily basis for various OLI scenes over a 16-day orbit cycle. It was found that the daily mean differences due to discrepancies in spatial sampling remain below 0.0015 (1) in absolute surface reflectance at subgranule scale (i.e., OLI scene size).We also found that the MODISVIIRS product intercomparisons appear to be minimally impacted when differences in the corresponding view zenith angles (VZAs) are within the range of -15deg to -35deg (VZA(sub v) - VZA(sub m)), where VIIRS and MODIS footprints resemble in size. In general, depending on the spatial heterogeneity of the OLI scene contents, per-grid-cell differences can reach up to 20.Further spatial analysis of the simulated NDVI and LSR products revealed that, depending on the user accuracy requirements for product intercomparisons, spatial aggregations may be used. It was found that if per-grid-cell differences on the order of 10(in LSR or NDVI) are tolerated, the product intercomparisons are expected to be immune from differences in spatial sampling.

VIIRS↗
NASA NTRS2021

Methodology to Assess the Changing Risk of Yield Failure Due to Heat and Drought Stress Under Climate Change

While the understanding of average impacts of climate change on crop yields is improving, few assessments have quantified expected impacts on yield distributions and the risk of yield failures. Here we present the relative distribution as a method to assess how the risk of yield failure due to heat and drought stress (measured in terms of return period between yields falling 15% below previous five year Olympic average yield) responds to changes of the underlying yield distributions under climate change. Relative distributions are used to capture differences in the entire yield distribution between baseline and climate change scenarios, and to further decompose them into changes in the location and shape of the distribution. The methodology is applied here for the case of rainfed wheat and grain maize across Europe using an ensemble of crop models under three climate change scenarios with simulations conducted at 25 km resolution. Under climate change, maize generally displayed shorter return periods of yield failures (with changes under RCP 4.5 between −0.3 and 0 years compared to the baseline scenario) associated with a shift of the yield distribution towards lower values and changes in shape of the distribution that further reduced the frequency of high yields. This response was prominent in the areas characterized in the baseline scenario by high yields and relatively long return periods of failure. Conversely, for wheat, yield failures were projected to become less frequent under future scenarios (with changes in the return period of −0.1 to +0.4 years under RCP 4.5) and were associated with a shift of the distribution towards higher values and a change in shape increasing the frequency of extreme yields at both ends. Our study offers an approach to quantify the changes in yield distributions that drive crop yield failures. Actual risk assessments additionally require models that capture the variety of drivers determining crop yield variability and scenario climate input data that samples the range of probable climate variation.

Climate risk assessment↗
NASA NTRS2023

Potomac River Basin Water Resources: Assessing Water Quality and Quantity in the National Capital Region Using NASA Earth Observations

The Potomac River Basin (PRB) is responsible for providing drinking water to over 5 million residents and plays a significant role in the health of the Chesapeake Bay. Therefore, it is important to understand the relationship between water quality, landcover, and the hydrological cycle within the PRB. The National Park Service (NPS) has monitored 37 streams within the National Park Units in Maryland, Virginia, West Virginia and Washington, D.C. This project aimed to help the NPS better understand trends in water quality to supplement their ability to monitor changes in the National Capital Region Network (NCRN). Google Earth Engine, ArcGIS Pro, R, and Python were used for data retrieval, visualization, and analysis. Earth observations included Landsat 5 TM and Landsat 8 OLI/TIRS imagery. Ancillary data included the USDA Cropland Data Layer, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), and soil moisture data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS). We compared Land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), precipitation and soil moisture data to water quality data provided by the NPS at a watershed level. LULC change maps were also generated for the PRB between 2008 and 2022. We found significant correlations between precipitation, soil moisture, NDVI, and water quality. Correlations were found between certain land use types and water quality metrics, but findings varied greatly between watersheds. These insights emphasize the imperative of strategic watershed management in preserving the integrity of key aquatic systems.

Landsat↗
DOE OSTI2018

Traits drive global wood decomposition rates more than climate

Wood decomposition is a major component of the global carbon cycle. Decomposition rates vary across climate gradients, which is thought to reflect the effects of temperature and moisture on the metabolic kinetics of decomposers. However, decomposition rates also vary with wood traits, which may reflect the influence of stoichiometry on decomposer metabolism as well as geometry relating the surface areas that decomposers colonize with the volumes they consume. In this paper, we combined metabolic and geometric scaling theories to formalize hypotheses regarding the drivers of wood decomposition rates, and assessed these hypotheses using a global compilation of data on climate, wood traits, and wood decomposition rates. Our results are consistent with predictions from both metabolic and geometric scaling theories. Approximately half of the global variation in decomposition rates was explained by wood traits (nitrogen content and diameter), whereas only a fifth was explained by climate variables (air temperature, precipitation, and relative humidity). These results indicate that global variation in wood decomposition rates is best explained by stoichiometric and geometric wood traits. Our findings suggest that inclusion of wood traits in global carbon cycle models can improve predictions of carbon fluxes from wood decomposition.

Hu, Zhenhong↗
DOE OSTI2016

Mapping Cheatgrass Across the Range of the Greater Sage-Grouse: Linking Biophysical, Climate and Remote Sensing Data to Predict Cheatgrass Occurrence

Increasing spread of invasive annual grasses, such as Bromus tectorum (cheatgrass), can contribute to increased fire frequency and hinder the reestablishment of native sagebrush, forbs, and grasses in subsequent years. Knowledge of the current distribution of cheatgrass on the landscape is a key component in planning and executing strategies to protect sagebrush ecosystems and sensitive wildlife species such as the Greater sage-grouse (Centrocercus urophasianus). Pacific Northwest National Laboratory (PNNL) worked with US Fish and Wildlife Service (FWS) to assemble and derive information to map cheatgrass occurrence across the historic range of sage-grouse. The information and map products described in this report can help land managers prioritize conservation efforts at the species’ range scale. We constructed an ecological model based on a suite of climatic and biophysical variables and satellite measures of peak NDVI (normalized difference vegetation index) – an index of vegetation greenness – to predict cheatgrass occurrence across the historic range of sage-grouse in the United States. More than 24,000 field measurements of cheatgrass cover across the study area were acquired from various agencies and research groups and reviewed for use in the modeling efforts. A subset of 6650 field measurement points were identified and verified for use in statistical analyses. For each measurement location we derived a suite of 50 biophysical and NDVI variables correlated with cheatgrass occurrence. Pairwise correlation of variables was examined to remove highly-correlated variables from the model. A total of 13 variables were retained for use in forward-stepping discriminant analysis and modeling. Discriminant scores were used to determine probability of cheatgrass occurrence, which was broken into two relative cover classes: 0% to 2% cover and > 2% cheatgrass cover. This report describes the data and methods used to develop the model and the cheatgrass occurrence map. We provide a brief discussion of the accuracy of classification, and describe the appropriate scale of use for map results. The range-wide map of occurrence will be made available online for FWS and partner agencies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

High Resolution Nature Runs and the Big Data Challenge

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

big data analysis↗
DOE OSTI2020

Meta-modeling strategy for data-driven forecasting

Accurately forecasting the weather is a key requirement for climate change mitigation. Data-driven methods offer the ability to make more accurate forecasts, but lack interpretability and can be expensive to train and deploy if models are not carefully developed. Here, we make use of two historical climate data sets and tools from machine learning, to accurately predict temperature fields. Furthermore, we are able to use low fidelity models that are cheap to train and evaluate, to selectively avoid expensive high fidelity function evaluations, as well as uncover seasonal variations in predictive power. This allows for an adaptive training strategy for computationally efficient geophysical emulation.

Skinner, Dominic J↗
DOE OSTI2022

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90°N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. As a result, this paper describes our library of least squared regression patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Physical and biological processes in the North Atlantic during the First GARP Global Experiment

The seasonal cycle of the phytoplankton productivity in the North Atlantic is investigated using monthly mean pigment composites generated by the Nimbus 7 CZCS for the period of December 1978 through December 1979. Data on the temporal and spatial distributions of surface pigments are compared with quantities derived from the First GARP Global Experiment wind data set, Climate Analysis Center SST fields, concurrent hydrogarphic station data, and climatological mixed-layer depths and heat fluxes. Time series at 11 selected sites throughout the North Atlantic basin are used to illustrate the relation of phytoplankton biomass to local physical forcing.

Mcclain, Charles R.↗