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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Evolving Obs4MIPs to Support Phase 6 of the Coupled Model Intercomparison Project (CMIP6)

Over the past four years, an initiative known as Observations for Model Intercomparison Projects (Obs4MIPs) has successfully completed its pilot phase by adopting a set of technical protocols (dataset format, metadata standards, and documentation requirements) for dataset contributions, producing datasets that conform to these standards and archiving them for distribution on the Earth System Grid Federation (ESGF) alongside the fifth phase of the Coupled Model Intercomparison Project (CMIP5) model output. This pilot phase of Obs4MIPs, initiated by the National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL) and the Department of Energy’s (DOE) Program for Climate Model Diagnosis and Intercomparison (PCMDI) at Lawrence Livermore National Laboratory, supported CMIP5 and provided a path to improve the coordination between observational communities and major climate modeling intercomparison projects such as CMIP. Obs4MIPs is now being embraced by the international community, with the World Climate Research Programme (WCRP) Data Advisory Council (WDAC) empaneling a task team to provide guidance and governance for Obs4MIPs at an international level, in conjunction with the existing NASA Science Working Group that is more tightly focused on NASA satellite data products. Here, following the example of the first DOE–NASA Obs4MIPs meeting, and with an initial design of CMIP6 being published, a meeting of over 50 experts in both climate modeling and satellite data from the United States, Europe, Japan, and Australia convened at NASA headquarters in Washington, D.C., for the purpose of planning the evolution of Obs4MIPs and its connection to the CMIP6 experiments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

I/O Parallelization for the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

The National Aeronautics and Space Administration (NASA) Data Assimilation Office (DAO) at the Goddard Space Flight Center (GSFC) has developed the GEOS DAS, a data assimilation system that provides production support for NASA missions and will support NASA's Earth Observing System (EOS) in the coming years. The GEOS DAS will be used to provide background fields of meteorological quantities to EOS satellite instrument teams for use in their data algorithms as well as providing assimilated data sets for climate studies on decadal time scales. The DAO has been involved in prototyping parallel implementations of the GEOS DAS for a number of years and is now embarking on an effort to convert the production version from shared-memory parallelism to distributed-memory parallelism using the portable Message-Passing Interface (MPI). The GEOS DAS consists of two main components, an atmospheric General Circulation Model (GCM) and a Physical-space Statistical Analysis System (PSAS). The GCM operates on data that are stored on a regular grid while PSAS works with observational data that are scattered irregularly throughout the atmosphere. As a result, the two components have different data decompositions. The GCM is decomposed horizontally as a checkerboard with all vertical levels of each box existing on the same processing element(PE). The dynamical core of the GCM can also operate on a rotated grid, which requires communication-intensive grid transformations during GCM integration. PSAS groups observations on PEs in a more irregular and dynamic fashion.

Lucchesi, Rob↗
DOE OSTI2018

Impacts of Future Climate-Driven Runoff on Six County WWT Facilities in New Jersey

The occurrence of extreme storms and associated flood hazards has increased in most regions in recent decades (Groisman et al. 2005, Alexander et al. 2006, Hegerl et al. 2011, Min et al. 2011). Extensive data on weather extremes since 1910 kept by the National Oceanic and Atmospheric Administration (NOAA)’s National Climatic Data Center have shown an increasing trend (reaching more than 20%) in U.S. one-day extreme precipitation events. This trend is expected to continue in the future, with likely intensification of heavy precipitation events (Kunkel et al. 2010, Westra et al. 2013). Recently, Argonne has performed dynamic downscaling from global climate model (GCM) projections with high-resolution regional climate model, Weather Research and Forecasting (WRF) and projected future climate conditions for the middle of century (2035-2064). For this project, we incorporated our future climate projections into our hydrological modeling to evaluate potential effects of changing climate on streamflow near the six county WWT facilities in New Jersey to improve our understanding of climate-driven precipitation impacts and use this information to inform the potential risk of precipitation-affected infrastructures. The information for the future projected hydrological flow discharges was also provided to support a storm surge study. The study was expanded later to quantify likelihood of river discharges near WWT facilities with a statistical approach and informed the flood risk contributed by the river discharges.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

A New ML-Based Adaptive Thinning Methodology to Improve the Impact of AIRS and CrIS Assimilation on Global Tropical Cyclone Forecasts

This work builds on previous research performed by this team to improve the forecast of Tropical Cyclones (TCs) by assimilating AIRS and CrIS radiances into the NASA Global Earth Observing System (GEOS). Past published work demonstrated that the assimilation of radiances with variable density was beneficial to TC forecasting in the GEOS. In the previous setup, a fixed-size moving square named 'TC domain' was activated by the so-called TC-vitals, an international real-time message accessible to all NWP forecasting centers, that documents the existence of a TC, its estimated position, and its size. The information from TC-vitals activated a switch in the GEOS, which allowed to reduce the distance used for thinning AIRS and CrIS data inside a 15 degrees by 15 degrees moving TC domain centered on the storm, so that more data were assimilated in the vicinity of the TC during its lifetime. The methodology produced improved TC analyses and led to better forecasts, particularly related to intensity, without damaging the global forecast skill. In the new version, the adaptive thinning methodology is based on a machine-learning technique. The technique searches for TCs and creates TC masks by using cloud-top temperatures from all geostationary satellites without the need for additional information. It is being trained against the International Best Track Archive for Climate Stewardship (IBTrACS) data base. Once a TC mask is created, a switch identical to the one used in the previous adaptive thinning method is activated, allowing the GEOS to ingest more data in the TC-shaped size-changing domain that follows the storm. As of today, the team has been able to successfully assimilate data inside the ML-detected TC domains. Future work includes an improved capability of reducing false alarm rates (i.e., cloud systems that are erroneously labeled as TCs).

Oreste Reale↗
DOE OSTI2023

Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger Over the Conterminous United States

Abstract Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Recent Changes in the Greenland Ice Sheet as Seen from Space

Many changes in the Greenland Ice Sheet have been reported in the recent scientific literature and have been attributed to various responses of the ice sheet due to regional (and global) warming. Because melting of the ice sheet would contribute approximately 7 m to sea-level rise, the lives and habitat of hundreds of millions of people worldwide would be directly and indirectly affected if continued ice-sheet melting occurs. As mean-annual global temperatures have increased, there has been an increasing focus on studying the Greenland Ice Sheet using available satellite data, and numerous expeditions have been undertaken. Regional "clear-sky" surface temperature increases since the early 1980s in the Arctic, measured using Advanced Very High Resolution Radiometer (AVHRR) infrared data, range from 0.57+/-0.02 C to 0.72+/-0.10 C per decade. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near O C during the melt season, and is thus vulnerable to more extensive melting if temperatures continue to increase. An increase in melting of the ice sheet would accelerate sea-level rise, an issue of increasing concern to billions of people worldwide. The surface temperature of the ice sheet has been studied in even greater detail using Moderate-Resolution Imaging Spectroradiometer (MODIS) data in the six individual drainage basins as well as for the ice sheet as a whole. Surface temperature trends in the decade of the 2000s have not been strong, according to the MODIS measurements. In addition to surface-temperature increases over the last few decades as measured by AVHRR, other changes have been observed such as accelerated movement of many of Greenland's outlet glaciers and sudden draining of supraglacial lakes. Decreasing mass of the ice sheet since (at least) 2002 has been measured using Gravity Recovery and Climate Experiment (GRACE) data, along with an build-up of ice at the higher elevations and a decrease of ice at the lower elevations as measured using airborne Lidar and Ice, Cloud and Land Elevation Satellite (ICESat) data. The seminar will address the above issues using a variety of NASA satellite data and ground observations.

Hall, Dorothy K.↗
NASA NTRS2008

AIRS Data Service at NASA Goddard Earth Sciences Data and Information Services (GES DISC) and Its Application to Climate Change Study

The Atmospheric Infrared Sounder (AIRS) instrument suite is designed to observe and characterize the entire atmospheric column from the surface to the top of the atmosphere in terms of surface emissivity and temperature, atmospheric temperature and humidity profiles, cloud amount and height, and the spectral outgoing infrared radiation on a global scale. The AIRS Data Support Team at the GES DISC provides data support to assist others in understanding, retrieving and extracting information from the AIRS/AMSU/HSB data products. Because a number of years has passed since its operation started, the amount of data has reached a certain level of maturity where we can address the climate change study utilizing AIRS data, In this presentation we will list various service we provide and to demonstrate how to utilize/apply the existing service to long-term and short-term variability study.

Won, Young-In↗
NASA NTRS1987

FIRE - The First ISCCP Regional Experiment

The First International Satellite Cloud Climatology Project Regional Experiment (FIRE) designed to study the roles of clouds, in particular marine stratocumulus and cirrus-cloud systems, in the global climate is discussed. The objectives of FIRE are: (1) to develop a cloud-classification scheme; (2) to validate and improve satellite cloud-retrieval techniques; (3) to improve cloud radiation models; (4) to collect cloud space/time statistics; (5) to improve cloud dynamics models; and (6) to validate and improve GCM cloud parameterizations. The methods used to acquire extended time data and intensive field observations are described. The extended time and intensive field data collected during the FIRE are to be archived in the NASA Pilot Climate Data System at Goddard Space Flight Center.

Cox, Stephen K.↗
NASA NTRS2020

Analyzing Changes in the Complexity of Climate in the Last Four Decades Using MERRA-2 Radiation Data

The energy balance of the Earth is controlled by the shortwave and longwave radiation emitted to space. Changes in the thermodynamic state of the system over time afect climate and are noticeable when viewing the system as a whole. In this paper, we study the changes in the complexity of climate in the last four decades using data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). First, we study the complexity of the shortwave and longwave radiation felds independently using Approximate Entropy and Sample Entropy, observing that the rate of complexity change is faster for shortwave radiation. Then, we study the causality of those changes using Transfer Entropy to capture the non-linear dynamics of climate, showing that the changes are mainly driven by the variations in shortwave radiation. The observed behavior of climatic complexity could be explained by the changes in cloud amount, and we research that possibility by investigating its evolution from a complexity perspective using data from the International Satellite Cloud Climatology Project (ISCCP).

Delgado-Bonal, Alfonso↗
NASA NTRS2004

Improved Mars Upper Atmosphere Climatology

The detailed characterization of the Mars upper atmosphere is important for future Mars aerobraking activities. Solar cycle, seasonal, and dust trends (climate) as well as planetary wave activity (weather) are crucial to quantify in order to improve our ability to reasonably depict the state of the Mars upper atmosphere over time. To date, our best information is found in the Mars Global Surveyor (MGS) Accelerometer (ACC) database collected during Phase 1 (Ls = 184 - 300; F10.7 = 70 - 90) and Phase 2 (Ls = 30 - 90; F10.7 = 90 - 150) of aerobraking. This database (100 - 170 km) consists of thermospheric densities, temperatures, and scale heights, providing our best constraints for exercising the coupled Mars General Circulation Model (MGCM) and the Mars Thermospheric General Circulation Model (MTGCM). The Planetary Data System (PDS) contains level 0 and 2 MGS Accelerometer data, corresponding to atmospheric densities along the orbit track. Level 3 products (densities, temperatures, and scale heights at constant altitudes) are also available in the PDS. These datasets provide the primary model constraints for the new MGCM-MTGCM simulations summarized in this report. Our strategy for improving the characterization of the Mars upper atmospheres using these models has been three-fold : (a) to conduct data-model comparisons using the latest MGS data covering limited climatic and weather conditions at Mars, (b) to upgrade the 15-micron cooling and near-IR heating rates in the MGCM and MTGCM codes for ad- dressing climatic variations (solar cycle and seasonal) important in linking the lower and upper atmospheres (including migrating tides), and (c) to exercise the detailed coupled MGCM and MTGCM codes to capture and diagnose the planetary wave (migrating plus non-migrating tidal) features throughout the Mars year. Products from this new suite of MGCM-MTGCM coupled simulations are being used to improve our predictions of the structure of the Mars upper atmosphere for the upcoming MRO aerobraking exercises in 2006. A Michigan website, containing MTGCM output fields from previous climate simulations, is being expanded to include new MGCM-MTGCM simulations addressing planetary wave influences upon thermospheric aerobraking fields (densities and temperatures). In addition, similar MTGCM output fields have been supplied to the MSFC MARSGRAM - 200X empirical model, which will be used in mission operations for conducting aerobraking maneuvers.

Bougher, S. W.↗
DOE OSTI2025

Assessing the performance of solar radiation management geoengineering simulations

Offsetting the global warming caused by anthropogenic increases in atmospheric greenhouse gases by deliberate injection of aerosols into the stratosphere is the most studied of solar radiation management geoengineering schemes. The long-term success or failure of such schemes in achieving their stated goals is assessed by comparing simulated geoengineered temperature, precipitation and tropical cyclones metrics to equivalent fields in the simulated targeted climate simulations. Results using available data sets from three single model stabilized climate target experiments and three multimodel climate change reduction experiments are presented and compared against a measure of internal variability. While all but one experimental scheme is successful in achieving their targeted global mean annual surface temperature, their success at regional scales varies significantly and is often larger than the internal variability metric used here.

climate model evaluation↗
DOE OSTI2022

Substantial influence of vapour buoyancy on tropospheric air temperature and subtropical cloud

The molar mass of water vapor is less than that of dry air, making humid air lighter than dry air at the same temperature and pressure. This effect is known as vapor buoyancy and has been considered negligibly small in large-scale climate dynamics. Here we use theory, reanalysis data, and a hierarchy of climate models to show that vapor buoyancy has a similar magnitude to thermal buoyancy in the tropical free troposphere. We further show that vapor buoyancy makes cold air rise and increases subtropical stratiform low clouds by up to 70% of its climatological value. However, some widely used climate models fail to represent vapor buoyancy in the governing equations. Further, this flaw leads to inaccurate simulations of cloud distributions—the largest uncertainty in predicting climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Understory plant communities show resistance to drought, hurricanes, and experimental warming in a wet tropical forest

Global climate change has led to rising temperatures and to more frequent and intense climatic events, such as storms and droughts. Changes in climate and disturbance regimes can have non-additive effects on plant communities and result in complicated legacies we have yet to understand. This is especially true for tropical forests, which play a significant role in regulating global climate. We used understory vegetation data from the Tropical Responses to Altered Climate Experiment (TRACE) in Puerto Rico to evaluate how plant communities responded to climate warming and disturbance. The TRACE understory vegetation was exposed to a severe drought (2015), 2 years of experimental warming (4°C above ambient in half of the plots, 2016–2017 and 2018–2019), and two major hurricanes (Irma and María, September 2017). Woody seedlings and saplings were censused yearly from 2015 to 2019, with an additional census in 2015 after the drought ended. We evaluated disturbance-driven changes in species richness, diversity, and composition across ontogeny. We then used Bayesian predictive trait modeling to assess how species responded to disturbance and how this might influence the functional structure of the plant community. Our results show decreased seedling richness after hurricane disturbance, as well as increased sapling richness and diversity after warming. We found a shift in species composition through time for both seedlings and saplings, yet the individual effects of each disturbance were not significant. At both ontogenetic stages, we observed about twice as many species responding to experimental warming as those responding to drought and hurricanes. Predicted changes in functional structure point to disturbance-driven functional shifts toward a mixture of fast-growing and drought-tolerant species. Our findings demonstrate that the tropical forest understory community is more resistant to climatic stressors than expected, especially at the sapling stage. However, early signs of changes in species composition suggest that, in a warming climate with frequent droughts and hurricanes, plant communities might shift over time toward fast-growing or drought-tolerant species.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Global Gridded Data from the Goddard Earth Observing System Data Assimilation System (GEOS-DAS)

The Goddard Earth Observing System Data Assimilation System (GEOS-DAS) timeseries is a globally gridded atmospheric data set for use in climate research. This near real-time data set is produced by the Data Assimilation Office (DAO) at the NASA Goddard Space Flight Center in direct support of the operational EOS instrument product generation from the Terra (12/1999 launch), Aqua (05/2002 launch) and Aura (01/2004 launch) spacecrafts. The data is archived in the EOS Core System (ECS) at the Goddard Earth Sciences Data and Information Services Center/Distributed Active Archive Center (GES DISC DAAC). The data is only a selection of the products available from the GEOS-DAS. The data is organized chronologically in timeseries format to facilitate the computation of statistics. GEOS-DAS data will be available for the time period January 1, 2000, through present.

Source record↗
NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban bias

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data -- the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data -- to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km x 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1 deg x 1 deg latitude-longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40 C for monthly minimum temperatures, near 0.25 C for monthly mean temperatures, and near 0.10 C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near -1.1 C for rural stations to 2.4 C for stations from the largest urban areas. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.

Epperson, David L.↗
NASA NTRS2019

Satellite Ocean Colour: Current Status and Future Perspective

Spectrally resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS, and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes the current status and future prospects in the field of ocean colour focusing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focusing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean colour are described and finally a summary of future perspectives is provided.

ocean colour↗
DOE OSTI2015

Extreme Weather Events and Interconnected Infrastructures: Toward More Comprehensive Climate Change Planning [Meeting challenges in understanding impacts of extreme weather events on connected infrastructures]

The President s Climate Change Action Plan calls for the development of better science, data, and tools for climate preparedness. Many of the current questions about preparedness for extreme weather events in coming decades are, however, difficult to answer with assets that have been developed by climate science to answer longer-term questions about climate change. Capacities for projecting exposures to climate-related extreme events, along with their implications for interconnected infrastructures, are now emerging.

54 ENVIRONMENTAL SCIENCES↗