Results for “climate data” — Page 30

DOE OSTI2016

A consistent prescription of stratospheric aerosol for both radiation and chemistry in the Community Earth System Model (CESM1)

Here we describe an updated parameterization for prescribing stratospheric aerosol in the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM1). The need for a new parameterization is motivated by the poor response of the CESM1 (formerly referred to as the Community Climate System Model, version 4, CCSM4) simulations contributed to the Coupled Model Intercomparison Project 5 (CMIP5) to colossal volcanic perturbations to the stratospheric aerosol layer (such as the 1991 Pinatubo eruption or the 1883 Krakatau eruption) in comparison to observations. In particular, the scheme used in the CMIP5 simulations by CESM1 simulated a global mean surface temperature decrease that was inconsistent with the GISS Surface Temperature Analysis (GISTEMP), NOAA's National Climatic Data Center, and the Hadley Centre of the UK Met Office (HADCRUT4). The new parameterization takes advantage of recent improvements in historical stratospheric aerosol databases to allow for variations in both the mass loading and size of the prescribed aerosol. An ensemble of simulations utilizing the old and new schemes shows CESM1's improved response to the 1991 Pinatubo eruption. Most significantly, the new scheme more accurately simulates the temperature response of the stratosphere due to local aerosol heating. Here, results also indicate that the new scheme decreases the global mean temperature response to the 1991 Pinatubo eruption by half of the observed temperature change, and modelled climate variability precludes statements as to the significance of this change.

58 GEOSCIENCES↗

NASA NTRS2009

Modeling Bird Migration under Climate Change: A Mechanistic Approach

How will migrating birds respond to changes in the environment under climate change? What are the implications for migratory success under the various accelerated climate change scenarios as forecast by the Intergovernmental Panel on Climate Change? How will reductions or increased variability in the number or quality of wetland stop-over sites affect migratory bird species? The answers to these questions have important ramifications for conservation biology and wildlife management. Here, we describe the use of continental scale simulation modeling to explore how spatio-temporal changes along migratory flyways affect en-route migration success. We use an individually based, biophysical, mechanistic, bird migration model to simulate the movement of shorebirds in North America as a tool to study how such factors as drought and wetland loss may impact migratory success and modify migration patterns. Our model is driven by remote sensing and climate data and incorporates important landscape variables. The energy budget components of the model include resting, foraging, and flight, but presently predation is ignored. Results/Conclusions We illustrate our model by studying the spring migration of sandpipers through the Great Plains to their Arctic breeding grounds. Why many species of shorebirds have shown significant declines remains a puzzle. Shorebirds are sensitive to stop-over quality and spacing because of their need for frequent refueling stops and their opportunistic feeding patterns. We predict bird "hydrographs that is, stop-over frequency with latitude, that are in agreement with the literature. Mean stop-over durations predicted from our model for nominal cases also are consistent with the limited, but available data. For the shorebird species simulated, our model predicts that shorebirds exhibit significant plasticity and are able to shift their migration patterns in response to changing drought conditions. However, the question remains as to whether this behavior can be maintained over increasing and sustained environmental change. Also, the problem is much more complex than described by the current processes captured in our model. We have taken some important and interesting steps, and our model does demonstrate how local scale information about individual stop-over sites can be linked into the migratory flyway as a whole. We are incorporating additional, species specific, mechanistic processes to better reflect different climate change scenarios

Smith, James A.↗

DOE OSTI2017

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks $-$ Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basins on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. Finally, the evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Performance Evaluation of Clouds and the Earth’s Radiant Energy System (CERES) Instruments in Generating A Stable Earth Radiation Budget Multi-Decadal Data Record

Clouds and the Earth’s Radiant Energy System (CERES) instrument is designed to measure the earth-reflected solar and earth-emitted longwave radiances, two components of the Earth Radiation Budget. CERES instrument measure radiances in 0.3 to 5.0 micron region with Shortwave (SW) sensor, 0.3 to >100 microns with Total (TOT) sensor, and either 8 - 12 micron with Window (WN) sensor or 5 – 35 micron with longwave (LW) sensor. The on-board internal calibration module (ICM) is used to monitor the stability of the sensors and to correct the pre-launch determined sensor gains for radiance calculations. Corrections for spectral changes occurring in different wavelength regions observed through vicarious studies were applied to sensor response function used in radiance determination. The resulting global flux long term trends from each instrument have shown consistent behavior and provided a stable global record of Top of the Atmosphere (TOA) shortwave and longwave fluxes for the Earth radiation budget climate data record.

Clouds and Earth Radiation Energy System (CERES)↗

NASA NTRSDate not supplied

Performance Evaluation of Clouds and the Earth’s Radiant Energy System (CERES) Instruments in Generating A Stable Earth Radiation Budget Multi-Decadal Data Record

Clouds and the Earth’s Radiant Energy System (CERES) instrument is designed to measure the earth-reflected solar and earth-emitted longwave radiances, two components of the Earth Radiation Budget. CERES instrument measure radiances in 0.3 to 5.0 micron region with Shortwave (SW) sensor, 0.3 to >100 microns with Total (TOT) sensor, and either 8 - 12 micron with Window (WN) sensor or 5 – 35 micron with longwave (LW) sensor. The on-board internal calibration module (ICM) is used to monitor the stability of the sensors and to correct the pre-launch determined sensor gains for radiance calculations. Corrections for spectral changes occurring in different wavelength regions observed through vicarious studies were applied to sensor response function used in radiance determination. The resulting global flux long term trends from each instrument have shown consistent behavior and provided a stable global record of Top of the Atmosphere (TOA) shortwave and longwave fluxes for the Earth radiation budget climate data record.

Earth Radiation Budget↗

DOE OSTI2013

Global vegetation model diversity and the risks of climate-driven ecosystem shifts

Climate change is modifying global biogeochemical cycles, and is expected to exert increasingly large effects in the future. How these changes will in turn affect and interact with the structure and function of particular ecosystems is unclear, however, both because of scientific uncertainties and the very diversity of global vegetation models in use. Writing in Environmental Research Letters, Warszawski et al. (1) aggregate results from a group of models, across a range of emissions scenarios and climate data, to investigate these risks. Although the models frequently disagree about which specific regions are at risk, they consistently predict a greater chance of ecosystem restructuring with more warming; this risk roughly doubles between 2 and 3 °C increases in global mean temperature. The innovative work of Warszawski et al. represents an important first step towards fully consistent multi-model, multi-scenario assessments of the future risks to global ecosystems.

climate change↗

DOE OSTI2018

Response of Tropical Terrestrial Gross Primary Production to the Super El Niño Event in 2015

The gross primary production (GPP) in tropical terrestrial ecosystems plays a critical role in the global carbon cycle and climate change. The strong 2015–2016 El Niño event offers a unique opportunity to investigate how GPP in the tropical terrestrial ecosystems responds to climatic forcing. This study uses two GPP products and concurrent climate data to investigate the GPP anomalies and their underlying causes. We find that both GPP products show an enhanced GPP in 2015 for the tropical terrestrial ecosystem as a whole relative to the multiyear mean of 2001–2015, and this enhancement is the net result of GPP increase in tropical forests and decrease in nonforests. We show that the increased GPP in tropical forests during the El Nino event is consistent with increased photosynthesis active radiation as a result of a reduction in clouds, while the decreased GPP in nonforests is consistent with increased water stress as a result of a reduction of precipitation and an increase of temperature. These results reveal the strong coupling of ecosystem and climate that is different in forest and nonforest ecosystems and provide a test case for carbon cycle parameterization and carbon-climate feedback simulation in models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗

DOE OSTI2023

Population Aging and Heat Exposure in the 21st Century: Which U.S. Regions Are at Greatest Risk and Why?

Abstract Background and Objectives The co-occurring trends of population aging and climate change mean that rising numbers of U.S. older adults are at risk of intensifying heat exposure. We estimate county-level variations in older populations’ heat exposure in the early (1995–2014) and mid (2050) 21st century. We identify the extent to which rising exposures are attributable to climate change versus population aging. Research Design and Methods We estimate older adults’ heat exposure in 3,109 counties in the 48 contiguous U.S. states. Analyses use NASA NEX Global Daily Downscaled Product (NEX-GDDP-CMIP6) climate data and county-level projections for the size and distribution of the U.S. age 69+ population. Results Population aging and rising temperatures are documented throughout the United States, with particular “hotspots” in the Deep South, Florida, and parts of the rural Midwest. Increases in heat exposure by 2050 will be especially steep in historically colder regions with large older populations in New England, the upper Midwest, and rural Mountain regions. Rising temperatures are driving exposure in historically colder regions, whereas population aging is driving exposure in historically warm southern regions. Discussion and Implications Interventions to address the impacts of temperature extremes on older adult well-being should consider the geographic distribution and drivers of this exposure. In historically cooler areas where climate change is driving exposures, investments in warning systems may be productive, whereas investments in health care and social services infrastructures are essential in historically hot regions where exposures are driven by population aging.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

The Impact of Drifting Orbits on the Monthly Regional TOA Flux Assuming Constant Meteorology

The NASA Clouds and the Earth's Radiant Energy System (CERES) gridded Single Scanner Footprint (SSF1deg) product provides TOA SW and LW monthly 1° regional all-sky fluxes, which are used to monitor the Earth’s energy balance. The CERES long-term climate data record relies on Terra and Aqua satellite sun-synchronous orbits that are maintained at 10:30 and 1:30 local equator crossing times (LECT), respectively. The Terra and Aqua satellites are expected to drift outside of their respective LECT during mid 2022. Both Terra and Aqua will drift over several years towards sunrise and sunset, respectively, and eventually will be deorbited. The CERES SSF1deg product monthly regional fluxes are based on the well calibrated and stable CERES observed fluxes and are temporally interpolated assuming constant meteorology between measurements to resolve the regional diurnal flux cycle to obtain a daily averaged flux. The drifting orbits may impact the monthly regional TOA flux over regions with systematic diurnal cycles, because the observations will shift in local time. The CERES project would like to determine the maximum Terra and Aqua LECT time shift before the monthly regional fluxes are diurnally impacted and become unreliable for long-term climate monitoring. To determine the impact of the drifting orbits on the SSF1deg monthly regional fluxes,15-minute Geostationary Earth Radiation Budget (GERB) broadband observed fluxes over the Meteosat geostationary satellite domain (±60° in longitude and latitude) are used as proxy CERES observations. The drifting orbit sampling pattern is achieved by simply incrementing the observation time by steps of 15 minutes from the CERES footprint time. For each 15-minute time interval, the CERES observed fluxes are replaced by the GERB observed fluxes. The-15 minute incremented monthly regional fluxes based on constant meteorology are compared to the reference 10:30 and 1:30 LECT fluxes. Regions with systematic diurnal cycles, include morning maritime stratus, where the clouds dissipate during the morning, and land afternoon convection, where clouds increase in the afternoon will impact the regional flux differences. Based on January and July 2010 GERB data, even a 15-minute LECT change caused regional monthly flux differences that would impact long term regional trend analysis. Results will be shown at the conference.

David Doelling↗

NASA NTRSDate not supplied

The Orbital Drift Impact on the Monthly Regional TOA Flux Assuming Constant Meteorology

The NASA Clouds and the Earth's Radiant Energy System (CERES) gridded Single Scanner Footprint (SSF1deg) product provides TOA SW and LW monthly 1° regional all-sky fluxes, which are used to monitor the Earth’s energy balance. The CERES long-term climate data record relies on Terra and Aqua satellite sun-synchronous orbits that are maintained at 10:30 and 1:30 local equator crossing times (LECT), respectively. The Terra and Aqua satellites are expected to drift outside of their respective LECT during mid 2022. Both Terra and Aqua will drift over several years towards sunrise and sunset, respectively, and eventually will be deorbited. The CERES SSF1deg product monthly regional fluxes are based on the well calibrated and stable CERES observed fluxes and are temporally interpolated assuming constant meteorology between measurements to resolve the regional diurnal flux cycle to obtain a daily averaged flux. The drifting orbits may impact the monthly regional TOA flux over regions with systematic diurnal cycles, because the observations will shift in local time. The CERES project would like to determine the maximum Terra and Aqua LECT time shift before the monthly regional fluxes are diurnally impacted and become unreliable for long-term climate monitoring. To determine the impact of the drifting orbits on the SSF1deg monthly regional fluxes,15-minute Geostationary Earth Radiation Budget (GERB) broadband observed fluxes over the Meteosat geostationary satellite domain (±60° in longitude and latitude) are used as proxy CERES observations. The drifting orbit sampling pattern is achieved by simply incrementing the observation time by steps of 15 minutes from the CERES footprint time. For each 15-minute time interval, the CERES observed fluxes are replaced by the GERB observed fluxes. The-15 minute incremented monthly regional fluxes based on constant meteorology are compared to the reference 10:30 and 1:30 LECT fluxes. Regions with systematic diurnal cycles, include morning maritime stratus, where the clouds dissipate during the morning, and land afternoon convection, where clouds increase in the afternoon will impact the regional flux differences. Based on January and July 2010 GERB data, even a 15-minute LECT change caused regional monthly flux differences that would impact long term regional trend analysis. Results will be shown at the conference.

D R Doelling↗

NASA NTRS2012

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Disasters Caused by Large Scale Rift Valley Fever Outbreaks

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of arthropod-borne diseases. We show that epidemics and epizootics of previously unpredictable Rift Valley fever are directly influenced by large scale flooding associated with the El Ni o/Southern Oscillation. This flooding affects the ecology of disease transmitting arthropod vectors through vegetation development and other bioclimatic factors. This information is now utilized to monitor, model, and map areas of potential Rift Valley fever outbreaks and is used as an early warning system for risk reduction of outbreaks to human and animal health, trade, and associated economic impacts. The continuation of such satellite measurements is critical to anticipating, preventing, and managing disease epidemics and epizootics and other climate-related disasters.

Anyamba, Assaf↗

DOE OSTI2026

Evaluation of daily gridded climate products using in situ FLUXNET data and tree growth modeling

Gridded climate data products have facilitated research in climate and ecology by providing meteorological data continuously across large spatial scales. However, the sensitivity of scientific outcomes to dataset choice remains poorly understood, and evaluation using station-based records can favor datasets built heavily on weather stations. Here, we evaluate seven high-resolution daily gridded datasets covering the contiguous United States using independent meteorology from the FLUXNET2015 dataset, with a focus on the implications of dataset choice for process-based tree growth modeling. We find that gridded products tend to capture temperature accurately while consistently overestimating the magnitude and frequency of precipitation and its extremes. Moreover, datasets vary in how they define a ‘day,’ which significantly affects temporal alignment with FLUXNET2015 observations. Despite differences among the datasets, the interannual variability in tree ring simulations is insensitive to dataset choice, likely because daily-scale biases are averaged out through accumulated growth across several months. However, inaccuracies in temperature and precipitation can significantly bias modeled xylem cell production, with systematically higher annual precipitation in the gridded datasets leading to greater xylem production compared to simulations using in situ data. Our results suggest that model applications, especially those that integrate to time scales longer than one day, are likely insensitive to climate dataset choice, but applications that are sensitive to daily climate variations or to absolute climate values need to carefully consider biases in gridded climate products.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Sources of Uncertainty in Predicting Land Surface Fluxes Using Diverse Data and Models

In the domain of predicting land surface fluxes, models are used to bring data from large observation networks and satellite remote sensing together to make predictions about present and future states of the Earth. Characterizing the uncertainty about such predictions is a complex process and one that is not yet fully understood. Uncertainty exists about initialization, measurement and interpolation of input variables; model parameters; model structure; and mixed spatial and temporal supports. Multiple models or structures often exist to describe the same processes. Uncertainty about structure is currently addressed by running an ensemble of different models and examining the distribution of model outputs. To illustrate structural uncertainty, a multi-model ensemble experiment we have been conducting using the Terrestrial Observation and Prediction System (TOPS) will be discussed. TOPS uses public versions of process-based ecosystem models that use satellite-derived inputs along with surface climate data and land surface characterization to produce predictions of ecosystem fluxes including gross and net primary production and net ecosystem exchange. Using the TOPS framework, we have explored the uncertainty arising from the application of models with different assumptions, structures, parameters, and variable definitions. With a small number of models, this only begins to capture the range of possible spatial fields of ecosystem fluxes. Few attempts have been made to systematically address the components of uncertainty in such a framework. We discuss the characterization of uncertainty for this approach including both quantifiable and poorly known aspects.

Dungan, Jennifer L.↗

NASA NTRS2014

Early Evaluation of the VIIRS Calibration, Cloud Mask and Surface Reflectance Earth Data Records

Surface reflectance is one of the key products fromVIIRS and as withMODIS, is used in developing several higherorder land products. The VIIRS Surface Reflectance (SR) Intermediate Product (IP) is based on the heritageMODIS Collection 5 product (Vermote, El Saleous, & Justice, 2002). The quality and character of surface reflectance depend on the accuracy of the VIIRS Cloud Mask (VCM), the aerosol algorithms and the adequate calibration of the sensor. The focus of this paper is the early evaluation of the VIIRS SR product in the context of the maturity of the operational processing system, the Interface Data Processing System (IDPS). After a brief introduction, the paper presents the calibration performance and the role of the surface reflectance in calibration monitoring. The analysis of the performance of the cloud mask with a focus on vegetation monitoring (no snow conditions) shows typical problems over bright surfaces and high elevation sites. Also discussed is the performance of the aerosol input used in the atmospheric correction and in particular the artifacts generated by the use of the Navy Aerosol Analysis and Prediction System. Early quantitative results of the performance of the SR product over the AERONET sites showthatwith the fewadjustments recommended, the accuracy iswithin the threshold specifications. The analysis of the adequacy of the SR product (Land PEATE adjusted version) in applications of societal benefits is then presented. We conclude with a set of recommendations to ensure consistency and continuity of the JPSS mission with the MODIS Land Climate Data Record.

Surface reflectance↗

DOE OSTI2021

Improving Short Term Predictability of Hydrologic Models with Deep Learning

Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2009

Climate Change and Sounder Radiometric Stability

Satellite instrument radiometric stability is critical for climate studies. The Atmospheric Infrared Sounder (AIRS) radiances are of sufficient stability and accuracy to serve as a climate data record as evidenced by comparisons with the global network of buoys. In this paper we examine the sensitivity of derived geophysical products to potential instrument radiometric stability issues due to diurnal, orbital and seasonal variations. Our method is to perturb the AIRS radiances and examine the impact to retrieved parameters. Results show that instability in retrieved temperature products will be on the same order of the brightness temperature error in the radiances and follow the same time dependences. AIRS excellent stability makes it ideal for examining impacts of instabilities of future systems on geophysical parameter performance.

Sounding↗

DOE OSTI2016

Projected impacts of climate change on hydropower potential in China

Hydropower is an important renewable energy source in China, but it is sensitive to climate change, because the changing climate may alter hydrological conditions (e.g., river flow and reservoir storage). Future changes and associated uncertainties in China's gross hydropower potential (GHP) and developed hydropower potential (DHP) are projected using simulations from eight global hydrological models (GHMs), including a large-scale reservoir regulation model, forced by five general circulation models (GCMs) with climate data under two representative concentration pathways (RCP2.6 and RCP8.5). Results show that the estimation of the present GHP of China is comparable to other studies; overall, the annual GHP is projected to change by -1.7 to 2 % in the near future (2020–2050) and increase by 3 to 6 % in the late 21st century (2070–2099). The annual DHP is projected to change by -2.2 to -5.4 % (0.7–1.7 % of the total installed hydropower capacity (IHC)) and -1.3 to -4 % (0.4–1.3 % of total IHC) for 2020–2050 and 2070–2099, respectively. Regional variations emerge: GHP will increase in northern China but decrease in southern China – mostly in south central China and eastern China – where numerous reservoirs and large IHCs currently are located. The area with the highest GHP in southwest China will have more GHP, while DHP will reduce in the regions with high IHC (e.g., Sichuan and Hubei) in the future. The largest decrease in DHP (in %) will occur in autumn or winter, when streamflow is relatively low and water use is competitive. Large ranges in hydropower estimates across GHMs and GCMs highlight the necessity of using multimodel assessments under climate change conditions. This study prompts the consideration of climate change in planning for hydropower development and operations in China, to be further combined with a socioeconomic analysis for strategic expansion.

13 HYDRO ENERGY↗