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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deforestation-induced climate change reduces carbon storage in remaining tropical forests

Abstract Biophysical effects from deforestation have the potential to amplify carbon losses but are often neglected in carbon accounting systems. Here we use both Earth system model simulations and satellite–derived estimates of aboveground biomass to assess losses of vegetation carbon caused by the influence of tropical deforestation on regional climate across different continents. In the Amazon, warming and drying arising from deforestation result in an additional 5.1 ± 3.7% loss of aboveground biomass. Biophysical effects also amplify carbon losses in the Congo (3.8 ± 2.5%) but do not lead to significant additional carbon losses in tropical Asia due to its high levels of annual mean precipitation. These findings indicate that tropical forests may be undervalued in carbon accounting systems that neglect climate feedbacks from surface biophysical changes and that the positive carbon–climate feedback from deforestation-driven climate change is higher than the feedback originating from fossil fuel emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Decline in Seasonal Snow during a Projected 20-Year Dry Spell

Snowpack loss in midlatitude mountains is ubiquitously projected by Earth system models, though the magnitudes, persistence, and time horizons of decline vary. Using daily downscaled hydroclimate and snow projections, we examine changes in snow seasonality across the U.S. Pacific Southwest region during a simulated severe 20-year dry spell in the 21st century (2051–2070) developed as part of the 4th California Climate Change Assessment to provide a “stress test” for water resources. Across California’s mountains, substantial declines (30–100% loss) in median peak annual snow water equivalent accompany changes in snow seasonality throughout the region compared to the historic period. We find that 80% of historic seasonal snowpacks transition to ephemeral conditions. Subsetting empirical-statistical wildfire projections for California by snow seasonality transition regions indicates a two-to-four-fold increase in the area burned, consistent with recent observations of high elevation wildfires following extended drought conditions. By analyzing six of the major California snow-fed river systems, we demonstrate snowpack reductions and seasonality transitions result in concomitant declines in annual runoff (47–58% of historical values). The negative impacts to statewide water supply reliability by the projected dry spell will likely be magnified by changes in snowpack seasonality and increased wildfire activity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

Risks of Synchronized Low Yields Are Underestimated in Climate and Crop Model Projections

Simultaneous harvest failures across major crop-producing regions are a threat to global food security. Concurrent weather extremes driven by a strongly meandering jet stream could trigger such events, but so far this has not been quantified. Specifically, the ability of state-of-the art crop and climate models to adequately reproduce such high impact events is a crucial component for estimating risks to global food security. Here we find an increased likelihood of concurrent low yields during summers featuring meandering jets in observations and models. While climate models accurately simulate atmospheric patterns, associated surface weather anomalies and negative effects on crop responses are mostly underestimated in bias-adjusted simulations. Given the identified model biases, future assessments of regional and concurrent crop losses from meandering jet states remain highly uncertain. Our results suggest that model-blind spots for such high-impact but deeply-uncertain hazards have to be anticipated and accounted for in meaningful climate risk assessments.

Atmospheric dynamics↗
DOE OSTI2020

Development and Evaluation of Occupancy-Aware Model Predictive Control for Residential Building Energy Efficiency and Occupant Comfort

The residential sector accounts for 25% of global primary energy consumption. Two methods have previously been proposed to reduce residential energy use associated with the provision of occupant thermal comfort: 1. Occupancy-based HVAC control, operating systems only during confirmed occupancy, and 2. model predictive control (MPC), harnessing a mathematical model and forecasts to find optimal operating strategies. Previous studies estimate the average energy savings of the two methods individually in the range of 21% and 16%, respectively. The research presented herein was carried out to evaluate the energy savings potential in residential buildings by combining both approaches across different climates, house vintages, and occupancy patterns. Occupancy and eight different physical modalities (e.g. CO2 and VOC) data were collected from five homes for time periods of 4–9 weeks. Collected data sets were used to train occupancy prediction models suggested by an extensive literature survey of occupancy model types. The trained prediction models were combined with MPC and detailed EnergyPlus building simulation models to evaluate residential building performance in terms of annual energy savings and thermal comfort, along with discomfort exceedance metrics. Multiple home types and regions were analyzed to understand regional and climate-dependent potential. Based on actual field data, the occupancy models had a prediction inaccuracy between 8% and 35% across the investigated homes. Average occupancy for the collected data ranged from 56% to 86%, a typical range reported in the literature. Building simulations were conducted for three control scenarios: conventional thermostatic control, occupancy-based, and occupancy-based MPC. The results indicate that all advanced strategies improve upon the conventional control, with some scenarios cutting energy use in half with only occasional incurrence of discomfort. The findings indicate that occupancy-aware model predictive residential building control has the potential to drastically reduce energy use and associated emissions while maintaining occupant comfort for both new and existing buildings.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
NASA NTRS2014

Geostationary Coastal and Air Pollution Events (GEO-CAPE) Sensitivity Analysis Experiment

Geostationary Coastal and Air pollution Events (GEO-CAPE) is a NASA decadal survey mission to be designed to provide surface reflectance at high spectral, spatial, and temporal resolutions from a geostationary orbit necessary for studying regional-scale air quality issues and their impact on global atmospheric composition processes. GEO-CAPE's Atmospheric Science Questions explore the influence of both gases and particles on air quality, atmospheric composition, and climate. The objective of the GEO-CAPE Observing System Simulation Experiment (OSSE) is to analyze the sensitivity of ozone to the global and regional NOx emissions and improve the science impact of GEO-CAPE with respect to the global air quality. The GEO-CAPE OSSE team at Jet propulsion Laboratory has developed a comprehensive OSSE framework that can perform adjoint-sensitivity analysis for a wide range of observation scenarios and measurement qualities. This report discusses the OSSE framework and presents the sensitivity analysis results obtained from the GEO-CAPE OSSE framework for seven observation scenarios and three instrument systems.

Lee, Meemong↗
DOE OSTI2025

Tracing the impacts of Mount Pinatubo eruption on regional climate using spatially-varying changepoint detection

Significant events, such as volcanic eruptions, can have global and long-lasting impacts on climate. These global impacts, however, are not uniform across space and time. Understanding how the Mt. Pinatubo eruption affects global and regional climate is of great interest for predicting the impact on climate due to similar events as well as understanding the possible effect of the stratospheric aerosol injections proposed to combat climate change. While many studies illustrated the impact of the Pinatubo eruption on a global scale, studies at a fine regional scale are scarce. Here, we propose a Bayesian spatially-varying changepoint detection and estimation method to trace the impact of Mt. Pinatubo eruption on regional climate. Our approach takes into account the diffusing nature and spatial correlation of the climate changes attributed to the volcanic eruption. We illustrate our method and demonstrate its advantages over an existing changepoint detection method through simulations. Finally, we apply our method to monthly stratospheric aerosol optical depth and surface temperature data from 1985 to 1995 to detect and estimate changepoints following the 1991 Mt. Pinatubo eruption. Our results quantitatively characterize the spatial pattern of the eruption’s impact on regional climate, complementing the previous studies on the global impact of the Pinatubo eruption.

Aerosol optical depth↗
DOE OSTI2022

Lithium-Ion Battery Testing Capable of Simulating “Ultralow” Lunar Temperatures

The first-ever lithium-ion battery cycler capable of testing the coin and pouch cells under ultralow (≤40 °C) temperatures down to -175 °C to simulate extreme climates found in the lunar and space missions, high-altitude air vehicles, polar regions of Earth, and military expeditionary missions is enthusiastically reported. The extremely cold temperatures are achieved through a tailored extreme low-temperature system (ELTS) with liquid nitrogen flow and the ability to reach temperatures between -175 and 25 °C, using 1 kΩ and 1 MΩ resistors at varying currents of 1–4 mA and 1–4 μA, respectively, is validated. Employing the ELTS, Li 4 Ti 5 O 12 (LTO)||Li cells with 1 m lithium bis(fluorosulfonyl)imide in cyclopentyl methyl ether as an electrolyte developed in this research group are charged and discharged at room temperature (cycling rate C/5), -20 (C/10), -40 (C/40), and -60 °C (C/50), which exhibited discharge capacities of 159.04, 119.12, 101.79, and 33.06 mAh g -1 , respectively. Moreover, the LTO||Li cell produced a reversible capacity of 7.12 mAh g -1 tested at -100 °C for the first time.

25 ENERGY STORAGE↗
DOE OSTI2022

Observations Indicate That Clouds Amplify Mechanisms of Southern Ocean Heat Uptake

Abstract The Southern Ocean has absorbed most of the excess heat associated with anthropogenic greenhouse gas emissions. Since Southern Ocean observations are sparse in certain regions and seasons, much of our knowledge of ocean heat uptake is based on climate model simulations. However, climate models still inadequately represent some properties of Southern Ocean clouds, and they have not identified the mechanisms by which clouds may affect Southern Ocean heat uptake (SOHU). Here, we use the ERA5 and JRA‐55 reanalyses to assess the influence of clouds and other atmospheric processes on SOHU from 1979 to 2020. We find that years with the highest SOHU between 45° and 65°S are dominated by ocean heat uptake anomalies during winter and spring, but not during summer or fall. Winter and spring cloud cover are up to 7% higher when SOHU is up to 5.5 W/m 2 higher than the climatological seasonal mean, with the largest increases in the South Pacific Ocean. Clouds also contain more liquid water. These changes in cloud properties increase downwelling longwave radiation, amplifying ocean heat uptake. Cloud changes are also concomitant with a more stable lower atmosphere, which suppresses turbulent heat fluxes out of the surface. Overall, we find that SOHU is likely not mediated by enhanced surface shortwave absorption over the observational time period. A better understanding of how atmospheric processes impact ocean heat uptake may help improve our understanding of ocean heat uptake mechanisms in the current generation of climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Dynamically Downscaled Hourly Future Weather Data with 12-km Resolution Covering Most of North America

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. The weather data is created for two emissions scenarios: RCP4.5 and RCP8.5 and spans two 10-year time slices in the future: 2045 - 2054 and 2085 - 2094. It offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide 20 years of future weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The data for RCP4.5 is still being processed and will be published soon.

Array↗
NASA NTRS2016

Using High-Resolution Airborne Remote Sensing to Study Aerosol Near Clouds

The horizontal space in between clear and cloudy air is very complex. This so-called twilight zone includes activated aerosols that are not quite clouds, thin cloud fragments that are not easily observable, and dying clouds that have not quite disappeared. This is a huge challenge for satellite remote sensing, specifically for retrieval of aerosol properties. Identifying what is cloud versus what is not cloud is critically important for attributing radiative effects and forcings to aerosols. At the same time, the radiative interactions between clouds and the surrounding media (molecules, surface and aerosols themselves) will contaminate retrieval of aerosol properties, even in clear skies. Most studies on aerosol cloud interactions are relevant to moderate resolution imagery (e.g. 500 m) from sensors such as MODIS. Since standard aerosol retrieval algorithms tend to keep a distance (e.g. 1 km) from the nearest detected cloud, it is impossible to evaluate what happens closer to the cloud. During Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS), the NASA ER-2 flew with the enhanced MODIS Airborne Simulator (eMAS), providing MODIS-like spectral observations at high (50 m) spatial resolution. We have applied MODIS-like aerosol retrieval for the eMAS data, providing new detail to characterization of aerosol near clouds. Interpretation and evaluation of these eMAS aerosol retrievals is aided by independent MODIS-like cloud retrievals, as well as profiles from the co-flying Cloud Physics Lidar (CPL). Understanding aerosolcloud retrieval at high resolution will lead to better characterization and interpretation of long-term, global products from lower resolution (e.g.MODIS) satellite retrievals.

aerosols near clouds↗
NASA NTRS2010

Studies of 21st-Century Precipitation Trends Over West Africa

West Africa includes a semi-arid zone between the Sahara Desert and the humid Gulf of Guinea coast, approximately between 10 N and 20 N, which is irrigated by summer monsoon rains. This article refers to the region as the Sahel. Rain-fed agriculture is the primary sustenance for Sahel populations, and severe droughts (in the 1970s and 1980s), therefore, have devastating negative societal impacts. The future frequency of Sahel droughts and the evolution of its hydrological balance are therefore of great interest. The article reviews 10 recent research studies that attempt to discover how climate changes will affect the hydrology of the Sahel throughout the 21st century. All 10 studies rely on atmosphere ocean global climate model (AOGCM) simulations based on a range of greenhouse gas emissions scenarios. Many of the simulations are contained in the Intergovernmental Panel on Climate Change archives for Assessment Reports #3 and #4. Two of the studies use AOGCM data to drive regional climate models. Seven studies make projections for the first half of the 21st century and eight studies make projections for the second half. Some studies make projections of wetter conditions and some predict more frequent droughts, and each describes the atmospheric processes associated with its prediction. Only one study projects more frequent droughts before 2050, and that is only for continent-wide degradation in vegetation cover. The challenge to correctly simulate Sahel rainfall decadal trends is particularly daunting because multiple physical mechanisms compete to drive the trend upwards or downwards. A variety of model deficiencies, regarding the simulation of one or more of these physical processes, taints models climate change projections. Consequently, no consensus emerges regarding the impact of anticipated greenhouse gas forcing on the hydrology of the Sahel in the second half of the 21st century.

Druyan, Leonard M.↗
DOE OSTI2025

Evaluation of historical precipitation interannual variability in CMIP6 over the United States

Interannual precipitation variability profoundly influences society via its effects on agriculture, water resources, infrastructure, and disaster risks. In this study, we use daily in situ precipitation observations from the global historical climatology network-daily (GHCN-D) to assess the ability of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, including the 50-member fifth-generation Canadian Earth System Model single model initial-condition large ensemble (CanESM5_SMILE), to realistically simulate historical interannual precipitation variability trends within 17 regions of the contiguous United States (CONUS). We assess how accurately the CMIP6 simulations align with observational data across annual, summer, and winter periods, focusing on four key hydrometeorological metrics, including interannual precipitation variability, relative interannual precipitation variability (coefficient of variation), annual mean precipitation, and annual wet day frequency. Our findings reveal that CMIP6 ensemble members generally reproduce the spatial patterns of observed trends in annual mean precipitation. In most regions, models agree well with the signs of observed changes in annual mean precipitation, though discrepancies in trend magnitude are evident. Further, observed trends in winter mean precipitation broadly exhibit a spatial pattern similar to that of the observed annual mean. However, analysis of the CanESM5_SMILE shows that trends in precipitation variability may primarily be the result of model-simulated internal variability, suggesting caution in interpreting multi-model single-realization ensemble results. Challenges in accurately simulating interannual precipitation variability underscore the need for ongoing model refinement and validation to enhance climate projections, especially in regions vulnerable to extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

Response to CO2 Transient Increase in the GISS Coupled Model: Regional Coolings in a Warming Climate

The (GISS) Goddard Institute for Space Studies coupled atmosphere-ocean model is used to investigate the effect of increased atmospheric CO2 by comparing a compounded 1 percent CO2 increase experiment with a control simulation. After 70 years of integration, the global surface air temperature in the 1 percent CO2 experiment is 1.43 C warmer. In spite of this global warming, there are two distinct regions, the northern Atlantic Ocean and the southern Pacific Ocean, where the surface air temperature is up to 4 C cooler. This situation is maintained by two positive feedbacks: a local effect on convection in the South Pacific and a non-local impact on the meridional circulation in the North Atlantic. The poleward transport of latent energy and dry static energy by the atmosphere is greater in the 1 percent CO2 experiment, caused by warming and therefore increased water vapor and greater greenhouse capacity at lower latitudes. The larger atmospheric transports tend to reduce upward vertical fluxes of heat and moisture from the ocean surface at high latitudes, which has the effect of stabilizing the ocean, reducing both convection and the thermohaline circulation. With less convection, less warm water is brought up from below, and with a reduced North Atlantic thermohaline circulation (by 30 percent at time of CO2 doubling), the poleward energy transport by the oceans decreases. The colder water then leads to further reductions in evaporation, decreases of salinity at high latitudes, continued stabilization of the ocean, and maintenance of reduced convection and meridional overturning. Although sea ice decreases globally, it increases in the cooling regions which reduces the overall climate sensitivity; its effect is most pronounced in the Southern Hemisphere. Tropical warming has been observed over the past several decades; if modeling studies such as this and others which have produced similar effects are valid, these processes may already be beginning.

Russell, Gary L.↗
NASA NTRS2015

Ocean-Atmosphere Coupled Model Simulations of Precipitation in the Central Andes

The meridional extent and complex orography of the South American continent contributes to a wide diversity of climate regimes ranging from hyper-arid deserts to tropical rainforests to sub-polar highland regions. In addition, South American meteorology and climate are also made further complicated by ENSO, a powerful coupled ocean-atmosphere phenomenon. Modelling studies in this region have typically resorted to either atmospheric mesoscale or atmosphere-ocean coupled global climate models. The latter offers full physics and high spatial resolution, but it is computationally inefficient typically lack an interactive ocean, whereas the former offers high computational efficiency and ocean-atmosphere coupling, but it lacks adequate spatial and temporal resolution to adequate resolve the complex orography and explicitly simulate precipitation. Explicit simulation of precipitation is vital in the Central Andes where rainfall rates are light (0.5-5 mm hr-1), there is strong seasonality, and most precipitation is associated with weak mesoscale-organized convection. Recent increases in both computational power and model development have led to the advent of coupled ocean-atmosphere mesoscale models for both weather and climate study applications. These modelling systems, while computationally expensive, include two-way ocean-atmosphere coupling, high resolution, and explicit simulation of precipitation. In this study, we use the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST), a fully-coupled mesoscale atmosphere-ocean modeling system. Previous work has shown COAWST to reasonably simulate the entire 2003-2004 wet season (Dec-Feb) as validated against both satellite and model analysis data when ECMWF interim analysis data were used for boundary conditions on a 27-9-km grid configuration (Outer grid extent: 60.4S to 17.7N and 118.6W to 17.4W).

Regional Climate Model↗
NASA NTRS2018

Climate Shifts Within Major Agricultural Seasons for +1.5 and +2.0 °C Worlds: HAPPI Projections and AgMIP Modeling Scenarios

This study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 °C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 °C between the +1.5 and +2.0 °C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO2 concentration that corresponds to the +1.5 °C World but overestimates the CO2 concentration for the +2.0 °C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO2 on crops in the +2.0 °C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5 °C and +2.0 °C worlds for adaptation planning in these climate stabilization targets.

Alexander C. Ruane↗
DOE OSTI2020

Future Warming and Intensification of Precipitation Extremes: A “Double Whammy” Leading to Increasing Flood Risk in California

This study focuses on quantifying future anthropogenic changes in surface runoff associated with extreme precipitation in California's Sierra Nevada. The method involves driving a land surface model with output from a high resolution regional atmospheric simulation of the most extreme atmospheric rivers (ARs). AR events were selected from an ensemble of global climate model simulations of historical and late 21st century climate under the “high-emission” RCP8.5 scenario. Average precipitation during the future ARs increases by ~25% but a much lower proportion falls as snow. The resulting future runoff increase is dramatic—nearly 50%, reflecting both the precipitation increase and simultaneous conversion of snow to rain. The “double whammy” impact on runoff is largest in the 2,000–2,500 m elevation band, where the snowfall loss and precipitation increase are both especially large. Overall, this huge increase in runoff during the most extreme AR events could present major flood control challenges for the region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessment of rainfall variability and future change in Brazil across multiple timescales

Abstract Rainfall variability change under global warming is a crucial issue that may have a substantial impact on society and the environment, as it can directly impact biodiversity, agriculture, and water resources. Observed precipitation trends and climate change projections over Brazil indicate that many sectors of society are potentially highly vulnerable to the impacts of climate change. The purpose of this study is to assess model projections of the change in rainfall variability at various temporal scales over sub‐regions of Brazil. For this, daily data from 30 CMIP5 models for historical (1900–2005) and future (2050–2100) experiments under a high‐emission scenario are used. We assess the change in precipitation variability, applying a band‐pass filter to isolate variability on daily, weekly, monthly, intra‐seasonal, and El Nino Southern Oscillation (ENSO) time scales. For historical climate, simulated precipitation is evaluated against observations to establish model reliability. The results show that models largely agree on increases in variability on all timescales in all sub‐regions, except on ENSO timescales where models do not agree on the sign of future change. Brazil will experience more rainfall variability in the future that is, drier or more frequent dry periods and wetter wet periods on daily, weekly, monthly, and intra‐seasonal timescales, even in sub‐regions where future changes in mean rainfall are currently uncertain. This may provide useful information for climate change adaptation across, for example, the agriculture and water resource sectors in Brazil.

Alves, Lincoln M.↗
NASA NTRS2007

Reconstructed Historical Land Cover and Biophysical Parameters for Studies of Land-Atmosphere Interactions within the Eastern United States

The local environment where we live within the Earth's biosphere is often taken for granted. This environment can vary depending on whether the land cover is a forest, grassland, wetland, water body, bare soil, pastureland, agricultural field, village, residential suburb, or an urban complex with concrete, asphalt, and large buildings. In general, the type and characteristics of land cover influence surface temperatures, sunlight exposure and duration, relative humidity, wind speed and direction, soil moisture amount, plant life, birds, and other wildlife in our backyards. The physical and biological properties (biophysical characteristics) of land cover help to determine our surface environment because they directly affect surface radiation, heat, and soil moisture processes, and also feedback to regional weather and climate. Depending on the spatial scale and land use intensity, land cover changes can have profound impacts on our local and regional environment. Over the past 350 years, the eastern half of the United States, an area extending from the grassland prairies of the Great Plains to the Gulf and Atlantic coasts, has experienced extensive land cover and land use changes that began with land clearing in the 1600s, led to extensive deforestation and intensive land use practices by 1920, and then evolved to the present-day landscape. Determining the consequences of such land cover changes on regional and global climate is a major research issue. Such research requires detailed historical land cover data and modeling experiments simulating historical climates. Given the need to understand the effects of historical land cover changes in the eastern United States, some questions include: - What were the most important land cover transformations and how did they alter biophysical characteristics of the land cover at key points in time since the mid-1600s? - How have land cover and land use changes over the past 350 years affected the land surface environment including surface weather, hydrologic, and climatic variability? - How do the potential effects of regional human-induced land cover change on the environment compare to similar changes that are caused by the natural variations of the Earth's climate system? To help answer these questions, we reconstructed a fractional land cover and biophysical parameter dataset for the eastern United States at 1650, 1850, 1920, and 1992 time-slices. Each land cover fraction is associated with a biophysical parameter class, a suite of parameters defining the biophysical characteristics of that kind of land cover. This new dataset is designed for use in computer models of land-atmosphere interactions, to understand and quantify the effects of historical land cover changes on the water, energy, and carbon cycles

Steyaert, Louis T.↗