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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Future Increases in Amazonia Water 1 Stress from CO2 Physiology and Deforestation

Several different drivers are contributing to climate change within the Amazon basin, including forcing from greenhouse gases and aerosols, plant physiology responses to rising CO 2 , and deforestation. Attribution among these drivers has not been quantified for Shared Socioeconomic Pathway (SSP) climate simulations. Here we identify the contribution of CO 2 physiology and deforestation to future hydroclimate change in the Amazon basin by combining information from four experiments and eight different Earth system models in Coupled Model Intercomparison Project Phase 6. Together, forcing from CO 2 physiology and deforestation account for about 44% of the projected annual precipitation decline, 48% of surface relative humidity decline and 11% of warming over the Amazon basin by 2100 for SSP3-7.0. Other Coupled Model Intercomparison Project Phase 6 SSP simulations have similar contributions from the two drivers. Insight from our attribution analysis can aid in identifying research priorities aimed at reducing uncertainty in future projections of water availability, carbon dynamics and wildfire risk.

Yue Li↗
NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗
NASA NTRS2011

Climate Forcing Reconstructions for Use in PMIP Simulations of the Last Millennium (v1.0)

Simulations of climate over the Last Millennium (850-1850 CE) have been incorporated into the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3). The drivers of climate over this period are chiefly orbital, solar, volcanic, changes in land use/land cover and some variation in greenhouse gas levels. While some of these effects can be easily defined, the reconstructions of solar, volcanic and land use-related forcing are more uncertain. We describe here the approach taken in defining the scenarios used in PMIP3, document the forcing reconstructions and discuss likely implications.

volcanoes↗
NASA NTRS2013

Long-term Ozone Changes and Associated Climate Impacts in CMIP5 Simulations

Ozone changes and associated climate impacts in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations are analyzed over the historical (1960-2005) and future (2006-2100) period under four Representative Concentration Pathways (RCP). In contrast to CMIP3, where half of the models prescribed constant stratospheric ozone, CMIP5 models all consider past ozone depletion and future ozone recovery. Multimodel mean climatologies and long-term changes in total and tropospheric column ozone calculated from CMIP5 models with either interactive or prescribed ozone are in reasonable agreement with observations. However, some large deviations from observations exist for individual models with interactive chemistry, and these models are excluded in the projections. Stratospheric ozone projections forced with a single halogen, but four greenhouse gas (GHG) scenarios show largest differences in the northern midlatitudes and in the Arctic in spring (approximately 20 and 40 Dobson units (DU) by 2100, respectively). By 2050, these differences are much smaller and negligible over Antarctica in austral spring. Differences in future tropospheric column ozone are mainly caused by differences in methane concentrations and stratospheric input, leading to approximately 10DU increases compared to 2000 in RCP 8.5. Large variations in stratospheric ozone particularly in CMIP5 models with interactive chemistry drive correspondingly large variations in lower stratospheric temperature trends. The results also illustrate that future Southern Hemisphere summertime circulation changes are controlled by both the ozone recovery rate and the rate of GHG increases, emphasizing the importance of simulating and taking into account ozone forcings when examining future climate projections.

tropospheric ozone↗
NASA NTRS1997

An Estimation of the Climatic Effects of Stratospheric Ozone Losses during the 1980s

In order to study the potential climatic effects of the ozone hole more directly and to assess the validity of previous lower resolution model results, the latest high spatial resolution version of the Atmospheric and Environmental Research, Inc., seasonal radiative dynamical climate model is used to simulate the climatic effects of ozone changes relative to the other greenhouse gases. The steady-state climatic effect of a sustained decrease in lower stratospheric ozone, similar in magnitude to the observed 1979-90 decrease, is estimated by comparing three steady-state climate simulations: 1) 1979 greenhouse gas concentrations and 1979 ozone, II) 1990 greenhouse gas concentrations with 1979 ozone, and III) 1990 greenhouse gas concentrations with 1990 ozone. The simulated increase in surface air temperature resulting from nonozone greenhouse gases is 0.272 K. When changes in lower stratospheric ozone are included, the greenhouse warming is 0.165 K, which is approximately 39% lower than when ozone is fixed at the 1979 concentrations. Ozone perturbations at high latitudes result in a cooling of the surface-troposphere system that is greater (by a factor of 2.8) than that estimated from the change in radiative forcing resulting from ozone depiction and the model's 2 x CO, climate sensitivity. The results suggest that changes in meridional heat transport from low to high latitudes combined with the decrease in the infrared opacity of the lower stratosphere are very important in determining the steady-state response to high latitude ozone losses. The 39% compensation in greenhouse warming resulting from lower stratospheric ozone losses is also larger than the 28% compensation simulated previously by the lower resolution model. The higher resolution model is able to resolve the high latitude features of the assumed ozone perturbation, which are important in determining the overall climate sensitivity to these perturbations.

MacKay, Robert M.↗
NASA NTRS1991

Springtime soil moisture, natural climatic variability, and North American drought as simulated by the NCAR Community Climate Model 1

Previous results concerning the role that summertime soil moisture reductions can play in amplifying or maintaining North American droughts are extended to include the role of springtime soil moisture reductions and the role that natural climatic variability, as expressed in soil moisture, can play. General circulation model (GCM) simulations with the NCAR Community Climate Model have been made with initial desert-like soil moisture anomalies imposed on 1 May and on 1 March. The May simulation maintained the imposed anomaly throughout the summer, while in the March simulation the anomaly was ameliorated within one month. Thus, the timing of soil moisture reductions may be crucial. A 10-year model control integration with prescribed sea surface temperatures yielded 1 year with late spring and summer soil moisture values similar to those of the 1 May anomaly simulation. This suggests that occasional widespread North American droughts may be an inherent feature of at least the GCM employed for this study. The results also demonstrate the important role played by moisture transport from the Gulf of Mexico in modulating or ameliorating drought conditions for much of the south-central United States, a topic that requires considerable further investigation.

Oglesby, Robert J.↗
NASA NTRS2022

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km^2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

soil moisture↗
NASA NTRS1981

Global and local fluctuations of winter and summer simulations with the GLAS climate model

Winter and summer simulations were carried out with an improved version of the GLAS general circulation model. An improved method of computing the boundary layer fluxes, and a more realistic specification of the albedo of snow and ice covered surfaces were used. Each particular diagnostic quantity was computed from the model data and each of the 15 years of observations in precisely the same way, wherever possible. The reported observational results are averaged over the 15 winters or summers, as appropriate.

Straus, D. M.↗
NASA NTRS2013

Historical and Future Black Carbon Deposition on the Three Ice Caps: Ice Core Measurements and Model Simulations from 1850 to 2100

Ice core measurements in conjunction with climate model simulations are of tremendous value when examining anthropogenic and natural aerosol loads and their role in past and future climates. Refractory black carbon (BC) records from the Arctic, the Antarctic, and the Himalayas are analyzed using three transient climate simulations performed with the Goddard Institute for Space Studies ModelE. Simulations differ in aerosol schemes (bulk aerosols vs. aerosol microphysics) and ocean couplings (fully coupled vs. prescribed ocean). Regional analyses for past (1850-2005) and future (2005-2100) carbonaceous aerosol simulations focus on the Antarctic, Greenland, and the Himalayas. Measurements from locations in the Antarctic show clean conditions with no detectable trend over the past 150 years. Historical atmospheric deposition of BC and sulfur in Greenland shows strong trends and is primarily influenced by emissions from early twentieth century agricultural and domestic practices. Models fail to reproduce observations of a sharp eightfold BC increase in Greenland at the beginning of the twentieth century that could be due to the only threefold increase in the North American emission inventory. BC deposition in Greenland is about 10 times greater than in Antarctica and 10 times less than in Tibet. The Himalayas show the most complicated transport patterns, due to the complex terrain and dynamical regimes of this region. Projections of future climate based on the four CMIP5 Representative Concentration Pathways indicate further dramatic advances of pollution to the Tibetan Plateau along with decreasing BC deposition fluxes in Greenland and the Antarctic.

Antarctic Regions↗
NASA NTRS2018

Seasonal Monitoring of Melt and Accumulation Within the Deep Percolation Zone of the Greenland Ice Sheet and Comparison with Simulations of Regional Climate Modeling

Increasing melt over the Greenland Ice Sheet (GrIS) recorded over the past several years has resulted in significant changes of the percolation regime of the ice sheet. It remains unclear whether Greenland's percolation zone will act as a meltwater buffer in the near future through gradually filling all pore space or if near-surface refreezing causes the formation of impermeable layers, which provoke lateral runoff. Homogeneous ice layers within perennial firn, as well as near-surface ice layers of several meter thickness have been observed in firn cores. Because firn coring is a destructive method, deriving stratigraphic changes in firn and allocation of summer melt events is challenging. To overcome this deficit and provide continuous data for model evaluations on snow and firn density, temporal changes in liquid water content and depths of water infiltration, we installed an upward-looking radar system (upGPR) 3.4 m below the snow surface in May 2016 close to Camp Raven (66.4779 deg N, 46.2856 deg W) at 2120 m a.s.l. The radar is capable of quasi-continuously monitoring changes in snow and firn stratigraphy, which occur above the antennas. For summer 2016, we observed four major melt events, which routed liquid water into various depths beneath the surface. The last event in mid-August resulted in the deepest percolation down to about 2.3 m beneath the surface. Comparisons with simulations from the regional climate model MAR are in very good agreement in terms of seasonal changes in accumulation and timing of onset of melt. However, neither bulk density of near-surface layers nor the amounts of liquid water and percolation depths predicted by MAR correspond with upGPR data. Radar data and records of a nearby thermistor string, in contrast, matched very well for both timing and depth of temperature changes and observed water percolations. All four melt events transferred a cumulative mass of 56 kg/(sq. m) into firn beneath the summer surface of 2015. We find that continuous observations of liquid water content, percolation depths and rates for the seasonal mass fluxes are sufficiently accurate to provide valuable information for validation of model approaches and help to develop a better understanding of liquid water retention and percolation in perennial firn.

Heilig, Achim↗
NASA NTRS2015

Simulated Impacts of Climate Change on Water Use and Yield of Irrigated Sugarcane in South Africa

Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated sugarcane production sites in South Africa (Malelane, Pongola and La Mercy) for current (1980-2010) and future (2070-2100) climate scenarios, using an approach based on the Agricultural Model Inter-comparison and Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating climate change impacts on sugarcane production. Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models (GCMs) assuming atmospheric CO2 concentration [CO2] of 734 ppm(A2 emissions scenario). Yield and water use were simulated using the DSSAT-Canegro v4.5 model. Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected to decline because of increased consumption of photo-assimilate for structural growth and maintenance respiration. Crop responses in canopy development and yield formation differed markedly between the crop cycles investigated. Possible agronomic implications of these results include reduced weed control costs due to shortened periods of partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to ripening and harvest practices to counter decreased cane quality and optimize productivity. Although the Delta climate data downscaling method is considered robust, accurate and easily-understood, it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications ought to be explored in future work. Shortcomings of the DSSAT-Canegro model include the simulated responses of phenological development, photosynthesis and respiration processes to high temperatures, and the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology refinements should improve the reliability of predicted climate change impacts on sugarcane yield.

Climate change↗
NASA NTRS2010

Using Transport Diagnostics to Understand Chemistry Climate Model Ozone Simulations

We demonstrate how observations of N2O and mean age in the tropical and midlatitude lower stratosphere (LS) can be used to identify realistic transport in models. The results are applied to 15 Chemistry Climate Models (CCMs) participating in the 2010 WMO assessment. Comparison of the observed and simulated N2O/mean age relationship identifies models with fast or slow circulations and reveals details of model ascent and tropical isolation. The use of this process-oriented N2O/mean age diagnostic identifies models with compensating transport deficiencies that produce fortuitous agreement with mean age. We compare the diagnosed model transport behavior with a model's ability to produce realistic LS O3 profiles in the tropics and midlatitudes. Models with the greatest tropical transport problems show the poorest agreement with observations. Models with the most realistic LS transport agree more closely with LS observations and each other. We incorporate the results of the chemistry evaluations in the SPARC CCMVal Report (2010) to explain the range of CCM predictions for the return-to-1980 dates for global (60 S-60 N) and Antarctic column ozone. Later (earlier) Antarctic return dates are generally correlated to higher (lower) vortex Cl(sub y) levels in the LS, and vortex Cl(sub y) is generally correlated with the model's circulation although model Cl(sub y) chemistry or Cl(sub y) conservation can have a significant effect. In both regions, models that have good LS transport produce a smaller range of predictions for the return-to-1980 ozone values. This study suggests that the current range of predicted return dates is unnecessarily large due to identifiable model transport deficiencies.

Strahan, S. E.↗
NASA NTRS2013

Influences of Regional Climate Change on Air Quality Across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations

Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture regional-scale changes in temperatures and precipitation. We use a regional climate model (RCM) to dynamically downscale the GCM's large-scale signal to investigate the changes in regional and local extremes of temperature and precipitation that may result from a changing climate. In this paper, we show preliminary results from downscaling the NASA/GISS ModelE IPCC AR5 Representative Concentration Pathway (RCP) 6.0 scenario. We use the Weather Research and Forecasting (WRF) model as the RCM to downscale decadal time slices (1995-2005 and 2025-2035) and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0. The regional climate change scenario is further processed using the Community Multiscale Air Quality modeling system to explore influences of regional climate change on air quality.

Air quality control↗
NASA NTRS1994

Simulation of the modern arctic climate by the NCAR CCM1

The National Center of Atmospheric Research (NCAR) Community Climate Model Version 1 (CCM1's) simulation of the modern arctic climate is evaluated by comparing a five-year seasonal cycle simulation with the European Center for Medium-Range Weather Forecasts (ECMWF) global analyses. The sea level pressure (SLP), storm tracks, vertical cross section of height, 500-hPa height, total energy budget, and moisture budget are analyzed to investigate the biases in the simulated arctic climate. The results show that the model simulates anomalously low SLP, too much storm activity, and anomalously strong baroclinicity to the west of Greenland and vice versa to the east of Greenland. This bias is mainly attributed to the model's topographic representation of Greenland. First, the broadened Greenland topography in the model distorts the path of cyclone waves over the North Atlantic Ocean. Second, the model oversimulates the ridge over Greenland, which intensifies its blocking effect and steers the cyclone waves clockwise around it and hence produces an artificial circum-Greenland trough. These biases are significantly alleviated when the horizontal resolution increases to T42. Over the Arctic basin, the model simulates large amounts of low-level (stratus) clouds in winter and almost no stratus in summer, which is opposite to the observations. This bias is mainly due to the location of the simulated SLP features and the negative anomaly of storm activity, which prevent the transport of moisture into this region during summer but favor this transport in winter. The moisture budget analysis shows that the model's net annual precipitation (P-E) between 70 deg N and the North Pole is 6.6 times larger than the observations and the model transports six times more moisture into this region. The bias in the advection term is attributed to the positive moisture fixer scheme and the distorted flow pattern. However, the excessive moisture transport into the Arctic basin does not solely result from the advection term. The contribution by the moisture fixer is as large as from advection. By contrast, the semi-Lagrangian transport scheme used in the CCM2 significantly improves the moisture simulation for this region; however, globally the error is as serious as for the positive moisture fixer scheme. Finally, because the model has such serious problems in simulating the present arctic climate, its simulations of past and future climate change for this region are questionable.

Bromwich, David H.↗
NASA NTRS2013

Uncertainty in Simulating Wheat Yields Under Climate Change

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

exhaust gases↗
NASA NTRS2020

The Impact of Planetary Rotation Rate on the Reflectance and Thermal Emission Spectrum of Terrestrial Exoplanets around Sunlike Stars

Robust atmospheric and radiative transfer modeling will be required to properly interpret reflected-light and thermal emission spectra of terrestrial exoplanets. This will help break observational degeneracies between the numerous atmospheric, planetary, and stellar factors that drive planetary climate. Here, we simulate the climates of earthlike worlds around the Sun with increasingly slow rotation periods, from earthlike to fully Sun-synchronous, using the ROCKE-3D general circulation model. We then provide these results as input to the Spectral Planet Model, which employs the Spectral Mapping Atmospheric Radiative Transfer model to simulate the spectra of a planet as it would be observed from a future space-based telescope. We find that the primary observable effects of slowing planetary rotation rate are the altered cloud distributions, altitudes, and opacities that subsequently drive many changes to the spectra by altering the absorption band depths of biologically relevant gas species(e.g.,HO2,O2, andO3). We also identify a potentially diagnostic feature of synchronously rotating worlds in mid-infraredHO2absorption/emission lines.

Exoplanet atmospheres↗
NASA NTRS2020

The Impact of Planetary Rotation Rate on the Reflectance and Thermal Emission Spectrum of Terrestrial Exoplanets Around Sun-L ike Stars

Robust atmospheric and radiative transfer modeling will be required to properly interpret reflected light and thermal emission spectra of terrestrial exoplanets. This will help break observational degeneracies between the numerous atmospheric, planetary, and stellar factors that drive planetary climate. Here we simulate the climates of Earth-like worlds around the Sun with increasingly slow rotation periods, from Earth-like to fully Sun-synchronous, using the ROCKE-3D general circulation model. We then provide these results as input to the Spectral Planet Model (SPM), which employs the SMART radiative transfer model to simulate the spectra of a planet as it would be observed from a future space-based telescope. We find that the primary observable effects of slowing planetary rotation rate are the altered cloud distributions, altitudes, and opacities which subsequently drive many changes to the spectra by altering the absorption band depths of biologically-relevant gas species (e.g., H2O, O2, and O3). We also identify a potentially diagnostic feature of synchronously rotating worlds in mid-infrared H2O absorption/emission lines.

Scott D Guzewich↗
NASA NTRS1993

Multiannual simulations with the Mars climate model

The martian atmospheric circulaton for 10 Mars years was simulated using a simplified three-dimensional climate model that is briefly described below. Analysis of these simulations reveals several interesting behaviors not previously recognized in other three-dimensional models. The first concerns the potential for interannual variability in the martian climate system. It is now known from the aperiodic frequency of planet-encircling storms, as well as differences in atmospheric water vapor from one year to the next, that the real planet exhibits interannual variations. The results of our simulations also show significant interannual variability and, interestingly, that variability maximizes during southern spring, the dust storm season. A second and related finding is that transient eddies in the southern hemisphere during winter are indeed weaker than their northern hemisphere counterparts as was found in more sophisticated models. However, southern hemisphere eddy activity rises dramatically during spring and is greater than that in the northern hemisphere at any season.

Haberle, R. M.↗