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NASA NTRS1997

Comparison of Ice Cloud Particle Sizes Retrieved From Satellite Data Derived From In Situ Measurements

Cloud microphysical parameterizations have attracted a great deal of attention in recent years due to their effect on cloud radiative properties and cloud-related hydrological processes in large-scale models. The parameterization of cirrus particle size has been demonstrated as an indispensable component in the climate feedback analysis. Therefore, global-scale, long-term observations of cirrus particle sizes are required both as a basis of and as a validation of parameterizations for climate models. While there is a global scale, long-term survey of water cloud droplet sizes (Han et al. 1994), there is no comparable study for cirrus ice crystals. In this paper a near-global survey of cirrus ice crystal sizes is conducted using ISCCP satellite data analysis. The retrieval scheme uses phase functions based upon hexagonal crystals calculated by a ray tracing technique. The results show that global mean values of D(e) are about 60 micro-m. This study also investigates the possible reasons for the significant difference between satellite retrieved effective radii (approx. 60 micro-m) and aircraft measured particle sizes (approx. 200 micro-m) during the FIRE I IFO experiment. They are (1) vertical inhomogeneity of cirrus particle sizes; (2) lower limit of the instrument used in aircraft measurements; (3) different definitions of effective particle sizes; and (4) possible inappropriate phase functions used in satellite retrieval.

Han, Qingyuan↗

NASA NTRS1997

Comparison of Ice Cloud Particle Sizes Retrieved from Satellite Data Derived from In Situ Measurements

Han, Qingyuan↗

DOE OSTI2020

An overview of the Energy Modeling Forum 33rd study: Assessing large-scale global bioenergy deployment for managing climate change

Previous studies have projected a significant role for bioenergy in decarbonizing the global economy and helping realize international climate goals such as limiting global average warming to 2°C or 1.5°C. However, with significant variability in bioenergy results and significant concerns about potential environmental and social implications, greater transparency and dedicated assessment of the underlying modeling and results and more detailed understanding of the potential role of bioenergy are needed. Stanford University’s Energy Modeling Forum (EMF) initiated a 33rd study (EMF-33) to explore the viability of large-scale bioenergy as part of a comprehensive climate management strategy. This special issue presents the papers of the EMF-33 study—a multi-year inter-model comparison project designed to understand and assess global, long-run, biomass supply and bioenergy deployment potentials and related uncertainties. Using a novel scenario design with independent biomass supply and bioenergy demand protocols, EMF-33 separately elucidates and explores the modeling of biomass feedstock supplies and bioenergy technologies and their deployment—revealing, comparing, and assessing the modeling that is suggesting that bioenergy could be a key climate containment strategy. This introduction provides an overview of the EMF-33 study design and the overview, thematic, and individual modeling team papers and types of insights that make up this special issue. By providing enhanced transparency and new detailed insights, we hope to inform policy dialogue about the potential role of bioenergy and facilitate new research.

Rose, Steven K.↗

NASA NTRS2015

Technical Report Series on Global Modeling and Data Assimilation, Volume 43: Initial Evaluation of the Climate - MERRA-2

The years since the introduction of MERRA have seen numerous advances in the GEOS-5 Data Assimilation System as well as a substantial decrease in the number of observations that can be assimilated into the MERRA system. To allow continued data processing into the future, and to take advantage of several important innovations that could improve system performance, a decision was made to produce MERRA-2, an updated retrospective analysis of the full modern satellite era. One of the many advances in MERRA-2 is a constraint on the global dry mass balance; this allows the global changes in water by the analysis increment to be near zero, thereby minimizing abrupt global interannual variations due to changes in the observing system. In addition, MERRA-2 includes the assimilation of interactive aerosols into the system, a feature of the Earth system absent from previous reanalyses. Also, in an effort to improve land surface hydrology, observations-corrected precipitation forcing is used instead of model-generated precipitation. Overall, MERRA-2 takes advantage of numerous updates to the global modeling and data assimilation system. In this document, we summarize an initial evaluation of the climate in MERRA-2, from the surface to the stratosphere and from the tropics to the poles. Strengths and weaknesses of the MERRA-2 climate are accordingly emphasized.

MERRA-2↗

DOE OSTI2021

Examining the Climate Effects of a Regional Nuclear Weapons Exchange Using a Multiscale Atmospheric Modeling Approach.

Abstract not provided.

Wagman, Benjamin Moore↗

NASA NTRS2010

Improving Global Mass Flux Solutions from Gravity Recovery and Climate Experiment (GRACE) Through Forward Modeling and Continuous Time Correlation

We describe Earth's mass flux from April 2003 through November 2008 by deriving a time series of mas cons on a global 2deg x 2deg equal-area grid at 10 day intervals. We estimate the mass flux directly from K band range rate (KBRR) data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. Using regularized least squares, we take into account the underlying process dynamics through continuous space and time-correlated constraints. In addition, we place the mascon approach in the context of other filtering techniques, showing its equivalence to anisotropic, nonsymmetric filtering, least squares collocation, and Kalman smoothing. We produce mascon time series from KBRR data that have and have not been corrected (forward modeled) for hydrological processes and fmd that the former produce superior results in oceanic areas by minimizing signal leakage from strong sources on land. By exploiting the structure of the spatiotemporal constraints, we are able to use a much more efficient (in storage and computation) inversion algorithm based upon the conjugate gradient method. This allows us to apply continuous rather than piecewise continuous time-correlated constraints, which we show via global maps and comparisons with ocean-bottom pressure gauges, to produce time series with reduced random variance and full systematic signal. Finally, we present a preferred global model, a hybrid whose oceanic portions are derived using forward modeling of hydrology but whose land portions are not, and thus represent a pure GRACE-derived signal.

Sabaka, T. J.↗

DOE OSTI2020

The effect of atmospheric transmissivity on model and observational estimates of the sea ice albedo feedback

The sea-ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS) – how much the surface albedo in sea-ice regions changes as the planet warms– and the radiative sensitivity (RS) – how much the top of atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite based RS is within the model range of RS that differs by a factor of two across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS which, in conjunction with the satellite-based RS yields an SIAF of 0.16 ± 0.04 W m-2 K-1. This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model specific RS and IS and find a model mean feedback parameter of 0.37 W m-2 K-1 which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.

Arctic, Antarctic, climate change, Climate & Earth↗

DOE OSTI2024

The effect of temperature on photosystem II efficiency across plant functional types and climate

Modeling terrestrial gross primary productivity (GPP) is central to predicting the global carbon cycle. Much interest has been focused on the environmentally induced dynamics of photosystem energy partitioning and how improvements in the description of such dynamics assist the prediction of light reactions of photosynthesis and therefore GPP. The maximum quantum yield of photosystem II (Φ PSIImax ) is a key parameter of the light reactions that influence the electron transport rate needed for supporting the biochemical reactions of photosynthesis. Φ PSIImax is generally treated as a constant in biochemical photosynthetic models even though a constant Φ PSIImax is expected only for non-stressed plants. We synthesized reported Φ PSIImax values from pulse-amplitude-modulated fluorometry measurements in response to variable temperatures across the globe. We found that Φ PSIImax is strongly affected by prevailing temperature regimes with declined values in both hot and cold conditions. To understand the spatiotemporal variability in Φ PSIImax , we analyzed the temperature effect on Φ PSIImax across plant functional type (PFT) and habitat climatology. The analysis showed that temperature's impact on Φ PSIImax is shaped more by climate than by PFT for plants with broad latitudinal distributions or in regions with extreme temperature variability. There is a trade-off between the temperature range within which Φ PSIImax remains maximal and the overall rate of decline of Φ PSIImax outside the temperature range such that species cannot be simultaneously tolerant and resilient to extreme temperatures. Our study points to a quantitative approach for improving electron transport and photosynthetic productivity modeling under changing climates at regional and global scales.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Weak Hydrological Sensitivity to Temperature Change over Land, Independent of Climate Forcing

We present the global and regional hydrological sensitivity (HS) to surface temperature changes, for perturbations to CO2, CH4, sulfate and black carbon concentrations, and solar irradiance. Based on results from ten climate models, we show how modeled global mean precipitation increases by 2-3% per kelvin of global mean surface warming, independent of driver, when the effects of rapid adjustments are removed. Previously reported differences in response between drivers are therefore mainly ascribable to rapid atmospheric adjustment processes. All models show a sharp contrast in behavior over land and over ocean, with a strong surface temperature-driven (slow) ocean HS of 3-5%/K, while the slow land HS is only 0-2%/K. Separating the response into convective and large-scale cloud processes, we find larger inter-model differences, in particular over land regions. Large-scale precipitation changes are most relevant at high latitudes, while the equatorial HS is dominated by convective precipitation changes. Black carbon stands out as the driver with the largest inter-model slow HS variability, and also the strongest contrast between a weak land and strong sea response. We identify a particular need for model investigations and observational constraints on convective precipitation in the Arctic, and large-scale precipitation around the Equator.

Samset, B. H.↗

DOE OSTI2024

Integrated assessment modeling of a zero-emissions global transportation sector

Currently responsible for over one fifth of carbon emissions worldwide, the transportation sector will need to undergo a substantial technological transition to ensure compatibility with global climate goals. Few studies have modeled strategies to achieve zero emissions across all transportation modes, including aviation and shipping, alongside an integrated analysis of feedbacks on other sectors and environmental systems. Here, we use a global integrated assessment model to evaluate deep decarbonization scenarios for the transportation sector consistent with maintaining end-of-century warming below 1.5 °C, considering varied timelines for fossil fuel phase-out and implementation of advanced alternative technologies. We highlight the leading low carbon technologies for each transportation mode, finding that electrification contributes most to decarbonization across the sector. Biofuels and hydrogen are particularly important for aviation and shipping. Our most ambitious scenario eliminates transportation emissions by mid-century, contributing substantially to achieving climate targets but requiring rapid technological shifts with integrated impacts on fuel demands and availability and upstream energy transitions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

The Energy Modeling Forum (EMF)-30 Study on Short-Lived Climate Forcers: Introduction and Overview

The Energy Modeling Forum (EMF)-30 inter-comparison study on short-lived climate forcers (SLCFs) focuses on black carbon (BC) and methane, two of the most important warming SLCFs. The potential impact on climate change of methane and BC-focused emission reductions in the transport and residential sectors were quantified using nine integrated assessment models and a simple climate model. These SLCF reductions reduced global temperatures by 0.18–0.26 °C in 2050 across the models for central climate parameters, with methane being the dominant contributor. The incremental impact of SLCF-focused reductions is much smaller when combined with a broader climate policy. Individual modeling groups examined the health benefits of air pollutant reductions, climate policy co-benefits, and quantified market impacts from air pollutant damages. Model structure and set-up impact on results are discussed along with areas for future work.

Smith, Steven J.↗

NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗

NASA NTRS2012

A Robust Response of Precipitation to Global Warming from CMIP5 Models

How precipitation responds to global warming is a major concern to society and a challenge to climate change research. Based on analyses of rainfall probability distribution functions of 14 state-of-the-art climate models, we find a robust, canonical global rainfall response to a triple CO2 warming scenario, featuring 100 250% more heavy rain, 5-10% less moderate rain, and 10-15% more very light or no-rain events. Regionally, a majority of the models project a consistent response with more heavy rain events over climatologically wet regions of the deep tropics, and more dry events over subtropical and tropical land areas. Results suggest that increased CO2 emissions induce basic structural changes in global rain systems, increasing risks of severe floods and droughts in preferred geographic locations worldwide.

Lau, K. -M.↗

DOE OSTI2022

Important role of stratospheric injection height for the distribution and radiative forcing of smoke aerosol from the 2019–2020 Australian wildfires

More than 1 Tg smoke aerosol was emitted into the atmosphere by the exceptional 2019–2020 southeastern Australian wildfires. Triggered by the extreme fire heat, several deep pyroconvective events carried the smoke directly into the stratosphere. Once there, smoke aerosol remained airborne considerably longer than in lower atmospheric layers. The thick plumes traveled eastward, thereby being distributed across the high and mid-latitudes in the Southern Hemisphere, enhancing the atmospheric opacity. Due to the increased atmospheric lifetime of the smoke plume, its radiative effect increased compared to smoke that remains in lower altitudes. Global models describing aerosol-climate impacts lack adequate descriptions of the emission height of aerosols from intense wildfires. Here, we demonstrate, by a combination of aerosol-climate modeling and lidar observations, the importance of the representation of those high-altitude fire smoke layers for estimating the atmospheric energy budget. Through observation-based input into the simulations, the Australian wildfire emissions by pyroconvection are explicitly prescribed to the lower stratosphere in different scenarios. Based on our simulations, the 2019–2020 Australian fires caused a significant top-of-atmosphere (TOA) hemispheric instantaneous direct radiative forcing signal that reached a magnitude comparable to the radiative forcing induced by anthropogenic absorbing aerosol. Up to +0.50 W m -2 instantaneous direct radiative forcing was modeled at TOA, averaged for the Southern Hemisphere (+0.25 W m -2 globally) from January to March 2020 under all-sky conditions. At the surface, on the other hand, an instantaneous solar radiative forcing of up to -0.81 W m -2 was found for clear-sky conditions, with the respective estimates depending on the model configuration and subject to the model uncertainties in the smoke optical properties. Since extreme wildfires are expected to occur more frequently in the rapidly changing climate, our findings suggest that high-altitude wildfire plumes must be adequately considered in climate projections in order to obtain reasonable estimates of atmospheric energy budget changes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Wind Energy Forecasting with the Weather Research and Forecasting Model

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Siemens Energy, Inc. (Siemens) to develop a wind resource forecasting tool. LLNL was to develop an independent high-resolution mesoscale modeling capability forecasting tool that could be implemented in conjunction with existing wind farm control and monitoring software to provide forecasting of wind resources using local observations of winds and temperature. Research with LLNL’s state-of-the-art large-eddy simulation meteorological prediction model, based on the community WRF model and innovative turbulence parameterizations, would improve that model’s applicability to large wind farms offshore and in complex terrain. The modeling capability would include uncertainty quantification. Finally, the application of the modeling tool and existing global climate change predictions would enable the delineation of the likely effects of climate change on wind resources. Siemens was to provide high time resolution hub-height wind speed and other meteorological data streams, including temperature profiles from wind farms, for LLNL to incorporate into the modeling system, to validate and tune this forecasting model for their locations of interest. These data streams would also be used for longer-term studies of correlations of wind resources to climate oscillations to indicate how long-term climate change trends may affect the available wind resource. Siemens would also provide information and observations of turbine wakes for incorporation into the modeling tool. By implementing state-of-the-art turbulence parameterizations into a simulation model and/or ensembles of simulation models, and by integrating real-time hub height wind speed and other meteorological datastreams from wind farms into that model or ensemble of models, LLNL would develop a forecasting tool that could be implemented by Siemens as an add-on to existing wind farm control and monitoring software to provide owners with useful resource forecasting. The desired outcome was that the accuracy level of the output would be sufficient to substantiate power output commitments. The final deliverable for this work would consist of a document outlining the algorithms and software tools that could be integrated into Siemens Wind Park Supervisor.

17 WIND ENERGY↗

DOE OSTI2020

Validation and Sensitivity Analysis of a 1-D Lake Model Across Global Lakes

Lakes have important influence on weather and climate from local to global scales. However, their prediction using numerical models is notoriously difficult because lakes are highly heterogeneous across the globe, but observations are sparse. In this study, we assessed the performance of a 1-D lake model in simulating the thermal structures of 58 lakes with diverse morphometric and geographic characteristics by following the phase 2a local lake protocol of the Inter-sectoral Impact Model Intercomparison Project (ISIMIP2a). After calibration, the root-mean-square errors (RMSE) were below 2 °C for 70% and 75% of the lakes for epilimnion and full-profile temperature simulations, with an average of 1.71 °C and 1.43 °C, respectively. The model performance mainly depended on lake shape rather than location, supporting the possibility of grouping model parameters by lake shape for global applications. Furthermore, through machine-learning based parameter sensitivity tests, we identified turbulent heat fluxes, wind-driven mixing and water transparency as the major processes controlling lake thermal and mixing regimes. Snow density was also important for modeling the ice phenology of high-latitude lakes. The relative influence of the key processes and the corresponding parameters mainly depended on lake latitude and depth. Turbulent heat fluxes showed a decreasing importance in affecting epilimnion temperature with increasing latitude. Wind-driven mixing was less influential to lake stratification for deeper lakes while the impact of light extinction, on the contrary, showed a positive correlation with depth. Our findings may guide improvements in 1-D lake model parameterizations to achieve higher fidelity in simulating global lake thermal dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Potential long-term, global effects of enhancing the domestic terrestrial carbon sink in the United States through no-till and cover cropping

Abstract Background Achieving a net zero greenhouse gas United States (US) economy is likely to require both deep sectoral mitigation and additional carbon dioxide removals to offset hard-to-abate emissions. Enhancing the terrestrial carbon sink, through practices such as the adoption of no-till and cover cropping agricultural management, could provide a portion of these required offsets. Changing domestic agricultural practices to optimize carbon content, however, might reduce or shift US agricultural commodity outputs and exports, with potential implications on respective global markets and land use patterns. Here, we use an integrated energy-economy-land-climate model to comprehensively assess the global land, trade, and emissions impacts of an adoption of domestic no-till farming and cover cropping practices based on carbon pricing. Results We find that the adoption of these practices varies depending on which aspects of terrestrial carbon are valued. Valuation of all terrestrial carbon resulted in afforestation at the expense of domestic agricultural production. In contrast, a policy valuing soil carbon in agricultural systems specifically indicates strong adoption of no-till and cover cropping for key crops. Conclusions We conclude that under targeted terrestrial carbon incentives, adoption of no-till and cover cropping practices in the US could increase the terrestrial carbon sink with limited effects on crop availability for food and fodder markets. Future work should consider integrated assessment modeling of non-CO 2 greenhouse gas impacts, above ground carbon storage changes, and capital and operating cost considerations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Snow-Cover Variability in North America in the 2000-2001 Winter as Determined from MODIS Snow Products

Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover maps have been available since September 13, 2000. These products, at 500-m spatial resolution, are available through the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado. By the 2001-02 winter, 5-km climate-modeling grid (CMG) products will be available for presentation of global views of snow cover and for use in climate models. All MODIS snow-cover products are produced from automated algorithms that map snow in an objective manner. In this paper, we describe the MODIS snow products, and show snow maps from the fall of 2000 in North America.

Hall, Dorothy K.↗