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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 55 records · Page 3

Changes in high-latitude surface energy balance driven by snowpack and vegetation dynamics under warmer climate

With rapid climate warming, expected changes in snowpack and vegetation will alter the seasonal surface albedo of high-latitude ecosystems. The extent to which these albedo changes may affect surface energy balances and thus soil temperatures is uncertain, but represents a potentially important component of ecosystem feedbacks to climate change. Here, we apply a well-tested process-rich ecosystem model, ecosys , to examine changes in seasonal surface albedo and soil temperature driven by climate-induced snowpack and vegetation changes across Alaska under a warmer twenty-first century climate. Under the Representative Concentration Pathway 8.5 climate change scenario, the modeled changes in surface albedo exhibited large seasonal and spatial variations. We found spring albedo decreases driven by increases in snow-free periods (>20 d) and an extended growing season length that resulted in greater gains in leaf area index (LAI) in most parts of Alaska. In contrast, we modeled increases in summer and winter albedo (despite modeled increases in LAI) across much of the boreal forest due to an increased proportion of aspen, which has a higher leaf albedo than the currently dominant black spruce. Modeled latent heat fluxes generally increase across the twenty-first century, particularly during the spring and summer. Overall, climate warming and changes in surface energy fluxes resulted in a 3.5 ± 0.50 °C increase in spatial- and annual-averaged top 10 cm surface soil temperatures across Alaskan ecosystems by the year 2100, with larger increases in tundra than boreal forest regions. We conclude that under warmer climates, seasonal variations in albedo and surface energy fluxes are particularly pronounced during the spring and summer, driven by changes in snowpack and vegetation dynamics.

54 ENVIRONMENTAL SCIENCES↗

Understanding Differences in California Climate Projections Produced by Dynamical and Statistical Downscaling

Abstract We compare historical and end‐of‐century temperature and precipitation patterns over California from one dynamically downscaled simulation using the Weather Research and Forecast (WRF) model and two simulations statistically downscaled using Localized Constructed Analogs (LOCA). We uniquely separate causes of differences between dynamically and statistically based future climate projections into differences in historical climate (gridded observations versus regional climate model output) and differences in how these downscaling techniques explicitly handle future climate changes (numerical modeling versus analogs). In these methods, solutions between different downscaling techniques differ more in the future compared to the historical period. Changes projected by LOCA are insensitive to the choice of driving data. Only through dynamical downscaling can we simulate physically consistent regional springtime warming patterns across the Sierra Nevada, while the statistical simulations inherit an unphysical signal from their parent Global Climate Model (GCM) or gridded data. The results of our study clarify why these different techniques produce different outcomes and may also provide guidance on which downscaled products to use for certain impact analyses in California and perhaps other Mediterranean regimes.

54 ENVIRONMENTAL SCIENCES↗

Dynamics of Water, Climate, and Infrastructure

Climate and its impacts on the natural environment, and on the ability of the natural environment to support population and the built environment, stands as a threat multiplier that impacts national and global security. The Water Intersections with Climate Systems Security (WICSS) Strategic Initiative is designed to improve understanding of water’s role in, among other topics, the connection of critical infrastructure to climate in light of competing national and global security interests (including transboundary issues and stability), and identifying research gaps aligned with Sandia, and Federal agency priorities. With this impetus in mind, the WICSS Strategic Initiative team conceptualized a causal loop diagram (CLD) of the relationship between and among climate, the natural environment, population, and the built environment, with an understanding that any such regionally focused system must have externalities that influence the system from beyond its’ control, and metrics for better understanding the consequences of the set of interactions. These are discussed in light of a series of worldviews that focus on portions of the overall systems relationship. The relationships are described and documented in detail. A set of reinforcing and balancing loops are then highlighted within the context of the model. Finally, forward-looking actions are highlighted to describe how this conceptual model can be turned into modeling to address multiple problems described under the purview of the Strategic Initiative.

54 ENVIRONMENTAL SCIENCES↗

Hydrostatic and Non‐Hydrostatic Baroclinic Instability in the Dynamical Core of the DOE Global Climate Model

Abstract The dynamical core of the Department of Energy global climate model is used to understand the role of non‐hydrostatic dynamics in the simulation of dry and moist baroclinic waves. To reduce computational cost, the Diabatic Acceleration and REscaling approach is adopted. Scale analysis and numerical simulations suggest that the model solution is not distorted by the change of spherical metric terms due to the change of Earth radius, neither are the inter‐scale interactions strongly altered as indicated by the analysis of spectral flux. Compared with hydrostatic simulations, the onset of baroclinic instability is delayed under non‐hydrostatic dynamics as the associated weaker vertical motions tend to increase the critical wavelength and narrow the range of waves that can be baroclinically unstable. During the development of baroclinic waves, non‐hydrostatic dynamics tends to induce vertical motions in the upper troposphere, accelerating the eastward propagation of upper‐level ridges through their impact on local vorticity tendency. These processes reduce the westward tilt of the vertical ridge axes and suppress the conversion of mean flow available potential energy to eddy kinetic energy, leading to weaker baroclinic eddies than in hydrostatic simulations. The contrasts between hydrostatic and non‐hydrostatic settings hold in supplemental experiments that vary the background flow Rossby number, the amount of water vapor content and vertical resolution. We also find that mesoscale and smaller‐scale activities can be considerably under‐represented when the vertical resolution is limited to that typically used in global climate models.

54 ENVIRONMENTAL SCIENCES↗

Impact of a dynamic grid mix and climate on operational carbon emissions modeling for different building typologies and climate zones

Calculating operational carbon emissions through a building’s lifecycle is complex due to the dynamic nature of influencing factors such as climate and energy grid mix. This paper introduces a novel methodology for modeling 30-year operational carbon impacts of buildings and applies this method to mid-rise office and residential typologies across various US climate zones. The method accounts for these temporal variabilities using new and scarcely cited data sources. Key findings indicate that future changes in the climate, while impactful, play a relatively modest role in operational carbon emissions compared to significant reductions with modeling scenarios using the projected decarbonization of the electricity grid. Here, the study also finds that using annual, month-hourly, or hourly grid emission factors have a minimal impact on carbon accounting, except in certain climates and program types where emission patterns do not align with a building’s energy consumption. Warmer climates like Miami, Florida and Tucson, Arizona, which rely heavily on cooling, demonstrate larger variations in carbon emissions when using higher temporal resolution emission factors. Ultimately, this study underscores the critical role of grid decarbonization in reducing long-term emissions and the importance of incorporating this variable in life cycle assessment (LCA) modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Evolving CO 2 rather than SST leads to a factor of ten decrease in GCM convergence time

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO 2 mixing ratio to equilibrate climate, has been implemented in a cloud resolving model. In this paper, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO 2 for SSTs ranging from 290 K to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

54 ENVIRONMENTAL SCIENCES↗

The Rate of Canopy Development Modulates the Link Between the Timing of Spring Leaf Emergence and Summer Moisture

Shifts in phenological timing have important implications for ecosystem processes, with spring leaf emergence as a dominant control of carbon, water, and energy cycling. Phenological events are predominantly determined by weather and climate, therefore dynamic in time and sensitive to climate feedbacks. Improving our understanding of how ecosystems respond to changes in phenological timing will enhance our ability to assess summer soil water availability, since the timing of spring leaf emergence may lead to soil moisture deficits later in the growing season. We leveraged data from five AmeriFlux towers in central and eastern United States to investigate the extent spring leaf emergence (i.e., start of spring, SoS ) influences rates at which forest canopies develop and how this impacts summer soil moisture ( θ JJA ) variability. Our results indicate that ecosystem processes, specifically gross primary production ( GPP ) and evapotranspiration ( ET ), exhibit compensatory responses to varying leaf emergence; with delayed spring-onset, the canopy developed more quickly, resulting in rapid GPP and ET increases, consistent across sites. Nonetheless, early SoS is a relatively good indicator for potential summer soil water deficits, particularly when it occurs together with meteorological conditions (i.e., lower-than-average precipitation, hot summer temperatures) that contribute to soil water deficits. When these meteorological conditions coincide with early SoS , θ JJA deficits are exacerbated. To the extent that these extreme conditions occur more frequently under future climate scenarios, the dynamics of spring phenology and hydroclimate may play an increasingly important role in portending the likelihood of summer water deficits, which are projected to become more severe.

54 ENVIRONMENTAL SCIENCES↗

A convection-permitting dynamically downscaled dataset over the Midwestern United States

Climate change is expected to have far-reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse-resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24-hr reinitialization strategy and a 12-hr spin-up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40-year Reanalysis project (NCEP) using a modified quantile mapping method. Bias-corrected 2-m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine-scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high-resolution climate dataset could be used for down-stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health.

54 ENVIRONMENTAL SCIENCES↗

Changes in the SST Seasonal Cycle in a Warmer North Pacific without Ocean Dynamical Feedbacks

Climate models project a significant intensification of the sea surface temperature (SST) seasonal cycle over the subpolar North Pacific due to global warming, with the shallower mixed layer widely recognized as the dominant factor. However, employing slab ocean experiments with only ocean–atmosphere thermal coupling, we find a substantial contribution from changes in surface heat flux to this seasonal cycle intensification. In particular, the stronger Newtonian cooling effect in winter acts as a more potent damping than in summer. This differential damping inhibits the warming in colder seasons, significantly contributing to the intensified SST seasonal cycle in the subpolar North Pacific. In addition, consistent phase shifts in the North Pacific are identified across CMIP6 models. In the northwest North Pacific, a phase advance is associated with anomalous heating in early spring, driven by enhanced warm atmospheric advection from lower latitudes and sea ice melting in marginal seas. In contrast, the southeast North Pacific exhibits a phase delay attributed to the anomalous cooling in spring relative to autumn. This cooling is due to weakened trade winds and increased presence of high clouds. In conclusion, the former leads to stronger evaporative cooling in spring, while the latter impedes shortwave radiation from reaching the ocean.

54 ENVIRONMENTAL SCIENCES↗

Fusing time-varying mosquito data and continuous mosquito population dynamics models

Climate change is arguably one of the most pressing issues affecting the world today and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under climate change scenarios. Many current models for disease transmission are continuous and autonomous, while mosquito data is discrete and varies both within and between seasons. This study uses an optimization framework to fit a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region, and a variance-based sensitivity analysis highlights the influence each parameter has on the peak magnitude and timing of the mosquito season. This method can easily extend to other geographic regions and be integrated into a larger disease transmission model. This method addresses the ongoing challenges of data and model fusion by serving as a link between discrete time-series data and continuous differential equations for mosquito-borne epidemiology models.

97 MATHEMATICS AND COMPUTING↗

Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

Abstract The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

54 ENVIRONMENTAL SCIENCES↗

Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region

Downscaling of global climate model (GCMs) simulations is a key element of regional-to-local-scale climate change projections that can inform impact assessments, long-term planning, and resource management in different sectors. Here, we conduct an intercomparison between statistically and dynamically downscaled GCMs simulations using the hybrid delta (HD) and the Weather Research and Forecast (WRF) Model, respectively, over the Midwest and Great Lakes region to 1) validate their performance in reproducing extreme daily precipitation (P) and daily maximum temperature (T max ) for summer and winter and 2) evaluate projections of extremes in the future. Our results show the HD statistical downscaling approach, which includes large-scale bias correction of GCM inputs, can reproduce observed extreme P and T max reasonably well for both summer and winter. However, raw historical WRF simulations show significant bias in both extreme P and T max for both seasons. Interestingly, the convection-permitting WRF simulation at 4-km grid spacing does not produce better results for seasonal extremes than the WRF simulation at 12 km using a parameterized convection scheme. Despite a broad similarity for winter extreme P projections, the projected changes in the future summer storms are quite different between downscaling methods; WRF simulations show substantial increases in summer extreme precipitation, while the changes projected by the HD approach exhibit moderate decreases overall. The WRF simulations at 4 km also show a pronounced decoupling effect between seasonal totals and extreme daily P for summer, which suggests that there could be more intense summer extremes at two different time scales, with more severe individual convective storms combined with longer summer droughts at the end of the twenty-first century.

54 ENVIRONMENTAL SCIENCES↗

Removing the Effects of Tropical Dynamics from North Pacific Climate Variability

Teleconnections from the Tropics energize variations of the North Pacific climate, but detailed diagnosis of this relationship has proven difficult. Simple univariate methods, such as regression on El Niño-Southern Oscillation (ENSO) indices, may be inadequate since the key dynamical processes involved -- including ENSO diversity in the Tropics, re-emergence of mixed layer thermal anomalies, and oceanic Rossby wave propagation in the North Pacific -- have a variety of overlapping spatial and temporal scales. Here we use a multivariate Linear Inverse Model to quantify tropical and extra-tropical multi-scale dynamical contributions to North Pacific variability, in both observations and CMIP6 models. In observations, we find that the Tropics are responsible for almost half of the seasonal variance, and almost three quarters of the decadal variance, along the North American coast and within the subtropical front region northwest of Hawaii. SST anomalies that are generated by local dynamics within the Northeast Pacific have much shorter time scales, consistent with transient weather forcing by Aleutian low anomalies. Variability within the Kuroshio-Oyashio Extension (KOE) region is considerably less impacted by the Tropics, on all time scales. Consequently, without tropical forcing the dominant pattern of North Pacific variability would be a KOE pattern, rather than the Pacific Decadal Oscillation (PDO). In contrast to observations, most CMIP6 historical simulations produce North Pacific variability that maximizes in the KOE region, with amplitude significantly higher than observed. Correspondingly, the simulated North Pacific in all CMIP6 models is shown to be relatively insensitive to the Tropics, with a dominant spatial pattern generally resembling the KOE pattern, not the PDO.

54 ENVIRONMENTAL SCIENCES↗

Warming Permafrost Model Intercomparision Project (WrPMIP): Pan-Arctic Perturbation Warming Simulations

Title: Warming Permafrost Model Intercomparision Project (WrPMIP): Pan-Arctic Perturbation Warming Simulations Description: WrPMIP Pan-Arctic simulations including historial baseline and two warming simulations. Models were perturbed similarly to known experimental warming trials that have been implemented across the Pan-Arctic over the last several decades. More information about the dataset can be found at the following links: https://warmingpermafrost.nau.edu/ The dataset is available at: https://esgf-node.ornl.gov/search/project=WrPMIP&mip_era=CMIP6&activity_id=WrPMIP

54 ENVIRONMENTAL SCIENCES↗

Decoding the dynamics of poleward shifting climate zones using aqua-planet model simulations

Growing evidence indicates that the atmospheric and oceanic circulation experiences a systematic poleward shift in a warming climate. However, the complexity of the climate system, including the coupling between the ocean and the atmosphere, natural climate variability and land-sea distribution, tends to obfuscate the causal mechanism underlying the circulation shift. Here, using an idealised coupled aqua-planet model, we explore the mechanism of the shifting circulation, by isolating the contributing factors from the direct CO 2 forcing, the indirect ocean surface warming, and the wind-stress feedback from the ocean dynamics. We find that, in contrast to the direct CO 2 forcing, ocean surface warming, in particular an enhanced subtropical ocean warming, plays an important role in driving the circulation shift. This enhanced subtropical ocean warming emerges from the background Ekman convergence of surface anomalous heat in the absence of the ocean dynamical change. Additionally, it expands the tropical warm water zone, causes a poleward shift of the mid-latitude temperature gradient, hence forces a corresponding shift in the atmospheric circulation and the associated wind pattern. The shift in wind, in turn drives a shift in the ocean circulation. Our simulations, despite being idealised, capture the main features of the observed climate changes, for example, the enhanced subtropical ocean warming, poleward shift of the patterns of near-surface wind, sea level pressure, storm tracks, precipitation and large-scale ocean circulation, implying that increase in greenhouse gas concentrations not only raises the temperature, but can also systematically shift the climate zones poleward.

54 ENVIRONMENTAL SCIENCES↗

Regional climate change: consensus, discrepancies, and ways forward

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.

54 ENVIRONMENTAL SCIENCES↗