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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantifying contributions of ozone changes to global and arctic warming during the second half of the twentieth century

Ozone is the third most important greenhouse gas in driving global warming, mainly due to increased tropospheric ozone. About 50% of the growth of global tropospheric ozone since preindustrial time occurred during 1955–2005, with continued growth since then. Furthermore, this study quantifies the relative contributions of ozone changes during 1955–2005 to total observed global and Arctic climate changes by comparing CESM1 historical simulations with all anthropogenic and natural radiative forcings including realistic ozone changes, and with the same forcings except with constant ozone or well-mixed greenhouse gases (WMGHG). Results indicate that ozone changes during 1955–2005 have strongly enhanced the downwelling longwave flux and increased net shortwave flux at the surface, and thus significantly contributed about 0.15 °C of global mean surface warming, roughly 21%, 26% and 16% of the observed, all-forcing and WMGHG-driven trends, respectively. In the Arctic in the same period, corresponding ozone-driven warming was about 0.63 °C, roughly 48%, 40% and 25% of the same three trends. During 1979–2005, these ozone changes have markedly added about 0.25 × 10 6 km 2 to the decrease in the Arctic sea ice extent (SIE), or roughly 25%, 48%, and 40% of the same three trends. Considering that the ozone-driven radiative forcing of about 0.22 (0.06) W·m –2 in 1955–2005 (1979–2005) was about 12% (6%) of the corresponding WMGHG forcing, ozone changes had contributed disproportionately to global and Arctic warming and Arctic sea ice decline during the second half of the twentieth century. Tropospheric ozone has shown relatively steady growth since 2006 and might have significantly contributed recent observed warming over the global and the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Market expectations of a warming climate

In this paper, we compare prices of financial derivatives whose payouts are based on future weather outcomes to CMIP5 climate model predictions as well as observed weather station data across eight cities in the US from 2001 through 2020. Derivative prices respond both to short-term weather forecasts for the next two weeks and longer-term warming trends. We show that the long-term trends in derivative prices are comparable to station-level data and climate model output. The one exception is February in the northeastern US, where financial markets price in a polar vortex-induced cooling effect, a recent scientific finding that was not present in the older CMIP5 climate output. When looking at the spatial and temporal heterogeneity in trends, futures prices are more aligned with climate model output than observed weather station trends, suggesting that market participants closely align their expectations with scientific projections rather than recent observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Long-term leaf C:N ratio change under elevated CO 2 and nitrogen deposition in China: Evidence from observations and process-based modeling

Climate change, elevating atmosphere CO 2 (eCO 2 ) and increased nitrogen deposition (iNDEP) are altering the biogeochemical interactions between plants, microbes and soils, which further modify plant leaf carbon-nitrogen (C:N) stoichiometry and their carbon assimilation capability. Many field experiments have observed large sensitivity of leaf C:N ratio to eCO 2 and iNDEP. However, the large-scale pattern of this sensitivity is still unclear, because eCO 2 and iNDEP drive leaf C:N ratio toward opposite directions, which are further compounded by the complex processes of nitrogen acquisition and plant-and-microbial nitrogen competition. Here, we attempt to map the leaf C:N ratio spatial variation in the past 5 decades in China with a combination of data-driven model and process-based modeling. These two approaches showed consistent results. Over different regions, we found that leaf C:N ratio had significant but uneven changes between 2 time periods (1960-1989 and 1990-2015): a 5% ± 8% increase for temperate grasslands in northern China, a 3% ± 6% increase for boreal grasslands in western China, and by contrast, a 7% ± 6% decrease for temperate forests in southern China, and a 3% ± 5% decrease for boreal forests in northeastern China. Additionally, the structural equation models indicated that the leaf C:N change was sensitive to ΔNDEP, ΔCO 2 and ΔMAT rather than ΔMAP and ecosystem types. In this work, process-based modeling suggested that iNDEP was the main source of soil mineral nitrogen change, dominating leaf C:N ratio change in most areas in China, while eCO 2 led to leaf C:N ratio increase in low iNDEP area. This study also indicates that the long-term leaf C:N ratio acclimation was dominated by climate constraint, especially temperature, but was constrained by soil N availability over decade scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Lack of a QBO-MJO Connection in Climate Models With a Nudged Stratosphere

The observed stratospheric quasi-biennial oscillation (QBO) and the tropospheric Madden-Julian oscillation (MJO) are strongly connected in boreal winter, with stronger MJO activity when lower-stratospheric winds are easterly. However, the current generation of climate models with internally generated representations of the QBO and MJO do not simulate the observed QBO-MJO connection, for reasons that remain unclear. Furthermore, this study builds on prior work exploring the QBO-MJO link in climate models whose stratospheric winds are relaxed toward reanalysis, reducing stratospheric biases in the model and imposing a realistic QBO. A series of ensemble experiments are performed using four state-of-the-art climate models capable of representing the MJO over the period 1980–2015, each with similar nudging in the stratosphere. In these four models, nudging leads to a good representation of QBO wind and temperature signals, however no model simulates the observed QBO-MJO relationship. Biases in MJO vertical structure and cloud-radiative feedbacks are investigated, but no conclusive model bias or mechanism is identified that explains the lack of a QBO-MJO connection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Rahimi, Stefan (ORCID:0000000331884462)↗
DOE OSTI2023

Thermal adaptation of soil microbial growth traits in response to chronic warming

ABSTRACT Adaptation of soil microbes due to warming from climate change has been observed, but it remains unknown what microbial growth traits are adaptive to warming. We studied bacterial isolates from the Harvard Forest Long-Term Ecological Research site, where field soils have been experimentally heated to 5°C above ambient temperature with unheated controls for 30 years. We hypothesized that Alphaproteobacteria from warmed plots have (i) less temperature-sensitive growth rates; (ii) higher optimum growth temperatures; and (iii) higher maximum growth temperatures compared to isolates from control plots. We made high-throughput measurements of bacterial growth in liquid cultures over time and across temperatures from 22°C to 37°C in 2–3°C increments. We estimated growth rates by fitting Gompertz models to the growth data. Temperature sensitivity of growth rate, optimum growth temperature, and maximum growth temperature were estimated by the Ratkowsky 1983 model and a modified Macromolecular Rate Theory (MMRT) model. To determine evidence of adaptation, we ran phylogenetic generalized least squares tests on isolates from warmed and control soils. Our results showed evidence of adaptation of higher optimum growth temperature of bacterial isolates from heated soils. However, we observed no evidence of adaptation of temperature sensitivity of growth and maximum growth temperature. Our project begins to capture the shape of the temperature response curves, but illustrates that the relationship between growth and temperature is complex and cannot be limited to a single point in the biokinetic range. IMPORTANCE Soils are the largest terrestrial carbon sink and the foundation of our food, fiber, and fuel systems. Healthy soils are carbon sinks, storing more carbon than they release. This reduces the amount of carbon dioxide released into the atmosphere and buffers against climate change. Soil microbes drive biogeochemical cycling and contribute to soil health through organic matter breakdown, plant growth promotion, and nutrient distribution. In this study, we determined how soil microbial growth traits respond to long-term soil warming. We found that bacterial isolates from warmed plots showed evidence of adaptation of optimum growth temperature. This suggests that increased microbial biomass and growth in a warming world could result in greater carbon storage. As temperatures increase, greater microbial activity may help reduce the soil carbon feedback loop. Our results provide insight on how atmospheric carbon cycling and soil health may respond in a warming world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Antecedent Hydrometeorological Conditions of Wildfire Occurrence in the Western U.S. in a Changing Climate

Abstract Wildfires have significant hydrological and ecological impacts in the western U.S. Using a high‐resolution regional climate simulation and wildfire observations for 1984–2018, this study investigates the antecedent hydrometeorological conditions (AHCs) of wildfires in the western U.S. During the warm season (April‐September), the wildfire AHCs feature diverse surface pressure (PS), soil moisture, and longwave/shortwave radiation (LW/SW) conditions. K‐means clustering classifies wildfires into four types with distinct AHCs: low‐PS‐type and high‐PS‐type with lower and higher PS anomalies, respectively, LW‐type featuring intense LW but weak SW anomalies, and wet‐soil‐type with wet soil anomalies. Each fire cluster represents 22%–27% of all the wildfires, featuring different combinations of climate and vegetation conditions and their diverse relations to regional hydrometeorological conditions, with wet‐soil‐type fires often exhibiting opposite correlations with AHCs compared to those of the other three types. In five major Köppen climate zones over the western U.S., clustering‐based predictions improve the seasonal wildfire prediction accuracy ( R 2 ) by 10% compared to prediction without classification. Such improvement comes from separating the opposite relationships between wet‐soil‐type fires and their seasonal AHCs from the other three types, along with separating LW‐type fires, which include most of the lightning‐ignited fires that occur more randomly. Increases in wildfire occurrence during 1984–2018 are dominated by the increases in the LW‐type fires, while the wet‐soil‐type fires have decreased, consistent with the long‐term drying in the western U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1

Abstract. This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1∘ horizontal resolution. We show that a factor-of-6 reduction of time step size in all major parts of the model leads to significant changes in the long-term mean climate. Examples of changes in 10-year mean zonal averages include the following: up to 0.5 K of warming in the lower troposphere and cooling in the tropical and subtropical upper troposphere, 1 %–10 % decreases in relative humidity throughout the troposphere, and 10 %–20 % decreases in cloud fraction in the upper troposphere and decreases exceeding 20 % in the subtropical lower troposphere. In terms of the 10-year mean geographical distribution, systematic decreases of 20 %–50 % are seen in total cloud cover and cloud radiative effects in the subtropics. These changes imply that the reduction of temporal truncation errors leads to a notable although unsurprising degradation of agreement between the simulated and observed present-day climate; to regain optimal climate fidelity in the absence of those truncation errors, the model would require retuning. A coarse-grained attribution of the time step sensitivities is carried out by shortening time steps used in various components of EAM or by revising the numerical coupling between some processes. Our analysis leads to the finding that the marked decreases in the subtropical low-cloud fraction and total cloud radiative effect are caused not by the step size used for the collectively subcycled turbulence, shallow convection, and stratiform cloud macrophysics and microphysics parameterizations but rather by the step sizes used outside those subcycles. Further analysis suggests that the coupling frequency between the subcycles and the rest of EAM significantly affects the subtropical marine stratocumulus decks, while deep convection has significant impacts on trade cumulus. The step size of the cloud macrophysics and microphysics subcycle itself appears to have a primary impact on cloud fraction in the upper troposphere and also in the midlatitude near-surface layers. Impacts of step sizes used by the dynamical core and the radiation parameterization appear to be relatively small. These results provide useful clues for future studies aiming at understanding and addressing the root causes of sensitivities to time step sizes and process coupling frequencies in EAM. While this study focuses on EAMv1 and the conclusions are likely model-specific, the presented experimentation strategy has general value for weather and climate model development, as the methodology can help researchers identify and understand sources of time integration error in sophisticated multi-component models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Implications of a Pervasive Climate Model Bias for Low‐Cloud Feedback

Abstract How low clouds respond to warming constitutes a key uncertainty for climate projections. Here we observationally constrain low‐cloud feedback through a controlling factor analysis based on ridge regression. We find a moderately positive global low‐cloud feedback (0.45 W , 90% range 0.18–0.72 W ), about twice the mean value (0.22 W ) of 16 models from the Coupled Model Intercomparison Project. We link this discrepancy to a pervasive model mean‐state bias: models underestimate the low‐cloud response to warming because (a) they systematically underestimate present‐day tropical marine low‐cloud amount, and (b) the low‐cloud sensitivity to warming is proportional to this present‐day low‐cloud amount. Our results hence highlight the importance of reducing model biases in both the mean state of clouds and their sensitivity to environmental factors for accurate climate change projections.

58 GEOSCIENCES↗
DOE OSTI2020

AeDES: a next-generation monitoring and forecasting system for environmental suitability of Aedes-borne disease transmission

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Aedes-borne diseases’ environmental suitability (AeDES) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of AeDES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Interannual variability and trends of summertime PM2.5-based air quality in the Intermountain West

Abstract Summertime air quality is a growing public health concern in the populated region of Northern Utah. Whereas winter air pollution is highly linked with local atmospheric temperature inversions associated with upper atmospheric high-pressure and radiational cooling in valleys, the relationship between climate factors and the frequency of poor air quality during summer is still unknown. Analyzing the last 20 years of data, we demonstrated that summertime unhealthy days (as defined by PM2.5 air quality index level) in Northern Utah highly correlate with the number of dry-hot days, wildfire size, and an upper atmospheric ridge over the Northwestern United States. The persistent atmospheric ridge enhances lightning-caused fire burned areas in northwestern states and then transports the wildfire smoke toward Northern Utah. Similarly, climate model simulations confirm observational findings, such as an increasing trend of the upper atmospheric ridge and summertime dry days in the northwestern states. Such metrics developed in this study could be used to establish longer-term monitoring and seasonal forecasting for air quality and its compounding factors, which is currently limited to forecasting products for only several days.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector

Abstract. Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios for ISIMIP phases 2 and 3. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various representative greenhouse gas concentration pathways (RCPs), all consistently bias-corrected on a 0.5∘ × 0.5∘ global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and using uncalibrated models for 17 500 lakes defined for all global grid cells containing lakes. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.

58 GEOSCIENCES↗
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 OSTI2025

The Hunga Volcanic Eruption Atmospheric Impacts Report

On 15 January 2022 a highly explosive eruption of the Hunga volcano occurred in the Kingdom of Tonga in the South Pacific Ocean (175°24’ W, 20°33’ S). The Volcanic Explosivity Index (VEI) 6 eruption originated from a shallow submarine vent, making it distinct from large subaerial eruptions of recent decades (e.g., 1982 El Chichón, 1991 Mt. Pinatubo). In particular, seawater enhanced explosivity and dampened sulfur dioxide (SO 2 ) emissions. The eruption was the culmination of ~1 month of precursory activity; however, the timing and size of the eruption were unexpected, partly due to the challenges of monitoring submarine volcanoes. The stratospheric hydration caused by the eruption was unprecedented in magnitude, altitude, and duration in the satellite record. This Executive Summary reflects the current assessment of the Hunga eruption and its impact on the climate system. We report key observations of the eruption and its aftermath, as well as simulations of its impact by global chemistry-climate models. The Hunga eruption had an unprecedented impact on the stratosphere and mesosphere due to the plume height and large water content, which increased the global stratospheric water vapour burden by 10%. Most of this water has remained in the atmosphere into 2025. However, Hunga’s net impact on surface climate was small compared to that of earlier large-magnitude volcanic eruptions, due to limited sulfate aerosol loading in the stratosphere and the high altitude of the water vapour injection.

58 GEOSCIENCES↗
DOE OSTI2023

Life Cycle of Major Sudden Stratospheric Warmings in the Southern Hemisphere from a Multimillennial GCM Simulation

Abstract Sudden stratospheric warmings (SSWs) are rare in the Southern Hemisphere (SH), making it difficult to study possible precursors or subsequent impacts. Using a multimillennial coupled climate model simulation producing 161 SSWs in the SH, we present a detailed study of their life cycle. We show that SH SSWs are predominantly displacement events forced by wave-1 planetary waves, and that a surface signature similar to the negative phase of the Southern Annular Mode (SAM) is detectable up to two months before the onset date, but there is a tendency for a transition from wave 1 before to zonally symmetric anomalies after onset. We identify a strong weakening of the Amundsen Sea low as one of the most prominent precursors, which weakens the climatological wave-2 and wave-3 stationary waves and strengthens wave-1 forcing. Compared to their northern counterparts, SH SSWs generally have a longer time scale, and while there is evidence of pre-onset forcing related to tropical sea surface temperatures, the Indian Ocean dipole is more important than El Niño–Southern Oscillation. Significance Statement Sudden stratospheric warmings (SSWs) are extreme events where the winter polar stratosphere warms within a few days to temperatures usually only experienced in summer. These events are rare in the Southern Hemisphere. Therefore, both the observational record and standard climate model simulations are not enough to understand how SSWs develop, or how they might change surface weather. Here we use very long global climate simulations that produce a large number of SSWs in the Southern Hemisphere to study the development and impact of these events. This includes possible precursors as well as the influence they have on surface weather after they occur.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Detecting impacts of surface development near weather stations since 1895 in the San Joaquin Valley of California

Abstract Temperature readings observed at surface weather stations have been used for detecting changes in climate due to their long period of observations. The most common temperature metrics recorded are the daily maximum (TMax) and minimum (TMin) extremes. Unfortunately, influences besides background climate variations impact these measurements such as changes in (1) instruments, (2) location, (3) time of observation, and (4) the surrounding artifacts of human civilization (buildings, farms, streets, etc.) Quantifying (4) is difficult because the surrounding infrastructure, unique to each site, often changes slowly and variably and is thus resistant to general algorithms for adjustment. We explore a direct method of detecting this impact by comparing a single station that experienced significant development from 1895 to 2019, and especially since 1970, relative to several other stations with lesser degrees of such development (after adjustments for the (1) to (3) are applied). The target station is Fresno, California (metro population ~ 15,000 in 1900 and ~ 1 million in 2019) situated on the eastern side of the broad, flat San Joaquin Valley in which several other stations reside. A unique component of this study is the use of pentad (5-day averages) as the test metric. Results indicate that Fresno experienced + 0.4 °C decade −1 more nighttime warming (TMin) since 1970 than its neighbors—a time when population grew almost 300%. There was little difference seen in TMax trends between Fresno and non-Fresno stations since 1895 with TMax trends being near zero. A case is made for the use of TMax as the preferred climate metric relative to TMin for a variety of physical reasons. Additionally, temperatures measured at systematic times of the day (i.e., hourly) show promise as climate indicators as compared with TMax and especially TMin (and thus TAvg) due to several complicating factors involved with daily high and low measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones

Unprecedented global vegetation greening during past decades is well known to affect annual and seasonal land surface temperatures (LST). However, the impact of observed vegetation cover change on diurnal LST across global climatic zones is not well understood. In this study, using global climatic time-series datasets, we investigated the long-term growing season daytime and nighttime LST changes globally and explored associated dominant contributors including vegetation and climate factors including air temperature, precipitation, and solar radiation. Results revealed asymmetric growing season mean daytime and nighttime LST warming (0.16 °C/10a and 0.30 °C/10a, respectively) globally from 2003 to 2020, as a result, the diurnal LST range (DLSTR) declined at 0.14 °C/10a. The sensitivity analysis indicated the LST response to changes in LAI, precipitation, and SSRD mainly concentrated during daytime instead of nighttime, however, which showed comparable sensitivities for air temperature. Combining the sensitivities results and the observed LAI and climate trends, we found rising air temperature contributes to 0.24 ± 0.11 °C/10a global daytime LST warming and 0.16 ± 0.07 °C/10a nighttime LST warming, turns to be the dominant contributor to the LST changes. Increased LAI cooled global daytime LST (–0.068 ± 0.096 °C/10a) while warmed nighttime LST (0.064 ± 0.046 °C/10a); hence LAI dominates declines in DLSTR trends (–0.12 ± 0.08 °C/10a), despite some daynight process variations across climate zones. In Boreal regions, reduced DLSTR was due to nighttime warming from LAI increases. In other climatic zones, daytime cooling, and DLSTR decline, was induced by increased LAI. Biophysically, the pathway from air temperature heats the surface through sensible heat and increased downward longwave radiation during day and night, while the pathway from LAI cools the surface by enhancing energy redistribution into latent heat rather than sensible heat during the daytime. These empirical findings of diverse asymmetric responses could help calibrate and improve biophysical models of diurnal surface temperature feedback in response to vegetation cover changes in different climate zones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantifying Uncertainty in Ice Particle Velocity–Dimension Relationships Using MC3E Observations

Abstract Ice particle terminal fall velocity ( V t ) is fundamental for determining microphysical processes, yet remains extremely challenging to measure. Current theoretical best estimates of V t are functions of Reynolds number. The Reynolds number is related to the Best number, which is a function of ice particle mass, area ratio ( A r ), and maximum dimension ( D max ). These estimates are not conducive for use in most models since model parameterizations often take the form , where ( α , β ) depend on habit and D max . A previously developed framework is used to determine surfaces of equally plausible ( α , β ) coefficients whereby ice particle size/shape distributions are combined with V t best estimates to determine mass- ( V M ) or reflectivity-weighted ( V Z ) velocities that closely match parameterized V M ,SD or V Z ,SD calculated using the ( α , β ) coefficients using two approaches. The first uses surfaces of equally plausible ( a , b ) coefficients describing mass ( M )–dimension relationships (i.e., ) to calculate mass- or reflectivity-weighted velocity from size/shape distributions that are then used to determine ( α , β ) coefficients. The second investigates how uncertainties in A r , D max , and size distribution N ( D ) affect V M or V Z . For seven of nine flight legs flown on 20 and 23 May 2011 during the Mesoscale Continental Convective Clouds Experiment (MC3E), uncertainty from natural parameter variability—namely, the variability in ice particle parameters in similar meteorological conditions—exceeds uncertainties arising from different A r assumptions or D max estimates. The combined uncertainty between A r , D max , and N ( D ) produced smaller variability in ( α , β ) compared to varying M ( D ), demonstrating M ( D ) must be accurately quantified for model fall velocities. Primary sources of uncertainty vary considerably depending on environmental conditions. Significance Statement Ice particle fall velocity is fundamental for numerous processes within clouds, and hence is a critical property that must be accurately represented in weather and climate models. Using aircraft observations of ice particle shapes and sizes obtained in clouds behind midlatitude thunderstorms, this work develops a new framework for estimating ice particle fall velocities and their uncertainty, including quantifying the importance of different uncertainty sources from cloud microphysics measurements. Natural parameter variability contributes the most uncertainty in ice particle fall velocity estimates, although other sources can also be important contributors to uncertainty in certain conditions. Additional work examining ice particle data is needed to further understand how dependent uncertainty in certain ice particle properties are to local environmental conditions.

Meteorology & Atmospheric Sciences↗