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Simulated precipitation changes in Central Asia since the Last Glacial Maximum
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Changes in dominant moisture sources and the consequences for hydroclimate on the northeastern Tibetan Plateau during the past 32 kyr
Lake Qinghai, located on the northeastern Tibetan Plateau north of the modern maximum summer monsoon extent, is well situated to record northward advances of the summer monsoon. Existing paleoclimate records contain conflicting evidence for the timing of summer monsoon advance into this region: an early arrival pre-Younger Dryas or a late arrival at the beginning of the Holocene. A 30-kyr-long leaf wax hydrogen isotope (n-alkanoic acid, δ2Hwax) record from Lake Qinghai helps to address this discrepancy by elucidating changes in the three main moisture sources in this region: southerly (summer monsoon), northwesterly, and local precipitation. Lake Qinghai δ2Hwax indicates that the arid glacial period was dominated by northwesterly moisture. Extremely arid conditions prevailed from 15 to 14 ka, likely because westerly winds were weakening and the summer monsoon had not yet reached this region. This arid period ended by 13.6 ka when small amounts of summer monsoon precipitation reached Lake Qinghai. Summer monsoon moisture subsequently retreated off of the northeastern Tibetan Plateau during the Younger Dryas and re-advanced in the early Holocene. Summer monsoon precipitation decreased progressively throughout the Holocene in response to decreasing summer insolation, and the modern northwesterly- and local-dominated moisture regime was attained ca. 2.6 ka. Lake Qinghai δ2Hwax demonstrates that the summer monsoon extent was dynamic during the past 30 kyr, responding dramatically to insolation and North Atlantic circulation changes. Moreover, Lake Qinghai δ2Hwax demonstrates that local and northwesterly air masses are important moisture sources to the northeastern Tibetan Plateau, and should be considered when reconstructing past hydroclimate in this region.
Cold surges and dust events: Establishing the link between the East Asian Winter Monsoon and the Chinese loess record
The Chinese loess/palaeosol succession is one of the most comprehensive and intensively studied archives of Neogene and Quaternary global palaeoclimate events. Its stratigraphic details are widely recognised to indicate close links to the history and function of the East Asian Winter Monsoon (EAWM) – one of the most active components of the Earth's climate system. But the formal meteorological links between the EAWM and dust emission, both in the present day and in the past, have not been established and with it, the veracity of the loess record as an indicator of the EAWM questioned. Here we show that present day major dust events over northern China, while largely occurring during spring, are nevertheless ‘conditioned’ by the strength of the preceding EAWM. We also demonstrate, for the first time, a close link between the occurrence of dust events and the strength of the EAWM. From these findings, linked to global-scale climate model simulations, we conclude that the Chinese loess succession provides a convincing proxy record of the strength of the East Asian Winter Monsoon.
Knowledge acquisition and innovation quality: The moderating role of geographical characteristics of technology
Acquiring and absorbing knowledge at different geographic levels is an important step in the technological learning and innovation processes for firms. Geographically bounded knowledge searching and spillover have been shown in various network formats, industries, geographic scopes, and country contexts. This study finds evidence of localized learning in citation networks in the solar photovoltaic balance-of-systems (PV BOS) innovations, and that the levels of localized learning vary based on the degree to which technologies are relevant to local contexts. Thus, this study proposes that the value of local and non-local knowledge on patent quality is contingent on technological characteristics. Further, leveraging the different degrees of local dependence on four types of technologies in the PV BOS industry, we find that the impact of local knowledge on innovation quality is higher for technologies that are more rooted in local contexts and that the impact of non-local knowledge is higher for technologies that are less relevant to local contexts.
Reflections on the anomalous ANITA events: the Antarctic subsurface as a possible explanation
The Antarctic Impulsive Transient Antenna (ANITA) balloon experiment was designed to detect radio signals initiated by high-energy neutrinos and cosmic ray (CR) air showers. These signals are typically discriminated by the polarization and phase inversions of the radio signal. The reflected signal from CRs suffer phase inversion compared to a direct ‘tau neutrino’ event. In this paper, we study subsurface reflection, which can occur without phase inversion, in the context of the two anomalous up-going events reported by ANITA. It is found that subsurface layers and firn density inversions may plausibly account for the events, while ice fabric layers and wind ablation crusts could also play a role. This hypothesis can be tested with radar surveying of the Antarctic region in the vicinity of the anomalous ANITA events. Future experiments should not use phase inversion as a sole criterion to discriminate between down-going and up-going events, unless the subsurface reflection properties are well understood.
The importance of sub-meter-scale snow roughness on conductive heat flux of Arctic sea ice
Abstract The conductive heat flux through the snow and ice is a critical component of the mass and energy budgets in the Arctic sea ice system. We use high horizontal resolution (3–15 cm) measurements of snow topography to explore the impacts of sub-meter-scale snow surface roughness on heat flux as simulated by the Finite Element method. Simulating horizontal heat flux in a variable snow cover modestly increases the total simulated heat flux. With horizontal heat flux, as opposed to simple 1D-vertical heat flux modeling, the simulated heat flux is 10% greater than that for uniform snow with the same mean snow thickness for a 31.5 × 21 m region of sea ice (the largest region we studied). Vertical-only (1D) heat flux simulates just a 6% increase for the same region. However, this is highly dependent on observation resolution. Had we measured the snow cover at 1 m horizontal spacing or greater, simulating horizontal heat flux would not have changed the net heat flux from that simulated with vertical-only heat flux. These findings suggest that measuring and modeling snow roughness at sub-meter horizontal scales may be necessary to accurately represent horizontal heat flux on level Arctic sea ice.
Beyond the Stokes approximation: shallow visco-elastic ice-sheet models
Abstract The hypothesis that ice-sheet evolution is only controlled by the long-term non-Newtonian viscous behavior of ice has been challenged by observations indicating that effects like brittle failure, stick-slip sliding, tides and wave action may affect ice-sheet evolution on sub-daily timescales. Over these timescales, the quasi-static-creep approximation is no longer appropriate and elastic effects become important. Simulating elastic effects in ice-sheet models over relevant timescales, however, remains challenging. Here, we show that by including a visco-elastic rheology and reintroducing the oft neglected acceleration term back into the ice-sheet stress balance, we can create a visco-elastic system where the velocity is locally determined and information propagates at the elastic wave speed. Crucially, the elastic wave speed can be treated like an adjustable parameter and set to any value to reproduce a range of phenomena, provided the wave speed is large compared to the viscous velocity. We illustrate the system using three examples. The first two examples demonstrate that the system converges to the steady-state viscous and elastic limits. The third example examines ice-shelf rifting and iceberg calving. This final example hints at the utility of the visco-elastic formulation in treating both long-term evolution and short-term environmental effects.
Observation and Simulation of Methane Plumes During the Morning Boundary Layer Transition
Abstract Methane (CH 4 ) contributes significantly to global warming. However, accurate identification of CH 4 sources for reducing CH 4 emissions is often hampered by inadequate accuracy and spatiotemporal coverage of CH 4 detection, and lack of accurate CH 4 forward modeling used in top‐down inversion systems. In this study, a field experiment was conducted in Pampa, Texas using two CH 4 sensors (LI‐COR and OGI camera) to detect CH 4 releases. We investigated whether high‐resolution simulations using the Weather Research and Forecasting (WRF) model with greenhouse gases (WRF‐GHG) could accurately simulate the CH 4 plumes in the presence of evolving atmospheric boundary layer from sunrise to noon. CH 4 plumes showed substantial variation in time. At a release rate of ∼17.5 kg hr −1 , the maximum enhancement of CH 4 measured by LI‐COR was 2.6 ppm at sunrise (7:36 a.m.), 250 m from the release location. Within half an hour after sunrise, this enhancement decreased to 0.3–0.4 ppm. The enhancement was 0.2 ppm by 10:00 a.m. and further dropped to less than 0.1 ppm after 11:30 a.m. Due to the low temperature at sunrise, the OGI camera failed to detect the CH 4 plume. The WRF‐GHG large‐eddy simulation (LES) with 32 m grid spacing successfully reproduced these CH 4 enhancements. In situ measurements together with numerical simulations illustrate the impact of the transition from a stable boundary layer in the early morning to a convective boundary layer at noon on the dispersion of CH 4 plumes. Additionally, CH 4 plumes from a cattle farm in Oklahoma are briefly examined using the same modeling approach.
Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah‐MP Land Surface Model: Model Development and Evaluation
Abstract Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C ( V cmax,25 ) and the leaf respiration rate at 25°C ( R 25 )) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R 25 and V cmax,25 vary substantially both temporally and spatially within the same PFT ( C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in V cmax,25 (73% and 54% of the variations in R 25 ). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO 2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.
Geospatial mapping of distribution grid with machine learning and publicly-accessible multi-modal data
Abstract Detailed and location-aware distribution grid information is a prerequisite for various power system applications such as renewable energy integration, wildfire risk assessment, and infrastructure planning. However, a generalizable and scalable approach to obtain such information is still lacking. In this work, we develop a machine-learning-based framework to map both overhead and underground distribution grids using widely-available multi-modal data including street view images, road networks, and building maps. Benchmarked against the utility-owned distribution grid map in California, our framework achieves > 80% precision and recall on average in the geospatial mapping of grids. The framework developed with the California data can be transferred to Sub-Saharan Africa and maintain the same level of precision without fine-tuning, demonstrating its generalizability. Furthermore, our framework achieves a R 2 of 0.63 in measuring the fraction of underground power lines at the aggregate level for estimating grid exposure to wildfires. We offer the framework as an open tool for mapping and analyzing distribution grids solely based on publicly-accessible data to support the construction and maintenance of reliable and clean energy systems around the world.
Urban land patterns can moderate population exposures to climate extremes over the 21st century
Abstract Climate change and global urbanization have often been anticipated to increase future population exposure (frequency and intensity) to extreme weather over the coming decades. Here we examine how changes in urban land extent, population, and climate will respectively and collectively affect spatial patterns of future population exposures to climate extremes (including hot days, cold days, heavy rainfalls, and severe thunderstorm environments) across the continental U.S. at the end of the 21st century. Different from common impressions, we find that urban land patterns can sometimes reduce rather than increase population exposures to climate extremes, even heat extremes, and that spatial patterns instead of total quantities of urban land are more influential to population exposures. Our findings lead to preliminary suggestions for embedding long-term climate resilience in urban and regional land-use system designs, and strongly motivate searches for optimal spatial urban land patterns that can robustly moderate population exposures to climate extremes throughout the 21st century.
Trade-offs in land-based carbon removal measures under 1.5 °C and 2 °C futures
Land-based carbon removals, specifically afforestation/reforestation and bioenergy with carbon capture and storage (BECCS), vary widely in 1.5 °C and 2 °C scenarios generated by integrated assessment models. Because underlying drivers are difficult to assess, we use a well-known integrated assessment model, GCAM, to demonstrate that land-based carbon removals are sensitive to the strength and scope of land-based mitigation policies. We find that while cumulative afforestation/reforestation and BECCS deployment are inversely related, they are both typically part of cost-effective mitigation pathways, with forestry options deployed earlier. While the CO 2 removal intensity (removal per unit land) of BECCS is typically higher than afforestation/reforestation over long time horizons, the BECCS removal intensity is sensitive to feedstock and technology choices whereas the afforestation/reforestation removal intensity is sensitive to land policy choices. Finally, we find a generally positive relationship between agricultural prices and removal effectiveness of land-based mitigation, suggesting that some trade-offs may be difficult to avoid.
Contemporary income inequality outweighs historic redlining in shaping intra-urban heat disparities in Los Angeles
The roots of intra-urban heat disparity in the U.S. often trace back to historical discriminatory practices, such as redlining, which categorized neighborhoods by race or ethnicity. In this study, we compare the relative impacts of historic redlining and current income inequality on thermal disparities in Los Angeles. A key innovation of our work is the use of land surface temperature data from the ECOSTRESS instrument aboard the International Space Station, enabling us to capture diurnal trends in urban thermal disparities. Our findings reveal that present-day income inequality is a stronger predictor of heat burden than the legacy of redlining. Additionally, land surface temperature disparities exhibit a seasonal hysteresis effect, intensifying during extreme heat events by 5−7 °C. Sociodemographic analysis highlights that African-American and Hispanic populations in historically and economically disadvantaged areas are often the most vulnerable. Our findings suggest that while the legacy of redlining may persist, the present-day heat disparities are not necessarily an immutable inheritance, where targeted investments and interventions can pave the way for a more thermally just future for these communities.
A baseline structure inventory with critical attribution for the US and its territories
Leveraging high performance computing, remote sensing, geographic data science, machine learning, and computer vision, Oak Ridge National Laboratory has partnered with Federal Emergency Management Agency (FEMA) to build a baseline structure inventory covering the US and its territories to support disaster preparedness, response, and recovery. The dataset contains more than 125 million structures with critical attribution, and is ready to be used by federal agencies, local government and first responders to accelerate on-the-ground response to disasters, further identify vulnerable areas, and develop strategies to enhance the resilience of critical structures and communities. Data can be freely and openly accessed through Figshare data repository, ESRI’s Living Atlas or FEMA’s Geodata platform.
ORBITaL-Net: A labeled training library for large-scale building feature extraction
Over the course of several years, nearly 1.5 million building outlines have been created from approximately 128,000 training tiles covering roughly 7,000 km 2 of very high-resolution multispectral overhead imagery, primarily dated between 2010 and 2020. This dataset, dubbed the Oak Ridge Building Image and TrAining Label Net (ORBITaL-Net), is designed for machine learning applications and is global in scope, with samples drawn from 72 countries across North America, South America, Africa, Europe, and Asia. ORBITaL-Net captures a great diversity in geographic setting, structural characteristics, land use (urban and rural), terrain, and imagery conditions. While the labeled building outlines are themselves valuable, the dataset’s true strength lies in the pairing of these labels with corresponding reference imagery, which is being released for open source use. Similar to SpaceNet and Replicable AI For Microplanning (ramp), this building outline dataset will allow the larger computer vision community from academia, government, and industry the opportunity to develop robust, scalable, and generalizable geospatial machine learning techniques. Unlike SpaceNet and ramp, which offer high resolution labels and imagery primarily for large urban cities, ORBITaL-Net is not focused on training samples from heavily populated areas but instead aims to capture the innate variability of conditions present in both the physical environment and imagery collections.