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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Carbon content of soil fractions varies with season, rainfall, and soil fertility across a lowland tropical moist forest gradient

Tropical forests contain some of the largest soil carbon (C) stocks on Earth, making them broadly relevant to terrestrial-climate feedbacks, yet our understanding of how their soil organic C (SOC) fractions vary over space and time is limited. We studied effects of season, fertility, and mean annual precipitation (MAP) on the C contents of soil fractions across 14 lowland forests in Panama. We measured free-debris, occluded-debris, and mineral-associated SOC fractions, as well as soluble C associated with each fraction. We hypothesized that mineral-associated SOC would be greatest in infertile, strongly weathered soils with large reactive mineral contents. We also hypothesized that the debris SOC fractions would accumulate during the dry season, reflecting seasonal increases in litterfall. To address this, we compared soil fractions in wet and dry seasons from fertile and infertile soils across a range of 1809 – 2864 mm MAP. The C content (mg C / g soil) of all soil fractions varied with fertility and MAP: specifically, free-debris SOC was greatest in wet, high-fertility soils, and occluded-debris SOC was greater in high-fertility than low-fertility soils. The mineral-associated SOC fraction, which contained the majority of bulk soil C, showed increasing C content with greater MAP in infertile sites, presumably driving similar spatial patterns in the bulk soil. Only the free-debris SOC fraction showed strong seasonal variation, increasing in mass during the dry season. Nitrogen behaved similarly to C. In summary, soil C contents increased with MAP in infertile sites but not fertile sites, driven by the mineral-associated SOC fraction. The dry season had greater free-debris SOC, but this seasonal trend was not apparent in bulk soil C, likely because of the small size of the free-debris SOC fraction. Furthermore, changes in the quantity and seasonality of precipitation, which are projected for tropical forests, might shift spatial and temporal patterns of soil C storage, which would in turn influence forest-climate feedbacks for this C-rich biome.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Planting miscanthus instead of row crops may increase the productivity and economic performance of farmed potholes

Abstract Climate change projections indicate that precipitation events in the central United States are expected to become more intense, more frequent in the spring, and less frequent in the summer. Such a precipitation shift could adversely impact crop yields, especially in subfield areas known as farmed potholes, which are highly susceptible to flooding and ponding, and crop death is more likely to occur, particularly early in the growing season. This suggests that planting alternative crops, such as more flood tolerant perennials, in these areas may be a more profitable option. Using observations of crop growth and yield along with ponding depth of a specific field and farmed pothole in the central United States, we developed a spatially explicit version of the agroecosystem model Agro‐IBIS to estimate water depth and crop yield. After evaluating the model, we conducted a case study for a specific farmed pothole with a range of future precipitation scenarios with Agro‐IBIS to simulate the effects of contemporary (2002–2016) and future precipitation on a conventional corn/soybean ( Zea mays L. and Glycine max Merr.) rotation and an alternative perennial miscanthus ( Miscanthus × giganteus Greef et Deu.) cropping system. The depth and frequency of ponding increased under most future precipitation scenarios. The corn/soybean rotation had greater total loss (i.e., no yield) on average (>30%) for all scenarios in comparison to miscanthus (<10%). Under one future precipitation scenario with increased spring precipitation, both the corn/soybean rotation and miscanthus simulations showed an increase in yield. A simple budget analysis indicated that it is more profitable to plant miscanthus instead of corn or soybeans where yields in farmed potholes are consistently poor. Our findings show that potholes can be individually modeled, and their influence on yield can be quantified for use in future management decisions dictated by change in climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Kelvin Waves during GOAmazon and Their Relationship to Deep Convection

The 2014–15 Observations and Modeling of the Green Ocean Amazon (GOAmazon) field campaign over the central Amazon near Manaus, Brazil, occurred in coordination with the larger Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA) project across Brazil. These programs provide observations of convection over the central Amazon on diurnal to annual time scales. Here, we address the question of how Kelvin waves, observed in satellite observations of deep cloud cover over the GOAmazon region during the 2014–15 time period, modulate the growth, type, and organization of convection over the central Amazon. The answer to this question has implications for improved predictability of organized systems over the region and representation of convection and its growth on local to synoptic scales in global models. Our results demonstrate that Kelvin waves are strong modulators of synoptic-scale low- to midlevel free-tropospheric moisture, integrated moisture convergence, and surface heat fluxes. These regional modifications of the environment impact the local diurnal cycle of convection, favoring the development of mesoscale convective systems. As a result, localized rainfall is also strongly modulated, with the majority of rainfall in the GOAmazon region occurring during the passage of these systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluation of Ice Nucleating Particles and Their Sources in the Central Arctic during MOSAiC

The Arctic is warming faster than any other region on Earth, causing glaciers to melt, frozen ground to thaw, and the Arctic Ocean's ice to shrink. These consequential changes induce feedback loops that exacerbate warming and affect weather and climate worldwide. Microscopic airborne particles called "aerosols" and clouds in the sky are crucial for regulating heat and light reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic, where they impact temperatures directly over the sea ice. Unique aerosols called "ice nucleating particles" (INPs), which play a significant role in cloud ice production, remain understudied. Understanding how ice forms in clouds is critical in the Arctic, as it affects cloud lifespan, interactions with heat and light, and precipitation. In this project, we conducted the first-ever observations of INPs in the middle of the Arctic over a whole year, covering the entire period when sea ice grows and melts. Furthermore, these are the first observations of INPs in different size ranges throughout the year, anywhere in the world. We use DNA sequencing to evaluate the presence of various types of microorganisms, while INP measurements on seawater, sea ice, snow, and meltwater samples help assess potential local Arctic sources of INPs. The results from this work are currently being used to improve the accuracy of Arctic cloud formation in various models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of DNA Extraction Efficiency in Diverse Algae Strains Using Commercial Kits and Lysis Approaches

Efficient DNA extraction is essential for accurately monitoring microalgae communities in large-scale cultivation systems such as raceway ponds and wastewater ponds. Traditional phenol chloroform extracts are a staple in microbiology but are obsolete for routine sampling due to its high toxicity reagents and time intensive setups. Commercial DNA extraction kits are more favorable for the microbes found in these ponds, but lack specific kits made for these communities. Little is known about which kits perform the best, leading researchers to use a variety of different kits with inconsistent results. This project compared one precipitation based commercial kit (Lucigen Masterpure) and five wash based kits (Monarch, Zymo Quick-DNA, and three Qiagen DNeasy kits) using four brackish algae strains to determine which methods yield the greatest quantity and quality of genomic DNA. Extractions were evaluated using the manufacturers protocol, and additional pretreatment options were administered before a single kit to compare its potential in being added routinely before extractions. Pretreatment options included both cryogenic freeze-thawing and heat incubation using enzymes. DNA was quantified using Qubit fluorometry and NanoDrop purity ratios. Overall, the Qiagen PowerWater kit provided the highest DNA yield and purity, but at a significantly higher cost then the precipitation-based kit (MasterPure). It was also noted that while the precipitation-based kit was significantly cheaper, provided similar results, it took significantly more time to complete a single run. Cryogenic pretreatment (6x cycles) increased average DNA yields by up to 80%, whereas enzymatic pretreatment most improved purity ratios without substantially improving quantity. The results suggest that it may be more cost and time efficient to use Qiagen kits with the addition of lysis pretreatments to procure better results. Future works includes developing a better system to efficiently collect multi variable data, and to upscale to artificial polycultures using similar methodologies alongside sequencing to confirm kit results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Wellbore Leakage Mitigation Using Advanced Mineral Precipitation Strategies (Final Scientific/Technical Report)

This research project, entitled Wellbore Leakage Mitigation using Advanced Mineral Precipitation Strategies, was directed toward advancing mineral precipitation technologies to seal wellbore leaks and other potential carbon dioxide (CO 2 ) leakage pathways, including compromised wellbore cement and fracture networks of varying geometries throughout the wellbore and surrounding formation. The advanced mineral sealing techniques were evaluated in the laboratory and the field to expand the range of temperature, pressure, and chemical conditions where mineral precipitation could be used. Rates of enzymatically or thermally induced calcium carbonate precipitation (EICP, TICP) were determined as well as rates of enzyme inactivation at elevated temperatures (>60 °C). Immobilization was shown to enhance the thermal stability of urease enzyme and provide protection from inhibition in low pH conditions. Mineral seals promoted by EICP and TICP were used to seal fractured composite (cement/sandstone) and shale cores in high pressure reactors operated at temperatures greater than 55 °C and 130 °C, respectively. A computational model was updated to model processes of EICP and TICP. The work resulted in a successful field test, which showed that EICP in the presence of low pH fluids (CO 2 -impacted brine) could promote permeability reduction and seal a channel in the near-wellbore environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

gaia: An R package to estimate crop yield responses to temperature and precipitation

gaia is an open-source R package designed to estimate crop yield shocks in response to annual weather variations and CO 2 concentrations at the country scale for 17 major crops. This innovative tool streamlines the workflow from raw climate data processing to projections of annual shocks to crop yields at the country level, using the response surfaces from an empirical econometric model developed and documented in Waldhoff et al. (2020), which leverages historical weather, CO 2 , and crop yield data for robust empirical fitting for 17 crops. gaia uses these response surfaces with monthly temperature and precipitation projections (e.g., from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (O’Neill et al., 2016) climate data bias-adjusted and statistically downscaled by the ISIMIP3BASD approach (Lange, 2019) in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) (Warszawski et al., 2014)) to project yield shocks that can be applied to agricultural productivity changes at the country level for use in multisectoral economic models. The historical and future projections use gridded, country-and-crop specific monthly growing season precipitation and temperature data, aggregated to the national level, and weighted by cropland area derived from the global Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) dataset (Portmann et al., 2010). These annual, country, and crop-specific yield shocks can be aggregated to different definitions of regions, crop commodities, and time periods, as needed by specific multisectoral economic models. gaia serves as a lightweight, powerful tool that can aid exploration of crop yield responses under a broad range of future climate projections, enhancing human-Earth system analysis capabilities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Quantifying the impact of climate change on erosion - 20397

The New York State Energy Research and Development Authority (NYSERDA) is the owner of the Western New York Nuclear Service Center (WNYNSC), a 1,351-ha (3,338-ac) site located approximately 48 km (30 mi) south of Buffalo, New York. In 1962, Nuclear Fuel Services, Inc. (NFS) entered into Agreements with the Atomic Energy Commission and New York State to construct the first commercial reprocessing plant of nuclear fuel in the United States. NFS, a private company, built and operated the spent fuel reprocessing plant and waste disposal facilities, processing 640 Mg (metric tons, or 705 short tons) of spent nuclear fuel from 1966 to 1972 under an Atomic Energy Commission license. Nuclear fuel reprocessing operations ended in 1972 and never reopened, leaving behind radioactive and chemical wastes. Erosion can play an important role in the fate and transport of waste at sites where disposal of long-lived waste is anticipated. The statistical characterization of key processes related to erosion is essential to the understanding of site stability through time. One of the key processes governing erosion is extreme precipitation and it is critical that trends in the distribution of extreme precipitation events through time be represented. The evidence of climate change is increasingly well documented and projected impacts on extreme precipitation events should be incorporated in performance assessment studies when relevant. Not evaluating future climate states in a performance assessment is contradictory to good modeling practice. Specifically, excluding climate change limits development of modeling information that could aid in effective decision making. The current climate literature provides both observational evidence and climate model projections of climate trends and/or climate change in the late 20. and early 21. centuries for North America and the northeast United States. In this work, this information was used to assess the impacts of potential changes in climate on erosion processes. The goal was to understand how projections of future climate relate to the performance of the WNYNSC through time. In the first stage of this work multi-temporal historical aerial images were analyzed in conjunction with orthophotography and Lidar data to develop probability distributions for variables representing important erosion processes. The information from these analyses was then used in conjunction with simulated data from the West Valley Erosion Working Group (EWG). Analysis of EWG simulations provides estimates for the change in erosion rates through time that is driven by changes in climate. The time-varying rates of erosion change were applied to the historical aerial imagery data in order to inform time-varying rates of erosion that are driven by changes in climate. Ultimately, this process resulted in the identification of locations for features like gully heads using both the Lidar dataset and projection of the estimated location from the historical aerial photo under consideration back to the Lidar dataset. The distance between the estimate of the location from the historical image and that of the Lidar dataset was the estimated distance the feature has moved. This distance was then divided by the number of years between the Lidar dataset and the year of the historical aerial image of interest to get a rate of movement through time. This was done for several dozen points on each historical aerial image. The analyses from the EWG were used to characterize the relative impact of climate change on the erosion rates. This relative impact was quantified by comparing the LEM based estimates of erosion that were derived using historical climate data with LEM based estimates of erosion that were derived using projections of future climate. The relative increase in the erosion rates was then applied to the historical estimates of erosion derived from the analysis of the historical aerial imagery. This approach was used since the LEM-based estimated of the historical erosion rates from the past century have a bias towards underestimating erosion. This underestimation is hypothesized to be a consequence of an inability of the LEMs to account for the impacts of land-use change (i.e. deforestation) which had been shown to significantly increase erosion in similar Northern hardwood forest ecosystems. In summary, gully head retreat rate and gully widening rate were characterized using statistical probability distributions from the historical aerial image analysis. Simulated data from the EWG was used to estimate the climatically-driven changes in erosion rates through time. The changes through time from the EWG were applied to the gully head retreat rate and gully widening rate characterized using the historical aerial image analysis. This information can be used to inform PPA models to project future risks from a given site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Projected runoff declines from plant physiological effects on precipitation

Abstract The impact of plants on runoff under high atmospheric CO 2 is a major uncertainty for future water resources. Theory and Earth system models (ESMs) suggest that stricter plant stomatal regulation under high CO 2 will reduce transpiration, potentially boosting runoff. Yet, across a 12-member ensemble of idealized ESM simulations that isolate plant responses to CO 2 , we show that lower transpiration robustly enhances runoff over only 5% of modelled global land area. Precipitation changes are five times more important than transpiration changes in driving runoff responses and are a significant signal of CO 2 physiological forcing over 31–57% of land areas across models. Crucially, ESMs largely disagree on where physiologically forced precipitation changes occur but agree that plant responses in most locations are as likely to reduce runoff as increase it. These results imply that large model uncertainties in precipitation responses, rather than transpiration responses, explain why ESMs disagree on plant physiologically driven runoff changes.

Lesk, Corey S. (ORCID:0000000308684040)↗
DOE OSTI2020

Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions

Simulated historical precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices. The model indices are evaluated against corresponding indices from the CPC unified gauge-based analyses of precipitation over seven geographical regions across the contiguous US (CONUS). The regions assessed match those in recent US National Climate Assessment Reports. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Most CMIP6 models overestimate the mean and variability of wet spell durations and underestimate the mean and variability of dry spell durations across the CONUS. Biases in interannual variability of most of the indices have similar patterns to those in corresponding mean biases. The median and interquartile model spreads in CMIP6 model biases are clearly smaller than those in CMIP5 model biases for wet spell durations. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. Depending on the index, extreme precipitation is slightly better in parts of the eastern half of the CONUS in CMIP6-MMM, otherwise, the biases in climatology and variability are similar to CMIP5-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Data and code for Daily and Multi-Day Extreme Rainfall Analysis Under Future Climates Using Stochastic Storm Transposition and NEX-GDDP-CMIP6 Over CONUS

This data package provides inputs, codes, and outputs for a comprehensive analysis of projected changes in extreme precipitation across 10 regions of the continental United States, using 34 downscaled Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections, Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) dataset. These models are part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a coordinated climate modeling framework widely used to assess climate change impacts. The analysis applies a stochastic storm transposition method to quantify changes in extreme rainfall under two Shared Socioeconomic Pathway (SSP) climate scenarios—SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions)—compared to historical conditions (1995–2014 vs. 2081–2100). The dataset includes rainfall depth estimates for extreme events with return periods from 2 to 500 years across multiple storm durations (1, 3, and 5 days) for each of the 10 U.S. regions. Weighted ensemble statistics are derived from individual ESM performance against historical precipitation patterns, enabling robust uncertainty quantification through both sign-based and permutation-test-based model agreement assessments. Key analyses address: (1) relative changes in extreme precipitation for each climate scenario, (2) differences between SSP scenarios (SSP5-8.5 vs. SSP2-4.5), (3) contrasts between rare and frequent events, and (4) variations between multi-day and daily storm durations. The workflow produces ensemble statistics—median, 5th, 25th, 75th, and 95th percentiles—along with model agreement metrics that identify regions and event types with robust climate change signals. The dataset includes: processed rainfall depth outputs (netCDF format) from the RainyDay Python package, ESM weights from historical performance evaluation using DayMet observations, ensemble statistics across all storm dimensions, and figures summarizing key findings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CMIP6 skill at predicting interannual to multi-decadal summer monsoon precipitation variability

Monsoons affect the economy, agriculture, and human health of two thirds of the world’s population. Therefore, predicting variations in monsoon precipitation is societally important. We explore the ability of climate models from the sixth phase of the Climate Model Intercomparison Project to predict summer monsoon precipitation variability by using hindcasts from the Decadal Climate Prediction Project (Component A). The multi-model ensemble-mean shows significant skill at predicting summer monsoon precipitation from one year to 6–9 years ahead. However, this skill is dependent on the model, monsoon domain, and lead-time. In general, the skill of the multi-model ensemble-mean prediction is low in year 1 but increases for longer-lead times and is largely consistent with externally forced changes. The best captured region is northern Africa for the 2–5 and 6–9 year forecast lead times. In contrast, there is no significant skill using the ensemble-mean over East and South Asia and, furthermore, there is significant spread in skill among models for these domains. By sub-sampling the ensemble we show that the difference in skill between models is tied to the simulation of the externally forced response over East and South Asia, with models with a more skilful forced response capable of better predictions. A further contribution is from skilful prediction of Pacific Ocean temperatures for the South Asian summer monsoon at longer lead-times. Therefore, these results indicate that predictions of the East and South Asian monsoons could be significantly improved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Distinct drivers of recent seasonal precipitation increase over Central Asia: roles of anthropogenic aerosols and greenhouse gases

Observational evidence reveals a pronounced wetting trend over Central Asia in recent decades, with the most substantial increases occurring during winter and summer. Yet the extent to which the drivers of these changes differ seasonally remains unknown. Here, we use single-forcing experiments from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) to examine the effects of various external forcings on winter and summer precipitation across Central Asia and to explore the physical mechanisms underlying seasonal precipitation changes. We find that greenhouse gas (GHG) forcing mainly increases winter precipitation by enhancing atmospheric moisture content through warming. In contrast, in summer, Asian sulfate aerosols enhance precipitation by modulating the westerly jet, which strengthens atmospheric moisture transport into the region. Asian black carbon exerts an opposing influence that partially offsets the sulfate-induced effect. Further attribution analysis based on CMIP6 simulations reinforces these sensitivity results and shows that GHG forcing is the primary driver of winter precipitation increases whereas anthropogenic aerosols dominate summer trends. Future CMIP6 projections suggest that under moderate- to high-emission scenarios, winter precipitation will continue to rise due to increasing GHG concentrations, while summer precipitation may decline across much of Central Asia as a result of reduced aerosol emissions following Asian clean air policies. These findings highlight a distinct seasonality in the drivers of recent precipitation increase and suggest a plausible divergence in future winter and summer precipitation trends.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Coupled model intercomparison project phase 6 (CMIP6) high resolution model intercomparison project (HighResMIP) bias in extreme rainfall drives underestimation of amazonian precipitation

Abstract Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3 h data were used as observations. Our results showed that eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Anthropogenic sulfate aerosol pollution in South and East Asia induces increased summer precipitation over arid Central Asia

Precipitation has increased across the arid Central Asia region over recent decades. However, the underlying mechanisms of this trend are poorly understood. Here, we analyze multi-model simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) to investigate potential drivers of the observed precipitation trend. We find that anthropogenic sulfate aerosols over remote polluted regions in South and East Asia lead to increased summer precipitation, especially convective and extreme precipitation, in arid Central Asia. Elevated concentrations of sulfate aerosols over remote polluted Asia cause an equatorward shift of the Asian Westerly Jet Stream through a fast response to cooling of the local atmosphere at mid-latitudes. This shift favours moisture supply from low-latitudes and moisture flux convergence over arid Central Asia, which is confirmed by a moisture budget analysis. High levels of absorbing black carbon lead to opposing changes in the Asian Westerly Jet Stream and reduced local precipitation, which can mask the impact of sulfate aerosols. This teleconnection between arid Central Asia precipitation and anthropogenic aerosols in remote Asian polluted regions highlights long-range impacts of anthropogenic aerosols on atmospheric circulations and the hydrological cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Non-stationary precipitation design standards for stormwater infrastructure modernization at USAF installations

The resilience of defense infrastructure systems to a changing climate is critical for national security. Climate induced recurrent flooding is already impacting over 20 U.S. Air Force installations, underscoring the urgency of revisiting precipitation standards and stormwater infrastructure design. Despite growing scientific knowledge and an expanding set of tools for updating outdated precipitation standards based on the assumption of climate stationarity, the adoption of climate informed analyses remain limited in practice. This study utilizes an existing framework to update Intensity (or Depth)-Duration-Frequency (DDF) curves using an ensemble of future climate projections. Change factors in precipitation estimates are derived and applied to six USAF installations across the U.S. The analysis is further extended to evaluate the implications of climate-informed DDFs on stormwater infrastructure performance and flood analysis at Tyndall AFB. Results indicate that the current design precipitation estimates are likely to become obsolete in all six USAF bases by the end of the century. The wide range of change factors across 32 GCM ensembles highlights the need to integrate uncertainty and evolving scientific data into infrastructure planning. The study also finds that the impacts of a changing climate vary spatially and temporally, emphasizing the value of localized analysis for infrastructure decision-making. The work advances ongoing DoD and societal efforts to implement adaptation strategies aimed at enhancing infrastructure resilience.

Intensity-duration-frequency curves↗
DOE OSTI2020

Permafrost thermal conditions are sensitive to shifts in snow timing

Abstract Changes in snow precipitation at high latitudes can significantly affect permafrost thermal conditions and thaw depth, potentially exposing more carbon-laden soil to microbial decomposition. A fully coupled process-based surface/subsurface thermal hydrology model with surface energy balance is used to analyze the impact of intra-annual variability in snow on permafrost thermal regime and the active layer thickness. In the four numerical scenarios considered, simulations were forced by the same meteorological data, except the snow precipitation, which was systematically altered to change timing of snowfall. The scenarios represent subtle shifts in snow timing, but the snow onset/melt days, the end of winter snowpack depth, and total annual snow precipitation are unchanged among scenarios. The simulations show that small shifts in the timing of snow accumulation can have significant effects on subsurface thermal conditions leading to active layer deepening and even talik formation when snowfall arrives earlier in the winter. The shifts in snow timing have a stronger impact on wetter regions, especially soil underneath small ponds, as compared to drained regions. This study highlights the importance of understanding potential changes in winter precipitation patterns for reliable projections of active-layer thickness in a changing Arctic climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantifying Heavy Precipitation throughout the Entire Tropical Cyclone Life Cycle

Abstract Tropical cyclones (TCs) and their associated precipitation can have devastating impacts on the areas affected, with outcomes ranging from mudslides to inland flash flooding. Previous studies have used a fixed radius around the TC to isolate storm-related precipitation. One previous study instead used a dynamic radius of 8 m s −1 winds, but the wind field of the TC can deteriorate or shift quickly after landfall or the onset of extratropical transition (ET). This study uses a dynamical radius derived from the 500-hPa geopotential height in and around the TC to define TC- and post-tropical cyclone (PTC)-related heavy precipitation, allowing for the analysis of precipitation with tropical origins after the official demise of the original TC. Climatologies are constructed, indicating a maximum in TC- and PTC-related heavy precipitation in the west North Pacific and a secondary maximum in the east North Pacific. PTC-related heavy precipitation accounts for as much as 40% of the annual heavy precipitation in the northwest portion of the west North Pacific basin and 3.13% of heavy precipitation globally. We observe that the major hurricane stage contributes on average 2.6% of the global TC- and PTC-related precipitation, while the less intense but more common tropical storm stages of the TC life cycle contribute 85.7% of this observed precipitation. This analysis framework can be further extended to assess model biases and climate projections of TC and PTC precipitation.

Meteorology & Atmospheric Sciences↗