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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Groundwater Volume Loss in Mexico City Constrained by InSAR and GRACE Observations and Mechanical Models

Abstract Groundwater withdrawal can cause localized and rapid poroelastic subsidence, spatially broad elastic uplift of low amplitude, and changes in the gravity field. Constraining groundwater loss in Mexico City, we analyze data from the Gravity Recovery and Climate Experiment and its follow‐on mission (GRACE/FO) and Synthetic Aperture Radar (SAR) Sentinel‐1A/B images between 2014 and 2021. GRACE/FO observations yield a groundwater loss of 0.85–3.87 km 3 /yr for a region of ∼300 × 600 km surrounding Mexico City. Using the high‐resolution interferometric SAR data set, we measure >35 cm/yr subsidence within the city and up to 2 cm/yr of uplift in nearby areas. Attributing the long‐term subsidence to poroelastic aquifer compaction and the long‐term uplift to elastic unloading, we apply respective models informed by local geology, yielding groundwater loss of 0.86–12.57 km 3 /yr. Our results suggest Mexico City aquifers have been depleting at faster rates since 2015, exacerbating the socioeconomic and health impacts of long‐term groundwater overdrafts.

58 GEOSCIENCES↗
DOE OSTI2023

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Geology↗
DOE OSTI2023

Observed impacts of large wind farms on grassland carbon cycling

Deployment of wind energy is an essential renewable energy source that mitigates climate change and reduces air pollution. Over the last several decades, wind energy development has increased worldwide, expanding from ~20 to ~900 GW (gigawatt) during 2001-2022. Nonetheless, researchers have identified unintended consequences of wind energy on microclimate via turbine-altered surface-atmosphere exchanges of energy, momentum, mass, and trace gases. Based on multi-source observations and models, researchers also have drawn some conclusions that wind farms could warm the land surface, especially at night, at regional and continental scales. Consequently, altered microclimates at wind farms may affect vegetation productivity and carbon sequestration, two critically important ecosystem services related to carbon dynamics; however, such potential impacts and driving mechanisms remain poorly understood. Wind energy deployment is increasing globally to meet carbon neutrality goals, with upscaling of onshore wind power capacity projected to grow from 542 GW in 2018 to 1787 and 5044 GW by 2030 and 2050, respectively. Furthermore, increased demand for wind energy deployment may lead to much larger wind farms in open, expansive landscapes. In turn, a large array of geographically clustered wind turbines could collectively modify local microclimate and amplify turbine-atmosphere interactions, which, if large enough, may produce detectable impacts on ecosystem dynamics. Thus, identifying and quantifying the potential impacts of wind farms on carbon-related ecosystem services may facilitate sustainable wind energy development globally.

17 WIND ENERGY↗
DOE OSTI2021

The Phase-Locking of Tropical North Atlantic and the Contribution of ENSO

The Tropical North Atlantic (TNA) is characterized by significant interannual variability in sea surface temperature (SST), which is phase-locked to the boreal spring. In this study, the phase-locking of TNA is investigated by adopting a linear stochastic-dynamical model (SDM) using seasonally modulated TNA feedbacks together with the seasonal modulation of ENSO forcing. In the observations, the role of local TNA feedbacks and ENSO forcing in TNA phase-locking are equivalently important with both preferring the peak of TNA variability to appear in the boreal spring. Besides, the seasonal modulation of TNA feedbacks and ENSO forcing strength are both mainly controlled by thermodynamic processes. In most climate models, the contribution of ENSO on TNA phase-locking is weaker than that in observations. The strength of ENSO-related TNA phase-locking is highly correlated with the relationship between ENSO and TNA, which is mainly determined by the amplitude of ENSO and its teleconnection patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Site-specific field management adaptation is key to feeding the world in the 21st century

Rapid climate change and growing population threaten global food security across the globe. Several studies have proposed early planting, increased irrigation, and increased fertilizer applications as climate adaptation strategies, yet none have considered combined and site-specific field management strategies as a comprehensive solution. Here, we analyzed non-irrigated wheat yield responses to climate change and field management adaptation using a mechanistic crop model evaluated against observed global non-irrigated wheat-yield over 3-year intervals spanning 13 years at 3749 sites (RMSE = 36 gC m -2 ). Early planting with later-maturing varieties provided the most benefit to future yields among the proposed field management adaptation strategies. Improved water use efficiency from increased CO 2 led to relatively low benefits of additional irrigation. Here we estimated that spatially heterogeneous adaption strategies had the potential to improve global wheat yields by 91% by 2100 compared to the present day. The yield improvements from combined field adaptation strategies were larger than the sum of improvements from the individual strategies. These synergistic benefits were shown to result from complementary processes regulating nutrient and water uptake, physiological tolerance to heat stress, and internal carbon and nutrient cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A New Paradigm for Observing and Modeling of Air-Sea Interactions to Advance Earth System Prediction

The protection of people, property, and environmental resources from extreme weather, seasonal patterns, and climate change drives the need for predictions of weather, ocean, and climate patterns that have skill and value at timescales longer than traditional 1-10-day forecasts, including outlooks spanning weeks to decades. Advancing Earth System Prediction (ESP) skill at this range of timescales requires improved observations, understanding, and modeling of the processes in the ocean boundary layer, the atmospheric boundary layer, and their interface. A new way of referring to this coupled system is the Air-Sea Transition Zone (ASTZ). The report that follows is framed by the paradigm that the ASTZ is a single entity that regulates the flow of energy and matter between the ocean and the atmosphere. The ASTZ is thus the medium through which the ocean and atmosphere respond to and influence one another across their often disparate scales of variability. ASTZ modeling, observing, and understanding needs are particularly acute because very few measurements exist over oceans, and even fewer span the entire ASTZ, even though oceans cover 70% of Earth’s surface and are the source of most of the rain and snow that falls on both the land and the oceans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Understanding water and energy fluxes in the Amazonia: Lessons from an observation‐model intercomparison

Abstract Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land‐atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat ( H ), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia—along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry‐season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%–30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%–15% of incoming solar radiation, compared to 0.15%–0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry‐season ET and associated cooling). These partially compensating model‐observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation ( R n ), the key driver of water and energy fluxes (LE ~ 0.6 R n and H ~ 0.15 R n ), though these biases varied among sites and models. A better representation of energy‐related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation–atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Restrepo‐Coupe, Natalia↗
DOE OSTI2022

Root responses to elevated CO 2 , warming and irrigation in a semi-arid grassland: Integrating biomass, length and life span in a 5-year field experiment

Plant roots mediate the impacts of environmental change on ecosystems, yet knowledge of root responses to environmental change is limited because few experiments evaluate multiple environmental factors and their interactions. Inferences about root functions are also limited because root length dynamics are rarely measured. Using a 5-year experiment in a mixed-grass prairie, we report the responses of root biomass, length and life span to elevated carbon dioxide (CO 2 ), warming, elevated CO 2 and warming combined, and irrigation. Root biomass was quantified using soil cores and root length dynamics were assessed using minirhizotrons. By comparing root dynamics with published results for soil resources and above-ground productivity, we provide mechanistic insights into how climate change might impact grassland ecosystems. In the upper soil layer, 0–15 cm depth, both irrigation and elevated CO 2 alone increased total root length by twofold, but irrigation decreased root biomass and elevated CO 2 had only small positive effects on root biomass. The large positive effects of irrigation and elevated CO 2 alone on total root length were due to increases in both root length production and root life span. The increased total root length and life span under irrigation and elevated CO 2 coincided with apparent shifts from water limitation of plant growth to nitrogen limitation. Warming alone had minimal effects on root biomass, length and life span in this shallow soil layer. Warming and elevated CO 2 combined increased root biomass and total root length by c. 25%, but total root length in this treatment was lower than expected if the effects of CO 2 and warming alone were additive. Treatment effects on total root length and root life span varied with soil depth and root diameter. Synthesis. Sub-additive effects of CO 2 and warming suggest studies of elevated CO 2 alone might overestimate the future capacity of grassland root systems to acquire resources. In this mixed-grass prairie, elevated CO 2 with warming stimulated total root length and root life span in deeper soils, likely enhancing plant access to more stable pools of growth-limiting resources, including water and phosphorus. Therefore, these root responses help explain previous observations of higher, and more stable, above-ground productivity in these projected climate conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Effective climate sensitivity distributions from a 1D model of global ocean and land temperature trends, 1970–2021

Abstract Current theoretically based Earth system models (ESMs) produce Effective Climate Sensitivities (EffCS) that range over a factor of three, with 80% of those models producing stronger global warming trends for 1970–2021 than do observations. To make a more observationally based estimate of EffCS, a 1D time-dependent forcing-feedback model of temperature departures from energy equilibrium is used to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. In response to two different radiative forcing scenarios, a full range of three model free parameters are evaluated to produce fits to a range of observed surface temperature trends (± 2σ) from four different land datasets and three ocean datasets, as well as deep-ocean temperature trends and borehole-based trend retrievals over land. Land-derived EffCS are larger than over the ocean, and EffCS is lower using the newer Shared Socioeconomic Pathways (SSP245, 1.86 °C global EffCS, ± 34% range 1.48–2.15 °C) than the older Representative Concentration Pathway forcing (RCP6, 2.49 °C global average EffCS, ± 34% range 2.04–2.87 °C). The strongest dependence of the EffCS results is on the assumed radiative forcing dataset, underscoring the role of radiative forcing uncertainty in determining the sensitivity of the climate system to increasing greenhouse gas concentrations from observations alone. The results are consistent with previous observation-based studies that concluded EffCS during the observational period is on the low end of the range produced by current ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Rising methane emissions from boreal lakes due to increasing ice-free days

Lakes account for about 10% of the boreal landscape and are responsible for approximately 30% of biogenic methane emissions that have been found to increase under changing climate. However, the quantification of this climate-sensitive methane source is fraught with large uncertainty under warming climate conditions. Only a few studies have addressed the mechanism of climate impact on the increase of northern lake methane emissions. This study uses a large observational dataset of lake methane concentrations in Finland to constrain methane emissions with an extant process-based lake biogeochemical model. We found that the total current diffusive emission from Finnish lakes is 0.12±0.03 Tg CH4 yr-1 and will increase by 26-59% by the end of this century depending on different warming scenarios. We discover that while warming lake water and sediment temperature plays an important role, the climate impact on ice-on periods is a key indicator of future emissions. We conclude that these boreal lakes remain a significant methane source under the warming climate within this century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An evaluation of size-resolved cloud microphysics scheme numerics for use with radar observations Part II: Condensation and evaporation

Accurate numerical modeling of clouds and precipitation is essential for weather forecasting and climate change research. While size-resolved (bin) cloud microphysics models predict particle size distributions without imposing shapes, results are subject to artificial size distribution broadening owing to numerical diffusion associated with various processes. Whereas Part I of this study addressed collision–coalescence, here we investigate numerical diffusion that occurs in solving condensation and evaporation. In a parcel model framework, all of the numerical schemes examined converge to one solution of condensation and evaporation as the mass grid is refined, and the advection-based schemes are recommended over the reassigning schemes. Including Eulerian vertical advection in a column limits the convergence to some extent, but that limitation occurs at a sufficiently fine mass grid, and the number of iterations in solving vertical advection should be minimized to reduce numerical diffusion. Insubstantial numerical diffusion in solving condensation can be amplified if collision–coalescence is also active, which in turn can be substantially diminished if turbulence effects on collision are incorporated. Here, large-eddy simulations of a drizzling stratocumulus field reveal that changes in moments of Doppler spectra obtained using different mass grids are consistent with those obtained from the simplified framework, and that spectral moments obtained using a mass grid designed to effectively reduce numerical diffusion are generally closer to observations. Notable differences between the simulations and observations still exist, and our results suggest a need to consider whether factors other than numerical diffusion in the fundamental process schemes employed can cause such differences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Extraordinary March 2022 East Antarctica “Heat” Wave. Part II: Impacts on the Antarctic Ice Sheet

Abstract Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30°–40°C temperature anomalies across the ice sheet. In Part I, we assessed the meteorological drivers that generated an intense atmospheric river (AR) that caused these record-shattering temperature anomalies. Here, we continue our large collaborative study by analyzing the widespread and diverse impacts driven by the AR landfall. These impacts included widespread rain and surface melt that was recorded along coastal areas, but this was outweighed by widespread high snowfall accumulations resulting in a largely positive surface mass balance contribution to the East Antarctic region. An analysis of the surface energy budget indicated that widespread downward longwave radiation anomalies caused by large cloud-liquid water contents along with some scattered solar radiation produced intense surface warming. Isotope measurements of the moisture were highly elevated, likely imprinting a strong signal for past climate reconstructions. The AR event attenuated cosmic ray measurements at Concordia, something previously never observed. Last, an extratropical cyclone west of the AR landfall likely triggered the final collapse of the critically unstable Conger Ice Shelf while further reducing an already record low sea ice extent. Significance Statement Using our diverse collective expertise, we explored the impacts from the March 2022 heat wave and atmospheric river across East Antarctica. One key takeaway is that the Antarctic cryosphere is highly sensitive to meteorological extremes originating from the midlatitudes and subtropics. Despite the large positive temperature anomalies driven from strong downward longwave radiation, this event led to huge amounts of snowfall across the Antarctic interior desert. The isotopes in this snow of warm airmass origin will likely be detectable in future ice cores and potentially distort past climate reconstructions. Even measurements of space activity were affected. Also, the swells generated from this storm helped to trigger the final collapse of an already critically unstable Conger Ice Shelf while further degrading sea ice coverage.

Wille, Jonathan D.↗
DOE OSTI2025

Opportunistic Short‐Term Water Uptake Dynamics by Subalpine Trees Observed via In Situ Water Isotope Measurements

Abstract Variations in tree water sources are important to understand in semi‐arid ecosystems because climatic shifts towards lower snowpack and increased drought affect water availability in subalpine forests of the western US. Here, we use daily in situ measurements of stable isotopes ( 2 H & 18 O) in soil and tree stem water, soil matric potential and sap flow to study tree water uptake dynamics. We instrumented three soil profiles down to 90 cm, as well as three aspen and engelmann spruce trees near Gothic, Colorado, in the East River watershed. We observed the fate of natural isotopic variations in rainfall, soil, and plants from June to October 2022, and in August 2023 we conducted a 2 H labeled irrigation experiment. Our observations showed that all studied aspen trees compensated for water scarcity in the shallow soil by shifting the dominant water source at 60(±20) cm to ⅔ of uptake from 90 cm within a few days of a dry period. Both species relied on snowmelt stored in the subsoil to sustain transpiration. Intense rainfall caused the plant water uptake to shift partially to top soil layers within 2 days. Spruce transpiration was lower and relied more on snowmelt, because rainfall infiltration was low in the spruce stand due to high canopy interception. Our findings highlight the important role of snowmelt stored in the deep soil layers for subalpine forest drought response and the dominant fate of monsoonal rainfall to become transpiration rather than recharging groundwater and streams in the Upper Colorado River. Plain Language Summary There is a need to understand how trees in mountainous regions respond to dry conditions that lead to water scarcity, because climate projections suggest that such conditions will become more frequent in the future. Here we present a novel data set of measurements of daily stable isotopes of water across soil profiles and in tree stems of aspen and spruce. Our data show that when the upper soil dried out, aspen trees shifted to using water from deeper layers (beneath 60 cm) to keep transpiring. For spruce trees the uptake pattern is less clear, but both types of trees mainly used snowmelt stored in the deeper soil layers to survive the dry summer. After heavy rain, aspen and spruce trees switched to using water from the top 20 cm of soil. However, for spruce, only some rain reached the soil because the dense tree canopy intercepted it, so spruce trees stayed more dependent on snowmelt and used less water overall. This study shows how important deep snowmelt water is for helping forests survive dry periods and suggests that most summer rain is quickly used by trees rather than replenishing streams and groundwater in the headwaters of the Colorado River. Key Points Tree water resources changed within a few days from snow dominated to higher share of rainfall as soils wetted up after a dry period Compensatory plant water uptake by aspen from the deep layer (90 cm), while uptake from soil depths that became drier (60 cm) declined Strong differences between water sources and availability beneath aspen and spruce, respectively

Sprenger, Matthias↗
DOE OSTI2021

Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE)

A grand challenge in hydrologic science is to understand why signals of climate change and variability, which are often visible in precipitation extremes at aggregate scales, are not consistently observed in the case of extreme flooding. However, a solution to this challenge may prove elusive unless the water cycle is viewed in an integrative manner. Thus, for riverine flooding, while Hortonian (infiltration excess) runoff may have stronger correlation with precipitation extremes and hence perhaps to warming trends or climate oscillators, Dunne (saturation excess) runoff may have a more complex relationships with time series of precipitation and with evaporation and transpiration, but rain-on-snow and snowmelt events may depend on land-surface and atmospheric temperatures. Atmospheric rivers and tropical cyclones lead to precipitation or flooding and are impacted by climate. Flooding assessments need to consider long-term baselines, evolving risk factors, coupled natural-human systems, and novel adaptation such as nature-inspired design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Land use for bioenergy: Synergies and trade-offs between sustainable development goals

Bioenergy aims to reduce greenhouse gas (GHG) emissions and contribute to meeting global climate change mitigation targets. Nevertheless, several sustainability concerns are associated with bioenergy, especially related to the impacts of using land for dedicated energy crop production. Cultivating energy crops can result in synergies or trade-offs between GHG emission reductions and other sustainability effects depending on context-specific conditions. Using the United Nations Sustainable Development Goals (SDGs) framework, the main synergies and trade-offs associated with land use for dedicated energy crop production were identified. Furthermore, the context-specific conditions (i.e., biomass feedstock, previous land use, climate, soil type and agricultural management) which affect those synergies and trade-offs were also identified. The most recent literature was reviewed and a pairwise comparison between GHG emission reduction (SDG 13) and other SDGs was carried out. A total of 427 observations were classified as either synergy (170), trade-off (176), or no effect (81). Most synergies with environmentally-related SDGs, such as water quality and biodiversity conservation, were observed when perennial crops were produced on arable land, pasture or marginal land in the ‘cool temperate moist’ climate zone and ‘high activity clay’ soils. Most trade-offs were related to food security and water availability. Previous land use and feedstock type are more impactful in determining synergies and trade-offs than climatic zone and soil type. This study highlights the importance of considering context-specific conditions in evaluating synergies and trade-offs and their relevance for developing appropriate policies and practices to meet worldwide demand for bioenergy in a sustainable manner.

09 BIOMASS FUELS↗
DOE OSTI2022

Detection and attribution of long-term and fine-scale changes in spring phenology over urban areas: A case study in New York State

Spring phenology plays an essential role in climate change, terrestrial ecosystem, and public health. Field-based monitoring and understanding of changes in spring phenology for long periods and in large regions are challenging due to the limited in-site observations. Space-based remotely sensed observations offer great potentials for monitoring decadal spring phenology changes from regional to global scales. However, the coarse-scale remotely sensed observations are insufficient to capture fine-scale spring phenology dynamics, especially in urban areas, and this makes it challenging for understanding the combined effects of climate change and urbanization on spring phenology. We derived the start of phenology season (SOS) in New York State using 30 m Landsat observations from 1990 to 2015 to understand the impact of the environment and urbanization on SOS. The results show that SOS for different years reveals heterogeneous spatial distribution. Most regions of New York State have been experiencing significant spring phenology changes in form of earlier onset of vegetation greening, ranging from 0.2 to 0.6 day/year during 1990 to 2015, and this trend varies slightly with latitudes and urbanization levels. Further, spatial correlation analysis shows that the increase in temperature and urbanization could both promote the advancement of SOS. However, the effect of urbanization (partial correlation coefficient (R) ranges from −0.289 to −0.542) on SOS is greater than the effect of temperature (R ranges from 0.006 to −0.192). The study generates a high spatio-temporal resolution spring phenology dataset for ecological, environmental and public health studies, especially in urban areas, and reveals the importance of better accounting for the urbanization effects when quantifying the SOS dynamics in phenology models.

Landsat↗
DOE OSTI2021

Interpretable Deep Learning for the Earth System with Fractal Nets

Focal Area 3: Explainable AI Our confidence in the projections made by Earth System Models (ESMs) depends on understanding them to be, in some important respects, faithful representations of the Earth system. Here we present an “explainable Artificial Intelligence (AI)” method that allows us to uncover the dynamical structure of the observed and modeled Earth system, discover hidden links across wide spatiotemporal scales, target model development efforts at poorly-represented dynamics, and optimize observed or modeled data collection to maximize predictive information. Science Challenge: Dynamical system science for the Earth system poses unique challenges given the large degree of internal climate variability. Thus, tools that help us understand how ESMs succeed and fail at representing these dynamics are crucial, particularly in relation to the observed system. Furthermore, the computational and memory constraints on ESM data output motivate in situ analysis of ESM dynamics, including automatic detection of dynamical shifts. Also, of key importance are procedures that leverage ESMs to optimize observational campaigns for improving process representation, reducing structural uncertainty and improving model skill.

54 ENVIRONMENTAL SCIENCES↗