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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Global burned area increasingly explained by climate change

Fire behaviour is changing in many regions worldwide. However, nonlinear interactions between fire weather, fuel, land use, management and ignitions have impeded formal attribution of global burned area changes. Here, in this work, we demonstrate that climate change increasingly explains regional burned area patterns, using an ensemble of global fire models. The simulations show that climate change increased global burned area by 15.8% (95% confidence interval (CI) [13.1–18.7]) for 2003–2019 and increased the probability of experiencing months with above-average global burned area by 22% (95% CI [18–26]). In contrast, other human forcings contributed to lowering burned area by 19.1% (95% CI [21.9–15.8]) over the same period. Moreover, the contribution of climate change to burned area increased by 0.22% (95% CI [0.22–0.24]) per year globally, with the largest increase in central Australia. Our results highlight the importance of immediate, drastic and sustained GHG emission reductions along with landscape and fire management strategies to stabilize fire impacts on lives, livelihoods and ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Interannual fires as a source for subarctic summer decadal climate variability mediated by permafrost thawing

Climate model simulations run under the Coupled Model Intercomparison Project Phase 6 (CMIP6) use an inhomogeneous biomass burning aerosol (BBA) emission dataset, which exhibits pronounced interannual variability from 1997–2014 due to the infusion of satellite data. Using the Community Earth System Model version 2 Large Ensemble (CESM2-LE) with original and smoothed CMIP6 BBA forcings, we show that the CMIP6 data inhomogeneity causes spurious decadal subarctic land warming. During years with reduced aerosol concentrations, increased solar radiation can trigger abrupt subarctic permafrost thawing, increased soil water drainage, upper soil drying, and subsequent surface warming. This slow process, which is further amplified by nonlinear cloud-aerosol interactions, cannot be completely offset during years of increased aerosol fluxes, thereby reddening surface temperature spectra in response to large-amplitude interannual aerosol forcing. More generally, our CESM2 experiments identify a pathway for generating decadal variability in high latitudes, involving interannual shortwave forcing and slow nonlinear soil responses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Projected changes in African easterly wave activity due to climate change

African easterly waves significantly influence regional hydroclimate, making it crucial to understand how global warming will impact their activity. Here, we investigate future changes in wave activity and assess the underlying mechanisms using an ensemble of Earth system models. We find a robust increase in wave activity over the Sahel–Sahara region by the end of the 21st century under two emission scenarios. This intensification is linked to increased baroclinicity associated with a strengthening of the meridional temperature gradient between the Guinea Coast and the Sahara. Our results also indicate that low-level warming enhances the waves by reinforcing monsoon flow, leading to increased convergence and vertical motion along the intertropical discontinuity. These energetic alterations significantly modify the conditions that currently produce these waves. Overall, our findings suggest that changes in wave activity could impact the transport of Saharan dust and mesoscale convective activity over the Sahel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying contributions of natural variability and anthropogenic forcings on increased fire weather risk over the western United States

Previous studies have identified a recent increase in wildfire activity in the western United States (WUS). However, the extent to which this trend is due to weather pattern changes dominated by natural variability versus anthropogenic warming has been unclear. Using an ensemble constructed flow analogue approach, we have employed observations to estimate vapor pressure deficit (VPD), the leading meteorological variable that controls wildfires, associated with different atmospheric circulation patterns. Our results show that for the period 1979 to 2020, variation in the atmospheric circulation explains, on average, only 32% of the observed VPD trend of 0.48 ± 0.25 hPa/decade (95% CI) over the WUS during the warm season (May to September). The remaining 68% of the upward VPD trend is likely due to anthropogenic warming. The ensemble simulations of climate models participating in the sixth phase of the Coupled Model Intercomparison Project suggest that anthropogenic forcing explains an even larger fraction of the observed VPD trend (88%) for the same period and region. These models and observational estimates likely provide a lower and an upper bound on the true impact of anthropogenic warming on the VPD trend over the WUS. During August 2020, when the August Complex “Gigafire” occurred in the WUS, anthropogenic warming likely explains 50% of the unprecedented high VPD anomalies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterizing the acceleration time of laser-driven ion acceleration with data-informed neural networks

Peak ion energy is an important figure-of-merit in short-pulse, laser-driven ion acceleration and is dependent on an associated acceleration time. Standard metrics for these quantities depend on analytical results such as the self-similar fluid model or empirical models based on relatively small experimental and simulation datasets. In this work we attempt to use a data-informed neural network (NN) as a surrogate model for a large ensemble of PIC simulations to investigate an effective acceleration time. We explore the application of a stacked convolutional and recurrent NN architecture for improved regression by incorporating the time dependencies of the data into the training process. Of particular note is how pretraining a network on lower fidelity data, e.g. 1D analytical results, greatly improves the network's ability to learn more complex, higher fidelity data. Finally, the dependency of the acceleration time on various laser and plasma parameters is explored.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Downwind control of oceanic air by land: the land wake and its sensitivity to CO 2

Oceans are well-known to be directly altered by global climate forcings such as greenhouse gas changes, but how oceans are indirectly influenced by land and its response to such forcings remains less explored. Here, we assess the present-day and projected future state of a little-explored feature of the climate system—a 'land wake' in relative humidity downwind of the east coast of North America, consisting of low-humidity continental air extending roughly 1000 km over the Atlantic ocean. The wake exists throughout the year, but is supported by high continental temperatures in summer and low continental moisture in winter. The wake is well represented in an ensemble of global climate models (GCMs), qualitatively matching reanalysis data. Under increasing atmospheric CO 2 , the land wake intensifies in GCM simulations through two pathways: the radiative effects of CO 2 on surface temperatures, and the biogeochemical effect of CO 2 on terrestrial vegetation. Vegetation responses to increased CO 2 alter the summer wake from Florida to Newfoundland, and both the radiative and biogeochemical effects of CO 2 drive reductions in coastal cloud cover. These changes illustrate the potential of rapidly changing terrestrial climate to influence coastal regions and the ocean environment downwind of continents through both light conditions and the energy balance of the surface ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations

Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter’s surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Further, our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗
DOE OSTI2025

BEAST

The Bilinear Ensemble Actuation Synthesis Toolkit (BEAST) is a computational platform that optimizes time-varying control signals to achieve a specified transfer of states governed by bilinear ensemble systems in which model parameters are subject to uncertainty.

Zlotnik, anatoly↗
DOE OSTI2020

Combining Emergent Constraints for Climate Sensitivity

A method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Separating the Influences of Low-Latitude Warming and Sea Ice Loss on Northern Hemisphere Climate Change

Abstract Analyzing a multimodel ensemble of coupled climate model simulations forced with Arctic sea ice loss using a two-parameter pattern-scaling technique to remove the cross-coupling between low- and high-latitude responses, the sensitivity to high-latitude sea ice loss is isolated and contrasted to the sensitivity to low-latitude warming. Despite some differences in experimental design, the Northern Hemisphere near-surface atmospheric sensitivity to sea ice loss is found to be robust across models in the cold season; however, a larger intermodel spread is found at the surface in boreal summer, and in the free tropospheric circulation. In contrast, the sensitivity to low-latitude warming is most robust in the free troposphere and in the warm season, with more intermodel spread in the surface ocean and surface heat flux over the Northern Hemisphere. The robust signals associated with sea ice loss include upward turbulent and longwave heat fluxes where sea ice is lost, warming and freshening of the Arctic Ocean, warming of the eastern North Pacific Ocean relative to the western North Pacific with upward turbulent heat fluxes in the Kuroshio Extension, and salinification of the shallow shelf seas of the Arctic Ocean alongside freshening in the subpolar North Atlantic Ocean. In contrast, the robust signals associated with low-latitude warming include intensified ocean warming and upward latent heat fluxes near the western boundary currents, freshening of the Pacific Ocean, salinification of the North Atlantic, and downward sensible and longwave fluxes over the ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Supersaturation, buoyancy, and deep convection dynamics

Motivated by recent discussions concerning differences of convective dynamics in polluted and pristine environments, the so-called convective invigoration in particular, this paper provides an analysis of factors affecting convective updraft buoyancy, such as the in-cloud supersaturation, condensate and precipitation loading, and entrainment. We use the deep convective period from simulations of daytime convection development over land discussed in our previous publications. An entraining parcel framework is used in the theoretical analysis. We show that for the specific case considered here, finite (positive) supersaturation noticeably reduces pseudo-adiabatic parcel buoyancy and cumulative convective available potential energy (cCAPE) in the lower troposphere. This comes from keeping a small fraction of the water vapor in a supersaturated state and thus reducing the latent heating. Such a lower-tropospheric impact is comparable to the effects of condensate loading and entrainment in the idealized parcel framework. For the entire tropospheric depth, loading and entrainment have a much more significant impact on the total CAPE. For the cloud model results, we compare ensemble simulations applying either a bulk microphysics scheme with saturation adjustment or a more comprehensive double-moment scheme with supersaturation prediction. We compare deep convective updraft velocities, buoyancies, and supersaturations from all ensembles. In agreement with the parcel analysis, the saturation-adjustment scheme provides noticeably stronger updrafts in the lower troposphere. For the simulations predicting supersaturation, there are small differences between pristine and polluted conditions below the freezing level that are difficult to explain by standard analysis of the in-cloud buoyancy components. By applying the piggybacking technique, we show that the lower-tropospheric buoyancy differences between pristine and polluted simulations come from a combination of temperature (i.e., latent heating) and condensate loading differences that work together to make polluted buoyancies and updraft velocities slightly larger when compared to their pristine analogues. Overall, the effects are rather small and contradict previous claims of a significant invigoration of deep convection in polluted environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Impacts of land use change and elevated CO 2 on the interannual variations and seasonal cycles of gross primary productivity in China

Climate change, rising CO 2 concentration, and landuse and land cover change (LULCC) are primary driving forces for terrestrialgross primary productivity (GPP), but their impacts on the temporal changesin GPP are uncertain. In this study, the effects of the three main factorson the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPPin China were investigated using 12 terrestrial biosphere models from theMulti-scale Synthesis and Terrestrial Model Intercomparison Project. Thesimulated ensemble mean value of China's GPP between 1981 and 2010, drivenby common climate forcing, LULCC and CO 2 data, was found to be7.4±1.8 Pg C yr -1 . In general, climate was the dominant controlfactor of the annual trends, IAV and seasonality of China's GPP. Theoverall rising CO2 led to enhanced plant photosynthesis, thusincreasing annual mean and IAV of China's total GPP, especially innortheastern and southern China, where vegetation is dense. LULCC decreasedthe IAV of China's total GPP by ~7%, whereas rising CO 2 induced an increase of 8 %. Compared to climate change andelevated CO 2 , LULCC showed less contributions to GPP's temporalvariation, and its impact acted locally, mainly in southwestern China.Furthermore, this study also examined subregional contributions to thetemporal changes in China's total GPP. Southern and southeastern Chinashowed higher contributions to China's annual GPP, whereas southwestern andcentral parts of China explained larger fractions of the IAV in China's GPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating an Earth system model from a water manager perspective

Abstract. The large spatial scale of global Earth system models (ESMs) is often cited as an obstacle to using the output by water resource managers in localized decisions. Recent advances in computing have improved the fidelity of hydrological responses in ESMs through increased connectivity between model components. However, the models are seldom evaluated for their ability to reproduce metrics that are important for and resonate with practitioners or that allow practitioners to situate higher-resolution model outputs within a cascade of uncertainty stemming from different models and scenarios. We draw on the combined experience of the author team and water manager workshop participants to identify salient water management metrics and evaluate whether they are credibly reproduced over the conterminous USA by the Community Earth System Model v2 (CESM2) Large Ensemble. We find that, while the exact values may not match the observations, aspects such as interannual variability can be reproduced by CESM2 for the mean wet day precipitation and length of dry spells. CESM2 also captures the proportion of total annual precipitation that derives from the heaviest rain days in watersheds that are not snow-dominated. Aggregating the 7 d mean daily runoff to two-digit Hydrological Unit Code (HUC2) watersheds also shows that rain-dominated regions capture the timing and interannual variability of annual maximum and minimum flows. We conclude that there is potential for far greater use of large-ensemble ESMs, such as CESM2, in long-range water management decisions to supplement high-resolution regional projections.

Tye, Mari R. (ORCID:0000000324911020)↗
NASA NTRS1972

Determination of bulk properties of tropical cloud clusters from large scale heat and moisture budgets, appendix B

The bulk properties of tropical cloud clusters, as the vertical mass flux, the excess temperature, and moisture and the liquid water content of the clouds, are determined from a combination of the observed large-scale heat and moisture budgets over an area covering the cloud cluster, and a model of a cumulus ensemble which exchanges mass, heat, vapor and liquid water with the environment through entrainment and detrainment. The method also provides an understanding of how the environmental air is heated and moistened by the cumulus convection. An estimate of the average cloud cluster properties and the heat and moisture balance of the environment, obtained from 1956 Marshall Islands data, is presented.

Yanai, M.↗
NASA NTRS2015

Assessing Disparities of Dengue Virus Transmission Risk across the US-Mexican Border Using a Climate Driven Vector-Epidemiological Model

Dengue fever is a mosquito‐borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Although transmission regularly occurs along the border region in Mexico, dengue virus transmission in bordering Arizona has not occurred. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input and Daymet for temperature and supplemental precipitation input, we modeled dengue transmission along a US‐Mexico transect using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Model runs were performed for 5 cities in Sonora, Mexico and southern Arizona. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. For cities with reported dengue case data, the top model simulations that best reproduced dengue case numbers were retained and their parameter values were extracted for comparison. These parameter values were used to run simulations in areas where dengue virus transmission does not occur or where dengue fever case data was unavailable. Additional model runs were performed to reveal how changes in climate or parameter values could alter transmission risk along the transect. The relative influence of climate variability and model parameters on dengue virus transmission is assessed to help public health workers prepare location specific infection prevention strategies.

Morin, Cory↗
NASA NTRS2019

Five Decades of Northern Land Carbon Uptake Revealed by the Interhemispheric CO2 Gradient

The global land and ocean carbon sinks have increased proportionally with increasing carbon dioxide emissions during the past decades. It is thought that Northern Hemisphere lands make a dominant contribution to the global land carbon sink; however, the long-term trend of the northern land sink remains uncertain. Here, using measurements of the interhemispheric gradient of atmospheric carbon dioxide from 1958 to 2016, we show that the northern land sink remained stable between the 1960s and the late 1980s, then increased by 0.5 ± 0.4 petagrams of carbon per year during the 1990s and by 0.6 ± 0.5 petagrams of carbon per year during the 2000s. The increase of the northern land sink in the 1990s accounts for 65% of the increase in the global land carbon flux during that period. The subsequent increase in the 2000s is larger than the increase in the global land carbon flux, suggesting a coincident decrease of carbon uptake in the Southern Hemisphere. Comparison of our findings with the simulations of an ensemble of terrestrial carbon models over the same period suggests that the decadal change in the northern land sink between the 1960s and the 1990s can be explained by a combination of increasing concentrations of atmospheric carbon dioxide, climate variability and changes in land cover. However, the increase during the 2000s is underestimated by all models, which suggests the need for improved consideration of changes in drivers such as nitrogen deposition, diffuse light and land-use change. Overall, our findings underscore the importance of Northern Hemispheric land as a carbon sink.

Carbon cycle↗
NASA NTRS2019

Seasonal Forecasting of the Quasi-Biennial Oscillation

The ability to seasonally forecast the Quasi-Biennial Oscillation (QBO) was examined using NASA S2S (Sub-seasonal to Seasonal), 9-month, retrospective forecasts. Validation of these forecasts showed that the S2S retrospective QBO forecasts improved skill in predicting the QBO amplitude and phase over a simple QBO phase propagation model at forecast lead times of 1 to 3 months. Results from an initial assessment of whether more accurate QBO forecasts can improve Northern Hemisphere winter sea level pressure forecasts showed no significant forecast improvement at a 1-month lead time, indicating the need for improved stratosphere-troposphere QBO coupling metrics and pathway identification. Overall, these results suggest that future improvements in representing the QBO in global models can increase the ensemble fraction of valid 1 to 3 month QBO forecasts and potentially extend useful QBO forecasts beyond 3 months.

Coy, Lawrence↗