Abstract Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers projected emissions. Here, we use a simple integrated assessment model, calibrated to century-scale data and expert assessments emissions, global economic growth, population make probabilistic through 2100. Under variety assumptions about fossil fuel resource levels decarbonization rates, our...

The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver sea-level changes. Anthropogenic change may drive a sizeable AIS tipping point with subsequent increases in coastal flooding risks. Many studies analyzing flood risks use simple models project future responses and its contributions. These analyses have provided new insights, but they are often silent on effects potentially processes such as Marine Ice Sheet Instability (MISI) or Cliff (MICI)....

Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional designed experiments remains an open area research. Contemporary analyses work on metrics that summarize collective properties microbiome, but such reductions preclude inference fine-scale effects environmental stimuli individual microbial taxa. Other approaches model...

Inference for spatial generalized linear mixed models (SGLMMs) high-dimensional non-Gaussian data is computationally intensive. The computational challenge due to the random effects and because Markov chain Monte Carlo (MCMC) algorithms these tend be slow mixing. Moreover, confounding inflates variance of fixed effect (regression coefficient) estimates. Our approach addresses both issues by replacing with a reduced-dimensional representation based on projections. Standard MCMC mix well...

Adjusting for an unmeasured confounder is generally intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on coherence between exposure and that ensure effect of estimable. specify our model assumptions spectral domain to allow different degrees confounding at resolutions. One assumption ensures identifiability present global scales dissipates local scales. show this equivalent adjusting global-scale by adding a spatially...

The aerosols over the Tibetan Plateau (TP) play an important role in radiative budget and hydrologic cycle Asia even northern hemisphere. Adjacent to major emission sources of air pollutants, transboundary pollutions transported TP due unique geographical location climatic characteristics, is exogenous driver multi-layer changes TP. influence boundary layer height (BLH) India pollution from 1980 2018 was investigated study. Results showed that pollutants more efficient within compared with...

People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. To make informed decisions on day-to-day activities, they interested in real-time information a localized scale. Publicly available, fine-scale, high-quality pollution measurements acquired using mobile monitors represent paradigm shift measurement technologies. A methodological framework utilizing these fine-scale provide maps and short-term quality...

Kriging is the predominant method used for spatial prediction, but relies on assumption that predictions are linear combinations of observations. often also additional assumptions such as normality and stationarity. We propose a more flexible prediction based Nearest-Neighbor Neural Network (4N) process embeds deep learning into geostatistical model. show 4N valid stochastic series new ways to construct features be inputs model neighboring information. Our framework outperforms some existing...

Sea-level rise is a key driver of projected flooding risks. The design strategies to manage these risks often hinges on projections that inform decision-makers about the surrounding uncertainties. Producing semi-empirical sea-level difficult, for example, due complexity error structure observations, such as time-varying (heteroskedastic) observation errors and autocorrelation data-model residuals. This raises question how neglecting impacts hindcasts projections. Here, we quantify this...

Diabetic retinopathy-related (DR-related) diseases are posing an increasing threat to eye health as the number of patients with diabetes mellitus that young increases significantly. The automatic diagnosis DR-related has benefited from rapid development image semantic segmentation and other deep learning technology.Inspired by architecture U-Net family, a neighbored attention (NAU-Net) is designed balance identification performance computational cost for DR fundus segmentation. In new...

The future behavior of the West Antarctic Ice Sheet (WAIS) may have a major impact on climate. For instance, ice sheet melt contribute significantly to global sea‐level rise. Understanding current state WAIS is therefore great interest. drained by fast‐flowing glaciers, which are contributors loss. Hence, understanding stability and dynamics glaciers critical for predicting sheet. Glacier driven interplay between topography, temperature, basal conditions beneath ice. A glacier model...

Abstract Dust–cloud–surface radiation interactions are a complex nonlinear relation referring to the influences of both atmospheric dust and dust-on-snow on surface albedo. A ‘Tiramisu’ snow event occurred 1 December 2018, in Urumqi, China, providing an excellent testbed for exploring comprehensive effect induced by those deposited atop fresh snowpack radiation. detailed analysis indicates that decrease albedo 0.17–0.26 (22%–34%) is contributed effects dust–cloud at synoptic scale this case....

Rapid changes in Earth's cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and projecting future of Arctic Antarctic ice sheets. However, these are typically subject large parametric uncertainties due poorly constrained model input parameters that govern simulated calibration provides formal statistical framework infer using observational data, quantify uncertainty projections parameters....

The escalating frequency and severity of global wildfires necessitate an in-depth understanding monitoring wildfire smoke impacts, specifically its contribution to fine particulate matter (PM2.5). We propose a data-fusion method study PM2.5 using satellite-derived plume indicators data. Our incorporates two types data, the high-quality but sparse Air Quality System (AQS) stations abundant less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists....

The future behavior of the West Antarctic Ice Sheet (WAIS) may have a major impact on climate. For instance, ice sheet melt contribute significantly to global sea level rise. Understanding current state WAIS is therefore great interest. drained by fast-flowing glaciers which are contributors loss. Hence, understanding stability and dynamics critical for predicting sheet. Glacier driven interplay between topography, temperature basal conditions beneath ice. A glacier model describes...

Rapid changes in Earth's cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and projecting future of Arctic Antarctic ice sheets. However, these are typically subject large parametric uncertainties, due poorly constrained model input parameters that govern simulated calibration provides formal statistical framework infer parameters, using observational data, quantify uncertainty projections...

Spatial generalized linear mixed models (SGLMMs) are popular and flexible for non-Gaussian spatial data. They useful interpolations as well fitting regression that account dependence, commonly used in many disciplines such epidemiology, atmospheric science, sociology. Inference SGLMMs is typically carried out under the Bayesian framework at least part because computational issues make maximum likelihood estimation challenging, especially when high-dimensional data involved. Here we provide a...

Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is quantify the risk of adverse health outcomes resulting from exposures such chemical mixtures identify which mixture constituents may be driving etiologic associations. variety statistical methods have been proposed address these research questions. However, they generally rely solely on measured exposure data available within a specific...