Minimal East Antarctic Ice Sheet retreat onto land during the past eight million years.

The East Antarctic Ice Sheet (EAIS) is the largest potential contributor to sea-level rise. However, efforts to predict the future evolution of the EAIS are hindered by uncertainty in how it responded to past warm periods, for example, during the Pliocene epoch (5.3 to 2.6 million years ago), when atmospheric carbon dioxide concentrations were last higher than 400 parts per million. Geological evidence indicates that some marine-based portions of the EAIS and the West Antarctic Ice Sheet retreated during parts of the Pliocene1,2, but it remains unclear whether ice grounded above sea level also experienced retreat. This uncertainty persists because global sea-level estimates for the Pliocene have large uncertainties and cannot be used to rule out substantial terrestrial ice loss 3 , and also because direct geological evidence bearing on past ice retreat on land is lacking. Here we show that land-based sectors of the EAIS that drain into the Ross Sea have been stable throughout the past eight million years. We base this conclusion on the extremely low concentrations of cosmogenic 10Be and 26Al isotopes found in quartz sand extracted from a land-proximal marine sediment core. This sediment had been eroded from the continent, and its low levels of cosmogenic nuclides indicate that it experienced only minimal exposure to cosmic radiation, suggesting that the sediment source regions were covered in ice. These findings indicate that atmospheric warming during the past eight million years was insufficient to cause widespread or long-lasting meltback of the EAIS margin onto land. We suggest that variations in Antarctic ice volume in response to the range of global temperatures experienced over this period-up to 2-3 degrees Celsius above preindustrial temperatures 4 , corresponding to future scenarios involving carbon dioxide concentrations of between 400 and 500 parts per million-were instead driven mostly by the retreat of marine ice margins, in agreement with the latest models5,6.

Balancing multiple stakeholder objectives for floodplain reconnection and wetland restoration.

Floodplain reconnection and wetland restoration projects are increasingly implemented to enhance flood resiliency, and these nature-based solutions can also achieve co-benefits of nutrient storage and improved habitats. Considering the multiple and sometimes incompatible objectives of stakeholders for uses of riverside lands, a decision-support tool linked to a hydraulic model would enable planners to simulate floodplain restoration scenarios while also quantifying and assessing the trade-offs between the stakeholder objectives to arrive at optimal restoration designs. We illustrate a simple ranking approach using an n-dimensional objective function to represent key stakeholders engaged in restoration. We applied our approach in a watershed in central Vermont (USA) that has been identified by regional and state-level stakeholders as an important location to mitigate flooding damages but also to improve water quality - all within a context of increasing development pressures on riparian lands and limited financial resources to accomplish restoration. Eleven different floodplain reconnection and wetland restoration modifications were combined in six scenarios and simulated with 2D Hydrologic Engineering Center's River Analysis System (2D HEC-RAS), along with a baseline (no-action) scenario. Only modest attenuation of peak flows for 2-, 25-, 50- and 100-year design storms was achieved by the floodplain restoration scenarios due to the steep setting, and flashy nature of the watershed. Yet, several scenarios of floodplain reconnection projects more than met the necessary annual phosphorus load reductions targeted under a Total Maximum Daily Load implementation plan. Our approach provided planners with a ranking of restoration scenarios that best met multiple stakeholder objectives and allowed effectiveness of alternate design scenarios to be quantified, justified, and visualized to promote consensus decision-making.

Quantifying the social benefits and costs of reducing phosphorus pollution under climate change.

Excess phosphorus loading to waterbodies has led to increasing frequency and severity of harmful algal blooms, negatively impacting economic activity and human health. While interventions to improve water quality can create large societal benefits, these investments are costly and the value of benefits is often unknown. Understanding the social and economic impacts of reduced phosphorus loading is critical for developing effective land use policies and for generating public and political support for these initiatives. Here, we quantify the social benefits and costs of improving water quality in Lake Champlain under a range of phosphorus reduction and climate change scenarios between 2016 and 2050. We use statistical models to link water quality outputs from an established integrated assessment model with three categories of benefits: tourism expenditures, property sales, and avoided human health impacts. We estimate the costs of reducing phosphorus loading using data reported by the State of Vermont. We find that under the most aggressive phosphorus reduction scenario, the total benefits of improved water quality are $55 to $60 million between 2016 and 2050. Over this 35 year time horizon, the combined benefits do not outweigh the costs under any scenario. If the time horizon is extended to 2100 or beyond, however, the benefits may exceed the costs if the applied discount rate is less than 3%. Importantly, we almost certainly underestimate the value of clean water, due to the omission of other types of benefits. Despite this uncertainty, our study provides a tractable framework for disentangling the complex relationships between water quality and human well-being, and illuminates the value of reductions in phosphorus loading to society.

Monitoring Perioperative Services Using 3D Multi-Objective Performance Frontiers.

The Acute Care Surgery model has been widely adopted by hospitals across the United States, with Acute Care Surgery services managing Emergency General Surgery patients that were previously being treated by General Surgery. In this analysis, we evaluate the impact of an Acute Care Surgery service model on General Surgery at the University of Vermont Medical Center using three metrics: under-utilized time, spillover time, and a financial ratio of work Relative Value Units over clinical Full Time Equivalents. These metrics are evaluated and used to identify three-dimensional Pareto optimality of General Surgery prior to and after the October 2015 tactical allocation to the Acute Care Surgery model. Our analysis was further substantiated using a Markov Chain Monte Carlo model for Bayesian Inference. We applied multi-objective Pareto and Bayesian breakpoint analysis to three operating room metrics to assess the impact of new operating room management decisions. In the two-dimensional space of Fig. 2, panel a), the post-tactical allocation front lies closer to the origin representing more optimal solutions for productivity and under-utilized time. The post-tactical allocation front is also closer to the origin for productivity and spillover time as shown in the two-dimensional space of Fig. 2, panel b). The results of the three-dimensional multi-objective analysis of Fig. 3 illustrate that the GS post-tactical allocation Pareto-surface is contained within a much smaller volume of space than the GS pre-tactical allocation Pareto-surface. The post-tactical allocation Pareto-surface is slightly lower along the z-axis, representing lower productivity than the pre-tactical allocation surface. This methodology might contribute to the external benchmarking and monitoring of perioperative services by visualizing the operational implications following tactical decisions in operating room management.

The Power Law in Operating Room Management.

The Acute Care Surgery model has been implemented by many hospitals in the United States. As complex adaptive systems, healthcare systems are composed of many interacting elements that respond to intrinsic and extrinsic inputs. Systems level analysis may reveal the underlying organizational structure of tactical block allocations like the Acute Care Surgery model. The purpose of this study is to demonstrate one method to identify a key characteristic of complex adaptive systems in the perioperative services. Start and end times for all surgeries performed at the University of Vermont Medical Center OR1 were extracted for two years prior to the transition to an Acute Care Surgery service and two years following the transition. Histograms were plotted for the inter-event times calculated from the difference between surgical cases. A power law distribution was fit to the post-transition histogram. The Kolmogorov-Smirnov test for goodness-of-fit at 95% level of significance shows the histogram plotted from post-transition inter-event times follows a power law distribution (K-S = 0.088, p = 0.068), indicating a Complex Adaptive System. Our analysis demonstrates that the strategic decision to create an Acute Care Surgery service has direct implications on tactical and operational processes in the perioperative services. Elements of complex adaptive systems can be represented by a power law distributions and similar methods may be applied to identify other processes that operate as complex adaptive systems in perioperative care. To make sustained improvements in the perioperative services, focus on manufacturing-based interventions such as Lean Six Sigma should instead be shifted towards the complex interventions that modify system-specific behaviors described by complex adaptive system principles when power law relationships are present.

Social-psychological determinants of farmer intention to adopt nutrient best management practices: Implications for resilient adaptation to climate change.

Successful adaptation to global climate change and enhancement of agricultural watersheds' resilience requires widespread use of Nutrient Best Management Practices (NBMPs) by farms of all sizes. In the US, adoption of many NBMP practices is voluntary and insufficient to achieve local and downstream conservation objectives. Despite evidence that both social-psychological factors and socio-economic factors influence farmer decision-making, very few studies of farmers' decision-making related to NBMP adoption combine these two factor groups in a theoretically rigorous way. To better understand farmers' management decisions, we test the social-psychological Theory of Planned Behavior (TPB) to determine the relative influence of attitudes, perceived social norms, and perceived behavioral control on adoption of nine NBMPs. A survey was designed by the research team and implemented by the U.S. Department of Agriculture-National Agricultural Statistics Service (USDA-NASS) in 2013, and replicated in 2016, on a stratified sample of 129 farmers (including panel data on 56 farmers). Farmers were located in the Missisquoi, and Lamoille River watersheds of the Lake Champlain Basin in the Northeast region of the United States. Survey responses revealed variation in past adoption of NBMPs was sensitive to practice type and farm size. We developed nine weighted structural equation models to test endogenous (social-psychological) and exogenous (policy, economic and demographic) predictors of farmer intention to adopt NBMPs. We found that perceived behavioral control had the largest effect size and strongest statistical significance on the farmers' expressed intentions to adopt NBMPs in the future. For a subset of NBMPs, perceived social norms and farmer attitudes toward these NBMPs were each also significant drivers of intention to adopt individual practices. Among the exogenous variables, we found that large farm size, college education, and having a conservation easement all had a positive influence on farmers' intention to adopt NBMPs. This study suggests that for widespread adoption of NBMPs, environmental managers, policy makers, and program developers should be attentive to farmers' perceived behavioral control, and support the design and execution of outreach and technical assistance programs that build on drivers of farmers' decision making.

Water Pollution and Environmental Concerns in Anesthesiology.

Medications administered by anesthesia health care providers and subsequently excreted into the water supply system have the potential to affect ecological systems. Presently, there is a lack of literature examining which medications or metabolites enter the waste stream. Further, their potential environmental impacts are often unknown or simply not considered as an externality of medical practice. Recent work examining the practice of anesthesiology has explored the solid waste stream, and the global warming potential of anesthetic gases, however the potential aquatic impacts remain unexplored. To address the potential for waterborne pollution and environmental toxicity, we extracted the total intravenous medications (by mass) administered by anesthesiologists in 2017 at The University of Vermont Medical Center (UVMMC), a mid-size regional Level 1 trauma center in Burlington, VT. The most commonly administered medications were: cefazolin, propofol, acetaminophen, sugammadex and lidocaine. To estimate the amount of each medication that entered the wastewater stream, we used published metabolism profiles to adjust from the total amount administered to the amount excreted unchanged or as prominent metabolites. For each medication we reviewed existing literature concerning their environmental fate and impacts in water. Due to the constraints of current knowledge, it is not possible to determine the exact fate and impacts of these drugs. Some medications, like propofol, have the potential for significant bioaccumulation and persistence. Others, such as lidocaine and acetaminophen, have short half-lives in the environment but their constant delivery and excretion result in pseudo-persistence. The current literature mostly assesses acute exposure at doses higher than could be expected in the environment on select species. While significant toxicities across a variety of species have been found repeatedly, chronic low dose exposures require further study for all the medications discussed. Finally, multi-drug impacts are likely to be more impactful than single-drug toxicities. While we cannot state definitive impacts, the pharmaceuticals most used in anesthesiology have a clear toxic potential and future studies should more closely examine the relative contribution of anesthesia to pharmaceutical pollution, as well as points of intervention for minimizing these unintended consequences of healthcare delivery.

A Tandem Evolutionary Algorithm for Identifying Causal Rules from Complex Data.

We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease.

Somtimes: self organizing maps for time series clustering and its application to serious illness conversations.

There is demand for scalable algorithms capable of clustering and analyzing large time series data. The Kohonen self-organizing map (SOM) is an unsupervised artificial neural network for clustering, visualizing, and reducing the dimensionality of complex data. Like all clustering methods, it requires a measure of similarity between input data (in this work time series). Dynamic time warping (DTW) is one such measure, and a top performer that accommodates distortions when aligning time series. Despite its popularity in clustering, DTW is limited in practice because the runtime complexity is quadratic with the length of the time series. To address this, we present a new a self-organizing map for clustering TIME Series, called SOMTimeS, which uses DTW as the distance measure. The method has similar accuracy compared with other DTW-based clustering algorithms, yet scales better and runs faster. The computational performance stems from the pruning of unnecessary DTW computations during the SOM's training phase. For comparison, we implement a similar pruning strategy for K-means, and call the latter K-TimeS. SOMTimeS and K-TimeS pruned 43% and 50% of the total DTW computations, respectively. Pruning effectiveness, accuracy, execution time and scalability are evaluated using 112 benchmark time series datasets from the UC Riverside classification archive, and show that for similar accuracy, a 1.8× speed-up on average for SOMTimeS and K-TimeS, respectively with that rates vary between 1× and 18× depending on the dataset. We also apply SOMTimeS to a healthcare study of patient-clinician serious illness conversations to demonstrate the algorithm's utility with complex, temporally sequenced natural language. Supplementary Information: The online version contains supplementary material available at 10.1007/s10618-023-00979-9.

Landfill intermediate cover soil microbiomes and their potential for mitigating greenhouse gas emissions revealed through metagenomics.

Landfills are a major source of anthropogenic methane emissions and have been found to produce nitrous oxide, an even more potent greenhouse gas than methane. Intermediate cover soil (ICS) plays a key role in reducing methane emissions but may also result in nitrous oxide production. To assess the potential for microbial methane oxidation and nitrous oxide production, long sequencing reads were generated from ICS microbiome DNA and reads were functionally annotated for 24 samples across ICS at a large landfill in New York. Further, incubation experiments were performed to assess methane consumption and nitrous oxide production with varying amounts of ammonia supplemented. Methane was readily consumed by microbes in the composite ICS and all incubations with methane produced small amounts of nitrous oxide even when ammonia was not supplemented. Incubations without methane produced significantly less nitrous oxide than those incubated with methane. In incubations with methane added, the observed specific rate of methane consumption was 0.776 +/- 0.055 µg CH4 g dry weight (DW) soil-1 h-1 and the specific rate of nitrous oxide production was 3.64 × 10-5 +/- 1.30 × 10-5 µg N2O g DW soil-1 h-1. The methanotrophs Methylobacter and an unclassified genus within the family Methlyococcaceae were present in the original ICS samples and the incubation samples, and their abundance increased during incubations with methane. Genes encoding particulate methane monooxygenase/ ammonia monooxygenase (pMMO) were much more abundant than genes encoding soluble methane monooxygenase (sMMO) across the landfill ICS. Genes encoding proteins that convert hydroxylamine to nitrous oxide were not highly abundant in the ICS or incubation metagenomes. In total, these results suggest that although ammonia oxidation via methanotrophs may result in low levels of nitrous oxide production, ICS microbial communities have the potential to greatly reduce the overall global warming potential of landfill emissions.

Microbiome assembly and stability during start-up of a full-scale, two-phase anaerobic digester fed cow manure and mixed organic feedstocks.

Carbon transformations during anaerobic digestion are mediated by complex microbiomes, but their assembly is poorly understood, especially in full-scale digesters. Gene-centric metagenomics combining functional and taxonomic classification was performed for an on-farm digester during start-up. Cow manure and organic waste pre-treated in a hydrolysis tank were fed to the methane-producing digester and the volatile solids loading rate was slowly increased from 0 to 3.5 kg volatile solids m-3 d-1 over one year. The microbial community in the anaerobic digester exhibited a high ratio of archaea, which were dominated by hydrogenotrophic methanogens. Bacteria in the anaerobic digester had a high abundance of genes for ferredoxin cycling, H2 generation, and more metabolically complex fermentations than in the hydrolysis tank. In total, the results show that a functionally stable microbiome was achieved quickly during start-up and that the microbiome created in the low-pH hydrolysis tank did not persist in the downstream anaerobic digester.

Unraveling associations between cyanobacteria blooms and in-lake environmental conditions in Missisquoi Bay, Lake Champlain, USA, using a modified self-organizing map.

Exploratory data analysis on physical, chemical, and biological data from sediments and water in Lake Champlain reveals a strong relationship between cyanobacteria, sediment anoxia, and the ratio of dissolved nitrogen to soluble reactive phosphorus. Physical, chemical, and biological parameters of lake sediment and water were measured between 2007 and 2009. Cluster analysis using a self-organizing artificial neural network, expert opinion, and discriminant analysis separated the data set into no-bloom and bloom groups. Clustering was based on similarities in water and sediment chemistry and non-cyanobacteria phytoplankton abundance. Our analysis focused on the contribution of individual parameters to discriminate between no-bloom and bloom groupings. Application to a second, more spatially diverse data set, revealed similar no-bloom and bloom discrimination, yet a few samples possess all the physicochemical characteristics of a bloom without the high cyanobacteria cell counts, suggesting that while specific environmental conditions can support a bloom, another environmental trigger may be required to initiate the bloom. Results highlight the conditions coincident with cyanobacteria blooms in Missisquoi Bay of Lake Champlain and indicate additional data are needed to identify possible ecological contributors to bloom initiation.

The StoryListening Project: Feasibility and Acceptability of a Remotely Delivered Intervention to Alleviate Grief during the COVID-19 Pandemic.

Background: The events surrounding the COVID-19 pandemic have created heightened challenges to coping with loss and grief for family and friends of deceased individuals, as well as clinicians who experience loss of their patients. There is an urgent need for remotely delivered interventions to support those experiencing grief, particularly due to growing numbers of bereaved individuals during the COVID-19 pandemic. Objective: To determine the feasibility and acceptability of the brief, remotely delivered StoryListening storytelling intervention for individuals experiencing grief during the COVID pandemic. Setting/Subjects: A single-arm pilot study was conducted in the United States. Participants included adult English-speaking family members, friends, or clinicians of individuals who died during the COVID-19 pandemic. All participants engaged in a televideo StoryListening session with a trained StoryListening doula. Measurements: Participants completed a brief follow-up telephone interview two weeks after the StoryListening session. We describe enrollment and retention data to assess feasibility and conducted a deductive thematic analysis of the follow-up interview data to assess acceptability. Results: Sixteen clinicians and 48 friends/family members enrolled in the study (n = 64; 75% enrollment), 62 completed a StoryListening session; 60 completed the follow-up interview. Participants reported that the intervention was useful and offered a valuable opportunity to process their grief experience. Conclusions: The StoryListening intervention is feasible and acceptable for friends/family members and clinicians who have experienced grief during COVID. Our intervention may offer an accessible first-line option to address the increasing wave of bereavement-related distress and clinician burnout in the United States.

An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences.

Context: Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. Purpose: To assess the feasibility of automating the identification of a conversational feature, Connectional Silence, which is associated with important patient outcomes. Methods: Using audio recordings from the Palliative Care Communication Research Initiative cohort study, we develop and test an automated measurement pipeline comprising three machine-learning (ML) tools-a random forest algorithm and a custom convolutional neural network that operate in parallel on audio recordings, and subsequently a natural language processing algorithm that uses brief excerpts of automated speech-to-text transcripts. Results: Our ML pipeline identified Connectional Silence with an overall sensitivity of 84% and specificity of 92%. For Emotional and Invitational subtypes, we observed sensitivities of 68% and 67%, and specificities of 95% and 97%, respectively. Conclusion: These findings support the capacity for coordinated and complementary ML methods to fully automate the identification of Connectional Silence in natural hospital-based clinical conversations.

Developing a Direct Observation Measure of Therapeutic Connection in Psilocybin-Assisted Therapy: A Feasibility Study.

Context: Measuring therapeutic connection during psilocybin-assisted therapy is essential to understand underlying mechanisms, inform training, and guide quality improvement. Purpose: To evaluate the feasibility of directly observing indicators of therapeutic connection during psilocybin administration encounters. Methods: We evaluated audio and video data from a recent clinical trial for observable expressions of therapeutic connection as defined in proposed best-practice competencies (i.e., empathic abiding presence and interpersonal grounding). We selected the first four 8-hour encounters involving unique participants, therapists, and gender pairs. Each video was independently coded by three members of an interprofessional six-person team. Using a structured checklist, coders recorded start-stop times, the audible (i.e., speech prosody or words) and visible (i.e., body movements, eye gaze, and touch) cues marking the event, and the qualities of the interaction (e.g., expression of awe, trust, distress, and calmness). We assessed feasibility by observing the frequency, distribution, and overlap of cues and qualities coders used to identify and define moments of therapeutic connection. Results: Among the 2074 minutes of video, coders recorded 372 moments of therapeutic connection. Eighty-three percent were identified by at least two coders and 41% by all three. Coders used a combination of audible and visual cues to identify therapeutic connection in 51% of observed events (190/372). Both the cues and qualities of therapeutic connection expressions varied over the course of psilocybin temporal effects on states of consciousness. Conclusion: Direct observation of therapeutic human connection is feasible, sensitive to changes in states of consciousness and requires evaluation of audible and visual data.

Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning.

Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance- This work advances the use of wearables for detecting childhood mental health disorders.

Identifying neuroanatomical and behavioral features for autism spectrum disorder diagnosis in children using machine learning.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that can cause significant social, communication, and behavioral challenges. Diagnosis of ASD is complicated and there is an urgent need to identify ASD-associated biomarkers and features to help automate diagnostics and develop predictive ASD models. The present study adopts a novel evolutionary algorithm, the conjunctive clause evolutionary algorithm (CCEA), to select features most significant for distinguishing individuals with and without ASD, and is able to accommodate datasets having a small number of samples with a large number of feature measurements. The dataset is unique and comprises both behavioral and neuroimaging measurements from a total of 28 children from 7 to 14 years old. Potential biomarker candidates identified include brain volume, area, cortical thickness, and mean curvature in specific regions around the cingulate cortex, frontal cortex, and temporal-parietal junction, as well as behavioral features associated with theory of mind. A separate machine learning classifier (i.e., k-nearest neighbors algorithm) was used to validate the CCEA feature selection and for ASD prediction. Study findings demonstrate how machine learning tools might help move the needle on improving diagnostic and predictive models of ASD.

Modeling the sensitivity of cyanobacteria blooms to plausible changes in precipitation and air temperature variability.

Many recent studies have attributed the observed variability of cyanobacteria blooms to meteorological drivers and have projected blooms with worsening societal and ecological impacts under future climate scenarios. Nonetheless, few studies have jointly examined their sensitivity to projected changes in both precipitation and temperature variability. Using an Integrated Assessment Model (IAM) of Lake Champlain's eutrophic Missisquoi Bay, we demonstrate a factorial design approach for evaluating the sensitivity of concentrations of chlorophyll a (chl-a), a cyanobacteria surrogate, to global climate model-informed changes in the central tendency and variability of daily precipitation and air temperature. An Analysis of Variance (ANOVA) and multivariate contour plots highlight synergistic effects of these climatic changes on exceedances of the World Health Organization's moderate 50 µg/L concentration threshold for recreational contact. Although increased precipitation produces greater riverine total phosphorus loads, warmer and drier scenarios produce the most severe blooms due to the greater mobilization and cyanobacteria uptake of legacy phosphorus under these conditions. Increases in daily precipitation variability aggravate blooms most under warmer and wetter scenarios. Greater temperature variability raises exceedances under current air temperatures but reduces them under more severe warming when water temperatures exceed optimal values for cyanobacteria growth more often. Our experiments, controlled for wind-induced changes to lake water quality, signal the importance of larger summer runoff events for curtailing bloom growth through reductions of water temperature, sunlight penetration and stratification. Finally, the importance of sequences of wet and dry periods in generating cyanobacteria blooms motivates future research on bloom responses to changes in interannual climate persistence.