Interpreting climate model projections of extreme weather events

AbstractThe availability of output from climate model ensembles, such as phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), has greatly expanded information about future projections, but there is no accepted blueprint for how this data should be utilized. The multi-model average is the most commonly cited single estimate of future conditions, but higher-order moments representing the variance and skewness of the distribution of projections provide important information about uncertainty. We have analyzed a set of statistically downscaled climate model projections from the CMIP3 archive to assess extreme weather events at a level aimed to be appropriate for decision makers. Our analysis uses the distribution of 13 global climate model projections to derive the inter-model standard deviation, skewness, and percentile ranges for simulated changes in extreme heat, cold, and precipitation by the mid-21st century, based on the A1B emissions scenario. These metrics provide information on overall confidence across the entire range of projections (via the inter-model standard deviation), relative confidence in upper-end versus lower-end changes (via skewness), and quantitative uncertainty bounds (derived from bootstrapping).Over our analysis domain, which covers the northeastern United States and southeastern Canada, some primary findings include: (1) greater confidence in projections of less extreme cold than more extreme heat and intense precipitation, (2) greater confidence in relatively conservative projections of extreme heat, and (3) higher spatial variability in the confidence of projected increases in heavy precipitation. In addition, we describe how a simplified bootstrapping approach can assist decision makers by estimating the probability of changes in extreme weather events based on user-defined percentile thresholds

Test of variables of attention (TOVA) as a predictor of early attention complaints, an antecedent to dementia

The goal of this study was to determine if impairments detected by the test of variables of attention (TOVA) may be used to predict early attention complaints and memory impairments accurately in a clinical setting. We performed a statistical analysis of outcomes in a patient population screened for attention deficit hyperactivity disorder or attention complaints, processing errors as measured by TOVA and the Wechsler Memory Scale (WMS-III) results. Attention deficit disorder (ADD) checklists, constructed using the Diagnostic and Statistical Manual of Mental Disorders 4th Edition criteria, which were completed by patients at PATH Medical, revealed that 72.8% of the patients had more than one attention complaint out of a total of 16 complaints, and 41.5% had more than five complaints. For the 128 males with a significant number of ADD complaints, individuals whose scores were significantly deviant or borderline (SDB) on TOVA, had a significantly greater number of attention complaints compared with normals for omissions (P < 0.02), response time (P < 0.015), and variability (P < 0.005), but not commissions (P > 0.50). For males, the mean scores for auditory, visual, immediate, and working memory scores as measured by the WMS-III were significantly greater for normals versus SDBs on the TOVA subtest, ie, omission (P < 0.01) and response time (P < 0.05), but not variability or commissions. The means for auditory, visual, and immediate memory scores were significantly greater for normals versus SDBs for variability (P < 0.045) only. In females, the mean scores for visual and working memory scores were significantly greater for normals versus SDBs for omissions (P < 0.025). The number of SDB TOVA quarters was a significant predictor for “impaired” or “normal” group membership for visual memory (P < 0.015), but not for the other three WMS-III components. For males, the partial correlation between the number of attention complaints and the number of SDB TOVA quarters was also significant (r = 0.251, P < 0.005). For the 152 females with a significant number of attention complaints, no significant differences between SDBs and normals were observed (P > 0.15). This is the first report, to our knowledge, which provides evidence that TOVA is an accurate predictor of early attention complaints and memory impairments in a clinical setting. This finding is more robust for males than for females between the ages of 40 and 90 years

Mutations in C16orf57 and normal-length telomeres unify a subset of patients with dyskeratosis congenita, poikiloderma with neutropenia and Rothmund–Thomson syndrome

Dyskeratosis congenita (DC) is an inherited poikiloderma which in addition to the skin abnormalities is typically associated with nail dystrophy, leucoplakia, bone marrow failure, cancer predisposition and other features. Approximately 50% of DC patients remain genetically uncharacterized. All the DC genes identified to date are important in telomere maintenance. To determine the genetic basis of the remaining cases of DC, we undertook linkage analysis in 20 families and identified a common candidate gene region on chromosome 16 in a subset of these. This region included the C16orf57 gene recently identified to be mutated in poikiloderma with neutropenia (PN), an inherited poikiloderma displaying significant clinical overlap with DC. Analysis of the C16orf57 gene in our uncharacterized DC patients revealed homozygous mutations in 6 of 132 families. In addition, three of six families previously classified as Rothmund–Thomson syndrome (RTS—a poikiloderma that is sometimes confused with PN) were also found to have homozygous C16orf57 mutations. Given the role of the previous DC genes in telomere maintenance, telomere length was analysed in these patients and found to be comparable to age-matched controls. These findings suggest that mutations in C16orf57 unify a distinct set of families which clinically can be categorized as DC, PN or RTS. This study also highlights the multi-system nature (wider than just poikiloderma and neutropenia) of the clinical features of affected individuals (and therefore house-keeping function of C16orf57), a possible role for C16orf57 in apoptosis, as well as a distinct difference from previously characterized DC patients because telomere length was normal

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm’s pa- rameters and data-related modeling choices are also both crucial and challenging

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p

Spatio-Temporal Variability in Remotely Sensed Vegetation Greenness Across Yellowstone National Park

The study&#8217;s objective was to quantify the responses of vegetation greenness and productivity to climate variability and change across complex topographic, climatic, and ecological gradients in Yellowstone National Park through the use of remotely sensed data. The climate change signal in Yellowstone was pronounced, including substantial warming, an abrupt decline in snowpack, and more frequent droughts. While phenological studies are increasing in Yellowstone, the near absence of long-term and continuous ground-based phenological measurements motivated the study&#8217;s application of remotely sensed data to aid in identifying ecological vulnerabilities and guide resource management in light of on ongoing environmental change. Correlation, time-series, and empirical orthogonal function analyses for 1982&#8211;2015 focused on Daymet data and vegetation indices (VIs) from the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The study&#8217;s key questions address unique time scales. First, what are the dominant meteorological drivers of variability in vegetation greenness on seasonal to interannual time scales? Key results include: (1) Green-up is the most elevation- and climate-sensitive phenological stage, with La Ni&#241;a-induced cool, wet conditions or an anomalously deep snowpack delaying the green-up wave. (2) Drought measures were the dominant contributors towards phenological variability, as winter&#8211;spring drought corresponded to enhanced April&#8211;June greening and spring&#8211;summer drought corresponded to reduced August&#8211;September greening. Second, how have patterns of productivity changed in response to climate change and disturbances? Key results include: (1) The park predominantly exhibited positive productivity trends, associated with lodgepole pine re-establishment and growth following the 1988 fires. (2) Landscapes which were undisturbed by the 1988 fires showed no apparent sign of warming-induced greening. This study motivates a systematic investigation of remote-sensing data across western parks to identify ecological vulnerabilities and support the development of climate change vulnerability assessments and adaptation strategies

A mechanism for abrupt climate change associated with tropical Pacific SSTs

ABSTRACT The tropical Pacific&apos;s response to transiently increasing atmospheric CO 2 is investigated using three ensemble members from a numerically efficient, coupled atmosphere-ocean GCM. The model is forced with a 1% yr Ϫ1 increase in CO 2 for 110 yr, when the concentration reaches 3 times the modern concentration. The transient greenhouse forcing causes a regionally enhanced warming of the equatorial Pacific, particularly in the far west. This accentuated equatorial heating, which is slow to arise but emerges abruptly during the last half of the simulations, results from both atmospheric and oceanic processes. The key atmospheric mechanism is a rapid local increase in the super-greenhouse effect, whose emergence coincides with enhanced convection and greater high cloud amount once the SST exceeds an apparent threshold around 27°C. The primary oceanic feedback is greater Ekman heat convergence near the equator, due to an anomalous near-equatorial westerly wind stress created by increased rising (sinking) air to the east (west) of Indonesia. The potential dependence of these results on the specific model used is discussed. The suddenness and far-ranging impact of the enhanced, near-equatorial warming during these simulations suggests a mechanism by which abrupt climate changes may be triggered within the Tropics. The extratropical atmospheric response in the Pacific resembles anomalies during present-day El Niño events, while the timing and rapidity of the midlatitude changes are similar to those in the Tropics. In particular, a strengthening of the Pacific jet stream and a spinup of the wintertime Aleutian low seem to be forced by the changes in the tropical Pacific, much as they are in the modern climate