A novel approach for selecting extreme climate change scenarios for climate change impact studies.

One of the main challenges in climate change impact assessment studies is selecting climate change scenarios. By focusing on selecting projected extremes in a high dimensional space, one is confronted with the shrinkage of ensemble size while preserving the projection spread. This study proposes a novel integrated computational geometry algorithm to select extreme climate change scenarios in a high dimensional space. A set of 12 prominent climate extremes indices were used (as input to the algorithm) out of the 27 core indices recommended by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI). The ETCCDI indices were projected by Coupled Model Intercomparison Project Phase 5 (CMIP5) for the period of 2081-2100 relative to the baseline period 1986-2005. The approach enables the user to shrink the initial selected ensemble into smaller sub-ensembles while still capturing a wide range of simulated changes for selected climatological variables. The conservation of the projection spread was evaluated using a robust validation method when the spread error was calculated for each simulation run. The developed algorithm was applied to three different regions where the geographical domain was narrowed-down from sub-continental (western North America) to its regional (Alberta, Canada), and local (Athabasca River basin, Alberta, Canada) subdomains. Results revealed that selected extreme scenarios can vary from one region to another within the same geographical domain in response to the spatial variation in climatic regime.

A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America.

We describe a spatially contiguous, temporally consistent high-resolution gridded daily meteorological dataset for northwestern North America. This >4 million km2 region has high topographic relief, seasonal snowpack, permafrost and glaciers, crosses multiple jurisdictional boundaries and contains the entire Yukon, Mackenzie, Saskatchewan, Fraser and Columbia drainages. We interpolate daily station data to 1/16° spatial resolution using a high-resolution monthly 1971-2000 climatology as a predictor in a thin-plate spline interpolating algorithm. Only temporally consistent climate stations with at least 40 years of record are included. Our approach is designed to produce a dataset well suited for driving hydrological models and training statistical downscaling schemes. We compare our results to two commonly used datasets and show improved performance for climate means, extremes and variability. When used to drive a hydrologic model, our dataset also outperforms these datasets for runoff ratios and streamflow trends in several, high elevation, sub-basins of the Fraser River.

Attribution of the Influence of Human-Induced Climate Change on an Extreme Fire Season.

A record 1.2 million ha burned in British Columbia, Canada's extreme wildfire season of 2017. Key factors in this unprecedented event were the extreme warm and dry conditions that prevailed at the time, which are also reflected in extreme fire weather and behavior metrics. Using an event attribution method and a large ensemble of regional climate model simulations, we show that the risk factors affecting the event, and the area burned itself, were made substantially greater by anthropogenic climate change. We show over 95% of the probability for the observed maximum temperature anomalies is due to anthropogenic factors, that the event's high fire weather/behavior metrics were made 2-4 times more likely, and that anthropogenic climate change increased the area burned by a factor of 7-11. This profound influence of climate change on forest fire extremes in British Columbia, which is likely reflected in other regions and expected to intensify in the future, will require increasing attention in forest management, public health, and infrastructure.

Efficacy and safety of liraglutide, a once-daily human glucagon-like peptide-1 receptor agonist, in African-American people with Type 2 diabetes: a meta-analysis of sub-population data from seven phase III trials.

AIM: To evaluate the efficacy and safety of the glucagon-like peptide-1 (GLP-1) receptor agonist liraglutide in African-American people with Type 2 diabetes. METHODS: Analyses were performed on patient-level data from individuals self-defined as African-American or non-African-American in seven phase III studies. Endpoints included change in HbA1c level, fasting plasma glucose level and body weight from baseline, proportion of patients reaching HbA1c target [< 53 mmol/mol (< 7.0%)], and incidence of hypoglycaemia and nausea. Analyses used data obtained after 26 weeks. Within-population comparisons of liraglutide were performed vs placebo for African-American and non-African-American patient groups. In addition, between-population comparisons with non-African-American patients were performed for each treatment. RESULTS: In African-American patients (n = 225), HbA1c was significantly reduced at 26 weeks with liraglutide 1.2 and 1.8 mg (-11 and -14 mmol/mol, respectively compared with placebo; P < 0.0001). There were also significant reductions in fasting plasma glucose (-2.4 and -3.1 mmol/l, respectively, compared with placebo; P < 0.0001). Statistically significant reductions in body weight were observed with 1.8 mg liraglutide (-2.1 kg compared with placebo; P = 0.0056), but not with 1.2 mg liraglutide (-0.26 kg; P = 0.7307). The P value for interaction between treatment and race was significant for body weight (P = 0.0355). The incidence of non-severe hypoglycaemia with liraglutide was low (11-15% of patients), and < 25% of patients receiving liraglutide experienced nausea. CONCLUSIONS: This meta-analysis suggests that liraglutide is well tolerated and efficacious for treatment of Type 2 diabetes in African-American patients, with an efficacy that was shown not to differ from that observed in non-African-American patients over 26 weeks.

Forecasting summertime surface-level ozone concentrations in the Lower Fraser Valley of British Columbia: an ensemble neural network approach.

Empirical models for predicting daily maximum hourly average ozone concentrations were developed for 10 monitoring stations in the Lower Fraser Valley (LFV) of British Columbia. According to data from 1991 to 1996, ensemble neural network models increased explained variance an average of 7% over multiple linear regression models using the same input variables. Without modification, all models performed poorly on days when the observed peak ozone concentration exceeded 82 parts per billion, the National Ambient Air Quality Objective. When numbers of extreme events in training data were increased using a histogram equalization process, models were able to forecast exceedances with improved accuracy. Modified generalized additive model (GAM) plots and associated measures of input variable importance and interaction were generated for a subset of the trained models and used to investigate relationships between input variables and ozone levels. The neural network models displayed a high degree of interaction among inputs, and it is likely the ability of these model types to account for interactions, rather than the nonlinearity of individual input variables, that explains their improved forecast skill. Inspection of GAM-style plots indicated that the relative importance of input variables in the ensemble neural network models varied with geographic location within the LFV. Four distinct groups of stations were identified, and rankings of inputs within the groups were generally consistent with physical intuition and results of prior studies.

Intraoperative radio-guided identification of parathyroid adenomas with the gamma probe: initial experiences in a community hospital.

Previous radiological imaging studies for identification of parathyroid adenomas have generally been unreliable. Currently, preoperative administration of Tc-99m sestamibi improves detection of parathyroid adenomas. Combining preoperative administration of sestamibi radionuclide with the gamma probe intraoperatively can successfully identify the exact location of parathyroid adenomas in a community hospital setting and facilitate a safe and efficient operation. A team approach, including surgeon, radiologist, and technologist, is recommended to facilitate mastery of the learning curve.