Publications Library

This special issue of Weather and Climate Extremes (WACE) includes a series of articles initiated during the 2014 WCRP summer school on the “Attribution and Prediction of Extreme Events”. The two-week summer school took place from 21st July to 4th August 2014 at the International Center for Theoretical Physics (ICTP) in Trieste, Italy, and was organized in the context of the WCRP Grand Challenge on Extremes.

PCIC is first and foremost a service provider that delivers an array of quantitative climate information for a variety of needs. This strategic plan details several service objectives for the coming five years that encompass the spectrum of information delivery from data to user-specific interpretation. The service objectives are laid out in section V.

Since the information that PCIC seeks to deliver to its users is generally not directly available “off the shelf”, it is necessary to make strategic investments in applied climate research and development in order to meet the service objectives. This plan therefore also outlines several strategic objectives that are required to achieve our service objectives.

PCIC has three applied research themes: Climate Analysis and Monitoring (CAM), Regional Climate Impacts (RCI) and Hydrologic Impacts (HI) with clearly defined research plans for the 2015-2019 period. These research plans are designed to guide future research activities of PCIC regional climate service delivery. Our commitment to collaboration and operational excellence further supports our service mandate.

The remainder of the strategic plan is structured as follows. It begins with a synopsis of PCIC, its history, governance and guiding principles. This is followed by descriptions of PCIC service objectives and modes of delivery for those services. The plan then summarizes the strategic goals that must be achieved to meet our service objectives.

Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.

Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can outperform the best forecast model. Simple average, median, Bates-Granger, constrained linear regression, and Bayesian model averaging optimized by expectation maximization showed better results compared with other methods over three basins. For the Rogue River basin, the intermediate and complex models outperformed most of the individual forecasts and the simple methods. Multimodeling techniques based on information criteria showed similar performances.

An attribution analysis of extreme temperature changes is conducted using updated observations (HadEX2) and multi-model climate simulation (CMIP5) datasets for an extended period of 1951–2010. Compared to previous HadEX/CMIP3-based results, which identified human contributions to the observed warming of extreme temperatures on global and regional scales, the current results provide better agreement with observations, particularly for the intensification of warm extremes. Removing the influence of two major modes of natural internal variability (the Arctic Oscillation and Pacific Decadal Oscillation) from observations further improves attribution results, reducing the model-observation discrepancy in cold extremes. An optimal fingerprinting technique is used to compare observed changes in annual extreme temperature indices of coldest night and day (TNn, TXn) and warmest night and day (TNx, TXx) with multi-model simulated changes that were simulated under natural-plus-anthropogenic and natural-only (NAT) forcings. Extreme indices are standardized for better intercomparisons between datasets and locations prior to analysis and averaged over spatial domains from global to continental regions following a previous study. Results confirm previous HadEX/CMIP3-based results in which anthropogenic (ANT) signals are robustly detected in the increase in global mean and northern continental regional means of the four indices of extreme temperatures. The detected ANT signals are also clearly separable from the response to NAT forcing, and results are generally insensitive to the use of different model samples as well as different data availability.

Extratropical explosive cyclones are rapidly intensifying low pressure systems with severe wind speeds and heavy precipitation, affecting livelihoods and infrastructure primarily in coastal and marine environments. This study evaluates how well the most recent generation of climate models reproduces extratropical explosive cyclones in the Northern Hemisphere for the period 1980–2005. An objective-feature tracking algorithm is used to identify and track cyclones from 25 climate models and three reanalysis products. Model biases are compared to biases in the sea surface temperature (SST) gradient, the polar jet stream, the Eady growth rate, and model resolution. Most models accurately reproduce the spatial distribution of explosive cyclones when compared to reanalysis data (R = 0.94), with high frequencies along the Kuroshio Current and the Gulf Stream. Three quarters of the models however significantly underpredict explosive cyclone frequencies, by a third on average and by two thirds in the worst case. This frequency bias is significantly correlated with jet stream speed in the inter-model spread (R ≥ 0.51), which in the Atlantic is correlated with a negative meridional SST gradient (R = −0.56). The importance of the jet stream versus other variables considered in this study also applies to the interannual variability of explosive cyclone frequency. Furthermore, models with fewer explosive cyclones tend to underpredict the corresponding deepening rates (R ≥ 0.88). A follow-up study will assess the impacts of climate change on explosive cyclones, and evaluate how model biases presented in this study affect the projections.

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods – bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) – are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis datasets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical datasets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

Retreat of mountain glaciers is a significant contributor to sea-level rise and a potential threat to human populations through impacts on water availability and regional hydrology. Like most of Earth’s mountain glaciers, those in western North America are experiencing rapid mass loss. Projections of future large-scale mass change are based on surface mass balance models that are open to criticism, because they ignore or greatly simplify glacier physics. Here we use a high-resolution regional glaciation model, developed by coupling physics-based ice dynamics with a surface mass balance model, to project the fate of glaciers in western Canada. We use twenty-first-century climate scenarios from an ensemble of global climate models in our simulations; the results indicate that by 2100, the volume of glacier ice in western Canada will shrink by 70 ± 10% relative to 2005. According to our simulations, few glaciers will remain in the Interior and Rockies regions, but maritime glaciers, in particular those in northwestern British Columbia, will survive in a diminished state. We project the maximum rate of ice volume loss, corresponding to peak input of deglacial meltwater to streams and rivers, to occur around 2020–2040. Potential implications include impacts on aquatic ecosystems, agriculture, forestry, alpine tourism and water quality.