Publications Library

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.

In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades.

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.

This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean–atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October–November–December) and late (January–February–March) winter monsoon periods. CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicable globally. Generally, CanCM4's skill in reproducing the climatology during winter is not as good as in other seasons because of the model's inability to simulate the regional synoptic circulations over the western Maritime Continent. In particular, the model fails to forecast the cold surges and Borneo vortex circulations that play critical roles in moisture horizontal advection. Moreover, its forecast skill during the early winter monsoon period is poorer than during the late period. Interestingly, forecast skill is enhanced when MOS models are applied as the MOS utilizes the predictive signals in the quasi-global predictors from the CanCM4 forecast system. The predictability can be traced to the conventional El Niño–Southern Oscillation (ENSO) and ENSO Modoki signals that are present in the CanCM4 forecast MOS predictor fields. The quasi-global sea-surface temperature and quasi-global sea-level pressure fields are found to be the most useful predictors. Interestingly, CanCM4 forecast signals associated with the Indian Ocean Dipole also contribute to the skill. Skill enhancement is particularly significant for northern Borneo during early monsoon periods in medium- and long-lead forecasts when the CanCM4 has minimal direct skill in the region.

Seven single-site statistical downscaling methods for daily temperature and precipitation, including four deterministic algorithms [analog model (ANM), quantile mapping with delta method extrapolation (QMD), cumulative distribution function transform (CDFt), and model-based recursive partitioning (MOB)] and three stochastic algorithms [generalized linear model (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model–Decision Centric (SDSM–DC] are evaluated at nine stations located in the mountainous region of Iran’s Midwest. The methods are of widely varying complexity, with input requirements that range from single-point predictors of temperature and precipitation to multivariate synoptic-scale fields. The period 1981–2000 is used for model calibration and 2001–2010 for validation, with performance assessed in terms of 27 Climate Extremes Indices (CLIMDEX). The sensitivity of the methods to large-scale anomalies and their ability to replicate the observed data distribution in the validation period are separately tested for each index by Pearson correlation and Kolmogorov–Smirnov (KS) tests, respectively. Combined tests are used to assess overall model performances. MOB performed best, passing 14.5 % (49.6 %) of the combined (single) tests, respectively, followed by SDSM, CaDENCE, and GLM [14.5 % (46.5 %), 13.2 % (47.1 %), and 12.8 % (43.2 %), respectively], and then by QMD, CDFt, and ANM [7 % (45.7 %), 4.9 % (45.3 %), and 1.6 % (37.9 %), respectively]. Correlation tests were passed less frequently than KS tests. All methods downscaled temperature indices better than precipitation indices. Some indices, notably R20, R25, SDII, CWD, and TNx, were not successfully simulated by any of the methods. Model performance varied widely across the study region.

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.

The Arctic has warmed significantly more than global mean surface air temperature over recent decades, as expected from amplification mechanisms. Previous studies have attributed the observed Arctic warming to the combined effect of greenhouse gases and other anthropogenic influences. However, given the sensitivity of the Arctic to external forcing and the intense interest in the effects of aerosols on its climate, it is important to examine and quantify the effects of individual groups of anthropogenic forcing agents. Here we quantify the separate contributions to observed Arctic land temperature change from greenhouse gases, other anthropogenic forcing agents (which are dominated by aerosols) and natural forcing agents. We show that although increases in greenhouse-gas concentrations have driven the observed warming over the past century, approximately 60% of the greenhouse-gas-induced warming has been offset by the combined response to other anthropogenic forcings, which is substantially greater than the fraction of global greenhouse-gas-induced warming that has been offset by these forcings. The climate models considered on average simulate the amplitude of response to anthropogenic forcings well, increasing confidence in their projections of profound future Arctic climate change.