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Stephen R. Sobie and Trevor Q. Murdock

Abstract

Knowledge from high-resolution daily climatological parameters is frequently sought after for increasingly local climate change assessments. This research investigates whether applying a simple postprocessing methodology to existing statistically downscaled temperature and precipitation fields can result in improved downscaled simulations useful at the local scale. Initial downscaled daily simulations of temperature and precipitation at 10-km resolution are produced using bias correction constructed analogs with quantile mapping (BCCAQ). Higher-resolution (800 m) values are then generated using the simpler climate imprint technique in conjunction with temperature and precipitation climatologies from the Parameter-Elevation Regression on Independent Slopes Model (PRISM). The potential benefit of additional downscaling to 800 m is evaluated using the “Climdex” set of 27 indices of extremes established by the Expert Team on Climate Change Detection and Indices (ETCCDI). These indices are also calculated from weather station observations recorded at 22 locations within southwestern British Columbia, Canada, to evaluate the performance of both the 10-km and 800-m datasets in replicating the observed quantities. In a 30-yr historical evaluation period, Climdex indices computed from 800-m simulated values display reduced error relative to local station observations than those from the 10-km dataset, with the greatest reduction in error occurring at high-elevation sites for precipitation-based indices.

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Stephen R. Sobie and Trevor Q. Murdock

Abstract

Information about snow water equivalent in southwestern British Columbia is used for flood management, agriculture, fisheries, and water resource planning. This study evaluates whether a process-based, energy balance snow model supplied with high-resolution statistically downscaled temperature and precipitation data can effectively simulate snow water equivalent (SWE) in the mountainous terrain of this region. Daily values of SWE from 1951 to 2018 are simulated at 1 km resolution and evaluated using a reanalysis SWE product (SNODAS), manual snow survey measurements at 41 sites, and automated snow pillows at six locations in the study region. Simulated SWE matches observed inter-annual variability well (R 2 > 0.8 for annual maximum SWE) but peak SWE biases of 20% to 40% occur at some sites in the study domain, and higher biases occur where observed SWE is very low. Modelled SWE displays lower bias compared to SNODAS reanalysis at most manual survey locations. Future projections for the study area are produced using 12 downscaled climate model simulations and used to illustrate the impacts of climate change on SWE at 1°C, 2°C, and 3°C of warming. Model results are used to quantify spring SWE changes at different elevations of the Whistler mountain ski resort, and the sensitivity of annual peak SWE in Metro Vancouver municipal watersheds to moderate temperature increases. The results illustrate both the potential utility of a process-based snow model, and identify areas where the input meteorological variables could be improved.

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Tongli Wang, Andreas Hamann, David L. Spittlehouse, and Trevor Q. Murdock

Abstract

This study addresses the need to provide comprehensive historical climate data and climate change projections at a scale suitable for, and readily accessible to, researchers and resource managers. This database for western North America (WNA) includes over 20 000 surfaces of monthly, seasonal, and annual climate variables from 1901 to 2009; several climate normal periods; and multimodel climate projections for the 2020s, 2050s, and 2080s. A software package, ClimateWNA, allows users to access the database and query point locations, obtain time series, or generate custom climate surfaces at any resolution. The software uses partial derivative functions of temperature change along elevation gradients to improve medium-resolution baseline climate estimates and calculates biologically relevant climate variables such as growing degree-days, number of frost-free days, extreme temperatures, and dryness indices. Historical and projected future climates are obtained by using monthly temperature and precipitation anomalies to adjust the interpolated baseline data for the location of interest. All algorithms used in the software package are described and evaluated against observations from weather stations across WNA. The downscaling algorithms substantially improve the accuracy of temperature variables over the medium-resolution baseline climate surfaces. Climate variables that are usually calculated from daily data are estimated from monthly climate variables with high statistical accuracy.

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Andreas Hamann, Tongli Wang, David L. Spittlehouse, and Trevor Q. Murdock

We present a comprehensive set of interpolated climate data for western North America, including monthly data for the last century (1901–2009), future projections from atmosphere–ocean general circulation models (A2, A1B, and B1 scenarios of the WCRP CMIP3 multimodel dataset), as well as decadal averages and multiple climate normals for the last century. For each of these time periods, we provide a large set of basic and derived biologically relevant climate variables, such as growing and chilling degree days, growing season length descriptors, frost-free days, extreme minimum temperatures, etc. To balance file size versus accuracy for these approximately 20,000 climate surfaces, we provide a stand-alone software solution that adds or subtracts historical data and future projections as medium-resolution anomalies (deviations) from the high resolution 1961–90 baseline normal dataset. The program further downscales the baseline data through a combination of bilinear interpolation and elevation adjustment using partial derivative functions. Observations from 3,353 weather stations were used to evaluate the climate estimates of our downscaling algorithms. We found that the algorithms substantially improved prediction accuracy of the monthly climate variables, especially for temperature variables. They eliminated up to 65% of the unexplained variance in observed monthly temperatures and reduced standard errors of climate estimates by up to 40%.

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Alex J. Cannon, Stephen R. Sobie, and Trevor Q. Murdock

Abstract

Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

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