Arelia T. Werner

Downscaling Extremes—An Intercomparison of Multiple Statistical Methods for Present Climate

AbstractFive statistical downscaling methods [automated regression-based statistical downscaling (ASD), bias correction spatial disaggregation (BCSD), quantile regression neural networks (QRNN), TreeGen (TG), and expanded downscaling (XDS)] are compared with respect to representing climatic extremes. The tests are conducted at six stations from the coastal, mountainous, and taiga region of British Columbia, Canada, whose climatic extremes are measured using the 27 Climate Indices of Extremes (ClimDEX; http://www.climdex.org/climdex/index.action) indices. All methods are calibrated from data prior to 1991, and tested against the two decades from 1991 to 2010. A three-step testing procedure is used to establish a given method as reliable for any given index. The first step analyzes the sensitivity of a method to actual index anomalies by correlating observed and NCEP-downscaled annual index values; then, whether the distribution of an index corresponds to observations is tested. Finally, this latter test is...

Uncertainties in Hydrologic and Climate Change Impact Analyses in Headwater Basins of British Columbia

AbstractThree headwater basins located across British Columbia (BC) were analyzed using a hydrologic model driven by five global climate models (GCMs) and three scenarios from the Special Report on Emissions Scenarios (SRES) to project future changes in seasonal water budgets and assess the uncertainty in the projections arising from GCMs, emissions scenarios, and hydrologic model parameterizations under two future time periods. Future projected changes in temperature are for annual increases of approximately +2°C by the 2050s and +3°C by the 2080s. The 2050s and 2080s precipitation projections are for increased winter precipitation in all basins and decreases in summertime precipitation for two of the three basins—with increases projected in the northeastern BC subwatershed. The study found that the hydrologic parameter uncertainty ranged up to 55%, (average 31%) for winter runoff anomalies, which was less than the uncertainty associated with GCMs and emissions scenarios that ranged up to 135% and 78% (a...

Downscaling Extremes: An Intercomparison of Multiple Methods for Future Climate

AbstractThis study follows up on a previous downscaling intercomparison for present climate. Using a larger set of eight methods the authors downscale atmospheric fields representing present (1981–2000) and future (2046–65) conditions, as simulated by six global climate models following three emission scenarios. Local extremes were studied at 20 locations in British Columbia as measured by the same set of 27 indices, ClimDEX, as in the precursor study. Present and future simulations give 2 × 3 × 6 × 8 × 20 × 27 = 155 520 index climatologies whose analysis in terms of mean change and variation is the purpose of this study. The mean change generally reinforces what is to be expected in a warmer climate: that extreme cold events become less frequent and extreme warm events become more frequent, and that there are signs of more frequent precipitation extremes. There is considerable variation, however, about this tendency, caused by the influence of scenario, climate model, downscaling method, and location. Th...

Terrestrial contribution to the heterogeneity in hydrological changes under global warming

This study investigates a physical basis for heterogeneity in hydrological changes, which suggests a greater detectability in wet than dry regions. Wet regions are those where atmospheric demand is less than precipitation (energy limited), and dry regions are those where atmospheric demand is greater than precipitation (water limited). Long-term streamflow trends in western North America and an analysis of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models at global scales show geographically heterogeneous detectability of hydrological changes. We apply the Budyko framework and state-of-the-art climate model data from CMIP5 to quantify the sensitivity and detectability of terrestrial hydrological changes. The Budyko framework quantifies the partitioning of precipitation into evapotranspiration and runoff components. We find that the terrestrial hydrological sensitivity is 3 times greater in regions where the hydrological cycle is energy limited rather than water limited. This additional source (the terrestrial part) contributes to 30–40% greater detectability in energy-limited regions. We also quantified the contribution of changes in the catchment efficiency parameter that oppose the effects of increasing evaporative demand in global warming scenarios. Incorporating changes to the catchment efficiency parameter in the Budyko framework reduces dry biases in global runoff change projections by 88% in the 21st century.

Evaluating Hydroclimatic Change Signals from Statistically and Dynamically Downscaled GCMs and Hydrologic Models

AbstractThis study analyzed potential hydroclimatic change in the Peace River basin in the province of British Columbia, Canada, based on two structurally different approaches: (i) statistically downscaled global climate models (GCMs) using the bias-corrected spatial disaggregation (BCSD) and (ii) dynamically downscaled GCM with the Canadian Regional Climate Model (CRCM). Additionally, simulated hydrologic changes from the GCM–BCSD-driven Variable Infiltration Capacity (VIC) model were compared to the CRCM integrated Canadian Land Surface Scheme (CLASS) output. The results show good agreements of the GCM–BCSD–VIC simulated precipitation, temperature, and runoff with observations, while the CRCM-simulated results differ substantially from observations. Nevertheless, differences (between the 2050s and 1970s) obtained from the two approaches are qualitatively similar for precipitation and temperature, although they are substantially different for snow water equivalent and runoff. The results obtained from th...

Potential near‐future carbon uptake overcomes losses from a large insect outbreak in British Columbia, Canada

The current capacity of northern high-latitude forests to sequester carbon has been suggested to be undermined by the potential increase in fire and insect outbreaks. Here we investigate the response of the terrestrial ecosystems in the province of British Columbia (BC), Canada, to the recent large mountain pine beetle (MPB) outbreak that started in 1999 as well as changing climate and continually increasing atmospheric CO2 concentration up to 2050, in a combined framework, using a process-based model. Model simulations suggest that the recent MPB outbreak results in BCs forests accumulating 328 Tg less carbon over the 1999–2020 period. Over this same period changing climate and increasing atmospheric CO2 concentration, however, yield enhanced carbon uptake equal to a cumulative sink of around 900–1060 Tg C, depending on the future climate change scenario, indicating that the reduced carbon uptake by land due to the MPB disturbance may already be surpassed by 2020.

Future Climate Change Impacts on Snow and Water Resources of the Fraser River Basin, British Columbia

AbstractChanges in air temperature and precipitation can modify snowmelt-driven runoff in snowmelt-dominated regimes. This study focuses on climate change impacts on the snow hydrology of the Fraser River basin (FRB) of British Columbia (BC), Canada, using the Variable Infiltration Capacity model (VIC). Statistically downscaled forcing datasets based on 12 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are used to drive VIC for two 30-yr time periods, a historical baseline (1980–2009) and future projections (2040–69: 2050s), under representative concentration pathways (RCPs) 4.5 and 8.5. The ensemble-based VIC simulations reveal widespread and regionally coherent spatial changes in snowfall, snow water equivalent (SWE), and snow cover over the FRB by the 2050s. While the mean precipitation is projected to increase slightly, the fraction of precipitation falling as snow is projected to decrease by nearly 50% in the 2050s compared to the baseline. Snow accumulation and snow-covered...

ClimDown: Climate Downscaling in R

PCIC’s overall downscaling algorithm is named Bias-corrected constructed analogues with quantile mapping reordering (BCCAQ) (Cannon, Sobie, and Murdock 2015; Werner and Cannon 2016). BCCAQ is a hybrid downscaling method that combines outputs from Constructed Analogues (CA) (Maurer et al. 2010) and quantile mapping at the fine-scale resolution. First, the CA and Climate Imprint (CI) (Hunter and Meentemeyer 2005) plus quantile delta mapping (QDM) (Cannon, Sobie, and Murdock 2015) algorithms are run independently. BCCAQ then combines outputs from the two by taking the daily QDM outputs at each fine-scale grid point and reordering them within a given month according to the daily CA ranks, i.e., using a form of Empirical Copula Coupling (Schefzik, Thorarinsdottir, and Gneiting 2013).