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The science of event attribution meets a mounting demand for reliable and timely information about the links between climate change and individual extreme events. Studies have estimated the contribution of human-induced climate change to the magnitude of an event as well as its likelihood, and many types of event have been investigated including heatwaves, floods, and droughts. Despite this progress, such approaches have been criticised for being unreliable and for being overly conservative. We argue that such criticisms are misplaced. Rather, a false dichotomy has arisen between “conventional” approaches and new alternative framings. We have three points to make about the choice of statistical paradigm for event attribution studies. First, different approaches to event attribution may choose to occupy different places on the conditioning spectrum. Providing this choice of conditioning is communicated clearly, the value of such choices depends ultimately on their utility to the user concerned. Second, event attribution is an estimation problem for which either frequentist or Bayesian paradigms can be used. Third, for hypothesis testing, the choice of null hypothesis is context specific. Thus, the null hypothesis of human influence is not inherently a preferable alternative to the usual null hypothesis of no human influence.

To plan for and adapt to the potential impacts of climate change, there is a need among communities in British Columbia for projections of future climate and climate extremes at a suitable, locally-relevant scale. This report summarizes work completed in 2012 by the Pacific Climate Impacts Consortium (PCIC) to this end. Commissioned by a group of municipalities and regional districts in the Georgia Basin (Figure 1), PCIC developed and analyzed a set of projections of future climate and climate extremes for the area. The full report, Georgia Basin, Projected Climate Change, Extremes and Historical Analysis, is available from PCIC’s online publications library.

Temperatures in the Capital Regional District (CRD) are warming. Global climate models project an average annual warming of about 3°C in our region by the 2050s. While that may seem like a small change, it is comparable to the difference between the warmest and coldest years of the past. The purpose of this report is to quantify, with the most robust projections possible, the related climate impacts (including changes to climate extremes) associated with warming. This climate information will then inform regional vulnerability and risk assessments, decision-making, and planning in the capital region, with a goal of improving resilience to climate change.

Estimates of surface snow water equivalent (SWE) in alpine regions with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC: ERA-Interim/Land, GLDAS-2, MERRA, MERRA-Land, GlobSnow and ERA-Interim. Relevant spatiotemporal covariates including survey date, year, latitude, longitude, elevation and grid cell elevation differences were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and correlations for April surveys were found using cross validation. The ANN using the three best performing SWE products (ANN3) had the lowest mean station MAE across the entire province, improving on the performance of individual products by an average of 53 %. Mean station MAEs and April survey correlations were also found for each of BC’s five physiographic regions. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all regions except for the BC Plains, which has relatively few stations and much lower accumulations than other regions. Subsequent comparisons of the ANN results with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to be superior over the entire VIC domain and within most physiographic regions. The superior performance of the ANN over individual products, product means, MLR and VIC was found to be statistically significant across the province.

Talk delivered by Katherine Pingree-Shippee at the 51st Annual Congress of the Canadian Meteorological and Oceanographic Society, in June of 2017.

A detection and attribution analysis on the multidecadal trend in snow water equivalent (SWE) has been conducted in four river basins located in British Columbia (BC). Monthly output from a suite of 10 general circulation models (GCMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used, including 40 climate simulations with anthropogenic and natural forcing combined (ALL), 40 simulations with natural forcing alone (NAT), and approximately 4200 yr of preindustrial control simulations (CTL). This output was downscaled to 1/16° spatial resolution and daily temporal resolution to drive the Variable Infiltration Capacity hydrologic model (VIC). Observed (manual snow survey) and VIC-reconstructed SWE, which exhibit declines across BC, are projected onto the multimodel ensemble means of the VIC-simulated SWE based on the responses to different forcings using an optimal fingerprinting approach. Results of the detection and attribution analysis shows that these declines are attributable to the anthropogenic forcing, which is dominated by the effect of increases in greenhouse gas concentration, and that they are not caused by natural forcing due to volcanic activity and solar variability combined. Anthropogenic influence is detected in three of the four basins (Fraser, Columbia, and Campbell Rivers) based on the VIC-reconstructed SWE, and in all basins based on the manual snow survey records. The simulations underestimate the observed snowpack trends in the Columbia River basin, which has the highest mean elevation. Attribution is supported by the detection of human influence on the cold-season temperatures that drive the snowpack reductions. These results are robust to the use of different observed datasets and to the treatment of low-frequency variability effects.

Two recently published articles explore how projected changes to climate and carbon dioxide in the atmosphere may affect grasslands in temperate regions and three crops in the United States. Addressing the first question in Nature Climate Change, Obermeier et al. (2017) find that the carbon dioxide fertilization effect in C3 grasslands is reduced when conditions are wetter, dryer or hotter than the conditions to which the grasses are adapted.

Publishing in Nature Communications, Schauberger et al. (2017) examine the second question. They find that yields for wheat, soy and corn decline at projected temperatures greater than 30°C, with reductions in yield of 22% for wheat, 40% for soy and 49% for corn. While carbon fertilization does reduce the loss in yields, the effect is much smaller than that of irrigation, suggesting that water stress at higher temperatures may be largely responsible for losses.

Warming of the climate is now unequivocal. The water holding capacity of the atmosphere increases by about 7% per °C of warming, which in turn raises the expectation of more intense extreme rainfall events. Meeting the demand for robust projections for extreme short-duration rainfall is challenging, however, because of our poor understanding of its past and future behaviour. The characterization of past changes is severely limited by the availability of observational data. Climate models, including typical regional climate models, do not directly simulate all extreme rainfall producing processes, such as convection. Recently developed convection-permitting models better simulate extreme precipitation, but simulations are not yet widely available due to their computational cost, and they have their own uncertainties. Attention has thus been focused on precipitation–temperature relationships in the hope of obtaining more robust extreme precipitation projections that exploit higher confidence temperature projections. However, the observed precipitation–temperature scaling relationships have been established almost exclusively by linking precipitation extremes with day-to-day temperature variations. These scaling relationships do not appear to provide a reliable basis for projecting future precipitation extremes. Until better methods are available, the relationship of the atmosphere's water holding capacity with temperature provides better guidance for planners in the mid-latitudes, albeit with large uncertainties.