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Main findings: Based on observations and modeling, the occurrence of a heatwave with maximum daily temperatures (TXx) as observed in the area 45–52 ºN, 119–123 ºW, was virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lie far outside the range of historically observed temperatures. This makes it hard to quantify with confidence how rare the event was. In the most realistic statistical analysis the event is estimated to be about a 1 in 1000 year event in today’s climate.There are two possible sources of this extreme jump in peak temperatures. The first is that this is a very low probability event, even in the current climate which already includes about 1.2°C of global warming -- the statistical equivalent of really bad luck, albeit aggravated by climate change. The second option is that nonlinear interactions in the climate have substantially increased the probability of such extreme heat, much beyond the gradual increase in heat extremes that has been observed up to now. We need to investigate the second possibility further, although we note the climate models do not show it. All numbers below assume that the heatwave was a very low probability event that was not caused by new nonlinearities. With this assumption and combining the results from the analysis of climate models and weather observations, an event, defined as daily maximum temperatures (TXx) in the heatwave region, as rare as 1 in a 1000 years would have been at least 150 times rarer without human-induced climate change. Also, this heatwave was about 2°C hotter than it would have been if it had occurred at the beginning of the industrial revolution (when global mean temperatures were 1.2°C cooler than today). Looking into the future, in a world with 2°C of global warming (0.8°C warmer than today which at current emission levels would be reached as early as the 2040s ), this event would have been another degree hotter. An event like this -- currently estimated to occur only once every 1000 years, would occur roughly every 5 to 10 years in that future world with 2°C of global warming.

The state of the future climate depends on human actions, primarily the emission of greenhouse gases and other industrial pollutants. This raises the questions: "What path are recent historical emissions following?" "What path would we be on, if we continue with business-as-usual, in the absence of further mitigation action?" And, "Are these paths reliable guides to future emissions?" One scenario that is commonly used in the scientific literature, RCP 8.5, is often referred to as "business-as-usual." Recently, some scientists have taken issue with this description, saying it is unrealistic and may hinder the goal of emissions reductions policy. Others argue that, in fact, RCP 8.5 is the scenario that most closely tracks cumulative emissions to date, that it is thus of the most use for planning out to the middle of the century. In this Science Brief, we unpack each of these arguments and evaluate what these differing perspectives can tell us about the ultimate objective of emissions scenarios as tools for exploring future climate change.

Pilot study for the development of stream flow design value projections and a prototype online tool. The BC Ministry of Transportation and Infrastructure supported PCIC in a pilot project to quantify design flood values (2-, 20-, 50-, 100- and 200-year events) for historical and future periods and make them accessible as a gridded product via PCIC’s Climate Explorer tool. As part of this work, PCIC has also been asked to calculate and supply the Melton Ratio as a gridded product. This study focuses on the Upper Fraser, a 34,200 km² region upstream of Prince George, BC, with primarily snow-dominated watersheds. This report was prepared for the Engineering Services Branch of the Engineering Systems Department of the Highway Services Department, Ministry of Transportation and Infrastructure, Government of British Columbia.

Increases in the intensity and frequency of hydroclimatic extremes associated with climate change can cause significant socioeconomic problems. Assessments of projected extremes using only a limited number of general circulation model (GCM) simulations can undermine the capacity to differentiate and communicate the contribution of internal climate variability (ICV) and external forcing and result in an underestimation of associated risks. In this study, we assess the impacts of climate change on extreme temperature and precipitation and quantify the contribution of internal variability over the Columbia, Fraser, Peace and Campbell River basins in northwestern North America (NWNA). Seven GCMs that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and a large ensemble of CanESM2 model simulations (50 members) are downscaled to 1/16° spatial resolution using Bias Correction Constructed Analogues with Quantile mapping reordering version 2 (BCCAQ2). Spatial and temporal changes of climate extreme indices, representing the frequency and intensity of extreme temperature and precipitation, are assessed over the historical (1981–2010) and future (2060–2089) periods under the Representative Concentration Pathway (RCP) 8.5. The influence of ICV on the estimated trends of extreme indices is characterised. Overall, both the frequency and intensity of extreme temperature and precipitation events are projected to increase in NWNA indicating more severe dry days and wet conditions in the future. High-elevation Rocky and the Coast Mountains are at larger risks of extreme precipitation, while the Columbia basin, which already faces drought issues, is expected to experience severe dry conditions. Internal climate variability plays a significant role, particularly in the trends of precipitation-related indices. The signal to internal noise ratio analyses suggest that higher elevations experience stronger forcing signals for precipitation-based indices compared to the other regions.

This Science Brief covers a paper in the Proceedings of the National Academy of Sciences, by Xu et al. (2020), who use global climate model (GCM) output, weather station data, estimates of historical global population density, and data on global gross domestic product (GDP), crop and livestock production, to determine if there has been a human climate niche. They determine that such a niche has existed. For the past 6000 years, human populations have lived largely in a fairly narrow range of climates and populations clustered around two temperature ranges, with most people living in a range of about 11°C to about 15°C for mean annual temperature and a smaller, but significant portion living in a range around 20°C to about 25°C.
They then examine how this niche may change in the future. They find that, under a high emissions scenario, this niche is projected to shift spatially more in the upcoming 50 years than it has in the past 6000, leaving a third of the projected future human population in regions where the mean annual temperature is greater than 29°C.

The January 2021 edition of the PCIC Update contains the following articles: Supporting Water Planning in BC, Developing Risk Assessment Tools for Salmon Habitat Management, and Engaging with the Agricultural Community. The staff profile is a joint one, highlighting the contributions of Jessie Booker and Cairo Sanders. It also contains PCIC staff news and publications.

This is PCIC's Corporate Report for 2019-2020.

This paper provides an updated analysis of observed changes in extreme precipitation using high quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of one day (Rx1day) and five-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including C. North-America, E. North-America, N. Central-America, N. Europe, Russian-Far-East, E.C. Asia, and E. Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a co-variate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percent change in extreme precipitation per Kelvin increase in GMST is 6.6% (5.1 to 8.2%, 5–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0 to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–2009 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.

The recurring devastation caused by extreme events underscores the need for reliable estimates of their intensity and frequency. Operational frequency and intensity estimates are very often obtained from generalized extreme value (GEV) distributions fitted to samples of annual maxima. GEV distributed random variables are “max-stable,” meaning that the maximum of a sample of several values drawn from a given GEV distribution is again GEV distributed with the same shape parameter. Long-period return value estimation relies on this property of the distribution. The data to which the models are fitted may not, however, be max-stable. Observational records are generally too short to assess whether max-stability holds in the upper tail of the observations. Large ensemble climate simulations, from which we can obtain very large samples of annual extremes, provide an opportunity to assess whether max-stability holds in a model-simulated climate and to quantify the impact of the lack of max-stability on very long period return-level estimates. We use a recent large ensemble simulation of the North American climate for this purpose. We find that the annual maxima of short-duration precipitation extremes tend not to be max-stable in the simulated climate, as indicated by systematic variation in the estimated shape parameter as block length is increased from 1 to 20 years. We explore how the lack of max-stability affects the estimation of very long period return levels and discuss reasons why short-duration precipitation extremes may not be max-stable.