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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.

Peatlands cover only about 3% of the Earth’s surface and store 15–30% of the Global soil carbon as a peat. However, human intervention and climate change threatens the stability of peatlands, owing to deforest, wildfire, mining, drainage, glacial retreat, and permafrost. In our study, we modified the SWAT model to couple snow, soil temperature and carbon dioxide emission. Then the modified SWAT was used for predicting snow depth, soil temperature at different depths and carbon dioxide emission from peatlands and other land uses at Athabasca river basin, Canada. The results of the study indicated that SWAT model estimated the daily snow depth with R2, NSE, RMSE and PBIAS values of 0.83, 0.76, 0.52 and −2.3 in the calibration period (2006–2007) and 0.79, 0.71, 0.97 and −3.6 for the validation period (2008–2009), respectively. The SWAT model also predicted soil temperature very well at three depths (5 cm, 10 cm and 30 cm). The simulation model results also confirmed that the modified SWAT model estimates the CO2 emission at Athabasca river basin with good model fit during calibration (R2 = 0.71, NSE = 0.67, RMSE = 2.6 and PBIAS = 3.2) and during validation (R2 = 0.63, NSE = 0.58, RMSE = 3.1 and PBIAS = 9.3). Overall, our result confirmed that SWAT model performed well in representing the dynamics of snow depth, soil temperature and CO2 emissions in the peatlands at the Athabasca river basin.

The August 2020 edition of the PCIC Update covers the following stories: the release of the 2019 in BC, in Climatological Context report; the New Plan2Adapt tool; and the Survey on Future Climate Data and Information Needs in the Building Sector. The staff profile is on Dr. Charles Curry. The Update covers a number of webinars, with BC ACARN, FPInnovations and those talks given by PCIC researchers at the 54th Annual CMOS Congress. The Update also included PCIC staff news and recent publications.

Landslide hazards in British Columbia are mainly caused by precipitation and can result in significant damage and fatalities. Anthropogenic climate change is expected to increase precipitation frequency and intensity in the winter, spring, and fall in British Columbia (BC), potentially resulting in increased frequency of landslide hazard. Quantifying the effect of changing precipitation on future landslide hazard across the varying topographic and climatic conditions in BC requires detailed projections of future precipitation. Here, the operational Landslide Hazard Assessment for Situational Awareness (LHASA) model is used with high-resolution, statistically downscaled daily precipitation to generate detailed simulations of landslide hazard in BC over the twenty-first century. Historical evaluation of the LHASA model is performed using a station-based, gridded observational precipitation dataset. Classification of observed landslide dates and locations as hazard events occurs as successfully as, or slightly better than, when LHASA is applied globally with satellite precipitation. Using the LHASA model with precipitation projections from 12 downscaled global climate models following RCP8.5 indicates that future landslide hazard frequency will increase from 16 days per year to 21 days per year (32%) on average by the 2050s for landslide susceptible regions in the province. Areas of the province currently with the most frequent landslide hazards (18 to 21 days per year), including the west coast and northern Rocky Mountains, are expected to see between 8 and 11 additional hazardous days (49 to 61% increases) per year. Most of the increased hazard frequency occurs during winter and fall, reflecting those seasons with the largest projected increases in single and multi-day precipitation. Risk assessments for regions in British Columbia vulnerable to landslides will need to account for increasing hazard due to climate change altered precipitation.