Publications Library

This PCIC Science Brief covers a recent paper by Sigmond and Fyfe (2016) that was published in Nature Climate Change. The authors investigate the causes of cooler winters over the early 2000s in North America and find that they vary by region. In the northwest, these cooler winters were largely due to a pattern of western cooling and central warming in the tropical Pacific Ocean. In central North America, the cooler winters were primarily due to changes in the northerly winds driven by increased sea level pressure on the west coast of North America.

Empirical downscaling is based in a statistical analysis of present climatic conditions for an area, usually recorded by a number of variables from weather stations. The result of this analysis is a set of recipes (algorithms) and parameters from which the present climate, or at least some of its crucial aspects, such as extreme temperature values, can be recovered.

For terrestrial British Columbia, precipitation averages and extremes can be simulated more accurately within individual regions by using gridded downscaling to increase the resolution of both global and regional climate models. In locations where the difference between observations and model simulations is large, bias correction tends to inflate the magnitude of high resolution projected extremes. The effect is minimal for average indices but is statistically significant in a subset of models for projected changes of 10 and 20-year return periods. Future work will focus on correcting the inflation of extremes in the downscaling method and extending the analysis to additional regions.

During 19–21 June 2013 a heavy precipitation event affected southern Alberta and adjoining regions, leading to severe flood damage in numerous communities and resulting in the costliest natural disaster in Canadian history. This flood was caused by a combination of meteorological and hydrological factors, which are investigated from weather and climate perspectives with the fifth generation Canadian Regional Climate Model. Results show that the contribution of orographic ascent to precipitation was important, exceeding 30 % over the foothills of the Rocky Mountains. Another contributing factor was evapotranspiration from the land surface, which is found to have acted as an important moisture source and was likely enhanced by antecedent rainfall that increased soil moisture over the northern Great Plains. Event attribution analysis suggests that human induced greenhouse gas increases may also have contributed by causing evapotranspiration rates to be higher than they would have been under pre-industrial conditions. Frozen and snow-covered soils at high elevations are likely to have played an important role in generating record streamflows. Results point to a doubling of surface runoff due to the frozen conditions, while 25 % of the modelled runoff originated from snowmelt. The estimated return time of the 3-day precipitation event exceeds 50 years over a large region, and an increase in the occurrence of similar extreme precipitation events is projected by the end of the 21st century. Event attribution analysis suggests that greenhouse gas increases may have increased 1-day and 3-day return levels of May–June precipitation with respect to pre-industrial climate conditions. However, no anthropogenic influence can be detected for 1-day and 3-day surface runoff, as increases in extreme precipitation in the present-day climate are offset by decreased snow cover and lower frozen water content in soils during the May–June transition months, compared to pre-industrial climate.

Plant and animal species have historically used movement to adapt to changes in the Earth’s climate, shifting their ranges across landscapes to stay within climatically suitable habitat. Species are using this strategy to adapt to present day climate change, but the current rate of change is so rapid that many species will have difficulty keeping pace. In addition, human land use (e.g., highways, cities, farms) presents significant barriers to wildlife movement across today’s landscapes. For this reason, enhancing habitat connectivity – the ability of species to move across the landscape – is a leading strategy for helping wildlife respond to climate change. And yet, significant challenges remain in translating this high-level strategy into specific, on-the-ground actions. The Washington-British Columbia Transboundary Climate-Connectivity Project was initiated to help address these challenges. The region spanning the border of Washington state, USA, and British Columbia, Canada, faces increasing development pressure and limited transboundary coordination of land and wildlife management, both of which may threaten habitat connectivity and limit the potential for wildlife movement in response to change. In addition, the effects of climate change may further reduce habitat connectivity, and species may need novel types of habitat connectivity to complete adaptive range shifts. This project paired scientists and practitioners from both sides of the border to collaboratively identify potential climate impacts and adaptation actions for transboundary habitat connectivity, using a diverse suite of case study species, a vegetation system, and a region. Case study assessments revealed that climate change is likely to have significant implications for transboundary habitat connectivity. The adaptation actions identified to address potential impacts varied by case study, but fell into two general categories: those addressing potential climate impacts on existing habitat connectivity and those addressing novel habitat connectivity needs for climate-induced shifts in species ranges. In addition, project partners identified priority spatial locations for implementing these actions, as well as additional research needed to improve assessment of climate impacts and adaptation actions for habitat connectivity. The project resulted in a suite of products designed in collaboration with project partners to ensure their relevance and ease of application to decision-making. These products include this project overview report, which describes the project’s rationale, partnerships, approach, key findings, lessons learned, and remaining needs; detailed, stand-alone appendices for each case study, which describe the assessment process and key findings for each, and include all materials used in the assessment; and an interactive project gallery on the online mapping platform, Data Basin, which includes project reports and associated assessment materials, including interactive and downloadable connectivity and climate datasets. In addition, project participants emerged with enhanced capacity and a transboundary community of practice for addressing climate change and habitat connectivity in their decisionmaking. However, ongoing support for transboundary capacity building, collaboration, and research will be needed to promote the future resilience of our shared species and ecosystems.

An anthropogenic signal is detected in Arctic Sea Ice Extent (SIE) with all ensembles for the annual time series and also for September and March separately. All forcings (anthropogenic and natural, ALL) are necessary to explain the occurrence of SIE events more extreme than the current record minima (2012 for Sep., 2015 for Mar.), but not yet sufficient. If the current trends continue, ALL forcing will become sufficient for the occurrence of such events. Arctic SIE presents a counterexample to the statement that individual extreme events cannot be attributed to human influence.

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the “models are statistically indistinguishable from the truth” paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951–2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ±± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (−0.01±0.02−0.01±0.02 K).

Our understanding of the impact of anthropogenic forcing on extremes remains limited. While we have relatively high confidence for temperature extremes and some confidence in precipitation extremes, as yet we can say relatively little about storms, droughts and floods. We are often very limited by data and, while models and methods can be improved, improving historical data is much harder. To progress, we need further methodological development and improved process understanding. Event attribution is increasingly being undertaken, but there is still much work to do to develop methods and capabilities, understand the implications of framing choices, and develop objective evaluation techniques.