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A climatological assessment of intense extratropical cyclones from the potential vorticity perspective
(Journal of Climate, 2019) Seiler, Christian
Extratropical cyclones (ETCs) are known to intensify due to three vertically interacting positive potential vorticity perturbations that are associated with potential temperature anomalies close to the surface (θB), condensational heating in the lower-level atmosphere (qsat), and stratospheric intrusion in the upper-level atmosphere (qtr). This study presents the first climatological assessment of how much each of these three mechanisms contributes to the intensity of extreme ETCs. Using relative vorticity at 850 hPa as a measure of ETC intensity, results show that in about half of all cases the largest contributions during maximum ETC intensity are associated with qsat (53% of all ETCs), followed by qtr (36%) and θB (11%). The relative frequency of storms that are dominated by qsat is higher 1) during warmer months (61% of all ETCs during warmer months) compared to colder months (50%) and 2) in the Pacific (56% of all ETCs in the Pacific) compared to the Atlantic (46%). The relative frequency of ETCs that are dominated by θB is larger 1) during colder months (13%) compared to warmer months (3%), 2) in the Atlantic (15%) compared to the Pacific (8%), and 3) in western (11%–20%) compared to eastern ocean basins (4%–9%). These findings are based on piecewise potential vorticity inversion conducted for intense ETCs that occurred from 1980 to 2016 in the Northern Hemisphere (3273 events; top 7%). The results may serve as a baseline for evaluating ETC biases and uncertainties in global climate models.
A comprehensive, high-resolution database of historical and projected climate surfaces for western North America
(Bulletin of the American Meteorological Society, 2013) Hamann, Andreas; Wang, Tongli; Spittlehouse, David L.; Murdock, Trevor Q.
With growing concern over global climate change, interpolated climate data have become increasingly important for biological research, natural resource management, and infrastructure planning. Virtually every study in the field of climate change impact and adaptation requires a variety of data that may include long-term reference data, historical time series, climate change projections, or information about recent climate trends. Such data are usually not easily accessible at the appropriate resolution, in a consistent format, and for a comprehensive set of relevant climate variables. Several comprehensive efforts exist to provide researchers as well as nonspecialists with such databases, typically in the format of gridded spatial data (E. H. Girvetz et al. reviewed such databases in their 2009 PLoS One article). However, gridded climate data with high resolution, many climate variables, long historical time series, and comprehensive future projections can become very large and difficult to use for researchers and resource managers.
A coupled streamflow and water temperature (VIC-RBM-CE-QUAL-W2) model for the Nechako Reservoir
(Journal of Hydrology: Regional Studies, 2022) Larabi, Samah; Schnorbus, Markus A.; Zwiers, Francis W.
Hydrological regulation affect both hydrological and thermal conditions in the reservoir and downstream reach, subsequently disrupting fish habitats. This paper aims at developing an integrated model simulating physical processes that govern the quantity and quality of inflow, reservoir, and outflow water of the Nechako Reservoir. Such a model would help stakeholders understand the response of in-reservoir water temperature stratification and downstream water temperature to changes in inflow and reservoir operation under future climate change.
A dynamical climate model–driven hydrologic prediction system for the Fraser River, Canada
(Journal of Hydrometeorology, 2015) Shrestha, Rajesh R.; Schnorbus, Markus A.; Cannon, Alex J.
Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.
A global, continental, and regional analysis of changes in extreme precipitation
(Journal of Climate, 2021) Sun, Qiaohong; Zhang, Xuebin; Zwiers, Francis W.; Westra, Seth; Alexander, Lisa V.
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 1-day (Rx1day) and 5-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 central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East 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 covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K 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 2000–09 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.
A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America
(Scientific Data, 2019) Schoeneberg (Werner), Arelia T.; Schnorbus, Markus A.; Shrestha, Rajesh R.; Cannon, Alex J.; Zwiers, Francis W.; Dayon, Gildas; Anslow, Faron
We describe a spatially contiguous, temporally consistent high-resolution gridded daily meteorological dataset for northwestern North America. This >4 million km2 region has high topographic relief, seasonal snowpack, permafrost and glaciers, crosses multiple jurisdictional boundaries and contains the entire Yukon, Mackenzie, Saskatchewan, Fraser and Columbia drainages. We interpolate daily station data to 1/16° spatial resolution using a high-resolution monthly 1971–2000 climatology as a predictor in a thin-plate spline interpolating algorithm. Only temporally consistent climate stations with at least 40 years of record are included. Our approach is designed to produce a dataset well suited for driving hydrological models and training statistical downscaling schemes. We compare our results to two commonly used datasets and show improved performance for climate means, extremes and variability. When used to drive a hydrologic model, our dataset also outperforms these datasets for runoff ratios and streamflow trends in several, high elevation, sub-basins of the Fraser River.
A new statistical approach to climate change detection and attribution
(Climate Dynamics, 2017) Ribes, Aurélien; Zwiers, Francis W.; Azaïs, Jean-Marc; Naveau, Philippe
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 K).
A short note on the use of daily climate data to calculate Humidex heat‐stress indices
(International Journal of Climatology, 2023) Diaconescu, Emilia; Sankare, Housseyni; Chow, Kenneth; Murdock, Trevor Q.; Cannon, Alex J.
The projected increase in the frequency and intensity of extreme heat events due to climate change means an associated increase in risk of heat‐related illnesses and mortality. Public health systems need to be prepared to identify and reduce the susceptibility of vulnerable populations to increased occurrence of heat‐related illness and stress. To facilitate this, climate services have begun developing climate change projections for heat‐stress indices based on exceedances of thresholds used operationally in meteorological heat warning systems. This task is complicated by the fact that heat‐stress indices are generally computed using hourly data whereas climate model outputs are often archived at daily or longer time steps. This study focuses on Humidex, a heat‐stress index used in heat alerts issued by the Meteorological Service of Canada. Several potential solutions for computing robust Humidex indices using daily data are examined, including a new approximation method. Indices obtained with the new method are compared with indices obtained using the classic method based on hourly data as well as with other two methods based on average daily values. The new approximation gives good estimations for humidex indices, while the daily‐average‐value methods present biases with respect to the hourly‐value method.
An evaluation of block-maximum-based estimation of very long return period precipitation extremes with a large ensemble climate simulation
(Journal of Climate, 2020) Ben Alaya, Mohamed A.; Zwiers, Francis W.; Zhang, Xuebin
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.
Anthropogenic influence on altitudinally amplified temperature change in the Tibetan Plateau
(IOP Science, 2024) Sun, Ying; Hu, Ting; Zhang, Xuebin
As the highest plateau on the Earth, the Tibetan Plateau (TP) has experienced rapid warming in the last decades, affecting natural ecosystem and water resources extending far beyond the plateau itself. A distinctive characteristic known as elevation-dependent warming (EDW) in the high mountain regions was particularly pronounced in the TP, whereby the magnitude of temperature warming was amplified with increasing altitudes. Different mechanisms have been proposed to explain this phenomenon, however, the link between the root cause of warming, human activities, and the EDW remains poorly understood. Here we used the homogenized observation and simulations by the newest climate models to discern human influence on both mean and extreme temperatures within the region. An optimal fingerprinting method was applied in a vertical space rather than in traditional horizontal space. We found that the long-term trends in mean and extreme temperature amplified with increasing elevation, with larger magnitude of trends at higher elevations. The response to external forcing, primarily driven by human activities, was robustly detected in altitudinal amplification of temperature increase, providing clear evidence of human causes of EDW. As warming increases, the EDW in the region will continue, with more pronounced EDW corresponding to larger magnitude of warming under a high emission scenario. These findings mark the first evidence of human influence on temperature across different vertical altitudes of climate system.
Assessing Potential Evapotranspiration Methods in Future Drought Projections across Canada
(Atmosphere-Ocean, 2024) Tam, Benita; Bonsal, Barrie; Zhang, Xuebin; Zhang, Qinxiao; Rong, Robin
Recently, concerns have arisen as to whether temperature-based proxy methods used to estimate potential evapotranspiration (PET) are reliable when examining future drought severity, especially in the context of a warmer climate. The objective of this study was to assess the effect of different PET approaches, focusing on proxies for radiation and humidity, on future Standardized Precipitation Evapotranspiration Index (SPEI) calculations across Canada. Using output from 22 CMIP6 global climate models (GCMs), seasonal and annual SPEI comparisons were carried out between the physically-based Penman-Monteith (PM) method and two approaches that incorporate temperature proxies to calculate radiation and/or humidity. These included the temperature-based Hargreaves (HG) approach and a PM method with derived humidity (PM-m). Results revealed that although the general patterns of SPEI projections across Canada were consistent among the methods, notable spatial and temporal differences were apparent. Specifically, both median and extreme SPEI projections based on the two temperature proxy methods revealed less annual and summer drying in much of central, eastern, and northern regions of Canada when compared to the physically based SPEI-PM. In extreme western regions (British Columbia, Yukon) these two methods, particularly HG, projected drier conditions. Differences of using temperature derived radiation and humidity were also most apparent in spring (and to a lesser degree, autumn), where the HG approach overestimated spring drying (and autumn wetting) over large regions of the country. Overall, differences tended to be more pronounced for the fully temperature-based HG approach during all periods considered. Results from this study strongly suggest that when possible, a physically-based approach be used when estimating PET to assess future drought projections. If a temperature proxy is used, the differences to a physically-based method should be understood and resultant implications be evaluated.
Assessing the influence of calibration methodology and model structure on glacio-hydrological simulations in the Cheakamus River Basin, British Columbia, Canada
(Journal of Hydrology X, 2022) Tsuruta, Kai; Schnorbus, Markus A.
As glaciers across the world continue to recede, there is a concern that their loss as a fresh water reservoir within mountainous basins will have a negative impact on stream temperatures and downstream water resources. Currently, there are relatively few glacio-hydrological models (GHMs) appropriate to study such phenomena and studies that have used GHMs generally acknowledge the high uncertainty associated with their simulations. Calibration techniques present a particular issue in GHMs as available glacier observations are limited and errors in the glacierized portion of a basin can be compensated by errors in the non-glacierized portion. Using as a study site the Cheakamus Basin in British Columbia, Canada, we 1) present a new, fully-coupled GHM, 2) analyze the effects different calibration techniques have on the model’s summer streamflow projections, and 3) compare the fully-coupled GHM results to projections using a one-way GHM. The calibration techniques studied vary in terms of glacier representation (dynamic/static), and glacier constraint (mass balance/thinning rates/thinning rates and area change). We find projected future climate forcings are sufficiently strong in the Cheakamus Basin so as to generally make the sign and significance of changes to the basin’s hydrology insensitive to the calibration and projection procedures studied. However, the variation among these procedures produces significant changes in the projected magnitude of future hydrological changes and therefore should be carefully considered in studies where precision beyond the sign and significance of change is required. Based on analysis of the variation within each procedure’s set of model outputs, we conclude 1) the two-way GHM has benefits over the one-way model, 2) calibration using dynamic glaciers and a thinning rate constraint is preferable for the new GHM, and 3) there is a need for additional studies on the uncertainties associated with the calibration of glacio-hydrological models.
Atmospheric rivers increase future flood risk in western Canada's largest Pacific river
(Geophysical Research Letters, 2019) Curry, Charles L.; Islam, Siraj ul; Zwiers, Francis W.; Déry, Stephen J.
Snow‐dominated watersheds are bellwethers of climate change. Hydroclimate projections in such basins often find reductions in annual peak runoff due to decreased snowpack under global warming. British Columbia's Fraser River Basin (FRB) is a large, nival basin with exposure to moisture‐laden atmospheric rivers originating in the Pacific Ocean. Landfalling atmospheric rivers over the region in winter are projected to increase in both strength and frequency in Coupled Model Intercomparison Project Phase 5 climate models. We investigate future changes in hydrology and annual peak daily streamflow in the FRB using a hydrologic model driven by a bias‐corrected Coupled Model Intercomparison Project Phase 5 ensemble. Under Representative Concentration Pathway (8.5), the FRB evolves toward a nival‐pluvial regime featuring an increasing association of extreme rainfall with annual peak daily flow, a doubling in cold season peak discharge, and a decrease in the return period of the largest historical flow, from a 1‐in‐200‐year to 1‐in‐50‐year event by the late 21st century. Plain Language Summary Snow‐covered areas of the globe are particularly sensitive to global warming. Future projections using global climate models generally show that as the ratio of snow to rain declines, river flows peak earlier in the year with reduced volume. These models also capture the phenomenon of “atmospheric rivers”: long, meandering plumes of water vapor often originating over the tropical oceans that bring sustained, heavy precipitation to the west coasts of North America and northern Europe. The present‐day frequency of landfalling atmospheric rivers on the Canadian west coast is projected to increase nearly fourfold by the late 21st century, with a proportionate increase in extreme rainfall events. Our work is the first to directly investigate the impact of these “rivers in the sky” on “rivers on the land” using climate model projections. Focusing on the Fraser River Basin, Canada's largest Pacific watershed, and using a business‐as‐usual industrial emissions scenario, we show that the basin transitions from one where peak flow results from spring snowmelt to one where peak flow is often caused by extreme rainfall. Our modeling suggests that extreme rainfall events resulting from atmospheric rivers may lead to peak annual floods of historic proportions, and of unprecedented frequency, by the late 21st century in the Fraser River Basin.
Attributing extreme fire risk in Western Canada to human emissions
(Climatic Change, 2017) Kirchmeier-Young, Megan C.; Zwiers, Francis W.; Gillett, Nathan P.; Cannon, Alex J.
Canada is expected to see an increase in fire risk under future climate projections. Large fires, such as that near Fort McMurray, Alberta in 2016, can be devastating to the communities affected. Understanding the role of human emissions in the occurrence of such extreme fire events can lend insight into how these events might change in the future. An event attribution framework is used to quantify the influence of anthropogenic forcings on extreme fire risk in the current climate of a western Canada region. Fourteen metrics from the Canadian Forest Fire Danger Rating System are used to define the extreme fire seasons. For the majority of these metrics and during the current decade, the combined effect of anthropogenic and natural forcing is estimated to have made extreme fire risk events in the region 1.5 to 6 times as likely compared to a climate that would have been with natural forcings alone.
Attributing northern high-latitude precipitation change over the period 1966–2005 to human influence
(Climate Dynamics, 2015) Wan, Hui; Zhang, Xuebin; Zwiers, Francis W.; Min, Seung-Ki
Using an optimal fingerprinting method and improved observations, we compare observed and CMIP5 model simulated annual, cold season and warm season (semi-annual) precipitation over northern high-latitude (north of 50°N) land over 1966–2005. We find that the multi-model simulated responses to the effect of anthropogenic forcing or the effect of anthropogenic and natural forcing combined are consistent with observed changes. We also find that the influence of anthropogenic forcing may be separately detected from that of natural forcings, though the effect of natural forcing cannot be robustly detected. This study confirms our early finding that anthropogenic influence in high-latitude precipitation is detectable. However, in contrast with the previous study, the evidence now indicates that the models do not underestimated observed changes. The difference in the latter aspect is most likely due to improvement in the spatial–temporal coverage of the data used in this study, as well as the details of data processing procedures.
Attribution and prediction of extreme events: Editorial on the special issue
(Weather and Climate Extremes, 2015) Seneviratne, Sonia I.; Zwiers, Francis W.
The investigation of extreme events and their relation to climate change and variability is arguably one of the most challenging areas in climate research. Extremes are also of continual concern amongst policy and decision makers, and in the media, because of the devastating impacts that can result from their occurrence. As highlighted in the recent IPCC Special Report on “Managing the risks of extreme events and disasters to advance climate change and adaptation” (SREX), climate extremes are by definition rare, often difficult to define, not well observed, and pose substantial challenges in their characterization (IPCC, 2012, Seneviratne et al., 2012). The scientific challenges are multidisciplinary, and point to the need for improved statistical analysis tools, deeper process understanding, and more extensive assessments of the societal and ecosystems impacts of extreme events. Despite, or maybe because of these issues, research on extremes is vibrant and is advancing rapidly (Seneviratne et al., 2012, Zwiers et al., 2013, Herring et al., 2014). Nevertheless, the magnitude and importance of research on extremes is so large that even greater levels of effort and amounts of expertise are urgently required across a broad range of disciplines.
Attribution of Arctic sea ice decline from 1953 to 2012 to influences from natural, greenhouse gas, and anthropogenic aerosol forcing
(Journal of Climate, 2018) Mueller, Bennit L.; Gillett, Nathan P.; Monahan, Adam H.; Zwiers, Francis W.
The paper presents results from a climate change detection and attribution study on the decline of Arctic sea ice extent in September for the 1953–2012 period. For this period three independently derived observational datasets and simulations from multiple climate models are available to attribute observed changes in the sea ice extent to known climate forcings. Here we direct our attention to the combined cooling effect from other anthropogenic forcing agents (mainly aerosols), which has potentially masked a fraction of greenhouse gas–induced Arctic sea ice decline. The presented detection and attribution framework consists of a regression model, namely, regularized optimal fingerprinting, where observations are regressed onto model-simulated climate response patterns (i.e., fingerprints). We show that fingerprints from greenhouse gas, natural, and other anthropogenic forcings are detected in the three observed records of Arctic sea ice extent. Beyond that, our findings indicate that for the 1953–2012 period roughly 23% of the greenhouse gas–induced negative sea ice trend has been offset by a weak positive sea ice trend attributable to other anthropogenic forcing. We show that our detection and attribution results remain robust in the presence of emerging nonstationary internal climate variability acting upon sea ice using a perfect model experiment and data from two large ensembles of climate simulations.
Attribution of extreme events in Arctic sea ice extent
(Journal of Climate, 2017) Kirchmeier-Young, Megan C.; Zwiers, Francis W.; Gillett, Nathan P.
Arctic sea ice extent (SIE) has decreased over recent decades, with record-setting minimum events in 2007 and again in 2012. A question of interest across many disciplines concerns the extent to which such extreme events can be attributed to anthropogenic influences. First, a detection and attribution analysis is performed for trends in SIE anomalies over the observed period. The main objective of this study is an event attribution analysis for extreme minimum events in Arctic SIE. Although focus is placed on the 2012 event, the results are generalized to extreme events of other magnitudes, including both past and potential future extremes. Several ensembles of model responses are used, including two single-model large ensembles. Using several different metrics to define the events in question, it is shown that an extreme SIE minimum of the magnitude seen in 2012 is consistent with a scenario including anthropogenic influence and is extremely unlikely in a scenario excluding anthropogenic influence. Hence, the 2012 Arctic sea ice minimum provides a counterexample to the often-quoted idea that individual extreme events cannot be attributed to human influence.
Attribution of extreme weather and climate‐related events
(WIREs Climate Change, 2016) Stott, Peter A.; Christidis, Nikolaos; Otto, Friederike E. L.; Sun, Ying; Vanderlinden, Jean‐Paul; Van Oldenborgh, Geert Jan; Vautard, Robert; von Storch, Hans; Walton, Peter; Yiou, Pascal; Zwiers, Francis W.
Extreme weather and climate‐related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human‐induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers.
Attribution of observed streamflow changes in key British Columbia drainage basins
(Geophysical Research Letters, 2017) Najafi, Mohammad R.; Zwiers, Francis W.; Gillett, Nathan P.
We study the observed decline in summer streamflow in four key river basins in British Columbia (BC), Canada, using a formal detection and attribution (D&A) analysis procedure. Reconstructed and simulated streamflow is generated using the semidistributed variable infiltration capacity hydrologic model, which is driven by 1/16° gridded observations and downscaled climate model data from the Coupled Model Intercomparison Project phase 5 (CMIP5), respectively. The internal variability of the regional hydrologic components using ~5100 years of streamflow was simulated using CMIP5 preindustrial control runs. Results show that the observed changes in summer streamflow are inconsistent with simulations representing the responses to natural forcing factors alone, while the response to anthropogenic and natural forcing factors combined is detected in these changes. A two‐signal D&A analysis indicates that the effects of anthropogenic (ANT) forcing factors are discernable from natural forcing in BC, albeit with large uncertainties.

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