PCIC Publications

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Improved optimal fingerprinting based on estimating equations reaffirms anthropogenic effect on global warming
(2025) Li, Yan; Wang, Tianying; Yan, Jun; Zhang, Xuebin
The optimal fingerprinting approach is central to detecting and attributing climate change. It utilizes a regression model with covariates that have measurement errors, linked by a shared covariance matrix with the regression error up to a known scale. The inferences about the regression coefficients are vital for making reliable detection and attribution statements, as well as for quantifying uncertainties in outcomes like attributable warming. Traditionally, this has involved the total least squares (TLS) method, which depends on accurately estimating the covariance matrix of the regression error. However, inaccuracies in this matrix’s estimation can lead to skewed scaling factor estimators and overly optimistic confidence intervals, potentially misrepresenting the accuracy of detection and attribution statements. The recent advent of an estimating equations approach, which offers more efficient point estimation with smaller possible variance and precise uncertainty quantification, prompts a critical reassessment of past climate change detection and attribution analyses. By applying this advanced method to HadCRUT5 observational data and CMIP6 multimodel simulations, our study reevaluates temperature detection and attribution at global and regional levels, strengthens the existing detection and attribution conclusions at the global scale, and provides evidence of the effect of anthropogenic forcings in various regions.
Should we think of observationally constrained multidecade climate projections as predictions?
(Science Advances, 2025) Li, Tong; Zwiers, Francis W.; Zhang, Xuebin
Empirical evidence indicates that the range of model-projected future warming can be successfully narrowed by conditioning the projected warming on past observed warming. We demonstrate that warming projections conditioned on the entire instrumental annual surface temperature record are of sufficiently high quality and should be considered as long-term predictions rather than merely as projections. We support this view by considering the skill of predicted 20- and 50-year lead temperature changes under the Shared Economic Pathway (SSP)1-2.6 and SSP5-8.5 emission scenarios in climates of different sensitivities. Using climate model simulations, we show that adjusting raw multimodel projections of future warming with the Kriging for Climate Change (KCC) method eliminates most biases and reduces the uncertainty of warming projections irrespective of the sensitivity of the climate being considered. Simpler methods, or using only the more recent part of the temperature record, provide less effective constraints. The high-skill future warming predictions obtained via KCC have a serious place in informing global climate policies.
Land temperature and hydrological conditions over B.C. in 2024
(Department of Fisheries and Oceans, 2025) Curry, Charles L.; Lang, Kristyn; Dah, Abigail
2024 was the second warmest year since 1940 in B.C., exceeded only by record warm temperatures in 2023. Snowpack increased from well below-normal to below-normal through the early winter, decreasing again to well below-normal by late spring. Compared to 2023 drought conditions were less severe overall; however, basins in the Northeast continued to experience extreme drought. The annual mean temperature in B.C. is increasing and can be distinguished from natural variability over the analyzed period of 1940-2024. Annual precipitation, however, exhibits no significant province-wide trend over that period.
Land temperature and hydrological conditions over B.C. in 2023
(Department of Fisheries and Oceans, 2024) Curry, Charles L.; Lang, Kristyn
In 2023, B.C. experienced record warm annual, summer and fall temperatures and well below-normal annual precipitation. Snowpack was generally below-normal through the winter, rapidly decreasing to well below-normal by June 1st due to early snowmelt across the province. In late summer and fall, severe drought conditions were experienced nearly everywhere in B.C., coinciding with record warm temperatures and below-normal precipitation. The trend in annual mean temperature in B.C. is positive and can be distinguished from natural variability over the analyzed period, 1950-2023. Annual precipitation, however, exhibits no significant trend over that period.
Constraining the entire Earth system projections for more reliable climate change adaptation planning
(Science Advances, 2025) Li, Chao; Zwiers, Francis W.; Zhang, Xuebin; Fischer, Erich M.; Du, Fujun; Liu, Jieyu; Wang, Jianyu; Liang, Yongxiao; Li, Tong; Yuan, Lina
The warming climate is creating increased levels of climate risk because of changes to the hazards to which human and natural systems are exposed. Projections of how those hazards will change are affected by uncertainties in the climate sensitivity of climate models, among other factors. While the level-of-global-warming approach can circumvent model climate sensitivity uncertainties in some applications, practitioners faced with specific adaptation responsibilities often find such projections difficult to use because they generally require time-oriented information. Earth system projections following specified emissions scenarios can, however, be constrained by applying the level-of-global-warming approach to observationally constrained warming projections to yield more reliable time-oriented projections for adaption planning and implementation. This approach also allows individual groups to produce consistent and comparable assessments of multifaceted climate impacts and causal mechanisms, thereby benefiting climate assessments at national and international levels that provide the science basis for adaptation action.
PCIC science brief: Projected changes in short-term climate variability induced by human activities
(Pacific Climate Impacts Consortium (PCIC), 2025-08) Pacific Climate Impacts Consortium (PCIC)
Internal climate variability occurs due to interactions between the parts of the Earth’s climate system and is an indelible feature of both observed and model-simulated climate . Anthropogenic climate change may alter the internal variability of the climate system which could, in turn, influence both the mean climate and extremes. Writing in the Journal of Climate, Coquereau and colleagues (2024) used global climate model simulations to examine how internal climate variability might change as the planet warms. In their article titled, “Anthropogenic Changes in Interannual-to-Decadal Climate Variability in CMIP6 Multiensemble Simulations,” the authors noted two regions in particular where changes in climate variability are manifested in future. The first is a decrease in temperature variability at higher latitudes, associated with the retreat of sea ice and the moderation of air temperature by the now exposed ocean surface. The second is an increase in the short-term variability of temperature and precipitation at low latitudes, which appears to reflect an increase in the frequency of the El Niño-Southern Oscillation. This Science Brief discusses these findings and what they might mean for the future climate in British Columbia.
PCIC update: June 2025
(Pacific Climate Impacts Consortium (PCIC), 2025) Pacific Climate Impacts Consortium (PCIC)
This issue of the PCIC Update contains stories about temperature anomalies, an update on the Pacific Climate Seminar Series, staff changes at PCICI. and PCIC's most recent publications. The most recent staff profile is on Dr. Tong Li.
The Washington-British Columbia transboundary climate-connectivity project: Climate impacts and adaptation actions for wildlife habitat connectivity in the transboundary region of Washington and British Columbia
(Climate Impacts Group, University of Washington, 2016) Krosby, M.; Michalak, J.; Robbins, T. O.; Morgan, H.; Norheim, R.; Mauger, G.; Murdock, Trevor Q.
Change point detection of flood events using a functional data framework
(Advances in Water Resources, 2020) Ben Alaya, Mohamed Ali; Ternyck, Camille; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.
Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.
Downscaling extremes—An intercomparison of multiple statistical methods for present climate
(Journal of Climate, 2012) Bürger, Gerd; Murdock, Trevor Q.; Schoeneberg (Werner), Arelia T.; Sobie, Stephen R.
Five statistical downscaling methods [automated regression-based statistical downscaling (ASD), bias correction spatial disaggregation (BCSD), quantile regression neural networks (QRNN), TreeGen (TG), and expanded downscaling (XDS)] are compared with respect to representing climatic extremes. The tests are conducted at six stations from the coastal, mountainous, and taiga region of British Columbia, Canada, whose climatic extremes are measured using the 27 Climate Indices of Extremes (ClimDEX; http://www.climdex. org/climdex/index.action) indices. All methods are calibrated from data prior to 1991, and tested against the two decades from 1991 to 2010. A three-step testing procedure is used to establish a given method as reliable for any given index. The first step analyzes the sensitivity of a method to actual index anomalies by correlating observed and NCEP-downscaled annual index values; then, whether the distribution of an index corresponds to observations is tested. Finally, this latter test is applied to a downscaled climate simulation. This gives a total of 486 single and 162 combined tests. The temperature-related indices pass about twice as many tests as the precipitation indices, and temporally more complex indices that involve consecutive days pass none of the combined tests. With respect to regions, there is some tendency of better performance at the coastal and mountaintop stations. With respect to methods, XDS performed best, on average, with 19% (48%) of passed combined (single) tests, followed by BCSD and QRNN with 10% (45%) and 10% (31%), respectively, ASD with 6% (23%), and TG with 4% (21%) of passed tests. Limitations of the testing approach and possible consequences for the downscaling of extremes in these regions are discussed.
Introduction to explaining extreme events of 2014 from a climate perspective
(Bulletin of the American Meteorological Society, 2015) Herring, Stephanie C.; Hoerling, Martin P.; Kossin, James P.; Peterson, Thomas C.; Stott, Peter A.
The field of event attribution faces challenging questions. Can climate change influences on single events be reliably determined given that observations of extremes are limited and implications of model biases for establishing the causes of those events are poorly understood? The scientific developments in this report—now in its fourth year—as well as in the broader scientific literature, suggest that “event attribution” that detects the effects of long-term change on extreme events is possible. However, because of the fundamentally mixed nature of anthropogenic and natural climate variability, as well as technical challenges and methodological uncertainties, results are necessarily probabilistic and not deterministic. As the science advances, other questions are emerging. For what types of events can event attribution provide scientifically robust explanations of causes? Is near-real-time attribution possible? And, how useful are science-based explanations of extremes for society? We consider these questions in more detail.
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).
How does dynamical downscaling affect model biases and future projections of explosive extratropical cyclones along North America’s Atlantic coast?
(Climate Dynamics, 2018) Seiler, Christian; Zwiers, Francis W.; Hodges, Kevin I.; Scinocca, John F.
Explosive extratropical cyclones (EETCs) are rapidly intensifying low pressure systems that generate severe weather along North America’s Atlantic coast. Global climate models (GCMs) tend to simulate too few EETCs, perhaps partly due to their coarse horizontal resolution and poorly resolved moist diabatic processes. This study explores whether dynamical downscaling can reduce EETC frequency biases, and whether this affects future projections of storms along North America’s Atlantic coast. A regional climate model (CanRCM4) is forced with the CanESM2 GCM for the periods 1981 to 2000 and 2081 to 2100. EETCs are tracked from relative vorticity using an objective feature tracking algorithm. CanESM2 simulates 38% fewer EETC tracks compared to reanalysis data, which is consistent with a negative Eady growth rate bias (−0.1 day). Downscaling CanESM2 with CanRCM4 increases EETC frequency by one third, which reduces the frequency bias to −22%, and increases maximum EETC precipitation by 22%. Anthropogenic greenhouse gas forcing is projected to decrease EETC frequency (−15%, −18%) and Eady growth rate (−0.2 day, −0.2 day), and increase maximum EETC precipitation (46%, 52%) in CanESM2 and CanRCM4, respectively. The limited effect of dynamical downscaling on EETC frequency projections is consistent with the lack of impact on the maximum Eady growth rate. The coarse spatial resolution of GCMs presents an important limitation for simulating extreme ETCs, but Eady growth rate biases are likely just as relevant. Further bias reductions could be achieved by addressing processes that lead to an underestimation of lower tropospheric meridional temperature gradients.
Evaluating hydroclimatic change signals from statistically and dynamically downscaled GCMs and hydrologic models
(Journal of Hydrometeorology, 2014) Shrestha, Rajesh R.; Schnorbus, Markus A.; Schoeneberg (Werner), Arelia T.; Zwiers, Francis W.
This study analyzed potential hydroclimatic change in the Peace River basin in the province of British Columbia, Canada, based on two structurally different approaches: (i) statistically downscaled global climate models (GCMs) using the bias-corrected spatial disaggregation (BCSD) and (ii) dynamically downscaled GCM with the Canadian Regional Climate Model (CRCM). Additionally, simulated hydrologic changes from the GCM–BCSD-driven Variable Infiltration Capacity (VIC) model were compared to the CRCM integrated Canadian Land Surface Scheme (CLASS) output. The results show good agreements of the GCM–BCSD–VIC simulated precipitation, temperature, and runoff with observations, while the CRCM-simulated results differ substantially from observations. Nevertheless, differences (between the 2050s and 1970s) obtained from the two approaches are qualitatively similar for precipitation and temperature, although they are substantially different for snow water equivalent and runoff. The results obtained from the five Coupled Global Climate Model, version 3, (CGCM3)-driven CRCM runs are similar, suggesting that the multidecadal internal variability is not a large source of uncertainty for the Peace River basin. Overall, the GCM–BCSD–VIC approach, for now, remains the preferred approach for projecting basin-scale future hydrologic changes, provided that it explicitly accounts for the biases and includes plausible snow and runoff parameterizations. However, even with the GCM–BCSD–VIC approach, projections differ considerably depending on which of an ensemble of eight GCMs is used. Such differences reemphasize the uncertain nature of future hydroclimatic projections.
Intercomparison of multi-model ensemble-processing strategies within a consistent framework for climate projection in China
(Science China Earth Sciences, 2023) Zhu, Huanhuan; Jiang, Zhihong; Li, Laurent; Li, Wei; Jiang, Sheng; Zhou, Panyu; Zhao, Weihao
Climate change adaptation and relevant policy-making need reliable projections of future climate. Methods based on multi-model ensemble are generally considered as the most efficient way to achieve the goal. However, their efficiency varies and inter-comparison is a challenging task, as they use a variety of target variables, geographic regions, time periods, or model pools. Here, we construct and use a consistent framework to evaluate the performance of five ensemble-processing methods, i.e., multi-model ensemble mean (MME), rank-based weighting (RANK), reliability ensemble averaging (REA), climate model weighting by independence and performance (ClimWIP), and Bayesian model averaging (BMA). We investigate the annual mean temperature (Tav) and total precipitation (Prcptot) changes (relative to 1995–2014) over China and its seven subregions at 1.5 and 2 °C warming levels (relative to pre-industrial). All ensemble-processing methods perform better than MME, and achieve generally consistent results in terms of median values. But they show different results in terms of inter-model spread, served as a measure of uncertainty, and signal-to-noise ratio (SNR). ClimWIP is the most optimal method with its good performance in simulating current climate and in providing credible future projections. The uncertainty, measured by the range of 10th-90th percentiles, is reduced by about 30% for Tav, and 15% for Prcptot in China, with a certain variation among subregions. Based on ClimWIP, and averaged over whole China under 1.5/2 °C global warming levels, Tav increases by about 1.1/1.8 °C (relative to 1995–2014), while Prcptot increases by about 5.4%/11.2%, respectively. Reliability of projections is found dependent on investigated regions and indices. The projection for Tav is credible across all regions, as its SNR is generally larger than 2, while the SNR is lower than 1 for Prcptot over most regions under 1.5 °C warming. The largest warming is found in northeastern China, with increase of 1.3 (0.6-1.7)/2.0 (1.4-2.6) °C(ensemble’s median and range of the 10th–90th percentiles) under 1.5/2 °C warming, followed by northern and northwestern China. The smallest but the most robust warming is in southwestern China, with values exceeding 0.9 (0.6–1.1)/1.5 (1.1–1.7) °C. The most robust projection and largest increase is achieved in northwestern China for Prcptot, with increase of 9.1%(-1.6–24.7%)/17.9% (0.5–36.4%) under 1.5/2 °C warming. Followed by northern China, where the increase is 6.0%(-2.6–17.8%)/11.8% (2.4–25.1%), respectively. The precipitation projection is of large uncertainty in southwestern China, even with uncertain sign of variation. For the additional half-degree warming, Tav increases more than 0.5 °C throughout China. Almost all regions witness an increase of Prcptot, with the largest increase in northwestern China.
Regional sources and sinks of atmospheric particulate selenium in the United States based on seasonality profiles
(Environmental Science & Technology, 2023) Lao, Isabelle Renee; Feinberg, Aryeh; Borduas-Dedekind, Nadine
Selenium (Se) is an essential nutrient for humans and enters our food chain through bioavailable Se in soil. Atmospheric deposition is a major source of Se to soils, driving the need to investigate the sources and sinks of atmospheric Se. Here, we used Se concentrations from PM2.5 data at 82 sites from 1988 to 2010 from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in the US to identify the sources and sinks of particulate Se. We identified 6 distinct seasonal profiles of atmospheric Se, grouped by geographical location: West, Southwest, Midwest, Southeast, Northeast, and North Northeast. Across most of the regions, coal combustion is the largest Se source, with a terrestrial source dominating in the West. We also found evidence for gas-to-particle partitioning in the wintertime in the Northeast. Wet deposition is an important sink of particulate Se, as determined by Se/PM2.5 ratios. The Se concentrations from the IMPROVE network compare well to modeled output from a global chemistry-climate model, SOCOL-AER, except in the Southeast US. Our analysis constrains the sources and sinks of atmospheric Se, thereby improving the predictions of Se distribution under climate change.
Possible increased frequency of ENSO-related dry and wet conditions over some major watersheds in a warming climate
(Bulletin of the American Meteorological Society, 2020) Sun, Qiaohong; Miao, Chiyuan; AghaKouchak, Amir; Mallakpour, Iman; Ji, Duoying; Duan, Qingyun
Predicting the changes in teleconnection patterns and related hydroclimate extremes can provide vital information necessary to adapt to the effects of the El Niño–Southern Oscillation (ENSO). This study uses the outputs of global climate models to assess the changes in ENSO-related dry/wet patterns and the frequency of severe dry/wet events. The results show anomalous precipitation responding asymmetrically to La Niña and El Niño, indicating the teleconnections may not simply be strengthened. A “dry to drier, wet to wetter” annual anomalous precipitation pattern was projected during La Niña phases in some regions, with drier conditions over southern North America, southern South America, and southern central Asia, and wetter conditions in Southeast Asia and Australia. These results are robust, with agreement from the 26 models and from a subset of 8 models selected for their good performance in capturing observed patterns. However, we did not observe a similar strengthening of anomalous precipitation during future El Niño phases, for which the uncertainties in the projected influences are large. Under the RCP4.5 emissions scenario, 45 river basins under El Niño conditions and 39 river basins under La Niña conditions were predicted to experience an increase in the frequency of severe dry events; similarly, 59 river basins under El Niño conditions and 61 river basins under La Niña conditions were predicted to have an increase in the frequency of severe wet events, suggesting a likely increase in the risk of floods. Our results highlight the implications of changes in ENSO patterns for natural hazards, disaster management, and engineering infrastructure.
Climate projections for the Okanagan region
(2020-02) Regional District of North Okanagan; Regional District of Central Okanagan; Regional District of Okanagan-Similkameen; Pinna Sustainability; Natural Resources Canada; Okanagan Basin Water Board; Pacific Climate Impacts Consortium (PCIC)
Climate change is challenging ecosystems, communities, and the economy. Wildfires, flooding, and drought have already overwhelmed local infrastructure, caused economic losses, and posed health risks to communities. Significant effort to reduce the reliance on fossil fuels as quickly as possible will slow, and has the potential to curb, climate change by the late century, making greenhouse gas emissions reductions a central part of any long-term adaptation strategy. Designing to current and future climate parameters is markedly more cost effective than reacting to climate shocks and stresses over time. This report is intended to support a local understanding of how climate across the Okanagan is projected to change, and inform regional planning on how to prepare for future climate events. This work is critical to maintaining wellbeing, including robust ecosystems, a thriving community, and a vibrant economy. Early efforts to prepare infrastructure and communities to climate change will reduce regional reliance on continued emergency management activations and support the ability of the region to thrive over time.
Climate change and watershed hydrology: Part I - Recent and projected changes in British Columbia
(Streamline, 2008) Pike, Robin G.; Spittlehouse, David L.; Bennett, Katrina E.; Egginton, V. N.; Tschaplinski, Peter J.; Murdock, Trevor Q.; Schoeneberg (Werner), Arelia T.
The climate of British Columbia is changing, and with these changes come many adjustments in watershed hydrology and ultimately in our use of water-related resources. For example, declining snowpacks are a concern because they affect many aspects of water resources, from instream flows for fish to community water supplies to soil moisture, ground water, and aquifer recharge (BCMOE 2007). Because British Columbia is hydrologically diverse, the local responses to the anticipated changes in precipitation and temperature will differ. As a guide to what might happen in the future, this article (Part I) summarizes historical temperature and precipitation trends and future climate scenarios for British Columbia. The accompanying article (Part II) discusses eight broad hydrologic implications of climate change in British Columbia.
Hydrologic models for forest management applications: Part 2: Incorporating the effects of climate change
(Streamline, 2009) Beckers, Jos; Pike, Robin G.; Schoeneberg (Werner), Arelia T.; Redding, Todd E.; Smerdon, Brian D.; Anderson, Axel
In Alberta and British Columbia, several detailed studies of climate trends, future climate predictions, and potential effects on hydrology have been conducted (e.g., Rodenhuis et al. 2007; Pike et al. 2008a, 2008b; Sauchyn and Kulshreshtha 2008; Walker and Sydneysmith 2008). These studies indicate that a changing climate will alter watershed processes, which in turn may affect many aspects of short- and long-term watershed management. From an operational perspective, watershed scale hydrologic models could be used to address a range of forest management uncertainties not limited to the assessment of future growing conditions, permanence of wetlands and small streams, and potential changes to flooding, low flow, and other disturbances (Pike et al. 2008b). However, using current hydrologic models to address such complex questions is expected to pose a number of challenges due to the inherent limitations of these models and data inadequacies that exist across British Columbia and Alberta.

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