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  • Source Publication: Journal of Climate, 30, 4113-4130, doi:10.1175/JCLI-D-16-0189.1 Authors: Naja , M.R., F.W. Zwiers and N.P. Gillett Publication Date: May 2017

    A detection and attribution analysis on the multidecadal trend in snow water equivalent (SWE) has been conducted in four river basins located in British Columbia (BC). Monthly output from a suite of 10 general circulation models (GCMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used, including 40 climate simulations with anthropogenic and natural forcing combined (ALL), 40 simulations with natural forcing alone (NAT), and approximately 4200 yr of preindustrial control simulations (CTL). This output was downscaled to 1/16° spatial resolution and daily temporal resolution to drive the Variable Infiltration Capacity hydrologic model (VIC). Observed (manual snow survey) and VIC-reconstructed SWE, which exhibit declines across BC, are projected onto the multimodel ensemble means of the VIC-simulated SWE based on the responses to different forcings using an optimal fingerprinting approach. Results of the detection and attribution analysis shows that these declines are attributable to the anthropogenic forcing, which is dominated by the effect of increases in greenhouse gas concentration, and that they are not caused by natural forcing due to volcanic activity and solar variability combined. Anthropogenic influence is detected in three of the four basins (Fraser, Columbia, and Campbell Rivers) based on the VIC-reconstructed SWE, and in all basins based on the manual snow survey records. The simulations underestimate the observed snowpack trends in the Columbia River basin, which has the highest mean elevation. Attribution is supported by the detection of human influence on the cold-season temperatures that drive the snowpack reductions. These results are robust to the use of different observed datasets and to the treatment of low-frequency variability effects.

  • Source Publication: Journal of Advances in Modeling Earth Systems, 9, 2, 1292-1306, doi:10.1002/2016MS000830 Authors: Ouali, D., F. Chebana and T.B.M.J. Ouarda Publication Date: Apr 2017

    The high complexity of hydrological systems has long been recognized. Despite the increasing number of statistical techniques that aim to estimate hydrological quantiles at ungauged sites, few approaches were designed to account for the possible nonlinear connections between hydrological variables and catchments characteristics. Recently, a number of nonlinear machine‐learning tools have received attention in regional frequency analysis (RFA) applications especially for estimation purposes. In this paper, the aim is to study nonlinearity‐related aspects in the RFA of hydrological variables using statistical and machine‐learning approaches. To this end, a variety of combinations of linear and nonlinear approaches are considered in the main RFA steps (delineation and estimation). Artificial neural networks (ANNs) and generalized additive models (GAMs) are combined to a nonlinear ANN‐based canonical correlation analysis (NLCCA) procedure to ensure an appropriate nonlinear modeling of the complex processes involved. A comparison is carried out between classical linear combinations (CCAs combined with linear regression (LR) model), semilinear combinations (e.g., NLCCA with LR) and fully nonlinear combinations (e.g., NLCCA with GAM). The considered models are applied to three different data sets located in North America. Results indicate that fully nonlinear models (in both RFA steps) are the most appropriate since they provide best performances and a more realistic description of the physical processes involved, even though they are relatively more complex than linear ones. On the other hand, semilinear models which consider nonlinearity either in the delineation or estimation steps showed little improvement over linear models. The linear approaches provided the lowest performances.

  • Source Publication: Nature Geoscience 10, 255–259, doi:10.1038/ngeo2911. Authors: Zhang, X., F.W. Zwiers, G.L. Hui Wan and A.J. Cannon Publication Date: Mar 2017

    Warming of the climate is now unequivocal. The water holding capacity of the atmosphere increases by about 7% per °C of warming, which in turn raises the expectation of more intense extreme rainfall events. Meeting the demand for robust projections for extreme short-duration rainfall is challenging, however, because of our poor understanding of its past and future behaviour. The characterization of past changes is severely limited by the availability of observational data. Climate models, including typical regional climate models, do not directly simulate all extreme rainfall producing processes, such as convection. Recently developed convection-permitting models better simulate extreme precipitation, but simulations are not yet widely available due to their computational cost, and they have their own uncertainties. Attention has thus been focused on precipitation–temperature relationships in the hope of obtaining more robust extreme precipitation projections that exploit higher confidence temperature projections. However, the observed precipitation–temperature scaling relationships have been established almost exclusively by linking precipitation extremes with day-to-day temperature variations. These scaling relationships do not appear to provide a reliable basis for projecting future precipitation extremes. Until better methods are available, the relationship of the atmosphere's water holding capacity with temperature provides better guidance for planners in the mid-latitudes, albeit with large uncertainties.

  • Source Publication: doi:10.1007/s00382-017-3634-9 Authors: C. Seiler, F. W. Zwiers, K. I. Hodges and J. F. Scinocca Publication Date: Mar 2017

    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−1). 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−1, −0.2 day−1), 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.

  • Authors: Norman J. Shippee, Christian Seiler and Francis Zwiers Publication Date: Jan 2017

    Presented by Norman Shippee at the American Meteorological Society's 97th Annual Meeting in January, 2017:  CanSIPS reproduces the overall spatial pattern of cyclone track density found in ERA-Interim. Individual models of CanCM3 and CanCM4 over and under-represent the observed storm track, respectively. CanSIPS mean storm track and spread of track density is well represented, though an overall overprediction of storm track density is evident across the North Pacific, particularly from 45 - 60 deg N. This overprediction of storm track density is present in all components of the CanSIPS system. Moderate to strong correlation between CanSIPS mean storm track density and ERA-Interim track density are found across the primary storm track in the North Pacific and in the primary formation regions for Atlantic cyclones affecting North America (Gulf of Mexico and Cape Hatteras) Most CanSIPS bias in the North Pacific is centralized in the exit regions of the Pacific storm track, localized to Gulf of Alaska and coastal BC.

  • Source Publication: Geoscientific Model Development, 9, 3751-3777 doi:10.5194/gmd-2016-78 Authors: Boer, G.J., D.M. Smith, C. Cassou, F. Doblas-Reyes, G. Danabasoglu, B. Kirtman, Y. Kushnir, M. Kimoto, G.A. Meehl, R. Msadek, W.A. Mueller, K. Taylor and F.W. Zwiers Publication Date: Jan 2017

    The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-toend decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours.

    Groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them. The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society

  • Source Publication: Journal of Climate, 30, 553-571, doi:10.1175/JCLI-D-16-0412.1 Authors: Kirchmeier-Young, M.C., F.W. Zwiers and N.P. Gillett Publication Date: Dec 2016

    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.

  • Source Publication: Delivered at the Fall Meeting of the American Geophysical Union, December 2016 Authors: Sobie, S.R. and T.Q. Murdock Publication Date: Dec 2016

    Regional and local governments in British Columbia are recognizing the need to obtain detailed information about the effects of future climate change in their communities. The Pacific Climate Impacts Consortium (PCIC) has been a source for relevant analysis and information focussed on climate projections and impacts in BC since 2005. Recently, PCIC has moved away from preparing reports directly for users and instead worked in a more collaborative framework with several communities. In this new format, PCIC supplies climate projection information and assistance with interpretation, while allowing users to develop assessments tailored to their individual needs. This new structure allows PCIC to be more relevant in informing adaption practices. Our goal is to describe the process and outcomes from several collaborative climate change assessment projects.

  • Source Publication: Presented at the 2016 Fall Meeting of the American Geophysical Union Authors: Curry, C.L. and F.W. Zwiers Publication Date: Dec 2016

    The Fraser River basin (FRB) of British Columbia is one of the largest and most important watersheds in Western North America, and is home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in June-July. However, while annual peak daily streamflow (APDF) during the spring freshet in the FRB is historically well correlated with basin-averaged, annual maximum snow water equivalent (SWEmax), there are numerous occurrences of anomalously large APDF in below- or near-normal SWEmax years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APDFs complicates future projections of streamflow magnitude and frequency.

    We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by both observations and an ensemble of CMIP3 climate models in an attempt to discover the proximate causes of anomalous APDF events in the FRB. At several hydrometric stations representing a range of elevations, the relative importance of a set of predictors characterizing the magnitude and timing of rainfall, snowfall, and temperature is examined within a regression framework. The results indicate that next to the magnitude of SWEmax, the rate of warming subsequent to the date of SWEmax is the most influential variable for predicting APDF magnitudes in the lower FRB. Finally, the role of large-scale climate modes of variability for APDF magnitude and timing in the basin will be briefly discussed.

  • Authors: Shumlich, M.J. and T.Q. Murdock Publication Date: Nov 2016

    Regional climate service providers such as the Pacific Climate Impacts Consortium (PCIC) have often produced “grey literature” scientific project reports and impact assessments for the regional stakeholders they serve. These reports are suitable for those with some experience in adaptation and climate science. However, for the broader audience of policymakers, planners and the general public these reports are often too technical to be of use. To address the inaccessibility of these reports and provide usable information for decision making, PCIC has taken three main approaches. The first of these approaches is producing high-level summary reports to accompany some of PCIC’s more technical project reports. It is challenging to provide plain language summaries of the important findings without misleading readers or glossing over the subtleties of climate change impacts. However, anecdotal feedback from users indicates that the availability of summary reports dramatically increases the usability of the information provided to them. The second approach is collaborating and co-writing project reports directly with our users. This approach fosters constant learning, improved understanding and strengthens two-way communication between PCIC and regional stakeholders. The third approach is to develop short, high-level extension notes called science briefs. These cover regionally-relevant findings from the scientific community, contextualizing them and discussing what they mean for the users PCIC serves. They also serve as a way for PCIC to address frequently-asked questions in an in-depth manner. This poster discusses the methods and communication principles PCIC employs in the development of these projects and some of the lessons that have been learned along the way.

    (Delivered at the 7th Annual Northwest Climate Conference in Stevenson, Washington, November 14-16, 2016.)

  • Source Publication: Bulletin of the American Meteorological Society, doi:10.1175/BAMS-D-16-0019.1 Authors: Myhre, G., P.M. Forster, B.H. Samset, O. Hodnebrog, J. Sillmann, O. Boucher, G. Faluvegi, D. Flaschner, T. Iversen, M. Kasoar, V. Kharin, A. Kirkevag, J.-F. Lamarque, D. Olivie, T. Richardson, D. Shindell, K.P. Shine, C. Weum Stiern, T. Takemura, A. Voulg Publication Date: Oct 2016

    PDRMIP investigates the role of various drivers of climate change for mean and extreme precipitation changes, based on multiple climate model output and energy budget analyses.

    As the global temperature increases with changing climate, precipitation rates and patterns are affected through a wide range of physical mechanisms. The globally averaged intensity of extreme precipitation also changes more rapidly than the globally averaged precipitation rate. While some aspects of the regional variation in precipitation predicted by climate models appear robust, there is still a large degree of inter-model differences unaccounted for. Individual drivers of climate change initially alter the energy budget of the atmosphere leading to distinct rapid adjustments involving changes in precipitation. Differences in how these rapid adjustment processes manifest themselves within models are likely to explain a large fraction of the present model spread and needs better quantifications to improve precipitation predictions. Here, we introduce the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where a set of idealized experiments designed to understand the role of different climate forcing mechanisms were performed by a large set of climate models. PDRMIP focuses on understanding how precipitation changes relating to rapid adjustments and slower responses to climate forcings are represented across models. Initial results show that rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers. The PDRMIP results are expected to dramatically improve our understanding of the causes of the present diversity in future climate projections.

  • Source Publication: Climatic Change, 136, 3, 571–586, doi:10.1007/s10584-016-1632-2 Authors: Najafi, M.R., F.W. Zwiers and N.P. Gillett Publication Date: Aug 2016

    While it is generally accepted that the observed reduction of the Northern Hemisphere spring snow cover extent (SCE) is linked to warming of the climate system caused by human induced greenhouse gas emissions, it has been difficult to robustly quantify the anthropogenic contribution to the observed change. This study addresses the challenge by undertaking a formal detection and attribution analysis of SCE changes based on several observational datasets with different structural characteristics, in order to account for the substantial observational uncertainty. The datasets considered include a blended in situ-satellite dataset extending from 1923 to 2012 (Brown), the National Oceanic and Atmospheric Administration (NOAA) snow chart Climate Data Record for 1968–2012, the Global Land Data Assimilation System version 2.0 (GLDAS-2 Noah) reanalysis for 1951–2010, and the NOAA 20th-century reanalysis, version 2 (20CR2) covering 1948–2012. We analyse observed early spring (March-April) and late spring (May-June) NH SCE extent changes in these datasets using climate simulations of the responses to anthropogenic and natural forcings combined (ALL) and to natural forcings alone (NAT) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The ALL-forcing response is detected in all of the observed records, indicating that observed changes are inconsistent with internal variability. The analysis also shows that the ALL-forcing simulations substantially underestimate the observed changes as recorded in the Brown and NOAA datasets, but that they are more consistent with changes seen in the GLDAS and 20CR2 reanalyses. A two-signal analysis of the GLDAS data is able to detect the influence of the anthropogenic component of the observed SCE changes separately from the effect of natural forcing. Despite dataset and modelling uncertainty, these results, together with the understanding of the causes of observed warming over the past century, provide substantial evidence of a human contribution to the observed decline in Northern Hemisphere spring snow cover extent.

  • Source Publication: Hydrology and Earth System Sciences, 20, 1483-1508, doi:10.5194/hess-20-1483-2016 Authors: Werner, A. T. and A.J. Cannon Publication Date: Aug 2016

    Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods – bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) – are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis data sets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical data sets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

  • Source Publication: Earth’s Future, 2, 3, 152‐160, doi: 10.1002/2013EF000159 Authors: Kumar, S., D. Lawrence, P. Dirmeyer, J. Sheffield Publication Date: Aug 2016

    The temporal variability of river and soil water affects society at time scales ranging from hourly to decadal. The available water (AW), i.e., precipitation minus evapotranspiration, represents the total water available for runoff, soil water storage change, and ground water recharge. The reliability of AW is defined as the annual range of AW between local wet and dry seasons. A smaller annual range represents greater reliability and a larger range denotes less reliability. Here we assess the reliability of AW in the 21st century climate projections by 20 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The multimodel consensus suggests less reliable AW in the 21st century than in the 20th century with generally decreasing AW in local dry seasons and increasing AW in local wet seasons. In addition to the canonical perspective from climate models that wet regions will get wetter, this study suggests greater dryness during dry seasons even in regions where the mean climate becomes wetter. Lower emission scenarios show significant advantages in terms of minimizing impacts on AW but do not eliminate these impacts altogether.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-016-3148-x Authors: Whan, K., F.W. Zwiers and J. Sillmann Publication Date: Aug 2016

    Regional climate models (RCMs) are the primary source of high-resolution climate projections, and it is of crucial importance to evaluate their ability to simulate extreme events under current climate conditions. Many extreme events are influenced by circulation features that occur outside, or on the edges of, RCM domains. Thus, it is of interest to know whether such dynamically controlled aspects of extremes are well represented by RCMs. This study assesses the relationship between upstream blocking and cold temperature extremes over North America in observations, reanalysis products (ERA-Interim and NARR), and RCMs (CanRCM4, CRCM5, HIRHAM5, and RCA4). Generalized extreme value distributions were fitted to winter minimum temperature (TNn) incorporating blocking frequency (BF) as a covariate, which is shown to have a significant influence on TNn. The magnitude of blocking influence in the RCMs is consistent with observations, but the spatial extent varies. CRCM5 and HIRHAM5 reproduce the pattern of influence best compared to observations. CanRCM4 and RCA4 capture the influence of blocking in British Columbia and the northeastern United States, but the extension of influence that is seen in observations and reanalysis into the southern United States is not evident. The difference in the 20-yr return value (20RV) of TNn between high and low BF in the Pacific Ocean indicates that blocking is associated with a decrease of up to 15°C in the 20RV over the majority of the United States and in western Canada. In northern North America the difference in the 20RV is positive as blocking is associated with warmer extreme cold temperatures. The 20RVs are generally simulated well by the RCMs.

  • Source Publication: Nature Climate Change, doi:10.1038/NCLIMATE2956 Authors: Sun, Y., X, Zhang, G. Ren, F.W. Zwiers and T. Hu Publication Date: Jul 2016

    China has warmed rapidly over the past half century and has experienced widespread concomitant impacts on water availability, agriculture and ecosystems. Although urban areas occupy less than 1% of China’s land mass, the majority of China’s observing stations are situated in proximity to urban areas, and thus some of the recorded warming is undoubtedly the consequence of rapid urban development, particularly since the late 1970s. Here, we quantify the separate contributions of urbanization and other external forcings to the observed warming. We estimate that China’s temperature increased by 1.44 °C (90% confidence interval 1.22–1.66 °C) over the period 1961–2013 and that urban warming influences account for about a third of this observed warming, 0.49 °C (0.12–0.86 °C). Anthropogenic and natural external forcings combined explain most of the rest of the observed warming, contributing 0.93 °C (0.61–1.24 °C). This is close to the warming of 1.09 °C (0.86–1.31 °C) observed in global mean land temperatures over the period 1951–2010, which, in contrast to China’s recorded temperature change, is only weakly affected by urban warming influences. Clearly the effects of urbanization have considerably exacerbated the warming experienced by the large majority of the Chinese population in comparison with the warming that they would have experienced as a result of external forcing alone.

  • Source Publication: Science Advances, 2, 7, e1501719 doi: 10.1126/sciadv.1501719 Authors: Weller, E., S.-K. Min, W. Cai, F.W. Zwiers, Y.-H Kim and D. Lee Publication Date: Jul 2016

    The Indo-Pacific warm pool (IPWP) has warmed and grown substantially during the past century. The IPWP is Earth’s largest region of warm sea surface temperatures (SSTs), has the highest rainfall, and is fundamental to global atmospheric circulation and hydrological cycle. The region has also experienced the world’s highest rates of sea-level rise in recent decades, indicating large increases in ocean heat content and leading to substantial impacts on small island states in the region. Previous studies have considered mechanisms for the basin-scale ocean warming, but not the causes of the observed IPWP expansion, where expansion in the Indian Ocean has far exceeded that in the Pacific Ocean. We identify human and natural contributions to the observed IPWP changes since the 1950s by comparing observations with climate model simulations using an optimal fingerprinting technique. Greenhouse gas forcing is found to be the dominant cause of the observed increases in IPWP intensity and size, whereas natural fluctuations associated with the Pacific Decadal Oscillation have played a smaller yet significant role. Further, we show that the shape and impact of human-induced IPWP growth could be asymmetric between the Indian and Pacific basins, the causes of which remain uncertain. Human-induced changes in the IPWP have important implications for understanding and projecting related changes in monsoonal rainfall, and frequency or intensity of tropical storms, which have profound socioeconomic consequences.

  • Source Publication: Climatic Change, 137, 1, 201–216, doi:10.1007/s10584-016-1669-2 Authors: Schar, C., N. Ban, E.M. Fischer, J. Rajczak, J. Schmidli, C. Frei, F. Giorgi, T.R. Karl, E.J. Kendon, A.M.G. Klein Tank, P.A. O'Gorman, J. Sillmann, X. Zhang and F.W. Zwiers Publication Date: Jul 2016

    Many climate studies assess trends and projections in heavy precipitation events using precipitation percentile (or quantile) indices. Here we investigate three different percentile indices that are commonly used. We demonstrate that these may produce very different results and thus require great care with interpretation. More specifically, consideration is given to two intensity-based indices and one frequency-based index, namely (a) all-day percentiles, (b) wet-day percentiles, and (c) frequency indices based on the exceedance of a percentile threshold.

    Wet-day percentiles are conditionally computed for the subset of wet events (with precipitation exceeding some threshold, e.g. 1 mm/d for daily precipitation). We present evidence that this commonly used methodology can lead to artifacts and misleading results if significant changes in the wet-day frequency are not accounted for. Percentile threshold indices measure the frequency of exceedance with respect to a percentile-based threshold. We show that these indices yield an assessment of changes in heavy precipitation events that is qualitatively consistent with all-day percentiles, but there are substantial differences in quantitative terms. We discuss the reasons for these effects, present a theoretical assessment, and provide a series of examples using global and regional climate models to quantify the effects in typical applications.

    Application to climate model output shows that these considerations are relevant to a wide range of typical climate-change applications. In particular, wet-day percentiles generally yield different results, and in most instances should not be used for the impact-oriented assessment of changes in heavy precipitation events.

  • Source Publication: Atmosphere-Ocean, doi:10.1080/07055900.2016.1158146. Authors: C.L. Curry, B. Tencer, K. Whan, A. J. Weaver, M. Giguère and E. Wiebe Publication Date: Jul 2016

    We evaluate the capacity of a regional climate model to represent observed extreme temperature and precipitation events and also examine the impact of increased resolution, in an effort to identify added value in this respect. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45 km (CRCM45) horizontal resolution driven at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) for the period 1973–1995. The simulations were evaluated using the spline-interpolated dataset ANUSPLIN, a daily observational gridded surface temperature and precipitation product with a nominal resolution of approximately 10 km. We examine a range of climate extremes, comprising the 10th and 90th percentiles of daily maximum (TX) and minimum (TN) temperatures, the 90th percentile of daily precipitation (PR90), and the 27 core Climate Daily Extremes (CLIMDEX) indices.

    Both simulations exhibit cold biases compared with observations over WCan, with the bias exacerbated at higher resolution, suggesting little added value for temperature overall. There are instances, however, of regional improvement in the spatial pattern of temperature extremes at the higher resolution of CRCM15 (e.g., the CLIMDEX index for the annual number of days when TX > 25°C). The high-resolution simulations also reveal similarly localized features in precipitation (e.g., rain shadows) that are not resolved at the 45 km resolution. With regard to precipitation extremes, although both simulations generally display wet biases, CRCM15 features a reduced bias in PR90 in all seasons except winter. This improvement occurs despite the fact that spatial and interannual variability of PR90 in CRCM15 is significantly overestimated relative to both CRCM45 and ANUSPLIN. We posit that these characteristics are the result of demonstrable differences between corresponding topographical datasets used in the gridded observations and CRCM, the resulting errors propagated to physical variables tied to elevation and the beneficial effect of subsequent spatial averaging. Because topographical input is often discordant between simulations and gridded observations, it is argued that a limited form of spatial averaging may contribute added value beyond that which has already been noted in previous studies with respect to small-scale climate variability.

  • Source Publication: Atmosphere-Ocean, in press. Authors: Curry, C.L., B. Tencer, K. Whan, A. J. Weaver, M. Giguère and E. Wiebe Publication Date: Jul 2016

    Currently in press.

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