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  • Source Publication: Bulletin of the American Meteorological Society, 95, 9 S1–S96 Authors: Stott, P.A., G.C. Hegerl, S.C. Herring, M.P. Hoerling, T.C. Peterson, X. Zhang and F.W. Zwiers Publication Date: Dec 2014
  • Source Publication: Environmental Research Letters, 9, 064023, doi:10.1088/1748-9326/9/6/064023 Authors: Sillmann, J., M.G. Donat, J.C. Fyfe and F.W. Zwiers Publication Date: Dec 2014

    The discrepancy between recent observed and simulated trends in global mean surface temperature has provoked a debate about possible causes and implications for future climate change projections. However, little has been said in this discussion about observed and simulated trends in global temperature extremes. Here we assess trend patterns in temperature extremes and evaluate the consistency between observed and simulated temperature extremes over the past four decades (1971–2010) in comparison to the recent 15 years (1996–2010). We consider the coldest night and warmest day in a year in the observational dataset HadEX2 and in the current generation of global climate models (CMIP5). In general, the observed trends fall within the simulated range of trends, with better consistency for the longer period. Spatial trend patterns differ for the warm and cold extremes, with the warm extremes showing continuous positive trends across the globe and the cold extremes exhibiting a coherent cooling pattern across the Northern Hemisphere mid-latitudes that has emerged in the recent 15 years and is not reproduced by the models. This regional inconsistency between models and observations might be a key to understanding the recent hiatus in global mean temperature warming.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Dec 2014

    Recent research by P.A. O’Gorman (2014), in the journal Nature, uses an ensemble of global climate model (GCM) simulations to examine the projected changes in both mean snowfall and daily snowfall extremes in a high greenhouse-gas emissions scenario. He finds that, while both mean snowfall and extreme snowfall decrease as the climate warms due to the influence of greenhouse gasses, the reduction in daily snowfall extremes is smaller than the reduction in mean snowfall. O’Gorman suggests, based on a simple physical model, that this may be due to snowfall extremes occuring near an optimal temperature that is insensitive to climate change.

  • Source Publication: Nature Climate Change, Advance Online Publication, doi:10.1038/nclimate2410. Authors: Sun, Y., X. Zhang, F.W. Zwiers, L. Song, H. Wan, t. Hu, H. Yin and G. Ren Publication Date: Dec 2014

    The summer of 2013 was the hottest on record in Eastern China. Severe extended heatwaves affected the most populous and economically developed part of China and caused substantial economic and societal impacts. The estimated direct economic losses from the accompanying drought alone total 59 billion RMB. Summer (June–August) mean temperature in the region has increased by 0.82 °C since reliable observations were established in the 1950s, with the five hottest summers all occurring in the twenty-first century. It is challenging to attribute extreme events to causes. Nevertheless, quantifying the causes of such extreme summer heat and projecting its future likelihood is necessary to develop climate adaptation strategies. We estimate that anthropogenic influence has caused a more than 60-fold increase in the likelihood of the extreme warm 2013 summer since the early 1950s, and project that similarly hot summers will become even more frequent in the future, with fully 50% of summers being hotter than the 2013 summer in two decades even under the moderate RCP4.5 emissions scenario. Without adaptation to reduce vulnerability to the effects of extreme heat, this would imply a rapid increase in risks from extreme summer heat to Eastern China.

  • Source Publication: Journal of Climate, doi:10.1175/JCLI-D-14-00636.1. Authors: Cannon, A.J. Publication Date: Dec 2014

    Logistical constraints can limit the number of Global Climate Model (GCM) simulations considered in a climate change impact assessment. When dealing with annual or seasonal variables, one can visualize and manually select GCM scenarios to cover as much of the ensemble’s range of changes as possible. Most environmental systems are sensitive to climate conditions, e.g., extremes, that cannot be described by a small number of variables. Instead, algorithms like k-means clustering have been used to select representative ensemble members. Clustering algorithms are, however, biased towards high-density regions of climate variable space and tend to select scenarios that describe the central tendency rather than the full spread of an ensemble. Also, scenarios selected via clustering may not be ordered, i.e., scenarios in the 5 cluster solution may not appear in the 6 cluster solution, which makes recommending a consistent set of scenarios to researchers with different needs difficult. Alternatively, an automated procedure based on a cluster initialization algorithm is proposed and applied to changes in 27 climate extremes indices between 1986-2005 and 2081-2100 from a large ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. Selections by the method are ordered and are designed to span the overall range of the ensemble. The number of scenarios required to account for changes spanned by at least 90% of the CMIP5 ensemble members is reported for 21 regions of the globe and compared with k-means clustering. On average, the proposed method requires 40% fewer scenarios to meet this threshold than does k-means clustering.

  • Source Publication: Water Resources Research, doi:10.1002/2014WR015279 Authors: Schnorbus, M.A. and A.J. Cannon Publication Date: Dec 2014

    A recent hydrological impacts study in British Columbia, Canada, used an ensemble of 23 climate change simulations to assess potential future changes in streamflow. These Coupled Model Intercomparison Project Phase 3 (CMIP3) simulations were statistically downscaled and used to drive the Variable Infiltration Capacity (VIC) hydrology model over several watersheds. Due to computational restrictions, the 23 member VIC ensemble is a subset of the full 136 member CMIP3 archive. Extending the VIC ensemble to cover the full range of uncertainty represented by CMIP3, and incorporating the latest generation CMIP5 ensembles, poses a considerable computing challenge. Thus, we extend the VIC ensemble using a computationally efficient statistical emulation model, which approximates the combined output of the two-step process of statistical downscaling and hydrologic modeling, trained with the 23 member VIC ensemble. Regularized multiple linear regression links projected changes in monthly temperature and precipitation with projected changes in monthly streamflow over the Fraser and Peace River watersheds. Following validation, the statistical emulator is forced with the full suite of CMIP3 and CMIP5 climate change projections. The 23 member VIC ensemble has a smaller spread than the full ensemble; however, both ensembles provide the same consensus estimate of monthly streamflow change. Qualitatively, CMIP5 shows a similar streamflow response as CMIP3 for snow-dominated hydrologic regimes. However, by end-century, the CMIP5 worst-case RCP8.5 has a larger impact than CMIP3 A2. This work also underscores the advantage of using emulation to rapidly identify those future extreme projections that may merit further study using more computationally demanding process-based methods.

  • Source Publication: Forest Ecolology and Management Authors: Alfaro, R.I., B. Fady, G.G. Vendramin, I.K. Dawson, R.A. Fleming, C. Sáenz‐Romero, R.A. Lindig‐Cisneros, T.Q. Murdock, B. Vinceti, C.M. Navarro, T. Skrøppa, G. Baldinelli, Y.A. El‐Kassaby and J. Loo Publication Date: Dec 2014

    The current distribution of forest genetic resources on Earth is the result of a combination of natural processes and human actions. Over time, tree populations have become adapted to their habitats including the local ecological disturbances they face. As the planet enters a phase of human-induced climate change of unprecedented speed and magnitude, however, previously locally-adapted populations are rendered less suitable for new conditions, and ‘natural’ biotic and abiotic disturbances are taken outside their historic distribution, frequency and intensity ranges. Tree populations rely on phenotypic plasticity to survive in extant locations, on genetic adaptation to modify their local phenotypic optimum or on migration to new suitable environmental conditions. The rate of required change, however, may outpace the ability to respond, and tree species and populations may become locally extinct after specific, but as yet unknown and unquantified, tipping points are reached. Here, we review the importance of forest genetic resources as a source of evolutionary potential for adaptation to changes in climate and other ecological factors. We particularly consider climate-related responses in the context of linkages to disturbances such as pests, diseases and fire, and associated feedback loops. The importance of management strategies to conserve evolutionary potential is emphasised and recommendations for policy-makers are provided.

  • Source Publication: Climate Dynamics, 45, 5, 1547-1564, doi:10.1007/s00382‐014‐2408‐x Authors: Christidis, N., P.A. Stott and F.W. Zwiers Publication Date: Nov 2014

    Regional warming due to anthropogenic influence on the climate is expected to increase the frequency of very warm years and seasons. The growing research area of extreme event attribution has provided pertinent scientific evidence for a number of such warm events for which the forced climate response rises above internal climatic variability. Although the demand for attribution assessments is higher shortly after an event occurs, most scientific studies become available several months later. A formal attribution methodology is employed here to pre-compute the changing odds of very warm years and seasons in regions across the world. Events are defined based on the exceedence of temperature thresholds and their changing odds are measured over a range of pre-specified thresholds, which means assessments can be made as soon as a new event happens. Optimal fingerprinting provides observationally constrained estimates of the global temperature response to external forcings from which regional information is extracted. This information is combined with estimates of internal variability to construct temperature distributions with and without the effect of anthropogenic influence. The likelihood of an event is computed for each distribution and the change in the odds estimated. Analyses are conducted with seven climate models to explore the model dependency of the results. Apart from colder regions and seasons, characterised by greater internal climate variability, the odds of warm events are found to have significantly increased and temperatures above the threshold of 1-in-10 year events during 1961–1990 have become at least twice as likely to occur.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-014-2408-x. Authors: Nikolaos Christidis, Peter A. Stott, Francis W. Zwiers Publication Date: Nov 2014

    Regional warming due to anthropogenic influence on the climate is expected to increase the frequency of very warm years and seasons. The growing research area of extreme event attribution has provided pertinent scientific evidence for a number of such warm events for which the forced climate response rises above internal climatic variability. Although the demand for attribution assessments is higher shortly after an event occurs, most scientific studies become available several months later. A formal attribution methodology is employed here to pre-compute the changing odds of very warm years and seasons in regions across the world. Events are defined based on the exceedence of temperature thresholds and their changing odds are measured over a range of pre-specified thresholds, which means assessments can be made as soon as a new event happens. Optimal fingerprinting provides observationally constrained estimates of the global temperature response to external forcings from which regional information is extracted. This information is combined with estimates of internal variability to construct temperature distributions with and without the effect of anthropogenic influence. The likelihood of an event is computed for each distribution and the change in the odds estimated. Analyses are conducted with seven climate models to explore the model dependency of the results. Apart from colder regions and seasons, characterised by greater internal climate variability, the odds of warm events are found to have significantly increased and temperatures above the threshold of 1-in-10 year events during 1961–1990 have become at least twice as likely to occur.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Nov 2014
  • Authors: The Pacific Climate Impacts Consortium Publication Date: Nov 2014

    In a recent paper in the journal Nature Climate Change, Meehl, Teng and Arblaster (2014) examine individual global climate model runs from models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) to see if any runs replicated the observed early-2000s hiatus in surface temperature warming. They found that those individual model runs that have Interdecadal Pacific Oscillation (IPO) values that matched with observed values successfully simulate the early 2000s hiatus. Using data available in the mid-1990s, they also apply a recently-developed climate prediction technique that uses modern global climate models (GCM), initialized with observations, to make so-called “decadal climate predictions” and find that both the negative phase of the IPO and the surface temperature hiatus could be predicted with this method, using only data that was available prior to the hiatus.

  • Authors: The Pacific Climate Impacts Consotrium Publication Date: Nov 2014
  • Source Publication: Water Resources Research, 50, 11, 8907–8926, doi:10.1002/2014WR015279. Authors: Markus A. Schnorbus and Alex J. Cannon Publication Date: Nov 2014

    A recent hydrological impacts study in British Columbia, Canada, used an ensemble of 23 climate change simulations to assess potential future changes in streamflow. These Coupled Model Intercomparison Project Phase 3 (CMIP3) simulations were statistically downscaled and used to drive the Variable Infiltration Capacity (VIC) hydrology model over several watersheds. Due to computational restrictions, the 23 member VIC ensemble is a subset of the full 136 member CMIP3 archive. Extending the VIC ensemble to cover the full range of uncertainty represented by CMIP3, and incorporating the latest generation CMIP5 ensembles, poses a considerable computing challenge. Thus, we extend the VIC ensemble using a computationally efficient statistical emulation model, which approximates the combined output of the two-step process of statistical downscaling and hydrologic modeling, trained with the 23 member VIC ensemble. Regularized multiple linear regression links projected changes in monthly temperature and precipitation with projected changes in monthly streamflow over the Fraser and Peace River watersheds. Following validation, the statistical emulator is forced with the full suite of CMIP3 and CMIP5 climate change projections. The 23 member VIC ensemble has a smaller spread than the full ensemble; however, both ensembles provide the same consensus estimate of monthly streamflow change. Qualitatively, CMIP5 shows a similar streamflow response as CMIP3 for snow-dominated hydrologic regimes. However, by end-century, the CMIP5 worst-case RCP8.5 has a larger impact than CMIP3 A2. This work also underscores the advantage of using emulation to rapidly identify those future extreme projections that may merit further study using more computationally demanding process-based methods.

  • Source Publication: Water Resources Research, 50, 11, 8907‐8926, doi: 10.1002/2014WR015279 Authors: Schnorbus, M.A. and A. J. Cannon Publication Date: Nov 2014

    A recent hydrological impacts study in British Columbia, Canada, used an ensemble of 23 climate change simulations to assess potential future changes in streamflow. These Coupled Model Intercomparison Project Phase 3 (CMIP3) simulations were statistically downscaled and used to drive the Variable Infiltration Capacity (VIC) hydrology model over several watersheds. Due to computational restrictions, the 23 member VIC ensemble is a subset of the full 136 member CMIP3 archive. Extending the VIC ensemble to cover the full range of uncertainty represented by CMIP3, and incorporating the latest generation CMIP5 ensembles, poses a considerable computing challenge. Thus, we extend the VIC ensemble using a computationally efficient statistical emulation model, which approximates the combined output of the two-step process of statistical downscaling and hydrologic modeling, trained with the 23 member VIC ensemble. Regularized multiple linear regression links projected changes in monthly temperature and precipitation with projected changes in monthly streamflow over the Fraser and Peace River watersheds. Following validation, the statistical emulator is forced with the full suite of CMIP3 and CMIP5 climate change projections. The 23 member VIC ensemble has a smaller spread than the full ensemble; however, both ensembles provide the same consensus estimate of monthly streamflow change. Qualitatively, CMIP5 shows a similar streamflow response as CMIP3 for snow-dominated hydrologic regimes. However, by end-century, the CMIP5 worst-case RCP8.5 has a larger impact than CMIP3 A2. This work also underscores the advantage of using emulation to rapidly identify those future extreme projections that may merit further study using more computationally demanding process-based methods.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Oct 2014
  • Source Publication: Nature Climate Change, 4, 1082–1085, doi:10.1038/nclimate2410. Authors: Ying Sun, Xuebin Zhang, Francis W. Zwiers, Lianchun Song, Hui Wan, Ting Hu, Hong Yin and Guoyu Ren Publication Date: Oct 2014

    The summer of 2013 was the hottest on record in Eastern China. Severe extended heatwaves affected the most populous and economically developed part of China and caused substantial economic and societal impacts. The estimated direct economic losses from the accompanying drought alone total 59 billion RMB. Summer (June–August) mean temperature in the region has increased by 0.82 °C since reliable observations were established in the 1950s, with the five hottest summers all occurring in the twenty-first century. It is challenging to attribute extreme events to causes. Nevertheless, quantifying the causes of such extreme summer heat and projecting its future likelihood is necessary to develop climate adaptation strategies. We estimate that anthropogenic influence has caused a more than 60-fold increase in the likelihood of the extreme warm 2013 summer since the early 1950s, and project that similarly hot summers will become even more frequent in the future, with fully 50% of summers being hotter than the 2013 summer in two decades even under the moderate RCP4.5 emissions scenario. Without adaptation to reduce vulnerability to the effects of extreme heat, this would imply a rapid increase in risks from extreme summer heat to Eastern China.

  • Source Publication: Bulletin of the American Meteorological Society, 95, 9, S1‐S96 Authors: Stott, P.A., G.C. Hegerl, S.C. Herring, M.P. Hoerling, T.C. Peterson, X. Zhang and F.W. Zwiers Publication Date: Sep 2014

    Attribution of extreme events is a challenging science and one that is currently undergoing considerable evolution. In this paper, 20 different research groups explored the causes of 16 different events that occurred in 2013. The findings indicate that human-caused climate change greatly increased the risk for he extreme heat waves assessed in this report. How human influence affected other types of events such as droughts, heavy rain events, and storms was less clear, indicating that natural variability likely played a much larger role in these extremes. Multiple groups chose to look at both the Australian heat waves and the California drought, providing an opportunity to compare and contrast the strengths and weaknesses of various methodologies. There was considerable agreement about the role anthropogenic climate change played in the events between the different assessments. This year three analyses were f evere storms and none found an anthropogenic signal. However, attribution assessments of these types of events pose unique challenges due to the often limited observational record. When human-influence for an event is not identified with the scientific tools available to us today, this means that if there is a human contribution, it cannot be distinguished
    from natural climate variability.

  • Source Publication: Bulletin of the American Meteorological Society, 95, 9 S1–S96. Authors: Stott, P.A., G.C. Hegerl, S.C. Herring, M.P. Hoerling, T.C. Peterson, X. Zhang and F.W. Zwiers Publication Date: Sep 2014

    Attribution of extreme events is a challenging science and one that is currently undergoing considerable evolution. In this paper, 20 different research groups explored the causes of 16 different events that occurred in 2013. The findings indicate that human-caused climate change greatly increased the risk for the extreme heat waves assessed in this report. How human influence affected other types of events such as droughts, heavy rain events, and storms was less clear, indicating that natural variability likely played a much larger role in these extremes. Multiple groups chose to look at both the Australian heat waves and the California drought, providing an opportunity to compare and contrast the strengths and weaknesses of various methodologies. There was considerable agreement about the role anthropogenic climate change played in the events between the different assessments. This year three analyses were of severe storms and none found an anthropogenic signal. However, attribution assessments of these types of events pose unique challenges due to the often limited observational record. When human-influence for an event is not identified with the scientific tools available to us today, this means that if there is a human contribution, it cannot be distinguished from natural climate variability.

  • Source Publication: Journal of Applied Meteorology and Climatology, 53, 2148‐2162, doi:10.1175/JAMC‐D‐13‐0361.1 Authors: Tencer, B., A.W. Weaver and F.W. Zwiers Publication Date: Sep 2014

    The occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Sep 2014

    In a recent article published in the journal Nature, Kossin et al. (2014) use satellite data and reanalysis products to see if there has been a shift in the latitudes at which tropical storms reach their maximum intensity over the 1982-2012 period. The authors find that, globally, the latitudes of maximum intensity have shifted poleward, 53 kilometres per decade in the Northern Hemisphere and 62 kilometres per decade in the Southern Hemisphere. This trend of poleward migration is evident in all ocean basins, except the North Indian Ocean basin, in homogenized satellite and so-called “best track” data. Kossin and colleagues note that this migration is apparently linked to: (1) the absolute difference between wind speeds in the upper and lower troposphere and (2) potential intensity. These have both experienced changes that can be linked to the expansion of the tropics, which is thought to be due, in part, to anthropogenic causes.

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