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  • Source Publication: Hydrological Processes, 28, 26, 6292–6308 doi: 10.1002/hyp.10113 Authors: Najafi M.R. and H. Moradkhani Publication Date: Dec 2014

    In this study the impact of climate change on runoff extremes is investigated over the Pacific Northwest (PNW). This paper aims to address the question of how the runoff extremes change in the future compared to the historical time period, investigate the different behaviors of the regional climate models (RCMs) regarding the runoff extremes and assess the seasonal variations of runoff extremes. Hydrologic modeling is performed by the variable infiltration capacity (VIC) model at a 1/8° resolution and the model is driven by climate scenarios provided by the North American Regional Climate Change Assessment Program (NARCCAP) including nine regional climate model (RCM) simulations. Analysis is performed for both the historical (1971–2000) and future (2041–2070) time periods. Downscaling of the climate variables including precipitation, maximum and minimum temperature and wind speed is done using the quantile-mapping (QM) approach. A spatial hierarchical Bayesian model is then developed to analyse the annual maximum runoff in different seasons for both historical and future time periods. The estimated spatial changes in extreme runoffs over the future period vary depending on the RCM driving the hydrologic model. The hierarchical Bayesian model characterizes the spatial variations in the marginal distributions of the General Extreme Value (GEV) parameters and the corresponding 100-year return level runoffs. Results show an increase in the 100-year return level runoffs for most regions in particular over the high elevation areas during winter. The Canadian portions of the study region reflect higher increases during spring. However, reduction of extreme events in several regions is projected during summer.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-014-2423-y Authors: Wan, H., X. Zhang, F. Zwiers and S. Min Publication Date: Dec 2014

    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.

  • Source Publication: Climate Dynamics, 43, 12, 3201‐3217, doi:10.1007/s00382‐014‐2098‐4 Authors: Gaitan, C.F., W.W. Hsieh, and A.J. Cannon Publication Date: Dec 2014

    Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.

  • Source Publication: Journal of Geophysical Research Atmospheres, doi:10.1002/2014JD022110 Authors: Kumar, S., P. Dirmeyer, D. Lawrence, T. DelSole, E. L. Altshuler, B. A. Cash, M. J. Fennessy, Z. Guo, J. L. Kinter III and D. M. Strauts Publication Date: Dec 2014

    Fully coupled global climate model experiments are performed using the Community Climate System Model version 4.0 for pre-industrial, present, and future climate to study the effects of realistic land surface initializations on sub-seasonal to seasonal climate forecasts. Model forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and/or model parameterizations. Findings suggest that realistic land surface initialization is important for climate predictability at sub-seasonal to seasonal time scales. We found the highest predictability for soil moisture, followed by evapotranspiration, temperature, and precipitation. The predictability is highest for the 16 to 30 days forecast period, and it progressively decreases for the second and third month forecasts. We found significant changes in the spatial distributions of temperature predictability in the present and future climate compared to the pre-industrial climate, although the spatial average changes for North America were rather small (

  • Source Publication: Journal of Geophysical Research: Atmospheres, 119, 23, 13,250–13,270, doi:10.1002/2014JD022110. Authors: Sanjiv Kumar, Paul A. Dirmeyer, David M. Lawrence, Timothy DelSole, Eric L. Altshuler, Benjamin A. Cash, Michael J. Fennessy, Zhichang Guo, James L. Kinter III and David M. Straus Publication Date: Dec 2014

    Fully coupled global climate model experiments are performed using the Community Climate System Model version 4.0 (CCSM4) for preindustrial, present, and future climate to study the effects of realistic land surface initializations on subseasonal to seasonal climate forecasts. Model forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and/or model parameterizations. Findings suggest that realistic land surface initialization is important for climate predictability at subseasonal to seasonal time scales. We found the highest predictability for soil moisture, followed by evapotranspiration, temperature, and precipitation. The predictability is highest for the 16 to 30 days forecast period, and it progressively decreases for the second and third month forecasts. We found significant changes in the spatial distributions of temperature predictability in the present and future climate compared to the preindustrial climate, although the spatial average changes for North America were rather small (

  • Source Publication: Hydrological Processes, 28, 14, 4294–4310, doi:10.1002/hyp.9997 Authors: Shrestha, R.R., D.L. Peters and M.A. Schnorbus Publication Date: Dec 2014

    It is a common practice to employ hydrologic models for assessing alterations to streamflow as a result of anthropogenically driven changes, such as riverine, land use, and climate change. However, the ability of the models to replicate different components of the hydrograph simultaneously is not clear. Hence, this study evaluates the ability of a standard hydrologic model set-up: Variable Infiltration Capacity (VIC) hydrologic model for two headwater sub-basins in the Fraser River (Salmon and Willow), British Columbia, Canada, with climate inputs derived from observations and statistically downscaled global climate models (GCMs); to simulate six general water resource indicators (WRIs) and 32 ecologically relevant indicators of hydrologic alterations (IHA). The results show a generally good skill of the observation-driven VIC model in replicating most of the WRIs and IHAs. Although the WRIs, including annual volume, centre of timing, and seasonal flows, and the IHAs, including maximum and minimum flows, were reasonably well replicated, statistically significant differences in some of the monthly flows, number and duration of flow pulses, rise and fall rates, and reversals were noted. In the case of GCM-driven results, additional monthly, maximum, and minimum flow indicators produced statistically significant differences. A number of issues with the model input/output data, hydrologic model parametrization and structure as well as downscaling methods were identified, which lead to such discrepancies. Therefore, there is a need to exercise caution in the use of model-simulated indicators. Overall, the WRIs and IHAs can be useful tools for evaluating changes in an altered hydrologic system, provided the skill and limitations of the model in replicating these indicators are understood

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-014-2408-x Authors: Christidis, N., P.A. Stott and F.W. Zwiers Publication Date: Dec 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: 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: 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.

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