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  • Source Publication: Geophysical Research Letters, 42, 24, 10,867–10,875, doi:10.1002/2015GL066858. Authors: Kumar, S., R.P. Allan, F.W. Zwiers, D.M. Lawrence and P.A. Dirmeyer Publication Date: Dec 2015

    A theoretically expected consequence of the intensification of the hydrological cycle under global warming is that on average, wet regions get wetter and dry regions get drier (WWDD). Recent studies, however, have found significant discrepancies between the expected pattern of change and observed changes over land. We assess the WWDD theory in four climate models. We find that the reported discrepancy can be traced to two main issues: (1) unforced internal climate variability strongly affects local wetness and dryness trends and can obscure underlying agreement with WWDD, and (2) dry land regions are not constrained to become drier by enhanced moisture divergence since evaporation cannot exceed precipitation over multiannual time scales. Over land, where the available water does not limit evaporation, a “wet gets wetter” signal predominates. On seasonal time scales, where evaporation can exceed precipitation, trends in wet season becoming wetter and dry season becoming drier are also found.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Dec 2015

    Publishing in the Reviews of Geophysics, Westra et al (2014) summarize the current state of research in the analysis of future changes to the intensity, frequency and duration of extreme rainfall. Their literature review highlights the complicated relationship between short duration extreme rainfall and atmospheric temperature. In some locations, such extreme precipitation does not simply scale with the ability of the atmosphere to hold moisture (i.e. at the Clausius-Clapyron rate of 6 to 7% per °C). Instead, at these locations the general pattern is that such a relationship is found to hold up to about 12 °C, but between 12 and 24 °C extreme precipitation appears to increase more strongly with warming. This is partly due to an increase in convective rainfall. However, above about 24 °C, the pattern at these locations is one in which the response of precipitation to increasing temperature appears to be weaker, eventually reversing. This may be due to decreased moisture availability at these temperatures, though Westra et al. note that “the mechanism that causes these moisture deficits remains to be investigated.” The authors also find that anticipated changes in sub-daily precipitation associated with a warming climate will “significantly affect the magnitude and frequency of urban and rural flash floods.”

    Compared to daily rainfall, Westra et al. find that sub-daily and sub-hourly rainfall are more sensitive to local surface temperatures. They also report that while sub-daily precipitation observations are too scarce to determine regional trends, geographic location will likely affect rates of change in daily precipitation extremes. In terms of making projections of future changes in these events, the authors find that, owing to the resolution of current global climate models, they are limited in their ability to simulate such precipitation events. In particular, the models are generally not run at sufficient resolution to accurately resolve the necessary convective processes, though some very high-resolution “convection permitting” regional climate models operate at a sufficient resolution to potentially be useful in projecting such extremes. One implication of these findings is that we cannot currently make credible projections of sub-daily rainfall events.

  • Source Publication: Hydrological Processes, 28, 4294–4310, doi: 10.1002/hyp.9997 Authors: Shrestha, R.R., D.L. Peters and M.A. Schnorbus Publication Date: Nov 2015

    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: Weather and Climate Extremes, 9, 57-67, doi:10.1016/j.wace.2015.05.001 Authors: Whan, K., J. Zscheischler, R., Orth, M. Shongwe, M., Rahimi, E.O. Asare and S.I. Seneviratne Publication Date: Nov 2015

    Land-atmosphere interactions play an important role for hot temperature extremes in Europe. Dry soils may amplify such extremes through feedbacks with evapotranspiration. While previous observational studies generally focused on the relationship between precipitation deficits and the number of hot days, we investigate here the influence of soil moisture (SM) on summer monthly maximum temperatures (TXx) using water balance model-based SM estimates (driven with observations) and temperature observations. Generalized extreme value distributions are fitted to TXx using SM as a covariate. We identify a negative relationship between SM and TXx, whereby a 100 mm decrease in model-based SM is associated with a 1.6 °C increase in TXx in Southern-Central and Southeastern Europe. Dry SM conditions result in a 2–4 °C increase in the 20-year return value of TXx compared to wet conditions in these two regions. In contrast with SM impacts on the number of hot days (NHD), where low and high surface-moisture conditions lead to different variability, we find a mostly linear dependency of the 20-year return value on surface-moisture conditions. We attribute this difference to the non-linear relationship between TXx and NHD that stems from the threshold-based calculation of NHD. Furthermore the employed SM data and the Standardized Precipitation Index (SPI) are only weakly correlated in the investigated regions, highlighting the importance of evapotranspiration and runoff for resulting SM. Finally, in a case study for the hot 2003 summer we illustrate that if 2003 spring conditions in Southern-Central Europe had been as dry as in the more recent 2011 event, temperature extremes in summer would have been higher by about 1 °C, further enhancing the already extreme conditions which prevailed in that year.

  • Source Publication: Earth’s Future, 2, 3, 152‐160, doi: 10.1002/2013EF000159 Authors: Kumar, S., D. Lawrence, P. Dirmeyer and J. Sheffield Publication Date: Nov 2015

    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.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Nov 2015

    This is the Pacific Climate Impacts Constortium's 2014-2015 Corporate Report.

  • Source Publication: Geology, 43, 23‐26, doi:10.1130/G36179.1 Authors: Ullman, D.J., A.E. Carlson, A.N. LeGrande, A.K. Moore, F.S. Anslow, M. Caffee, K.M. Syverson, and J.M. Licciardi Publication Date: Nov 2015

    Establishing the precise timing for the onset of ice-sheet retreat at the end of the Last Glacial Maximum (LGM) is critical for delineating mechanisms that drive deglaciations. Uncertainties in the timing of ice-margin retreat and global ice-volume change allow a variety of plausible deglaciation triggers. Using boulder 10Be surface exposure ages, we date initial southern Laurentide ice-sheet (LIS) retreat from LGM moraines in Wisconsin (USA) to 23.0 ± 0.6 ka, coincident with retreat elsewhere along the southern LIS and synchronous with the initial rise in boreal summer insolation 24–23 ka. We show with climate-surface mass balance simulations that this small increase in boreal summer insolation alone is potentially sufficient to drive enhanced southern LIS surface ablation. We also date increased southern LIS retreat after ca. 20.5 ka likely driven by an acceleration in rising isolation. This near-instantaneous southern LIS response to boreal summer insolation before any rise in atmospheric CO2 supports the Milanković hypothesis of orbital forcing of deglaciations.

  • Source Publication: Climate Dynamics, 45, 7, 1713-1726 doi:10.1007/s00382‐014‐2423‐y Authors: Wan, H., X. Zhang, F.W. Zwiers and S.K. Min Publication Date: Oct 2015

    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 Symposium 2014 – Findings and Recommendations. Bulletin of the American Meteorological Society, 96, ES145–ES147, doi:10.1175/BAMS-D-15-00003.1 Authors: Asrar, G, S. Bony, O. Boucher, A. Busalacchi, A. Cazenave, M. Dowell, G. Flato, G. Hegerl, E. Källén, T. Nakajima, A. Ratier, R. Saunders, J. Slingo, B. Sohn, J. Schmetz, B. Stevens, P. Zhang and F. Zwiers Publication Date: Sep 2015

    The Climate Symposium 2014, organized by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the World Climate Research Programme (WCRP), was entitled “Climate Research and Earth Observation from Space—Climate Information for Decision Making.” Session topics revolved around the six Grand Science Challenges of the WCRP and addressed the specific need for, and role of, climate observations from space. Based on the presentations and discussions at the symposium, the Science Programme Committee identified main findings and recommendations, which are presented in this summary.

  • Source Publication: Weather and Climate Extremes special issue, 9, 47-56, doi:10.1016/j.wace.2015.04.001 Authors: Mueller, B., M.C. Hauser, C. Iles, R. Haque Rimi, F.W. Zwiers and H. Wan Publication Date: Sep 2015

    Human-induced increases in atmospheric greenhouse gas concentrations have led to rising global temperatures. Here we investigate changes in an annual temperature-based index, the growing season length, defined as the number of days with temperature above 5 °C. We show that over extratropical regions where wheat and maize are harvested, the increase in growing season length from 1956 to 2005 can be attributed to increasing greenhouse gas concentrations. Our analyses also show that climate change has increased the probability of extremely long growing seasons by a factor of 25, and decreased the probability of extremely short growing seasons. A lengthening of the growing season in regions with these mostly rain-fed crops could improve yields, provided that water availability does not become an issue. An expansion of areas with more than 150 days of growing season into the northern latitudes makes more land potentially available for planting wheat and maize. Furthermore, double-cropping can become an alternative to current practices in areas with very long growing seasons which are also shown to increase with a warming climate. These results suggest that there is a strong impact of anthropogenic climate change on growing season length. However, in some regions and with further exacerbated climate change, high temperatures may already be or may become a limiting factor for plant productivity.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Sep 2015

    Stories in this newsletter: Metro Vancouver Climate Extremes Projections, Whistler Climate Extremes Projections, the Ministry of Transportation and Infrastructure Technical Circular, PCIC Climatologist Faron Anslow On the Recent Storms, PCIC Welcomes New Lead, Planning and Operations, Launch of the 2015-2016 Seminar Series.

  • Source Publication: Journal of Climate 28.17, 6938-6959, doi:10.1175/JCLI-D-14-00754.1. Authors: Cannon, A.J., S.R. Sobie and T.Q. Murdock Publication Date: Sep 2015

    Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

  • Source Publication: Weather and Climate Extremes, 9, 2-5, doi:10.1016/j.wace.2015.08.003 Authors: Seneviratne, S.I. and F.W. Zwiers Publication Date: Aug 2015

    This special issue of Weather and Climate Extremes (WACE) includes a series of articles initiated during the 2014 WCRP summer school on the “Attribution and Prediction of Extreme Events”. The two-week summer school took place from 21st July to 4th August 2014 at the International Center for Theoretical Physics (ICTP) in Trieste, Italy, and was organized in the context of the WCRP Grand Challenge on Extremes.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Jul 2015

    In a recent paper published in Science, Karl et al. (2015) revise the National Oceanic and Atmospheric Administration’s (NOAA) surface temperature data set and examine temperature trends in the updated data. The authors use a sea surface temperature data set that has been corrected for biases in sea surface data that arise due to the difference in measurements from ships and buoys, and the authors incorporate a much larger amount of data from land-based observations.
    They find that the global warming trend in the updated data set over the 1998-2012 period is just over double of that in the old data set, about 0.086 °C per decade, compared to 0.039 °C per decade. This is largely due to the corrections in sea surface temperature measurements. The updated data shows a statistically significant global warming trend over the 1998-2012 period and the authors note that their results “do not support the notion of a ‘slowdown’ in the increase of global surface temperature.”

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Jul 2015

    PCIC is first and foremost a service provider that delivers an array of quantitative climate information for a variety of needs. This strategic plan details several service objectives for the coming five years that encompass the spectrum of information delivery from data to user-specific interpretation. The service objectives are laid out in section V.

    Since the information that PCIC seeks to deliver to its users is generally not directly available “off the shelf”, it is necessary to make strategic investments in applied climate research and development in order to meet the service objectives. This plan therefore also outlines several strategic objectives that are required to achieve our service objectives.

    PCIC has three applied research themes: Climate Analysis and Monitoring (CAM), Regional Climate Impacts (RCI) and Hydrologic Impacts (HI) with clearly defined research plans for the 2015-2019 period. These research plans are designed to guide future research activities of PCIC regional climate service delivery. Our commitment to collaboration and operational excellence further supports our service mandate.

    The remainder of the strategic plan is structured as follows. It begins with a synopsis of PCIC, its history, governance and guiding principles. This is followed by descriptions of PCIC service objectives and modes of delivery for those services. The plan then summarizes the strategic goals that must be achieved to meet our service objectives.

  • Authors: The Pacific Climate Impacts Consortium Publication Date: Jul 2015
  • Source Publication: Journal of Hydrometeorology, 16, 1273–1292, doi:10.1175/JHM‐D‐14‐0167.1 Authors: Shrestha, R.R., M.A. Schnorbus and A.J. Cannon Publication Date: Jun 2015

    Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions

  • Source Publication: Journal of Hydrometeorology, 16, 1273–1292, doi:http://dx.doi.org/10.1175/JHM-D-14-0167.1 Authors: Shrestha, R.R., M.A. Schnorbus and A.J. Cannon Publication Date: Jun 2015

    Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.

  • Source Publication: Journal of Hydrologic Engineering, 04015043, doi: 10.1061/(ASCE)HE.1943-5584.0001250 Authors: Najafi M.R. and H. Moradkhani Publication Date: Jun 2015

    Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can outperform the best forecast model. Simple average, median, Bates-Granger, constrained linear regression, and Bayesian model averaging optimized by expectation maximization showed better results compared with other methods over three basins. For the Rogue River basin, the intermediate and complex models outperformed most of the individual forecasts and the simple methods. Multimodeling techniques based on information criteria showed similar performances.

  • Source Publication: Journal of Hydrology, 525, 352-361, doi: 10.1016/j.jhydrol.2015.03.045 Authors: Najafi M.R. and H. Moradkhani Publication Date: Jun 2015

    In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades.

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