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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.

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.

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.

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.

Early Holocene summer warmth drove dramatic Greenland ice sheet (GIS) retreat. Subsequent insolation-driven cooling caused GIS margin readvance to late Holocene maxima, from which ice margins are now retreating. We use 10Be surface exposure ages from four locations between 69.4°N and 61.2°N to date when in the early Holocene south to west GIS margins retreated to within these late Holocene maximum extents. We find that this occurred at 11.1 ± 0.2 ka to 10.6 ± 0.5 ka in south Greenland, significantly earlier than previous estimates, and 6.8 ± 0.1 ka to 7.9 ± 0.1 ka in southwest to west Greenland, consistent with existing 10Be ages. At least in south Greenland, these 10Be ages likely provide a minimum constraint for when on a multicentury timescale summer temperatures after the last deglaciation warmed above late Holocene temperatures in the early Holocene. Current south Greenland ice margin retreat suggests that south Greenland may have now warmed to or above earliest Holocene summer temperatures.

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.

To improve local understanding and manage the impacts of atmospheric river events, the B.C. Ministry of Environment commissioned work to summarize the current state of knowledge pertaining to BC on this topic and conduct a multi-agency qualitative risk assessment.
In April 2013, scientists and researchers gathered in Victoria, B.C. to review and summarize the current state of knowledge on atmospheric rivers. As a result of their efforts, the Pacific Climate Impacts Consortium and Pinna Sustainability produced this "Atmospheric River State of Knowledge Report."

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.

A recent meta-analysis published in the journal Nature Climate Change, by Challinor et al. (2014) examines 1,722 crop model simulations, run using global climate model output under several emissions scenarios, to evaluate the potential effects of climate change and adaptation on crop yield. The authors find that, without adaptation, projected corn, rice and wheat production is reduced when areas experience 2.0 °C or more of local warming, with losses greater in the second half of the century due to larger changes in climate. Crop-level adaptations are projected to be able to increase yields by an average of 7-15% when compared to similar scenarios that do not utilize adaptation. Projections indicate that adaptation may be more successful for wheat and rice than for corn. Though less data is available on yield variability, Challinor et al. find that it is likely to increase.