Evaluation of mechanisms of hot and cold days in climate models over Central Europe

Changes in intensity, frequency, and location of temperature extreme events are a focus for many studies that often rely on simulations from climate models to assess changes in temperature extremes. Given the use of climate models for attributing such events to human and natural influences and for projecting future changes, an assessment of the capability of climate models to properly simulate the mechanisms associated with temperature extreme events is necessary. In this study, known mechanisms and relevant meteorological variables are explored in a composite analysis to identify and quantify a climatology of synoptic weather patterns related to hot and cold seasonal temperature extreme events over Central Europe. The analysis is based on extremes that recur once or several times per season for better sampling. Weather patterns from a selection of CMIP5 models are compared with patterns derived from the ERA interim reanalysis. The results indicate that climate models simulate mechanisms associated with temperature extreme events reasonably well, in particular circulation-based mechanisms. The amplitude and average length of events is assessed, where in some cases significant deviations from ERA interim are found. In three cases, the models have on average significantly more days per season with extreme events than ERA interim. Quantitative analyses of physical links between extreme temperature and circulation, relative humidity, and radiation reveal that the strength of the link between the temperature and the variables does not vary greatly from model to model and ERA interim

The timing of anthropogenic emergence in simulated climate extremes

Determining the time of emergence of climates altered from their natural state by anthropogenic influences can help inform the development of adaptation and mitigation strategies to climate change. Previous studies have examined the time of emergence of climate averages. However, at the global scale, the emergence of changes in extreme events, which have the greatest societal impacts, has not been investigated before. Based on state-of-the-art climate models, we show that temperature extremes generally emerge slightly later from their quasi-natural climate state than seasonal means, due to greater variability in extremes. Nevertheless, according to model evidence, both hot and cold extremes have already emerged across many areas. Remarkably, even precipitation extremes that have very large variability are projected to emerge in the coming decades in Northern Hemisphere winters associated with a wettening trend. Based on our findings we expect local temperature and precipitation extremes to already differ significantly from their previous quasi-natural state at many locations or to do so in the near future. Our findings have implications for climate impacts and detection and attribution studies assessing observed changes in regional climate extremes by showing whether they will likely find a fingerprint of anthropogenic climate change

Super Storm Desmond: a process-based assessment

“Super” Storm Desmond broke meteorological and hydrological records during a record warm year in the British-Irish Isles (BI). The severity of the storm may be a harbinger of expected changes to regional hydroclimate as global temperatures continue to rise. Here, we adopt a process-based approach to investigate the potency of Desmond, and explore the extent to which climate change may have been a contributory factor. Through an Eulerian assessment of water vapour flux we determine that Desmond was accompanied by an Atmospheric River (AR) of severity unprecedented since at least 1979, on account of both high atmospheric humidity and high wind speeds. Lagrangian air-parcel tracking and moisture attribution techniques show that long-term warming of North Atlantic sea surface temperatures (SSTs) has significantly increased the chance of such high humidity in ARs in the vicinity of the BI. We conclude that, given exactly the same dynamical conditions associated with Desmond, the likelihood of such an intense AR has already increased by 25% due to long-term climate change. However, our analysis represents a first-order assessment, and further research is needed into the controls influencing AR dynamics

Climatic warming in China during 1901-2015 based on an extended dataset of instrumental temperature records

Monthly mean instrumental surface air temperature (SAT) observations back to the nineteenth century in China are synthesized from different sources via specific quality-control, interpolation, and homogenization. Compared with the first homogenized long-term SAT dataset for China by Cao et al. (2013), which contained 18 stations mainly located in the middle and eastern part of China, the present dataset includes homogenized monthly SAT series at 32 stations, with an extended coverage especially towards western China. Missing values are interpolated by using observations at nearby stations including those from neighboring countries. Cross validation shows that the mean bias error (MBE) is generally small and falls between 0.45°C and -0.35°C. Multiple homogenization methods and available metadata are applied to assess the consistency of the time series and to adjust inhomogeneity biases. The homogenized annual mean SAT series show a range of trends between 1.1 and 4.0°C/century in northeastern China, between 0.4 and 1.9°C/century in southeastern China, and between 1.4 and 3.7°C/century in western China to the west of 105E (from the beginning years of the stations to 2015). The unadjusted data include unusually warm records during the 1940s and hence tend to underestimate the warming trends at a number of stations. The mean SAT series for China based on the Climate Anomaly Method shows a warming trend of 1.56°C/century during 1901-2015, larger than those based on other currently available datasets

Attribution of extreme precipitation in the lower reaches of the Yangtze River during May 2016

May 2016 was the third wettest May on record since 1961 over central eastern China based on station observations, with total monthly rainfall 40% more than the climatological mean for 1961–2013. Accompanying disasters such as waterlogging, landslides and debris flow struck part of the lower reaches of the Yangtze River. Causal influence of anthropogenic forcings on this event is investigated using the newly updated Met Office Hadley Centre system for attribution of extreme weather and climate events. Results indicate that there is a significant increase in May 2016 rainfall in model simulations relative to the climatological period, but this increase is largely attributable to natural variability. El Ni ̃no years have been found to be correlatedwith extreme rainfall in the Yangtze River region in previous studies—the strong El Ni ̃no of 2015–2016 may account for the extreme precipitation event in 2016. However, on smaller spatial scales we find that anthropogenic forcing has likely played a role in increasing the risk of extreme rainfall to the north of the Yangtze and decreasing it to the south