Ensemble modelling of the East African 2014 long rains season suggests no anthropogenic influence on the likelihood of low rainfall but clear signals in other drivers of drought.
The Himalayan snowstorm of October 2014 resulted from the unusual merger of a tropical cyclone with an upper trough, and their collective changes under climate warming have increased the odds for similar events.
A comparison of observations and multiple global climate models indicates human influence has increased the chance of extreme hot springs in Korea such as the 2014 event by two to three times.
CMIP5 models suggest that human influence has increased the probability of regional high SST extremes over the western tropical and northeast Pacific Ocean during the 2014 calendar year and summer.
Northeast Asia experienced a severe drought in summer 2014. Sea surface temperature forcing may have increased the risk of low precipitation, but model biases preclude reliable attribution to anthropogenic forcing.
Anthropogenic forcing may have contributed to an 11-fold increase in the chance of the 2014 hot spring in northern China.
New climate simulations suggest that the extremely active 2014 Hawaiian hurricane season was made substantially more likely by anthropogenic forcing, but that natural variability of El Niño was also partially involved.
The absence of western North Pacific tropical cyclone activity during August 2014 was apparently related to strong easterly wind anomalies induced by combined negative intraseasonal and Pacific decadal oscillation phases.
The record dry spell over Singapore–Malaysia was caused by the southward contraction of the intertropical convergence zone. Within present evidence, there is no clear attribution to climate change.
The January 2014 floods paralyzed nearly all of Jakarta, Indonesia. The precipitation events that lead to these floods were not very unusual but show positive trends in the observed record.
The risk of an extreme 5-day July rainfall event over Northland, New Zealand, such as was observed in early July 2014, has likely increased due to anthropogenic influence on climate.
Climate model simulations for 2014 indicate anthropogenic climate change very likely increased the likelihood of hot and very hot November days in Brisbane by at least 25% and 44% respectively.
Anthropogenic climate change very likely increased the likelihood of prolonged heat waves like that experienced in Adelaide in January 2014 by at least 16%. The influence for Melbourne is less clear.
The record warm Australian spring of 2014 would likely not have occurred without increases in CO2 over the last 50 years working in concert with an upper-level wave train.
Anthropogenic activity has increased the risk of Australian heatwaves during late autumn similar to the 2014 event by up to 23 fold, compared to climate conditions under no anthropogenic influence.
It is likely that human influences on climate increased the odds of the extreme high pressure anomalies south of Australia in August 2014 that were associated with frosts, lowland snowfalls and reduced rainfall.
The record maximum of Antarctic sea ice resulted chiefly from anomalous winds that transported cold air masses away from the Antarctic continent, enhancing thermodynamic sea ice production far offshore.
This special supplement on explaining extreme events has now published 79 papers over the past four years. Over half of these papers have shown that human-caused climate change influenced an event's frequency and/or intensity in a substantial manner. It could be argued that because all of these events occurred in the context of a warmer world, there are impacts on all extremes whether or not the influence is detectable with current methods and available observations. While potentially true, to make attribution results informative to adaptation decisions, scientists must take on the questions of whether the risk or magnitudes of such events have increased or decreased, by how much, and what level of confidence supports the claims. This is the challenge the authors who have contributed to this report have taken on. The summary table (Table 34.1) is provided to give readers a general overview of their results. However, it is a highly simplified categorization of the results and does not include information about the size of the signal detected and the confidence in the results. This information is present within each individual report, and provides essential context for understanding and interpreting results for any individual event.