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Atmospheric rivers (ARs) are narrow and elongated regions of enhanced horizontal water vapour transport. Considerable research on understanding Northern Hemisphere ARs and their relationship with extreme precipitation has shown that ARs have a strong association with heavy rainfall and flooding. While there has been very little work on ARs in the Southern Hemisphere, global climatologies suggest that ARs are equally as common in both hemispheres. New Zealand in particular is located in a region of high AR frequency. This study aims to test the hypothesis that ARs play a significant role in heavy precipitation and flooding events in New Zealand. We used a recently developed AR identification method and daily station data across New Zealand to test for the concurrence of ARs and extreme rainfall. We found that, at each of the eleven stations analysed, at least seven to all ten of the top ten heaviest precipitation days between 1980 and 2018 were associated with AR conditions. Nine of the ten most damaging floods in New Zealand between 2007 and 2017 occurred during AR events. These results have important implications for understanding extreme rainfall in New Zealand, and ultimately for predicting some of the most hazardous events in the region. This work also highlights that more research on ARs in New Zealand is needed.

Weather and climate service providers around the world are looking to issue assessments of the human role in recent extreme weather events. For this attribution to be of value, it is important that vulnerability is acknowledged and questions are framed appropriately.

Hurricane Harvey is one of the costliest tropical cyclones in history. In this paper, we use a probabilistic event attribution framework to estimate the costs associated with Hurricane Harvey that are attributable to anthropogenic influence on the climate system. Results indicate that the “fraction of attributable risk” for the rainfall from Harvey was likely about at least a third with a preferable/best estimate of three quarters. With an average estimate of damages from Harvey assessed at about US$90bn, applying this fraction gives a best estimate of US$67bn, with a likely lower bound of at least US$30bn, of these damages that are attributable to the human influence on climate. This “bottom-up” event-based estimate of climate change damages contrasts sharply with the more “top-down” approach using integrated assessment models (IAMs) or global macroeconometric estimates: one IAM estimates annual climate change damages in the USA to be in the region of US$21.3bn. While the two approaches are not easily comparable, it is noteworthy that our “bottom-up” results estimate that one single extreme weather event contributes more to climate change damages in the USA than an entire year by the “top-down” method. Given that the “top-down” approach, at best, parameterizes but does not resolve the effects of extreme weather events, our findings suggest that the “bottom-up” approach is a useful avenue to pursue in future attempts to refine estimates of climate change damages.

An important and under-quantified facet of the risks associated with human-induced climate change emerges through extreme weather. In this paper, we present an initial attempt to quantify recent costs related to extreme weather due to human interference in the climate system, focusing on economic costs arising from droughts and floods in New Zealand during the decade 2007–2017. We calculate these using previously collected information about the damages and losses associated with past floods and droughts, and estimates of the “fraction of attributable risk” that characterizes each event. The estimates we obtain are not comprehensive, and almost certainly represent an underestimate of the full economic costs of climate change, notably chronic costs associated with long-term trends. However, the paper shows the potential for developing a new stream of information that is relevant to a range of stakeholders and research communities, especially those with an interest in the aggregation of the costs of climate change or the identification of specific costs associated with potential liability.

As greenhouse gases continue to accumulate in Earth’s atmosphere, the nature of extreme weather events (EWEs) has been changing and is expected to change in the future. EWEs have contributions from anthropogenic climate change as well as from natural variability, which complicates attribution statements. EWERAM is a project that has been funded through the New Zealand Ministry of Business, Innovation and Employment Smart Ideas programme to develop the capability to provide, within days of an EWE having occurred over New Zealand, and while public interest is still high, scientifically defensible statements about the role of climate change in both the severity and frequency of that event. This is expected to raise public awareness and understanding of the effects of climate change on EWEs.A team of researchers from five institutions across New Zealand are participating in EWERAM. EWE attribution is a multi-faceted problem and different approaches are required to address different research aims. Although robustly assessing the contribution of changes in the thermodynamic state to an observed event can be more tractable than including changes in the dynamics of weather systems, for New Zealand, changes in dynamics have had a large impact on the frequency and location of EWEs. As such, we have initiated several lines of research to deliver metrics on EWE attribution, tailored to meet the needs of various stakeholders, that encompass the effects of both dynamical and thermodynamical changes in the atmosphere. This presentation will give an overview of EWERAM and present the methodologies and tools used in the project.

Previous studies evaluating anthropogenic influences on the meteorological drivers of drought have found mixed results owing to (1) the complex physical mechanisms which lead to the onset of drought, (2) differences in the characteristics and timescales of drought for different regions of the world, and (3) different approaches to the question of attribution. For a mid-latitude, temperate climate like New Zealand, strongly modulated by oceanic influences, summer droughts last on the order of three months, and are less strongly linked to persistent temperature anomalies than continental climates. Here, we demonstrate the utility of a novel approach for characterizing the meteorological conditions conducive to extreme drought over the North Island of New Zealand, using the January-March 2013 event as a case study. Specifically, we consider the use of self-organizing map (SOM) techniques in a multi-member coupled climate model ensemble to capture changes in daily circulation, between two 41-year periods (1861-1901 and 1993-2033). Comparisons are made with seasonal pressure and precipitation indices. Our results demonstrate robust (>99% confidence) increases in the likelihood of observing circulation patterns like those of the 2013 drought in the recent-climate simulations when compared with the early-climate simulations. Best-guess estimates of the fraction of attributable risk range from 0.2 to 0.4, depending on the metric used and threshold considered. Contributions to uncertainty in these attribution statements are discussed.

A new climate modelling project has been developed for regional climate simulation and the attribution of weather and climate extremes over Australia and New Zealand. The project, known as weather@home Australia-New Zealand, uses public volunteers' home computers to run a moderate-resolution global atmospheric model with a nested regional model over the Australasian region. By harnessing the aggregated computing power of home computers, weather@home is able to generate an unprecedented number of simulations of possible weather under various climate scenarios. This combination of large ensemble sizes with high spatial resolution allows extreme events to be examined with more robust estimates of uncertainty. This paper provides an overview of the weather@home Australia-New Zealand project, including initial evaluation of the regional model performance. The model is seen to be capable of resolving many climate features that are important for the Australian and New Zealand regions, including the influence of El Niño-Southern Oscillation on driving natural climate variability. To date, 75 model simulations of the observed climate have been successfully integrated over the period 1985–2014 in a time-slice manner. In addition, multi-thousand member ensembles have also been generated for the years 2013, 2014 and 2015 under climate scenarios with and without the effect of human influences. All data generated by the project is freely available to the broader research community.