Search

We present a deep neural network based single site stochastic precipitation generator (SPG), capable of producing realistic time series of daily and hourly precipitation. The neural network outputs a wet day probability and precipitation distributions in the form of a mixture model. The SPG was tested in four different locations in New Zealand, and we found it accurately reproduced the precipitation depth, the autocorrelations seen in the original data, the observed dry-spell lengths and the seasonality in precipitation. We present two versions of the hourly and daily SPGs: (i) a stationary version of the SPG that assumes that the statistics of the precipitation are time independent (ii) a non-stationary version that captures the secular drift in precipitation statistics resulting from climate change. The latter was developed to be applicable to climate change impact studies, especially, studies reliant on SPG projections of future precipitation. We highlight many of the pitfalls associated with the training of a non-stationary SPG on observations alone, and offer an alternative method that replicates the secular drift in precipitation seen in a large-ensemble regional climate model. The SPG runs several orders of magnitude faster than a typical regional climate model, and permits the generation of very large ensembles of realistic precipitation time series under many climate change scenarios, these ensembles will also contain many extreme events not seen in the historical record.

Extreme temperature events (ETEs) have evolved alongside the warming climate over most parts of the world. This study provides a statistical quantification of how human influences have increased the likelihood and frequencies of ETEs in New Zealand, depending on the synoptic weather type. We use the simulation under pre-industrial conditions (natural scenarios with no rise in greenhouse gases (GHGs)) and present-day conditions (anthropogenic scenarios) from the weather@home regional climate model. The ensembles of simulations under these two scenarios are used to identify how increases in GHG concentrations have impacted the frequency and intensity of ETEs based on their connection to different large-scale circulation patterns derived using Self Organizing Maps (SOMs). Over New Zealand, an average 2-3 fold rise in frequencies of extremes occurs irrespective of seasons due to elevated GHG concentrations with a mean temperature increase close to 1℃. For some synoptic situations, the frequency and intensity of ETEs are enhanced. In particular, for low-pressure centers to the northeast of New Zealand, the frequency of occurrence of daily temperature extremes has increased by a factor of 7 between anthropogenic and natural simulations for the winter season, though these synoptic patterns rarely occur. For low-pressure centers to the northwest of New Zealand, we observe extreme temperatures frequently in both anthropogenic and natural simulations which we attribute to warm air advection from the tropics. The frequency of occurrence of these synoptic patterns has also increased by a factor of 2 between the natural and anthropogenic simulations. For these synoptic states, the extremes are observed in the North Island and along the east coast of the country with the highest temperature along the Canterbury coast and Northland. However, the change between the natural and anthropogenic simulations is largest on the west coast along the Southern Alps.