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New Zealand's National Institute of Water and Atmospheric Research publishes climate normals for New Zealand that are used for reporting the regional state of the climate, climate extremes and variability. Temperature and precipitation patterns are affected by both anthropogenic climate change and natural climate variability which in turn affect the climatological normal values calculated every decade. This study investigates how New Zealand's normals for temperature and precipitation have shifted over time at the national, regional and seasonal scales from the 1941–1970 period to the 1991–2020 period. Contrary to WMO recommendations, but aligned with many other countries, New Zealand's climate normals have traditionally not undergone homogenisation. The impact of introducing some homogenisation in the latest 1991–2020 normals, in contrast to historical non‐homogenized station normals, has been assessed using a new homogenized "Seventeen‐Station" temperature series from 1941 to 2020. We demonstrate that interpolating sparse non‐homogenized normals spatially to a grid can produce significant erroneous patterns and therefore undermine the accuracy of the conclusions drawn when using such normals. We find that the historical non‐homogenized temperature normals have a consistent negative bias in the long‐term trend at national, regional and seasonal scales. Our analysis of homogeneity tested precipitation showed consistent decreases at a national scale across all normal periods relative to the 1951–1980 precipitation normal. We also highlight how fixed period temperature and precipitation normals do not fully reflect the current state of a climate that is influenced by decadal variability and global warming. To derive normals fit for use in a changing climate it is suggested that automated methods for broad data homogenisation be developed along with alternative methods to derive normals that account for a non‐stationary climate. [ABSTRACT FROM AUTHOR]

An end of summer snowline (EOSS) photographic dataset for Aotearoa New Zealand contains over four decades of equilibrium line altitude (ELA) observations for more than 50 index glaciers. This dataset provides an opportunity to create a climatological ELA reference series that has several applications. Our work screened out EOSS sites that had low temporal coverage and also removed limited observations when the official survey did not take place. Snowline data from 41 of 50 glaciers in the EOSS dataset were retained and included in a normalised master snowline series that spans 1977–2020. Application of the regionally representative normalised master snowline series in monthly and seasonally resolved climate response function analyses showed consistently strong relationships with austral warm-season temperatures for land-based stations west of the Southern Alps and the central Tasman Sea. There is a trend towards higher regional snowlines since the 1990s that has been steepening in recent decades. If contemporary decadal normalised master snowline series trends are maintained, the average Southern Alps snowline elevation will be displaced at least 200 m higher than normal by the 2025–2034 decade. More frequent extremely high snowlines are expected to drive more extreme cumulative mass-balance losses that will reduce the glacierised area of Aotearoa New Zealand. [ABSTRACT FROM AUTHOR]

After the first detection of myrtle rust (Austropuccinia psidii) on mainland New Zealand in May 2017, the Ministry for Primary Industries sought information about how weather conditions would affect regional and seasonal risk of disease establishment to help plan the incursion response. Using internationally published information, a pathogen- process model was developed to predict infection, latent period and sporulation in relation to weather variables (temperature, relative humidity and solar radiation). This Myrtle Rust Process Model (MRPM) was implemented by the National Institute of Water and Atmospheric Research Limited using numerical weather model data to produce weekly maps of potential risk. Predicted risk was greatest in northern North Island and decreased further south, but was still substantial in coastal areas of the north-western South Island during summer and autumn. Risk was low in southern coastal areas of the South Island and the lowest risk occurred in mountainous areas, particularly in the South Island. Retrospective analysis of surveillance data showed that the MRPM accurately predicted geographic risk and it is currently in use for tactical planning of incursion surveillance and organism management. [ABSTRACT FROM AUTHOR]