MPI | Climate Matching Tool

This tool compares climates from different locations under current and future climate scenarios


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Upload occurrences

The table needs to have longitude and latitude of occurrences. Download example for format

Select weather station and climate

Select comparison climate

Map View

Upload occurrences

The table needs to have longitude and latitude of occurrences. Download example for format

Select climate group one

Select climate group two



This app presents results from climate match comparisons between different locations under historical and future climate scenarios.

It was developed to support the work of the Plant Risk Assessment and Biosecurity Intelligence teams from the New Zealand Ministry for Primary Industries.

How the tool works

This app has three main features:
  • It provides results from climate match comparisons between NZ and the world. Users can choose to display results from various NZ/world pairs of climate scenarios.
  • Species occurrence records can be uploaded to the app, which then provides information about the climatic similarities of the uploaded locations to NZ.
  • It provides climate match results between sets of locations outside NZ. These are referred to as "weather stations" (though see Methods for further details).

The future climate scenarios include projections for 2030, 2050, and 2070 under several Shared Socioeconomic Pathways.


All climate data were sourced from "Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315" here.

Historical climate data are averages for 1970-2000 ("1985") released January 2020. Future climate scenarios include projected 20 year averages from the HadGEM3-GC31-LL Global Circulation Model for 2021-2040 ("2030"), 2041-2060 ("2050"), and 2061-2080 ("2070") under Shared Socioeconomic Pathways (SSP) 126 ("low emissions"), 245 ("medium emissions") and 585 ("high emissions").

The locations chosen for making climatic comparisons between points outside NZ coincide with locations of weather stations used by WorldClim to develop its global climate surfaces (details in Fick & Hijmans (2017)).
However, the data used for climate matching were extracted from WorldClim's climate surfaces rather than separately from each weather station. The aim was to use WorldClim data for locations where interpolation errors should be small.

Climatic similarities were calculated using the "Match Climates Regional" algorithm of Hearne Software's CLIMEX-DYMEX package at the default settings (maximum temperature, minimum temperature and precipitation each weighted at one).
Details of the algorithm are in "Kriticos DJ, Maywald GF, Yonow T, Zurcher EJ, Herrmann NI, Sutherst R 2015. Climex Version 4: Exploring the effects of climate on plants, animals and diseases. CSIRO, Canberra." here.

Climatic similarities between NZ and the world were calculated using 2.5 minute resolution data for NZ and 5 minute data for the rest of the world. Climatic similarities between weather station locations were calculated using 2.5 minute resolution data.

The displayed Köppen-Geiger data are from Beck, H.E., N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Nature Scientific Data, 2018 here.

The displayed altitude layer uses 5 minute resolution data from WorldClim here.

The NZ crop area layers are from AgriBase.

The kauri layer represents the potential distribution of kauri. It was created using kauri distribution from ecosat database and the environments classification from LENZ database.

Limitations of location data

The current version of the app enables users to see climatic similarities (CMIs) between 989 prioritised locations, distributed worldwide, under various different climate scenarios. The locations were chosen to coincide with weather stations to minimise interpolation error. It would have been ideal if the app could have presented climatic similarities between all climate station locations for which data were available (approximately 10000). However, this was beyond the resources available for app development. Thus, when a location of interest is unavailable, the recommendation is to use the closest location with similar altitude and Köppen-Geiger classification instead (the altitude and Köppen-Geiger layers be selected from the map tab). In the future we hope to expand the list of sites/weather stations substantially.

Further information

When documenting use of these maps, please cite one or both of the following papers. These articles also provide information about the Composite Match Index used by this tool to compare climates between locations, which is the 'match climates regional' algorithm of the Climex software package.
  • Phillips CB, Kean JM, Vink C, Berry J 2018. Utility of the CLIMEX match climates regional algorithm for pest risk analysis: An evaluation with non-native ants in New Zealand. Biological Invasions 20, 777-791.
  • Roigé M, Phillips CB 2020. Validation and uncertainty analysis of the match climates regional algorithm (CLIMEX) for pest risk analysis. Ecological Informatics.

Development of this tool has been funded by Better Border Biosecurity, AGMARDT and the Ministry for Primary Industries.

Design and co-development by Epi-interactive.

For more information contact,,

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