NZ Wave Data Tool: Model Projections

Step 1: Select a model

Step 2: Select variables to export

For an explanation of these variables, please click here

Step 3: Select data points

To select a subset of data points, use the polygon or rectangle tool. Making a new selection will replace the previous selection.

0 data points selected.

Step 4: Select a time range

Adjust the time range here or by dragging the ends of the timeseries export range control at the bottom.

Timeseries range:

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0 total rows for export. Estimated (worst-case) time to export: about 1 minute

Past and Future GCMs simulations for New Zealand

João Albuquerque, Jose A. A. Antolínez, Fernando J. Méndez, Giovanni Coco

Here we provide three time-slices of high resolution wave climate data for the New Zealand waters. We developed a set of historical and projected wave climatologies from 3 global climate models (GCM) and two projected pathways. The models were chosen based on their skill to represent the past atmospheric patterns of the wave generation basins New Zealand is exposed to.

Reconstructed bimodal boundaries from windsea and swell waves together with GCM winds provided the forcing for a SWAN grid around New Zealand. Waves were downscaled in non-stationary mode through 4 levels of nesting, storing both partitioned and integrated parameters. The historical boundaries were obtained from 20 years (1986–2006) of wave data from three GCM (ACCESS1-0, CNRM-CM5 and MIROC5). The future wave climate boundaries and wind forcing are from two 20-year (2026–2046, 2080–2100) ensembles of wave climate projections from the same GCMs under two different representative concentration pathways (RCP 4.5, RCP 8.5).

An assessment of the anomalies between the past and future GCMs provides us insightful information about the potential changes in the future wave climate of New Zealand. This dataset will be of great value to a number of future studies on risk assessment and mitigation of coastal hazards. The full description of data and methods will be available shortly in Albuquerque et al., 2020 (in prep).

We acknowledge funding from a New Zealand GNS-Hazard Platform grant to Giovanni Coco.We also acknowledge NeSI for providing us the computing power necessary to perform the simulations and the Centre for eResearch for assisting us on the development of this web-interface. We would appreciate if you let us know that you are using the data.