Feature article

Introducing Climate Explorer

Figure 1.

Figure 2.

Figure 3.

Climate Explorer is NIWA’s new web-based tool to help monitor the New Zealand climate. It makes available an array of climate analysis maps, data sets, and line plots that will enable viewers to understand background climate features, and to keep close tabs on how the current climate is progressing compared with last year or the historical mean.

In addition to historical and current data, a powerful new service offered by Climate Explorer is probabilistic forecasts of weather anomalies for the next two weeks. These will provide valuable guidance to support planning for weather dependent activities.

Some examples from Climate Explorer are shown below. For more information, log into the web address given above, or contact Andrew Tait: a.tait@niwa.co.nz

Figure 1: Rainfall accumulation plots show the amount of rain that has fallen since 1 July in the current July to June growing season (red curve) compared to last season (blue curve) and the historical 90, 50, and 10 percentile accumulations. The vertical bars show the historical mean monthly rainfall (grey), and total rainfall for last season (light blue), and the current season (red).

Figure 2: Rainfall maps show the amount of rain for the last 15 days or for the month to date. The maps give the historical normal rainfall for the period (left), the observed anomaly (centre), and the estimated rainfall occurrence in the period in the current year.

Figure 3: Forecasts of likely climatic conditions over the next two weeks are created from multi-model runs for different weather parameters – in this example, the temperature of the earth at 10 cm depth. The red line represents the historical mean earth temperature for the period, and the thick black curve shows the median temperatures predicted by the models. The width of the grey band behind the black curve is a measure of the spread of forecast temperatures predicted by the models – the narrower the band, the better the agreement between models, and hence the more confident we can be about the forecast.