User Guide

The Climate theme tabs provide the first level of customisation where users can select from seven major climate themes. Each theme presents a selection of related climate variables and metrics. The climate theme tabs are located on top of the Dashboard.

The climate themes that can be selected via the tabs on the Dashboard include:

• Mean climate: The long-term average of the climate over the time-period of interest.
• Heatwaves: Prolonged extreme heat events which impact many sectors of the Australian community. Understanding how heatwave characteristics may change across Queensland in future is of critical importance to understanding potential impacts on health, wellbeing and mortality.
• Extreme temperature indices: Extreme temperature indices are used to monitor changes and variability in extreme temperature events over time. These indices are often important for adaptation decision-making in the health, agriculture and infrastructure sectors.
• Extreme precipitation indices: Extreme precipitation indices are meaningful tools to understand changes and variability in water supply over time. The indices offer insights to inform water management, agriculture and emergency services, among other sectors.
• SPI-drought indices: Australia is the driest continent with highly variable rainfall regimes, and understanding trends and patterns in future droughts is pivotal for Queensland communities (based on the 12-month Standardized Precipitation Index or SPI).
• SPI-wetness indices: Wet events (e.g. heavy rainfall, floods) are part of Queensland’s variable climate and understanding future changes in wetness is paramount for reducing the future impacts of flood events. In Queensland, wetness is usually associated with specific drivers such as La Niña and extreme events (e.g. convective storms, low pressure systems and tropical cyclones) (also based on the 12-month Standardized Precipitation Index or SPI).
• Fire weather indices: Australian landscapes are fire prone and have been severely damaged by bushfires in the recent past. Fire weather is defined as the climate conditions conducive to bushfires. Understanding how these conditions are projected to change in future is essential to build preparedness for these natural disasters. This theme includes seven categories of fire danger using the McArthur Forest Fire Danger Index (FFDI).

The drop-down menus towards the top left of the window allow you to select the variable of interest, region, emissions scenario, season and year (20-year time period). The blue information dots link to pop-ups with definitions of the key terms and further explanations.

You can select regions using a number of different categories depending on your scale of interest: local government areas, bio-regions, regional plan areas, major river basins and disaster districts.

For emissions scenario, you can select between two Representative Concentration Pathways (RCPs). RCP8.5 is a high emissions scenario while RCP4.5 is a moderate emissions scenario. CoastAdapt provides a useful explainer for the RCPs. Using RCP4.5 and 8.5 together provides realistic lower and upper bounds to use for climate risk assessments.

Four future time periods are available:

• 2020-2039, labelled as ‘2030’
• 2040-2059, labelled ‘2050’
• 2060-2079, labelled ‘2070’
• 2080-2099, labelled ‘2090’.

Using a combination of the tabs and drop-downs, you can select from 49 different climate variables listed below. Full descriptions of these variables are provided in a separate factsheet.

Mean climate

• Mean temperature – seasonally averaged mean air temperature (°C)
• Maximum temperature – seasonally averaged maximum daily air temperature (°C)
• Minimum temperature – seasonally averaged minimum daily air temperature (°C)
• Precipitation – seasonally averaged daily precipitation (mm/day)
• Pan evaporation – seasonally averaged potential pan evaporation rate (mm)
• Relative humidity – seasonally averaged relative humidity (%)
• Surface wind – seasonally averaged horizontal wind speed at 10m above ground (m/s)
• Solar radiation – seasonally averaged downwelling shortwave radiation at ground (W/m2).

Heatwaves

• Heatwave peak temperature - maximum temperature of the hottest day of the season’s hottest heatwave (°C)
• Heatwave frequency – number of heatwave days relative to number of days in a season
• Heatwave duration – average duration of all heatwave events in the season (days)
• Maximum heatwave duration – length of the longest heatwave of the season (days).

Extreme temperature indices

• Hot days – seasonal count of days with maximum temperature > 35°C (days)
• Hot nights – seasonal count of nights with minimum temperature > 20°C (days)
• Very hot days – seasonal count of days with maximum temperature > 40°C (days)
• Warm spell duration – seasonal count of days with at least 4 consecutive days when daily maximum temperature > 90th percentile (days)
• Cold spell duration – seasonal count of nights with at least 4 consecutive nights when daily minimum temperature < 10th percentile (days)
• Cold nights - seasonal count of nights with minimum temperature < 5°C (days).

Extreme precipitation indices

• Maximum 1-day precipitation – seasonal maximum 1-day total precipitation (mm)
• Maximum 5-day precipitation – seasonal maximum consecutive 5-day total precipitation (mm)
• Extremely wet day precipitation – seasonal total precipitation when daily precipitation > 99th percentile (mm)
• Simple daily intensity – seasonal total precipitation divided by the number of wet days (mm/day)
• Consecutive dry days – maximum number of consecutive days with daily precipitation < 1 mm (days)
• Consecutive wet days - maximum number of consecutive days with daily precipitation ≥ 1 mm (days).

SPI-drought indices

• Duration of moderate droughts – average duration of droughts where the SPI ≤ -1 (months)
• Frequency of moderate droughts – average number of drought events per year where the SPI ≤ -1
• Percent time in moderate drought – average number of months per year in drought as a percentage of the 20-year period where the SPI ≤ -1 (%)
• Duration of severe droughts – average duration of droughts where the SPI ≤ -1.5 (months)
• Frequency of severe droughts – average number of drought events per year where the SPI ≤ -1.5
• Percent time in severe drought – average number of months per year in drought as a percentage of the 20-year period where the SPI ≤ -1.5 (%)
• Duration of extreme droughts – average duration of droughts where the SPI ≤ -2 (months)
• Frequency of extreme droughts – average number of drought events per year where the SPI ≤ -2
• Percent time in extreme drought – average number of months per year in drought as a percentage of the 20-year period where the SPI ≤ -2 (%).

SPI-wetness indices

• Duration of moderate wet – average duration of wet where the SPI ≥ 1 (months)
• Frequency of moderate wet – average number of wet events per year where the SPI ≥ 1
• Percent time in moderate wet – average number of months per year in wet where the SPI-12 ≥ 1 (%)
• Duration of severe wet – average duration of wet where the SPI ≥ 1.5 (months)
• Frequency of severe wet – average number of wet events per year where the SPI ≥ 1.5
• Percent time in severe wet – average number of months per year in wet where the SPI-12 ≥ 1.5 (%)
• Duration of extreme wet – average duration of wet where the SPI ≥ 2 (months)
• Frequency of extreme wet – average number of wet events per year where the SPI ≥ 2
• Percent time in extreme wet – average number of months per year in wet where the SPI ≥ 2 (%).

Fire weather indices

• Low fire risk days – seasonal count of days with an FFDI between 0 and 5
• Moderate fire risk days – seasonal count of days with an FFDI between 5 and 12
• High fire risk days – seasonal count of days with an FFDI between 12 and 24
• Very high fire risk days – seasonal count of days with an FFDI between 24 and 50
• Severe fire risk days – seasonal count of days with an FFDI greater than 50
• 95th percentile fire risk days – seasonal count of days with an FFDI greater or equal to the 95th percentile for the reference period (1986-2005)
• 99th percentile fire risk days – seasonal count of days with an FFDI greater or equal to the 99th percentile for the reference period (1986-2005).

The map section of the Dashboard allows you to pan and zoom to a region of interest. It displays the result of the customisations undertaken via climate theme tabs and drop-down menus, one climate variable at a time, with a legend to help interpret the data displayed. Importantly, the map displays the result for the model ensemble average only – that is the averaged future scenario from the 11 different downscaled climate models. When a region is selected, users can hover a mouse over an individual 10x10km grid cell to trigger a popup with data for that grid cell.

The map display is useful for gaining a quick understanding of regional variation.

The output charts on the right of the Dashboard window display data for the selected region, variable, scenario, and future time periods relative to the reference period (1986 – 2005).

These charts and data are useful for building regional summaries, for providing information to include in reports, and for ‘first pass’ or ‘scan’ climate risk assessments. As you select different options via the menu, you will see these charts update in response to your selections. When clicking over the plot elements, a pop up is shown with the statistics for individual models, as well as the model average and percentiles.

• The top chart shows seasonal variation for the selected year.
• The bottom chart shows change over time (2030, 2050, 2070 and 2090).
• The heavy line is the average for the ensemble of 11 models used in the simulations, but the chart also shows the range of model outputs to help understand uncertainty. Full descriptions of the 11 models used are provided in a separate factsheet.
• Using the buttons at the bottom of the panel, you can download the charts and underlying data in different formats, as both images and data tables.

There are three different switch or slider controls for changing the chart appearance; Mean, Range and Models. These sliders can be toggled on or off using the buttons at the top of the output chart. Switching 'Mean' to 'On' will show the multi-model ensemble mean as a thick bar (this is the default option). Turning on 'Range' will show the range in the ensemble as a green bar. Switching 'Model' to 'On" will show thin bars for individual models.

The ability to download the ensemble averages of the simulations for each variable in shapefile format allows users to easily combine it with other spatial data of interest using a Geographic Information System (GIS). The shapefile button will download the currently selected variable in the popular shapefile format that can be read by any GIS application.

The attribute table for the shapefile provided for each climate variable includes data for each combination of the four time slices (2030, 2050, 2070 and 2090) and for all seasons, including:

• Annual
• Summer (labelled ‘djf’ for December, January and February)
• Autumn (‘mam’ for March, April and May)
• Winter (‘jja’ for June, July and August)
• Spring (‘son’ for September, October and November)
• Dry (‘mjjaso’ for May - October)
• Wet (‘ndjfma’ for November - April).

The Heatwaves case study summarises the expected effects of climate change on the frequency and intensity of heatwaves, and the implications these changes may have on our health, lifestyles, infrastructure, services and industries.

For example, the figure shown above is a chart featured in the case study that integrates historical observations and projections for the South East Queensland region. This work suggests that the mean number of heatwave days (expressed as a percentage of the year) will increase from less than two over the reference period to about 17 in 2070 and about 25 in 2090. There is a similar trend for heatwave duration which may increase from four days during the reference period to about 20 days in 2070 and over 30 days in 2090 (depending on future greenhouse gases emissions).

The heatwave case study also includes an interactive map allowing users to view projection information for heatwave frequency and duration for each local government area.

The Water security case study explores the potential effects of climate change on our water supply and water security, and how these effects can be managed.

The figure above provides an example of the information available from the water security case study. For the Burnett River catchment, the timeseries shows a clear increase in the aridity index. While the streamflow trend shows a decline in the average level, there is some volatility in the range and projected maximum values.