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There is evidence suggesting that fire activity in southeast Australia has increased in the past two decades. There is also anecdotal evidence that pyrocumulonimbus events have become more common, although the observed record is short. We explore the extent to which possible changes in fire and pyrocumulonimbus occurrence in southeast Australia can be explained by changes in the number of extreme (above the 95th percentile) days per year of two fire-weather indices, the Forest Fire Danger Index (FFDI) and the continuous Haines index (C-Haines). For the period 1958 through 2020, we show that there is a dependence between the number of extreme FFDI days per year and burned area for forested parts of southeast Australia. To a lesser extent, there is a relationship between the FFDI and pyrocumulonimbus occurrences. We find an increase in the occurrence of extreme FFDI days from the late 1990s, with up to 24 more extreme days per year. In the southwest of the state of Victoria, there are concomitant increases in temperature and wind speed, and decreases in relative humidity. We show that the structure of C-Haines has not changed, but extreme days more frequently coincide with extreme FFDI days. This suggests that possible observed increases in pyrocumulonimbus occurrences may partly be explained as a consequence of the increases in co-occurring extreme FFDI and C-Haines days, rather than due to changes in the atmospheric stability. We also find that changes in the atmospheric circulation are consistent with those seen in the fire weather indices. Increased surface and mid-tropospheric pressure since 1998 are consistent with a poleward shift of the storm track. In particular, an increase in anticyclonic anomalies in the Tasman Sea is indicative of enhanced blocking activity. Although blocking frequency in these longitudes has decreased overall, the proportion of extreme FFDI days that are considered blocked has increased.

The Earth has warmed over the past century. The warming rate (amount of warming over a given period) varies in time and space. Observations show a recent increase in global mean warming rate, which is initially maintained in model projections, but which diverges substantially in future depending on the emissions scenario followed. Scenarios that stabilize forcing lead to much lower warming rates, as the rate depends on the change in forcing, not the amount. Warming rates vary spatially across the planet, but most areas show a shift toward higher warming rates in recent decades. The areal distribution of warming rates is also changing shape to include a longer tail in recent decades. Some areas of the planet are already experiencing extreme warming rates of about 1 °C/decade. The fat tail in areal distribution of warming rates is pronounced in model runs when the forcing and global mean warming rate is increasing, and indicates a climate state more prone to regime transitions. The area-proportion of the Earth displaying warming/cooling trends is shown to be directly related to the global mean warming rate, especially for trends of length 15 years and longer. Since the global mean warming rate depends on the forcing rate, the proportion of warming/cooling trend areas in future also depends critically on the choice of future forcing scenario. Keywords: Climate variability, Climate projection, Transient response, Extreme warming

Climate influences marine ecosystems on a range of time scales, from weather-scale (days) through to climate-scale (hundreds of years). Understanding of interannual to decadal climate variability and impacts on marine industries has received less attention. Predictability up to 10 years ahead may come from large-scale climate modes in the ocean that can persist over these time scales. In Australia the key drivers of climate variability affecting the marine environment are the Southern Annular Mode, the Indian Ocean Dipole, the El Nino/Southern Oscillation, and the Interdecadal Pacific Oscillation, each has phases that are associated with different ocean circulation patterns and regional environmental variables. The roles of these drivers are illustrated with three case studies of extreme events—a marine heatwave in Western Australia, a coral bleaching of the Great Barrier Reef, and flooding in Queensland. Statistical and dynamical approaches are described to generate forecasts of climate drivers that can subsequently be translated to useful information for marine end users making decisions at these time scales. Considerable investment is still needed to support decadal forecasting including improvement of ocean-atmosphere models, enhancement of observing systems on all scales to support initiation of forecasting models, collection of important biological data, and integration of forecasts into decision support tools. Collaboration between forecast developers and marine resource sectors—fisheries, aquaculture, tourism, biodiversity management, infrastructure—is needed to support forecast-based tactical and strategic decisions that reduce environmental risk over annual to decadal time scales.