Pandora Hope

Contributors to the Record High Temperatures Across Australia in Late Spring 2014

Data. Monthly maximum temperatures from the Australian Water Availability Project (AWAP) gridded dataset (Jones et al. 2009) were analyzed on a 0.25° grid over Australian land points. Observed sea surface temperatures from Hurrell et al. (2008) for 1981 and Reynolds et al. (2002) from 1982 onwards were used for the ENSO index (based on Nino-3.4 SSTs: 5°N–5°S, 170°–120°W) and the Indian Ocean dipole mode index [IOD: western pole (10°S–10°N, 50°–70°E); eastern pole (10°S–0°, 90°–110°E); Saji et al. 1999]. The SAM was calculated as the first EOF of mean sea level pressure (MSLP) anomalies over 20°–75°S (e.g., Lim et al. 2011) from the ERA-Interim reanalysis (Dee et al. 2011). Soil moisture estimates are from Raupach et al. (2009) for the upper-layer (<0.2m). Global mean temperatures were from the U.K. Met Office HadCRUT4 (version 4.3.0.0; www. metoffice.gov.uk/hadobs/hadcrut4/).

ENSO teleconnections with Australian rainfall in coupled model simulations of the last millennium

El Niño-Southern Oscillation is the major source of interannual rainfall variability in the Australian region, with the strongest influence over eastern Australia. The strength of this regional ENSO–rainfall teleconnection varies in the observational record. Climate model simulations of the “last millennium” (850–1850 C.E.) can be used to quantify the natural variability of the relationship between ENSO and Australian rainfall on decadal and longer time scales, providing a baseline for evaluating future projections. In this study, historical and last millennium (LM) simulations from six models were obtained from the Coupled Model Intercomparison Project Phase 5 and Palaeoclimate Modelling Intercomparison Project Phase 3. All models reproduce the observed negative correlation between September to February (SONDJF) eastern Australian rainfall and the NINO3.4 index, with varying skill. In the LM simulations, all models produce decadal-scale cooling over eastern Australia in response to volcanic forcing, as well as a long-term cooling trend. Rainfall variability over the same region is not strongly driven by external forcing, with each model simulating rainfall anomalies of different phase and magnitude. SONDJF eastern Australian rainfall is strongly correlated with ENSO in the LM simulations for all models, although some models simulate periods when the teleconnection weakens substantially for several decades. Changes in ENSO variance play a role in modulating the teleconnection strength, but are not the only factor. The long-term average spatial pattern of the ENSO–Australian rainfall teleconnection is similar in the LM and historical simulations, although the spatial pattern varies over time in the LM simulations.

What Caused the Record-Breaking Heat Across Australia in October 2015?

The Event. In 2015, Australia experienced another exceptionally warm spring, making the spring seasons of 2013, 2014, and 2015 the three warmest from 105 years of record (Trewin 2013). In 2015, October was the most extreme month (Fig. 24.1a), with the largest monthly mean daily maximum temperature (AusTmax) anomaly (+3.44°C, relative to 1961–90; 33.54°C absolute) of any month, surpassing the September 2013 AusTmax record of +3.41°C. The monthly mean daily minimum temperature was also a record high for October (+2.34°C), and the fourth largest positive anomaly of any month. More than half of the continent (54.7%) recorded the highest-on-record October maximum temperatures, exceeding the previous record of 22.3% in 1988. The heat was particularly focused in the south (Fig. 24.1a), associated with a number of weather systems that encouraged surface northerlies from the continental interior during the month (Australian Bureau of Meteorology 2015). Temperatures in the north were relatively cool. At inland locations, temperatures were consistently above average, leading to record warm monthly averages, but no daily records being set.

Extreme monthly rainfall over Australia in a changing climate

Motivated by the important impacts of extreme rainfall, this study extends the CSIRO and BoM (2015) analyses and projections of 20-year means and daily extremes to rainfall on the monthly timescale. Frequency distributions for monthly rainfall rates simulated by 40 CMIP5 models for the 1986-2005 period are compared with those from the AWAP 0.25° gridded observational data. Distributions spatially-averaged over Australian regions provide a signature of seasonal rainfall. Composites of months in the top and lowest deciles for each grid point and each of the four seasons are then evaluated, along with the frequency of rainfall rates exceeding thresholds ranging from 0.5 mm d to 8 mm d. The simulated changes by 2080-2099 under the RCP8.5 scenario for the various rainfall statistics are assessed. Maps of the ensemble mean of changes of the lowest and top deciles, as a percentage of the 1986-2005 base, partly reflect the tendency for increased mean rain in summer and autumn, with decreases in winter and spring. There is also a change in the frequency distribution, with the top decile rainfall tending to increase and the lowest decile to decrease. Bar graphs are used to represent the range of change across the models, for each of four seasons and four regions. In most cases the bars for each statistic cover both declines and increases, but there is again a shift towards the positive in the progression from lowest decile to top decile. The changes are consistent with a broadening of the distribution of monthly amounts. Model spatial resolution is not a major influence on the changes. These projections for monthly rainfall statistics should be applicable to a range of climate impacts.

Assessing the Climate Response to Major Surface Inundation: Lake Eyre, Australia

A World Meteorological Organization assessment of freshwater resources noted that approximately 1.7 billion people, or one-third of the world’s population, live in countries that are water-stressed (Stockholm Environment Institute 1997) (defined as using more than 20% of their renewable water supply, a commonly used indicator of water stress), and that this number is projected to increase to around 5 billion by 2025. As a result, projects to ‘drought-proof’ regions vulnerable to water stress are clearly of interest to many people and governments.