Current and future climate of the North west including extreme events
The points under each project describe the first major deliverables that are currently being addressed and are by no means the full four-year research plan for IOCI Stage 3.
Project 2.1: Have Asian aerosols caused the rainfall increase in north west Australia? (Leon Rotstayn)
Australia's natural rainfall variability is substantially driven by natural oscillations or "modes" in the surrounding ocean basins, of which the El Nino Southern Oscillation (ENSO) is the best known. If a climate model is to be used to investigate the drivers of Australian rainfall changes, it is important that the model has a good simulation of these modes of variability. We have used a technique called empirical orthogonal teleconnections (EOTs) to evaluate the simulation of Australian rainfall variability in a current climate simulation of the CSIRO Mk3.6 climate model and several other models (see Rotstayn et al., 2009 for further details). The Mk3.6 model is to be used to study whether the increased rainfall in north-western and central Australia can be attributed to forcing from Asian aerosol pollution. The figure shows the leading mode of rainfall variability from observations (top left panel) and five climate models, including the Mk3.6 model and its predecessor (Mk3.5). The observations show an "ENSO-related" rainfall mode, centred over eastern Australia. This is well captured by the Mk3.6 model, whereas other models shown incorrectly locate this mode over northern WA or the Northern Territory. The improved simulation in Mk3.6 relative to Mk3.5 is important, because if the ENSO-related mode is located over northern WA, the response of the modelled rainfall there to a change of forcing (such as aerosols or greenhouse gases) is likely to be unrealistically dominated by the response of ENSO to that forcing.
- Refine the high-resolution CSIRO climate model (Mk3.5A) so that it achieves a stable, high-resolution simulation of present global climate, including aerosols, and an evaluation of this simulation. (The inclusion of aerosols is non-trivial task.)
- Describe the observed climate changes, the modelled changes and their limitations; and then incorporate time-varying aerosol forcing into model with the aim of capturing the observed rainfall increases with increased confidence.
- Investigate the effects of aerosols and greenhouse gases on past and future rainfall trends in the North-West using the Mk3.5A model.
Figure 2.1.1: Leading mode of annual rainfall variability over Australia, from observations (Bureau of Meteorology), the CSIRO Mk3.6 and Mk3.5 climate models, and three leading international models: HadGEM1 (United Kingdom), GFDL CM2.1 (USA) and MIROC 3.2, medium resolution (Japan).
The observations show an "ENSO-related" rainfall mode, centred over eastern Australia. This is well captured by the Mk3.6 model, whereas other models shown incorrectly locate this mode over northern WA or the Northern Territory. The improved simulation in Mk3.6 relative to Mk3.5 is important, because if the ENSO-related mode is located over northern WA, the response of the modelled rainfall there to a change of forcing (such as aerosols or greenhouse gases) is likely to be unrealistically dominated by the response of ENSO to that forcing.
Rotstayn, L.D., M.A. Collier, Y. Feng, H.B. Gordon, S.P. O'Farrell, I.N. Smith, J. Syktus, 2009. Improved simulation of Australian climate and ENSO-related rainfall variability in a GCM with an interactive aerosol treatment. Submitted to Int. J. Climatol.
Project 2.2: Tropical Cyclones in the North West (John McBride and Hamish Ramsay)
- Write and submit a research paper on the climatology and interannual variability of tropical cyclones in the Indian Ocean that affect the Western Australian coastline.
- Develop a statistical forecast model for tropical cyclone activity along the Western Australian coastline (2-week lead time)
Figure 2.2.1: The spatial pattern of correlation between sea surface temperature (SST), averaged over September to November, and the number of tropical cyclones (TCs) from November-April in the Western Australian main development region (MDR), using data from 1969 to 2007. Warmer (cooler) than average SSTs in the central tropical Pacific during Spring - El Niño (La Niña) - results generally in suppressed (enhanced) TC development over the MDR region. The strong, positive correlation between MDR SST and the number of TCs is also attributed to changes in the tropical Pacific SST associated with El Niño and La Niña events.
Project 2.3: Statistical Downscaling for the North West (Steve Charles)
Statistical downscaling uses large-scale predictors such as mean sea-level pressure to build a statistical model of the expected local rainfall or temperature on a certain day. Few downscaling models have been developed for the tropics. To develop such a tool the major predictors must first be identified. In the tropics, unlike in the sub-tropics, mean sea-level pressure is not necessarily representative of the features aloft, thus the winds must be used at several levels to best describe the atmosphere.
- Analyse and model historical multi-site daily rainfall, for a network of high quality stations in the North West, to provide an understanding of the dominant patterns of rainfall variability and their trends and shifts over recent decades.
- Calibrate statistical downscaling models to identify the dominant large-scale atmospheric drivers of the historical rainfall variability, trends and shifts.
- Assess climate models for their ability to reproduce these drivers, thus allowing selection of the better performing climate models to be used for generating climate change projections of multi-site rainfall for the region to 2100.
Figure 2.3.1: The mean summer (December to March) winds are shown at three levels, from the surface (c) through levels higher up (b) and (a). The shaded regions correspond to the mean sea-level pressure at the surface (c), (contour interval is 2hPa and the yellow shaded area is values below 1008 hPa), and the height of the atmosphere at the designated pressure level in (a), (contour interval 10 m, and heights below 3130 m shaded yellow) and (b), (contour interval is 8 m and regions below 1492 m shaded yellow). The data is from the NCEP/NCAR reanalyses.
Previous work assessing which are the best predictors for local tropical rainfall and temperature from global climate models found that while upper level level winds are useful, direct model results of the variables predicted add further skill. For example, direct model output large-scale rainfall is a good predictor for local rainfall.
Timbal, B., Li, Z. and Fernandez, E., 2008: The Bureau of Meteorology Statistical Downscaling Model Graphical User Interface: user manual and software documentation. CAWCR technical report, 4, 90pp. http://www.cawcr.gov.au/publications/technicalreports.php
Project 2.4: Physical-Statistical Modelling of Extreme Events (Debbie Abbs, Yun Li, Mark Palmer and Eddy Campbell)
- Develop climate change projections of the intensity-frequency-duration characteristics of extreme temperature events, with an evaluation of the potential for forecast skill at seasonal time scales.
- Document available data resources and their spatial and temporal patterns.
- Develop new methods for identifying atmospheric predictors influencing rainfall and temperature extremes.
Identify predictors identified north-west WA.