Downscaled GCM climate projections of fire weather over Victoria, Australia. Part 1*: evaluation of the MACA technique
Scott Clark A , Graham Mills A , Timothy Brown B , Sarah Harris C E and John T. Abatzoglou D
+ Author Affiliations
- Author Affiliations
A School of Earth, Atmosphere and Environment, Monash University, 1-131 Wellington Road, Clayton, Vic. 3800, Australia.
B Desert Research Institute, 2215 Raggio Parkway, Reno, NV 89512, USA.
C Bushfire Management, Country Fire Authority, 8 Lakeside Drive, Burwood East, Vic. 3151, Australia.
D School of Engineering, University of California – Merced, 5200 North Lake Road, Merced, CA 95343, USA.
E Corresponding author. Email: sarah.harris@cfa.vic.gov.au
International Journal of Wildland Fire 30(8) 585-595 https://doi.org/10.1071/WF20174
Submitted: 10 November 2020 Accepted: 7 May 2021 Published: 3 June 2021
Abstract
Anthropogenic climate change is expected to cause an increase in fire danger over south-eastern Australia during the 21st century, primarily driven by increased surface temperature. Studies of future fire weather in Victoria, Australia, have so far mostly utilised direct output from general circulation models, which have inadequate resolution for resolving the dynamics of local fire danger and are prone to substantial biases that may affect the seasonality of dry fuels. In this paper, we assess the ability of the Multivariate Adaptive Constructed Analogs (MACA) method to downscale output from general circulation models over Victoria, and replicate statistical attributes of fire danger indices. We find that climatological descriptors of meteorological variables of wind, temperature and humidity are captured extremely well, and fields on extreme fire days are well captured. We find that the method works very well for statistically downscaling fire weather elements over Victoria and provides a vehicle to assess the regional variation of fire weather projections over Victoria.
Keywords: GCM, MACA, statistical downscaling, fire weather, Victoria, Australia, FFDI, model evaluation.
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