Past and future temperature
extremes and vegetation in
Western Australia
T.J. Lyons
Environmental Science
Murdoch University
Evans, B.J and T.J. Lyons, 2013: Bioclimatic extremes drive forest mortality in
southwest, Western Australia Climate, 1, 28-52; doi:10.3390/cli1020028
Evans, B.J and T.J. Lyons, 2013: Bioclimatic extremes drive forest mortality in
southwest, Western Australia Climate, 1, 28-52; doi:10.3390/cli1020028
Evans, B.J and T.J. Lyons, 2013: Bioclimatic extremes drive forest mortality in
southwest, Western Australia Climate, 1, 28-52; doi:10.3390/cli1020028
Maximum temperature - February
Maximum temperature – February 2050
Minimum temperature - February
Minimum temperature – February 2050
Times series of Australian areally averages
Alexander, L.V. and J.M. Arblaster, 2009: Assessing trends in observed and modelled climate extremes over Australia in relation to
future projections Int. J. Climatol., 29, 417-435.
Lyons, T.J., 2002: Clouds prefer native vegetation. Meteorology and
Atmospheric Physics, 80, 131-140.
CORDEX AustralAsia Domain
Dynamic downscaling
Soils
Synoptic forcing
Land use
Topography
Mesoscale
meteorological
model
Sea surface
temperatures
Climate 20C
ERA-INT
CCSM
CSIRO
ECHAM5
MIROC
IPCC AR4 Projected Climate CMIP3
CCSM-A2
MIROC-A2
ECHAM5-A2
CSIRO-A2
IPCC AR5 Projected Climate CMIP5
CSIRO
CCSM4
ECHAM6
MIROC
ACCESS
All simulations are 362 sequential months
to preserve variables such as soil moisture
and require approximately 8 hours per
simulated month on 12 nodes (144 CPUs)
with 23GB memory per node, producing
42948 GB of storage per 30 year simulation
Regional climate models are sensitive to the forcing data used,
as well as different model physics options – validation of
model set up against 2010 observations
Kala, J., J. Andrys, T.J. Lyons, I.J. Foster and B.J. Evans, 2014: Sensitivity of WRF to driving data and physics options on a seasonal timescale for the southwest of Western Australia. Climate Dynamics (revised)
2010 Observations
Kala, J., J. Andrys, T.J. Lyons, I.J. Foster and B.J. Evans, 2014: Sensitivity of WRF to driving data and physics options on a seasonal timescale for the southwest of Western Australia. Climate Dynamics (revised)
WRF realisation of 2010
at different grid resolutions
Kala, J., J. Andrys, T.J. Lyons, I.J. Foster and B.J. Evans, 2014: Sensitivity of WRF to driving data and physics options on a seasonal timescale for the southwest of Western Australia. Climate Dynamics (revised)
Validation of 30 year climate simulation against
gridded Bureau of Meteorology observations
5 km x 5 km grid point comparison
30 year climatology
Minimum temperature
Perkins Skill
Score measures
the agreement
between
probability
density
functions at
each grid point
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
Maximum temperature
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
Precipitation
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
Absolute Seasonal PPT Bias (mm/month)
D02
10 km
DJF
MAM
JJA
SON
D03
5 km
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
Tropical Nights (TR) Annual count of nights where TMIN > 20°C - simulation is
significantly overestimating the southerly extent of the TR index
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American meteorological Society Annual meeting, Atlanta.
Frost Days (FD) Annual count of nights where TMIN < 2°C. Spatial distribution of
frost susceptible areas is well simulated but modeled number of FD is generally higher
than observed, especially in the western Wheatbelt.
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American meteorological Society Annual meeting, Atlanta.
Summer Days (SU) Annual count of days where TMAX > 34°C. Spatial distribution
and overall count of SU is very well simulated.
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
Max 1 Day Rainfall Intensity (RX1d) Relative (%) bias (wrf-obs) for RX1d for domains
D02 and D03. With the exception of the eastern perimeter, RX1d is well simulated.
D03 reduces the negative bias in the north west corner however bias sees a small
increase throughout the domain at this higher resolution.
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
Longest Run of Consecutive Dry Days (CDD) Tends to underestimate dry days in the
south of the domain. Strong positive bias introduced into northern grid in D03 which
suggests that this higher resolution domain is not capturing all of the rainfall events.
D02 shows the stronger simulation performance.
Percent
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
SPII: Simple Precipitation Intensity Index
mm/rain day
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
RX5d: Maximum 5 Day Rainfall Intensity Underestimating the highest intensity
rainfall periods and hence the contribution of these periods to overall rainfall
mm
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American meteorological Society Annual meeting, Atlanta.
PRCPTOT: Annual Number of Wet Days
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American meteorological Society Annual meeting, Atlanta.
R95pTOT: Annual rainfall when intensity exceeds the 95th percentile
Andrys, J., T. Lyons and J. Kala, 2014: Validation of WRF downscaling capabilities over Western Australia to detect rainfall and
temperature extremes. American Meteorological Society Annual meeting, Atlanta.
Summary of Findings
WRF is able to capture the spatial structure of precipitation extreme metrics
at the 5km resolution
Simulation tends to underestimate the magnitude and therefore contribution
of high intensity rainfall in the coastal regions.
Statistical distribution of diurnal events simulated with skill however
minimum temperatures are slightly skewed to the right (generally warmer
than observed) and maximum temps are skewed left (generally colder
than observed).
Colder extremes (Frost Days) of temperature were modeled with skill as were
hot extremes (Summer Days).
Initial indication of projected changes – CSIRO model
Base climate 1979-1999
300C
350C
400C
Projected climate 2029-2049
Probability of daily maximum temperatures exceeding threshold
Based on generalised extreme value (GEV) distribution
00C
50C
Probability of daily minimum temperatures exceeding threshold
100C
Heat stress 1989-1999
Cold stress
Heat stress 2029-2039
Frost days 1989-1999
Frost days 2029-2039
Changes in variance are significant
Under a warming drying climate in the southwest
high resolution simulations of downscaled general
circulation models highlight an increase in heat and cold stress
Current observations illustrate that under these conditions
native vegetation is more susceptible to pest attack
.