Download Queensland Climate Change Centre of Excellence (QCCCE)

QCCCE & ACRE
Jozef Syktus
Queensland Climate Change Centre of
Excellence (QCCCE)
Department of Environment and Resource
Management
Structure
Director
Policy Development
Executive Director
OCC
(Greg Withers)
Director
Policy Coordination
& Implementation
Climate System
Research
Office of Climate Change
Director
QCCCE
(Lynne Turner)
Climate Impacts ,
Applications &
Mitigation
Information &
Knowledge
Special Projects
Purpose of QCCCE
• Has a whole-of-government focus.
• Provides scientific advice, information and data on climate
change and climate variability.
• Informs government policy and assists Queensland communities
better understand, forecast, prepare for and adapt to climate
variability and climate change.
• Provides advice to the OCC regarding practical measures to
further reduce and offset the State’s greenhouse gas emissions.
Climate System Research - Key area projects will
address:
 Climate system modelling using global and regional climate
models to produce seasonal climate forecasts, simulate historical
climate in Australian region and produce future projections of
climate change.
 Research causes and mechanisms impacting the Queensland
climate variability and changes during the historical times,
including climate extremes (attribution of regional climate change)
and in future.
 Construction of future climate change scenarios and
downscaling these scenarios to produce regional and local climate
change projections.
Information and Knowledge Theme
Key deliverables
• Provide access to high quality information and knowledge
systems to underpin science, decision making and policy
development including:
 SILO (climate database – interpolated and station)
 AussieGRASS (environmental calculator – spatial
simulator at 5 km grid over Australia)
 The ‘Climate Monitor’ (online updates- seasonal
conditions outlook)
QCCCE Activities, Projects and Tools
 Simulation of historical & future climate using global coupled and uncoupled
GCMs (C20C, Impact of LCLU on Australian climate, AR5)
 Dynamical downscaling of data from the global climate models for Queensland
region (both for climate change and seasonal forecasting)
 Analysis of IPCC model datasets and datasets from other modelling centres.
 Attribution of historical trends and changes in Queensland region
 Global and regional seasonal climate forecasts (operational since 1998 &
contributing to IRI in New York)
International & national collaboration (CSIRO, BoM, International Research Institute
for Climate and Society, Hadley Centre, Walker Institute)
 Analysis, synthesis and delivery of research to stakeholders (Regional Water
Supply Strategy, 2050 review, regional climate change projections for Qld)
 CSIRO/QCCCE modeling contribution to AR5
 Impact of climate extremes on the Queensland economy and society
 Generation of climate change scenarios
Challenges
Information Delivery
QCCCE
Attribution of drying trends in Queensland
Modelling
Statistical
 Changes in TC environment
in Queensland region?
Downscaling
 Improved understanding of changes in SH circulation in
Global
CC
CC
recent
decades
and in future (ENSO, SAM,Regional
Hadley
Projections
Scenarios
and Walker Circulation)
 Improved characterization
Dynamical of extremes
Statistical (droughts,
Downscaling
Calibration
Research bushfires, floods)
Research
heatwaves,
IPCC,
CSIRO
Improved regional projections
Rainfall Relative to Historical Records
Australia– July 1992 to June 2003
Percentiles
Red = Last 10
years are in the
bottom 10% of
all previous
ten July to
June year
periods from
1890
AussieGrass/SILO
system at QNR&M
Current trends in rainfall:
Western Australia 1900-2007
Trend in Annual Rainfall
1970-2007
50mm decrease per decade
1900
2000
Eastern Australia 1900-2007
50mm increase per decade
Source: Bureau
of Meteorology
1900
2000
Observed Trends in Hydrological Cycle
1993 to 2003
• Observed trends for the 1993 to 2003 period show strong decline in
rainfall, soil moisture and runoff, mainly during summer (NDJFM)
season!
• These trends have occurred in spite of prolonged La Nina’s
(1998 to 2001) when typically the water storage is replenished.
The Challenge: Reducing uncertainty
White areas are where less than 66% of the models agree in the sign of the
change and stippled areas are where more than 90% of the models agree in the
sign of the change
Precipitation increases very likely in high latitudes
Decreases likely in most subtropical land regions
The current challenge.
Changes in Mean Climate – summer
(November – March)
Long-term average
climate response:
• hotter & drier for
modern land cover
conditions
• Impact strongest in
summer
• near surface wind
speed increase
DJF temperature anomaly for the
2002-2003 El Niño event
Simulated temperature change oC
Modern - PreEuropean
Observed temperature anomaly oC
(2002/03 – 1951-2000)
Amplified temperature response during El Nino’s eg. 20022003 summer with fragmented vegetation cover
Changes in Climate Extremes – annual
average during 1951-2003 period
(a)
(b)
Land clearing leads to an increase in
climatic extremes, as shown by
a) increase in frequency of hot days,
(stronger impact in summer)
b) increase in frequency of dry days,
c) Reduced daily rainfall intensity,
(c)
(d)
c) Reduced no of wet days
Note: red (increase), blue (decrease),
closed (significant), open (not significant)
These changes coincide with areas of
land cover change, and have occurred in
the vicinity of Murray Darling Basin,
Australia’s agricultural production zone.
Standarized Precipitation Index – drought severity & duration index
Drought duration index 1951-2003
Hot days (tmax >35oC) - DJF
(a
4
Probability
distribution functions
(PDF) of percentage of
hot days during
summer (DJF) with
tmax  35C over
1951-2003 of preEuropean and modern
day vegetation
conditions
3
pre-European (blue)
2
modern-day (red)
3
3
2
probability (%)
probability (%)
pre-European
present day
1
0
2
1
0
0
10
20
30
40
0
10 20 30 40 50 60
summer (DJF) hot days (%)
summer(DJF) hot days (%)
30
20
10
0
0
2
4
6
8 10 12 14
summer (DJF) hot days (%)
probability (%)
probability (%)
probability (%)
3
2
1
0
1
0
0
10 20 30 40 50 60
summer (DJF) hot days (%)
0
10
20
30
40
50
summer (DJF) hot days (%)
Hot days >35oC summer (NDJFM)
2002/03
1982/83
NSW
Victoria
Dry days summer (NDJFM)
2002/03
1982/83
NSW
Victoria
9
8
Tropical Cyclone Numbers 10-28oS, 142-153oE
7
TC numbers
6
post 1999-00
data not part of
HQ dataset
5
4
3
2
1
0
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
Year
89
19
91
19
93
19
95
19
97
19
99
19
01
20
Source: CBoM
03
20
VWS Trend Difference O3 – SST 1961-2003
JFM (m/s per 100 yrs)
Observed (ERA40) and simulated
trends in Mean Sea Level Pressure
Regional Impact of Multiple Forcing
Observed trends in the Southern Hemisphere Polar
Vortex and Blocking Frequency from Reanalysis
Linear trends in Dec-May zonal winds &
shift towards positive phase of SAM and
positive trend in ZW3
Linear trends in Dec-May blocking
Renwick, 2004
How 20th Century Reanalysis
can help?
• Need to understand the changes in weather statistics
in Qld region – attribution of drying trends e.g.
Hadley cell, STR, sub-tropical jet stream
• Better sampling to investigate climate extremes
• Extended dataset to investigate the changes in the
environment for TC formation e.g. wind shear
• Improved sampling and uncertainty – blocking, storm
tracks, trends in SAM
Potential options for
improvement
• Dynamical downscaling over Australian region
• CCAM variable resolution global AGCM
• Use initial conditions from reanalysis and SST & sea
ice to run CCAM in weather forecast mode
Dynamical
Downscalling
CCAM
C128
~20kms
DJF Rainfall (1951-1970) - downscalled from
CSIRO T63 Mk3.5 coupled model & Obs
Average from 6 member ensemble
JJA Rainfall (1951-1970) - downscalled from
CSIRO T63 Mk3.5 coupled model & Obs
Average from 6 member ensemble
DJF 2m temperature (1951-1970) downscalled from CSIRO T63 Mk3.5
coupled model & Obs
Average from 6 member ensemble
JJA 2m temperature (1951-1970) downscalled from CSIRO T63 Mk3.5
coupled model & Obs
Average from 6 member ensemble
Potential options for use of
Reanalysis output
• SILO – interpolated historical climate surfaces at
5km grid over Australian continent
• Daily surfaces used to drive AussieGrass spatial
simulation model over the past 120 years
• Pre- 1957 very sparse data especially in west
• Climatology used instead
• Potential to use reanalysis data to blend with station
data as an input into interpolation
CLIMARC Origins
• A jointly funded collaborative project known as
CLIMARC - "Computerising the Australian Climate
Archives" - was established in 1999 to address these
issues. For 64 sites at 51 key climate locations
across Australia, the project involved the data entry
and quality control of more than 40,000 monthly
climate records, some going back as far as 1858.
The CLIMARC project was completed in 2002, and
the computerised data integrated with the existing
ADAM climate record.
Low number of available observations prior
to 1957:
Climarc stations
CLIMARC Stations
Low
station
density
for
direct
spline
interpol
ation
14016
-15
31010
3002
29004
2011
31011
30018
15087
33001
4002
4032
-20
33002 33045
34002
30045
33047 33046
38003
36030
15540
-25
35027
6062
39015
44022
7046
39039
40264
41023
41038
8051
8050
46037
17031
-30
12039
10093
56017
48013 52026
17024
55023
55024
48030
18011
18012
18044
21046
9541
9518
18070
9500
-35
65016
63004
76077
73127
74128 74114
72150
72151
78031
26021
26020
90015
-40
91057
91049
115
120
125
130
135
140
145
150
Capital cities
and some
other sites
already
punched
What did we do
• Construct a base line average (contain detail e.g.
coastal, & topographic gradients) 1957-1987
• Calculate anomaly (daily value for station – daily
mean)
• Interpolate anomaly
• Add to mean
• Cross-validation suggests method works ok, much
better than mean (but still not good enough in the
west)
Example
Maximum temperature observations (left) for the example date 15
January, 1925, and corresponding anomaly-interpolated surface (right).
Gridded data used for Ecology and Hydrology
Vapour pressure surfaces for 15 January 1900
Would gridded reanalysis data
improve ?
Direct spline interpolation
Anomaly spline
interpolation
How to use results from ACRE
re-analysis data
• Test re-analysis grids vs Interpolated data (check for
biases etc)
• Use re-analysis data as a co-variate in spatial
interpolation especially pre 1915
• Use re-analysis data to help identify errors
• Pre 1890 – what is possible?
Issues & questions
• What are practical ways to increase data quantity
from SH used by the reanalysis?
• Pre-satellite SST & sea ice used to drive reanalysis
• Representation of historical radiative forcings in the
reanalysis e.g. IPCCC AR5 is releasing the
comprehensive forcing history of GHG, ozone,
aerosols …
• Evaluation and validation of reanalysis