QCCCE & ACRE

Jozef Syktus

Queensland Climate Change Centre of Excellence (QCCCE)

Department of Environment and Resource Management

Structure Executive DirectorOCC

(Greg Withers)

DirectorPolicy Development

DirectorPolicy Coordination& Implementation

DirectorQCCCE

(Lynne Turner)

Climate System Research

Climate Impacts , Applications &

Mitigation

Information & Knowledge

Special Projects

Office of Climate Change

• 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.

Purpose of QCCCE

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.

• Provide access to high quality information and knowledge systems to underpin science, decision making and policy development including:

Key deliverables

Information and Knowledge Theme

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)

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

QCCCE Activities, Projects and Tools

Challenges

QCCCEModelling

IPCC,CSIRO

Global CCProjections

Regional CCScenarios

Statistical Downscaling

Dynamical Downscaling

StatisticalCalibration

Research Research

Information Delivery

Attribution of drying trends in Queensland Changes in TC environment in Queensland region? Improved understanding of changes in SH circulation in

recent decades and in future (ENSO, SAM, Hadley

and Walker Circulation) Improved characterization of extremes (droughts,

heatwaves, bushfires, floods) Improved regional projections

Rainfall Relative to Historical Records

Australia– July 1992 to June 2003Percentiles

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:

Trend in Annual Rainfall 1970-2007

Western Australia 1900-2007

Eastern Australia 1900-2007

1900 2000

50mm decrease per decade

50mm increase per decade

1900 2000

Source: Bureau of Meteorology

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 the2002-2003 El Niño event

Simulated temperature change oCModern - PreEuropean

Observed temperature anomaly oC (2002/03 – 1951-2000)

Amplified temperature response during El Nino’s eg. 2002-2003 summer with fragmented vegetation cover

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) 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.

Changes in Climate Extremes – annual average during 1951-2003 period(a) (b)

(c) (d)

Standarized Precipitation Index – drought severity & duration index

Drought duration index 1951-2003

Probability distribution functions (PDF) of percentage of hot days during summer (DJF) with tmax 35C over 1951-2003 of pre-European and modern day vegetation conditions

pre-European (blue)

modern-day (red)

Hot days (tmax >35oC) - DJF

0

10

20

30

0 2 4 6 8 10 12 14

summer (DJF) hot days (%)

prob

abili

ty (

%)

0

1

2

3

0 10 20 30 40

pre-Europeanpresent day

summer(DJF) hot days (%)

prob

abili

ty (

%)

0

1

2

3

0 10 20 30 40 50 60

summer (DJF) hot days (%)

pro

babili

ty (

%)

0

1

2

3

0 10 20 30 40 50 60

summer (DJF) hot days (%)

prob

abili

ty (

%)

0

1

2

3

4

0 10 20 30 40 50

summer (DJF) hot days (%)

prob

abili

ty (

%)

(a

Hot days >35oC summer (NDJFM) Dry days summer (NDJFM)

2002/03 1982/83 2002/03 1982/83

NSW

Victoria Victoria

NSW

Tropical Cyclone Numbers 10-28oS, 142-153oE

Source: CBoM

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

Renwick, 2004

Linear trends in Dec-May zonal winds & shift towards positive phase of SAM and

positive trend in ZW3

Linear trends in Dec-May blocking

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

CCAM C128~20kms

Dynamical Downscalling

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

115 120 125 130 135 140 145 150

-40

-35

-30

-25

-20

-15

CLIMARC Stations

20113002

40024032

6062

7046

80508051

95009518

9541

1009312039

14016

15087

15540

17024

17031

1801118012

18044

18070

21046

26020

26021

29004

30018

30045

31010

31011

33001

33002 33045

3304633047

34002

350273603038003

39015

3903940264

4102341038

44022

4603748013

48030

52026

5502355024

56017

6300465016

7215072151

731277411474128

76077

78031

90015

91049

91057

Capital cities and some other sites already punched

Low station density for direct spline interpolation

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

Direct spline interpolation Anomaly spline interpolation

Would gridded re-analysis data improve ?

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