QCCCE & ACRE Jozef Syktus Queensland Climate Change Centre of Excellence (QCCCE) Department of Environment and Resource Management

Structure 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 QCCCE Modelling IPCC, CSIRO Global CC Projections Regional CC Scenarios Statistical Downscaling Dynamical Downscaling Statistical Calibration 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 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: Trend in Annual Rainfall Western Australia Eastern Australia mm decrease per decade 50mm increase per decade 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 the El Niño event Simulated temperature change o C Modern - PreEuropean Observed temperature anomaly o C (2002/03 – ) Amplified temperature response during El Nino’s eg 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 period (a)(b) (c) (d)

Standarized Precipitation Index – drought severity & duration index Drought duration index

Probability distribution functions (PDF) of percentage of hot days during summer (DJF) with tmax  35  C over of pre- European and modern day vegetation conditions pre-European (blue) modern-day (red) Hot days (tmax >35 o C) - DJF (a

Hot days >35 o C summer (NDJFM) Dry days summer (NDJFM) 2002/ / / /83 NSW Victoria NSW

Tropical Cyclone Numbers o S, o E Source: CBoM

VWS Trend Difference O 3 – SST 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 20 th 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 ( ) - downscalled from CSIRO T63 Mk3.5 coupled model & Obs Average from 6 member ensemble

JJA Rainfall ( ) - downscalled from CSIRO T63 Mk3.5 coupled model & Obs Average from 6 member ensemble

DJF 2m temperature ( ) - downscalled from CSIRO T63 Mk3.5 coupled model & Obs Average from 6 member ensemble

JJA 2m temperature ( ) - 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 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 The CLIMARC project was completed in 2002, and the computerised data integrated with the existing ADAM climate record.51 key climate locations

Low number of available observations prior to 1957:

Climarc stations Capital cities and some other sites already punched Low station density for direct spline interpol ation

What did we do Construct a base line average (contain detail e.g. coastal, & topographic gradients) 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