Regional Climate Modelling Techniques: Impacts on Regional Climate - PowerPoint PPT Presentation

Regional Climate Modelling Techniques: Impacts on Regional Climate Change Climate Adaptation Flagship Jack Katzfey, M. Chattopadhyay, J. McGregor, K. Nguyen and M. Thatcher CAWCR, CMAR, Aspendale GH2011

Outline • Uncertainty and downscaling • Our dynamical downscaling approach (and why) • Some results – CFT and PCCSP • Summary Regional Climate Modelling

Cascade of uncertainty? Regional Climate Modelling

Cascade of uncertainty? Need for ensembles Regional Climate Modelling

Cascade of uncertainty? But is this really true? Regional Climate Modelling

Cascade of uncertainty? Increased resolution But is this Additional surface forcing really true? I think not! Regional Climate Modelling

Regional Climate Modelling • SRES - RCP Scenario selection • Good current climate – necessary? • Good variability – yes GCM • Patterns of climate change? (Whetton) selection • Determine climatology for each month • For whole run, subtract bias for each month Bias correct • Preserves variability and change signal SSTs Regional Climate Modelling

Large-scale bias-correction • In addition to fixing biases, Surface temperature average allows simulation to have more 115 E to 155 E, 40 S to 10 S 3 year running average realistic weather systems and Model uncertainty plus change (3.7°C) how they may change in response to climate change • Affect on downscaling later Model uncertainty/error (2.3°C) Example of SST bias in a GCM Spread of change signal (1.4°C) Same mean Regional Climate Modelling

Regional Climate Modelling • Region of interest – domain • Resolution • Surface specification – land use/orography RCM setup • Physics/parameterisations chosen • Time period • Outputs • Forcing? Simulation • New domain/resolution/surface inputs • Forcing from courser resolution simulation Multiple downscaling Regional Climate Modelling

Regional Climate Modelling Approaches Lateral boundary influence High None Variable Global Limited area resolution high-resolution High Low Computational expense • Also need to consider: • Domain size • Resolution • Two-way interaction • Internal variability Regional Climate Modelling

Stretched grid – No lateral boundaries Conformal Cubic Atmospheric Model (CCAM) Full atmospheric model – like GCMs Allows interaction with larger scales Example of variable resolution CCAM grid – 60 km over Australia 11 Regional Climate Modelling

Bias adjustment of sea surface temperatures • Sea surface temperatures main influence on climate (ENSO, climate change) • Dommenget, Dietmar, 2009: The Ocean’s Role in Continental Climate Variability and Change. J. Climate , 22 , 4939–4952 • Can we improve our representation of the current climate by fixing the biases? (yes) 12 Regional Climate Modelling

Comparison of downscaling approaches: July rainfall (1970-1979) Note the similarities of CCAM with uncorrected SSTs to the GCM (RHS) CCAM with bias-corrected SSTs is more similar to the observed (LHS) GCM Observed CCAM, SST, NF CCAM, bcSST, NF Regional Climate Modelling

Bias adjustment of sea surface temperatures • CCAM is an atmosphere only model – require GCMs to provide projections of sea surface temperatures • Does using one downscale model decrease spread of climate change signal? (no) 14 Regional Climate Modelling

Ensemble Mean and Spread: Changes in DJF precipitation The spread of climate change signals in CCAM is similar to that in the GCMs 6 CCAM mean 6 GCM mean 2085-1980 change 6 CCAM sdev 6 GCM sdev 2070-2100 change spread (Std.Dev.) Regional Climate Modelling

Adding more information with higher resolution • Can multiply downscale • CCAM within itself • Other RCMs • CCAM uses a digital filter to forcing large-scale information from coarser resolution run into higher resolution run 16 Regional Climate Modelling

Multiple downscaling to higher resolution Simulated annual rainfall for Tasmania at different resolutions Global Model CCAM 60 km CCAM 14 km Spectral Bias-correc. forcing Increased Increased resolution resolution Climate Futures for Tasmania project Regional Climate Modelling

DJF rainfall: percentage change Change in rainfall 1970:1999 to 2070:2099 6 model mean GCM 60 km RCM 14 km RCM Climate Futures for Tasmania project 18 Regional Climate Modelling

JJA rainfall: percentage change Change in rainfall 1970:1999 to 2070:2099 6 model mean GCM 60 km RCM 14 km RCM Climate Futures for Tasmania project 19 Regional Climate Modelling

Ensembles Change in annual rainfall 1961:1990 to 2070:2099 14 km results Although mean changes, pattern fairly consistent Climate Futures for Tasmania project 20 Regional Climate Modelling

Downscaling for the PCCSP • Can multiply downscale CCAM within itself • Use digital filter to forcing large-scale information from coarser resolution run into higher resolution run 21 Regional Climate Modelling

Downscaling activities for PCCSP CSIRO UK GFDL Miroc GFDL GCMs Echam5 Mk3.5 Hadcm2 cm21 mr cm20 Bias adjust SSTs CCAM x 2 60 km 60 km 60 km 60 km 60 km 60 km (1961-2099) 8km Zetac + Statistical Stat. WRF (15 cities, 8km RegCM 1980-2065) 8km MM5 + Extra and + Stat. Dynamical CCAM Stat. PRECIS Downscaling (7 countries, (1980-2000,2045-2065) 1980-2000,2045-2065,2080-2099)

PCCSP domains • 8 km domains in red • Extra DDS domain in green

8 km Orography CCAM 8 km CRU 50 km CCAM 60 km PNG JJA Rainfall TRMM 25 km GCM

Lamap, Vanuatu: 20 th Century Validation Precipitation (mm/d) Station Obs (black), CMAP (red), CRU (blue), GPCP (green) and TRMM (cyan) Station Obs (black), CCAM 8m(red)+2xsdev(gray), Station Obs (black), CCAM 8m(red)+2xsdev(gray), Stats Downscale(blue)+2xsdev(tan) CCAM 60km(blue)+2xsdev(yellow), GCM(green)+2xsdev(tan)

Vanuatu : Lamap 20 th Century PDF Precipitation f r e q u e n c y Sqrt(mm/day) Sqrt(mm/day) Temperature f r e q u e n c y o C o C Station Obs (black), CCAM 8m(red)+2xsdev(gray), Station Obs (black), CCAM 8m(red)+2xsdev(gray), CCAM Stats Downscale(blue)+2xsdev(tan) 60km(blue)+2xsdev(yellow), GCM(green)+2xsdev(tan)

Extra downscaling – NCEP2 – DJF rainfall Preliminary results CM AP GPCP CCAM Zetac - JFM WRF mm/day RegCM _E M M 5 PRECIS

Key results • Dynamical downscaling provides physically-based and more detailed representation of the regional climate • Bias-correction of sea surface temperatures significantly improves the representation of the current climate • Ensemble-based downscaling • Multiple GCMs • Multiple resolutions • Multiple RCMs (including different model set-ups) Regional Climate Modelling

Key results • Dynamical downscaling • Needs to be done carefully • Results may depend upon method, GCMs downscaled, model set-up, resolution, etc. • Projections based upon dynamical downscaling • Physically-based patterns of change • May help reduce some of the uncertainty by providing physically-based patterns of change, but need to understand physical causes of patterns of change • Only sub-sample of the full range of GCMs • Still based upon projections of GCMs, so uncertainty still exists Regional Climate Modelling

CAWCR, CMAR Jack Katzfey MMA Team Leader Phone: +61 3 9239 4562 Email: jack.katzfey@csiro.au Web: www.csiro.au/cmar Thank you Contact Us Phone: 1300 363 400 or +61 3 9545 2176 Email: Enquiries@csiro.au Web: www.csiro.au