Tailored hydroclimate projections –

an Australian case study

Dr Marie EkströmResearch Fellow

School of Earth and Ocean Sciences

Cardiff UniversityDavid Perry at Australian Bureau of Meteorology

Setting the scene

Underpinning experiences:• National Climate Projections for

Australia (2013-2015)

• Hydroclimate projections supporting water supply and demand planning for the state of Victoria (2015-2017)

• ‘Refining Australia's water futures’ funded by the Australian Government's National Environmental Science Programme (2016-2018)

Talking points:

• The hydroclimate projection concept

• Method choices explained

• Common method related challenges

Australian hydroclimate projections

Key differences to climate projections

Often commissioned by a region or state with particular method specifications

(their representation of planning horizon, seasonality representation, their impact model, their criteria GCM skill)

Greater format guidance based on stakeholder needs (essentially a product delivery)

Example: Runoff projections developed as part of the

‘Victorian climate Initiative’

(funded by the Victorian Government, CSIRO and BoM)

Adaptive framework for water resources planning and management in Victoria

LONG-TERM PLANNING

Balancing supply &

demand for the next 50 years with an

appropriate buffer

PLANNING TOOLS:

• Regional Sustainable Water

Strategies

• Water Supply-Demand

Strategies

SHORT-TERM PLANNING

Managing variability in

supply from year to year based on forward

outlooks

PLANNING TOOLS:

• Drought Response Plans

• Emergency Management Plans

• Annual Operating Plans

• Water Conservation &

Efficiency Plans

• Permanent Water Saving Plans

Projection domain

Projections required for all topographic drainage basins in Victoria.

Estimates based on distributed hydrological modelling (5km resolution) across all contributing basins.

6 |

Target periods, baseline and emission scenario

• Use CMIP5 runs for RCP8.5 (high emission future) for scenarios

• Hydrological input data provided by empirical scaling. Scaling factors derived from GCMs and applied to observations; seasonal scaling for PET, daily scaling for rainfall.

• Projections for target dates of 2040 (2031–2050) and 2065 (2056–2075). Change relative to 1986-2005, plus a historical scenario.

• Historical observations 1975–2014, samples annual and decadal variability

7 |

Method choices explained: RCP8.5

For the Australian water sector, low emission scenarios are generally not considered relevant to adaptation planning.

IPCC, Special report Global Warming of 1.5 C

“Human-induced warming reached approximately 1°C (likely between 0.8°C and 1.2°C) above pre-industrial levels in 2017, increasing at 0.2°C (likely between 0.1°C and 0.3°C) per decade (high confidence)”

FIGURE 4.2.1: LINEAR TREND IN AUSTRALIAN MEAN TEMPERATURE FROM THE AUSTRALIAN CLIMATE OBSERVATIONS REFERENCE NETWORK (ACORN-SAT) CALCULATED FOR THE ENTIRE PERIOD 1910 TO 2013 (SOURCE: BOM, 2014A).

Method choices explained: Why GCM ensemble?

Source: Melbourne Water 2016 Large-scale circulation patterns and weather systems that shape Australia’s climate

The Australian climate is much more variable compared to that of UK

Range of runoff results – RCP8.5 @2065

Uncertainty in GCM response a major source of uncertainty (here 42 GCMs)

What about downscaled products?

Median rainfall change [%] in mid-late 20th century following a high-emission scenario.• Finer resolution simulations

look similar to observations, but generally have distributional errors compared to observed data.

• Different downscaling products can give very different regional estimates

• Added value needs to be demonstrated.

[%]

Potter, N.J., Ekström, M., et al., 2018. Change-signal impacts in downscaled data and its influence hydroclimate projections. Journal of Hydrology.

Method choices explained: when history is ‘worse’ compared to future model simulations

•Inability to replicate some of the recent

seasonal changes in

rainfall

•Presence of historical step changes in

Victoria’s climate variables

•Inability to model long

duration droughts

Historical based scenario

Relevant for supply systems that are deemed to be vulnerable to climate variability and climate change, and where recent trends in rainfall and streamflow reductions are more severe than GCM projections would suggest.

Projected changes [%] in annual runoff under RCP8.5 relative to 1986-2005

[%]

Application ready data sets: 42 change factors based on 20 year time periods, applied to 40 year time period.

Persistent method related challenges in producing hydroclimate projections

• Always a trade-off with respect to what uncertainty is better sampled.

• Breaking ‘physical connectivity’ in the water cycle by running hydrological simulations off-line.

• The unfortunate need of bias-correction of model output (necessary until hydrological community is happy with process representation in land surface component of climate models).

GCM and downscaled output (and other relevant science)

Knowledge of plausible regional change

Datasets for application

Sub-setInforms

Needs to be representative of

Provides context for

Key take home message!

Thank you,

Marie Ekström

ekstromm@cardiff.ac.uk

Method choices explained: when history is ‘worse’ compared to future model simulations

• the low confidence in GCMs’ ability to model long duration droughts (and other GCM limitations)

• the inability to replicate some of the recent seasonal changes in autumn rainfall (and the magnitude of cool season rainfall decline)

• the presence of historical step climate changes in Victoria’s climate variables

=> Need to have a scenario that represents change as seen in observational data. Relevantfor supply systems that are deemed to be vulnerable to climate variability and climate change, or where recent trends in rainfall and streamflow reductions are more severe than GCM projections would suggest

Indicator Big River at Jamieson

(wet catchment)

Wimmera River at Eversley

(dry catchment)

Inflow to Melbourne’s four major harvesting

reservoirsAverage annual flow over current climate baseline, July 1975 to June 2015

324 GL 23.6 GL 506 GL

Average annual flow over post-1997 step climate change baseline, July 1997 to June 2015

231 GL 6.1 GL 423 GL

Average annual flow over Millennium Drought, July 1997 to June 2009

194 GL 3.2 GL 376 GL

% change in average annual flow for post-1997 step climate change (relative to current climate baseline)

-29% -74% -16%

% change in average annual flow for Millennium Drought (relative to current climate baseline)

-40% -87% -26%

Projected change in runoff from GCMs at Year 2040 -29% to +10% -32% to +12%

-29% to +10% (Yarra)-28% to +10%

(Thomson)Projected change in runoff from GCMs at Year 2065 -42% to +1% -53% to +12%

-44% to +1% (Yarra)-42% to +2% (Thomson)

Rainfall-runoff modelling

19 |

• 5km gridded data from the Australian Water Availability Project (AWAP): temperature, vapour pressure, radiation combined to give potential evapotranspiration

• Model used: SIMHYD with routing (GR4J also developed with similar results)

• 90 calibration catchments (red) taken from the Australian Water Resources Assessment (AWRA) calibration catchments

• Model parameters in ungauged areas determined by nearest neighbour (grey polygons)

Model evaluation

Model evaluation involve assessing each model's skill on reproducing current climate.

Why?

(1) Helps to understand model’s projected future changes and confidence in them

(2) May lead to exclusion of some models, but not picking best models

What is tested?

Skill of reproducing seasonal climatological averages of rainfall and temperature and mean sea level pressure

Teleconnections (e.g. ENSO)

Large scale processes such as Southern Annual Mode (SAM), etc…

Typical metrics used to assess skill: temporal standard deviation; spatial correlation; root-mean-square-error (rmse).

Model Low

M-S

core

for

PR

and T

AS

Low

ST

RM

SE

Low

score

for

clim

ate

featu

res

Low

EN

SO

– r

ain

fall

tele

connection

No M

onsoon

weste

rlie

s

Wet season r

ain

fall

not

good s

patially

Rain

fall

rela

tionship

to B

lockin

g n

ot

good

IOD

spatial variabili

ty

too low

or

too h

igh

Sim

ple

MJO

skill

not

good

ACCESS1-0 X 1 ACCESS1-3 X X 2 BNU-ESM, X X X 3 CanCM4 X 1 CanESM2 X 1 CCSM4, X 1 CESM1-BGC, X 1 CESM1-WACCM, X X X 3 CMCC-CESM, X 1 CMCC-CMS X 1 CSIRO-Mk3-6-0 X X 2 GFDL-CM3 X 1 GFDL-ESM2G X X X 3 GISS-E2-H, X X X X 4 GISS-E2-H-CC, X X X 3 GISS-E2-R X X X 3 HadCM3 X X 2 HadGEM2-ES X 1 INMCM4 X X X 3 IPSL-CM5A-LR X X X 3 IPSL-CM5A-MR X X X 3 IPSL-CM5B-LR X 1 MIROC-ESM X X X X X 5 MIROC-ESM-CHEM X X X X 4 MPI-ESM-LR X 1 MPI-ESM-MR X 1 MPI-ESM-P X 1 MRI-CGCM3 X 1 NorESM1-M, X 1 NorESM1-ME, X 1

Higher value = scoring low on multiple testsMoise, et al. 2015. Evaluation of CMIP3 and CMIP5 Models … Australian Meteorological and Oceanographic Journal 65, 19-53

Available climate change information

Dataset Emissionscenario

Current climate Future climate Ensemble size

Empirical scaling RCP8.5 1986-2005 2060-2079 42

Nonhomogenous Hidden Markov Model (NHMM)

RCP8.5 1986-2005 2060-2079 19

Statistical analogues (SDM) RCP8.5 1986-2005 2060-2079 22

Conformal Cubic Atmosphere Model (CCAM)

RCP8.5 1986-2005 2060-2079 6

NARCliM projections(using WRF)

SRES A2 1990-2009 2056-2075 12

Actual:- Statistical analogues daily data set currently only available on monthly resolution,

hot homogenised in space or time. Employ annual and seasonal scaling.

Physical plausible change is the credibility we give a method to capture all aspects of a changing climate under climate change.

1) The ability of methods to reflect change in local variable when there is change to the prevailing atmospheric circulation and the chemical and physical state of the atmosphere,

2) There should be no unexplainable bias compared to projected changes in the source material (usually the GCM output) and the downscaled projections should be physically plausible.

Climate realism is the ability of the downscaled data to ‘look’ like observed climate, in terms of: magnitude, distributional qualities, spatial and temporal qualities.

needed for applicability

needed for physical credibility

… and note that climate realism ≠ physical plausible change!

Two characteristics of downscaled projections