Dr. Andrew Watkins (abw@bom.gov.au)MCV Climate Week 17 November 2015

Improved Seasonal Forecast Service

Climate Outlooks now and in the future

The Bureau's Climate Prediction service

• ENSO Wrap Up• ENSO Tracker• Model Summary• Weekly Tropical Climate Note• Tropical Cyclone outlook• Northern Rainfall Onset• Seasonal Outlooks• Videos• Briefings/engagement/ministerials/high level advice

• 5% of GDP ($58B) exposed to annual climate variability • Bureau (and climate science can influence Australia’s ability to respond

effectively • Potential value of climate forecasts is >$1.5B• Departures from “normal” are increasing due to global warming

The Bureau's Climate Outlook service

1996

The Climate Outlook service – Seasonal Outlookhttp://www.bom.gov.au/climate/outlooks/ 1989-now

1989 2015

The Climate Outlook service – Seasonal Outlookhttp://www.bom.gov.au/climate/outlooks/

• Temperature and rainfall• Can add more variables • Model is reliable• Engaging and intuitive• Large user base• Operationally supported• State of the art/science

Skill of the model – seasonal (rainfall)Lead time 10 days [Multiple lead times possible 0-50days, 5 day increments]

Hindcast Forecast

Skill of the model – monthly (rainfall)Lead time 10 days [Multiple lead times possible 0-50days, 5 day increments]

Hindcast Forecast

Skill of the model – fortnightly (rainfall)Lead time 0 days

Hindcast Forecast

Skill of the model – seasonal (rainfall) vs statisticalLead time 10 days [Multiple lead times possible 0-50days, 5 day increments]

System Period BSS REL RES PC

Statistical 1981-2010 0.3% 0.0028 0.0028 51.7% (45.4,58.1)

POAMA-lagged (10 day) 1981-2010 5.2% 0.0015 0.014 54.0% (53.0,69.2)

Statistical 2000-2011 2.7% 0.0022 0.0078 58.3% (47.8,68.7)

POAMA-lagged (10 day) 2000-2011 5.0% 0.0049 0.017 64.0% (47.5,78.7)

Statistical 1950-1979 1.3% 0.00035 0.00338 51.2% (45.5-56.2)

Statistical 1950-1999 0.55% 0.00095 0.00228 50.3% (45.9-54.8)

Statistical 1980-1999 -0.77% 0.00385 0.00173 49.1% (41.9-56.7)

Charles et al., (2015)

Areas for improvement…

• Coarse 250km grid resolution• Limited compatibility with decision support models• No explicit climate change signal• Gap between days and months• Skill remains modest• Model differs from weather model

SEASONAL OUTLOOKSBETTER

Finermodel detail

More outlook periods

Higher outlookskill

World classservice

Bigger userreturns

Moving from 250 km to 60 km

resolution

meaningmore localised information

by accounting for local conditions

Australia:120 to2000 grid points

Seamless: filling the gap between

7-day and monthly outlooks

Outlooks updated weekly

Season MonthFortnightWeek

Likely 10% improvement in outlook accuracy

meaningthe best outlooks for Australia

of all international models

meaninginformation is clear, concise and

available when and where you need it+ Not only rainfall and temperature

More intelligence possible:• Evaporation• Humidity• Wind

• Drought• Extremes• Tropical Cyclones

Reduce losses: agricultural production lost from 2010-11 La Niña:

More than $2 billion

Potential value of improved seasonal forecasts:

More than $1 billion per yearABARES Centre for International Economics 2014

Improved resolution

POAMA-2 ACCESS-S

Australian topography

• Resolution improves from 250 km to 60 km

• Resolves the Great Dividing Range, Tasmania, WA Darling Ranges, Pilbara (Tom Price)

Improved resolution• Resolution improves from 250 km to 60 km

• Resolves the Great Dividing Range, Tasmania, WA Darling Ranges, Pilbara (Tom Price)

Met

ress

Better model climate• Able to provide more realistic climate patterns

• Link to decision support tools (e.g., fire models, crop models etc)

August Mean Rainfall

POAMA-2 Observations ACCESS-S

mm/dayy

Better model climate• Able to provide more realistic climate patterns

• Link to decision support tools (e.g., fire models, crop models etc)

August Mean RainfallPOAMA-2 Observations ACCESS-S

mm/day

Better model climate• Able to provide more realistic weather sequences/climate patterns

• Link to decision support tools (e.g., fire models, crop models etc)

mm/day

More accurate outlooks

• Early testing shows improved accuracy for rainfall

• Better predictions of El Niño / La NiñaNINO3.4: all start months

El Niño forecast accuracyRainfall forecast accuracy

New model

Heat extremes case studies

September 2013

Monthly temperature forecast

Source: ABC online

Decision support tools

Where to next?

• 2015: Obtain feedback on current service and priorities for improvement

→ Develop service solutions

• 2016: Test deployment of new model

• 2017: First deployment of new outlook service (including multi-week)

• 2018: Further upgrade to model (physics, initial conditions)

• 2019: New outlooks service deployed

Thank you

Questions?