Climate forecasting advances and agricultural risk management in Australia

Prof Roger Stone. International Centre for Applied Climate Science ResearchUniversity of Southern Queensland, Toowoomba, Australia.

Meat and Livestock Australia

International Conference on Promoting Weather and Climate Information for Agriculture and Food Security. Antalya, Turkey 7-9 April, 2014.

Key points:

•High levels of Climate variability in Australia.

•Main ‘drivers’ (causes) of this variability?

•Seasonal climate forecasting developments – new generation of GCMs.

•Seasonal forecast model “verification” - skill assessment of independent forecasts, as issued operationally or quasi-operationally.

•Critical links to agricultural applications..but when is it best to apply seasonal forecasting outputs?

•The issue of using seasonal forecasting (‘switching them on’) for agricultural applications only during periods of known/likely high skill – eg core ENSO periods.

•Some examples!

Climate issues dominate - Australia has the world’s highest levels of year to year climate variability – which is increasing!…

Variability of Annual rainfall

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Australia S. Africa Germany France NZ India UK Canada China USA Russia

Country

Coe

ffici

ent (

%)

(100 years of data for Australia and generally also for the other countries)

(Love, 2005)

– especially in northern and eastern Australia.

The main contributor? Conditions in the Tropical Pacific Ocean (example from October 1988)

December 1991 – (El Niño event)

Need to consider other important ‘drivers’ - eg the latitude of the sub-tropical ridge (Pittock, 1975)

Correlation: SOI with rainfall Correlation L STR with rainfall

Decision type (eg. only) Frequency (year)

Logistics (eg. scheduling of planting / harvest operations)

Intraseasonal (>0.2) (MJO)

Tactical crop management (eg. fertiliser/pesticide use)

Intraseasonal (0.2-0.5)

Crop type (eg. wheat or chickpeas); irrigation planning; irrigation scheduling

Seasonal (0.5-1.0) (ENSO)

Crop sequence (eg. long or short fallows) Interannual (0.5-2.0)

Crop rotation (eg. winter or summer crop) Annual/biennial (1-2 (QBO))

Crop industry (eg. grain or cotton, phase farming) Decadal (~10)(STR)

Agricultural industry (eg. crop or pasture) Interdecadal (10-20)

Landuse (eg. Agriculture or natural system) Multidecadal (20+)

Landuse and adaptation of current systems Climate change

Agricultural/water resource systems operate on many time scales – links to key climate drivers = opportunities for preparedness

(Meinke and Stone, 2005).

Queensland state output – focussing on specific regions – and linking to targeted requirements –pasture growth forecasts include antecedent soil moisture conditions and pasture growth modelling.

Commonwealth Bureau of Meteorology output – standard output

Operational climate services – some very useful examples

Australian BoM POAMA excellent operational output: example

SOI phase-based output (Queensland Government and to fit agricultural applications.

Opportunities for global assessments (eg value in commodity trading)..

(Stone et el, Nature, 1996).

Or to provide the capability of statistical systems ‘downscaled’ to a location.

The Value of Forecast skill assessment: – Real time hit rate verifications (courtesy BoM) – forecasts of statistical seasonal rainfall across all years studied.

BoM forecast model (2c)JJA 2000 – JAS 2005

WLD SST phase schemeJFM 2000 – JAS 2005

SOI analogues schemeJFM 2001 – JAS 2005

SOI phase schemeSON 1997 – JAS 2005

3-cat. (+5/-5) SOI strat. scheme SON 1997/JAS 2005

Temporal skill: verification-skill assessment over time (SOIP) Queensland (courtesy BoM, 2011)

SOI-based forecasts assessed over time - Queensland

(NCC/BoM, 2011)SOI-based forecasts over time - NSW

‘Per cent consistent /correct’ skill assessment over time (SOIP) for Queensland – courtesy: BoM,2011. Circled periods are mostly those coinciding with an El Niño or La Niña event.

SOI-based forecasts over time: Queensland

Skill in seasonal climate forecasting for GCMs? When best to apply these systems? Comparison of a number of general circulation models -including POAMA 1.5 and 2.4 - and their forecast skill over Australia: 1980-2005.

(Langford and Hendon, 2013)

POAMA - MAM above median rainfall: zero lag hit rates POAMA - JJA above median rainfall: zero lag hit rates

POAMA - SON above median rainfall: zero lag hit ratesPOAMA - DJF above median rainfall: zero lag hit rates

Accuracy score for above median rainfall forecast (fraction of hits and correct negatives compared to total forecasts) for P2.4).

GCM temporal skill assessment – pastoral regions verification – all GCMs – Queensland (USQ, 2014).

%

NSW/VIC region forecast verification – all GCMs – zero/one month lag (USQ, 2014)

%

NSW VIC region 3-month lag verification (USQ, 2014).

%

Queensland region – now compare GCM POAMA with SOI-based statistical system

NSW VIC GCM/POAMA compared with SOI-based system (USQ, 2014)

The key value of long-lead times provided by GCMs (eg 2010/11) (ECMWF )

Note: these types of forecasts can be made very early in the season –August to October example from 2010 issued in April, 2010.

What about extreme rainfall? Probability of being in the ‘upper quintile’.. Example for 2010/11

Key value – linkages between key climate indicators and crop yields:

Mean /std production levels associated with ENSO – example for sorghum and wheat /Australia (Hansen and Stone, USQ, 2012)

Also applies to other regions Mean/std Corn production RSA

and Palm Kernels (global) associated with ENSO (Hansen

and Stone, 2012)

WA

NT

SA

NSW

VIC

TAS

Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data

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#WA

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Roma

Dalby

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Goondi windi

Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data

(a) (b)

Links to trading decisions Decisions being made by grain exporting authority: forecasting agricultural commodities: Use of the larger spatial scale model ‐ ‘OzWheat ’ ‐ to produce pprobabilistic of exceeding long‐term median wheat yields for every wheat producing district in Australia issued in July 2001 and July 2002, respectively  ‐ (2002 was an ‘El Niño year’) (Potgieter, 2010).

July 2001 July 2002

Local scale issues - relationship between annual variation in the SOI and annual Moree Plains wheat yield (Stone and Donald, 2007) – the key is the need to modify actions ahead of impacts. - links to innovative insurance systems..

“Climate forecast information has no value unless it changes a management decision”

Utilising climate forecasts in decision making (Hammer, 2000).

‘How much Nitrogen to apply given current low soil moisture

levels and low probability of sufficient in-crop rainfall?

“Deciding which variety to plant given low rainfall probability

values and high risk of damaging frost at anthesis?

Understand decisions across the value chain

Understanding climate related issues across the whole value chain

The CanePlant

Sugarcane Production

Harvest & Transport

Raw Sugar Milling

Marketing & Shipping

• Best use of scarce/costlywater resources

• Better decisions onfarm operations

• Improved planningfor wet weather

disruption• Best cane supply

arrangements- crush start and

finish times

• Better schedulingof mill operations- crop estimates- early seasoncane supply

• Better marketing decisions basedon likely sugar quality

• More effective forward sellingbased on likely crop size

• Improved efficiency of sugarshipments based on supplypattern during harvest season

Y.L. Everingham, R.C. Muchow, R.C. Stone,N.G. Inman-Bamber, A. Singels, C.N. Bezuidenhout (2002)

Further examples of applications: 20th Century Reanalysis has provided improved long-term assessment of the return periods and trends of the

Accumulated Heat Load Index (AHLI).

• Utilising the 20th Century Reanalysis Process, the AHLI has now been calculated from the 100 year reanalysis data – results shows that in the Darling Downs region the AHLI has reached extreme risk levels on six occasions in 100 years – and the number of high or extreme values appears to be increasing. 

•The ALHI also used to calculate recovery times from excessive heat load (EHL). (The best recovery from heat load occurs when the HLI below 74 between four to six hours during the night. ‐ longer periods may be needed if exposure to EHL has been prolonged). 

ALHI/ALHU for the Darling Downs region,Australia

Applications for seasonal climate forecasting of the extremes and key components of heat stress –this offers the capability for improved preparedness for climate extremes: the ability to forecast extreme levels for the coming season of Maximum Temperatures:

Example: describing the risk of Maximum Temp being in the highest 20% of possible values for core summer period following earlier onset of an El Niño period (Stone and Marcussen, 2012).

In this example, note the regions shaded dark orange/ red which have greatly increased risk of excessive maximum temperatures with this pattern.

The ability to forecast extremely high levels of Minimum Temperatures for the ensuing season/three months:

Example: describing the risk of Minimum Temp being in the highest 20% of possible values for the core summer period following earlier onset of an El Niño period (Stone and Marcussen, 2012).

In this example, note the regions shaded dark orange / red which have greatly increased risk of excessive maximum temperatures with this pattern.

The ability to forecast extreme levels of high Relative Humidity for the ensuing season/three months:

Example: describing the risk of RH% being in the highest 20% of possible values for core summer period following earlier onset of a La Niña period (Stone and Marcussen, 2012).

In this example, note the regions shaded dark orange/red which have greatly increased risk of excessive RH% in this pattern.

Combining all elements ‐ Forecasting Accumulated Heat Load Units for the coming season using key climate systems – El Niño and La Niña patterns are critical.

Number of excessive heat load units exceeding critical thresholds according to ENSO types – example for St George, southern inland Queensland.

‘Warm episodes’ = El Niño pattern in the Pacific Ocean at end of December – mean number of heat load units Jan to March=28.

‘Cool episodes’ = La Niña pattern in the Pacific Ocean at end of December - mean number of heat units Jan to March=19.

Summary and conclusions.•Seasonal climate forecasting can provide high value to agricultural decision‐making in Australia (and elsewhere) ‐ key aspect to link seasonal climate forecasting to important agricultural management decisions.

•Temporal skill assessment suggests statistical seasonal climate forecasting systems (eg SOI‐based) provide useful skill, especially during ENSO episodes – periods with a strong signal.  Key aspect is to show and explain this result to users.

•Analysis of statistical forecast systems (eg SOI-based) suggest highest skill has generally been obtained in core ENSO periods, especially for eastern Australia: but that knowledge of skill periods can be incredibly valuable to users.

•GCMs offer potential to capture climate change effects and other less quantifiable systems (eg QBO) and their impact on rainfall and temperature, crop/pasture growth, key valueappears to be provided in ENSO periods through provision of enhanced lead times. 

•GCM analysis: GCMs appear to mostly ‘track one another’ ‐ provide equivalent skill ‐ (POAMA may do better the southern/western region of Australia).  However, statistical seasonal forecasts (based on SOI) are still providing useful skill and should not be discarded.  

•Question: Do we ‘switch‐on’ seasonal forecasts only in ENSO/strong signal periods/years? ‐does this rule apply in other world regions? Needs continued comprehensive analysis over longer time periods.  

THANK YOU