Transcript Meinke - Brohan.org

Models as bio-indicators – why we need good
climate records
Holger Meinke and many other colleagues
CWE, Plant Sciences Group, Wageningen University, Netherlands
APSRU, Australia
Converting the vagrancies of weather and climate
into real options for risk management …
… is a long and treacherous task
that requires science without
disciplinary dominance, good
partnerships and lots of patients.
Uncertainty = randomness
with unknowable probabilities
Risk = randomness with
knowable probabilities
Real Options = values flexible
& adaptive mgt in response to
uncertainty (in contrast to
discounted cash flow
analysis)
Knight, F.H., 1921
Background and Motivation
•
•
The 20th century was the century of
analysis based on new discoveries and
exploring biological systems in ever
increasing detail (from discovery of DNA
to mapping of the human genome),
creating new disciplines in the process
The 21st century is rapidly becoming the
century of synthesis with much greater
emphasis on holistic approaches and
creating new insights at the interfaces of
disciplines (transdisciplinary)
Analysis
Climate
factors
(eg rainfall,
MSLP, SST)
Action
Impacts
Impacts
Integration
and
with other
adaptation issues –
policy, risk
management
Climate risk
mgt
mainstreamed,
increased
‘climate
knowledge’
Two sides of the same coin
Adaptive Capacity
Probability density
Vulnerability
Climatic outcome (e.g., rainfall, production)
Without adaptation
With adaptation
Source: IRI
Example of an application
•
Simulation models are ideal diagnostic tool for
identifying broadly based seasonal to annual climatic
drivers
•
Hence, we used simulated wheat yield as an integrative
quality of rainfall over a long period
•
Contrary to traditional climatic analyses we started with
impacts to investigate processes (model as a bioindicator)
Potgieter, A.B, Hammer, G.L., Meinke, H., Stone, R.C. and Goddard, L., 2005.
Spatial variability in impact on Australian wheat yield reveals three putative
types of El Niño. Journal of Climate, 18: 1566-1574.
Probability of exceeding longterm median wheat yields in El
Niño years for every wheat-producing district in Australia
simulated with a process-based model that uses daily
temperature and rainfall records as input and has ‘memory’ at
a range of time scales.
Legend:
0-10%
10-20%
20-30%
30-40%
40-50%
50-60%
60-70%
70-80%
80-90%
90-100%
No data
Now conduct a
multivariate
NT analysis
on these simulated
district wheat yields to
reveal spatial impact
WA
patterns
#
Emerald
Rom a
#
Dalby
#
Goondi wi ndi
#
SA
NSW
VIC
Dual purpose application
1.0
1946
1997
1941
1972
1977
1925
1993
1969
1992
1951
1991
1987
1953
1911
1905
1982
1914
1994
1965
1957
1919
2002
1940
1902
Linkage Distance
Multivariate Analysis
3.5
3.0
2.5
2.0
1.5
Footprint 1
Group 2
Footprint 2
Group 1
Footprint 3
Group 3
0.5
0.0
Average standardised shire
wheat yield relative to all years
Legend :
< -2
-2 - -1
-1 - -0.5
-0.5 - 0.5
0.5 - 1
1-2
>2
NT
QLD
WA
SA
NSW
Legend :
< -2
-2 - -1
-1 - -0.5
-0.5 - 0.5
0.5 - 1
1-2
>2
VIC
NT
Footprint 1
TAS
QLD
(6 years)
WA
SA
NSW
Legend :
< -2
-2 - -1
-1 - -0.5
-0.5 - 0.5
0.5 - 1
1-2
>2
VIC
Footprint 2
NT
TAS
(9 years)
QLD
WA
SA
NSW
Footprint 3
VIC
9 years
TAS
Legend :
< -2
-2 - -1
-1 - -0.5
-0.5 - 0.5
0.5 - 1
1-2
>2
NT
Average standardised shire
rainfall relative to all years
QLD
WA
SA
NSW
Legend :
< -2
-2 - -1
-1 - -0.5
-0.5 - 0.5
0.5 - 1
1-2
>2
VIC
NT
Footprint 1
TAS
QLD
(6 years)
WA
SA
NSW
Footprint 2
Legend :
< -2
-2 - -1
-1 - -0.5
-0.5 - 0.5
0.5 - 1
1-2
>2
VIC
NT
TAS
(9 years)
QLD
WA
SA
NSW
Footprint 3
VIC
9 years
TAS
FP1
FP2
FP3
FP1
FP2
FP3
SPCZ
Footprints of El Niño
• The evolution of the EN events (timing of onset and locations of
the major SST and MSLP anomalies) is highly variable but can
be clustered into 3 distinct groups.
• Over Australia MSLP anomalies were shifted southward affecting
the latitude of the sub-tropical ridge, which has significant
association with rainfall variability.
• The shift in tilt of the line of separation between warm and cool
SST pools from years in FP1 and FP2 to those in FP3 suggests
linkage to the location of the South Pacific Convergence Zone,
which has significant association with decadal variability in
rainfall.
Results
 The results indicate that climate system drivers that are responsible
for variation in EN events and decadal rainfall patterns.
 The associations suggest that variability in impact is most likely
forced by differences in the temporal evolution and spatial extent of
the ocean/atmosphere patterns. Features outside the tropical
Pacific are also contributing.
 Plausible mechanisms need to be investigated further by linking of
crop models to GCM (lead to better targeted predictive agricultural
systems).
We need good, longterm, (daily) surface
records …
… but NOT to analyse in ever greater detail
quantities that decision makers have no
control over (e.g. rainfall).
We really need them as input into biophysical
decision models that provide REAL OPTIONS
for decision making.
Towards ‘Real Options’ for policy:
Exposure vs Coping Capacity
Biophysical
Multiple dimensions
high vulnerability
moderate vulnerability
low vulnerability
Meinke et al., 2006. Actionable climate knowledge – from
analysis to synthesis. Climate Research, 33: 101-110.
What usually happens - A case of market
failure
Local,
longterm
weather /
climate data
Dynamic climate
model
Sector
specific
demand for
climaterelated
forecasts and
risk
assessments
Supply of
climate
variables in
ever
increasing
detail
Bio-physical model
Local,
longterm
weather /
climate data
Dynamic climate
model
Sector specific
demand for
climate-related
forecasts and
risk
assessments
Spatialtemporal
impact
on BP
quantity
Statistical model
categorising
impacts (PC or
pattern analysis)
Global patterns
of fundamental
climate drivers
Statistical model
clustering global
climate indicators
Spatialtemporally
coherent
impacts
Bio-physical model
Local,
longterm
weather /
climate data
Dynamic climate
model
Adaptive
capacity
creation via
provision of
REAL
OPTIONS
Spatialtemporal
impact
on BP
quantity
Statistical model
categorising
impacts (PC or
pattern analysis)
Global patterns
of fundamental
climate drivers
Statistical model
clustering global
climate indicators
Spatialtemporally
coherent
impacts
Beyond Impact - the business case for Action





Adaptation and preparedness have emerged as THE biggest issues
for a post-Kyoto world – not many know how to do it.
The excellent SUPPLY of scientific information and insights will
remain without impact (diffuse and unfocused) in the absence of
clear user DEMAND for climate services.
Policy makers AND practitioners need access to relevant information
for informed discussions or debates.
We need to become more transdisciplinary and problem oriented in
our approaches to science – without disciplinary dominance.
We need good modelling tools for all sectors, not just climate.
References
Howden et al. 2007. Adapting agriculture to climate change. PNAS, 104(5), 19691–19696
Lo et al. 2007. Probabilistic forecasts of the onset of the North Australian wet season. MWR,
135, 3506-3520.
Maia et al. 2007. Inferential, non-parametric statistics to assess quality of probabilistic
forecast systems. MWR, 135, 351-362.
Meinke et al. 2006. Actionable climate knowledge – from analysis to synthesis. Climate
Research, 33: 101-110. Open access at http://www.int-res.com/articles/cr_oa/c033p101.pdf .
Meinke, H. et al. 2007. Climate predictions for better agricultural risk management. Aust. J.
Agric. Res., 58, 935-938.
Nelson, R. et al. 2007. From rainfall to farm incomes - transforming policy advice for
managing climate risk in Australia. Aust. J. Agric. Res., 58, 1004-1012.
Potgieter, A.B, Hammer, G.L., Meinke, H., Stone, R.C. and Goddard, L., 2005. Spatial variability
in impact on Australian wheat yield reveals three putative types of El Niño. Journal of
Climate, 18: 1566-1574.
Methods
•
•
ENSO classification – combined ocean (Niñ0 3.4 SST Trenberth, 1997) and atmosphere (SOI - Ropelewski & Jones , 1987)
classification.
Multivariate analysis on simulated shire wheat yields
(Potgieter et al. , 2002)
•
•
Align standardised weighted wheat yields with
standardised shire rainfall
Mapped 3-monthly SST & MSLP (Smith & Reynolds, 2003)
Year SOI SST Year SOI SST Year SOI SST
Using the SST time series for Niño
3.4, a year was classified as EN if
the 5-month running mean was ≥
0.5 for six or more months
between April and December
(Trenberth, 1997).
Using the SOI time series, a year
was classified as EN if the 3month running mean was ≤ –5.5
for six or more months between
April and December (Ropelewski
and Jones, 1987).
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