Transcript Gameda_CAgM Nov08 CC Scenarios

Climate Change Scenarios for Agriculture
Sam Gameda and Budong Qian
Eastern Cereal and Oilseed Research Centre
Agriculture and Agri-Food Canada
Ottawa, Canada
Objective
• Review some of the climate change scenarios being
developed for agricultural impact and adaptation
assessments
• Present AAFC research on climate variability and
change in a Canadian context
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Climate Change Scenarios
• Range of efforts on developing and using climate
change scenarios
– Global, Regional
• IPCC AR4
• EU, ENSEMBLES (PRUDENCE, STARDEX, MICE)
– National
• US
– Effects of Climate Change on Agriculture, Land Resources, Water Resources and
Biodiversity (2008) Climate Change Science Program
– Climate Change Impacts for the Conterminous US (Climatic Change 2005 (Vol 69))
• UK, Climate Impacts Program
– UKCIP02, UKCIP08
• Developing Countries
– UNDP Climate Change Country Profiles (52 countries)
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Highlights
• ENSEMBLES – Research and Application Function
– Probabilistic estimate of uncertainty in future climate
• Seasonal, decadal, +
– Tool for statistical downscaling
– Regional climate data sets
Work linked to
Evaluation of, and recommendation on, systematic errors in
GCM and RCM modelling
- higher resolution dynamical and/or statistical downscaling to
provide projections and hindcasts
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Highlights
• PRUDENCE
– High resolution climate change scenarios for 2071-2100 for Europe using
regional climate models
– Estimates of variability and level of confidence in the scenarios
• STARDEX
– Intercomparisons of statistical, dynamical and statistical-dynamical
downscaling methods
• Reconstruction of observed extremes
• Construction of scenarios of extremes
• MICE
– Direct use of climate models
• Evaluate capacity of climate models to reproduce observed extremes
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Highlights
• UK Climate Impacts Program
– Scenarios Gateway page
• Guidance on scenario use and development
• Access to maps and datasets
• Canada – Climate Change Scenarios Network (CCSN)
– Network of researchers providing scenarios and advice to the
impacts and adaptation community
– Provision of CRCM output
– On-line automated statistical downscaling tool, based on
SDSM
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General Characteristics
• Global and regional changes in mean values
– Annual, seasonal, (monthly)
• Useful for determining broad changes, e.g.
– Growing season length
– Moisture availability
– Broad vulnerability to pests, disease
• Limitations for determining crop dynamics, pest hazard
cycles
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AAFC Climate Change Scenarios Research
• Background on climate change and scenario
development
• AAFC weather generator
• Findings on agroclimatic indices and extremes
• Links to crop response
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Downscaling
• Output from the nearest GCM grid point used at
times to evaluate impacts of future climate
• However, downscaling is required to construct
realistic regional or local scenarios from GCM
outputs
• There are two main approaches to downscaling dynamical and statistical
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Dynamical approaches
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High-resolution atmosphere-only GCMs
Nested regional climate models (RCMs)
Formulated using physical principles
Computationally expensive
Parameterization schemes for processes at subgrid scales may be operating outside the range
for which they were designed
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Statistical approaches
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Regression-type models.
Weather generators.
Weather classification.
Analogue methods.
Computationally inexpensive; function at finer scales than
dynamical methods; applicable to parameters that cannot be
directly obtained from RCM outputs
• Require observational data at the desired scale for a long
enough period; assume that the derived cross-scale relationships
remain valid in a future climate; cannot effectively accommodate
regional feedbacks and can lack coherency among multiple
climate variables under some approaches.
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AAFC-WG
• An unconditional weather generator
• Richardson-type weather generator
• Precipitation occurrence is simulated by a two
state second order Markov chain
• Precipitation amounts, temperatures and
radiation are simulated by empirical distributions
of the observed data
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AAFC-WG (continued)
• Validated for diverse Canadian climates
• Has been compared with other stochastic
weather generators – LARS-WG
• Evaluated for extreme daily values
• Developed schemes for perturbing weather
generator parameters based on GCM-simulated
changes in the statistics of daily climate
variables
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Climate Change Scenarios Data
Two sets of daily climate scenarios data
• CGCM1 IS92a GHG+A and HadCM3 A2.
• On 0.5°grids for south of 60°N
• For the future time period of 2040-2069
• Values of daily Tmax, Tmin, P and Rad
• Generated by AAFC-WG
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Scenarios data for ecodistricts
• Two data sets for CGCM1 IS92a (GHG+A) and
HadCM3 A2.
• Developed with the “delta” method.
• For the future period of 2040-2069.
• Daily Tmax, Tmin, precipitation and Rad.
• Centroids of ecodistricts where daily weather
data are available at neighbouring stations.
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Some evaluations using the scenarios data
• Agroclimatic indices (e.g. frost-free days, last
frost day in spring and first frost day in fall, GDD,
EGDD, CHU, precipitation deficit)
• Annual and growing-season extreme values of
daily Tmax, Tmin and precipitation, their 10yr,
20yr and 50yr return values
• Relative changes to 1961-1990 baseline climate
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Last Frost in Spring
(2040-2069)
CGCM1
HadCM3
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Last Frost in Spring
(Changes)
CGCM1
HadCM3
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First Frost in Fall
(2040-2069)
CGCM1
HadCM3
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First Frost in Fall
(Changes)
CGCM1
HadCM3
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Frost-Free Days
(Changes)
CGCM1
HadCM3
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Precipitation Deficit/Surplus
(2040-2069)
CGCM1
HadCM3
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Precipitation Deficit/Surplus
(Changes)
CGCM1
HadCM3
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JJA Number of days Tmax≥30˚C
(2040-2069)
CGCM1
HadCM3
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JJA Number of days Tmax≥30˚C
(Changes)
CGCM1
HadCM3
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DJF Number of days Tmin≤-20˚C
(2040-2069)
CGCM1
HadCM3
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DJF Number of days Tmin≤-20˚C
(Changes)
CGCM1
HadCM3
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CHU versus grain corn yields in eastern Canada
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Yield (t ha-1)
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y = 0.00583x - 8.23
R2 = 0.86
P <0.001
10
8
6
4
2200
2400
2600
2800
3000
3200
3400
CHU
Corn yields increase about 0.6 t ha-1 for each increase of 100 CHU
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CHU versus soybeans yields in eastern Canada
4.5
Yield (t ha-1)
4.0
3.5
y = 0.00133x - 0.68
R2 = 0.74
P < 0.001
3.0
2.5
2.0
2200
2600
3000
3400
3800
CHU
Soybean yields increase about 0.13 t ha-1 for each increase of 100 CHU
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Barley yields versus Effective Growing Degree-Days above 5ºC (EGDD)
2 - Row Barley
6 - Row Barley
5.5
6.5
5.0
5.5
-1
Yield (t ha )
-1
Yield (t ha )
6.0
5.0
4.5
4.0
3.5
3.0
1000
y = -0.0016x + 7.57
R2 = 0.26 P < 0.013
1200
1400
4.5
4.0
3.5
1600
EGDD
1800
2000
2200
3.0
1000
y = -0.00098x + 5.84
R2 = 0.16 (NS)
1200
1400
1600
1800
2000
2200
EGDD
Increasing EGDD by 400 units reduces yield of 6-row and 2-row
barley about 0.6 and 0.4 t ha-1, respectively
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Barley yields versus water deficit (PE – P)
6 - Row Barley
2 - Row Barley
6.5
5.5
6.0
Yield (t ha -1)
Yield (t ha -1)
5.0
5.5
5.0
4.5
4.0
2
y = -0.0001x + 0.0253x + 4.19
4.0
y = -0.000145x 2 + 0.0258x + 3.48
3.5
2
3.5
4.5
R = 0.18
2
R = 0.41
3.0
3.0
-20
20
60
100
DEFICIT (mm)
140
180
-20
20
60
100
DEFICIT (mm)
140
180
Average corn yields vs CHU – USA Locations
2i
Yield (t/ha)
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2i
1
5
5
4 4
4 5
10
2i
3 3 1i
5
5
6i
1i
2i
55
5
4 1i
5
4
4
6 3
6i
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1i
6i
6i
6i
6
6
6
3
6
6i
6
1
3
4
2
1 = Illinois
2 = Nebraska
3 = Indiana
4 = Iowa
5 = Ohio
6 = Missouri
i = irrigated
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(based on average yield
of top 10 hybrids in field
trials, 4 to 8 yrs data,
1994-2001)
6
2
2
6
3000
3500
4000
CHU
Agriculture and
Agri-Food Canada
4500
5000
Corn yields vs Water Deficits – USA Locations
Yield (t/ha)
14
3
1
15
5
6
3
5
1
4
3
10
3
6
5
1 = Illinois
2 = Nebraska
3 = Indiana
4 = Iowa
5 = Ohio
6 = Missouri
6
4 4
4
4 6 56 6
5
3
4
6
5
5
2
5
5
(based on average
yield of top 10 hybrids
in field trials, 4 to 8 yrs
data, 1994-2001)
4
6
2
2
6
100
150
200
250
Water Deficit (mm)
Agriculture and
Agri-Food Canada
300
350
Planned scenarios
• New data sets for CGCM3 (A2, A1B, B1) and
HadCM3 (A1B, B1)
• For the future period of 2040-2069
• Gridded and/or ecodistrict scales
• Continuous 2000-2099 data for a range of
stations
• Daily Tmax, Tmin, precipitation and Rad.
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Summary
 There is a need for a shift from scenarios based on annual, seasonal, or monthly
climate values, to daily ones for agricultural impact assessments.
 AAFC-WG was suitable for the development of daily climate scenarios, and scenarios
of extremes.
 Earlier last frost in spring and later first frost in fall with a longer growing season are
projected.
 There would be an increase in crop heat units under climate change.
 Larger precipitation deficits can be expected, especially in the Prairies.
 An increase in extremely hot days in summer is foreseen.
 Increased crop heat units will likely result in increased production of corn and
soybeans, but decreased barley yields.
 Crop response may be more sensitive to extremes. We will be carrying out more
studies on the impacts of climate extremes. We will make use of crop modelling for this
purpose.
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THANK YOU!