CCAFS Date: 10/10/2012 Place: CIAT

1. Cali, Colombia CIAT
10/10/2012
Carlos Navarro, Julian Ramirez,
Andy Jarvis, Peter Laderach

2. Contents
• Brief about climate
& agricultre
• Climate
data, availability,
difficulties, options
• Our databases

3. We know…
• Any agroecosystem respond to changes of
anthropogenic factors, biotics, abiotics.
• Weather and climate predictability is fairly limited.
• The climate will change.
• Each system is an specific case.
• Crops are very sensitive to climatic conditions

4. Climate & Agriculture
– Multiple variables
– Very high spatial
–T°
• Max,
resolution • Min,
Less importance
More certainty
• Mean
– Mid-high temporal (i.e.
monthly, daily)
resolution –Prec
– Accurate weather
forecasts and climate – HR
projections – Radiation
– High certainty – Wind
• Both for present and – …….
future

5. We don’t know… What are the conditions in 30, 50,
100 years?
• How our system
respond to these
conditions?
• When, where and
what type of change
requiere to adapt?
• Who should plan?
>> UNCERTAINTIES
Who should leads
the process ? Who
should run?

6. Needs Limitations

7. Emission Scenarios
Pessimistic
Intermediate
“Bussiness as usual”
P
Economic P
E
E
Global Regional
P P
E E
Perfect World Environmental Optimistic

8. In agriculture, the
different emission
scenarios are not
important ... by
2030 the difference
between the
scenarios is
minimal

9. GCM “Global Climate Model”
GCMs are the only way
Using the past to learn
we can predict the future
for the future
climate

10. What are saying the models?
Atmospheric concentrations
Variations of the Earth’s surface
temperature: 1000 to 2100
Anthropogenic changes lead to changes in weather

11. Resolutions
• Horizontal
resolution 100 to
300 km
• 18 and 56 vertical
levels
Global scale 
Regional or local scale 

12. Model Country Atmosphere Ocean
BCCR-BCM2.0 Norway T63, L31 1.5x0.5, L35
CCCMA-CGCM3.1 (T47) Canada T47 (3.75x3.75), L31 1.85x1.85, L29
CCCMA-CGCM3.1 (T63) Canada T63 (2.8x2.8), L31 1.4x0.94, L29
CNRM-CM3 France T63 (2.8x2.8), L45 1.875x(0.5-2), L31
CSIRO-Mk3.0 Australia T63, L18 1.875x0.84, L31
CSIRO-Mk3.5 Australia T63, L18 1.875x0.84, L31
GFDL-CM2.0 USA 2.5x2.0, L24 1.0x(1/3-1), L50
GFDL-CM2.1 USA 2.5x2.0, L24 1.0x(1/3-1), L50
GISS-AOM USA 4x3, L12 4x3, L16
GISS-MODEL-EH USA 5x4, L20 5x4, L13
GISS-MODEL-ER USA 5x4, L20 5x4, L13
IAP-FGOALS1.0-G China 2.8x2.8, L26 1x1, L16
INGV-ECHAM4 Italy T42, L19 2x(0.5-2), L31
INM-CM3.0 Russia 5x4, L21 2.5x2, L33
IPSL-CM4 France 2.5x3.75, L19 2x(1-2), L30
MIROC3.2-HIRES Japan T106, L56 0.28x0.19, L47
MIROC3.2-MEDRES Japan T42, L20 1.4x(0.5-1.4), L43
MIUB-ECHO-G Germany/Korea T30, L19 T42, L20
MPI-ECHAM5 Germany T63, L32 1x1, L41
MRI-CGCM2.3.2A Japan T42, L30 2.5x(0.5-2.0)
NCAR-CCSM3.0 USA T85L26, 1.4x1.4 1x(0.27-1), L40
NCAR-PCM1 USA T42 (2.8x2.8), L18 1x(0.27-1), L40
UKMO-HADCM3 UK 3.75x2.5, L19 1.25x1.25, L20
UKMO-HADGEM1 UK 1.875x1.25, L38 1.25x1.25, L20
Uncertainties!

13. Difficulty 1. They differ on resolution

14. • Difficulty 2. They differ in availability (via IPCC)
WCRP CMIP3 A1B-P A1B-T A1B-Tx A1B-Tn A2-P A2-T A2-Tx A2-Tn B1-P B1-T B1-Tx B1-Tn
BCCR-BCM2.0 OK OK OK OK OK OK OK OK OK OK OK OK
CCCMA-CGCM3.1-T63 OK OK NO NO NO NO NO NO OK OK NO NO
CCCMA-CGCM3.1-T47 OK OK NO NO OK OK NO NO OK OK NO NO
CNRM-CM3 OK OK NO NO OK OK NO NO OK OK NO NO
CSIRO-MK3.0 OK OK OK OK OK OK OK OK OK OK OK OK
CSIRO-MK3.5 OK OK OK OK OK OK OK OK OK OK OK OK
GFDL-CM2.0 OK OK OK OK OK OK OK OK OK OK OK OK
GFDL-CM2.1 OK OK OK OK OK OK OK OK OK OK OK OK
GISS-AOM OK OK OK OK NO NO NO NO OK OK OK OK
GISS-MODEL-EH OK OK NO NO NO NO NO NO NO NO NO NO
GISS-MODEL-ER OK OK NO NO OK OK NO NO OK OK NO NO
IAP-FGOALS1.0-G OK OK NO NO NO NO NO NO OK OK NO NO
INGV-ECHAM4 OK OK NO NO OK OK NO NO NO NO NO NO
INM-CM3.0 OK OK OK OK OK OK OK OK OK OK OK OK
IPSL-CM4 OK OK NO NO OK OK NO NO OK OK NO NO
MIROC3.2.3-HIRES OK OK OK OK NO NO NO NO OK OK OK OK
MIROC3.2.3-MEDRES OK OK OK OK OK OK OK OK OK OK OK OK
MIUB-ECHO-G OK OK NO NO OK OK NO NO OK OK NO NO
MPI-ECHAM5 OK OK NO NO OK OK NO NO OK OK NO NO
MRI-CGCM2.3.2A OK OK NO NO OK OK NO NO OK OK NO NO
NCAR-CCSM3.0 OK OK OK OK OK OK OK OK OK OK OK OK
NCAR-PCM1 OK OK OK OK OK OK OK OK OK OK OK OK
UKMO-HADCM3 OK OK NO NO OK OK NO NO OK OK NO NO
UKMO-HADGEM1 OK OK NO NO OK OK NO NO NO NO NO NO

15. Difficulty 3. Limited ability to represent present
climates
Relying on a single GCM is dangerous!

16. How I can use this information?
Options
Downscaling by
Needs statistical or
To increase resolution,
dynamical
uniformise, provide
methods..
Problem high resolution and
Even the most contextualised data
precise GCM is
too coarse
(~100km)

17. The Delta Method
• Use anomalies and discard baselines
in GCMs
– Climate baseline: WorldClim
– Used in the majority of studies
– Takes original GCM timeseries
– Calculates averages over a baseline and
future periods (i.e. 2020s, 2050s)
– Compute anomalies
– Spline interpolation of anomalies
– Sum anomalies to WorldClim

19. Stations by
variable:
• 47,554
precipitation
Mean annual
temperature (ºC) • 24,542
-30.1
tmean
30.5
• 14,835
tmax y tmin
Sources:
•GHCN
•FAOCLIM
Annual •WMO
precipitation (mm)
0 •CIAT
•R-Hydronet
12084
•Redes nacionales

20. RCM PRECIS Providing REgional Climates for
– They use outputs of Impacts Studies
GCMs
– Area are limited .. Need
boundary conditions.
– Performs calculations
of atmospheric
dynamics and solve
equations for each grid.
– Daily data
– Resolution varies between 25-
50km
– More than 170 output variables

21. Method + -
*Easy to implement * Change variable only at big scale
Statistical *  resolutions * Variables do not change their relations
downscaling *Apply to all GCMs with time
*Uniforme baseline *  variables
*Few platforms (PRECIS, CORDEX)
* Robust
*Many processes and stockages
Dynamic *Apply to GCMs if data
*Limited resolution (25-50km)
downscaling available
*Missing development
* variables
*Dificulty to quantify uncertainties

22. We need models to quantify the impacts and
adaptation options for effective design
Based on process
GCMs
Statistical Downscaling
MarkSim
Dynamical downscaling:
Regional Climate Model
Based on niches DSSAT
Probability
EcoCrop
Statistical
Downscaling
Environmental gradient Effective
MaxEnt adaptation
options

23. Changes in climate affect the adaptability of crops…
There will be
winners…
Number of crops with more than 5% gain
…But much
more losers in
developing
countries
Number of crops with more than 5% loss

24. http://ccafs-climate.org

26. • Downscaling is inevitable.
• Continuous improvements are
being done
• Strong focus on uncertainty
analysis and improvement of
baseline data
• We need multiple approaches to improve the
information base on climate change scenarios
 Development of RCMs (multiple: PRECIS not enough)
 Downscaling empirical, methods Hybrids
 We tested different methodologies