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CIAT’s experience in climate modeling; Scenarios of future climate change
Carlos Navarro, Julián Ramírez, Andy Jarvis
IntroClimate Data
Why do you need?Who needs them?Disadvantages?
ProblemsLimited knowledgeComplexity of the climate systemUnsuitable model resolutionsData provide fine-scale future climateUncertainties
• Any agroecosystem respond to changes of:
– Anthropogenic factors (socials),
– biotics (pest, diseases)– abiotics (weather, soilss)
• Weather and climate predictability is fairly limited.
• The climate will change.• Each system is an specific case.
We know..
• What are the conditions in 30, 50, 100 años?
We don’t know
• How our system respond to these conditions?
• When, where and what type of change requiere to adapt?
• Who should plan? Who should leads the process ? Who should run?
>> UNCERTAINTIES
• Agriculture demands:– Multiple variables– Very high spatial resolution– Mid-high temporal (i.e.
monthly, daily) resolution– Accurate weather forecasts
and climate projections– High certainty
• Both for present and future
Climate & Agriculture
Predicting impacts of climate change
Emissions Scenarios from population, energy, economics models
Concentrations Carbon cycle and chemistry models
Global Climate Change Global Climate models
Regional Detail Regional climate models
Impacts Impact models
Emission scenariosEconomic
Environmental
Global Regional
PESSIMIST
OPTIMISTAlmost Unreal
Emissions scenarios are plausible representations of future emissions of substances that are radiatively active (Jones 2004)
Prediction Models GCMs
GCMs are the only means we have to predict future climates…
They are calibrate front the past (using time series CRU-UEA), and
proyected future
Global scale Regional or local scale
Main features• Horizontal resolution 100 to 300 km • 18 and 56 vertical levels
GCMs y Resoluciones
Model Country Atmosphere OceanBCCR-BCM2.0 Norway T63, L31 1.5x0.5, L35CCCMA-CGCM3.1 (T47) Canada T47 (3.75x3.75), L31 1.85x1.85, L29CCCMA-CGCM3.1 (T63) Canada T63 (2.8x2.8), L31 1.4x0.94, L29CNRM-CM3 France T63 (2.8x2.8), L45 1.875x(0.5-2), L31CSIRO-Mk3.0 Australia T63, L18 1.875x0.84, L31CSIRO-Mk3.5 Australia T63, L18 1.875x0.84, L31GFDL-CM2.0 USA 2.5x2.0, L24 1.0x(1/3-1), L50GFDL-CM2.1 USA 2.5x2.0, L24 1.0x(1/3-1), L50GISS-AOM USA 4x3, L12 4x3, L16GISS-MODEL-EH USA 5x4, L20 5x4, L13GISS-MODEL-ER USA 5x4, L20 5x4, L13IAP-FGOALS1.0-G China 2.8x2.8, L26 1x1, L16INGV-ECHAM4 Italy T42, L19 2x(0.5-2), L31INM-CM3.0 Russia 5x4, L21 2.5x2, L33IPSL-CM4 France 2.5x3.75, L19 2x(1-2), L30MIROC3.2-HIRES Japan T106, L56 0.28x0.19, L47MIROC3.2-MEDRES Japan T42, L20 1.4x(0.5-1.4), L43MIUB-ECHO-G Germany/Korea T30, L19 T42, L20MPI-ECHAM5 Germany T63, L32 1x1, L41MRI-CGCM2.3.2A Japan T42, L30 2.5x(0.5-2.0)NCAR-CCSM3.0 USA T85L26, 1.4x1.4 1x(0.27-1), L40NCAR-PCM1 USA T42 (2.8x2.8), L18 1x(0.27-1), L40UKMO-HADCM3 UK 3.75x2.5, L19 1.25x1.25, L20UKMO-HADGEM1 UK 1.875x1.25, L38 1.25x1.25, L20
GCMs y Resolutions
Uncertainties!
• First, they differ on resolution
Dificulties
• Second: 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 OKCCCMA-CGCM3.1-T63 OK OK NO NO NO NO NO NO OK OK NO NOCCCMA-CGCM3.1-T47 OK OK NO NO OK OK NO NO OK OK NO NOCNRM-CM3 OK OK NO NO OK OK NO NO OK OK NO NOCSIRO-MK3.0 OK OK OK OK OK OK OK OK OK OK OK OKCSIRO-MK3.5 OK OK OK OK OK OK OK OK OK OK OK OKGFDL-CM2.0 OK OK OK OK OK OK OK OK OK OK OK OKGFDL-CM2.1 OK OK OK OK OK OK OK OK OK OK OK OKGISS-AOM OK OK OK OK NO NO NO NO OK OK OK OKGISS-MODEL-EH OK OK NO NO NO NO NO NO NO NO NO NOGISS-MODEL-ER OK OK NO NO OK OK NO NO OK OK NO NOIAP-FGOALS1.0-G OK OK NO NO NO NO NO NO OK OK NO NOINGV-ECHAM4 OK OK NO NO OK OK NO NO NO NO NO NOINM-CM3.0 OK OK OK OK OK OK OK OK OK OK OK OKIPSL-CM4 OK OK NO NO OK OK NO NO OK OK NO NOMIROC3.2.3-HIRES OK OK OK OK NO NO NO NO OK OK OK OKMIROC3.2.3-MEDRES OK OK OK OK OK OK OK OK OK OK OK OKMIUB-ECHO-G OK OK NO NO OK OK NO NO OK OK NO NOMPI-ECHAM5 OK OK NO NO OK OK NO NO OK OK NO NOMRI-CGCM2.3.2A OK OK NO NO OK OK NO NO OK OK NO NONCAR-CCSM3.0 OK OK OK OK OK OK OK OK OK OK OK OKNCAR-PCM1 OK OK OK OK OK OK OK OK OK OK OK OKUKMO-HADCM3 OK OK NO NO OK OK NO NO OK OK NO NOUKMO-HADGEM1 OK OK NO NO OK OK NO NO NO NO NO NO
Dificulties
• Third: limited ability to represent present climates
Dificulties
DownscalingOptions
• Even the most precise GCM is too coarse (~100km)
• To increase resolution, uniformise, provide high resolution and contextualised data
• Different methods exist… from interpolation to neural networks and RCMs– DELTA (empirical-statistical)– DELTA-VAR (empirical-statistical)– DELTA-STATION (empirical-statistical)– RCMs (dynamical)– …
Statistical Downscaling
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
The delta method
Delta- Station• Most similar to original methods in
WorldClim (Saenz-Romero et al. 2009)– Climate baseline: weather stations– Calculate anomalies over specific periods
(i.e. 2020s, 2050s) in coarse GCM cells– “Update” weather station values using
GCM cell anomalies within a neighborhood (400 km)
– Inverse distance weighted– Use thin plate smoothing splines with
LAT,LON,ALT as covariates for interpolation
Statistical Downscaling
Delta- Var
Delta-VAR (Mitchell et al. 2005• AKA pattern scaling
– Climate baseline: CRU– Provided by Tyndall Centre (UK)– Use captured variability in GCMs (MAGICC)
and anomalies– Run a new GCM pattern at a higher
resolution (CLIMGEN)– Calculate averages over specific periods
using the GCM scaled time-series
Statistical Downscaling
Disaggregation• Similar to the delta method, but does not use
interpolation– Climate baseline: CRU, WorldClim– Calculate anomalies over periods in GCM cells– Sum anomalies to climate baseline
Statistical Downscaling
• RCMs (Giorgi 1990)– Climate baseline: GCM boundary
conditions– Develop complex numerical
models to simulate climate behaviour
RCMs PRECISDynamical Downscaling
– “Nest” the RCM into a coarse resolution model (GCM) and apply equations to re-model processes in a limited geographic domain
– Resolution varies between 25-50km– Takes several months to process– Requires a new validation (on top of the GCM validation)
Which one is the best?Method Pros Cons
Delta
*Quick to implement* resolution*Applicable to ALL GCMs*Uniformise baselines
* Assumes changes only occur at broad scales* Assumes variables don’t change relationships in time* variables
Delta-VAR*Quick to implement*Applicable to ALL GCMs*Uniformise baselines* Reproduces GCM pattern
* Max. 50km resolution (CRU)* Reduces original variance in GCMs* variables
Delta-STATION
* Relatively quick to implement* More robust interpolation* Any resolution* Applicable to ALL GCMs
* Assumes changes only occur at broad scales* Assumes variables don’t change relationships in time* Each station represents changes in a 400km range * variables
RCMs
* Most climatologically robust*Applicability depends upon availability of GCM BC* variables
*Few platforms (PRECIS)*Massive storage and processing*Limited resolution (25-50km)*More development is required*Uncertainties difficult to assess
CIAT Experience
• Empirically downscaled, disaggregated for the whole globe at 1km to 20km
• Dinamically downscaled (PRECIS) for South America
• 20 GCM for 2050, 9 for 2020 (Stanford data) downscaled a 20km, 5km, 1km
• 7 GCMs with information Tyndall
Our Databases
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Reaching users globally
http://ccafs-climate.org
• Our in-house capacity:– Four 8-core processing servers in a blade array
under Windows (empirical downscaling)– Two 24-core and 1-8core processing servers in a
blade array under Linux (PRECIS)– ~100TB storage
• Compresing and publishing data is a lengthy process and requires massive storage and network capacity (esp. 1km global datasets)
Capabilities and limitations
Downscaled 30 seg = 100% Resample 2.5min, 5min, 10min = 100%Convert to ascii and compress 30 seg = 30 % (19/63)Convert to ascii and compress resampled = 100%Compress grids resampled = 100%Publising compressed asciis and grids = 0%
Downscaled GCMs 7 periods for 63 models (≈ 20 GCMs x 3 scenarios)
Downscaled 30 seg = 100% Resample 2.5min, 5min, 10min = 100%Convert to ascii and compress 30 seg = 33 % (21/63)Convert to ascii and compress resampled = 100%Compress grids Resamples = 100%Publising compressed asciis and grids = 0%
Dissagregated GCMs 7 periods for 63 models (≈ 20 GCMs x 3 scenarios)
DownscalingIn process..
In process..
PRECIS
• Region: Andes• Resolution 50 km• Grid : 151 x 153
A quick comparison
1 interpolation (37 steps)
x 7 periods x 20 GCMs
= 1 week
210 weeks
x 3 scenarios
÷ 4 servers÷ 4 processes
= 26 weeks= 6 months!!
1 PRECIS run (10 year)
x 15 periodsx 1 GCM
= 2 weeks
30 weeks
x 1 scenario
÷ 3 servers÷ 2 processes
= 5 weeks
= 300 weeks = 6 years!!
x 20 GCM sx 3 scenarios
Hypothetically..
In process..
0% (0/14y)
0%(0/26y)
0% (0/30y)
0% (0/30y)
0% (0/30y)
0% (0/30y)
0% (0/30y)
0% (0/111y)
0% (0/150y)
77% (116/150y)
97% (145/150y)
100% (150/150y)
100% (150/150y)HadCM3Q16 (SRES – A1B)
HadCM3Q0 (SRES – A1B)
ECHAM 5 (SRES – A1B)
HadCM3Q3 (SRES – A1B)
ECHAM 4 (SRES – A2)
ECHAM 4 (SRES – B2)
HadAM3P (Baseline)
HadAM3P (SRES – A2)
HadAM3P (SRES – B2)
ERA40 (Reanalisys)
NCEP:R2 (Reanalisys)
ERA – Interim (Reanalisys)
ERA 15 (Reanalisys)
In process..
TEMP. (JJA) RAINFALL (JJA)
Ethiopia
What´s next? Validation GCMs
Post-processing
Cell maps
Comparison with in situ
data
GCM vs Stations
R2 Vs. Lat / Alt
Format conversion
Averages/sums,
monthly/annual
Histograms R2, RMSQ,
RMSE, slope
RMSQ vs. Lat / Alt
RMSQ vs. Lat / Alt
What´s next? Validation GCMs
What’s next?
Seiler 2009
Baseline Average 1961 – 1990 Total Precipitation (mm/yr)
Máx: 4151.01
Mín: 3.454
Máx: 4724.028
Mín: 1.1344
Máx: 4796.844
Mín: 1.1839
Máx: 28.8573
Mín: -8.3415
Máx: 28.99
Mín: -9.22
Máx: 30.541
Mín: -7.413
Legend
t_2070_2099
Value
High : 9,22064
Low : 1,65393
t_2070_2099
Value
High : 7,92572
Low : 1,1236
Legend
t_2070_2099
Value
High : 9,22064
Low : 1,65393
t_2070_2099
Value
High : 7,92572
Low : 1,1236
Legend
t_2070_2099
Value
High : 9,22064
Low : 1,65393
t_2070_2099
Value
High : 7,92572
Low : 1,1236
Baseline Average Annual Mean Temperature (°C)
ECHAM5 HadCM3Q0 HadCM3Q16
High : 28,7984
Low : -24,2223
High : 28,7984
Low : -24,2223
High : 28,7984
Low : -24,2223
ECHAM5 HadCM3Q0 HadCM3Q16
MRI Validation
0 100 200 300 400 500 600 7000
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200
300
400
500
600
700
R² = 0.280058915841597
R² = 0.707312312307941
Observed vs. Modeled Acumulated Monthly Rainfall (Mean Monthly 1979-2003)
GHCN Stations (mm/month)
MRI
Dat
aset
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m/m
onth
)
-5 0 5 10 15 20 25 30 35-5
0
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25
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R² = 0.900919390026699R² = 0.991495346306954
Observed vs. Modeled Mean Monthly Temperature (Mean Monthly 1979-2003)
MRI Datasets (°C)
GHC
N S
tatio
ns (°
C)
What’s next?
MRI
Jan Feb Mar Apr May Jun Jul Jul Sep Oct Nov Dec20
40
60
80
100
120
140
160Observed vs. Modeled Rainfall Cycle
(Mean monthly 1979-2003)
MRI Datasets GHCN Stations
Month
Prec
ipit
ation
(mm
/mon
th)
Jan Feb Mar Apr May Jun Jul Jul Sep Oct Nov Dec17
18
19
20
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23Modeled and Observed Temperature Cycle
(Mean monthly 1979-2003)
MRI Datasets
GHCN Stations
Month
Tem
pera
ture
(°C/
mon
th)
What’s next?
• Improve baseline data and metadata• Gather and process AR5 projections• Downscale with desired methods• Evaluate and assess uncertainties• Publish all datasets and results• Use the AMKN platform to link climate data,
and modelling outputs
What’s next?
CCAFS Climate data strategy
• CIAT and CCAFS data to be one single product (other datasets are being added)
• Downscaling is inevitable, so we are aiming to report caveats on the methods
• Continuous improvements are being done• Strong focus on uncertainty analysis and
improvement of baseline data• Reports and publications to be pursued…
grounding with climate science
In summary
