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Climate Data for Sustainable South-America
Project (SUSA/SAL)
SUSA/SAL annual meeting 2016
Prajal Pradhan, Wei Wenig, Desirée Dörr, David Landholm, ,
Jürgen Kropp
1st
to 3rd
March, Cali
Background
• A goal of the project is to investigate impacts of climate
change on crop/natural species and ecosystem services.
• Such investigation is important to identify of land-use based
adaptation needs.
• WP2 is designed to fulfil this goal based on the three output
indicators.
• The first one is to provide climate projection data based on
the new carbon emission scenarios, Representative
Concentration Pathways (RCPs).
2P. Pradhan, RDII Climate Impact and Vulnerability
3P. Pradhan, RDII Climate Impact and Vulnerability
Objective
• To report on available climate data that can be used.
• First, brief description on crop models and required input
climate data.
• Second, available climate data for baseline conditions.
• Third, an overview on data of climatic conditions based on
future projections.
• Finally, our options and suggestions on climate input data.
Crop models
4P. Pradhan, RDII Climate Impact and Vulnerability
Model
Spatial
resolution
Temporal
resolution
Input data
Maxent
30 arc
seconds
(~ 1 x 1 km)
Monthly
19 bioclimatic variables as
described by Hijmans et al.
(2005) and solar radiation
Ecocrop
30 arc seconds
(~ 1 x 1 km)
Monthly
maximum and minimum
temperature, precipitation,
and length of growing season
DSSAT
5 arc minutes
(~ 10 x 10 km)
Daily
Solar radiation, maximum
and minimum temperature
and precipitation, relative
humidity, and wind seed
Baseline data
• Climate Research Unit data (CRU TS series)
o most used climate baseline data for global vegetation and
agricultural dynamics simulation
o data on various weather parameters available
o time period 1901-2013
o spatial resolution of 0.5° (~50 X 50 km)
• WorldClim dataset (Hijmans et al. 2005)
o mostly used for ecological niche modelling
o provides a set of global climate layers including 19 bioclimatic
variables derived from monthly temperature and rainfall data
o for time period 1950-2000
o a spatial resolution up to 30 arc seconds (~ 1 x 1 km)
5P. Pradhan, RDII Climate Impact and Vulnerability
Future projections
• Climate projections are based on Global Circulation Models
(GCMs) driven by different forcing scenarios.
• IPCC’s AR5 uses Representative Concentration Pathways
(RCPs) as forcing scenarios.
• RCPs (RCP 2.6 , RCP 4.5, RCP 6 & RCP 8.5) are based on the 21st
century peak/stabilization value of the radiative forcing.
• Coupled Model Intercomparison Project Phase 5 (CMIP5) uses
≈ 40 GCMs to understand the future climatic conditions.
6P. Pradhan, RDII Climate Impact and Vulnerability
Downscaling required
• Spatial resolution of GCMs is roughly 1 to 2 degrees.
• Crop niche models are simulated on a finer resolution.
• Processing/downscaling of GCMs outputs are a prerequisite to
be utilized as inputs for crop models.
• Different methods consisting of interpolation and bias
correction use to process GCMs outputs.
• Regional climate models (RCMs) are used to dynamically
downscale GCMs outputs to regional resolution.
7P. Pradhan, RDII Climate Impact and Vulnerability
WorldClim data
• provides climate projection data in a very fine resolution up
to 30 arc seconds (~ 1 x 1 km) for around 20 GCMs
• use a simple delta method
• based on projected change in a weather variable and on the
high resolution baseline climate data
8P. Pradhan, RDII Climate Impact and Vulnerability
ISI-MIP input data
Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP)
input data
•provides a consistent framework to compare climate impact
projections
•initiated and partly performed by the PIK
•provides the projected climate data at 0.5 degree spatial
resolution for 5 GCMs participated in CMIP5
•interpolation and bias correction based on a complex transfer
functions
9P. Pradhan, RDII Climate Impact and Vulnerability
ISI-MIP Setup: Choice of GCMs
10P. Pradhan, RDII Climate Impact and Vulnerability
CMIP3/CMIP5 model genealogy
Knutti et al. 2013 GRL
5 GCMs chosen
based on two
criteria (variable
availability and
ranged of predicted
T and P):
• HadGEM2-ES
• MIROC-ESM-
CHEM
• IPSL-CM5A-LR
• GFDL-ESM-2M
• NorESM1-M
Coordinated Regional Climate Downscaling
Experiment (CORDEX)
• regional downscaling measures based on regional dynamics
• statistical downscaling
• dynamic downscaling that use nested regional models
• Statistical downscaling uses empirically based models
according to regional historical data.
• The nesting regional models account for local climate physics.
• provides physically based regional climate projections,
recognizing spatial variance of climate forcing such as
orography.
• This is relatively important for a local scale crop model.
11P. Pradhan, RDII Climate Impact and Vulnerability
CORDEX
• two domains covers the our
case study regions
• available data is limited for
the both domains
• Spatial resolution 0.44
degrees (~ 50 x 50 km)
• further interpolation and
bias correction required
12P. Pradhan, RDII Climate Impact and Vulnerability
Options and suggestions (Baseline condition)
Both CRU TS and WorldClim data can be used
•CRU TS data available at daily temporal resolution but has a
course spatial resolution of 0.5°
•GAEZ uses downscaled CRU TS data to 5 minutes resolution
•WorldClim finer spatial resolution up to 30 arc seconds but
temporal resolution is limited to monthly values
•additionally provides data on nineteen bioclimatic variables
that are often used for ecological modeling
13P. Pradhan, RDII Climate Impact and Vulnerability
Options and suggestions
(Future climatic condition)
• an ideal situation is to use a multi-model approach
considering
• different climate scenarios (4 RCPs)
• all GCMs
• different downscaling approaches
• due to resource and time constrain we suggest followings:
• to consider the best case and worst case scenarios (RCP 2.6 and
RCP 8.5)
• to consider an ensemble of five core GCMs used for ISI-MIP or
AgMIP or 20 GCMs used for AgMIP
• fine resolution WorldClim, ISI-MIP data
• CORDEX data with preprocessing
14P. Pradhan, RDII Climate Impact and Vulnerability
Way forward
• to start investigation using the WorldClim data considering
the ensemble of five GCMs that are used in the ISI-MIP and
two GHGs forcing scenarios (RCP 2.6 and RCP 8.5)
• to explore possibilities of obtaining climate data used in the
ISI-MIP in 5 arc minutes resolutions from IIASA (PIK)
• to explore options of obtaining CORDEX data for case study
regions and to process it into finer resolution (PIK and CIAT)
• By such multi-model ensemble, we can provide a sound
scientific understanding on possible impacts of climate on
crop suitability, covering a wide range of uncertainties.
15P. Pradhan, RDII Climate Impact and Vulnerability
Further collaboration
• Verify local people perception with weather station data
(Gisella Susana Cruz Garcia and others)
• Analysis of weather station data for Colombia and Peru
(Carlos Eduardo Navarro Racines, Marcela Orjuela Baquero,
ect.)
• Other collaborations ……
16P. Pradhan, RDII Climate Impact and Vulnerability
Thank you very much!!!
Please provide your comments and
suggestions!!!
17P. Pradhan, RDII Climate Impact and Vulnerability

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10. Presentación corta: Climate data

  • 1. Climate Data for Sustainable South-America Project (SUSA/SAL) SUSA/SAL annual meeting 2016 Prajal Pradhan, Wei Wenig, Desirée Dörr, David Landholm, , Jürgen Kropp 1st to 3rd March, Cali
  • 2. Background • A goal of the project is to investigate impacts of climate change on crop/natural species and ecosystem services. • Such investigation is important to identify of land-use based adaptation needs. • WP2 is designed to fulfil this goal based on the three output indicators. • The first one is to provide climate projection data based on the new carbon emission scenarios, Representative Concentration Pathways (RCPs). 2P. Pradhan, RDII Climate Impact and Vulnerability
  • 3. 3P. Pradhan, RDII Climate Impact and Vulnerability Objective • To report on available climate data that can be used. • First, brief description on crop models and required input climate data. • Second, available climate data for baseline conditions. • Third, an overview on data of climatic conditions based on future projections. • Finally, our options and suggestions on climate input data.
  • 4. Crop models 4P. Pradhan, RDII Climate Impact and Vulnerability Model Spatial resolution Temporal resolution Input data Maxent 30 arc seconds (~ 1 x 1 km) Monthly 19 bioclimatic variables as described by Hijmans et al. (2005) and solar radiation Ecocrop 30 arc seconds (~ 1 x 1 km) Monthly maximum and minimum temperature, precipitation, and length of growing season DSSAT 5 arc minutes (~ 10 x 10 km) Daily Solar radiation, maximum and minimum temperature and precipitation, relative humidity, and wind seed
  • 5. Baseline data • Climate Research Unit data (CRU TS series) o most used climate baseline data for global vegetation and agricultural dynamics simulation o data on various weather parameters available o time period 1901-2013 o spatial resolution of 0.5° (~50 X 50 km) • WorldClim dataset (Hijmans et al. 2005) o mostly used for ecological niche modelling o provides a set of global climate layers including 19 bioclimatic variables derived from monthly temperature and rainfall data o for time period 1950-2000 o a spatial resolution up to 30 arc seconds (~ 1 x 1 km) 5P. Pradhan, RDII Climate Impact and Vulnerability
  • 6. Future projections • Climate projections are based on Global Circulation Models (GCMs) driven by different forcing scenarios. • IPCC’s AR5 uses Representative Concentration Pathways (RCPs) as forcing scenarios. • RCPs (RCP 2.6 , RCP 4.5, RCP 6 & RCP 8.5) are based on the 21st century peak/stabilization value of the radiative forcing. • Coupled Model Intercomparison Project Phase 5 (CMIP5) uses ≈ 40 GCMs to understand the future climatic conditions. 6P. Pradhan, RDII Climate Impact and Vulnerability
  • 7. Downscaling required • Spatial resolution of GCMs is roughly 1 to 2 degrees. • Crop niche models are simulated on a finer resolution. • Processing/downscaling of GCMs outputs are a prerequisite to be utilized as inputs for crop models. • Different methods consisting of interpolation and bias correction use to process GCMs outputs. • Regional climate models (RCMs) are used to dynamically downscale GCMs outputs to regional resolution. 7P. Pradhan, RDII Climate Impact and Vulnerability
  • 8. WorldClim data • provides climate projection data in a very fine resolution up to 30 arc seconds (~ 1 x 1 km) for around 20 GCMs • use a simple delta method • based on projected change in a weather variable and on the high resolution baseline climate data 8P. Pradhan, RDII Climate Impact and Vulnerability
  • 9. ISI-MIP input data Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) input data •provides a consistent framework to compare climate impact projections •initiated and partly performed by the PIK •provides the projected climate data at 0.5 degree spatial resolution for 5 GCMs participated in CMIP5 •interpolation and bias correction based on a complex transfer functions 9P. Pradhan, RDII Climate Impact and Vulnerability
  • 10. ISI-MIP Setup: Choice of GCMs 10P. Pradhan, RDII Climate Impact and Vulnerability CMIP3/CMIP5 model genealogy Knutti et al. 2013 GRL 5 GCMs chosen based on two criteria (variable availability and ranged of predicted T and P): • HadGEM2-ES • MIROC-ESM- CHEM • IPSL-CM5A-LR • GFDL-ESM-2M • NorESM1-M
  • 11. Coordinated Regional Climate Downscaling Experiment (CORDEX) • regional downscaling measures based on regional dynamics • statistical downscaling • dynamic downscaling that use nested regional models • Statistical downscaling uses empirically based models according to regional historical data. • The nesting regional models account for local climate physics. • provides physically based regional climate projections, recognizing spatial variance of climate forcing such as orography. • This is relatively important for a local scale crop model. 11P. Pradhan, RDII Climate Impact and Vulnerability
  • 12. CORDEX • two domains covers the our case study regions • available data is limited for the both domains • Spatial resolution 0.44 degrees (~ 50 x 50 km) • further interpolation and bias correction required 12P. Pradhan, RDII Climate Impact and Vulnerability
  • 13. Options and suggestions (Baseline condition) Both CRU TS and WorldClim data can be used •CRU TS data available at daily temporal resolution but has a course spatial resolution of 0.5° •GAEZ uses downscaled CRU TS data to 5 minutes resolution •WorldClim finer spatial resolution up to 30 arc seconds but temporal resolution is limited to monthly values •additionally provides data on nineteen bioclimatic variables that are often used for ecological modeling 13P. Pradhan, RDII Climate Impact and Vulnerability
  • 14. Options and suggestions (Future climatic condition) • an ideal situation is to use a multi-model approach considering • different climate scenarios (4 RCPs) • all GCMs • different downscaling approaches • due to resource and time constrain we suggest followings: • to consider the best case and worst case scenarios (RCP 2.6 and RCP 8.5) • to consider an ensemble of five core GCMs used for ISI-MIP or AgMIP or 20 GCMs used for AgMIP • fine resolution WorldClim, ISI-MIP data • CORDEX data with preprocessing 14P. Pradhan, RDII Climate Impact and Vulnerability
  • 15. Way forward • to start investigation using the WorldClim data considering the ensemble of five GCMs that are used in the ISI-MIP and two GHGs forcing scenarios (RCP 2.6 and RCP 8.5) • to explore possibilities of obtaining climate data used in the ISI-MIP in 5 arc minutes resolutions from IIASA (PIK) • to explore options of obtaining CORDEX data for case study regions and to process it into finer resolution (PIK and CIAT) • By such multi-model ensemble, we can provide a sound scientific understanding on possible impacts of climate on crop suitability, covering a wide range of uncertainties. 15P. Pradhan, RDII Climate Impact and Vulnerability
  • 16. Further collaboration • Verify local people perception with weather station data (Gisella Susana Cruz Garcia and others) • Analysis of weather station data for Colombia and Peru (Carlos Eduardo Navarro Racines, Marcela Orjuela Baquero, ect.) • Other collaborations …… 16P. Pradhan, RDII Climate Impact and Vulnerability
  • 17. Thank you very much!!! Please provide your comments and suggestions!!! 17P. Pradhan, RDII Climate Impact and Vulnerability