10. Presentación corta: Climate data

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


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


Model Spatial resolution

Temporal resolution Input data

Maxent

30 arc seconds

(~ 1 x 1 km)

Monthly19 bioclimatic variables as described by Hijmans et al. (2005) and solar radiation

Ecocrop30 arc seconds

(~ 1 x 1 km)Monthly

maximum and minimum temperature, precipitation, and length of growing season

DSSAT5 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 availableo time period 1901-2013o spatial resolution of 0.5° (~50 X 50 km)

• WorldClim dataset (Hijmans et al. 2005)o mostly used for ecological niche modellingo provides a set of global climate layers including 19 bioclimatic

variables derived from monthly temperature and rainfall datao for time period 1950-2000o a spatial resolution up to 30 arc seconds (~ 1 x 1 km)


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.


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.


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


ISI-MIP input dataInter-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


ISI-MIP Setup: Choice of GCMs


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.


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


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


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


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.


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 ……


Thank you very much!!!

Please provide your comments and suggestions!!!


10. Presentación corta: Climate data

Science

Transcript of 10. Presentación corta: Climate data