Climate Modelling,CC d ata & Agricu l tu re

Carlos NavarroJ. Ramirez, A. Jarvis, K. Loheto

Observed changes - Atmosphere

¿Why we are sure that climate is changing?

IPCC, 2013

Future Challenges

Population1.Grow of the

2. Food security

3.Produce

more & better

… con less water, land and resourcesHow we could prepare

For thefuture?

How to predict the future?

Economic

Environmental

Global Regional

Pessimistic“Bussiness as usual”

OptimisticPerfect World

IntermediateP

E

P

E

P

E

P

E

Emission Scenarios

GCMs are the only way we can predict the future

climate

Using the past to learn for the future

The ModelsGCM “Global Climate Model”

Variations of the Earth’s surface temperature: 1000 to 2100

What are saying the models?

Anthropogenic changes lead to changes in weather

Atmospheric concentrations

GCMs

GCM - Limitations

Global scale Regional or local scale

Resolutions

• Horizontal resolution 100 to 300 km • 18 and 56 vertical levels

How I can use this information?

Problem

Needs

OptionsDownscaling by

statistical or dynamical methods..

To increase resolution, uniformise, provide high resolution and contextualised dataEven the most

precise GCM is too coarse (~100km)

GCM - Limitations

CCAFS-ClimateUsers

Actualized Feb 2015

Visits % New Visits New Visits Bounce Rate Pages / Visit Avg. Session Duration

93,266% of Total:

100.00% (93,266)

51.29%Avg for View:

51.19% (0.20%)

47,836% of Total:

100.20% (47,741)

41.39%Avg for View:

41.39% (0.00%)

3.77Avg for View:

3.77 (0.00%)

00:04:39Avg for View:

00:04:39 (0.00%)

http://ccafs-climate.org

Options – Statistical Methods

• Progressive climate change over agriculture (22%),

• Ecology and species distribution (58%)

• Climate dynamics (3%)• Ecosystem Services (5%)• Non-academic (i.e. policy

making, food security, and adaptation planning (12% )

CCAFS-ClimateCitations

Significant impact by putting climate change

information into the hands of non-climate scientists

and next users which represent up to 19% of all

CCAFS-Climate users.

> 300 Publications

CORDEX Dynamical Downscaled Data

Undefined periods Prec, Tmax, Tmin, Bioclim + otrhers0.44deg (~50km)At least 2 CORDEX Domains

2014

ETA Dynamical Downscaled Data

4 GCM - 2 SCENARIOS,4 future periods.0.33deg (~40km)South America

4 RCP106 GCM (about 25 models per RCP)4 future periods5 climatological variables 4 spatial resolutions (the highest at 1 Km2)

Full set of CMIP5 Delta Method Downscaled Data

CMIP5 Raw and Processed Daily Data with several bias-correction Methodologies (Online processing)

2015

DSSAT (.wtg)

APSIM (json)

Others (ascii)

2030’s, 2050’s, 2070’s, 2080’sPrec, Tmax, Tmin, RsdsRaw Resolution

Extractions online in formats of interest to

Crop Modelers

CCAFS-ClimateData Strategy

We are focused now in increase its use amongst

crop modelers

GCMs

Effective adaptation options

MarkSim

DSSAT

Statistical Downscaling

Dynamical downscaling:Regional Climate Model

EcoCropStatistical Downscaling

MaxEnt

We need models to quantify the impacts and adaptation options for effective design

Based on niches

Prob

abili

ty

Environmental gradient

Based on process

Impacts

Part IGrid-base data for Future Climate Conditions

CCAFS Climate http://ccafs-climate.org

• Description: The CCAFS-Climate data portal provides users global and regional future high-resolution climate datasets that can help assess the impacts of climate change in a variety of fields related to biodiversity, agriculture in others.

• Data provided: Most common spatial raster (i.e. ESRI-Ascii, ESRI-Grid) up to 30 arc-sec of resolution (1 km2) for whole world.

• Possible output format after conversion: Shapefiles, KML (classified by ranges).

• What kind of information we could get?. For example, changes in monthly temperature and precipitation by 2030 or 2050 for a specific location, municipality, region, country or continent.

• Example dataset prepared for Hackaton: Average conditions in a climate change scenario by 2030, 2050 for whole Africa at 0.5 degrees resolution (30 minutes).

CCAFS Analogueshttp://ccafs-analogues.org • Description: The Climate Analogues approach

can identify areas that experience statistically similar climatic conditions, but which may be separated temporally and/or spatially. In essence, the approach allows you to glimpse into the future by locating areas whose climate today is similar to the projected future climate of a place of interest (i.e. where can we find today the future climate of Nairobi, Kenya?), or vice-versa.

• Data provided: Spatial raster (i.e. GeoTiff) up to 30 arc-sec of resolution (1 km2) for whole world.

• Possible output format after conversion: Shapefiles, KML (classified by ranges of percent).

• What kind of information we could get?. For example, similarity of one location in an smalle village with the climate of whole Kenya in present, past or future conditions.

• Example dataset prepared for Hackaton: NA. The teams must be use the portal.

Part IIGrid-base Data for current conditions

WorldClimhttp://worldclim.org • Description: WorldClim is a set of global

climate layers (climate grids) with a spatial resolution of about 1 square kilometer.

• Data provided: Spatial raster (i.e. ESRI-Grid, BIL) up to 30 arc-sec of resolution (1 km2) for whole world.

• Possible output format after conversion: Shapefiles, KML (classified by ranges of percent).

• What kind of information we could get?. For example, current climatology for a specific location, municipality, region, country or continent.

• Example dataset prepared for Hackaton: Monthly precipitation and temperature resampled to 0.5 deg (30 minutes) of resolution.

STRMhttp://srtm.csi.cgiar.org/ • Description: The CGIAR-CSI GeoPortal is

able to provide SRTM 90m Digital Elevation Data for the entire world.

• Data provided: Spatial raster (i.e. ESRI-Ascii, Tiff) up to 90 metters of resolution for whole world.

• Possible output format after conversion: Shapefiles, KML (classified by ranges of percent).

• What kind of information we could get?. For example, altitude (masl) for a specific location, municipality, region, country or continent.

• Example dataset prepared for Hackaton: Altitude at 30 sec (1 Km2) of resolution for Africa.

TAMSAThttp://www.met.reading.ac.uk/tamsat/about/

• Description: TAMSAT stands for Tropical Applications of Meteorology using SATellite data and ground-based observations. They use satellite imagery, calibrated against ground observations for estimating rainfall for Africa.

• Data provided: NetCDF files at 4 Km of resolution for whole Africa.

• Possible output format after conversion: Shapefiles, KML (classified by ranges of percent).

• What kind of information we could get?. For example, current wheather conditions for a municipality, region, country or continent.

• Example dataset prepared for Hackaton: NA. Teams must be download the data.

Part IIIPoint-base Data for current conditions

GHCN / GSODhttp://gis.ncdc.noaa.gov/ • Description: The Global Historical

Climatology Network (GHCN) and Global Summary of the Day are an integrated databases of climate summaries from land surface stations across the globe that have been subjected to a common suite of quality assurance reviews.

• Data provided: Wheather series at daily and monthly time-step .

• Possible output format after conversion: CSV, TXT, or similar text base format.

• What kind of information we could get?. For example, wheather information for 2000 to present at daily scale.

• Example dataset prepared for Hackaton: NA. Users must be use the portal.

Part IVMiscellaneous Data(Including Off-line Resources)

AGTRIALShttp://www.agtrials.org/ • Description: Agtrials.org is an information

portal developed by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) which provides access to a database on the performance of agricultural technologies at sites across the developing world. It builds on decades of evaluation trials, mostly of varieties, but includes any agricultural technology for developing world farmers.

• Data provided: Many agronomic variables by trial.

• Possible output format after conversion: CSV, TXT, or similar text base format.

• What kind of information we could get?. For example, historical yield of cassava by trial site.

• Example dataset prepared for Hackaton: NA. Users must be use the portal.

AMKNhttp://amkn.org/ • Description: The AMKN is a platform for

accessing and sharing current agricultural adaptation and mitigation knowledge from the CGIAR and its partners. It provides a visual display of farmers’ on-the-ground climate realities and transforms hard research data into interactive multimedia that can be easily understood by all users.

• Data provided: Blogpost, photo sets, videos.

• Possible output format after conversion: NA.

• What kind of information we could get?. For example, testimonial of a farmer about his management practices.

• Example dataset prepared for Hackaton: NA. Users must be use the portal.

Daily Climate Change Projections

• Data provided: Text plain files including climate change projections by few models.

• Possible output format after conversion: CSV, TXT, or similar text base format.

• What kind of information we could get?. For example, future climate conditions for a location (specifying the coordinates) at daily time-step, year by year from 2015-2100.

• Example dataset prepared for Hackaton: Few models, extracted by Carlos Navarro.9 /4 /2 0 2 8 1 /1 7 /2 0 3 0 6 /1 /2 0 3 1 1 0 /1 3 /2 0 3 2 2 /2 5 /2 0 3 4 7 /1 0 /2 0 3 5 1 1 /2 1 /2 0 3 6 4 /5 /2 0 3 8 8 /1 8 /2 0 3 9 1 2 /3 0 /2 0 4 0 5 /1 4 /2 0 4 2

-10

0

10

20

30

40

50

60

70

Value (mm/day)

http://ccafs-climate.org/data_bias_corrected/

Contact: Carlos Navarro-Racinesc.e.navarro@cgiar.org

Thank youBlessings