Date post: | 15-Jan-2017 |
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Environment |
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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/