mer f - climate information portals, nairobi aug 2012
TRANSCRIPT
Introduction to the climate information portals
Julian Ramirez / Andy Jarvis / Carlos Navarro / Flora Mer
• Agriculture demands:– Multiple variables– Very high spatial
resolution– Mid-high temporal (i.e.
monthly, daily) resolution
– High certainty– Both for present and
future
Climate & Agriculture
Gaps in the climate system representationClimatic data of good
quality
Climate models with limited performanceEvaluation of climate
change impacts
Large uncertainties
>> INCERTIDUMBRE
Which climate data is used to assess agricultural impacts?
Climate data sources
Local weather stations
GHCNThe most used
data sourceGSOD
WCL-WS
Climate model outputs
GCM data
GCM data more used than the
others.
RCM dataSatellite imagery
WorldClim
GCM dataGCM at coarse resolution
Downscaling to have better resolution
(Ramirez and Challinor, 2012)
PR
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PR
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Comparison (R2 based) of interpolated climatology (WorldClim, The University of East Anglia Climatic Research Unit dataset (CRU)), and each of the GCMs (average 1961-1990 period) for each of the countries in the study area for mean temperature (left) and precipitation (right) for the annual mean. All R2 values were statistically significant at p < 0.001. (Ramirez and Challinor, 2012)
How to study the accuracy of climate model outputs?
Projections of future global average annual precipitation for A1B scenarios from donwscaled data.
24 GlobalCirculation Models (GCMs)
Uncertainties?
Projections of future global average annual temperature for A1B scenarios from donwscaled data.
24 GlobalCirculation Models (GCMs)
Downscaling by statistical
method or dynamic method
Increase resolution, uniformity… Provide
data with high resolution to assess
impact studies on agricultural systems,
…
Still the more precise GCM is too coarse
(100km).
Statiscical downscaling
Dynamicdownscaling
Delta method
Dissagregation
PRECIS
CORDEX
…Which are Regional Climate Model (RCM)
- For the whole world at 1km to 20km- 20 GCMs for 2050, 9 for 2020 dowscaled to 20km, 5km, 1km
• Use anomalies and discard baselines in GCMs– Climate baseline: WorldClim– Used in the majority of studies– Takes original GCM time series– Calculates averages over a baseline and
future periods (i.e. 2020s, 2050s)– Compute anomalies– Spline interpolation of anomalies– Sum anomalies to WorldClim
• Similar to the delta method, but does not use interpolation– Climate baseline: WorldClim– Calculate anomalies over periods in GCM cells– Sum anomalies to climate baseline
• Region: Andes• Resolution 50 km• Grid : 151 x 153
In Latin America
-The Coordinated Regional Downscaling Experiment in Africa-
http://start.org/cordex-africa/
Method + -
Statistical downscaling
*Easy to implement* resolutions*Apply to all GCMs*Uniforme baseline
* Change variable only at big scale* Variables do not change their relations with time* variables
Dynamic downscaling
* Robust*Apply to GCMs if data available* variables
*Few platforms (PRECIS, CORDEX)*Many processes and stockages*Limited resolution (25-50km)*Missing development*Dificulty to quantify uncertainties
CCAFS provides these data
Our climate portal http://ccafs-climate.org
http://ccafs-climate.org
- Less access to internet- Data heavy to download
• Improve baseline data and metadata• process and assess AR5 predictions (RCP 4.5)• Downscale with desired methods• Evaluate and assess uncertainties• Publish all datasets and results
• 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