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)

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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