Decision and Policy Analysis Program – DAPA
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Climate Change in Colombian High Plains

(taken from “Aumentaría el calor en la Altillanura en 2030″  http://www.futuro-orinoquia.org/index.php/2012/02/calor-altillanura-2030)

NP Llanos10_loThe Colombian High Plains future climate scenario was the result of both one statistical process based in sub-selection of Global Climate Models (GCM) and one uncertainty analysis.  The analysis showed that it would be an increase on mean annual temperature about 1.3°C  in 2030 and 2.3 °C in 2050, and a slight increase on total rainfall about 30mm and 70mm in 2030 and 2050, respectively.  This projection of climate change came from the generation scenarios for the region, which has been one of the most relevant results in the first year of the project ‘Modelo para evaluar los riesgos del cambio climático y generar estrategias de adaptación y mitigación en la Altillanura’.  Results were presented at a workshop held on January 31, 2012 in Villavicencio, it was attended by researchers from CORPOICA, CIAT, IDEAM and Alexander Von Humboldt Institute.

The project aims to strengthen the producers, development planners and researchers capacity to analyze the risks of change and climate variability on agricultural activities. It also seeks to assess the socio-economic and environmental adaptation and mitigation responses of agriculture in the region.

We made significant progress during this first stage of the agreement:

Generating of climate baseline: it was defined the climate baseline scenario in order to determine the scenarios and projections of climate change, which generally comprise an average period of 30 years. For the case study, pattern of temperatures and precipitation in the region was substantially improved.
Generating climate change scenarios in the Hig Plains.
Start of the crop and pests modeling.

For climate modeling we got two significant results: first, an improved climate baseline, which included a collection of information from weather stations in the region (‘Instituto de Hidrología, Meteorología y Estudios Ambientales’ [IDEAM], Food and Agriculture Organization of the United Nations [FAO] and other sources) and the generation of surfaces with high spatial resolution (scale necessary to estimate the weather conditions at regional level) resulting from this information. This process was based on the methodology described by WorldClim (www.worldclim.org).

Second result of this topic was to generate climate change scenarios for the region.  The most common way to generate climate change scenarios is using Global Climate Models (GCM) to simulate the present climate and a possible future.  GCMs are the best currently available scientific tool to simulate the global climate system response to a change in the composition of the atmosphere.  Although GCMs information has many advantages, the spatial and temporal resolution of these models is low and because of this, it is necessary to reduce the scale, either by statistical or dynamic.  This also became part of the methodological development of the project, getting climate surfaces of 1 km spatial resolution (at the equator), useful to predict regional-scale phenomena.

After the process of statistical sub-selection of GCMs and uncertainty analysis, the scene for the Colombian High Plains future was recreated; results showed that it would be a significant increase on mean annual temperature about 1.3 °C in 2030 to 2.3 °C in 2050 and a slight increase on total rainfall about 30mm and 70mm in 2030 and 2050, respectively.

Meanwhile, for modeling crops were selected soybean and natural rubber (those represent annual and perennial crops) to generate preliminary ecological niche models.  For such modeling, it was based on the information provided by crop experts and climate data generated for the region.  So far, we have done analysis of the potential distribution of soybean for current situation and 2050, seeking to assess the climate change impacts that would have areas where crops grows today and where they has growth potential climatic factors.

For this new stage of the project, we expect to continue with improvements in the climatological baseline, which could include daily data entry and exploring other alternatives for generation of future scenarios, perhaps with Regional Climate Model projections (RCM) or by incorporating data of the Fifth Assessment Report of IPCC-AR5-(Intergovernmental Panel on Climate Change), which data processing has already started within CIAT.

 

 

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