cambio climatico en los andes

Climate change and its impact on agriculture Jorge Rubiano and Mark Mulligan, King’s College London

Impacts on the Agricultural sector with focus on Oil Palm in Colombia,[object Object]

BFPANDES : Focus on producing useful outputs capacity built in local students, institutions/stakeholders through training, workshops, tools, dissemination (b) freely available report, maps and baseline data diagnosing current status of water poverty, water productivity, environmental security and their social and institutional context along with likely future impacts (http://www.bfpandes.org) . Released at upcoming conf. The AguAAndes Policy Support System – a simple, accessible web based tool for understanding the likely impact of particular scenarios of change and policy options on water and water poverty in detail in any Andean catchment . Batteries included! -all data supplied. (http://www.policysupport.org/links/aguaandes).

The AGUAANDES POLICY SUPPORT SYSTEM ,[object Object]

All data supplied (1km or 1 Ha.)

Testable climate and land use scenarios and policy options e.g. dam buildingSimTerra : the most detailed global databases, tiled + Detailed grid –based process models + Tools to test scenarios and policy options http://www.policysupport.org/links/aguaandes

The Andes ‘basin’ (all basins above 500 masl) and the 13 key CPWF sub-basins Context: Transnational, globally important Heterogeneous (hyper humid to hyper arid) Steep slopes, competing demands on land use Environmentally sensitive Population of 108 million (Col, Ecu, Peru, Bol ,2008) Pop growth 2.5% p.a. (1980-2005) Highly urbanised (<20% of population is rural) 49 million considered poor (income<essential needs) Agricultural area and fertiliser use increasing since the 1960s, 40-80% national water use is agricultural Hydropower is important Complex water legislation Climate change

Don’t we have enough to deal with : why also worry about climate change? ...because climate change changes everything and policy support based on current climate can be rendered irrelevant if it does not take climate change into account

So what will happen? Who knows? What can we do? ...use our best guess. A general circulation model (GCM)

How a general circulation model sees the Earth. (a) spatially crude - the Hadley centre model is one of the best and has only 7 cells for the whole of Colombia (b) averaged - earth surface properties such as altitude, vegetation are averaged over hundreds of square kilometres (c) simplified - the complexity of atmospheric, terrestrial, biospheric and oceanic processes is highly simplified Even at this level of abstraction, resolving complex atmospheric dynamics over the whole earth with a GCM requires the most powerful computers available and is still computationally limited.

So how can we reduce uncertainty? Use many models and see what they agree and disagree on:

A wide and deep analysis of climate change in Latin America Method: Anomalies (1950-2000 to 2040-2069) from 17 GCMs interpolated and applied to WorldClim 1km climate surfaces (Ramirez and Jarvis). A2 scenario used (Business as usual), second worst case. ,[object Object]

Mean, worst case, best case scenarios analysed

Model disagreement and uncertainty analysed

Seasonality of change compared

Water balance model run for mean scenario

Implications for water productivity analysed,[object Object]

Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs miroc3_2_hires ipsl_cm4 miroc3_2_medres miub_echo_g mpi_echam5 miroc3_2_medres miub_echo_g mpi_echam5 miroc3_2_hires ncar_pcm1 mri_cgcm2_3_2a mri_cgcm2_3_2a ncar_pcm1 °C ....the magnitude as well as the spatial pattern vary considerably (for the same scenario) between different models

Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs bccr_bcm2_0 cccma_cgcm2 cccma_cgcm3_1 cnrm_cm3 cccma_cgcm3_t_t63 mm/yr csiro_mk3_0 gfdl_cm2_1 giss_aom gfdl_cm2_0 hccpr_hadcm3 For precipitation there is disagreement on the direction of change as well as the magnitude. All models indicate wetting in the Andes... Data source : Ramirez, J.; Jarvis, A. 2008. High Resolution Statistically Downscaled Future Climate Surfaces. International Centre for Tropical Agriculture, CIAT. Available at: http://gisweb.ciat.cgiar.org/GCMPage/home.html

Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs miroc3_2_hires ipsl_cm4 miroc3_2_medres miub_echo_g mpi_echam5 ncar_pcm1 mri_cgcm2_3_2a mm/yr ...many models indicate considerable drying in parts of N Colombia, Venezuela and the Amazon

Change AR4 A2a (BasU) (1961-90) to 2050 – mean of 17 different GCMs °C Temperature increases least on the coast (<2°C) Around 2 °C in the Andes Closer to 3 °C in the Amazon mm/yr Precipitation changes greatest in the Andes and West A few hundred mm/yr more in the N Andes, W Amazon, Llanos A few hundred mm/yr less in the S Andes, Guyana shield, E Brazil

Pessimist  AR4 A2a (1961-90) to 2050 – worst case of 17 different GCMs in each pixel Worst case = highest temperature increase Worst case temperature 8 °C except coasts Guyana shield especially prone to high change °C mm/yr Worst case = greatest rainfall decrease Andes generally showing increase, rarely decrease Significant drying in forested NE Amazon and north of Colombia

Optimist  AR4 A2a (1961-90) to 2050 – best case of 17 different GCMs in each pixel °C Best case = lowest temperature increase For NW Amazon best case is liitle change, slight cooling Central (dry) Andes even best case is 2 °C warming mm/yr Best case = greatest increase in precip For most areas greatest increase 200 mm/yr A few areas in Andes/Amazon with significant increases

Uncertainty AR4 A2a (1961-90) to 2050 – SD of 17 different GCMs in each pixel °C Standard deviation (SD) of results for 17 GCMs used as a measure of uncertainty Low temperature uncertainty at high latitudes, coasts and mountains Much greater certainty (low variability between model predictions) in the Andes mm/yr Low rainfall uncertainty in S and SE and parts of Andes Much greater uncertainty in NE Brazil and Amazons

So what will happen? Who knows? It will be warmer and wetter but each GCM gives a different spatial pattern Mean of 17 models warming is highest in the lowlands Mean of 17 models wetting is highest in the Andes Worst case is 6-8 °C warming throughout and wetting of 200-600 mm/yr in the W and S, drying of 600-1000 mm/yr in the NE Best case is 2 °C in the high Andes, less elsewhere and an increased rainfall of at least 200mm/yr throughout Uncertainty in temperature change is low in the Andes (the model agree) and is greatest in the Amazon Uncertainty in rainfall change follows a complex pattern but is much greater in the N than the S And what about seasonality....

Seasonal change : temperature : mean of 17 models J F M A M J J A S O °C At southern latitudes temperature Change is much greater in N,D,J,F In the equatorial regions change is much greater in J,J,A,S N D

Seasonal change : precipitation : mean of 17 models J F M A M J J A S O mm/yr Precipitation change (drying and wetting) is greatest and most spatially variable in J,J,A,S,O N D

So what are the implications for agriculture? Method: Use monthly anomalies (mean of 17 models) to force FIESTA hydrological model at Andes scale Look into implications for evapotranspiration and water balance Examine current distribution of productivity from 10 years of 10-daily remote sensing data Associate current productivity to current climate Draw implications for impacts of climate change scenario Change question from what will the future be like? to how much change can the systems stand?

cambio climatico en los andes

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