Water for Agriculture in Latin America and the Caribbean Under a Changing Climate

Robert Oglesby1,2,3, Clinton Rowe1,3, Azar Abadi1, and Rachindra Mawalagedara1,3,*

1Department of Earth and Atmospheric Sciences, University of Nebraska-Lincoln2School of Natural Resources, University of Nebraska-Lincoln3Daugherty Water for Food Institute, University of Nebraska

*Former DWFI Postdoctoral researcher, now at ISU

Why Latin America and the Caribbean?

Potential Risks

• Regions potentially at severe risk due to future climate change

• Need for properly resolved surface climate in the region, due to its complex topography and nearness to oceans

• Existing knowledge gaps in dynamical downscaling

Extreme Events

• Latin America and the Caribbean are regions presently at grave risk to a variety of extreme climate events.

• These include flooding rains, damaging winds, drought, heat waves, and in high elevation mountainous regions, excessive snowfalls.

• Such extreme events are likely to become even worse under projections of future climate changes

Why use a Regional Climate Model?

Our results suggest that for proper simulation of both mean climate and extreme events:

• A spatial resolution of 4 km is required in regions of complex mountainous topography.

• A somewhat coarser resolution of 12 km is adequate in regions without much topographic relief and where differing land cover accounts for most of the spatial heterogeneity.

Leung et al., 2012

Background: What we have done so far

• Weather Research and Forecasting (WRF) model simulations for Mesoamerica andthe Caribbean.

• More comprehensive series of individualized simulations for Guatemala, Honduras and for Bolivia.

• Regional and country-level workshops

• Conducted a number of workshops to provide training to local users charged with addressing climate change impacts for their countries.

High-Resolution Climate Change Scenarios for Mesoamerica

Spatial resolution

of domains

d01: 36 km

d02: 12 km

d03-d06: 4 km

d01

Effects of Elevation on Temperature

GUATEMALA

Spatial resolution

of domains

d01: 36 km

d02: 12 km

d03: 4 km

d01

d02

d03

Historic Simulations for Model Verification

• Purpose is to verify model capabilities by comparing to actual station observations

• WRF driven by NCEP reanalyses as a proxy for real large-scale forcing

• Two 10-year periods simulated• 1971-1980

• 2001-2010

Climate Change Downscaling Simulations

• Purpose is to compute changes forced by increasing greenhouse gas forcing

• Forced by NCAR CCSM4 GCM simulation of the RCP 8.5 emission scenario

• Two 10-year periods simulated• ‘Present-day’ control (2011-2020)

• Future climate change (2061-2070)

Simulations for Guatemala

July

TemperatureGCM

WRF

Mean Temperature (PD) Difference (FT – PD)

PD: 2011-2020

FT: 2061-2070

July

PrecipitationGCM

WRF

Mean Precipitation (PD) Difference (FT – PD)

PD: 2011-2020

FT: 2061-2070

BOLIVIAd01

Spatial resolution

of domains

d01: 36 km

d02: 12 km

d03: 4 km

Model Scenario Years # years

NCEP 1979-2012 33

CCSM4 RCP 2.6 2006-2020 2066-2080 30

RCP 4.5 2006-2040 2066-2080 50

RCP 8.5 2006-2020 2056-2080 40

ECHAM RCP 2.6 2006-2020 2066-2080 30

RCP 4.5 2006-2020 2066-2080 30

RCP 8.5 2006-2020 2056-2080 40

MIROC RCP 2.6 2006-2020 2066-2080 30

RCP 4.5 2006-2020 2066-2080 30

RCP 8.5 2006-2020 2056-2080 40

A comprehensive set of WRF simulations were carried out for Bolivia

Temperature Differences for July

Scenario: RCP8.5Domain Resolution: 4 km Difference = (2066-2075) – (2006-2015)

Precipitation Differences for July

Scenario: RCP8.5Domain Resolution: 4 km Difference = (2066-2075) – (2006-2015)

Lessons Learned

• Engaging countries across the region has been very productive, but we see a real need for more specialized training and simulations for individual countries.

• The LAC region contains a tremendous reservoir of talent. Our approach both allows this talent to develop expertise and, most importantly, convey that new knowledge to policy-makers.

• It is crucial at some point to have the technical and policy people together in the same room.

• The technical people are highly motivated and highly skilled, but often lacking in basic climate expertise. Back home, they wear several disciplinary hats.

Model output download

• ability to select subset of output• temporally

• spatially

• by variable