AgMIP Climate Team Activities for Integrated Assessments Alex Ruane and the AgMIP Climate Team AgMIP Sub-Saharan Africa Project Kickoff Workshop September.

AgMIP Climate Team Activities for Integrated Assessments

Alex Ruane and the AgMIP Climate TeamAgMIP Sub-Saharan Africa Project Kickoff Workshop

September 10-14, 2012

[email protected]

1

Overview of Tasks• Characterize climate in integrated assessment region

• Identify at least one historical time series for region

• Produce climate time series for each crop modeling

location within the region

• Produce future scenarios for each crop modeling location

2

Characterize Regional Climate• Machakos Region Annual Temperature

− From WorldClim dataset

− For larger regions we may use bias-corrected MERRA series

(Background climate data set)

3

Characterize Regional Climate• Machakos Region Annual Precipitation

− From WorldClim dataset

4

Characterize Regional Climate• Machakos Region Climate Zones and station dataset

− 1980-2010 data from Katumani, Kenya

5

Identify Climate Series• Katumani, Kenya station

− Find its climate zone and compare

climatology to other zones

− If more than one station are

available, assign each crop modeling

location to a specific time series

6

Climate Zone MeansZone Alt Tann Tjan Tfeb Tmar Tapr Tmay Tjun Tjul Taug Tsep Toct Tnov Tdec 1 293 25.81 26.60 27.37 27.79 26.96 25.68 24.42 23.58 23.80 24.68 26.01 26.46 26.41 2 448 25.35 25.89 26.80 27.22 26.50 25.34 23.88 23.06 23.37 24.55 25.96 26.01 25.57 3 311 26.03 26.57 27.43 27.90 27.20 26.01 24.54 23.84 24.11 25.13 26.56 26.71 26.35 4 598 24.60 25.16 26.13 26.48 25.75 24.59 23.11 22.22 22.56 23.87 25.28 25.23 24.77 5 456 25.21 26.12 26.89 27.24 26.28 25.06 23.79 22.86 23.08 24.04 25.42 25.90 25.79 6 644 24.27 24.89 25.77 26.14 25.35 24.24 22.83 21.90 22.24 23.47 24.87 24.95 24.53 7 788 23.56 24.28 25.23 25.51 24.72 23.51 22.03 21.06 21.46 22.76 24.18 24.18 23.81 8 931 22.73 23.38 24.32 24.60 23.89 22.73 21.22 20.29 20.70 22.00 23.40 23.33 22.92 9 1093 21.78 22.46 23.32 23.59 22.89 21.76 20.26 19.36 19.77 21.09 22.47 22.38 22.01 10 1236 21.04 22.01 22.66 22.82 22.01 20.80 19.40 18.53 19.03 20.30 21.65 21.73 21.53 11 1248 20.85 21.61 22.39 22.63 21.92 20.79 19.31 18.42 18.85 20.16 21.54 21.45 21.18 12 1593 19.24 19.90 20.67 20.93 20.31 19.28 17.75 16.86 17.31 18.72 20.07 19.62 19.45 13 1551 19.18 20.02 20.73 20.94 20.22 19.11 17.66 16.73 17.15 18.48 19.88 19.72 19.56 14 1903 16.96 18.07 18.58 18.76 17.90 16.66 15.47 14.45 14.88 16.07 17.41 17.67 17.58 15 2581 12.83 13.97 14.48 14.62 13.70 12.43 11.44 10.33 10.80 11.91 13.15 13.53 13.58 16 3198 9.18 10.18 10.80 10.94 10.02 8.82 7.78 6.78 7.26 8.32 9.50 9.82 9.9617 3595 6.89 7.80 8.44 8.58 7.72 6.56 5.52 4.56 5.06 6.06 7.20 7.50 7.6818 4192 3.57 4.40 5.05 5.15 4.35 3.30 2.25 1.45 1.85 2.85 3.85 4.05 4.25

Note: this demonstrates the approach but the climate zones are slightly different than original Machakos run

Produce Baseline Climate Series for Each Crop Modeling Location

• For each climate zone produce a time series where day-by-day

variability comes from Katumani but 30-year mean comes from climate

zone using delta method to impose spatial variability in the mean

7

Produce Future Climate Series for Each Crop Modeling Location

• For each climate zone produce future time series scenarios using

climate model projections

8

Produce Climate Series for Each Crop Modeling Location

• Look at the baseline and future scenarios

9