MarkSim GCM is a tool developed for generating plausible daily weather data to inform agricultural impact models based on future climatic scenarios. It utilizes a Markov weather simulator calibrated with data from over 10,000 weather stations and will enhance its capacity in Version 2 by incorporating data from more than 50,000 sites globally. The model forecasts temperature and rainfall impacts based on various emissions scenarios, while also highlighting the uncertainties in climate projections and variability.

1. MarkSim GCM: generating plausible weather data for future climates September 2011

2. MarkSim GCM A tool to generate daily data that are characteristic (to some extent) of future climatologies for any point on the globe, to drive agricultural impact models

3. A Markov weather simulator that generates simulated daily weather for any point in the tropics Runs off interpolated climate surfaces Model fitted to 10,000 weather stations World climates classified into 701 groups Climate group is known for any pixel on map Some model parameters estimated by regressions within climate groups based on the pixels’ climate from the interpolated surface MarkSim ™

4. Number of MarkSim rainfall stations per half-degree grid cell (as in early 2010) Still some major gaps in coverage, particularly in the tropical areas of Africa Version 2 of MarkSim is under development, and will be based on >50,000 sites globally with historical daily rainfall data

5. Multi-model global averages of surface warming (relative to 1980-99) for the SRES scenarios The future is uncertain: which GCM, which emissions scenario?

6. Projected mean impacts on global temperatures of three different scenarios Global mean warming from the IPCC multi-model ensemble mean for three periods relative to 1980–1999 (Wilby et al. 2009, data source IPCC 2007) 0.56 0.47 0.37 Committed warming (emissions stabilised at 2000 levels) 1.79 1.29 0.66 B1 (“low” emissions) 2.65 1.75 0.69 A1B (“medium” emissions) 3.13 1.65 0.64 A2 (“high” emissions) 2080-2099 2046-2065 2011-2030 Scenario

7. Atmosphere-Ocean General Circulation Models used in MarkSim GCM4 AVR Average climatology of the above 6 AOGCMs Ensemble average MIR Center for Climate System Research (University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC), Japan MIROC3.2 (medres) INM Institute for Numerical Mathematics, Moscow, Russia INMCM3_0 ECH Max Planck Institute for Meteorology, Germany ECHam5 CSI Commonwealth Scientific and Industrial Research Organisation (CSIRO) Atmospheric Research, Melbourne, Australia CSIRO-Mk3_5 CNR Météo-France/Centre National de Recherches Météorologiques, France CNRM-CM3 BCC Bjerknes Centre for Climate Research (University of Bergen, Norway) BCCR_BCM2.0 Code Institution Model Name

8. How different are the projections of rainfall and temperature among the various GCMs? One good place to find out: http://www.ipcc-data.org/maps/

9. June precipitation anomalies (relative to the 20th century control 1961-1990 30-year normal) for 2046-2065 for SRES A2 and four GCMs www.ipcc-data.org

10. Annual rainfall changes (mm) from 2000 to 2050, A2 CNR CSI ECH MIR

11. Where do the MarkSim model parameters come from? From climate grids, or from the user directly: Monthly rainfall amounts Monthly average max and min temperatures From the climate typing clusters: Number of rain days per month Monthly correlation matrix of raindays per month Baseline probits of a wet day following three dry days and the “lag parameters” Derived parameters: Monthly solar radiation

12. Where do the MarkSim model parameters come from? From climate grids, or from the user directly: Monthly rainfall amounts Monthly average max and min temperatures From the climate typing clusters: Number of rain days per month Monthly correlation matrix of raindays per month Baseline probits of a wet day following three dry days and the “lag parameters” Derived parameters: Monthly solar radiation Grids of possible future climates from GCMs, from RCMs

13. MarkSim GCM Climate model outputs: Calculate “long-term” monthly means for rainfall and max & min temperatures from daily output:  20-or 30-year monthly averages, say for 2041-2060 (the “2050s”) Downscale spatially using “unintelligent” downscaling (e.g. the “delta” method):  interpolate from the coarse (often 200-300 km grids) spatial resolution of the climate models to a higher resolution and add differences to a baseline climatology such as WorldClim (www.worldclim.org)

14. What to use for “observations”? Could use www.worldclim.org

15. Where do the MarkSim model parameters come from? From climate grids: Monthly rainfall amounts Monthly average max and min temperatures From the climate typing clusters: Number of rain days per month Monthly correlation matrix of raindays per month Baseline probits of a wet day following three dry days and the “lag parameters” Derived parameters: Monthly solar radiation With future climatologies of rainfall and max & min temps, we could then generate the remaining parameters and simulate “plausible” daily data for these climatologies

16. Where do the MarkSim model parameters come from? From climate grids: Monthly rainfall amounts Monthly average max and min temperatures From the climate typing clusters: Number of rain days per month Monthly correlation matrix of raindays per month Baseline probits of a wet day following three dry days and the “lag parameters” Derived parameters: Monthly solar radiation But: remember Cape Town? Differences between coarse-grid statistical downscaling and RCM-based downscaling We could do much better using future climatologies derived in different / better ways

17. MarkSim GCM “ All” that is needed from a climate model is a set of long-term mean monthly data for rainfall and max & min temperatures for a specific time slice and GHG emissions scenario Climate typing is then used to generate the remaining parameters MarkSim’s climate clustering is based on current climate types: climates are changing through time, and the climate cluster to which any point belongs often changes into the future

18. MarkSim GCM MarkSim asymptotically matches monthly means of rainfall (whether current or future); but concerning changes in climate variability, the only information in MarkSimGCM relates to the current variability of the fitted cluster – there is no other information available What this means: GCMs are climate models, not weather models; the methods used in MarkSimGCM cannot capture future (unknown) variability in weather (although they might do better with RCM-derived climatologies)  Much care is needed in how we use the outputs of MarkSimGCM and similar tools

19. MarkSim is a climate typer – so shifts in climate cluster between now and 2050, say, may result in shifts in weather variability (associated with the new cluster) – but not much if any reason to suppose that this may be realistic Use ensembles of GCM-scenario combinations, look at the variation in mean response, and present this variability (uncertainty) The general approach may be OK; the weakest link is the future climatologies. Much more advisable to use a better climate downscaling approaches + weather generation? MarkSim GCM

21. The tool allows you to select one of the three scenarios, and one of 6 climate models (or their average)

22. Select climate model Select emissions scenario Select the centre year of the time slice and number of years of data wanted Select location (the ILRI cafeteria in Nairobi)

23. … graphed … After running the model, the daily data can be viewed directly … … or downloaded as a ZIP file to the user’s computer

24. Region Dec-Jan Jun-Aug Limitations and uncertainties associated with these data GCM consistency in regional precipitation projections for 2090-2099 (SRES A1B). IPCC, 2007 Large decrease (>20%) Inconsistent Southern Africa Inconsistent Small increase (5-20%) East Africa Inconsistent Inconsistent West Africa Inconsistent Small decrease (5-20%) Sahara

25. http://gismap.ciat.cgiar.org/MarkSimGCM/ Demo