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CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.
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MarkSim GCM: generating plausible weatherdata for future climates
September 2011
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
™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
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
Multi-model global averages of surface warming (relative to 1980-99) for the SRES scenarios
The future is uncertain: which GCM, which emissions scenario?
Scenario 2011-2030
2046-2065
2080-2099
A2 (“high” emissions) 0.64 1.65 3.13
A1B (“medium” emissions) 0.69 1.75 2.65
B1 (“low” emissions) 0.66 1.29 1.79
Committed warming (emissions stabilised at 2000 levels)
0.37 0.47 0.56
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)
Atmosphere-Ocean General Circulation Models used in MarkSim GCM4
Model Name Institution Code
BCCR_BCM2.0 Bjerknes Centre for Climate Research (University of Bergen, Norway)
BCC
CNRM-CM3 Météo-France/Centre National de Recherches Météorologiques, France
CNR
CSIRO-Mk3_5 Commonwealth Scientific and Industrial Research Organisation (CSIRO) Atmospheric Research, Melbourne, Australia
CSI
ECHam5 Max Planck Institute for Meteorology, Germany ECH
INMCM3_0 Institute for Numerical Mathematics, Moscow, Russia INM
MIROC3.2 (medres)
Center for Climate System Research (University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC), Japan
MIR
Ensemble average
Average climatology of the above 6 AOGCMs AVR
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/
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
Annual rainfall changes (mm) from 2000 to 2050, A2
CNR CSI
ECH MIR
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
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
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)
What to use for “observations”?Could use www.worldclim.org
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
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
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
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
• 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
The tool allows you to select one of the three scenarios, and one of 6 climate models (or their average)
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)
… graphed …
After running the model, the daily data can be viewed directly …
… or downloaded as a ZIP file to the user’s computer
Region Dec-Jan Jun-Aug
Sahara Small decrease(5-20%)
Inconsistent
West Africa Inconsistent Inconsistent
East Africa Small increase (5-20%)
Inconsistent
Southern Africa Inconsistent Large decrease (>20%)
Limitations and uncertainties associated with these data
GCM consistency in regional precipitation projections for 2090-2099 (SRES A1B). IPCC, 2007
http://gismap.ciat.cgiar.org/MarkSimGCM/
Demo