SRESA1B experiments

CCSM case names:
b30.040a b30.040b b30.040c b30.040d b30.040e b30.040b.ES01 b30.040f.ES01 b30.040g.ES01 b30.040g (years 2000-2099)
b30.044a b30.044b b30.044c b30.044d b30.044e (years 2100-2199)
b30.044b.ES01 (years 2100-2449)
b30.044f.ES01 b30.044g.ES01 (years 2100-2349)


Ozone: After 2000, tropospheric 3D monthly patterns for year t same as year 2000 patterns but scaled by Q(t)/Q(2000), where Q global mean radiative forcing from the IPCC TAR and reproduced in Wigley et al. (2002). After 2000, stratospheric ozone recovers consistent with Montreal Protocol as in the NOAA dataset.
GHGs: As in SRES.
Sulfates: A discontinuity arose at the year 2000 in the sulfate datasets. This was due to the SRES datasets for the future scenarios overpredicting sulfate amounts after 1990, such that switching from the relatively accurate historical datasets to the future datasets at the year 2000 resulted in a discontinuity. Therefore, new future scenario SOx emission datasets were generated for each scenario. Anomalies of the future SOx emissions versus the year 2000 SOx values were created for each SRES scenario. These anomalies were added to the Smith year 2000 data. The anomaly was distributed across the two vertical levels in the same ratio as the values of the two vertical levels of the Smith year 2000 data, making sure emission did not go below zero anywhere. Sulfate data was generated for the following dates (year-month) and interpolated in time: 1990-07, 2000-01, 2000-07, 2010-07, 2020-07, 2030-07, 2040-07, 2050-07, 2060-07, 2070-07, 2080-07, 2090-07, 210007. Since the corrections were done on a grid cell basis, this could result in negative values for emissions in some of the scenarios for some regions. These differences could be considered as initial conditions; for example, they did not reflect long-term changes in what emissions would be in 2100. Therefore, we added (on a grid cell basis) the difference between both year 2000 datasets, which made the year 2000 data continuous. Then we added 4/5 of that difference in 2010, 3/5 of the difference in 2020, etc., and then from 2050 onward the SO2 was identical to the SRES scenarios. In that way there were no negative values or regions with abnormally low emissions. We then created a two-level ratio of the difference that was the same as the ratio of each level of the historical data to 2000 data, to the total historical 2000 data, and then we applied the 4/5, 3/5, 2/5, 1/5 weighting of that to the SRES values from 2010 to 2100. Thus, the emissions over most of the century were then identical to the SRES values. These future emissions are described by Smith et al. (2005).
Carbon aerosols: Geographical distributions are scaled by future SO2 amounts instead of keeping carbon aerosols constant after year 2000. Carbon aerosol scaling datasets were generated for each scenario as: f(year N) = SO2(year N)/SO2(year 2000), where SO2 is the globally averaged SO2 value. Data were generated for the following dates (year- month) and interpolated in time: 2000-01, 2000-07, 2010-07, 2020-07, 2030-07, 2040-07, 2050-07, 2060-07, 2070-07, 2080-07, 2090-07, 2100-07. These values were created by using the sulfate inventories, globally integrated, and dividing by the year 2000 global integral.
Sea salt and dust: held at year 2000 values.

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greenhouse gases
Sulfate emissions Ozone concentration Other aerosols
(black and organic carbon,
sea salt, dust)
scaling factor