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The GCM projection ensemble percentiles are selected from the average value of scalars over the
analysis area (i.e., Massachusetts and the Connecticut and Merrimack Watersheds) for each of 22 GCMs.
The formula used for determining the X percentile ensemble member is: Xth % = (X - 1) * X% +1. For
example, when 22 GCMs are used for the ensemble of precipitation projections, the 10th percentile
model is defined by (22-1)* 10% +1 = 3.1. For simplicity, the 3rd place GCM value is chosen in this case
as the low percentile climate change value.
In the precipitation part of the study, the GCMs selected for the 10th, 50th and 90th percentiles are:
10th% = CCSM45
50th% = IPSL_CM5A_MR
90th% = MIROC5
The percent change in magnitude for the 10th, 50th and 90th percentile GCMs is subsequently derived by
multiplying the cell-specific scalar by the global mean warming under a specific emission scenario (RCP
in this case) and time period. The percent change in magnitude is then applied to higher resolution
spatially-explicit observed data (recorded precipitation depth in recent years) to capture the variability
within a GCM cell (0.5 degree x 0.5 degree) and to project future precipitation depth for the 100-year
return interval event. In this case, National Oceanic and Atmospheric Administration (NOAA) Atlas 14
data are used. Because of the higher resolution of the NOAA Atlas data, this application step results in a
final raster resolution of about 0.5 mile (0.8 km) as depicted on the projected precipitation depth maps.
For example, if a climate station shows that the historic 24-hour 100-year return interval precipitation
event is 100 mm and the raster cell (projected percent change) has a value of 30% (increase of 30%),
and then the projected future precipitation depth is:
100 x 1.3 = 130 mm
Please note that moderate changes in future precipitation (both percent change and absolute depth
change) may not demonstrate themselves on the maps in different outer years or scenarios. As the
precipitation map legend shows, the legend classification entails a range of values in each class.
Sometimes it may appear that the class (color) doesn’t change in different outer years or scenarios on
the maps, nevertheless, it doesn’t necessary mean the values don’t change. On the contrary, they could
be changing but just within the bounds of the data in that class. On the other side, a very small change
(e.g. 0.1 inch) can result in a change in class and thus color on a map if the value is on the high end of
the class range to begin with.
High Temperature
Similar to precipitation projections, temperature projections are developed for GCMs falling closest to
the idealized 10th, 50th, and 90th percentile scalars from the 40 GCMs that are available in SimCLIM
software for monthly temperature change analysis. The percentiles are selected from the average
annual value of change in temperature by 2100 (in comparison to the baseline) under the highest
emission scenario (RCP 8.5) over the analysis area (and Massachusetts state boundaries and the
Connecticut and Merrimack watersheds) for each model.
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