Options for Improving Climate Modeling to Assist Water Utility ...

Water Utility Climate Alliance White Paper
Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change
eastern North America (IPCC Chapter 11, Supp. Material; range over all seasons and
over the 25th to 75th percentiles of model biases; these regions include the United States
and the southern tier of Canada). The median biases among models go from a low of
2°C to a high of +0.4°C, depending on region and season.
The diurnal temperature range (the difference of daily maximum and minimum
temperatures) is generally less well simulated, with most CMIP3 models showing too
small a diurnal range. Over the United States, these biases are largest in the West, with
typical models showing 5°C too small a range (Solomon et al., 2007, Chapter 8, Supp.
Material).
Precipitation is generally less well simulated than temperature:
“[C]orrelation between models and observations is 50 to 60% for seasonal means on
scales of a few hundred kilometers” (CCSP 3.1).
Seasonal (three-month average) model biases in the mean precipitation climatology range
from 25% to +25% over central and eastern North America, and from +21% to +103%
over western North America (IPCC Chapter 11, Supp. Material; range over all seasons
and over the 25th to 75th percentile of model biases). The median biases in western North
America are particularly large, ranging from +28% (Summer) to +98% (Winter). Median
biases in the other regions range between 16% and +21%.
The average diurnal cycle in precipitation (e.g., the timing of thunderstorms) is very
poorly modeled in many convective regions, including the central United States.
Precipitation biases are generally largest in the Tropics. Because of teleconnections
through the atmospheric circulation, this affects precipitation patterns around the globe,
including over the United States.
The North American monsoon, which is an important source of summer precipitation in
the southwest United States, is generally not well simulated in the GCMs (Lin et al.,
2008).
Studies that look at model performance for many variables at once yield some useful insights:
Climate model simulations have generally improved since the early 1990s in their ability
to simulate the mean climate and seasonal cycle (Reichler and Kim, 2008).
Despite the increase in model performance over the last two decades, the range of global
climate projections has not greatly narrowed. However, the understanding of these
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