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
Precipitation, wind, cloudiness, ocean currents, air, and water temperatures – these and other
climate variables evolve in time and space governed by physical, chemical, and biological
processes. We refer to these as mechanistic processes to distinguish them from purely empirical
relationships that may be found in data without reference to an underlying mechanism. The
mechanistic processes included in the climate models are quite varied – from evapotranspiration
to cloud formation, the transport of heat and water vapor by the wind, infiltration of surface
water into the soil, turbulent mixing of air and of the ocean waters, and so on. To the climate
modeler, these processes all have one thing in common: they can be expressed in terms of
mathematical equations derived from a combination of scientific laws, empirical data, and
observations. These equations are then converted into computer code, along with information
about the Earth’s geography – such as the distribution of vegetation and soil types and a digital
elevation model of topography – to form the basis for a climate model. AOGCMs are driven by
the observed time history and future projected changes in incoming solar radiation, GHG
concentrations, and aerosols given off by volcanic eruptions and human activities.
The variables of a climate model are marched forward at discrete time intervals, or “timesteps.”
Timesteps can range from a few minutes to an hour, depending on the spatial resolution of the
model. As a result, GCMs simulate hourly and daily weather, and climate statistics are computed
from climate models just as they are from observations.
Because of the complexity of the mathematical equations in climate models, these equations can
only be solved approximately, even on the most powerful supercomputers. To determine the
most precise results within this limitation, GCMs typically divide the globe – the atmosphere and
the oceans – into a horizontal and vertical grid, creating so-called “grid boxes” or “grid cells”
(Figure 3.2). The finer the grid, the higher the spatial resolution, and the more computer power
required to run the simulations. The horizontal resolution is typically cited as representative of a
component model’s overall spatial and temporal resolution. The individual component models do
not typically have the same spatial resolution or timestep, and may in fact have very different
grid representations to better accommodate their unique physical processes. These differences
are taken into account when the components are coupled.
Many climate phenomena, such as thunderstorms, take place at spatial scales smaller than a
model grid cell – be it in a GCM or even a RCM. The idea of parameterization is to account for
the effect that small-scale processes have within the grid cell through a representation of the
phenomena. One way to accomplish this is through a simplified mechanistic model, or
conceptual model. For example, given the grid-scale temperature lapse rate and moisture
convergence in the atmosphere, a conceptual model of atmospheric convection can compute the
expected total amount of convective rainfall within the grid cell, which then affects the moisture
and heat fluxes at the grid scale.
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