Carbon Dioxide and Earth's Future Pursuing the ... - Magazooms

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described it, “the cloud parameterization problem is overwhelmingly complicated,” and “cloud
parameterization developers,” as they referred to them, were still “struggling to identify the
most important processes on the basis of woefully incomplete observations.” And to drive this
point home, they said “there is little question why the cloud parameterization problem is taking
a long time to solve: It is very, very hard.” In fact, the four scientists concluded that “a sober
assessment suggests that with current approaches the cloud parameterization problem will not
be ‘solved’ in any of our lifetimes.”
So is all hope lost with respect to models ever being able to correctly forecast floods and
drought if they cannot correctly reproduce clouds and precipitation? Not entirely.
The shining hope of the climate-modeling community resides in something Randall et al. called
“cloud system-resolving models” or CSRMs, which can be compared with single-column models
or SCMs that can be “surgically extracted from their host GCMs.” These advanced models, as
they describe them, “have resolutions fine enough to represent individual cloud elements, and
space-time domains large enough to encompass many clouds over many cloud lifetimes.” Of
course, these improvements mean that “the computational cost of running a CSRM is hundreds
or thousands of times greater than that of running an SCM.” Nevertheless, in a few more
decades, according to Randall et al., “it will become possible to use such global CSRMs to
perform century-scale climate simulations, relevant to such problems as anthropogenic climate
change.” In the interim, however, they remain far from ready for prime time, as evidenced in a
study conducted four years later by Zhou et al. (2007).
Noting that CSRMs “still need parameterizations on scales smaller than their grid resolutions
and have many known and unknown deficiencies,” and to help stimulate progress in these
areas, Zhou et al. compared the cloud and precipitation properties observed by instruments
deployed in the Clouds and Earth’s Radiant Energy System (CERES) and Tropical Rainfall
Measuring Mission (TRMM) systems against simulations obtained from the three-dimensional
Goddard Cumulus Ensemble (GCE) model during the South China Sea Monsoon Experiment
(SCSMEX) field campaign of 18 May-18 June 1998. And as a result of that analysis, the nine
researchers reported that: (1) “the GCE rainfall spectrum includes a greater proportion of heavy
rains than PR (Precipitation Radar) or TMI (TRMM Microwave Imager) observations,” (2) “the
GCE model produces excessive condensed water loading in the column, especially the amount
of graupel as indicated by both TMI and PR observations,” (3) “the model also cannot simulate
the bright band and the sharp decrease of radar reflectivity above the freezing level in
stratiform rain as seen from PR,” (4) “the model has much higher domain-averaged OLR
(outgoing longwave radiation) due to smaller total cloud fraction,” (5) “the model has a more
skewed distribution of OLR and effective cloud top than CERES observations, indicating that the
model’s cloud field is insufficient in area extent,” (6) “the GCE is ... not very efficient in
stratiform rain conditions because of the large amounts of slowly falling snow and graupel that
are simulated,” and finally, in summation, that (7) “large differences between model and
observations exist in the rain spectrum and the vertical hydrometeor profiles that contribute to
the associated cloud field.”
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