  Data required to calibrate uncertain GCM parameterizations are often only
available in limited regions or time periods, for example, observational data
from field campaigns, or data generated in local high-resolution simulations.
This raises the question of where and when to acquire additional data to be
maximally informative about parameterizations in a GCM. Here we construct a new
ensemble-based parallel algorithm to automatically target data acquisition to
regions and times that maximize the uncertainty reduction, or information gain,
about GCM parameters. The algorithm uses a Bayesian framework that exploits a
quantified distribution of GCM parameters as a measure of uncertainty. This
distribution is informed by time-averaged climate statistics restricted to
local regions and times. The algorithm is embedded in the recently developed
calibrate-emulate-sample (CES) framework, which performs efficient model
calibration and uncertainty quantification with only $\mathcal{O}(10^2)$ model
evaluations, compared with $\mathcal{O}(10^5)$ evaluations typically needed for
traditional approaches to Bayesian calibration. We demonstrate the algorithm
with an idealized GCM, with which we generate surrogates of local data. In this
perfect-model setting, we calibrate parameters and quantify uncertainties in a
quasi-equilibrium convection scheme in the GCM. We consider targeted data that
are (i) localized in space for statistically stationary simulations, and (ii)
localized in space and time for seasonally varying simulations. In these
proof-of-concept applications, the calculated information gain reflects the
reduction in parametric uncertainty obtained from Bayesian inference when
harnessing a targeted sample of data. The largest information gain typically,
but not always, results from regions near the intertropical convergence zone
(ITCZ).
