In:
Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 18, No. 17 ( 2018-09-11), p. 13031-13053
Abstract:
Abstract. Observational constraint of simulated aerosol and cloud
properties is an essential part of building trustworthy climate models for
calculating aerosol radiative forcing. Models are usually tuned to achieve
good agreement with observations, but tuning produces just one of many
potential variants of a model, so the model uncertainty cannot be determined.
Here we estimate the uncertainty in aerosol effective radiative forcing (ERF)
in a tuned climate model by constraining 4 million variants of the
HadGEM3-UKCA aerosol–climate model to match nine common observations
(top-of-atmosphere shortwave flux, aerosol optical depth, PM2.5, cloud
condensation nuclei at 0.2 % supersaturation (CCN0.2), and
concentrations of sulfate, black carbon and organic carbon, as well as
decadal trends in aerosol optical depth and surface shortwave radiation.) The
model uncertainty is calculated by using a perturbed parameter ensemble that
samples 27 uncertainties in both the aerosol model and the physical climate
model, and we use synthetic observations generated from the model itself to
determine the potential of each observational type to constrain this
uncertainty. Focusing over Europe in July,
we show that the aerosol ERF uncertainty can be reduced by about 30 % by
constraining it to the nine observations, demonstrating that producing
climate models with an observationally plausible “base state” can
contribute to narrowing the uncertainty in aerosol ERF. However, the
uncertainty in the aerosol ERF after observational constraint is large
compared to the typical spread of a multi-model ensemble. Our results
therefore raise questions about whether the underlying multi-model
uncertainty would be larger if similar approaches as adopted here were
applied more widely. The approach presented in this study could be used to
identify the most effective observations for model constraint. It is hoped
that aerosol ERF uncertainty can be further reduced by introducing
process-related constraints; however, any such results will be robust only if
the enormous number of potential model variants is explored.
Type of Medium:
Online Resource
ISSN:
1680-7324
DOI:
10.5194/acp-18-13031-2018
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2018
detail.hit.zdb_id:
2092549-9
detail.hit.zdb_id:
2069847-1
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