Global aerosol simulations using NICAM.16 on a 14 km grid spacing for a climate study: improved and remaining issues relative to a lower-resolution model
Authors: Goto Daisuke , Sato Yousuke , Yashiro Hisashi , Suzuki Kentaroh , Oikawa Eiji , Kudo Rei , Nagao Takashi M. , Nakajima Teruyuki 
Published: August 2020

Abstract. High-performance computing resources allow us to conduct
numerical simulations with a horizontal grid spacing that is sufficiently
high to resolve cloud systems on a global scale, and high-resolution models
(HRMs) generally provide better simulation performance than low-resolution
models (LRMs). In this study, we execute a next-generation model that is
capable of simulating global aerosols using version 16 of the Nonhydrostatic
Icosahedral Atmospheric Model (NICAM.16). The simulated aerosol
distributions are obtained for 3 years with an HRM using a global 14 km grid
spacing, an unprecedentedly high horizontal resolution and long integration
period. For comparison, a NICAM with a 56 km grid spacing is also run as an
LRM, although this horizontal resolution is still high among current global
aerosol climate models. The comparison elucidated that the differences in
the various variables of meteorological fields, including the wind speed,
precipitation, clouds, radiation fluxes and total aerosols, are generally
within 10 % of their annual averages, but most of the variables related to
aerosols simulated by the HRM are slightly closer to the observations than
are those simulated by the LRM. Upon investigating the aerosol components,
the differences in the water-insoluble black carbon and sulfate
concentrations between the HRM and LRM are large (up to 32 %), even in the
annual averages. This finding is attributed to the differences in the
aerosol wet deposition flux, which is determined by the conversion rate of
cloud to precipitation, and the difference between the HRM and LRM is
approximately 20 %. Additionally, the differences in the simulated aerosol
concentrations at polluted sites during polluted months between the HRM and
LRM are estimated with normalized mean biases of −19 % for black carbon
(BC), −5 % for sulfate and −3 % for the aerosol optical thickness (AOT).
These findings indicate that the impacts of higher horizontal grid spacings
on model performance for secondary products such as sulfate, and complex
products such as the AOT, are weaker than those for primary products, such
as BC. On a global scale, the subgrid variabilities in the simulated AOT and
cloud optical thickness (COT) in the 1∘×1∘
domain using 6-hourly data are estimated to be 28.5 % and 80.0 %,
respectively, in the HRM, whereas the corresponding differences are 16.6 %
and 22.9 % in the LRM. Over the Arctic, both the HRM and the LRM generally
reproduce the observed aerosols, but the largest difference in the surface
BC mass concentrations between the HRM and LRM reaches 30 % in spring (the
HRM-simulated results are closer to the observations). The vertical
distributions of the HRM- and LRM-simulated aerosols are generally close to
the measurements, but the differences between the HRM and LRM results are
large above a height of approximately 3 km, mainly due to differences in the
wet deposition of aerosols. The global annual averages of the effective
radiative forcings due to aerosol–radiation and aerosol–cloud interactions
(ERFari and ERFaci) attributed to anthropogenic aerosols in the HRM are
estimated to be -0.293±0.001 and -0.919±0.004 W m−2,
respectively, whereas those in the LRM are -0.239±0.002 and
-1.101±0.013 W m−2. The differences in the ERFari
between the HRM and LRM are primarily caused by those in the aerosol burden,
whereas the differences in the ERFaci are primarily caused by those in the
cloud expression and performance, which are attributed to the grid spacing.
The analysis of interannual variability revealed that the difference in
reproducibility of both sulfate and carbonaceous aerosols at different
horizontal resolution is greater than their interannual variability over
3 years, but those of dust and sea salt AOT and possibly clouds were the
opposite. Because at least 10 times the computer resources are required for
the HRM (14 km grid) compared to the LRM (56 km grid), these findings in
this study help modelers decide whether the objectives can be achieved using
such higher resolution or not under the limitation of available
computational resources.