[1] Shortwave direct aerosol radiative forcing (DARF) is derived at the top of the atmosphere (TOA) and at the surface under clear-sky, cloudy-sky, and all-sky conditions using data of space-borne CALIOP lidar and MODIS sensor. We investigate four scenarios for evaluating the DARF: clear-sky, the case that aerosols exist above clouds, the case that aerosols exist below high-level clouds, and the case that aerosols are not detected by CALIOP in cloudy-sky condition. The cloudy-sky DARF is estimated by the latter three scenarios. The all-sky DARF is the combination of clear-sky and cloudy-sky DARF weighted by the cloud occurrence. They are then compared with DARF calculated by a global aerosol model, SPRINTARS. The results show that the TOA forcing over desert regions caused by dust with single scattering albedo (SSA) of 0.92 is positive regardless of cloud existence, due to high solar surface albedo. Off southern Africa, smoke aerosols with SSA of 0.84 above low-level clouds are observed and simulated and the annual mean TOA cloudy-sky DARF is estimated at more than +3 Wm À2 , consistent with past studies. Aerosols with SSA of 0.96 within optically thin clouds cause a TOA negative forcing, while that within optically thick clouds cause a TOA positive forcing. This indicates that aerosols within optically thick clouds cause positive forcing in our radiative transfer calculation, regardless of SSA. Annual zonal averages of DARF from 60 S to 60 N under clear-sky, cloudy-sky, and all-sky are À2.97, +0.07, and À0.61 Wm À2 from CALIOP and À2.78, +1.07, and À0.58 Wm À2 from SPRINTARS.Citation: Oikawa, E., T. Nakajima, T. Inoue, and D. Winker (2013), A study of the shortwave direct aerosol forcing using ESSP/CALIPSO observation and GCM simulation,

In this study, all‐sky ShortWave Direct Aerosol Radiative Forcing (SWDARF) at the top of atmosphere is estimated using the method of Oikawa et al. (2013) applied to two generations of Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 products, i.e., version 2 (V2) and version 3 (V3), and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud product. The estimated SWDARF in Oikawa et al. (2013) was based on CALIPSO V2 product, which contained significant errors in cloud clearing and low‐altitude aerosols. This error was corrected in V3, resulting in greatly improved and significantly different aerosol and cloud distributions. In clear‐sky conditions, the magnitude of aerosol optical thickness underestimation becomes smaller and SWDARF becomes more negative using the V3 product. In addition, above‐cloud aerosols, which cause positive SWDARF, are less frequently detected and below‐cloud aerosols are more frequently detected in the V3 product than in the V2 product, so that cloudy‐sky SWDARF becomes more negative using the V3 product. From these results, clear‐sky, cloudy‐sky, and all‐sky SWDARFs become more negative using the V3 product than the V2 product. The magnitude of negative SWDARF using the V3 product is more than twice as large as the V2 product under all‐sky conditions due to V3 improvements in the lidar retrieval algorithms. Considering the uncertainties of aerosol and cloud measurements, annual zonal averages of clear‐sky, cloudy‐sky, and all‐sky SWDARFs from 60°S to 60°N are estimated as −4.0 ± 0.2, −1.1 ± 0.3, and −2.1 ± 0.2 Wm−2 from the V3 product.

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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.

We developed an algorithm to derive extinction coefficients for four aerosol components (water-soluble, dust, sea salt, black carbon) from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. The algorithm was applied to the nine-year data for 2007–2015 and the results were compared to CALIOP standard product (CALIOP-ST) and MODerate resolution Imaging Spectroradiometer (MODIS) standard product (MODIS-ST). Comparisons of the total aerosol optical thickness (AOT) showed that MODIS-ST was the largest, followed by CALIOP-ST (Ver.4), and our product. CALIOP-ST (Ver.3) showed a similar magnitude to ours.