This article reviews the development of a global non-hydrostatic model, focusing on the pioneering research of the Non-hydrostatic Icosahedral Atmospheric Model (NICAM). Very high resolution global atmospheric circulation simulations with horizontal mesh spacing of approximately O (km) were conducted using recently developed supercomputers. These types of simulations were conducted with a specifically designed atmospheric global model based on a quasi-uniform grid mesh structure and a non-hydrostatic equation system. This review describes the development of each dynamical and physical component of NICAM, the assimilation strategy and its related models, and provides a scientific overview of NICAM studies conducted to date.

An alternatively spliced variant of Smad2 with a deletion of exon 3 (Smad2⌬exon3) is found in various cell types. Here, we studied the function of Smad2⌬exon3 and compared it with those of wild-type Smad2 containing exon 3 (Smad2(wt)) and Smad3. When transcriptional activity was measured using the p3TP-lux construct, Smad2⌬exon3 was more potent than Smad2(wt), and had activity similar to Smad3. Transcriptional activation of the activin-responsive element (ARE) of Mix.2 gene promoter by Smad2⌬exon3 was also similar to that by Smad3, and slightly less potent than that by Smad2(wt). Phosphorylation by the activated transforming growth factor-␤ type I receptor and heteromer formation with Smad4 occurred to similar extents in Smad2⌬exon3, Smad2(wt), and Smad3. However, DNA binding to the activating protein-1 sites of p3TP-lux was observed in Smad2⌬exon3 as well as in Smad3, but not in Smad2(wt). In contrast, Smad2(wt), Smad2⌬exon3, and Smad3 efficiently formed ARE-binding complexes with Smad4 and FAST1, although Smad2(wt) did not directly bind to ARE. These results suggest that exon 3 of Smad2 interferes with the direct DNA binding of Smad2, and modifies the function of Smad2 in transcription of certain target genes.

[1] This article introduces an international regional experiment, East Asian Regional Experiment 2005 (EAREX 2005), carried out in March-April 2005 in the east Asian region, as one of the first phase regional experiments under the UNEP Atmospheric Brown Cloud (ABC) project, and discusses some outstanding features of aerosol characteristics and its direct radiative forcing in the east Asian region, with some comparison with the results obtained in another ABC early phase regional experiment, ABC Maldives Monsoon Experiment (APMEX) conducted in the south Asian region. Time series of aerosol optical thickness (AOT), single scattering albedo (SSA), aerosol extinction cross section profile and CO concentration shows that air pollutants and mineral dust were transported every 5 to 7 days in the EAREX region to produce SSA values at wavelength of 700 nm from 0.86 to 0.96 and large clear-sky shortwave forcing efficiency at 500 nm from 60 W m À2 to 90 W m À2 , though there are some unexplained inconsistencies depending on the evaluation method. The simulated whole-sky total forcing in the EAREX region is À1 to À2 W m À2 at TOA and À2 to À10 W m À2 at surface in March 2005 which is smaller in magnitude than in the APMEX region, mainly because of large cloud fraction in this region (0.70 at Gosan versus 0.51 at Hanimadhoo in the ISCCP total cloud fraction). We suggest there may be an underestimation of the forcing due to overestimation of the simulated cloudiness and aerosol scale height. On the other hand, the possible error in the simulated surface albedo may cause an overestimation of the magnitude of the forcing over the land area. We also propose simple formulae for shortwave radiative forcing to understand the role of aerosol parameters and surface condition to determine the aerosol forcing. Such simple formulae are useful to check the consistency among the observed quantities.

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Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid spacings of ∼ 200 km, while satellite observations of aerosol use so-called pixels of ∼ 10 km. Ground site or airborne observations relate to even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid spacing) over either the large areas of global model grid boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field of view of ground sites ("perfect" observations). Our analysis suggests that instantaneous rootmean-square (RMS) differences of perfect observations from perfect global models can easily amount to 30-160 %, for a range of observables like AOT (aerosol optical thickness), extinction, black carbon mass concentrations, PM 2.5 , number densities and CCN (cloud condensation nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a threefold reduction for AOT), than for others (e.g. a twofold reduction for surface black carbon concentrations), but significant RMS differences remain (10-75 %). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

Abstract. The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation error for a range of timescales and length scales (semi-annually down to sub-daily, 300 to 50 km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site or in situ remote sensing (PM 2.5 , black carbon mass or number concentrations), satellite remote sensing with imagers or lidar (extinction). We show that observational coverage (a measure of how dense the spatiotemporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly gridded satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. However, temporal collocation of data (possible when observations are compared to model data or other observations), combined with temporal averaging, can be very effective at reducing representation errors. We also show that ground-based and wideswath imager satellite remote sensing data give rise to similar representation errors, although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce, even with substantial temporal averaging.

Objective. To clarify the glucose-6-phosphate isomerase (GPI)-specific CD4؉ T cell lineage involved in GPI-induced arthritis and to investigate their pathologic and regulatory roles in the induction of the disease.Methods. DBA/1 mice were immunized with GPI to induce arthritis. CD4؉ T cells and antigen-presenting cells were cocultured with GPI, and cytokines in the supernatant were analyzed by enzyme-linked immunosorbent assay. Anti-interferon-␥ (anti-IFN␥) monoclonal antibody (mAb), anti-interleukin-17 (anti-IL-17) mAb, or the murine IL-6 receptor (IL-6R) mAb MR16-1 was injected at different time points, and arthritis development was monitored visually. After MR16-1 was injected, percentages of Th1, Th2, Th17, and Treg cells were analyzed by flow cytometry, and CD4؉ T cell proliferation was analyzed using carboxyfluorescein diacetate succinimidyl ester.Results. GPI-specific CD4؉ T cells were found to be differentiated to Th1 and Th17 cells, but not Th2 cells. Administration of anti-IL-17 mAb on day 7 significantly ameliorated arthritis (P < 0.01), whereas administration of anti-IFN␥ mAb exacerbated arthritis.Neither anti-IL-17 mAb nor anti-IFN␥ mAb administration on day 14 ameliorated arthritis. Administration of MR16-1 on day 0 or day 3 protected against arthritis induction, and MR16-1 administration on day 8 significantly ameliorated existing arthritis (P < 0.05). After administration of MR16-1, there was marked suppression of Th17 differentiation, without an increase in Th1, Th2, or Treg cells, and CD4؉ T cell proliferation was also suppressed.Conclusion. IL-6 and Th17 play an essential role in GPI-induced arthritis. Since it has previously been shown that treatment with a humanized anti-IL-6R mAb has excellent effects in patients with rheumatoid arthritis (RA), we propose that the IL-6/IL-17 axis might also be involved in the generation of RA, especially in the early effector phase.