The Goddard Convective-Stratiform Heating (CSH) algorithm has been used to retrieve latent heating (LH) associated with clouds and cloud systems in support of the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission. The CSH algorithm requires the use of a cloud-resolving model (CRM) to simulate LH profiles to build look-up tables. This paper describes the current V6 CSH and its differences/similarities versus the previous V5 CSH. Long-term CRM simulations were conducted to identify the impact of the CRM resolution and convective-stratiform separation method on LH structure/profiles. The TRMM and GPM Combined radar-radiometer algorithm-derived surface rain rates and their associated precipitation properties were the input to the CSH algorithm.

CSH V6-retrieved regional LH profiles in the tropics and subtropics display the classic signatures of heating in the convective region and heating over cooling in the stratiform region. Because there is no direct measurement of LH structure, the performance of the CSH V6 algorithm is examined by comparing its vertically integrated heating (or equivalent surface rain rate) against the surface rain rate derived from the TRMM/GPM Combined algorithm. The CSH three-month and zonal mean equivalent surface rain rates are in good agreement with the Combined rain rates over the Inter Tropical Convergence Zone region; the agreement is best over the ocean. CSH three-month and zonal mean equivalent surface rain rates are larger than the Combined rain rates over land in both the tropics and subtropics. CSH three-month mean equivalent surface rain rates also have local differences with the Combined rain rates that can be smoothed by area averaging to larger horizontal resolutions (from the CSH standard grid of 0.25° × 0.25° to 0.5° × 0.5° or 1.0° × 1.0°). CSH equivalent surface rain rates have more light rain rates but less larger rates compared to the GPM Combined surface rain rates.

The near-real-time merged satellite and in-situ data global daily sea surface temperature (SST) of the Japan Meteorological Agency (hereinafter abbreviated as R-MGD) is subjected to filtering out short-time-scale fluctuations from observations prior to the analysis time. Therefore, the rapid SST change due to the passage of tropical cyclones (TCs) is thought to cause biases. Here, the biases in the R-MGD with respect to in-situ observations were quantified along the passage of TCs in the western North Pacific. First, we examined a case study on the approach of three successive TCs in August–September 2020. The R-MGD had positive biases of > 2°C just after the passage of three TCs, and negative biases were observed after one week of the last TC's passage. The comparison of the R-MGD with a moored buoy indicates that the biases can be explained by short-term fluctuations filtered out and the SST prior to the analysis time in R-MGD analysis. Second, the composite analysis from May 2015–October 2020 indicates that the statistically significant biases at the observation points ranged between −1 days and +4 days for positive biases and between +7 days and +14 days for negative biases relative to the time of the closest approach of a TC within 500 km. The positive SST bias is largely associated with cold subsurface water and intense TCs, being pronounced in the mid-latitude, except around the Kuroshio and Kuroshio extension regions. The assimilation of in-situ observations recorded within 72 h prior to the R-MGD analysis time through additional optimal interpolation alleviates these biases because this process redeems short-time-scale fluctuations. The impact on TC forecasts and the validity of the optimal interpolation experiment against the independent observations were also investigated.

Temporal variations of atmospheric radon-222 (²²²Rn) observed at four Japan Meteorological Agency stations in Japan by the Meteorological Research Institute were analyzed using an online Global Spectral Atmosphere Model-Transport Model (GSAM-TM). Monthly and diurnal variations and a series of synoptic high-²²²Rn events were extracted from 5 years to 12 years of ²²²Rn observations during 2007–2019. Observed seasonal patterns of winter maxima and summer minima, driven mainly by monsoons, were well reproduced by the GSAM-TM based on existing ²²²Rn emission inventories, but their absolute values were generally underestimated, indicating that our understanding of ²²²Rn emission processes in East Asia is lacking. The high-resolution model (∼ 60 km mesh) demonstrated that observed consecutive high-²²²Rn peaks at several-hour timescales were caused by two ²²²Rn streams from different regions and were not well resolved by the low-resolution model (∼ 200 km mesh). GSAM-TM simulations indicate that such cold-front-driven events are sometimes accompanied by complicated three-dimensional atmospheric structures such as stratospheric intrusion over the front, significantly affecting distributions of atmospheric components. A new calculation approach using hourly ²²²Rn values normalized to daily means was used to analyze the diurnal ²²²Rn cycle, allowing diurnal cycles in winter to be extracted from ²²²Rn data that are highly variable due to sporadic continental ²²²Rn outflows, which tend to obscure the diurnal variations. Normalized diurnal cycles of ²²²Rn in winter are consistent between observations and model simulations, and seem to be driven mainly by diurnal variations of planetary boundary layer height (PBLH). These results indicate that ²²²Rn in the near-surface atmosphere, transported from remote source regions, could vary diurnally by up to 10 % of the daily mean mainly owing to local PBLH variations, even without significant local ²²²Rn emissions.

Atmospheric nitrous oxide (N₂O) contributes to global warming and stratospheric ozone depletion, so reducing uncertainty in estimates of emissions from different sources is important for climate policy. In this study, we simulate atmospheric N₂O using an atmospheric chemistry-transport model (ACTM), and the results are first compared with the in situ measurements. Five combinations of known (a priori) N₂O emissions due to natural soil, agricultural land, other human activities, and sea–air exchange are used. The N₂O lifetime is 127.6 ± 4.0 yr in the control ACTM simulation (range indicates interannual variability). Regional N₂O emissions are optimized using Bayesian inverse modeling for 84 partitions of the globe at monthly intervals, using measurements at 42 sites around the world covering 1997–2019. The best estimated global land and ocean emissions are 12.99 ± 0.22 TgN yr⁻¹ and 2.74 ± 0.27 TgN yr⁻¹, respectively, for 2000–2009, and 14.30 ± 0.20 TgN yr⁻¹ and 2.91 ± 0.27 TgN yr⁻¹, respectively, for 2010–2019. On regional scales, we find that the most recent ocean emission estimation, with lower emissions in the Southern Ocean regions, fits better with that predicted by the inversions. Marginally higher (lower) emissions than the inventory/model for the tropical (extratropical) land regions are estimated and validated using independent aircraft observations. Global land and ocean emission variabilities show a statistically significant correlation with El Niño Southern Oscillation (ENSO). Analysis of regional land emissions shows increases over America (Temperate North, Central, and Tropical), Central Africa, and Asia (South, East, and Southeast) between the 2000s and 2010s. Only Europe as a whole recorded a slight decrease in N₂O emissions due to the chemical industry. Our inversions suggest revisions to seasonal emission variations for three of the 15 land regions (East Asia, Temperate North America, and Central Africa), and the Southern Ocean region. The terrestrial ecosystem model (Vegetation Integrative SImulator for Trace Gases) can simulate annual total emissions in agreement with the observed N₂O growth rate since 1978, but the lag-time scales of N₂O emissions from nitrogen fertilizer application may need to be revised.

Typhoon Jongdari (2018) took an unusual track along the circumference of an upper-tropospheric cold low (UTCL) before making landfall in Japan on 29 July. To investigate the effects of atmosphere–ocean interactions and interactions between the UTCL and Jongdari on the storm's track, numerical simulations were conducted with a 3-km-mesh nonhydrostatic atmosphere model and an atmosphere–wave–ocean coupled model, using different initial conditions created by adopting different start times of numerical integration. The UTCL was characterized by high potential vorticity (PV), low pressure, and low relative humidity on the 355-K isotherm surface. While the UTCL moved southwestward north of Jongdari from 25 to 27 July, simulation results indicate that Jongdari traveled counterclockwise along the circumference of the UTCL. After Jongdari moved westward, the coupled model clearly simulated sea surface cooling along the track. Jongdari weakened after making landfall while the UTCL also weakened south of Japan. In particular, latent heat flux from the sea and the resulting humidification of the upper troposphere through the convection affected the UTCL. When Jongdari redeveloped over the ocean south of Kyushu, some simulations showed that Jongdari merged with the UTCL there as a result of high PV in Jongdari and relatively low upper-tropospheric PV near the UTCL. Ocean coupling helped sustain the uppertropospheric PV near the UTCL and weakened the column of elevated PV associated with Jongdari, which affected the location of the tropopause folding transformed from the UTCL by lowering the PV column of Jongdari and weakening the upper-tropospheric outflow from the center. Because the steering flow of Jongdari was affected by the geostrophic-balanced cyclonic circulation created by the UTCL, a larger difference of the atmospheric initial conditions between the initial times had a stronger influence on track and intensity simulations of both Jongdari and UTCL than ocean coupling.

Previous studies suggest the nature of the air–sea interaction of the tropical intraseasonal oscillation (ISO) can strongly influence our understanding and simulation of the ISO characteristics. In this study, we assess the representation of the surface components in three of the most up-to-date reanalyses, namely, the fifth generation of the European Centre for Medium-Range Weather Forecasts' (ECMWF) reanalysis (ERA5), ERA-interim (ERAi), and Japanese global atmospheric reanalysis (JRA55), to identify which reanalysis dataset is more suitable for investigating air–sea interaction associated with the ISO and to quantify the intraseasonal biases of related variables for simulating the ocean responses. All three reanalyses well capture the ISO convective characteristics in terms of the spatial patterns and the propagation features, although the amplitude of the outgoing longwave radiation is severely underestimated (by ∼ 40–60 %, depending on region and season) in JRA55. Out of the two ERA reanalysis datasets, our results indicate the ERA5 may serve as a better ocean forcing dataset, as the ERAi largely underestimates the magnitudes of the ISO-related precipitation and 10 m winds (of summer ISO or boreal summer ISO) but overestimates the latent heat flux (of winter ISO or the Madden–Julian oscillation). JRA55, while having comparable amplitude biases to ERA5 in variables except precipitation, generally shows larger phase biases in comparison with the two ERA reanalyses.

Synoptic-scale variabilities of atmospheric CO₂ and CH₄ observed at Yonagunijima (Yonaguni Island, YON, 24.47°N, 123.01°E) during winter (from January to March) in 1998–2020 were examined. The monthly mean variability ratios (ΔCO₂/ΔCH₄) based on correlation slopes within 24 h time windows showed a clear increasing trend, which is mainly attributed to the unprecedented increase in the fossil fuel-derived CO₂ (FFCO₂) emissions from China. A similar increasing trend of the ΔCO₂/ΔCH₄ ratio had been reported for the observation at Hateruma Island (HAT, 24.06°N, 123.81°E), located at approximately 100 km east of YON. Nevertheless, the absolute values for YON were 34 % larger than those for HAT. Additionally, the monthly average in February 2020 for YON showed no marked change, whereas that for HAT showed an abrupt considerable decrease associated with the FFCO₂ emission decrease in China presumably caused by the COVID-19 lockdown. Investigating the diurnal variations, we found that the local influences were larger at YON, especially during daytime, than at HAT. Using nighttime data (20-6 LST) and a longer time window (84 h), we succeeded in reducing the local influences and the resulting monthly mean ΔCO₂/ΔCH₄ ratio showed considerable similarity to that observed at HAT including the abrupt decrease in February 2020. These results convinced us that the ΔCO₂/ΔCH₄ ratio could be successfully used to investigate the relative emission strength in the upwind region.

In the discretization of the primitive equations for numerical calculations, a formulation of a three-dimensional spectral model that uses the spectral method not only in the horizontal direction but also in the vertical direction is proposed. In this formulation, the Legendre polynomial expansion is used for the vertical discretization. It is shown that semi-implicit time integration can be efficiently done under this formulation. Then, a numerical model based on this formulation is developed and several benchmark numerical calculations proposed in previous studies are performed to show that this implementation of the primitive equations can give accurate numerical solutions with a relatively small degrees of freedom in the vertical discretization. It is also shown that, by performing several calculations with different vertical degrees of freedom, a characteristic property of the spectral method is observed in which the error of the numerical solution decreases rapidly when the number of vertical degrees of freedom is increased. It is also noted that an alternative to the sponge layer can be devised to suppress the reflected waves under this formulation, and that a “toy” model can be derived as an application of this formulation, in which the vertical degrees of freedom are reduced to the minimum.