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Toward the achievement of reliable global kilometer-scale (k-scale) climate simulations, we improve the Nonhydrostatic ICosaherdral Atmospheric Model (NICAM) by focusing on moist physical processes. A goal of the model improvement is to establish a configuration that can simulate realistic fields seamlessly from the daily-scale variability to the climatological statistics. Referring to the two representative configurations of the present NICAM, of which each has been used for climate-scale and sub-seasonal-scale experiments, we try to find the appropriate partitioning of fast/local and slow/global-scale circulations. In a series of sensitivity experiments at 14-km horizontal mesh, (1) the tuning of terminal velocities of rain, snow, and cloud ice, (2) the implementation of turbulent diffusion by the Leonard term, and (3) enhanced vertical resolution are tested. These tests yield reasonable convection triggering and convection-induced tropospheric moistening, and result in better performance than in previous NICAM climate simulations. In the mean state, double Intertropical Convergence Zone bias disappears, and the zonal contrast of equatorial precipitation, top-of-atmosphere radiation balance, vertical temperature profile, and position/strength of subtropical jet are dramatically better reproduced. Variability such as equatorial waves and the Madden–Julian oscillation (MJO) is spontaneously realized with appropriate spectral power balance, and the Asian summer monsoon, boreal-summer MJO, and tropical cyclone (TC) activities are more realistically simulated especially around the western Pacific. Meanwhile, biases still exist in the representation of low-cloud fraction, TC intensity, and precipitation diurnal cycle, suggesting that both finer spatial resolutions and the further model development are warranted.

The reduction behavior of the surface oxide on Cu particles under a reducing gas atmosphere was investigated for a pressureless sinter joining. We conducted x-ray thermodiffraction analysis and simultaneous thermogravimetry, differential thermal analysis, and mass spectroscopy (TG–DTA–MS) under a reducing atmosphere to investigate the reduction and subsequent sintering behaviors of copper particles at different oxygen concentrations. The shear strength of the pressureless sinter joint decreased with increasing oxygen concentration. The thermodiffraction results revealed that the reduction onset of Cu2O started at the same temperature (220°C), whereas the reaction markedly persisted at higher oxygen concentrations. Cu sintering progressed significantly after the reduction due to generation of Cu nanoparticles. The TG–DTA–MS results indicated that H2O formation temperature associated with the reduction depends on the oxygen concentration, consistent with the thermodiffraction results. The surface oxides were found to play an important role in pressureless sinter joining via nanoparticle formation, while the presence of a large amount of oxide delayed the reduction and subsequent sintering.

This article reviews the first version of the NICAM–LETKF JAXA Research Analysis (NEXRA), a weather research analysis system, which was developed from the fusion of the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the local ensemble transform Kalman filter (LETKF) using various observational data, such as NCEP PREPBUFR, radiances from Advanced Microwave Sounding Unit (AMSU)-A, and precipitation obtained from Global Satellite Mapping of Precipitation (GSMaP). The development of NEXRA started in 2017 as a collaboration between the Japan Aerospace Exploration Agency (JAXA), the Atmosphere and Ocean Research Institute at the University of Tokyo (AORI), and the RIKEN Center for Computational Science (R-CCS). NEXRA routinely provides 2-D graphical weather information on the web site for general users and ensemble analysis data for research purposes. Routine operation of the first version of NEXRA started on the JAXA Supercomputer System Generation 2 (JSS2) in June 2018 and ended in February 2021 when JSS2 ceased operation. Routine operation of the second version of NEXRA (NEXRA_2.0) started in April 2021 on the JAXA Supercomputer System Generation 3 (JSS3). This article reviews the development and operation of the first version of NEXRA and describes the use of the ensemble analysis data produced by NEXRA as the atmospheric initial conditions for predictability research in the analysis of three events: the record-breaking rainfall in Japan that occurred in July 2018, Typhoon Krosa in August 2019, and the rapid intensification of Typhoon Haishen in August 2020. Finally, some details of the collaborations between NEXRA and other projects and of the development of the current version of NEXRA (NEXRA_2.0) are given, and a future vision for NEXRA is outlined.

The Nonhydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16), modified for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP). Major updates of NICAM.12, a previous version used for climate simulations, included updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. External forcings were updated to follow the protocol of the HighResMIP. A series of short-term sensitivity experiments were performed to determine and understand the impacts of these various model updates on the simulated mean states. The NICAM16-S simulations demonstrated improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. Some long-standing biases, such as the double intertropical convergence zone and smaller low cloud amount, still exist or are even worse in some cases, suggesting further necessity for understanding their mechanisms, upgrading schemes and parameter settings, and enhancing horizontal and vertical resolutions.

The stable water isotopes (SWIs) (δ18O and δD) are used as an indicator of the intensity of the atmospheric hydrological cycle due to their large variability in time and space. SWIs are used for investigating the model’s bias and uncertainty. In this study, we developed a new global storm-resolving model equipped with SWIs (NICAM-WISO). We applied the new model to conduct three current climate simulations using a single-moment cloud microphysics scheme, without any convection parameterization scheme: CTRL, LRES, and HRES. These simulations used the same physical process but at a different horizontal resolution (LRES, 224 km; CTRL, 56 km; HRES, 14 km). We conducted the simulations on the supercomputer Fugaku. CTRL reproduced the seasonal means of the atmospheric hydrological cycle, as well as precipitation isotopic ratios. However, all simulation results have three types of biases. First, in tropical ocean regions, the model had a negative bias in precipitation isotopic ratios; this was caused by a negative bias in vapor isotopic ratios for the middle troposphere, which resulted from excess condensation biases during upward transportation and high-frequency deep convection. Second, all simulations overestimated precipitation isotopic ratios in the East Asia summer monsoon region due to low precipitation in the region caused by a shift in the moisture convergence zone from eastern China to the western Pacific Ocean. Third, in cold continental regions such as Siberia, Greenland, and Antarctica, the model had a positive bias in precipitation isotopic ratios due to a moisture bias and a low temperature effect; these regions also had a large positive bias in terms of precipitation deuterium excess. A particularly large bias was observed in ice clouds with low ice water content, indicating uncertainties in the vapor deposition process. Together, these results suggest that stable water isotopes are helpful for identifying biases associated with cloud microphysics and the atmospheric hydrological cycle. The unique constraints of stable water isotopes revealed cloud microphysics uncertainty and biases in the hydrological simulations.

Molecular hydrogen, H2, is one of the most abundant trace gases in the atmosphere. The main known chemical source of H2 in the atmosphere is the photolysis of formaldehyde and glyoxal. Recent laboratory measurements and ground-state photochemistry calculations have shown other aldehydes photodissociate to yield H2 as well. This aldehyde photochemistry has not been previously accounted for in atmospheric H2 models. Here, we used two atmospheric models to test the implications of the previously unexplored aldehyde photochemistry on the H2 tropospheric budget. We used the AtChem box model implementing the nearly chemically explicit Master Chemical Mechanism at three sites selected to represent variable atmospheric environments: London, Cabo Verde and Borneo. We conducted five box model simulations per site using varying quantum yields for the photolysis of 16 aldehydes and compared the results against a baseline. The box model simulations showed that the photolysis of acetaldehyde, propanal, methylglyoxal, glycolaldehyde and methacrolein yields the highest chemical production of H2. We also used the GEOS-Chem 3-D atmospheric chemical transport model to test the impacts of the new photolytic H2 source on the global scale. A new H2 simulation capability was developed in GEOS-Chem and evaluated for 2015 and 2016. We then performed a sensitivity simulation in which the photolysis reactions of six aldehyde species were modified to include a 1 % yield of H2. We found an increase in the chemical production of H2 over tropical regions where high abundance of isoprene results in the secondary generation of methylglyoxal, glycolaldehyde and methacrolein, ultimately yielding H2. We calculated a final increase of 0.4 Tg yr−1 in the global chemical production budget, compared to a baseline production of ∼41 Tg yr−1. Ultimately, both models showed that H2 production from the newly discovered photolysis of aldehydes leads to only minor changes in the atmospheric mixing ratios of H2, at least for the aldehydes tested here when assuming a 1 % quantum yield across all wavelengths. Our results imply that the previously missing photochemical source is a less significant source of model uncertainty than other components of the H2 budget, including emissions and soil uptake.

Atmospheric deep moist convection can organize into cloud systems, which impact the Earth's climate significantly. High-resolution simulations that correctly reproduce organized cloud systems are necessary to understand the role of deep convection in the Earth's climate system. However, there remain issues regarding convergence with respect to grid spacing. To investigate the resolution necessary for a reasonable simulation of deep convection, we conducted grid-refinement experiments using state-of-the-art atmospheric models. We found that the structure of an updraft ensemble in an organized cloud system converges at progressively smaller scales as the grid spacing is reduced. The gap between two adjacent updrafts converges to a particular distance when the grid spacing becomes as small as 1/20-1/40 of the updraft radius. We also found that the converged inter-updraft distance value is not significantly different between Reynolds-averaged Navier-Stokes simulations and large eddy simulations for grid spacings in the terra incognita range.

The Non-hydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. Here, we present its recent updates for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP) and their impact on the simulated mean states using 56-14km mesh model. Major updates include updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. A short-term sensitivity experiments demonstrate improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. The decadal climate simulations further reveal an improvement in the genesis and structure of the tropical cyclones compared with those with the previous model. Finally, we will address outlook toward the cloud-resolving climate simulation based on a fresh benchmark result on supercomputer Fugaku, a flagship supercomputer in Japan.

The stable water isotopes (SWIs) (δ18O and δD) are used as an indicator of the intensity of the atmospheric hydrological cycle due to their large variability in time and space. Although data about vapor isotope ratio with high frequency and high resolution are now available by satellite observations and spectroscopic analyses, there is some room for discussion on the variability of isotope ratios in vapor and precipitation related to cloud microphysical processes.Here, we incorporated SWI tracer into the latest version of a global cloud system resolving model (the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)), iso-NICAM, and investigated the contribution of cloud microphysical processes to the variability of isotope ratios in precipitation and vapor. One of the merits used NICAM is that its physical process can cover from low spatial resolution to high spatial resolution. We conducted two mode simulations (GCM and CRM). The GCM mode simulation is based on the Arakawa-Schubert scheme as convective parameterization and a large-scale condensation scheme as the cloud physical process. In contrast, the CRM mode simulation is based on the a single-moment bulk cloud microphysics scheme with 6 water categories as cloud microphysical scheme, convective parameterization scheme was not used. These simulations are set to about 223 km of horizontal mesh resolution and 78 vertical layers. We conducted an AMIP-type climate experiment for one year from 1979.The simulated precipitation δ18O showed the latitude effect pattern (high δ18O in low latitude region, low δ18O in high latitude region), but those values in the CRM mode was slightly lower than that in the GCM mode . The simulated precipitation δ18O in the CRM mode was lower in high altitude or inland regions compared with those in the GCM mode . Besides, the precipitation d-excess in the CRM mode shows large spatial variability compared with the GCM mode. Although the low spatial resolution was set in this study, these simulations indicated cloud microphysical processes are important for understanding the variability of isotope physics. We will conduct these simulations with finer spatial resolution and a more extended simulation period.

Recent advancement of supercomputing enables us to conduct a climate simulation by using a global model with horizontal grid spacing of a few kilometers. We may need to tune the model in order to conduct a reliable simulation. In order to test feasibility of a few kilometer climate simulation in near future, we conducted one-year simulation from June 2004 to May 2005 by using Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with horizontal grid spacing of 28 km, 14 km, 7 km, and 3.5 km, and evaluated their simulation performances. In general, global models have shown weak wind speed of tropical cyclones compared to its central sea level pressure due to insufficient horizontal resolution. As expected, the 3.5 km simulation showed improvement of this bias. As for simulated mean state, globally annual mean precipitation tended to be decreased with finer horizontal resolution in NICAM. Compared with observation (Global Precipitation Climatology Project V2.2; 2.71 mm day-1), 7 km and 3.5 km simulations underestimated the global mean precipitation (2.54 mm day-1 and 2.67 mm day-1), while 14 km and 28 km simulations overestimated (2.84 mm day-1 and 2.78 mm day-1). The 3.5 km simulation showed the best performance for reproducing globally annual mean precipitation. However, the 3.5 simulation showed underestimation of the South Pacific Convergence Zone. In order to conduct a reliable simulation, we need to improve performance of the 3.5 km global model. This demands extensive computing resources. The supercomputer Fugaku will give us extensive computing resources for addressing this issue.

In parallel with the new Japanese flagship supercomputer, Fugaku, we have continued improving a nonhydrostatic icosahedral atmospheric model (NICAM). Here, we introduce the results of our system-application co-design since 2014. Fugaku's CPU (A64FX) is based on the Arm instruction-set architecture. This 48-core many-core CPU is equipped with 32GB of HBM2 memory, showing data transfer performance comparable to GPUs. We have implemented kernel-level optimizations to take advantage of Fugaku's high memory performance. Among them, we recognized trade-offs related to ensuring memory locality and parallelism, and register allocation. We improved the application's average arithmetic intensity through detailed loop-by-loop performance measurements and reduced memory pressure by actively using single-precision operations. We also redesigned the data layout and the file I/O component of the ensemble data assimilation (DA) system and achieved good scalability in the atmospheric simulation and DA. We performed a global 3.5km mesh, 1024-member ensemble simulation, and DA using 82% of the Fugaku system (131,072 nodes, 6,291,456 cores). In this world's most massive ensemble DA benchmark experiment, the simulation and the DA achieved 29 PFLOPS and 79 PFLOPS of effective performance.

We have been carrying out the FLAGSHIP 2020 Project to develop the Japanese next-generation flagship supercomputer, the Post-K, recently named “Fugaku”. We have designed an original many core processor based on Armv8 instruction sets with the Scalable Vector Extension (SVE), an A64FX processor, as well as a system including interconnect and a storage subsystem with the industry partner, Fujitsu. The “co-design” of the system and applications is a key to making it power efficient and high performance. We determined many architectural parameters by reflecting an analysis of a set of target applications provided by applications teams. In this paper, we present the pragmatic practice of our co-design effort for “Fugaku”. As a result, the system has been proven to be a very power-efficient system, and it is confirmed that the performance of some target applications using the whole system is more than 100 times the performance of the K computer.

Numerical weather prediction (NWP) supports our daily lives. Weather models require higher spatiotemporal resolutions to prepare for extreme weather disasters and reduce the uncertainty of predictions. The accuracy of the initial state of the weather simulation is also critical; thus, we need more advanced data assimilation (DA) technology. By combining resolution and ensemble size, we have achieved the world’s largest weather DA experiment using a global cloud-resolving model and an ensemble Kalman filter method. The number of grid points was $\sim$4.4 trillion, and 1.3 PiB of data was passed from the model simulation part to the DA part. We adopted a data-centric application design and approximate computing to speed up the overall system of DA. Our DA system, named NICAM-LETKF, scales to 131,072 nodes (6,291,456 cores) of the supercomputer Fugaku with a sustained performance of 29 PFLOPS and 79 PFLOPS for the simulation and DA parts, respectively.

NICAM, a nonhydrostatic global atmospheric model with an icosahedral grid system, has been developed for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16) modified for Coupled Model Intercomparison Project Phase 6 (CMIP6). Major updates from NICAM.12, a previous version used for climate simulations, include updates of a cloud microphysics scheme and a land model, an introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag, and improvement of coupling between cloud microphysics and radiation schemes. External forcings were updated to follow a protocol of CMIP6 High Resolution Model Intercomparison Project (HighResMIP). A series of short-term sensitivity experiments were performed to check and understand impacts of the model updates on the simulated mean states. Improvements in the ice water content, the high cloud amounts, the surface air temperature over the Arctic region, the location and the strength of zonal mean subtropical jet, and shortwave radiation over the Africa and the South Asia were found in the NICAM16-S simulations. Some long-standing biases such as the double intertropical convergence zone and smaller low cloud amounts still exist or even worsen in some cases, suggesting further necessity for understanding their mechanisms and upgrading schemes and/or their parameter settings as well as for enhancing horizontal and vertical resolutions.

In precipitation science, satellite data have been providing precious, fundamental information, while numerical models have been playing an equally important role. Data assimilation integrates the numerical models and real-world data and brings synergy. We have been working on assimilating the GPM data into the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) using the Local Ensemble Transform Kalman Filter (LETKF). We continue our effort on “Enhancing Precipitation Prediction Algorithm by Data Assimilation of GPM Observations” funded by JAXA, following successful completion of the 3-year project titled “Enhancing Data Assimilation of GPM Observations” from April 2016 to March 2019. The project first started in April 2013 on “Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements”, where we developed a global data assimilation system NICAM-LETKF from scratch. This presentation will provide a summary of the past 7-year effort with more emphasis on the recent achievements, including JAXA’s near-real-time analysis called NEXRA (NICAM-LETKF JAXA Research Analysis) and new theoretical developments of Local Particle Filter to treat highly non-Gaussian distributions of precipitation variables in data assimilation.

Dealing with extreme scale earth system models is challenging from the computer science perspective, as the required computing power and storage capacity are steadily increasing. Scientists perform runs with growing resolution or aggregate results from many similar smaller-scale runs with slightly different initial conditions (the so-called ensemble runs). In the fifth Coupled Model Intercomparison Project (CMIP5), the produced datasets require more than three Petabytes of storage and the compute and storage requirements are increasing significantly for CMIP6. Climate scientists across the globe are developing next-generation models based on improved numerical formulation leading to grids that are discretized in alternative forms such as an icosahedral (geodesic) grid. The developers of these models face similar problems in scaling, maintaining and optimizing code. Performance portability and the maintainability of code are key concerns of scientists as, compared to industry projects, model code is continuously revised and extended to incorporate further levels of detail. This leads to a rapidly growing code base that is rarely refactored. However, code modernization is important to maintain productivity of the scientist working with the code and for utilizing performance provided by modern and future architectures. The need for performance optimization is motivated by the evolution of the parallel architecture landscape from homogeneous flat machines to heterogeneous combinations of processors with deep memory hierarchy. Notably, the rise of many-core, throughput-oriented accelerators, such as GPUs, requires non-trivial code changes at minimum and, even worse, may necessitate a substantial rewrite of the existing codebase. At the same time, the code complexity increases the difficulty for computer scientists and vendors to understand and optimize the code for a given system. Storing the products of climate predictions requires a large storage and archival system which is expensive. Often, scientists restrict the number of scientific variables and write interval to keep the costs balanced. Compression algorithms can reduce the costs significantly but can also increase the scientific yield of simulation runs. In the AIMES project, we addressed the key issues of programmability, computational efficiency and I/O limitations that are common in next-generation icosahedral earth-system models. The project focused on the separation of concerns between domain scientist, computational scientists, and computer scientists. The key outcomes of the project described in this article are the design of a model-independent Domain-Specific Language (DSL) to formulate scientific codes that can then be mapped to architecture specific code and the integration of a compression library for lossy compression schemes that allow scientists to specify the acceptable level of loss in precision according to various metrics. Additional research covered the exploration of third-party DSL solutions and the development of joint benchmarks (mini-applications) that represent the icosahedral models. The resulting prototypes were run on several architectures at different data centers.

This chapter describes the authors’ effort on ensemble data assimilation of satellite precipitation measurements. The Local Ensemble Transform Kalman Filter (LETKF) was implemented with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), and JAXA’s GSMaP (Global Satellite Mapping of Precipitation) data were assimilated at 112-km resolution.

Reducing the computational cost of weather and climate simulations would lower electric energy consumption. From the standpoint of reducing costs, the use of reduced precision arithmetic has become an active area of research. Here the impact of using single-precision arithmetic on simulation accuracy is examined by conducting Jablonowski and Williamson’s baroclinic wave tests using the dynamical core of a global fully compressible nonhydrostatic model. The model employs a finite-volume method discretized on an icosahedral grid system and its mesh size is set to 220, 56, 14, and 3.5 km. When double-precision arithmetic is fully replaced by single-precision arithmetic, a spurious wavenumber-5 structure becomes dominant in both hemispheres, rather than the expected baroclinic wave growth only in the Northern Hemisphere. It was found that this spurious wave growth comes from errors in the calculation of gridcell geometrics. Therefore, an additional simulation was conducted using double precision for calculations that only need to be performed for model setup, including calculation of gridcell geometrics, and single precision everywhere else, meaning that all calculations performed each time step used single precision. In this case, the model successfully simulated the growth of the baroclinic wave with only small errors and a 46% reduction in runtime. These results suggest that the use of single-precision arithmetic will allow significant reduction of computational costs in next-generation weather and climate simulations using a fully compressible nonhydrostatic global model with the finite-volume method.

A bonding method utilizing redox reactions of metallic oxide microparticles achieves metal-to-metal bonding in air, which can be alternative to lead-rich high-melting point solder. However, it is known that the degree of the reduction of metallic oxide microparticles have an influence on the joint strength using this bonding method. In this paper, the reduction behavior of CuO paste and its effect on Cu-to-Cu joints were investigated through simultaneous microstructure-related x-ray diffraction and differential scanning calorimetry measurements. The CuO microparticles in the paste were gradually reduced to submicron Cu2O particles at 210–250°C. Subsequently, Cu nanoparticles were generated instantaneously at 300–315°C. There was a marked difference in the strengths of the joints formed at 300°C and 350°C. Thus, the Cu nanoparticles play a critical role in sintering-based bonding using CuO paste. Furthermore, once the Cu nanoparticles have formed, the joint strength increases with higher bonding temperature (from 350°C to 500°C) and pressure (5–15 MPa), which can exceed the strength of Pb-5Sn solder at higher temperature and pressure.