Key Points:
Key Points:
· We improve a global nonhydrostatic atmospheric model focusing on resolution-independent errors that can exist even in k-scale climate runs
· Key improvements are retuning of cloud microphysics properties, consideration of grid-scale turbulent mixing, and increased vertical layers
How Can We Improve the Seamless Representation of Climatological Statistics and Weather Toward Reliable Global K-Scale Climate Simulations?
Abstract Toward the achievement of reliable global kilometer-scale climate simulations, we improve the Nonhydrostatic ICosahedral Atmospheric Model 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 Nonhydrostatic ICosahedral Atmospheric Model, each of which 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 fourteen-kilometer horizontal resolution, we test (a) the tuning of terminal velocities of rain, snow, and cloud ice, (b) the implementation of turbulent diffusion by the Leonard term, and (c) enhanced vertical resolution. These tests yield reasonable convection triggering and convection-induced tropospheric moistening, and result in better performance than in previous Nonhydrostatic ICosahedral Atmospheric Model 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 reproduced dramatically better. Variability such as equatorial waves and the Madden-Julian oscillation is spontaneously realized with appropriate spectral power balance, and the Asian summer monsoon, boreal-summer Madden-Julian oscillation, and tropical cyclone activities are more realistically simulated especially around the western Pacific. Meanwhile, biases still exist in the representation of low-cloud fraction, tropical cyclone intensity, and precipitation diurnal cycle, suggesting that both higher spatial resolutions and further model development are warranted.
Plain Language Summary In the near future, increasing computational power will make it possible to perform a global kilometer-scale "cloud-resolving" model simulation on the climate time scale, which is expected to reduce the uncertainty of cloud-related processes in the climate system. In this sense, it is important to make global cloud-resolving models more reliable tools in the evaluation and prediction of the variabilities over a wide range of spatio-temporal scales. With this perspective, we improve a Japanese global cloud-resolving model, the Nonhydrostatic Atmospheric Icosahedral Model, to achieve the realistic representation of both weather phenomena and climatological features in long-term simulations. We revise the Nonhydrostatic Atmospheric Icosahedral Model by the reconsideration of cloud microphysics properties, the implementation of diffusion processes around strong convection cores, and increased vertical layers. These revisions lead to the substantial improvements in the climatological mean precipitation distributions, radiative energy balance at the top of the atmosphere, westerly jets in the mid-latitude, and temperature fields. We also find that weather phenomena such as the Asian summer monsoon and tropical cyclone genesis are simulated more realistically. We expect that, in addition to the above model improvements, kilometer-scale horizontal resolutions can resolve a part of remaining issues of the representation of tropical cyclone intensity and precipitation diurnal cycle.
One. Introduction
One. Introduction
In Earth's atmosphere, deep convection is a fundamental element of phenomena over a wide range of spatio-temporal scales. While individual deep convective clouds have a O(one)-kilometer spatial scale and a short lifetime (within a few hours), they play a significant role in redistributing heat, water, and momentum and in exciting atmospheric waves. They are thus tightly coupled with the global atmospheric circulation driven by latent and radiative heating and by momentum transportation. Also, deep convection is often organized at O(one thousand) to O(ten thousand) kilometers scales, as observed as mesoscale convective systems, tropical cyclones, convectively coupled equatorial waves, and the Madden-Julian oscillation, all of which greatly modulate both local and global weather patterns. Hence, better treatment of deep convection in climate models is expected to achieve the seamless representation of both the climatological mean states and variabilities.
Simulating deep convection globally and explicitly without any convective parameterizations is one of the strategies for improving the accuracy of global climate models. Such "global convection-resolving model simulations" have become possible at kilometer-scale resolutions thanks to the recent increase in computing power, and they have succeeded in reproducing weather variability especially at the sub-seasonal to seasonal scale. In pioneering work, researchers used the Nonhydrostatic Icosahedral Atmospheric Model with three point five-kilometer and seven-kilometer resolutions to realistically simulate the eastward migration of Madden-Julian oscillation convection for about a month and the Madden-Julian oscillation-related tropical cyclone genesis. In addition, researchers recently compared forty-day simulations by nine global models at less than five-kilometer grid spacing and showed that kilometer-scale models can reasonably represent the large-scale circulation and tropical cyclone activities at least at the sub-seasonal scale.
Beyond the sub-seasonal to seasonal scale, a ten-year global climate simulation with explicit treatment of deep convection (i.e., global convection-resolving model-mode climate simulation) is an important milestone in climate modeling. It has already been performed by researchers, although they adopted fourteen-kilometer grid spacing, at which individual cumulus systems cannot be fully resolved. In CMIP6 HighResMIP simulations by Nonhydrostatic ICosahedral Atmospheric Model, the mean radiation distributions, which are important to the climate, can be optimized especially by refining the cloud microphysics and radiation schemes. This success is attributed to the better representation of the amount of cloud ice and high clouds originating from explicit deep convection. In addition, global convection-resolving model-mode climate simulations may help reduce the uncertainties in the statistics of tropical cyclones and cloud amounts in various climate regimes.
Although GCRM-mode climate simulations are expected to be able to reproduce both realistic climatological statistics and individual weather disturbances seamlessly, this has not yet been achieved, at least in fourteen-kilometer mesh NICAM climate simulations. For example, the amplitude of the simulated MJO is much weaker than the observations, and the simulated MJO tends to fail to propagate into the western Pacific, which is an exaggerated "barrier effect" of the Maritime Continent as seen in many other conventional GCMs. In addition, some TC tracks are unrealistically represented, in that TCs generated over the eastern Pacific tend to cross the dateline. The climatological mean states also have some long-standing biases, such as the double intertropical convergence zone and smaller low-cloud amount, especially in NICAM HighResMIP simulations.
One may expect that k-scale resolutions can solve the aforementioned issues in coarser-resolution climate simulations, and several studies have shown aspects of this to be partly true: a tendency for deep convective characteristics to converge at eight hundred seventy-meter resolution, a realistic large-scale circulation in a four-month one point four-kilometer resolution simulation, and the subtropical low-cloud amount close to the observation at two point five-kilometer resolution. Wedi et al. also reported, however, that the model biases in the MJO or tropical precipitation are not reduced even at one point four-kilometer resolution. This situation holds true for NICAM, as indicated by the forty-day (from one June two thousand four) mean precipitation for the observation and fourteen- and three point five-kilometer simulations under the HighResMIP configuration. The biases of the excess of precipitation are common at both resolutions over the ITCZ and Indian Ocean. In addition, meridional splitting of the precipitation band around the dateline and the shortage of precipitation over the western Pacific remain or are emphasized in the three point five-kilometer simulation.
Such resolution-independent errors (at least within order one to ten kilometer resolutions) in the representation of large-scale weather disturbances and climate patterns suggest the importance of better understanding and improving mesoscale approximately one hundred kilometers and corresponding temporal-scale moist physics even in, or rather because of, the GCRM framework at k-scale resolutions. GCRMs aim to directly solve local responses (e.g., precipitation, mesoscale circulations) to the moisture and cloud evolution and to obtain larger-scale patterns by accumulating those local responses. However, a model at order one to ten kilometer resolutions still has scale-independent uncertainties in physics-dynamics coupling. In that sense, large-scale fields simulated by GCRMs at k-scale resolutions can depend heavily on local model physics, as implied by Miura, although this problem might be resolved if resolutions were fine enough to capture cloud eddies. In this situation, it is non-trivial whether the accumulation of local responses leads to the accurate simulation of equilibrium states such as the climate, because a model physics may not have enough degrees of freedom to satisfy two key aspects: better representation of individual large-scale convective variations for realistic weather, and better energy balance for realistic equilibrium states. In fact, one NICAM configuration that works well for S two S-scale simulations (e.g., good MJO hindcasts) does not provide a realistic equilibrium of the radiative energy budget at climate scales, so another configuration that improves climatological mean radiative fields has been established. Note that the S two S-scale-mean state trade-off has also been reported and discussed in the context of tuning of the local convective sensitivity to tropospheric moisture in conventional GCMs.
In convection-resolving simulations, where cloud formation is directly coupled to local dynamics, the moisture-convection-radiation relationship and its impact on the large-scale circulations are controlled by explicit cloud microphysics and turbulent diffusion, as well as by model resolutions. In fact, Miura showed that the MJO reproducibility in GCRMs is sensitive to cloud microphysics parameters, such as the velocity of falling rain and snow, that can affect the vertical profile of moisture and clouds. The impacts of microphysics are also confirmed in the TC development and diurnal convection over the Maritime Continent. In addition, the choice and parameter settings of turbulent schemes can influence the favored spatio-temporal scale of convective organization by changing the efficiency of the subgrid-scale horizontal/upward moisture transport. Furthermore, vertical resolutions have a large impact on the amount of tropical high clouds, which truly governs the mean radiation balance.
Given that parameter tuning and better treatment of unresolvable physics in GCRMs can determine model performance over various temporal scales and that mesoscales seem to have significant impacts on synoptic-to-large-scale behaviors, it is essential to consider the model physics required for reproducing both realistic climatological statistics and weather before entering the k-scale world. Thus, in the present study, using a series of one-year sensitivity experiments with a fourteen-kilometer horizontal mesh, which effectively resolves structures at scales of more than one hundred kilometers, we aim to obtain a model that can seamlessly and realistically reproduce atmospheric variabilities and equilibrium states ranging from the precipitation diurnal cycle to the global circulation. Specifically, we reconsider the cloud microphysics, turbulent diffusion, and vertical resolution, and examine their impacts on the moisture-convection relationship. Then, we provide a model configuration that can achieve the best possible representation of wide spatio-temporal-scale fields, in anticipation of a reliable k-scale climate simulation.
This paper is organized as follows. Section Two describes the model, the experimental design of sensitivity experiments toward model improvement, and the observational data sets for model evaluation. Section Three provides a comparison of the moisture transport via deep convection, the one-year mean states, and the disturbances in two representative settings, one of which emphasizes the better radiation distributions in climate-scale simulations and the other the better MJO representation in S2S-scale simulations. Based on this comparison, we introduce the major model updates and their impacts on the moisture-convection relationship and convective organization in Section Four. Section Five presents a comprehensive examination of the impacts of the model updates on the mean precipitation, radiation, and large-scale circulations and disturbances such as the MJO, equatorial waves, TCs, and precipitation diurnal cycle. In Section Six, we discuss a possible reason for the mitigation of double ITCZ and weak MJO biases, which are representatives of long-standing problems in NICAM climate simulations. A summary and concluding remarks are given in Section Seven.