High-resolution downscaling is vital to project climate extremes and their future changes by resolving fine topography reasonably well, which is a key to represent local climatology and impacts of weather extremes. A direct dynamical downscaling with a regional climate model (RCM) embedded within an atmosphere-ocean coupled general circulation model (AOGCM) is commonly used but is subject to systematic biases in their present-day simulations of AOGCM, which may cause unexpected effects on future projections and lead to difficult interpretation of climate change. In a high-resolution atmospheric general circulation model (AGCM)-RCM system, the present-day climate in AGCM is forced by observed sea surface temperature (SST) and sea-ice distribution. Then, the future climate is calculated with the “future” boundary conditions (SST and sea-ice), which are created by adding their future changes projected by AOGCM to the observed present-day values, besides the future radiative forcing. This system is one of methods to minimize the effects of such biases. A Meteorological Research Institute AGCM with 20-km grids is successfully applied to project future changes in weather extremes such as tropical cyclones and rain systems that cause heavy rainfall and strong winds. Regional downscaling with 5-km mesh RCM is then performed over certain area to investigate local extreme rainfall events and their future changes. In this paper, we review various downscaling methods and try to rationalize a use of high-resolution AGCM-RCM system.
The sampling downscaling (SmDS) in which a regional atmospheric model is integrated for sampled periods was performed for summertime Hokkaido. Selected are top two and bottom two years of the general circulation model projection onto the first singular value decomposition mode where heavy precipitation in southern Hokkaido is correlated with the moisture flux convergence in the synoptic field. The SmDS result integrated for the four years successfully reproduces the dynamical downscaling for 30 years, in terms of climatological precipitation and the 99-percentile value of daily precipitation. This indicates that SmDS can be applied to the environment where local precipitation is mostly controlled by synoptic climate patterns. A further statistical consideration in this study supports the notion. It is also demonstrated that SmDS selects a group of years where extreme events likely occur another group of years where they rarely occur.
Dynamical downscaling (DDS) is performed using regional climate models (RCMs) with global atmospheric states as the input, but there is no consensus among researchers on how to define and estimate the resolvable scale of the various climatic variables obtained by DDS. Sources of RCM uncertainties, including both internal model and intermodel variability, have been assessed by performing ensemble simulations and model intercomparisons, sometimes under the controversial assumption that model bias is independent of the climatic state. Compared with low-resolution global climate simulations, DDS can add value in several ways. For example, because they consider high-resolution topographic data, RCMs can often capture mesoscale phenomena and can better represent climate dynamics. Another downscaling method, empirical statistical downscaling (ESD), is complementary to DDS because it is based on a different philosophy (i.e., sources of information) and on a mostly different set of assumptions. More collaboration and communication should be encouraged among those who develop models, those who use models and perform downscaling, those who use downscaling data, and those who make decisions based on the scientific results provided by models. In addition, ensemble experiments should be devised that can more effectively benefit impact studies. Using DDS and ESD, separately or in combination, users can maximize the utility of local climate information.
Reanalysis data sets have been widely used in regional climate dynamical downscaling studies. In this study, we test the use of various reanalysis data sets in constraining dynamical downscaling by assessing the reconstruction skill of the Yellow Sea coastal winds using the COSMO model in Climate Mode (CCLM) with 7-km resolution. Four reanalysis forcing data sets are used as lateral boundary conditions and internal large-scale constraints (spectral nudging): the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set (NCEP1) is downscaled to an intermediate domain with 55-km resolution (CCLM_55km), ERA-interim reanalysis data set (ERAint), NCEP climate forecast system reanalysis data set (CFSR), and Japanese 55-year reanalysis data set (JRA55). Several statistical analysis methods are employed to assess the modeled winds through comparison with observed offshore wind data from 2006, and it is found that the downscaled simulations yield good quality wind speed products. However, they all tend to overestimate observed low wind speeds and underestimate observed high wind speeds. Furthermore, the quality of the modeled wind direction is strongly associated with the wind speed intensities, exhibiting a much better reproduction of wind direction at strong wind speeds than at light wind speeds. The downscaling simulations driven by ERAint, JRA55, and CFSR are consistent with each other in the reproduction of local wind speed and direction; the simulations driven by ERAint and JRA55 are slightly better for strong winds and those driven by CFSR are better for light winds. All three simulations generate local wind estimates that are superior to those of the simulation driven by CCLM_55km. This superiority reflects the better quality of the CFSR, ERAint, and JRA55 reanalyses with regard to assimilated local observations compared with the CCLM_55km hindcast, which exploits only upper-air large scale NCEP1 wind fields.
This study investigated potential future climate changes over East Asia with a focus on temperature variation and changes in precipitation as well as future changes in the East Asian monsoon system. The current and future climate projection scenarios are downscaled over East Asia using the regional spectral model (RSM). The representative concentration pathways (RCPs) 2.6 and 8.5 scenarios driven by the Hadley Center Global Environmental Model version 2 (HG2) are used to provide large-scale forcing for the RSM downscaling simulations. Simulations were conducted for the current climate from 1980 to 2005 and two types of future climate between 2020 and 2100. Near-future (2025-2050) and far-future (2075-2100) climate simulations are compared with the current (1980-2005) climatology to investigate climatic change over East Asia. The RSM well captures the precipitation and temperature distribution related to the East Asian summer and winter monsoon and mesoscale mountain range with added values, although the wet and cold biases are aggravated in the RSM downscaling. Additionally, short time-scale phenomena such as daily mean temperature and daily precipitation are more accurately reproduced by the RSM than by the HG2. From future climate projection, the increasing temperature trends of each RCP scenario are consistently reproduced in the RSM downscaling; in particular, the result from the RCP 8.5 experiment shows a significantly steeper trend with increasing temperature. Meanwhile, the East Asian monsoon is intensified in the future climate projection by the strengthening North Pacific subtropical high and Okhotsk high in summer and intensified Siberian high in winter. These changes lead to an increase in precipitation for summer and a decrease for winter.
The characteristics of diurnal precipitation variability are evaluated over the tropical Maritime Continent (MC) from both satellite observations and high-resolution simulations performed using the Weather Research and Forecasting (WRF) model. Simulations using the Kain-Fritsch convective scheme showed a slight improvement in representing the precipitation diurnal cycle compared with those using the other two schemes examined here. The influence of boundary forcing was compared between the National Center for Environmental Prediction-Final Analysis (NCEP-FNL) and the Norwegian Earth System Model (NorESM). In these experiments, simulations with NCEP-FNL data as lateral boundary conditions outperformed those with NorESM boundary conditions. All WRF simulations exaggerated the amplitude of diurnal precipitation over the land. However, the WRF captured the principal shape of the observed diurnal cycle well. An empirical orthogonal function (EOF) analysis was applied and the first two modes from satellite data explained up to about 80 % of the total diurnal variance. The results confirm that the land-sea breeze circulation plays a significant role in the diurnal cycle of precipitation. The radiatively induced land-sea breeze circulation and its timing were well reproduced in the WRF simulations. These results suggest that higher resolution simulations that reproduce˜heterogeneous local-scale processes are likely necessary in order to resolve diurnal variability of precipitation and its future changes over the MC.
Because tornadoes cause enormous damage to humans and societies, projecting plausible future changes in tornado activity is an important research focus. We used the results of climate experiments under the A1B emissions scenario with a 20-km-mesh and a high-resolution atmospheric global circulation model to project future changes in the frequency of conditions conducive to the generation of strong tornadoes (F2 or greater on the Fujita scale) in Japan. We found that this frequency is likely to double in future in almost all areas of the Japanese Islands in spring and on the Japan Sea side of the Japanese Islands in summer due to intensification of atmospheric instability caused by an increase in the water-vapor mixing ratio and a temperature rise in the lower troposphere. In contrast, the frequency of strong vertical wind shear, which is conducive to tornadogenesis, was projected to hardly change or decrease slightly. Comparison with climate fields generated by a 60-km-mesh 12-member ensemble experiment showed that future changes in tornadogenesis-favorable environmental conditions projected by the 20-km-mesh experiment were highly reliable. Moreover, we found that the predicted future changes were robust when we used other thresholds for the environmental parameters that created conditions conducive to strong tornadoes. Our results indicate that there will be a significant increase in the frequency of strong tornadoes in Japan in the future.
The detectability of climate change signals such as a precipitation change depends on temporal and spatial averaging scales. The present study aims to clarify the dependence of the detectability on the two averaging scales by analyzing the difference in daily precipitation between present (1979-2003) and future (2075-2099) climates. The dataset for the analysis is obtained from an atmospheric general circulation model. The robustness of the precipitation change signal is evaluated with the signal-to-noise ratio (SNR), which is often used in statistical tests to detect climate change signals. The SNR is increased and the detectability of the precipitation change signal is enhanced with increases in the two averaging scales. When either averaging scale is increased (decreased) with a constant SNR, the other averaging scale needs to be decreased (increased); this is the trade-off relation between the two averaging scales. The trade-off relation is obtained quantitatively and provides useful information for climate change impact assessments using various temporal and spatial scales or resolutions. The characteristics of the trade-off relation are found to differ qualitatively among the tropics, mid-latitudes, and subpolar regions and to derive from the precipitation power spectrum representing spatio-temporal scales of precipitation-related meteorological phenomena, e.g., baroclinic waves.
This study uses the non-hydrostatic regional climate model (NHRCM) to simulate and project rainfall and tropical cyclone (TC) activity over Vietnam. The simulated precipitation shows that climatic heavy rainfall centers are well captured in the seasonal march. In near and far future, the projected rainfall by NHRCM using outputs of the Meteorological Research Institute atmospheric general circulation model 3.2 with RCP8.5 scenario will clearly decrease in Northwest and Central Vietnam in June-August, while it will remarkably increase in Northeast and Central Vietnam in September-November. The model underestimates TC number and activity area in the first half of the TC season but slightly overestimates in the second half as compared to the best track. Projected TCs indicate a decrease in both TC number and activity area in near and far future. Moreover, the maximum TC number occurs one month late as compared to the present climate, whereas TC number remarkably decreases in July-August in far future. Rainfall induced by TCs increases in North Vietnam in the projected climate as compared to the baseline period. It also increases in mid-Central Vietnam in near future but decreases in southern Central Vietnam in near and far future. Conversely, non-TC rainfall is likely to decrease in North Vietnam in future and in mid-Central Vietnam in near future but increase in southern Central Vietnam in far future.
Rice is an important commodity in the Philippines. In the Cagayan Valley (CV), rice production provides employment to more than half of the region’s population and any climate variability and change can cause negative impacts on crop production and people’s livelihoods. This paper attempts to understand projected climate changes in seasonal rainfall and mean temperature (2011-2040) to inform climate change adaptation planning in CV. The climate change projections were provided to crop and water resource modeling, agricultural market modeling, food insecurity vulnerability analysis, community-based climate change adaptation planning, and policy simulation. The results are presented for the Provinces of Cagayan, Isabela, Nueva Vizcaya, and Quirino based on the statistical downscaling of three global climate models (BCM2, CNCM3, and MPEH5) and two emission scenarios (A1B and A2). A spatial interpolation technique was utilized in interpolating downscaled climate projections at weather stations to grids, and subsequently aggregated to administrative provinces. Results obtained in the downscaling showed anticipated significant climate changes from 2011 to 2040 in terms of rainfall and temperatures relative to 1971-2000. Consistent signals of climate change were found in many seasons and variables, whereas conflicting signs of changes were found in a few cases. A larger warming effect is projected for a daily minimum temperature than that for the maximum temperature, thus reducing diurnal temperature range. Precipitation is projected to increase in general in the Valley. Regarding seasonality, dry months (March-April-May) will continue to remain dry but during the rainy season, July and November are likely to become more notable wet months. There are also indications of an increasing frequency of heavy rainfall events, prolonged dry spell events and extreme daytime temperatures (especially in Aparri).
Regional climate models have been useful in climate studies and in downscaling climate projections from global climate models, especially for areas characterized by complex topography and coastline features, such as the Philippines. However, several factors may affect model skill, such as uncertainties related to the boundary conditions and model configuration. This study evaluates the performance of the non-hydrostatic regional climate model (NHRCM) over the Philippines. Present-day climate simulations at 50 km resolution are conducted using two sets of boundary conditions (ECMWF ERA-Interim and the NCEP/NCAR Reanalysis Project NNRP1), as well as two convective parameterization schemes in the model (Grell and Kain-Fritsch). Results show that the seasonal changes in the spatial distribution of temperature, rainfall, and winds over the Philippines are simulated reasonably well. NHRCM has an overall cold and dry bias over land, the degree of which depends on the boundary condition and the convective scheme used. After adjusting the simulated temperature because of the difference in topography, the temperature differs from that observed by -0.90°C to -0.42°C on average. The rainfall bias in NHRCM ranges from -62.13 % to -25.20 %. Regardless of the boundary condition, the Grell scheme results in the lowest temperature bias with high skill scores, while the Kain-Fritsch scheme gives the lowest rainfall bias with high correlation and skill scores. The boundary conditions also influence model skill, such that the model bias is lower for temperature when ERA-Interim is used, but lower for rainfall with NNRP1. NHRCM represents the seasonal cycles of temperature and rainfall for all regions, but tends to generate more occurrences of cold and dry months. Improvements in the model are still possible, but these results indicate the potential of the model to be used for providing essential information for describing historical and future changes in the Philippine climate.
The model-resolution sensitivity of simulated intensifying and deepening rates of an extremely intense tropical cyclone (TC), Typhoon Ida (1958), was investigated using the Japan Meteorological Agency/Meteorological Research Institute nonhydrostatic atmospheric model with horizontal resolutions of 20, 10, 5, and 2 km. The results revealed great differences in the intensifying and deepening rates and their associated structural changes among simulations. The typhoon simulated by a finer horizontal resolution resulted in a greater maximum intensity associated with more rapid intensification. The differences were also revealed in the hourly precipitation pattern, the radius of maximum wind speed at 2-km altitude (RMW) and its shrinking behavior, near-surface inflow, and the axisymmetrization of the inner core. Only the cloud-resolving 2-km model, with explicit microphysics, could reproduce the observed maximum intensity and extreme intensification rate of the typhoon realistically because the model could produce the deep, intense, and upright updrafts inside RMW around the vorticity-rich area over the strong near-surface inflow. The results suggest that the appropriate horizontal resolution of the model should be used in dynamical downscaling experiments to examine extremely intense TCs with extremely high intensifying rates.
This paper clarifies that the skillful time-scale characteristic of a model is one of the key factors to reproduce the amount precipitation at a specific location with the model. A comparison with data from an operational weather station of the Japan Meteorological Agency in Tokyo (Ote-machi) revealed that a model needed That a model requires 5-km-grid resolution and below to represent the power spectrum of hourly precipitation. A model with a higher resolution is probably needed to simulate hourly precipitation in Tokyo during the summer monsoon season.
The atmosphere-ocean-coupled regional downscaling system of the Regional Spectral Model for the atmosphere and the Regional Ocean Modeling System (RSM-ROMS) was used to improve the downscaling simulation accuracy, particularly of coastal areas, and a dynamical downscale of the historical global reanalysis data for the East Asian region over 25 years was conducted. The results showed that in the coupled run, the sea surface temperature (SST) tended to show large-scale discrepancy from reality, basically because the models remain imperfect. On the other hand, for net heat flux, precipitation, and surface air temperature, the coupled run showed positive improvement compared with the uncoupled run. The improvement in these three variables and the degradation in SST were also apparent for event-based (one-month) averages. This inconsistency between the impacts on SST and the other variables may indicate that there is room to improve the model system further, particularly in the coupling and/or boundary layer processes for both the atmosphere and ocean.