Local Sea-Level Rise Caused by Climate Change in the Northwest Pacific Marginal Seas Using Dynamical Downscaling

Global climate models (GCMs) have limited capacity in simulating spatially non-uniform sea-level rise owing to their coarse resolutions and absence of tides in the marginal seas. Here, regional ocean climate models (RCMs) that consider tides were used to address these limitations in the Northwest Pacific marginal seas through dynamical downscaling. Four GCMs that drive the RCMs were selected based on a performance evaluation along the RCM boundaries, and the latter were validated by comparing historical results with observations. High-resolution (1/20°) RCMs were used to project non-uniform changes in the sea-level under intermediate (RCP 4.5) and high-end emissions (RCP 8.5) scenarios from 2006 to 2100. The predicted local sea-level rise was higher in the East/Japan Sea (EJS), where the currents and eddy motions were active. The tidal amplitude changes in response to sea-level rise were significant in the shallow areas of the Yellow Sea (YS). Dynamically downscaled simulations enabled the determination of practical sea-level rise (PSLR), including changes in tidal amplitude and natural variability. Under RCP 8.5 scenario, the maximum PSLR was ∼85 cm in the YS and East China Sea (ECS), and ∼78 cm in the EJS. The contribution of natural sea-level variability changes in the EJS was greater than that in the YS and ECS, whereas changes in the tidal contribution were higher in the YS and ECS. Accordingly, high-resolution RCMs provided spatially different PSLR estimates, indicating the importance of improving model resolution for local sea-level projections in marginal seas.

Hydrologic modeling by means of a hybrid downscaling approach: an application to the Sai Gon–Dong Nai Rivers Basin

The spatial and temporal availability and reliability of hydrological data are substantial contribution to the accuracy of watershed modeling; unfortunately, such data requirements are challenging and perhaps impossible in many regions of the world. In this study, hydrological conditions are simulated using the hydrologic model-WEHY, whose data input are obtained from a hybrid downscaling technique to provide reliable and high temporal and spatial resolution hydrological data. The hybrid downscaling technique is coupled a hydroclimate and a machine learning models; wherein the global atmospheric reanalysis data, including ERA-Interim, ERA-20C, and CFSR are used for initial and boundary conditions of dynamical downscaling utilizing the Weather Research and Forecasting model (WRF). The machine learning model (ANN) then follows to further downscale the WRF outputs to a finer resolution over the studied watershed. An application of the combination of mentioned techniques is applied to the third-largest river basin in Vietnam, the Sai Gon–Dong Nai Rivers Basin. The validation of hybrid model is in the ‘satisfactory’ range. After the estimation of geomorphology and land cover within the watershed, WEHY's calibration and validation are performed based on observed rainfall data. The simulation results matched well with flow observation data with respect to magnitude for both the rising and recession time segments. In comparison among the three selected reanalysis data sets, the best calibration and validation results were obtained from the CFSR data set. These results are closer to the observation data than those using only the dynamic downscaling technique in combination with the WEHY model.

Inter-Comparison of Two Regional Climate Models (RegCM and WRF) in Downscaling CFSv2 For the Seasonal Prediction of Indian Summer Monsoon

The efficacy of two latest versions of regional climate models (RegCM and WRF) for simulating the Indian summer monsoon (JJAS) is tested in this study. The CFSv2 hindcast outputs are downscaled over the Indian monsoon domain for 11 contrasting monsoon seasons using the regional models. The April start ensembles of the CFSv2 are averaged to generate the initial and lateral boundary conditions for driving the WRF and RegCM. The regional models perform better in simulating the Indian summer monsoon features better than the parent CFSv2 model. The rainfall pattern as well as the intensities are improved with the dynamical downscaling and the errors in the rainfall are minimized over the GCM hindcast. On comparing the two regional models, the RegCM overestimates the rainfall during the excess and normal monsoon seasons. The RCMs improve the skill of rainfall prediction as compared to the GCM and WRF shows better skill in particular. One peculiar finding of this study is that the daily rainfall biases averaged over all the years of simulation shows that the two RCMs show similar biases with RegCM showing stronger biases occasionally. It may be implied that the errors from GCM in the form of the ICBC might be influencing the simulation in the RCMs. The upper air and surface parameters analysis shows that the WRF performs better in representing the semi-permanent features of the Indian summer monsoon which may be helping in improving the rainfall over the RegCM. The wind pattern as well as the relative humidity along the vertical column of the atmosphere are captured better in the WRF model. Diagnostics of CAPE & vertically integrated moisture transport supports the finding of the rainfall being simulated better in the WRF model.

Added value of high-resolution climate projections over South Korea on the scaling of precipitation with temperature

This study evaluates the resolution dependency of scaling precipitation with temperature from the perspective of the added value of high-resolution (5-km) dynamical downscaling using various kinds of long-term climate change projections over South Korea. Three CMIP5 Global Climate Models (GCMs) with different climate sensitivities, and one pseudo global warming (PGW) experiment, are downscaled by Weather Research and Forecasting (WRF) one-way double nested modeling system with convective parameterization for the reference (1976-2005) and future (2071-2100) periods under RCP8.5 scenario. A detailed comparison of the driving GCM/PGW, 20-km mother simulation, and 5-km nested simulation demonstrates improved representation of precipitation with increasing resolution not only in the spatial pattern and magnitude for both the mean and the extremes, but also in a more realistic representation of extreme precipitation’s sensitivities to temperature. According to the projected precipitation changes downscaled from both GCM ensemble and PGW, there will be intensified precipitation, particularly for the extremes, over South Korea under the warming, which is primarily contributed by CP increase that shows higher temperature sensitivity. This study also compares the extreme precipitation-temperature scaling relations within-epoch (apparent scaling) and between-epoch (climate scaling). It confirms that the magnitude and spatial pattern of the two scaling rates can be quite different, and the precipitation change over Korea under global warming is mainly controlled by thermodynamic factors.

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100)

Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979–2014 and future scenarios (SSP245 and SSP585) for 2015–2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth’s future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

Dynamical Downscaling of Temperature Variations over the Canadian Prairie Provinces under Climate Change

In this study, variations of daily mean, maximum, and minimum temperature (expressed as Tmean, Tmax, and Tmin) over the Canadian Prairie Provinces were dynamically downscaled through regional climate simulations. How the regional climate would increase in response to global warming was subsequently revealed. Specifically, the Regional Climatic Model (RegCM) was undertaken to downscale the boundary conditions of Geophysical Fluid Dynamics Laboratory Earth System Model Version 2M (GFDL-ESM2M) over the Prairie Provinces. Daily temperatures (i.e., Tmean, Tmax, and Tmin) were subsequently extracted from the historical and future climate simulations. Temperature variations in the two future periods (i.e., 2036 to 2065 and 2065 to 2095) are then investigated relative to the baseline period (i.e., 1985 to 2004). The spatial distributions of temperatures were analyzed to reveal the regional impacts of global warming on the provinces. The results indicated that the projected changes in the annual averages of daily temperatures would be amplified from the southwest in the Rocky Mountain area to the northeast in the prairie region. It was also suggested that the projected temperature averages would be significantly intensified under RCP8.5. The projected temperature variations could provide scientific bases for adaptation and mitigation initiatives on multiple sectors, such as agriculture and economic sectors over the Canadian Prairies.