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Open Access Publications from the University of California

Scholarly Works (6 results)

  • Article
  • Peer Reviewed
LBL Publications (2019)
The global climate change has resulted in not only warmer climate conditions but also more frequent extreme weather events, such as heat waves. However, the impact of heat waves on the indoor environment has been investigated in a limited manner. In this research, the indoor thermal environment is analyzed using a building performance simulation tool for a typical residential building in multiple cities in China, over a time period of 60 years using actual measured weather data, in order to gain a better understanding of the effect of heat wave events. The simulation results were used to analyze the indoor environment during hot summers. A new kind of weather data referred to as the typical hot year was defined and selected based on the simulated indoor environment during heat waves. The typical hot-year weather data can be used to simulate the indoor environment during extreme heat events and for the evaluation of effective technologies and strategies to mitigate against the impact of heat waves on the energy demand of buildings and human health. The limitations of the current study and future work are also discussed.
    Cover page: A novel approach for selecting typical hot-year (THY) weather data
    • Article
    • Peer Reviewed
    LBL Publications (2017)
    Weather has significant impacts on the thermal environment and energy use in buildings. Thus, accurate weather data are crucial for building performance evaluations. Traditionally, typical year data inputs are used to represent long-term weather data. However, there is no guarantee that a single year represents the changing climate well. In this study, the long-term representation of a typical year was assessed by comparing it to a 55-year actual weather data set. To investigate the weather impact on building energy use, 559 simulation runs of a prototype office building were performed for 10 large cities covering all climate zones in China. The analysis results demonstrated that the weather data varied significantly from year to year. Hence, a typical year cannot reflect the variation range of weather fluctuations. Typical year simulations overestimated or underestimated the energy use and peak load in many cases. With the increase in computational power of personal computers, it is feasible and essential to adopt multiyear simulations for full assessments of long-term building performance, as this will improve decision-making by allowing for the full consideration of variations in building energy use.
      Cover page: Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China
      • Article
      • Peer Reviewed
      LBL Publications (2021)
      In accordance with the developing economy and growing population, an increasing number of tall buildings have been constructed over the last 20 years. In 2017, there were 144 new buildings worldwide that were 200 m or greater in height; 50% of these buildings were in China. Due to the vertical gradient impact of meteorological parameters, the energy performance of tall buildings differs from that of general buildings. Few studies exist on vertical meteorological changes using measured data at different heights. Most studies on dynamic energy simulation simulate meteorological parameters using models. This study explores vertical meteorological patterns using hourly dry-bulb temperature, relative humidity, and wind speed data from 2007 to 2017 for a 325 m meteorological tower in Beijing. The temperature decreased significantly with increasing altitude (~0.9 °C per 100 m), while the daily temperature difference decreased with increasing altitude. The absolute humidity did not change significantly with height. The wind speed increased with altitude at approximately 2 m/s per 100 m. The building simulation showed that the annual heating load at a height of 320 m increased by 85% from that at 8 m; the annual cooling load decreased by 20%. Such significant differences in thermal loads for 300-m-tall buildings confirm the need to consider vertical meteorological differences in building performance simulations for tall buildings. A greater number of measurement points at different heights improve the simulation accuracy. Guidance on selecting the heights for vertical metrological measurements is provided based on the influences of building thermal loads.
        Cover page: Vertical meteorological patterns and their impact on the energy demand of tall buildings
        • Article
        • Peer Reviewed
        LBL Publications (2021)
        Climate models are critical tools to study earth system processes and are often further downscaled to provide refined-resolution data sets for regional water-energy impact analyses. In this study, the Variable-Resolution Community Earth System Model (VR-CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR-CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR-CESM simulates +2°C warm bias in the low-elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR-CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water-energy studies.
          Cover page: Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling
          • Article
          • Peer Reviewed
          LBL Publications (2020)

          Climate models have been used to study water resources and regional hydrologic responses to climate change, but climate model outputs must be downscaled to provide relevant regional data. However, the accuracy of this regional data is limited by uncertainties across and within downscaling methods, uncertainty across global outputs, and discontinuities at downscaled boundaries. A new alternative to traditional downscaling is a variable resolution model that incorporates fine-resolution regions directly into a coarse-resolution, global climate simulation in order to capture contiguous dynamics across resolution boundaries. In this study, we used the Variable-Resolution Community Earth System Model (VR-CESM) to generate one-eighth degree (14 km) fine-resolution outputs for the western U.S. and eastern China from 1970-2006.

           

          We focus our evaluation on precipitaiton, temperature, snow pack, solar radiation, and wind. We compare the model outputs with remote-sensing-based precipitation data, and both reanalysis and gridded weather station data for precipitation and temperature. VR-CESM generally has a cold bias in winter and a warm bias in summer in the western U.S., which compensate each other to reduce the annual bias. In eastern China, however, the sign of temperature biases are more consistent throughout the year with cold biases in the higher mountains and warm biases throughout most of the rest of the region. Precipitation biases are dependent upon reference data, and show slight overestimation in high mountain regions in both the U.S. and China with respect to gridded weather station data. Simulated snow cover in the western U.S. is reasonable compared to remote sensing data, but snow cover and snow water equivalent have larger biases when compared to reanalysis data. In eastern China there are widespread snow cover biases compared to remote sensing data. VR-CESM underestimates downward shortwave radiation to a greater degree in summer than in winter, and underestimates surface layer windspeed over mountains to a greater degree than in other areas. Comparison between VR-CESM and a coarser simulation (1-degree Beijing Climate Center model) shows reduced precipitation biases in the mountainous regions with finer resolution, indicating the value of variable-resolution modeling for reigonal studies.

            Cover page: Evaluating fine-resolution, regional outputs of a variable resolution global climate model
            • Article
            • Peer Reviewed
            LBL Publications (2023)
            The East Asian summer monsoon (EASM) is unique among monsoon systems that it features meridional evolution of the summer monsoon. In this study, we evaluate the performances of a Variable-Resolution Community Earth System Model (VR-CESM) regionally refined over eastern China (14 km) in reproducing the seasonal evolution of EASM precipitation over China. Compared with reference datasets, VR-CESM shows better performance than the corresponding globally uniform coarse-resolution model CESM (quasi-uniform 1°), especially over western China where complex local topography exists. The northward monsoon migration is closely related to low-level southerly flows and vertical moisture advection, which are more reasonably simulated in VR-CESM. The four critical timings of the EASM (monsoon onset, withdrawal, peak, and duration) are also better captured in VR-CESM than in CESM. The corresponding spatial Pearson correlation coefficients of the four critical timings with respect to reference datasets are about 0.1 higher in VR-CESM than those in CESM. Both models are most accurate in simulating monsoon onset and least accurate at simulating the monsoon peak. The overestimated zonal thermal contrast in CESM is responsible for the earlier monsoon onset and excessive precipitation in September over the Yangtze River valley. Finer resolution in VR-CESM, especially over the Tibetan Plateau (TP), appears to be a main factor in simulating better zonal thermal contrast and seasonal evolution of the EASM.
              Cover page: Advantages of a variable‐resolution global climate model in reproducing the seasonal evolution of East Asian summer monsoon
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