Title: Projections of daily mean surface temperature over the Beijing-Tianjin-Hebei region through a stepwise cluster downscaling method

Journal: Theoretical and Applied Climatology

DOI: https://doi.org/10.1007/s00704-020-03172-w

Abstract: A stepwise cluster analysis (SCA) method is developed to downscale the daily mean surface temperature of 23 meteorological stations over the Beijing-Tianjin-Hebei (BTH) region in North China. Through comparisons of three evaluation indicators, including correction coefficients (CORRs), root mean square errors (RMSEs), and mean relative errors (MREs), between model simulations and observations, the SCA is proven to be capable of simulating the daily mean temperature in the baseline period (1961–2005). The projections of mean surface temperature under RCP4.5 and RCP8.5 scenarios are then developed using the SCA model. The results show that the mean surface temperature at most stations would keep increasing in future throughout the twenty-first century. The largest magnitude and rate of temperature increase are reported in the northwest mountains of the BTH region (especially obvious in winter), suggesting that the response to climate change in highland regions is more sensitive than that in low elevations. It is also reported that the projected changes in mean surface temperature in coastal regions are smaller than those in plain areas. This could be due to ocean’s moderating influence in the HadGEM2-ES. In terms of seasonal changes in surface temperature, the smallest magnitude of warming is expected in summer while the highest is likely to occur in winter, especially under RCP8.5 emission scenarios. Furthermore, the annual cycle analysis for temperature changes suggests that the highest amplitude of warming is expected in cold months (i.e., February or December) while the smallest is likely to occur in warm months (i.e., July or August).

Title: Evaluating the added values of regional climate modeling over China at different resolutions

Journal: Science of The Total Environment

DOI: https://doi.org/10.1016/j.scitotenv.2020.137350

Abstract: Previous studies have suggested that dynamical downscaling to global climate models can produce improved climate simulations at regional and local scales. However, the expensive computational requirements of dynamical downscaling inevitably add a limit to the spatial resolution of the resulting regional climate simulations. In order to find a balance between computational requirements and simulation improvements, it is extremely important to investigate how the spatial resolution of regional climate simulation affects the added values of dynamical downscaling; yet, it is still not well understood. Therefore, in this study, we conduct long-term climate simulations for the entire country of China with the PRECIS regional climate model at two different spatial resolutions (i.e., 25 and 50 km). The purpose is to evaluate whether a fine-resolution model simulation, given its considerable requirements for computational resources, would add more valuable information for understanding regional climatology than a coarse-resolution model simulation. Our results show that the PRECIS can reasonably reproduce the spatial distribution of seasonal and monthly mean temperature and precipitation over the most of regions in China. However, in the process of downscaling, RCM with higher resolution cannot always produce more accurate output. In regard to precipitation simulations, compared with the host GCM, it is difficult to determine exactly a homogeneous improvement of performance in downscaling, both in terms of spatial patterns as well as magnitude of errors. For interannual variability, variations in temperature are closer to observation than precipitation and the high-resolution R25 has better skills over the northwest than R50. Moreover, except for the west, it is shown that PRECIS is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet days and simple precipitation intensity index in spatial distribution.

The following paper about projections of daily mean surface temperature over the Beijing-Tianjin-Hebei region in China has recently been accepted for publication by Theoretical and Applied Climatology:

Guo, J., G. Huang, X. Wang, and C. Lu. Projections of daily mean surface temperature over the Beijing-Tianjin-Hebei region through a stepwise cluster downscaling method. Theoretical and Applied Climatology , accepted on February 26, 2020.

More details will come soon once the paper is published.

The following paper about the evaluation of the added values of regional climate modeling over China at different resolutions has recently been accepted for publication by Science of the Total Environment:

Guo, J., G. Huang, X. Wang, Y. Wu, Y. Li, R. Zheng and L. Song.
Evaluating the added values of regional climate modeling over China at different resolutions. Science of the Total Environment, accepted on February 14, 2020.

More details will come soon once the paper is published.

Title: Projected changes in wind speed and its energy potential in China using a high‐resolution regional climate model

Journal: Wind Energy

DOI: https://doi.org/10.1002/we.2417

Abstract: Following its commitment to Paris Agreement in 2015, China has started to explore potential renewable energy solutions with low carbon emissions to mitigate global warming. Though wind energy is one of the most cost‐effective solutions and has been favored for climate policy development around the world, its high sensitivity to climate change raises some critical issues for the long‐term effectiveness in providing sustainable energy supply. Particularly, how wind speed and its energy potential in China will change in the context of global warming is still not well understood. In this paper, we simulate the near‐surface wind speed over China using the PRECIS regional climate modeling system under different RCP emission scenarios for assessing the possible changes in wind speed and wind energy availability over China throughout the 21st century. Overall, the PRECIS model can reasonably reproduce the mesoscale climatological near‐surface wind speed and directions as documented in reanalysis data across most regions of China, while some local discrepancies are reported in the southwestern regions. In the future, the annual mean wind speed would be decreasing in most regions of China, except for a slightly increase in the southeast. The expected changes in wind speed are characterized with different amplitudes and rates under different RCP emission scenarios. The changes in the spatial distribution of wind speed seem to be sensitive for RCP climate emission scenarios, especially in the late 21st century. The spatiotemporal changes in wind energy potential exhibit a similar behavior to those in near‐surface wind speed, but the magnitudes of these changes are larger. In general, the wind power density is expected to increase by over 5% in winter in the major wind fields in China (ie, Northwest, Northcentral and Northeast), while significant decreases (by about 6% on average) are projected for other seasons (ie, spring, summer and autumn). By contrast, the wind energy potential in the northeast would increase over most months in the year, especially in winter and summer. The results of this research are of great importance for understanding where and to what extent the wind energy can be utilized to contribute renewable energy system development in China in support of its long‐term climate change mitigation commitment.