Paper Accepted by Remote Sensing

The following paper about the water cycle projections over China has been accepted for publication by Remote Sensing.

Lu, C., G. Huang, G. Wang, J. Zhang, X. Wang, and T. Song. Long-term Projection of Water Cycle Changes over China using the RegCM. Remote Sensing, accepted on September 23, 2021.

More details will come soon once the paper is published.

Paper Published in Earth’s Future

Title: Possibility of Stabilizing the Greenland Ice Sheet

Journal: Earth’s Future


Abstract: Recent acceleration in the retreat of the Greenland ice sheet under a warming climate has caused unprecedented challenges and threats to coastal communities due to the rising sea level and increasing storm surges. This raises a critical question from a climate mitigation perspective: Would there still be a chance to stabilize the Greenland ice sheet if the carbon reduction goals of the Paris Agreement could be met? Here, we show that there is indeed a possibility for stabilizing the Greenland ice sheet with the low-emission scenario of RCP2.6. In particular, RCP2.6 would potentially limit the warming in Greenland below 1°C within next 30 years and constrain its loss of ice sheet coverage below 10%. After 2050, the annual mean temperature in Greenland is likely to be stabilized and no further loss is expected to its ice sheet. However, the effective window for this chance will be closing after 2020. If no effective carbon reduction policies are being taken now, we are very likely to enter a continuous warming pathway and lose the chance of stabilizing the Greenland ice sheet.

Paper Accepted by Earth’s Future

The following paper about the possibility of stabilizing the Greenland ice sheet has been accepted for publication by Earth’s Future.

Wang, X., A. Fenech, and A. Farooque. Possibility of stabilizing the Greenland ice sheet. Earth’s Future, accepted on July 4, 2021.

More details will come soon once the paper is published.

Paper Published in Climate Dynamics

Ensemble Projection of City-Level Temperature Extremes with Stepwise Cluster Analysis

Journal: Climate Dynamics


Abstract: Climate change can cause property damage and deaths in cities. City-scale climate projections are essential for making informed decisions towards climate change mitigation and adaptation at city levels. This study aims at developing ensemble projections of temperature extremes at the city-level and quantifying the contributions of various factors to the resulting uncertainty of the ensemble projections. The city of Toronto will be used here as an example to demonstrate the effectiveness of the proposed research framework. In particular, the stepwise cluster analysis (SCA) model will be used to perform climate downscaling to three GCM datasets (GFDL, IPSL, and MPI) under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) in order to generate city-level climate projections for the city of Toronto. The SCA model is demonstrated to be capable of capturing the inter- and intra-annual variations of the daily maximum, mean, and minimum temperatures in the studied city. The results suggest that mean temperatures in Toronto are projected to increase at the rate of 0.15 and 0.5 °C/decade under RCP4.5 and RCP8.5, respectively, while no significant warming trend is detected for RCP2.6. In terms of temperature extremes, extreme warm events are projected to increase while extreme cold events decrease under all emission scenarios. The decrease in the heating demand is two to four times larger than the increase in the cooling demand, indicating a decrease in the city’s total energy use. The projected warming might be beneficial for the urban growers because of the significant increases in the growing season length and growing degree days; however, the residents of the city of Toronto are likely to experience simultaneous increases in the intensity, duration, and frequency of heatwave events in future summers. Because of the warming, coldwave events in winters are likely to become less frequent and be shorter in duration, but their intensity is expected to increase significantly. Through decomposition of the resulting uncertainty of the ensemble projections, emission scenario is found to be the dominant factor for the uncertainty associated with urban climate projection.

Paper Published in Water Resources Research

Title: Vine Copula Ensemble Downscaling for Precipitation Projection Over the Loess Plateau Based on High‐Resolution Multi‐RCM Outputs

Journal: Water Resources Research


Abstract: A vine copula‐based ensemble downscaling (VCED) framework is proposed to jointly downscale the projected precipitation from multiple regional climate models (RCMs). This approach can effectively reduce the biases inherent to precipitation projections from different RCMs and thus provide more reliable ensemble projections. The proposed approach was applied to RCM projections over the Loess Plateau of China, which features complex topography and various climatic zones. Precipitation projections from 7 RCMs were used, and 21 sets of downscaling results were obtained. The performance of the VCED in reproducing historical precipitation across the Loess Plateau was evaluated using mean absolute error (MAE), the Taylor diagram, and the rank histogram (RH). The proposed VCED approach was found to be more effective than quantile mapping and bivariate copula methods in achieving robust precipitation projections. Overall flat RH diagrams indicate that the ensemble prediction and observations have strong consistency in distribution. Future precipitation changes of two 30‐year periods (i.e., the 2050s and 2080s) under two Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and RCP 8.5) over the Loess Plateau were then analyzed after postdownscaling processes. The results show that the average annual precipitation over the Loess Plateau may increase by 8.4%–11.4% under the RCP 4.5 scenario and by 9.3%–17.5% under RCP 8.5. The projected precipitation in the south‐central parts of the Loess Plateau would be significantly reduced whereas those of the other parts be significantly increased.