Title: Long-Term Projection of Water Cycle Changes over China Using RegCM

Journal: Remote Sensing

DOI: https://doi.org/10.3390/rs13193832

Abstract: The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.

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.

Title: Possibility of Stabilizing the Greenland Ice Sheet

Journal: Earth’s Future

DOI: https://doi.org/10.1029/2021EF002152

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.

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.

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

Journal: Climate Dynamics

DOI: https://doi.org/10.1007/s00382-021-05644-9

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.