Wenchao Chu

Most glaciers and ice caps (GIPs) are out of balance with the current climate, exhibiting continuous retreat and thinning. These changes impact regional runoff and contribute to sea level rise, causing glacier-related hazards. In this study, we estimate the area and volume losses for current GIPs to reach equilibrium through modelling the steady state accumulation area ratios (AAR0) and time-averaged AAR of each GIP. The modelled global average AAR0 is 0.541 ± 0.082, which is lower than most previous studies due to the inclusion of numerous unobserved GIPs. Ice caps exhibit a higher AAR0 (0.612 ± 0.11) compared to glaciers (0.538 ± 0.08). For regional distribution, the largest AAR0 appears in northern Arctic Canada (0.608 ± 0.114) and low latitude areas (0.570 ± 0.067), while the smallest AAR0 occurs in Central Europe (0.519 ± 0.066) and north Asia (0.522 ± 0.071). Accounting for debris-cover reveals a decrease in AAR0 due to reduced sub-debris melting, while considering the frontal ablation of marine-terminating glaciers leads to an increase in AAR0. Assessing the imbalance between global GIPs and the current (2000-2019) climate, we project an additional loss of 23 ± 6% in area and 29 ± 8% in volume. This corresponds to a sea level rise equivalent of 128 ± 34 mm. The Antarctic and subantarctic are the primary contributor to global mean sea level rise, accounting for 60 ± 20 mm. The GIPs in central and eastern Himalaya, as well as the Mt. Hengduan exhibit significant instability, characterized by an average imbalance ratio (AAR/AAR0) of 0.72 ± 0.25.

Mountain glaciers provide us a window into past climate changes and landscape evolution, but the pattern of glacier evolution at centennial or suborbital timescale remains elusive, especially in monsoonal Himalayas. We simulated the glacier evolution in Bhutanese Himalaya (BH), a typical monsoon-influenced region, during the Little Ice Age (LIA) using the Open Global Glacier Model driven by six paleoclimate datasets and their average. Compared with geomorphologically mapped glacial landforms, the model can well capture the patterns of glacier length change. Simulation results revealed four glacial substages (the 1270s, 1470s, 1710s, and 1850s) during LIA in the study area. Statistically, a positive correlation between the number of glacial substages and glacier slope was found, indicating that the occurrence of glacial substages might be a result from heterogeneous responses of glaciers to climate change. Monthly climate change analysis and sensitivity experiments indicated that the summer temperature largely dominates the regional glacier evolution during the LIA in BH.

Mountain glaciers provide us a window into past climate change and landscape evolution, but the pattern of glacier evolution at centennial or suborbital timescale remains elusive, especially in monsoonal Himalayas. We simulated the glacier evolution in Bhutanese Himalaya, a typical monsoon influenced region, during the Little Ice Age (LIA), using the Open Global Glacier Model and six paleo-climate datasets. Compared with the mapped glacial landforms, the model can well capture the glacier length changes, especially for the experiment driving by the GISS climate dataset, but overestimates the changes in glacier area. Simulation results reveal four glacial substages at 1270s–1400s, 1470s–1520s, 1700s–1710s, and 1820s–1900s in the study area. From further analysis, a negative correlation between the number of the substages and glacier length was found, which suggests that the number and occurrence of glacial substages are regulated by the heterogeneous responses of glaciers to climate change. In addition, the changes in summer temperature dominated the glacier evolution in this region during the LIA.

Scale-aware parameterizations of subgrid scale physics are essentials for multiscale atmospheric modeling. A single-ice (SI) microphysics scheme and Gaussian probability-density-function (Gauss-PDF) macrophysics scheme were implemented in the single-column Global-to-Regional Integrated forecast System model (SGRIST) and they were tested using the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) and the Atmospheric Radiation Measurement Southern Great Plains Experiment in 1997 (ARM97). Their performance was evaluated against observations and other reference schemes. The new schemes simulated reasonable precipitation with proper fluctuations and peaks, ice, and liquid water contents, especially in lower levels below 650 hPa during the wet period in the TWP-ICE. The root mean square error (RMSE) of the simulated cloud fraction was below 200 hPa was 0.10/0.08 in the wet/dry period, which showed an obvious improvement when compared to that, i.e., 0.11/0.11 of original scheme. Accumulated ice water content below the melting level decreased by 21.57% in the SI. The well-matched average liquid water content displayed between the new scheme and observations, which was two times larger than those with the referencing scheme. In the ARM97 simulations, the SI scheme produced considerable ice water content, especially when convection was active. Low-level cloud fraction and precipitation extremes were improved using the Gauss-PDF scheme, which displayed the RMSE of cloud fraction of 0.02, being only half of the original schemes. The study indicates that the SI and Gauss-PDF schemes are promising approaches to simplify the microphysics process and improve the low-level cloud modeling.

Shanghai is the biggest metropolis in China, and its local temperature change is affected not only by global warming but also by urbanization. Integrating the Mann-Kendall test, EMD (Empirical Mode Decomposition), Cross Wavelet Analysis and statistical methods, we studied the response of the local temperature change in Shanghai to global warming and urbanization. The results indicate that the local temperature at Shanghai present a significant warming trend under the background of global warming over the past 135 years. The local temperature at Shanghai displays 2-year, 6-year, 15-year, 23-year and 68-year periodic fluctuation, whereas global temperature shows 4-year, 9-year, 15-year, 23-year and 68-year cyclic variation. Although the two cycles are not exactly the same, they show some comparability. Urbanization facilitated the warming process of Shanghai. In the most recent 50 years, temperature difference between urban and suburban Shanghai has increased nearly 0.4 °C. The related indicators of urban development, such as population, built-up area, Gross Domestic Product (GDP), energy consumption and number of vehicles show significantly positive correlation with the temperature difference between urban and suburban area. In addition, the frequency of extreme high temperature has become higher, whereas the frequency of extreme low temperature has become lower over the most recent 55 years.