Glacier surface melting can be described using energy-balance models. We conducted a surface energy budget experiment to quantify surface energy fluxes and to identify factors affecting glacial melt in the ablation zone of Laohugou glacier No. 12, western Qilian mountains. The surface energy budget was calculated based on data from an automatic weather station, and turbulent fluxes calculated using the bulk-aerodynamic approach were corrected using measurements from an eddy-covariance system. Simulated mass balances were validated by stake observations. Net shortwave radiation was the primary component of the surface energy balance (126Wm^–2), followed by sensible heat flux. Net longwave radiation (–45Wm^–2) and latent heat flux (–12.8 Wm^–2) represented heat sinks. The bulk-aerodynamic method underestimated sensible and latent heat fluxes by 3.4 and 1.2 W m^–2, respectively. The simulated total mass balance of –1703mmw.e. exceeded the observed total by 90 mm w.e. Daily positive accumulated temperature and low albedo were the main factors accelerating glacier melt. An uncertainty assessment showed that mass balance was very sensitive to albedo and varied by 36% when albedo changed by 0.1.

In this study, 2-m or near-surface air temperature (T2m) products from atmospheric reanalysis ERA5 and the regional climate model RACMO2.3p2 over Greenland are compared with observations from staffed stations and Automated Weather Stations (AWS). The results show the following: 1) Greenland experienced decadal periods of both cooling and warming during 1958–2020, with an inflection point around the mid-1990s, and no significant warming after ∼2005 except in the north and northeast. 2) In the full time series, the magnitude of the warming increases gradually from south to north, with peak warming found along the northeastern coast. 3) The most intense warming occurred in autumn and winter, notably in the northeast. 4) The correlations of T2m with the large-scale circulation indices NAO and GBI are highly significant, but they gradually weaken from southwestern to northeastern Greenland. Under the background of Greenland rapidly warming, the shift from positive to negative NAO (negative to positive GBI) is critical to the sudden warming in Greenland since the mid-1990s.

The Qilian Mountains (QMs) act as the “water tower” of the Hexi Corridors, playing an important role in the regional ecosystem security and economic development. Therefore, it is of great significance to understand the spatiotemporal characteristics of precipitation in the QMs. This study evaluated the performance of 21 models of phase 6 of the Coupled Model Intercomparison Project (CMIP6) from 1959 to 1988 based on ERA5 and in situ datasets. In addition, the precipitation changing trend from 2015 to 2100 was projected according to four shared socioeconomic pathways (SSPs): namely, SSP126, SSP245, SSP370, and SSP585. The results have shown the following: (1) all CMIP6 models could reflect the same precipitation changing trend, based on the observed datasets (−2.01 mm·10a⁻¹), which was slightly lower than that of ERA5 (2.82 mm·10a⁻¹). Multi-mode ensemble averaging (MME) showed that the projected precipitation-change trend of the four scenarios was 5.73, 9.15, 12.23, and 16.14 mm·10a⁻¹, respectively. (2) The MME and ERA5 showed the same precipitation spatial pattern. Also, during the period 1959–1988, the MME in spring, summer, autumn and winter was 130.07, 224.62, 95.96, and 29.07 mm, respectively, and that of ERA5 was 98.57, 280.77, 96.85, and 22.64 mm, respectively. The largest precipitation difference in summer was because of strong convection and variable circulation. (3) From 2015 to 2100, the snow-to-rain ratio was between 0.1 and 1.1, and the snow-to-rain ratio climate tendency rate was concentrated in the range of −10~0.1 mm·10a⁻¹. Both of these passed the significance test (p < 0.05). The projected rainfall of all four SSPs all showed an increasing trend with values of 6.20, 11.31, 5.64, and 20.41 mm·10a⁻¹, respectively. The snowfall of the four SSPs all showed a decreasing trend with values of 0.42, 2.18, 3.34, and 4.17 mm·10a⁻¹, respectively.