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Abstract. Water resources, which are considerably affected by land use/land cover (LULC) and climate changes, are a key limiting factor in highly vulnerable ecosystems in arid and semi-arid regions. The impacts of LULC and climate changes on water resources must be assessed in these areas. However, conflicting results regarding the effects of LULC and climate changes on runoff have been reported in relatively large basins, such as the Jinghe River basin (JRB), which is a typical catchment (> 45 000 km 2 ) located in a semi-humid and arid transition zone on the central Loess Plateau, northwest China. In this study, we focused on quantifying both the combined and isolated impacts of LULC and climate changes on surface runoff. We hypothesized that under climatic warming and drying conditions, LULC changes, which are primarily caused by intensive human activities such as the Grain for Green Program, will considerably alter runoff in the JRB. The Soil and Water Assessment Tool (SWAT) was adopted to perform simulations. The simulated results indicated that although runoff increased very little between the 1970s and the 2000s due to the combined effects of LULC and climate changes, LULC and climate changes affected surface runoff differently in each decade, e.g., runoff increased with increased precipitation between the 1970s and the 1980s (precipitation contributed to 88 % of the runoff increase). Thereafter, runoff decreased and was increasingly influenced by LULC changes, which contributed to 44 % of the runoff changes between the 1980s and 1990s and 71 % of the runoff changes between the 1990s and 2000s. Our findings revealed that large-scale LULC under the Grain for Green Program has had an important effect on the hydrological cycle since the late 1990s. Additionally, the conflicting findings regarding the effects of LULC and climate changes on runoff in relatively large basins are likely caused by uncertainties in hydrological simulations.

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Clouds’ efficiency at reflecting solar radiation and trapping the terrestrial radiation is strongly modulated by the diurnal cycle of clouds (DCC). Much attention has been paid to mean cloud properties due to their critical role in climate projections; however, less research has been devoted to the DCC. Here we quantify the mean, amplitude, and phase of the DCC in climate models and compare them with satellite observations and reanalysis data. While the mean appears to be reliable, the amplitude and phase of the DCC show marked inconsistencies, inducing overestimation of radiation in most climate models. In some models, DCC appears slightly shifted over the ocean, likely as a result of tuning and fortuitously compensating the large DCC errors over the land. While this model tuning does not seem to invalidate climate projections because of the limited DCC response to global warming, it may potentially increase the uncertainty of climate predictions.

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The local role that land‐atmosphere interactions play in the rainfall process has been often explored by investigating the initiation of moist convection as the top of the atmospheric boundary layer (ABL) crosses the lifting condensation level (LCL). However, this LCL crossing alone is not a sufficient indicator of the probability and intensity of subsequent convective precipitation, which is instead better characterized by the added consideration of the so‐called convective available potential energy (CAPE). In this study, both the LCL crossing and CAPE are jointly considered as the primary indicators of the occurrence and intensity of moist convection in order to analyze the land‐atmosphere interactions through a simple soil‐plant system and a zero‐dimensional mixed‐layer model. The approach is explored using the free atmospheric conditions observed at the Central Facility in the Southern Great Plains, where the ABL analysis shows both dry and wet soil can be conducive to early moist convection depending on atmospheric conditions but CAPE always tends to be larger under wetter soil conditions. The combination of the two indicators, LCL crossing and CAPE, further allows us to classify free atmosphere and soil moisture regimes into positive and negative feedback regimes for moist convection.