Annual precipitation anomalies over eastern China are characterized by a north–south dipole pattern, referred to as the “southern flooding and northern drought” pattern (SF/ND), fluctuating on decadal time scales. Previous research has suggested possible links with oceanic forcing, but the underlying physical mechanisms by which sea surface temperature (SST) variability impacts the dipole pattern remains unclear. Idealized atmospheric general circulation model experiments conducted by the U.S. CLIVAR Drought Working Group are used to investigate the role of historical SST anomalies associated with Pacific El Niño–Southern Oscillation (ENSO)-like and the Atlantic multidecadal oscillation (AMO) patterns in this dipole pattern. The results show that the Pacific SST pattern plays a dominant role in driving the decadal variability of this dipole pattern and the associated atmospheric circulation anomalies, whereas the Atlantic SST pattern contributes to a much lesser degree. The direct atmospheric response to the Pacific SST pattern is a large-scale cyclonic or anticyclonic circulation anomaly in the lower troposphere occupying the entire northern North Pacific. During the warm phase of the Pacific SST pattern, it is cyclonic with northwesterly wind anomalies over northern China pushing the monsoon front to the south and consequently SF/ND. During the cold phase of the Pacific SST pattern, the circulation anomaly reverses with southeasterly winds over northern China allowing the monsoon front and the associated rainband to migrate northward, resulting in southern drought and northern flooding. The Atlantic SST pattern plays a supplementary role, enhancing the dipole pattern when the Pacific SST and Atlantic SST patterns are in opposite phases and weakening it when the phases are the same.

The equation of one-dimensional thermal conduction and convection in soil is solved analytically by applying the traditional harmonic method (HM) and the Laplace transform method (LTM). A simple method to determine accurate values of soil heat diffusivity and liquid water flux density is given. Using this method, we determined the soil thermal diffusivity, k = 0.85 × 10⁻⁶ m² s⁻¹, and liquid water flux density, W = 4.3 × 10⁻⁶ m³ s⁻¹ m⁻², for the Naqu site in the summer of 1998. An experimental evaluation of the proposed methods is also given.

Afforestation efforts in China resulted in significant changes in vegetation coverage over the Loess Plateau during 2001–2009. While regional climate conditions dominate the distribution of major vegetation types, human activities, primarily afforestation/reforestation and the resultant land use/land cover (LULC) changes (LULCC) and their impacts, are the focus of this study. A new attribution method was developed and applied to observed data for investigating the interrelationships between climate variation and LULCC. Regional climate (temperature and precipitation) changes are attributed to climate variation and LULCC; LULCC is attributed to climate variation and human activities. Climate attribution analysis indicated a larger contribution ratio (based on comparison of standard deviations of each contributing factor-induced climate changes and that of total change) from climate variation than from LULCC (0.95 from climate variation vs. 0.35 from LULCC) for variations in temperature. Impacts on precipitation indicated more spatial variations than those on temperature. The spatial variation of LULCC impacts on precipitation implied that human activities might have larger impacts on precipitation in the region’s arid north than in its humid south. Using both leaf area index (LAI) and areal coverage of each of the major land types, LULCC attribution analysis suggested that LULCC observed in the 2000s resulted primarily from human activities rather than climate variations (0.99 contribution ratio from human activities vs. 0.26 from climate variation).

Adverse environmental impacts from deforestation are a growing area of concern in climate change discussions. The Chinese government has implemented a series of policies, such as the Grain for Green Project, in an attempt to mitigate the impacts. This study takes a regional perspective to report land use/land cover changes over the Loess Plateau region from 2001 to 2009. MODIS data were used in analyzing both the conversions among and the resulting changes in different land types. Government statistical census data and observed climate data were also incorporated in the analysis. A general consistency is shown in both remotely sensed and census data. With the implementation of various projects, including the Grain for Green Project, the total areas covered by grassland, cropland, and forests have increased by 19.2 % (6.05 × 10⁴ km²), 33.7 % (5.80 × 10⁴ km²), and 19.6 % (3.08 × 10⁴ km²), respectively, during the 9-year period. While climatic conditions, particularly annual precipitation totals, usually dominate the distribution of vegetation, it is found that socioeconomic polices and human activities contribute to the increase in overall greenness and to vegetation growth (e.g., LAI increased by 16.8 % (0.10) overall). However, the feedback of land use/land cover to regional climate is complicated and cannot be easily distinguished from natural climate variations based on short-term observational data alone. To better isolate the effects, further analysis and modeling studies are suggested.

The Global Precipitation Measurement (GPM) mission has generated global precipitation products of improved accuracy and coverage that are promising for advanced hydrological and meteorological studies. This study evaluates three Integrated Multi-satellitE Retrievals for GPM (IMERG) Hourly products, including the Early-, Late-, and Final-run products (IMERG-HE, IMERG-HL, and IMERG-HF, respectively), over Sichuan Basin of China. This highly complex terrain of the steep mountainous region offers further scrutiny on the quality and applicability of the data. The China Meteorological Precipitation Analysis (CMPA) data from January 2016 to December 2018 are used as the reference for the evaluation. Results show that: (1) At grid scale, IMERG-HL and IMERG-HF outperform IMERG-HE in terms of correlation coefficient (CC) and root-mean-square error (RMSE), but IMERG-HL has smaller relative bias (RB) than that of the IMERG-HF (by 21.16%). IMERG-HF presents the highest probability of detection (POD = 0.52) and critical success index (CSI = 0.32), except for high false alarm ratio (FAR) for light precipitation. (2) At regional scale, IMERG-HF outperforms IMERG-HE and IMERG-HL in annual evaluation in all the metrics except for the serious overestimation as shown in RB (20.18%, 3.84%, and 4.97%, respectively). Its accumulative precipitation deviation mainly comes from moderate precipitation events (1–10 mm/h), while better detection capability is seen in light precipitation (<1 mm/h). Seasonally, IMERG-HF performs the best in winter, while IMERG-HL performs the best in the other seasons. (3) IMERG-HF captures the peak precipitation more accurately in all seasons. In reproducing the diurnal cycle, IMERG-HF performs better in winter, while IMERG-HL performs better in summer and autumn, and IMERG-HE in spring. However, all three products overestimate the early morning precipitation (01:00–08:00 local standard time) of the diurnal cycle in spring, summer, and autumn.