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Vegetation dynamic is sensitive to climatic warming, and is affected by individual or combined climatic factors at different temporal scale with different intensity. Previous studies have unraveled the relationships between vegetation condition and individual climatic factors; however, it is unclear whether the effects of single or combined climatic factors on vegetation dynamic was dominant for different temporal scales, vegetation types, and climatic regions. The objective of this study was to explore the scale-specific univariate and multivariate controls on vegetation over the period 1982–2015 using bivariate wavelet coherency (BWC), multiple wavelet coherence (MWC), and multiple empirical model decomposition (MEMD). The results indicated that the significant vegetation dynamics were mainly located at scales of 1, 0.5, and 0.3 years. The combined explanatory power of the seven climatic factors on the vegetation were greater at the short-term and long-term scales, while the individual climatic factor might affect vegetation dynamic in the seasonal and medium-term scales at some climatic regions. The combined effect of climatic factors in grassland of Tibetan Plateau (TP) and Tempera grassland of Inner Mongolia (TGIM) regions were the greatest, which were 65.06% and 59.53%, respectively. The explanatory powers of climate for crop dynamics between temperate humid & subhumid Northeast China (THSNC) and TP, warm-temperate humid & subhumid North China (WHSNC) and subtropical humid Central & South China (SHCSC), and TGIM and temperate & warm-temperate desert of Northwest China (TWDNC) were equivalent, which were around 47%, 45%, and 39%, respectively. Farming practices in cropland could alleviate the spatial variation of the relationships between climate and vegetation, while enhance the temporal difference of their relationships. Additionally, the dominant influencing factor among different regions varied greatly in the medium-term scale. Collectively, the results might provide alternative perspective for understanding vegetation evolution in response to climatic changes in China.

Northeast China with seasonally frozen soil is quite sensitive to global warming. The changes in soil freezing and thawing processes initiated by global warming could alter the hydrological cycle of agricultural fields. A paired-plot experiment was conducted in frozen agricultural soils in Northeast China to examine the impacts of simulated warming on soil freezing and thawing processes and on soybean production. Infrared radiators were used to simulate global warming, rising surface soil temperature (5 cm depth) by 2.86 °C. We showed that, artificial warming caused the freeze duration shortened by 22 days, and the thaw duration shortened by 17 days resulting in the mean duration of soil freezing-thawing significantly shortened by 39 days and the maximum frost depth reduced by about 40 cm. Simulated warming had no significant effect on the average annual freeze-thaw cycle frequency. Warming induced a larger water accumulation in the 0–100 cm soil layer during 2014–2015 soil freezing period. In the dry year of 2015, warming did not significantly affect surface soil moisture during period from sowing date to VC (soybean cotyledon) date. Thus, warming-induced an increase in soybean yield in the dry year may be attributable to the positive effect of enhanced soil temperature on soybean growth (aboveground dry matter accumulation) and consequent on soybean production. In the wet year of 2014, warming decreased surface soil moisture from sowing date to the date of VC stage because warming advanced the soil thaw-end date in 20–60 cm layer by 15 days. This decline in surface soil water availability may potentially offset the positive effects of increased soil temperature on soybean yield, thus warming effects on soybean production was neutral in the wet year. Our findings highlight the potential role of seasonally soil freezing and thawing dynamics in regulating soybean to global warming and suggested that warming effects on soil water dynamics during soil freezing and thawing periods, and subsequent on the surface soil water availability at the early vegetative stage and soybean production were associated with the hydrological year. We conclude that under current precipitation patterns, the no response of soil surface water availability to warming during early vegetative growth, coupled with warming-mediated increases in soil temperature, might improve soybean production during dry years in Northeast China.

To extend the historical temperature record in the permafrost region of Northeast China, we reconstruct the regional ground surface temperature (GST) for the past four centuries based on a network of Dahurian larch (Larix gmelinii Rupr.) tree-ring width chronologies. Seven standard tree-ring chronologies, which correlate well with each other, are averaged to create a regional mean chronology. GST is the major limiting factor for tree growth in this region. The optimum range of GSTs is from 30 May to 26 August (summer GST), identified by combining the days on which tree growth was strongly influenced by the daily GST data. The summer GST was significantly correlated with the regional mean chronology (r = 0.704, p

Rapid urbanization has resulted in the permanent conversion of large areas of cropland and natural vegetation to impervious surfaces and therefore greatly modified land surface properties and land-atmosphere interactions. This study sought to examine the urbanization process using Landsat images from 2001 to 2010 in metropolitan JingjinTang (JJT), a rapidly expanding urban cluster in northern China. We aggregated the original results of land use data as fractional cover information in 1 km and 10 km grids. Annual and seasonal land surface temperatures (LSTs) were processed from Moderate Resolution Imaging Spectroradiometer products. We used moving window and gradient analysis methods to examine the differences in LST between urban and other land types, further identifying LST increases in gradients of urbanization levels. Urban extent increased by 1.6 times, and approximately 45% newly developed areas were converted from croplands during this process. Emerging urban land in JJT has caused approximately 0.85 ± 0.68°C warming in terms of annual mean LST, and the greatest warming occurred in the summer. An increase in urban land of 10% in a 1 km grid in JJT would cause approximately a 0.21°C increase in annual LST. Urbanization also led to increases in daytime LSTs and nighttime LSTs by approximately 1.03 ± 1.38°C and 0.78 ± 1.02°C, respectively. The warming trend induced by urbanization exhibits clear seasonal and diurnal differences, and this warming trend is most likely caused by the cumulative effects of changes in land properties, radiation storage, and anthropogenic heat release by urbanization.

Droughts caused by a lack of precipitation are one of the major factors limiting agri- cultural crop production. It is thus important to assess the risk of such droughts in order to reduce their effect on agriculture. In the present study, the drought risk for crop production was assessed through an integrated approach that analyzed the relationship between crop yield and drought on the Henan Plain, China. We used the calibrated CERES-Wheat model to simulate 2 levels of wheat yield, the yield potential and the water-limited yield potential, at 66 weather stations. The yield gap between the yield potential and the water-limited yield potential was used as an indicator of the effects of a precipitation deficit on crop production under rain-fed conditions. A strong linear relationship between the yield gap and the amount of precipitation in the growing season was observed for each station during the period 1962�2009. A uniform criterion for drought severity thresholds for the entire Henan Plain was constructed based on the yield gap. For each station, the growing-season precipitation thresholds associated with different drought severities were then calculated based on the linear relationship between the yield gap and the amount of precipitation in the growing season. Drought frequencies derived from changes in the amount of precipitation during the growing season were also examined for all stations and spatially interpolated over the plain. The results showed diverse spatial patterns of frequency with respect to different drought types. Light droughts often occurred in the southern region, and moderate droughts occurred more frequently in the western and eastern regions. Severe drought displayed a generally decreasing trend from north to south.

In order to explore the trends in sunshine duration in the low-latitude belt of South China, long-term sunshine duration, total clouds, low clouds, and visibility were investigated for the period 1961–2005. The results show significant declines in sunshine duration, occurring on the average of −3.2% and −2.8% per decade under all-sky and clear-sky conditions, respectively. It is noted that increased air aerosol loading due to rapid socio-economic development is the drive behind the sharp declines in sunshine duration in the study area. Though cloud is the primary regulator of sunshine duration under all-sky conditions, sunshine is strongly correlated with visibility under clear-sky conditions. Relational analysis between sunshine and cloud amount suggests 0.4% change in clouds per decade, which is well in agreement with the trend of decline in sunshine duration under all-sky conditions in the study area. Increasing low-cloud opacity could be the primary factor driving the decline in sunshine duration in the low-latitude belt of South China.

The seasonal patterns of total cloud amount (TCA) responsible for El Nino/La Nina-Southern Oscillation (ENSO) Sea Surface Temperature (SST) anomalies were investigated using the ISCCP-D2 cloud and NOAA OI.v2 SST datasets for the period of July 1983 to June 2008. The results show three main ENSO-sensitive regions obtained by spatial overlapping of seasonal correlations, two in the western tropical Pacific and one in the central tropical Pacific. These regions were named WTP1, WTP2, and CTP. In all three regions, except the JJA (June–August) WTP2, the TCA changes were significantly correlated with the Nino 3.4 anomalies during the four seasons (December–January–February (DJF), March–April–May, JJA, and September–October–November (SON)). Remarkable differences in the seasonal variability of TCA were observed in these regions. In the WTP1, the DJF TCA always remained the highest value among the four seasons in all years. In the WTP2, the maximum TCA occurred during JJA in most years. In the CTP, the extreme value of TCA was mainly observed in DJF or SON near the peak time of ENSO. Seasonal cross-correlation analyses also showed significant relations between TCA and Nino 3.4 SST in these regions, which may be helpful for forecasting the evolution of ENSO.