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We identified the climate zones where the climate has highest variation similarity to aid to climate data selection. The calculation of climate-growth correlations is the analytical foundation to study climate change influence on tree growth in dendrochronology. However, the majority of climate data used in climate-growth correlation analyses are not directly recorded on the sample sites, but obtained from nearby weather stations. We used a sample site in Saihanba region as a case study to address how correlation bias may occur if nearby climate products have no high correlation with the climate in the sample site. Temperatures in the sample site and from other data resources were highly correlated, suggesting that small potential bias in growth-temperature correlations when using temperatures from nearby climate stations. However, precipitation had large spatial variability, resulting in low correlation between precipitation of the sample site and precipitation from other resources. Large biases in growth-precipitation analysis would be expected when using precipitation from nearby stations, suggesting that precipitation records should be carefully chosen. To aid in this selection, we used a cluster analysis and multiple data-products across China to identify regions where station climate do and do not reflect accurately site conditions, and classified temperature and precipitation zones where climate has high correlation among grid cells of the same climate zone based on similarity of the macroclimate using a ~ 2.5 km resolution gridded climate dataset. Using climate stations located in the same cluster as the sample sites would help to prevent or reduce correlation biases in growth-climate analyses. The generated temperature and precipitation zones are freely available to download as GeoTIFF files in the online supplementary materials (Fig. 1S and Fig. 2S).

Forests play an important role in sequestrating atmospheric carbon dioxide (CO2). Therefore, in order to understand the spatial–temporal variations and controlling mechanisms of global forest carbon (C) storage under future climate change, an improved individual-based forest ecosystem carbon budget model and remote sensing outputs in this study were applied to investigate the spatial–temporal dynamics of global forest (vegetation+soil) C storage in the future climate change scenario. The results showed that in the future RCP4.5 (representative concentration pathways) climate scenario, the total C storage per unit area per year in vegetation and soil of the global forest ecosystem showed a trend of first decreasing and then increasing from 2006 to 2100, with an average of 22.77 kg C m−2 year−1. However, the evolution trends of C storage changes in vegetation and soil were different. Moreover, the average soil C storage per unit area per year was 2.87 times higher than the average vegetation C storage. The impact of climate change on total C storage in vegetation and soil of the global forest ecosystems was positive, showing an obvious increase during 2006–2100. The total C storage varied significantly in spatial distribution. Spatially, the vegetation C storage and the soil organic C storage were projected to decrease significantly in most parts of South America and southern Africa in the Southern Hemisphere and increase in eastern North America, western Asia, and most areas of Europe in the Northern Hemisphere. Especially in the middle and high latitude regions of the Northern Hemisphere, the total forest C stock was projected to increase by 30–90% from 2046 to 2100. In the future, in these areas where forest C reserves were predicted to be reduced, it was suggested to increase afforestation, prohibit deforestation, and develop projects to increase forest C. Sustainable forest managements also offered opportunities for immediate mitigation and adaptation to climate change. Our findings provided not only a projection of C storage of global forest ecosystem responses to future climate change but also a useful methodology for estimating forest C storage at global levels.

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

Precipitation is the dominant factor that controls vegetation growth and land-use practices in the arid and semiarid Mongolian Plateau (MP), so the spatiotemporal heterogeneity of precipitation change has been an important scientific question in the region. This study investigated the spatiotemporal characteristics of annual and seasonal precipitation across the entire MP based on monthly precipitation data from 136 meteorological stations during 1961–2014 by using a modified Mann–Kendall test, Sen’s slope, Morlet Wavelet Transform, and geostatistical methods. Results show the following: 1) Annual precipitation decreased slightly from 1961 to 2014. Stations with positive and negative trends were 41.9% and 58.1%, respectively. Significant positive trends were mainly in the southwestern and northeastern regions of the plateau, whereas significant negative trends were in the northern and southeastern regions. 2) Precipitation decreased at rates of −5.65 and −0.41 mm/decade in summer and autumn, respectively, but increased at 1.91 and 0.51 mm/decade in spring and winter. The contribution of spring and winter precipitation to the annual amount increased significantly, but that of summer precipitation decreased significantly. 3) A large majority of stations (80.2%) showed decreasing trends in summer, whereas 89.7% and 83.1% of stations showed increasing trends in spring and winter. The spatial distribution of trend magnitude in seasonal precipitation amount was strongly heterogeneous. 4) By climatic zones, precipitation increased in humid and arid zones, but decreased in a semiarid zone. On the whole, the MP experienced a drying trend, with significant regional differentiation and seasonal variations.

The green vegetation fraction (GVF) and surface albedo are important land surface parameters often used for validation of climate and land surface models that are influenced largely by environmental gradients and human activities. In this study, fine resolution GVF and albedo values derived from Landsat Thematic Mapper/Enhanced Thematic Mapper Plus images from 1990 to 2000 were used to examine the relationship of both GVF and albedo values to the spatial gradients of parameters related to dramatic urbanization in the Greater Guangzhou metropolitan area, Guangdong Province, in South China. Moderate resolution GVF and albedo datasets derived from the MODIS Collection 5 product were used to analyze the seasonal variation of GVF and albedo with rapid urban expansion from 2001 to 2007. The results show that the shortwave albedo had a clear declining trend from the urban center to natural land in 1990. However, no obvious trend in shortwave albedo change was observed along urban–rural gradients caused by the expansion of low-albedo urban buildings and more heterogeneous land cover patterns in 2000. A threshold of GVF (~0.21) was estimated for determining the change of albedo associated with vegetation fraction. Vegetation cover modified by urban expansion changed surface reflectance and influenced the surface energy balance. It is suggested that a large portion of energy absorbed in an urban area is likely to be converted to thermal energy that heating up is near the surface and emitted as longwave radiation.

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.