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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 are facing climate changes such as warmer temperatures, accelerated snowmelt, increased drought, as well as changing diurnal temperature ranges (DTR) and cloud cover regimes. How tree growth is influenced by the changes in daily to monthly temperatures and its associations with droughts has been extensively investigated, however, few studies have focused on how changes in sub-daily temperatures i.e., DTR, influence tree growth during drought events. Here, we used a network of Larix principis-rupprechtii tree-ring data from 1989 to 2018, covering most of the distribution of planted larch across North China, to investigate how DTR, cloud cover and their interactions influence the relationship between drought stress and tree growth. DTR showed a negative correlation with larch growth in 95 % of sites (r

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.

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland's boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May-September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.

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

Boreal forests are facing profound changes in their growth environment, including warming-induced water deficits, extended growing seasons, accelerated snowmelt, and permafrost thaw. The influence of warming on trees varies regionally, but in most boreal forests studied to date, tree growth has been found to be negatively affected by increasing temperatures. Here, we used a network of Pinus sylvestris tree-ring collections spanning a wide climate gradient the southern end of the boreal forest in Asia to assess their response to climate change for the period 1958-2014. Contrary to findings in other boreal regions, we found that previously negative effects of temperature on tree growth turned positive in the northern portion of the study network after the onset of rapid warming. Trees in the drier portion did not show this reversal in their climatic response during the period of rapid warming. Abundant water availability during the growing season, particularly in the early to mid-growing season (May-July), is key to the reversal of tree sensitivity to climate. Advancement in the onset of growth appears to allow trees to take advantage of snowmelt water, such that tree growth increases with increasing temperatures during the rapidly warming period. The region's monsoonal climate delivers limited precipitation during the early growing season, and thus snowmelt likely covers the water deficit so trees are less stressed from the onset of earlier growth. Our results indicate that the growth response of P. sylvestris to increasing temperatures strongly related to increased early season water availability. Hence, boreal forests with sufficient water available during crucial parts of the growing season might be more able to withstand or even increase growth during periods of rising temperatures. We suspect that other regions of the boreal forest may be affected by similar dynamics.

Knowledge of spatio-temporal groundwater recharge (GR) is crucial for optimizing regional water management practices. Daily potential GR at 58 sites over the Chinese Loess Plateau (CLP) during 1981–2099 was simulated using the HYDRUS-1D and robust model inputs. The objective was to explore the impacts of soil, vegetation, and climate on potential GR at a regional scale. The median potential GR over the CLP during 1981–2010 was 1.8 cm, accounting for 4.1% of the annual precipitation (P). As dominated by P, the annual potential GR decreased from 18.8 cm (28% of P) at the southeast to 0.0 cm at the northwest. Temporally, consistent low-intensity of GR interspersed with extreme rainfall-induced high GR, being episodic or seasonal depending on sites and years. The lag (average of 5 months) between deep drainage at 3 m depth and rainfall was controlled by climate (i.e., P). From 1981 to 2010, annual potential GR significantly decreased as a result of increased ETp and leaf area index (LAI) over time. A warmer and wetter CLP at the end of this century as predicted by the HadCM3 model may decrease the potential GR because of the increased ETp. This study highlights the dominance of water input factor (P) on the spatial GR and water output factors (ETp and LAI) on the temporal GR. Measures such as water-saving practices and land use optimization should be taken to mitigate climate change effect on groundwater recharge.