2020). Complexity revealed in the greening of the Arctic. Nature Climate Change, 10 pp. 106-117.For guidance on citations see FAQs.

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset . Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.

Field observations and time series of vegetation greenness data from satellites provide evidence of changes in terrestrial vegetation activity over the past decades for several regions in the world. Changes in vegetation greenness over time may consist of an alternating sequence of greening and/or browning periods. This study examined this effect using detection of trend changes in normalized difference vegetation index (NDVI) satellite data between 1982 and 2008. Time series of 648 fortnightly images were analyzed using a trend breaks analysis (BFAST) procedure. Both abrupt and gradual changes were detected in large parts of the world, especially in (semi-arid) shrubland and grassland biomes where abrupt greening was often followed by gradual browning. Many abrupt changes were found around large-scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niñ o event.The net global figure -considered over the full length of the time series -showed greening since the 1980s. This is in line with previous studies, but the change rates for individual short-term segments were found to be up to five times higher. Temporal analysis indicated that the area with browning trends increased over time while the area with greening trends decreased. The Southern Hemisphere showed the strongest evidence of browning. Here, periods of gradual browning were generally longer than periods of gradual greening. Net greening was detected in all biomes, most conspicuously in croplands and least conspicuously in needleleaf forests. For 15% of the global land area, trends were found to change between greening and browning within the analysis period. This demonstrates the importance of accounting for trend changes when analyzing long-term NDVI time series.

Considerable evidence exists that current global temperatures are higher than at any time during the past millennium. However, the long-term impacts of rising temperatures and associated shifts in the hydrological cycle on the productivity of ecosystems remain poorly understood for mid to high northern latitudes. Here, we quantify species-specific spatiotemporal variability in terrestrial aboveground biomass stem growth across Canada's boreal forests from 1950 to the present. We use 873 newly developed tree-ring chronologies from Canada's National Forest Inventory, representing an unprecedented degree of sampling standardization for a largescale dendrochronological study. We find significant regional-and species-related trends in growth, but the positive and negative trends compensate each other to yield no strong overall trend in forest growth when averaged across the Canadian boreal forest. The spatial patterns of growth trends identified in our analysis were to some extent coherent with trends estimated by remote sensing, but there are wide areas where remote-sensing information did not match the forest growth trends. Quantifications of tree growth variability as a function of climate factors and atmospheric CO 2 concentration reveal strong negative temperature and positive moisture controls on spatial patterns of tree growth rates, emphasizing the ecological sensitivity to regime shifts in the hydrological cycle. An enhanced dependence of forest growth on soil moisture during the late-20th century coincides with a rapid rise in summer temperatures and occurs despite potential compensating effects from increased atmospheric CO 2 concentration. drought impacts | climate change | dendrochronology | normalized difference vegetation index | ecology C ircumpolar boreal forests are estimated to store ∼53.9 Pg of carbon or ∼14% of terrestrial vegetation biomass (1). These regions are currently experiencing accelerated changes, including warmer and longer growing seasons, tree line expansion, species migration, increased frequency and severity of drought, and increases in the frequency and severity of disturbances (2-10). These changes create uncertainty about the boreal forests' future role in the global carbon cycle (11). Adding to this uncertainty is the discrepancy over recent changes in the productivity of boreal and other northern latitude forests. Some empirical evidence suggests increases in the forest productivity (12)(13)(14), whereas other studies suggest decreasing productivity over the last decades (7,8,(15)(16)(17). Furthermore, inversion and process-based ecosystem models indicate large carbon sinks (7,8), whereas field-based bottom-up approaches suggest smaller carbon sinks or small carbon sources (3, 18), or large sinks (19). Quantifying