Summer warming explains widespread but not uniform greening in the Arctic tundra biome

Abstract

Arctic warming can influence tundra ecosystem function with consequences for climate feedbacks, wildlife and human communities. Yet ecological change across the Arctic tundra biome remains poorly quantified due to field measurement limitations and reliance on coarse-resolution satellite data. Here, we assess decadal changes in Arctic tundra greenness using time series from the 30 m resolution Landsat satellites. From 1985 to 2016 tundra greenness increased (greening) at ~37.3% of sampling sites and decreased (browning) at ~4.7% of sampling sites. Greening occurred most often at warm sampling sites with increased summer air temperature, soil temperature, and soil moisture, while browning occurred most often at cold sampling sites that cooled and dried. Tundra greenness was positively correlated with graminoid, shrub, and ecosystem productivity measured at field sites. Our results support the hypothesis that summer warming stimulated plant productivity across much, but not all, of the Arctic tundra biome during recent decades.

Fig. 1. Spatial extent of Arctic tundra and locations of field and Landsat sample sites.

a The Arctic can be subdivided into the minimally vegetated High Arctic, moderately vegetated Low Arctic, and southern mountainous Oro Arctic. Landsat NDVI_max was compared against three metrics of plant productivity measured at field sites around the Arctic. b, c Number of Landsat sampling sites within a 50 × 50 km² grid cell that were used for assessing NDVI_max trends and correlations with summer temperatures from 1985 to 2016 and 2000 to 2016. It was not possible to assess NDVI_max trends or correlations in the eastern Eurasian Arctic from 1985 to 2016 owing to the lack of Landsat data prior to circa 2000. Arctic tundra without adequate data for Landsat assessment is shown in black. Projection: Lambert Azimuthal Equal Area.

Fig. 2. Tundra greenness and summer air temperature time series and covariation.

Left panels show changes in mean Landsat NDVI_max [unitless] anomalies for the Arctic and each zone from 1985 to 2016 (a) and 2000 to 2016 (d). Middle panels show changes in mean summer warmth index [SWI; °C] anomalies from 1985 to 2016 (b) and 2000 to 2016 (e) derived from five temperature data sets. Right panels show the relationship between mean Arctic NDVI_max and SWI anomalies from 1985 to 2016 (c) and 2000 to 2016 (f). Spearman’s correlation coefficients (r_s) relating NDVI_max and SWI are provided for each period. Narrow error bands and whiskers depict 95% confidence intervals derived from Monte Carlo simulations (n = 10³). Note that while mean SWI time series are based on pan-Arctic data, the NDVI_max time series, and NDVI_max–SWI relationships are based on sites where Landsat data were available from 1985 to 2016 (a, c) and 2000 to 2016 (d, f), as shown in Fig. 1.

Fig. 3. Tundra greenness trends and correlations with summer air temperatures summarized by Arctic zone.

Left panels show the percent of sites in the Arctic and each zone that exhibited a positive trend [green], a negative trend [brown], or no trend [gray] in Landsat NDVI_max from 1985 to 2016 (a) and 2000 to 2016 (c). Right panels show the percent of sites that exhibited a positive correlation [green], a negative correlation [brown], or no correlation [gray] between annual NDVI_max and the summer warming index (SWI) from 1985 to 2016 (b) and 2000 to 2016 (d). In all panels, dark and light shades denote significance levels of trends or correlations [dark shades: α = 0.05; light shades: α = 0.10). The sample size is provided above each bar.

Fig. 4. Tundra greenness and summer air temperature trends and correlations across the Arctic.

Top panels (a–d) depict Landsat NDVI_max trends, summer warmth index (SWI) trends, and NDVI_max–SWI correlations from 1985 to 2016, while bottom panels (e–h) depict trends and correlations from 2000 to 2016. Trends in tundra greenness were inferred at random sampling sites (Fig. 1b, c) using NDVI_max time series and Mann–Kendall trend tests. The percent of sites with positive (a, e) and negative (b, f) trends [α = 0.10] was summarized within 50 × 50 km² grid cells. c, g Changes in annual SWI derived using an ensemble of five temperature data sets. d, h Mean Spearman’s correlation (r_s) between annual NDVI_max and SWI among sites within each 50 × 50 km² grid cell. The maps also depict areas in the Arctic that are barren [mean NDVI_max < 0.10; dark gray] or lacked adequate Landsat data for trend and correlation assessments [black].

Fig. 5. Primary environmental predictors of tundra greenness trends.

Variable importance and partial dependence of the six most important variables for predicting site-level Landsat NDVI_max trend categories from 2000 to 2016 using Random Forests. The three NDVI_max trend categories included browning, no trend, and greening that were based on trend direction and significance (α = 0.10). a Variable importance was characterized by the mean decrease in accuracy, where a higher value indicates that a variable is more important to the classification. b Partial dependency plots illustrate how each predictor variable affects class probability while accounting for the mean effect of other predictors in the model. The top six predictor variables included changes (∆) in summer warmth index (2000–2016), summer soil moisture (2000–2016), and annual mean soil temperature (2003–2016), as well as elevation, summer warmth index in 2000, and annual mean soil temperature in 2003. Soil temperature data were for 1 m depth and were not available prior to 2003. Boxplot lines and whiskers in a depict median estimates and 95% confidence intervals derived from Monte Carlo simulations (n = 10³), as do solid lines and error bands in b.