Europe witnessed a strong increase in climate variability and extremes over the past decades that caused degradation of ecological and societal resilience. Cascading feedbacks like the 2003 and 2010 heat waves were considered environmental response to anthropogenically reinforced climate anomalies. Land-use intensification, landcover change, and forest degradation further increased the pressure on the ecosystem’s functionalities. Conversely, recent satellite observations emphasized positive vegetation and landcover reflectance trends as a response to rising atmospheric CO₂ input and global warming, particularly in the northern latitudes. However, these global trends obfuscate the intensified regional divergence of climate variability and extremes and risk underestimating future climate induced feedbacks. Consequently, long-term climate trends and variability must be analysed together with annual and interannual anomalies to understand regional feedback mechanisms and surface cover changes. This article presents long-term NDVI (Normalized Difference Vegetation Index) monthly time series analyses compared to multicomponent environmental background variability to highlight European-scale greening trends over the period 2001–2020. Significant warming and persistent hot droughts affect regional vegetation trends in central Europe compared to prolonged phenological phases and reduced snow-cover in north-eastern Europe.
Research Article
Synchronous dynamics of spectral greening trends and regional climate variability across Europe
https://doi.org/10.21203/rs.3.rs-1457891/v1
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NDVI
greening trend
climate change
drought
landcover change
spatial modelling
Droughts are among the most severe environmental disasters, enhancing ecological and socio-economic conflicts (Geist and Lambin 2004; Godfray et al. 2010; Gupta et al. 2020; Kaczan and Orgill-Meyer 2020; Lambin and Geist 2006; Lesk et al. 2016; Schmidhuber and Tubiello 2007; Xu et al. 2020). Particularly long-term drought spells, hot drought events, and mega-heatwaves like in 2003 and 2010 provoke persistent landcover transformations that amplify faunal and floral aggravation and desertification processes, and force socio-cultural adaptation (Barriopedro et al. 2011; Fischer et al. 2021; Lin et al. 2020; Luterbacher et al. 2004; Miralles et al. 2014; Pereira et al. 2017; Quesada et al. 2012; Rasmijn et al. 2018; Schumacher et al. 2019; Sousa et al. 2020; Zhou et al. 2019). Human-induced surface degradation from crop production and extensive cattle breeding further contributes to current landcover changes (Burrell et al. 2020; Harris 2010). Parallel to this, however, the number of severe flood events and the frequency of wet spells increased over the past decades with significant regional variability across Europe (Breinl et al. 2020; Dai et al. 1998; Dietze et al. 2022; Zolina et al. 2010). The development of rapidly occurring and long-lasting extremes poses particularly high challenges on the ecosystem’s adaptive mechanisms. Due to the accelerated speed, intensification, and persistence of dry and wet spell transitions, severe water shortages or massive oversupply occur within short term periods. Recent results by Fischer et al. (2021) emphasized the strong increase in climate extremes probability and the short return periods of record-shattering events during the forthcoming decades (Fischer et al. 2021).
Parallel to this, satellite observations and vegetation response monitoring detected enhanced global spectral greening and a generally positive trend in surface reflectance patterns, which showed that previously degraded landscapes were affected by anthropogenic signals superimposed on causative precipitation trends (Cortés et al. 2021; Forzieri et al. 2017; Herrmann et al. 2005; Myers-Smith et al. 2020; Piao et al. 2020). Apparently, complex and scale-dependent vegetation response to global warming is strongly coupled to regional climatic feedbacks and topographic variables (Forzieri et al. 2017; Myers-Smith et al. 2020; Piao et al. 2020). Whereas anthropogenic reinforcements of spectral greening trends are most likely linked to intensified land-use management (Chen et al. 2019; de Jong et al. 2012; Piao et al. 2020). Crop production accounts for over one third of the green-leaf area increase, highlighting the spatial and seasonal variability of global greening trends (Chen et al. 2019; Winkler et al. 2021). Extensive monoculture cultivation, on the other hand, increases the yield vulnerability to climate extreme events and persistent heat waves, which in turn causes massive harvest loss (Brunner et al. 2018; Gampe et al. 2021). A general spectral greening trend is likely to obfuscate regional vegetation response anomalies during persistent drought periods and to underestimate local environmental feedbacks. In general, the increase in globally observed surface reflectance is biased by multiple components, including latitudinal gradients and snow cover variability, wildfire development, land-use strategies, and forest decline – making it particularly difficult to trace regional response to global warming on the continental scale. For this reason, long-term trend observation of surface reflectance patterns through remote sensing tools can provide a suitable resource to evaluate global impacts of climate change feedbacks. However, tracing regional ecosystem response models must include annual and interannual anomaly detection to highlight the increase of annual variability masked by overall trends.
This paper evaluates multiannual greening trends, regional vegetation response, and surface cover reflectance anomalies across Europe using long-term monthly vegetation indices (NDVI) and monthly climate and environmental variables (Beaudoing et al. 2020; Harris et al. 2020; Kumar et al. 2006; Peters-Lidard et al. 2007; Rodell et al. 2004) (Table 1). Monitoring vegetation change through remote sensing techniques has become a common tool since the introduction of the NDVI and the availability of medium resolution satellite imagery covering the past two decades (Cortés et al. 2021; Tucker 1979; Tucker et al. 1986). Monthly MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI imagery were analysed to detect vegetation anomalies compared to background spectral greening and browning trends, precipitation and temperature variability and environmental covariates over the period 2001–2020. Rank-based correlation between environmental variables and NDVI time-series was performed to test the predictive models for significant interdependencies between climate variables and landcover change. Eventually, the models were compared to annual and monthly anomalies to detect regional feedbacks and to highlight the strong temporal variability of dry and wet spells underlying vegetation signals of spectral greening and browning as well as surface reflectance trends across Europe.
The analyses span the period between January 2001 and December 2020. Spatial and temporal subsets of NDVI imagery and environmental parameters from the CRU (Climate Research Unit) (Harris et al. 2020) and GLDAS (Global Land Data Assimilation System) (Beaudoing et al. 2020) datasets were chosen to highlight vegetation response to climate change on a multiscale and multiparameter level. Corine landcover data (CLC) (https://land.copernicus.eu/pan-european/corine-land-cover, last accessed, 26th of October 2021) was used to measure the degree of surface transformation and to compare trends over cropland and forest cover. Annual, seasonal, and monthly vegetation anomalies were calculated for each spatial and temporal subset. Growing season differentiations in NDVI were calculated for the periods March-May (MAM), June-August (JJA), September-November (SON), and December-February (DJF, including December 2000). All temporal and spatial subsets were analysed for vegetation anomalies and spectral greening and browning trends based on the MODIS NDVI time-series with 240 single images. The NDVI is a method to measure vegetation vigour from satellite imagery and acts as a vegetation performance indicator where higher values indicate higher photosynthetic activity (Justice et al. 1985; Tucker 1979). Vegetation indices are used to distinguish photosynthetic activity of vegetation from other land surfaces. The index is based on the reflection characteristics of near infrared (NIR) and the absorption of red radiation (Red) (Anyamba and Tucker 2005; Tucker 1979; Tucker et al. 1991). All statistical analyses were performed using the R-environment (R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/). All code underlying the analyses are stored as supplementary data to this article.
2.1 MODIS NDVI monthly dataset
To measure the relationship between temporal variability of climate variables, geographical location, and vegetation feedback, a global NDVI monthly time series dataset (2000–2020) was downloaded from the Earthdata server of the United States Geological Survey (USGS) (https://lpdaac.usgs.gov/products/mod13c2v006/, last accessed 16th of April 2021) (Didan 2015). The NDVI data consist of cloud-free composites with 0.05° geographic grid resolution (5,600 m), covering the period 02/2000–12/2020 at a monthly return period. The data comes in HDF4 file format (hierarchical data format), which stores scientific data into various subsets and can be read, classified, and saved as single files using the R code from the supplementary data (Code_1). Annual composites, annual mean, and growing season subsets were calculated and cropped to the extent of the study area, defined as the extent of the European coastline vector (available from the EEA (European Environment Agency, https://www.eea.europa.eu/data-and-maps/data/eea-coastline-for-analysis-1/gis-data/europe-coastline-shapefile/at_download/file, last accessed 26th of October 2021). All spatial features < 106 m² and parts of Africa and the polar region were removed from the mask layer. The shapefiles are stored as auxiliary data to this article. Code_3 reprojects and crops the extracted MODIS single files to the extent of the study area.
2.2 CRU climate datasets
Multiannual average monthly total precipitation and temperature (2001–2020) was accessed via the data portal of the Climate Research Unit (CRU) of the University of East Anglia (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/cruts.2103051243.v4.05/pre/, last accessed 26th of October 2021) (Harris et al. 2020). The .nc files were extracted to single raster datasets using the R-package ncdf4 (Pierce 2019) and the code available to this article (Code_2). Annual sums were created and anomalies (Code_4) as well as trend analyses were performed (Code_5).
2.3 Global Land Data Assimilation System (GLDAS)
GLDAS climate and environmental datasets were downloaded from the Goddard Earth Sciences Data and Information Services Center (GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.1 (GLDAS_NOAH025_M, https://disc.gsfc.nasa.gov; last accessed 17th of December 2021) (Beaudoing et al. 2020; Rodell et al. 2004). The .nc files from January 2001 to December 2020 were extracted and stored as annual sums. Trend analysis and anomalies were calculated for annual sums covering the period 2001–2020. Variables underlying the analysis are listed in Table 1.
Tab. 1: GLDAS (Global Land Data Assimilation System, Version 2) variables and units (Beaudoing et al. 2020; Kumar et al. 2006; Peters-Lidard et al. 2007; Rodell et al. 2004).
2.4 Corine landcover classification
CLC datasets were downloaded from the Copernicus server (https://land.copernicus.eu/pan-european/corine-land-cover, last accessed, 26th of October 2021). The raster data was reprojected to WGS84 EPSG:4326 at a resolution of 0.05°. The CLC legend was acquired from the European Environment Agency (EEA) (https://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2/corine-land-cover-classes-and/clc_legend.csv, last accessed, 26th of October 2021) and the grid codes were extracted for each surface type, which distinguish artificial built-up (1–11), agricultural areas (12–22), and forest cover and semi-natural areas (23–34). Cropland, and forest/semi-natural areas of each year were vectorized and used as mask layers to crop the annual NDVI raster. The mask layers are stored as auxiliary data to this article.
2.5 Anomaly calculations
The arithmetic mean (m) and the first standard deviation (SD) were calculated from aggregated raster stacks (Code_4). SD was subtracted from and added to the mean value (m-SD; m + SD) to create the range of the standard deviation for the reference period. The (m + SD) was subtracted from each single year and growing period value and all values ≤ 0 were removed for the upper limits of the standard deviation range. According to the lower limits of the standard deviation range, (m-SD) was subtracted from each year and growing period value and all values ≥ 0 were removed. Both parts were merged to show negative and positive trends, compared to the long-term temporal series of 20 years (Code_4 and Code_7).
2.6 Raster trend analysis
A time series analysis was conducted using a pixel-wise raster analysis on monthly NDVI, precipitation, temperature, and GLDAS data to detect trends in vegetation change and explanatory covariates (Brandt et al. 2014) (Code_5). For annual greening/browning trend analysis, monthly data was aggregated to annual sums and the slope was calculated to identify trends. P-values were extracted and all values > 0.05 (0.5) were masked to highlight a confidence level of 95% (50%). Analogue NDVI analyses were run on a monthly and seasonal basis and for different climate zones (Beck et al. 2018) as well as for crop production areas, natural forests, and topographic parameters with 500 m ranges to highlight regional and zonal vegetation trends. Boxplots and density curves of NDVI trends with 95% confidence level visualize multiannual, seasonal, and monthly greening and browning trends across Europe. Temperature, Precipitation, and GLDAS variables were equally processed and are stored as supplementary data to this article.
2.7 Rank-based pair-wise correlation
To correlate time-series trend parameters, the variables were packed into raster stacks using monthly totals and annual sums. Due to different spatial resolution, the NDVI stacks were cropped to the extent of the CRU/GLDAS datasets and resampled to a 0.5°x0.5° resolution. The Spearman rank-based correlation method was chosen to perform pair-wise correlation between NDVI and each environmental variable over the period 2001–2020 (Code_6). The code returns a set of raster layers of correlation values.
3.1 Vegetation response to global warming across Europe
NDVI trend analyses over the period 2001–2020 highlight regional vegetation and landcover change as response to climate variability in Europe (Fig. 1). Particularly strong vegetation greening and positive surface reflectance trends can be detected in central, southern, and towards the north-eastern parts of Europe. Western Europe and large parts of France, the Iberian Peninsula, Italy, and the British Islands show weaker but still significant greening. Eastern Europe shows a strongly negative vegetation response to climate variability and an increasing browning trend with significant peaks in south-eastern Ukraine and Russia. The strong greening trends of northern and eastern Europe match the temperature trends of the region during the past 20 years. Due to feedback mechanism of global warming and heat waves, the snow cover over northern Europe is decreasing, which influences the surface reflectance patterns indicating positive greening trends. These are, however, more likely to represent reduced snow cover in spring (Bormann et al. 2018; Dye and Tucker 2003; Zhang et al. 2020). Less and scattered as well as darkened snow cover resulting from particle deposition further amplifies solar heating of snowpacks and contributes to increased snow melt (Flanner et al. 2009). During spring, these interactions can lead to changes in surface reflectance that are not caused by an earlier onset of the phenological phase but by removed or scattered snow cover (Dye and Tucker 2003). Reverse browning trends, on the other hand, can be linked to enhanced fire activity (Eckert et al. 2015; Forkel et al. 2013).
In general, significant warming can be detected in most parts of continental Europe and particularly in the central and north-eastern parts. A strong increase in total precipitation further amplifies vegetation response in north-eastern Europe and large parts across Turkey, Greece, and toward the Black Sea. However, parts of central Europe show continuous greening signals despite increasing temperature and decreasing precipitation trends. Continental eastern Europe is more directly related to drying-up and increasing temperature trends.
Considering European vegetation response to climate change mechanisms on multiannual scales, a monthly, seasonal, and regional variability in trend behaviour can be detected. Particularly positive reflectance trends occur during November and December, which emphasize the strong temperature increase and a shift in snow cover duration in north-eastern continental climate zones (Fig. 2). A very strong warming signal during February further decreases snow cover and enhances the positive spectral reflectance (Fig. 3). The region shows constant greening throughout spring and summer, compared to a significant browning trend in eastern Europe and France and a less significant greening in central Europe. Browning trends increase significantly during September and October, affecting large parts of central-eastern and western Europe. Particularly the western part shows negative vegetation response, which can be traced back to strong negative precipitation trends and an increase in mean temperature from July onwards (Fig. 4). An increase in winter heavy precipitation has been predicted in western Europe for future climate models, particularly affecting the UK and parts of south-western Scandinavia (Chan et al. 2018; Christidis et al. 2021; Ketzler et al. 2021; Whan et al. 2020). The simulated increase in winter precipitation is predominantly visible during December, whereas spring and early summer rainfall decreases significantly. Despite the changes in mean precipitation trends, the annual variability strongly increases, confirming recent results (Chan et al. 2018; Zhang et al. 2021).
3.2 Correlation and attribution of climatic variables to spectral greening
Spatial correlation between temperature and landcover reflectance patterns shows strong significance with increasing temperatures and vegetation response across central and north-eastern Europe (Fig. 5). Precipitation trends are not significant in the north-eastern part and the increasing spectral greening signal is linked to the temperature trends and reduced snow coverage (Fig. 6). Soil moisture correlates positively with enhanced vegetation response but is spatially connected to a significant increase in precipitation. Eastern Europe and southern Russia and Ukraine show significantly positive correlation between precipitation and soil moisture decrease, and increased browning trends. The eastern parts of the Black Sea, however, reveal reverse correlation.
In central Europe, and particularly across Germany and central France, regional warming correlates strongly with increased greening trends. But vegetation response is negatively connected to the drying-up process and a very significant decrease in annual total precipitation stands against the positive spectral greening. Here, the monthly variability shows more positive feedbacks. In the southern parts of Europe, the correlation is regionally diverse. In central and southern Italy and Spain, total precipitation is strongly decreasing, which is not entirely mirrored in the greening trends – probably linked to growing irrigation measures (Cramer et al. 2018; Pool et al. 2021). Soil temperature and evapotranspiration are strongly increasing, and soil moisture and canopy surface water show negative trends, particularly affecting the infiltration rate and the deeper soil layers (see supplementary data to this article for trend analyses of GLDAS variables).
3.3 Trends in European climate variability and extremes
Recently, the discussion about cause and effect of the local and regional anthropogenic overprint on the ecosystem’s functionalities, fuelled by global climate change feedbacks, has been reinforced by the severe gradient of continental European flooding and drought spells (Ciais et al. 2005; Harris 2010; Herrmann and Hutchinson 2005; Lin et al. 2020; Zahradníček et al. 2015). Particularly the severe drought episodes of 2003, 2010, and between 2018 and 2020, followed by dramatic flooding events across Europe in summer 2021, have entered the political and economic debate (Brun et al. 2020; Büntgen et al. 2021; Cramer et al. 2018; Dirmeyer et al. 2021; Hari et al. 2020; Kahle et al. 2022; Kempf and Glaser 2020). Temperatures are constantly increasing across Europe and particularly over terrestrial surfaces. According to the 2021 IPCC report, the year 2020 has been marked the warmest on instrumental record (Masson-Delmotte et al. 2021), which contributes to the enhanced temperature trend of the observation period presented in this article. Compared to the IPCC, the past two decades show stronger regional trend variability, e.g. across the Iberian Peninsula, that demonstrates less positive temperature trends than over the reference period 1960–2020 used by the IPCC (Masson-Delmotte et al. 2021). Temperature anomalies, on the other hand, are less variable here with a tendency towards more hot year anomalies compared to colder events (Fig. 7). Precipitation anomalies, however, increase in both, positive and negative annual sums, and subsequent hot and dry year periods are expected to occur more frequently by the mid of the 21st century (Gampe et al. 2021; Vogel et al. 2021).
The strongest occurrence in positive temperature anomalies can be detected across Turkey, central France, south-western Germany, and extensive parts of Belarus, Ukraine, and Russia. In addition to this, negative temperature anomalies are lacking, which underlines the trend towards prolonged hot drought periods and strongly increasing annual average temperatures – affecting tree growth and enhance increased die-back (Del Martinez Castillo et al. 2022). Across northern Russia, both, positive and negative temperature anomalies are frequent and negative anomalies prevail in the Balkans, which strengthen the risk of crop failure during periods of either hot droughts or intensified cold waves (Piticar et al. 2018; Vogel et al. 2019). Regional temperature extremes are furthermore accompanied by growing negative precipitation anomalies, particularly across central Europe, the Adriatic, north-western parts of the Iberian Peninsula, and the Alpine region. Where no negative anomalies prevail, a trend towards lower precipitation rates poses a particular threat to regional croplands and the water balance of the forests and meadows. Across central Europe, NDVI anomalies show strongly negative and only partly positive occurrences, which stand against the rising spectral greening trend over the reference period. Accumulated anomalies are rather attributed to increasing numbers of drought periods and clearly indicate the rapidly rising vulnerability of vegetation response to an increase in extreme weather patterns.
Extreme heatwaves as in 2003 in central Europe and in 2010 over Russia have been traced back to atmospheric patterns that caused a massive increase in surface temperature (Di Capua et al. 2021; Liu et al. 2020). Constant warming over an increasingly desiccated land surface caused persistent thick hot-air masses that maintained surface warming and led to further heat accumulation and soil desiccation, amplifying the heatwave cascading effects (Christian et al. 2020; Miralles et al. 2014). Particularly the southern parts, which experience a significant increase in heat supply and soil temperature and a strong reduction in soil moisture are currently the most productive and intensely exploited wheat cultivation areas (Di Paola et al. 2018). Considering the trend toward increased soil moisture availability and temperature across the northern and north-eastern parts of the country, the land-use patterns are most likely to shift in the future. This can cause strong surface transformation and a reverse trend in soil moisture availability under rapidly increasing surface and soil temperature.
The observed response in plant growth and composition, and a shift towards arid-tolerant species does not necessarily mean a recovery in total ecological diversity (Herrmann and Tappan 2013; Ibrahim et al. 2018; Zida et al. 2020). Partly, this trend is attributed to increased atmospheric CO2 concentration, precipitation totals, the spread of agricultural exploitation, and increased warming of the permafrost in the Northern Hemisphere (Peng et al. 2020; Piao et al. 2020; Winkler et al. 2021). Global spectral greening trends and phenological shifts over the past two decades were thought to emerge from increased greenhouse gas concentrations and higher temperatures, causing higher biological activity and intensified carbon sink in higher latitudes in the Northern Hemisphere (de Jong et al. 2012; Menzel et al. 2020; Piao et al. 2019; Rosbakh et al. 2021; Zhou et al. 2001). Results from high-resolution satellite LAI (Leaf Area Index) analyses identified forest regrowth following land abandonment as main driving factor of EU-wide spectral greening (Buitenwerf et al. 2018). On the other hand, grassland and shrubs were particularly affected by an inverse trend and according to de Jong and colleagues (2012), semi-arid regions are more vulnerable to browning trends after rapid greening caused by short-term climate variability (de Jong et al. 2012). In combination with persistent hot drought periods, the carbon uptake can be drastically reduced by decreased gross primary production (GPP), particularly affecting grassland and cropland in the northern temperate climate zone (Gampe et al. 2021). General grassland and cropland response to CO2 increase is particularly sensitive to soil moisture availability, soil type and texture (Fay et al. 2012) and an increasing soil moisture trend across north-eastern Europe correlates with strong greening signals and reduced snow cover – indicating positive surface reflectance trends. Climate variability and particularly the precipitation rate after the growing season of the preceding year until August of the current year has been shown to control productivity patterns (Li et al. 2013). Recent results have further narrowed down the temporal corridor of European species’ phenology during spring and summer to the previous months (Menzel et al. 2006; Wu et al. 2021) – which emphasizes the cause and effect in vegetation response to soil moisture and precipitation deficits during persistent heat waves in warm-dry regions (Klesse et al. 2018). Particularly the Mediterranean suffers from decreasing precipitation trends and rising temperatures. Cramer et al. (2018) highlighted the strong coupling of temperature increase and potential precipitation decline of up to 30% by 2080, accompanied by a 10–20% increase in heavy rainfall events (Cramer et al. 2018) and an increase in heat wave related harvest loss of 3%/yr (Brás et al. 2021). In addition, Winkler et al. (2021) emphasized the strong latitudinal gradient of climate change effects on biomes and highlighted the linkages between northern greening and warming trends compared to southern precipitation variability and subsequent browning (Winkler et al. 2021). Parallel to this, the number of extreme years with heat waves or pronounced cold spells is growing, which will raise questions regarding a calculable risk of future agricultural strategies. Central Europe is particularly affected, showing a strong increase in negative vegetation anomalies, which are explained by the increase in mean annual temperature and the decrease in total precipitation. These trends will intensify in the future and a reversal of the greening trend in the wake of prolonged droughts could lead to massive surface atmosphere-interactions and regional climate amplifications. Further trend analyses of climate variables are needed to enhance the knowledge of regional environmental feedbacks. Monitoring surface transformations over long temporal periods is mostly limited by the scale and detailed ground-based measurements can be carried out only locally, which produces high spatial resolution datasets but lacks the comparison of global environmental response to global feedbacks (Cortés et al. 2021).
Code availability
All code underlying the analyses are available as supplement data to this article.
Data availability
All data underlying the results of this article are publicly available on the internet.
Competing interests statement
The author declares no conflict of interest
The author has no relevant financial or non-financial interests to disclose
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