Climate extremes can trigger exceptional responses in terrestrial ecosystems, for instance by altering growth or mortality rates. Such effects are often manifested in reductions in net primary productivity (NPP). Investigating a Europe-wide network of annual radial tree growth records confirms this pattern: we find that 28 % of tree ring width (TRW) indices are below two standard deviations in years in which extremely low precipitation, high temperatures or the combination of both noticeably affect tree growth. Based on these findings, we investigate possibilities for detecting climate-driven patterns in long-term TRW data to evaluate state-of-the-art dynamic vegetation models such as the Lund-Potsdam-Jena dynamic global vegetation model for managed land (LPJmL). The major problem in this context is that LPJmL simulates NPP but not explicitly the radial tree growth, and we need to develop a generic method to allow for a comparison between simulated and observed response patterns. We propose an analysis scheme that quantifies the coincidence rate of climate extremes with some biotic responses (here TRW or simulated NPP). We find a relative reduction of 34 % in simulated NPP during precipitation, temperature and combined extremes. This reduction is comparable to the TRW response patterns, but the model responds much more sensitively to drought stress. We identify 10 extreme years during the 20th century during which both model and measurements indicate high coincidence rates across Europe. However, we detect substantial regional differences in simulated and observed responses to climatic extreme events. One explanation for this discrepancy could be the tendency of tree ring data to originate from climatically stressed sites. The difference between model and observed data is amplified by the fact that dynamic vegetation models are designed to simulate mean ecosystem responses on landscape or regional scales. We find that both simulation results and measurements display carry-over effects from climate anomalies during the previous year. We conclude that radial tree growth chronologies provide a suitable basis for generic model benchmarks. The broad application of coincidence analysis in generic model benchmarks along with an increased availability of representative long-term measurements and improved process-based models will refine projections of the long-term carbon balance in terrestrial ecosystems.
Extreme climate events are known to trigger exceptional responses in
terrestrial ecosystems
(Reyer et al., 2012; Smith,
2011; Zscheischler et al., 2014a, c). Understanding
which ecosystem processes exceed their natural range of variability in the
wake of environmental extremes is crucial for anticipating the fate of land
ecosystems under climate change scenarios (Cotrufo et al., 2011;
Jentsch et al., 2011). For instance, anomalous ecosystem responses induced
by drought events (Schwalm et al., 2012) may decrease the
economic returns from forest ecosystems (Hanewinkel et al., 2013)
or lead to substantial net CO
Water stress and high temperatures reduce evapotranspiration and
productivity in many mid- and low-latitude areas
(Granier et al., 2007). Yet, the
general applicability of such studies is challenged by the different climate
responses of forests across biomes and tree species
(Babst
et al., 2013b; Granier et al., 2007; Lindner et al., 2010). Furthermore, the
extent to which increasing amounts of atmospheric CO
The growing recognition of the role that climate extremes play in land ecosystems (e.g. Williams et al., 2014; Zscheischler et al., 2013) and the implications for global carbon cycling requires developing new data analysis tools. While there is a large body of literature on the quantification of extremes in climate variables, we lack techniques to quantify extremes in biospheric responses and most importantly a solid framework for linking climatic and biospheric extreme events (Smith, 2011). A suitable methodological approach in this direction requires detecting both instantaneous and lagged responses of a biospheric variable (e.g. tree ring width index – TRW – and net primary productivity – NPP) to climatic extremes (e.g. temperature, precipitation or the combination of both). We therefore propose a generic method to evaluate the impacts of climate extremes on biospheric variables; this method quantifies the coincidence rates of extremes in long climatic and biospheric time series (> 50 years). Thereby, we create a unit-free metric that enables us to compare different measures of vegetation productivity. We apply coincidence analysis, a method that was put forward by Donges et al. (2011) in a different context. We exemplify our approach by evaluating a set of European tree ring data and the output from a dynamic vegetation model (LPJmL) model.
We focus on exploring the potential of annual radial growth increments (tree ring chronologies) for model evaluation purposes. This data source is recognized as one of the few opportunities for quantifying ecosystem responses to multiple extreme events on long-term timescales (Babst et al., 2012). Tree ring chronologies can, with certain restrictions, be regarded as proxies for the variability in stand-scale productivity and offer a possibility to relate long-term tree growth to climate fluctuations and extremes on regional to continental scales (e.g. Babst et al., 2012; Battipaglia et al., 2010). Likewise, tree rings show pronounced lagged effects and a positive relationship with previous fall's climate (Wettstein et al., 2011). Depending on their sign, climate anomalies in this season may either enhance or mitigate the impact of extremes on forest growth in the subsequent year because they directly affect the growing season length and, related to this, the replenishment of carbon storage (Kuptz et al., 2011). Also, the interaction of carbon accumulation with seed production (i.e. mast years) may sometimes lead to low-growth anomalies regardless of climatic conditions. Such masting events and non-climatic drivers of forest growth (e.g. management or disturbances) may challenge the interpretation of biotic responses to climate extremes because they alter carbon allocation patterns (Mund et al., 2010). Nevertheless, tree ring chronologies are widely regarded as robust and very unique long-term indicators of biospheric responses to climate anomalies (Babst et al., 2014b; Jones et al., 2009; Pederson et al., 2014).
Despite extensive observational studies, the impacts of extreme events under current and past environmental conditions remain insufficiently documented. This is a natural consequence of the low occurrence probability of the events along with chronically scarce long-term observations (Innes, 1998; Smith, 2011). Hence, it is difficult to project the impacts of expected changes in frequencies and intensities of extreme events (Barriopedro et al., 2011; Field et al., 2012) on the terrestrial carbon cycle (Reichstein et al., 2013). In this context terrestrial biosphere models play a crucial role in quantifying the impact of climate extremes on the terrestrial carbon cycle and, most importantly, on NPP (Keenan et al., 2012; Zscheischler et al., 2014b; Williams et al., 2014). One prerequisite is, however, that models are well tested for their capacity to reproduce the relevant signatures of extreme impacts in the recent past. Year-to-year variation and impacts of extreme events in these models are best reflected by simulated NPP, and we analyse the impact of climate extremes on simulated NPP within our coincidence framework, thereby considering the role of lagged events.
Our scale-free approach allows us to directly compare response patterns identified in the observed tree rings with simulated productivity, which is a straightforward way of testing models for their capacity to reproduce the relevant signatures of extreme impacts in the recent past. Indeed, it has been recognized that it is essential for advanced modelling studies to converge to suitable benchmarks for testing terrestrial biosphere models (cf. Dalmonech and Zaehle, 2013; Kelley et al., 2013; Luo et al., 2012).
For instance, Luo et al. (2012) conclude that suitable model benchmarks are characterized by “objectivity, effectiveness, and reliability for evaluating model performance”. Hence, the goal has to be a suite of metrics that can embrace characteristic response functions. Along these lines, our study evaluates the potential of the coincidence analysis framework to become an element of a generic model benchmarking system. We address this issue by working on the following specific questions:
do state-of-the-art dynamic vegetation models agree with observed responses to climate extremes? how can long-term observations help us understanding biotic responses to extreme events?
We compiled TRW chronologies from 606 sites across
Europe and parts of northern Africa (10
We use the WATCH-ERA-Interim (European global atmospheric Re-Analysis data from the Water and Global Change EU-Project) daily climate data at 0.25 latitude/longitude
resolution based on downscaled WATCH climate data (Weedon
et al., 2011) for the years 1901–2001 and extended to 2010 using downscaled
ERA-Interim climate data
(Dee et al., 2011).
Daily temperature, precipitation and solar radiation were used to drive the
model runs. For the coincidence analysis with TRW and simulated NPP, we
calculate mean annual temperature (
Simulations of monthly NPP are performed with the dynamic global vegetation
model LPJmL (Bondeau et al., 2007; Sitch et al.,
2003) with a fully coupled carbon and water cycle (Gerten et al.,
2004). The model is driven by temperature, radiation, precipitation and
atmospheric CO
For the present study, we ran LPJmL in its natural vegetation mode not
considering land management and land-use change. Process-based simulation of
fire is included by the so-called SPITFIRE model, which is coupled to LPJmL
(Thonicke et al., 2010). Simulation runs were performed at
0.25
To determine the length of the growing season (GS
To determine the length of the growing season for each simulated grid cell
(GS
The coincidence analysis requires pairing each point in the TRW data set with
local
To obtain pairs of time series for the comparison of simulated NPP with
The TRW data set consists of 606 time series at selected measurement sites throughout Europe. For comparison with simulated NPP, we select the corresponding grid cell centres nearest to the measurement sites.
For our analysis, we search for coincidences (Donges et al., 2011) between specific percentiles in the pairs of biotic and climate time series. In the case of TRW and NPP, values smaller than the 10th percentiles were used (low-productivity extremes). In the climate records, all values exceeding the 90th percentiles of mean growing season temperature (hot extremes) and being less than the 10th percentile of the total growing season precipitation (dry extremes) were defined as extreme events. This combination of climatic and biotic extremes tests the link between extremely high temperature, low precipitation or the combination of both in causing low-growth responses at all sites. At alpine or boreal sites, particularly high temperatures may even lead to better growth conditions (e.g. Jolly et al., 2005). Similarly, extremely low temperatures during the growing season could cause low-growth extremes, e.g. in the Alps or the boreal zone (e.g. Babst et al., 2012). We therefore interpret our results carefully regarding these issues.
To obtain the number of coincidences,
Example of coincidence analysis between a time series of
Autocorrelations as well as the specific shape of the distribution of
amplitudes in the considered climatological and biotic time series can have
a profound influence on the observed bivariate coincidence rates. To control
for these effects and assess the statistical significance of the computed
coincidence rates
To identify years with a pronounced European-wide forest response to climate extremes, i.e. years that yield a high number of coincidences across the continent, we take the sum over all coincidences at significant sites/grid cells occurring during a specific year and divide it by the number of all significant sites/grid cells, again yielding a number between 0 and 1 (0 if no coincidences occur at significant sites/grid cells and 1 if all significant sites show a coincidence in the year considered). As “European-wide extreme years” we define all values one standard deviation above the average annual significant coincidence rate.
Histograms of deviations of growth responses of TRW
Extreme years during the period 1901–2010 as detected by the coincidence analysis for TRW and NPP (only at TRW sites) with precipitation (upper two rows), temperature (middle two rows) and combined precipitation and temperature (lower two rows) extremes. Black bars indicate that extreme years were detected in both TRW and in NPP.
To estimate the potential downregulation of forest growth by extreme events, we
assume that years with coincidences of extremes in
In this section we first discuss the general picture of the impact of climate extremes on measured TRW and modelled NPP and then focus on patterns of spatial and temporal coincidence rates at significant sites/grid cells.
To estimate potential effects of extreme events on tree growth and
productivity, we quantified TRW and NPP anomalies in years with extreme
climate conditions. Generally carbon losses are strongest during combined
precipitation and temperature extremes, particularly for TRW. A total of 815
TRW extremes significantly coincide with
Map of coincidence rates between extremes in simulated NPP and
precipitation for
To analyse the responses of forest growth to drought and heat extremes in
models and observations, it is necessary to first evaluate whether the
timing of the climate-driven reductions in TRW and simulated NPP events match
reasonably well. In this context, we determine European-wide extreme years as
described in Sect. 2.4. For both TRW and simulated NPP, we identify the
years 1911, 1921, 1945, 1947, 1976 and 2003 (Fig. 3, dark grey boxes) as
dry extremes with substantial biotic impacts. Extremely hot years are
detected in 1934, 1945, 1947, 1949, 1950, 2002 and 2003 (Fig. 3).
Coincidences of combined
Map of tree ring sites and coincidence rates at each site. Coincidence rates between extremes in TRW and precipitation for
Significant coincidence rates of TRW (red dots) and simulated NPP
at TRW sites (blue dots) with
In the next step, we focus on the regional patterns of biotic responses
revealed by coincidence analysis. The value of tree ring records as model
benchmarks under climate extremes will depend crucially on the matches in
these spatial patterns. Figure 4 identifies areas where simulated NPP of
broadleaved and needle-leaved trees shows significant coincidences with
precipitation, temperature and combined temperature and precipitation
extremes, respectively. Figure 5 shows the analogue picture for TRW.
Generally, we find more significant grid cells with high coincidence rates
between simulated NPP and precipitation (
Drought conditions may not only result from a lack of rainfall but also from
high temperature, which drives vapour pressure deficit in dry areas such as the
Mediterranean region (Williams et al., 2012). Therefore, we also show the
coincidence rates
Instantaneous (
Zonal patterns become more obvious through binning of the results (Fig. 6).
For both, simulated and observed growth responses, we find a
To further assess the growth responses to climate extremes found in models
and observations, it is necessary to analyse their dynamics. Lagged biotic responses to extreme events are of particular
interest. We therefore compare
the coincidence rate
We present a simple method for detecting impacts of extreme events in time series of climate and forest growth that is based on coincidence analysis. The coincidence metric is viewed as a “unit-free”, neutral measure for biotic responses to climate impacts. The method is general and independent of units and does not require attempts to convert tree ring width to NPP for comparison with model output; instead, we can compare the results of the coincidence analysis to test for possible causal relationships between extreme climate and extreme growth responses.
Tree rings are long-term observational time series related to forest
productivity and are thus valuable archives for improving our
process understanding of forest responses to extreme events and, thus, for
evaluating dynamic vegetation models. Our study shows that low
precipitation, high temperature and combined extremes lead to substantial
losses in forest productivity, which is
Our study has shown the potential of standardized tree ring data to be used for the evaluation of dynamic global vegetation models abilities to simulate growth responses to climate extremes. Earlier model evaluation studies have lacked this type of analysis. As climate extremes can have long-lasting impacts, DGVMs need to be able to simulate such effects and capture the processes that are responsible for multiyear lagged effects. The combination of improved DGVMs and the method of coincidence analysis can then be applied to quantify the impacts of extreme events, e.g. on the long-term fate of the global carbon balance.
This work emerged from the CARBO-Extreme project (grant agreement no. 226701) of the European Community's 7th framework program. M. Wiedermann has been supported by the German Federal Ministry for Science and Education via the BMBF Young Investigators Group CoSy-CC2 (grant no. 01LN1306A). M.D. Mahecha acknowledges support by the GEOCARBON project (grant agreement no: 283080) of the European Community's 7th framework program. J.F. Donges thanks the German National Academic Foundation and the Stordalen Foundation for financial support. F. Babst acknowledges funding from the Swiss National Science Foundation (grant PBSKP2_144034). We thank two anonymous reviewers for their constructive comments to the manuscript. Edited by: C. A. Williams