Coincidences of climate extremes and anomalous vegetation responses: comparing tree ring patterns to simulated productivity

Abstract

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

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

“…The idea of exploring lag times was introduced by several studies in the past (see, e.g. Davis, 1984;Braswell et al, 1997), and it has been adopted in various studies more recently (Anderson et al, 2010;Kuzyakov and Gavrichkova, 2010;Chen et al, 2014;Rammig et al, Number of consecutive days per month over 90th percentile CLD (Consecutive low-value days)…”

Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

“…(here: globally) (e.g., Ledford and Tawn, 1996;Bae et al, 2003;Donges et al, 2016). Applications of the socalled co-occurrence or coincidence analysis can be found in Donges et al (2011b), Rammig et al (2015), Zscheischler et al (2015), Guanche et al (2016), and Siegmund et al (2016). For comparing the algorithms, we are interested in the information that at least one variable is above a certain threshold.…”

“…Multivariate approaches in geoscience make use of anomalies occurring simultaneously in multiple data streams, often referred to as coincidences or co-exceedances (e.g., Donges et al, 2011b;Rammig et al, 2015;Zscheischler et al, 2015;Donges et al, 2016;Guanche et al, 2016;Siegmund et al, 2016). An alternative is the copula approach introduced to the field by Schoelzel and Friedrichs (2008) and Durante and Salvadori (2010).…”

Influence of wood density in tree-ring-based annual productivity assessments and its errors in Norway spruce

“…Apart from the bias in productivity caused by a sample focusing on faster-growing trees (Nehrbass-Ahles et al, 2014), the productivity at stand level would probably generate an overestimation related to a decreased wood density as trees producing larger rings would be sampled. Another issue in using the tree-ring parameters (width and density) to produce annual productivity estimations is the presence of autocorrelation or carry-over effects in the series, which are reflected in the derived productivity estimations but are generally not observed in the measured or modelled carbon fluxes (Babst et al, 2014a, b;Rammig et al, 2015).…”