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Ecosystem functioning is enveloped by hydrometeorological variability
Abstract
Terrestrial ecosystem processes, and the associated vegetation carbon dynamics, respond differently to hydrometeorological variability across timescales, and so does our scientific understanding of the underlying mechanisms. Long-term variability of the terrestrial carbon cycle is not yet well constrained and the resulting climate–biosphere feedbacks are highly uncertain. Here we present a comprehensive overview of hydrometeorological and ecosystem variability from hourly to decadal timescales integrating multiple in situ and remote-sensing datasets characterizing extra-tropical forest sites. We find that ecosystem variability at all sites is confined within a hydrometeorological envelope across sites and timescales. Furthermore, ecosystem variability demonstrates long-term persistence, highlighting ecological memory and slow ecosystem recovery rates after disturbances. However, simulation results with state-of-the-art process-based models do not reflect this long-term persistent behaviour in ecosystem functioning. Accordingly, we develop a cross-time-scale stochastic framework that captures hydrometeorological and ecosystem variability. Our analysis offers a perspective for terrestrial ecosystem modelling and paves the way for new model–data integration opportunities in Earth system sciences.
Supplementary resource (1)
... Because most land surface models are driven by leaf-level processes (Pappas et al., 2013), the emergent discrepancy in post-drought recovery between observations of stem growth (tree rings) and model-simulated net primary productivity is to be expected (Anderegg et al., 2015;Kolus et al., 2019). A likely explanation relates to a mismatch in the auto-correlation structures of land surface model output and tree-ring observations used as benchmarks Pappas et al., 2017). This highlights the need for robust quantification of these legacy effects to better constrain land surface model predictions of tree growth (aboveground woody NPP) responses to extreme drought events and, ultimately, improve process representation in these models. ...
... Ring-width time series often exhibit significant partial auto-correlation at higher orders (e.g., 2, …, n). In contrast, time series of climate, precipitation in particular, are much less auto-correlated (Pappas et al., 2017) as illustrated by the black time series in Fig. 1a. When a time series with strong positive auto-correlation (growth) is predicted by another time series with weak auto-correlation (climate), the residuals will have an auto-correlation structure that is very similar to the predictand. ...
... This will help set appropriate expectations for growth recovery following drought. Prior knowledge about auto-correlation structure or variability decay (as described by Pappas et al., 2017Pappas et al., , 2020 of a target species should be the basis for predictions and hypotheses about recovery (Lloret et al., 2011). That is, the shorter the overall memory or carry-over effect (described by 1-n) of a tree species, the faster it should recover compared to a tree species with greater (given equal ). ...
Drought legacies in radial tree growth are an important feature of variability in biomass accumulation and are widely used to characterize forest resilience to climate change. Defined as a deviation from normal growth, the statistical significance of legacy effects depends on the definition of “normal”—expected growth under average conditions—which has not received sufficient scrutiny.
We re‐examined legacy effect analyses using the International Tree‐Ring Data Bank (ITRDB) and then produced synthetic tree‐ring data to disentangle four key variables influencing the magnitude of legacy effects. We hypothesized that legacy effects (i) are mainly influenced by the auto‐correlation of the radial growth time series (phi), (ii) depend on climate‐growth cross‐correlation (rho), (iii) are directly proportional to the inherent variability of the growth time series (standard deviation, SD), and (iv) scale with the chosen extreme event threshold.
Using a data simulation approach, we were able to reproduce observed lag patterns, demonstrating that legacy effects are a direct outcome of ubiquitous biological memory. We found that stronger legacy effects for conifers compared to angiosperms is a consequence of their higher auto‐correlation, and that the detectability of legacy effects following rare drought events at individual sites is compromised by strong background stochasticity.
Synthesis . We propose two pathways forward to improve the assessment and interpretation of legacy effects: First, we highlight the need to account for auto‐correlated residuals of climate‐growth regression models a posteriori, thereby retrospectively adjusting expectations for “normal” growth variability. Alternatively, we recommend including lagged climate variables in regression models a priori. By doing so, the magnitude of detected legacy effects is greatly reduced and biological memory is directly attributed to antecedent climatic drivers. We argue that future analyses should focus on understanding the functional reasons for how and why key statistical parameters describing this biological memory differ across species and sites. These two pathways should also stimulate improved process‐based representation of vegetation carbon dynamics in mechanistic models.
... relative tree water content, Martinez-Vilalta et al. 2018). Indeed, similarly to other geophysical phenomena (Mandelbrot and Wallis 1969), the statistical properties of spatiotemporal ecophysiological observations can be used to quantify short-and long-term persistence (statistical memory, hereafter 'memory') of the underlying processes (Pappas et al. 2017) and to infer early-warning signals for critical transitions (Dakos et al. 2012, Scheffer et al. 2015. Power spectra (Kleinen et al. 2003) as well as changes in second-order statistics (Carpenter and Brock 2006) of time series describing ecosystem processes could provide robust indicators of ecological transitions, such as, for example, early-warning signals of drought-induced tree mortality (Camarero et al. 2015, Rogers et al. 2018, Cailleret et al. 2019. ...
... Power spectra (Kleinen et al. 2003) as well as changes in second-order statistics (Carpenter and Brock 2006) of time series describing ecosystem processes could provide robust indicators of ecological transitions, such as, for example, early-warning signals of drought-induced tree mortality (Camarero et al. 2015, Rogers et al. 2018, Cailleret et al. 2019. Such statistics are widely applied in several disciplines including metrology ('allan deviation graph'; Allan 1966), hydrology (Hosking 1984, Pelletier andTurcotte 1997;and 'climacogram';Koutsoyiannis 2015), climatology (Mitchell 1976, Koscielny-Bunde et al. 1998, Markonis and Koutsoyiannis 2012 and terrestrial carbon cycle (Katul et al. 2001, Stoy et al. 2005, Mahecha et al. 2007, Paschalis et al. 2015, Pappas et al. 2017), but have not been yet exploited in an ecophysiological context. A systematic characterization of the temporal variability in tree water use and growth across a continuum of time scales could provide important insights not only into tree performance but also into species resilience to environmental changes, as detailed below. ...
... hourly, daily, seasonal and inter-annual variability (Fig. 1), can reveal the strength of the autocorrelation structure (memory) of the data ('aggregated variance analysis'; Papoulis 1965, Beran 1994, Taqqu et al. 1995, Koutsoyiannis 2015. Applying this approach to ecophysiological time series, species-specific differences in tree water use and growth may be pinpointed and species resilience to environmental stressors may be inferred by the strength of memory in the examined data ( Fig. 1; Pappas et al. 2017). The rationale behind this approach is that species which exhibit high variability in ecophysiological variables describing tree vitality (e.g. ...
Tree growth is an indicator of tree vitality and its temporal variability is linked to species resilience to environmental changes. Second‐order statistics that quantify the cross‐scale temporal variability of ecophysiological time series (statistical memory) could provide novel insights into species resilience. Species with high statistical memory in their tree growth may be more affected by disturbances, resulting in lower overall resilience and higher vulnerability to environmental changes. Here, we assessed the statistical memory, as quantified with the decay in standard deviation with increasing time scale, in tree water use and growth of co‐occurring European larch Larix decidua and Norway spruce Picea abies along an elevational gradient in the Swiss Alps using measurements of stem radius changes, sap flow and tree‐ring widths. Local‐scale interspecific differences between the two conifers were further explored at the European scale using data from the International Tree‐Ring Data Bank. Across the analysed elevational gradient, tree water use showed steeper variability decay with increasing time scale than tree growth, with no significant interspecific differences, highlighting stronger statistical memory in tree growth processes. Moreover, Norway spruce displayed slower decay in growth variability with increasing time scale (higher statistical memory) than European larch; a pattern that was also consistent at the European scale. The higher statistical memory in tree growth of Norway spruce in comparison to European larch is indicative of lower resilience of the former in comparison to the latter, and could potentially explain the occurrence of European larch at higher elevations at the Alpine treeline. Single metrics of resilience cannot often summarize the multifaceted aspects of ecosystem functioning, thus, second‐order statistics that quantify the strength of statistical memory in ecophysiological time series could complement existing resilience indicators, facilitating the assessment of how environmental changes impact forest growth trajectories and ecosystem services.
... GVM predictions are primarily driven by photosynthesis, and their parameterization is based on temporally highly resolved but short-term observations from eddy covariance and remote-sensing systems or from laboratory experiments (13,14). Accordingly, GVMs are capable of simulating short-term forest carbon uptake with precision, whereas annual or longer-term ecosystem processes are insufficiently represented (13,(15)(16)(17). This problem is evidenced by model-data discrepancies (12,(15)(16)(17)(18)(19)(20) and the considerable spread in the annual climate sensitivity of current GVM ensembles (4). ...
... Accordingly, GVMs are capable of simulating short-term forest carbon uptake with precision, whereas annual or longer-term ecosystem processes are insufficiently represented (13,(15)(16)(17). This problem is evidenced by model-data discrepancies (12,(15)(16)(17)(18)(19)(20) and the considerable spread in the annual climate sensitivity of current GVM ensembles (4). Refined carbon allocation schemes (21), as well as better representation of growth processes and their climate response in GVMs, have been highlighted as a way to improve the models and refine projections of the forest carbon balance (13,(18)(19)(20). ...
... This is likely because trees can access carbohydrate reserves to sustain growth, leading to lag effects that are evidenced by significant correlations with previous year's climate (Fig. 2). Hence, the climate response of tree growth is less instantaneous compared with that of photosynthesis (7,(15)(16)(17)(18)(19)(20). Carbohydrate reserves are now starting to be implemented in GVMs, and our global climate response maps can serve as independent benchmarks for the climate response simulated with refined carbon allocation schemes (21). ...
Energy and water limitations of tree growth remain insufficiently understood at large spatiotemporal scales, hindering model representation of interannual or longer-term ecosystem processes. By assessing and statistically scaling the climatic drivers from 2710 tree-ring sites, we identified the boreal and temperate land areas where tree growth during 1930–1960 CE responded positively to temperature (20.8 ± 3.7 Mio km ² ; 25.9 ± 4.6%), precipitation (77.5 ± 3.3 Mio km ² ; 96.4 ± 4.1%), and other parameters. The spatial manifestation of this climate response is determined by latitudinal and altitudinal temperature gradients, indicating that warming leads to geographic shifts in growth limitations. We observed a significant ( P < 0.001) decrease in temperature response at cold-dry sites between 1930–1960 and 1960–1990 CE, and the total temperature-limited area shrunk by −8.7 ± 0.6 Mio km ² . Simultaneously, trees became more limited by atmospheric water demand almost worldwide. These changes occurred under mild warming, and we expect that continued climate change will trigger a major redistribution in growth responses to climate.
... Again the connection of the climacospectrum with the CEPLT emerges through log-log derivatives. Specifically, combining (41), (45) and (47) we find that # ( ) = 1 + # ( ) = 1 + 2 ( ) − 2; hence: ...
... Of course stochastic treatment is not advisable in this case, but often such periodic behaviours appear as components of stochastic processes. Obviously the second-order characteristics of such processes are affected by periodic components and therefore we need to know which equations should be superimposed in those of the pure stochastic process (see also [40,41]). As an example of such a case, Figure 4h shows the behaviour of a process defined as the average of a FHK with H = M = 0.8 and a harmonic oscillation. ...
... Connections of microscale turbulence and macroscale atmospheric phenomena are also a broad field for future studies. Further applications, also extending similar recent works in hydrology, geophysics and ecosystems [30,40,41], would enrich our knowledge on natural behaviours. ...
While the modern definition of entropy is genuinely probabilistic, in entropy production the classical thermodynamic definition, as in heat transfer, is typically used. Here we explore the concept of entropy production within stochastics and, particularly, two forms of entropy production in logarithmic time, unconditionally (EPLT) or conditionally on the past and present having been observed (CEPLT). We study the theoretical properties of both forms, in general and in application to a broad set of stochastic processes. A main question investigated, related to model identification and fitting from data, is how to estimate the entropy production from a time series. It turns out that there is a link of the EPLT with the climacogram, and of the CEPLT with two additional tools introduced here, namely the differenced climacogram and the climacospectrum. In particular, EPLT and CEPLT are related to slopes of log-log plots of these tools, with the asymptotic slopes at the tails being most important as they justify the emergence of scaling laws of second-order characteristics of stochastic processes. As a real-world application, we use an extraordinary long time series of turbulent velocity and show how a parsimonious stochastic model can be identified and fitted using the tools developed.
... Therefore, the predicted reactions to the mean change in climate components such as air temperature and rainfall might be misleading if their variability is neglected (Heimann and Reichstein, 2008). Terrestrial ecosystem carbon (C) cycling is heavily influenced by climate variability on timescales ranging from hours to decades and longer rather than the average climate change (Messori et al., 2019;Pappas et al., 2017). Climate variability affects biogeochemical processes that determine the net ecosystem carbon dioxide (CO 2 ) exchange at multiple timescales (Heimann and Reichstein, 2008). ...
... The tight interrelation between stronger interannual variations in the global averaged atmospheric CO 2 and El Niño-Southern Oscillation provides empirical evidence for a short-term response of the C cycle to global climate variability (Heimann and Reichstein, 2008). Understanding how climate variability, by affecting specific ecological and biogeochemical processes, influences C-cycling patterns is therefore indispensable (Pappas et al., 2017). Decadal, annual, and seasonal warming/warming hiatus/cooling variations in temperature therefore are crucial for both terrestrial ecosystem processes and must be accounted for in atmospheric studies (Heimann and Reichstein, 2008;Messori et al., 2019). ...
The response of soil microbial decomposition of soil organic carbon (C) to temperature variation against an average warming background is of great importance to understand how climate change affects the ecosystem C cycling. In this study, a warming and step-wised stop-warming experiment was conducted to examine whether the response of soil respiration (Rs) and heterotrophic respiration (Rh) persists post-warming and to understand the underlying mechanisms. The treatment plots (10 plots) were warmed (~1.5 °C at 10 cm soil depth) in 2017, then warming was stopped in one group (5 plots) in 2018 (WS18) and stopped in another group (the remaining 5 plots) in 2019 (WS19). Plant biomass, soil microbial biomass, and soil microbial community composition were measured from 2017 to 2019.On average, warming increased Rh by 28% in 2017. The Rh in WS18 was stilled increased by 26% in 2018, which was lower than the warming induced increase in Rh in WS19 at the same period. The Rh in WS18 showed no difference with the control and that in WS19 was higher than the control in only June in 2019. Aboveground biomass of WS18 and WS19 increased by 20% and 29%, respectively in 2017, and they were still increased by 12% and 17% in 2019 even the warming stopped one two years and one years, respectively. Belowground biomass, microbial biomass, and diversity indices showed no significant differences among treatments in 2018 or 2019. The fungal community was significantly different among WS18, W19, and the control in both 2018 and 2019. The relative abundance of Ascomycetes, which made the largest contribution to the differences in the fungal community, was negatively correlated with Rh (r = −0.4, n = 30, p < 0.05). Our results indicate a warming legacy effect on the microbial decomposition of soil organic C, resulting from the increase in plant productivity and fungal community change when warming stopped.
... Here, we propose a framework for jointly analysing ESO and ESM simulation results across 73 a continuum of spatiotemporal scales, building upon previous cross-scale analyses (Markonis 74 and Koutsoyiannis, 2013Hanel et al., 2017;Pappas et al., 2017;Papalexiou et al., 2020). 75 ...
... Pappas et al., 2017) The aggregated variance method is a graphical 97 method that examines how second-order statistics (i.e., variance, standard deviation) of a time 98 series change when the time series is aggregated (averaged or summed) across different scales. 99It offers a straightforward quantification of variability (e.g., using the variance or the standard 100 deviation as a metric of variability) and thus provides a very intuitive interpretation of the 101 emerged patterns.laws ...
Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatiotemporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. Here, we propose a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatiotemporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatiotemporal continuum. The proposed cross-scale framework for integrating multi-source data in Earth system sciences is already developed as a stand-alone R package that is freely available to download.
... Life has transformed the Earth, mediating fluxes of elements and energy from the smallest to the largest spatial scales (Schramski et al., 2015;Le Qu er e et al., 2016). The diversity and distributions of plant, animal and microbial life reflect evolutionary and ecological processes constrained by broadscale abiotic gradients of energy, resources and meteorological conditions on land (Hawkins et al. 2003;Kreft & Jetz 2007;Pappas et al. 2017) and in the oceans (Vallina et al. 2014;Woolley et al. 2016;Frainer et al. 2017;Tr eguer et al. 2018). Even while the distribution of biodiversity reflects gradients of energy and limiting resources, it also contributes to how effectively those gradients are exploited to confer ecosystem functioning, such as variability in the rates of primary and secondary production (Baldocchi 2014;Niu et al. 2017;Pappas et al. 2017;Jia et al. 2018). ...
... The diversity and distributions of plant, animal and microbial life reflect evolutionary and ecological processes constrained by broadscale abiotic gradients of energy, resources and meteorological conditions on land (Hawkins et al. 2003;Kreft & Jetz 2007;Pappas et al. 2017) and in the oceans (Vallina et al. 2014;Woolley et al. 2016;Frainer et al. 2017;Tr eguer et al. 2018). Even while the distribution of biodiversity reflects gradients of energy and limiting resources, it also contributes to how effectively those gradients are exploited to confer ecosystem functioning, such as variability in the rates of primary and secondary production (Baldocchi 2014;Niu et al. 2017;Pappas et al. 2017;Jia et al. 2018). Yet, understanding how feedbacks between biodiversity and ecosystem functioning occur, and vary from local to biogeographic scales, is a major challenge (Enquist et al. 2003(Enquist et al. , 2007Grace et al. 2007;Gross & Cardinale 2007;Violle et al. 2014;Guidi et al. 2016;Maestre et al. 2016;Tr eguer et al. 2018;Bagousse-Pinguet et al. 2019), one that is urgent to resolve as biodiversity change occurs at multiple scales in response to climate warming, species introductions and habitat degradation (Reichstein et al. 2014;Snelgrove et al. 2014;Isbell et al. 2017;Chase et al. 2019). ...
A rich body of knowledge links biodiversity to ecosystem functioning (BEF), but it is primarily focused on small scales. We review the current theory and identify six expectations for scale dependence in the BEF relationship: (1) a nonlinear change in the slope of the BEF relationship with spatial scale; (2) a scale‐dependent relationship between ecosystem stability and spatial extent; (3) coexistence within and among sites will result in a positive BEF relationship at larger scales; (4) temporal autocorrelation in environmental variability affects species turnover and thus the change in BEF slope with scale; (5) connectivity in metacommunities generates nonlinear BEF and stability relationships by affecting population synchrony at local and regional scales; (6) spatial scaling in food web structure and diversity will generate scale dependence in ecosystem functioning. We suggest directions for synthesis that combine approaches in metaecosystem and metacommunity ecology and integrate cross‐scale feedbacks. Tests of this theory may combine remote sensing with a generation of networked experiments that assess effects at multiple scales. We also show how anthropogenic land cover change may alter the scaling of the BEF relationship. New research on the role of scale in BEF will guide policy linking the goals of managing biodiversity and ecosystems. We address the challenge of scale for biodiversity and ecosystem functioning (BEF) research. We review current theory and identify six expectations for scale dependence in the BEF relationship. We suggest directions for synthesis that combine theoretical and empirical methods and suggest their application to human transformed landscapes.
... The terrestrial biosphere is a major component of the global C-cycle ( Ciais et al 2013), and is heavily influenced by climate variability on timescales ranging from hours to decades and longer (e.g. Urbanski et al 2007, Pappas et al 2017. In turn, the control exerted by the C-cycle on atmospheric composition and surface-atmosphere fluxes implies that changes in the C-cycle can affect climate on a variety of scales (e.g. ...
... An intuitive summary of this complex web of interactions is provided by Pappas et al (2017), who constructed an ecosystem variability measure by combining a range of C-cycle metrics. The analysis of the spectral variability in climate drivers and C-cycle metrics showed that ecosystem variability in extratropical forests is confined within a hydrometeorological envelope, with precipitation defining the lower limit and energy (namely, temperature and radiation) the upper limit of plausible variability regimes. ...
The terrestrial biosphere is a key component of the global carbon cycle and is heavily influenced by climate. Climate variability can be diagnosed through metrics ranging from individual environmental variables, to collections of variables, to the so-called climate modes of variability. Similarly, the impact of a given climate variation on the terrestrial carbon cycle can be described using several metrics, including vegetation indices, measures of ecosystem respiration and productivity and net biosphere-atmosphere fluxes. The wide range of temporal (from sub-daily to paleoclimatic) and spatial (from local to continental and global) scales involved requires a scale-dependent investigation of the interactions between the carbon cycle and climate. However, a comprehensive picture of the physical links and correlations between climate drivers and carbon cycle metrics at different scales remains elusive, framing the scope of this contribution. Here, we specifically explore how climate variability metrics (from single variables to complex indices) relate to the variability of the carbon cycle at sub-daily to interannual scales (i.e. excluding long-term trends). The focus is on the interactions most relevant to the European terrestrial carbon cycle. We underline the broad areas of agreement and disagreement in the literature, and conclude by outlining some existing knowledge gaps and by proposing avenues for improving our holistic understanding of the role of climate drivers in modulating the terrestrial carbon cycle.
... Climate policy relies heavily on predictions from earth system models, including their crucial DGVM sub-components required to model terrestrial carbon fluxes, water exchange, and energy balances (Boucher et al., 2016). Current DGVMs struggle, however, to simulate forest growth and its climate response accurately, particularly at annual or longer time scales (Anderegg et al., 2015;Pappas et al., 2017;Rollinson et al., 2017;Tei et al., 2017). Hence, we see great potential for both tree-ring observations and ecophysiological models of tree growth to help evaluate and improve DGVMs. ...
... Past research has revealed a large spread in the ability of different DGVMs to reproduce patterns observed in tree rings. Besides being exceedingly sensitive to climate variability Klesse et al., 2018), modeled NPP tends to recover much more quickly after extreme events (Anderegg et al., 2015) and lacks the memory effects that are commonly observed in tree-ring observations also in non-extreme years (Pappas et al., 2017). Accordingly, neither the significant correlations with previous year's climate, nor the positive auto-correlation structure of most tree-ring time series are simulated accurately. ...
The demand for large-scale and long-term information on tree growth is increasing rapidly as environmental change research strives to quantify and forecast the impacts of continued warming on forest ecosystems. This demand, combined with the now quasi-global availability of tree-ring observations, has inspired researchers to compile large tree-ring networks to address continental or even global-scale research questions. However, these emergent spatial objectives contrast with paleo-oriented research ideas that have guided the development of many existing records. A series of challenges related to how, where, and when samples have been collected is complicating the transition of tree rings from a local to a global resource on the question of tree growth. Herein, we review possibilities to scale tree-ring data (A) from the sample to the whole tree, (B) from the tree to the site, and (C) from the site to larger spatial domains. Representative tree-ring sampling supported by creative statistical approaches is thereby key to robustly capture the heterogeneity of climate-growth responses across forested landscapes. We highlight the benefits of combining the temporal information embedded in tree rings with the spatial information offered by forest inventories and earth observations to quantify tree growth and its drivers. In addition, we show how the continued development of mechanistic tree-ring models can help address some of the non-linearities and feedbacks that complicate making inference from tree-ring data. By embracing scaling issues, the discipline of dendrochronology will greatly increase its contributions to assessing climate impacts on forests and support the development of adaptation strategies.
https://authors.elsevier.com/c/1XWsP-4PRq7xR (free access until Sep 27)
... For an application to a proper cyclo-stationary scheme using the SMA model (i.e., abbreviated as CSMA) see in Dimitriadis et al. (2018a). Another simple and robust method is to directly generate the dependence structure of the periodic process using periodic stochastic models (for such applications in ecosystems see Pappas et al., 2017). ...
... However, natural processes with HK behaviour abound in literature. For example, turbulent processes exhibit such long-term persistent behaviour (e.g., Dimitriadis et al., 2016a, and references therein), recently in ecosystem variability (Pappas et al., 2017) as well as most geophysical processes as verified in several cases (Koutsoyiannis, 2003;O'Connell et al., 2016;, and specifically in key hydrometeorological processes such as: river discharge and stage (Hurst, 1951;Koutsoyiannis et al., 2008;Markonis et al., 2017); solar radiation and wind speed Tsekouras and Koutsoyiannis, 2014;Koudouris et al., 2017); precipitation Dimitriadis et al., 2016a;; paleoclimatic temperature reconstructions ; temperature and dew point Lerias et al., 2016) and thus, humidity; potential evapotranspiration which can be adequately evaluated only by temperature and deterministic extraterrestrial radiation (Tegos et al., 2017) and therefore, a similar Hurst parameter as in temperature is expected; but also other renewable-energy related processes, such as wave energy and period , as well as processes used in energy production and management (Chalakatevaki et al., 2017;Papoulakos et al, 2017;Mayrogeorgios et al., 2017), but also weather finance models (Karakatsanis et al., 2017). Interestingly, in most of the aforementioned processes (if treated properly within a robust physical and statistical framework, e.g. by adjusting the process for sampling errors as well as discretization and bias effects) the Hurst parameter is estimated at the range 0.8 to 0.85, as indicated by Hurst ...
The high complexity and uncertainty of atmospheric dynamics has been long identified through the observation and analysis of hydroclimatic processes such as temperature, dew-point, humidity, atmospheric wind, precipitation, atmospheric pressure, river discharge and stage etc. Particularly, all these processes seem to exhibit high unpredictability due to the clustering of events, a behaviour first identified in Nature by H.E. Hurst in 1951 while working at the River Nile, although its mathematical description is attributed to A. N. Kolmogorov who developed it while studying turbulence in 1940. To give credits to both scientists this behaviour and dynamics is called Hurst-Kolmogorov (HK). In order to properly study the clustering of events as well as the stochastic behaviour of hydroclimatic processes in general we would require numerous of measurements in annual scale. Unfortunately, large lengths of high quality annual data are hardly available in observations of hydroclimatic processes. However, the microscopic processes driving and generating the hydroclimatic ones are governed by turbulent state. By studying turbulent phenomena in situ we may be able to understand certain aspects of the related macroscopic processes in field. Certain strong advantages of studying microscopic turbulent processes in situ is the recording of very long time series, the high resolution of records and the controlled environment of the laboratory. The analysis of these time series offers the opportunity of better comprehending, control and comparison of the two scientific methods through the deterministic and stochastic approach.
In this thesis, we explore and further advance the second-order stochastic framework for the empirical as well as theoretical estimation of the marginal characteristic and dependence structure of a process (from small to extreme behaviour in time and state). Also, we develop and apply explicit and implicit algorithms for stochastic synthesis of mathematical processes as well as stochastic prediction of physical processes. Moreover, we analyze several turbulent processes and we estimate the Hurst parameter (H >> 0.5 for all cases) and the drop of variance with scale based on experiments in turbulent jets held at the laboratory. Additionally, we propose a stochastic model for the behaviour of a process from the micro to the macro scale that results from the maximization of entropy for both the marginal distribution and the dependence structure. Finally, we apply this model to microscale turbulent processes, as well as hydroclimatic ones extracted from thousands of stations around the globe including countless of data.
The most important innovation of this thesis is that, to the Author’s knowledge, a unique framework (through modelling of common expression of both the marginal density distribution function and the second-order dependence structure) is presented that can include the simulation of the discretization effect, the statistical bias, certain aspects of the turbulent intermittent (or else fractal) behaviour (at the microscale of the dependence structure) and the long-term behaviour (at the macroscale of the dependence structure), the extreme events (at the left and right tail of the marginal distribution), as well as applications to 13 turbulent and hydroclimatic processes including experimentation and global analyses of surface stations (overall, several billions of observations).
A summary of the major innovations of the thesis are: (a) the further development, and extensive application to numerous processes, of the classical second-order stochastic framework including innovative approaches to account for intermittency, discretization effects and statistical bias; (b) the further development of stochastic generation schemes such as the Sum of Autoregressive (SAR) models, e.g. AR(1) or ARMA(1,1), the Symmetric-Moving-Average (SMA) scheme in many dimensions (that can generate any process second-order dependence structure, approximate any marginal distribution to the desired level of accuracy and simulate certain aspects of the intermittent behaviour) and an explicit and implicit (pseudo) cyclo-stationary (pCSAR and pCSMA) schemes for simulating the deterministic periodicities of a process such as seasonal and diurnal; and (c) the introduction and application of an extended stochastic model (with an innovative identical expression of a four-parameter marginal distribution density function and correlation structure, i.e. g(x;C)=λ/[(1+|x/a+b|^c )]^d, with C=[λ,a,b,c,d]), that encloses a large variety of distributions (ranging from Gaussian to powered-exponential and Pareto) as well as dependence structures (such as white noise, Markov and HK), and is in agreement (in this form or through more simplified versions) with an interestingly large variety of turbulent (such as horizontal and vertical thermal jet of positively buoyancy processes using laser-induced-fluorescence techniques as well as grid-turbulence generated within a wind-tunnel), geostatistical (such as 2d rock formations), and hydroclimatic processes (such as temperature, atmospheric wind, dew-point and thus, humidity, precipitation, atmospheric pressure, river discharges and solar radiation, in a global scale, as well as a very long time series of river stage, and wave height and period). Amazingly, all examined physical processes (overall 13) exhibited long-range dependence and in particular, most (if treated properly within a robust physical and statistical framework, e.g. by adjusting the process for sampling errors as well as discretization and bias effects) with a mean long-term persistence parameter equal to H ≈ 5/6 (as in the case of isotropic grid-turbulence), and (for the processes examined in the microscale such atmospheric wind, surface temperature and dew-point, in a global scale, and a long duration discharge time series and storm event in terms of precipitation and wind) a powered-exponential behaviour with a fractal parameter close to M ≈ 1/3 (as in the case of isotropic grid-turbulence).
... Vegetation growth, indicated by satellite or tree ring proxies, is a key variable signifying the quantity and quality of essential ecosystem services such as carbon sequestration, mitigation of anthropogenic climate change and the provision of food and fibre for human consumption. Yet, vegetation growth is known to vary from year to year (Pappas et al., 2017). While small variations are expected and do not raise concerns, large and extreme negative anomalies in vegetation growth could lead to severely impaired ecosystem services (Felton, 2021;Piao et al., 2019;McDowell et al., 2020). ...
Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite-derived vegetation index (NDVI) data and supporting tree-ring chronologies, we identify periods of NEGs from 1981-2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi-arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region- and biome-dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non-dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate-specific disaster prevention and mitigation plans among different regions of the globe in a changing climate.
... Globally, the temporal variability of precipitation will likely become larger than ever 6 , which may lead to a higher frequency of drought events. The implications of increased drought frequency on soil greenhouse gas emissions and plant nutrient availability, and thus for ecosystem services as a whole 7 , can be severe. Understanding if soil community composition can shift in response to changing conditions 8 and how this can affect ecosystem dynamics is, therefore, crucial to better predict the future alterations of biogeochemical cycles. ...
Climate change is altering the frequency and severity of drought events. Recent evidence indicates that drought may produce legacy effects on soil microbial communities. However, it is unclear whether precedent drought events lead to ecological memory formation, i.e., the capacity of past events to influence current ecosystem response trajectories. Here, we utilize a long-term field experiment in a mountain grassland in central Austria with an experimental layout comparing 10 years of recurrent drought events to a single drought event and ambient conditions. We show that recurrent droughts increase the dissimilarity of microbial communities compared to control and single drought events, and enhance soil multifunctionality during drought (calculated via measurements of potential enzymatic activities, soil nutrients, microbial biomass stoichiometry and belowground net primary productivity). Our results indicate that soil microbial community composition changes in concert with its functioning, with consequences for soil processes. The formation of ecological memory in soil under recurrent drought may enhance the resilience of ecosystem functioning against future drought events.
... Field-based measurements of tree growth are required to develop and evaluate ecosystem models quantifying the role of forests in the global carbon cycle. Interannual variation in the growth responses of terrestrial forest ecosystems to fluctuating environmental conditions is an aspect of the global carbon cycle that is difficult to predict (Luo and Chen 2015;Pappas et al. 2017). Given the annual resolution and temporal depth of tree-ring data, there is great potential for improving the predictive capacity of these models in the simulated magnitude and dynamics of forest carbon uptake (Babst et al. 2014a, 2014b, Metsaranta and Kurz 2012Teets et al. 2018;Metsaranta et al. 2018a). ...
Understanding the magnitude and cause of variation in tree growth and forest productivity is central to sustainable forest management. Measurements of annual growth rings allow assessments of individual tree, tree population and forest ecosystem vulnerabilities to drought stress or other changing forest disturbance regimes (insects, diseases, fire), which can be used to identify areas at greatest risk of forest losses. Given a heightened demand for tree-ring data, we consolidated and synthesized tree-ring studies and datasets gathered over the past 30 years in Canada by scientists with the Canadian Forest Service and research partners. We incorporated these datasets into a data repository that currently contains tree-ring measurements from 40,206 tree samples from 4,594 sites and 62 tree species from all Canadian provinces and territories. Through our synthesis, we demonstrate the value of such large ensembles of tree-ring data for identifying patterns in tree growth over large spatial scales by mapping pan-Canadian drought sensitivity. Overall, we found high coherence in the samples analysed; low coherence was generally limited to data- poor regions and species. Drought sensitivity was widespread across species and regions: 34% of sampled trees displayed a significant positive relationship between annual growth increment and summer soil moisture index. Dependence upon water availability in species Picea mariana, Pinus banksiana, Pinus contorta, and Pseudotsuga menziesii was more strongly expressed in the warmest regions of the species’ range; for species Picea glauca and Populus tremuloides, drought sensitivity was stronger in the driest regions. This unprecedented consolidation and synthesis of tree-ring data will enable new research initiatives (e.g., meta-analyses) aimed at improved understanding of the drivers, patterns, and implications of changes in tree growth, as well as facilitating new research collaborations in earth and environmental sciences. Amongst other things, there is a need for expanding the spatial distribution of sites across Canada’s northern regions, increasing the number of samples collected from older stands and angiosperm species, and integrate datasets from studies that evaluate the effects of silvicultural experiments, including provenance and progeny trials, on tree growth.
... At our study site, direct climatic controls on meristematic activity at sub-monthly time scales [e.g., Girardin et al., 2016a;Babst et al., 2019] and potential nutrient limitation [e.g., Fernández-Martínez et al., 2014] are likely more influential determinants of boreal tree growth than C input. Lagged effects between photosynthesized C and its investment to woody tissues [e.g., Cuny et al., 2015;Pappas et al., 2017] as well as direct climatically-driven sink limitations can shape the interannual aboveground boreal tree growth. These findings corroborate existing literature Fig. 4. (a) Seasonal dynamics of forest-stand needle phenology at the site based on vegetation canopy greenness index during 2011 to 2015 from the PhenoCam imagery. ...
The boreal biome accounts for approximately one third of the terrestrial carbon (C) sink. However, estimates of its individual C pools remain uncertain. Here, focusing on the southern boreal forest, we quantified the magnitude and temporal dynamics of C allocation to aboveground tree growth at a mature black spruce (Picea mariana)-dominated forest stand in Saskatchewan, Canada. We reconstructed aboveground tree biomass increments (AGBi) using a biometric approach, i.e., species-specific allometry combined with forest stand characteristics and tree ring widths collected with a C-oriented sampling design. We explored the links between boreal tree growth and ecosystem C input by comparing AGBi with eddy-covariance-derived ecosystem C fluxes from 1999 to 2015 and we synthesized our findings with a refined meta-analysis of published values of boreal forest C use efficiency (CUE). Mean AGBi at the study site was decoupled from ecosystem C input and equal to 71 ± 7 g C m–2 (1999–2015), which is only a minor fraction of gross ecosystem production (GEP; i.e., AGBi / GEP ≈ 9 %). Moreover, C allocation to AGBi remained stable over time (AGBi / GEP; –0.0001 yr–1; p-value=0.775), contrary to significant trends in GEP (+5.72 g C m–2 yr–2; p-value=0.02) and CUE (–0.0041 yr–1, p-value=0.007). CUE was estimated as 0.50 ± 0.03 at the study area and 0.41 ± 0.12 across the reviewed boreal forests. These findings highlight the importance of belowground tree C investments, together with the substantial contribution of understory, ground cover and soil to the boreal forest C balance. Our quantitative insights into the dynamics of aboveground boreal tree C allocation offer additional observational constraints for terrestrial ecosystem models that are often biased in converting C input to biomass, and can guide forest-management strategies for mitigating carbon dioxide emissions.
... For timescales < 1 week we used 1 h chamber observations, noting that sparse daytime-only observations of concentrations and 1 h fluxes may underestimate short-term variability (σ init ). We use the climacogram to test whether the variability of the diffusive CH 4 flux is contained within meteorological variability, as for terrestrial ecosystem processes (Pappas et al., 2017). ...
Lakes and reservoirs contribute to regional carbon budgets via significant emissions of climate forcing trace gases. Here, for improved modelling, we use 8 years of floating chamber measurements from three small, shallow subarctic lakes (2010–2017, n=1306) to separate the contribution of physical and biogeochemical processes to the turbulence-driven, diffusion-limited flux of methane (CH4) on daily to multi-year timescales. Correlative data include surface water concentration measurements (2009–2017, n=606), total water column storage (2010–2017, n=237), and in situ meteorological observations. We used the last to compute near-surface turbulence based on similarity scaling and then applied the surface renewal model to compute gas transfer velocities. Chamber fluxes averaged 6.9±0.3 mg CH4 m−2 d−1 and gas transfer velocities (k600) averaged 4.0±0.1 cm h−1. Chamber-derived gas transfer velocities tracked the power-law wind speed relation of the model. Coefficients for the model and dissipation rates depended on shear production of turbulence, atmospheric stability, and exposure to wind. Fluxes increased with wind speed until daily average values exceeded 6.5 m s−1, at which point emissions were suppressed due to rapid water column degassing reducing the water–air concentration gradient. Arrhenius-type temperature functions of the CH4 flux (Ea′=0.90±0.14 eV) were robust (R2≥0.93, p<0.01) and also applied to the surface
CH4 concentration (Ea′=0.88±0.09 eV). These results imply
that emissions were strongly coupled to production and supply to the water column. Spectral analysis indicated that on timescales shorter than a month, emissions were driven by wind shear whereas on longer timescales variations in water temperature governed the flux. Long-term monitoring efforts are essential to identify distinct functional relations that govern flux variability on timescales of weather and climate change.
... Research on sub-annual tree-ring widths (i.e. earlywood and latewood) and xylogenesis could serve to bridge the temporal scale between legacy effects in GPP and legacy effects in tree rings (Pappas et al. 2017;Szejner et al. 2018), though care should be taken as the relationship between cell expansion (i.e. radial growth) and biomass accumulation (i.e. ...
Multi‐year lags in tree drought recovery, termed ‘drought legacy effects’, are important for understanding the impacts of drought on forest ecosystems, including carbon (C) cycle feedbacks to climate change. Despite the ubiquity of lags in drought recovery, large uncertainties remain regarding the mechanistic basis of legacy effects and their importance for the C cycle. In this review, we identify the approaches used to study legacy effects, from tree rings to whole forests. We then discuss key knowledge gaps pertaining to the causes of legacy effects, and how the various mechanisms that may contribute these lags in drought recovery could have contrasting implications for the C cycle. Furthermore, we conduct a novel data synthesis and find that legacy effects differ drastically in both size and length across the US depending on if they are identified in tree rings versus gross primary productivity. Finally, we highlight promising approaches for future research to improve our capacity to model legacy effects and predict their impact on forest health. We emphasise that a holistic view of legacy effects – from tissues to whole forests – will advance our understanding of legacy effects and stimulate efforts to investigate drought recovery via experimental, observational and modelling approaches. This review synthesizes current research on legacy effects, reviews the evidence behind hypothesized mechanisms for drought legacies, and discusses key knowledge gaps pertaining to how they impact the carbon cycle. Additionally, through a novel data synthesis this study demonstrates that lags in drought recovery are much more severe when quantified in tree rings versus gross primary productivity.
... Time series decomposition allows us to extract different frequencies such as annual, intra-annual, and interannual oscillations from vegetation and climate time series. Such approaches have proven useful, e.g., to characterize at what scales vegetation responses are dampened or amplified in comparison with their climate forcing (Stoy et al., 2009), how ecosystem variability is confined by hydrometeorological variability (Pappas et al., 2017), what scales of variability need to be considered to relate forcing variables and vegetation state comprehensively (Katul et al., 2001;Braswell et al., 2005), or to remove confounding effects from processes acting on longer timescales than the process in question (Mahecha et al., 2010b). However, to date most studies employing time series decomposition to study vegetation dynamics have focused on disentangling timescales from minutes to a few years based on flux data (Stoy et al., 2009;Katul et al., 2001;Mahecha et al., 2007Mahecha et al., , 2010c. ...
Climate variables carry signatures of variability at multiple timescales. How these modes of variability are reflected in the state of the
terrestrial biosphere is still not quantified or discussed at the global scale. Here, we set out to gain a global understanding of the relevance
of different modes of variability in vegetation greenness and its covariability with climate. We used >30 years of remote sensing records of
the normalized difference vegetation index (NDVI) to characterize biosphere variability across timescales from submonthly oscillations to decadal
trends using discrete Fourier decomposition. Climate data of air temperature (Tair) and precipitation (Prec) were used to characterize
atmosphere–biosphere covariability at each timescale.
Our results show that short-term (intra-annual) and longer-term (interannual and longer) modes of variability make regionally highly important
contributions to NDVI variability: short-term oscillations focus in the tropics where they shape 27 % of NDVI variability. Longer-term
oscillations shape 9 % of NDVI variability, dominantly in semiarid shrublands. Assessing dominant timescales of vegetation–climate covariation,
a natural surface classification emerges which captures patterns not represented by conventional classifications, especially in the
tropics. Finally, we find that correlations between variables can differ and even invert signs across timescales. For southern Africa for example,
correlation between NDVI and Tair is positive for the seasonal signal but negative for short-term and longer-term oscillations,
indicating that both short- and long-term temperature anomalies can induce stress on vegetation dynamics. Such contrasting correlations between timescales exist for 15 % of vegetated areas for NDVI with Tair and 27 % with Prec, indicating global relevance of scale-specific
climate sensitivities.
Our analysis provides a detailed picture of vegetation–climate covariability globally, characterizing ecosystems by their intrinsic modes of
temporal variability. We find that (i) correlations of NDVI with climate can differ between scales, (ii) nondominant subsignals in climate
variables may dominate the biospheric response, and (iii) possible links may exist between short-term and longer-term scales. These heterogeneous
ecosystem responses on different timescales may depend on climate zone and vegetation type, and they are to date not well understood and do not always
correspond to transitions in dominant vegetation types. These scale dependencies can be a benchmark for vegetation model evaluation and for
comparing remote sensing products.
... As a result, a few bursts of positive extremes in terms of productivity can strongly modify the annual budget and long-term dynamics . Therefore, to quantify the interannual dynamics of veg- between research groups and the use of proxy data to extend the length of the time series (e.g., tree rings) are necessary to better inform models (Babst et al., 2018;Pappas, Mahecha, Frank, Babst, & Koutsoyiannis, 2017). ...
Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of ten terrestrial biosphere models to reproduce observed sensitivity of ecosystem productivity to rainfall changes at ten sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are: (a) Inter-model variation is generally large and model agreement varies with time scales. In severely water limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent gross primary productivity. In more mesic sites model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) time scales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.
... by hydrometeorological variability ( Pappas et al., 2017), what scales of variability need to be considered to relate forcing variables and vegetation state comprehensively ( Katul et al., 2001), or to remove confounding effects from processes acting on longer time scales than the process in question ( Mahecha et al., 2010b). However, to date most studies employing time-series 55 decomposition to study vegetation dynamics have focused on disentangling time scales from minutes to few years based on flux data ( Stoy et al., 2009;Katul et al., 2001;Mahecha et al., 2007Mahecha et al., , 2010c). ...
Climate variables carry signatures of variability at multiple time scales. How these modes of variability are reflected in the state of the terrestrial biosphere is still not quantified, nor discussed at the global scale. Here, we set out to gain a global understanding of the relevance of different modes of variability in vegetation greenness and its co-variability with climate. We used > 30 years of remote sensing records of Normalized Difference Vegetation Index (NDVI) to characterize biosphere variability across time scales from sub-monthly oscillations to decadal trends using discrete Fourier decomposition. Climate data of air temperature (Tair) and precipitation (Prec) were used to characterize atmosphere-biosphere co-variability at each time scale.
Our results show that short-term (intra-annual) and longer-term (inter-annual and longer) modes of variability make regionally highly important contributions to NDVI variability: Short-term oscillations focus in the tropics where they shape 27 % of NDVI variability. Longer-term oscillations shape 9 % of NDVI variability, dominantly in semi-arid shrublands. Assessing dominant time scales of vegetation-climate co-variation, a natural surface classification emerges which captures patterns not represented by conventional classifications, especially in the tropics. Finally, we find that correlations between variables can differ and even invert signs across time scales. For southern Africa for example, correlation between NDVI and Tair is positive for the seasonal signal, but negative for short-term and longer-term oscillations, indicating that both short and long-term temperature anomalies can induce stress on vegetation dynamics. Such contrasting correlations between time scales exist for 15 % of vegetated area for NDVI with Tair, and 27 % with Prec, indicating global relevance of scale-specific climate sensitivities.
Our analysis provides a detailed picture of vegetation-climate co-variability globally, characterizing ecosystems by their intrinsic modes of temporal variability. We find that (i) correlations of NDVI with climate can differ between scales, (ii) non-dominant sub-signals in climate variables may dominate the biospheric response, and (iii) possible links may exist between short-term and longer-term scales. These heterogeneous ecosystem responses on different time scales may depend on climate zone and vegetation type, and are to date not well understood, nor always correspond to transitions in dominant vegetation types. These scale dependencies can be a benchmark for vegetation model evaluation and for comparing remote sensing products.
... These knowledge gaps in vegetation-climate-topography interactions in the Alpine region hinder our ability to assess the impacts of climate and land use changes on mountain ecosystems (Viviroli et al., 2011;Voepel et al., 2011). Additionally, recent research has shown that the cross-scale temporal variability of ecosystem processes is enveloped by the variability of hydrometeorological variables (Pappas, Mahecha, Frank, Babst, & Koutsoyiannis, 2017). Distributed ecohydrological models offer the opportunity to explore the cross-scale variability of these processes not only in time but also in space. ...
Mountain ecosystems are experiencing rapid warming resulting in ecological changes worldwide. Projecting the response of these ecosystems to climate change is thus crucial, but also uncertain due to complex interactions between topography, climate and vegetation. Here, we performed numerical simulations in a real and a synthetic spatial domain covering a range of contrasting climatic conditions and vegetation characteristics representative of the European Alps. Simulations were run with the mechanistic ecohydrological model Tethys‐Chloris to quantify the drivers of ecosystem functioning and to explore the vulnerability of Alpine ecosystems to climate change. We correlated the spatial distribution of ecohydrological responses with that of meteorological and topographic attributes and computed spatially explicit sensitivities of net primary productivity, transpiration, and snow cover to air temperature, radiation and water availability. We also quantified how the variance in several ecohydrological processes, such as transpiration, quickly diminishes with increasing spatial aggregation, which highlights the importance of fine spatial resolution for resolving patterns in complex topographies. We conducted controlled numerical experiments in the synthetic domain to disentangle the effect of catchment orientation on ecohydrological variables, such as streamflow. Our results support previous studies reporting an altitude threshold below which Alpine ecosystems are water‐limited in the drier inner‐Alpine valleys and confirm that the wetter areas are temperature‐limited. High‐resolution simulations of mountainous areas can improve our understanding of ecosystem functioning across spatial scales. They can also locate the areas that are the most vulnerable to climate change and stimulate future measurement campaigns.
... Therefore, although they may capture some of the variability in vegetation biomass (e.g. Myneni et al., 2001;Pappas et al., 2017), they contribute to the plant C source perspective, which has been prevalent in the literature. Direct observations of growth are mostly limited to aboveground (e.g. ...
Contents I. II. III. IV. V. VI. References The increase in atmospheric CO2 in the future is one of the most certain projections in environmental sciences. Understanding whether vegetation carbon assimilation, growth, and changes in vegetation carbon stocks are affected by higher atmospheric CO2 and translating this understanding in mechanistic vegetation models is of utmost importance. This is highlighted by inconsistencies between global‐scale studies that attribute terrestrial carbon sinks to CO2 stimulation of gross and net primary production on the one hand, and forest inventories, tree‐scale studies, and plant physiological evidence showing a much less pronounced CO2 fertilization effect on the other hand. Here, we review how plant carbon sources and sinks are currently described in terrestrial biosphere models. We highlight an uneven representation of complexity between the modelling of photosynthesis and other processes, such as plant respiration, direct carbon sinks, and carbon allocation, largely driven by available observations. Despite a general lack of data on carbon sink dynamics to drive model improvements, ways forward toward a mechanistic representation of plant carbon sinks are discussed, leveraging on results obtained from plant‐scale models and on observations geared toward model developments.
... Certain studies, as are those of Markonis and Koutsoyiannis (2013) and of Pappas and Koutsoyiannis (2017) have utilized a tool named combined climacogram in order to jointly visualize the climacogram of time-series recorded on different scales, on the same log-log plot of aggregated variance (or standard deviation), against the averaged scale. ...
Long records and reconstructions of oceanic variables as the Sea Level (SL)
and the Sea Surface Temperature (SST) portray the intrinsic variability they
inhibit through multiple time scales. In this study we examine the Long Term
Persistence (or Long Range Dependence) that these variables exhibit by using
observed and reconstructed proxies on time scales spanning from 1 month
to 10 million years. We also associate their variability with periodic or
oscillation processes such as the Milankovitch cycles for the SL variable and
the ENSO phenomenon for the SST variable. Simple and parsimonious tools
derived from Stochastic Methods, such as the climacogram and the Hurst
exponent, which can be easily reproduced with basic elements of probabilities
and statistics, are utilized for the purposes of this study.
... Another reason for the observation-model sensitivity discrepancy might be that nonstructural carbohydrates (frequently described as the carbon reserve pool) are not represented in most state-of-theart DGVMs. This factor may help explain the high sensitivity to current year's growing season climate of the models and is demonstrated by (i) significantly different autocorrelative properties of modeled and observed time series ( Figure S7), (ii) lower ecosystem persistence in models (Pappas et al., 2017), (iii) the rather immediate NPP recovery after climate extremes compared to that of tree ring observations (Anderegg et al., 2015), and (iv) missing lag effects of modeled NPP time series compared to observed tree ring widths (Zhang et al., 2017). ...
The response of forest growth to climate variability varies along environmental gradients. A growth increase and decrease with warming is usually observed in cold-humid and warm-dry regions, respectively. However, it remains poorly known where the sign of these temperature effects switches. Here we introduce a newly developed European tree-ring network that has been specifically collected to reconstruct forest aboveground biomass increment (ABI). We quantify, how the long-term (1910-2009) inter-annual variability of ABI depends on local mean May-August temperature and test, if a dynamic global vegetation model (DGVM) ensemble reflects the resulting patterns. We find that sites at 8°C mean May-August temperature increase ABI on average by 5.7±1.3 %, whereas sites at 20°C decrease ABI by 3.0±1.8 % m-2 y-1 ∆°C-1. A threshold temperature between beneficial and detrimental effects of warming and the associated increase in water demand on tree growth emerged at 15.9±1.4°C mean May-August temperature. Because inter-annual variability increases proportionally with mean growth rate – i.e. the coefficient of variation stays constant – we were able to validate these findings with a much larger tree-ring dataset that had been established following classic dendrochronological sampling schemes. While the observed climate sensitivity pattern is well reflected in the DGVM ensemble, there is a large spread of threshold temperatures between the individual models. Also, individual models disagree strongly on the magnitude of climate impact at the coldest and warmest locations, suggesting where model improvement is most needed to more accurately predict forest growth and effectively guide silvicultural practices.
... We suggest that the divergence in crown characteristics (morphological and physiological; Williams et al. 2017) may not only be beneficial for ecosystem productivity, but could also result in complementarity in light-and water-use strategies, with pronounced implications for boreal forest resilience to projected changes in climate (e.g., water-and heat-induced stress). The contrasting light-and water-use strategies that individual boreal tree species employ reveal that, although the boreal forest is characterized by low species diversity, its functional diversity may enhance boreal forest resilience to short-term (i.e., climate extremes such as droughts and heatwaves) and long-term (i.e., shifts in precipitation and temperature norms; e.g., Pappas et al. 2017) hydrometeorological variability. ...
Water stress has been identified as a key mechanism of the contemporary increase in tree mortality rates in northwestern North America. However, a detailed analysis of boreal tree hydrodynamics and their interspecific differences is still lacking. Here we examine the hydraulic behaviour of co-occurring larch (Larix laricina) and black spruce (Picea mariana), two characteristic boreal tree species, near the southern limit of the boreal ecozone in central Canada. Sap flux density (Js), concurrently recorded stem radius fluctuations and meteorological conditions are used to quantify tree hydraulic functioning and to scrutinize tree water-use strategies. Our analysis revealed asynchrony in the diel hydrodynamics of the two species with the initial rise in Js occurring 2 h earlier in larch than in black spruce. Interspecific differences in larch and black spruce crown architecture explained the observed asynchrony in their hydraulic functioning. Furthermore, the two species exhibited diverging stomatal regulation strategies with larch and black spruce employing relatively isohydric and anisohydric behaviour, respectively. Such asynchronous and diverging tree-level hydrodynamics provide new insights into the ecosystem-level complementarity in tree form and function, with implications for understanding boreal forests' water and carbon dynamics and their resilience to environmental stress.
To seek stochastic analogies in key processes related to the hydrological cycle, an extended
collection of several billions of data values from hundred thousands of worldwide stations is used
in this work. The examined processes are the near-surface hourly temperature, dew point, relative
humidity, sea level pressure, and atmospheric wind speed, as well as the hourly/daily streamflow
and precipitation. Through the use of robust stochastic metrics such as the K-moments and a secondorder climacogram (i.e., variance of the averaged process vs. scale), it is found that several stochastic
similarities exist in both the marginal structure, in terms of the first four moments, and in the secondorder dependence structure. Stochastic similarities are also detected among the examined processes,
forming a specific hierarchy among their marginal and dependence structures, similar to the one
in the hydrological cycle. Finally, similarities are also traced to the isotropic and nearly Gaussian
turbulence, as analyzed through extensive lab recordings of grid turbulence and of turbulent buoyant
jet along the axis, which resembles the turbulent shear and buoyant regime that dominates and
drives the hydrological-cycle processes in the boundary layer. The results are found to be consistent
with other studies in literature such as solar radiation, ocean waves, and evaporation, and they can
be also justified by the principle of maximum entropy. Therefore, they allow for the development
of a universal stochastic view of the hydrological-cycle under the Hurst–Kolmogorov dynamics,
with marginal structures extending from nearly Gaussian to Pareto-type tail behavior, and with
dependence structures exhibiting roughness (fractal) behavior at small scales, long-term persistence
at large scales, and a transient behavior at intermediate scales.
Land carbon cycle components in an Earth system model (ESM) play a crucial role in the projections of forest ecosystem responses to climate/environmental changes. Evaluating models from the viewpoint of observations is essential for an improved understanding of model performance and for identifying uncertainties in their outputs. Herein, we evaluated the land net primary production (NPP) for circumboreal forests simulated with 10 ESMs in Phase 5 of the Coupled Model Intercomparison Project by comparisons with observation‐based indexes for forest productivity, namely, the composite version 3G of the normalized difference vegetation index (NDVI3g) and tree‐ring width index (RWI). These indexes show similar patterns in response to past climate change over the forests, i.e., a one‐year time lag response and smaller positive responses to past climate changes in comparison with the land NPP simulated by the ESMs. The latter showed overly positive responses to past temperature and/or precipitation changes in comparison with the NDVI3g and RWI. These results indicate that ESMs may overestimate the future forest NPP of circumboreal forests (particularly for inland dry regions, such as inner Alaska and Canada, and eastern Siberia, and for hotter, southern regions, such as central Europe) under the expected increases in both average global temperature and precipitation, which are common to all current ESMs.
Long-term persistence (LTP) of annual river runoff is a topic of ongoing hydrological research, due to its implications to water resources management. Here, we estimate its strength, measured by the Hurst coefficient H, in 696 annual, globally distributed, streamflow records with at least 80 years of data. We use three estimation methods (maximum likelihood estimator, Whittle estimator and least squares variance) resulting in similar mean values of H close to 0.65. Subsequently, we explore potential factors influencing H by two linear (Spearman's rank correlation, multiple linear regression) and two non-linear (self-organizing maps, random forests) techniques. Catchment area is found to be crucial for medium to larger watersheds, while climatic controls, such as aridity index, have higher impact to smaller ones. Our findings indicate that long-term persistence is weaker than found in other studies, suggesting that enhanced LTP is encountered in large-catchment rivers, were the effect of spatial aggregation is more intense. However, we also show that the estimated values of H can be reproduced by a short-term persistence stochastic model such as an auto-regressive AR(1) process. A direct consequence is that some of the most common methods for the estimation of H coefficient, might not be suitable for discriminating short- and long-term persistence even in long observational records.
Elevated atmospheric CO2 concentrations are expected to enhance photosynthesis and reduce stomatal conductance, thus increasing plant water use efficiency. A recent study based on eddy covariance flux observations from Northern Hemisphere forests showed a large increase in inherent water use efficiency (IWUE). Here, we used an updated version of the same dataset and robust uncertainty quantification to revisit these contemporary IWUE trends. We tested the hypothesis that the observed IWUE increase could be attributed to interannual trends in plant functional traits, potentially triggered by environmental change. We found that IWUE increased by ca. 1.3% yr-1, which is less than previously reported but still larger than theoretical expectations. Numerical simulations with the T&C ecosystem model using temporally static plant functional traits cannot explain this increase. Simulations with plant functional trait plasticity, i.e., temporal changes in model parameters such as specific leaf area and maximum Rubisco capacity, match the observed trends in IWUE. Our results show that trends in plant functional traits, equal to 1.0% yr-1, can explain the observed IWUE trends. Thus, at decadal or longer time scales, trait plasticity could potentially influence forest water, carbon and energy fluxes with profound implications for both the monitoring of temporal changes in plant functional traits and their representation in Earth system
The terrestrial carbon sink, as of yet unidentified, represents 15-30% of annual global emissions of carbon from fossil fuels and industrial activities. Some of the missing carbon is sequestered in vegetation biomass and, under the Kyoto Protocol of the United Nations Framework Convention on Climate Change, industrialized nations can use certain forest biomass sinks to meet their greenhouse gas emissions reduction commitments. Therefore, we analyzed 19 years of data from remote-sensing spacecraft and forest inventories to identify the size and location of such sinks. The results, which cover the years 1981-1999, reveal a picture of biomass carbon gains in Eurasian boreal and North American temperate forests and losses in some Canadian boreal forests. For the 1.42 billion hectares of Northern forests, roughly above the 30th parallel, we estimate the biomass sink to be 0.68 0.34 billion tons carbon per year, of which nearly 70% is in Eurasia, in proportion to its forest area and in disproportion to its biomass carbon pool. The relatively high spatial resolution of these estimates permits direct validation with ground data and contributes to a monitoring program of forest biomass sinks under the Kyoto protocol.
The terrestrial carbon sink, as of yet unidentified, represents 15–30% of annual global emissions of carbon from fossil fuels and industrial activities. Some of the missing carbon is sequestered in vegetation biomass and, under the Kyoto Protocol of the United Nations Framework Convention on Climate Change, industrialized nations can use certain forest biomass sinks to meet their greenhouse gas emissions reduction commitments. Therefore, we analyzed 19 years of data from remote-sensing spacecraft and forest inventories to identify the size and location of such sinks. The results, which cover the years 1981–1999, reveal a picture of biomass carbon gains in Eurasian boreal and North American temperate forests and losses in some Canadian boreal forests. For the 1.42 billion hectares of Northern forests, roughly above the 30th parallel, we estimate the biomass sink to be 0.68 ± 0.34 billion tons carbon per year, of which nearly 70% is in Eurasia, in proportion to its forest area and in disproportion to its biomass carbon pool. The relatively high spatial resolution of these estimates permits direct validation with ground data and contributes to a monitoring program of forest biomass sinks under the Kyoto protocol.
The eddy-covariance (EC) micro-meteorological technique and the ecology-based biometric methods (BM) are the primary methodologies to quantify CO2 exchange between terrestrial ecosystems and the atmosphere (net ecosystem production, NEP) and its two components, ecosystem respiration and gross primary production. Here we show that EC and BM provide different estimates of NEP, but comparable ecosystem respiration and gross primary production for forest ecosystems globally. Discrepancies between methods are not related to environmental or stand variables, but are consistently more pronounced for boreal forests where carbon fluxes are smaller. BM estimates are prone to underestimation of net primary production and overestimation of leaf respiration. EC biases are not apparent across sites, suggesting the effectiveness of standard post-processing procedures. Our results increase confidence in EC, show in which conditions EC and BM estimates can be integrated, and which methodological aspects can improve the convergence between EC and BM.
Primary productivity of terrestrial vegetation is expected to increase under the influence of increasing atmospheric carbon dioxide concentrations ([CO2]). Depending on the fate of such additionally fixed carbon, this could lead to an increase in terrestrial carbon storage, and thus a net terrestrial sink of atmospheric carbon. Such a mechanism is generally believed to be the primary global driver behind the observed large net uptake of anthropogenic CO2 emissions by the biosphere. Mechanisms driving CO2 uptake in the Terrestrial Biosphere Models (TBMs) used to attribute and project terrestrial carbon sinks, including that from increased [CO2], remain in large parts unchanged since those models were conceived two decades ago. However, there exists a large body of new data and understanding providing an opportunity to update these models, and directing towards important topics for further research. In this review we highlight recent developments in understanding of the effects of elevated [CO2] on photosynthesis, and in particular on the fate of additionally fixed carbon within the plant with its implications for carbon turnover rates, on the regulation of photosynthesis in response to environmental limitations on in-plant carbon sinks, and on emergent ecosystem responses. We recommend possible avenues for model improvement and identify requirements for better data on core processes relevant to the understanding and modelling of the effect of increasing [CO2] on the global terrestrial carbon sink.
The integration of functional traits into vulnerability assessments is a promising approach to quantitatively capture differences in species sensitivity and adaptive capacity to climate change, allowing the refinement of tree species distribution models. In response to a clear need to identify traits that are responsive to climate change and applicable in a management context, we review the state of knowledge of the main mechanisms, and their associated traits, that underpin the ability of boreal and temperate tree species to persist and/or shift their distribution in a changing climate. We aimed to determine whether current knowledge is sufficiently mature and available to be used effectively in vulnerability assessments. Marshalling recent conceptual advances and assessing data availability, our ultimate objective is to guide modellers and practitioners in finding and selecting sets of traits that can be used to capture differences in species’ ability to persist and migrate. While the physiological mechanisms that determine sensitivity to climate change are relatively well understood (e.g. drought-induced cavitation), many associated traits have not been systematically documented for North American trees and differences in methodology preclude their widespread integration into vulnerability assessments (e.g. xylem recovery capacity). In contrast, traits traditionally associated with the ability to migrate and withstand fire are generally well documented, but new key traits are emerging in the context of climate change that have not been as well characterized (e.g. age of optimum seed production). More generally, lack of knowledge surrounding the extent and patterns in intraspecific trait variation -as well as co-variation and interaction among traits - limit our ability to use this approach to assess tree adaptive capacity. We conclude by outlining research needs and potential strategies for the development of trait-based knowledge applicable in large-scale modelling efforts, sketching out important aspects of trait data organization that should be part of a coordinated effort by the forest science community.
L’utilisation des traits fonctionnels dans l’évaluation de la vulnérabilité est une approche prometteuse pour intégrer de manière quantifiable les différences de sensibilité des espèces et leur capacité d’adaptation aux changements climatiques, améliorant ainsi les modèles de répartition des arbres. Afin d’identifier dans un contexte d’aménagement les traits clés qui sont affectés par les changements climatiques, nous examinons l’état des connaissances sur les principaux mécanismes – ainsi que leurs traits associés — qui régissent la capacité des arbres des forêts boréales et tempérées à persister ou à migrer. Nous avons tenté de déterminer si les connaissances actuelles sont suffisamment matures et disponibles pour être utilisées efficacement dans le contexte des évaluations de vulnérabilité. En synthétisant les avancées conceptuelles les plus récentes et en évaluant la disponibilité des données, nous avons comme objectif principal de guider les modélisateurs et autres intervenants dans la sélection de traits fonctionnels pouvant servir à caractériser les différences dans la capacité des espèces à persister et à migrer face à un climat en changement. Par exemple, malgré que l’on comprenne assez bien les mécanismes physiologiques qui déterminent la sensibilité aux changements climatiques (ex., cavitation induite par la sécheresse), un grand nombre de traits associés à ces mécanismes n’ont pas été systématiquement documentés pour les arbres d’Amérique du Nord. Nous constatons également des différences dans les méthodologies utilisées pour mesurer ces traits (ex., capacité de rétablissement des xylèmes), ce qui nuit à l’intégration des traits dans l’évaluation de la vulnérabilité. Pour leur part, les traits traditionnellement associés à la capacité de migrer et de résister au feu sont généralement bien documentés; cependant, de nouveaux traits clés émergeant dans le contexte des changements climatiques demeurent peu documentés (ex., l’âge de la production optimale de graines). De façon générale, le manque de connaissance entourant la variabilité intraspécifique des traits, ainsi que sur la covariation et l’interaction entre traits, est limitant dans nos évaluations de la capacité adaptative des arbres. Nous concluons en soulignant des besoins précis en matière de recherche et en identifiant certaines avenues possibles pour le développement des connaissances liées aux traits applicables dans des projets de modélisation à grande échelle. Nous soulignons finalement l’importance de certains aspects de la gestion de données de traits qui devraient faire partie de tout effort coordonné de documentation par la communauté scientifique du milieu forestier.
Long-term persistence or Hurst-Kolmogorov behaviour has been identified in many hydroclimatic records. Such time series are intriguing because they are the hallmark of multi-scale dynamical processes that govern the system from which they arise. They are also highly relevant for water resource managers because these systems exhibit persistent, for example, multi-decadal, mean shifts or extremes clustering that must be included into any long-term drought management strategy. During recent years the growing number of palaeoclimatic reconstructions has allowed further investigation of the long-term statistical properties of climate and an understanding of their implications for the observed change. Recently, the consistency of the proxy data for precipitation was strongly doubted, when their persistence property was compared to the corresponding estimates of instrumental records and model results. The latter suggest that droughts or extremely wet periods occur less frequently than depicted in the palaeoclimatic reconstructions. Here, we show how this could be the outcome of a varying scaling law and present some evidence supporting that proxy records can be reliable descriptors of the long-term precipitation variability.
Ecological resilience is the ability of a system to persist in the face of perturbations. Although resilience has been a highly influential concept, its interpretation has remained largely qualitative. Here we describe an emerging family of methods for quantifying resilience on the basis of observations. A first set of methods is based on the phenomenon of critical slowing down, which implies that recovery upon small perturbations becomes slower as a system approaches a tipping point. Such slowing down can be measured experimentally but may also be indirectly inferred from changes in natural fluctuations and spatial patterns. A second group of methods aims to characterize the resilience of alternative states in probabilistic terms based on large numbers of observations as in long time series or satellite images. These generic approaches to measuring resilience complement the system-specific knowledge needed to infer the effects of environmental change on the resilience of complex systems.
The old principle of parsimonious modelling of natural processes has regained its importance in the last few years. The inevitability of uncertainty and risk, and the value of stochastic modelling in dealing with them, are also again appreciated, after a period of growing hopes for radical reduction of uncertainty. Yet in stochastic modelling of natural processes several families of models are used which are often non-parsimonious, unnatural or artificial, theoretically unjustified and, eventually, unnecessary. Here we develop a general methodology for more theoretically justified stochastic processes, which evolve in continuous time and stem from maximum entropy production considerations. The discrete-time properties thereof are theoretically derived from the continuous-time ones and a general simulation methodology in discrete time is built, which explicitly handles the effects of discretization and truncation. Some additional modelling issues are discussed with focus on model identification and fitting, which are often made using inappropriate methods.
The impacts of climate extremes on terrestrial ecosystems are poorly understood but important for predicting carbon cycle feedbacks to climate change. Coupled climate-carbon cycle models typically assume that vegetation recovery from extreme drought is immediate and complete, which conflicts with the understanding of basic plant physiology. We examined the recovery of stem growth in trees after severe drought at 1338 forest sites across the globe, comprising 49,339 site-years, and compared the results with simulated recovery in climate-vegetation models. We found pervasive and substantial "legacy effects" of reduced growth and incomplete recovery for 1 to 4 years after severe drought. Legacy effects were most prevalent in dry ecosystems, among Pinaceae, and among species with low hydraulic safety margins. In contrast, limited or no legacy effects after drought were simulated by current climate-vegetation models. Our results highlight hysteresis in ecosystem-level carbon cycling and delayed recovery from climate extremes.
Copyright © 2015, American Association for the Advancement of Science.
Three common stochastic tools, the climacogram i.e. variance of the time averaged process over averaging time scale, the autocovariance function and the power spectrum are compared to each other to assess each one’s advantages and disadvantages in stochastic modelling and statistical inference. Although in theory, all three are equivalent to each other (transformations one another expressing second order stochastic properties), in practical application their ability to characterize a geophysical process and their utility as statistical estimators may vary. In the analysis both Markovian and non Markovian stochastic processes, which have exponential and power-type autocovariances, respectively, are used. It is shown that, due to high bias in autocovariance estimation, as well as effects of process discretization and finite sample size, the power spectrum is also prone to bias and discretization errors as well as high uncertainty, which may misrepresent the process behaviour (e.g. Hurst phenomenon) if not taken into account. Moreover, it is shown that the classical climacogram estimator has small error as well as an expected value always positive, well-behaved and close to its mode (most probable value), all of which are important advantages in stochastic model building. In contrast, the power spectrum and the autocovariance do not have some of these properties. Therefore, when building a stochastic model, it seems beneficial to start from the climacogram, rather than the power spectrum or the autocovariance. The results are illustrated by a real world application based on the analysis of a long time series of high-frequency turbulent flow measurements.
The coarse-grained spatial representation of many terrestrial ecosystem models hampers the importance of local scale heterogeneities. To address this issue, we combine a range of observations (forest inventories, eddy flux tower data, remote sensing products) and modeling approaches with contrasting degrees of abstraction. The following models are selected: (i) LPJ, a well-established, area-based, Dynamic Global Vegetation Model (DGVM); (ii) LPJ-GUESS, a hybrid, individual-based approach that additionallyconsiders plant population dynamics in greater detail; and (iii) D-LPJ, a spatially explicit version of LPJ, operating at a fine spatial resolution (100 m × 100 m), which uses an enhanced hydrological representation accounting for lateral connectivity of surface and subsurface water fluxes. By comparing model simulations with a multivariate data-set available at the catchment scale, we argue that: (i) local environmental and topographic attributes that are often ignored or crudely represented in DGVM applications exert a strong control on terrestrial ecosystem response; (ii) the assumption of steady-state vegetation and soil carbon pools at the beginning of simulation studies (e.g., under “current conditions”), as embedded in many DGVM applications, is in contradiction with the current state of many forests that are often out of equilibrium; (iii) model evaluation against vegetation carbon fluxes does not imply an accurate simulation of vegetation carbon stocks. Having gained insights about the magnitude of aggregation-induced biases due to smoothing of spatial variability at the catchment scale, we discuss the implications of our findings with respect to the global scale modeling studies of carbon cycle and we illustrate alternative ways forward.
Extreme droughts, heat waves, frosts, precipitation, wind storms and other climate extremes may impact the structure, composition, and functioning of terrestrial ecosystems, and thus carbon cycling and its feedbacks to the climate system. Yet, the interconnected avenues through which climate extremes drive ecological and physiological processes and alter the carbon balance are poorly understood. Here we review literature on carbon-cycle relevant responses of ecosystems to extreme climatic events. Given that impacts of climate extremes are considered disturbances, we assume the respective general disturbance-induced mechanisms and processes to also operate in an extreme context. The paucity of well-defined studies currently renders a quantitative meta-analysis impossible, but permits us to develop a deductive framework for identifying the main mechanisms (and coupling thereof) through which climate extremes may act on the carbon cycle. We find that ecosystem responses can exceed the duration of the climate impacts via lagged effects on the carbon cycle. The expected regional impacts of future climate extremes will depend on changes in the probability and severity of their occurrence, on the compound effects and timing of different climate extremes, and on the vulnerability of each land-cover type modulated by management. Though processes and sensitivities differ among biomes, based on expert opinion we expect forests to exhibit the largest net effect of extremes due to their large carbon pools and fluxes, potentially large indirect and lagged impacts, and long recovery time to re-gain previous stocks. At the global scale, we presume that droughts have the strongest and most widespread effects on terrestrial carbon cycling. Comparing impacts of climate extremes identified via remote sensing vs. ground-based observational case studies reveals that many regions in the (sub-)tropics are understudied. Hence, regional investigations are needed to allow a global upscaling of the impacts of climate extremes on global carbon-climate feedbacks. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.
The land and ocean absorb on average just over half of the anthropogenic emissions of carbon dioxide (CO2) every year. These CO2 "sinks" are modulated by climate change and variability. Here we use a suite of nine dynamic global vegetation models (DGVMs) and four ocean biogeochemical general circulation models (OBGCMs) to estimate trends driven by global and regional climate and atmospheric CO2 in land and oceanic CO2 exchanges with the atmosphere over the period 1990-2009, to attribute these trends to underlying processes in the models, and to quantify the uncertainty and level of inter-model agreement. The models were forced with reconstructed climate fields and observed global atmospheric CO2; land use and land cover changes are not included for the DGVMs. Over the period 1990-2009, the DGVMs simulate a mean global land carbon sink of -2.4 +/- 0.7 PgC yr(-1) with a small significant trend of -0.06 +/- 0.03 PgC yr(-2) (increasing sink). Over the more limited period 1990-2004, the ocean models simulate a mean ocean sink of -2.2 +/- 0.2 PgC yr(-1) with a trend in the net C uptake that is indistinguishable from zero (-0.01 +/- 0.02 PgC yr(-2)). The two ocean models that extended the simulations until 2009 suggest a slightly stronger, but still small, trend of 0.02 +/- 0.01 PgC yr(-2). Trends from land and ocean models compare favourably to the land greenness trends from remote sensing, atmospheric inversion results, and the residual land sink required to close the global carbon budget. Trends in the land sink are driven by increasing net primary production (NPP), whose statistically significant trend of 0.22 +/- 0.08 PgC yr(-2) exceeds a significant trend in heterotrophic respiration of 0.16 +/- 0.05 PgC yr(-2) - primarily as a consequence of widespread CO2 fertilisation of plant production. Most of the land-based trend in simulated net carbon uptake originates from natural ecosystems in the tropics (0.04 +/- 0.01 PgC yr(-2)), with almost no trend over the northern land region, where recent warming and reduced rainfall offsets the positive impact of elevated atmospheric CO2 and changes in growing season length on carbon storage. The small uptake trend in the ocean models emerges because climate variability and change, and in particular increasing sea surface temperatures, tend to counteract the trend in ocean uptake driven by the increase in atmospheric CO2. Large uncertainty remains in the magnitude and sign of modelled carbon trends in several regions, as well as regarding the influence of land use and land cover changes on regional trends.
The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18–28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time-series data and facilitating new hypothesis generation and additional research.
Significance
Understanding and accurately predicting how global terrestrial primary production responds to rising atmospheric CO 2 concentrations is a prerequisite for reliably assessing the long-term climate impact of anthropogenic fossil CO 2 emissions. Here we demonstrate that current carbon cycle models underestimate the long-term responsiveness of global terrestrial productivity to CO 2 fertilization. This underestimation of CO 2 fertilization is caused by an inherent model structural deficiency related to lack of explicit representation of CO 2 diffusion inside leaves, which results in an overestimation of CO 2 available at the carboxylation site. The magnitude of CO 2 fertilization underestimation matches the long-term positive growth bias in the historical atmospheric CO 2 predicted by Earth system models. Our study will lead to improved understanding and modeling of carbon–climate feedbacks.
Forests strongly affect climate through the exchange of large amounts of atmospheric CO2. The main drivers of spatial variability in net ecosystem production (NEP) on a global scale are, however, poorly known. As increasing nutrient availability increases the production of biomass per unit of photosynthesis and reduces heterotrophic respiration in forests, we expected nutrients to determine carbon sequestration in forests. Our synthesis study of 92 forests in different climate zones revealed that nutrient availability indeed plays a crucial role in determining NEP and ecosystem carbon-use efficiency (CUEe; that is, the ratio of NEP to gross primary production (GPP)). Forests with high GPP exhibited high NEP only in nutrient-rich forests (CUEe = 33 ± 4%; mean ± s.e.m.). In nutrient-poor forests, a much larger proportion of GPP was released through ecosystem respiration, resulting in lower CUEe (6 ± 4%). Our finding that nutrient availability exerts a stronger control on NEP than on carbon input (GPP) conflicts with assumptions of nearly all global coupled carbon cycle–climate models, which assume that carbon inputs through photosynthesis drive biomass production and carbon sequestration. An improved global understanding of nutrient availability would therefore greatly improve carbon cycle modelling and should become a critical focus for future research.
Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510-758 ppm of CO2), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.
Various aspects of the biochemistry of photosynthetic carbon assimilation in C3 plants are integrated into a form compatible with studies of gas exchange in leaves. These aspects include the kinetic properties of ribulose bisphosphate carboxylase-oxygenase; the requirements of the photosynthetic carbon reduction and photorespiratory carbon oxidation cycles for reduced pyridine nucleotides; the dependence of electron transport on photon flux and the presence of a temperature dependent upper limit to electron transport. The measurements of gas exchange with which the model outputs may be compared include those of the temperature and partial pressure of CO2(p(CO2)) dependencies of quantum yield, the variation of compensation point with temperature and partial pressure of O2(p(O2)), the dependence of net CO2 assimilation rate on p(CO2) and irradiance, and the influence of p(CO2) and irradiance on the temperature dependence of assimilation rate.
External forcing and internal dynamics result in climate system
variability ranging from sub-daily weather to multi-centennial trends
and beyond. State-of-the-art palaeoclimatic methods routinely use
hydroclimatic proxies to reconstruct temperature (for example, refs , ),
possibly blurring differences in the variability continuum of
temperature and precipitation before the instrumental period. Here, we
assess the spectral characteristics of temperature and precipitation
fluctuations in observations, model simulations and proxy records across
the globe. We find that whereas an ensemble of different general
circulation models represents patterns captured in instrumental
measurements, such as land-ocean contrasts and enhanced low-frequency
tropical variability, the tree-ring-dominated proxy collection does not.
The observed dominance of inter-annual precipitation fluctuations is not
reflected in the annually resolved hydroclimatic proxy records.
Likewise, temperature-sensitive proxies overestimate, on average, the
ratio of low- to high-frequency variability. These spectral biases in
the proxy records seem to propagate into multi-proxy climate
reconstructions for which we observe an overestimation of low-frequency
signals. Thus, a proper representation of the high- to low-frequency
spectrum in proxy records is needed to reduce uncertainties in climate
reconstruction efforts.
We assess the magnitude of decadal to multidecadal (D2M) variability in
Climate Model Intercomparison Project 5 (CMIP5) simulations that will be
used to understand, and plan for, climate change as part of the
Intergovernmental Panel on Climate Change's 5th Assessment Report. Model
performance on D2M timescales is evaluated using metrics designed to
characterize the relative and absolute magnitude of variability at these
frequencies. In observational data, we find that between 10% and 35% of
the total variance occurs on D2M timescales. Regions characterized by
the high end of this range include Africa, Australia, western North
America, and the Amazon region of South America. In these areas D2M
fluctuations are especially prominent and linked to prolonged drought.
D2M fluctuations account for considerably less of the total variance
(between 5% and 15%) in the CMIP5 archive of historical
(1850-2005) simulations. The discrepancy between observation and
model based estimates of D2M prominence reflects two features of the
CMIP5 archive. First, interannual components of variability are
generally too energetic. Second, decadal components are too weak in
several key regions. Our findings imply that projections of the future
lack sufficient decadal variability, presenting a limited view of
prolonged drought and pluvial risk.
This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). © 2013 Royal Meteorological Society
The terrestrial biosphere is a key component of the global carbon cycle and its carbon balance is strongly influenced by climate. Continuing environmental changes are thought to increase global terrestrial carbon uptake. But evidence is mounting that climate extremes such as droughts or storms can lead to a decrease in regional ecosystem carbon stocks and therefore have the potential to negate an expected increase in terrestrial carbon uptake. Here we explore the mechanisms and impacts of climate extremes on the terrestrial carbon cycle, and propose a pathway to improve our understanding of present and future impacts of climate extremes on the terrestrial carbon budget.
Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to monitoring global vegetation dynamics and for modeling exchanges of energy, mass and momentum between the land surface and planetary boundary layer. LAI and FPAR are also state variables in hydrological, ecological, biogeochemical and crop-yield models. The
generation, evaluation and an example case study documenting the utility of 30-year long data sets of LAI and FPAR are described in this article. A neural network algorithm was first developed between the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI and
FPAR products for the overlapping period 2000–2009. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with
previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.
Despite decades of work on climate change biology, the scientific community remains uncertain about where and when most species distributions will respond to altered climates. A major barrier is the spatial mismatch between the size of organisms and the scale at which climate data are collected and modeled. Using a meta-analysis of published literature, we show that grid lengths in species distribution models are, on average, ~10,000-fold larger than the animals they study, and ~1,000-fold larger than the plants they study. And the gap is even worse than these ratios indicate, since most work has focused on organisms that are significantly biased toward large size. This mismatch is problematic because organisms do not experience climate on coarse scales. Rather, they live in microclimates, which can be highly heterogeneous and strongly divergent from surrounding macroclimates. Bridging the spatial gap should be a high priority for research and will require gathering climate data at finer scales, developing better methods for downscaling environmental data to microclimates, and improving our statistical understanding of variation at finer scales. Interdisciplinary collaborations (including ecologists, engineers, climatologists, meteorologists, statisticians, and geographers) will be key to bridging the gap, and ultimately to providing scientifically grounded data and recommendations to conservation biologists and policy makers. This article is protected by copyright. All rights reserved.
We review observational, experimental, and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied, although potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational, and/or modeling studies have the potential to overcome important caveats of the respective individual approaches.
The NCEP-DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the "50-year" (1948-present) NCEP-NCAR Reanalysis Project. NCEP-DOE AMIP-II reanalysis covers the "20-year" satellite period of 1979 to the present and uses an updated forecast model, updated data assimilation system, improved diagnostic outputs, and fixes for the known processing problems of the NCEP-NCAR reanalysis. Only minor differences are found in the primary analysis variables such as free atmospheric geopotential height and winds in the Northern Hemisphere extratropics, while significant improvements upon NCEP-NCAR reanalysis are made in land surface parameters and land-ocean fluxes. This analysis can be used as a supplement to the NCEP-NCAR reanalysis especially where the original analysis has problems. The differences between the two analyses also provide a measure of uncertainty in current analyses.
This contribution illustrates results from a large-scale application of the Joint Research Centre Two-stream Inversion Package (JRC-TIP), using MODIS broadband visible and near-infrared white sky surface albedos as inputs. The discussion focuses on products (based on the mean and one-sigma values of the probability distribution functions (PDFs)) obtained during the summer and winter. This paper discusses the retrieved model parameters including the effective leaf area index (LAI), the background brightness, and the scattering efficiency of the vegetation elements. The similarity between the derived LAI seasonal maps and earlier distributions of this variable comforts us in the quality of the albedo products as well as in the ability of the JRC-TIP to interpret the latter meaningfully. The opportunity to generate global maps of new products, such as the background albedo, underscores the advantages of using state of the art algorithmic approaches capable of fully exploiting accurate satellite remote sensing data sets. The detailed analyses of the retrieval uncertainties highlight the central role and contribution of the LAI, the main process parameter to interpret radiation transfer observations over vegetated surfaces. The estimation of the radiation fluxes that are absorbed, transmitted, and scattered by the vegetation layer and its background is achieved on the basis of the retrieved PDFs of the model parameters. Results from this latter step are discussed in a companion paper.
Carbohydrates provide the building blocks for plant structures as well as versatile resources for
metabolic processes. The nonstructural carbohydrates (NSC), mainly sugars and starch, fulfil
distinct functional roles, including transport, energy metabolism and osmoregulation, and
provide substrates for the synthesis of defence compounds or exchange with symbionts involved
in nutrient acquisition or defence. At the whole-plant level, NSC storage buffers the asynchrony
of supply and demand on diel, seasonal or decadal temporal scales and across plant organs.
Despite its central role in plant function and in stand-level carbon cycling, our understanding of
storage dynamics, its controls and response to environmental stresses is very limited, even after a
century of research. This reflects the fact that often storage is defined by what we can measure,
that is, NSC concentrations, and the interpretation of these as a proxy for a single function,
storage, rather than the outcome of a range of NSC source and sink functions. Newisotopic tools
allow direct quantification of timescales involved in NSC dynamics, and show that NSC-C fixed
years to decades previously is used to support tree functions. Here we review recent advances,
with emphasis on the context of the interactions between NSC, drought and tree mortality.
The sensitivity of soil organic carbon (SOC) to changing environmental conditions represents a critical uncertainty in coupled carbon cycle-climate models. Much of this uncertainty arises from our limited understanding of the extent to which root-microbe interactions induce SOC losses (through accelerated decomposition or priming) or indirectly promote SOC gains (via protection through interactions with mineral particles). We developed a new SOC model to examine priming and protection responses to rising atmospheric CO 2. The model captured disparate SOC responses at two temperate free-air CO 2 enrichment (FACE) experiments. We show that stabilization of new carbon in protected SOC pools may equal or exceed microbial priming of old SOC in ecosystems with readily decomposable litter and high clay content (for example, Oak Ridge). In contrast, carbon losses induced through priming dominate the net SOC response in ecosystems with more resistant litters and lower clay content (for example, Duke). The SOC model was fully integrated into a global terrestrial carbon cycle model to run global simulations of elevated CO 2 effects. Although protected carbon provides an important constraint on priming effects, priming nonetheless reduced SOC storage in the majority of terrestrial areas, partially counterbalancing SOC gains from enhanced ecosystem productivity.
Vegetation and the water cycles are inherently coupled across a wide range of spatial and temporal scales. Water availability interacts with plant ecophysiology and controls vegetation functioning. Concurrently, vegetation has direct and indirect effects on energy, water, carbon, and nutrient cycles. To better understand and model plant–water interactions, highly interdisciplinary approaches are required. We present an overview of the main processes and relevant interactions between water and plants across a range of spatial scales, from the cell level of leaves, where stomatal controls occur, to drought stress at the level of a single tree, to the integrating scales of a watershed, region, and the globe. A review of process representations in models at different scales is presented. More specifically, three main model families are identified: (1) models of plant hydraulics that mechanistically simulate stomatal controls and/or water transport at the tree level; (2) ecohydrological models that simulate plot‐ to catchment‐scale water, energy, and carbon fluxes; and (3) terrestrial biosphere models that simulate carbon, water, and nutrient dynamics at the regional and global scales and address feedback between Earth's vegetation and the climate system. We identify special features and similarities across the model families. Examples of where plant–water interactions are especially important and have led to key scientific findings are also highlighted. Finally, we discuss the various data sources that are currently available to force and validate existing models, and we present perspectives on the evolution of the field. WIREs Water 2016, 3:327–368. doi: 10.1002/wat2.1125
This article is categorized under: Water and Life > Nature of Freshwater Ecosystems
Science of Water > Hydrological Processes
Earth System Models (ESMs) typically use static responses to temperature to calculate photosynthesis and respiration, but experimental evidence suggests that many plants acclimate to prevailing temperatures. We incorporated representations of photosynthetic and leaf respiratory temperature acclimation into the Community Land Model (CLM), the terrestrial component of the Community Earth System Model. These processes increased terrestrial carbon pools by 20 Pg C (22%) at the end of the twenty-first century under a business-as-usual (RCP8.5) climate scenario. Including the less certain estimates of stem and root respiration acclimation increased terrestrial carbon pools by an additional 17 Pg C (~40% overall increase). High latitudes gained the most carbon with acclimation, and tropical carbon pools increased least. However, results from both of these regions remain uncertain; few relevant data exist for tropical and boreal plants or for extreme temperatures. Constraining these uncertainties will produce more realistic estimates of land-carbon feedbacks throughout the twenty-first century.
This paper explores the effects from averaging weather station data onto a grid on the first four statistical moments of daily minimum and maximum surface air temperature (SAT) anomalies over the entire globe. The Global Historical Climatology Network-Daily (GHCND) and the Met Office Hadley Centre GHCND (HadGHCND) datasets from 1950 to 2010 are examined. The GHCND station data exhibit large spatial patterns for each moment and statistically significant moment trends from 1950 to 2010, indicating that SAT probability density functions are non-Gaussian and have undergone characteristic changes in shape due to decadal variability and/or climate change. Comparisons with station data show that gridded averages always underestimate observed variability, particularly in the extremes, and have altered moment trends that are in some cases opposite in sign over large geographic areas. A statistical closure approach based on the quasi-normal approximation is taken to explore SAT's higher-order moments and point correlation structure. This study focuses specifically on relating variability calculated from station data to that from gridded data through the moment equations for weighted sums of random variables. The higher-order and nonlinear spatial correlations up to the fourth order demonstrate that higher-order moments at grid scale can be determined approximately by functions of station pair correlations that tend to follow the usual Kolmogorov scaling relation. These results can aid in the development of constraints to reduce uncertainties in climate models and have implications for studies of atmospheric variability, extremes, and climate change using gridded observations.
Although the analysis of flux data has increased our understanding of the interannual variability of carbon inputs into forest ecosystems, we still know little about the determinants of wood growth. Here, we aimed to identify which drivers control the interannual variability of wood growth in a mesic temperate deciduous forest.
We analysed a 9-yr time series of carbon fluxes and aboveground wood growth (AWG), reconstructed at a weekly time-scale through the combination of dendrometer and wood density data.
Carbon inputs and AWG anomalies appeared to be uncorrelated from the seasonal to interannual scales. More than 90% of the interannual variability of AWG was explained by a combination of the growth intensity during a first ‘critical period’ of the wood growing season, occurring close to the seasonal maximum, and the timing of the first summer growth halt. Both atmospheric and soil water stress exerted a strong control on the interannual variability of AWG at the study site, despite its mesic conditions, whilst not affecting carbon inputs.
Carbon sink activity, not carbon inputs, determined the interannual variations in wood growth at the study site. Our results provide a functional understanding of the dependence of radial growth on precipitation observed in dendrological studies.
For plants to grow they need resources and appropriate conditions that these resources are converted into biomass. While acknowledging the importance of co-drivers, the classical view is still that carbon, that is, photosynthetic CO2 uptake, ranks above any other drivers of plant growth. Hence, theory and modelling of growth traditionally is carbon centric. Here, I suggest that this view is not reflecting reality, but emerged from the availability of methods and process understanding at leaf level. In most cases, poorly understood processes of tissue formation and cell growth are governing carbon demand, and thus, CO2 uptake. Carbon can only be converted into biomass to the extent chemical elements other than carbon, temperature or cell turgor permit.
Copyright © 2015. Published by Elsevier Ltd.
Terrestrial ecosystems play a crucial role in the global carbon cycle and in the regulation of climate change. Anthropogenic CO2 emissions increased from 2.4 Pg C in 1960 to 8.7 Pg C per year in 2008 while terrestrial ecosystems absorbed roughly 30% during that period (Le Quere et al., 2009). If that absorption capacity were to change, in either direction, it would have a large impact on atmospheric CO2 concentrations, resulting in a strong feedback effect on climate (Denman et al., 2007, Friedlingstein et al., 2006). It is, therefore, imperative to accurately predict dynamics of the terrestrial carbon cycle in order to accurately predict future changes in the Earth's climate. Here, we examine the current state of the art of predictive modeling of the global carbon cycle, and outline how an understanding of the intrinsic predictability of its components can be used to guide future experimental research and develop the next generation of carbon cycle models.This article is protected by copyright. All rights reserved.
[1] Dynamic vegetation models have been widely used for analyzing ecosystem dynamics and their interactions with climate. Their performance has been tested extensively against observations and by model intercomparison studies. In the present analysis, Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), a state-of-the-art ecosystem model, was evaluated by performing a global sensitivity analysis. The study aims at examining potential model limitations, particularly with regard to long-term applications. A detailed sensitivity analysis based on variance decomposition is presented to investigate structural model assumptions and to highlight processes and parameters that cause the highest variability in the output. First- and total-order sensitivity indices were calculated for selected parameters using Sobol's methodology. In order to elucidate the role of climate on model sensitivity, different climate forcings were used based on observations from Switzerland. The results clearly indicate a very high sensitivity of LPJ-GUESS to photosynthetic parameters. Intrinsic quantum efficiency alone is able to explain about 60% of the variability in vegetation carbon fluxes and pools for a wide range of climate forcings. Processes related to light harvesting were also found to be important together with parameters affecting forest structure (growth, establishment, and mortality). The model shows minor sensitivity to hydrological and soil texture parameters, questioning its skills in representing spatial vegetation heterogeneity at regional or watershed scales. In the light of these results, we discuss the deficiencies of LPJ-GUESS and possibly that of other, structurally similar, dynamic vegetation models and we highlight potential directions for further model improvements.
Attempts to combine biometric and eddy‐covariance ( EC ) quantifications of carbon allocation to different storage pools in forests have been inconsistent and variably successful in the past.
We assessed above‐ground biomass changes at five long‐term EC forest stations based on tree‐ring width and wood density measurements, together with multiple allometric models. Measurements were validated with site‐specific biomass estimates and compared with the sum of monthly CO 2 fluxes between 1997 and 2009.
Biometric measurements and seasonal net ecosystem productivity ( NEP ) proved largely compatible and suggested that carbon sequestered between January and July is mainly used for volume increase, whereas that taken up between August and September supports a combination of cell wall thickening and storage. The inter‐annual variability in above‐ground woody carbon uptake was significantly linked with wood production at the sites, ranging between 110 and 370 g C m ⁻² yr ⁻¹ , thereby accounting for 10–25% of gross primary productivity ( GPP ), 15–32% of terrestrial ecosystem respiration ( TER ) and 25–80% of NEP .
The observed seasonal partitioning of carbon used to support different wood formation processes refines our knowledge on the dynamics and magnitude of carbon allocation in forests across the major European climatic zones. It may thus contribute, for example, to improved vegetation model parameterization and provides an enhanced framework to link tree‐ring parameters with EC measurements.
Studies using idealized ensemble data assimilation systems have shown that flow-dependent background-error covariances are most beneficial when the observing network is sparse. The computational cost of recently proposed ensemble data assimilation algorithms is directly proportional to the number of observations being assimilated. Therefore, ensemble-based data assimilation should both be more computationally feasible and provide the greatest benefit over current operational schemes in situations when observations are sparse. Re- analysis before the radiosonde era (pre-1931) is just such a situation. The feasibility of reanalysis before radiosondes using an ensemble square root filter (EnSRF) is examined. Real surface pressure observations for 2001 are used, subsampled to resemble the density of observations we estimate to be available for 1915. Analysis errors are defined relative to a three-dimensional variational data assimilation (3DVAR) analysis using several orders of magnitude more observations, both at the surface and aloft. We find that the EnSRF is computationally tractable and considerably more accurate than other candidate analysis schemes that use static background-error covariance estimates. We conclude that a Northern Hemisphere reanalysis of the middle and lower troposphere during the first half of the twentieth century is feasible using only surface pressure observations. Expected Northern Hemisphere analysis errors at 500 hPa for the 1915 observation network are similar to current 2.5-day forecast errors.
Climate variability and global change studies are increasingly focused on understanding and predicting regional changes of daily weather statistics. Assessing the evidence for such variations over the last 100 yr requires a daily tropospheric circulation dataset. The only dataset available for the early twentieth century consists of error-ridden hand-drawn analyses of the mean sea level pressure field over the Northern Hemisphere. Modern data assimilation systems have the potential to improve upon these maps, but prior to 1948, few digitized upper-air sounding observations are available for such a "reanalysis." We investigate the possibility that the additional number of newly recovered surface pressure observations is sufficient to generate useful weather maps of the lower-tropospheric extratropical circulation back to 1890 over the Northern Hemisphere, and back to 1930 over the Southern Hemisphere. Surprisingly, we find that by using an advanced data assimilation system based on an ensemble Kalman filter, it would be feasible to produce high-quality maps of even the upper troposphere using only surface pressure observations. For the beginning of the twentieth century, the errors of such upper-air circulation maps over the Northern Hemisphere in winter would be comparable to the 2-3-day errors of modern weather forecasts.
A growing number of studies investigated the feedback between the carbon cycle and the climate system. Modeling studies evolved from analysis based on simple land or ocean carbon cycle models to comprehensive Earth System Models accounting for state-of-the-art climate models coupled to land and ocean biogeochemical models. So far, there is a general agreement that climate change negatively affects the oceanic uptake of carbon. On land there was a similar agreement until recently where new studies showed that warming could reduce nitrogen limitation to growth, reducing the amplitude, or even changing the sign of, the land feedback. In parallel, alternative approaches used the observational record of atmospheric CO2 and temperature, on time scales ranging from interannual to millennial, to estimate the climate–carbon cycle feedback. These studies confirmed that at the global scale, warming leads to a release of CO2 from the land/ocean system to the atmosphere. Whether these observations can strongly constrain the magnitude of the feedback under future climate change is still under investigation.
*Second-generation Dynamic Global Vegetation Models (DGVMs) have recently been developed that explicitly represent the ecological dynamics of disturbance, vertical competition for light, and succession. Here, we introduce a modified second-generation DGVM and examine how the representation of demographic processes operating at two-dimensional spatial scales not represented by these models can influence predicted community structure, and responses of ecosystems to climate change. *The key demographic processes we investigated were seed advection, seed mixing, sapling survival, competitive exclusion and plant mortality. We varied these parameters in the context of a simulated Amazon rainforest ecosystem containing seven plant functional types (PFTs) that varied along a trade-off surface between growth and the risk of starvation induced mortality. *Varying the five unconstrained parameters generated community structures ranging from monocultures to equal co-dominance of the seven PFTs. When exposed to a climate change scenario, the competing impacts of CO(2) fertilization and increasing plant mortality caused ecosystem biomass to diverge substantially between simulations, with mid-21st century biomass predictions ranging from 1.5 to 27.0 kg C m(-2). *Filtering the results using contemporary observation ranges of biomass, leaf area index (LAI), gross primary productivity (GPP) and net primary productivity (NPP) did not substantially constrain the potential outcomes. We conclude that demographic processes represent a large source of uncertainty in DGVM predictions.
Dynamic global vegetation models ( DGVMs ) are powerful tools to project past, current and future vegetation patterns and associated biogeochemical cycles. However, most models are limited by how they define vegetation and by their simplistic representation of competition.
We discuss how concepts from community assembly theory and coexistence theory can help to improve vegetation models. We further present a trait‐ and individual‐based vegetation model ( aDGVM 2) that allows individual plants to adopt a unique combination of trait values. These traits define how individual plants grow and compete. A genetic optimization algorithm is used to simulate trait inheritance and reproductive isolation between individuals. These model properties allow the assembly of plant communities that are adapted to a site's biotic and abiotic conditions.
The a DGVM 2 simulates how environmental conditions influence the trait spectra of plant communities; that fire selects for traits that enhance fire protection and reduces trait diversity; and the emergence of life‐history strategies that are suggestive of colonization–competition trade‐offs.
The a DGVM 2 deals with functional diversity and competition fundamentally differently from current DGVMs. This approach may yield novel insights as to how vegetation may respond to climate change and we believe it could foster collaborations between functional plant biologists and vegetation modellers.
