Drought, Heat, and the Carbon Cycle: a Review

Purpose of the Review Weather and climate extremes substantially affect global- and regional-scale carbon (C) cycling, and thus spatially or temporally extended climatic extreme events jeopardize terrestrial ecosystem carbon sequestration. We illustrate the relevance of drought and/or heat events (“DHE”) for the carbon cycle and highlight underlying concepts and complex impact mechanisms. We review recent results, discuss current research needs and emerging research topics. Recent Findings Our review covers topics critical to understanding, attributing and predicting the effects of DHE on the terrestrial carbon cycle: (1) ecophysiological impact mechanisms and mediating factors, (2) the role of timing, duration and dynamical effects through which DHE impacts on regional-scale carbon cycling are either attenuated or enhanced, and (3) large-scale atmospheric conditions under which DHE are likely to unfold and to affect the terrestrial carbon cycle. Recent research thus shows the need to view these events in a broader spatial and temporal perspective that extends assessments beyond local and concurrent C cycle impacts of DHE. Summary Novel data streams, model (ensemble) simulations, and analyses allow to better understand carbon cycle impacts not only in response to their proximate drivers (drought, heat, etc.) but also attributing them to underlying changes in drivers and large-scale atmospheric conditions. These attribution-type analyses increasingly address and disentangle various sequences or dynamical interactions of events and their impacts, including compensating or amplifying effects on terrestrial carbon cycling.publishedVersio

Have precipitation extremes and annual totals been increasing in the world’s dry regions over the last 60 years?

Daily precipitation extremes and annual totals have increased in large parts of the global land area over the past decades. These observations are consistent with theoretical considerations of a warming climate. However, until recently these trends have not been shown to consistently affect dry regions over land. A recent study, published by Donat et al. (2016), now identified significant increases in annual-maximum daily extreme precipitation (Rx1d) and annual precipitation totals (PRCPTOT) in dry regions. Here, we revisit the applied methods and explore the sensitivity of changes in precipitation extremes and annual totals to alternative choices of defining a dry region (i.e. in terms of aridity as opposed to precipitation characteristics alone). We find that (a) statistical artifacts introduced by data pre-processing based on a time-invariant reference period lead to an overestimation of the reported trends by up to 40 %, and that (b) the reported trends of globally aggregated extremes and annual totals are highly sensitive to the definition of a "dry region of the globe". For example, using the same observational dataset, accounting for the statistical artifacts, and based on different aridity-based dryness definitions, we find a reduction in the positive trend of Rx1d from the originally reported +1.6 % decade−1 to +0.2 to +0.9 % decade−1 (period changes for 1981–2010 averages relative to 1951–1980 are reduced to −1.32 to +0.97 % as opposed to +4.85 % in the original study). If we include additional but less homogenized data to cover larger regions, the global trend increases slightly (Rx1d: +0.4 to +1.1 % decade−1), and in this case we can indeed confirm (partly) significant increases in Rx1d. However, these globally aggregated estimates remain uncertain as considerable gaps in long-term observations in the Earth's arid and semi-arid regions remain. In summary, adequate data pre-processing and accounting for uncertainties regarding the definition of dryness are crucial to the quantification of spatially aggregated trends in precipitation extremes in the world's dry regions. In view of the high relevance of the question to many potentially affected stakeholders, we call for a well-reflected choice of specific data processing methods and the inclusion of alternative dryness definitions to guarantee that communicated results related to climate change be robust.Have precipitation extremes and annual totals been increasing in the world’s dry regions over the last 60 years?publishedVersio

Detecting impacts of extreme events with ecological in situ monitoring networks

Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with  ≈  100 randomly placed sites in Europe yield a  ≥  90 % chance of detecting the eight largest (typically very large) extreme events; but only a  ≥  50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere

Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave

Combined droughts and heatwaves are among those compound extreme events that induce severe impacts on the terrestrial biosphere and human health. A record breaking hot and dry compound event hit western Russia in summer 2010 (Russian heatwave, RHW). Events of this kind are relevant from a hydrometeorological perspective, but are also interesting from a biospheric point of view because of their impacts on ecosystems, e.g., reductions in the terrestrial carbon storage. Integrating both perspectives might facilitate our knowledge about the RHW. We revisit the RHW from both a biospheric and a hydrometeorological perspective. We apply a recently developed multivariate anomaly detection approach to a set of hydrometeorological variables, and then to multiple biospheric variables relevant to describe the RHW. One main finding is that the extreme event identified in the hydrometeorological variables leads to multidirectional responses in biospheric variables, e.g., positive and negative anomalies in gross primary production (GPP). In particular, the region of reduced summer ecosystem production does not match the area identified as extreme in the hydrometeorological variables. The reason is that forest-dominated ecosystems in the higher latitudes respond with unusually high productivity to the RHW. Furthermore, the RHW was preceded by an anomalously warm spring, which leads annually integrated to a partial compensation of 54% (36% in the preceding spring, 18% in summer) of the reduced GPP in southern agriculturally dominated ecosystems. Our results show that an ecosystem-specific and multivariate perspective on extreme events can reveal multiple facets of extreme events by simultaneously integrating several data streams irrespective of impact direction and the variables' domain. Our study exemplifies the need for robust multivariate analytic approaches to detect extreme events in both hydrometeorological conditions and associated biosphere responses to fully characterize the effects of extremes, including possible compensatory effects in space and time

Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

Accurate model representation of land-atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented with a steadily evolving body of mechanistic theory provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates "readable" models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (Random Forests, Support Vector Machines, Artificial Neural Networks, and Kernel Ridge Regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-east England. We find that the GEP retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components; the identification of a "general" terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data rich era, complementing more traditional modelling approaches

Detecting impacts of extreme events with ecological in situ monitoring networks

Extreme hydrometeorological conditions typically impact ecophysiological processes of terrestrial vegetation. Satellite based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in-situ observations. The question is if the density of existing ecological in-situ networks is sufficient for analyzing the impact of extreme events, or what are expected event detection rates of ecological in-situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small network, but also reveal a linear decay of detection probabilities towards smaller extreme events in log-log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the largest 8 (typically very large) extreme events; but only a ≥ 50 % chance of capturing the largest 39 events. These finding are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation issues and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in-situ networks can capture extreme events of a given size. Consistent with our theoretic considerations, we find that today's systematic network designs (i.e. NEON) reliably detects the largest extremes. But the extreme event detection rates are not higher than they would be achieved by randomly designed networks. Spatiotemporal expansions of ecological in-situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor their impacts in the terrestrial biosphere

Vegetation modulates the impact of climate extremes on gross primary production

Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. Factors such as the duration, timing, and intensity of extreme events influence the magnitude of impacts on ecosystem processes such as gross primary production (GPP), i.e., the ecosystem uptake of CO2. Preceding soil moisture depletion may exacerbate these impacts. However, some vegetation types may be more resilient to climate extremes than others. This effect is insufficiently understood at the global scale and is the focus of this study. Using a global upscaled product of GPP that scales up in situ land CO2 flux observations with global satellite remote sensing, we study the impact of climate extremes at the global scale. We find that GPP in grasslands and agricultural areas is generally reduced during heat and drought events. However, we also find that forests, if considered globally, appear in general to not be particularly sensitive to droughts and heat events that occurred during the analyzed period or even show increased GPP values during these events. On the one hand, normal-to-increased GPP values are in many cases plausible, e.g., when conditions prior to the event have been particularly positive. On the other hand, however, normal-to-increased GPP values in forests may also reflect a lack of sensitivity in current remote-sensing-derived GPP products to the effects of droughts and heatwaves. The overall picture calls for a differentiated consideration of different land cover types in the assessments of risks of climate extremes for ecosystem functioning.ISSN:1726-4170ISSN:1726-417

Interannual global carbon cycle variations linked to atmospheric circulation variability

One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of global C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatiotemporal sea level pressure (SLP) fields. C-cycle variability is diagnosed from the global detrended atmospheric CO2 growth rate and the land CO2 sink from 16 dynamic global vegetation models and two atmospheric inversions in the Global Carbon Budget 2018. We use a regularized linear regression model, which represents a statistical learning technique apt to deal with the large number of atmospheric circulation predictors (p >= 800, each representing one pixel-based time series of SLP anomalies) in a relatively short observed record (n < 60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in the atmospheric CO2 growth rate and the global land sink, with Pearson correlations between reference and predicted values between 0.70 and 0.84 for boreal winter SLP anomalies. This is comparable to or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The spatial patterns of regression coefficients of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Nino-Southern Oscillation variability. We also identify another important region in the western Pacific, roughly corresponding to the West Pacific pattern.We further evaluate the influence of the time series length on the predictability of IAV and find that reliable estimates of global C-cycle IAV can be obtained from records of 30-54 years. For shorter time series (n < 30 years), however, our results show that conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices.ISSN:2190-4987ISSN:2190-497

A joint framework for studying compound ecoclimatic events

Extreme weather and climate events have direct impacts on ecosystems and can further trigger ecosystem disturbances, often having impacts that last longer than the event's duration. The projected increased frequency or intensity of extreme events could thus amplify ecological impacts and reduce the biosphere's CO2 mitigation potential, but multiple feedbacks between ecosystems and climate extremes are often not considered in risk assessments. In this Perspective, we propose a systemic framework to analyse the causal relationships between climate extremes, disturbance regimes and ecosystems, building on two broadly used perspectives: climate risk assessment and disturbance ecology. Each has strengths and limitations, as each perspective places a different - and partly disjointed - focus on the physical and ecological processes that drive high-impact ecological events. We unify these approaches into a framework (compound ecoclimatic events) that decomposes events into climatic drivers, stressors, environmental factors, impacts and their sources of variability, and further incorporates feedbacks between ecosystem processes and stressors. This framework can be used to develop ecoclimatic storylines to better understand the role of each factor in influencing high-impact events; to incorporate uncertainties associated with internal climate and ecological variability, with scenario definitions, and with epistemic uncertainties; and to quantify the human fingerprint on high-impact ecoclimatic events.ISSN:2662-138

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

Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no "gold standard" for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.</p