Jakob Zscheischler

Climate extremes and the carbon cycle

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.

Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts

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 the 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. Although 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 regain 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.

A few extreme events dominate global interannual variability in gross primary production

Understanding the impacts of climate extremes on the carbon cycle is important for quantifying the carbon-cycle climate feedback and highly relevant to climate change assessments. Climate extremes and fires can have severe regional effects, but a spatially explicit global impact assessment is still lacking. Here, we directly quantify spatiotemporal contiguous extreme anomalies in four global data sets of gross primary production (GPP) over the last 30 years. We find that positive and negative GPP extremes occurring on 7% of the spatiotemporal domain explain 78% of the global interannual variation in GPP and a significant fraction of variation in the net carbon flux. The largest thousand negative GPP extremes during 1982?2011 (4.3% of the data) account for a decrease in photosynthetic carbon uptake of about 3.5?Pg?C?yr?1, with most events being attributable to water scarcity. The results imply that it is essential to understand the nature and causes of extremes to understand current and future GPP variability.

Detection and attribution of large spatiotemporal extreme events in Earth observation data

article i nfo Latest climate projections suggest that both frequen cy and intensity of climate extremes will be substan- tially modified over the course of the coming decades. As a consequence, we need to understand to what extent and via which pathways climate extremes affect the state and functionality of terrestrial ecosystems and the associated biogeochemical cycles on a global scale. So far the impacts of climate extremes on the terrestrial biosphere were mainly investigated on the basis of case studies, while global assessments are widely lacking. In order to facilitate global analysis of this kind, we present a methodological framework that firstly de- tects spatiotemporally contiguous extremes in Earth observations, and secondly infers the likely pathway of the preceding climate anomaly. The approach does not require long time series, is computationally fast, and easily applicabletoavarietyofdatasetswithdifferentspatialandtemporalresolutions.Thekeyelementofouranalysis strategy is to directly search in the relevant observations for spatiotemporally connected components exceeding a certain percentile threshold. We also put an emphasis on characterization of extreme event distribution, and scrutinize the attribution issue. We exemplify the analysis strategy by exploring the fraction of absorbed photo- synthetically active radiation (fAPAR) from 1982 to 2011. Our results suggest that the hot spots of extremes in fAPAR lie in Northeastern Brazil, Southeastern Australia, Kenya and Tanzania. Moreover, we demonstrate that the size distribution of extremes follow a distinct power law. The attribution framework reveals that extremes in fAPAR are primarily driven by phases of water scarcity.

Impact of large‐scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data

Understanding the role of climate extremes and their impact on the carbon (C) cycle is increasingly a focus of Earth system science. Climate extremes such as droughts, heat waves, or heavy precipitation events can cause substantial changes in terrestrial C fluxes. On the other hand, extreme changes in C fluxes are often, but not always, driven by extreme climate conditions. Here we present an analysis of how extremes in temperature and precipitation, and extreme changes in terrestrial C fluxes are related to each other in 10 state-of-the-art terrestrial carbon models, all driven by the same climate forcing. We use model outputs from the North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). A global-scale analysis shows that both droughts and heat waves translate into anomalous net releases of CO2 from the land surface via different mechanisms: Droughts largely decrease gross primary production (GPP) and to a lower extent total respiration (TR), while heat waves slightly decrease GPP but increase TR. Cold and wet periods have a smaller opposite effect. Analyzing extremes in C fluxes reveals that extreme changes in GPP and TR are often caused by strong shifts in water availability, but for extremes in TR shifts in temperature are also important. Extremes in net CO2 exchange are equally strongly driven by deviations in temperature and precipitation. Models mostly agree on the sign of the C flux response to climate extremes, but model spread is large. In tropical forests, C cycle extremes are driven by water availability, whereas in boreal forests temperature plays a more important role. Models are particularly uncertain about the C flux response to extreme heat in boreal forests.

Record dry summer in 2015 challenges precipitation projections in Central Europe

Central Europe was characterized by a humid-temperate climate in the 20th century. Climate change projections suggest that climate in this area will shift towards warmer temperatures by the end of the 21st century, while projected precipitation changes are highly uncertain. Here we show that the 2015 summer rainfall was the lowest on record since 1901 in Central Europe, and that climate models that perform best in the three driest years of the historical time period 1901–2015 project stronger drying trends in the 21st century than models that perform best in the remaining years. Analyses of precipitation and derived soil moisture reveal that the 2015 event was drier than both the recent 2003 or 2010 extreme summers in Central Europe. Additionally there are large anomalies in satellite-derived vegetation greenness. In terms of precipitation and temperature anomalies, the 2015 summer in Central Europe is found to lie between historical climate in the region and that characteristic of the Mediterranean area. Even though the models best capturing past droughts are not necessarily generally more reliable in the future, the 2015 drought event illustrates that potential future drying trends have severe implications and could be stronger than commonly assumed from the entire IPCC AR5 model ensemble.

A submonthly database for detecting changes in vegetation-atmosphere coupling

Land-atmosphere coupling and changes in coupling regimes are important for making precise future climate predictions and understanding vegetation-climate feedbacks. Here we introduce the Vegetation-Atmosphere Coupling (VAC) index which identifies regions and times of concurrent strong anomalies in temperature and photosynthetic activity. The different classes of the index determine whether a location is currently in an energy-limited or water-limited regime, and its high temporal resolution allows to investigate how these regimes change over time at the regional scale. We show that the VAC index helps to distinguish different evaporative regimes. It can therefore provide indirect information about the local soil moisture state. We further demonstrate how the index can be used to understand processes leading to and occurring during extreme climate events, using the 2010 heat wave in Russia and the 2010 Amazon drought as examples.

Quantifying changes in climate variability and extremes: Pitfalls and their overcoming

Hot temperature extremes have increased substantially in frequency and magnitude over past decades. A widely used approach to quantify this phenomenon is standardizing temperature data relative to the local mean and variability of a reference period. Here we demonstrate that this conventional procedure leads to exaggerated estimates of increasing temperature variability and extremes. For example, the occurrence of ‘2-sigma extremes’ would be overestimated by 48.2% compared to a given reference period of 30 years with time-invariant simulated Gaussian data. This corresponds to an increase from a 2.0% to 2.9% probability of such events. We derive an analytical correction revealing that these artifacts prevail in recent studies. Our analyses lead to a revision of earlier reports [e.g. Huntingford et al., 2013]: For instance we show that there is no evidence for a recent increase in normalized temperature variability. In conclusion, we provide an analytical pathway to describe changes in variability and extremes in climate observations and model simulations.

Dependence of drivers affects risks associated with compound events

Compound extremely hot and dry summers become more frequent with higher CO2 concentrations due to stronger interdependence. Compound climate extremes are receiving increasing attention because of their disproportionate impacts on humans and ecosystems. However, risks assessments generally focus on univariate statistics. We analyze the co-occurrence of hot and dry summers and show that these are correlated, inducing a much higher frequency of concurrent hot and dry summers than what would be assumed from the independent combination of the univariate statistics. Our results demonstrate how the dependence structure between variables affects the occurrence frequency of multivariate extremes. Assessments based on univariate statistics can thus strongly underestimate risks associated with given extremes, if impacts depend on multiple (dependent) variables. We conclude that a multivariate perspective is necessary to appropriately assess changes in climate extremes and their impacts and to design adaptation strategies.

A guided hybrid genetic algorithm for feature selection with expensive cost functions

We present a guided hybrid genetic algorithm for feature selection which is tailored to minimize the number of cost function evaluations. Guided variable elimination is used to make the stochastic backward search of the genetic algorithm much more efficient. Guiding means that a promising feature set is selected from a population and suggestions (for example by a trained Random Forest) are made which variable could be removed. It uses implicit diversity management and is able to return multiple optimal solutions if present, which might be important for interpreting the results. It uses a dynamic cost function that avoids prescribing an expected upper limit of performance or the number of features of the optimal solution. We illustrate the performance of the algorithm on artificial data, and show that the algorithm provides accurate results and is very efficient in minimizing the number of cost function evaluations.