The Department of Earth System Science (ESS) focuses on how the atmosphere, land, and oceans interact as a system, and how the Earth will change over a human lifetime.
El Niño-Southern Oscillation (ENSO) exhibits diverse characteristics in spatial pattern, peak intensity, and temporal evolution. Here we develop a three-region multiscale stochastic model to show that the observed ENSO complexity can be explained by combining intraseasonal, interannual, and decadal processes. The model starts with a deterministic three-region system for the interannual variabilities. Then two stochastic processes of the intraseasonal and decadal variation are incorporated. The model can reproduce not only the general properties of the observed ENSO events, but also the complexity in patterns (e.g., Central Pacific vs. Eastern Pacific events), intensity (e.g., 10–20 year reoccurrence of extreme El Niños), and temporal evolution (e.g., more multi-year La Niñas than multi-year El Niños). While conventional conceptual models were typically used to understand the dynamics behind the common properties of ENSO, this model offers a powerful tool to understand and predict ENSO complexity that challenges our understanding of the twenty-first century ENSO.
Bacterial communities in the organic leaf litter layer and bulk (mineral and organic) soil are sensitive to environmental change. However, despite close interactions between these communities, the leaf litter layer has historically been studied in isolation from the bulk soil. Whether bacterial response to environmental change is uniform throughout the surface soil remains unclear. Here, we simultaneously characterized how bacterial community composition in three surface soil layers (the leaf litter layer, 0–2 cm of bulk soil, and 0–10 cm of bulk soil) responded to a wildfire burning through a 13-year drought simulation in two adjacent ecosystems, a grassland and coastal sage scrubland. We found that bacterial communities in all three surface soil layers were distinct in composition and varied with drought, ecosystem type, and temporal variation. Moreover, the impact of these environmental changes on bacterial community composition decreased with depth in the surface soil. Bacterial response to drought was three-fold higher in the leaf litter layer than in the top 10 cm of bulk soil, with the drought treatment explaining 4.8% and 1.6% of the compositional variation, respectively. Wildfire altered bacterial composition in the leaf litter layer but not within the top 10 cm of bulk soil. Further, previous exposure of the bacterial communities in the leaf litter layer to drought did not influence its response to the wildfire. Thus, considering soil depth when assessing the impact of environmental conditions on the surface soil microbiome may improve predictions about the degree to which microbial communities, and therefore soil carbon, will respond to future environmental change.
Multi-year El Niño events induce severe and persistent floods and droughts worldwide, with significant socioeconomic impacts, but the causes of their long-lasting behaviors are still not fully understood. Here we present a two-way feedback mechanism between the tropics and extratropics to argue that extratropical atmospheric variability associated with the North Pacific Oscillation (NPO) is a key source of multi-year El Niño events. The NPO during boreal winter can trigger a Central Pacific El Niño during the subsequent winter, which excites atmospheric teleconnections to the extratropics that re-energize the NPO variability, then re-triggers another El Niño event in the following winter, finally resulting in persistent El Niño-like states. Model experiments, with the NPO forcing assimilated to constrain atmospheric circulation, reproduce the observed connection between NPO forcing and the occurrence of multi-year El Niño events. Future projections of Coupled Model Intercomparison Project phases 5 and 6 models demonstrate that with enhanced NPO variability under future anthropogenic forcing, more frequent multi-year El Niño events should be expected. We conclude that properly accounting for the effects of the NPO on the evolution of El Niño events may improve multi-year El Niño prediction and projection.
Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near-real-time approach for tracking contributions from deforestation, forest, agricultural, and savanna fires to burned area and emissions and apply the approach to the 2019 fire season in South America. Across the southern Amazon, 19,700 deforestation fire events accounted for 39% of all satellite active fire detections and the majority of fire carbon emissions (63%; 69 Tg C). Multiday fires accounted for 81% of burned area and 92% of carbon emissions from the Amazon, with many forest fires burning uncontrolled for weeks. Most fire detections from deforestation fires were correctly identified within 2 days (67%), highlighting the potential to improve situational awareness and management outcomes during fire emergencies.
The Southern Ocean surrounding Antarctica is a region that is key to a range of climatic and oceanographic processes with worldwide effects, and is characterised by high biological productivity and biodiversity. Since 2013, the International Bathymetric Chart of the Southern Ocean (IBCSO) has represented the most comprehensive compilation of bathymetry for the Southern Ocean south of 60°S. Recently, the IBCSO Project has combined its efforts with the Nippon Foundation - GEBCO Seabed 2030 Project supporting the goal of mapping the world's oceans by 2030. New datasets initiated a second version of IBCSO (IBCSO v2). This version extends to 50°S (covering approximately 2.4 times the area of seafloor of the previous version) including the gateways of the Antarctic Circumpolar Current and the Antarctic circumpolar frontal systems. Due to increased (multibeam) data coverage, IBCSO v2 significantly improves the overall representation of the Southern Ocean seafloor and resolves many submarine landforms in more detail. This makes IBCSO v2 the most authoritative seafloor map of the area south of 50°S.
LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. We show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.
Soil moisture is a major driver of microbial activity and thus, of the release of carbon (C) into the Earth's atmosphere. Yet, there is no consensus on the relationship between soil moisture and microbial respiration, and as a result, moisture response functions are a poorly constrained aspect of C models. In addition, models assume that the response of microbial respiration to moisture is the same for all ecosystems, regardless of climate history, an assumption that many empirical studies have challenged. These gaps in understanding of the microbial respiration response to moisture contribute to uncertainty in model predictions. We review our understanding of what drives microbial moisture response, highlighting evidence that historical precipitation can influence both responses to moisture and sensitivity to drought. We present two hypotheses, the ‘climate history hypothesis’, where we predict that baseline moisture response functions change as a function of precipitation history, and the ‘drought legacy hypothesis’, in which we suggest that the intensity and frequency of historical drought have shaped microbial communities in ways that will control moisture responses to contemporary drought. Underlying mechanisms include biological selection and filtering of the microbial community by rainfall regimes, which result in microbial traits and trade-offs that shape function. We present an integrated modelling and empirical approach for understanding microbial moisture responses and improving models. Standardized measures of moisture response (respiration rate across a range of moistures) and accompanying microbial properties are needed across sites. These data can be incorporated into trait-based models to produce generalized moisture response functions, which can then be validated and incorporated into conventional and microbially explicit ecosystem models of soil C cycling. Future studies should strive to analyse realistic moisture conditions and consider the role of environmental factors and soil structure in microbial response. Microbes are the engines that drive C storage and are sensitive to changes in rainfall. A greater understanding of the factors that govern this sensitivity could be a key part of improving predictions of soil C dynamics, climate change and C-climate feedbacks. Read the free Plain Language Summary for this article on the Journal blog.
Decomposer fungi play a fundamental role in terrestrial ecosystem dynamics. In the southwestern United States, climate change is causing more frequent and severe droughts, which may alter fungal community composition and activity. Investigating relationships between fungal traits may improve the prediction of fungal responses to drought. In this dual field and laboratory experiment, we examine whether trade-offs occur between traits associated with drought. Specifically, we test the hypothesis that fungi sort into lifestyles specializing in growth yield, resource acquisition, and drought stress tolerance (“YAS” framework). For the field experiment, we constructed microbial “cages” containing sterilized litter and 1 of 10 fungal isolates. These cages were placed in long-term drought and control plots in a southern Californian grassland for 6 and 12 months. We measured fungal hyphal length per unit litter mass loss for growth yield, the potential activities of four extracellular enzymes for resource acquisition, and the ability to grow in the drought versus control plots for drought stress tolerance. We compared these results with a laboratory microcosm experiment constructed with the same fungal isolates and that measured the same fungal traits. The field experiment corroborated our laboratory results, in that no trade-offs were observed between growth yield and resource acquisition traits. However, in contrast to the laboratory experiment, drought tolerance was negatively related to extracellular enzyme activity and growth yield in the field, implying a trade-off. Despite this observed trade-off in the field, growth yield was not hindered by drought. We propose a modification to the YAS framework, by combining the growth yield and resource acquisition lifestyles, which may be more appropriate for this arid system. This joint laboratory and field approach contextualizes a theoretical framework in microbial ecology and improves understanding of fungal community response to climate change.