Department of Earth System Science

Natural gas is the primary fuel used in U.S. residences, yet little is known about its consumption patterns and drivers. We use daily county-level gas consumption data to assess the spatial patterns of the relationships and the sensitivities of gas consumption to outdoor air temperature across U.S. households. We fitted linear-plus-plateau functions to daily gas consumption data in 1000 counties, and derived two key coefficients: the heating temperature threshold (Tcrit) and the gas consumption rate change per 1°C temperature drop (Slope). We identified the main predictors of Tcrit and Slope (like income, employment rate, and building type) using interpretable machine learning models built on census data. Finally, we estimated a potential 2.47 million MtCO2 annual emission reduction in U.S. residences by gas savings due to household insulation improvements and hypothetical behavioral change toward reduced consumption by adopting a 1°C lower Tcrit than the current value.

Microbes are essential for the functioning of all ecosystems, and as global warming and anthropogenic pollution threaten ecosystems, it is critical to understand how microbes respond to these changes. We investigated the climate response of Sphingomonas, a widespread gram-negative bacterial genus, during an 18-month microbial community reciprocal transplant experiment across a Southern California climate gradient. We hypothesized that after 18 months, the transplanted Sphingomonas clade and functional composition would correspond with site conditions and reflect the Sphingomonas composition of native communities. We extracted Sphingomonas sequences from metagenomic data across the gradient and assessed their clade and functional composition. Representatives of at least 12 major Sphingomonas clades were found at varying relative abundances along the climate gradient, and transplanted Sphingomonas clade composition shifted after 18 months. Site had a significant effect (PERMANOVA; P < 0.001) on the distribution of both Sphingomonas functional (R² = 0.465) and clade composition (R² = 0.400), suggesting that Sphingomonas composition depends on climate parameters. Additionally, for both Sphingomonas clade and functional composition, ordinations revealed that the transplanted communities shifted closer to the native Sphingomonas composition of the grassland site compared with the site they were transplanted into. Overall, our results indicate that climate and substrate collectively determine Sphingomonas clade and functional composition.IMPORTANCESphingomonas is the most abundant gram-negative bacterial genus in litter-degrading microbial communities of desert, grassland, shrubland, and forest ecosystems in Southern California. We aimed to determine whether Sphingomonas responds to climate change in the same way as gram-positive bacteria and whole bacterial communities in these ecosystems. Within Sphingomonas, both clade composition and functional genes shifted in response to climate and litter chemistry, supporting the idea that bacteria respond similarly to climate at different scales of genetic variation. This understanding of how microbes respond to perturbation across scales may aid in future predictions of microbial responses to climate change.

Soil carbon (C) responses to environmental change represent a major source of uncertainty in the global C cycle. Feedbacks between soil C stocks and climate drivers could impact atmospheric CO2 levels, further altering the climate. Here, we assessed the reliability of Earth system model (ESM) predictions of soil C change using the Coupled Model Intercomparison Project phases 5 and 6 (CMIP5 and CMIP6). ESMs predicted global soil C gains under the high emission scenario, with soils taking up 43.9 Pg (95% CI: 9.2–78.5 Pg) C on average during the 21st century. The variation in global soil C change declined significantly from CMIP5 (with average of 48.4 Pg [95% CI: 2.0–94.9 Pg] C) to CMIP6 models (with average of 39.3 Pg [95% CI: 23.9–54.7 Pg] C). For some models, a small C increase in all biomes contributed to this convergence. For other models, offsetting responses between cold and warm biomes contributed to convergence. Although soil C predictions appeared to converge in CMIP6, the dominant processes driving soil C change at global or biome scales differed among models and in many cases between earlier and later versions of the same model. Random Forest models, for soil carbon dynamics, accounted for more than 63% variation of the global soil C change predicted by CMIP5 ESMs, but only 36% for CMIP6 models. Although most CMIP6 models apparently agree on increased soil C storage during the 21st century, this consensus obscures substantial model disagreement on the mechanisms underlying soil C response, calling into question the reliability of model predictions.

Global change is currently impacting ecosystems and their contributions to people (i.e. ecosystem services). These impacts have consequences for societies and human well-being, especially in Africa. Historically, efforts have focused on assessing global change from a social or biophysical perspective, treating them as separate entities. Yet, our understanding of impacts to social-ecological systems remains limited, particularly in the Global South, due to a lack of data, tools, and approaches accounting for social and ecological aspects of ecosystem services. This is especially relevant for cultural ecosystem services as they are less tangible. We use a simple indicator and important provider of a multitude of cultural ecosystem services, birding, to understand how climate, biodiversity, and land use change will impact cultural ecosystem services across Africa. We explore how emerging tools and data can overcome limitations in mapping and modeling cultural ecosystem services, particularly in analyzing human preferences and behavior at large spatiotemporal scales and in data-poor regions. Leveraging crowdsourced data from eBird and using machine learning techniques we map and model recreational birding to assess the underlying social-ecological relationships and the impact of future climate and environmental change. We show that bird species richness, protected areas, accessibility, and max temperature contribute most to birding suitability across the continent. Further, we show spatial shifts in the suitability of birding under three future climate scenarios (SSP126, 370, and 585). Models suggest climate and biodiversity change will increasingly constrain the flow of birding related cultural ecosystem services across Africa. This has implications for human-nature interactions, development of countries, management of protected areas, and overall human well-being in the future. More generally, we highlight opportunities for crowdsourced datasets and machine learning to integrate non-material ecosystem services in models and thus, enhance the understanding of future impacts to ecosystem services and human well-being.

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

During wildfires and fossil fuel combustion, biomass is converted to black carbon (BC) via incomplete combustion. BC enters the ocean by rivers and atmospheric deposition contributing to the marine dissolved organic carbon (DOC) pool. The fate of BC is considered to reside in the marine DOC pool, where the oldest BC 14C ages have been measured (>20,000 14C y), implying long-term storage. DOC is the largest exchangeable pool of organic carbon in the oceans, yet most DOC (>80%) remains molecularly uncharacterized. Here, we report 14C measurements on size-fractionated dissolved BC (DBC) obtained using benzene polycarboxylic acids as molecular tracers to constrain the sources and cycling of DBC and its contributions to refractory DOC (RDOC) in a site in the North Pacific Ocean. Our results reveal that the cycling of DBC is more dynamic and heterogeneous than previously believed though it does not comprise a single, uniformly old 14C age. Instead, both semilabile and refractory DBC components are distributed among size fractions of DOC. We report that DBC cycles within DOC as a component of RDOC, exhibiting turnover in the ocean on millennia timescales. DBC within the low-molecular-weight DOC pool is large, environmentally persistent and constitutes the size fraction that is responsible for long-term DBC storage. We speculate that sea surface processes, including bacterial remineralization (via the coupling of photooxidation of surface DBC and bacterial co-metabolism), sorption onto sinking particles and surface photochemical oxidation, modify DBC composition and turnover, ultimately controlling the fate of DBC and RDOC in the ocean.

Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a "top 10" list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

The lifetime of tropospheric O3 is difficult to quantify because we model O3 as a secondary pollutant, without direct emissions. For other reactive greenhouse gases like CH4 and N2O, we readily model lifetimes and timescales that include chemical feedbacks based on direct emissions. Here, we devise a set of artificial experiments with a chemistry-transport model where O3 is directly emitted into the atmosphere at a quantified rate. We create 3 primary emission patterns for O3, mimicking secondary production by surface industrial pollution, that by aviation, and primary injection through stratosphere–troposphere exchange (STE). The perturbation lifetimes for these O3 sources includes chemical feedbacks and varies from 6 to 27 days depending on source location and season. Previous studies derived lifetimes around 24 days estimated from the mean odd-oxygen loss frequency. The timescales for decay of excess O3 varies from 10 to 20 days in northern hemisphere summer to 30 to 40 days in northern hemisphere winter. For each season, we identify a single O3 chemical mode applying to all experiments. Understanding how O3 sources accumulate (the lifetime) and disperse (decay timescale) provides some insight into how changes in pollution emissions, climate, and stratospheric O3 depletion over this century will alter tropospheric O3. This work incidentally found 2 distinct mistakes in how we diagnose tropospheric O3, but not how we model it. First, the chemical pattern of an O3 perturbation or decay mode does not resemble our traditional view of the odd-oxygen family of species that includes NO2. Instead, a positive O3 perturbation is accompanied by a decrease in NO2. Second, heretofore we diagnosed the importance of STE flux to tropospheric O3 with a synthetic “tagged” tracer O3S, which had full stratospheric chemistry and linear tropospheric loss based on odd-oxygen loss rates. These O3S studies predicted that about 40% of tropospheric O3 was of stratospheric origin, but our lifetime and decay experiments show clearly that STE fluxes add about 8% to tropospheric O3, providing further evidence that tagged tracers do not work when the tracer is a major species with chemical feedbacks on its loss rates, as shown previously for CH4

Anthropogenic climate change has increased the frequency of drought, wildfire, and invasions of non-native species. Although high-severity fires linked to drought can inhibit recovery of native vegetation in forested ecosystems, it remains unclear how drought impacts the recovery of other plant communities following wildfire. We leveraged an existing rainfall manipulation experiment to test the hypothesis that reduced precipitation, fuel load, and fire severity convert plant community composition from native shrubs to invasive grasses in a Southern California coastal sage scrub system. We measured community composition before and after the 2020 Silverado wildfire in plots with three rainfall treatments. Drought reduced fuel load and vegetation cover, which reduced fire severity. Native shrubs had greater prefire cover in added water plots compared to reduced water plots. Native cover was lower and invasive cover was higher in postfire reduced water plots compared to postfire added and ambient water plots. Our results demonstrate the importance of fuel load on fire severity and plant community composition on an ecosystem scale. Management should focus on reducing fire frequency and removing invasive species to maintain the resilience of coastal sage scrub communities facing drought. In these communities, controlled burns are not recommended as they promote invasive plants.

Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed climate-invariant ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.