Earth & Environmental Sciences

Biogeography is a dynamic field that has transformed dramatically over the last few decades from being necessarily descriptive to become a rigorous science. Major recent areas of growth have included phylogenetics and phylogeography, microbial biogeography and metagenomics, and macroecology. However, the welcome recent deluge of massive amounts of data, in particular from genomics, museum specimens, and field observations, as well as environmental information, is posing a huge challenge to the field. The society has several key roles, not only to serve as a home for researchers in the field and enabling interaction among them, but also: (1) to provide a forum to facilitate awareness and use of rapidly developing tools and data; (2) to encourage a solid foundation in organismal research, with emphasis on field and museum based resources; (3) to promote global connections; and (4) to cultivate interdisciplinarity, such that the predictive capabilities of the field can be used to inform management and policy.

Despite its importance for determining global carbon fluxes, leaf respiration remains poorly constrained in land surface models (LSMs). We tested the sensitivity of the Energy Exascale Earth System Model Land Model - Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) to variation in the canopy gradients of leaf maintenance respiration (R_dark). We ran global and point simulations varying the canopy gradient of R_dark to explore the impacts on forest structure, composition, and carbon cycling. In global simulations, steeper canopy gradients of R_dark lead to increased understory survival and leaf biomass. Leaf area index (LAI) increased up to 77% in tropical regions compared with the default parameterization, improving alignment with remotely sensed benchmarks. Global vegetation carbon varied from 308 Pg C to 449 Pg C across the ensemble. In tropical forest simulations, steeper gradients of R_dark had a large impact on successional dynamics. Results show the importance of canopy gradients in leaf traits and fluxes for determining plant carbon budgets and emergent ecosystem properties such as competitive dynamics, LAI, and vegetation carbon. The high-model sensitivity to canopy gradients in R_dark highlights the need for more observations of how leaf traits and fluxes vary along light micro-environments to inform critical dynamics in LSMs.

Mountains create and enhance their own clouds, which both scatter and absorb shortwave radiation from the sun and absorb and re-emit land surface and atmospheric longwave radiation. However, the impacts of clouds on the surface radiation balance in high elevation snowy mountain terrain are poorly explored. In this study, we use data collected by the SAIL field campaign and partner organizations in the upper elevations (2,880 m.a.s.l) of the Upper Colorado River Basin (UCRB) over a 21-month period from September 2021 to June 2023 to estimate Cloud Radiative Forcing (CRF) in the shortwave, longwave, and the net effect. Longwave warming effects dominate during the winter when snow albedos are high (0.8–0.9) and the background atmospheric precipitable water vapor is low ((Formula presented.) 0.5 cm), yielding a maximum monthly average net CRF of +34.7 W·m−2, meaning that clouds increase the net radiation relative to clear skies during this time period. The sign of net CRF switches in the warm season as snow recedes, sun-angles increase, and the North American monsoon arrives, yielding a minimum monthly average net CRF of −47.6 W·m−2 with hourly minima of −600 W·m−2. The sign of net CRF is typically positive, even at solar noon, when the surface is snow covered, except for a brief period over melting, low-albedo snow (0.5–0.6) impacted by dust impurities. Sensitivity tests elucidate the role of the surface albedo on the net CRF. The results suggest that net CRF will increase in magnitude and lead to a more persistent cooling effect on the surface net radiation budget as the snow cover declines.

Many atmospheric river detection tools (ARDTs) have been developed over the past few decades to identify atmospheric rivers (ARs). Different ARDTs have been observed to capture a variety of frequencies, shapes, and sizes of ARs. Due to this, questions have arisen about the underlying phenomena associated with the detected ARs: do all ARDTs detect the same meteorological phenomena? In this paper, we assess eight ARDTs and investigate the underlying synoptic scale phenomena during landfalling ARs along the west coast of North America. We find that during landfalling AR events, prevalent low-pressure and high-pressure systems converge and enhance moisture influx toward the landfalling site. We identify that all eight ARDTs identify AR conditions associated with baroclinic waves, with the region of intense integrated vapor transport (IVT) located downstream of the upper level (500 hPa) trough. The magnitude of IVT is enhanced by the strength of the pressure gradients in the confluence region. Although the ARDTs assessed agree on the general phenomena, there are however subtle differences in each ARDT per the clustering analysis we performed. We conclude that the eight ARDTs identify similar underpinning synoptic scale meteorological phenomena.

Microbial carbon use efficiency (CUE) is a key microbial trait affecting soil organic carbon (SOC) dynamics. However, we lack a unified and predictive understanding of the mechanisms underpinning the temperature response of microbial CUE, and, thus, its impacts on SOC storage in a warming world. Here, we leverage three independent soil datasets (n = 618 for microbial CUE; n = 591 and 660 for heterotrophic respiration) at broad spatial scales to investigate the microbial thermal response and its implications for SOC responses to warming. We show a nonlinear increase and decrease of CUE and heterotrophic respiration, respectively, in response to mean annual temperature (MAT), with a thermal threshold at ≈15 °C. These nonlinear relationships are mainly associated with changes in the fungal-to-bacterial biomass ratio. Our microbial-explicit SOC model predicts significant SOC losses at MAT above ≈15 °C due to increased CUE, total microbial biomass, and heterotrophic respiration, implying a potential abrupt transition to more vulnerable SOC under climate warming.

Uncultivated but abundant soil microorganisms have untapped potential for producing broad ranges of natural products, as well as for bioremediation. However, cultivating soil microorganisms while maintaining a broad microorganism diversity to enable phenotyping and functional analysis of as diverse individual isolates as possible remains challenging. In this study, we developed and tested the ability of several culture media formulations that contain defined soil metabolites or soil extracts to maintain microorganism diversity during culture. We also assessed their performance in microfluidic droplet cultivation where single-soil microorganism isolates were encapsulated and cultivated in picoliter-volume water-in-oil emulsion droplets to enable clonal growth needed for downstream functional analyses. Our results show that droplet cultivation with media supplemented by soil extract or soil metabolites enables the recovery of soil microorganisms with higher diversity (up to 1.5-fold higher richness) compared to bulk cultivation methods. Importantly, 1.7-fold more of less abundant (<1%) phyla and 11-fold more of unique genera were recovered, demonstrating the utility of this method for interrogating highly diverse soil microorganisms for broad ranges of applications.IMPORTANCEAlthough soil microorganisms hold a significant value in bioproduction and bioremediation, only a small fraction-less than 1%-can be cultured under specific media and cultivation conditions. This indicates that there are ample opportunities in harvesting the diverse environmental microorganisms if isolating and recovering these uncultured microorganisms are possible. This paper presents a new cultivation technique composed of isolating single-soil microorganism cell from an in situ soil microorganism community in microfluidic droplets and conducting in-droplet cultivation in media supplemented by soil extract or soil metabolites. This method enables the recovery of a broader diversity of the original microorganism community, laying the groundwork for a high-throughput phenotyping of these diverse microorganisms from their natural habitats.

Midwestern U.S. extreme precipitation is associated with multiple storm types including mesoscale convective systems (MCSs) and/or training thunderstorms, tropical cyclone (TC) remnants, and winter storms. Anthropogenic warming is expected to increase climatological precipitation globally, however, there may be little correspondence with regional storm-based changes. Furthermore, uncertainty remains in precipitation-temperature scaling due to use of convective parameterization in most global models. In this study, we investigated historically impactful extreme precipitation events from multiple types of Midwest storms using the Weather Research and Forecasting model at convection-permitting resolution. We simulated five-member ensembles of historical hindcasts and experiments representing the storms in the future using the pseudo-global warming method. We found that future precipitation changes depend on storm type, with increases near Clausius-Clapeyron (CC) for winter storms, no consensus for MCSs and/or training thunderstorms, and sub-CC increases for TC remnants. This research highlights the importance of considering storm type in future extreme precipitation projections.

We analyze large-scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k-means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies two days before the onset, from October to March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as Northwest continental negative height anomaly, Eastward positive “Pacific-North American Pattern (PNA),” Westward negative “PNA,” and Prominent Alaskan ridge. These four clusters, shown in multiple variables, evolve very differently and have differing links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula-based framework for predicting PEx events, we find LSMP indices that are useful predictors of NorCal PEx events, with moisture-based variables being the best predictors of PEx events at least 6 days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance tested. To ensure statistical rigor, the LSMPs analyzed here (with the modified acronym) include local tests of both significance and consistency, which are not always featured in the literature on large-scale meteorological patterns.