Department of Geography

This study identifies 38 cases of windthrows in the Amazonia to explore the relationship between windthrows and the characteristics (storm passing time, cloud top temperature, and maximum precipitation) of mesoscale convective systems (MCSs) that produced them. Most of windthrow cases in this study occurred in August and September. The storm passing time is positively correlated with the size of windthrows. MCSs with colder cloud top temperature (with a mean at 206 K)—indicating deeper convection—resulted in large windthrows, while those with warm cloud top (with a mean above 230 K) resulted in relatively small windthrows except for windthrows in the western Amazonia. No significant relationship is found between maximum precipitation intensity and the area of windthrows.

The recovery of long-term climate proxy records with seasonal resolution is rare because of natural smoothing processes, discontinuities and limitations in measurement resolution. Yet insolation forcing, a primary driver of multimillennial-scale climate change, acts through seasonal variations with direct impacts on seasonal climate¹. Whether the sensitivity of seasonal climate to insolation matches theoretical predictions has not been assessed over long timescales. Here, we analyse a continuous record of water-isotope ratios from the West Antarctic Ice Sheet Divide ice core to reveal summer and winter temperature changes through the last 11,000 years. Summer temperatures in West Antarctica increased through the early-to-mid-Holocene, reached a peak 4,100 years ago and then decreased to the present. Climate model simulations show that these variations primarily reflect changes in maximum summer insolation, confirming the general connection between seasonal insolation and warming and demonstrating the importance of insolation intensity rather than seasonally integrated insolation or season duration^2,3. Winter temperatures varied less overall, consistent with predictions from insolation forcing, but also fluctuated in the early Holocene, probably owing to changes in meridional heat transport. The magnitudes of summer and winter temperature changes constrain the lowering of the West Antarctic Ice Sheet surface since the early Holocene to less than 162 m and probably less than 58 m, consistent with geological constraints elsewhere in West Antarctica^4-7.

Windthrows (trees uprooted and broken by winds) are common across the Amazon. They range in size from single trees to large gaps that lead to changes in forest dynamics, composition, structure, and carbon balance. Yet, the current understanding of the spatial variability of windthrows is limited. By integrating remote sensing data and geospatial analysis, we present the first study to examine the occurrence, area, and direction of windthrows and the control that environmental variables exert on them across the whole Amazon. Windthrows are more frequent and larger in the northwestern Amazon (Peru and Colombia), with the central Amazon (Brazil) being another hot spot of windthrows. The predominant direction of windthrows is westward. Rainfall, surface elevation, and soil characteristics explain the variability (20%-50%) of windthrows but their effects vary regionally. A better understanding of the spatial dynamics of windthrows will improve understanding of the functioning of Amazon forests.

Tropical forest dynamics play a crucial role in the global carbon, water, and energy cycles. However, realistically simulating the dynamics of competition and coexistence between different plant functional types (PFTs) in tropical forests remains a significant challenge. This study aims to improve the modeling of PFT coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations and (2) whether machine learning (ML)-based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted three ensembles of ELM-FATES experiments at a tropical forest site near Manaus, Brazil. By comparing the ensemble experiments without (Exp-CTR) and with (Exp-OBS) consideration of observed trait relationships, we found that accounting for these relationships slightly improves the simulations of water, energy, and carbon variables when compared to observations but degrades the simulation of PFT coexistence. Using ML-based surrogate models trained on Exp-CTR, we optimized the trait parameters in ELM-FATES and conducted another ensemble of experiments (Exp-ML) with these optimized parameters. The proportion of PFT coexistence experiments significantly increased from 21 % in Exp-CTR to 73 % in Exp-ML. After filtering the experiments that allow for PFT coexistence to agree with observations (within 15 % tolerance), 33 % of the Exp-ML experiments were retained, which is a significant improvement compared to the 1.4 % in Exp-CTR. Exp-ML also accurately reproduces the annual means and seasonal variations in water, energy, and carbon fluxes and the field inventory of aboveground biomass. This study represents a reproducible method that utilizes machine learning to identify parameter values that improve model fidelity against observations and PFT coexistence in vegetation demography models for diverse ecosystems. Our study also suggests the need for new mechanisms to enhance the robust simulation of coexisting plants in ELM-FATES and has significant implications for modeling the response and feedbacks of ecosystem dynamics to climate change.

Forest mortality caused by convective storms (windthrow) is a major disturbance in the Amazon. However, the linkage between windthrows at the surface and convective storms in the atmosphere remains unclear. In addition, the current Earth system models (ESMs) lack mechanistic links between convective wind events and tree mortality. Here we find an empirical relationship that maps convective available potential energy, which is well simulated by ESMs, to the spatial pattern of large windthrow events. This relationship builds connections between strong convective storms and forest dynamics in the Amazon. Based on the relationship, our model projects a 51 ± 20% increase in the area favorable to extreme storms, and a 43 ± 17% increase in windthrow density within the Amazon by the end of this century under the high-emission scenario (SSP 585). These results indicate significant changes in tropical forest composition and carbon cycle dynamics under climate change.

Stem respiration is a quantitatively important, but poorly understood component of ecosystem carbon cycling in terrestrial ecosystems. However, a dynamic stem gas exchange system for quantifying real-time stem carbon dioxide (CO₂) efflux (E_s) is not commercially available resulting in limited observations based on the static method where air is recirculated through a stem enclosure. The static method has limited temporal resolution, suffers from condensation issues, requires a leak-free enclosure, which is often difficult to verify in the field, and requires physically removing the chamber or flushing it with ambient air before starting each measurement.•With the goal of improving our quantitative understanding of biophysical, physiological, biochemical, and environmental factors that influence diurnal E_s patterns, here we present a custom system for quantifying real-time stem E_s in remote tropical forests.•The system is low cost, lightweight, and waterproof with low power requirements (1.2-2.4 W) for real-time monitoring of stem E_s using a 3D printed dynamic stem chamber and a 12V car battery. The design offers control over the flow rate through the stem chamber, eliminates the need for a pump to introduce air into the chamber, and water condensation issues by removing water vapor prior to CO₂ analysis.•Following a simple CO₂ infrared gas analyzer (IRGA) calibration and match procedure with a 400-ppm standard, we quantified diurnal E_s observations over a 24-hours period during the summer growing season from an ash tree (Fraxinus sp.) in Fort Collins, Colorado. The results are consistent with previous laboratory and field studies that show E_s can be suppressed during the day relative to the night.

While it is recognized that farming alternatively is inherently knowledge intensive, in the United States, farmer knowledge has been widely overlooked and under-documented within the scientific literature. Farmer knowledge of soil in particular is understudied in the US, especially given that healthy soils have been identified as the basis for resilient agriculture. Applying an exploratory, case study approach, we interviewed 13 organic farmers based in Yolo County, California to understand how organic farmers in this region acquire knowledge about their soils, to document what organic farmers in this region know about their soils, and to share key management practices organic farmers use to build soil health in the region. We found the organic farmers in this study acquire knowledge about their farming systems primarily through direct observation, personal experience, experimentation, and inherited wisdom. To evaluate soil health, farmers in this study cited using a range of indicators, including soil structure, crop health, growth habits of weeds, and soil biology. We found that these organic farmers possess extensive place-based knowledge of their local farming systems, and that this knowledge base represents an important source for innovation and adaptive management in scientific and policy-making contexts.

The warmer early Pliocene climate featured changes to global sea surface temperature (SST) patterns, namely a reduction in the Equator-pole gradient and the east-west SST gradient in the tropical Pacific, the so-called "permanent El Niño". Here we investigate the consequences of the SST changes to silicate weathering and thus to atmospheric CO2 on geological timescales. Different SST patterns than today imply regional modifications of the hydrological cycle that directly affect continental silicate weathering in particular over tropical "hotspots"of weathering, such as the Maritime Continent, thus leading to a "weatherability pattern effect". We explore the impact of Pliocene-like SST changes on weathering using climate model and silicate weathering model simulations, and we deduce CO2 and temperature at carbon cycle equilibrium between solid Earth degassing and silicate weathering. In general, we find large regional increases and decreases in weathering fluxes, and the net effect depends on the extent to which they cancel. Permanent El Niño conditions lead to a small amplification of warming relative to the present day by 0.4 °C, suggesting that the demise of the permanent El Niño could have had a small amplifying effect on cooling from the early Pliocene into the Pleistocene. For the reduced Equator-pole gradient, the weathering increases and decreases largely cancel, leading to no detectable difference in global temperature at carbon cycle equilibrium. A robust SST reconstruction of the Pliocene is needed for a quantitative evaluation of the weatherability pattern effect.

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in Amazon forests. Images from medium-resolution optical satellites combined with extensive field data have allowed researchers to assess patterns of windthrow tree-mortality and to monitor forest recovery over decades of succession in different regions. Although satellites with high spatial-resolution have become available in the last decade, they have not yet been employed for the quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November 2015. Remote sensing estimates of windthrow tree-mortality were produced from Spectral Mixture Analysis and evaluated with forest inventory data (i.e., ground true) by using Generalized Linear Models. Field measured windthrow tree-mortality (3 transects and 30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Although the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3%, p < 0.001), Landsat 8 had the most accurate results and efficiently captured field-observed variations in windthrow tree-mortality across the entire gradient of disturbance (Sentinel 2 and WorldView 2 produced the second and third best results, respectively). As expected, mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of model fits showed the opposite pattern. We suggest that this reflects the influence of a relatively minor disturbance, such as defoliation and crown damage, and the fast growth of natural regeneration, which were not measured in the field nor can be captured by coarser resolution imagery. Our results validate the reliability of Landsat imagery for assessing plot-to-landscape patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Satellites with high spatial resolution can improve estimates of windthrow severity by allowing the quantification of crown damage and mortality of lower canopy and understory trees. However, this requires the validation of remote sensing metrics using field data at compatible scales.