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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 829 records · Page 46
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DOE OSTI2020

Rescuing Legacy Seismic Data FAIR’ly

The Earth’s interconnected and dynamical systems operate on a spectrum of time scales from millions of years to fractions of a second. While the evolution of the Earth and its deep interior are beyond the time span of human observations, understanding of many natural phenomena operating on human times scales have benefited from direct scientific observation. Continuous processes and those that are repeated over time shape the environment we live in. Furthermore, as we are faced with unprecedented changes to climate, understanding the deeper patterns and trends in natural systems through time have taken on new importance (Research Data Alliance, 2019). The call to reuse data is driven not only by economics but also by the recognition of their complete uniqueness (observations of natural systems are not repeatable) and scientific value in enhancing current understandings as well as potential new discoveries especially in the era of big data. These data are part of the historical record and our scientific heritage (American Geophysical Union, 2019) not only in explicitly recording earth observations but implicitly recording, and thus providing the evidence that addresses the manner in which science was conducted.

58 GEOSCIENCES↗
DOE OSTI2026

Rates of Sea‐Level Rise Are Highly Sensitive to Ice Viscosity Parameters in Model Benchmarks

Glacier flow plays a major role in current and future rates of globally averaged sea-level rise. The viscosity of glacial ice, controlling the rate of flow, decreases as stress increases and is highly sensitive to the value of the stress exponent, $n$, in the constitutive equation for viscous flow. Glaciologists and climate modelers almost exclusively assume $n=3$ when modeling ice flow and projecting sea-level rise through forward modeling. However, recent work suggests that $n\approx 4$ better fits observations, prompting the question: How sensitive are projections of sea-level rise to the value of $n$? We use an established community ice flow model and standard benchmark experiments designed as an idealized representation of Pine Island Glacier, West Antarctica. While initializing an $n=3$ model to match observations of an $n=4$ ice sheet is possible, we find that incorrectly assuming $n=3$ when in fact $n=4$ dramatically underestimates rates of sea-level rise. The scale of this error grows nonlinearly with the magnitude of the climate forcing, acting to increase projection uncertainties. Additionally, we find that models often account for this stress-dependent rheology mismatch during model initialization in a way that masks this rheological effect in the short term while leaving model outputs vulnerable to larger biases in longer-term projections. Initializations to observations of Pine Island Glacier display similar rheology-mismatch fingerprints to our idealized example.

climate sensitivity↗
DOE OSTI2026

N 2 Onet: a global collaborative network facilitating advances in measurement, modeling, and mitigation of agricultural soil nitrous oxide emissions

Nitrogen (N) fertilizer supports global food production, but its use and overuse drive emissions of nitrous oxide (N 2 O), a potent and long-lived greenhouse gas. Understanding the drivers of N 2 O fluxes remains elusive, making it difficult to predict emissions in time and space and to develop and evaluate ways to lower emissions through management. Major scientific uncertainties underlying the understanding of the drivers of N 2 O fluxes identified in a workshop of N 2 O emissions experts include poor process-based understanding of controls on soil N 2 O emissions in the field; insufficient data to reduce uncertainty in N 2 O budgets from the field to regional scales, including N 2 O emission measurements and importantly, field-scale N balances; and high uncertainty in model predictions of soil N 2 O emissions across environmental and management conditions. To reduce these uncertainties, we present the concept of N 2 Onet, a global collaborative initiative to accelerate advances in N 2 O measurement, analyses, and mitigation. N 2 Onet will serve as an observational network of supersites with multi-scale measurements; a database hub for N 2 O flux and ancillary data; and a catalyst for community building, information sharing, and training. By coalescing and coordinating the global community of researchers, N 2 Onet will provide a roadmap for reducing N 2 O emissions from agriculture worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation and projection of long period return values of extreme daily precipitation in the CMIP5 and CMIP6 models

Using a non-stationary Generalized Extreme Value statistical method, we calculate selected extreme daily precipitation indices and their 20 year return values from the CMIP5 and CMIP6 climate models over the historical and future periods. We evaluate model performance of these indices and their return values in replicating similar quantities calculated from multiple gridded observational products. Difficulties in interpreting model quality in the context of observational uncertainties are discussed. Projections are framed in terms of specified global warming target temperatures rather than at specific times and under specific emissions scenarios. The change in framing shifts projection uncertainty due to differences in model climate sensitivity from the values of the projections to the timing of the global warming target. At their standard resolutions, we find there are no meaningful differences between the two generations of models in their quality or projections of simulated extreme daily precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Assessing Radiative Feedbacks and Their Contribution to the Arctic Amplification Measured by Various Metrics

Arctic amplification (AA), characterized by a more rapid surface air temperature (SAT) warming in the Arctic than the global average, is a major feature of global climate warming. Various metrics have been used to quantify AA based on SAT anomalies, trends, or variability, and they can yield quite different conclusions regarding the magnitude and temporal patterns of AA. This study examines and compares various AA metrics for their temporal consistency in the region north of 70°N from the early twentieth to the early 21st century using observational data and reanalysis products. We also quantify contributions of different radiative feedback mechanisms to AA based on short-term climate variability in reanalysis and model data using the Kernel-Gregory approach. Albedo and lapse rate feedbacks are positive and comparable, with albedo feedback being the leading contributor for all AA metrics. The net cloud feedback, which has large uncertainties, depends strongly on the data sets and AA metrics used. By quantifying the influence of internal variability on AA and related feedbacks based on global climate model ensemble simulations, we find that water vapor and cloud feedbacks are most heavily affected by internal variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating Large‐Storm Dominance in High‐Resolution GCMs and Observations Across the Western Contiguous United States

Abstract Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance.

Environmental Sciences & Ecology↗
DOE OSTI2023

Evaluation of native Earth system model output with ESMValTool v2.6.0

Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool needs to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as “CMORization”. While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing of “native” climate model output, i.e., operational output produced by running the climate model through the standard workflow of the corresponding modeling institute. This is achieved by an extension of ESMValTool's preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitude–longitude grid. Extensions to ESMValTool's regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids. Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; at the moment, only surface variables are available), EC-Earth3, EMAC, ICON, and IPSL-CM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added.

58 GEOSCIENCES↗
DOE OSTI2024

Are turbulence effects on droplet collision–coalescence a key to understanding observed rain formation in clouds?

Rain formation is a critical factor governing the lifecycle and radiative forcing of clouds and therefore it is a key element of weather and climate. Cloud microphysics–turbulence interactions occur across a wide range of scales and are challenging to represent in atmospheric models with limited resolution. Based on past experiments and idealized numerical simulations, it has been postulated that cloud turbulence accelerates rain formation by enhancing drop collision–coalescence. We provide substantial evidence for significant impacts of turbulence on the evolution of cloud droplet size distributions and rain formation by comparing high-resolution observations of cumulus congestus clouds with state-of-the-art large-eddy simulations coupled with a Lagrangian particle-based microphysics scheme. Turbulent coalescence must be included in the model to accurately represent the observed drop size distributions, especially for drizzle drop sizes at lower heights in the cloud. Turbulence causes earlier rain formation and greater rain accumulation compared to simulations with gravitational coalescence only. The observed rain size distribution tail just above cloud base follows a power law scaling that deviates from theoretical scalings considering either a purely gravitation collision kernel or a turbulent kernel neglecting droplet inertial effects, providing additional evidence for turbulent coalescence in clouds. In contrast, large aerosols acting as cloud condensation nuclei (“giant CCN”) do not significantly impact rain formation owing to their long timescale to reach equilibrium wet size relative to the lifetime of rising cumulus thermals. Overall, turbulent drop coalescence exerts a dominant influence on rain initiation in warm cumulus clouds, with limited impacts of giant CCN.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Use of Satellite, Surface Observations and Numerical Weather Prediction Model Data to Improve Cloud Base Height and Cloud Base Vertical Velocity Estimation

Cloud base height (CBH) and cloud base vertical velocity (CBVV) are important variables that impact the overall climate in a region as they influence the formulation, longevity, and evolution of clouds. Retrieval of both parameters have long used ground instrumentation (e.g., Doppler lidar (DL), ground base radar); however, retrieving CBH from satellites is particularly challenging given that space-based instruments only observe cloud tops. In this manuscript, CBH is retrieved using a multi-linear regression equation, while CBVV used a random forests model. Both retrievals combine satellite and numerical weather prediction data. The satellite data used are the Visible Infrared Imaging Radiometer Suite imagery, while measurements of CBH and CBVV include DL and radiosonde data at the Southern Great Plains (SGP) Atmospheric Radiation Measurement observatory. Data from 83 summer days (May-August) in 2018–2021 featuring cumulus clouds forced by solar heating were examined and used to train the models, with years 2022–2023 used for validation. Various spatial domains were defined with one large (2.4° longitude by 2.0° latitude) SGP domain being split into smaller sections (smallest being 0.99° and 0.61° longitude and latitude respectably). CBH and CBVV values obtained from the DL as compared to the models show root mean square errors between 150 and 200 m, with CBVV values between 0.45 and 1 ms -1 . Finally, it was found that the CBH formulation performs well over all domains, while the CBVV retrievals become less accurate due to more turbulence being introduced into the observations as the number of DL stations decreases in the smaller domains.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Environmental controls on ecosystem-scale cold-season methane and carbon dioxide fluxes in an Arctic tundra ecosystem

Abstract. Understanding the processes that influence and control carbon cycling in Arctic tundra ecosystems is essential for making accurate predictions about what role these ecosystems will play in potential future climate change scenarios. Particularly, air–surface fluxes of methane and carbon dioxide are of interest as recent observations suggest that the vast stores of soil carbon found in the Arctic tundra are becoming more available to release to the atmosphere in the form of these greenhouse gases. Further, harsh wintertime conditions and complex logistics have limited the number of year-round and cold-season studies and hence too our understanding of carbon cycle processes during these periods. We present here a two-year micrometeorological data set of methane and carbon dioxide fluxes, along with supporting soil pore gas profiles, that provide near-continuous data throughout the active summer and cold winter seasons. Net emission of methane and carbon dioxide in one of the study years totalled 3.7 and 89 g C m−2 a−1 respectively, with cold-season methane emission representing 54 % of the annual total. In the other year, net emission totals of methane and carbon dioxide were 4.9 and 485 g C m−2 a−1 respectively, with cold-season methane emission here representing 82 % of the annual total – a larger proportion than has been previously reported in the Arctic tundra. Regression tree analysis suggests that, due to relatively warmer air temperatures and deeper snow depths, deeper soil horizons – where most microbial methanogenic activity takes place – remained warm enough to maintain efficient methane production whilst surface soil temperatures were simultaneously cold enough to limit microbial methanotrophic activity. These results provide valuable insight into how a changing Arctic climate may impact methane emission, and highlight a need to focus on soil temperatures throughout the entire active soil profile, rather than rely on air temperature as a proxy for modelling temperature–methane flux dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Century-long timelines of herbarium genomes predict plant stomatal response to climate change

Abstract Dissecting plant responses to the environment is key to understanding whether and how plants adapt to anthropogenic climate change. Stomata, plants’ pores for gas exchange, are expected to decrease in density following increased CO 2 concentrations, a trend already observed in multiple plant species. However, it is unclear whether such responses are based on genetic changes and evolutionary adaptation. Here we make use of extensive knowledge of 43 genes in the stomatal development pathway and newly generated genome information of 191 Arabidopsis thaliana historical herbarium specimens collected over 193 years to directly link genetic variation with climate change. While we find that the essential transcription factors SPCH, MUTE and FAMA, central to stomatal development, are under strong evolutionary constraints, several regulators of stomatal development show signs of local adaptation in contemporary samples from different geographic regions. We then develop a functional score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the past centuries, suggesting a genetic component contributing to this change. This approach combining historical genomics with functional experimental knowledge could allow further investigations of how different, even in historical samples unmeasurable, cellular plant phenotypes may have already responded to climate change through adaptive evolution.

Environmental Sciences & Ecology↗
DOE OSTI2026

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 4.

To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 1.0 in 2017, followed by 2.0 in 2018, and 3.0 in 2021. This new release of FRED 4.0 now has 213,941 observations of 238 root traits, for a combined total of roughly 3.4 million data fields for root traits and ancillary data together. FRED 4.0 has 39.8% more root trait observations than FRED 3.0 and a 34.4% increase in unique data sources. This release of FRED 4.0 also includes significant increases in geographic regions that have long been underrepresented in global datasets, notably in the tropical low latitudes. Ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently with root trait observations. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. Despite this fine-root focus, FRED accepts data collected from roots of all sizes and contains observations of many root classes including coarse roots. Data collection will continue for the foreseeable future. The FRED4_Entire_Database_2026.csv file is the flat csv data file for FRED 4.0, and the FRED4_dd.csv file is the data dictionary of all columns available in FRED, including column IDs, column names, definitions, and unit (where applicable).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Transcriptional Response of Soil Bacteria to Long-Term Warming and Short-Term Seasonal Fluctuations in a Terrestrial Forest

Terrestrial ecosystems are an important carbon store, and this carbon is vulnerable to microbial degradation with climate warming. After 30 years of experimental warming, carbon stocks in a temperate mixed deciduous forest were observed to be reduced by 30% in the heated plots relative to the controls. In addition, soil respiration was seasonal, as was the warming treatment effect. We therefore hypothesized that long-term warming will have higher expressions of genes related to carbohydrate and lipid metabolism due to increased utilization of recalcitrant carbon pools compared to controls. Because of the seasonal effect of soil respiration and the warming treatment, we further hypothesized that these patterns will be seasonal. We used RNA sequencing to show how the microbial community responds to long-term warming (~30 years) in Harvard Forest, MA. Total RNA was extracted from mineral and organic soil types from two treatment plots (+5°C heated and ambient control), at two time points (June and October) and sequenced using Illumina NextSeq technology. Treatment had a larger effect size on KEGG annotated transcripts than on CAZymes, while soil types more strongly affected CAZymes than KEGG annotated transcripts, though effect sizes overall were small. Although, warming showed a small effect on overall CAZymes expression, several carbohydrate-associated enzymes showed increased expression in heated soils (~68% of all differentially expressed transcripts). Further, exploratory analysis using an unconstrained method showed increased abundances of enzymes related to polysaccharide and lipid metabolism and decomposition in heated soils. Compared to long-term warming, we detected a relatively small effect of seasonal variation on community gene expression. Together, these results indicate that the higher carbohydrate degrading potential of bacteria in heated plots can possibly accelerate a self-reinforcing carbon cycle-temperature feedback in a warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Climate change and the future productivity and distribution of crab in the Bering Sea

Crab populations in the eastern Bering Sea support some of the most valuable fisheries in the United States, but their future productivity and distribution are uncertain. Here, we explore observed changes in the productivity and distribution for snow crab, Tanner crab, and Bristol Bay red king crab. We link historical indices of environmental variation and predator biomass with observed time series of centroids of abundance and extent of crab stock distribution; we also fit stock–recruit curves including environmental indices for each stock. We then project these relationships under forcing from global climate models to forecast potential productivity and distribution scenarios. Our results suggest that the productivity of snow crab is negatively related to the Arctic Oscillation (AO) and positively related to ice cover; Tanner crab’s productivity and distribution are negatively associated with cod biomass and sea surface temperature. Aspects of red king crab distribution and productivity appear to be related to bottom temperature, ice cover, the AO, and/or cod biomass. Projecting these relationships forward with available forecasts suggests that Tanner crab may become more productive and shift further offshore, red king crab distribution may contract and move north, and productivity may decrease for snow crab as the population contracts northward.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Prediction of annual soil respiration from its flux at mean annual temperature

Accurately scaling soil respiration (SR, the soil-to-atmosphere flow of CO2) across time and space is important to constrain and understand ecosystem to global scale SR, a major terrestrial carbon flux to the atmosphere. Year-round SR measurements are however expensive and sometimes impossible to perform. Bahn et al. (2010) developed an approach to estimate annual SR (SRannual) from the flux measured at mean soil temperature (SRMAST), but the robustness of this approach needs to be evaluated in diverse ecosystem types and climatic conditions globally. We used a global soil respiration database (SRDB-V4, with 823 SR observations worldwide) to test the capability of SRMAST to predict SRannual. SRMAST estimated using a variety of methods all showed clear relationships with annual SRannual. Two single-rate methods (i.e., using the single SR rate most close to mean annual soil temperature, or the single SR rate most close to mean annual air temperature) showed the most pronounced divergence from the true SRannual, but errors significantly decreased when using multiple SR rates within 1 ? of the mean annual soil temperature to estimate SRMAST. SRannual was most closely correlated with SRMAST estimated via a Q10 relationship, but this method has a potential autocorrelation issue that we explore and discuss. Air temperature data are much more widely available than is soil temperature, and we found that SR at mean annual air temperature (SRMAAT) can be used to predict SRannual as well. This study builds on Bahn et al. (2010) to demonstrate that SR measured at both annual mean soil and air temperature can be used to predict annual SR, with well-quantified errors. This capability could be used to reduce SR measurement frequency required for estimating SRannual and greatly decrease cost, factors that are generally important but especially in lower-income countries and cold, inaccessible regions.

Soil respiration, temperature, modeling↗
DOE OSTI2023

Detecting Permafrost Active Layer Thickness Change From Nonlinear Baseflow Recession

We report permafrost underlies about one fifth of the global land area and affects ground stability, freshwater runoff, soil chemistry, and surface-atmosphere gas exchange. The depth of thawed ground overlying permafrost (active layer thickness) has broadly increased across the Arctic in recent decades, coincident with a period of increased streamflow, especially the lowest flows (baseflow). Mechanistic links between active layer thickness and baseflow have recently been explored using linear reservoir theory, but most watersheds behave as nonlinear reservoirs. We derive theoretical nonlinear relationships between long-term average saturated soil thickness $\bar{η}$(proxy for active layer thickness) and long-term average baseflow. When applied to 38 years of daily streamflow data for the Kuparuk River basin on the North Slope of Alaska, the theory predicts $\bar{η}$ increased 0.17 ± 0.22[2σ] cm a -1 between 1983 and 2020 (6.4 ± cm total). The rate of increase nearly doubled to 0.29 ± 0.31 cm a -1 between 1990 and 2020, during which time local field measurements from Circumpolar Active Layer Monitoring sites indicate the active layer increased 0.31 ± 0.22 cm a -1 . The predicted rate of increase more than doubled again between 2002 and 2020, outpacing a near doubling of observed active layer thickening, consistent with trends in terrestrial water storage inferred from Gravity Recovery and Climate Experiment satellite gravimetry and Modern-Era Retrospective Analysis for Research and Applications climate reanalysis. Overall, hydrologic change is accelerating in the Kuparuk River basin, and we provide a theoretical framework for estimating basin-scale changes in active layer water storage from streamflow measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Experimental Soil Warming Impacts Soil Moisture and Plant Water Stress and Thereby Ecosystem Carbon Dynamics

Experimental soil heating experiments have found a consistent increase in soil-surface CO 2 emissions ( F s ), but inconsistent soil organic carbon (SOC) responses. Interpretation of heating effects is complicated by spatial heterogeneity and soil moisture, nitrogen availability, and microbial and plant responses. Here we applied a mechanistic ecosystem model to interpret heating impacts on a California forest subjected to 1 m deep, 4°C heating. The model accurately simulated control-plot CO 2 fluxes, SOC stocks, fine root biomass, soil moisture, and soil temperature, and the observed increases in F s and decreases in fine root biomass. We show that a complex suite of interactions can lead to a consistent increase in F s (~17%) over the 5-year study period, with very small changes in SOC stocks (<1%). Modeled increases in leaf water stress from soil drying reduced GPP and NPP. The resulting reduction in leaf and fine root allocation increased fine root litter inputs to the soil and reduced root exudation. Soil heating led to about a 50% larger increase in root autotrophic respiration than in heterotrophic respiration, with the heating effect on both these fluxes decreasing over the simulation period. Increased heterotrophic respiration led to increased soil N availability and plant N uptake. These heating responses are mechanistically linked, of magnitudes that can affect ecosystem dynamics, and long-term observations of them are rarely made. Therefore, we conclude that a coupled observational and mechanistic modeling framework is needed to interpret manipulation experiments, and to improve projections of climate change impacts on terrestrial ecosystem carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ENSO Recharge Oscillator Theory Integrating the Southward Wind Shift

The El Niño–Southern Oscillation (ENSO) is one of the most‐well understood climate phenomena, and the Recharge Oscillator (RO) theory is widely used to conceptualize its physics in observations and models. ENSO‐associated equatorial zonal wind anomalies shift southward in boreal winter, contributing to ENSO termination. Thus far, this effect has not been explicitly described in the RO framework. Here we derive a new form of the RO under the low‐frequency limit that incorporates the seasonal migration of zonal wind anomalies. In our theory, the Bjerknes feedback depends on the central latitude of zonal wind anomalies, decaying exponentially as winds shift away from the equator. Meanwhile, wind anomalies centered off the equator induce equatorial wave adjustment and thereby have a delayed effect on sea surface temperature anomalies. A stochastic RO simulation with a prescribed wind shift reproduces about half the amplitude of ENSO seasonal synchronization as well as ENSO combination tones.

54 ENVIRONMENTAL SCIENCES↗