Weather at the mid-latitudes are governed by cyclones and anticyclones mostly migrating eastward. These weather systems cause the jet streams to undulate, the meandering patterns are known as the Rossby waves. Occasionally, Rossby waves bring forth localized extreme weather phenomena. An example of finite-amplitude wave phenomenon is atmospheric blocking, which is often associated with heat waves and droughts. Recent development of a finite-amplitude local wave activity (FALWA) theory by Nakamura and collaborators enables comprehensive analysis of the dynamics of finite-amplitude Rossby waves observed in climate data, which helps understand the drivers of their life cycles. Despite the simplicity of interpretation it brings about, to apply the FALWA diagnostic to climate data requires more involved calculations than the traditional Eulerian framework. This article introduces the open-source Python package falwa which encapsulates the FALWA diagnostics implemented on gridded climate data presented in the authors' previous publications. It reviews the essence of the FALWA theory, the corresponding components in the package that implements the calculations, and where users can find sample notebooks to start with. It aims to serve as a road map for new users to easily navigate through this package. The latter half of this article documents the practices of the developers, which includes the documentation tools, continuous integration practice, and repository maintenance using automated GitHub functionalities. The authors also discuss existing numerical issues and future improvement plans. Hopefully, this open-source project encourages wider use of the FALWA diagnostics on climate data and model output by minimizing the impediment of the complex numerical computations.

A document by Xiaochen Zhao. Click on the document to view its contents.

Clumped isotope thermometry (T(∆47)) of soil carbonates provides an estimate of soil temperature at the time of mineral formation. Historically, that temperature has been interpreted to represent a warm-season soil temperature based on modern calibration studies largely done in (very) coarse-grained soils. Additionally, T(∆47) gives us an estimate of the oxygen isotope composition of soil water (δ18Ow) in the past, but previous calibration work has not generated independent δ18Ow datasets with which to understand these archives. Here, we present a modern calibration study of pedogenic carbonate clumped isotope thermometry in three soils in Colorado and Nebraska, USA, that have a fine-medium grain size, contain clay, and are representative of many carbonate-bearing paleosols preserved in the rock record. At two of the three sites, Briggsdale, CO and Seibert, CO, T(∆47) overlaps with mean annual air temperature (MAAT), and the calculated δ18Ow overlaps within uncertainty with measured δ18Ow at carbonate bearing depths. At the third site in Oglala National Grassland, NE, mean T(∆47) is 9 – 10°C warmer than MAAT, and the calculated δ18Ow has a significantly higher isotope value than any observations of δ18Ow. At all three sites, even in the fall season, δ18Ow values at carbonate bearing depths indicate little to no evaporative enrichment of heavy isotopes. Moreover, δ18Ow values overlap with precipitation isotope values from spring precipitation. Altogether, these data indicate that soil grain size affects pedogenic carbonate formation, and highlight a need for continued research in modern systems that emulate key features of the geologic record.

We conduct a high-resolution seismic tomography for the crustal P and S-wave velocities of Yunnan region in southwestern China. Waveforms recorded at 128 broadband stations from 131 regional earthquakes of moment magnitudes 3.9−5.5 occurring between 2009 and 2021 are used to obtain traveltime residuals by the cross-correlation between records and synthetics. Using the regional community velocity model SWChinaCVM‐1.0 as the initial model, we carry out a three-stage iterative adjoint tomography, progressing from the longer period band of 50–20 s to shorter-period bands of 30–10 s and 30–5 s. The final model shows general consistency in the spatial patterns of P- and S-wave velocity anomalies. Widespread low-velocity anomalies with high-Vp/Vs ratios in the mid and lower crust in the region suggest a mix of weak materials of the mid-lower crustal flow from under the Tibetan Plateau with hot materials of the upwelling from the deep mantle plume that led to the Emeishan Large Igneous Province. Localized velocity and Vp/Vs ratio anomalies also reveal that the Lijiang-Xiaojinhe Fault Zone appears to be confined in the upper crust, while the Anninghe-Zemuhe Fault Zone and the Xiaojiang Fault Zone are both whole-crust structures reaching the Moho interface. The Red River Fault Zone, a whole-crust fault, separates the Yangtze Craton to the northeast from the Indo-China Block to the southwest. The main fault zones, the decoupling between the crustal and uppermost mantle parts, and the wide-spreading weak mid-lower crustal materials mutually interact, all contributing to the tectonic evolution of the entire region.

The Penultimate Deglaciation (PDG) saw warming from the Penultimate Glacial Maximum to the Last Interglacial (LIG). We present 18-kyr transient simulations through the PDG into the LIG using a fully-coupled three-dimensional ocean-atmosphere model forced with PMIP4 transient boundary conditions. We perform sensitivity experiments to disentangle the roles of the multiple drivers. We also perform steady-state simulations with matched boundary conditions to explore Earth-system memory. We focus on time series of global temperature, Atlantic Meridional Overturning Circulation (AMOC) strength and Asian monsoon precipitation. We demonstrate a tight coupling of these Earth-system components, with meltwater-driven AMOC variability driving the timing of changes in global temperature and Asian monsoon strength. The magnitude of glacial-interglacial warming is dominated by CO2 and ice sheets, but the timing is highly sensitive to orbital forcing and meltwater fluxes. Transient and steady-state global temperatures can differ by up to 2.0 °C during the deglaciation. Relative to steady state, transient simulations find delayed multi-millennial global warming, a marginally cooler early LIG, and differences in AMOC hysteresis. The 127 ka timeslice is more sensitive to disequilibrium effects compared to later stages of the LIG. The occurrence of an interstadial in the early PDG depends on the profile of meltwater forcing. The timing of final AMOC recovery is accelerated by ice-sheet retreat and orbital forcing. Indian monsoon strengthening is primarily driven by orbital forcing and CO2, but the timing is linked to AMOC recovery. Indian Monsoon variability is qualitatively most closely simulated using the meltwater forcing profile derived from ice-rafted debris.

The typical coarse resolution of Earth system models (ESMs) ($\sim$100 km) is insufficient to represent atmospheric features critical for aerosols and aerosol-cloud interactions (ACI), contributing to uncertainties in climate predictions. Significant efforts have been made to develop next-generation ESMs for global kilometer-scale resolutions. However, the behavior of aerosol and ACI parameterizations at kilometer scales within a global ESM framework is unclear, and model evaluation at such high resolutions is computationally infeasible. To address this challenge, aerosol and ACI in the Energy Exascale Earth System Model (E3SM) are evaluated at a convection-permitting 3.25 km resolution using the regionally refined mesh (RRM) capability. Kilometer-scale E3SM simulations are performed in four geographical regions with distinct aerosol and cloud conditions. These kilometer-scale simulations are compared with coarse-resolution E3SM simulations and are evaluated against ground-based, aircraft, and satellite measurements. Results show that increasing model resolution moderately improves the multivariable relationships related to ACI, such as the cloud condensation nuclei number versus cloud droplet number (N$\mathrm{_d}$), and N$\mathrm{_d}$ versus the liquid water path. However, its impact on accurately predicting aerosol properties varies by region. Overall, the differences between E3SM simulations at different resolutions are smaller than the differences between model simulations and observations. These results suggest that increasing resolution is insufficient to improve the simulation of aerosol and ACI with existing process representations. Improved process representations are required to achieve more accurate simulations of aerosol and ACI at global kilometer scales.

Convection-permitting dynamical downscaling (CPDD) allows for an explicit representation of the storm-scale generators of tornadoes, hail, severe thunderstorm winds, and locally heavy precipitation. Possible changes in such hazardous convective weather (HCW) due to human-induced climate change are therefore projected with higher confidence using CPDD than with analyses of relatively coarse global climate models (GCM). However, the computational resources necessary for CPDD are significant and therefore CPDD-based future projections of HCW have tended to be based on a single experiment, and thus absent of uncertainty measures otherwise determined with an ensemble of experiments via an ensemble of GCMs. Herein we present “environment-informed” CPDD as a means to efficiently generate a CPDD ensemble driven by different GCMs. This variant of CPDD is applied only to a subset of days and geographical domains over which the meteorological conditions potentially favor HCW; unnecessary model integrations on meteorologically unfavorable days and domains are thereby eliminated. The selection procedure also accounts for GCM biases.The temporal and geospatial occurrence of historical HCW over the United States is demonstrated from the perspective of environment-informed CPDD as applied to eight different GCMs and ERA5 reanalysis. The overall geographical distributions in HCW vary considerably from downscaled GCM to GCM, thus demonstrating the value of an ensemble. The efficiency in which HCW is realized in favorable environments also varies considerably across the eight downscaled GCMs.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) collects thermal observations from the International Space Station to support evapotranspiration (ET) research at fine spatial resolutions (70 m x 70 m). Initial ET estimates from ECOSTRESS Collection 1 have been used in a wide range of scientific studies and applications, though subsequent analyses identified areas for improvement. This study provides an overview of updates to ECOSTRESS Collection 2 ET and presents an accuracy assessment of ET and auxiliary variables against in situ data from AmeriFlux. Key updates in Collection 2 include: four independent model estimates of ET and improved auxiliary forcing data. We find the multi-model ensemble ET estimate achieves a root mean square error (RMSE) of 109 Wm-2 for instantaneous observations and 1.5 mm/day for daily retrievals. When considering uncertainty in energy balance closure approaches for site-level data, the RMSE improves to 48 Wm-2 for instantaneous ET. We observe variable performance based on time of day of ECOSTRESS image acquisition, climate and vegetation type. Evaluation of auxiliary data highlight limitations in down-scaled net radiation and relative humidity, contributing to a diurnal hysteresis in ET estimates. We provide accuracy metrics and model sensitivity to auxiliary data to facilitate user confidence, data adoption, interpretation, and applications. ECOSTRESS is the only instrument capable of providing ET at different times of day at high spatial scales; thus, this work is an important step toward enhancing the capabilities of satellite-driven ET models in resolving diurnal ET variations and guiding directions for future improvements.

A document by Noya Vainbrand. Click on the document to view its contents.

The Earth surface Mineral dust source InvesTigation (EMIT) is a visible-to- shortwave infrared imaging spectrometer currently aboard the International Space Station. Derivations of fractional cover from spectral unmixing algorithms have provided insights into various ecosystem functions. In the case of EMIT, they will be used by multiple global Earth systems models to constrain the sign of dust-related radiative forcing. This study aims to evaluate the efficacy of different approaches for estimating fractional cover and quantifying the corresponding uncertainty, and serves as a model to encapsulate the true error budget for EMIT. We simulated surface reflectance from a spectral library compiled from various drylands to generate millions of candidate spectra made up of different random fractions of nonphotosynthetic vegetation (NPV), green vegetation (GV), and soil. Simulated spectra were used as-is but we also tested the impact of atmospheric conditions/surface reflectance retrieval by using them to calculate top-of-atmosphere radiance then using the current EMIT surface reflectance retrieval algorithm to estimate apparent surface reflectance. We tested approaches to unmixing these simulated spectra using multiple strategies for dealing with spectrum brightness, within-class spectral variability, and library selection. We also incorporated a Monte Carlo approach to stabilize fractional cover retrievals and quantify uncertainty. The best spectral unmixing approaches produced mean absolute error <0.10 for NPV and soil and <0.06 for GV with uncertainties ≤ ± 0.02 for all classes. We named this innovative approach EndMember Combination Monte Carlo, E(MC) ², unmixing and found that our fractional cover retrievals are insensitive to atmospheric residuals in the surface reflectance data.

A document by Katie Spellman. Click on the document to view its contents.

Python has become the leading programming language in a wide variety of sciences including remote sensing. Developments in N-dimensional array handling using numpy, and particularly xarray and its integration with projects like Dask, Jax, and CUDA allow for intuitive and scalable analysis of multi-dimensional data in Python. Spatial data types can now be handled by projects like rasterio and GDAL for gridded spatial data formats like GeoTIFF, and geopandas for spatial vectors. Once gridded arrays are accessible, they can be passed to tools like scikit-learn for machine learning or for use in deep-learning with packages like Keras to predict labels. Together these packages provide all the core components for an end-to-end raster and remote sensing operations.

Many iron meteorites sample the metallic cores of planetesimals, providing a unique opportunity to study early Solar System planetary processes. Modeling the chemical evolution during crystallization of these iron meteorite groups provides evidence that both S and P were present as light elements in planetesimal cores. As fractional crystallization of these cores proceeded, the concentrations of both S and P in the residual metallic liquid in the core increased, and eventually, liquid immiscibility in the Fe-Ni-S-P system would be expected to be encountered in many cases. For example, the IIAB and IIG irons may be related in this way. Considerable previous experimental work has determined element partitioning behaviors between solid metal and liquid metal as a function of the S and P concentrations of the metallic liquid, but few experimental studies have examined trace element partitioning behavior after the onset of liquid immiscibility. In this work, we present new experimental results that determine element partitioning behavior between two coexisting immiscible metallic liquids in the Fe-Ni-S-P system, as may be experienced during the later stages of the evolution of planetesimal cores.

The radiation parameterization is one of the computationally most expensive components of Earth system models (ESMs). To reduce computational cost, radiation is often calculated on coarser spatial or temporal scales, or both, than other physical processes in ESMs, leading to uncertainties in cloud-radiation interactions and thereby in radiative temperature tendencies. One way around this issue is the emulation of the radiation parameterization using machine learning which is usually faster and has good accuracy in a high dimensional parameter space. This study investigates the development and interpretation of a machine learning based radiation emulator using the ICOsahedral Non-hydrostatic (ICON) model with the RTE-RRTMGP radiation code which calculates radiative fluxes based on the atmospheric state and its optical properties. With a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture, which can account for vertical bidirectional auto-correlation, we can accurately emulate shortwave and longwave heating rates with a mean absolute error of $0.049~K/d\,(2.50\%)$ and $0.069~K/d\,(5.14\%)$ respectively. Further, we analyse the trained neural networks using Shapley Additive exPlanations (SHAP) and confirm that the networks have learned physical meaningful relationships among the inputs and outputs. Notably, we observe that the local temperature is used as a predictive source for the longwave heating, consistent with physical models of radiation. For shortwave heating, we find that clouds reflect radiation, leading to reduced heating below the cloud.

In the Southern Ocean, subsurface chlorophyll maxima (SCMs) can indicate deep accumulations of phytoplankton. Recent observations of subsurface chlorophyll fluorescence maxima (SFMs) from a large network of biogeochemical Argo (BGC-Argo) floats suggest that Southern Ocean SCMs are widespread. However, the attribution of SFMs to SCMs is not trivial, and SFMs are often observed without the presence of subsurface biomass maxima (SBMs), where biomass is quantified by particulate organic carbon. Consequently, it is questionable if these widespread SFMs represent increased phytoplankton biomass or if they are formed by intracellular processes that alter chlorophyll fluorescence, without a concurrent increase in biomass, such as photo-acclimation or non-photochemical quenching. This study builds confidence in the interpretation of SFMs as SCMs and finds their widespread occurrence of SCMs in the Southern Ocean during summer. We identify SCMs from ship-based chlorophyll sampling and SFMs from fluorometers using a distributional shape-based clustering method which achieves consistent results between ship and BGC-Argo float datasets. Ship data reveal a 15 % disagreement in the identification of SFMs as SCMs. We attribute these uncertainties to non-photochemical quenching corrections and increases in chlorophyll fluorescence yields with depth. In the overlying waters above these SCMs we find increased non-algal contributions to bio-optical POC in the upper mixed layer. These non-algal stocks obscure deep accumulations of phytoplankton biomass and result in the decoupling of SBMs from SCMs in a way that cannot be explained by increases in intracellular chlorophyll fluorescence with depth.

The benthic biolayer is a reaction hotspot that contributes disproportionately to whole-stream reactions, including aerobic respiration and contaminant transformation. Quantifying the relative contribution of the biolayer to whole-stream mass transformation remains challenging because it requires that hyporheic zone solute transport and reaction heterogeneity are explicitly captured within a single upscaled modeling framework. Here, we use field experiments and modeling to quantify mass transformation in the biolayer relative to other stream compartments. We co-injected and monitored several fluorescent tracers, including the reactive tracer resazurin, into a controlled experimental stream whose 8.5 cm hyporheic zone is isolated from groundwater. We characterized reactive transport in the water column and at multiple hyporheic zone depths by simultaneously fitting concentration time series measured at these locations to a new mobile-immobile model, using resazurin-to-resorufin transformation as an indicator of aerobic bioreactivity. Results show that the biolayer transformed 2× more resazurin to resorufin than all other stream compartments combined, and over half of all reactions occurred within 2 advective travel times through the reach. This hotspot and hot moment behavior is attributed to the biolayer’s propensity to rapidly acquire, transiently retain, and rapidly degrade stream-borne solutes. Model analysis shows that mass transformation is highest in the biolayer across a wide range of biolayer structural properties, including scenarios when the biolayer is less reactive than deeper regions of the hyporheic zone. Together, our results point to the biolayer as a common feature of streams and rivers that should be considered in network-scale models of river corridor biogeochemistry.

Super magnetic storm occurred on October 10, 2024, and a super substorm occurred at the beginning of the storm just after the shock arrival at 1520 UT. As a result, red aurora were photographed at multiple points over wide region of Japan from 1700 UT. A new Bayesian analysis enables us to estimate the time variation of the most probable height and latitude of the red aurora based on the citizen science dataset in combination of POES/MetOp satellite datasets of electron precipitation boundary. We found that the top height of red aurora extended to ~850 km and the red aurora shifted toward low latitude according to the storm development. The ultra-high altitude of the red aurora can be evidence of rapid atmospheric heating and the atmospheric expansion.

The Southern Ocean (SO) south of 35°S represents a small source of natural inorganic carbon for the atmosphere but a major sink of anthropogenic carbon. The magnitude of the total (natural plus anthropogenic) carbon sink strongly depends on the rate at which carbon is subducted below the mixed layer. We use a global ocean model at eddying resolution under preindustrial and historical conditions to provide a detailed view of total and anthropogenic dissolved inorganic carbon (DIC) pathways across and within the time-varying mixed layer of five physically consistent regions. Within each region, subduction fluxes at the mixed layer base are decomposed into advective and diffusive contributions to determine which process dominates. Total DIC is found to be obducted south of the Antarctic Circumpolar Current (ACC), transferred northward within the mixed layer and subducted north of the ACC. This results in a net obduction of 11.2 PgC/year, with advective processes dominating the total transfer (67%). Anthropogenic carbon is taken up in all regions but anthropogenic DIC is mainly subducted north of the ACC, the carbon taken up in the south being advected northward within the mixed layer before being subducted. This subduction (1.05 PgC/year) is achieved mainly through advection and diffusion, which dominate respectively north and south of the Subantarctic Front. Advective subduction fluxes show strong zonal variations and are increased near major topographic features and boundary currents. Our results suggest that we need to untangle advective and diffusive pathways regionally in order to understand how carbon subduction will evolve.