We propose a methodology for evaluating the performance of climate models based on the use of the Wasserstein distance. This distance provides a rigorous way to measure quantitatively the difference between two probability distributions. The proposed approach is flexible and can be applied in any number of dimensions; it allows one to rank climate models taking into account all the moments of the distributions. Furthermore, by selecting the combination of climatic variables and the regions of interest, it is possible to highlight the deficiencies of each of the models under study. The Wasserstein distance thus enables a comprehensive evaluation of climate model skill. We apply this approach to a selected number of physical fields, ranking the models in terms of their performance in simulating them, as well as pinpointing their weaknesses in the simulation of some of the selected physical fields in specific areas of the Earth.

Microbursts are impulsive (<1s) injections of electrons (few keV to >MeV) from the outer radiation belt into the atmosphere, primarily caused by nonlinear scattering by chorus waves. Although attempts have been made to quantify their contribution to outer belt electron loss, the uncertainty in the overall size and duration of the microburst region is typically large, so that their contribution to outer belt loss is uncertain. We combine datasets that measure chorus waves (Van Allen Probes (RBSP), Arase, ground-based VLF stations) and microburst (>30 keV) precipitation (FIREBIRD II and AC6 CubeSats, POES/MetOp) to determine the size of the microburst-producing chorus source region beginning on 5 December 2017. We show that the lower/upper limits on the long-lasting (~30 hours) microburst precipitation region is 4 to 8 MLT and 2 to 8.5 L. We conclude that microbursts likely represent a major loss source of outer radiation belt electrons for this event.

Small tsunamis caused by a ship and a supercell thunderstorm that occurred on the coast of Norway on 30 June 2019 are compared. Free waves are generated in either case, at shallow depth changes of the ocean area or fjord. The generation occurs at subcritical speed and is similar in the two cases. How shorter waves appear in the generation process are discussed. The ship moving in Oslofjorden causes upstream waves where a leading part is followed by shorter waves of essential extension and amplitude. The latter, of period of one third of the main waves, are caused by the short bow and stern. The fast moving thunderstorm generated a free wave trough that was reflected from the coast 22 minutes prior to arrival of the forced wave. This period triggered resonant oscillations in fjords and caused damage. Reflected free waves had period just over 30 minutes.

High rate Global Navigation Satellite System (GNSS) deformation time series capture a broad spectrum of earthquake strong motion signals for rapid contributions to hazard warnings and assessment, but experience regular sporadic noise that can be difficult to distinguish from true seismic signals. Previous studies developed methods for automatically detecting these signals but most rely on various external inputs to differentiate true signal from noise. In this study we generated a dataset of high rate GNSS time differenced carrier phase (TDCP) velocity time series concurrent in space and time with expected seismic surface waves from known seismic events. TDCP velocity processing has increased sensitivity relative to traditional geodetic displacement processing without requiring sophisticated corrections. We trained, validated and tested a random forest machine learning classifier. We find our supervised random forest classifier outperforms the existing detection methods in stand-alone mode by combining frequency and time domain features into decision criteria. We optimized the classifier on a balance of sensitivity and false alerting. Within a 100km epicentral radius, the classifier automatically detects 86% of events greater than MW5.0 and 98% of events greater than MW6.0. The classifier model has typical detection latencies seconds behind S-wave arrivals when run in real-time mode on “unseen” events. We conclude the performance of this model provides sufficient confidence to enable these valuable ground motion measurements to run in stand-alone mode for development of edge processing, geodetic infrastructure monitoring and inclusion in operational ground motion observations and models.

A three-dimensional physical-biogeochemical ocean numerical model with eddy-permitting horizontal resolution was applied to simulate upper-ocean carbon cycle variabilities in the Tropical Maritime Continent (TMC) over the last decade (2010–2019). Forced by atmospheric and oceanic reanalysis products with high temporal resolution, the model showed robust consistency with the observed seasonality of pCO2 and atmospheric CO2 sink/source characteristics across the modeling domain. Within the TMC, the model results indicated strong CO2 degassing along the west of Sumatra-south of Java associated with the seasonal cycle of the upwelling system in the area. While acting as a full-year atmospheric CO2 source, the TMC exhibited pronounced interannual modulation in both pCO2 and sea-air CO2 flux over the last decade. Large-scale anomalous strong CO2 degassing from 2015 to 2016 in response to the evolution of the 2015/2016 El Niño was observed from the simulation results. Modulations related to the Indian Ocean Dipole (IOD), on the other hand, were confined along the west of Sumatra-south of Java with a higher magnitude compared with anomalies related to El Niño/La Niña. Simulation results also captured the asymmetric response of the upper-ocean carbon cycle to the IOD over the last decade, where anomalies during negative IOD (nIOD) were notably strong despite being indicated as weak nIOD events by the Dipole Mode Index.

Land evapotranspiration (ET) and lake evaporation are important water budget components, representing the main processes of energy and water exchange between the earth and the atmosphere, and thus can influence the regional-scale hydrological cycles. Based on several long-term and comprehensive land-atmosphere interaction measurements over the Tibetan Plateau, the total amounts of land ET and lake evaporation are estimated by a combination of satellite products and meteorological data, the results show that: (1) the total ET amount has an average annual value of 1.238±0.058×103 km3. The trends of annual ET amount show high variable in spatial distributions, with an increasing trend in the east plateau and a decreasing trend in the west plateau. (2) As for the lake surface, lake ice phenology are clearly presented by MODIS 8-day snow cover products, and they show large spatial variability in the duration of ice-free season. The estimated Bowen ratio and evaporation amounts show acceptable accuracies, and display opposite spatial distributions, with the latter being higher in the southern part than in the northern part. On the TP, a lake with a higher elevation, a smaller area and a larger latitude mostly corresponds to a shorter ice-free season (a lower total net radiation), a larger Bowen ratio and finally a lower evaporation amount. The multi-year average evaporation amounts are listed, with the total water evaporated from lake surface being approximately 29.4±1.2 km3 year-1 for the studied 75 lakes and 51.7±2.1 km3 year-1 for all Plateau lakes included. (3) To further explore the land/lake-atmosphere interaction processes in detail over data-limited regions of the TP and supported by the “Third Pole Environment (TPE) program, 16 comprehensive observation and research stations have been constructed over all kinds of landscapes and in different regions of the TP in 2021. These data have provided significance for future research on plateau- and regional-scale water budget, hydrological cycle and water resources management.

Strong radar reflections have been previously mapped at the base of the Martian South Polar Layered Deposits (SPLD). Here, we analyze laboratory measurements of dry and briny samples to determine the cause of this radar return. We find that liquid vein networks consisting of brines at the grain boundaries of ice crystals can greatly enhance the electrical conductivity, thereby causing strong radar reflection. A liquid brine concentration of 2.7–6.0 vol% in ice is sufficient to match the electrical properties of the basal reflection as observed by MARSIS. When brine is mixed with sediments, we find that the brine-ice mixture in the pores must be 2–5 times more concentrated in salt, increasing the brine concentration to 6.3–29 vol%. Thus, our best fit of the median observed MARSIS value suggests a salt-bulk sample concentration of ~6 wt%, which is ~8 larger than that of the Phoenix landing site. To form brine, the basal reflector must reach a temperature greater than the eutectic temperature of calcium perchlorate of 197.3±0.2 K. Colder metastable brines are possible, but it is unclear if brines can remain metastable for millions of years. Additionally, grey hematite with a concentration of 33.2-59.0 vol% possess electrical properties that could cause the observed radar returns. However, such concentrations are 2-3 times larger than anywhere currently mapped on Mars. We also demonstrate that brines mixed with high-surface-area sediments, or dry red hematite, jarosite, and ilmenite cannot create the observed radar returns at low temperatures.

The ionosphere contains many small-scale electron density variations that are under represented in smooth physics-based or climatological models. This can negatively impact the results of Observation System Simulation Experiments, which use a truth model to simulate data. This paper addresses this problem by using ionosonde data to study ionospheric variability and build a new truth model with empirically-driven variations. The variations are studied for their amplitude, horizontal and vertical size, and temporal extent. Results are presented for different local times, seasons, and at two different points in the solar cycle. We find that these departures from a smooth background are often as large as 25\% and are most prevalent near 250 km in altitude. They have horizontal spatial extents that vary from a few hundred to a few thousand kilometers, and typically have the largest horizontal extent at high altitudes. Their vertical extents follow the same pattern of being larger at high altitudes, but they only vary from 10s of km up to 200 km in vertical size. Temporally, these variations can last for a few hours. The procedure for using these spatial and temporal distributions to add empirically-driven variance to a smooth truth model is outlined. This process is used to make a truth model with representative variations, which is compared to ionosonde data as well as GPS Total Electron Content (TEC) data that was not used to inform the model. The new model resembles the data much better than the smooth models traditionally used.

This study reports two different properties of ionospheric perturbations detected to the west and south of the Korean Peninsula after the Hunga-Tonga volcanic eruption on 15 January 2022. Transient wave-like total electron content (TEC) modulations and intense irregular TEC perturbations are detected in the west and south of the Korean Peninsula, respectively, about eight hours after the eruption. The TEC modulations in the west propagate away from the epicenter with a speed of 302 m/s. Their occurrence time, propagation direction and velocity, and alignment with the surface air pressure perturbations indicate the generation of the TEC modulations by Lamb waves generated by the eruption. The strong TEC perturbations and L band scintillations in the south are interpreted in terms of the poleward extension of equatorial plasma bubbles (EPBs). We demonstrate the association of the EPBs with volcanic eruption using the EPB occurrence climatology derived from Swarm satellite data.

Acetone is an abundant volatile organic compound with important influence on ozone and atmospheric self-cleaning processes. The budget of acetone is influenced by various sources and sinks. Direct sources include anthropogenic, terrestrial vegetation, oceanic, and biomass burning emissions, while chemistry forms acetone from other compounds. Sinks include deposition onto the land and ocean surfaces, as well as chemical loss. The GISS Earth System Model, ModelE, is capable of simulating a variety of Earth system interactions. Previously, acetone had a very simplistic representation in the ModelE chemical scheme. This study assesses a greatly improved acetone tracer scheme, in which acetone's sources, sinks and atmospheric transport are now tracked in 3 dimensions. Extensive research was conducted to assess how well past literature supported the new global acetone budget. Anthropogenic, vegetation, biomass burning, and deposition schemes fit well with previous studies. While their net fluxes were well-supported, source and sink terms for chemistry and the ocean were overestimated and underestimated, respectively. In iterations of the chemistry scheme, it was found that the production of acetone from hydrocarbon oxidation is a strong leverage to the overall chemical source. Spatial distributions reveal that ocean uptake of acetone dominates northern latitudes, while production is mainly in mid-southern latitudes. Ocean surface conditions influence ocean-acetone interactions and will be considered when modifying the ocean scheme in future work. The seasonality of acetone-related processes was also studied in conjunction with field measurements around the world. These comparisons show promising results, but have shortcomings at urban locations, since the model's resolution is too coarse to capture high-emission areas. Overall, an analysis of the acetone budget aids the development of the tracer in the GISS ModelE, a crucial step to parameterizing the role of acetone in the atmosphere.

Self-propelled motion is an agnostic biosignature that is observed widely, yet motility of microbes in their natural environments is sparsely studied. In this study we use a Digital Holographic Microscope (DHM) for in situ imaging of aquatic samples in extreme environments to investigate motility and morphology as biosignatures. Samples were collected from glaciovolcanic ice caves, glacial runoff, hot springs, and mixed glacial and hot spring samples.The transport and deposition of materials and heat from the volcanic subsurface in glaciovolcanic caves may be similar in some respects to the eruption processes of the plumes of Enceladus. Through different tracking methods, we identified concentrations of organisms, morphologies, swimming patterns, speeds, and turn angles. In every type of sample we looked we were able to identify motile organisms. Methods for distinguishing active swimming from Browian motion and drift are considered. Field work was done over two deployments in collaboration with the Thermal High-voltage Ocean-penetrating Research platform (THOR) science team and EELS robotics team. This work and these collaborations intend to inform future off-world extant life detection missions of the utility of DHM and motility as an investigation tool and biosignature, respectively.

A physically rational model for river width is critical to predict macroscopic landscape evolution driven by fluvial sediment transport. Growing evidence suggests that rivers widen until the stress exerted by the fluid on the bed surface is close to the critical entrainment stress of the bank material. In this study, we test the limits of this model as a closure assumption in dynamically evolving river systems. We consider a simple laboratory channel with a fixed water discharge, monodisperse bed material, no sediment supply, and an initial relief that was sufficiently large to guarantee a finite transport capacity. Over time, the transport capacity approaches zero through changes in channel morphology. Concurrent measurements of width and sediment load highlight departures from theory that mirror empirical trends in bankfull alluvial rivers. We suggest that lateral instability limits channel width at high sediment loads.

Historically, the sparseness of in situ open-ocean wave and weather observations has severely limited the forecast skill of weather over the ocean with major social and economic consequences for coastal communities and maritime industries. Ocean surface waves, specifically, are important for the interaction between atmosphere and ocean, and thus key in modeling weather and climate processes. Here, we investigate the improvements achievable from a large distributed sensor network combined with advances in assimilation strategies. Wave spectra from a global network of over 600 Sofar Spotter buoys are assimilated into an operational global wave forecast via optimal interpolation to update model spectra to best fit observations. We demonstrate end-to-end improvements in forecast skill of significant wave height of 38\%, and up to 45\% for other bulk parameters. This shows distributed observations of the air-sea interface, with advances in assimilation strategies, can reduce uncertainty in forecasts to dramatically improve earth system modeling.

Mineral dust alters cloud microphysical properties by acting as ice-nucleating particles (INPs). The effects of anthropogenic pollution aging on the ice nucleation activity (INA) of mineral dust are still controversial. Such effects were investigated by verifying the chemical aging of airborne size-resolved Asian dust particles via particle chemistry and morphology analyses and comparing the immersion mode INP properties of aged and normal Asian dust. The INP concentrations and ice nucleation active site densities of chemically aged supermicron dust particles (1.0-10.0 μm) were nearly equal to or slightly higher than those of normal Asian dust, which were 0.70-2.45 times and 0.64-4.34 times at -18 ℃, respectively. These results reveal that anthropogenic pollution does not notably change the INP concentrations and does not impair the INA of Asian dust. Our work provides direct observational evidence and clarifies the non-suppression effect of anthropogenic pollution on the INA of airborne East Asian dust.

Because climate change is both a physical and social phenomenon, personal experience has been considered the first step to entail how individuals perceive climate change risk and which actions can be promoted to reduce their vulnerability. Considering that agriculture is affected by climate change in several ways, farmers can provide first-hand observations of climate change impacts and suggest better adaptation options. However, modeling farmers’ behavior is a non-trivial task: personal experience is well recognized as a complex non-linear, multi-variate process due to the high heterogeneity and uncertainties in human cognition and decision-making processes. Furthermore, individual understandings of climate change are always contextualized within broader considerations, meaning that farmers are not ‘blank slates’ receiving information about climate change, but that information is always and inevitably filtered through values and worldviews. Despite the burgeoning of research on climate change, information about farmers’ awareness and risk perception is not geographically homogenized and varies substantially among countries and regions. For example, studies from Global North regions are scarce and emphasize how farmers characterize themselves rather than how they perceive and react to climate change. Drawing on farmers’ surveys in the Lombardy region (Italy), we provide an empirical study to pre-test the triple-loop analysis of farmers’ behavior regarding climate change: awareness, perceived impacts, and adaptation measures and barriers. Applying descriptive statistics and considering socio-economic data and farm characteristics, we address two main research questions: 1) What are farmers’ perceptions of climatic impacts and which responses do they promote? 2) How do personal experience and attitude change is conditioning farmers’ adaptation capacity? Obtained results from accurate bottom-up knowledge on farmers’ behavior may increase policy-makers’ and managers’ understanding of climate change and re-think local policies, which is essential to address agricultural risks in climate change hotspots.

Classification of weather systems provides a simple description of atmospheric circulations and bridges the gap between large-scale atmospheric conditions and local-scale environmental variables. However, the existing classification methods are challenged due to lack of labels and inaccurate similarity measures between data samples. In this letter, we propose a self-supervised Spatiotemporal Contrastive Learning (SCL) framework for the classification of weather systems without manual labels. In particular, we operate both spatial and temporal augmentation on multivariate meteorological data to fully explore temporal context information and spatial stability in accordance with synoptic nature. With the classification results, we apply a statistical downscaling method based on analog forecasting for the assessment and comparison of classification results. The experimental results demonstrate that the proposed SCL model outperforms traditional classification methods.

Many theories exist to predict the growth of Microcystis, one major type of toxic cyanobacteria that form harmful algal blooms. However, the impacts of suspended particles, which are ubiquitous in freshwater, on Microcystis growth have not been fully understood. Here, we show that a smectite clay can inhibit the growth of Microcystis aeruginosa, a typical toxic freshwater cyanobacterium, through physical clay-cell interactions. We grow M. aeruginosa under identical growth conditions in three nutrient solutions: one pure solution, one with a synthetic and transparent clay, and another one chemically modified by clay but with clay particles removed. Cells in pure solution and chemically-modified solution grow equally well, while cells in solutions with the physical presence of clay do not grow nor produce pigments. Microscopic imaging of clay-cell interactions suggests that the inhibition of the growth of M. aeruginosa by clay is due to the physical encapsulation of cells in clay.

We investigated historical changes in temperature and phytoplankton in Fuxian Lake (FX) and Dianchi Lake(DC), two plateau lakes in Southwest China. We detected sterols and alkyl diols as algal biomarkers (dinoflagellates, diatoms, eustigmatophytes, cyanobacteria, and chlorophyta) in the sediment cores by using GC-MS. We characterized sedimentary organic matter (SOM) via Rock-Eval analysis to obtain TOC, S2, the hydrogen index (HI), and refractory carbon (RC), and measured nutrients such as total phosphorus (TP) and total nitrogen (TN) in the sediment cores. The results showed that the sedimentation rates measured by 210Pb dating ranged from 0.02 to 0.30 g cm-2 yr-1 and 0.07 g cm-2 yr-1 in FX and DC, respectively. In the past 100 years, total phytoplankton biomarker concentrations ranged from 2.25 µg g-1 to 59.1 µg g-1 and from 2.20 µg g-1 to 76.4 µg g-1 in FX and DC respectively. Primary productivity derived from the S2 and HI parameters showed an increasing trend in FX, while it showed a trend of first increasing and then decreasing in DC. Cyanobacteria and chlorophyta were the two dominant phylum in the two lakes. Algal growth was controlled primarily by temperature and nutrient availability. Principal component analysis (PCA) and multiple linear regression analysis showed that the major factors controlling the historical change in phytoplankton growth were TN and temperature in FX and DC.