GRL

Diagnosing the role of internal variability over recent decades is critically important for both model validation and projections of future warming. Recent research suggests that for 1980–2022 internal variability manifested as Global Cooling and Arctic Warming (i-GCAW), leading to enhanced Arctic Amplification (AA), and suppressed global warming over this period. Here we show that such an i-GCAW is rare in CMIP6 large ensembles, but simulations that do produce similar i-GCAW exhibit a unique and robust internally driven global surface air temperature (SAT) trend pattern. This unique SAT trend pattern features enhanced warming in the Barents and Kara Sea and cooling in the Tropical Eastern Pacific and Southern Ocean. Given that these features are imprinted in the observed record over recent decades, this work suggests that internal variability makes a crucial contribution to the discrepancy between observations and model-simulated forced SAT trend patterns.

Recent distributed acoustic sensing (DAS) experiments in ocean areas throughout the world have accumulated records for various wavefields. However, there are few tsunami records because tsunami observation depends on the DAS experimental period and its location. From continuous DAS records, we found tsunami signals at a frequency band of 5–30 mHz, which correspond to high-frequency components of tsunamis and their propagation velocities differ from low-frequency tsunamis. We estimated time series of the tsunami excitations at the source using the DAS records, which are consistent with those using records of ocean-bottom absolute pressure gauges. Our study suggests that DAS records can be used for detecting tsunami propagations in the regions where other geophysical instruments are not available, and contribute to elucidating their excitation mechanisms.

Strengthening aquatic resilience to prevent adverse shifts is critical for preserving global freshwater biodiversity and advancing Sustainable Development Goals. Nonetheless, understanding the long-term trends and underlying causes of lake ecosystem resilience at a global scale remains elusive. Here, we employ an innovative framework, integrating satellite-derived water quality indices with early warning signals and machine learning techniques, to investigate the dynamics of resilience in 1,049 lakes worldwide during 2000–2018. Our results indicate that 46.7% of lakes are experiencing a significant decline in resilience, particularly since the early 2010s, closely associated with higher human population density and anthropogenic eutrophication. In contrast, most lakes situated in alpine regions exhibit an increase in resilience, probably benefiting from climate warming and wetting. Together, this study provides a novel way to monitor lake resilience and predict undesired transitions, and reveals a widespread erosion in the ability of lakes to withstand stressors associated with global change.

Variability of ice microphysical properties like crystal size and density in cirrus clouds is important for climate through its impact on radiative forcing, but challenging to represent in models. For the first time, recent laboratory experiments of particle growth (tied to crystal morphology via deposition density) are combined with a state-of-the-art Lagrangian particle-based microphysics model in large-eddy simulations to examine sources of microphysical variability in cirrus. Simulated particle size distributions compare well against balloon-borne observations. Overall, microphysical variability is dominated by variability in the particles' thermodynamic histories. However, diversity in crystal morphology notably increases spatial variability of mean particle size and density, especially at mid-levels in the cloud. Little correlation between instantaneous crystal properties and supersaturation occurs even though the modeled particle morphology is directly tied to supersaturation based on laboratory measurements. Thus, the individual thermodynamic paths of each particle, not the instantaneous conditions, control the evolution of particle properties.

Chorus subpackets are the wave packets with modulated amplitudes in chorus waves, commonly observed in the magnetospheres of Earth and other planets. Nonlinear wave-particle interactions have been suggested to play an important role in subpacket formation, yet the corresponding electron dynamics remain not fully understood. In this study, we have investigated the electron trapping through cyclotron resonance with subpackets, using a self-consistent general curvilinear plasma simulation code simulation model in dipole fields. The electron trapping period has been quantified separately through electron dynamic analysis and theoretical derivation. Both methods indicate that the electron trapping period is shorter than the subpacket period/duration. We have further established the relation between electron trapping period and subpacket period through statistical analysis using simulation and observational data. Our study demonstrates that the nonlinear electron trapping through cyclotron resonance is the dominant mechanism responsible for subpacket formation.

Satellite laser ranging (SLR) is a well-established geodetic technique for measuring the low-degree time-variable gravity field for decades. However, its application in mass change estimation is limited by low spatial resolution, even for global mean ocean mass (GMOM) change which represents one of the largest spatial scales. After successfully correcting for signal leakage, for the first time, we can infer realistic GMOM changes using SLR-derived gravity fields up to only degree and order 5. Our leakage-corrected SLR GMOM estimates are compared with those from the Gravity Recovery and Climate Experiment (GRACE) for the period 2005 to 2015. Our results show that the GMOM rate estimates from SLR are in remarkable agreement with those from GRACE, at 2.23 versus 2.28 mm/year, respectively. This proof-of-concept study opens the possibility of directly quantifying GMOM change using SLR data prior to the GRACE era.

Machine learning-based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three-dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL-based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities.

Eruptive dynamics is influenced by gas escape from the ascending magma. Gas pathways form in the magma via bubble coalescence, leading to gas channeling. Magmatic crystals play a key role in gas channel formation. This work constrains experimentally decompression-induced coalescence in high-crystallinity silicic magmas without external deformation, focusing on low gas content and bimodal crystal size (microlites and phenocrysts). All percolating samples have permeabilities of 10−14 m2 at bulk porosities of 7–10 vol% and bulk crystallinities up to 75 vol%. Our results demonstrate the possibility of coalescence-related outgassing at high pressure (120–350 MPa) and without external strain, which corresponds to magma stagnating deep in a volcanic conduit. Channeling at such low gas content implies that bimodal crystallinity favors effusive over explosive volcanic behavior. It may also be the missing physical mechanism explaining gas transfer across magmatic systems despite high melt viscosity and low or absent magma extrusion.

Composite analyses of NOAA satellite-based outgoing longwave radiation data and ERA5 reanalysis data for nearly six solar maximum periods support the existence of a response of tropical convection and precipitation to short-term (∼27-day) solar ultraviolet variations. Following solar UV peaks, the response consists of an increase in average convection and precipitation in the equatorial Indian Ocean and a decrease in the western and central tropical Pacific, with maximum amplitude at a lag of 4 to 8 days. The opposite occurs following short-term solar UV minima. The observed responses are most detectable when the Madden-Julian oscillation (MJO) is active and appear to be related to a reduced ability of the MJO to propagate across the Maritime Continent barrier following solar UV peaks relative to UV minima. A similar behavior has previously been found when the stratospheric quasi-biennial oscillation is in its westerly phase relative to its easterly phase.

The hydrological and biogeochemical properties of the hyporheic zone in stream and riverine ecosystems have been extensively studied over the past two decades. Although it is widely acknowledged that sediment heterogeneity can influence biogeochemical reactions, little effort has been made to understand the role of heterogeneity on the spatiotemporal variability of riverbed redox conditions under changing flow dynamics at different timescales. Here we integrate a mechanistic model and field data to demonstrate that heterogeneity in permeability plays a vital role in modulating sediment redox conditions at both seasonal (annual) and event (daily-to-weekly) timescales, whereas heterogeneity in particulate organic carbon (POC) content only has a comparable influence on redox conditions at the seasonal timescale. These findings underscore the importance of accurately characterizing sediment heterogeneity, in terms of permeability and POC content, in quantifying biogeochemical dynamics in the riverbed and hyporheic zones of riverine ecosystems.

Since volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io's surface have been monitored from both spacecraft and ground-based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a ground-based telescope. These images, acquired by the SHARK-VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io's trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io's surface using adaptive optics at visible wavelengths.

La Palma, Canary Islands, had its largest historical eruption in 2021. From January 2022 to May 2023 there were >2,100 seismic events, primarily at depths ≤20 km, prompting us to update the deformation and modeling study, using interferometric synthetic aperture radar observations and a last generation interpretation tool. We detect the evolution of the remaining magmatic body in the SW portion of the island, with arrival of new magma moving into the oceanic crust out to sea, and a pressurized zone in the central-eastern area, at regions of structural weakness. The current source characteristics have some similarities to the early stage dynamics prior to the 2021 eruption. Operational and multidisciplinary studies must continue to monitor either their stabilization or growth and destabilization. The ability to identify magma ascent using only deformation data over short time periods allows us to characterize unrest patterns and provide new insights into volcanic processes.

The evolution of coastal wetlands is a complex process which is difficult to forecast, made more complicated by the addition of changing climatic conditions. Here, long term ecological and geomorphological data are coupled to geotechnical measurements at a coastal wetland in North Inlet estuary, South Carolina. The coupled methodology is presented and discussed in context of understanding coastal wetland system evolution in a changing climate. Specifically, the root shear strength of Spartina alterniflora across a range of elevations was investigated using a cone penetrometer test. Elevation, shear strength, and biomass are shown to be critically interconnected. Root strength was shown to decrease with increased inundation time and decreased elevation (i.e., mudflats). Conversely, the data set illustrates the importance of maintaining key elevation ranges in relation to sea-level to optimize wetland resilience.

The climate benefit of blue carbon sequestered by mangrove forests can be partially offset by CH4 emission, but this offset is rarely assessed using multi-year high-frequency measurements. Here, four-year eddy covariance measurements were used to examine temporal patterns of CH4 flux and its blue carbon offset (i.e., reduced climate benefit) in a subtropical estuarine mangrove in China. We found both diel and seasonal CH4 fluxes were mainly driven by soil temperature and tidal activities, showing greater nighttime emission. On average, one-tenth of CO2 uptake was offset by CH4 emission using the sustained-flux global warming potential metric at a 20-year time horizon, while this offset could vary over an order of magnitude due to asynchronous fluxes of CH4 and CO2 across diel and seasonal cycles. These results highlight the significant contribution of nighttime emission to mangrove CH4 budget and the importance of asynchronous flux variations in assessing mangrove's climate benefit.

Tornadoes, as highly destructive weather events, require accurate detection for effective decision-making. Traditional radar-based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi-Task Identification Network (MTI-Net), leveraging Doppler radar data to enhance tornado recognition. MTI-Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI-Net, we introduce a novel backbone network of Multi-Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI-Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI-Net in handling small-scale tornado events.

The role of internal variability in generating an apparent link between autumn Barents-Kara sea (BKS) ice and the winter North Atlantic Oscillation (NAO) has been intensely debated. In particular, the robustness and causality of the link has been questioned by showing that BKS-NAO correlations exhibit nonstationarity in both reanalysis and climate model simulations. We show that the lack of ice observations means nonstationarity cannot be confidently assessed using reanalysis prior to 1961. Model simulations are used to corroborate an argument that forced nonstationarity could result from ice edge changes due to global warming. Consequently, the observed change in BKS-NAO correlations since 1960 might not be purely a result of internal variability and may also reflect that the ice edge has moved. The change could also reflect the availability of more accurate ice observations. We discuss potential implications for analysis based on coupled climate models, which exhibit large ice edge biases.

West Africa continues to host a growing number of low and intermediate-magnitude earthquakes (M2-5) along its passive margins, and its continental interior. Earthquake activity in these regions raises the need to comprehend the causes and the tectonic controls of the seismicity. Unfortunately, such studies are rare. Here, we apply single-station inversion techniques to constrain fourteen focal mechanisms, computed after compiling a set of high-quality waveforms. We describe the connection between seismicity, the contemporary stress field, anthropogenic activity and Holocene fault scarps in the region. Our results indicate transpressive stresses acting on the inherited brittle structures in the passive margins. We also observe a compressive regime in the intracontinental failed rifts. We attribute the seismicity to the reactivation of “weak” faults in the Neoproterozoic and Mesozoic failed rifts, the passive transform structures, and the intracratonic Precambrian brittle shear zones.

We use neural networks and large climate model ensembles to explore predictability of internal variability in sea surface temperature (SST) anomalies on interannual (1–3 years) and decadal (1–5 and 3–7 years) timescales. We find that neural networks can skillfully predict SST anomalies at these lead times, especially in the North Atlantic, North Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The spatial patterns of SST predictability vary across the nine climate models studied. The neural networks identify “windows of opportunity” where future SST anomalies can be predicted with more certainty. Neural networks trained on climate models also make skillful SST predictions in reconstructed observations, although the skill varies depending on which climate model the network was trained. Our results highlight that neural networks can identify predictable internal variability within existing climate data sets and show important differences in how well patterns of SST predictability in climate models translate to the real world.

The electric fields of subauroral polarization streams (SAPS) have been suggested to affect energetic charged particles' dynamics in the inner magnetosphere, though their role on radiation belt electrons has never been properly quantified. A moderate geomagnetic storm on 2015-09-07 caused the deep injection of 10–100s of keV electrons in Earth's inner magnetosphere to low L* (L* < 4). Using a 2-D test particle tracer, we present the effects of electric fields given by the Volland-Stern model, a SAPS (Goldstein et al., 2005, https://doi.org/10.1029/2005ja011135) model, and a modified SAPS model on the energetic electron deep injections. The modified SAPS model reflects the SAPS electric field observations by the Van Allen Probes and is supported by Defense Meteorological Satellite Program observations. Simulations suggest that the SAPS electric field pushes 10–20 MeV/G electrons Earthward to L* ∼ 2.7 in 2.5 hr, much deeper compared to the Volland-Stern electric field.

Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. In this study, we show that a snow fence experiment on High-Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo-erosion gully in the landscape. We estimate that 1.1–3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilized aquatic carbon. Our results show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss, highlighting the need for improved model representation.