Wiley

Geophysical Research Letters

Published by Wiley and American Geophysical Union

Online ISSN: 1944-8007

·

Print ISSN: 0094-8276

Journal websiteAuthor guidelines

Top-read articles

419 reads in the past 30 days

Location of sampling sites (a–e), spatiotemporal variability of sediment denitrification/anammox, (f and g) in China's marginal seas, and their interaction with temperature from this study, (h and i) and historical researches including our data, and (j and k). All sampling sites from this study are annotated using numbers from low to high latitudes. The color bar shows the water depth of <80 m. Different seasons are separated by vertical dotted lines in (f). Error bars represent the standard deviation of triplicates. All data in (h)–(k) are listed in Supplementary Table S2 in Supporting Information S1. Significant differences (p < 0.05) among the groups are indicated as different letters on the boxes in (h–k). NA represents not applicable.
The seasonal spatial distributions of sediment denitrification (a–f, unit in mmol N m⁻² d⁻¹) and N2O production (g–j, unit in μmol N m⁻² d⁻¹) in the marginal seas of China. Significant differences among the groups are indicated as different letters on the boxes (p < 0.05).
The conceptual diagram of sediment nitrate respiration‐derived carbon and nitrogen coupled biogeochemical cycles in China marginal seas. Bohai Sea/Yellow Sea (YS), East China Sea (ECS)/TS, and Northern South China Sea (NSCS) represent the Bohai/YS, ECS/Taiwan Strait, and NSCS, respectively. The orange and black arrows indicate N and C flows, respectively.
Sedimentary Nitrate Respiration Potentially Offsets the Climatic Benefits From CO2 Uptake by Marginal Seas

March 2025

·

421 Reads

·

·

·

[...]

·

Download

Aims and scope


Geophysical Research Letters is an open access journal that publishes high-impact, innovative, and timely communications-length articles on major advances spanning all of the major geoscience disciplines. Papers should have broad and immediate implications meriting rapid decisions and high visibility.

Recent articles


Sampling locations (modified from Hu et al., 2020). A push core (ROV05, green dot) and three piston cores PC01 (blue dot), PC02 (yellow dot), and S05 (black dot) were recovered at or near the Haima cold seeps (stars).
Geochemical parameters of carbon and sulfur in pore water and the solid phase of the studied cores. (a) Sulfate (SO4²⁻) and methane (CH4), (b) hydrogen sulfide (H2S), (c) dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) concentrations, (d) total organic carbon (TOC) contents, (e) δ¹³C values of DIC, DOC, and carbonate nodules, (f) total sulfur (TS) contents, (g) chromium reducible sulfur (CRS) contents, and (h) δ³⁴S values of CRS. CH4 concentrations adopted from a push core from the Haima seeps (Niu et al., 2023), and H2S concentrations adopted from the ROV04 push core from the Haima seeps in this study. SO4²⁻, DIC, DOC concentrations, and δ¹³C values for the push core ROV05 were taken from Hu et al. (2021) and Gong et al. (2023). SO4²⁻, H2S, and DOC concentrations and TOC and TS contents of cores PC01, PC02, and S05 were taken from Hu et al. (2023). Dotted lines mark the positions of the sulfate‐methane transition zone of cores ROV05 (green), PC01 (blue), and PC 02 (brown). Core S05 represents the reference core, while ROV05, PC01, and PC02 are cores influenced by methane seepage; mbsf, meters below the seafloor.
Geochemical parameters of reactive iron oxides (FeR) and organic carbon bound to FeR (OC‐FeR) in the studied cores. (a) FeR contents, (b) OC‐FeR contents, (c) percentage of OC‐FeR relative to TOC (fOC‐FeR), (d) molar ratio of OC‐FeR to FeR (OC:Fe), (e) δ¹³C values of TOC (δ¹³CTOC), (f) δ¹³C values of OC‐FeR (δ¹³COC‐FeR), and (g) average abundance of FeR and OC‐FeR. Dotted lines mark the positions of the sulfate‐methane transition zone in cores ROV05 (green), PC01 (blue), and PC 02 (brown); mbsf, meters below the seafloor. The orange band in panel (g) represents the average values of the seep‐impacted cores PC01 from 0 to 5.1 m below the seafloor (mbsf) and PC02 from 0 to 5.3 mbsf. Data of FeR and OC‐FeR were compared between the average values and the reference core S05 within the same depth interval.
Stability of Reactive Iron‐Bound Organic Carbon During Sulfidization of Iron Oxides: Insights From Methane‐Seep Sediments
  • Article
  • Full-text available

April 2025

·

61 Reads

·

Kai Li

·

Johan C. Faust

·

[...]

·

Plain Language Summary Marine sediments represent the largest organic carbon (OC) sink on Earth. Preservation of OC in marine sediments, often associated with reactive iron oxides (FeR), has garnered considerable attention for its potential to protect OC from degradation. The majority of marine OC bound to FeR is found in continental margin sediments, where a significant part of FeR undergoes extensive sulfidization to form authigenic iron sulfide minerals. However, the stability of OC bound to FeR during the diagenetic transformation of iron phases in marine sulfidic sediments remains poorly understood. Our study shows that a 42% decrease in FeR during early diagenesis leads to an only 6.3% reduction in OC bound to FeR in sulfidic sediments. This result suggests that bonding of OC to FeR is stable in marine sulfidic sediments during sulfidization of reactive iron oxides. Such a finding implies that bonding of OC with FeR may have been a key mechanism for OC burial in past anoxic oceans, where euxinic conditions and sulfidization of reactive iron oxides occurred more commonly. Our findings thus have far‐reaching implications to enhance our understanding of the crucial role of OC bound to FeR within the geological carbon cycle.


Overview of the dipolarization front from MMS1. (a–b) MMS1 location in geocentric solar magnetospheric coordinates. (c) Bz component of the magnetic field. (d) Bx and By components of the magnetic field. (e) The plasma number density. (f) Magnitude of ion flow velocity. (g) Omnidirectional differential energy fluxes of ions. (h) Omnidirectional differential energy fluxes of electrons. The blue shaded area represents the magnetic depression region.
Detailed analysis of the magnetic depression (a–b) Magnetic field magnitudes measured by MMS1. (c) Criterion of interchange instability (ICI), which indicates that the magnetic hole (MH) is generated by the ICI. (d–f) Phase space density as a function of electron energy in the omnidirectional, perpendicular, and parallel to the magnetic field. The blue, red, and black lines represent the intervals before the MH, inside the MH, and after the MH.
Energy conversion associated with the MH from MMS1. (a–c) The relative positions of the four MMS satellites in the XY, XZ, and YZ coordinates. (d) BL component of the magnetic field measured by MMS1. (e) Electron velocity. (f–h) L, M, and N components of the current density. (i–k) L, M, and N components of the terms in Ohm's law. The yellow region represents the measurement error. (l) Energy conversion. (m) The accumulation of the energy conversion.
Two‐dimensional image of the interchange instability (ICIs) at the dipolarization fronts. (a) Bz component of the magnetic field measured by four spacecraft. (b)–(d) Vex, Vey, and Vez components of the electron flow velocity after removing the background flow velocity. (e) Two‐dimensional image of the ICIs. The table lists the normal directions of the boundaries obtained via the MVA method.
Strong Energy Conversion by a Magnetic Hole Behind a Dipolarization Front

April 2025

·

8 Reads

Z. Y. Xu

·

H. S. Fu

·

W. D. Fu

·

[...]

·

J. B. Cao

Dipolarization fronts (DFs) have been widely reported in the Earth's magnetotail and are suggested to play an important role in energy conversion. Magnetic holes (MHs) are also usually observed near DFs, and recent spacecraft observations suggest that they can be excited by interchange instability (ICI). However, whether the MHs near DFs could contribute to energy conversion is still unknown. Here, by using the Magnetospheric Multiscale mission observations, we find a sub‐ion scale MH behind a DF. We present a two‐dimensional illustration of the MH, revealing that such an MH was generated by the ICI. Inside this MH, a significant energy conversion up to ∼2 nW/m³ (higher than typical observations near DFs) is caused by the local electron vortex current inside the MH and the background electric field on the DF. This study improves our understanding of energy injection during substorms and energy conversion near DFs.


Ascent fraction (α $\alpha $, red lines)—defined as the fraction of the tropical domain (20° ${}^{\circ}$S to 20° ${}^{\circ}$N) with negative (ascending) vertical velocity at 500 hPa over the climatological year, calculated at a monthly frequency—and estimated ascent fraction adjusted for entrainment (αeste ${\alpha }_{\mathit{est}}^{e}$, purple lines, see Section 3.2 for details) vs. SST perturbation for patches centered at different longitudes: (a) 100° ${}^{\circ}$E; (b) 140° ${}^{\circ}$E; (c) 180° ${}^{\circ}$E; and (d) 220° ${}^{\circ}$E.
(a) Seasonal cycle of control simulation climatological monthly values of simulated ascent fraction (α $\alpha $; red markers) and estimated ascent fraction calculated without (αest ${\alpha }_{\mathit{est}}$; blue markers) and with (αeste ${\alpha }_{\mathit{est}}^{e}$; purple markers) an entrainment adjustment. Also shown with corresponding horizontal lines are the ascent fractions as calculated on a monthly frequency over 12 months. Note that the entrainment parameter is chosen such that the red and purple horizontal lines are equal (see Text S2 in Supporting Information S1). (b) Instability index, Φ ${\Phi }$, for February of the control simulation (color scale) including the zero contour marked as a dashed line. The zero contour of vertical velocity at 500 hPa is also shown (solid line). (c) as for (b) but showing the entrainment‐adjusted instability index, Φe ${{\Phi }}^{e}$. (d) Entrainment‐adjustment, ϵˆh∗e $\widehat{{\epsilon}}{h}^{\ast e}$, for the same month (color scale) with the zero contour of vertical velocity as in (b).
(a, b): Joint distributions of the entrainment‐adjusted instability index in the control (x‐axes) and +2 K perturbation simulations (y‐axes) for warming patches centered at (a) 180° ${}^{\circ}$E and (b) 220° ${}^{\circ}$E. The colorscale indicates the density of each bin. Red lines and right hand y‐axes show the control instability index distribution directly above the patch. (c, d): For all SST perturbations, the proportion of the domain falling within each quadrant. Quadrants are defined as “convective regime” [top‐right of (a, b)]; “subsidence regime” [bottom‐left]; “convective margins regime” [bottom‐right]; and “subsidence margins regime [top‐left]. (e, f): As for (a, b), but here the colorscale indicates the change in net cloud radiative effect ΔCREnet $\left({\Delta }{\mathrm{C}\mathrm{R}\mathrm{E}}_{\mathit{net}}\right)$ in instability index space. (g, h): Area‐weighted contributions from each quadrant in (e, f) to the tropics‐wide change in net cloud radiative effect for each SST perturbation (black lines indicate the total changes).
(a) Adjusted instability index (colors) in February for the control simulation. Purple contour indicates region where SSTs have been warmed by ≥ ${\ge} $0.4 K in the +4 K simulation for the warming patch centered at 180° ${}^{\circ}$E. Hatching shows gridpoints identified to be in the subsidence (diagonal hatching) regime, convective regime (vertical hatching), or convective margins regime (dotted hatching) (see main text for definitions) after the +2 K perturbation. (b) As (a), but here showing results for perturbation simulation with a +2 K SST warming patch centered at 220° ${}^{\circ}$E.
Circulation and Cloud Responses to Patterned SST Warming

April 2025

·

2 Reads

Anna Mackie

·

Michael P. Byrne

·

Emily K. Van de Koot

·

Andrew I. L. Williams

Plain Language Summary Recent advances have demonstrated the importance of spatial patterns in tropical sea surface warming for determining how Earth's tropical energy balance responds to climate change. Sensitivity of the energy balance is higher when warming is concentrated in regions where air is generally ascending, such as the west Pacific, than in regions where air is generally descending, such as in the east Pacific. This variation in sensitivity across regions depends on the degree to which surface warming is communicated to the upper atmosphere, and subsequently whether low clouds brighten in the east Pacific. What is less well understood is how the atmospheric circulation—the movement of air—responds to these surface warming patterns, and how these circulation changes may be coupled to clouds and energy balance. Using climate simulations where only a specified patch of the tropical ocean is warmed, we demonstrate that if ascent regions are directly warmed these regions tend to contract in area. But there is little change in circulation if descent regions are warmed. We develop a simple conceptual model which provides insight into the mechanisms of these circulation changes and demonstrate that cloud changes can be decomposed into the responses from individual circulation regimes.


(a) A typical space hurricane auroral structure in the Northern Hemisphere observed by Defense Meteorological Satellite Program (DMSP) F16 from 19:47 to 19:53 UT on 7 June 2013. From top to bottom is the DMSP F16 in situ plasma observation: (b) field‐aligned current (FAC, positive represents upward), (c) ions horizontal cross‐track velocity, (d) electron differential energy flux, and (e) ion differential energy flux. The magenta arrow points to the space hurricane. The red dotted line marks the ion velocity reversal (center of the space hurricane). The red arrows point to the upward FAC, ions convection reversal and Inverted‐V electron acceleration.
Statistical results of (a) MLAT‐MLT location of space hurricane centers, (b) monthly distribution of space hurricanes, (c, d) IMF By and Bz condition distribution of background (black bars) and space hurricanes (magenta bars), (e, f) AE and SYM‐H indexes of background (black dots) and space hurricanes (magenta diamonds). Noting that the magenta plot in panel (a) covers the 126 space hurricane centers, and it is the same for the other figures.
Statistical results of Defense Meteorological Satellite Program (DMSP) and Gravity Field and Steady‐State Ocean Circulation Explorer (GOCE). From top to bottom: (a–c) DMSP cross‐track ion velocity V, (d–f) GOCE cross‐track horizontal neutral wind U, (g–i) 12 point moving standard deviation of vertical neutral wind σWz, and (j–l) 12 point moving standard deviation of neutral mass density σρn. Noting that σρn represent the percentage of deviation to origin value. The left, middle, and right columns correspond to background, space hurricane, and their difference, respectively. The magenta lines correspond to the region of the space hurricanes in Figure 2a. The black solid lines and dotted lines in Figures 2e and 2f represent the contours of 400 and −200 m/s ion velocity. The warm and cold colors represent sunward and antisunward, respectively.
Statistical results of electron energy flux (Je) measured by Defense Meteorological Satellite Program satellites. The top, middle bottom rows show the Je in total, Je with an energy >500 eV and Je with an energy <500 eV, respectively. The left, middle, and right columns correspond to background, space hurricane and their difference, respectively. The magenta lines in the middle and right column correspond the same region as in Figure 3. Please notice that there is a logarithmic color scale in this figure.
Statistical results of Pedersen conductance (top), electric field (middle), and Joule heating (bottom). The left, middle, and right columns correspond to background, space hurricane, and their difference, respectively. The magenta lines in the middle and right column correspond to the same region as in Figure 3. Please notice that there is a logarithmic color scale in panels (g–i).
How Do Space Hurricanes Disturb the Polar Thermosphere: A Statistical Survey

Zhi‐Feng Xiu

·

Yu‐Zhang Ma

·

Qing‐He Zhang

·

[...]

·

Sheng Lu

Plain Language Summary The space hurricane is a cyclonic auroral structure over the Earth's polar cap region, under northward interplanetary magnetic field conditions. During otherwise extremely quiet geomagnetic periods, it can inject a substantial amount of energy and particles into the polar upper atmosphere. Joule heating plays a crucial role in the energy budget, leading to significant atmospheric disturbances. Utilizing in situ observations from Defense Meteorological Satellite Program satellites and the Gravity Field and Steady‐State Ocean Circulation Explorer satellite, we conducted a statistical survey that investigates how space hurricanes disturb the polar thermosphere. Space hurricanes influence the average neutral horizontal wind, which displays a pattern similar to clockwise plasma convection. Enhanced Joule heating, caused by increased electric fields and Pedersen conductance associated with space hurricanes, generates notable disturbances in neutral density and vertical winds within the polar cap. These findings reveal the thermospheric characteristics of space hurricanes, which are of great significance for understanding polar ionosphere‐thermosphere coupling during quiet geomagnetic conditions.


The 5 Human Development Index regions over which equity is defined in the penalty P $\mathcal{P}$.
MSE (black) and P $\mathcal{P}$ (blue) for each neural network ensemble trained to predict (a) DTR and (b) TAS with varying α $\alpha $. (b, d) Show the same as (a, c), respectively, but only for α≤0.25 $\alpha \le 0.25$. Error bars represent standard error of the ensemble mean.
MSE (black) and P $\mathcal{P}$ (blue) for each neural network ensemble trained to predict DTR with varying α $\alpha $. The square and triangular points represent MSE over land and ocean, respectfully. Error bars represent standard error of the ensemble mean.
MSE for (a) DTR and (b) TAS in each Human Development Index region for several values of α $\alpha $. The black square line shows the mean of the colored bars, or simply the MSE over land, and the black triangle line shows the MSE over the ocean.
MSE of the neural network's DTR predictions averaged over the test data for (a) α=0 $\alpha =0$, (b) 0.05, (c) 0.1, and (d) 0.25. Values over the ocean are masked.
Enforcing Equity in Neural Climate Emulators

April 2025

·

1 Read

William Yik

·

Sam J. Silva

Neural network emulators have become an invaluable tool for climate prediction tasks but do not have an inherent ability to produce equitable predictions (e.g., predictions which are equally accurate across different regions or groups of people). This motivates the need for explicit internal representations of fairness. To that end, we draw on methods for enforcing physical constraints in emulators and propose a custom loss function which punishes predictions of unequal quality across any prespecified regions or category, here defined using Human Development Index. This loss function weighs a standard error metric against another which captures inequity between groups, allowing us to adjust the priority of each. Our results show that emulators trained with our loss function provide more equitable predictions. We empirically demonstrate that an appropriate selection of an equity priority can minimize loss of performance, mitigating the tradeoff between accuracy and equity.


Ozone Pollution Extremes in Southeast China Exacerbated by Reduced Uptake by Vegetation During Hot Droughts

April 2025

·

10 Reads

Meiyun Lin

·

Yuanyu Xie

·

Isabelle De Smedt

·

Larry W. Horowitz

Using a decade of observations and chemistry‐climate model simulations (2014–2023), we highlight the key role of biosphere‐atmosphere interactions in driving late summer–autumn ozone pollution extremes over Southeast China during hot droughts. In the 2019 and 2022 droughts, stomatal closure in the Yangtze River Basin, caused by soil moisture deficits, led to ∼60% reductions in ozone deposition rates to vegetation, aligning with reduced photosynthesis inferred from satellite remote sensing of solar induced fluorescence. Ozone production increased due to higher isoprene emissions from heat stress, NOx‐rich airflow from North China, and enhanced solar radiation. Soil drought intensified temperatures and increased isoprene emissions by 27%, but these only had marginal impact on ozone (<5 ppbv) in South China, where ozone formation is NOx‐limited. Reduced ozone uptake by drought‐stressed vegetation played a dominant role, driving 10–20 ppbv increases in daily maximum 8‐hr average ozone concentrations and a threefold rise in events exceeding 100 ppbv.


Cloud liquid droplet effective radius response to volcanic aerosols. (a) MODIS‐AQUA and (b) MODIS‐Terra liquid cloud droplet effective radius anomaly in October 2014, 2002–2022 baseline. (c–f) WRF‐Chem liquid cloud droplet effective radius anomaly due to volcanic emissions (VOLC—noVOLC anomaly, October 2014 average) using the (c) ARG02 (d) TE14 (e) BL95 and (f) LMDZ6 ACI parameterizations. Above each panel, reff,avg ${r}_{eff,avg}$ gives the regionally averaged reff ${r}_{\mathit{eff}}$ in October 2014, observed or modeled in the VOLC simulation.
Liquid water path response to volcanic aerosols. (a) MODIS‐AQUA and (b) MODIS‐Terra liquid water path radius anomaly observed in October 2014, 2002–2022 baseline. (c–f) WRF‐Chem liquid water path anomaly due to volcanic emissions (VOLC—NOVOLC anomaly), October 2014 average, using the (b) ARG02 (c) TE14 (d) BL95 and (e) LMDZ6 ACI parameterizations. BL95 and LMDZ6 do not include the second indirect effect.
Sensitivity of volcanic aerosol‐cloud‐interactions to the non‐volcanic aerosol background concentration. (left) effective radius anomaly during the eruption (right) indirect shortwave radiative effect of the eruption at top‐of‐atmosphere. All values are given as percentage changes from the unperturbed reference simulations.
Aerosol Background Concentrations Influence Aerosol‐Cloud Interactions as Much as the Choice of Aerosol‐Cloud Parameterization

April 2025

·

7 Reads

Louis Marelle

·

Gunnar Myhre

·

Jennie L. Thomas

·

Jean‐Christophe Raut

Plain Language Summary Particles suspended in the atmosphere (aerosols) play a key role in cloud formation. These aerosol‐cloud interactions have a major but uncertain influence on climate. We compare four different ways to calculate aerosol‐cloud interactions in a numerical atmospheric model. We compare model results to observed changes in clouds measured from satellites during the Holuhraun eruption in Iceland in 2014, which released large amounts of volcanic gases forming atmospheric aerosols. We find that all four approaches reproduce the observed reduction in cloud droplet sizes during the eruption, but that they disagree on its intensity and its impacts on the Earth's energy budget. An earlier study found that aerosol‐cloud interactions did not significantly increase the amount of liquid water in the clouds; using a more recent version of the satellite observations we find that large increases are possible. We also show that the eruption's impacts on the Earth's energy budget strongly depend on non‐volcanic aerosols already present in the atmosphere: doubling non‐volcanic aerosols reduces the impacts by ∼30% 30%{\sim} 30\%. Aerosol biases in climate models can be far greater, indicating that this could be a major source of uncertainty for aerosol‐cloud interactions and for understanding past, present and future climates.


Field site. (a) Pond and surroundings (0.5 m elevation contours), with red box showing region mapped in panel (b). (b) Bathymetry and instrument locations 1–9: current meters (circles), water temperature profiles (triangles), and sediment temperature (squares). Northing and easting measured from 68.626° ${}^{\circ}$N, 149.597° ${}^{\circ}$W. Dashed and dotted lines show transects plotted in panel (c). (c) Vertical cross section showing instruments [symbols as in panel (b), projected normally onto vertical plane through dashed line in panel (b)] and bathymetry [blue and brown show water and sediment along dashed transect in panel (b), while dotted line shows bed along dotted transect in panel (b)].
Profiles at mid‐pond (a–c, locations 5, 6, Figure 1), eastern (d–f, locations 8, 9) and western (g–i, locations 1, 3) locations. Horizontal dotted lines indicate local bed elevation. Temperature (a, d, g) increased each day and decreased each night (vertical dashed and solid lines mark 19:00 and 05:00). Buoyancy frequency (b, e, h) showed the nightly development of surface mixed layers (all N2≤3×10−4 ${N}^{2}\le 3\times 1{0}^{-4}$ s−2 ${\mathrm{s}}^{-2}$ plotted dark blue) and daily re‐stratification. However, surface mixed layers never reached the deepest parts of the pond (b). Turbulent dissipation (c, f, i) was relatively intense in the surface mixed layer, but below detection limit (all ϵ≤10−9 ${\epsilon}\le 1{0}^{-9}$ W/kg plotted dark blue) at greater depths. Thick black curve (the smallest depth where location 5 ϵ<3×10−9 ${\epsilon}< 3\times 1{0}^{-9}$ W/kg) marks the base of the region of active turbulent mixing.
Night‐time bottom water renewal process hypothesized (a) and tested using a bottom layer heat balance (b). Panel (a) color shows water temperature (colormap resembles Figures 2a, 2d, and 2g). A surface mixed layer is characterized by free convection (thin black arrows), whereas the underlying bottom layer is relatively quiescent. Colder, denser water flows down the sloping bed from the eastern (right) end of the pond (white arrow). This water flows to the bottom of the pond, displacing previous bottom water upward (thick black arrows). Panel (b) Rate of bottom water warming dTb/dt $d{T}_{b}/dt$ versus difference between temperatures of inflowing eastern cold water Ti ${T}_{i}$ and displaced water To ${T}_{o}$ (definitions in panel a). All data from 29‐day deployment plotted, with each dot a 1.5‐hr running mean. During night‐time (large black circles), cooling in the deep pond dTb/dt<0 $\left(d{T}_{b}/dt< 0\right)$ was proportional to Ti−To ${T}_{i}-{T}_{o}$, consistent with Equation 1. During day‐time (small gray circles), warming was common. Regression through night‐time values yields a slope of 0.13 hour−1 ${\text{hour}}^{-1}$ (line). From Equation 1, this slope equals F/V $F/V$, suggesting a night‐time bottom water production rate F=3.8 $F=3.8$ m3hour−1 ${\mathrm{m}}^{3}{\text{hour}}^{-1}$.
(a) Estimated Darcy velocity of water downwelling into the pond bed and (b) maximum pond water depth. In panel (a), triangles, circles and squares respectively indicate estimates for western, mid‐pond and eastern locations (2, 4 and 7, Figure 1).
Thermal Overturning Circulation in an Arctic Pond

April 2025

·

4 Reads

Stephen M. Henderson

·

Sally MacIntyre

In a 1.2‐m‐deep arctic permafrost pond, early‐summer bottom‐water renewal was dominated by thermal overturning circulation, rather than wind‐driven overturning or vertical turbulent mixing. Three high‐resolution current profilers measured turbulent dissipation rates. Three dense temperature logger arrays measured stratification. A turbulent surface mixed layer grew thicker with nightly cooling and thinner with daily warming. However, both day and night, turbulence was inhibited in a stratified layer that separated the surface mixed layer from the deeper pond. Nightly cooling, likely intensified in shallow regions of the pond, generated 10‐cm‐thick cold layers, which flowed down the sloping bed to renew bottom waters. A heat balance suggests sufficient flow to replace most bottom water each night. Groundwater flows were too slow to influence this circulation, but likely advected significant heat into sediments near the pond's western end. Bottom water renewal may influence greenhouse gas emissions and heat transport in the evolving permafrost landscape.


Tectonic setting of south‐central Alaska and northwestern Canada. Black and blue dashed lines represent the Yakutat (Eberhart‐Phillips et al., 2006) and Wrangell slabs (Yang & Gao, 2020). Red triangles mark the Quaternary volcanoes, including the Aleutian Arc (AA), the Buzzard Creek‐Jumbo Dome volcanoes (BC‐JD), the Wrangell volcanic field (WVF), the Prindle volcano (PV), and the Alligator Lake volcanic complex (ALVC). DVG—the Denali volcanic gap. Gray lines depict the major faults, including the Tintina Fault (TF), the Hines Creek Fault (HCF), the Denali Fault (DF), and the Border Ranges Fault (BRF). Color‐coded dots denote earthquakes with magnitudes ≥3.5 (https://earthquake.usgs.gov/data/comcat). Thick white line represents the country border. The inset map shows seismic stations used in this study.
(a)–(r) Comparisons between observed (black) and synthetic waveforms generated from the initial (green) and final (red) models for a regional earthquake (2022/01/08 08:16:45 GMT). Waveforms are filtered at 10–100 s and normalized by the maximum amplitude of the observed waveforms. (s) and (t) Phase delay measurements and cross‐correlation coefficients from the initial (green circles) and final (red circles) models. (u) Locations of the earthquake (red star) and seismic stations (black squares).
Shear‐wave velocities at multiple depths. The low‐ and high‐velocity features are denoted as LVs and HVs. Black triangles mark the Quaternary volcanoes. The horizontal slices are smoothed by applying a low‐pass filter with a window size of 60 km at 28–42 km depths, 80 km at 76–111 km depths, and 100 km at 130–168 km depths. Magenta lines in (f) mark the profile locations in Figure 4.
(a)–(e) Shear‐wave velocity profiles at depths of 5–200 km. Black dots are subduction‐related earthquakes with magnitudes ≥3.5. Red triangles mark the Quaternary volcanoes. Gray lines represent the CRUST1.0 Moho (Laske et al., 2013). Black dashed line represents the Slab2 plate interface (Hayes et al., 2018). Magenta lines represent the Vs = 4.3 km/s contours and white lines represent the Vs = 4.5 km/s contours. There is no exaggeration of the vertical profiles. The profiles are smoothed by applying the same smoothing factors at each depth as used in Figure 3.
Controls of Slab Subduction and Tearing on the Magmatism of Wrangell Volcanoes in South‐Central Alaska

April 2025

·

7 Reads

Meng Liu

·

Haiying Gao

This study integrates data from all broadband seismic stations in Alaska and northwestern Canada in 1999–2022 to construct a shear‐wave velocity model for south‐central Alaska and northwesternmost Canada, using ambient noise wave propagation simulation and inversion. Our model reveals three key features, including (a) the presence of the subducting Yakutat slab with apparent velocity reductions near the trench and within its flat segment, (b) two slab segments beneath the Wrangell volcanic field, differing in steepness, depth, and seismic velocity, and aligning spatially with the northwestern and southeastern volcano clusters, and (c) the existence of slab windows between the Yakutat and Wrangell slabs and between the northwestern and southeastern portions of the Wrangell slab. Our findings reinforce that the Wrangell volcanoes are predominantly influenced by subduction‐related magmatism. Furthermore, the two slab windows could have induced asthenospheric upwelling, contributing to the volcanism in the Wrangell clustered volcanoes.


I. River network observed by the DISR camera at the Huygens landing site (10.25° ${}^{\circ}$S 192.5° ${}^{\circ}$W). (a) Mosaic showing the landing site (X) (E. Karkoschka, https://pds‐atmospheres.nmsu.edu/PDS/data/hpdisr_0001/EXTRAS/MOSAICS/MOSIACS_PNG/). (b) Pebble bed photographed by the DISR camera shortly after the landing (courtesy of ESA/NASA/JPL/University of Arizona). (c) Orthorectified image superimposed on the DTM (Daudon et al., 2020). The vertical amplitude is 400 m (× ${\times} $1.5). Dark streams are interpreted as fluvial valleys and bright units as topographic features (Daudon et al., 2020; Perron et al., 2006). Orange arrows indicate the investigated river. Green dots () and blue dots () mark the upstream and downstream edges of the channel. II. River network at the south pole as observed by the Cassini Radar instrument. The image is the despeckled SAR image of the T55 radar swath (Lucas, Aharonson, et al., 2014). The rivers appear bright and meandering in the valley (Le Gall et al., 2010). The bright terrain around the valley is mountainous. The orange arrows indicate the river studied, the green dot () the upstream limit of the section studied, and the blue dot () the downstream limit.
Measured slope S $S$ as a function of dimensionless width W/L $W/L$, with W $W$ the measured width and L $L$ the characteristic length (see Equation 3) for natural terrestrial (Métivier et al., 2017; Seizilles et al., 2013) and two Titan rivers. The vertical colorbar corresponds to the logarithm of the dimensionless Archimedes number. The black lines correspond to the threshold theory calculated for two extreme values of the critical Shields number found on Earth (θt ${\theta }_{t}$ = 0.02 for a gravel‐bed river and θt ${\theta }_{t}$ = 0.4 for a sand‐bed river). Error bars for Titan rivers are due to parameter variability (S, W and L), summarized in Table 1.
Log‐log plot of dimensionless discharge Q∗=Qw1/2gds5 ${\mathrm{Q}}^{\ast }=\frac{{Q}_{w}^{1/2}}{\sqrt{gd{s}^{5}}}$ of terrestrial and Titan rivers as a function of dimensionless width W∗=Wds ${\mathrm{W}}^{\ast }=\frac{W}{ds}$ (ρf−ρsρf $\frac{{\rho }_{f}-{\rho }_{s}}{{\rho }_{f}}$)1/4. Dots correspond to alluvial rivers (∼ ${\sim} $2,600) and squares to bedrock rivers (∼ ${\sim} $35). Orange dots correspond to laboratory data (Baynes et al., 2020; Métivier et al., 2017) while green dots correspond to natural rivers (Birch et al., 2023; Métivier et al., 2017; Phillips et al., 2022). The thick gray line indicates the threshold theory (Equation 4) calculated with the parameters in Table 1, while the two thin gray lines represent the uncertainty in the calculation of the river width at the threshold. This uncertainty is due to the intrinsic variability of the two dimensionless coefficients (cf ${c}_{f}$ and μt ${\mu }_{t}$) and the critical Shields number θt ${\theta }_{t}$. The blue areas represent the estimated discharge at two Titan sites with their uncertainties (resulting from uncertainties in drainage areas, river widths and transported grain sizes) for the HLS and the south pole. The shaded bands represent the discharge estimates calculated from GCM precipitation rates for the two study sites (Barth & Rafkin, 2010; Hueso & Sánchez‐Lavega, 2006; Lora et al., 2015; Mitchell et al., 2011; Rafkin et al., 2022; Schneider et al., 2012) (see also Section S7 in Supporting Information S1). Uncertainties in these discharge estimates arise from both variability between model predictions and drainage area estimates.
Inferring Discharge From River Geometry on Titan

April 2025

·

59 Reads

Titan's dense atmosphere, composed mainly of methane and nitrogen, maintains a methane cycle that shapes its surface. Like water on Earth, methane precipitation erodes Titan's surface, carving river networks at all latitudes, as revealed by the Cassini‐Huygens mission. On Earth, it is well known that laboratory and natural rivers exhibit a power‐law relationship between their bankfull geometry and water discharge, as described by the threshold theory. Here, we investigate this hydraulic‐geometric relationship on two rivers on Titan, one near the equator and the other at the south pole. We hypothesize that this relationship can be applied to any river, and test it for the first time on extraterrestrial rivers. Having shown that Titan's rivers are consistent with the threshold theory, we use this relationship to estimate river discharge from bankfull geometry. As a perspective, we then use these discharges to infer precipitation rates, which could help to better understand Titan's climate.


(a) Unitless amplitude and (b) phase of the annual harmonic of SSS from OISSS data. Phase indicates the month of maximum SSS. Blank areas are where the annual harmonic is not significant at the 95% confidence level.
(a) Mode amplitudes for EOF modes 1 and 2 for the OISSS data set. Blue curve is mode 1. Red curve is mode 2. (b) Same as panel (a), but zoomed in on the years 2012–2013 to show the phase of the seasonal cycle of each of these modes. Note different y‐axis limits in panels (a, b). (c) Spatial pattern of mode 1 for OISSS, multiplied by the approximate amplitude of the OISSS mode 1 curve in panel (a) (150) to make this panel comparable to Figure 1a. (d) Same as panel (c) but for mode 2, multiplied by 100.
(a) Blue curves, left‐hand axis: reconstructed global mean SSS anomaly using the first two EOF modes computed from measurements taken from different distances from the coast. The solid curve is from 100 km distance. There are dashed and dotted curves on the figure that are not very visible, denoting 200 and 500 km distance respectively. Black curves, right‐hand axis: The range, as given by the maximum in 1 year minus the minimum in the previous year. The legend relates the curves to the distances, 100 km (solid), 200 km (dashed) and 500 km (dotted). (b) Blue curve, left axis: Full‐ocean reconstructed global mean SSS anomaly using the first two EOF modes. Black curve, right axis: The range, as given by the maximum in 1 year minus the minimum in the previous year. The OISSS data set is used for all these curves. Note different y‐axis limits for each panel.
Global mean SSS from the three data sets as a function of time. (Red, blue, green, black) curves are from the (tropics, 20°S–20°N; southern hemisphere, 60°S–20°S; northern hemisphere, 20°N–60°N; and global, 60°S–60°N). These curves are marked by the letters (“TR,” “SH,” “NH” and “GL”) in panel (a). (a) OISSS. (b) CCI + SSS. (c) EN4. (d) Green curve is the same as the green curve in panel (a). Black curve is the maximum mean SSS each year minus the minimum from the previous year, using the right‐hand axis.
Intensifying Seasonality of the Global Water Cycle as Indicated by Sea Surface Salinity

F. M. Bingham

·

E. Bayler

Plain Language Summary Sea surface salinity (SSS) changes result from of rainfall, evaporation and processes internal to the ocean over the course of a year. Global average SSS becomes greatest in March and reaches a minimum in September, indicating that fresh water is leaving and entering the surface ocean and being exchanged with land or the ocean interior. The magnitude of this exchange is equivalent to about 3 cm of water averaged over the global ocean. Evidence collected since satellite SSS observations began in 2010 indicate that the magnitude of the annual cycle's exchange rate has increased substantially, equating to about 1.0 cm of extra water. As the Earth warms, the atmosphere holds more water and is better able to evaporate it from the ocean surface and transport it onto land. Consequently, the increasing seasonality of SSS is a direct consequence of climate change and an indicator of an accelerating global water cycle.


Zonal mean (a) precipitation over land (mm/day) and (b) surface soil saturation (−) in several data sets: ERA5 reanalysis (Hersbach et al., 2020), simulated results from the Coupled Model Intercomparison Project (CMIP6) (Eyring et al., 2016; O’Neill et al., 2016), and, for soil saturation, satellite products (NNsm AMSR‐E and AMSR2 series) (Yao et al., 2021). To compute soil saturation, volumetric water content was scaled using wilting point and field capacity quantities from the HiHydroSoil (v2.0) database (Simons et al., 2020). These precipitation patterns are broadly consistent with prior work (Huffman et al., 2023). Inset gray shading represents the global fraction of land area by latitude (referencing the right‐hand y $y$‐axis). See Figures S2 and S3 in Supporting Information S1 for precipitation and soil saturation for each individual CMIP6 ensemble member.
(a) Schematic of the conceptual model where P(t) $P(t)$ is precipitation, E(t) $E(t)$ is evapotranspiration, Q(t) $Q(t)$ is the sum of runoff and drainage, and Δz ${\Delta }z$ is the thickness of the soil layer. Example response curves for (b) Q(t) $Q(t)$ and (c) E(t) $E(t)$ are shown for both the simple model case (blue line) and the modified simple model case (red line).
Variations on the simple theory generated using ERA5 data from 1970 to 2000. The mean soil saturation is shown in panel (a); the simple model is shown in panel (b); the intermediate model (e.g., globally constant parameter values) is shown in panel (c); and the maximally tuned model is shown in panel (d).
Directly simulated trends in soil saturation compared with theory using CMIP6 data for SSP2‐4.5. Specifically, directly simulated δs/sδT $\delta s/s\delta T$ (a) is compared to the simple model (b), the intermediate model (e.g., globally constant parameter values) (c), and the maximally tuned model (d) following Figure 3. Trends are shown between a reference period (1970–2000) and projections for the following century (2070–2100). Gray regions in panel (a) indicate areas with no soil moisture s $s$. The simple model and its variants reproduce fractional changes in s $s$ that cannot be explained by P $P$ or Rn ${R}_{n}$ alone.
Climate‐Scale Variability in Soil Moisture Explained by a Simple Theory

April 2025

·

32 Reads

Tara Gallagher

·

There is no basic explanation for soil moisture variability in the current climate, and models diverge on the sign of expected changes in a warming world. Here, we present a diagnostic physical theory for soil moisture at large scales. The theory is radically simpler than published alternatives, dependent only on precipitation and surface net radiation with no free parameters. Minor variations improve its performance. The theory answers two basic questions: (a) Why does soil moisture exhibit a W‐shaped latitudinal profile, even though precipitation over land does not? Poleward declines in net radiation resolve this discrepancy. (b) Why does soil moisture decrease with warming in some regions where precipitation increases? The theory predicts this phenomenon where fractional increases in net radiation exceed those in precipitation. Common alternative mechanisms, which invoke changes in vapor pressure deficit or plant responses to CO2 CO2{\text{CO}}_{2}, are inessential to explaining first‐order changes in soil moisture with warming.


Ground measurements of atmospheric properties during the new particle formation event on 12 September 2016. (a) Particle size distribution. (b) Ntot, N<30, and Stot. (c) CO mixing ratio and refractory black carbon concentration. (d) Precipitation rate and vertical velocity.
Monthly statistics (a) N<30, (b) CO, (c) refractory black carbon, and (d) absorption coefficient during the LASIC campaign. Boxes represent 25th and 75th percentile range, black lines represent median values, white circles represent mean values, and red circles represent median values during new particle formation events.
(a) Correlation between monthly cumulative precipitation and N<30. (b) Correlation between monthly average ambient temperature and N<30. (c) Correlation between monthly cumulative precipitation and Stot. (d) Vertical velocity observed under different newly formed particle fractions (N<30/Ntot) for different altitudes above ground level during the LASIC campaign. Colors represent the intensity of vertical velocity. Negative vertical velocity indicates downward air motion.
Vertical profiles of (a) Ntot,air and NAcc,air, (b) CO and O3 mixing ratios, and (c) potential temperature and liquid water content observed on 5 September 2017 between 16:00 and 17:00 UTC at ∼200 km southeast of Ascension Island. (d) Aerosol size distributions measured at Ascension Island on September 5 and 6, 2017.
New Particle Formation Events Over the Southeast Atlantic Coincide With the African Biomass Burning Season

April 2025

·

13 Reads

We investigated the occurrence and evolution of new particle formation (NPF) events over the southeast Atlantic. The studied region is under the influence of the long‐range transport of aerosols and gases during the southern African biomass burning season, from June to October every year. Interestingly, NPF was observed to coincide with the African biomass burning season, although wet removal of pre‐existing aerosols is needed during these NPF events. Surface and airborne measurements show that these NPF events likely occurred in the upper region of the marine boundary layer, and the newly formed aerosols were further transported to the surface via vertical air motions. Using a box model, we predicted that a large fraction of these particles could grow to sizes related to cloud condensation nuclei. Our study shows that NPF can occur over the southeast Atlantic, and the African biomass‐burning plume likely contributed to the NPF occurrence.


(a) Maximum azimuthal‐mean 10‐m wind speed and (b) 6‐2 km vertical tilt for CTL (black), TC48 (blue), TC72 (green), and TC96 (red). Dots in panels (a)–(b) indicate the initial moments of the respective experiments. (c)–(h) Same as (a)–(b), but for experiments initialized with the fully asymmetric components (black lines), the WN‐1 asymmetric components (red lines), and the axisymmetric component only (blue lines) of the TC structure at 48 hr (c)–(d), 72 hr (e)–(f), and 96 hr (g)–(h). Dots in panels (c)–(h) mark the onset of intensification for each experiment. Note that the times shown on the X‐axis are adjusted relative to those in CTL. A 6‐hr running averaging is applied to the intensity and tilt evolution.
(a) Hodographs of the initial vertical tilt and (b)–(g) the initial asymmetric vorticity and wind fields at 2‐km altitude for experiments modifying the phase and amplitude. The wind fields here are removed an average within a radius of 70 km. The black arrow in panel (a) shows the easterly vertical wind shear. Evolution of (h) the maximum azimuthal‐mean 10‐m wind speed and (i) 6–2 km vertical tilt in the above experiments. Dots indicate the onset of intensification for each experiment. Note that the times shown on the X‐axis are adjusted relative to those in CTL. A 6‐hr running averaging is applied to the intensity and tilt evolution.
(a) The trajectories of the 6‐2 km vortex tilt for experiments modifying the phase and amplitude of initial WN‐1 asymmetries. Hollow circles represent the location of initial moment (72 hr relative to CTL), while solid circles indicate the locations at which intensification begins. (b)–(g) WN‐1 asymmetric vorticity and wind fields at 2 km altitude for above experiments at the onset of intensification. Note that the times shown in panel (a) are adjusted relative to those in CTL. A 6‐hr running averaging is applied to the tilt evolution and the WN‐1 asymmetries, except for (c) which shows the result after 1 hr of model integration.
(a)–(b) Evolution of (a) the maximum azimuthal‐mean 10‐m wind speed and (b) the 6–2 km vertical tilt. The thick black line represents the results of TC72‐WN0+1, while thin purple (gray) lines denote experiments with smaller (larger) biases in initial asymmetries. Purple (gray) shading indicates the range from minimum to maximum values for experiments with smaller (larger) biases at each hour. (c)–(d) Maximum (orange bars) and mean (red bars) absolute intensity errors relative to TC72‐WN0+1 from 72 to 132 hr for experiments with biases in the (c) amplitude and (d) phase of the initial asymmetries. Note that the times shown on the X‐axis are adjusted relative to those in CTL. A 6‐hr running averaging is applied to the intensity and tilt evolution.
Importance of Initial Vortex Wavenumber‐1 Asymmetries to Tropical Cyclone Intensification: Idealized Numerical Experiments

April 2025

·

34 Reads

Plain Language Summary A critical aspect of predicting tropical cyclone (TC) intensity using numerical models is the initialization of the vortex structure. Current operational hurricane models (e.g., HWRF and HAFS) typically initialize TC vortices with inner‐core asymmetries directly derived from the previous 6‐hr forecast fields. However, these asymmetric components are often not accurate due to limitations of observations. Idealized simulations in this study demonstrate the significant impact of inner‐core wavenumber‐1 asymmetries in the initial TC structure on intensity evolution. TC intensification occurs only when the wavenumber‐1 asymmetries reach a specific configuration relative to the axisymmetric circulation. Otherwise, inaccurate or missing wavenumber‐1 asymmetries in the initial vortex require additional time to adjust the vertical structure of TC vortices, delaying the timing of intensification onset. These findings highlight the potential importance of accurately incorporating wavenumber‐1 asymmetries into initial conditions to improve TC intensity forecasts in operational hurricane models.


Schematic of calcite formation conditions, sample map and isotopic composition. (a) Cartoon showing ice sheet response during a cold period (blue boxes in inset graph of temperature vs. time); reduced ocean forcing allows for a shallow ice surface slope which reduces flushing of subglacial waters, causing them to be isolated from each other and making calcite formation less likely, (b) cartoon showing ice sheet response during a warm period (red boxes in inset graph of temperature vs. time); increased ocean forcing causes ice loss which leads to steepening of ice surface slope and consequent ice acceleration and enhanced flushing of subglacial waters, making calcite formation more likely, and (c) base map shows modeled δ¹⁸O of basal ice (Gasson et al., 2016). Circles indicate δ¹⁸O values of calcites measured in this study and the squares indicate minimum δ¹⁸O values of ice cores. The large difference in the δ¹⁸O values of the calcites compared with the modeled δ¹⁸O of local basal ice shows the relative oxygen isotope depletion of precipitate‐forming waters.
Comparison between calcite ages and Antarctic temperature. (a) Filtered ΔδD record from EPICA Dome C (EDC) ice core, a proxy for Antarctic temperature, using AICC2012 chronology (Parrenin et al., 2013) covering the last 240 ka. Data are filtered as in Cheng et al. (2016) to remove the orbital‐scale signal and amplify millennial‐scale peaks in warming (Antarctic Isotopic Maximum (AIMs)). AIMs are indicated with red circles, and calcite ages ± 2σ uncertainty are indicated by both the blue vertical bars and black open circles, (b) schematic of coincidence calculation used to compare calcite ages to AIMs. 1. Coincidence is defined as the area of overlap between the probability density functions representing the date and uncertainty of the calcite age and the warm peak time respectively. 2. Coincidences are added together for each age and associated warm peak, for real warm peaks (red) and 10,000 different sets of synthetic warm peaks (blue). 3. Coincidence of ages with real warm peaks is compared to distribution of coincidences with random synthetic warm peaks, (c) coincidences between calcite ages and randomly generated warm peaks for 10,000 model simulations (blue histogram) compared with coincidence between calcite dates and observed warm peaks from (b) (red line). A p‐value <0.05 indicates statistical correlation between the timing of calcite formation and Southern Ocean warming; our calculated p‐value is 0.02.
Comparison of environmental conditions at times of calcite formation. Each panel shows a histogram of climate forcing data for the last 260 ka (grey) and a histogram of a subsample of those climate forcing parameters at time indices where we have a dated calcite (blue). Any significant skew in the blue histogram compared to the grey histogram shows climate conditions where calcite preferentially forms. P‐value results of a KS‐test comparing calcite forming parameters to all parameters are shown for each climate forcing parameter; a Kolmogorov‐Smirnov test gives the probability that two distributions are random samples of the same parent distribution, and we use it in this context to determine whether there is a systematic difference between the climate parameters represented at the times when calcite formed and the climate parameters represented across the entire record. The p‐values are color coded by whether they fall above or below the 0.05 threshold for statistical significance. Climate forcings include (a) δD from EPICA Dome C ice core (a proxy for Antarctic temperature) with AICC2012 chronology, (b) June 21 insolation at 65°N, (c) integrated summer energy at 65°N, (d) sea level, (e) atmospheric CO2, and (f) global benthic δ¹⁸O. Yellow boxes indicate peak millennial power of respective climate parameters, as described by (Barker & Knorr, 2021).
Subglacial Precipitates Record Antarctic Ice Sheet Response to Southern Ocean Warming

April 2025

·

16 Reads

Plain Language Summary Calcium carbonate, also known as calcite, forms under the Antarctic ice sheet when warm waters from the Southern Ocean interact with the edges of the ice sheet. The formation ages of these rocks can be measured using ²³⁴U‐²³⁰Th geochronology and are thought to record periods of time when meltwaters from the interior of the ice sheet were brought to the edges by ice acceleration that occurred in response to ocean warming. We measured ²³⁴U‐²³⁰Th ages and other geochemical data from a collection of 38 Antarctic calcite precipitates. We used a Monte Carlo model to assess the coincidence between the calcite ages and millennial‐scale (meaning they are smaller temperature fluctuations that happen within glacial‐interglacial cycles) warm peaks in the paleoclimate record, and we found that they have a statistically significant correlation with periods of Southern Ocean warming, which strengthens the argument for the connection between ocean warming and ice acceleration. We also show that the calcite dates tend to cluster during periods when millennial‐scale temperature fluctuations are more pronounced and total global ice volume is high, which is consistent with the idea that the ice sheet could be more likely to respond to climate change under those conditions.


Mean outgoing longwave radiation variance of equatorial Kelvin waves of 30 days (a) before and (b) after the South China Sea summer monsoon onset. (c, d) Are the same as (a, b), but for the mixed Rossby‐gravity‐tropical depression waves.
The leading extended empirical orthogonal function mode of 10‐day high‐pass filtered outgoing longwave radiation (OLR) anomalies for 1 month (a–c) before and (d–f) after the South China Sea summer monsoon onset during 1979–2022, which explains 3.2% and 2.8% of the variance, respectively. To display a larger domain and the full horizontal winds, the outgoing longwave radiation and low‐level winds are regressed onto the normalized PC1. Wind vectors below the 90% confidence level are masked.
Regressions of the outgoing longwave radiation (shading, units in W m⁻²) and 850 hPa winds (vectors, units in m s⁻¹) on lag days (a) −2, (b) 0, and (c) +2 for 1 month before the South China Sea summer monsoon (SCSSM) onset. (d–f) Are the same as (a–c), but for 1 month after the SCSSM onset. These anomalies have been subjected to a 10‐day high‐pass Butterworth filter to extract the synoptic‐scale variability. These regressions are based on the mixed Rossby‐gravity‐tropical depression filtered 850 hPa meridional winds at the base point of (150°E, 0°E).
Composite synoptic‐scale eddy kinetic energy (EKE) (units in m² s⁻²) at 850 hPa for (a) 1 month before the South China Sea summer monsoon (SCSSM) onset and (b) 1 month after the SCSSM onset. (c) Is calculated as (b) minus (a), which highlights the changes after the monsoon onset. (d–f) Are the same as (a–c), but for the composite synoptic‐scale EKE tendencies (units in 10⁻⁶ m² s⁻³) due to barotropic energy transitions (from mean kinetic energy to EKE). The hatched areas in panels (c, f) highlight the differences that are significant at the 90% confidence level.
Composite winds at 850 hPa (vectors, units in m s⁻¹) and outgoing longwave radiation (shading, units in W m⁻²) for 1 month (a) before and (b) after the South China Sea summer monsoon onset. (c) Is calculated as (b) minus (a), and only the winds significant at 90% confidence level are shown. The composited zonal winds are highlighted by the red contours, with intervals of 3 m s⁻¹ in panels (a, b) and 2 m s⁻¹ in panel (c) and the zero lines thickened.
Distinctive Features of Tropical Waves Before and After the South China Sea Summer Monsoon Onset

April 2025

·

49 Reads

Plain Language Summary Previous studies have shown that the South China Sea summer monsoon onset is important on the large‐scale: signify the establishment of summer monsoon over East Asia‐Southeast Asia‐western North Pacific, the arrival of the main rainy season in these locations, and the adjustment of the atmospheric circulation from winter‐type to summer‐type. We find that the changes in atmospheric mean flow before and after monsoon onset can have a significant modulating effect on the synoptic‐scale perturbations gestated in them. Prior to the monsoon onset, the most obvious permutations are those propagating eastward near the equator. In contrast, after the monsoon onset, the northwestward propagating tropical disturbances off‐the‐equator become more active. Specifically, tropical depressions and tropical cyclones occur more frequently and begin to affect Southeast and East Asia. These distinctive features of tropical waves can be understood in terms of the energy conversion between the mean circulation and the disturbances.


(a) The comparison between retrieved and parameterized κ values (κret and κpra). The relative error (RE) of κpra to κret (RE = (κpra – κret)/κret × 100%) is depicted. The dotted lines mean that the absolute value of RE is equal to 20%. (b) The RE of estimated f (RH) based on κpra compared to the measured f (RH) at different RH levels, represented in box plots. In a box plot, the upper, middle, and lower lines denote the upper, middle, and lower quartiles respectively. The upper and lower bounds of the box indicate the maximum and minimum values.
(a) The comparison between the estimated NCCN and measured NCCN at different SSs, where the estimated NCCN is calculated using Equation 7 and dry aerosol optical data. (b) The mean absolute percentage error (MAPE) between the estimated NCCN and measured NCCN at different SSs, where the estimated NCCN is calculated using Equation 7 and wet aerosol optical data (MAPE = mean (|(NCCN_estimated–NCCN_measured)/NCCN_measured|). (c) The comparison between estimated and measured NCCN at 0.2% SS across varying RHs. (d) The ratios of the extinction coefficients and extinction Ångström exponents at high RH conditions to their values at dry conditions, represented as σep,RH/σep,dry and αep,RH/αep,dry, respectively. The solid colored lines in panels (a, c) represent fitting lines, with k values denoting their slopes.
(a) The MAPE between the estimated NCCN and measured NCCN at 0.2% SS, where the estimated NCCN is calculated using Equation 7 with the derived σep,RH and αep,RH for three different κ values (κret, κmean, and κpra). (b) The relationships and fitting curves between aRH or bRH and RH for κmean‐dependent calculation. (c) The relationships and fitting curves between aRH or bRH and RH for κpra‐dependent calculation.
(a) Same as Figure 2b but at the Shanghai site. The MAPE between the estimated NCCN and measured NCCN at different SSs, where the estimated NCCN is calculated using Equation 7 and wet aerosol optical data. (b) The MAPE between the estimated NCCN and measured NCCN at different SSs, where the estimated NCCN is calculated using Equation 8 with the derived σep,RH and αep,RH for κmean.
The Role of Relative Humidity in Estimating Cloud Condensation Nuclei Number Concentration Through Aerosol Optical Data: Mechanisms and Parameterization Strategies

April 2025

·

28 Reads

The number concentration of cloud condensation nuclei (NCCN) is vital for quantifying aerosol‐cloud interactions. Estimating NCCN using aerosol optical properties is essential for obtaining continuous NCCN data. This study highlights the significant impact of relative humidity (RH) on NCCN estimation through aerosol optical data, especially at low supersaturations (SS). When RH exceeds a threshold (e.g., 60% at 0.2% SS), NCCN estimation shifts from underestimation to overestimation, with the overestimation degree increasing with RH. Including RH in the estimation formula can effectively reduce this bias, although the aerosol optical hygroscopicity parameter is found to have a minimal effect on NCCN estimation. Based on these insights, a new parameterization scheme for NCCN estimation is proposed, which can significantly reduce NCCN estimation bias when using wet aerosol optical data at high RH levels (40%–90%).


(a) Study sites and distributed temperature profiling (DTP) probe locations (red circles). Contour lines on the site map indicate 20 m elevation intervals. (b) Schematic and example photos of snow DTP probes. (c) Example of snow depth estimation based on the mean daily high‐frequency temperature variability. The right panel shows the temperature time series from 5 to 10 February 2023 (gray area in left panel) to illustrate contrasting temperature signals below and above the snow surface. (d)–(g) Classified time series of snow depth and ground interface temperature, along with median air temperature (AT) across all locations. Each group is represented by the median (solid lines) and 25%–75% interquartile range (shaded areas), with the number of locations showing in the legends. In the snow depth panels, the dashed black lines represent the R² between late‐winter snow depth and mean daily snow depth before snowmelt across all locations, calculated only on days when more than 30 locations had snow depth greater than zero.
Influences of vegetation and topography on variability in snow accumulation at various time during the snow season. (a) and (b) R² between mean daily snow depth and vegetation height, topographic position index (TPI), or both variables at T27 (a) and K64 (b) during snow accumulation periods in WY22 and WY23. R² was calculated only on days when more than 30 locations had snow depth greater than zero. (c)–(h) Relationship between late‐winter snow depth and vegetation height or TPI, with color representing TPI or vegetation height, across two sites and two years.
Spatial variability in snow‐free dates and snow insulation in the snowmelt period. (a)–(d) Relationship between snow‐free date and late‐winter snow depth at the two study sites and years. (e)–(f) Relationship between snow‐induced thawing n‐factor and late‐winter snow depth (e) or snow‐free dates (f) at the two study sites and years.
Quantification of the impact of snow depth on snow insulation. (a) and (b) Relationship between snow‐induced freezing n‐factor and late‐winter snow depth (a) or ATE‐weighted snow depth (b), fitted with y = e−ax; solid markers represent example locations with snow depth time series shown in panel (c). (d) Relationship between the difference of mean annual ground interface temperature and mean annual air temperature, and ATE‐weighted snow depth, fitted with y = be−ax + c.
Advancing the Understanding of Snow Accumulation, Melting, and Associated Thermal Insulation Using Spatially Dense Snow Depth and Temperature Time Series

April 2025

·

46 Reads

Plain Language Summary In Arctic regions, the snow plays an important role in regulating ground temperatures and influencing different earth processes. Acting like a blanket, the snow keeps the ground warm during the cold season and prevents heat from moving into ground during snowmelt. Snow depth and timing of snow accumulation and melt events can vary greatly from place to place, leading to significant differences in ground temperatures. We analyzed snow depth and ground interface temperatures collected continuously for 112 locations at two small subarctic sites in Alaska over two years. Our results showed that vegetation and topography strongly influenced snow depth, but their relationships changed over time and varied between sites. We also found that differences in late‐winter snow depth led to variable snow‐free dates, and local air temperature further complicated this. Finally, we developed a new metric to better estimate how snow insulated the ground, incorporating daily snow depth and air temperature throughout the entire cold season. This research advances our understanding of snow dynamics and insulation effects over space and time, which is vital for evaluating how well we are capturing snow and permafrost processes in Earth system models.


Maps of the area change in 35 years on the global 1° grid of the 1985/2020 stage of mangroves. The 35‐year change in area is calculated by subtracting the 1985 mangrove distribution from that in 2020 (Unit: ha). Light pink to dark pink indicates losses ranging from small to large, while light green to dark green indicates increases ranging from small to large. The 35‐year percentage change is obtained by dividing the difference between the 2020 and 1985 values by the 1985 value (Unit: %), with light red to dark red representing increasing loss percentages and light green to green representing increasing gain percentages.
Mangrove changes in northern and southern hemispheres: (a) Distribution of mangroves in the northern and southern hemispheres in 1985, (b) distribution of mangroves in the northern and southern hemispheres in 2020, (c) mangrove distribution by 1° latitude, and (d) mangrove changes from 1985 to 2020 by 1° latitude.
Global Declines in Mangrove Area and Carbon‐Stock From 1985 to 2020

April 2025

·

46 Reads

Mangroves are one of Earth's “blue lungs” due to their exceptional carbon‐storage capabilities amidst rapidly increasing carbon dioxide. Despite providing numerous ecological services, their global distribution and carbon‐storage capacities have severely declined over the past 35 years (1985–2020). Here, we quantify spatio‐temporal changes in global and national carbon‐stocks that include this period. We found that global mangrove area decreased from 17.35 million‐hectares in 1985 (carbon‐storage of 6.84 Pg) to 13.61 million‐hectares in 2020 (carbon‐storage of 5.72 Pg). Significant losses occurred in Saudi Arabia and Indonesia, with a global reduction of 21.6% in area and 16.5% in carbon‐stocks. Potential maximum loss of accumulated carbon‐storage in mangroves was equivalent to 4.13 Pg of CO2, accounting for 0.4% of the global cumulative fossil CO2 emissions (1,009 Pg) during 1985–2020. This study provides more comprehensive and accurate statistics, maps, and insights on estimating and reducing mangrove carbon emissions to support global and national protection policies.


Petermann Glacier on 10 April 2023 with (a) location, (b) Landsat‐9 image and 2021 “ice grounding zone” (orange) (Ciraci et al., 2023), landings at Center Lake and West Lake, the rift zone, and (c) drilling camp. (d) Grounding line Remote Operated Vehicle (GROV) instrument with multibeam sonar (multibeam echo sounders) module. (e) GROV deployed in a borehole with a fiber‐optic tether. Ice draft elevation color coded from −360 to −60 m from (f) GROV with 10‐m contour levels (black) and (g) derived from a World View (WV) Digital Elevation Models (DEM) acquired on 05/10/2023 assuming flotation. Labeled profiles are shown in Figure 3. Two flow lines (purple) in (b) help document the eastbound migration of the center channel.
Ocean properties of Petermann Ice Shelf, Greenland with (a) temperature (° ${}^{\circ}$C, black) and salinity (psu, red) vs. depth, with inset zooming on the top 200 m; (b) temperature/salinity plot with Gade mixing line for ice and seawater (T0=−92.5° ${T}_{0}=-92.5{}^{\circ}$C, slope = 2.66°C/psu), with depth markings (black circles with labels); and (c) Turner angle (degrees, black) and melt water fraction (‰, red) every meter (a median filter of 9 m was applied to reduce noise). DC for diffusive convection, Stable in temperature and salinity, SF for salt fingering, and Unstable in both temperature and salinity.
Ice draft elevation of Petermann Ice Shelf, Greenland color‐coded from −360 to −60 m from GROV with elevation profiles collected along (a)–(b) North; (c)–(d) East; (e)–(f) Channel; (g)–(h) Cliff, (i)–(j) West profiles in Figure 1. In (b), (d), (f), (h), and (j), ice draft elevation from GROV is black, derived from WV DEM (05/10/2023) is red; and from BedMachinev5 is dotted blue. Ice surface from WV is black. GROV trajectory is green.
Grounding Line Remote Operated Vehicle (GROV) Survey of the Ice Shelf Cavity of Petermann Glacier, Greenland

April 2025

·

13 Reads

The melting of glaciers by ocean waters along the ice sheet periphery is a major physical process driving glacier evolution in a changing climate. Using a fiber‐optic‐tethered Grounding line Remote Operated Vehicle, we explore the ice shelf cavity of Petermann Glacier, in Northwestern Greenland, with an interferometric multibeam sonar operating with 360° {}^{\circ} viewing capability. We detect a uniform seafloor at 820 m depth, 200 m deeper than anticipated. The ice shelf base reflects spatial variations in ice melt with no apparent signature at the surface, including widespread ice terraces interrupted by 30–40 m ice cliffs connected to a smooth, central basal channel that deviates by many 10 m's from flotation and experiences differential melt along its sides. Water stratification at the base of the center channel is prone to diffusive convection instead of a fully‐developed turbulent state. The results illustrate the critical importance of exploring cavities in situ.


Left: 2D histogram of the oxygen to proton OH $\left[\frac{O}{H}\right]$ abundance versus the solar wind bulk velocity. The magenta curve represents the empirical model function (see equations in text). Middle: Normalized histogram of the data‐model deviation in logarithmic space. Right: Histogram of the measured ACE 1.1 (plain black) and modeled (dashed magenta) values for the OH $\left[\frac{O}{H}\right]$ abundance, normalized to the total number of points in each set. The shaded area represents the data distribution effectively covered by the empirical model. The green dot‐dashed line shows the histogram of the Whittaker and Sembay (2016) empirical function. The blue dotted line shows the result of convolving the magenta curve with the distribution of the data‐model deviation.
Same as Figure 1, except for the C6+ ${\mathrm{C}}^{6+}$/O (top) and C5+ ${\mathrm{C}}^{5+}$/O (bottom) abundances versus the C65 ratio.
Same as Figure 1, except for the O8+ ${\mathrm{O}}^{8+}$/O abundance versus the O86 ratio (top) and O7+ ${\mathrm{O}}^{7+}$/O abundance versus the O76 ratio (bottom). The green dot‐dashed lines show the histograms of the Whittaker and Sembay (2016) empirical functions.
Same as Figure 1, except for the Ne9+ ${\text{Ne}}^{9+}$/O (top) and Mg11+ ${\text{Mg}}^{11+}$/O (bottom) abundances versus the O76 ratio.
2D histogram of the O7+/O6+ ${\mathrm{O}}^{7+}/{\mathrm{O}}^{6+}$ versus the C5+C6+/C5+ ${\mathrm{C}}^{6+}/{\mathrm{C}}^{5+}$ ion ratio from the ACE SWICS 1.1 (left) and 2.0 (right) data. The red plain line represents the coronal hole (C. H.)—streamer wind type separation according to von Steiger and Zurbuchen (2015). The Outlier (dot‐dashed) and the Upper Depleted Wind (UDW) (dashed) type limits from Zhao et al. (2017, 2022) are also marked (See details in text). Adapted and expanded from Koutroumpa (2024) and references therein.
Empirical Functions for Highly Charged Ion Abundances in Solar Wind Charge Exchange Models: Addressing Post‐2011 ACE Data Limitations

April 2025

·

3 Reads

Upcoming imaging missions—NASA's LEXI and ESA/CAS's SMILE—will target solar wind charge exchange X‐ray (SWCX) emission from Earth's magnetosheath. This emission is generated by highly charged ions colliding with neutrals in Earth's exosphere. Accurate SWCX models require data on exospheric neutral densities, as well as solar wind flux and composition. The Advanced Composition Explorer (ACE) Solar Wind Ionic Composition Spectrometer (SWICS) provided the needed solar wind composition data from 1998 until an instrument anomaly in 2011 limited its outputs. To address this, we developed empirical functions using ion ratios (O7+/O6+,O8+/O6+,C6+/C5+ O7+/O6+,O8+/O6+,C6+/C5+{\mathrm{O}}^{7+}/{\mathrm{O}}^{6+},{\mathrm{O}}^{8+}/{\mathrm{O}}^{6+},{\mathrm{C}}^{6+}/{\mathrm{C}}^{5+}) still available from ACE, partially compensating for missing composition data. The results underscore the need for a new mission to measure solar wind composition and support future SWCX analysis efforts.


A Statistical Study of the Decreased TEC Region During Summer at Northern High Latitudes

April 2025

·

11 Reads

Plain Language Summary The polar ionosphere is filled with non‐uniform plasma densities. In addition to higher plasma density regions, many depleted structures, such as ionization troughs, polar holes, and auroral cavities, are observed at the F‐region of the polar ionosphere. In the summer hemisphere, most of the high‐latitude ionosphere is under the sunlit ionization. However, large area with decreased total electron content (TEC) is found in the summer hemisphere. Based on an 11‐year Global Navigation Satellite System TEC data, for the first time, we investigated the spatial distribution of the polar ionospheric TEC at high latitudes in the northern hemisphere. This decreased TEC region occurs mainly in regions above 70° magnetic latitude for moderate and high solar activity. The lower‐TEC region is predominantly located in the dawn and midnight sectors. The depth of the decreased TEC region is deeper for higher Kp than for low Kp. The result is expected for further understanding of the summer polar ionosphere.


(a–b) Show the ground uplift, in terms of vertical velocity Usism ${\mathrm{U}}_{\mathrm{s}\mathrm{i}\mathrm{s}\mathrm{m}}$ at the epicenter for the Sanriku‐Oki and Noto earthquakes, respectively. (c–d) Show Usism ${\mathrm{U}}_{\mathrm{s}\mathrm{i}\mathrm{s}\mathrm{m}}$ distribution with epicentral distance for the two earthquakes, respectively. (e) Shows the sound speed profiles from the NRLMSISE00 model and (f) shows the ionospheric electron number density profiles from the IRICORE model.
Simulation results for the Sanriku‐oki in the upper panels and the Noto in the lower panel: In panels (a), (c), the red‐blue pixmap shows the amplitude uy $\left({\mathrm{u}}_{\mathrm{y}}\right)$ of AGWs with time and altitude. The filled green circle corresponds to the sound‐ray travel time y/s $y/s$. The black open circle is at the altitude of the secondary maxima. In panels (b), (d), the red curves show the % $\%$ electron density disturbances n(t)−ntp/nmax $\left(\left(\mathrm{n}(\mathrm{t})-\mathrm{n}\left({\mathrm{t}}_{\mathrm{p}}\right)\right)/{\mathrm{n}}_{\max }\right)$ with time and altitude, n(t),ntp,nmax $\left(\mathrm{n}(\mathrm{t}),\mathrm{n}\left({\mathrm{t}}_{\mathrm{p}}\right),{\mathrm{n}}_{\max }\right)$ correspond to the electron density at the current time, the previous time and the maximum value of electron density. The black highlighted curve is at the altitude of the strongest electron density disturbance. The green curve corresponds to the ambient electron density profile.
Observation and Simulation comparison for the Sanriku‐Oki earthquake: (a) The observed ΔTEC ${\Delta }\mathrm{T}\mathrm{E}\mathrm{C}$ disturbances that is, CSID from five receiver‐G07 pairs where numbers denote receiver stations at the top of each curve, (b) The ΔTEC ${\Delta }\mathrm{T}\mathrm{E}\mathrm{C}$ disturbances that is, CSID from observation (red curve) and simulation (black curve), (c) The CSID detection time estimation from observation (red‐square) and simulation (black circle).
Observation and Simulation comparison for the Noto earthquake: (a) The observed ΔTEC ${\Delta }\mathrm{T}\mathrm{E}\mathrm{C}$ disturbances that is, CSID from five receiver‐G16 pairs where numbers denote receiver stations at the top of each curve, (b) The ΔTEC ${\Delta }\mathrm{T}\mathrm{E}\mathrm{C}$ disturbances that is, CSID from observation (red curve) and simulation (black curve), (c) The CSID detection time estimation from observation (red‐square) and simulation (black circle).
The First Near‐Real‐Time Compatible Numerical Method for Co‐Seismic Ionospheric Disturbances Simulation

April 2025

·

58 Reads

Numerous studies demonstrated a huge potential for ionospheric total electron content (TEC) measurements to be included in earthquake and tsunami risk assessments. However, up to now, only a few methodologies or tools can be used in near‐real‐time (NRT) for these purposes. For the first time, this study presents a method allowing for fast simulations of Co‐Seismic Ionospheric Disturbances (CSID) or ionoquakes. The new analytical simulation code models the propagation of acoustic‐gravity waves (AGWs) generated by a co‐seismic uplift of the ground/seafloor, by resolving the governing equations in the time‐altitude‐horizontal domain. The method models the near‐field CSID in about 60 s of simulation time, that is, before the waves are detected in the ionospheric TEC measurements. The developed method is, therefore, among the first most promising products in Ionospheric Seismology that can be used for NRT applications, such as tsunami early warning.


(a) Hydrothermal ring‐shear apparatus at Utrecht University (after Verberne et al. (2020)). (b) A simplified stratigraphic column of the Changning shale gas field (after Lu et al. (2021)), the mineral composition and assumed in situ condition for each gouge sample. σn – applied normal stress. Pf – applied pore pressure. T‐temperature. (c) (a − b) value of each type of sample retrieved from the results of velocity‐step test which were fitted with a RSF law with the aging law with one state variable using the open‐source software RSFit3000 (Skarbek & Savage, 2019). (d) Typical procedure of the NSO experiment in the present study.
Effects of oscillation frequencies, amplitudes, and load‐point velocities on the frictional properties of Longmaxi shale and Baota limestone. (a)–(c) Definition of ∆τvs $\mathit{{\increment}}{\tau }_{vs}$, ∆τvw $\mathit{{\increment}}{\tau }_{vw}$, and Δ ${\Delta }$ τw, which are used to characterize the fault frictional properties. ∆τvs $\mathit{{\increment}}{\tau }_{vs}$, ∆τvw $\mathit{{\increment}}{\tau }_{vw}$, and Δ ${\Delta }$ τw as a function of oscillation frequencies in both experiments are shown in panels (d–g) while effects of oscillation amplitudes are shown in panels (h–k).
Effects of stressing rates (product of oscillation frequency and amplitude) on (a) ∆τvs $\mathit{{\increment}}{\tau }_{vs}$ for Longmaxi shale or ∆τvw $\mathit{{\increment}}{\tau }_{vw}$ for Baota limestone and (b) ∆τw $\mathit{{\increment}}{\tau }_{w}$ for two gouge samples. Panels (c) and (d) show the ∆τvs $\mathit{{\increment}}{\tau }_{vs}$ and ∆τw $\mathit{{\increment}}{\tau }_{w}$ for all velocity‐strengthening gouge samples, respectively. The data for all load‐point velocities are included. The bubble size refers to the oscillation amplitudes adopted in the corresponding experiment. VS, velocity‐strengthening; VW, velocity‐weakening.
(a) ∆τvs $\mathit{{\increment}}{\tau }_{vs}$ and (b) ∆τw $\mathit{{\increment}}{\tau }_{w}$ modeled using the extended CNS or LD92 model as a function of stressing rate. Root Mean Square Error (RMSE) is calculated to quantify the distinction between the experimental and modeled data. The experimental data (u1161) is included for comparison, which is derived from the experiment conducted with the same pressure and temperature conditions. Panels (c–e) show the results of a parameter sensitivity analysis with the shear stiffness of the fault loading system, load‐point velocity, and the background normal stress, respectively.
Effects of Periodic Normal Stress Oscillations on Frictional Properties of Simulated Natural Fault Gouges Under In Situ P‐T Conditions

April 2025

·

65 Reads

Induced earthquakes are occasionally associated with stress oscillations resulting from periodic industrial activities. Yet, the effects of stress oscillations on fault friction under realistic subsurface conditions are not fully clear. We conducted normal stress oscillations experiments using simulated fault gouges derived from the major reservoirs and caprocks of the Changning shale gas field under in situ conditions. Our experimental results reveal that most gouges are velocity‐strengthening under quasi‐static loading. Interestingly, after applying normal stress oscillation, they show similar shear stress evolution with different oscillation amplitudes, frequencies and load‐point velocity, suggesting a negligible effect of the rock composition and the applied P‐T conditions. We successfully reproduce our experimental results using an extended CNS (Chen‐Niemeijer‐Spiers) model and the model proposed by Linker and Dieterich (1992, https://doi.org/10.1029/92jb00017). Therefore, these experimental observations and friction models can be reliably extrapolated to more heterogeneous field cases and other regions with similar lithology distribution.


(a) 27 bifurcations in 11 rivers (red circles) were selected for study, (b) circle diameters scale with the number of bifurcations, ranging from 1(Kuskokwim River, Mekong River, Amazon River, Rhine River, and Mezcalapa River) to 10 (Athabasca River, Embarras River, Peace River, des Rochers River, and Slave River), (c) four bifurcations are measured in the Saskatchewan River, Canada. The yellow lines represent Surface Water and Ocean Topography (SWOT) River Database (SWORD, Altenau et al., 2021). If zoom‐in of the Saskatchewan River, we could see that SWORD is not able to capture the multiple channels 100% due to computational cost of SWOT data processing. The lower panel shows that all three types of river bifurcations (e)–(f) are studied. These are Mid‐channel bar (e, 112.87°E, 29.79°N), bifurcation‐confluence loop (f, 161.35°W, 60.83°N), and distributary (g, 111.59°W, 58.38°N). Red lines are extracted channel width lines in branches for discharge partitioning calculation and sensitivity analysis. The distance between the two width lines is 10% of the smaller branch channel's width right downstream of the bifurcation node (Wsmall).
(a) Relationship between in‐situ measured and estimated discharge ratio using direct width, (b) width equation found in Section 3.1, and (c) remotely sensed width and sinuosity. Three types of bifurcations, including bifurcation‐confluence loop (blue dots, N = 3), distributary (orange dots, N = 10), and mid‐channel bar (green dots, N = 10), are shown here. Error distributions of discharge ratio estimates based on direct channel width (Ew), width equation (Ewe), and width × sinuosity (Ews) (d). EΨ quantifies the proportion of discharge entering the bifurcation that is routed incorrectly. For instance, an EΨ of 0.1 suggests that the partitioning estimate inaccurately routed 10% of the flow at the bifurcation.
Longitudinal changes in error metric EΨ at different locations downstream of 23 bifurcation nodes in 11 rivers. The X‐axis represents downstream distance, increasing by 0.1 × Wsmall, where the distance between the two width lines is 10% of the smaller branch channel's width immediately downstream of the bifurcation node (Wsmall). The Y‐axis represents error estimation: when E is 0, the estimated partitioning at the bifurcation matches in‐situ measured partitioning exactly. Positive/negative values of E indicate over/underestimation flow entering the larger branch, respectively.
Exceptions where width‐based methods are not effective for estimating discharge partitioning at channel bifurcations. For example, the bifurcation has nearby tributary, (a Parana River, 58.58°W 27.30°S), the bifurcation has wide but very shallow channel due to an ongoing avulsion, (b Athabasca River, 111.54°W 58.36°N), and the bifurcations have multiple downstream branches, (c Amazon River, 73.19°W 3.90°S, and (d) des Rochers River, 111.22°W 58.72°N). Landsat (a) and (c) and Planet (b) and (d) images whose dates same or close to the in‐situ discharge measurements were used for channel width and sinuosity calculations. Relationship between width estimated versus in situ discharge ratio for these four sites using (e) direct width, (f) width equation shown in Section 3.1, and (g) remotely sensed width and sinuosity.
Remote Sensing of Discharge Ratios at River Channel Bifurcations

April 2025

·

14 Reads

River channel bifurcations are crucial for distributing water and sediment on floodplains and deltas, but estimating discharge ratios between branches remains challenging. Using satellite imagery and in‐situ discharge data, we demonstrate that bifurcate channel widths can estimate discharge ratios at 23 of 27 bifurcations in 11 rivers worldwide, with good accuracy (R² = 0.80) in 26 of 33 measurements. An empirical width‐discharge equation derived from 5,740 United States Geological Survey gauging stations further improves accuracy (R² = 0.82). For best results, branch widths should be measured within one channel width of the bifurcation. The method is ineffective in cases influenced by tributaries, avulsion, or multiple branches. We conclude that channel width is effective for estimating discharge ratios, especially when paired with an empirical width‐discharge equation, potentially enhancing river discharge estimates from the Surface Water and Ocean Topography satellite mission, which currently lacks flow partitioning capabilities for bifurcations.


Journal metrics


4.6 (2023)

Journal Impact Factor™


38%

Acceptance rate


9.0 (2023)

CiteScore™


32 days

Submission to first decision


1.2 (2023)

Immediacy Index


0.13397 (2023)

Eigenfactor®


$2,900 / £2,200 / €2,450

Article processing charge

Editors