Geophysical Research Letters

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Statistical results of Auroral kilometric radiation (AKR) samples observed by the Arase satellite from 23 May 2017 to 31 July 2019. (a) The number of AKR samples for each region with the interval of 1 in L and 1 hr in magnetic local time (MLT) for the whole Mlat (−40°–40°). (b) The occurrence rate of AKR for each region with the interval of 1 in L and 1 hr in magnetic local time (MLT) for the whole Mlat (−40°–40°).
(a) The number of Auroral kilometric radiation (AKR) samples in the northern hemisphere as a function of MLT and λ. (b) The number of satellite passings in the northern hemisphere. (c) The occurrence rate in the northern hemisphere. (d–f) The same as (a–c) except for the southern hemisphere.
The number of Auroral kilometric radiation (AKR) samples as a function of magnetic local time (MLT) and λ under different SYM‐H indexes in both hemispheres. (a–b) The number in the northern hemisphere for SYM‐H > −10 nT and SYM‐H ≤ −10 nT. (c–d) The same as (a–b) except for the southern hemisphere.
Results of Auroral kilometric radiation (AKR) samples observed by the Arase satellite. (a) The number of AKR samples with different fp in the northern hemisphere (blue) and southern hemisphere (red). (b) The averaged wave amplitude with different fp in the northern hemisphere (blue) and southern hemisphere (red).
  • Fuliang XiaoFuliang Xiao
  • Jiawen TangJiawen Tang
  • Sai ZhangSai Zhang
  • [...]
  • Satoko NakamuraSatoko Nakamura
Plain Language Summary Auroral kilometric radiation (AKR) is a strong radio emission with kilometric wavelength at the Earth. They have a potential for accelerating electrons to relativistic energies or scattering electrons into the atmosphere, leading to serious damage to spacecrafts or ozone destruction. Because the parallel electric field contributing to AKR generation should be different in the northern and southern hemispheres, it is necessary to study the distribution characteristics of AKR in two hemispheres. Here, we examine the data of Arase satellite from 23 March 2017 to 31 July 2019, and find that the distributions of AKR samples in two hemispheres are asymmetric. The occurrence rate in the southern hemisphere is greater than that in the northern hemisphere. More AKR samples in the northern (southern) hemisphere occur from dusk to midnight (pre‐midnight to dawn). More AKR samples in the northern (southern) hemisphere are observed in the frequency range of ≤300 kHz (>300 kHz). This study provides more information about AKR in the magnetosphere.
A microinjection event on 4 August 2016. (a) Energy spectrogram of energetic electron flux between 20:00 and 23:20. (b) Pitch angle spectrogram of energetic electron flux between 100 and 300 keV. (c) AE index and Sym‐H index. (d) Magnitudes of electric field and magnetic field. (e) Energy spectrogram of energetic electron flux between 22:15 and 22:55. (f) Pitch angle spectrogram of energetic electron flux between 100 and 300 keV. (g) Residual pitch angle spectrogram of (f).
The ultralow‐frequency wave in the microinjection event on 4 August 2016. (a) Wave components of the magnetic field. (b) Wave components of the electric field. (c) Wavelet spectrogram of Er. (d) Electron fluxes of different energy channels.
Phase relationship between the filtered Er and residual electron flux. (a) Band‐pass filtered Er. (b) Band‐pass filtered residual electron flux.
Microinjection event on 9 August 2016. (a) Energy spectrogram of energetic electron flux. (b) Band‐pass filtered Er. (c) Band‐pass filtered residual electron flux.
Plain Language Summary In the dusk to midnight plasma sheet, the fluxes of counter‐streaming energetic electrons were often observed to fluctuate at the period about 2 ∼ 6 min, with the higher energy fluxes coming to peaks earlier than those at lower energies. These phenomena were considered to be caused by repetitive injections of electrons with smaller time scale than the substorm‐associated injections, so were named “microinjections.” In this paper, we analyze two microinjection events and find that they are likely to result from the drift resonance between local electrons and Ultralow‐frequency compressional toroidal waves. To verify this hypothesis, we extend the present theory for toroidal mode drift resonance from only considering electrons with 90° pitch angles to including all bouncing electrons. We find that the predicted phase relationships of electron fluxes and the wave electric field correspond well with observations. The phase difference between the electron fluxes and the electric field equals to −90° or +90° at the resonant energy and increases with energy. Therefore, we propose that the compressional toroidal mode drift resonance may work as the generation mechanism of microinjections in the two events.
Zhao, P., Sprenger, M., Barzegar, R., Tang, X., & Adamowski, J. (2022). Similar isotopic biases of plant stem bulk water from different water sources by cryogenic vacuum distillation demonstrated through rehydration experiments. Geophysical Research Letters, 49, e2021GL096474. The above article from Geophysical Research Letters, published online on 30 March 2022 in Wiley Online Library (, has been withdrawn by agreement among the authors, the Editor‐in‐Chief Harihar Rajaram, the American Geophysical Union, and Wiley Periodicals, LLC. The withdrawal has been agreed because the authors discovered, after acceptance of the article but before final publication, some accidental transcription/typo errors in the data (for some δ²H and δ¹⁸O values) which would affect some of the results and cannot be sufficiently corrected to the authors' satisfaction.
(a) Location of observed H2O enhancements on 14 and 15 January. (b) Location of maximum H2O on 15–18 January. Lines display back trajectories from these measurements to the eruption time. Triangles mark the volcano location. (c) H2O profiles associated with locations shown in (a). The temperature profile (red dashed line) is the average of the temperature profiles retrieved by the Microwave Limb Sounder (MLS) at those locations. (d) H2O profiles associated with locations shown in (b). The 2005–2021 January–February–March mean plus 100 standard deviation values (μ + 100σ) are also shown in (c) and (d). (e) Measured (solid lines) and simulated (with and without considering SO2, dotted and dashed lines, respectively) radiances at the mixing ratio maxima for the enhanced profiles shown in (d) (colored lines) as well as for background conditions at the same pressure levels (gray lines). Note that this MLS spectrometer is centered on the 183.3 GHz H2O spectral line. Most MLS spectrometers observe emissions from two separate spectral regions: the “lower sideband” (LSB) and “upper sideband” (USB) as indicated for selected channels.
Profiles with maximum (a) SO2 and (c) HCl on 16 and 17 January. All of these measurements lie downwind of the HT‐HH volcano. (b) Measured (solid lines) and simulated (dashed) SO2 radiances at the mixing ratio maxima for the enhanced profiles (colored lines) as well as for background conditions at the same pressure levels (gray lines). (d) Same as (b) but for differences between measured radiances and those simulated without HCl (solid lines) as well as estimated HCl signatures (from differences between simulations, see legend; dashed lines). All enhancements shown fail the QS.
Time series of quality‐screened maximum H2O, SO2, and HCl mixing ratios at different pressure levels. SO2 maxima at 14 hPa and HCl maxima at 31 hPa disregarding QS after the HT‐HH eruption are shown in pink. Similarly, H2O maxima disregarding QS are shown in pink for each level. Prior to the HT‐HH eruption, we show QS data to avoid displaying retrieval artifacts, but no other injections failing the QS are found in the record.
(a) Maps of H2O at selected pressure levels for illustrative days after the eruption. Stippling indicates regions where a majority of the retrievals do not pass the QS. The volcano location is indicated by a triangle. (b) Meridional (30°S to 5°N) and (c) zonal mean anomalies for the same days. Colored contours show anomalies using all Microwave Limb Sounder H2O retrievals, while line contours display the same anomalies based only on QS data; differences indicate regions where many measurements do not pass QS. The volcano location is shown by dashed vertical lines; dashed horizontal lines indicate the level of the map on each day.
(a) The atmospheric tape recorder (zonal mean H2O anomalies in the tropics). (b) Time series of near‐global (60°S to 60°N) H2O at 100 and 31 hPa. H2O abundances are based on GOZCARDS (Froidevaux et al., 2015) and Microwave Limb Sounder data.
Following the 15 January 2022 Hunga Tonga‐Hunga Ha'apai eruption, several trace gases measured by the Aura Microwave Limb Sounder (MLS) displayed anomalous stratospheric values. Trajectories and radiance simulations confirm that the H2O, SO2, and HCl enhancements were injected by the eruption. In comparison with those from previous eruptions, the SO2 and HCl mass injections were unexceptional, although they reached higher altitudes. In contrast, the H2O injection was unprecedented in both magnitude (far exceeding any previous values in the 17‐year MLS record) and altitude (penetrating into the mesosphere). We estimate the mass of H2O injected into the stratosphere to be 146 ± 5 Tg, or ∼10% of the stratospheric burden. It may take several years for the H2O plume to dissipate. This eruption could impact climate not through surface cooling due to sulfate aerosols, but rather through surface warming due to the radiative forcing from the excess stratospheric H2O.
An example of the giant undulation and the ion density profile obtained from Defense Meteorological Satellite Program (DMSP) F16. The top panel shows the 5‐point smoothed (20 s) ion density measured by DMSP F16 Retarding Potential Analyzer with the ionospheric projection of the plasmapause (PP) region marked by the vertical red and blue dashed lines, which is also marked as a black rectangle on the trajectory in the bottom panel. And the vertical green dashed line represents the center of the PP. The bottom panel shows the aurora image with the sawteeth boundary in the altitude‐adjusted corrected geomagnetic MLAT‐MLT grids. The logarithmically scaled ion density is overlaid on the DMSP trajectory, whose beginning is marked by a black diamond symbol.
The Gauss fittings (red lines) of root‐mean‐square errors (black dashed lines) for (a) HEQ, (b) WEQ, (c) LGU, (g–h) DPPEQ, and (i–j) LPP with 1σ (blue), 2σ (green), 3σ (orange) confidence intervals.
Histograms of the occurrences of the projected giant undulations and the plasmapause with the number of cases and the simplified definition of parameters indicated in right upper corners. These parameters are (a) HEQ, (b) LGU (red) and LPP (blue), (c) WEQ, (d) LGU–LPP, (e) DPPEQ, and (f) GPP. The data in panels (a, c, and e) and (b–d) was binned in 0.2 and 0.5 RE intervals, respectively. And the data in panel (f) was binned in 0.5 interval.
Fitting plots of relationships between parameters of the projected giant undulations and the plasmapause with the number of cases marked at right upper corners: (a) HEQ versus DPPEQ, (b) HEQ versus GPP, (c) WEQ versus DPPEQ, (d) WEQ versus GPP, (e) LGU versus LPP, and (f) WEQ versus HEQ. The gray dots in each panel show the raw data. The solid diamonds in each panel show the binned average of raw data in 0.1 RE (panels a and c), 0.25 RE (panel f), 0.3 (panel e) and 0.3 (panels b and d), respectively. The red line in each panel represents the linear fitting to the binned data with the corresponding equation and the correlation coefficient shown at the upper part. The blue and green lines in each panel show +1σ and −1σ uncertainties of the red line with their corresponding equations shown at the upper part, respectively.
Plain Language Summary The magnetosphere‐plasmasphere‐ionosphere energy coupling in geospace is one of the key focuses in the space physics and space weather researches. When the energy, mass and moment in solar wind enter into the magnetosphere, the interactions between the enhanced convection electric field and the corotation electric field will change the configurations of plasmapause (PP). A recent study found that the thin and sharper PP may be conductive to excite PP surface waves, which generate giant undulations (GUs) on the equatorward boundary of the diffuse aurora, and drive outward‐propagating ultra‐low frequency waves. However, it remains unclear how the configurations of PP exactly control the characteristics of plasmapause surface waves (PSWs) including amplitudes and wavelengths during their sunward transportation. In this letter, we report the correlations between projected GUs and the PP configurations during the propagation process of PSWs based on GUs' images and the corresponding PP crossings of Defense Meteorological Satellite Program satellites between 2005 and 2019. These results would provide both physical insights and model constrains on the magnetosphere‐plasmasphere‐ionosphere energy coupling and the generation mechanisms of GUs and PSWs.
Understanding the partitioning of snow and rain contributing to either catchment streamflow or evapotranspiration (ET) is of critical relevance for water management in response to climate change. To investigate this partitioning, we use endmember splitting and mixing analyses based on stable isotope (18 O) data from nine headwater catchments in the East River, Colorado. Our results show that one third of the snow partitions to ET and 13% of the snowmelt sustains summer streamflow. Only 8% of the rainfall contributes to the summer streamflow, because most of the rain (67%) partitions to ET. The spatial variability of precipitation partitioning is mainly driven by aspect and tree cover across the sub-catchments. Catchments with higher tree cover have a higher share of snow becoming ET, resulting in less snow in summer streamflow. Summer streamflow did not contain more rain with higher rainfall sums, but more rain was taken up in ET.
Spatial distribution of trends (°C/decade) in diurnal temperature range (DTR) during 1951–2018. Trends in (a) observed DTR (Obs); and trends in CMIP6 multimodel ensemble mean simulations with: (b) anthropogenic and natural forcing (ALL), (c) natural forcing only (NAT), (d) anthropogenic forcing (ANT), (e) greenhouse gas forcing only (GHG), and (f) anthropogenic aerosol forcing only (AER). Black points indicate that the trends are significant at the 95% confidence level. The inset histograms summarize the percentage of the land surface showing a significant decrease (SD), decrease (d), increase (i), and significant increase (SI) in DTR, respectively.
Optimized scaling factors and their 95% confidence intervals during 1951–2018. The observations are regressed onto anthropogenic and natural forcing (ALL) simulations by one‐way detection, onto anthropogenic forcing (ANT) and natural forcing (NAT) by two‐way detection, and onto greenhouse gas (GHG), anthropogenic aerosol forcing (AER), OANT, and NAT by four‐way detection. The horizontal black and red dashed lines indicate zero and unity, respectively. When the 95% confidence interval of the scaling factor lies above zero this indicates the corresponding signal can be detected in the observed diurnal temperature range, and a scaling factor larger than one suggests that the simulated trend underestimates the observed one.
Observed annual diurnal temperature range trends and their drivers during 1951–2018. Attributable change is calculated by multiplying the annual trends by the corresponding scaling factors. Error bars are the corresponding 95% confidence intervals.
Projected changes in diurnal temperature range (DTR) under different scenarios. (a–c) Spatial distribution of trends during 1951–2100 (°C/century) for SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. (d–o) Probability density function (PDF) of mean DTR changes (°C) for the late twenty‐first century (2071–2100) compared with the period of 1951–1980. (p–r) Anomalies of annual time series of global DTR during 1951–2100. The data for the period of 1951–2014 is derived from the anthropogenic and natural forcing (ALL) experiments, and the data for 2015–2100 are derived from (p) SSP1‐2.6 (q) SSP2‐4.5, and (r) SSP5‐8.5 scenarios. The trend values are calculated as the linear trend of the projections multiplied by the corresponding scaling factor of ALL experiments shown in Figure 2.
Plain Language Summary Contrary to rising temperatures, the diurnal temperature range has been decreasing over the past several decades. Although the impacts of humans on global warming have been widely demonstrated, formal detection and attribution of the impacts of human‐made greenhouse gases (GHG) and aerosols on the DTR are still lacking. Our results suggest that human impacts on the DTR are clearly detectable, separately from natural changes. Human‐made greenhouse gases are the dominant factor controlling decreases in the DTR worldwide. In contrast, anthropogenic aerosols (AER) are the dominant contributor for Europe and have led to an abnormal increase in the DTR in this region. If human emissions continue, we expect to see further decreases in the DTR in most regions. Our first quantification of human impacts on the global and regional DTR has significant implications for climate change assessments and future climate projections.
A schematic of overlapping cold wakes (upper panel) and the evolving vertical temperature profiles (lower panel) at the overlapping location (the gray dot in the upper panel). Cold wake 1 and cold wake 2 are two successive cold wakes induced by the same tropical cyclone. ΔSST1 (ΔSST2) is the SST cooling for the cold wake 1 (2).
Annually accumulated Tropical cyclone (TC)‐induced sea surface temperature (SST) cooling (unit: °C) averaged over 1982–2016 with double counting (DC, a) and without DC (NDC, b). The contour lines of climatological seasonal amplitude of SST are added in panel (b) for comparison. The magnitude of SST seasonal cycle is calculated as the difference between maximum and minimum climatological monthly SST. Annually accumulated TC‐induced diffusivity (unit: cm²/s) averaged over 1982–2016 with DC (c) and without DC (d). The area‐weighted average SST cooling (blue) and diffusivity (red) within 40°S–40°N over 1982–2016 with DC (e) and without DC (f). Dots: AVHRR‐only, diamonds: AVHRR + AMSR for inter‐validation. RT stands for relative trend (linear trend divided by the mean value). Linear trends are obtained from a least square method and P‐values using Student's t test are shown. Note the different scales in (a–d).
Area‐weighted averages of the annually accumulated Tropical cyclone (TC)‐induced sea surface temperature (SST) cooling (ΔSST, unit: °C, blue) and diffusivity (unit: cm²/s, red) in major ocean regions from 1982 to 2016. All the results are without DC. Absolute trends (unit: °C/decade for SST cooling and cm²/s/decade for diffusivity) and relative trends (unit:/decade, in the parentheses) are labeled. (a) GL: global; (b) NH: Northern Hemisphere; (c) SH: Southern Hemisphere; (d) NWP: Northwest Pacific,100°E−180°, 0°–40°N; (e) NEP: Northeast Pacific, 180°–90°W, 0°–40°N; (f) NA: North Atlantic, 90°W–30°E, 0°–40°N; (g) NI: North Indian Ocean, 30°E−100°E, 0°–40°N; (h) SP: South Pacific, 135°E−70°W, 40°S–0°; (i) SI: South Indian Ocean, 10°E−135°E, 40°S–0°. Linear trends are obtained from a least square method. Trends with P‐values less than 0.05 are marked with asterisks.
Time series of standardized factors influencing the Tropical cyclone (TC)‐induced sea surface temperature (SST) cooling and diffusivity, including upper ocean stratification (red), annual average TC translation speed (yellow), TC intensity (green), TC number (blue), TC lifetime (pink) and TC size (violet) from 1982 to 2016 in ocean basins defined the same as in Figure 3. The original time series are smoothed using a 5‐year moving average. Dashed lines are linear fits using a least square method. Linear trends with P‐values less than 0.05 are marked with asterisks.
Tropical cyclones (TCs) are an important source of turbulent mixing for the upper ocean at low latitudes, causing sea surface cooling and subsurface warming. A new estimate of annually accumulated sea surface cooling and upper ocean diapycnal diffusivity induced by TCs is obtained by using quantified cold wake sizes, which were largely ignored by previous studies. Both the annually accumulated tropical cyclone‐induced sea surface cooling and upper ocean diffusivity on a global scale show a significant decreasing trend over the period of 1982–2016, at a rate of −0.09 ± 0.03 °C/decade and −0.03 ± 0.01 cm²/s/decade respectively. The strengthening of ocean stratification with global warming contributes to the decrease of sea surface cooling and mixing, while the changes of tropical cyclone characteristics (such as translation speed, intensity, number, lifetime and size) contribute differently in various ocean basins.
The two‐branch proposed model. The blue dotted box is the deep learning branch which takes in waveforms as the input with convolutional neural network (CNN) layers as the hidden layers, and a fully connected layer (FC) to flatten out the features. The green dotted box contains the physics‐based feature branch, which can take in P/S ratio measurements with or without the ML–MC measurements (adding ML–MC is shown in Supporting Information S1). Features from these two branches will be concatenated and pass another FC layer to make a decision. The small texts on top of the layer block represent the feature maps in CNN or number neurons in FC. 500 × 1@64 represents 64 feature maps with dimension 500 × 1.
Classification performance metrics. (a) cases where testing data is from the same region as training data, that is, Source Physics Experiment (SPE), Bighorn Arch Seismic Experiment (BASE), and Mount St. Helens (MSH) (20% of the total data saved as testing data). (b) cases where testing data is from a different region, Salton Seismic Imaging Project (SSIP), rather than the three training regions (c – f) The Receiver Operating Characteristic curve using training data from any of the three regions and testing on the new fourth region for five random initialization with mean (solid lines) and standard deviation (shaded areas). The blue curves show the designed model with deep learning and physics parameters branches. The orange and green curves are the model only with the deep learning or physics parameters branch. The Area Under the Curve (AUC) is shown in the legend. WF – Waveform, PS – P/S ratio.
Average of the normalized Gradient‐weighted Class Activation Mapping weights for the earthquake and explosion records across different distance bins (bin size 20 km). Rows a and b are weights on the time series. The blue thick lines are the average weights and the vertical thin blue lines are the standard deviation in the bins. The vertical green and red lines are the average of the estimated P and S arrivals using the same regional velocity models as in the calculation of the P/S ratios. For each panel, the horizontal axis is the time in seconds starting 5 s before the arrival of P wave. The vertical axis is the normalized weight. The rows c and d are weights on the spectrograms. Color shows the weights, from blue (0) to red (clipped at 0.5). Vertical green and red lines are the average of the estimated P and S arrivals using the same regional velocity models as in the calculation of the P/S ratios. For other distance bins, please refer to Figures S14–S17 in Supporting Information S1.
The average of the top five frequency bins across different distances corresponding to Figures S16 and S17 in Supporting Information S1 and Physics‐based branch error visualization. (a) For the earthquake records. (b) For the explosion records. The blue lines are for the P wave while the orange lines are for the S wave. (c) P/S ratio error histogram for earthquake records with 0.1 step bins. The gray bars are all the earthquake records, and the cyan colored bars are the wrongly estimated records in each bin. The blue line with dots is the percentage calculated using the cyan bar over gray bar, that is, the error rate. (d) P/S ratio error histogram for earthquake records with 0.1 step bins. Same as (c), but for the explosion records.
This paper combines the power of deep‐learning with the generalizability of physics‐based features, to present an advanced method for seismic discrimination between earthquakes and explosions. The proposed method contains two branches: a deep learning branch operating directly on seismic waveforms or spectrograms, and a second branch operating on physics‐based parametric features. These features are high‐frequency P/S amplitude ratios and the difference between local magnitude (ML) and coda duration magnitude (MC). The combination achieves better generalization performance when applied to new regions than models that are developed solely with deep learning. We also examined which parts of the waveform data dominate deep learning decisions (i.e., via Grad‐CAM). Such visualization provides a window into the black‐box nature of the machine‐learning models and offers new insight into how the deep learning derived models use data to make decisions.
Air temperatures are predicted to increase at an accelerated rate, and we need to understand how these faster rates of change could impact ecological communities in the future. Density distributions show how rates of significant (p < 0.05) summer (June–September) warming trends are distributed across the northern mid‐ and high‐latitude land area (>30°N) for the 1970–2019 period (left, green; Menne et al., 2018) and predicted for the latter half of this century (Shared Socio‐Economic Pathway 7.0, CMIP6 mean of all model ensembles (Dix et al., 2019)).
(a) La Perouse Glacier study area in June 2018 showing past ice margin positions during the historical period of retreat. Stars mark locations of air temperature sensors, and tree‐ring sampling locations, and yellow star marks the location where the annual record of June–September air temperature was estimated in Figure 3. (b) Rate of warming by month measured along the glacier‐to‐forest transect.
Gray lines show annual summer temperatures recorded in (a) Yakutat and (b) Sitka. Bold black lines depict the climate bordering the La Perouse Glacier's terminus based on the microclimatic‐offset rate estimated in Figure 2b, and the distance to the ice margin over time. Ice margin distance from 1948 to 2021 is based on remote sensing and field mapping, while the ice margin distance from 1855 to 1948 is based on historical accounts and cross‐dated glacier‐killed trees.
Mean and standard deviation of rates of warming near the La Perouse Glacier compared to historical and future rates of change over different 50‐year periods elsewhere. Periglacial accentuation of warming trends exceeds the magnitude of decadal‐scale warming documented during most historic examples of climate change, but it is similar to the accelerated rates predicted for coming decades (see Figure 1).
The responses of yellow‐cedar trees provide an example of how the accentuated climatic changes caused by proximity to a glacier can provide insights into how trees respond to rapid warming and cooling events. (a) Annual growth rates and 20‐year low‐pass filter of Alaska yellow‐cedar trees (from Gaglioti et al. [2021]). During the mid‐ to late 19th century, the advancing glacier's microclimate began depressing the growth rates of cedar trees. Following 1895, an overall warming trend caused by glacier retreat enhanced their growth. (b) The impacts of the changing periglacial microclimate occurred in a time‐transgressive manner with growth slowdowns occurring sooner and being more pronounced in trees growing closer to the advancing ice edge. The star indicates when the buried forest was overtaken by outwash gravels and advancing ice (Gaglioti et al., 2019).
Plain Language Summary There is growing concern that the unprecedented rate of future climate warming will interrupt the ecosystem services on which human society depends. To assess this concern, it helps to study natural communities that have experienced exceptionally high rates of climate change in the past. Here we show that as an Alaska glacier advanced and retreated over the last 166 years, the changes in air temperatures near its margins were greatly amplified relative to the temperature changes in the surrounding region. These near‐glacier changes in air temperature were similar to the rates of warming that are predicted to occur elsewhere on Earth by the year 2100. We then use tree rings to provide one example of how this natural experiment at the fluctuating margin of a glacier provides a way to assess the future impacts of rapid, high magnitude climate changes on forests. Across the planet, many glaciers are now in retreat, and other glacier‐margin ecosystems can be used as natural laboratories for studying global‐change biology.
Medians of distributions of daily number concentrations of ice crystals that are larger than 5 μm (in L⁻¹; left y‐axis) as a function of temperature for the time period 2006–2016. The temperature scale (x‐axis) is not continuous; it comprises distinct temperature bins of 10°C each. The temperature offset between each consecutive point is artificial and introduced only for readability of the plot. Different colors indicate the surfaces over which these clouds exist; sea ice (in turquoise) and open ocean (in blue). Two different seasons are presented (cold: DJF, SON and warm: MAM, JJA) and five geographical regions (latitude belts). The 95% confidence intervals are displayed as the error bars. The numbers of samples used to calculate the medians are shown in gray (right y‐axis).
Plain Language Summary The Arctic region is particularly affected by climate change, its warming is 2–3 times larger than global average during recent decades. One of the contributors to this “Arctic Amplification” may be the Arctic clouds and in particular the mixed phase type, where ice and supercooled liquid coexist at temperatures lower than 0°C. Aerosols play a significant role in cloud formation, since without the presence of some effective particles, the ice crystals could not form at all at temperatures between 0°C and roughly −40°C. In this study, we use a new satellite data set which provides an important cloud quantity, the amount of ice crystals in the clouds. Although this data set is limited to pure ice clouds, it can prove useful for understanding the behavior of Arctic clouds. What we find here is that Arctic low‐level clouds show larger quantities of ice crystals over sea ice than over ocean and we think that this can be attributed to the amount and type of aerosols related to each surface. This finding contradicts a previous hypothesis, which stated that more ice crystals would possibly form over ocean because of the presence of highly ice effective aerosols there.
Representative X‐ray diffraction (XRD) patterns and phase relations in Fe‐C‐H system at high P‐T conditions. (a) Phase relations in Fe‐C‐H system at 10.7 GPa in run‐1. (b) Phase relations in Fe‐C‐H system at 24.7 GPa in run‐2. (c) Phase relations in Fe‐C‐H system at 42.6 GPa in run‐3. Inset: Caked diffraction pattern at 42.6 GPa, 1885 K. Crystalline peaks of Fe, FeHx, Fe3C, and diamond were marked using the phase bars below the XRD patterns (d) Stability P‐T field of Fe alloys in Fe‐C‐H system when C and H were in excess from this and previous studies (Hirose et al., 2019; Narygina et al., 2011). FeC and FeH represent Fe carbide and hydride phases, respectively.
Representative X‐ray diffraction patterns showing the melting in Fe‐C and Fe‐C‐H systems. (a) Evolution of melting in Fe‐C system at 28.5 GPa in run‐7 (b) Evolution of melting in Fe‐C‐H system at 30.6 GPa in run‐6. X‐ray patterns at elevated temperatures were normalized by offsetting and matching the intensities of the low‐angle integration region. The gradually elevated background (pink shaded area) was caused by the thermal diffuse scattering due to the onset of melting. Crystalline peaks of fcc Fe, hcp FeHx, Fe3C, Diamond and KCl were marked by phase bars.
Eutectic melting temperature as a function of pressure in Fe‐C and Fe‐C‐H systems compared with geotherms. (a) Eutectic melting points in the Fe‐C system (red squares) and Fe‐C‐H system (blue squares and hexagons) compared with the melting temperature of Fe‐C, Fe‐H, and Fe3C in previous studies. Gray open symbols: pentagon: Fe‐C (Okamoto, 1992), diamonds: Fe‐C, multi anvil press (MAP) (Hirayama et al., 1993), triangles: Fe‐C, MAP (Fei & Brosh, 2014), hexagons: Fe‐C, laser‐heated diamond anvil cell (LHDAC) (Lord et al., 2009), crosses: Fe‐H, MAP (Sakamaki et al., 2009), circles: Fe‐H, LHDAC (Hirose et al., 2019) and dashed lines: Fe, Fe‐C, Fe3C, FeH, LHDAC (Anzellini et al., 2013; Hirose et al., 2019; Liu, Li, et al., 2016; Liu, Lin, et al., 2016; Morard et al., 2017; Zhang et al., 2016). (b) The fitted melting curve with confidence interval band (solid and dashed (extrapolation) red and blue lines) compared with mantle geotherm (yellow band (Ohtani, 2015), green line K2010 (Katsura et al., 2010) and brown line S2011 (Frost et al., 2022; Stixrude & Lithgow‐Bertelloni, 2011) and cold slab temperature (blue band: the upper boundary represents the temperature of cold slab surface and the lower boundary represents the average temperature of the cold slab (Kirby et al., 1996; Ohtani, 2015; Syracuse et al., 2010)). UM: upper mantle; LM: lower mantle; TZ: transition zone.
Cartoon illustrating the proposed reaction and melting under mantle conditions. Diamonds are produced from interaction between C and H‐bearing phases in the subducting slab and Fe metal in the ambient mantle (formed through disproportionation of Fe²⁺)(D J Frost et al., 2004; Rohrbach et al., 2007) or Fe in the subducted slab (formed through serpentinization of oceanic peridotite)(Smith et al., 2021). Fe‐C‐H melts are formed in the mantle. With continuous replenishment of C and H from the subducting slab, diamonds grow in those metallic melts.
Plain Language Summary Deep carbon and hydrogen cycles (cycles between the surface and deep Earth) have significant influence on the physical and chemical evolution of our habitable planet, which affects the long‐term climate and ecosystem evolution. Subduction of oceanic floor carries carbon‐ and hydrogen‐rich species such as carbonates, hydrous minerals, and organic compounds into the deep Earth, where these species may react with the metallic iron there and form Fe‐C‐H alloy. In this study, we reproduced the extreme high‐pressure and high‐temperature conditions of the deep Earth using a technique called laser‐heated diamond anvil cell. Combined with high‐energy X‐ray diffraction, we studied the phase relation and melting behavior of the Fe‐C‐H system simultaneously at high pressure and high temperature. We find that the melting temperatures of the Fe‐C‐H system is lower than the temperatures of the mantle at depth, indicating Fe‐C‐H alloy may be molten along mantle geotherm. Therefore, the mobility of carbon and hydrogen in the deep mantle is enhanced, facilitating the cycling of deep carbon and hydrogen. Further, the substitution of carbon by hydrogen in carbon‐rich alloys may account for the formation of diamonds of deep origin, and the Fe‐C‐H melts can provide the fluid environment to grow large diamonds in deep Earth.
Linear trends of summer rainfall amount of different intensities during 1961–2018, (a) total rainfall, (b) light rainfall, (c) moderate rainfall, (d) heavy rainfall, (e) rainstorm and (f) extreme rainfall. The unit of trend is mm/decade. Black circles report stations with statistically significant trends.
Annual time series plots of summer rainfall anomalies of different intensities over urban (red) and rural stations (blue) during 1961–2018, (a) total rainfall, (b) light rainfall, (c) moderate rainfall, (d) heavy rainfall, (e) rainstorm, and (f) extreme rainfall. The anomalies were calculated by removing the multiyear climatological summertime mean of baseline period (1961–1990). The shaded areas represent one standard deviation above and below the means. Statistics of the linear regressions for 1961–2018 are reported.
Spatial changes of summer (a–c) total rainfall and (d–f) strong‐intensity rainfall derived from WRF simulation comparison: S2000‐S1990 (left column), S2010‐S2000 (middle column) and S2010‐S1990 (right column). The Taihu watershed is outlined in purple. The black polygons represent urban extent after the corresponding urbanization, that is, urban extent of 2000 in S2000‐S1990 and urban extent of 2010 in S2010‐S2000 and S2010‐S1990.
Same as Figure 3 but for summer mean (a–c) planetary boundary layer (PBL) height, (d–f) maximum convective available potential energy (MCAPE), (g–i) lifted condensation level (LCL), and (j–l) level of free convection (LFC). The units of PBL, LCL and LFC are meters (m), and unit of MCAPE is J/kg.
In this study, we investigated the urbanization‐induced summer rainfall changes in the Yangtze River Delta (YRD) by analyzing long‐term observations and numerical simulations. The observation‐based analysis showed that long‐term urbanization increased the region's summer rainfall, particularly through the intensification of heavy rainfall, which is noted as the urban rain island (URI) effect. A series of numerical sensitivity experiments with three historical land use and land cover scenarios (1990, 2000, and 2010) were designed to further understand the urbanization impacts on rainfall. The observed URI effect was well reproduced by the numerical simulations, and on average, urban expansion during 1990–2010 increased summer rainfall over urban areas by 51.91 mm. The URI effect slightly weakened in the late stage of urbanization (2000–2010) compared to the early stage (1990–2000). We conclude that the strengthening of precipitation‐inhibiting effects during the late period offset the precipitation‐enhancing effects, which led to the weakening of the URI effect.
Maps of precipitation, rock type, and landslide locations. (a) 30‐year mean water year precipitation (m/yr) with period WY1990‐WY2019 calculated from Parameter‐elevation Regressions on Independent Slopes Model data. (b) Simplified geologic map showing the areal extent of the Franciscan mélange rock unit. (c) Location of active landslides identified with our interferometric synthetic aperture radar analyses. Well‐studied landslide groups labeled Eel = Eel River, BH = Berkeley Hills, CSAF = Central San Andreas Fault, PBL = Portuguese Bend landslide. (d–i) Precipitation Ratio (total WY precipitation/30‐year mean precipitation) for WY2015‐WY2020. Red colors correspond to drier than average years and blue colors correspond to wetter than average years. Yellow circles in (d) show landslides selected for detailed time series analyses.
Landslide and precipitation time series for 38 selected landslides in California. (a) Cumulative displacement time series projected onto the downslope direction and separated by water year (WY). The time series for each landslide are smoothed using a moving median temporal filter. Solid lines correspond to landslides occurring within the Franciscan mélange rock unit. Colors correspond to 30‐year mean water year precipitation (WY1990‐WY2019) for each landslide. Inset shows the location of the selected landslides on the 30‐year mean precipitation map. (b) Cumulative precipitation time series for each landslide separated by WY and colored by 30‐year mean WY precipitation.
Landslide response to changes in precipitation. (a–d) Maps of velocity and precipitation ratio by water year. Brown to green color symbols correspond to velocity ratio values for each landslide. Red to blue colors in background correspond to precipitation ratio. Symbols correspond to landslide type. Rock type is shown by black or gray symbol border color. (e–h) Velocity ratio as a function of precipitation ratio for selected landslides. (i–l) Velocity ratio as a function of estimated landslide thickness. Error bars show the uncertainty in the velocity ratio. Red to blue colors correspond to the 30‐year mean water year precipitation (WY1990‐WY2019) for each landslide. Symbols correspond to landslide type. Rock type is shown by black or gray symbol border color. We calculated the velocity ratio uncertainty using standard error propagation and assumed nil uncertainty in the precipitation data.
Plain Language Summary Landslides are often triggered by precipitation and as a result are sensitive to local climate conditions. Climate change is impacting precipitation patterns worldwide and therefore will likely have a profound influence on landslide activity over the coming decades. Here we use standardized open‐access satellite radar data to assess landslide sensitivity to precipitation across a large rainfall gradient in California between 2015 and 2020. During this time period, our study area experienced some of the wettest and driest years on record, which is a precipitation pattern that is predicted to become the norm over the next century in California. We found that landslides in both wet regions of northwestern California and dry regions of southwestern California were similarly sensitive to seasonal and multi‐year changes in precipitation. These landslides moved faster than average during wet years and slower than average during dry years. Our findings further confirm landslide sensitivity to climate change under diverse hydroclimate conditions and highlight the need to establish a long time series of landslide behaviors that can be used to better predict future landslide activity.
Arctic observations of the diffusive staircase. (a) Map of the Arctic Ocean indicating the study region (Beaufort Gyre Region, BGR), the Northwind Ridge (NWR), as well as the Atlantic Water (AW, red lines) and Pacific Water (PW, blue lines) inflows. (b) BGR showing locations (dots with color indicating year) of all ITP profiles analyzed between 2004 and June 2020; gray and black squares mark the locations of the representative profiles (color coded accordingly) shown in (c–e) from the central and eastern BGR, respectively. (c) Potential temperature (°C) versus depth, with the diffusive‐convective staircase shown in the inset (indicated by gray boxes). Prominent thermohaline intrusions around the core of the Atlantic Water Layer are indicated. (d) Salinity versus depth, with the staircase in the inset. (e) Smoothed buoyancy frequency N² (s⁻²) versus depth (smoothing over 40 data points, approximately 10 m). Black dots in panels (c–e) indicate the depth of NP2 ${N}_{P}^{2}$.
Staircase variability in the Beaufort Gyre Region. Map of (a) the Atlantic Water potential temperature maximum (°C) with the depth at which salinity = 34 overlain (the Northwind Ridge, NWR, is labeled), (b) the peak buoyancy frequency NP2 ${N}_{P}^{2}$ (s⁻²), (c) mean layer thickness (m), (d) the potential temperature variance (°C²) in a 100‐m depth region centered at the Atlantic Water potential temperature maximum, and (e) bulk density ratio Rρ. (f) Mean layer thickness (m) against NP2 ${N}_{P}^{2}$ (s⁻²) (gray dots) and binned in seven 0.25 × 10⁻⁴ s⁻² increments, with error bars indicating ±1 standard deviation in binned mixed layer thickness. Only bins with at least 10 data points are included.
Schematic indicating the west to east gradient in staircase layer thickness in context with Atlantic Water propagation (including via thermohaline intrusions) and Beaufort Gyre circulation. Thin staircase layers, run‐down intrusions, and low values of overlying stratification NP2 $\left({N}_{P}^{2}\right)$ generally occur together, while thick layers, prominent intrusions, and high NP2 ${N}_{P}^{2}$ are generally observed together.
Arctic Ocean waters sourced from the Atlantic contain a vast amount of heat. In the Arctic’s Beaufort Gyre, diffusive convection is the primary mechanism by which this heat is transported vertically. This mixing process is characterized by a “staircase” where convective layers are separated by interfaces in temperature and salinity. It is not well‐understood what governs layer thickness, which is an important parameter in heat transport. Here we relate staircase properties to the background water‐mass structure of the Beaufort Gyre via analysis of Ice‐Tethered Profiler observations. We find that staircase layer thicknesses vary with intrusive features below the staircase and the stratification overlying the staircase. We relate these features to the pathway of anomalously warm Atlantic Water in the Beaufort Gyre. Results suggest that intrusive features in context with the Gyre’s large‐scale geostrophic flow may be key to understanding layer thicknesses and the propagation of warm waters into the Gyre.
(a) P‐ and (b) S‐wave velocities and (c) density of superhydrous phase B (SuB) as a function of pressure and temperature. Open and solid colored circles correspond to our experimental data collected in cycles 1–3 up to 750 K, and cycles 4–8 up to 900 K, respectively.
(a) Longitudinal (VP) and (b) shear (VS) wave velocities, and (c) density of hydrous (1.2 wt% of H2O) and dry pyrolite. Solid blue lines represent hydrous pyrolite for superhydrous phase B (SuB) with an ideal composition (e.g., 5.8 wt.% H2O) along a cold slab geotherm; the higher and lower bounds of the blue shadow represent the variation of water content in SuB from 0 wt.% to 10 wt.% H2O, respectively. The broken green lines represent the decomposition of SuB = 34% h‐Rw +19% H2O (ice VII) + 47% MgO (upper mantle) and SuB = 18% phase D + 2% Brg + 80% MgO (lower mantle) (Komabayashi & Omori, 2006). Solid green and red lines represent dry pyrolite along cold slab and mantle geotherms respectively; for example, red shadow represent the velocities and density variation upon increasing temperature. Velocity and density contrasts ΔW = 100 × [Wdry − Whydrous]/Wdry, where W = (d) VP, (e) VS, and (f) density; solid blue and red lines represent the contrasts between cold hydrous pyrolite and dry pyrolite along cold slab and mantle geotherms, respectively; broken green and red lines represent the contrasts between cold hydrous pyrolite upon decomposition of SuB and dry pyrolite along cold slab and mantle geotherm, respectively.
Plain Language Summary Water can be transported into the deep Earth by cold subducting slabs in the form of hydrous minerals. Superhydrous phase B (SuB) is proposed to be one of the most important water holders in the mantle transition zone (MTZ) and upper lower mantle (ULM). Here we investigated the sound velocities of SuB up to 21 GPa and 900 K using ultrasonic interferometry combined with synchrotron X‐ray techniques in a multi‐anvil apparatus. Based on our velocity and density data, we determined an equation of state for SuB up to the pressures of the MTZ and temperatures relevant to a cold geotherm. Our new elasticity data allowed to calculate the variation of velocities and density of the hydrated mantle as a function of depth, up to the P and T conditions of the MTZ and extrapolate those properties to the ULM. The results show that the presence of the hydrated region with SuB as the main water carrier could explain low VP and low VS anomalies observed by seismological studies in cold regions while it would rather be associated with low VP and high Vs anomalies in hot regions.
Time‐mean (a) eddy kinetic energy (EKE), (b) EKEa, (c) EKEo and (d) EKEg averaged over the upper 100 m in the Community Earth System Model (CESM). The boxes in (b) encompass different regions for regional analysis. The abbreviations WBCE, STG, SPG, and SO stand for the western boundary currents and extensions, subtropical gyres, subpolar gyres, and Southern Ocean, respectively. The region within 5°S–5°N is masked by gray. The results for the integration over the entire water column are provided in the Figure S4 of Supporting Information S1.
Time‐mean (a–c) C(PM,PE), Co(PM,PE), Ca(PM,PE) and (d–f) C(PE,KE), Co(PE,KE), Ca(PE,KE) averaged over the upper 100 m in the CESM. The region within 5°S–5°N or shallower than 100 m is masked by gray. The results for the integration over the entire water column are provided in the Figure S5 of Supporting Information S1.
(a) Vertical profiles of time‐mean C(PE,KE) (black), Co(PE,KE) (blue), Ca(PE,KE) (red), and CTTW(PE,KE) (green dashed) averaged within 5°–70° (GLO). (b–e) Same as (a) but for the western boundary currents and extensions, STG, subpolar gyres, and Southern Ocean, respectively. (f–j) Same as (a–e) but for the seasonal difference (winter minus summer). The surface boundary layer (SBL) depth is marked by a gray line.
(a) Vertical profiles of time‐mean and quasi‐global‐mean CTTW(PE,KE) (black) and its decomposition into CTTWwindPE,KE ${C}_{\text{TTW}}^{\text{wind}}\left({P}_{E},{K}_{E}\right)$ (blue), CTTWgeoPE,KE ${C}_{\text{TTW}}^{\text{geo}}\left({P}_{E},{K}_{E}\right)$ (green) and CTTWageoPE,KE ${C}_{\text{TTW}}^{\text{ageo}}\left({P}_{E},{K}_{E}\right)$ (orange). The red dashed line denotes the sum of CTTWgeoPE,KE ${C}_{\text{TTW}}^{\text{geo}}\left({P}_{E},{K}_{E}\right)$ and CTTWageoPE,KE ${C}_{\text{TTW}}^{\text{ageo}}\left({P}_{E},{K}_{E}\right)$. (b) Same as (a) but for its seasonal difference (winter minus summer). (c) Seasonal difference of normalized quasi‐global‐mean |τ′g| $\vert {{\boldsymbol{\tau }}^{\prime }}_{g}\vert $ (black), Km (red) and Sg (blue). Each quantity is normalized by its time‐mean value to show its relative change.
Plain Language Summary The swirling mesoscale (100–1,000 km) eddies are a prominent feature in the upper ocean. Their vigorous currents are powered primarily by releasing the potential energy stored in the tilting isopycnals (surface of constant density). To date, this release is prevailingly attributed to baroclinic instability, a process lowering the center of gravity by slumping isopycnals in a frictionless manner. However, based on a state‐of‐the‐art global climate simulation resolving mesoscale eddies, we demonstrate that frictional forces play an important role in converting the potential energy to kinetic energy of mesoscale eddies. It generates an overturning flow that is directed upwards on the buoyant side and downwards on the dense side, lowering the center of gravity. This frictionally driven overturning flow is stronger in winter than in summer as a result of active turbulent mixing induced by intense sea surface cooling and wind stirring in winter. Accordingly, the conversion of potential energy to kinetic energy of mesoscale eddies exhibits a distinct seasonal cycle in the global ocean.
Deployment of the AQG‐B in the summit craters area of Mt. Etna volcano. (a) Sketch map of Mt. Etna showing the position of PDN, SLN, and MNT stations. The shaded yellow area encloses all the possible horizontal positions of the mass source (Figure 4 and text). Inset: aerial photo of the PDN observatory (Google Maps). (b) Picture of the AQG‐B. The height of the sensor head is 100 cm. (c) Panorama looking NW, from the slope to the S of the observatory, showing the proximity of PDN to the summit craters. (d) Section (not in scale) crossing the observatory and showing the installation configuration of the AQG‐B.
Sensitivity analysis on data from the AQG‐B. (a) Spectrogram of the vertical‐component seismic signal from the Pizzi Deneri station (EPDN seismic station; 1 August–3 December 2020). The green and orange boxes mark periods of low and high volcanic tremor amplitude, respectively. (b) Power spectral density of the seismic velocity recorded at EPDN, during the phases of low (green) and high (orange) tremor amplitude marked in the spectrogram. Black curves indicate the new high and low noise models from Peterson (1993). (c) Allan deviation of the corrected gravity data from the AQG‐B at PDN, during the low (green) and high (orange) tremor phases. The green and orange dashed lines in the Allan plot indicate a sensitivity of 1,200 and 1,600 nm/s²/τ $\tau $1/2, respectively.
Gravity time series and wavelet coherence analysis. (a) Gravity time series from PDN (1 August–3 December 2020), after removal of the perturbing effects shown in panels (b–f) of Figure S1 in Supporting Information S1. Data are averaged over 10‐min (gray curve), 1‐hr (black curve) and 5‐hr (red curve) intervals. (b) Gravity time series from SLN, after removal of Earth tide, atmospheric pressure, ground tilt and polar motion. Data are averaged over 10‐s (gray curve) and 10‐min (black curve) intervals. (c) Wavelet coherence between the signals in panels (a and b) Black arrows indicate the relative phase relationship (in‐phase pointing right, anti‐phase pointing left, and one signal leading the other by 90° pointing straight up or down). The cone of influence, where edge effects may distort the results, is shown as a lighter shade.
Gravity time series during high‐coherence phases and derivation of mass source depths. (a and b) Gravity time series from PDN (black curves) and SLN (red curves), during the first (h‐c 1) and second (h‐c 2) subintervals of high coherence. As for h‐c 2, the available signal from MNT is also shown (blue curve in panel (b). (c and d) Scatterplots of SLN against PDN, during h‐c 1, and SLN against PDN and SLN against MNT, during h‐c 2. For each scatterplot, the slope of the best‐fitting line (s.) and the correlation coefficient (c. f.) are reported. (e) PDN/SLN (red area) and MNT/SLN (blue area) gravity amplitude ratio versus depth of the mass source. The calculations are performed for all the possible positions of the mass source enclosed in the yellow area of Figure 1a. Dashed segments mark the values of the observed amplitude ratios during h‐c 1 and h‐c 2.
Plain Language Summary Mass redistributions occurring in the Earth's interior, for example, when a magma batch is displaced through the feeding system of an active volcano, may induce tiny changes in gravity over time, measurable on the ground. Measurement of such changes requires high‐precision devices, namely, the gravimeters, which can detect variations as small as one part in 10⁹ of the gravity acceleration on Earth. However, standard gravimeters are not ideally suited for use in harsh field conditions, especially when continuous measurements are the target. Recent advances in quantum technology have allowed the development of a portable gravimeter which can successfully operate under field conditions. Here we present the world's first application of this quantum gravimeter for monitoring and studying an active volcano. The device was deployed only 2.5 km away from the summit active craters of Mt. Etna volcano (Italy) and has provided a high‐quality gravity time series. Inspection of this time series highlighted gravity changes which are reflective of bulk volcanic processes, involving magma and exsolved gas in the upper part of Mt. Etna's plumbing system.
Map showing the Ross Sea location in Antarctica and the sites where the BC006 and BC010 sediment cores were collected on the continental slope in 2017. The color bar on the right represents the elevation of the Antarctic continent (upper) and the water depth of the Ross Sea (lower).
Plots of the concentration depth distribution of total organic carbon (TOC) and Pyrogenic carbon (PyC) and the ratio of PyC/TOC in sediment cores BC006 (a) and BC010 (b); the grain size characteristics of the two cores (c); the depth distributions of Δ¹⁴C values of TOC and PyC and their ¹⁴C ages in cores BC006 (d) and BC010 (c); the depth distributions of the δ¹³C values of TOC and PyC in cores BC006 (f) and BC010 (j).
Plot of concentrations of Pyrogenic carbon (PyC) versus total organic carbon (TOC) (a) and the Δ¹⁴C values of PyC versus TOC (b) in BC006 and BC010 sediment cores collected in the Ross Sea, Antarctica. Plot of the values of δ¹³C versus Δ¹⁴C for TOC (c) and PyC (or BC) (d) in sediments of the Ross Sea (, this study), Arctic Ocean (, Ren et al., 2019), Santa Monica Basin (, Masiello & Druffel, 2003), and deep trenches in the North Pacific (, Zhang et al., 2022). The solid line is the regression fit to the data.
Comparative plot of ¹⁴C ages of total organic carbon (TOC) and Pyrogenic carbon (PyC) in the two sediment cores BC006 and BC010 in the Ross Sea, Antarctic, with those in the Arctic Ocean (AO, Ren et al., 2019), East China Sea (ECS, Wang & Li, 2007), Atacama Trench (AT), Kermadec Trench (KT), and other trenches, including the Mariana Trench, Mussau Trench, New Britain Trench, and Bougainville Trench in the North Pacific Ocean (TRs, Zhang et al., 2022), Santa Monica Basin (SMB, Masiello & Druffel, 2003), Northeastern Pacific (NP) and Southern Ocean (SO) (Masiello & Druffel, 1998). The open triangles (▽) represent the maximum, open circles (○) represent the minimum, and open squares (□) represent the average ages of TOC and PyC in the sediment cores.
Plain Language Summary A large fraction of pyrogenic carbon (PyC) is produced from incomplete combustion of biomass fires on Earth and is widely distributed on land and in the oceans. This study presents the first evidence of both radiocarbon and stable carbon isotopes in the PyC preserved in the slope sediments of the Ross Sea in Antarctica. The results revealed that PyC accounted for a significant fraction (10.0%–28.0%) of the sedimentary total organic carbon (TOC) buried in the sediments, and both the Δ¹⁴C and δ¹³C values of PyC showed distinctive differences compared with those of TOC. The well‐defined δ¹³C (−10.9‰ to −17.2‰) and Δ¹⁴C (−415‰ to −843‰) values of PyC in the sediments revealed that in ancient times, PyC was produced from wildfires of C4 vegetation in the Southern Hemisphere and was transported in the atmosphere to Antarctica. The isotopic records of PyC preserved in the Ross Sea sediments provide meaningful evidence for environmental changes.
(a) Annual Arctic and global mean temperature anomaly, with respect to 1961–1990 mean, according to HadCRUT5.0 (black and red solid lines) and their piece‐wise linear approximations (dashed lines LinFit). (b and c) The maximum (red columns) difference between 21 years moving trends after and before the breaking point identifies 1986 and 1999 as times of changing trends in mean Arctic temperature. (d) The values of Arctic Amplification index obtained using the HadCRUT5.0 (red), GISS (black), NOAA NCEI (gray), and HadCRUT/CW (blue) data, and their step‐like approximation (black horizontal lines).
The Arctic Amplification (AA) (red line) and its two standard deviations (yellow lines) uncertainties calculated from the mean HadCRUT5 temperature data and its one hundred realizations, together with the AA from the ensemble mean of CMIP6 simulations with the first member of each model (black dashed line) and its one standard deviation uncertainties (gray dashed lines). The observed near constant periods of the AA are shown as horizontal solid black lines.
Correlation coefficient 1970–2010 between the Arctic Amplification (AA) calculated from the observed (HadCRUT5) temperatures and the first realization of the model simulations. Red columns show a positive correlation, blue columns show a negative correlation. (b) The same for the first and second model realizations. Gray columns show correlation coefficient between the observed AA and the first model realizations; the yellow columns between the second realization and observed AA.
CMIP6 model simulations using the ensemble mean of the first five realizations of each model. (a)The Arctic temperature difference between the model simulated and observed warming between the mean of 2010–2020 and 1961–1990. The red columns are positive differences and the blue columns are negative differences. (b) The same for mean global temperature. (c) Correlation coefficient between the AA calculated using the model and the HadCRUT5 temperature data. (d) Models' order (skill measure) using the procedure described in the text. (e) The AA according to the HadCRUT5 data (red line), the average of Arctic Amplification (AA) of the four models with the lowest order number (the highest skill—the best four models) shown as a solid black line, the AA as an average of the four models with the highest order number (the worst skills—the worst four models) as a dotted gray line.
While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the “best” CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time.
(a) Bathymetry and major fronts of the Southern Ocean. Gray contours show 1,000, 2,000, and 3,000 m isobaths. Fronts are the Subantarctic Front (blue), Polar Front (orange), Southern Antarctic Circumpolar Current Front (green), and the Southern Boundary (red). (b) Base‐10 logarithm of eddy kinetic energy (EKE) [log10 m² s⁻²]. Black solid lines show the Antarctic Circumpolar Current (ACC) boundaries used in this study. Black dotted lines denote regions of high EKE. Standing meanders are labeled by the corresponding bathymetric feature: Crozet Plateau (CrP), Kerguelen Plateau (KP), Campbell Plateau (CP), East Pacific Rise (EPR), and Drake Passage (DP). (c) Spatial distribution of float profiles containing oxygen data across the Southern Ocean within the ACC; Δlatitude = 1.25°, Δlongitude = 2.5°. Black dotted lines show the ACC boundaries used in this study. (d) Histogram of the number of float profiles as a function of longitude within the ACC boundaries in panel (b). Profiles categorized as low‐EKE are in orange, with high‐EKE profiles in blue. Standing meanders are labeled the same as in panel (b). (e) Histogram of the number of float profiles at a given latitude within the ACC boundaries in panel (b). Colors are the same as in panel (d).
Probability density functions across the full Antarctic Circumpolar Current of ΔAOU [μmol kg⁻¹] where (a) Δh = 100 m, (b) Δh = 400 m, (c) Δh = 700 m. (d) Median in ΔAOU [μmol kg⁻¹] at values of Δh. (e) Variance of AOU [μmol² kg⁻²] on potential density surfaces. (f) Locations of profiles used to create probability density functions of ΔAOU [μmol kg⁻¹] at Δh = 300 m at (i) Drake Passage, (ii) Crozet Plateau, (iii) Kerguelen Plateau, (iv) Campbell Plateau, and (v) Eastern Pacific Rise. In all panels, blue colors denote high‐eddy kinetic energy (EKE) regions and orange colors denote low‐EKE regions.
Absolute salinity (SA)‐conservative temperature (CT) diagrams. Average apparent oxygen utilization (AOU) for each SA‐CT position at Δh = 300 m in (a) the low‐eddy kinetic energy (EKE) regions and (b) the high‐EKE regions. (c) Difference in AOU between the low‐ and high‐EKE regions. Joint histogram of profile locations at at Δh = 300 m in (d) the low‐EKE regions, and (e) the high‐EKE regions. (f) Difference in joint histograms between the low‐ and high‐EKE regions. Gray contours are potential density [kg m⁻³], with the black contour at 27.2 kg m⁻³. In all panels, only where there were more than five points at a given CT‐SA value that could be averaged are shown. ΔCT = 0.2°C, ΔSA = 0.025 g kg⁻¹.
(a) Snapshot of Finite‐size Lyapunov Exponents (FSLEs) from 1 March 2020 centered on the Crozet Plateau region of the Antarctic Circumpolar Current (ACC). Blue regions are high‐eddy kinetic energy (EKE) while orange are low‐EKE, using the EKE definition defined in the methods. (b) Probability density function of maximum stretching FSLE in the high‐ (blue) and low‐ (orange) EKE regions across the full ACC. Gray lines are expanded in panel (c). (c) Probability density function of maximum stretching FSLE in the Kergulen Plateau (red), the Crozet Plateau (black), the Campbell Plateau (yellow), Drake Passage (green), and the Eastern Pacific Rise (EPR; blue). Vertical lines represent 75th percentiles with the same colors as above. (d) Plot of the mode of the FSLE in the high‐EKE region versus the differences in the high and low medians of the ΔAOU probability density functions (δAOU) at Δh = 250 m. Error bars are standard deviations of δAOU and colors are the same as in panel (c).
Plain Language Summary The circulation of the Southern Ocean is dominated by the eastward‐flowing Antarctic Circumpolar Current (ACC). The characteristics of the ACC are not uniform around the Southern Ocean. Rather, when the ACC encounters underwater mountain ranges the flow is diverted, which causes these regions to be more energetic through the generation of ocean eddies in a process similar to atmospheric storm tracks. Numerical models have suggested that the exchange of properties, such as heat and carbon dioxide, between the atmosphere and the interior ocean is enhanced in these energetic regions. In this study, data from freely floating robotic floats in the Southern Ocean is used to observe the vertical structure of dissolved oxygen. Transfer of properties between the ocean's surface and the interior ocean preferentially occurs in high energy regions of the ACC. Most previous work has relied on numerical models of the ocean that, due to computational limits, do not represent all aspects of the ACC's energetic regions. This study has implications for how the Southern Ocean's ability to take up excess carbon dioxide from the atmosphere will evolve in the future.
Dust (a), anthropogenic (b), and wildfire (c) dFe deposition fluxes (μmol/m²/year) and standard experiment surface ocean (0–10 m) dFe concentration (d; μmol/m³) and δ⁵⁶Fediss (g; ‰) for 2014. Absolute (e; μmol/m³) and relative (f; %) change in dFe concentration, in δ⁵⁶Fediss (h; ‰), and for primary production (i; mmolC/m³/year); calculated by subtracting the experiment without anthro‐Fe from the standard experiment (see Table S1 in Supporting Information S1). Red boxes indicate regions analyzed in Section 3.2.
Seasonal variability (year 2014) in dFe deposition (a; nmol/m²/day) for a subpolar, Fe‐limited region (Region 1) and a subtropical, nitrogen‐limited region (Region 2; see Figure 1), and the corresponding simulated surface ocean (0–10 m) primary production (b; mmolC/m³/day), phytoplankton Fe uptake (c; nmolFe/m³/day), dFe concentration (d; μmol/m³), and δ⁵⁶Fediss (e, f; ‰) for experiments with (red) and without (black) anthro‐Fe deposition (difference in blue). For δ⁵⁶Fediss, the contribution of δ⁵⁶Fe fractionation and source endmember effects is shown. See Table S1 in Supporting Information S1 for details on attribution.
Effect of external dFe sources and fractionating processes on surface ocean (0–10 m) δ⁵⁶Fediss (a–f; ‰, average for 2014), calculated by subtracting δ⁵⁶Fediss of experiments with muted δ⁵⁶Fe effects from standard experiment δ⁵⁶Fediss (see Section 2 and Table S1 Supporting Information S1). Panels (a–f) sum to the overall δ⁵⁶Fediss distribution (Figure 1g), with a <0.02‰ discrepancy due to hydrothermal dFe.
Effect of anthro‐Fe deposition on different ocean biological regimes (a), in the North Pacific (b), and at global scale (c). Note that the depicted regions are illustrative of the present day, and may change if, for instance, nutrient input patterns change (e.g., shifts in aeolian deposition). In hatched areas, the effect of anthro‐Fe is weaker and limited to months of highest productivity.
Plain Language Summary Iron released into the atmosphere by anthropogenic activities (e.g., combustion, metal industry) can get transported to open ocean areas, where it can fertilize biological production upon deposition. The distinctively‐light isotopic signatures of such anthropogenic iron have been used to trace its oceanic impact, and disentangle its contribution from that of other external iron sources. However, this approach is complicated by fractionation during surface ocean processing, which can affect the dissolved iron isotopic signature. To quantify the impact of anthropogenic iron on surface ocean iron and its isotopes, we added iron deposition from anthropogenic and other (dust, wildfire) sources to a global ocean model which incorporates iron isotopes. Focusing on the North Pacific, we find the impact of anthropogenic iron varies in time and space, whereby changes in iron concentration and isotopic signatures are distinct and also differ from the footprint of atmospheric deposition. These discrepancies relate to differences in biology, specifically the productivity of a surface ocean system, and whether this productivity is limited by the availability of iron. We find dissolved iron isotopic signatures to be useful to trace anthropogenic iron, provided that fractionating (biological) processes and the impact of other external iron sources are accounted for.
2008–2010 June, July, and August relationship between idealized tracer χ40−50 and daily maximum 2‐m temperature T from GEOS‐Chem simulations. (a) Daily regression slope dχ40−50/dT. Regressions that are not statistically significant are hatched (p > 0.05). (b) Mean meridional gradient of tracer ∂ϕχ40−50. (c) Mean meridional gradient of temperature ∂ϕT. (d) Gradient ratio ∂ϕχ40−50/∂ϕT. Regions with |∂ϕT| < 0.2 K/° are hatched (backslash), superimposed on forward slash from (a). (e) Scatter plot of ∂ϕχ40−50/∂ϕT versus dχ40−50/dT averaged over 10° latitude × 20° longitude domains, colored by ∂ϕT as in (c). Symbols show regions binned by |∂ϕT|. Dashed line shows the 1:1 slope. RMSE between gradient ratio and regression for each |∂ϕT| bin including outliers (Figure S3a in Supporting Information S1) is indicated.
Idealized tracer scatter plots averaged over 10° latitude × 20° longitude regions. (a) June, July, and August (JJA) gradient ratio ∂ϕχ20−30/∂ϕT versus regression dχ20−30/dT, colored by ∂ϕT with three |∂ϕT| bins (in K/°) shown by symbols. (b) Same as (a) but for DJF ∂ϕχ40−50/∂ϕT versus dχ40−50/dT. (c) JJA ∂ϕχ40−50/∂ϕQ versus dχ40−50/dQ, colored by specific humidity gradient ∂ϕQ with three |∂ϕQ| bins (in g/kg/°) shown by symbols. (d) JJA ∂ϕχ20−30/∂ϕχ40−50 versus dχ20−30/dχ40−50, colored by ∂ϕχ40−50 with three |∂ϕχ40−50| bins (in ppm/°) shown by symbols. Dashed lines show the 1:1 slope. RMSE between gradient ratio and regression for each bin including outliers (not shown) is shown in brackets.
Same as Figure 1, but for 2008–2010 June, July, and August O3‐T relationship from GMI simulations. (a) Daily regression slope dO3/dT. (b) Mean meridional gradients of ozone ∂ϕO3. (c) Mean meridional gradients of temperature ∂ϕT. (d) Gradient ratio ∂ϕO3/∂ϕT. (e) ∂ϕO3/∂ϕT versus dO3/dT. Open symbols and RMSE in brackets are from the transport‐only simulation. See Figure S3b in Supporting Information S1 for outliers.
The daily variation of ground‐level ozone (O3), a harmful pollutant, is positively correlated with air temperature (T) in many midlatitude land regions in the summer. The observed temporal regression slope between O3 and T (dO3/dT) is referred to as the “ozone‐climate change penalty” and has been proposed as a way to predict the impact of future climate warming on O3 from observations. Here, we use two chemical transport models to show that the spatial variation of dO3/dT is primarily determined by simultaneous meridional advection of O3 and T. Furthermore, the sign and magnitude of dO3/dT can be approximated by their climatological meridional gradient ratio (O3 gradient divided by T gradient). Consideration of expected changes in the meridional gradients of T and O3 due to climate change indicates that dO3/dT will likely change. Caution is needed when using the observed climate penalty to predict O3 changes.
Experiments in a water‐recirculating flume to visualize the release of fluorescent dye from the sediment into the surface water. Refractive‐index‐matched sediment and translucent vegetation dowels were used. A green, fluorescent dye was injected into the sediment and a blue lamp was used to excite the dye.
The concentration of the fluorescent dye in the sediment, represented by the fluorescence intensity of the emitted green light, decays over time. The flow was started at time = 0 hr. The black and red symbols represent the fluorescence intensities relative to the background intensities in channels without vegetation and with vegetation of volume fraction ϕv=0.05 ${\phi }_{v}=0.05$, at flow velocities of 4.0 and 3.6 cm/s, respectively. The black and red solid curves represent the fits of the measurements to the mass transfer model (Equations 1 and 2) with R2=0.99 ${R}^{2}=0.99$ for both cases. The model fits were conducted when the streamwise fluorescence intensity decreased uniformly (see Text S3 in Supporting Information S1 for details). For these two experiments, the horizontal area of the sediment‐water interface ASWI=0.19m2 ${A}_{SWI}=0.19\,{\mathrm{m}}^{2}$; the sediment porosity ϕs=0.3 ${\phi }_{s}=0.3$; the volume of pore space in the sediment Vol,s=1200±9mL ${V}_{ol,s}=1200\pm 9\,\text{mL}$; and the volume of surface water Vol,w=2830L ${V}_{ol,w}=2830\,\,\mathrm{L}$. The fitted parameters are VH ${V}_{H}$ and background image intensity. Dashed lines show the model predictions.
(a) The effective hyporheic exchange velocity VH ${V}_{H}$ versus mean flow velocity U $U$ for cases without vegetation (black) and with vegetation of a volume fraction ϕv=0.05 ${\phi }_{v}=0.05$ (red). The black solid line (y=(0.2x+0.4)×10−4 $y=(0.2x+0.4)\times {10}^{-4}$) and the red‐dashed line (y=(1.1x−0.3)×10−4 $y=(1.1x-0.3)\times {10}^{-4}$) represent linear fits to measurements without and with vegetation with R2=0.89 ${R}^{2}=0.89$ and R2=0.95 ${R}^{2}=0.95$, respectively. (b) VH ${V}_{H}$ versus the total near‐bed turbulent kinetic energy kt ${k}_{t}$ for cases without vegetation (black) and with vegetation (red). The black solid line (y=(7.2x+8.2)×10−5 $y=(7.2x+8.2)\times {10}^{-5}$) and the red‐dashed line (y=(7.2x+4.7)×10−5 $y=(7.2x+4.7)\times {10}^{-5}$) represent linear fits of the measurements without and with vegetation with R2=0.92 ${R}^{2}=0.92$ for both cases.
Plain Language Summary The exchange of contaminants and nutrients between surface and subsurface water in the hyporheic zone of rivers and wetlands controls water quality as well as the metabolism of benthic microbes and the associated biogeochemical cycle. Vegetation, which is ubiquitous in aquatic ecosystems, has been found to affect the surface‐subsurface exchange and as such impact water quality and stream biogeochemical cycle. However, how vegetation impacts this exchange remains unclear, making it difficult to predict the contaminant transport and biogeochemical cycle in streams, lakes, and coastal areas with vegetation. In this study, we directly visualized the release of a fluorescent dye from the transparent sediment into the surface water in a water‐recirculating tank filled with translucent vegetation. We discovered that vegetation can significantly increase the exchange in the hyporheic zone. Furthermore, we proposed a model to predict the impacts of vegetation on hyporheic exchange. We believe that this finding will help improve predictions of contaminant transport and biogeochemical cycle in streams and other aquatic ecosystems. The results of this study will also help ecologists design stream restoration projects that use vegetation to increase the retention and degradation of contaminants in sediment.
(a‐c) Step‐by‐step explanation of quantile line plots (see subplot titles) (d‐f) Magnitude estimate development for three different predictability models: Gutenberg‐Richter (GR) (not predictable during growth phase), skewed GR (not point‐predictable, but deviation from prior already during growth phase) and Gaussian (predictable). For each model, we use the same hypothetical event with prototypical triangular moment rate functions representing the first order moment release history; for predictable models to be viable, second order features of the moment rate function would differ between smaller and larger events. The dashed black line indicates the cumulative moment release. An additional visualization of the options at a fixed time is shown in Figure S3a in Supporting Information S1.
Distribution of events and histograms for magnitude distribution for the three STF datasets. The events are color coded by their data set. Ye et al. is plotted on top of USGS, on top of SCARDEC. This might lead to few events not being visible due to overlaps.
(a) Probability density functions (PDFs) calculated from the source time function (STF) model just before onset, and at 2, 4, 6 and 8 s after onset. Colored ticks on the PDFs indicate 0.05, 0.2, 0.5, 0.8, 0.95 quantiles. (b‐f) Example predictions from the STF model visualized by the 0.05, 0.2, 0.5, 0.8, 0.95 quantiles over time. (b) shows the same event as (a) The lower right gives information on the event. The black dashed line shows the moment released so far, that is, the trivial lower bound. The bottom plots show the STFs. The upper right indicates the STF database used. For a step‐by‐step explanation on the quantile plots, see Figures 1a–1c.
Average predicted Probability density functions (PDFs) based on source time function (STFs) (a)‐(f) and teleseismic waveforms (g)‐(h) grouped by magnitude bin. Left column shows results at time t after onset PM|Ot $\left(\mathbb{P}\left(M\vert {O}_{t}\right)\right)$, right column after cumulative moment equals magnitude M¯ $\bar{M}$ PM|OM¯ $\left(\mathbb{P}\left(M\vert {O}_{\bar{M}}\right)\right)$. The STF model has been trained on the SCARDEC data set and evaluated on each STF data set. See Figure S8 in Supporting Information S1 for STF results from a neural network trained with the USGS data set. PDFs were truncated in visualization to avoid overlap between different times/base magnitudes. Black dotted lines in (b, d, f, g) indicate the current base magnitude. The apparent skew between buckets in panel (b) and prediction difference in (g) for M¯=6.0 $\bar{M}=6.0$ likely results from SCARDEC processing artifacts. For determining tM¯ ${t}_{\bar{M}}$ in (h) we used the SCARDEC data set. See Figure S12 in Supporting Information S1 for plots with the other STF datasets. Events differ between panels (g) and (h): (h) only includes those events present in both the teleseismic data set and SCARDEC (∼3,500 events) and (g) all of the former (∼38,000 events).
Plain Language Summary Earthquakes are among the most destructive natural hazards known to humankind. While earthquakes can not be predicted, it is possible to record them in real‐time and provide warnings to locations that the shaking has not reached yet. Warning times usually range from few seconds to tens of seconds. For very large earthquakes, the rupture itself, which is the process sending out the seismic waves, can have a similar duration. Whether the final size of the earthquake, its magnitude, can be determined while the rupture is still ongoing is an open question. Here we show that this question is inherently probabilistic ‐ how likely is an event to become large? We develop a formulation of rupture predictability in terms of conditional probabilities and a framework for estimating these from data. We apply our approach to two observables: moment rate functions, describing the energy release over time during a rupture, and seismic waveforms at distances of several thousand kilometers. The final earthquake magnitude can only be predicted after the moment rate peak, at approximately half the event duration. Even then, it is impossible to foresee future subevents. Our results suggests that ruptures exhibit a universal initiation behavior, independent of their size.
(a) Photoproduction rate of Carbonyl sulfide (COS) in 0.2‐μm‐filtered seawater collected from the surface of the Indian Ocean. Production rates were calculated by dividing the net (light‐dark) COS concentration produced in the photochemical experiments by the incubation time. The value above each error bar is the mean production rate of COS. Error bars denote the standard deviation of measurements from multiple quartz bottles (n = 3). (b) Photochemical production rate of COS in the samples plotted against the initial absorption coefficient at 350 nm (a350). The vertical error bars denote the standard deviation of replicate samples (n = 3).
(a) Carbonyl sulfide (COS) concentration measured in 0.2‐μm‐filtered Indian seawater added 50 μM cysteine exposed to simulated sunlight with varying cutoff filters for 1 hr in quartz bottles. Full indicates no cutoff filter and dark indicates samples wrapped in aluminum foil. The value above each error bar is the concentration of COS. Error bars denote the standard deviation of multiple bottles for each spectral treatment (n = 3). (b) Relative contribution of ≤320 nm solar radiation (UVB) and 400 > λ > 320 nm solar radiation (UVA) to the total photochemical production of COS.
(a) Carbonyl sulfide (COS) concentration measured in 0.2‐μm‐filtered Indian seawater with and without 50 μM cysteine in quartz bottles. (b) COS concentration profiles in 0.2‐μm‐filtered Indian seawater added 50 μM cysteine in quartz bottles with different nitrate concentrations under sunlight conditions. (c) Comparison of the COS production rate in 0.2‐μm‐filtered Indian seawater added 50 μM cysteine in quartz bottles with different nitrate concentrations; (d) COS concentration measured in 0.2‐μm‐filtered Indian seawater added 50 μM cysteine in quartz bottles as well as added 50 μM cysteine and 500 μM nitrate with and without isopropyl alcohol as the •OH scavenger exposed to sunlight for 5 hr in quartz bottles; (e) the photochemical mechanism of COS in surface seawater. Error bars denote the standard deviation of measurements from multiple quartz bottles (n = 3).
Carbonyl sulfide (COS) plays an important role in the sulfur cycle and climate change. Yet, much remains unknown about the photochemical mechanisms of COS in nutrient‐rich seawater. We measured the photochemical production rates of COS in the surface seawater of the Indian Ocean under sunlight irradiation. The photochemical production of COS was mainly initiated by ultraviolet (UV) radiation with UVA contributing approximately 68% to the total COS production. Using cysteine, a typical proxy of dissolved organic sulfur, the effect of enhanced nitrate concentration on COS formation was conducted in authentic seawater during simulated sunlight irradiation, indicating the enhancement of the COS formation with increasing nitrate concentrations. This result revealed that the generation of hydroxyl radical with nitrate photolysis plays a key role in the COS formation process. These findings improve our understanding of the marine COS photoproduction cycle and the impact of nitrate on the COS photochemical production in surface seawater.
Distribution of intensity change (units: kt/24 hr) and ΔR34 (units: km/24 hr). Green and purple dots denote rapid growth (RG) and rapid shrinkage (RS) events identified by the isolation forest algorithm, respectively. Gray circles denote the events not identified as RG or RS. Dashed and solid lines denote the thresholds of the 95th (5th) and 90th (10th) percentiles. The histogram on the right shows the probability distribution of ΔR34 (units: %).
Distributions of (a) lifetime maximum size (LMS) and (b) lifetime maximum integral kinetic energy (LMIKE) for Atlantic hurricanes. IKE was computed using the wind profile derived using Holland et al. (2010). Gray histograms and black lines depict the distribution of all tropical cyclones (TCs). Orange, green, and purple lines show the probability distribution functions (PDFs) for the TCs that undergo rapid growth (RG) defined as the 90th (≥75 km/24 hr) and 95th (≥103 km/24 hr) percentiles, and TCs undergoing rapid intensification (RI; ≥30 k/24 hr), respectively. The PDFs were smoothed using kernel density estimation with a bandwidth of 0.2. Vertically dashed line denotes the 90th percentile of LMIKE (≥82 TJ).
Life cycle of tropical cyclone (TC) outer size and intensity. Composite of the evolution of (a) R34 and (b) Vmax for rapid growth of outer size (RG) and non‐RG TCs (c and d) are the same with (a and b), but for the RI and non‐RI TCs. The solid lines represent average values, while the shadows represent one standard deviation.
Composite maps of environmental factors. Composite fields of (a–c) 600‐hPa relative humidity (RH, units: %); (d–f) 850‐hPa radial inflow (units: m/s); and (g–i) 700‐hPa Eady growth rate (σ, units: day–1) for (a, d, and g) RG (b, e, and h) non‐RG and (c, f, and i) the difference between RG and non‐RG events. The data was obtained from the ERA5 reanalysis. The units for x‐ and y‐axis are degrees. Stippled areas in the right panel indicate that the differences are statistically significant at the 99% confidence level based on Student's t‐test. The two circles are placed at radii of 5° and 10° from the tropical cyclone center.
Plain Language Summary The destructiveness of tropical cyclones (TCs) is mainly caused by their strong winds. The maximum wind speed, namely TC intensity, and its evolution has been intensively studied, including rapid intensification (RI). However, the extent of strong winds and its growth remains little‐explored. We investigate, for the first time, the rapid growth (RG) of the gale‐force wind radius (R34) for Atlantic hurricanes. We define RG by the 90th percentile of R34 changes, which is equivalently at least 75 km/24 hr. This threshold is supported by an objective anomaly detection algorithm, the isolation forest. There are 88% of the TCs (15/17) with large lifetime maximum size (larger than 400 km) undergoing RG. Among the 11 TCs with high destructive potential (kinetic energy higher than 82TJ), nine show RG, while only five undergo RI. The TCs with RG also show a more discernible size life cycle than those without RG. Our analysis highlights the crucial role of the rapid growth of TC outer size in changing the TC overall destructive potential, which is found to be at least as important as the widely recognized TC rapid intensification.
Comparison of plume‐slab termination and interaction. (a) Instance of plume‐driven subduction termination. The mantle plume deflects around the descending slab, cuts through it, and hits the lithosphere near the subduction zone, terminating subduction. (b) Instance of plume‐subduction zone interaction where a plume hits the lithosphere near the subduction zone and interacts with the slab but subduction is not terminated.
Surface heat flow (scaled up from 2D to global values) over time for Model 1 (left) and Model 2 (right). Vertical, red dashed lines denote when termination occurs. Spikes in heat flow represent onset of plate tectonic‐like convection after a period of stagnant lid. Plume‐driven subduction termination leads to periods of stagnant lid in episodic models. Similar terminations occur in Model 2 while remaining mobile.
Instance of plume‐driven termination from Model 1. Columns a, b, and c, show temperature, viscosity, and total accumulated strain, respectively. The subducting slab thickness before plume interaction is ∼60 km. The slab is fully weakened and damaged as shown by column c and has an internal slab temperature of ∼900 K as seen in column (a).
Slices through the tomographic model of Portner et al. (2020) for the South American subduction zone, reinterpreted (contours) but generally following the scenario of Portner et al. (2017); Portner et al. (2020). A‐C are profiles perpendicular to the trench with added interpretation. A1‐A2 shows the subducting slab underlay by a slow anomaly interpreted to be a plume, and possible smaller plume west of the trench. Further south, B1‐B2 shows the plume rising through a hole in the descending slab resulting in the flattening of the slab dip angle (Portner et al., 2017). C1‐C2 shows the remnant of the plume head to the south and the end of the plume‐slab interaction.
Plain Language Summary The main driving force of mantle convection is the subduction of cold, lithospheric slabs. Mantle plumes that rise from the bottom of the mantle are typically not considered as important to plate tectonics, even though they have been suggested to initiate subduction, for example, here, we use 2‐D computer models of mantle convection, with the particular addition of a deformation memory for rocks. We show that mantle plumes can actually stop subduction if certain criteria are met. The weakening behavior of the subducting slab and the overall slab thickness/age are the main criteria for deciding if a plume can stop subduction. We compare our findings to present‐day subduction zones that show indications of possible plume‐slab interactions. One such case is South America, and we consider how this mechanism may play out in a more complicated system. Our findings have implications for early Earth plate tectonics and perhaps present‐day subduction.
The first panel corresponds to the Ap index (geomagnetic activity). The second and third panel correspond to the NO concentration between 10⁻³ hPa and 10⁻² hPa, averaged over the latitude band 60–70°N for both the Medium energy electrons (MEE) (blue line) and no‐MEE (green line) run, as well as their difference (black line). The fourth and fifth panel correspond to the zonal wind velocity between 10⁻¹ hPa and 10⁻² hPa, averaged over the latitude band 60–70°N for both the MEE (blue line) and no‐MEE (green line) run, as well as their difference (black line).
The upper plot corresponds to the zonal wind velocity for the noMEE run. The lower plot shows the zonal wind velocity for the Medium energy electrons (MEE) run. The red color represents positive velocities, associated with eastward winds, while blue represents negative velocities, associated with westward winds. The data is averaged over the latitude band 60–70°N with a daily resolution. The two colored lines represent the mesopause altitude (coldest altitude) for both runs, the blue line corresponds to the MEE run, and the green line to the noMEE run.
Upper plots (from left to right): Zonal wind velocity difference between the MEE and the noMEE run, and temperature difference between both runs. Bottom plots (from left to right): NO VMR relative difference, and O3 absolute difference. For all plots, the data is averaged over the latitude band 60–70°N with a daily resolution. The two colored lines represent the mesopause altitude (coldest altitude) for both runs, the blue line corresponds to the MEE run, and the green line to the noMEE run.
OH absolute difference, the data is averaged over the latitude band 60–70°N with a daily resolution. The two colored lines represent the mesopause altitude (coldest altitude) for both runs. The blue line corresponds to the Medium energy electrons (MEE) run, and the green line to the noMEE run.
Medium energy electron (MEE) (30–1,000 keV) precipitation enhances the production of nitric (NOx) and hydrogen oxides (HOx) throughout the mesosphere, which can destroy ozone (O3) in catalytic reactions. The dynamical effect of the direct mesospheric O3 reduction has long been an outstanding question, partly due to the concurrent feedback from the stratospheric O3 reduction. To overcome this challenge, the Whole Atmosphere Community Climate Model version 6 is applied in the specified dynamics mode for the year 2010, with and without MEE ionization rates. The results demonstrate that MEE ionization rates can modulate temperature, zonal wind and the residual circulation affecting NOx transport. The required fluxes of MEE to impose dynamical changes depend on the dynamical preconditions. During the Northern Hemispheric winter, even weak ionization rates can modulate the mesospheric signal of a sudden stratospheric warming event. The result provides a first step in a paradigm shift for the understanding of the MEE direct effect.
Plasma wave observations of Van Allen Probe A during 21–22 UT on 16 October 2017. (a) SYM‐H (blue) and AE (red) indices from 12 UT on 16 October 2017 to 12 UT on 17 October 2017 (b–d) Frequency‐time spectrograms of magnetic field spectral intensity, ellipticity, and wave normal angle obtained by the EMFISIS WFR. (e–g) Frequency‐time spectrograms of magnetic field spectral intensity, ellipticity, and wave normal angle obtained by the EMFISIS MAG. (h) Integral wave amplitude of MS waves (blue) and EMIC waves (red) (i and j) Scatterplots of the magnetic field spectral intensities of EMIC waves and MS waves during 21:15 UT–21:30 UT and the mean profile (thick black curves). In Figures 1b–1d, the black lines represent lower hybrid resonance frequency fLHR, 0.5 fLHR, and the equatorial proton gyrofrequency fcP, respectively. The white lines denote fcP in Figures 1e–1g.
Energetic electron differential flux observations by Van Allen Probes during the periods of 12:00–12:40 UT, 18:20–19:00 UT, and 21:00–21:50 UT on 16 October 2017. Differential fluxes of radiation belt energetic electrons measured by the MagEIS instrument ((a) 108 keV, (b) 235 keV, (c) 470 keV, (d) 597 keV, and (e) 749 keV) and by the REPT instrument ((f) 1.8 MeV and (g) 3.4 MeV) as a function of pitch angle and time. The black shading in Figure 2c represents the Interval C of 21:15–21:30 UT on 16 October 2017 when the concurrent EMIC waves and MS waves are observed. The black shading in Figures 2a and 2b represents the Interval A and B when the Van Allen Probe A passes the exact same L‐shell range as Interval C.
Two‐dimensional plots of bounce‐averaged diffusion coefficients (from left to right: <Dαα>, <DEE>, and |<DαE>|) as a function of equatorial pitch angle αeq and electron kinetic energy Ek for (a–c) Magnetosonic (MS) waves only, (d–f) Electromagnetic ion cyclotron (EMIC) waves only, (g–i) the sum of MS and EMIC waves only, and (j–l) MS and EMIC waves simultaneously.
The temporal evolution of simulated electron PSDs at selected energies under the impact of Electromagnetic ion cyclotron (EMIC) waves only, Magnetosonic (MS) waves only, and EMIC and MS waves simultaneously in 160 min. In Figures 4e–4h, the solid lines indicate the simulated results and the dashed lines display the initial electron PSDs. The dots in Figures 4e and 4f–4h represent the electron PSDs observed during periods of ∼18:32–18:43 UT (Interval B in Figure 2) and 21:15–21:30 UT on 16 October (Interval C in Figure 2) correspondingly.
Plain Language Summary Electromagnetic ion cyclotron (EMIC) waves and Magnetosonic (MS) waves are commonly observed in the Earth's magnetosphere and play important roles in energetic electron dynamics. Usually, EMIC waves and MS waves scatter electrons at different energies and pitch angles. The peak frequency of the H⁺ band EMIC wave is usually much lower than the equatorial proton gyrofrequency. Recently, a different type of H⁺ band EMIC waves, named unusual high‐frequency EMIC waves since their peak frequency is close to the equatorial proton gyrofrequency, has been reported. Studies on the unusual high‐frequency EMIC waves found that these waves are capable of scattering sub‐MeV and MeV electrons that can also be influenced by MS waves. Moreover, observations confirm that unusual high‐frequency EMIC waves can be well connected with MS waves. In the present study, we quantitatively investigate the combined scattering effect of both wave modes on radiation belt electrons and simulate the evolution of the electron phase space densities under the impact of both waves. The simulation results show similar evolution trends as the observations, indicating the importance of incorporating these two waves and evaluating their combined effects on radiation belt particle dynamics.
Velocity (a) and density (b) as a function of pressure for molten lunar black glass. Different markers in (a) represent different experimental runs: circle‐T2441, square‐T2484, diamond‐T2544, and different colors correspond to different temperatures: blue—1,736K, green—1,827K, orange—1,919K, and magenta—2,010K. Blue, green, and red curves are fitting results at 1,773K, 1,873K, and 1,973K isotherms, respectively. Previous density measurements shown in (b) include Sakamaki et al. (2010) (X‐ray absorption), Van Kan Parker et al. (2012) (X‐ray absorption + molecular dynamics simulations), Circone and Agee (1996) (Sink‐float experiments), and Vander Kaaden et al. (2015) (Sink‐float experiments, reanalysis of the data from Circone and Agee (1996)). All the original density data from previous studies are plotted in (b) with the corresponding temperature color‐coded based on the color bar. The blue, green, and red solid curves are density profiles calculated in this study for 1,773K, 1,873K, and 1,973K isotherms, respectively. The dashed, dash‐dot, and dotted blue curves are the density curves at 1,773K from Sakamaki et al. (2010), Van Kan Parker et al. (2012), and Vander Kaaden et al. (2015), respectively.
Comparison of the density of Fe‐Ti‐rich melt and velocity of Fe‐Ti‐rich melt‐bearing mantle with lunar seismic models. The density of molten black glass (a) is calculated along its liquidus (Wagner & Grove, 1997) and is compared with the density of its equilibrium olivine (Ol) and orthopyroxene (Opx) (see Text S5 in Supporting Information S1) as well as the lunar density models (Garcia et al., 2011, 2019; Khan et al., 2014; Matsumoto et al., 2015; Weber et al., 2011). The Vp (b), Vs (c), and Vp/Vs ratio (d) of Fe‐Ti‐rich melt‐bearing mantle are calculated using the equilibrium geometry model (Takei, 2002) along a lunar thermal profile (Khan et al., 2014) (Text S5 in Supporting Information S1), and are compared with most recent lunar seismic models (Garcia et al., 2011, 2019; Khan et al., 2014; Matsumoto et al., 2015; Weber et al., 2011). The colored thick lines are modeled results with different melt fractions. (b), (c), and (d) share the same legend. The gray shaded area represents the initial depth of the Fe‐Ti‐rich ilmenite layer (Hess & Parmentier, 1995), the cyan shaded region represents the source for lunar picritic glasses from multiple‐saturation experiments (Elkins Tanton et al., 2002), the black star is the depth for the source of lunar black glass estimated by experiments (Wagner & Grove, 1997), and the red shaded area represents the region for the deep melt layer above lunar core‐mantle boundary.
Schematic illustration of the post magma‐ocean lunar evolution. After magma ocean crystallization, part of the shallow Fe‐Ti‐rich layer underwent efficient overturn and sank to the bottom of the mantle (red solid arrow), becoming a stable partially molten layer until the present day. Some inefficiently overturned Fe‐Ti‐rich domains would be introduced to the mantle during the overturn process, becoming part of the source for Fe‐Ti‐enriched lunar volcanisms (dark brown dashed and dotted arrows). Melting of regular mantle cumulates without contribution from inefficiently overturned Fe‐Ti‐rich domains leads to the formation of Fe‐Ti‐poor mare basalts and glasses (light brown solid arrow). The orange shaded area represents the source region for mare volcanisms, and the blue curve indicates the minimum depth of density crossover between lunar melts and mantle. See text for detailed discussion.
Plain Language Summary The Moon may have formed from a lunar magma ocean (LMO). During the late stage of LMO, a layer of dense Fe‐Ti‐rich phases would crystallize, causing the lunar mantle overturn due to gravitational instability. Such a process could introduce Fe‐Ti‐rich bodies into the lunar interior, where they may become part of the source for Fe‐Ti‐enriched lunar basalts and pyroclastic volcanic glasses. Apollo seismic data have implied the potential presence of a partially molten region in the deep lunar interior, likely corresponding to the melting of those overturned Fe‐Ti‐rich bodies. However, whether such a molten layer could be stable and match with lunar seismic observations is still not well‐known. In this study, we have experimentally determined the sound velocity of a lunar Fe‐Ti‐rich melt for the first time, up to conditions of the deep lunar mantle. Our new data help tightly constrain the velocity and density profiles of Fe‐Ti‐rich melt in the lunar interior. By comparing with lunar seismic observations, we find that a partial melt layer with at least 20% overturned Fe‐Ti‐rich melt could be stable above the lunar core‐mantle boundary.
Topographic map of the studied area with 5‐m elevation contours, showing the extent of the Quiock Creek watershed (white line) and the hydrological network (blue lines). Seismic profiles (colored lines), piezometric wells (light blue dots) and direct samples from Buss et al. (2010) (red dot) are also represented. The knickzone is delineated with dashed white line, and its location along the stream is shown with a green star. This map, and the following, are projected in the Universal Transverse Mercator geographic coordinate system, zone 20 N. The top left inset shows the location of Basse‐Terre island in the Lesser Antilles.
(a) 3D view of the inferred subsurface water saturation profiles in the Quiock catchment. The hydrological network (blue lines), the extent of the watershed (black line) and the location of the knickpoint (green star) are also represented (b–f) P‐wave velocity VP and (g–k) water saturation profiles, with the blue arrows indicating the location of the stream. Horizontal distance = 0 m corresponds to the label position in (a). VP contours corresponding to the bottom of saprolite (1,200 m/s, white solid line) and the transition between weathered and fractured bedrock (2,700 m/s, white dashed line) are shown in (b–f). Black contour lines in (g–k) correspond to the inferred water table (W = 0.9). The petrophysical model was calibrated by comparing soil sample analysis from Buss et al. (2010) (red dot in a) with the closest seismic data point at 170 m along P5 (red star in a, c, and h).
(a) Digital elevation model (DEM) of the Quiock Creek watershed. (b) Interpolated depth of the weathering front (WF). (c) Interpolated depth of the water table (WT). (d) Difference between water table and weathering front elevations. These maps are overlaid with 5‐m contours. The extent of the catchment and the seismic lines are shown in black, and the hydrological network in blue. (e) Comparison of average water table levels observed in piezometric wells (light blue dots in c) with interpolated water table depths. See Supporting Information S1 for details about error bar estimation. (f) Interpretive cross section of the stream topographic profile (black line) computed from the 5‐m DEM. It highlights the location of the water table (in blue) and displays the structure of the CZ with specific VP contours describing the weathering front (in orange), and the transition zone between weathered and fractured bedrock (in brown). Colored dots and associated error bars correspond to the average depth of each interface, extracted from geophysical profiles. Empty brown squares with no error bars correspond to the maximum investigation depth at which fractured bedrock is not reached. See Supporting Information S1 for details about error bar estimation.
Plain Language Summary Infiltration of rainwater into the subsurface chemically alters and breaks down rock at depth, thus creating porous space able to store life‐sustaining water for overlying ecosystems. Information about the structure and the water content of this invisible compartment is difficult to obtain. Here we use minimally invasive geophysical techniques to image this subsurface porous layer, and map the depth of the weathered zone and the water table. We applied this approach across forested slopes of Basse‐Terre island (Guadeloupe, France), which is representative of volcanic tropical landscapes with strong weathering and erosion activity. We use petrophysical relationships to convert our geophysical measurements into estimates of porosity and water saturation. We then apply spatial interpolation techniques to extend our local estimates across the entire watershed. Our novel approach provides unique insights on both the physical structure and water content of the subsurface at such a scale.
Maps of stations (a) and events (b) used in this study. (a) Red triangles denote SEISConn stations. The black dashed line A‐A’ is the projected profile line. Colored lines show the surface boundaries of the eastern edge of Laurentia (blue), the Mesozoic Hartford rift basin (yellow), and the western edge of Avalonia (red). U.S. state names are shown with abbreviations (CT, Connecticut; MA, Massachusetts; NY, New York; RI, Rhode Island). (b) Red triangle represents the center of the SEISConn array. Red stars denote earthquakes used to construct migration images, within the epicentral distance limits of 30° and 90° (black dashed circles). Blue filled stars mark the three events used to construct Figure S5a in Supporting Information S1.
Scattered wavefield migration images of S‐wave velocity perturbations δββ $\left(\frac{\boldsymbol{\delta }\boldsymbol{\beta }}{\boldsymbol{\beta }}\right)$ constructed using different phases. Lighter regions have positive velocity perturbations and darker regions have negative velocity perturbations. The horizontal line visible in each image at 35 km depth is an artifact from the 1‐D background velocity model (see Text S1 in Supporting Information S1). (a) Migration image generated using forward‐scattered Ps phase, band‐pass filtered between 0.2 and 2.0 Hz. (b) Migration image generated using backscattered phases (PPs, PSp, PSsv, PSsh), band‐pass filtered between 0.03 and 1.0 Hz. (c) Composite migration image generated using both forward‐scattered and backscattered phases, band‐pass filtered between 0.03 and 1.0 Hz. (d) Uncertainty in the composite migration image derived from the bootstrap resampling. Lighter regions have smaller uncertainty and darker regions have larger uncertainty.
(a) Annotated version of Figure 2a, the migration image made with only Ps phase band‐passed filtered between 0.2 and 2.0 Hz. Markers denote corresponding features observed in single‐station stacked receiver functions band‐passed between 0.2 and 2.0 Hz (Luo et al., 2021). (b) Annotated version of Figure 2c, the composite migration image made with all available phases, band‐passed filtered between 0.03 and 1.0 Hz. (c) Schematic diagram showing one possible model to explain observed features. Solid black lines are hypothesized crustal boundaries, not constrained by the migration. NAA—Northern Appalachian Anomaly. Plotting conventions for migration results are as in Figure 2. Colored dashed lines highlight the Moho discontinuity (yellow), a west‐dipping elongate feature in the mantle lithosphere (blue), and a strong low‐velocity anomaly beneath the eastern portion of the array (red).
Estimates of the relative S‐wave velocity contrast (unitless) across the Moho discontinuity. The black solid line and shaded region show the result and uncertainty estimate from bootstrap resampling. Colored dashed lines show results from individual migration images based on the same set of 56 events but with different frequency contents, as shown by the legend.
Plain Language Summary Tectonic processes in the geologic past, such as the formation and breakup of supercontinents, modified the deep structures of the crust and upper mantle beneath eastern North America. In this study, we use a seismic imaging technique based on scattered wavefield back‐projection to investigate deep structures beneath southern New England. This imaging technique, which relies on seismic wave energy from distant earthquakes, is capable of resolving km‐scale structures when applied to data from closely spaced seismometers (∼10 km station spacing). We image an abrupt, step‐like change of the crustal thickness beneath southern New England; the details of this feature suggest a complicated tectonic history during the formation of the Appalachian Mountains. A west‐dipping interface in the upper mantle suggests the presence of a relict slab beneath southern New England, associated with a past subduction event. A region of low seismic velocity in the upper mantle beneath southeastern New England may reflect past impingement of a mantle plume or modern upwelling of asthenospheric mantle.
Probability (a) of occurrence of compound snow drought and heatwave (CSDHW) events and Arid Index map (b) showing arid, transitional, and humid regions across the world. Statistically significant grid boxes at 95% confidence level are dotted on maps.
The fractions (a) and spatial coverage trends (b–q) of compound snow drought and heatwave (CSDHW) events across the world during 1981–2020. The red and blue stack areas represent the dry‐ and warm‐type CSDHW events, respectively, the sum of which represents all CSDHW events. The linear annual trends in dry‐type, warm‐type, and all CSDHW events are represented in dashed, dotted and solid lines, respectively. The number on the left indicates the estimated linear slope based on the least‐squares method, and the asterisk denotes the statistically significant trend (p < 0.05) based on the Mann‐Kendall test. GLB, Globe; NEU, North Europe; CEU, Central Europe; MED, South Europe/Mediterranean; NAS, North Asia; EAS, East Asia; SAU, South Australia/New Zealand; TIB, Tibetan Plateau; CAS, Central Asia; WAS, West Asia; WSA, West Coast South America; ENA, East North America; CNA, Central North America; WNA, West North America; ALA, Alaska/N.W. Canada; CGI, Canada/Greenland/Iceland.
Heatwave probability (HWP, left column) and heatwave severity (HWS, right column) in dry‐ and warm‐type compound snow drought and heatwave: HWP distribution under the type of dry (a) and warm (c) snow droughts; HWS distribution under the type of dry (b) and warm (d) snow droughts; HWP (e) and HWS (f) statistics under dry (red) and warm (blue) snow droughts. Statistically significant grid boxes at 95% confidence level are dotted on maps (a and c). The limits of statistics (e and f) represent the upper and lower quartiles and the circles represent the mean values. Statistics labeled with different letters (“A” and “B”) indicate a significant difference, and letter “B” represents a greater mean value than letter “A.” Two statistics that are both labeled with “A” have no obvious difference.
Soil moisture (SM) anomaly (a and b) and vapor pressure deficit (VPD) anomaly (c and d) as well as their correlations with heatwave severity (e and f) in the first snow‐free month under dry (left column) and warm (right column) snow drought conditions. Asterisks in a–d denote that the SM (VPD) of snow‐drought grid boxes is significant (p < 0.05) lower (higher) than those of all grid boxes based on the two‐sample t‐test. Asterisks in e and f represent that the correlation is statistically significant (p < 0.05). Linear trends have been removed from all the variables.
The compound of late winter snow droughts and early spring heatwaves (CSDHW) could dramatically affect ecosystems and water availability, but has not been systematically investigated. Here we present a comprehensive assessment of CSDHW events and possible driving mechanisms. We find that 7% of the snow-covered area experiences significant (p < 0.05) CSDHW events, and an average of 35% of snow droughts are followed by heatwaves during 1981–2020. The spatial extent of CSDHW is asymmetrically enlarging, with a significant increase in Eurasia and a relatively high fluctuation in North America. Specifically, the warm-type CSDHW (i.e., snow drought with normal or above-average precipitation followed by heatwave) occurs more frequently, with spatial coverage increasing faster than the dry-type CSDHW (i.e., snow drought with below-average precipitation followed by heatwave). In comparison, dry snow drought is more likely to be followed by heatwave due to intensified soil drought and atmospheric aridity.
Observed time series (solid lines) of annual global tropical cyclone (TC) activity from 1980 to 2021 including (a) number of named storms (magenta), number of named storms excluding short‐lived storms (green), hurricanes (blue), and category 3+ hurricanes (purple) and (b) named storm days (magenta), hurricane days (blue), category 3+ hurricane days (purple), and accumulated cyclone energy (ACE) (black; 10⁴ kt²). Dashed lines indicate the corresponding climatological mean and dotted lines indicate the mean ± one standard deviation. (c) Standard deviation divided by the mean for various TC metrics for each basin. (d) The percent contribution of each basin to global TC activity for various TC metrics, with the percentage contributions for named storms and ACE labeled.
Correlation coefficients (Spearman rank; ρ) between climate mode indices and (a) number of named storms, (b) number of named storms excluding short‐lived storms, (c) named storm days, and (d) accumulated cyclone energy. Climate indices are averaged January–December for global tropical cyclone (TC) activity, May–November for TC activity in Northern Hemisphere basins, and November–April for TC activity in Southern Hemisphere basins. Statistically significant (p = 0.05) correlations are labeled.
Boxplots of January–December‐averaged (a) El Niño–Southern Oscillation Longitude Index (ELI) (°E) and (b) Atlantic Meridional Mode (AMM) for years in which the bottom (blue), middle (black), and top (red) percentiles of annual global named storm days and accumulated cyclone energy (ACE) were observed, over the years 1980–2021. Boxplots of annual global (c) named storm days and (d) ACE (10⁴ kt²) for years in which the January–December averaged ELI (°E; magenta), Niño 3.4 index (red), and AMM index (blue) were observed within the bottom, middle, and top percentiles over the years 1980–2021 (a–d) Boxplots show the minimum, 1st quartile, median, 3rd quartile, and maximum of the metric on the y‐axis (c and d) Solid and dashed black lines denote the mean and mean ± one standard deviation, respectively, for the TC metric.
Composites of January–December‐averaged sea‐surface temperature (°C) corresponding to years in which the annual global named storm days were in the (a) bottom percentiles minus the 1980–2021 climatology and (b) top percentiles minus the 1980–2021 climatology. Similar to (a and b), but for (c and d) 600 hPa relative humidity (%) and (e and f) zonal vertical wind shear (m s⁻¹) between 850 hPa and 200 hPa.
Plain Language Summary Although the number of global tropical cyclones (TCs) has been relatively constant from year‐to‐year in recent decades, the reason remains unknown. It is important to understand what can lead to global TC frequency variations because of its link with TC impacts. We investigated years in which observed global TC activity deviated from the 1980–2021 average. We found that global TC activity is significantly linked with ocean variability, most strongly with El Niño–Southern Oscillation (ENSO). La Niña, which is marked by cool eastern equatorial Pacific sea‐surface temperature (SST) anomalies, is associated with less global TC activity, and vice versa for El Niño. A new physically‐based index for ENSO, the ENSO Longitude Index (ELI), explains annual global named storm days and ACE as well as the SST anomaly‐based Niño 3.4 index. This is because the ELI accounts for the nonlinear response of thunderstorm activity to SST, accounts for changes in the background SST state associated with the seasonal cycle and/or climate change, and better captures ENSO's spatial diversity than Niño 3.4. This research reveals that reliable future projections of ENSO are necessary, but not sufficient, to understand whether global TC frequency may change in the future.
Time series of P and T measured at specific depths in Tenmile geyser in (a) 2011, (b) 2013, (c) 2014, and (d) 2015; measurement data in 2011 has a lower resolution than others. In 2013, 2014, and 2015, time series of Pair and Tair were also measured. The gray box, including one natural eruption and four artificially induced eruptions, is enlarged in Figure 2b.
(a) Conceptual diagram delineating one cycle of eruptive processes. The length of each arrow represents the magnitude of the corresponding flow qualitatively. (b) Measurement data from the gray box in Figure 1d, including one naturally driven and four artificially induced geyser eruptions. (c) Changes in P, T, and v (upward flow as positive value) during the naturally driven geyser. (d) Changes in P and T during four artificially induced eruptions; the gray boxes represent durations of pumping. During the last two eruptions (16:30–17:30), an action camera (GoPro) was set at a depth of approximately 30 m to observe the evolution of CO2 bubbles in the well (Figure S5 in Supporting Information S1 and Movie S1).
Relationship between atmospheric conditions (Pair and Tair) and lengths of IBEs (LIBE). (a) Two quantile density contours based on Pair and Tair from the whole observational data set are plotted in a 1‐day cycle with LIBE (109 points). The polynomial fitting curve based on middle time in intervals between two consecutive eruptions (IBEs) is delineated by a black line with a 95% confidence interval (two dashed lines; the statistics are summarized in Table S1 of Supporting Information S1). (b) Two transducers were installed in different depths with Δh. (c) Double box based on Pair and Tair measured during each IBE; the colors of boxes indicate the φIBE/Δt values. The vertical and lateral boundaries for boxes represent the 25th and 75th percentiles of Pair and Tair during each IBE, respectively.
CO2 generation from time series of ΔP' and electrical conductivity (EC) and T profiles. (a) Time series of P measured at depths of 6.6 and 17.8 m and their differences (ΔP'). (b–d) EC and T profiles were measured at times indicated in (a) Profiles before (13:34 and 17:35) and after (14:07 and 18:11) eruptions are plotted in b and d; four profiles (14:07, 15:33, 16:33, and 17:35) measured within the intervals between two consecutive eruption are plotted in (c) The grayness of boxes in a and lines in b–d are intended to convey proximity to the next eruption. Other ΔP' and temperature, level, and conductivity data are shown in Figures S7 and S8 of Supporting Information S1, respectively.
CO2‐driven cold‐water geysers periodically ejecting cold water are rare. Although coalescence and expansion of ascending CO2 bubbles can explain the eruption process, the triggering conditions and eruption cycle remain unclear. To clarify the triggering conditions, hydrostatic pressure in the well was decreased by pumping to induce eruptions. All four pumping tests successfully induced eruptions by decreasing the pressure of ∼10⁴ Pa. In the absence of artificial perturbations, similar reductions in pressure were observed during the intervals between two consecutive eruptions (IBEs). During IBE, the atmospheric pressure (Pair) and temperature (Tair) controlled the generation of the CO2 bubbles which directly induced the pressure reduction in the well. Especially under the persistent low Pair and high Tair, the length of IBE showed a minimum value of 3.90 hr during field observations. We suggest that the atmospheric perturbations are the causes of the changes in geyser periodicity, given consistent geological and hydraulic conditions.
Selected XRD patterns from in situ observations during heating (BRD03). The heating rate was 10 K/min. The numbers represent the Miller indices. Brg, bridgmanite; Au, gold. The vertical bars indicate the peak positions calculated for bridgmanite (red) and gold (blue) at ambient conditions. A magnified X‐ray diffraction pattern at 673 K is shown in the inset.
Amorphization kinetics of bridgmanite. (a) Plots of the peak intensity and lattice volume (V/V0) of bridgmanite as a function of time. Data were taken from the run with the target temperature of 473 K (BRD05). The heating rate was 50 K/min (shaded area). The temperature was maintained starting at 0 s after the initial temperature increase. The error bars of the peak intensity reflect the standard deviations (1σ) derived from various diffraction peaks. (b) Peak intensities as a function of time at temperatures ranging from 453–533 K. The heating rate varied from 50–80 K/min, except for run S14, which was immediately heated to 533 K from room temperature. The curves are the results of the fitting of kinetic data using the Avrami rate equation [Equation 1]. The gray line shows the kinetics assuming n = 1 in Equation 1. (c) Graph showing ln(ln(1/(1− X))) as a function of ln (t) for the kinetic data at constant temperatures in the range of 453–533 K. The solid lines are based on a fixed n value of 0.05. The back‐transition kinetics from Mg2SiO4 wadsleyite to olivine at 1273 K (Ming et al., 1991) with an n value of 1.5 are also provided for comparison.
High‐temperature behavior of bridgmanite. (a) Bridgmanite peak intensities as a function of the temperature. The error bars reflect the standard deviations (1σ) derived from various diffraction peaks. (b) Relative volume (V/V0) as a function of the temperature. The dashed line shows the thermal expansion extrapolated from the data bellow 400 K assuming a linear correlation. The amorphization started at ∼400 K and clearly changed the peak intensities and trend of the thermal expansion. (c) Apparent pressure on bridgmanite grains. Uncertainties are due to the errors of the thermal expansion coefficients, which were obtained from various independent runs. At high temperatures, the data are scattered because of the weak peak intensities.
Back‐scattered electron images of bridgmanite before and after amorphization. (a) Starting material for the experiments. (b) Partially‐amorphized bridgmanite after high‐temperature annealing at 453 K (BRD06). Amorphous phases are darker regions within the grains.
Bridgmanite/amorphous fraction as a function of the time and temperature. (a), Amorphization kinetics of bridgmanite at 473 K based on an Avrami coefficient n of 0.05 (solid line). The dashed line shows an example of kinetics when an n value of 1 is applied. (b), Temperature dependence of the bridgmanite/amorphous fraction at a time scale of ∼10² s. The blue curve represents the volume fraction between bridgmanite and the amorphous fraction based on the temperature dependence of the rate constant k and the data of run BRD03. The residual temperature was estimated to be ∼600 K based on the assumption that 10 mol.% of bridgmanite are preserved (dashed line).
Bridgmanite, the most abundant mineral in the Earth's lower mantle, can be found in meteorites that experienced instantaneous high shock pressure during parent body impact. However, the presence of bridgmanite in meteorites is unusual because bridgmanite grains should be amorphized under residual post‐shock temperatures at ambient pressure. Here, we report the results of time‐resolved synchrotron X‐ray diffraction measurements at high temperatures to analyze the amorphization mechanisms and kinetics of bridgmanite. The thermal expansion coefficient of bridgmanite before the amorphization is 2.1 × 10⁻⁵ K⁻¹. At higher temperatures, our results show that the significant volume expansion due to the amorphization induces static stress that can reach up to ∼0.5 GPa, which prevents the progress of the amorphization. This time‐insensitive amorphization kinetics may have enabled the preservation of bridgmanite in the shocked meteorite that fell on Earth. Also, the reaction progress estimated based on the amorphous fraction provides the residual post‐shock temperature.
Yutu‐2 route and Lunar Penetrating Radar data. (a) The Yutu‐2 route within the first 15 lunar days. (b) The acquired CH‐2B LPR data. The red arrows indicate the positions to extract rover turning data. (c–e) Show the details of the rover route, the yellow arrows indicate the turning positions. A coordinate is established in (e) to analyze the acquired data when the rover was turning. The rover heading direction when the rover started to turn is selected as the x‐axis.
The Lunar Penetrating Radar polarimetric attributes processing results. (a) The instantaneous amplitude profile of migrated LPR image. (b) The surface‐like scattering component (ps) corresponding to the signals from continuous rough interfaces in (d) or the surfaces of boulders in (e). (c) The volume scattering component (pv) corresponding to the signals from different sizes of boulders of strata interiors in (f and g). The curved arrows in (d–g) represent the schematic propagating paths of electromagnetic waves transmitted by LPR. A and B indicate the boundaries of the newly discovered sandwich structure in the paleo‐crater.
The regolith maturity and analyses. (a) The regolith maturity within 0–24 m depth. The black curve in (b and c) represent the average maturity of 0–2 m and 12–24 m depths along the Yutu‐2 route, respectively. The red and blue curves in (b and c) denote the average ps and pv within the same depth ranges. The red numbers 1–6 indicate the regions with unusual strong surface‐like scattering and the blue numbers 7–14 mark the regions with anomalously strong volume scattering. (d–f) Are the distribution of regolith maturity of three‐layered regions of different depths. (d) The near‐surface region. (0–2 m) (e) the fine‐grained regolith layer (2–12 m). (f) The coarse‐grained ejecta layer (12–24 m).
Fine Evolution of the Regolith at the CE‐4 landing site. (a) Impact events of different extents reshaped the structure of coarse‐grained regolith. (b) Erosion and small‐scale impactions constantly modified the lunar surface. (c) Coverage of Finsen ejecta. (d) Formation of fine‐grained regolith. (e) Delivery of fragments by distal impact events. (f) Current regolith structure. The grains of the rocks represent the maturities of regolith. The orange‐colored anomalies represent the superposition of multiple ejecta from nearby caters and the gray‐colored anomalies denote the Finsen ejecta. The green arrows and the dashed arrows represent the single and multiple impact events, respectively; whereas the undulated arrows and the gray arrows indicate the accumulations of multi‐source materials and Finsen ejecta, respectively.
Plain Language Summary The entire lunar surface is considered to be covered by a layer of regolith. The study of regolith weathering degree almost focused on the near‐surface because the traditional techniques cannot detect the regolith at large depths. The Chang’E‐4 lunar penetrating radar (LPR) can detect the regolith structure within dozens of meters depth. However, previous publication of LPR results only use the macro layering model to interpret the regolith structure. This study innovatively extracts new properties from the LPR data acquired while the rover was turning and estimated the quantitative maturity of regolith within ∼24 m depth at lunar farside. We found that the LPR can mainly detect the subsurface rock fragments that survived weathering and rarely the interfaces of strata because the materials of different strata are mixed at interfaces and make it gradational. Our results also reveal the spatial difference of weathering in the regolith. We investigate the formations and material compositions of several interesting regions with unusual weathering degrees. Based on these new insights, we establish a model to illustrate the weathering process of the regolith at the CE‐4 landing site.
Observation of the DL structure and E|| turbulence by the MMS3 satellite on May 5, 2018. (a, b) The MMS locations in the X‐Z and X‐Y planes in GSE coordinates. (c) Magnetic field in GSE coordinates. (d) Parallel electric field in burst mode (black) and electron density (blue). (e) Current density in field‐aligned coordinates. (f) Differential energy fluxes of electrons and parallel electron temperature (white). (g)–(i) Electron 2D VDF for three different time durations, with the black lines denoting the contours of the phase space density. Here, the velocity plane is defined as (VB,VE×B ${\mathbf{V}}_{\mathbf{B}},\,{\mathbf{V}}_{\mathbf{E}\times \mathbf{B}}$).
Acceleration of beam electrons. (a) Electron 1D VDF in the parallel direction, derived from the electrons with α < 45°. The white dotted line denotes the drift velocity of the electron beam. (b) E|| measured by EDP, the highest resolution HMFE data (65 kHz sampling, red) are plotted on top of the burst data (8 kHz sampling, black). (c) E⊥ in burst mode (8 kHz sampling). (d) Potential (black) and changes in the averaged parallel energy (blue). (e)–(h) Four snapshots of the electron 1D VDF in S1–S4 for the time durations denoted in (a).
Thermalization of beam electrons. (a) The power spectrum density of HMFE E||; the two black dotted lines denote 0.25 kHz, and 4 kHz. (b) Filtered HMFE E|| waveform with a bandwidth of 0.25–4 kHz. (c) RMS value of the electric fields in the frequency ranges of 0.25–4 kHz (black) and 4–32 kHz (blue). (d) The equivalent parallel temperature of beam electrons (black) and the Omni parallel electron temperature (blue).
Plain Language Summary Electron acceleration and thermalization in the plasma sheet (PS) of the Earth's magnetotail are fundamental research topics of magnetospheric physics. Theoretical analyses and numerical simulations have revealed that beam electrons can be accelerated and thermalized by the structure of double layer (DL). Direct observation of PS electron acceleration and thermalization is essential to demonstrate the theoretical prediction. Due to the low time and energy resolutions of observing electrons on previous satellites, it was very difficult to display the detailed evolutionary processes of electron acceleration and thermalization by DL structures. Using 3D electron phase‐space distributions with a time resolution of 30 ms and electric field data in burst mode by the magnetospheric multiscale satellites, for the first time, we provide a complete and direct observation of the detailed evolution of the acceleration and thermalization of magnetotail beam electrons by a DL structure, which indicates the energy exchange process between nonlinear electric field structures and electrons in the Earth's PS.
(a) The four NAAMES cruise tracks in the western North Atlantic Ocean. Colored dots show IFCB sample locations; blue = NAAMES01 in November 2015; orange = NAAMES02 in May‐June 2016; yellow = NAAMES03 in August‐September 2017; and purple = NAAMES04 in March‐April 2018 (total n = 4,328). Black squares show locations of water samples taken for HPLC analysis (n = 205). Scenes 1 and 2 from NAAMES02 further analyzed with satellite data are outlined in red boxes. (b) Example diatom images collected underway during NAAMES02. Black 10 μm scale bars on each image are equivalent. Genera/categories of each image: A—Chaetoceros sp., B—likely Guinardia sp., C—Pseudo‐nitzschia sp., D—order Naviculales, E—Chaetoceros sp., F—Corethron sp., G—unidentified pennate, H—Thalassiosira sp., I—unidentified centric, J—Guinardia sp., K—Rhizosolenia sp.
(a) Diatom carbon estimated from IFCB imagery (x‐axis; Section 2.2) and from accessory pigment‐based methods (y‐axis; Equations 2–5). Y‐axis error bars show the range of possible values when converting from accessory pigment‐based Chl a values to diatom carbon (Equation 5, Section 2.3). (b) Chl a versus diatom carbon estimated from IFCB imagery across all four NAAMES cruises (gray dots, n = 1,449), and using the equations in Table S1 in Supporting Information S1 (colored lines). Shaded areas around the lines represent the range of values in diatom carbon with minimum and maximum C:Chl values applied (Section 2.3). Black line shows the fit described by Equation 6 of this study. Cdiat_IFCB error bars in both (a) and (b) represent the combined uncertainty in particle biovolume estimates, uncertainties in the conversion from cell volume to carbon, and statistical counting errors.
Scene 1 diatom carbon estimated from satellite data, with in situ imagery‐based data points from May 11 to 13, 2016 overlain. (a) Cdiat estimated using fDiatH11 from Hirata et al. (2011) (Equation 2) converted to units of carbon (mg m⁻³) (Equation 5). Cdiat mean, median, and SD are 19.9, 10.2, and 23.1 mg C m⁻³, respectively. (b) Cdiat estimated using Equation 6 of this study. Cdiat mean, median, and SD = 3.5, 1.5, and 6.7 mg C m⁻³, respectively. (c) Diatom carbon estimated with the three‐parameter neural network model described in this study (Section 2.4). Cdiat mean, median, and SD = 5.6, 2.3, and 9.7 mg C m⁻³, respectively. Regions of missing data not seen in the other two panels are the result of neural network inputs from satellite data falling outside the range of in situ data used to train the neural network.
Scene 2 diatom carbon estimated from satellite data. Panels as in Figure 3, but for the Scene 2 region with imagery‐based in situ data from May 16 to 23, 2016. (a) Cdiat mean, median, and SD are 16.6, 7.0, and 22.6 mg C m⁻³, respectively. (b) Cdiat mean, median, and SD = 4.1, 1.1, and 10.3 mg C m⁻³, respectively. (c) Cdiat mean, median, and SD = 6.5, 2.7, and 10.8 mg C m⁻³, respectively.
Estimating the biomass of phytoplankton communities via remote sensing is a key requirement for understanding global ocean ecosystems. Of particular interest is the carbon associated with diatoms given their unequivocal ecological and biogeochemical roles. Satellite‐based algorithms often rely on accessory pigment proxies to define diatom biomass, despite a lack of validation against independent diatom biomass measurements. We used imaging‐in‐flow cytometry to quantify diatom carbon in the western North Atlantic, and compared results to those obtained from accessory pigment‐based approximations. Based on this analysis, we offer a new empirical formula to estimate diatom carbon concentrations from chlorophyll a. Additionally, we developed a neural network model in which we integrated chlorophyll a and environmental information to estimate diatom carbon distributions in the western North Atlantic. The potential for improving satellite‐based diatom carbon estimates by integrating environmental information into a model, compared to models that are based solely on chlorophyll a, is discussed.
(a) Image and (b) stereogrammetric digital elevation model (DEM) of the small lobe of Arrokoth (now named Weeyo), with (c) representative topographic profiles below showing asymmetric shape of the 7‐km‐wide crater Sky; note vertical offset for clarity. The asymmetry is plausibly the result of oblique impact, which is accentuated for large impacts into convex shapes. Image and DEM are in orthographic projection centered on the visible disk of Weeyo; updated from Schenk et al. (2021). (d) Geomorphological map of Weeyo overlain on (e) portion of New Horizons observation CA06 (33 m/pixel, phase angle 32.5°). Units are labeled and colored as shown in the legend. Adapted from Spencer et al. (2020).
Crater counts on Charon presented as R‐plots (binned differential plots normalized by a D⁻³ distribution), for (a and b) the high‐resolution swath of Vulcan Planitia (VP) imaged by New Horizons and (c and d) VP generally, based on Singer et al. (2021). (a and c) also show the range of size‐frequency distribution slopes inferred for small craters on Charon by Morbidelli et al. (2021) from their study of Arrokoth's craters. (c and d) show that dividing VP into sections nearer and farther from the terminator (toward lower right) does not compromise identification of small craters (north is up).
Monte Carlo distribution of impactor sizes and velocities that could have created Sky crater on Arrokoth. (a) Color code indicates the linear momentum brought in by the impactor. Velocities are drawn from the distribution of impact speeds and diameters for small KBOs from Greenstreet et al. (2019); see Figure S7 in Supporting Information S1 for additional details. Here, equal densities of 500 kg m⁻³ are assumed for impactor and target, with compaction crater scaling following Equation 2 for n = 0.7 and Yc = 100 kPa. Inset shows point count density of the distribution. (b) Weeyo linear momentum component into and away from Wenu.
Schematic effects of Sky crater formation on Arrokoth. Presuming Sky post‐dates the merger of the two lobes (McKinnon et al., 2020), the momentum imparted to Weeyo (SL) is sufficient to disrupt the neck or join between the two lobes. End‐member responses are shown at right, with some combination of shear and compressive dissipation limiting lobe motion at the neck being the most likely outcome (see text). Arrokoth's present rotation state about its center‐of‐mass is clockwise in this view (see Keane et al., 2022).
Plain Language Summary It has become apparent over the last few years that small asteroids and comets are very underdense compared with the materials they are made of. This means that their total porosities are likely quite high, in excess of 70%, both as tiny voids within particles (so‐called microscopic porosity) and spaces between particles (macroscopic porosity). But none are likely as porous as the distant denizens of the Kuiper belt such as Arrokoth (visited by the New Horizons spacecraft in 2019). This paper concerns impact craters on Arrokoth and similar small bodies, and the rather unusual effects expected. Imagine a fluffy (fine powder) snowball striking a much larger fluffy snowball, only that the snow is not pure ice but a mixture of porous icy, rocky, and carbon‐rich particles. Even at high velocities (>100 s of m/s) craters should mostly form by compacting pore space and pushing material away from the impact point, not the traditional blasting of ejecta back into space. Similar to crush‐up of an automobile bumper, compaction helps to protect from the potentially catastrophic effects of large impacts, such as complete disruption of the target or breakup of bilobate bodies like Arrokoth, and should be incorporated in future collisional evolution studies.
Plain Language Summary Eddy kinetic energy has been increasing in the Southern Ocean over the past few decades. These changes in the eddy field are of great importance because they play a crucial role in modulating the ocean circulation response to surface forcing. However, the EKE changes are inhomogeneous in the Southern Ocean. To understand the pattern of these changes, we analyze the satellite altimeter record over a period of 28 years since 1993 and carry out a set of idealized simulations. We find that the change of EKE is more related to the mean-flow than to localized wind changes. The increasing wind stress contributes to increasing EKE by intensifying the circumpolar mean flow, with local wind stress playing a minor role in the pattern of EKE changes. Strong EKE variations are generally confined downstream of major topographic features, suggesting strong modulation by topography. This study indicates the change of EKE depends on the combination of wind stress, mean-flow and topography in the Southern Ocean.
Phase‐averaged intra‐tidal variabilities of (a) current speed profile, (b) dissipation rate and buoyancy flux, (c) flux Richardson number RfII ${R}_{f}^{\mathit{II}}$ and gradient Richardson number, (d) buoyancy Reynolds number, and (e) turbulent Froude number. Error bars indicate the standard deviation of the data in each bin. Note that data for late ebb (after 7 Hour Relative to High Water) was omitted given low water level and flow velocity.
RfII ${R}_{f}^{\mathit{II}}$ as a function of Reb (after Monismith et al., 2018, incorporating additional datasets). The closed dots represent mean values of RfII ${R}_{f}^{\mathit{II}}$ binned by Reb with error bars indicating standard deviation of the data in each bin. Data from other estuaries are also shown (Holleman et al., 2016; MacDonald & Geyer, 2004). Other observational data are covariance and microstructure profile data (Monismith et al., 2018; blue lines), as well as data from quiescent sites and energetic sites in Mediterranean Sea (Vladoiu et al., 2021; blue symbols). Black lines are parameterizations based on data from shelf seas (Bluteau et al., 2013), lakes (Bouffard & Boegman, 2013) and atmospheric boundary layer (Lozovatsky & Fernando, 2013). Black symbols are DNS data from Shih et al. (2005), Chung and Matheou (2012), Zhou et al. (2017), Salehipour and Peltier (2015), Brethouwer et al. (2007), and Arthur et al. (2017). Results from laboratory experiments in green color represent homogeneous, shear (Rohr & Van Atta, 1987) and grid stirring, unsheared turbulence.
RfII ${R}_{f}^{\mathit{II}}$as a function of Ri for covariance data of Monismith et al. (2018) and estuarine data of Holleman et al. (2016) and present study. The closed dots represent mean values of RfII ${R}_{f}^{\mathit{II}}$ binned by Ri with error bars indicating standard deviation of the data in each bin. Curves outside the main graph are PDFs of RfII ${R}_{f}^{\mathit{II}}$ and Ri for these three datasets.
RfII ${R}_{f}^{\mathit{II}}$ as a function of Frt. The closed red dots represent mean values of RfII ${R}_{f}^{\mathit{II}}$ binned by Frt with error bars indicating standard deviation of the data in each bin. Observational data from deep ocean by Ijichi et al. (2020) are shown in the form of binned averages and standard deviations in blue colored symbols. Green triangles represent observations from Fraser river estuary by MacDonald and Geyer (2004). Different DNS datasets of Shih et al. (2005), Maffioli et al. (2016), and GV19 are presented as asterisks, diamonds, and triangles, respectively. Black solid lines represent (6). The best fit line based on laboratory data by G. Ivey and Imberger (1991) is indicated by black dashed line. Curves outside the main graph are PDFs of Rf and Frt for Ijichi et al. (2020, blue) and the present results (red).
The flux Richardson number Rf, also called the mixing efficiency of stratified turbulence, is important in determining geophysical flow phenomena such as ocean circulation and air-sea transports. Measuring Rf in the field is usually difficult, thus parameterization of Rf based on readily observed properties is essential. Here, estimates of Rf in a strongly turbulent, sediment-stratified estuarine flow are obtained from measurements of covariance-derived turbulent buoyancy fluxes (B) and spectrally fitted values of the dissipation rate of turbulent kinetic energy (ε). We test scalings for Rf in terms of the buoyancy Reynolds number (Reb), the gradient Richardson number (Ri), and turbulent Froude number (Frt). Neither the Reb-based nor the Ri-based scheme is able to describe the observed variations in Rf, but the Frt-based parameterization works well. These findings support further use of the Frt-based parameterization in turbulent oceanic and estuarine environments.
Picture and diagram of the setup. In the picture foreground: the polarimeter Grand Cru. We only use two of its four channels, one at a time, for observing the polarization at 391.4 nm (purple) and 427.8 nm (blue). In the picture background (on the table): the vacuum chamber. Inside, the sphere is surrounded by a black cache in order to avoid any internal reflection on the glass chamber. Below the table, a vacuum pump creates a primary vacuum of about 5 Pa. One generator injects electric current in the Planeterrella while a second one is used to make the sphere rotate on itself with a period T0 = 4 min. During operation, the room is fully dark and a thick cover sheet wraps the full setup in order to prevent any external light pollution. The diagram (not to scale) shows the main dimensions and the geometry of the experimental setup. One channel of the instrument (on the right), is pointing toward the Planeterrella through the glass chamber (thin circle) and then the hole in the black cache (thick line). Inside the vacuum chamber, the electrons are produced at the cathode (red minus sign), and move toward the anode sphere (blue plus sign).
Polarization parameters at 427.8 nm. Upper panel: light intensity in arbitrary units. Middle panel: Degree of Linear Polarization in %. Lower panel: Angle of Linear Polarization in degrees. Left panels: without a magnet inside the sphere. Right panels: with a horizontal magnet inside the sphere. Uncertainties on the raw data (black dots) are indicated by the gray shaded area. Uncertainties on the data after averaging over T1 are given by the width of the blue curve.
A series of experiments have shown recently that several auroral lines are polarized, when observed from the ground. However, this polarization may be caused by indirect light sources (from the ground or the sky) scattered in the lower atmosphere by Rayleigh and Lorenz‐Mie scattering, or during the crossing of the ionospheric current sheets. Here, we present polarization measurements of the N2+ ${N}_{2}^{+}$ blue (427.8 nm) and purple (391.4 nm) emissions in a laboratory confined setting that excludes any light pollution or scattering. We show that both lines are polarized, at a level comparable to that of the natural auroral observations. Our results furthermore show that the Degree of Linear Polarization depends on the magnetic conditions. This set of experiments confirms in a controlled environment the polarization of auroral emissions and constitutes a strong evidence in favor of auroral emission already polarized in the upper atmosphere.
  • Young Hong ShinYoung Hong Shin
  • C. K. ShumC. K. Shum
  • Carla BraitenbergCarla Braitenberg
  • [...]
  • Byung‐Dal SoByung‐Dal So
  • A. RaniA. Rani
  • A. Basu SarbadhikariA. Basu Sarbadhikari
  • D. R. HoodD. R. Hood
  • [...]
  • S. KarunatillakeS. Karunatillake
Large-scale CO2 sequestration into geological formations has been suggested to reduce CO2 emissions from industrial activities. However, much like enhanced geothermal stimulation and wastewater injection, CO2 sequestration has a potential to induce earthquake along weak faults, which can be considered a negative impact on safety and public opinion. This study shows the physical mechanisms of potential seismic hazards along basement faults driven by CO2 sequestration under variation in geological and operational constraints. Specifically we compare the poroelastic behaviors between multiphase flow and single-phase flow cases, highlighting specific needs of evaluating induced seismicity associated with CO2 sequestration. In contrast to single-phase injection scenario, slower migration of the CO2 plume than pressure pulse may delay accumulation of pressure and stress along basement faults that may not be mitigated immediately by shut-in of injection. The impact of multiphase flow system, therefore, needs to be considered for proper monitoring and mitigation strategies.
Top-cited authors
E. Rignot
  • University of California, Irvine
John Gosling
  • University of Colorado Boulder
Michiel Roland Van den Broeke
Andre Balogh
  • Imperial College London
David Mccomas
  • Southwest Research Institute