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Geophysical Research Letters

Published by Wiley and American Geophysical Union

Online ISSN: 1944-8007

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Print ISSN: 0094-8276

Disciplines: Earth and space science

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Figure 1. Pb-Sn phase diagram. 
Figure 2. Sketch of the directional solidification furnace. The crosses are the rough locations of the ingot section centers for which we computed the density. 
Figure 3. Micrographs of horizontal slices at a height of 63 mm, 40X magnification (the bars in all micrographs are 1 mm), polished to 3 μm, and etched with 2% nital for 30 s. The Pb-rich phase is dark, the eutectic phase light. (a) A directionally solidified ingot with the smaller temperature gradient, showing cross-shaped, Pb-rich dendrites. (b) A directionally solidified (with the smaller temperature gradient) and then annealed ingot, showing coarsening as the secondary arms have become more globular. 
Figure 4 of 5
Figure 5 of 5
Partial melting of a Pb-Sn mushy layer due to heating from above, and implications for regional melting of Earth's directionally solidified inner core

August 2015

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High-impact, short-format reports with immediate implications spanning all Earth and space sciences.

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Committed Ice Loss in the European Alps Until 2050 Using a Deep‐Learning‐Aided 3D Ice‐Flow Model With Data Assimilation
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December 2023

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19 Reads

Modeling the short‐term (<50 years) evolution of glaciers is difficult because of issues related to model initialization and data assimilation. However, this timescale is critical, particularly for water resources, natural hazards, and ecology. Using a unique record of satellite remote‐sensing data, combined with a novel optimisation and surface‐forcing‐calculation method within the framework of the deep‐learning‐based Instructed Glacier Model, we are able to ameliorate initialization issues. We thus model the committed evolution of all glaciers in the European Alps up to 2050 using present‐day climate conditions, assuming no future climate change. We find that the resulting committed ice loss exceeds a third of the present‐day ice volume by 2050, with multi‐kilometer frontal retreats for even the largest glaciers. Our results show the importance of modeling ice dynamics to accurately retrieve the ice‐thickness distribution and to predict future mass changes. Thanks to high‐performance GPU processing, we also demonstrate our method's global potential.
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Regional and Teleconnected Impacts of Solar Radiation‐Topography Interaction Over the Tibetan Plateau
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December 2023

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48 Reads

Solar radiation‐topography interaction plays an important role in surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of solar radiation‐topography interaction over the TP. Land‐atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for solar radiation‐topography interaction improves E3SM simulation of surface climate. The winter cold bias in air temperature decreases from −4.57 to −3.79 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP's solar radiation‐topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts.

Sea Surface Salinity Strongly Weakens ENSO Spring Predictability Barrier

December 2023

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16 Reads

Previous studies suggested that tropical sea surface salinity (SSS) can influence tropical Pacific sea surface temperature (SST) through mixing and entrainment and thus it may be a signal for El Niño‐Southern Oscillation (ENSO) prediction. This paper explores the influence of SSS on ENSO spring predictability barrier (SPB) using an empirical dynamic model ‐ Linear Inverse Model (LIM). By coupling and decoupling SSS in the LIM, we find that tropical Pacific SSS plays a significant role in weakening both Central‐Pacific and Eastern‐Pacific ENSO SPB. The evolution of optimal initial structure also shows the importance of SSS dynamics in ENSO. We found an SSS mode that plays the dominant role in SSS impacting ENSO prediction. By the analysis of lead‐lag correlation, we find that this mode can induce easterlies during the spring, which finally leads to a La Niña‐like SST pattern in the winter through zonal advective and thermocline feedbacks.

Organic Matter Matters—The Imaginary Conductivity of Sediments Rich in Solid Organic Carbon

December 2023

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35 Reads

Solid organic matter (OM) is a biogeochemically relevant constituent of soils and sediments. It also affects sediments' geophysical properties, but is often overlooked in hydro‐ and biogeophysical approaches for the characterization of the shallow subsurface. Here, we explore the potential of spectral induced polarization (SIP) to delineate OM‐rich zones in the subsurface and provide insights into the mechanisms that drive OM‐polarization using measurements on both field cores and artificial OM‐sand mixtures. Both, field samples and artificial mixtures showed a linear relationship between the total organic carbon (TOC) content and charge storage (imaginary conductivity). The high cation exchange capacity of OM drives the increase in polarization and can help in delineating potentially microbially active OM‐rich zones in cores or field surveys. To avoid misinterpretation of SIP data in unconsolidated media, we strongly suggest quantifying TOC content in sediment samples to accompany the interpretation of field surveys.

Does Downscaling Improve the Performance of Urban Ozone Modeling?

December 2023

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26 Reads

Increasing the model resolution is expected to be one way for improving air quality forecasts in urban areas. In this study, we evaluate the model performance in a large city at various resolutions to examine the best resolution for air pollution simulations. The comparison with measurements at a station near the traffic emissions shows the advantage of using high resolutions for capturing the extreme values. The statistical evaluation indicates that the highest model resolution (33 m) provides the best results for NO X concentration distributions near the traffic roads, while the improvement for roadside O 3 with decreasing grid spacing stops at a certain point. The best model performance for the areas with a distance to the pollution sources is with the resolution of 100–300 m, at which the transport errors are equivalent to the emission biases.

Figure 1. (a) The elevation map of Peninsular Malaysia. (b) The summary map of casualty and displacements. (c) The domain average daily precipitation over the entire Peninsular Malaysia in December, obtained from ECMWF Reanalysis v5 enhanced for the land component (ERA5-Land) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). (d) Anomaly of 3-day precipitation by comparing the 16-18 December 2021 event to the 1950-2021 records using ERA5-Land data. Locations with significant positive anomaly (with statistical significance at the 95% confidence level based on a one-tailed z test) are marked with black dots. (e) Is same as (d), but the data set is changed to CHIRPS in 1981-2021.
Figure 2. (a-h) The MCS tracking results during the flood event at different key time steps, where the Super Typhoon Rai, MCS1, and MCS2 are labeled. (i) The time series of area of MCS1 (green solid line) and MCS2 (blue solid line), and their respective precipitation rate under the cloud shield (feature precipitation rate; dash lines). (j) The time series of hourly precipitation rate averaged over the entire Peninsular Malaysia (peninsular precipitation rate) from total (yellow solid line), MCS1 (green solid line), and MCS2 (blue solid line), as well as their fractional contribution to total precipitation (dash lines).
Figure 3. (a) The 3-day average of MSLP (contour lines), IVT (filled contours), and wind at 750 hPa (vectors) during the 16-18 December 2021 Peninsular Malaysia flood. The critical time steps in the evolution of synoptic features are shown for (a) 16 December 00:00, (b) 16 December 12:00, (c) 17 December 10:00, and (d) 18 December 01:00. The boundary of Peninsular Malaysia is highlighted in red.
Figure 4. The return period of extreme 3-day precipitation in December over Peninsular Malaysia estimated based on generalized extreme value (GEV) distribution with the input of (a) ERA5-Land precipitation and (b) CHIRPS precipitation. (c) The time series of 3-day minimum mean sea level pressure (MSLP) in December over the peninsula, where the dotted lines indicate the events whose MSLP is lower than the 2021 event. (d) The time series of 3-day mean integrated water vapor transport (IVT) averaged over the peninsula, where the dotted lines indicate the events whose IVT is higher than the 2021 event. (e) The joint histogram of MSLP and IVT. (f) The spatial distribution of the locations of tropical depression (defined as the minimum MSLP over the peninsula) and the corresponding IVT.
Revealing the Key Drivers Conducive to the “Once‐In‐A‐Century” 2021 Peninsular Malaysia Flood

December 2023

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24 Reads

In December 2021, Super Typhoon Rai caused significant devastation to the South Philippines and East Malaysia. In the meantime, an unprecedented flood event occurred in Peninsular Malaysia at 2,000 km west of the typhoon's path, causing comparable socioeconomic impacts as Rai. Record‐breaking 3‐day precipitation was received by Peninsular Malaysia during 16–18 December. Based on the storm tracking results, this study identified two mesoscale convective systems (MCSs) that were directly responsible for the flooding. The two MCSs were directly initiated by a tropical depression and sustained by an elongated easterly water vapor corridor originating from the Super Typhoon Rai. The return period and joint frequency analysis of key drivers indicate that the 3‐day downpour was more severe than a “once‐in‐a‐century” event. Historical records suggest such anomalous moisture channel has become more frequent in Southeast Asia, which alarms heightened attention in forecasting winter flood.

Anthropogenic Aerosols Offsetting Ocean Warming Less Efficiently Since the 1980s

December 2023

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6 Reads

Greenhouse gases and aerosols play a major role in controlling global climate change. Greenhouse gases drive a radiative imbalance which warms the ocean, while aerosols cool the ocean. Since 1980, the effective radiation felt by the planet due to anthropogenic aerosols has leveled off, global ocean cooling due to aerosols has decelerated, and greenhouse gas‐driven ocean warming has accelerated. We explore the deceleration of aerosol‐driven ocean cooling by quantifying a time‐ and spatially varying ocean heat uptake efficiency, defined as the change in the rate of global ocean heat storage per degree of cooling surface temperature. In aerosol‐only simulations, ocean heat uptake efficiency has decreased by 43 ± 14% since 1980. The tropics and sub‐tropics have driven this decrease, while the coldest fraction of the ocean continues to sustain cooling and high ocean heat uptake efficiency. Our results identify a growing trend toward less efficient ocean cooling due to aerosols.

The Role of Wet Processes in Extratropical Thermal Stratification During the Glacial Period

December 2023

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5 Reads

In this study, we differentiate wet processes from dry processes in shaping the extratropical thermal stratification during the Last Glacial Maximum. Our findings indicate that even during the dry glacial period the influence of wet processes on thermal stratification cannot be overlooked. The applicability of dry and wet baroclinic adjustment theory strongly depends on the seasonality rather than the glaciation as the warm season is characterized by a weaker meridional temperature gradient and increased precipitation than the cold season. Despite that the baroclinic adjustment theory based on effective static stability can be proficiently applied to all seasons, the classical dry baroclinic adjustment theory may be better suited for use during relatively cold seasons. These findings have important implications for understanding processes governing the extratropical thermal stratification, particularly in the context of cold climate.

Surface Temperature Pattern Scenarios Suggest Higher Warming Rates Than Current Projections

December 2023

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11 Reads

Atmosphere‐ocean general circulation models (AOGCMs) struggle to reproduce recently observed sea surface temperature (SST) trend patterns. Here, we quantify the relevance of this SST pattern uncertainty to global‐mean temperature projections through convolving Green's functions with SST pattern scenarios that differ from the ones AOGCMs produce by themselves. We find that future SST pattern uncertainty has a significant impact on projections, such as increasing total model uncertainty by 40% in a high‐emissions scenario by 2085. A reversal of the current cooling trend in the East Pacific over the next few decades could lead to a period of global‐mean warming with a 60% higher rate than currently projected. SST pattern uncertainty works through a destabilization of the shortwave cloud feedback to affect temperature projections. It is critical for climate change impact, adaptation, and mitigation assessments to incorporate this previously unaccounted for uncertainty until we trust the evolution of SST patterns in AOGCMs.

Gravitational Constraints on the Earth's Inner Core Differential Rotation

December 2023

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8 Reads

The differential axial rotation of the solid inner core (IC) is suggested by seismic observations and expected from core dynamics models. A rotation of the IC by an angle α takes its degree 2, order 2 topography (peak‐to‐peak amplitude δh ) out of its gravitational alignment with the mantle. This creates a gravity variation of degree 2, order 2 proportional to δh and to α . Here, we use gravity observations from Satellite Laser Ranging, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On to reconstruct the time‐variable S 2,2 Stokes coefficient. We show that for δh = 90 m, S 2,2 provides upper bounds on α of 0.09°, 0.3°, and 0.4° at periods of ∼4, ∼6, and ∼12 years, respectively. These are overestimates, as our reconstructed S 2,2 signal likely remains polluted by hydrology, although viscous relaxation of the IC can permit larger amplitudes.

Multi‐Objective Ensemble‐Processing Strategies to Optimize the Simulation of the Western North Pacific Subtropical High in Boreal Summer

December 2023

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7 Reads

The western North Pacific Subtropical High (WNPSH) in boreal summer is a major atmospheric player affecting East Asian climate, but its simulation in state‐of‐the‐art climate models is still largely biased. Here we use a multi‐objective optimization strategy, the Pareto optimality, to incorporate multiple physical constraints in processing multi‐model simulations provided by the Coupled Model Intercomparison Project Phase 6. We aim to improve the simulation of WNPSH by this practice. Sea surface temperatures from three tropical oceanic basins are found highly related to WNPSH, and thus used as constraints. We also present an ameliorated strategy, which takes a subset of the raw Pareto optimality by imposing conditions of smallest errors. Results show that the overestimate of WNPSH is effectively corrected. The two multi‐objective optimization schemes both perform better than the traditional approach, revealing the importance of implementing physically based links in processing multi‐model ensemble simulations.

Foundations for Universal Non‐Gaussian Data Assimilation

December 2023

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4 Reads

In many applications of data assimilation, especially when the size of the problem is large, a substantial assumption is made: all variables are well‐described by Gaussian error statistics. This assumption has the advantage of making calculations considerably simpler, but it is often not valid, leading to biases in forecasts or, even worse, unphysical predictions. We propose a simple, but effective, way of replacing this assumption, by making use of transforming functions, while remaining consistent with Bayes' theorem. This method allows the errors to have any value of the skewness and kurtosis, and permits physical bounds for the variables. As such, the error distribution can conform better to the underlying statistics, reducing biases introduced by the Gaussian assumption. We apply this framework to a 3D variational data assimilation method, and find improved performance in a simple atmospheric toy model (Lorenz‐63), compared to an all‐Gaussian technique.

Strongly Nonlinear Effects on Determining Internal Solitary Wave Parameters From Surface Signatures With Potential for Remote Sensing Applications

December 2023

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34 Reads

The inversion of remote sensing signatures of internal solitary waves (ISWs) can retrieve dynamic characteristics in the ocean interior. However, the presence of ubiquitous large‐amplitude ISWs poses challenges to the commonly used weakly nonlinear methods for parameter retrieval. Through laboratory experiments, we establish a relationship between surface features and internal characteristics of ISWs by the remote sensing imaging mechanism. The results demonstrate that strong nonlinearity significantly influences the retrieval of ISWs, primarily manifested in the calculation of wave‐induced velocities and the applicability of ISW solutions. A fully nonlinear model Dubreil–Jacotin–Long equation is used in the retrieval and has been tested under different conditions. Mooring observations indicate that the determination of ISW parameters from satellite images is affected by the complexity of in situ stratification, but additional remote sensing information such as surface velocities enables us to perform retrievals even if the real‐time measurement of pycnocline depth is not available.

Figure 1. (a) Distributions of selected three seismic arrays A1, A2 and A3 and great circle paths from 110 events. The aperture of the three arrays ranges from 20 to 150 km. (b) Normalized waveform comparison between translational acceleration (m/s 2 ) and retrieved rotational velocity (rad/s) using the A2 array from the earthquake: M 8.2-99 km SE of Perryville in 2021, Alaska (the red line path in the lower left corner of a). (c) Correlation coefficients of all events between translation and retrieved rotation of the A2 array.
Figure 2. Backazimuth deviation and calculated dispersion points. Solid red line is the theoretical phase velocity from isotropic PREM model. (a) Deviations between the great circle path direction and the azimuths calculated by the horizontal rotation components (Tang et al., 2023) of the A2 array in the period range of 120-250 s (b), (c) and (d) are the observed velocity (black lines) of the three arrays calculated using only the data of the azimuth deviations (red dotted line) within 5° in (a). The 1σ uncertainty is about 0.1 km/s.
Figure 3. Variation of azimuth-dependent phase velocity. Red lines are the best-fit 2ψ curves. Blue lines are the best-fit curves when 2ψ and 4ψ terms are included. Green lines are the best-fit 1ψ curves. (a), (b) and (c) are the smoothed phase velocity (black points) at three different periods from the A1, A2, and A3 array, respectively. The 1σ uncertainty (about 0.1 km/s) is estimated in an azimuth bin based on Equations 2 and 3.
Figure 4. Comparison of fast wave directions from different methods. The solid black line and dashed black line represent the S-wave fast wave directions jointly retrieved by SKS and surface waves (Marone & Romanowicz, 2007) at depth of 200 and 300 km, respectively. The solid blue line and dashed blue line represent the Rayleigh wave fast directions retrieved by the amplitude ratio between translation and rotation at depth of about 200 and 300 km, respectively. The black arrow represents the APM directions (Gripp & Gordon, 2002). The green lines represent the fast directions of SKS splitting (Becker et al., 2012). The dashed black lines with arrows are the horizontal mantle flow streamlines retrieved by geodetic data (Barbot, 2020). Yellow lines represent the fast direction of Rayleigh wave in the upper lithosphere estimated from the beamforming method (Alvizuri & Tanimoto, 2011) and blue shaded arrows are estimated plate motion directions from the amplitude ratio method.
Anisotropy and Deformation Processes in Southern California From Rotational Observations

December 2023

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46 Reads

Seismic anisotropy in the upper mantle reveals geodynamic processes and the tectonic evolution of the Earth. The two most powerful methods, surface wave tomography, and shear-wave splitting observations, cannot investigate the deep local anisotropy with good vertical and lateral resolution, resulting in poor constraints on plate deformation processes of the complex plate boundary beneath the Southern California region. Here, we show that the amplitude ratio of translational displacement and rotation makes it possible to retrieve the local anisotropy in the upper mantle. Azimuthal anisotropy in the asthenosphere is well determined and resolved in lateral and vertical directions. The fast axis retrieved from amplitude observations indicates the local rapid changes in plate deformation and complex pattern of mantle flow, which is compatible with the distributions of horizontal mantle flow illuminated by geodetic measurements, providing new insights on geodynamic processes of the Southern California region.

Dependencies of (a) Nd∗ ${N}_{d}^{\ast }$, (b) LWC*, (c) Ni∗ ${N}_{i}^{\ast }$, (d) IWC*, and (e) liquid mass fraction (LMF) on normalized cloud width (x*). The dots indicate the mean, and the error bars represent 25th—75th percentiles. (f) The average LMF in cloud narrower and wider than 1 km, and the average LMF between 3 and 6 km, and that between 6 and 9 km, respectively.
(a) Fraction of the clouds in which the liquid mass fraction has a positive (green), negative (orange) or no obvious (blue) trend toward the edge. (a) For the clouds narrower than 1 km, and (b) for the clouds wider than 1 km.
Dependencies of mean volume radius rv on droplet concentration Nd measured by Forward Scattering Spectrometer Probe (a) near cloud edge (x* between −0.2 and 0) in all cases, and (b) in the clouds in which the liquid mass fraction decreases toward the edge. The gray dots indicate the 1 Hz data, the black line indicates the average value and the error bars indicate 25%–75% percentiles. (c) Histogram of the time scale of turbulent mixing (blue) and the phase relaxation time of water calculated using Equation 11 (orange) and 13 (green).
(a) Ice particle size distributions at x* ≤ −0.4 (blue), −0.4 < x* ≤ −0.2 (orange), and x* > −0.2 (green) in clouds (a) narrower than <1 km, and (b) wider than 1 km.
Liquid‐Ice Mass Partitioning Across the Edge of Mixed‐Phase Cumulus Clouds

November 2023

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30 Reads

Plain Language Summary In mixed‐phase clouds, the coexistence of liquid and ice has significant impacts on the cloud lifecycle and radiative properties, but the phase partitioning in mixed‐phase cloud is still not fully understood. In cumulus clouds, one of the major factors controlling the microphysics across cloud edges is the entrainment of dry air into cloud. During the mixing process, the liquid‐ice partitioning is determined by the mixing type (homogeneous or inhomogeneous), the evaporation (growth) of liquid, and sublimation (growth) of ice. Currently, there is a lack of observational evidence for the main mixing mechanism that controls the phase partitioning. In this study, the liquid‐ice mass partitioning across the edge of shallow to moderately deep cumulus clouds are analyzed using airborne in situ measurements. The results show the liquid mass fraction remains similar across the cloud, and inhomogeneous mixing dominates the phase partitioning. The results improve our understanding on the role of entrainment, and are useful in evaluating model simulations.

Summer precipitation (shaded; units: mm d⁻¹) and moisture transport (vector; units: kg m⁻¹ s⁻¹). Top panels represent differences between Set B and Set A. Bottom panels represent differences between Set C and Set A. From left to right the coastal mountain altitudes are 0, 2, and 4 km, respectively. In each panel, only the areas with confidence levels >95% (the Student's t‐test) are labeled for the water vapor transport changes and dotted for the precipitation changes. The red rectangle denotes the location of the Songliao Basin.
Summer precipitation change (mm d⁻¹) between experiments in Set B and Set A (a) and between experiments in Set C and Set A (b). In Panel a, precipitation changes for coastal mountain altitudes of 0 and 2 km are little. To clearly show bar plot in a, the left two bar heights are amplified, so the exact values of summer precipitation change for each condition are presented below or over their corresponding bars, respectively.
Zonal‐vertical cross sections of total diabatic heating during summer (shaded; units: K d⁻¹) and vertical velocity (vectors; units: ×0.01 Pa s⁻¹) averaged within 20–40°N. Top panels: differences between experiments in Set B and Set A. Bottom panels: differences between experiments in Set C and Set A. From left to right: coastal mountain altitudes are 0, 2 and 4 km, respectively.
Geopotential heights (shaded; units: m) and winds (vector; units: m s⁻¹) at 775 hPa. Top panels: differences between experiments in Set B and Set A. Bottom panels: differences between experiments in Set C and Set A. From left to right: coastal mountain altitudes are 0, 2, and 4 km, respectively. In each panel, only the areas with confidence levels >95% (the Student's t‐test) are labeled for wind difference and dotted for the geopotential heights changes.
Coastal Mountains Amplified the Impacts of Orbital Forcing on East Asian Climate in the Late Cretaceous

November 2023

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38 Reads

Plain Language Summary Tectonic events and solar insolation are the two important factors impacting variations of the climate system in the geological past. Regional climate responses to variations in the radiation from the sun over 10⁴–10⁵ years were often magnified or dampened by tectonic events. Cretaceous sedimentary records in East Asia suggest that East Asian climate was influenced by the solar insolation. Geological evidence showed that a mountain range existed along the East Asian coast then. Would this mountain range modulate impacts of solar insolation on East Asian climate? Our modeling results show that the influence of solar insolation on East Asian climate can be amplified by the coastal mountain range, depending on the mountain elevation. When the coastal mountain range is ∼2 km high, the amplification effects become significant. When its altitude reaches ∼4 km, the response of East Asian climate to solar insolation is considerably strengthened, and such a condition is supported by the rhythm induced by the climate variation due to solar insolation archived in the Cretaceous strata in the Songliao Basin. Thus, we speculate that the East Asian coastal mountains might have reached an altitude more than 2 km in the Late Cretaceous.

Map of integrated water vapor (color shading: kg/m²) with mean sea level pressure (gray contours: hPa) and averaged wind speed between 300 and 900 hPa (gray vectors: m/s) from ERA5 at 1300 UTC on 22 February 2023. Hatched area indicates the atmospheric river (AR) defined by the AR algorithm. Black line shows the track of the R/V Shirase during JARE64. Purple dot indicates the position of launch of the cloud particle sensor sonde on 22 February 2023. Cyan square shows the position of Japan's Syowa Station.
Vertical profiles of (a) air temperature (°C), (b) water vapor mixing ratio (g/kg), (c) wind speed (m/s: black) and wind direction (°: gray), (d) cloud particle sensor signal voltage (V) with degree of polarization, and (e) log10 (total particle count) (/L) at 1300 UTC on 22 February 2023.
(a) Map of the 8‐day mean chlorophyll‐a concentration (mg/m³) during 18–26 February 2023. Red and blue lines show 3‐day backward trajectories of air masses arriving at the height of 4.5 km (BT4.5; blue) and 6.5 km (BT6.5; red) at the time of the cloud particle sensor (CPS) observation for the ensemble mean (thick) and 27 ensemble members (thin) with Global Forecast System meteorology on a 0.25° × 0.25° latitude/longitude grid. Plots show positions of air masses at 0000 UTC on 20 February (dots), 21 February (triangles), and 22 February 2023 (squares) for each ensemble member. Time series of the height (km) above ground level of the air masses arriving at the height of (b) 4.5 km and (c) 6.5 km at the time of the CPS observation for the ensemble mean (thick) and 27 ensemble members (thin). The 3‐day backward trajectories were computed using National Oceanographic and Atmospheric Administration's hybrid single‐particle Lagrangian integrated trajectory model.
Time–height sections of the 27‐member ensemble mean dimethylsulfide (DMS:×10⁻¹¹ kg/kg) at the air mass position of (a) BT4.5 and (b) BT6.5 with Global Forecast System meteorology data during the backward trajectory period. Blue and red contours show the height (km) of (a) BT4.5 and (b) BT6.5 for the ensemble mean (thick) and 27 ensemble members (thin). Time series of (c) simulated wave height (m) from ERA5 and (d) observed chlorophyll‐a concentration (mg/m³) from MODIS–Aqua satellite at the air mass position of BT4.5 (blue) and BT6.5 (red) for the ensemble mean (thick) and 27 ensemble members (thin).
Ice Cloud Formation Related to Oceanic Supply of Ice‐Nucleating Particles: A Case Study in the Southern Ocean Near an Atmospheric River in Late Summer

November 2023

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9 Reads

Plain Language Summary Polar region clouds play a key role in Earth's climate. Knowledge of the cloud phase (i.e., liquid water, ice, or mixed) is important for determining the surface heat budget because the reflection of solar radiation at the cloud top depends on cloud phase. Although the development of numerical climate models allows the investigation of clouds globally, there still is a cloud phase (water or ice) bias in the models. Therefore, an observational study is required to investigate cloud formation environments. During a cruise in the SO by a research ship, ice clouds were observed at relatively high temperatures in the mid‐troposphere at high‐latitudes. Analysis of the trajectory of the air mass at the ice formation height indicated that the air mass had traveled from the surface in the mid‐latitudes to the mid‐troposphere over high‐latitudes. Biogenic material emitted from the ocean under strong wind and high wave conditions was transferred to the air mass near the surface in the mid‐latitudes and eventually reached the mid‐troposphere at the observation point. These results suggest that marine biogenic material transported from the mid‐latitudes influenced ice cloud formation under relatively high temperatures.

Power plant emission rate estimates compared to in situ emission rates in scenario 1: rs = 300 m. (a): Emission rate estimates. (b): Emission rate estimation errors. The average estimate error is the average of all the 47 estimate percentage errors, as shown in the dashed horizontal line in the bottom figure.
A power plant plume example. Time: 08 April 2020 17:07:56 UTC. Location: Four Corners Power Plant, (36.6862, −108.4775). (a) Left: plume figure in a zoomed‐out view (generated by the matched filter method); (a) right: plume figures in the circle study area of rc = 563.5 m, with and without RGB basemap. Background noise is outlined by red rectangles. Note that both two starting radius (rs = 300 m and rs = 100 m) return the same rc value in this example. (b) Top: ΔIME over radius; (b) bottom: emission rate estimate Q as a function of radius rc.
Liquefied natural gas plume examples. (a) Sabine Pass terminal. Point source A‐E are from fuel combustion and point source F‐H are from flare combustion. (b) Cameron terminal. Point source A‐C are from fuel combustion.
Measuring Carbon Dioxide Emissions From Liquefied Natural Gas (LNG) Terminals With Imaging Spectroscopy

November 2023

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11 Reads

Plain Language Summary The natural gas (NG) system is an important source of carbon dioxide (CO2) emissions. Rising U.S. NG production and international energy demand led to a rapid growth of liquefied natural gas (LNG) exports. This makes it increasingly important to assess the CO2 emissions along the LNG supply chain, especially during gas liquefaction at LNG export terminals. However, existing inventories only provide annual/monthly data for some major LNG terminals from operators, which lack measurement‐based in situ validation. Here we introduce a top‐down CO2 measuring method using remote sensing imaging spectroscopy, which can provide an independent third‐party data source above the detection threshold with uniform measuring technology across all infrastructure. Additionally, the independent measurements from this method would help evaluate the magnitude and variation of existing emission inventories. When combined with remote sensing methane detection, it can further monitor the carbon emissions more efficiently along the NG supply chain. This could be achieved by retrieving atmospheric CO2 and CH4 simultaneously from the same remote sensing campaign. This study also shows the mapping and quantification capability of imaging spectroscopy on the plumes with emission rate of 100–3,000 t CO2/hr, implying a broader application potential in CO2 top‐down detection.

Observations between 16:10–19:10 on 26 August 2015. Timeseries are shown for (a) the solar wind dynamic pressure, P [nPa], (indigo) and the north‐south interplanetary magnetic field component, BZ, (pink); (b) the Sym H index [nT]; (c) the Van Allen Probes A L value (indigo) and magnetic local time (pink); (d) the electron density, n [cm⁻³] (indigo) and the Alfvén speed, VA [km s⁻¹] (pink); (e) the radial magnetic field component, Br [nT]; (f) the azimuthal magnetic field component, Bϕ [nT]; and (g) the compressional magnetic field component, B‖ [nT]. Panels (h–j) show the power spectral density, P [nT² Hz⁻¹], for the radial, azimuthal, and compressional magnetic field components. The value of P is indicated by color, and the power is plotted as a function of time on the x‐axis, with frequency, f [mHz], on the y axis. For each time step, the frequency at the maximum in P (for frequencies between 2 and 12 mHz) is marked by the pink profile.
Magnetic field and electron density observations between 17:25–19:25 on 26 August 2015. Panel (a) shows the magnetic field vectors in the radial (x‐direction) and azimuthal (y‐direction) as a function of time, using a 5 min sampling interval. The magnitude of the vectors correspond to the scale shown on the right of the panel. Color coding above the panel describes the angle of the vector measured from the y‐axis. Green for 0 ± 22.5°; indigo for 45 ± 22.5°; and pink for 90 ± 22.5°. Panel (b) shows the electron density, n [cm⁻³], as a function of time. Panels (c–f) show hodograms, where the radial and azimuthal field components, Br and Bϕ [nT], respectively, are plotted for a 30 min interval. The midpoint time of the interval is labeled at the top of each panel. The color of the trace indicates the time from the start (light blue) to the end (dark purple) of the interval. The dashed lines in each panel show the major axis.
Data and model timeseries between 17:55 and 18:55 on 26 August 2015. Panel (a) shows electron density, n [cm⁻³]. Panel (b) shows the range of polarisation dependent modeled frequencies on the spacecraft's field line, and panel (c) shows the power spectral density P [nT² Hz⁻¹] of the transverse magnetic field component. For each time step, the frequency at the maximum in P is marked in pink (for frequencies between 2 and 12 mHz).
Van Allen Probes Observations of a Three‐Dimensional Field Line Resonance at a Plasmaspheric Plume

November 2023

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24 Reads

Field Line Resonances (FLRs) are a critical component in Earth's magnetospheric dynamics, associated with the transfer of energy between Ultra Low Frequency waves and local plasma populations. In this study we investigate how the polarisation of FLRs are impacted by cold plasma density distributions during geomagnetic storms. We present an analysis of Van Allen Probe A observations, where the spacecraft traversed a storm time plasmaspheric plume. We show that the polarisation of the FLR is significantly altered at the sharp azimuthal density gradient of the plume boundary, where the polarisation is intermediate with significant poloidal and toroidal components. These signatures are consistent with magnetohydrodynamic modeling results, providing the first observational evidence of a 3D FLR associated with a plume in Earth's magnetosphere. These results demonstrate the importance of cold plasma in controlling wave dynamics in the magnetosphere, and have important implications for wave‐particle interactions at a range of energies.

(a) Positive phase of the NP4 modes shown in units of correlation (r) between each mode and observed 500 mb geopotential height anomalies. (b) Example showing the forecast mode amplitude for 50 ensemble members out to 30 days lead, where green (yellow) shading indicates when the 70% consensus is reached for the positive (negative) phase, white indicates uncertainty, and the red line gives the observed mode amplitude. (c) Validated WY2022 forecasts of the NP4 mode phase after filtering, where each individual forecast is displayed on a diagonal line, the x‐axis gives the target date from 1 November to 28 February, the y‐axis gives the lead time from 1 to 30 days, red (blue) shading indicates that a mode was correctly forecast to be in the positive (negative) phase, yellow indicates error, and gray represents forecasts classified as uncertain. (d) Observed and forecast AR behavior during WY2022 for different coastal regions, where the top four panels (blue stem plots) show observed daily AR IVT, and the bottom panels show AR forecasts for each region at different lead times. Each forecast is shown on a diagonal line, blue indicates above normal AR probability forecasts (wet), yellow/orange shows low probability forecasts (dry), gray indicates uncertainty (no forecast, NF).
(a) Accuracy of the mode phase forecasts out to 30 days, where purple shows accuracy using the ensemble mean, orange shows accuracy of the filtered forecasts, green shows accuracy of forecasts removed by filtering, the black dashed line gives the 95th percentile of the climatological distribution using random resampling, and gray shading shows the area below the 95th percentile indicating no significant skill. Lines have been smoothed using a 3‐day running mean. Black arrows are discussed in the text. (b–d) Hindcast skill assessment of heat waves in three regions, SAWs in Southern California, and ARs at four coastal latitudes, respectively. The y‐axis shows observed event frequency following three forecast categories: low (blue), above normal (orange), and much above normal (red) probability. The sign of the BSS (±) is shown along the bottom, highlighted in yellow. The gray shaded area in (b–d) gives the 10th–90th percentiles of the resampled distribution over the hindcast period (2001–2020). Red, orange, and blue markers are weighted by the log of the sample size (n = 10 to 451), and filled markers indicate statistically significant skill (90% level using resampling).
Subseasonal Prediction of Impactful California Winter Weather in a Hybrid Dynamical‐Statistical Framework

November 2023

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8 Reads

Plain Language Summary California winter weather can alternate between very wet conditions from atmospheric rivers making landfall along the Pacific coast to hot, dry, and windy conditions brought by Santa Ana winds blowing in from the Southwest interior. Atmospheric rivers are important for water resources while also causing flooding, whereas Santa Ana winds are often associated with wildfire, especially following prolonged dry periods. Preparing for these types of weather events is important for managing resources and protecting life and property, yet reliable forecasts beyond about 7–10 days remain a challenge. We have developed a new prediction system that combines information about approaching atmospheric weather patterns from weather forecast models along with historical information relating those patterns to impacts over California to predict the likelihood of impactful weather at 1–4 weeks lead time. By extending the window of opportunity to take management action, this new approach should aid in resource and emergency planning in water, land, and fire sectors as well as protecting residents through improved warning systems.

Optimized analog forecasting method and interpretable neural network architecture. The analog forecasting method can be described in three steps: (1) identify a state of interest (SOI) and a library of potential analogs. (2) Determine which maps are the most similar. (3) Make a prediction using the best analog(s). In the blue box, we show our weighted‐mask approach for determining the similarity of two maps. The weighted mask is multiplied by the SOI and a potential analog before computing the mean squared error (MSE). In the red box, the interpretable neural network architecture is shown. Two input samples are multiplied by a matrix of trainable weights and the MSE is computed. This MSE is then converted to a predicted difference in the sample targets using a group of fully‐connected dense layers. Note that the weighted mask has the same dimensions as the input field(s), despite the coarser resolution in this figure.
Weighted mask and example for multi‐year predictions of North Atlantic sea surface temperature (SST). (a) Weighted mask, as learned by the interpretable neural network. (b) Standardized SST anomalies for a sample state of interest (SOI). (c) Standardized SST anomalies for the best analog associated with the SOI. (d) Weighted SOI. (e) Weighted best analog.
Analog forecasts of North Atlantic sea surface temperature. (a) Skill scores for our weighted mask analog forecast and other baselines. (b) Weighted mask analog forecasts for 200 years of MPI‐GE simulations, including the mean prediction from the top‐10 analogs, the spread of these predictions, and the truth values.
Weighted mask and skill scores for seasonal predictions of El Niño Southern Oscillation. (a) Weighted mask. (b) Skill scores for our weighted mask analog and other baselines.
Analog forecasting skill of El Niño Southern Oscillation when various regions are occluded or isolated. (a) As in Figure 4a, but the lowest 95 percent of weights are set to zero. Four regions of focus are highlighted by the colored boxes. (b) Skill scores for analog forecasts when each region is occluded from the mask (top) and when the region is isolated to make a forecast (bottom).
Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

November 2023

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13 Reads

Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill.

Monthly sea ice area differences between models and observations averaged from 1991 to 2009. Positive (blue) values indicate positive model biases. Models are ranked by the mean absolute error (MAE) of the 12 months; those above the black dashed line have an MAE less than 2 × 10⁶km² are considered to be in the best‐performing model group.
DISO metrics between the best performing models, as shown in Figure 1, and observations for each sea ice concentration budget component in (a–d) winter and (e–h) spring. Error bars denote one standard deviation. The four models that participated in all the CMIP6 historical, OMIP1 and OMIP2 experiments are marked with colored symbols. DISO is a dimensionless metric, with lower values indicating closer to observed data.
A comparison of the winter (the upper four rows) sea ice concentration budget components, (e1–e4) sea level pressure (SLP) overlaid with the ice drift vectors, (f1) net heat flux, (f2–f4) departures from (f1), and (g1–g4) sea ice thickness (SIT) of the (a1–g1) observations and the CMCC‐CM2‐SR5 (a2–g2) historical (a3–g3) OMIP1 and (a4–g4) OMIP2 experiments. All subplots are averaged over the winter of 1991–2009, except for (g1), the SIT averaged from May and June 2004–2006. (e1–e4) SLP from ERA5, historical experiment, CORE‐II and JRA55‐do. (f1) ERA5 net heat flux, (f2–f4) historical experiment, CORE‐II and JRA55‐do minus (f1). The net heat flux is negative when the surface loses heat to the atmosphere. The ridging areas are enclosed by cyan curves (d1–d4).
The area integral of div as a proportion of total sea ice change versus total sea ice volume for (a) winter and (b) spring. The blue dashed lines are linear regressions for all models, and the CCs and p‐values are marked in blue. The solid black line is the regression after removing the two models with significantly unrealistic sea ice thickness patterns (marked by the black boxes). The gray horizontal line is the percentage contribution of the observed div, the models fall in the gray‐filled region are overestimating sea ice divergence.
Geophysical Research Letters - 2023 - Nie - Differences Between the CMIP5 and CMIP6 Antarctic Sea Ice Concentration Budgets

November 2023

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81 Reads

Plain Language Summary Current state‐of‐the‐art climate models do not reproduce the total area of Antarctic sea ice and its trends as observed. This impedes the use of climate models to understand changes in Antarctic sea ice over the past decades and to project its future. Separating how much of the simulated sea ice change is due to freezing and melting, and how much is due to ice transport allows us to identify physical causes for model biases, which helps us to optimize models. We examined the climate models that have simulated near‐realistic sea ice areas between 1991 and 2009 and found significant improvements in their simulation of sea ice processes in the latest generation of models compared to the previous generation, although there is still much room for further improvement. As sea ice thickness and velocity interact, a key limitation of the state‐of‐the‐art Antarctic sea ice simulation may stem from the general underestimation of ice thickness. This could be a critical issue to be targeted on the way toward increasingly skillful climate projections.

Map of the study area and data set. (a) A topographic map of the DVP and surrounding area shows a plume track (pink shade), compound magma flow (“C”), and simple flow (“S”) (Sen, 2001; Sen & Chandrasekharam, 2011). (b) A map showing 12 broadband stations (red inverted triangles) that operated during 2020–2022 along with major tectonic features and another station ISRP operated during 2018–2022. (c) Selected Receiver Functions (RFs) with varying distance and back azimuth, and (d) Topography and station locations.
(a) Velocity model for the crust. Red circles‐ Moho from H‐κ stacking; white circles‐ Moho from joint inversion. HVL‐ High‐velocity layer interpreted as a densified crust, LVL‐ Low‐velocity layer interpreted as possible fossil magma chamber, LC‐Lower crust (Vs > 3.8–4.0 km/s), UL‐ Underplated layer (Vs > 4.0 km/s above Moho), PF‐Panvel Flexure. (b) In the upper mantle (40–160 km) the velocity variation (%) is presented relative to ak135 (Kennett et al., 1995). LAB‐Lithosphere‐Asthenosphere Boundary. Note three prominent LVLs at 50–60 km and 90–110 km depths.
(a) Comparison of velocity models of the DVP (this study) with that of eastern Dharwar craton (EDC) and western Dharwar craton (WDC) after (Borah et al., 2014), and global model (Christensen, 1996; Rudnick & Gao, 2003). (b) Proportional velocity reduction (Vs/Vs°) versus melt volume fraction. Horizontal lines indicate proportional velocity reductions in the LVL for the region. W: blue line is an analytical relationship of Watanabe (1993) for randomly oriented triangular melt tubes. Ts: Red dashed line is an analytical relationship of Taylor and Singh (2002) for the slow propagation direction in a medium containing perfectly aligned oblate spheroids of aspect ratio 10.
Cryptic Magma Chamber in the Deccan Traps Imaged Using Receiver Functions and Surface Wave Dispersion

November 2023

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40 Reads

Plain Language Summary The Deccan volcanism occurred around 65 Ma ago when India and Seychelles, moving northward, interacted with the Reunion plume, leading to increased temperature and hence melting at the upper mantle. The buoyant magma moved upward and ponded toward the crust‐mantle boundary. When the overlying crust failed due to high overpressure and/or magma buoyancy, the magma ascended via dikes and assimilated into a shallow crust. The process is expected to produce significant crust and upper mantle modifications due to increased heat transfer and chemical transformation during magma ascent. Existing geophysical knowledge of the Deccan traps does not provide the signature of a magma ascent path and evidence for crustal transformations. Using data from a recent 160 km‐long broadband seismological experiment, we construct a detailed model of the crust and uppermost mantle, showing a complete magma plumbing system. The result shows evidence for lithospheric thinning and well‐preserved deformation in the shallow mantle in the form of sulfide melt, magma ponding at the crust‐mantle boundary beneath the coastal basin, an extensive low‐velocity layer in the upper/mid crust possibly representing the horizontally elongated frozen magma reservoir, and a densified high‐velocity layer in the shallow crust (4–8 km depth) representing basaltic mafic intrusions.

Walker circulation intensification for 1971–2013. (a, b) Linear trends in SLP (shading) and surface winds (vector) for the Japanese 55‐Year Reanalysis (JRA55) data (Kobayashi et al., 2015) and the ensemble mean of the MIROC6 AMIP experiment. The stippling denotes that the SLP trends are statistically significant at the 95% level. The wind trends are plotted over the Pacific, with those significant at the 95% level in black and the 90% level in gray. (c) Time series of the Walker index in JRA55 (blue) and the AMIP (red), with the linear trends by dashed lines. The positive value indicates strengthening in the Pacific Walker circulation. The ensemble range (maximum and minimum) is shown by shading. The correlation coefficient between the time series is indicated in the panel.
Attribution of the Pacific Walker circulation trend to the SST pattern and uniform warming. (a) Linear trend of the Walker index in the AMIP‐trend experiments (red) and the difference from the trend in AMIP (blue). The trend in AMIP+4K (purple) is shown for comparison. The error bar represents the maximum‐minimum range. The orange dot with the gray line indicates the mean and the standard deviation of the trends from the CMIP6 multi models. (b) As in (a) but for the trend in the global hydrological budgets from the AMIP‐trend experiments: ΔM (blue), ΔP‐Δq (light green), and ΔP‐αΔT (green), all presented as a fractional change against their climatological mean. We used precipitable water as q whereas the convective mass flux derived from parameterization schemes and integrated for 925–700 hPa as M. (c) Scatter plot between the 35 years trends in the mass flux (ΔM) and the Walker index (ΔW), respectively. Their correlation coefficient, regression line, and sensitivity to warming are shown in the panel.
Trend patterns induced by the SST trend pattern and uniform warming. (a) Linear trends for 1971–2013 in precipitation (shading) and surface winds (vector) in Global Precipitation Climatology Project (GPCP) observation (Adler et al., 2018) and the JRA55, and (b) as in (a) but for the ensemble mean of the AMIP experiment. The precipitation trends statistically significant at the 95% level are stippled. The wind trends are plotted for the magnitude greater than 1 m s⁻¹ in JRA55 and 0.5 m s⁻¹ per 35 years in the AMIP. (c, d) As in (b) but for the trends in the AMIP+4Ktrend and their difference from the AMIP.
Attribution of the Pacific Walker circulation trend to the regional SST trend. (a) Linear trend of the Walker index in the AMIP experiment (left) and the trend due to direct radiative forcing (RF), SST trend in the equatorial band (EQ), Northern Hemisphere (NH), Sothern Hemisphere (SH), and their sum. The fractional percentage of each contribution is shown by numbers. The orange dot with the gray line indicates the trend due to RF obtained from AMIP‐piForcing experiments by 8 models. (b) As in (a) but for the attribution of the Walker index trend in the AMIP‐EQtrend into the trend due to the Indian Ocean (IND), western Pacific (WP), eastern Pacific (EP), and Atlantic (ATL) SST trends. See Section 4 for the description of the experiments.
Green's function of the Pacific Walker circulation (PWC) to local SST warming. Response of the annual‐mean Walker index to a unit of SST warming at each grid box, obtained from the CAM4 Green's function experiment. The units are Pa K⁻¹. Positive values indicate that the PWC strengthens in response to an SST increase there and vice versa for negative values.
Two Competing Drivers of the Recent Walker Circulation Trend

November 2023

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55 Reads

Plain Language Summary The large‐scale overturning circulation in the tropical Pacific known as the Pacific Walker circulation is the heart of the general circulation of the atmosphere. Most of the future climate projections by climate models suggest that the Walker circulation weakens with surface warming. However, observations show that the circulation has strengthened over the past decades despite ongoing global warming. We show, using atmosphere model simulations for 1979–2013 that the Walker circulation strengthening is well reproduced and it weakens when a hypothetical uniform surface warming trend was imposed on the sea surface temperature (SST) data that force the model. The weakening of the Walker circulation occurs at a rate of about 8% per degree warming, but this effect cannot overcome the SST pattern effect that intensifies the circulation. Further attribution simulations show that the past strengthening is explained directly by the SST warming pattern in the narrow equatorial band, about one‐third of which is induced by the warming of the Indian Ocean.

(a) Study region with seismic stations (red inverted triangles) used in this study. Yellow triangles show the location of volcanoes (Syracuse & Abers, 2006). The converging direction (N76°E) of the Nazca plate toward the South America plate is shown in an orange arrow (Norabuena et al., 1998). (b) The navy inverted triangles show shear wave splitting (SWS) stations. Colored bars on the stations show the observed SWS results of Deng et al. (2017) (blue), Eakin et al. (2014, 2015) (orange), Long et al. (2016) (yellow), Lynner and Beck (2020) (purple), Polet et al. (2000) (green), and Reiss et al. (2018) (cyan). (c) Our inferred SWS for the stations used in this study as shown in (a) (red inverted triangle). The black solid lines labeled as a‐a′ and b‐b′ are the cross‐sectional lines that are used in Figure 2. For (b) and (c), the length of the light blue bar represents the SWS time while its orientation represents the fast polarization direction of the inferred SWS.
(a) Isotropic and (b) prescribed anisotropy tomography slices at 160 km showing targeted sub‐slab low‐velocity anomalies beneath the Nazca Ridge denoted as “A.” Cross‐sectional views of (c) isotropic and (d) prescribed anisotropy tomographies along the profile a‐a′ in (a). High‐velocity anomalies denoted as “C” in only (d) show possible artifact velocity anomalies from considering inappropriate anisotropy structure for sub‐slab low‐velocity anomalies beneath the Nazca Ridge. (e) Isotropic and (f) prescribed anisotropy tomography slices at 310 km showing targeted sub‐slab low‐velocity anomalies beneath the Iquique Ridge denoted as “B.” Cross‐sectional views of (g) isotropic and (h) prescribed anisotropy tomographies along the b‐b′ in (e). The thick dashed gray line in (a), (b), (e), and (f) shows the trench while thin dashed gray lines show the iso‐depth contours of the subducted Nazca Plate by Scire et al. (2016). The solid black line in (c), (d), (g), and (h) shows the slab contour suggested by Slab2 of Hayes et al. (2018).
Estimated mantle temperature for the sub‐slab low‐velocity anomalies of (a) isotropic and (b) prescribed anisotropy tomographies beneath the Nazca Ridge based on the 160‐km‐depth slice (same temperature color bar); (c) isotropic and (d) prescribed anisotropy tomographies beneath the Iquique Ridge based on the 310‐km‐depth slice (same temperature color bar). The temperature contour is indicated in black. (e) A schematic view of inferred mantle structure for sub‐slab low‐velocity anomalies beneath the Nazca Plate. The blue structure represents high‐velocity anomalies that may show slab structure while the orange structure represents low‐velocity anomalies. The navy solid and dashed lines on the topography present coastal and trench lines, respectively. Our study suggests that subslab low‐velocity anomalies found beneath the Nazca Ridge and Iquique Ridge come from warm temperatures and anisotropic mantle, respectively.
The Role of Subslab Low‐Velocity Anomalies Beneath the Nazca Ridge and Iquique Ridge on the Nazca Plate and Their Possible Contribution to the Subduction Angle

November 2023

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35 Reads

Plain Language Summary Understanding the subduction process and how the mantle flows is pivotal in understanding the planetary evolution of Earth. Yet, several subduction characteristics remain unsolved, and the angle of plate subduction, which is often categorized into <30° (shallow), ∼30–35° (normal), >35° (steep), is one of those. Subduction of an oceanic ridge on a plate has been proposed as a cause of a shallow subduction angle since the thick crust of the ridge is less dense than the surrounding mantle. However, normal angles have been observed in some cases where oceanic ridges are subducting. In this study, we compare the Nazca Ridge and the Iquique Ridge, on the Nazca Plate subducting beneath South America. The subduction angles of the Nazca and Iquique Ridges are shallow and normal, respectively. When we incorporate directional variations in seismic wave velocities, which are produced by mantle flow, in seismic tomographic imaging, we find that the subducting oceanic ridge may not be a primary factor producing shallow angle subduction. Instead, the warm mantle beneath the Nazca Ridge may provide the buoyancy to support the Nazca Plate. Comparably, since the mantle beneath the Iquique Ridge is not warm, the subduction angle would stay normal.