Application of apatite (U-Th)/He thermochronology has been hindered by incomplete understanding of single-grain age dispersion often displayed by samples, particularly those from older, slowly cooled settings. To assess the capability of continuous ramped heating (CRH) to explain dispersion, we performed a study on an apatite suite from Cathedral Rocks in the Transantarctic Mountains (TAM) that have high age dispersion. Examining 132 apatite grains from a total of six samples, we confirmed earlier apatite (U-Th)/He results showing that measured AHe ages have at least three-fold intra-sample dispersion with no obvious relationships between ages and effective uranium concentration (eU) or grain size. CRH results on these apatites yielded two groups. Those with younger ages, characterized by single-peak incremental 4He gas-release curves, displayed simple volume diffusion behavior. In contrast, grains with older ages generally show anomalous gas release in the form of sharp spikes and / or extended gas-release at high temperatures (i.e., >= 800 °C). Well-behaved apatites still show considerable age dispersion that exceeds what grain size, radiation damage, and analytical uncertainty can explain, but this dispersion appears to be related to variations in 4He diffusion kinetics. The screened AHe ages from well-behaved younger apatite grains together with kinetic information from these grains suggest that the sampled region experienced slow cooling prior to rapid cooling (rock exhumation) beginning ca. 35 Ma. This interpretation is consistent with other studies indicative of an increase in exhumation rates at this time, possibly related to the initiation of glaciation at the Eocene-Oligocene climate transition. An attempt to correct anomalous older apatite ages by simply removing extraneous gas-release components is proposed yielded some ages that are too young for the samples’ geologic setting, suggesting that the factors that lead to anomalous laboratory release behavior can impact both the expected radiogenic component as well as those that are extraneous. From our observations we conclude that: (1) CRH analysis can serve as a routine screening tool for AHe dating and offers opportunities to reveal first-order kinetic variations; (2) model-dependent age correction may be possible but would require some means of estimating the broad proportions of 4He components incorporated into grains before and after closure to diffusion, and (3) interpretation of highly dispersed AHe ages requires assessment of individual-grain diffusion kinetics beyond that predicted by radiation-damage models. We also infer that many apatite grains contain imperfections of varying kinds that contribute significantly to kinetic variability beyond that associated with radiation damage.

The lithosphere-asthenosphere system is fundamental to our understanding of mantle convection and plate tectonics. Seismic and electromagnetic methods are our primary means of determining its structure and physical properties. These independent constraints with different sensitivities to Earth’s properties hold promise for understanding the system. Here we use the shear velocity model from Rayleigh waves and the MT based resistivity model from near the equatorial Mid-Atlantic Ridge. Cross-plots of the models suggest a linear or near-linear trend that is also in agreement with petrophysical predictions. We therefore map the MT model to a new shear-wave starting model using the petrophysical relationship, which is then used to re-invert for shear-wave velocity. The resulting shear-wave velocity model fits the phase velocity data, and the correlation coefficient between the shear velocity and resistivity models is increased. Much of the model can be predicted by expectations for a thermal half-space cooling model, although some regions require a combination of higher temperatures, volatiles, or partial melt. We use the petrophysical predictions to estimate the melt fraction, melt volatile content, and temperature structure of the asthenospheric anomalies. We find up to 4% melt, with the lowest resistivities and shear velocities explained by up to 20% water or 20% CO2 in the melt or ~1% nearly pure sulfide melt, depending on the set of assumptions used. Melt is required in punctuated anomalies over broad depth ranges, and also in channels at the base of the lithosphere. Melt in the asthenosphere is dynamic, yet persistent on geologic time scales.

In this paper, we present a novel approach to improve the accuracy of TEC prediction through data augmentation. Prior works that adopt various deep-learning-based approaches suffer from two major problems. First, from a deep model perspective: LSTM models exhibit low performance on long-term data dependency, while self-attention-based methods ignore the temporal nature of time series, which results in an information utilization bottleneck. Second, the existing TEC actual data is limited and existing generative models fail to generate sufficient high-quality datasets. Our work leverages a two-stage deep learning framework for TEC prediction, stage 1: a time series generative model synthesis of sufficient data close to real data distribution, and stage 2: an Anto-correlation-based transformer to model temporal dependencies by presenting series-wise connections. Experiment on the 2018 TEC testing benchmark demonstrates that our method improves the accuracy by a large margin. The models trained on synthetic data had a notably lower RMSE of 1.17 TECU, while the RMSE for the IRI2016 model was 2.88 TECU. Our results show that the model significantly reduces monthly RMSE, displaying higher reliability in mid, high, low latitudes. Our model shows higher reliability and significantly reduces monthly RMSE and latitude RMSE. However, although our model performs better than IRI2016, low latitudes RMSE needs improvement, as values are generally above 2.5 TECU. This finding has important implications for the development of advanced TEC prediction models and highlights the potential of transformer models trained on synthetic data for a range of applications in ionospheric research and satellite communication systems.

The WIVERN (WInd VElocity Radar Nephoscope) mission, currently under the Phase-0 of the ESA Earth Explorer program, promises to provide new insight in the coupling between winds and microphysics by globally observing, for the first time, vertical profiles of horizontal winds in cloudy areas. The objective of this work is to explore the potential of the WIVERN conically scanning Doppler 94GHz radar for filling the wind observation gap inside tropical cyclones. To this aim, realistic WIVERN notional observations of TCs are produced by combining the CloudSat 94GHz radar reflectivity observations from 2007 to 2009 with ECMWF co-located winds. Despite the short wavelength of the radar (3mm), which causes strong attenuation in presence of large amount of liquid hydrometeors, the system can profile most of the tropical cyclones, particularly the cloudy areas above the freezing level and the precipitating stratiform regions. The statistical analysis of the results shows that, (i) because of its lower sensitivity, a nadir pointing WIVERN would detect 80% of the clouds (60% of winds with 3m/s accuracy) observed by CloudSat, (ii) but thanks to its scanning capability, WIVERN would actually provide 52 times more observations of clouds (40~times more observations of horizontal winds) than CloudSat in TCs, (iii) this corresponds to more than 400 (300) million observations of clouds (accurate winds) every year. Such observations could be used in data assimilation models in order to improve numerical weather prediction and by modellers in order to shed light on the physical processes underpinning the evolution of tropical cyclones.

Floods are among the most destructive natural hazards on Earth. In paleohydrology, sediments are generally considered as one of the best archives to extend flood records to pre-historical timescales. Doing so requires being able to identify flood deposits from sediment archives and decipher between flood types. The latter is particularly important in glacierized regions, where meteorological floods frequently co-occur with Glacial Lake Outburst Floods (GLOFs). In Patagonia, GLOFs are particularly pronounced in the Baker River watershed (48°S), where 23 events occurred between 2008 and 2020. Since 1976, the same region experienced three intense rain-on-snow events. To identify the sedimentary signature of these flood events, ten sediment cores collected in the fjord immediately downstream of the Baker River (Martínez Channel) were investigated and compared to the recent flood history of the river. Results show that sediments accumulate on the fjord head delta at 2.0 to 3.4 cm yr-1 and that GLOF deposits can be distinguished from background sediments by their finer grain size (5.98 ± 0.82 μm) and lower organic carbon content (0.31 ± 0.06%), reflecting the release and transport in suspension of high amounts of glacial rock flour. Our results also show that the rain-on-snow events that occur in summer, and therefore primarily affect the glacierized part of the watershed, have the same sedimentary signature as GLOFs. In contrast, rain-on-snow events occurring in winter have a distinct coarse and organic-rich signature, reflecting sediment input from the non-glacierized part of the watershed. In summary, this study shows that (a) GLOF deposits in fjord sediments are distinct from typical flood turbidites and are best identified by their low grain size and total organic carbon content, and (b) the sedimentary signature of rain-on-snow floods in partially glacierized watersheds depends on the season during which they occur. We anticipate that our findings will contribute to a better interpretation of flood records from partially glacierized watersheds.

On 13 November 2017, a strong continental-scale Saharan dust outbreak was observed in satellite imagery over Mindelo, Cape Verde, located about 650 km off the coast of Senegal in West Africa. Horizontal visibility was reduced to 1100 m leading to major disruptions of the local air traffic. Dust mobilization was already observed over the foothills of the Saharan Atlas Mountains at 0600 UTC on 10 November 2019 but did not appear clearly in SEVIRI pink dust images in the subsequent days. In this study, we examined the multi-scale dynamical processes associated with this particular dust storm using ECMWF ERA-Interim reanalysis data, newly performed very-high resolution WRF-CHEM simulations with horizontal grids of 18 km and less, NAAPS aerosol forecast output, ship-based observation dataset from the North Atlantic Expedition MSM 68/2, as well as surface observations, and upper-air soundings from weather stations in North Africa. Our analyses of this storm highlights the following meteorological processes: (1) the event was associated with a typical Harmattan surge, i.e., the post-frontal strengthening of the northerly winds behind an eastward moving cyclone, (2) a series of earlier Rossby Wave Breaking events (RWBs) made the environment favorable for the Harmattan surge, (3) the dust storm was composed of two distinct dust surges, (4) the dust aerosol from the first surge was later mixed with the dust from the second surge while simultaneously propagating south-westward and later westward, (5) the PBL became adiabatic along the leading edge of the leftover cold front between the southern/southeastern flank of the Atlas Mountains and western/northwestern flank of the Hoggar Mountains, and (6) vertical dust mixing then occurred due to very strong surface heating associated with the development of a deep daytime PBL in the region behind the cold front. The results of this research demonstrate that very-high spatial resolution WRF-CHEM model can resolve the dynamical processes and realistically simulate large-scale North African dust storm.

Weather and climate forecast predictability relies on Land-Atmosphere (L-A) interactions occurring at different time scales. However, evaluation of L-A coupling parameterizations in current land surface models (LSMs) is challenging since the physical processes are complex, and large-scale observations are scarce and uncommon. Recent advancements in satellite observations, in this light, provide a unique opportunity to evaluate the models’ performances at large spatial scales. Using 5-year soil moisture memory (SMM) from Soil Moisture Active and Passive (SMAP) observations, we evaluate L-A coupling performances in 4 prevailing LSMs with both coupled and offline simulations. Multi-model mean comparison at the global scale shows that current LSMs tend to overestimate SMM that is controlled by water-limited processes and vice versa. Large model spreads in SMM are also observed between individual models. The SMM biases are highly dependent on models’ parameterizations, while showing minor relevance to the models’ soil layer depths or the models’ online/offline simulating schemes. Further analyses of two important terrestrial water cycle-related variables indicate current LSMs may underestimate soil moisture that is directly available for evapotranspiration and global flood risks. Finally, a comparison of two soil moisture thresholds indicates that the soil parameters employed in LSMs play an essential role in producing the model’s biases. The satellite estimation of ET at the water-limited stage and soil hydraulic parameters provides readily available information to constrain LSMs, which are essentially important to improve the models’ L-A coupling simulations, as well as other land surface processes such as terrestrial hydrological cycles.

Quantifying mixing rates in the Arctic Ocean is critical to our ability to predict heat flux, freshwater distribution, and circulation. However, turbulence measurements in the Arctic are sparse, and cannot characterize the high spatiotemporal variability typical of ocean mixing. Using year-round temperature and salinity data from Ice-Tethered Profiler (ITP) instruments between 2004 and 2018, we apply a finescale parameterization to obtain pan-Arctic estimates of turbulent dissipation and mixing rates at unprecedented space-time resolution. Building on previous work that used ITP data to identify double-diffusive staircases and analyze the associated convective mixing, we apply the finescale parameterization only where these step-like thermohaline structures are not present and mixing is expected to be internal wave-dominated. We find that the inferred wave-driven dissipation and mixing rates are generally low, but highly variable in both space and time, displaying significant regional differences between the shelves and central basins, as well as a small seasonal cycle. We detect no statistically significant interannual trend in mixing rate estimates over the period examined, with the exception of a small increase in the Canada Basin immediately below the mixed layer. The joint consideration of turbulent dissipation rates and stratification imply varied Arctic Ocean mixing regimes, which are most often not appropriately characterized as isotropic turbulence. Where justified, we infer turbulent heat fluxes out of the Atlantic Water layer that are mostly small, but also exhibit a distinct regional dependence.

Agronomic technological advancements provide more precise means to establish methodologies that can estimate yield response in many different ways. We are experimenting with a new image-based technique to predict the yield response of Canola using the rate of ground cover accumulation. Our trial was composed of row spacing and seeding rate as factors that influence the growth and the spatial distribution to evaluate its influence on the yield. Using the Visible Band Difference Vegetation Index (VDVI) from digital images, we estimated the ground cover and modelled the change over time. We regressed ground cover accumulation and integrated the function to calculate the area under the curve to regress against yield. Preliminary analysis indicates that the green ground cover accumulation overtime is sufficiently correlated with the yield (F=168.1, p=2.2e-16, R2=0.4694). Further, our results suggest the amount of green ground cover accumulation over time is dependent on the seeding density and row spacing. The analysis shows the higher seeding densities, 40 plants/m2 and above, acquire biomass rapidly, and the most stable yield predictions with ground cover are likely reached at similar plant densities. The most stable yield predictions in-relation to row spacing obtained from either 0.3m, 0.45m or 0.6m spacing (R-squares 0.94, 0.93, and 0.89, respectively). We are further experimenting to understand what growth period of the crop is most suitable for ground cover based yield predictions. Our primary target is to develop a high throughput image-based methodology to estimate the yield response using the ground cover accumulation rate for on-farm precision agronomy.

Inverse problems are ubiquitous in hydrological modelling for parameter estimation, system understanding, sustainable water resources management, and the operation of digital twins. While statistical inversion is especially popular, its sampling-based nature often inhibits its application to computationally costly models, which has compromised the use of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, e.g., for spatially distributed (partial) differential equation based models. In this study we introduce multilevel GLUE (MLGLUE), which alleviates the computational burden of statistical inversion by utilizing a hierarchy of model resolutions. Inspired by multilevel Monte Carlo, most parameter samples are evaluated on lower levels with computationally cheap low-resolution models and only samples associated with a likelihood above a certain threshold are subsequently passed to higher levels with costly high-resolution models for evaluation. Inferences are made at the level of the highest-resolution model but substantial computational savings are achieved by discarding samples with low likelihood already on levels with low resolution and low computational cost. Two example inverse problems, using a rainfall-runoff model and groundwater flow model, demonstrate the substantially increased computational efficiency of MLGLUE compared to GLUE as well as the similarity of inversion results. Findings are furthermore compared to inversion results from Markov-chain Monte Carlo (MCMC) and multilevel delayed acceptance MCMC, a corresponding multilevel variant, to compare the effects of the multilevel extension. All examples demonstrate the wide-range suitability of the approach and include guidelines for practical applications.

While the Madden Julian Oscillation (MJO) is known to influence the midlatitude circulation and its predictability on subseasonal-to-seasonal (S2S) timescales, little is known how this connection may change with anthropogenic warming. This study investigates changes in the causal pathways between the MJO and the North Atlantic Oscillation (NAO) within historical and SSP585 simulations of the CESM2-WACCM coupled climate model. Two data-driven approaches are employed, namely, the STRIPES index and graphical causal models. These approaches collectively indicate that the MJO’s influence on the North Atlantic strengthens in the future, consistent with an extended jet-stream. In addition, the graphical causal models allow us to distinguish the causal pathways associated with the teleconnections. While both a stratospheric and tropospheric pathway connect the MJO to the North Atlantic in CESM2-WACCM, the strengthening of the MJO-NAO causal connection over the 21st century is shown to be due exclusively to teleconnections via the tropospheric pathway.

The Angstrom-Prescott (A-P) model is widely suggested for estimating solar radiation (Rs) in areas without measured or deficiency of data. The coefficients of this model must be locally calibrated, to calculate evapotranspiration (ET) correctly. The aim of this research was calibration and validation of the coefficients of the A-P model at six meteorological stations across arid and semi-arid regions of Iran. This model was improved by adding the air temperature and relative humidity terms. Besides, the coefficients (’a’ and ‘b’) of the A-P model and improved models was calibrated using some optimization algorithms including Harmony Search (HS) and Shuffled Complex Evolution (SCE). Performance indices, i.e., Root Mean Square Error (RMSE), Mean Bias Error (MBE), and coefficient of determination (R2) were used to analyze the models ability in estimating Rs. The results indicated that the performance of the A-P model had more precision and less error than improved models in all the stations. In addition, the best results were obtained for the A-P model with the SCE algorithm. The RMSE varies between 0.82 and 2.67 MJ m-2 day-1 for the A-P model with the SCE algorithm in the calibration phase. In the SCE algorithm, the values of RMSE had decreased about 4% and 7% for Mashhad and Kerman stations in the calibration phase compared to the HS algorithm, respectively. In other words, the highest decrease of RMSE is related to Kerman station.

Rn and CO2 in-depth, as a proxy for pre-seismic activity Hovav Zafrir1,4, Uri Malik1, Elad Levintal2, Noam Weisbrod2, Yochai Ben Horin3, Zeev Zalevsky4, Nimrod Inbar5 1Geological Survey of Israel, 32 Yesha’ayahu Leibowitz, Jerusalem 9371234, Israel, 2The Zuckerberg Institute for Water Research, Ben-Gurion University, 8499000 Sede Boqer, Israel, 3Soreq Nuclear Research Center, Yavne 81800, Israel, 4Faculty of Engineering, Bar Ilan University, Ramat-Gan 52900, Israel, 5Ariel University, Ariel 40700, Israel. (First author e-mail: [email protected]; [email protected]). Abstract The method of long-term monitoring of subsurface gases in shallow to deep boreholes assumes that the climatic influence on geo-physicochemical parameters is limited since its energy decreases with the increase in the thickness of the geological cover. Hence, the monitoring of radon (Rn), CO2 and other constituents above and below the water table in deep boreholes enables to eliminate the climatic-induced periodic contributions, from the residual portion of the signals that are associated with the regional geodynamic processes, as have been proved by us recently for radon(*). Monitoring of radon and CO2 at a depth of several tens of meters along the Dead Sea Fault Zone, between the Dead Sea and the Hula Valley has led to a clear discovery of the phenomenon that both gases are affected by an underground tectonic activity related to the pre-seismic processes of producing earthquakes, even if they are weak. The pre-seismic processes even if not all end with earthquakes, cause the movement of gases in the subsurface geologic media and creating non-periodic signals that are wider than 20 to 24 hours. Hence, monitoring of any other natural gas at depth may show a similar expansion signal and may serve as a precursor for earthquakes. The necessary conditions needed to explore anomalous signals of gases that induced by pre-seismic processes at the depth, as accumulation and relaxation of lithospheric stress and strain, are: a) setup of a monitoring system within boreholes airspace, drilled to active faults, b) verify that there is at least one gas with concentration level few times above the conventional background level of the regional subsurface content, c) utilizing high sensitive detectors to recover changes in the gas content, with detection limit of few percents of the local average (As an example: for radon, the required content is at least 1kBq/m3 and the required sensitivity is better than 5%). (*) Zafrir, H., Ben Horin Y., Malik, U., Chemo, C., and Zalevsky, Z., 2016, Novel determination of radon-222 velocity in deep subsurface rocks and the feasibility to using radon as an earthquake precursor, J. Geophys. Res. Solid Earth, 121, 6346–6364, doi: 10.1002/2016JB013033.

Wind and solar energy technologies are, by their nature, variable. Variations in resource availability, based on weather patterns, occur on intra-day to inter-annual time scales. Many energy system models optimize over a single year of input weather and electricity demand data. Energy system planners need increased understanding of the variability in generation potential across multiple years and how this could impact model results. A system achieving 100% reliability modeled using Year A data will not necessarily achieve 100% reliability when applied to Year B data unless an overbuild safety margin is added. We demonstrate: 1) model results can vary significantly based on the year of data used, 2) adding wind and solar does not necessarily reduce the predictability of meeting reliability targets year-to-year and can improve predictability in many cases, and 3) we illustrate a method to derive safety margins to predictably meet 100% reliability year after year and find the least-cost option.

In organic soils the availability of electron acceptors determines the ratio of CO2 to CH4 formation under anoxic conditions. While typically only inorganic electron acceptors are considered, the importance of electron accepting capacities of organic matter is increasingly acknowledged. Redox properties of organic matter are yet only investigated for a limited set of peat and reference materials. Therefore, we incubated 60 peat samples of 15 sites located in five major peatland regions covering a variety of both bog and fen samples and characterized their capacities to serve as electron acceptor for anaerobic CO2 production. We quantified CO2 and CH4 formation, and changes in available EAC in anoxic incubations of 56 days. On the time scale of our experiment, on average 36.5 % of CO2 could be attributed to CH4 formation, assuming an CO2/CH4 ratio for methanogenesis of 1:1. Regarding the remaining CO2 formed, for which a corresponding electron acceptor would be needed, we could on average explain 70.8 % by corresponding consumption of EAC from both organic and inorganic electron acceptors, the latter contributing typically less than 0.1 %. When the initial EAC was high, CO2 formation from apparent consumption of EAC was high and outweighed CO2 formation from methanogenesis. A rapid depletion of available EAC resulted in a higher share of CO2 from CH4 formation. Our study demonstrates that EAC provides the most important redox buffer for competitive suppression of CH4 formation in peat soils. Moreover, electron budgets including EAC of organic matter could largely explain anaerobic CO2 production.

Surface waves on Earth’s magnetopause have a controlling effect upon global magnetospheric dynamics. Since spacecraft provide sparse in situ observation points, remote sensing these modes using ground-based instruments in the polar regions is desirable. However, many open conceptual questions on the expected signatures remain. Therefore, we provide predictions of key qualitative features expected in auroral, ionospheric, and ground magnetic observations through both magnetohydrodynamic theory and a global coupled magnetosphere-ionosphere simulation of a magnetopause surface eigenmode. These show monochromatic oscillatory field-aligned currents, due to both the surface mode and its non-resonant Alfvén coupling, are present throughout the magnetosphere. The currents peak in amplitude at the equatorward edge of the magnetopause boundary layer, not the open-closed boundary as previously thought. They also exhibit slow poleward phase motion rather than being purely evanescent. We suggest the upward field-aligned current perturbations may result in periodic auroral brightenings. In the ionosphere, convection vortices circulate the poleward moving field-aligned current structures. Finally, surface mode signals are predicted in the ground magnetic field, with ionospheric Hall currents rotating perturbations by approximately (but not exactly) 90º compared to the magnetosphere. Thus typical dayside magnetopause surface modes should be strongest in the East-West ground magnetic field component. Overall, all ground-based signatures of the magnetopause surface mode are predicted to have the same frequency across L-shells, amplitudes that maximise near the magnetopause’s equatorward edge, and larger latitudinal scales than for field line resonance. Implications in terms of ionospheric Joule heating and geomagnetically induced currents are discussed.

For catchment-scale streamflow estimation, recently, the preference is shifting towards the use of innovative remote sensing (RS)-based approaches at the remote gauging stations. In this context, this study evaluates the performances of three RS-based models designed with the near-infrared (NIR) bands of Landsat images at the Jenapur outlet of the Brahmani River basin in eastern India. These RS-based models are designed using the spectral behavior of land (C) and water (M) pixels in the NIR region of the electromagnetic spectrum in the presence and absence of water surrounding the streamflow gauging station. Further, the computed pixel ratio (C/M) is used as a parameter for the discharge estimation, in which four years (2009-2013) of Landsat images are used during calibration and three years (2014-2016) of these images are used during validation. Model-I uses the C/M method in which a box-matrix is conceptualized to analyze the optimal location of the land pixel (C0); and subsequently, the time series of C/M is calibrated with the in-situ discharge (Q) time series. The best pixel ratio (C0/M) time series is preprocessed with an exponential smoothing filter to derive the best filtered-pixel ratio (C0/M*) time series, which is used in the regression model to estimate the river discharge. Model-II corresponds to the multi-pixel ratio (MPR) method, where a 3×3 window is used to calculate the average reflectance of both the C and M pixels within the box-matrix, and subsequently, to obtain the best pixel (C0ʹ/Mʹ) ratio as in the case of Model-I to develop the spectral relationship between C0ʹ/Mʹ and Q time series. Model-III uses both the C/M and water width-based function to estimate the streamflow. The performance evaluation of the models is carried out using the Nash-Sutcliffe efficiency, Percentage bias, and Mean absolute error, which reveals that the model performance varies in the order: Model-III > Model-II > Model-I. This proposed RS-based discharge estimation model framework has the potential to be used in many world- rivers with varying cross-sections.

We compute stress drops for earthquakes in Northern Chile recorded between 2007to 2021. By applying two different analysis techniques, (1) the spectral ratio (SR) method and (2) the spectral decomposition (SDC) method, a stress drop map for the subduction zone consisting of 51,510 stress drop values is produced. We build an extended set of empirical Green’s function (EGF) events for the SR method by systematic template matching. Outputs are used to compare with results from the SDC approach, where we apply cell-wise obtained global EGF’s to compensate for the structural heterogeneity of the subduction zone. We find a good consistency of results of the two methods. The increased spatial coverage and quantity of stress drop estimates from the SDC method facilitate a consistent stress drop mapping of the different seismo-tectonic domains. Albeit differences of median stress drops are relatively small, strike-perpendicular depth sections clearly reveal systematic variations, with earthquakes within the upper plate, along the interface and at intermediate depth exhibiting distinct values. In particular, interface seismicity is characterized by the lowest observed median value, whereas upper plate earthquakes show noticeably higher stress drop values. Intermediate depth earthquakes show comparatively high average stress drop and a rather strong depth-dependent in-crease of median stress drop.  Additionally, we observe spatio-temporal variability of stress drops related to the occurrence of the two megathrust earthquakes in the study region. The here presented study is the first coherent large scale 3D stress drop mapping of the Northern Chilean subduction zone. It provides an important component for further detailed analysis of the physics of earthquake ruptures.