Carbon capture and storage (CCS) is forecast to play a significant role towards CO2 emissions reduction. Cost-effective and simplified monitoring will be essential for rapid adoption and growth of CCS. Seismic imaging methods are regularly utilized to monitor low-velocity anomalies generated by injection of CO2 in the subsurface. In this study we generate active and passive synthetic seismic datasets at different stages of CO2 injection in the subsurface based on geologically constrained subsurface models of the Pelican storage site in the Gippsland Basin, Australia. We apply full waveform inversion (FWI) and wave-equation dispersion (WD) inversion to seafloor deployed distributed acoustic sensing (DAS) data to reconstruct the low-velocity anomalies. We model both strain (DAS) and displacement datasets for the active data component of the study and show that they result in similar reconstruction of the CO2 anomaly. FWI based time-lapse imaging of active data yields the most accurate results. However, this approach is expensive and also suffers from complex issues because of the near-onshore location of the storage site. Alternatively inverting passive data results in only minor differences, but can still effectively monitor changes in the subsurface, and assist in monitoring the CO2 plume at the reservoir depth. Furthermore, we demonstrate the capability of WD for inverting Scholte-waves derived from ambient noise for shallow detection of CO2 in the unlikely event of a leakage. Therefore, we propose a mixed mode monitoring strategy where passive data is utilised for routine monitoring while active surveys are deployed only when further investigation is required.

The region of northern Borneo in South East Asia sits within a post-subduction setting formed by the recent termination of two sequential but opposed subduction systems. In this study we use seismic data from a recent temporary array deployment to image the crustal velocity structure beneath northern Borneo using a two-stage Bayesian trans-dimensional tomography scheme, in which period dependent phase velocity maps are first generated, and then used to build a 3-D shear wave model through a series of 1-D inversions. In the second stage, we also apply an Artificial Neural Network to solve the 1D inverse problem, which results in a smoother 3-D model compared to the TransD approach without sacrificing data fit. Our shear wave velocity model reveals a complex crustal structure. Under the Crocker Range, a heterogeneous velocity structure likely represents remnants of early Miocene subduction, including underthrust continental crust from subsequent continent-continent collision. In the east we observe high velocities that are interpreted to be igneous rocks in the crust generated by melting due to mid Miocene Celebes Sea subduction and later decompression melting as well as a low velocity zone that could represent underthrust sediment or duplexes from Celebes Sea subduction. A low velocity zone in the lower crust is present in a region of apparent crustal thinning. Our preferred explanation for this anomaly is remnant thermal upwelling within a failed rift that represents the on-shore continuation of the extension of the Sulu Sea, most likely caused by rollback of the Celebes Sea slab.

Crustal perturbations related to seismic activity can generally be observed with the occurrence of a large magnitude event. For less energetic seismic sequences though, the associated transient crustal variations are questionably measurable, and their observation gets easily obscured by relatively stronger perturbations such as the ones related to hydrological processes. In this study we reveal the significant role that terrestrial water-storage variations play in governing temporal crustal changes in the tectonically active Northern Apennines of Italy, and discuss the potential of accounting for its correction in order to monitor the relatively weaker transient perturbations caused by local seismic swarms. This area is characterized by an extensive level of low-energetic seismic activity, typically clustered in time and space, of which three main seismic swarms outstand during the 12-year period of study (2010-2021). Our analysis compares independent observations and processing methods of GNSS measurements and ambient seismic noise recordings. We adopt a multivariate statistical approach to discriminate between independent sources of ground deformation, and seismic noise cross-correlation analysis to monitor relative seismic-velocity variations. The result shows how the perturbation effects produced by variations in total water content are dominant in both time series of ground deformations and seismic-velocity variations. After correcting for the water-related variation effects, our monitoring results reveal perturbations in the crustal properties whose activation time and depth range correlate with the occurrences of the seismic swarms.

Machine learning (ML) is increasingly considered the solution to environmental problems where only limited or no physico-chemical process understanding is available. But when there is a need to provide support for high-stake decisions, where the ability to explain possible solutions is key to their acceptability and legitimacy, ML can come short. Here, we develop a method, rooted in formal sensitivity analysis (SA), that can detect the primary controls on the outputs of ML models. Unlike many common methods for explainable artificial intelligence (XAI), this method can account for complex multi-variate distributional properties of the input-output data, commonly observed with environmental systems. We apply this approach to a suite of ML models that are developed to predict various water quality variables in a pilot-scale experimental pit lake. A critical finding is that subtle alterations in the design of an ML model (such as variations in random seed for initialization, functional class, hyperparameters, or data splitting) can lead to entirely different representational interpretations of the dependence of the outputs on explanatory inputs. Further, models based on different ML families (decision trees, connectionists, or kernels) seem to focus on different aspects of the information provided by data, although displaying similar levels of predictive power. Overall, this underscores the importance of employing ensembles of ML models when explanatory power is sought. Not doing so may compromise the ability of the analysis to deliver robust and reliable predictions, especially when generalizing to conditions beyond the training data.

Heat and drought events are increasing in frequency and intensity, posing significant risks to natural and agricultural areas with uncertain effects on the net ecosystem CO2 exchange (NEE). We modified the Vegetation Photosynthesis and Respiration Model to include soil moisture impacts on the gross ecosystem exchange (GEE) and respiration (RECO) fluxes and determine the temporal variability of NEE over south-western Europe for 2001-2022. Warming temperatures lengthen growing seasons causing an increase in GEE which is mostly compensated by a similar increment in RECO, resulting in a modest annual increase of net carbon sink of 0.80 gC/m2year but with high spatial and annual variability. The heatwave of 2022 reduced NEE by 78.5 TgC, a 27% decrease from the mean. The interannual variability is more influenced by drought in temperate humid regions than in Mediterranean semi-arid regions. These results emphasize the vulnerability of the net carbon sink as drying trends could revert the NEE trends, as it is happening for croplands in the French Central Massif.

Drought intensity is commonly characterized using meteorologicly-based metrics that struggle to provide insight into water deficits within deeper hydrologic systems. In contrast, Global Positioning System (GPS) displacements are sensitive to both local and regional hydrologic-storage fluctuations. While a few studies have leveraged this sensitivity to produce geodetic drought indices, hydrologic drought characterization using GPS is not commonly accounted for in drought assessment and management. To motivate this application, we produce a new geodetic drought index (GDI) and quantify its ability to characterize hydrologic drought conditions in key surface and sub-surface hydrologic reservoirs across California. In northern California, the GDI exhibits a strong regional association with reservoir storage at the 1-month time scale (correlation coefficient: 0.83) and groundwater levels at the 3-month time scale (correlation coefficient: 0.87), along with moderate associations with stream discharge at the daily (instantaneous) time scale (correlation coefficient: 0.50). Groundwater in southern California is best characterized with a 12-month GDI (correlation coefficient: 0.77), and reservoir storage is optimized with the 3-month GDI (correlation coefficient: 0.72). Differences between northern and southern California reveal that the GDI is sensitive to unique aquifer and drainage basin characteristics. In addition to capturing long-term hydrologic trends, rapid changes in the GDI initiate during clusters of large atmospheric river events that closely mirror fluctuations in traditional hydrologic and meteorological observations. We show that GPS-based hydrologic drought indices provide a significant opportunity to improve drought assessment, in California and beyond, by improving our understanding of the hydrologic cycle.

The Tropical West Pacific (TWP) is the key entry point of air into the stratosphere during Northern Hemispheric winter. Our previous work showed how transport to the TWP troposphere is modulated by the movement of the Intertropical Convergence Zone, allowing transport of polluted air masses from tropical Asia during spring (Müller et al., 2023a, Müller et al. 2023b). Here, we present insights on air mass transport to the upper troposphere/lower stratosphere (UTLS) in this region using the ozone and relative humidity time series (2016-2023) from the Palau Atmospheric Observatory (PAO), located in the center of the tropical warm pool (7°N, 134°E). We further give an overview of all observations performed at the PAO during the ACCLIP summer campaign 2022, including ozone, CFH and COBALD sonde and lidar measurements.In analogy to Müller et al. 2023b, Lagrangian backward trajectories calculated are used to identify transport from remote locations. The time series already allows some interpretation regarding interannual variability mainly to the ENSO cycle. Here, we show anomalies during the 2016 strong El Nino and potentially the El Nino in the upcoming winter 2023. The PAO proves to be an excellent site to study UTLS composition and dynamics in the TWP region.

Wind renewable energy (WRE) is an essential component of the global sustainable energy portfolio. Recently, there has been increasing discussion on the potential supplementation of this conventional mitigation portfolio with Solar Radiation Modification (SRM). However, the impact of SRM on conventional mitigation measures has received limited attention to date. In this study, we explore one part of this impact, the potential effect of one type of SRM, Stratospheric Aerosol Injections (SAI), on WRE. Using hourly output from the Earth System Model CNRM-ESM2-1, we compare WRE potential under a medium emission scenario (SSP245) and a high emission scenario (SSP585) with an SRM scenario that has SSP585 baseline conditions and uses SAI to offset warming to approximately SSP245 global warming levels. Our results suggest that SAI may affect surface wind resources by modifying large-scale circulation patterns, such as a significant poleward jet-shift in the Southern Hemisphere. The modeled total global WRE potential is negligibly reduced under SAI compared to the SSP-scenarios. However, regional trends are highly variable, with large increases and decreases in WRE potential frequently reaching up to 16 % across the globe with SAI. This study highlights potential downstream effects of SRM on climatic elements, such as wind patterns, and offers perspectives on its implications for our mitigation efforts.

Precipitation data collected from sparse monitoring stations in numerous karst basins pose a challenge for hydrologic models to accurately capture spatial and temporal correlation between precipitation and karst spring discharge, hindering the development of robust simulation models. To address this data scarcity issue, this study employes a coupled deep learning model that integrates a variation autoencoder (VAE) for augmenting precipitation data and a long short-term memory (LSTM) network for karst spring discharge prediction. The VAE contributes by generating synthetic precipitation data through an encoding-decoding process. This process generalizes the observed precipitation data by deriving joint latent distributions with improved preservation of temporal and spatial correlations in the data. The combined VAE-generated precipitation and observation data are used to train and test the LSTM for predicting the spring discharge. Applied to Niangziguan spring catchment in northern China, our coupled VAE/LSTM model demonstrated significantly higher predictive accuracy compared to a LSTM model using only field observations. We also explored temporal and spatial correlations in the observed data and the impact of different ratios of VAE-generated precipitation data to actual data on model performances. Additionally, our study evaluated the effectiveness of VAE-augmented data on various deep learning models and compared VAE with other data augmentation techniques. Our study demonstrates that the VAE offers a novel approach to address data scarcity and uncertainty, improving learning generalization and predictive capability of various hydrological models.

We produce realistic sedimentary formations consisting of ripple deposits with varying grain size contrast and wettability in a meter-scale slab chamber. Then, we conduct multiphase flow experiments through these structures and measure the infiltration patterns, capillary heterogeneity trapping, and overall trapping performance. When we alter the ripple bedform architecture, variations in trapped saturation and capillary heterogeneity trapping (10% to 20% increment) are exhibited. Similar growth in trapping performance is also observed when grain size contrast increases. Finally, wettability changes (water- to oil-wet) can increase nonwetting saturation and capillary heterogeneity trapping up to 5% and 10-20%, respectively. These results emphasize the importance of correctly characterizing the impact of small-scale heterogeneities and wettability changes.

The strip-like bulge is a storm-time conjugate ionospheric plasma density enhancement that extends widely (over 150° in longitude) in the zonal dimension but occupies only 1°~5° in latitude. Based on in-situ measurements of 11 low earth orbit (LEO) satellites, this study statistically investigates the bulge structures of geomagnetic storms driven by 136 interplanetary coronal mass ejections (ICMEs) during 2000~2021. The statistical results show that the strip-like bulges are initiated at the end of the storm main phase and can persist for more than 60 hours. The spatial and temporal coverage of the strip-like bulge varies from storm to storm. However, the bulges do exhibit occurrence preferences: stronger storms, the Asian-Pacific sector (with eastward magnetic declination), the nightside of the dawn-dusk terminator, and solar minimum periods. A quiet time density enhancement called midlatitude extra peaks could be recognized as a precursor of the strip-like bulge. The plasmaspheric compression shares some similar occurrence features with the strip-like bulge, indicating a field-aligned downward and cross-L inward intrusion of the plasmaspheric ions. The local net ion drifts partly support this scenario with downward/inward being the most dominant but not unique pattern, other diverse net ion drift configurations exist but their impact on the strip-like bulges remains unclear.

Hypoxia is increasing in coastal oceans. This is because eutrophication has increased oxygen consumption, while less oxygen is replenished to the bottom under stronger stratification. Quantifying these biogeochemical and physical drivers is important for management and predicting future trends. By using observations from the Pearl River Estuary (PRE) region (10-70 m deep) and similar coastal systems, this paper introduces a simple analysis to quantify both the biogeochemical and physical drivers of hypoxia. We show that in the PRE region, sediment respires >60% of organic matter produced in the water column, leading to high sediment oxygen uptake (average 41.1±16.3 mmol m-2 d-1) and shallow oxygen penetrations (2-7 mm). The sediment’s effect on the bottom oxygen loss becomes stronger with the reducing thickness of the bottom boundary layer. We then construct a generic mass-balance model to quantify oxygen loss, determine timescales of hypoxia formation, and explain within- and cross-system variabilities.

A document by Benjamin Rabe. Click on the document to view its contents.

Highlights：The tropical lower stratosphere water vapour (SWV) experiences a drying process during 1984-2020 in both linear and nonlinear perspectives.The Indo-Pacific warm pool (IPWP) is the main factor in such drying of the tropical stratosphere.IPWP leads the coldest point region cooler modulating tropical SWV entry by enhancing equatorial waves.ABSTRACTA decreasing trend in the tropical (30°S~30°N) stratospheric water vapour (SWV) entry in recent four decades (from 1984 to 2020) is detected based on the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) measurements and the ERA5 reanalysis dataset using linear regression and Ensemble Empirical Mode Decomposition (EEMD) analysis. With the concurrent warming of the SST, the Indo-Pacific warm pool (IPWP) appears to be the most significant region among the tropical oceans based on correlation analysis. More than 43% of the decreasing tropical lower SWV trend is likely to be related to the IPWP sea surface temperature (SST) warming. To validate this relationship, two groups of idealized runs are carried out with version 4 of the Whole Atmosphere Community Climate Model (WACCM4) and version 5 of the Community Atmosphere Model (CAM5). Both simulations agree with the observational-based linkage. The IPWP-SST-warming forced simulations show that the temperature in the tropical tropopause has decreased at the rate of around 0.318 K per decade in the coldest point region, as the tropical convection over the IPWP has become more vigorous and excited stronger equatorial waves to produce adiabatic cooling around tropopause. This cooling tropical tropopause leads to a dehydrating tropical lower stratosphere at the rate of 0.025 ppmv per decade, as expected by the freeze-drying mechanism. These results imply the substantial warming trend of IPWP is an important factor for the long-term trend of the tropical SWV entry under climate change, and a better representation of this relationship in the model is critical for the SWV projection under future climate scenarios.Keywords: Stratospheric water vapour; Indo-Pacific warm pool; Trend; Tropopause; Coldest point regionIntroductionThe stratospheric water vapour (SWV) mainly originates from the troposphere: the moist air parcels at the bottom of the troposphere ascend, reaching the tropical tropopause layer (TTL) between 14–18.5 km, experiencing a severe dehydration process at the TTL (because the TTL has the coldest temperature in the lower atmosphere) (Gettelman and Forster 2002; Fueglistaler et al. 2009), then arriving in the stratosphere. The SWV is suggested to contribute significantly to global climate change by altering the infrared opacity of the atmosphere (e.g., Soden and Held 2006), providing a strong positive feedback at +0.3 W/(m2·K) to global warming (Dessler et al. 2013). When the SWV increases, it subsequently leads to warming in the troposphere and cooling in the stratosphere, and the warmer troposphere will, in turn, increase the SWV (Rind and Lonergan 1995; de F. Forster and Shine 1999; Solomon et al. 2010; Dessler et al. 2013; Fu et al. 2015), and by this positive feedback, the increase of the SWV will accelerate the rate of increase in global surface temperature and vice versa. Meanwhile, the SWV participates in stratospheric chemical processes as the primary source of stratospheric hydrogen oxide radicals. For example, it strongly affects heterogeneous chemistry on cold sulfate aerosol and the formation of polar stratospheric clouds, which promote chlorine activation and polar ozone loss (Evans et al. 1998; Shindell 2001; Stenke and Grewe 2005; Tian et al. 2009). So, it is critical to understand the decadal and long-term SWV variability and the relevant physical processes.Previous studies have already documented that the TTL temperature is very important for SWV variability. Because the TTL is the main area where air enters the stratosphere, changes in SWV are largely related to the tropical SWV entry and the TTL temperature largely determines the SWV entry values (Brewer 1949; Randel et al. 1998; Scaife et al. 2003; Fueglistaler et al. 2005; Rosenlof and Reid 2008; Schoeberl and Dessler 2011; Grise and Thompson 2012; Dessler et al. 2013). Therefore, the multi-timescale variations of the SWV ranging from daily to decadal timescales (e.g., Randel et al. 2004; Fueglistaler and Haynes 2005; Fujiwara et al. 2010; Hegglin et al. 2014) can be traced to TTL temperature variations (e.g., Randel et al. 2007; Rosenlof and Reid 2008; Randel 2010; Fueglistaler et al. 2013; Randel and Jensen 2013). As a layer between the stratosphere and troposphere at about 14–18.5 km (Fueglistaler et al. 2009), the TTL temperature is affected by both the stratospheric (top-down) and tropospheric (bottom-up) processes, including variability of the Brewer-Dobson circulation (BDC, a stratospheric mean meridional circulation), the quasi-biennial oscillation (QBO), and tropical convection (bottom-up). Because the TTL temperature and SWV entry values are generally the results of the interplay between the top-down and bottom-up processes, the SWV’s interpretation (e.g., Hegglin et al. 2014) and prediction (e.g., Gettelman et al. 2010) are complex. In the tropical troposphere, anomalous deep convection induces upward motion near the tropopause and thereby results in a cooling of the TTL (Highwood and Hoskins 1998). And deep convection is usually associated with the El Nino Southern Oscillation (ENSO), Asian Monsoon, and Madden-Julian Oscillation exert tropical planetary waves including the equatorial Rossby wave and Kelvin wave. In the stratosphere, the acceleration of the BDC causes the adiabatic cooling of the TTL through the enhanced large-scale vertical ascending motions, and vice versa (Holton et al. 1995; Thompson and Solomon 2005). Fu et al. (2010) also documented that the strength of the BDC is a main factor driving the seasonal variability of the TTL temperature. In short, the TTL temperature variability is driven by both the tropospheric (bottom-up) processes and the stratospheric (top-down) (Kumar et al. 2014).By using balloon-borne water vapour profiles above Washington DC and Boulder, Oltmans et al. (2000) observed an increase of lower SWV by about 1% per year during the 1960s and 1990s. This agrees with the Third Assessment Report of the IPCC, which reported water vapour in the lower stratosphere is likely to have increased by about 10% per decade since the beginning of the observational record. Dessler et al. (2013) showed observational evidence for stratospheric water vapour feedback—a warmer climate increases stratospheric water vapour, and because stratospheric water vapour is itself a greenhouse gas, this leads to further warming. Lin et al. (2017) found that the tropical tropopause layer will become warmer in response to carbon dioxide increase and surface warming. A few numerical studies reported that a moist stratosphere occurs under global warming scenario by using the forcing of quadrupling CO2 in different general circulation models (Zhang and Huang 2014; Banerjee et al. 2019; Li and Newman 2020; Wang and Huang 2020; Xia et al. 2021b). Keeble et al. (2021) suggested that CMIP6 multi-model mean SWV mixing ratios in the tropical lower stratosphere have increased by ∼0.5 ppmv from the pre-industrial to the present-day period and are projected to increase further by the end of the 21st century. Keeble et al. (2021) further pointed out that the largest SWV increases (∼2 ppmv) are simulated under the future scenarios with the highest assumed forcing pathway (e.g., SSP5-8.5).However, the increasing trend of the SWV seems stopped or becomes blurred after the 1990s. Although the SWV above Boulder is reported to increase during the periods of 1992 to 2002 in the balloon water vapour data (Randel et al. 2004). This increasing trend is not reproduced well by satellite data. Randel et al. (2004) also documented that the SWV near Boulder, Colorado (40°N) and the tropical mean (60°S-60°N) SWV have no significant trend in 1992-2002 based on the HALOE data. Besides, the Fifth Assessment Report of the IPCC (IPCC5, 2014) documented that the near-global satellite measurements of SWV show substantial variability but small net changes for 1992-2011, i.e., the satellite data show no clear trends for the SWV. Randel et al. (2006) even reported that the near-global SWV after 2001 decreased (or had persistent low values beginning in 2001), and this near-global SWV decrease is attributed to the enhanced tropical upwelling after 2001. Hurst et al. (2011) analyzed the balloon-borne SWV over Boulder, Colorado, then reported the multi-decadal variability of the SWV: the SWV increased by an average of 1.0 ± 0.2 ppmv (27 ± 6%) during 1980-2010, but in 2001-2005, it has an opposite trend to other periods. Recently, another strong drop is reported in the tropical SWV (10°S-10°N, similar to the SWV drop observed in the year ~2000) was observed in 2011-2012 (Urban et al. 2014). Hegglin et al. (2014) using observation data revised by transfer function found a negative trend in the lower and mid-stratosphere. Dessler et al. (2014) revealed that water vapour entering the stratosphere has no firm evidence of trend. Konopka et al. (2022) suggested that the stratosphere has become wetter after 2000. Tao et al. (2023) found that SWV has a robust multi-decadal variation and short-term trends in SWV are closely related to this multi-decadal variation. These imply that there are great uncertainties in the trends of SWV and the trends are sensitive to the period focused on.Many studies have suggested that tropical oceans have a crucial effect on the stratosphere (e.g., Hu et al. 2014; Hu and Guan 2018; Xie et al. 2020b; Xie et al. 2020a; Xia et al. 2021a). As an important component of the stratosphere, the SWV is no exception. Scaife et al. (2003) have pointed out that a positive trend in water vapour of around 0.1% per year and ENSO effects appear to explain no more than about one-tenth of the long-term trend by using model and observational data. Tropical SST variability, especially ENSO, has been known to be an important factor in determining the amount of water vapour being uplifted to the upper tropospheric region and the lower stratosphere by altering tropical convection (Su et al. 2006; Rosenlof and Reid 2008; Liang et al. 2011; Xie et al. 2012; Garfinkel et al. 2013a; Garfinkel et al. 2013b; Avery et al. 2017; Su et al. 2020). The tropical SST has been suggested to contribute to the drop of the lower stratospheric water vapour during 2000 (Brinkop et al. 2016; Ding and Fu 2018). The Indo-Pacific Warm Pool (IPWP) has been also documented as a vital region that affects the stratosphere and tropical lower SWV (Xie et al. 2014; Xie et al. 2018; Zhou et al. 2018). The warm phase of IPWP causes a drier lower stratosphere and vice versa. Zhou et al. (2021) further pointed out that such impact has seasonality and hemispheric differences. Almost the entire tropical ocean shows a warming long-term trend in SST over the last century (Deser et al. 2010). The tropical western Pacific is warm faster than the eastern Pacific in observations (Cane et al. 1997; Kanamitsu et al. 2002; Compo and Sardeshmukh 2010; Zhang et al. 2010; Li et al. 2017). There is widespread warming across the tropical Indian Ocean basin and SSTs have reached 28°C in the western Indian Ocean, which has expanded the Indo-Pacific warm pool region defined by the 28°C isotherm westward (Roxy et al. 2015). It is quite possible that the zonally asymmetric warming trend of tropical oceans will further influence the SWV entry and provide the principal source for its decadal trend. Previous studies have shown the impact of IPWP on interannual variability of tropical lower SWV and discussed its seasonal and hemispheric differences. However, the impact of IPWP on tropical SWV entry over longer time periods is unclear, especially when the IPWP is substantially warming in the past forty years. Therefore, in this study, we seek to understand the impact of IPWP continuous warming on tropical SWV entry in recent decades.In short, the decadal or long-term changes of the tropical SWV during the past decades seem not to strictly follow the presumed long-term increasing trend under global warming. So, we try to revisit and interpret the decadal or long-term change of the SWV with longer observations for the period 1984-2020 and explore the potential impact of IPWP warming on it. The remainder of the paper is arranged as follows. The data, model, and methods we used are presented in Section 2. In section 3, we revisit the long-term trend of SWV for the period 1984-2020, then focus on its link with the warming of IPWP. Section 4 is a summary of the principal findings.Data, model and methodsWater vapor dataStratospheric Water and OzOne Satellite Homogenized (SWOOSH) is a merged data set that ranges from 1984 to the present containing multiple satellite data and it not only offers values of SWV but also provides some ancillary information like standard deviation, number of data points and others (Davis et al. 2016). In addition, it also supplies a combined product which is a weighted mean value from the available satellite measurements mentioned above and it has a fabulous advantage that when one satellite measurement is missing, others will be filled in by using different algorithms. SWOOSH has been used in some studies (Hardiman et al. 2015; Gilford et al. 2016). Our study used this combined product with 31 pressure levels from 316 to 1 hPa and the version of SWOOSH is v2.7.In addition to SWOOSH, water vapour of ERA5 was also analyzed for comparison. ERA5 which covers the period from 1979 to the present, is the latest global atmospheric reanalysis product obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). Based on the 4D-Var data assimilation scheme and Integrated Forecast System (IFS) CY41R2, it covers the earth with a 30 km horizontal grid and 137 hybrid sigma/pressure levels from the surface to 0.01 hPa (Hersbach et al. 2020). And the resolution we used is 0.25° × 0.25° in the horizontal and 37 levels from 1000 hPa to 1 hPa in the vertical. Because ERA5 has a cold bias in the lower stratosphere during 2000 and 2006, ERA5.1 is applied in this special time.Meteorological dataBesides ERA5, the major meteorological data we also used is Japanese 55-year Reanalysis (JRA-55). JRA-55 is conducted by Japan Meteorological Agency (JMA) and JRA5 is a comprehensive climate dataset with the applicant of 4D-Var. It covers the period from 1958, coinciding with the establishment of the global radiosonde observing system. The resolution we used is 1.25° × 1.25° in the horizontal and 37 levels from 1000 hPa to 1 hPa in the vertical.Another relevant meteorological data is sea surface temperature, and Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) data set was applied in our study. It is mainly from Met Office Marine Data Bank (MDB), and it also covers data from Global Telecommunications System (GTS) since 1982. To enhance the coverage of data, when data from MDB is missing, the monthly median SSTs for 1871-1995 from the Comprehensive Ocean-Atmosphere Data Set (COADS) (now ICOADS) are included (Rayner et al. 2003). And its resolution is 1° × 1°.ModelThe Community Earth System Model version 1 (CESM1), developed by National Center for Atmosphere Research (NCAR), can simulate the state of climate from the past to the future. It consists of several relatively independent component models, including atmosphere, ocean, land, land ice, sea ice, and so on, and all of them have their own spatial resolutions. Besides, CESM1 has a central coupler that can exchange energy and information between different component models (Hurrell et al. 2013). In our study, we used version 5 of the Community Atmosphere Model (CAM5) to design experiments, which is one of the vital component models of CESM1, to simulate global atmosphere activity. CAM5 can not only run as one of the component models of CESM1 but also run as an independent model. It applies 30 vertical levels from the ground to 3.64 hPa. Version 4 of the Whole Atmosphere Community Climate Model (WACCM4), a comprehensive numerical model based on CAM, was also used in the experiment design. It can extend from the surface to the thermosphere, namely, from the surface to 5.1× 10-6 hPa (about 140 kilometers) with 66 vertical levels. In our study, the finite-volume dynamical core was applied in both CAM5 and WACCM4. We used the 1.9° × 2.5° medium resolution version which includes 96 longitude and 144 latitude points.CAM5 and WACCM4 are employed to investigate how and to what extent SWV entry changes in response to IPWP warming. WACCM4 can capture the negative relationship between the IPWP SST anomalies and the tropical SWV entry (Xie et al. 2018; Zhou et al. 2018). Two groups of experiments are carried out, which involve a control group forced by observed SST from 1955 to 2005 (E0, E1) and another forced by the observed SST in the IPWP region only (E2, E3). Details of transient experiments can be referred to in Table 1. Due to the limitation of WACCM4, E0 and E2 run only from 1955 to 2005. So, although CAM5 in E1 and E3 run from 1900 to 2005, we use the same period as WACCM4 in E0 and E2. We evaluate the two modes using control runs (E0, E1) by comparing them with the observational data and reanalysis data. Both can represent the structures in SWV and tropical tropopause temperature, with the pattern correlations exceeding 0.7 between observed and simulated climatology (Fig. S1 and Fig. S2).

In this study we apply electron tomography to characterize 3D dislocation microstructures in two highly sheared quartz mylonite specimens from the Moine and Main Central thrusts which were deformed extensively by dislocation creep in the presence of water. Both specimens show dislocation activity with dislocation densities of the order of 3-4.1012 m-2 and evidence of recovery from the presence of subgrain boundaries. slip occurs predominantly on pyramidal and prismatic planes ( basal glide is not active). [c] glide is not significant. On the other hand, we observe a very high level of activation of glide in the{10-10},{10-11}, {11-2n} (n=1,2) and even {21-31} planes. Approximately 60% of all dislocations involve climb with a predominance of mixed climb, a deformation mechanism characterized by dislocations moving in a plane intermediate between the glide and the climb planes. This atypical mode of deformation demonstrates comparable glide and climb efficiency under natural deformation conditions. It promotes dislocation glide in planes atypical of quartz structure, probably by inhibiting lattice friction. Our quantitative characterization of the microstructure enables us to assess the strain that dislocations can generate. We show that the contribution of glide produced by the observed dislocations is sufficient to satisfy the von Mises-Taylor criterion. Hence, activation of climb is not necessary to provide additional strain components, but it contributes to the magnitudes of strains achieved. On the basis of this characterization, we propose a numerical modelling approach for attempting to characterize the local stress state that gave rise to the observed microstructure.

In slow spreading environments, oceanic crust is formed by a combination of magmatic and tectonic processes. Using full waveform inversion applied to active-source ocean bottom seismometer data, we reveal the presence of a strong lateral variability in the 40 – 48 Ma old oceanic crust at the slow-spreading Mid-Atlantic Ridge in the equatorial Atlantic Ocean. Over a 120 km-long section between the St Paul fracture zone and the Romanche transform fault, we observe four distinct 20-30 km long crustal segments. The segment affected by the St Paul FZ consists of three layers, 2-km thick layer with velocity <6 km/s, 1.5 km thick middle crust with velocity 6-6.5 km/s, and an underlying layer with velocity ~7 km/s in the lower crust. The segment associated with an abyssal hill morphology contains high velocity ~7 km/s from a shallow depth of 2 – 2.5 km below basement, indicating the presence of primitive gabbro. The segment associated with a low basement morphology seems to have 5.5 – 6.5 km/s velocity starting near the basement extending down to ~4 km depth, indicating chemically distinct crust. The segment close to the Romanche transform fault, a normal oceanic crust with velocity 4.5-5 km/s near the seafloor increasing to 7 km/s at 4 km depth indicates a magmatic origin. The four distinct crustal segments have a good correlation with the overlying seafloor morphology. The observed strong crustal heterogeneities could result from alternate tectonic and magmatic processes along the ridge axis, possibly modulated by chemical variations in the mantle.

There are various hotspots in the Atlantic ocean, which are underlain by mantle plumes that likely cross the mantle and originate at the core-mantle boundary. We use teleseismic core-diffracted shear waves to look for an Ultra-Low Velocity Zone (ULVZ) at the potential base of central Atlantic mantle plumes. Our data set shows delayed postcursory phases after the core-diffracted shear waves. The observed patterns are consistent in frequency dependence, delay time, and scatter pattern with those caused by mega-ULVZs previously modelled elsewhere. Synthetic modelling of a cylindrical structure on the core-mantle boundary below St. Helena provides a good fit to the data. The preferred model is 600 km across and 20 km high, centred at approximately 15° South, 15° West, and with a 30% S-wave velocity reduction. Significant uncertainties and trade-offs do remain to these parameters, but a large ULVZ is needed to explain the data. The location is west of St. Helena and south of Ascension. Helium and neon isotopic systematics observed in samples from this region could point to a less-outgassed mantle component mixed in with the dominant signature of recycled material. These observations could be explained by a contribution from the Large Low Shear Velocity Province (LLSVP). Tungsten isotopic measurements would be needed to understand whether a contribution from the mega-ULVZ is also required at St. Helena or Ascension.

The modelling of coastal Directional Wave Spectra (DWSs) often requires downscaling techniques integrating DWSs from open ocean boundaries. Dynamic downscaling methods reliant on numerical wave models are often computationally expensive. In coastal areas, wave dynamics are strongly influenced by the topography, implying that once the DWSs at the open ocean boundary are known, the DWSs at various locations along the coast are almost determined. This property can be utilized for statistical downscaling of coastal DWSs. This study presents a deep learning approach that can compute coastal DWSs from open ocean DWSs. The performance of the proposed downscaling model was evaluated using both numerical wave model data and buoy data in the Southern California Bight. The results show that the deep learning approach can effectively and efficiently downscale coastal DWSs without relying on any predefined spectral shapes, thereby holding promise for coastal wave climate studies.