Studies of element partitioning between suspended sediment and water with increased seawater mixing are sparse, particularly in Bangladesh. However, these studies are important for understanding elemental cycling, pollutant transport, and impacts on aquaculture and sensitive ecosystems in estuaries and tidal deltas such as the Sundarbans mangrove forest in Southwest Bangladesh. Thus, water samples collected within the upper 1m of the water column along a transect of well-mixed tidal channels in Southwest Bangladesh during the dry season were analyzed for dissolved and suspended sediment element concentrations and other geochemical parameters. While most elements in the suspended load were close to or depleted relative to upper continental crust (UCC), several trace elements such as Sb, As, Cd and Se were slightly enriched. Additionally, most trace elements in the dissolved load were well above world average riverine concentrations, particularly Se and As. Dissolved load Ba and Se displayed mostly conservative mixing trends with seawater. Barium was likely originally sourced from sediment desorption and groundwater exfiltration, while Se may have been anthropogenically sourced from the city of Khulna or farther upstream. Dissolved As did not display conservative mixing trends, and may ultimately be geogenic in origin, possibly from groundwater. Ni and Co show trends consistent with desorption from competitive seawater cation exchange along the transect, similar to a study in the nearby Hooghly Estuary in West Bengal. Collectively, our results show that combined anthropogenic and natural influences on trace element distributions in coastal environments are important to quantify for continual protection of natural areas and better understanding of trace element discharge to global oceans.

In the Jovian magnetosphere, an electric current system within the ‘current sheet’ generates a magnetic field, which is comparable to or dominating the Jovian intrinsic field in the nightside magnetosphere. However, update of an existing model of the magnetospheric field using newly acquired data by Galileo and Juno have never been conducted since it was first formulated in 1997. Here we used the data by Voyager 1/2, Galileo and Juno to revise the current sheet model as well as the magnetospheric field model based on each spacecraft data. We derived models that reproduced each data well, and revealed long-term variations of both current sheet and magnetospheric field over several decades. The updated models were found useful to detect dynamic events in the magnetosphere such as magnetopause deformation and plasmoid generation. They can also be used as external fields necessary for probing into the Galilean icy moons by electromagnetic induction methods.

All rivers undergo changes in channel geometry downstream systematically. A river adjusts its channel geometry (width,depth,cross-sectional area)within the limitations imposed by discharge, channel lithology. As not much studies on channel geometry on large monsoon-fed rivers. Therefore, in this study an attempt will be made to understand the fluvial geomorphological characteristics and variation for 2 decades of major Indian Peninsular River namely Krishna, in terms of channel geometry, discharge. Present study focusses on channel geometry changes downstream for a large Peninsular River . The Krishna River is the third largest river in India.Cross-sectional data for a downstream gauging sites during 1990-2010is downloaded from India Water Resources Information System (India-WRIS) WebGIS. portal. Channel Geometry parameters in terms of Channel Cross-Section Area,Width,Depth,Wetted Perimeter,Hydraulic Radius,Form Ratio are derived from the data involving 9 sites on main river. The Graphs indicate as the maximum discharge increases with corresponding increase in Cross-Sectional Area,Width,Depth,Wetted Perimeter,Form Ratio,Velocity,Hydraiulic Radius is observed with corresponding decrease in Mean Velocity downstream.Channel forms upstream to downstream becomes less steep.APower-log regression equation carried out to evaluate the level of confidence between the parameters.

It is now well-established through multiple event and statistical studies that the solar wind at 1 AU contains contains periodic, mesoscale (L~100-1000Mm) structures in the proton density. Composition variations observed within these structures and remote sensing observations of similar structures in the young solar wind indicate that at least some of these periodic structures originate in the solar atmosphere as a part of solar wind formation. Viall et al. [2008] analyzed 11 years of data from the Wind spacecraft near L1 and demonstrated a recurrence to the observed length scales of periodic structures in the solar wind proton density. In the time since that study, Wind has collected 14 additional years of solar wind data, new moment analysis of the Wind SWE data is available, and new methods for spectral background approximation have been developed. In this study, we analyze 25 years of Wind data collected near L1 and produce occurrence distributions of statistically significant periodic length scales in proton density. The results significantly expand upon the Viall et al. [2008] study, and further shows a possible relation of the length scales to solar “termination” events.

A document by Jun Tsuchiya. Click on the document to view its contents.

“Model-driven” and “data-driven” approaches together have helped humans understand the principles of geophysical phenomena for a long time. With increasingly available geophysical data, a new data-driven technique, i.e., deep learning, has played an important role in the accurate prediction of complex system states and relieving the curse of dimensionality in large temporal and spatial geophysical applications. In this article, we review the basic concepts of and recent advances in data-driven deep learning approaches in a variety of geophysical scenarios. Explorational geophysics including data processing and imaging, are the main focus. Deep learning applications in the geosciences including the Earth interior, earthquakes, water resources, atmospheric science, satellite remote sensing, and space sciences are also reviewed. A coding tutorial and a summary of tips for rapidly exploring deep learning are presented for beginners and interested readers of geophysics. Several promising directions are provided for future research involving deep learning in geophysics, such as unsupervised learning, transfer learning, multimodal deep learning, federated learning, uncertainty estimation, and active learning.

Microwave signals, for example, those from Global Navigation Satellite Systems (GNSS) and very long baseline interferometry, are affected by tropospheric turbulence in such a way that the random fluctuations of the atmospheric index of refraction correlate the phase measurements. These atmospheric correlations are an important error source in space geodetic techniques. For computational reasons, they are neglected in positioning applications, to the detriment of a trustworthy description of the precision, and rigorous test statistics. Fortunately, modelling such correlations is possible by combining concepts from electromagnetic wave propagation in a random medium and the Kolmogorov turbulence theory. In this contribution, we will process single differences GNSS phase observations from a 300 m baseline between two different receivers linked to a common clock. After a preprocessing to filter additional error contributions, such as multipath, we will study the power spectral density of the phase residuals. We will estimate its low and high cutoff frequencies with an adapted unbiased Whittle maximum likelihood estimator. These cutoff frequencies – as predicted by turbulence theory – are related directly to the scale lengths of turbulence, i.e. the size of the eddies that correlate the GNSS observations. The study of their dependencies with the satellite geometry, day of the year, or time of the day provides new insights into the two- and three-dimensional atmospheric turbulence in the atmosphere. In addition, it contributes to improving the stochastic description of random effects impacting GNSS phase observations.

Rapid increasing industries and city expansions have caused severe air pollution in the Guanzhong Basin (GZB), China in recent decades. Observations reveal that, although implementation of strict mitigation measures since 2014 has considerably reduced particulate matter (PM) pollution, the ozone (O) pollution during the warm season has continuously deteriorated in the basin. Simulations in May and August 2018 have been conducted using the WRF-Chem model to examine spatial and seasonal variations of the O formation regimes as well as source attributions in the GZB. The model generally performs well in simulating meteorological parameters, O, NO, and fine PM against measurements. The identified O formation regimes in cities of the GZB are all VOCs-sensitive in May and become more NO-sensitive in August. Sensitivity studies have shown that the power plants source generally suppresses the Oformation considerably in May and enhances it slightly in August due to its high NO and low VOCs emissions. The residential, transportation and industry sources increase the O concentration in May and August. Moreover, the transportation and industry sources play an increasingly important role in August but opposite for the residential source. The variation of O formation regimes and source attributions from May to August is caused by intensification of solar radiation, which not only promotes photochemical processes, also increases temperature and further enhances biogenic emissions and vertical exchange in the planetary boundary layer. The present study can provide guidelines to devise the effective O abatement strategies suitable for local situations.

The Earth’s F region ionosphere is dominated by the collision between atomic oxygen and its first positive ion. An accurate corresponding collision frequency model is necessary to understand the ionosphere. However, the widely used classic Banks theoretical model typically provides a collision frequency that is 30% lower than the expectation from ionospheric observations. Accordingly, the classic collision frequency is often adjusted by multiplying it by a constant known as the Burnside factor. This correction-factor model adopted the classic model as its basis due to a misunderstanding that the classic model was based on a laboratory experiment; that is, the correction factor was originally meant to compensate for laboratory contamination. In this study, we construct a collision frequency model based on the laboratory experiment. We find that the resultant laboratory-based model is consistent with ionospheric observations. In this construction, we have determined that the impact of laboratory contamination is small (7%) and is mostly canceled by a misinterpretation regarding the conventional definitions of energy. Thus, the 30% difference is mainly caused by a theoretical error in the classic model itself. This error is energy-dependent and corrected by the later wide-energy theoretical model. Thus, the classic model cannot be corrected by a constant and should be replaced by the later model.

The Madden-Julian Oscillation (MJO) exhibits evident interannual variations, which have been mostly attributed to modulations of El Niño-Southern Oscillation (ENSO) in previous studies. However, whether the basin-wide warming/cooling of the Indian Ocean (IO) independent of ENSO could affect the MJO remains elusive. Here, we show that the MJO tends to initiate more westward and only propagate confined over the IO under warm conditions during boreal summer. In contrast, a cold IO results in an eastward-shifted MJO initiation and smooth propagation across the Maritime Continent (MC)-western Pacific (WP). The genesis location is mainly associated with warm ocean beneath the MJO, which accumulate more during warm conditions. Additionally, the warming of the IO induces anomalous anticyclone and stronger vertical wind shears over the WP, which synergistically lead to stronger Rossby wave while weaker Kelvin wave components, thereby inhibiting the MJO eastward propagation. Moreover, the suppressed convection and thus low-level Kelvin easterly wind anomalies over the MC-WP region become weaker with the warming of the entire IO. Consequently, the premoistening dominated by the high-frequency synoptic-scale meridional advection decreases and causes nonpropagating MJO during boreal summer. These findings highlight that interannual variations of the MJO could be attributed by considering independent modulation effects of the IO.

Every year, the area of the Arctic sea-ice decreases in the boreal spring and summer and reaches its yearly minimum in the early autumn. The continuous satellite-based time series shows that the September area has decreased from 4.5*10^6 km^2 in 1979, to 2.8*10^6 km^2 in 2020. The decline has been approximately linear in global mean surface temperature, with a rate of loss of 2.7*10^6 km^2 per degree C of global warming. In the CMIP6 ensemble, however, we find that the majority of the models that reach an Arctic sea-ice free state in the SSP585 runs shows an accelerated loss of sea-ice for the last degree of warming compared to the second last degree of warming, which implies an increased sensitivity of the sea-ice to temperature changes. Both in the observational and CMIP6 data, we find that the decline in September sea-ice area is approximately proportional to the area north of which the zonal average temperature in spring and summer is lower than a critical threshold Tc. The Arctic amplification implies that the zonally averaged temperatures increase relative to the global temperatures, and with rates increasing with latitude. Linear extrapolation of the zonally averaged temperatures predicts that, with further warming, the September sea-ice area will depend non-linearly on global temperature, the sensitivity will increase and the September sea-ice area may become less that 1*10^6 km^2 for global warming between 0.9 and 1.6 degrees C above the current temperature. As a result of accelerated sea-ice loss, the average evolution of the sea-ice area among the CMIP6 models before the loss of the summer sea-ice shows an increase in the year-to-year fluctuations in minimum ice cover in the next decade. This implies exceptional accumulation of extreme events with very low or no sea-ice at all even before the final loss of the sea-ice. Likewise, an apparent short-term recovery of the sea-ice loss might be observable due to the increasing fluctuations.

Equatorial Pacific decadal variability (EPDV) modulates global climate. Although EPDV is suggested to be generated by both air-sea thermodynamically coupled slab ocean models (SOM) and fully coupled dynamic ocean models (DOM), the reason of EPDV simulated by the two distinct hierarchies of models remains unclear. This ambiguity arises from a gap in the dynamical framework between SOM and DOM. To fill the gap, we conducted a novel experiment (Clim-tau) that retains only the effects of thermodynamic coupling and mean ocean current on EPDV (without anomalous ocean current). We showed that in Clim-tau, thermodynamic-driven EPDV as in SOM is largely damped by equatorial Pacific mean upwelling; whereas involving anomalous ocean current as in DOM, the damped EPDV will be further amplified. Finally, we discussed the role of ocean dynamics in the observed EPDV. Our study highlights that SOM may misinterpret the physical mechanisms in the regions where ocean dynamics is important.

Key points: • The detrimental impact of preadsorbed water on the methane adsorption capacity and rate is more pronounced than that of water vapor • The distributions of adsorbed methane and water in shale pores were compared between the SAWM and PMMS • Water vapor preferentially enters mesopores (1.5-20 nm), and preadsorbed water mainly occupies micropores (0.3-1.5 nm) Abstract Water plays an essential role in shale gas migration and adsorption, and most studies on the influence of water on shale gas adsorption refer only to moisture-equilibrated shales. To investigate the impact of water vapor on methane adsorption in shales, three experiments were conducted and compared: (1) pure methane adsorption onto dry shale (PMD), (2) pure methane adsorption onto moisture-equilibrated shale (PMMS), and (3) simultaneous adsorption of water vapor and methane (SAWM) onto shale. Comparison of the experimental results reveals that the detrimental impact of water vapor on methane adsorption is inferior to that of preadsorbed water. Two mechanisms, i.e., water blocking and adsorption competition, are responsible for the reduction and difference in the methane adsorption capacity and adsorption rate between the PMMS and SAWM. Compared to the PMD, the methane adsorption capacity decreases by 81-96% in the PMMS, and by 20-49% in the SAWM. Methane adsorption equilibrium is achieved the fastest in the PMD. Before the equilibration degree reaches 95%, methane adsorption during the SAWM progresses more rapidly, while the reverse occurs when the equilibration degree exceeds 95%. The pore size distribution and water film thickness calculations indicate that the impacts of water vapor in the SAWM on micro-to mesopores are weaker than those of preadsorbed water. In the PMMS, adsorbed water mainly

Uncertainty quantification and characterization in changing climate scenarios can have a direct impact on the efforts to mitigate and adapt. Chaotic and non-linear nature of atmospheric processes results in high sensitivity to initial conditions resulting in considerable variability. Multiple model ensembles of Earth System Models are often used to visualize the role of parametric uncertainties in mean and extreme attributes of precipitation trends in various time horizons. However, studies quantifying the role of internal variability in controlling extreme precipitation statistics in decadal and interdecadal scales are limited. In this study, we use a thirty one-member ensemble of Community Earth System Model Large ensemble project and thirty-one ensembles from Coupled Model Intercomparison Project 5 (CMIP5) to quantify the relative contribution of uncertainty due to internal variability in the depth and volatility of Indian Summer Monsoon Rainfall extremes of different durations and frequencies. We find that in the short-term and long-term, the role of internal variability in extreme precipitation indices is comparable to the uncertainty arising from structural differences in the model captured through multiple model ensembles. Further, we show that combining outputs from multiple initial condition runs generated to span the range of internal climate variability can help us reduce uncertainty in infrastructure design relevant Depth Duration and Frequency (DDF) curves.

In stratified oligotrophic waters, phytoplankton communities forming the deep chlorophyll maximum (DCM) are isolated from atmospheric iron sources above and remineralized iron below. Reduced supply leads to a minimum in dissolved iron (dFe) near 100 m, but it is unclear if iron limits growth at the DCM. Here, we propose that natural iron addition events occur regularly with the passage of mesoscale eddies, which alter the supply of dFe and other nutrients relative to the supply of light, and can be used to test for iron limitation at the DCM. This framework is applied to two eddies sampled in the North Pacific Subtropical Gyre. Observations in an anticyclonic eddy center indicated downwelling of iron-rich surface waters, leading to increased dFe at the DCM but no increase in productivity. In contrast, uplift of isopycnals within a cyclonic eddy center increased supply of both nitrate and dFe to the DCM, and led to dominance of picoeukaryotic phytoplankton. Iron addition experiments did not increase productivity in either eddy, but did enhance leucine incorporation at ambient light in the cyclonic eddy, a potential indicator of iron stress among Prochlorococcus. Rapid cycling of siderophores and low dFe:nitrate uptake ratios also indicate that a portion of the microbial community was stressed by low iron. However, near-complete nitrate drawdown in this eddy, which represents an extreme case in nutrient supply compared to nearby Hawaii Ocean Time-series observations, suggests that recycling of dFe in oligotrophic ecosystems is sufficient to avoid iron limitation in the DCM under typical conditions.

The annual mean net surface heat fluxes (NSHFs) from the ocean to the atmosphere play an important role in driving both atmospheric circulations and oceanic meridional overturning circulations. Those generated by historical forcing simulations using the UK HadGEM3-GC3.1 coupled climate model are shown to be relatively independent of resolution, for model horizontal grid spacings between 1 and 1/12 degree, and to agree well with those based on the DEEPC analyses for the period 2000-2009. Interpretations of the geographical patterns of the NSHFs are suggested that are based on relatively simple dynamical ideas. As a step toward investigation of their validity, we examine the contributions to the rate of change of the active tracers (potential temperature, salinity and potential density) from the main terms in their prognostic equations as a function of the active tracer and latitude. We find that the main contributions from vertical mixing occur in “near surface” layers and that, except at high latitudes, the time-mean advection of potential temperature and density is well anti-correlated with the sum of the surface fluxes and vertical diffusion. By contrast, the tracer budget for the salinity has at least four terms of comparable magnitude. The heat input by latitude bands is shown to be dominated by the NSHFs, the time-mean advection, and the equatorial Pacific. Expressions for global integrals of the salt and heat content tendencies due to advection as a function of salinity and potential temperature respectively are derived and shown to make contributions that should not be neglected.

North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable locations in the world’s oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25-year leadtimes. Layer-wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream-North Atlantic Current region for accurate predictions. Additionally, CESM1-trained NNs do not need additional transfer learning to successfully predict the phasing of multidecadal variability in an observational dataset, suggesting consistency in physical processes driving NASST variability between CESM1 and observations.

Nonmigrating tidal diagnostics of SABER temperature observations in the ionospheric dynamo region reveal a large amount of variability on time-scales of a few days to weeks. In this paper, we discuss the physical reasons for the observed short-term tidal variability using a novel approach based on Information theory and Bayesian statistics. We diagnose short-term tidal variability as a function of season, QBO, ENSO, and solar cycle and other drivers using time dependent probability density functions, Shannon entropy and Kullback-Leibler divergence. The statistical significance of the approach and its predictive capability is exemplified using SABER tidal diagnostics with emphasis on the responses to the QBO and solar cycle. Implications for F-region plasma density will be discussed.