Reducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from sparse pCO2 observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions’ ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO2 fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real- world observations. The power of a testbed is that the perfect reconstruction is known for each of the 100 original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a commonly used neural-network approach can skillfully reconstruct air-sea CO2 fluxes when and where it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 39% [15%:58%, interquartile range] overestimation of amplitude, and phasing is only moderately correlated with known truth (r=0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% [3%:34%]. Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink.

Atmospheric rivers (AR) are large and narrow filaments of poleward horizontal water vapor transport. Because of its direct relationship with horizontal vapor transport, extreme precipitation, and overall AR impacts over land, the AR size is an important characteristic that needs to be better understood. Current AR detection and tracking algorithms have resulted in large uncertainty in estimating AR sizes, with areas varying over several orders of magnitude among different detection methods. We develop and implement five independent size estimation methods to characterize the size of ARs that make landfall over the west coast of North America in the 1980-2017 period and reduce the range of size estimation from ARTMIP. ARs that originate in the Northwest Pacific (WP) (100$^\circ$E-180$^\circ$E) have larger sizes and are more zonally oriented than those from the Northeast Pacific (EP) (180$^\circ$E-240$^\circ$E). ARs become smaller through their life cycle, mainly due to reductions in their width. They also become more meridionally oriented towards the end of their life cycle. Overall, the size estimation methods proposed in this work provide a range of AR areas (between 7x10$^{11}$m$^2$ and 10$^{13}$ m$^2$) that is several orders of magnitude narrower than current methods estimation. This methodology can provide statistical constraints in size and geometry for the AR detection and tracking algorithms; and an objective insight for future studies about AR size changes under different climate scenarios.

We present a near-real-time (NRT) scenario of analysis of ionospheric response to the 15 January 2022 Hunga Tonga-Hunga Ha’apai eruption by using GNSS data. We introduce a new method to determine instantaneous velocities using an interferometric approach and using the time derivative of the total electron content (TEC). Moreover, for the first time, we propose a novel method that automatically estimates the propagation velocity of disturbances from near-real-time travel-time diagrams. By using our new methods, we analyzed the dynamics of co-volcanic ionospheric disturbances generated by the Hunga-Tonga eruption, and we estimated the first propagation velocity to be ~800-950 m/s, which subsequently decreased to ~600 m/s. We demonstrate that our approach can be used to detect, analyze and identify the complexity of a natural hazard event. Also, it is important to note that our new methods can perform at a low spatial resolution and 30-sec cadence data.

The auroral electrojet is traditionally measured remotely with magnetometers on ground or in low Earth orbit (LEO). The sparse distribution of measurements, combined with a vertical distance of some 100 km to ground and typically >300 km to LEO satellites, means that smaller scale sizes can not be detected. Because of this, our understanding of the spatiotemporal characteristics of the electrojet is incomplete. Recent advances in measurement technology give hope of overcoming these limitations by multi-point remote detections of the magnetic field in the mesosphere, very close to the electrojet. We present a prediction of the magnitude of these disturbances, inferred from the spatiotemporal characteristics of magnetic field-aligned currents. We also discuss how Zeeman magnetic field sensors (Yee et al., 2020) onboard the Electrojet Zeeman Imaging Explorer (EZIE) satellites will be used to essentially image the equivalent current at unprecedented spatial resolution. The electrojet imaging is demonstrated by combining carefully simulated measurements with a spherical elementary current representation using a novel inversion scheme.

Super-Earth and super-Venus exoplanets may have similar bulk compositions but dichotomous surface conditions and mantle dynamics. Vigorous convection within their metallic cores may produce dynamos and thus magnetospheres if the total heat flow out of the core exceeds a critical value. Earth has a core-hosted dynamo because plate tectonics cools the core relatively rapidly. In contrast, Venus has no dynamo and its deep interior probably cools slowly. Here we develop scaling laws for how planetary mass affects the minimum heat flow required to sustain both thermal and chemical convection, which we compare to a simple model for the actual heat flow conveyed by solid-state mantle convection. We found that the required heat flows increase with planetary mass (to a power of ~0.8–0.9), but the actual heat flow may increase even faster (to a power of ~1.6). Massive super-Earths are likely to host a dynamo in their metallic cores if their silicate mantles are entirely solid. Super-Venuses with relatively slow mantle convection could host a dynamo if their mass exceeds ~1.5 (with an inner core) or ~4 (without an inner core) Earth-masses. However, the mantles of massive rocky exoplanets might not be completely solid. Basal magma oceans may reduce the heat flow across the core-mantle boundary and smother any core-hosted dynamo. Detecting a magnetosphere at an Earth-mass planet probably signals Earth-like geodynamics. In contrast, magnetic fields may not reliably reveal if a massive exoplanet is a super-Earth or a super-Venus. We eagerly await direct observations in the next few decades.

Using data from the Van Allen Probe and Swarm-Bravo satellites, evidence for a persistent population of slow magnetosonic waves in the ionosphere is presented. Dispersion relations from two-fluid analyses of waves in warm plasma are used to interpret and explicate these observations. These waves appear to be continuously present and globally distributed. Their amplitudes systematically decrease with increasing altitude. The amplitudes are also correlated with longitude in a manner consistent with the global distribution of lightning strikes. Evidence for narrow resonances in the Swarm data consistent with doppler shifted Schumann resonance frequencies is presented. In addition, nearly dispersionless fast magnetosonic waves are sometimes also seen. A new method for the analysis of these waves suggests they show the existence of “foamy” plasma bubble “roots” at the base of the ionosphere.

The long-term substitution of mineral phosphorus (P) fertilizers by manure fertilizers combined with nitrogen (N) fertilizer can effectively reduce water pollution and slow down the consumption of P rock resources. Knowledge is needed about how those affect soil P forms to understand soil P cycling and plant P uptake. However, these mechanisms have not been fully elucidated. Therefore, we studied the effects of long-term fertilization on soil P forms in different soil depths. The results of the 40 years field experiments showed that fertilization can significantly improve the P extraction efficiency using NaOH-EDTA in different soil depths. The majority of P was in the form of orthophosphate for all the treatments, which constituted 14.9-483.6 mg/kg (0-20 cm), 24.1-224.4 mg/kg (20-40 cm), and 23.7-59.5 mg/kg (40-60 cm). The proportion of organic P forms was significantly increased by long-term fertilization in 20-40 cm soil layer. The concentrations of all P compounds in topsoil increased significantly under the treatment of the combination of manure fertilizer and mineral fertilizers (N and P). The addition of manure fertilizer extremely increased the stereoisomers of IHP (myo-IHP, scyllo-IHP, neo-IHP, and D-chiro-IHP) and the proportion and concentration of corrected diesters. Fertilization year had no significant difference in IHP stereoisomers and phosphate diester. The results indicate that in the long-term, different fertilization treatments and different soil depths had the best overall effect, which could significantly increase yield and the content of orthophosphate directly absorbed by crops and IHP stereoisomers that could be used by plants in the long run.

This paper examines the physical controls of extratropical humidity and clouds by isolating the effects of cloud physics factors in an idealized model. The Held-Suarez dynamical core is used with the addition of passive water vapor and cloud tracers, allowing cloud processes to be explored cleanly. Separate saturation adjustment and full cloud scheme controls are used to consider the strength of advection-condensation theory. Three sets of perturbations to the cloud scheme are designed to test the model’s sensitivity to the physics of condensation, sedimentation, and precipitation formation. The condensation and sedimentation perturbations isolate two key differences between the control cases. First, the sub-grid-scale relative humidity distribution assumed for the cloud macrophysics influences the location and magnitude of the extratropical cloud maxima which interrupt the isentropic transport of moisture to the polar troposphere. Second, within the model’s explicit treatment of cloud microphysics, re-evaporation of hydrometeors moistens and increases clouds in the lower troposphere. In contrast, microphysical processes of precipitation formation (specifically, the ratio of accretion to autoconversion) have negligible effects on humidity, cloudiness, and precipitation apart from the strength of the large-scale condensation and formation cycle. Additionally, counterintuitive relationships—such as cloud condensate and cloud fraction responding in opposing directions—emphasize the need for careful dissection of physical mechanisms. In keeping with advection-condensation theory, circulation sets the patterns of humidity, clouds, and precipitation to first order, with factors explored herein providing secondary controls. The results substantiate the utility of such idealized modeling and highlight key cloud processes to constrain.

A three-dimensional shear velocity model for the crust and upper mantle beneath the central United States is presented by inverting Rayleigh wave phase velocities from 20s to 100s periods. These phase velocities were determined using regional and teleseismic earthquakes recorded by the Northern Embayment Lithospheric Experiment stations, the CERI New Madrid Seismic Network, the Earthscope Transportable Array, and the Ozark Illinois INdiana Kentucky Flexible Array. A low Vs anomaly is imaged in the mantle below the Reelfoot Rift, which is the uppermost portion of connected low-velocity zones dipping toward the southwest below the rift and to the northwest below the Illinois Basin. According to the analysis in previous tomographic studies using both Vp and Vs anomalies, the elevation of temperature and the enrichment of iron, water, and orthopyroxene contents are required factors to explain the reduced seismic velocities. These low-velocity zones are produced by silica-rich fluids rising from the stalled Farallon slab. Two weak zones characterized by low Vs are imaged below the Ste. Genevieve and the Wabash Valley seismic zones. The low-velocity, weak areas may be responsible for stress concentration and thus the generation of intraplate seismicity.

Over the last several decades, the study of Earth surface processes has progressed from a descriptive science to an increasingly quantitative one due to advances in theoretical, experimental, and computational geosciences. The importance of geomorphic forecasts has never been greater, as technological development and global climate change threaten to reshape the landscapes that support human societies and natural ecosystems. Here we explore best practices for developing socially-relevant forecasts of Earth surface change, a goal we are calling “earthcasting”. We suggest that earthcasts have the following features: they focus on temporal (~1 to ~100 years) and spatial (~1 m to ~10 km) scales relevant to planning; they are designed with direct involvement of stakeholders and public beneficiaries through the evaluation of the socioeconomic impacts of geomorphic processes; and they generate forecasts that are clearly stated, testable, and include quantitative uncertainties. Earthcasts bridge the gap between Earth surface researchers and decision-makers, stakeholders, researchers from other disciplines, and the general public. We investigate the defining features of earthcasts and evaluate some specific examples. This paper builds on previous studies of prediction in geomorphology by recommending a roadmap for (i) generating earthcasts, especially those based on modeling; (ii) transforming a subset of geomorphic research into earthcasts; and (iii) communicating earthcasts beyond the geomorphology research community. Earthcasting exemplifies the social benefit of geomorphology research, and it calls for renewed research efforts toward further understanding the limits of predictability of Earth surface systems and processes, and the uncertainties associated with modeling geomorphic processes and their impacts.

Controls on the deformation pattern (shortening mode and tectonic style) of orogenic forelands during lithospheric shortening remain poorly understood. Here, we use high-resolution 2D thermomechanical models to demonstrate that orogenic crustal thickness and foreland lithospheric thickness significantly control the shortening mode in the foreland. Pure-shear shortening occurs when the orogenic crust is not thicker than the foreland crust or thick, but the foreland lithosphere is thin (< 70-80 km, as in the Puna foreland case). Conversely, simple-shear shortening, characterized by foreland underthrusting beneath the orogen, arises when the orogenic crust is much thicker. This thickened crust results in high gravitational potential energy in the orogen, which triggers the migration of deformation to the foreland under further shortening. Our models present fully thick-skinned, fully thin-skinned, and intermediate tectonic styles in the foreland. The first tectonics forms in a pure-shear shortening mode whereas the others require a simple-shear mode and the presence of thick (> ~4 km) sediments that are mechanically weak (friction coefficient < ~0.05) or weakened rapidly during deformation. The formation of fully thin-skinned tectonics in thick and weak foreland sediments, as in the Subandean Ranges, requires the strength of the orogenic upper lithosphere to be less than one-third as strong as that of the foreland upper lithosphere. Our models successfully reproduce foreland deformation patterns in the Central and Southern Andes and the Laramide province.

Southern Hemisphere (SH) Stratospheric Warming Events (SWEs) are usually associated with a negative phase of the tropospheric Southern Annular Mode (SAM) during the following summer. In contrast, using ensemble climate model simulations we show that the anomalously high ozone concentrations typically occurring during SWEs can force periods of persistent positive tropospheric SAM in austral spring by increasing lower stratospheric static stability and changing troposphere-to-stratosphere wave propagation. Eventually, the tropospheric SAM switches sign to its negative phase in late spring/early summer, but this ‘downward propagation’ of the stratospheric signal does not occur in simulations without seasonal cycle. We find that the downward propagation is forced both dynamically by adiabatic heating and radiatively by increased shortwave absorption by ozone due to the seasonal cycle. Capturing this ozone forcing mechanism in models requires the inclusion of interactive ozone, which has important implications for the predictive skill of current seasonal forecasting systems.

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte datasets. Datasets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence (AI/ML) are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?

Tight oil and gas reservoirs have attracted an increasing amount of attentions and have become one of the focus of research field in recent years. Tight sandstones have complex pore structures and narrow pores and throats with pore sizes varying from nanometers to micrometers, and studying flow mechanisms in tight sandstones is of great importance to tight oil/gas reservoir development. Reconstructing digital rock, which can comprehensively represent the petrophysical properties of tight sandstone, is key to simulating the fluid flow in micro/nanopores. This paper proposes a new method of reconstructing 3D digital rock from CT images of tight sandstones based on a deep convolutional generative adversative network (DCGAN), and 3D convolution in the generator and discriminator are adopted to realize reconstruction from 1D data to a 3D digital rock model. The model adopts pore area, volume, spatial distribution and connectivity, Fréchet inception distance score to evaluate the proposed model. Studies show that when the training effect is slightly poor, the generated digital rock model will exhibit noise, which can be reduced by postprocessing; when the training effect is good, DCGAN can accurately reconstruct the 3D digital rock model of tight sandstones, and the reconstructed digital rock is very consistent with the pore size, geometric structure, and connectivity of natural tight sandstones. When multiple 3D tight sandstone CT images are used for training, the DCGAN can learn the pore structure characteristics of entire tight sandstone bodies, which have strong heterogeneous, and the porosity distribution obtained from the generated digital rock is similar to that of the original tight sandstone.

High-resolution underwater glider data collected in the Gulf of Oman (2015-16), combined with reanalysis datasets, describe the spatial and temporal variability of the mixed layer during winter and spring. We assess the effect of surface forcing and submesoscale processes on upper ocean buoyancy and their effects on mixed layer stratification. Episodic strong and dry wind events from the northwest (Shamals) drive rapid latent heat loss events which lead to intraseasonal deepening of the mixed layer. Comparatively, the prevailing southeasterly winds in the region are more humid, and do not lead to significant heat loss, thereby reducing intraseasonal upper ocean variability in stratification. We use this unique dataset to investigate the presence and strength of submesoscale flows, particularly in winter, during deep mixed layers. These submesoscale instabilities act mainly to restratify the upper ocean during winter through mixed layer eddies. The timing of the spring restratification differs by three weeks between 2015 and 2016 and matches the sign change of the net heat flux entering the ocean and the presence of restratifying submesoscale fluxes. These findings describe key high temporal and spatial resolution drivers of upper ocean variability, with downstream effects on phytoplankton bloom dynamics and ventilation of the oxygen minimum zone.

On average, two tropical storms or cyclones enter the Mozambique Channel between the African mainland and Madagascar each year. Their impact can be devastating. The tropical cyclone Idai, which hit land in Mozambique in 2019, was one of the deadliest storms on record in the Southern Hemisphere. Previous studies have found that the tracks and strengths of tropical storms and cyclones are difficult to predict more than a few days ahead. An extension of this forecast horizon would be crucial for enabling authorities to take precautionary actions. Here, the ability of a state-of-the-art ensemble prediction model to predict Idai and 38 other tropical systems is assessed. It is found that the minimum sea level pressure (SLP) associated with Idai was only skilfully predicted at lead times up to three days. When considering all the systems, less than a quarter of the ensemble members predicted lower minimum SLP than the MERRA-2 reanalysis at lead times of five days and longer. However, several variables are found to be useful precursors of tropical storms and cyclones, and some of these are skilfully predicted at long lead times. In particular, area-averaged anomalies of geopotential height and specific humidity at 500 hPa and SLP one week before the passage of storms are significantly correlated with minimum SLP anomalies during the storms. As these precursor variables are skilfully predicted by the model at lead times of 10–12 days, it should be possible to include their forecasted values in hybrid statistical–dynamical prediction systems at lead times beyond a few days. An additional interesting finding is that warm sea surface temperature anomalies and weak vertical wind shear, which are generally considered to be favourable for tropical storms and cyclones, do not qualify as precursors of the systems investigated here.

Improved imaging of the spatio-temporal growth of fault slip is crucial for understanding the driving mechanisms of earthquakes and faulting. This is especially critical to properly evaluate the evolution of seismic swarms and earthquake precursory phenomena. Fault slip inversion is an ill-posed problem and hence regularisation is required to obtain stable and interpretable solutions. An analysis of compiled finite fault slip models shows that slip distributions can be approximated with a generic elliptical shape, particularly well for M≤7.5 events. Therefore, we introduce a new physically-informed regularisation to constrain the spatial pattern of slip distribution. Our approach adapts a crack model derived from mechanical laboratory experiments and allows for complex slipping patterns by stacking multiple cracks. The new inversion method successfully recovered different simulated time-dependent patterns of slip propagation, i.e., crack-like and pulse-like ruptures, directly using wrapped satellite radar interferometry (InSAR) phase observations. We find that the new method reduces model parameter space, and favours simpler interpretable spatio-temporal fault slip distributions. We apply the proposed method to the 2011 March-September normal-faulting seismic swarm at Hawthorne (Nevada, USA), by computing ENVISAT and RADARSAT-2 interferograms to estimate the spatio-temporal evolution of fault slip distribution. The results show that (1) aseismic slip might play a significant role during the initial stage, and (2) this shallow seismic swarm had slip rates consistent with those of slow earthquake processes. The proposed method will be useful in retrieving time-dependent fault slip evolution and is expected to be widely applicable to studying fault mechanics, particularly in slow earthquakes.

Idealized models can reveal insights into Earth’s climate system by reducing its complexities. However, their potential is undermined by the scarcity of fully coupled idealized models with components comparable to contemporary, comprehensive Earth System Models. To fill this gap, we compare and contrast the climates of two idealized planets which build on the Simpler Models initiative of the Community Earth System Model (CESM). Using the fully coupled CESM, the Aqua configuration is ocean-covered except for two polar land caps, and the Ridge configuration has an additional pole-to-pole grid-cell-wide continent. Contrary to most sea surface temperature profiles assumed for atmosphere-only aquaplanet experiments with the thermal maximum on the equator, the coupled Aqua configuration is characterized by a global cold belt of wind-driven equatorial upwelling, analogous to the eastern Pacific cold tongue. The presence of the meridional boundary on Ridge introduces zonal asymmetry in thermal and circulation features, similar to the contrast between western and eastern Pacific. This zonal asymmetry leads to a distinct climate state from Aqua, cooled by ~2{degree sign}C via the radiative feedback of clouds and water vapor. The meridional boundary of Ridge is also crucial for producing a more Earth-like climate state compared to Aqua, including features of atmospheric and ocean circulation, the seasonal cycle of the Intertropical Convergence Zone, and the meridional heat transport. The mean climates of these two basic configurations provide a baseline for exploring other idealized ocean geometries, and their application for investigating various features and scale interactions in the coupled climate system.