A document by Srishti Singh. Click on the document to view its contents.

Soil health is the capability of soil to provide ecosystem services. These can be quantified through multiple separate indicators (N-mineralization, water infiltration, aggregate stability, etc.) or by a single proxy that integrates many soil processes. Two commonly used integrative measurements are the soil 24h-respiration test (CO2burst) and the visual evaluation of soil structure (VESS). Both are fast, but capture only a part of whole phenomenon of soil health. Soil redox potential is a promising soil and plant health indicator. The redox potential is controlled by soil chemical oxidation-reduction reactions and therefore integrates several processes. However, this method has been tested only on a few soils. In this study, we evaluated redox by comparing it with other established soil health indicators on 35 fields in Finland. Based on the results, redox correlated well with soil biological activity, structure, and texture. Soils with good structure had an oxidized redox status. A low redox state was connected to high biological activity. The carbon farming practices resulted in lower oxidation. A combination of redox and pH could be used to classify soils. The analysis supports the use of redox as a soil health indicator, but further research is needed in identifying the processes and properties the redox is an indicator for.

Drought alone and with associated abiotic stress such as heat and nutrient deficiency leads to significant agricultural crop loss. Thus, with changing climatic conditions, it is important to develop resilience measures in agricultural systems against drought stress. In this review, we discuss the modifications in plants while responding to drought giving special focus on roots as they are the primary sense organs in this context. Prospects of genomic crop improvement by pointing out the focus areas to engineer root system architecture and genomic regions involved in the related traits are also discussed. We have also listed instruments and software facilitating high throughput phenotyping of root system in field conditions as the phenotyping of root system architecture in the field is a challenge.

Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such genomic prediction in sugarcane presents an interesting case for machine learning methods, which are purportedly able to deal with high levels of complexity in prediction. Here we investigate deep learning networks (DL), including Multilayer networks (MLP) and convolution neural networks (CNN), and Random Forest (RF) for genomic prediction in sugarcane. The data set was 2912 sugarcane clones, scored for 26,086 genome wide SNP markers, with final assessment trial (FAT) data for total cane harvested (TCH), Commercial cane sugar (CCS) and Fibre content. The clones in the latest trial (2017) were used as a validation set. We compared performances of these methods to GBLUP extended to include dominance and epistatic effects. The prediction accuracies from GBLUPs were 0.37 for TCH, 0.37 for CCS and 0.48 for Fibre, while the DL models had accuracies of 0.33 for TCH prediction, 0.38 for CCS prediction and 0.43 for Fibre. Optimised RF achieved a prediction accuracy of 0.35 for TCH, 0.38 for CCS and 0.48 for Fibre. Both DL and RF predictions were more accurate additive GBLUP but generally lower than extended GBLUP. Finally, we identified a partially shared distribution of SNP selections between RF and GBLUP models. We conclude RF may have some utility for genomic prediction for crops with highly complex genomes, particularly if non-additive interactions can be captured with clonal propagation.

Soil moisture influences near-surface air temperature by partitioning downwelling radiation into latent and sensible heat fluxes, through which dry soils generally lead to higher temperatures. The strength of this coupled soil moisture-temperature (SM-T) relationship is not spatially uniform, and numerous methods have been developed to assess SM-T coupling strength across the globe. These methods tend to involve either idealized climate-model experiments or linear statistical methods which cannot fully capture nonlinear SM-T coupling. In this study, we propose a nonlinear machine learning-based approach for analyzing SM-T coupling and apply this method to various mid-latitude regions using historical reanalysis datasets. We first train convolutional neural networks (CNNs) to predict daily maximum near-surface air temperature (TMAX) given daily SM and geopotential height fields. We then use partial dependence analysis to isolate the average sensitivity of each CNN’s TMAX prediction to the SM input under daily atmospheric conditions. The resulting SM-T relationships broadly agree with previous assessments of SM-T coupling strength. Over many regions, we find nonlinear relationships between the CNN’s TMAX prediction and the SM input map. These nonlinearities suggest that the coupled interactions governing SM-T relationships vary under different SM conditions, but these variations are regionally dependent. We also apply this method to test the influence of SM memory on SM-T coupling and find that our results are consistent with previous studies. Although our study focuses specifically on local SM-T coupling, our machine learning-based method can be extended to investigate other coupled interactions within the climate system using observed or model-derived datasets.

A document by Siddhartha Bishnu. Click on the document to view its contents.

\justify Several methods have been developed to quantify the oceanic accumulation of anthropogenic carbon dioxide (CO$_2$) in response to rising atmospheric CO$_2$. Yet, we still lack a corresponding estimate of the changes in the total oceanic dissolved inorganic carbon (DIC). In addition to the increase in anthropogenic CO$_2$, changes in DIC also include alterations of natural CO$_2$. Once integrated globally, changes in DIC reflect the net oceanic sink for atmospheric CO$_2$, complementary to estimates of the air-sea CO$_2$ exchange based on surface measurements. Here, we extend the MOBO-DIC machine learning approach by \citeA{keppler_mapped_2020} to estimate global monthly fields of DIC at 1$^{\circ}$ resolution over the top 1500 m from 2004 through 2019. We find that over these 16 years and extrapolated to cover the whole global ocean down to 4000 m, the oceanic DIC pool increased close to linearly at an average rate of 3.2$\pm$0.7 Pg C yr$^{-1}$. This trend is statistically indistinguishable from current estimates of the oceanic uptake of anthropogenic CO$_2$ over the same period. Thus, our study implies no detectable net loss or gain of natural CO$_2$ by the ocean, albeit the large uncertainties could be masking it. Our reconstructions suggest substantial internal redistributions of natural oceanic CO$_2$, with a shift from the mid-latitudes to the tropics and from the surface to below $\sim$200 m. Such redistributions correspond with the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. The interannual variability of DIC is strongest in the tropical Western Pacific, consistent with the El Ni$\tilde{n}$o Southern Oscillation.

Tree training systems for temperate fruit have been developed throughout history by pomologists to improve light interception, fruit yield, and fruit quality. These training systems direct crown and branch growth to specific configurations. Quantifying crown architecture could aid the selection of trees that require less pruning or that naturally excel in specific growing/training system conditions. Regarding peaches [Prunus persica (L.) Batsch], access tools such as branching indices (BIs) have been developed to characterize tree crown architecture. However, the required branching data to develop these indices are difficult to collect. Traditionally, branching data have been collected manually, but this process is tedious, time-consuming, and prone to human error. These barriers can be circumnavigated by utilizing terrestrial LiDAR (TLS) to obtain a digital twin of the real tree. TLS generates three-dimensional (3D) point clouds of the tree crown, wherein every point contains 3D coordinates (x, y, z). To facilitate the use of these tools for peach, we selected four young peach trees scanned in 2021 and 2022. These four young trees were then modeled and quantified using the open-source software TreeQSM. As a result, “in silico” branching and biometric data for the young peach trees were calculated to demonstrate the capabilities of TLS phenotyping of peach tree-crown architecture. The comparison and analysis of field measurements (in situ) and in silico branching data (BD), biometric data, and residual ground truth data were utilized to determine the reconstructive model’s reliability as a source substitute for field measurements. Mean average deviation (MAD) when comparing young tree height was approx. 8.2%, with crown volume (crV) was approx. 7.6% across both 2021 and 2022. All point clouds of the young trees in 2022 showed residuals < 10mm to cylinders fitted to all branches, and mean surface coverage >50% across all branching orders.

The total meridional heat transport (MHT) is relatively stable across different climates. Nevertheless, the strength of individual processes contributing to the total transport are not stable. Here we investigate the MHT and its main components especially in the atmosphere, in five coupled climate model simulations from the Deep-Time Model Intercomparison Project (DeepMIP). These simulations target the Early Eocene Climatic Optimum (EECO), a geological time period with high CO2 concentrations, analogous to the upper range of end-of-century CO2 projections. Preindustrial and early Eocene simulations at a range of CO2 levels (1x, 3x and 6x preindustrial values) are used to quantify the MHT changes in response to both CO2 and non-CO2 related forcings. We found that atmospheric poleward heat transport increases with CO2, while the effect of non-CO2 boundary conditions (e.g., paleogeography, land ice, vegetation) is causing more poleward atmospheric heat transport on the Northern and less on the Southern Hemisphere. The changes in paleogeography increase the heat transport via transient eddies at the mid-latitudes in the Eocene. The Hadley cells have an asymmetric response to both the CO2 and non-CO2 constraints. The poleward latent heat transport of monsoon systems increases with rising CO2 concentrations, but this effect is offset by the Eocene topography. Our results show that the changes in the monsoon systems’ latent heat transport is a robust feature of CO2 warming, which is in line with the currently observed precipitation increase of present day monsoon systems.

Volcanic glass and its mixture with smectite are commonly observed in shallow parts of subduction zones. As volcanic glass layers often act as a glide plane to induce mass transportation such as submarine landslides, and because its alteration product, smectite, is one of the frictionally weakest geological materials, the frictional characteristics of volcanic glass-smectite mixtures are important for fault slip behavior in shallow parts of subduction zones. We performed a series of friction experiments on volcanic glass-smectite mixtures with different smectite contents at various velocity conditions from 10 μm/s to 1 m/s under an effective normal stress of 5 MPa and pore pressure of 10 MPa. In general, friction coefficients negatively depend on the smectite content at any velocity tested. We found that samples with smectite contents of 15-30 % showed a drastic slip-weakening behavior at intermediate velocities of 1-3 mm/s with a characteristic slip displacement of ~0.1 m. Finite element method modeling shows that thermal pressurization does not contribute to the observed weakening behavior. We propose that gouge fluidization or compaction-induced pore pressure increase may be the cause of the weakening. The slip-weakening behavior at intermediate velocities enlarges a critical nucleation length for frictional instability to 1-30 km, or prevent acceleration to seismic slip velocities. Therefore, gouges with minor amount of clay, such as subducting volcanic ash layers, may contribute to the occurrence of the at shallow depths in subduction zones.

Simulating whole atmosphere dynamics, chemistry, and physics is computationally expensive. It can require high vertical resolution throughout the middle and upper atmosphere, as well as a comprehensive chemistry and aerosol scheme coupled to radiation physics. An unintentional outcome of the development of one of the most sophisticated and hence computationally expensive model configurations is that it often excludes a broad community of users with limited computational resources. Here, we analyze two configurations of the Community Earth System Model Version 2, Whole Atmosphere Community Climate Model Version 6 (CESM2(WACCM6)) with simplified “middle atmosphere” chemistry at nominal 1 and 2 degree horizontal resolutions. Using observations, a reanalysis, and direct model comparisons, we find that these configurations generally reproduce the climate, variability, and climate sensitivity of the 1 degree nominal horizontal resolution configuration with comprehensive chemistry. While the background stratospheric aerosol optical depth is elevated in the middle atmosphere configurations as compared to the comprehensive chemistry configuration, it is comparable between all configurations during volcanic eruptions. For any purposes other than those needing an accurate representation of tropospheric organic chemistry and secondary organic aerosols, these simplified chemistry configurations deliver reliable simulations of the whole atmosphere that require 35% to 86% fewer computational resources at nominal 1 and 2 degree horizontal resolution, respectively.

Oceanic emissions of nitrous oxide (N2O) account for roughly one-third of all natural sources to the atmosphere. Hot-spots of N2O outgassing occur over oxygen minimum zones (OMZs), where the presence of steep oxygen gradients surrounding anoxic waters leads to enhanced N2O production from both nitrification and denitrification. However, the relative contributions from these pathways to N2O production and outgassing in these regions remains poorly constrained, in part due to shared intermediary nitrogen tracers, and the tight coupling of denitrification sources and sinks. To shed light on this problem, we embed a new, mechanistic model of the OMZ nitrogen cycle within a three-dimensional eddy-resolving physical-biogeochemical model of the ETSP, tracking contributions from remote advection, atmospheric exchange, and local nitrification and denitrification. Our results indicate that net N2O production from denitrification is approximately one order of magnitude greater than nitrification within the ETSP OMZ. However, only ~30% of denitrification-derived N2O production ultimately outgasses to the atmosphere in this region (contributing ~34% of the air-sea N2O flux on an annual basis), while the remaining is exported out of the domain. Instead, remotely-produced N2O advected into the OMZ region accounts for roughly half (~56%) of the total N2O outgassing, with smaller contributions from nitrification (~7%). Our results suggests that, together with enhanced production by denitrification, upwelling of remotely-derived N2O (likely produced via nitrification in the oxygenated ocean) contributes the most to N2O outgassing over the ETSP OMZ.

Seismological data can provide timely information for slope failure hazard assessments, among which rockfall waveform identification is challenging for its high waveform variations across different events and stations. A rockfall waveform does not have typical body waves as earthquakes do, so researchers have made enormous efforts to explore characteristic function parameters for automatic rockfall waveform detection. With recent advances in deep learning, algorithms can learn to automatically map the input data to target functions. We develop RockNet via multitask and transfer learning; the network consists of a single-station detection model and an association model. The former discriminates rockfall and earthquake waveforms. The latter determines the local occurrences of rockfall and earthquake events by assembling the single-station detection model representations with multiple station recordings. RockNet achieves macro F1 scores of 0.990 and 0.981 in terms of discriminating earthquakes and rockfalls from other events with the single-station detection and association models, respectively.

River flows change on timescales ranging from minutes to millennia. These variations influence fundamental functions of ecosystems, including biogeochemical fluxes, aquatic habitat, and human society. Efforts to describe temporal variation in river flow—i.e., flow regime—have resulted in hundreds of unique descriptors, complicating interpretation and identification of global drivers of flow dynamics. Here, we used a cross-disciplinary analytical approach to investigate two related questions: 1. Is there a low-dimensional structure that can be used to simplify descriptions of streamflow regime? 2. What catchment characteristics are most associated with that structure? Using a global database of daily river discharge from 1988-2016 for 3,120 stations, we calculated 189 traditional flow metrics, which we compared to the results of a wavelet analysis. Both quantification techniques independently revealed that streamflow data contain substantial low-dimensional structure that correlates closely with a small number of catchment characteristics. This structure provides a framework for understanding fundamental controls of river flow variability across multiple timescales. Climate was the most important variable across all timescales, especially those lasting several weeks, and likely contributes as much as dams in controlling flow regime. Catchment area was critical for timescales lasting several days, as was human impact for timescales lasting several years. In addition, both methods suggested that streamflow data also contain high-dimensional structure that is harder to predict from a small number of catchment characteristics (i.e. is dependent on land use, soil structure, etc.), and which accounts for the difficulty of producing simple hydrological models that generalize well.

The lower cell of the meridional overturning circulation (MOC) is sourced by dense Antarctic Bottom Water (AABW), which forms and sinks around Antarctica and subsequently fills the abyssal ocean. For the MOC to ‘overturn’, these dense waters must upwell through mixing with lighter waters above. Here, we investigate the processes underpinning such mixing, and the resulting water mass transformation, using an observationally forced, high-resolution numerical model of the Drake Passage in the Southern Ocean. In the Drake Passage, the mixing of dense AABW formed in the Weddell Sea with lighter deep waters transported from the Pacific Ocean by the Antarctic Circumpolar Current is catalysed by energetic flows impinging on rough topography. We find that multiple topographic interaction processes act to facilitate mixing of the two water masses, ultimately resulting in upwelling of waters with neutral density greater 28.19 kg m-3, and downwelling of the lighter waters above. In particular, we identify the role of sharp density interfaces between AABW and overlying waters, and find that the dynamics of the interfaces’ interaction with topography can enhance mixing. Such sharp interfaces between water masses have been observed in several parts of the global ocean, but are unresolved and unrepresented in ocean and climate models. We suggest that they are likely to play an important role in abyssal dynamics and mixing, and therefore require further exploration.

Climate influences on below-ground plant traits seldom receive due attention. Climate change is varying the availability of resources, soil physical properties, rainfall events, soil mineral weathering and leaching intensity which collectively determines soil physical and chemical properties. Soil constraints – acidity (pH<6), salinity(pH≤8.5), sodicity and dispersion (pH>8.5) are major causes of wheat yield loss in arid and semi-arid cropping systems. To cope with changing environment, plants employ adaptive strategies such as phenotypic plasticity; a key multifaceted trait, to promote shifts in phenotypes. Adaptive strategies are complex, determined by key functional traits and Genotype × Environment interactions. The understanding of molecular basis of stress tolerance is particularly challenging for plasticity traits. Advances in sequencing and high-throughput genomics technologies has identified functional alleles in gene-rich regions, haplotypes, candidate genes, mechanisms and in silico gene expression profiles at various growth developmental stages. Our review focuses on favourable alleles for enhanced gene expression, QTLs and epigenetic regulation of plant responses to soil constraints including heavy metal stress and nutrient limitations. A strategy is then described for quantitative traits in wheat by investigating significant alleles, functional characterization of variants, followed by gene validation using advanced genomic tools and marker development for molecular breeding and genome editing. Also, the review highlights the progress of gene editing in wheat, multiplex gene editing and novel alleles for smart control of gene expression. Integration of these genomic technologies will be effective to enhance plasticity traits and stabilizing wheat yields on constrained soils in the face of climate change.

In the absence of consistent meteorological data on Mars, the morphology of dunes can be employed to study its atmosphere. Specifically, barchan dunes, which form under approximately unimodal winds, are reliable proxies for the dominant wind direction. Here, we characterize near-surface winds on Mars from the morphology of >106 barchans mapped globally on the planet by a convolutional neural network. Barchan migration is predominantly aligned with the global circulation: northerly at mid-latitudes and cyclonic near the north pole, with the addition of an anti-cyclonic north-polar component that likely originates from winds emerging from the ice cap. Locally, migration directions deviate from regional trends in areas with high topographic roughness. Notably, obstacles <100 km such as impact craters are efficient at deflecting surface winds. Our database, which provides insights into planetary-scale aeolian processes on modern-day Mars, can be used to constrain global circulation models to assist with predictions for future missions.

Microseismicity is expected in potash mining due to the associated rock-mass response. This phenomenon is known, but not fully understood. To assess the safety and effciency of mining operations, producers must quantitatively discern between normal and abnormal seismic activity. In this work, statistical aspects and clustering of microseismicity from a Saskatchewan, Canada, potash mine are analyzed and quantified. Specifically, the frequency-magnitude statistics display a rich behavior that deviates from the standard Gutenberg-Richter scaling for small magnitudes. To model the magnitude distribution, we consider two additional models, i.e., the tapered Pareto distribution and a mixture of the tapered Pareto and Pareto distributions to fit the bi-modal catalog data. To study the clustering aspects of the observed microseismicity, the nearest-neighbor distance (NND) method is applied. This allowed the identification of potential cluster characteristics in time, space, and magnitude domains. The implemented modeling approaches and obtained results will be used to further advance strategies and protocols for the safe and effcient operation of potash mines.