The Carrascoy and Palomares faults are two major active faults of the Eastern Betic Shear Zone (SE Iberia), both controlling conspicuous mountain fronts. However, the area in between both faults, corresponding to the Mazarron Graben (MG), is a nearly flat plain bounded by a relief of smooth hills whose tectonic origin and evolution remains uncertain. By means of a morphotectonic analysis, geophysical survey and paleoseismological trenching we point out that this is area of distributed deformation controlled by folds of variable amplitude nucleated in high angle reverse faults with sinistral component without a well-defined deformation front. The MG developed a marine basin during the Upper Miocene evolving into an alluvial environment with calcrete pedogenic development through the Pleistocene, which formed a tableland landscape that favors the identification of tectonic structures. In this study we demonstrate how some of the ancient normal faults controlling the graben were reactivated as reverse during the late Middle Pleistocene within a regional frame of positive tectonic inversion. Such inversion is evidenced by several emblematic structures: (i) presence of harpoon folding, and (ii) newly formed high angle reverse faults, which dips increase and ruptures become younger backwards on the hanging wall. Based on the timing of the observed deformation, we also suggest that the onset of the regional tectonic inversion might be related to the tectonic evolution of the neighboring Carrascoy and Palomares faults, producing a local stress tensor varying dramatically from extension to compression within the neotectonic period in a regional convergence tectonic frame.

Permafrost underlies approximately one fifth of the global land area and affects ground stability, freshwater runoff, soil chemistry, and surface‑atmosphere gas exchange. The depth of thawed ground overlying permafrost (active layer thickness, ALT) has broadly increased across the Arctic in recent decades, coincident with a period of increased streamflow, especially the lowest flows (baseflow). Mechanistic links between ALT and baseflow have recently been explored using linear reservoir theory, but most watersheds behave as nonlinear reservoirs. We derive theoretical nonlinear relationships between long‑term average saturated soil thickness η (proxy for ALT) and long-term average baseflow. The theory is applied to 38 years of daily streamflow data for the Kuparuk River basin on the North Slope of Alaska. Between 1983–2020, the theory predicts that η increased 0.11±0.17 [2σ] cm a-1, or 4.4±6.6 cm total. The rate of change nearly doubled to 0.20±0.24 cm a-1 between 1990–2020, during which time field measurements from CALM (Circumpolar Active Layer Monitoring) sites in the Kuparuk indicate η increased 0.31±0.22 cm a-1. The predicted rate of change more than doubled again between 2002–2020, mirroring a near doubling of observed ALT rate of change. The inferred increase in η is corroborated by GRACE (Gravity Recovery and Climate Experiment) satellite gravimetry, which indicates that terrestrial water storage increased ~0.80±3.40 cm a-1, ~56% higher than the predicted increase in η. Overall, hydrologic change is accelerating in the Kuparuk River basin, and we provide a theoretical framework for estimating changes in active layer water storage from streamflow measurements alone.

Superseded by: Sun resonant forcing of Mars, Moon, and Earth seismicityhttps://arxiv.org/abs/2301.10800

Aquatic vegetation modifies hydrodynamics, turbulence structure, sediment transport, and ecological processes in marine ecosystems. Recent turbulence models for vegetated flows have focused on open channel unidirectional flows. However, the unsteadiness and turbulent structure of oscillatory flows often prevent the direct application of such models in wave-dominated environments. We investigate Turbulent Kinetic Energy (TKE) connected to the flow structure in oscillatory flows through aquatic vegetation. Using an oscillatory tunnel, we test vegetation densities up to $\phi=0.10$ with wave periods between 2.1-5.3 s and wave amplitudes between 2-10 cm. Our measurements show a nonlinear relation between the TKE inside the canopy and vegetation density due to the change from the stem- to canopy-scale dominated regime. We observe that $ah\geq 0.8$ marks a threshold for this transition: a reduction of wake TKE inside the canopy and an increase of shear TKE at the top of the canopy. This transition is characterized by increasing frequency and intensity of sweeps and ejections near the bed and at the canopy top. We developed a two-equation predictor for TKE at the top of the canopy using the “short-cut” TKE transfer first proposed by \citeA{finnigan2000turbulence} where canopy-scale eddies convert TKE into stem-scale eddies via the work against vegetation drag. For near-bed TKE, we adapt \citeA{tanino2008lateral}’s model to predict the maximum TKE values on oscillatory flows. These two predictors provide easy-to-use tools suitable for wave-dominated environments to accurately estimate TKE levels inside the canopy for estimating sediment transport rates and mass exchange across the canopy.

To accelerate plant breeding genetic gain, spatial heterogeneity must be considered. Previously, design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments. This study proposes a two-stage approach for improving agronomic trait genomic prediction (GP) using high-throughput phenotyping (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index (NDVI) is measured using a multi-spectral MicaSense camera and ImageBreed. The first stage separates additive genetic effects from local environmental effects (LEE) present in the NDVI throughout the growing season. Considered NDVI LEE (NLEE) are spatial effects from univariate/multivariate two-dimensional splines (2DSpl) and separable autoregressive (AR1) models, as well as permanent environment (PE) effects from random regression models (RR). The second stage leverages the NLEE within genomic best linear unbiased prediction (GBLUP) in two distinct implementations, either modelling an empirical plot-to-plot covariance (L) for random effects or modelling fixed effects (FE). Testing on Genomes-to-Fields (G2F) hybrid maize (Zea mays) field experiments in 2017, 2019, and 2020 for grain yield (GY), grain moisture (GM), and ear height (EH) improves heritability and model fit equally-or-greater than spatial corrections; however, genotypic effect estimation across replicates is not significantly improved. Electrical conductance (EC), elevation, and curvature from a 2019 soil survey significantly improve GP model fit, but less than NLEE. Soil EC and curvature are most correlated to univariate 2DSpl NLEE. Defining L significantly improves genomic heritability and model fit more than setting FE, and RR NLEE can most significantly improve GP for GY and GM.

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

Stable water isotopes in inland Antarctic ice cores are powerful paleoclimate proxies; however, their relationship with dynamical atmospheric circulations remains controversial. Using a water isotope climate model (MIROC5-iso), we assessed the influence of the Last Glacial Maximum (LGM; ~21,000 years ago) sea surface temperatures (SST) and sea ice (SIC) on Antarctic precipitation isotopes (δ18Op) through atmospheric circulation. The results revealed that the synoptic circulation mostly maintained southward moisture transport, reaching inland Antarctica. The steepened meridional SST gradient in the mid-latitudes increased δ18Op in inland Antarctica by enhancing the baroclinic instability and synoptic moisture transport. In contrast, enhanced SIC reduced the atmospheric humidity around Antarctica and lowered δ18Op through extensive surface cooling and transport from the ocean. These findings elucidate the isotopic proxies and enable us to constrain the southern hemisphere atmospheric circulation, including the westerlies, using ice cores during past climates, including the LGM.

Bangladesh, part of Indo-Gangetic-Brahmaputra Plain, is frequently affected by floods and droughts. As the economy of Bangladesh is still agriculture based, effective measurement of soil moisture will not only strengthen the irrigation management but also improve the hydrological modelling and drought prediction. But only nine agro-meteorological stations of Bangladesh measure the soil moisture four times a month which creates a vacuum to scientifically manage her water resources. SMAP (Soil Moisture Active Passive) satellite of NASA provides an unprecedented opportunity for full scale measurement of soil moisture over this region. Field measurements of soil moisture from April 2015 were used to assess the effectiveness of the SMAP’s measurement over the North West Region of Bangladesh which suffers from frequent dry spells. Initially the Root mean squared error (RMSE) between the SMAP and observed soil moisture were found to vary between 12.28 to 16.72% for the available stations. The results showed a bias in SMAP data and it was significantly reduced using bias correction. Later multiple linear regression, based on supplementary climate data in addition to SMAP observations, was applied to obtain an improved estimate of soil moisture and the RMSE were reduced to 1.19 to 3.18%. Lastly, different machine learning techniques (i.e. ANN, SVR, XGBoost etc.) were used to reduce the bias further. This study demonstrates a promising potential of using the SMAP data in soil moisture estimation over Bangladesh for its effective water resources management.

We investigate the applicability of deep learning methods for reconstructing daily weather data. Inspired by video inpainting, we propose a novel method, WeRec3D, which utilizes a three-dimensional convolutional neural network. Our approach was developed iteratively by evaluating six modeling improvement techniques. The resulting method reduces the validation error to 48% compared to the baseline. Additionally, we demonstrate the impact of the spatial distribution of observations on reconstruction accuracy and propose a potential integration with the analogue resampling method. WeRec3D is trained and validated in a self-supervised manner using ERA5’s surface temperature and pressure data over Europe. On a hold-out set from 1950 to 1954, the validation results in an MAE of 1.11 °C and 199 Pa. As a case study, we reconstruct the 1807 heat wave and validate it using a leave-one-out method in space. Compared to the original data, the reconstructed time series exhibit a correlation of at least 0.91, with a maximum normalized RMSE and standard deviation delta of 0.58 and 0.51 respectively. To the best of our knowledge, this is the first study to investigate weather reconstruction using deep learning algorithms, proposing video inpainting as a novel approach for reconstructing missing weather information.

Previous work has established that warming is associated with an increase in dry static stability, a weakening of the tropical circulation, and a decrease in the convective mass flux. Using a set of idealized simulations with specified surface warming and super-parameterized convection, we find support for these previous conclusions. We use an energy and mass balance framework to develop a simple diagnostic that links the fractional area covered by the region of upward motion to the strength of the mean circulation. We demonstrate that the diagnostic works well for our idealized simulations, and use it to understand how changes in tropical ascent area and the strength of the mean circulation relate to changes in heating in the ascending and descending regions. We show that the decrease in the strength of the mean circulation can be explained by the relatively slow rate at which atmospheric radiative cooling intensifies with warming. In our simulations, decreases in tropical ascent area are balanced by increases in non-radiative heating in convective regions. Consistent with previous work, we find a warming-induced decrease in the mean convective mass flux. However, when we condition by the sign of the mean vertical motion, the warming-induced changes in the convective mass flux are non-monontonic and opposite between the ascending and descending regions.

Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top-of-atmosphere radiation balance and stratospheric water vapor budget. These cirrus are often associated with deep convection, which global climate models must parameterize and struggle to accurately simulate. By comparing high-resolution global storm-resolving models from the Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison that explicitly simulate deep convection to satellite observations, we assess how well these models simulate deep convection, convectively generated cirrus, and deep convective injection of water into the TTL over representative tropical land and ocean regions. The DYAMOND models simulate deep convective precipitation, organization, and cloud structure fairly well over land and ocean regions, but with clear intermodel differences. All models produce frequent overshooting convection whose strongest updrafts humidify the TTL and are its main source of frozen water. Inter-model differences in cloud properties and convective injection exceed differences between land and ocean regions in each model. We argue that global storm-resolving models can better represent tropical cirrus and deep convection in present and future climates than coarser-resolution climate models. To realize this potential, they must use available observations to perfect their ice microphysics and dynamical flow solvers.

According to the 2020 FEMA Fact Sheet (FEMA, 2020), Puerto Rico and Virgin Islands are located in a high risk region for earthquake damage. In the last year (05-jul-2019 to 05-jul 2020) the Puerto Rico Seismic Network (PRSN) recorded 136 earthquakes with magnitudes greater than 4.0 and two events had magnitudes greater than or equal to 6.0. The last update of the seismic hazard maps for Puerto Rico and the US Virgin Islands was performed in 2003. According to Mueller et al. (2010), the Bunce fault, the Muertos Trough and related fault structures were not included in the hazard map. The Great Southern Puerto Rico fault, was considered largely quiescent, but Piety et al. (2008) through paleoseismic studies, evidenced activity in this one. Otherwise, offshore faults in southern Puerto Rico were not considered, because their rates of activity were poorly known and assumed to be most likely small. However, starting in December 2019 the Southwestern Puerto Rico (SWPR) Seismic Sequence has been active and continues to be active in these southern offshore faults. According to the StEER technical report (Miranda et al., 2020), the events that occurred between December 2019 and January 2020 caused damage to approximately 10,000 structures and more than 8,000 people were displaced. Based on these facts, and considering that previously unknown tectonic fault structures have been revealed by the SWPR Seismic Sequence (Lopez et al., 2020b), a reevaluation of the seismic threat to Puerto Rico is required. This should include the seismic activity that, according to the PRSN catalog, consists of more than 50,000 events since 2002 to date. This new seismic hazard evaluation will include multiple earthquakes with magnitudes greater than M 5 which were not part of the catalog in 2001. The purpose of this study is generating a new Ground Motion Prediction Equation (GMPE) that incorporates the current seismicity for the region, which unlike the local model used in Mueller et al. (2010) the data will be used directly to obtain the ground motion relationships (Motazedian et al., 2005). This new data now permits to obtain a GMPE from a regression analysis. In the first stage of the research, the aim is generating a preliminary GMPE based on crustal seismicity (events less than 25 km deep) which represents more than 60% of the total registered earthquakes in the PRSN catalog.

The in-situ magnetospheric exploration of the four large planets of our solar system had started with Pioneer 10’s flyby of Jupiter in Dec. 1973. The second collection of field, particle and radio data of the gas giant was carried out by Pioneer 11 in Dec. 1974, before this spacecraft made its closest approach to Saturn in Sep. 1979. Around the same period, Voyager 1 (2) flew by Jupiter in Mar. (Jul.) 1979 then Saturn in Nov. (Aug.) 1980 (1981). As of today, only Voyager 2 visited the magnetospheres of Uranus (Jan. 1986) and Neptune (Aug. 1989). Galileo had remained the only spacecraft to orbit an outer planet for several years (1995 - 2003) until the arrival of Juno at Jupiter in 2016. Between 2004 and 2017, the Cassini mission had provided a wealth of in-situ data pertinent to the study of magnetospheric particles at Saturn. In this paper, we present our current understanding of the processes that shape the spatial distributions of energetic electrons trapped in the magnetospheres of Jupiter (L < 6), Saturn (L < 15) and Uranus (L < 15) obtained by combining multi-instrument analyses of data from past missions (Pioneer, Voyager, Galileo, Cassini) and computational models of charged particle fluxes. To determine what controls the energy and spatial distributions throughout the different magnetospheres, we compute the time evolution of particle distributions with the help of a diffusion theory particle transport code that solves the governing 3-D Fokker-Planck equation. Particle, field and wave datasets are either used to provide model constraints, assist in modeling physical processes, or validate our simulation results. We first emphasize our latest results regarding the relative (or coupled) role of mechanisms at Saturn, including the radial transport and interactions of electrons with Saturn’s dust/neutral/plasma environments and waves, as well as particle sources from high-latitudes, interchange injections, and outer magnetospheric region. The lessons learned from our modeling of electron distributions at Saturn are used to identify the processes that may be missing in our modeling of Jupiter’s energetic electron environment or those in need to be implemented using new modeling concepts. Our first physics-based modeling of electron populations at Uranus is also assessed with our data-model comparison approach.

Cotton production in the United States has reduced since 1950. The U.S. cotton industry is committed to sustainable cotton production practices that reduce water, land, and energy usage and soil loss while improving soil health and cotton yield. Various climate-smart agriculture strategies have been planned that boost yields and may lower operating costs. Cultivars, soil types, management strategies, pests and diseases, climate, and weather patterns impact crops in complex and nonlinear ways and make crop yield prediction difficult. This is where machine learning (ML) comes in. In this work (Fig.1), we aim to accurately predict cotton yield using an ML method incorporating the effects of climate change, soil variety, cultivars, and the nitrogen from NH4, NO3, and Urea. Two types of cotton yield data—field data and synthetic data—were used. The field data was collected in the 1980s and early 1990s across the southern cotton belt. The dataset consisted of different soil types, cultivars, and amounts of nitrogen. However, these data do not reflect the most recent effects of climate change over the past few years. To address this issue, six years of cotton yield data were generated using the process-based cotton model, GOSSYM. This dataset helps train the ML algorithm with the climate change effect and more precisely predict cotton yield. We concentrated on three southern states: Texas, Mississippi, and Georgia. For each state, three different locations in the cotton-producing counties were chosen. Weather data from 2017 to 2022 for each location were generated using the POWER Data Access Viewer web interface. The same planting and harvest dates were selected for all cases. For each case, the accumulated heat unit (AHU) was calculated from the weather data and used as one of the inputs to the ML model. Instead of applying time series weather data, calculating AHU simplified the scenario, and we were able to reduce the number of computations. Soil types, cultivars, and amounts of nitrogen were varied to create combinations of inputs likely to correspond to the range of farm input factors currently being experienced in these locations. We then developed a Random Forest Regressor to predict the yield. The results show the use of this method is highly accurate (~89%), with R2 averaging around 0.82 and a root mean square error of 117.03 kg/ha.

A document by Matthew Lobo. Click on the document to view its contents.

The gap between governmental policy and actions taken by local officials often exists and this gap could be even more severe when it is climate adaptation policy. On the other hand, most of existing studies tend to pay more attention to the structure and the performance of the model rather than the practical operation procedure. To minimize the gap and to evaluate the effectiveness of the adaptation policy, a cross-scale and cross-sector adaptation assessment model, Agriculture and Hydrology assessment model (AgriHydro), is proposed in this study. AgriHydro is established physically to embed top down policies and bottom up local actions and combine with the standard assessment framework, Climate Adaptation 6-Steps and Climate Risk Template, to support decision-making. For sub-models of the AgriHydro, the Aquacrop crop model and the system dynamic water distribution model were calibrated and integrated to form the feedback mechanism. The weather generator (WthGen) modular including climate change scenarios is also established and coupled into the AgriHydro to form a fully functional adaptation assessment tool. An actual application for analyzing the adaptation policy for water supply and crop yield in Taoyuan City of Taiwan is implemented due to the diversity and the complexity of Taoyuan City in land use and water system. The results reflect the possible trade-off of future risks between water resource and agriculture fields under different climate and social scenarios. Moreover, this study shows that the proposed model can provide the linkage to the standard assessment framework, which allows a consist understanding among users on risk and model output. This enables AgriHydro to become a practical and operational tool for decision making and ready for further application.

Land use sector is important in stabilizing global mean temperature rise to 2 °C or less, because land use changes are likely to affect the climate system. Changes in the extent and magnitude of local-to-regional climate by anthropogenic modifications of land use are still largely debated. In this study, we simulate and analyze the climate response to different ranges of idealized extreme land cover changes with two regional climate models (COSMO-CLM v.4.8 and WRF v.3.9.1 in EURO-CORDEX (European branch of the international Coordinated Regional climate Downscaling Experiment-CORDEX initiative) domain. Different experiments are envisioned in this study, including a control run and simulations based on idealized extensive deforestation (replacement of today forestland to bare land or herbaceous vegetation) or afforestation (conversion of today cropland to evergreen needle-leaf forest or deciduous broad-leaf forest). The simulations also include more realistic land cover changes across different land cover classes. The investigated parameters will be the changes of temperature, precipitation, and frequency of temperature extremes at both the entire EURO-CORDEX domain (regional scale) and the changed grids (local scale). Results will also be compared to observation data gathered from satellite retrievals. In the grid cell affected by land cover change, we expect to find temperature changes that are more significant than in non-affected areas. A latitudinal pattern and seasonal variability should also emerge. Of particular interest will be the understanding of the spatial patterns of the climate response to the individual types of land cover changes, their sensitivity to space and location, and the analysis of possible correlations with key vegetation and climate parameters. As biophysical effects from land cover changes shape European climate in different ways, further developments and better understanding of land-climate interactions can ultimately assist decision makers to modulate land management strategies at different scales in light of climate change mitigation and adaptation.

Projected changes in climate are likely to affect not only its mean state but also its variability. As such, improving our understanding of the spectrum of climate variability and how different feedbacks in the climate system influence it is of vital importance. We perform a process-based examination of variability with respect to changing orbital insulation, ice coverage, and land/sea distribution during the Last Glacial Maximum and the Holocene. To this end, we adapt a two-dimensional energy balance model [Zhuang, North & Stevens, 2017] to run transient simulations. The model is forced by carbon dioxide and solar insolation changes for the last Glacial cycle. We evaluate the model’s ability to reproduce changes in local to global, seasonal to millennial temperature distributions during the Last Glacial Maximum and the Holocene. We compare the simulated states and the transient evolution to those obtained by comprehensive coupled climate models. Finally, we test the mean-state dependence of temperature variability over a large range of model configurations and discuss implications for future climate.