Induced seismicity observed during Enhanced Geothermal Stimulation (EGS) at Otaniemi, Finland is modelled using both statistical and physical approaches. The physical model produces simulations closest to the observations when assuming rate-and-state friction for shear failure with diffusivity matching the pressure build-up at the well-head at onset of injections. Rate-and-state friction implies a time dependent earthquake nucleation process which is found to be essential in reproducing the spatial pattern of seismicity. This implies that permeability inferred from the expansion of the seismicity triggering front (Shapiro, 1997) can be biased. We suggest a heuristic method to account for this bias that is independent of the earthquake magnitude detection threshold. Our modelling suggests that the Omori law decay during injection shut-ins results mainly from stress relaxation by pore pressure diffusion. During successive stimulations, seismicity should only be induced where the previous maximum of Coulomb stress changes is exceeded. This effect, commonly referred to as the Kaiser effect, is not clearly visible in the data from Otaniemi. The different injection locations at the various stimulation stages may have resulted in sufficiently different effective stress distributions that the effect was muted. We describe a statistical model whereby seismicity rate is estimated from convolution of the injection history with a kernel which approximates earthquake triggering by fluid diffusion. The statistical method has superior computational efficiency to the physical model and fits the observations as well as the physical model. This approach is applicable provided the Kaiser effect is not strong, as was the case in Otaniemi.
In the coming decades, the frequency of coastal flooding will increase due to sea-level rise and changes in climate extremes. We force the Global Tide and Surge Model (GTSM) with a climate model ensemble from the CMIP6 High Resolution Model Intercomparison Project (HighResMIP) to produce global projections of extreme sea levels (defined as tides and storm surge) from 1950 to 2050. This is the first time that an ensemble of global ~25km resolution climate models is used for this purpose, which increases the credibility of projected storm surges. Here we validate the historical simulations (1985-2014) against the ERA5 climate reanalysis. The overall performance of the HighResMIP ensemble is good with mean bias smaller than 0.1 m. However, there is a strong large-scale spatial bias. Future projections for the high emission SSP5-8.5 scenario indicate changes up to 0.1 m or 20% in 10-year return period surge level from 1951-1980 to 2021-2050. Increases are seen in parts of the coastline of the Caribbean, Madagascar and Mozambique, Alaska, and northern Australia, whereas the Mediterranean region may see a decrease. The full dataset underlying this analysis, including timeseries and statistics, is openly available on the Climate Data Store and can be used to inform broad-scale assessment of coastal impacts under future climate change.
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.
\justifying The atmospheric radiative transfer calculations are among the most time-consuming components of the numerical weather prediction (NWP) models. Deep learning (DL) models have recently been increasingly applied to accelerate radiative transfer modeling. Besides, a physical relationship exists between the output variables, including fluxes and heating rate profiles. Integration of such physical laws in DL models is crucial for the consistency and credibility of the DL-based parameterizations. Therefore, we propose a physics-incorporated framework for the radiative transfer DL model, in which the physical relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. It is also found that the prediction accuracy was improved with the physic-incorporated layer. In addition, we trained and compared various types of deep learning model architectures, including fully connected (FC) neural networks (NNs), convolutional-based NNs (CNNs), bidirectional recurrent-based NNs (RNNs), transformer-based NNs, and neural operator networks, respectively. The offline evaluation demonstrates that bidirectional RNNs, transformer-based NNs, and neural operator networks significantly outperform the FC NNs and CNNs due to their capability of global perception. A global perspective of an entire atmospheric column is essential and suitable for radiative transfer modeling as the changes in atmospheric components of one layer/level have both local and global impacts on radiation along the entire vertical column. Furthermore, the bidirectional RNNs achieve the best performance as they can extract information from both upward and downward directions, similar to the radiative transfer processes in the atmosphere.
Himalayas hydroclimate is a lifeline for South Asia’s most densely populated region. Every year flooding in the Himalayan rivers is usual during monsoon, which impacts millions of inhabitants of the Himalayas and downstream regions. Recent studies demonstrate the role of melting glaciers and snow, in the context of global warming, along with monsoonal rain causing recurrent floods. Here, we highlight the interannual variability in the eastern Himalayan hydroclimate as a natural hazard using observed reanalysis for the last 43 years (1979-2021). We found anomalous extreme years with eight dry years and eight wet years after removing the climate change signal. Monsoon rainfall is a significant contributor, and melting snow is not a potential contributor to these anomalous extreme years. The variability of Himalayan monsoonal rainfall is strongly regulated by local monsoonal Hadley circulation associated with Walker circulation. Our findings demonstrate mechanisms associated with Himalayan wet and dry response. The insights provided in this study underscore the impact of natural variability-driven challenging events that could be predictable. Thus, this mechanism could improve the predictability of the Himalayas floods.
Some programming languages are easy to develop at the cost of slow execution, while others are lightning fast at run time but are much more difficult to write. Julia is a programming language that aims to be the best of both worlds—a development and production language at the same time. To test Julia’s utility in scientific high-performance computing (HPC), we built an unstructured-mesh shallow water model in Julia and compared it against an established Fortran-MPI ocean model, MPAS-Ocean, as well as a Python shallow water code. Three versions of the Julia shallow water code were created, for: single-core CPU; graphics processing unit (GPU); and Message Passing Interface (MPI) CPU clusters. Comparing identical simulations revealed that our first version of the single-core CPU Julia model was 13 times faster than Python. Further Julia optimizations, including static typing and removing implicit memory allocations, provided an additional 10–20x speed-up of the single-core CPU Julia model. The GPU-accelerated Julia code is extremely fast, with a speed-up of 230-380x compared to the single-core CPU Julia code if communication with the GPU occurs every 10 time steps. Parallelized Julia-MPI performance was identical to Fortran-MPI MPAS-Ocean for low processor counts, and ranges from 2x faster to 2x slower for higher processor counts. Our experience is that Julia development is fast and convenient for prototyping, but that Julia requires further investment and expertise to be competitive with compiled codes. We provide advice on Julia code optimization for HPC systems.
Discrepancies exist in global temperature evolution from the Last Glacial Maximum to the present between model simulations and proxy reconstruction. This debate is critical for understanding and evaluating current global warming on a longer timescale. Here we report a branched GDGTs-based temperature reconstruction from the sediments of Huguangyan Maar Lake in southeast China and validate it using historical documentary evidence and instrumental data. The reconstructed mean annual air temperature (MAAT) indicates distinct changes during the last deglaciation (Oldest Dryas, Bølling-Allerød, Younger Dryas). During the Holocene, temperatures gradually increased from the end of the Younger Dryas to ~7.0 ka BP, followed by a decrease in recent decades. However, our terrestrial temperature record differs from model simulations and proxy sea surface temperature records of the Holocene. We conclude that ice volume or ice sheet is the most prominent forcing that controlled the regional temperature evolution from the Last Glacial Maximum to the beginning of the middle Holocene; while the temperature variations during the middle and late Holocene were mainly regulated by several possible factors, such as oceanic and atmospheric circulation, and external drivers (solar and volcanic activity).
Slow, aseismic slip plays a crucial role in the initiation, propagation and arrest of large earthquakes along active faults. In addition, aseismic slip controls the budget of elastic strain in the crust, hence the amount of energy available for upcoming earthquakes. The conditions for slow slip include specific material properties of the fault zone, pore fluid pressure and geometrical complexities of the fault plane. Fine scale descriptions of aseismic slip at the surface and at depth are key to determine the factors controlling the occurrence of slow, aseismic versus rapid, seismic fault slip. We focus on the spatial and temporal distribution of aseismic slip along the North Anatolian Fault, the plate boundary accommodating the 2 cm/yr of relative motion between Anatolia and Eurasia. Along the eastern termination of the rupture trace of the 1944 M7.3 Bolu-Gerede earthquake lies a segment that slips aseismically since at least the 1950’s. We use Sentinel 1 time series of displacement and GNSS data to provide a spatio-temporal description of the kinematics of fault slip. We show that aseismic slip observed at the surface is coincident with a shallow locking depth and that slow slip events with a return period of 2.5 years are restricted to a specific section of the fault. In the light of historical measurements, we discuss potential rheological implications of our results and propose a simple alternative model to explain the local occurrence of shallow aseismic slip at this location.
The atmospheric Green’s function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the "pattern effect." To better study this method, this paper presents a protocol for creating atmospheric Green’s functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere-only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming vs. cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green’s function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation.
The progenitor of SARS-CoV-2 remains unknown, according to a preliminary report released on 9 June 2022 by the WHO panel. Jesse Bloom pondered about the SARS-CoV-2 emergence long before December 2019, putting in check the joint WHO-China report. In addition, a rare conflict of interest occurred: ‘Mr. Inattention’ was a member of the team that the WHO sent to China in 2021 to investigate the COVID-19 origin. The presence of ‘Mr. Inattention’ provides evidence that WHO overlooked a troubling possibility: apparently, there were those who had at least planned to develop full-length infectious clones of bat SARS-related coronaviruses, with insertion of a fragment (proteolytic cleavage site) of this virus into bat coronaviruses, such a cleavage site being able to interact with furin, an enzyme expressed in human cells. Some moral threshold may have been damaged, threatening civilizational security and public health, given the hypothesis of an unnatural origin of SARS-CoV-2. In other words, there is a possibility of a lab-associated origin of this novel pathogen. This makes it illegal to patent vaccines against COVID-19 in Brazil and all other 192 member states of the World Intellectual Property Organization (WIPO), at least as long as such suspicion exists.
Surface water nutrient pollution, the primary cause of eutrophication, remains a major environmental concern in Western Lake Erie despite intergovernmental efforts to regulate nutrient sources. The Maumee River Basin has been the largest nutrient contributor. The two primary nutrients sources are inorganic fertilizer and livestock manure applied to croplands, which are later carried to the streams via runoff and soil erosion. Prior studies on nutrient source attribution have focused on large watersheds or counties at long time scales. Source attribution at finer spatiotemporal scales, which enables more effective nutrient management, remains a substantial challenge. This study aims to address this challenge by developing a portable network model framework for phosphorus source attribution at the subwatershed (HUC-12) scale. Since phosphorus release is uncertain, we combine excess phosphorus derived from manure and fertilizer application and crop uptake data, flow dynamics simulated by the SWAT model, and in-stream water quality measurements into a probabilistic framework and apply Approximate Bayesian Computation to attribute phosphorus contributions from subwatersheds. Our results show significant variability in subwatershed-scale phosphorus release that is lost in coarse-scale attribution. Phosphorus contributions attributed to the subwatersheds are on average lower than the excess phosphorus estimated by the nutrient balance approach adopted by environmental agencies. Phosphorus release is higher during spring planting than the growing period, with manure contributing more than inorganic fertilizer. By enabling source attribution at high spatiotemporal resolution, our lightweight and portable model framework is suitable for broad applications in environmental regulation and enforcement for other regions and pollutants.
Vegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modelled by Land Surface Models (LSMs), which are an essential part of numerical weather prediction and data assimilation. However, most current LSMs implemented specifically in weather forecasting systems use climatological vegetation indices, and land use/land cover datasets in these models are often outdated. In this study, we update land surface data in the ECMWF land surface modelling system ECLand using Earth observation-based time varying leaf area index and land use/land cover data, and evaluate the impact of vegetation dynamics on model performance. The performance of the simulated latent heat flux and soil moisture is then evaluated against global gridded observation-based datasets. Updating the vegetation information does not always yield better model performances because the model’s parameters are adapted to the previously employed land surface information. Therefore we recalibrate key soil and vegetation-related parameters at individual grid cells to adjust the model parameterizations to the new land surface information. This substantially improves model performance and demonstrates the benefits of updated vegetation information. Interestingly, we find that a regional parameter calibration outperforms a globally uniform adjustment of parameters, indicating that parameters should sufficiently reflect spatial variability in the land surface. Our results highlight that newly available Earth-observation products of vegetation dynamics and land cover changes can improve land surface model performances, which in turn can contribute to more accurate weather forecasts.
We report the first detection of unrest at Socompa, Northern Chile, a stratovolcano which has recorded no eruptions since ~7,200 years ago. We measure deformation at and around Socompa using Interferometric Synthetic Aperture Radar (InSAR) observations between Jan 2018 and Oct 2021. We find that, whilst initially inactive, Socompa shows a steady uplift (17.5 mm/yr) from Dec 2019, independently recorded by near-field continuous Global Positioning System (GPS) data. The data can be fit with pressure increase in an ellipsoidal source region stretching from 1.9 to 9.5 km, with a volume change rate of ~5.8×106 m3/yr. Our observations of the onset of uplift preclude the possibility that a nearby Mw 6.8 deep intraslab earthquake on 3rd June 2020 triggered the unrest. The deformation signal we detect indicates the initiation of unrest at Socompa, after at least two decades without measurable deformation, and many thousands of years without volcanic activity.
High-oil tobacco varieties have been recently engineered to produce increased leaf oil content for future food and fuel needs. An engineered variety of Nicotiana tabacum produces ~30 percent of leaf dry weight in lipids in the form of triacylglycerol (TAG), a significant increase relative to the less than 1 percent storage oil normally found in wild-type leaves. This high-oil tobacco also accumulates oil bodies in stomatal guard cells. In order to understand the impact of oil on guard cell shape, aperture, and dynamics, we have co-opted computer vision tools in PlantCV to create an accurate, flexible, and high-throughput method for microscopy image analysis of stomata. To this end, leaf impressions are made with silicone putty; clear nail polish peels of the putty impressions are imaged using light microscopy. Binary thresholding followed by point-and-click regions of interest and morphology calculations provide stomatal counts, aperture, and other shape characteristics. Applying this method to high-oil tobacco demonstrated reduced stomatal aperture but the same number of stomata per unit leaf area, providing a mechanistic explanation of high-oil tobacco responses to high temperature and water deficit stresses.
Mass recycling from subduction to magmatic extrusion shapes our habitable environment and Earth’s interior. Subducted igneous crust may form pyroxenites before participating magmatism, but the deep journey of associated carbonates remains unclear. Here we report new Mg-isotope data for ~89 to 81 Ma basaltic rocks in Langshan area, central Asia (δ26Mg = -0.391 to -0.513 ‰) with a synthesis for post-110 Ma basalts across eastern Asian continent. The merged low-δ26Mg basaltic province normally interpreted as derivations from carbonated sources paradoxically displays geochemical signatures (low Ca/Al and high K2O contents) resembling partial melts of uncarbonated sources. Negative correlations of δ26Mg vs TiO2 and FCKANTMS, the proxy of pyroxenitic melts, and adiabatic melting modeling suggest presence of Mg-isotopically light source pyroxenites transformed from decarbonated altered oceanic crust. This may explain ubiquitous pyroxenitic contributions in many low-δ26Mg basaltic suites and has significant implication for deep carbon cycling.
The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2021. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures. We built XIS with a computationally tractable workflow for extensibility to future years, and we used weighted evaluation to fairly assess performance in sparsely monitored regions. The weighted root mean square error (RMSE) of predictions in site-level cross-validation for 2021 was 1.89 K for the minimum daily temperature, 1.27 K for the mean, and 1.72 K for the maximum. We obtained higher RMSEs in earlier years with fewer ground monitors. Comparing to three leading gridded temperature models in 2021 at thousands of private weather stations not used in model training, XIS had at most 49% of the mean square error for the minimum temperature and 87% for the maximum. In a national application, we report a stronger relationship between minimum temperature in a heatwave and social vulnerability with XIS than with the other models. Thus, XIS-Temperature has potential for reconstructing important environmental exposures, and its predictions have applications in environmental justice and human health.
Atmospheric methane’s rapid growth from 2006 to the present is unprecedented in the observational record. Isotopic evidence implies the growth is mainly driven by an increase in biogenically-sourced emissions, both from wetlands and ruminants, and waste. A significant part of methane’s current rise may come not from direct anthropogenic emissions and land use changes, but rather from a combination of natural biogenic feedback responses, occurring in response to the anthropogenic forcing. Although microbial emissions from agricultural and waste have increased between 2006-2020 by about 35 Tg/yr, perhaps 35-40 Tg/yr of the recent net growth in methane emissions may have been driven by natural biogenic processes, especially wetland feedbacks to climate change. Modelling comparison between the biogenic component of methane’s growth and isotopic shift in the 15 years from 2007-2022, and the global-scale climate reorganisations during the transitions from glacial to interglacial periods in the Pleistocene, shows that the modern growth event is comparable to or greater than the scale and speed of methane’s growth and isotopic shift during past glacial/interglacial termination events. It remains possible that current changes are related to decadal- or centennial-scale variability in precipitation and temperature and remain within the range of Holocene variability, or due to direct anthropogenic actions. But, though any current transition will differ greatly from the past glacial-interglacial changes, it is also possible methane’s remarkable growth and isotopic shift that began in 2006 may be a first indicator that a very large-scale reorganisation of the natural climate and biosphere system is under way.