Temperature is central for ocean science but is still poorly sampled on the deep ocean. Here, we show that Distributed Acoustic Sensing (DAS) technology can convert several kilometer long seafloor fiber-optic (FO) telecommunication cables into dense arrays of temperature anomaly sensors with milikelvin (mK) sensitivity, allowing us to monitor oceanic processes such as internal waves and upwelling with unprecedented detail. We validate our observations with oceanographic in-situ sensors and an alternative FO technology. Practical solutions and recent advances are outlined to obtain continuous absolute temperatures with DAS at the seafloor. Our observations grant key advantages to DAS over established temperature sensors, showing its transformative potential for thermometry in ocean sciences and hydrography.
The cryptocurrency sector is increasingly integrated into the global financial system. The world’s transition to a digital economy, facilitated by major technological breakthroughs, has several benefits. But as the demand for exchanging and investing in digital currencies is growing , the world must pay careful attention to the hidden and overlooked environmental impacts of this growth. The dramatic increase in the price of Bitcoin (BTC) over the last year and the resulting global race for BTC mining is turning the cryptocurrency market turning into one of the world’s leading polluting sectors. Yet, our knowledge about the environmental footprints of mining BTC is very limited. To address this hap, this study provides the first estimates of the carbon, water and land footprints of BTC mining around the world.
Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording the total solar irradiance (TSI). Some of these measurements last for more than a decade. It is then mandatory to merge them to obtain a seamless record whose duration exceeds that of the individual instruments. Climate models can be better validated using such long TSI records which can also help provide stronger constraints on past climate reconstructions (e.g.,back to the Maunder minimum). We propose a 3-stepmethod based on data fusion, including a stochastic noise model to take into account short and long-term correlations. Compared with previous products, the difference in terms of mean value over the whole time series and at the various solar minima are below 0.2W/m2. Next, we model the frequency spectrum of this 41-year TSI composite time series with a Generalized Gauss-Markov model to help describing an observed flattening at high frequencies. It allows us to fit a linear trend into these TSI time series by joint inversion with the stochastic noise model via a maximum-likelihood estimator. Our results show that the amplitude of such trend is∼−0.009±0.010 W/(m2.yr) for the period 1980-2021. These results are compared with the difference of irradiance values estimated from two consecutive solar minima. We conclude that the trend in these com-posite time series is mostly an artefact due to the coloured noise.
Near-Earth asteroids and meteoroids constitute various levels of impact danger to our planet. On the one end, billions of events associated with small-sized meteoroids have resulted in trivial effects. On the other end, the occurrences of large-sized asteroidal collisions that can cause mass extinctions and may wipe out the modern human civilization are extremely rare. In addition, large near-Earth asteroids are being monitored constantly for accurate and precise predictions of potential hazardous visits to our planet. However, small asteroids and large meteoroids can still often go under the radar and cause bolide explosions with potential of significant damage to communities on the ground. To facilitate management of bolide hazard, a number of scholarly works have been dedicated to estimation of frequencies of bolide events from a global perspective for planetary defense and mitigation. Nevertheless, few of the existing bolide frequency models were developed for local hazard management. In this presentation, the author introduces two recently developed frequency models for local management of bolide hazard. The first one, called the Dome model, computes the expected frequency of bolide explosions within a dome-shaped volume around a location. The second one, called the Coffee Cup model, is for a column-shaped volume above an area. Both models are based on empirical calibrations with historical data on energy, latitude, altitude, and frequency of bolide events. The modeling results indicate a linearly decreasing trend of frequency of bolide events from south to north latitudinally around the globe. The presented models can be applied to any location or area on Earth, including the entire surface of the planet.
This study uses a newly-developed firebrand spotting parameterization in simulations of the Marshall Fire (2021) to demonstrate that without fire spotting, wind-driven fire simulations cannot reproduce the behavior of some fires. The Marshall Fire, the most destructive in Colorado’s history, took mere hours to cause nearly half a billion dollars in damage and destroy over 1000 homes. In wind-driven events that occur in the wildland-urban interface, the model’s ability to spot is critical for modeling fire spread over water streams and urban features such as highways. Without ignition of fire spots, the simulated Marshall Fire cannot advance. In cases when spotting significantly contributes to fire spread, the process’ nonlinear nature is a source of uncertainty to modeling fire behavior that can broaden the model’s ensemble spread and possibly produce a more realistic probability of outcomes. The results in this study corroborate the importance of representing fire spotting in atmosphere-fire behavior coupled models, such as WRF-Fire.
Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the ‘signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. Although this procedure originated in LiDAR-based estimation of mean tree and canopy height, ground noise filtering has remained prevalent in LiDAR pre-processing, even as modelers have shifted focus to forest aboveground biomass (AGB) and related characteristics for which ground returns may actually contain useful information about stand density and openness. In particular, ground returns may be helpful for making accurate biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. We applied several ground noise filtering thresholds while mapping two regions within New York State, one a forest-dominated area and the other a mixed-use landscape. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling. By fitting random forest models to each of these predictor sets, we found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds. The relative inferiority of AGB models based on filtered LiDAR returns was much greater for the mixed land-cover study area than for the contiguously forested study area. Our results suggest that ground filtering should be avoided when mapping biomass, particularly when mapping heterogeneous and highly patchy landscapes, as ground returns are more likely to represent useful ‘signal’ than extraneous ‘noise’ in these cases.
Coastal interfaces blend processes dominated by upland region hydrology and ocean hydrodynamics (tides, winds, waves, baroclinic fluctuations, among others). These areas tend to be vulnerable to flooding, a matter of concern considering that around 40% of the world’s population lives within 100 km of the ocean. Specifically, The US East and Gulf of Mexico Coasts are heavily affected by extratropical storms every year with catastrophic consequences. Models that integrate the dynamics of both oceans and river networks are needed in order to better improve flood forecast systems in coastal areas. Due to their spatial and temporal scale differences, traditional models solve river and ocean hydrodynamics independently. As a first step toward unifying coastal interface modeling, we designed an ADCIRC-based model that uses unstructured, highly variable-sized triangular meshes that can accurately represent both ocean basins and inland river networks. This meshing technique allows for incorporating features that control the dynamics of the nearshore area, such as barrier islands, jetties, and dredged channels. We analyze how mesh design impacts water level estimations in the deep ocean as well as inland rivers. Accuracy in the deep ocean is sensitive primarily to bathymetry in areas with high energy dissipation, whereas water level prediction within river networks depends on both bathymetry and resolution. While a minimum resolution in the order of a hundred meters is enough to accurately predict water level for most rivers with tidal influence, smaller tributaries require resolutions down to tens of meters. Future research will use these findings to build precipitation and rainfall-runoff into the model for a more comprehensive understanding of the coastal interface hydrodynamics.
The main sources of the ambient seismic wavefield in the microseismic frequency band (peaking in the ~0.04-0.5 Hz range) are the earth’s oceans, namely wind-driven surface gravity waves (SGW) coupling oscillations into the seafloor and the upper crust underneath. Cyclones (e.g. hurricanes, typhoons) and other atmospheric storms are efficient generators of high ocean waves with complex but distinct microseismic signatures. In this study, we perform a polarization (i.e. 3-component) beamforming analysis of microseismic (0.05-0.16 Hz) retrograde Rayleigh and Love waves during major Atlantic hurricanes using a virtual array of seismometers in North America. Oceanic hindcasts and meteorological data are used for comparison. No continuous generation of microseism along the hurricane track is observed but rather an intermittent signal generation at specific oceanic locations along the track. Both seismic surface wave types show clear cyclone-related microseismic signatures and are consistent with a colocated generation at near-coastal or shallow regions, however the Love wavefield is comparatively less coherent. We identify two different kind of signals: a) intermittent signals that originate with a constant spatial lag at the trail of the hurricanes and b) signals remaining highly stationary in direction of arrival even days after the hurricane passed the presumable source region. This high complexity highlights the need for further studies to unravel the interplay between site-dependent geophysical parameters and SGW forcing at depth, as well as the potential use of cyclone microseisms as passive natural sources.
Energy, transport, urbanization and burning are responsible for changes in atmospheric BC. This work uses direct solar atmospheric column measurements of single scatter albedo [SSA] retrieved at multiple wavelengths from AERONET at 68 Asian sites over 17 years. A MIE model is solved across the wavelengths using a core-shell mixing approximation to invert the probabilistic BC, shell size, and UV SSA. Orthogonal patterns are obtained for urban, biomass burning [BB], and long-range transport [LRT] conditions, which are used to analyze and attribute source types of BC across the region. Large urban areas (thought to be dominated by urban BC) are observations during targeted times (shorter than seasonally) to yield significant contributions from non-urban BC. BB and LRT are observed to dominate Beijing and Hong Kong 2 months a year. LRT is observed during the clean Asian Monsoon season in both Nepal and Hong Kong, with sources identified from thousands of kilometers away. Computing the shift in shell size required to constrain the results approximates secondary aerosol growth in-situ, and subsequently aerosol lifetime, which is found to range from 11 days to a month, implying both a significant amount of BC above the boundary layer, and that BC generally has a longer lifetime than PM2.5. These findings are outside of the range of most modeling studies focusing on PM2.5, but are consistent with independent measurements from SP2 and modeling studies of BC that use core-shell mixing together with high BC emissions.
Soil moisture is a critical component in many meteorological, hydrological, and agricultural applications, and understanding its spatial and temporal dynamics is vital for the understanding of these processes. Satellite-based remote sensing offers the ability to synoptically capture this spatiotemporal information over large areas, compared to more site-based in-situ field measurements. In this study, we use Sentinel-1 SAR imagery of the River Thames catchment, United Kingdom, over the period 2015 - 2020. A backscatter normalisation process is applied to account for the use of multiple satellite viewing geometries. A change-detection algorithm utilising backscatter power is then applied to the timeseries, to estimate relative surface soil moisture (rSSM) across the study area. To determine information across the large river watershed, smaller sub-catchments, and intra-field scales, the rSSM time series is replicated at multiple spatial scales (1 km, 500m, 250m, and 100m). Although positive biases are present during the growing season of arable farmland, comparison with rainfall data and in-situ soil moisture probes shows there is good agreement with the temporal cycle of soil moisture. These data are being used to evaluate natural flood management by land use and management across a wide area to better understand relationships between surface wetness and water storage in relation to land cover and underlying geology for the Landwise project (Landwise-NFM.org).
Climate is a key factor affecting water and energy resources. To quantify its effect, researchers have developed statistical and mechanistic models that simulate the interactions of the Water-Energy-Climate Nexus (WECN). In this work, we address two limitations of current WECN models, including the need to (1) account for the impact of climate on both demand and supply of water and energy, and (2) consider the influence of multiple climate variables in addition to temperature and precipitation. For this aim, we build upon our existing model that simulates in coupled fashion the water and energy systems of the metropolitan region of Phoenix, AZ. We design multidecadal storylines of future climate using an ensemble of downscaled general circulation models from CMIP6, along with possible energy infrastructure expansion scenarios. We develop a hydrologic model to account for the climate impacts on surface water supply sources. We constrain electricity supply based on water availability and temperature-dependent water intensities for power production. In parallel, we consider the effect of climate on demand through multilinear regressions between per-capita water and energy demand and several climate variables. This work advances the simulation of WECN interactions by integrating statistical and mechanistic models of water and energy systems with climate and hydrologic models. While the modeling framework is tested in the Phoenix metropolitan region, our findings provide useful insights that support WECN modeling efforts in other regions.
Land cover information is fundamental in numerical weather prediction and climate modelling because of its impact on the land surface heat, momentum, and moisture fluxes. A new land cover (LC) dataset for the European region is introduced here for the WRF (Weather Research and Forecasting) model coupled with the Noah-MP surface scheme. As part of the Copernicus program the satellite-based Coordination of Information on the Environment (CORINE) LC dataset is available for most of the European continent at high resolution (100 m). This dataset provides a more detailed land cover classification compared to the default WRF LC database over Europe. Its potential applications range from urban numerical studies to regional climate modelling. The CORINE dataset is incorporated into WRF at two different resolutions of 0.00208333° and 0.00416666°. Furthermore, the original 44-category CORINE LC for the WRF model is converted to the USGS LC categories for applications where less detailed but still up-to-date information is desired. It is shown that the application of the CORINE LC dataset not only affects near-surface temperatures (by ≈1 °C on average and ≈3-6 °C over urban areas) but precipitation, snow cover, and wind speed as well.
Transport of sediment particles from the source of their origin to a deposition area is of utmost importance, especially in catchments very prone to erosion. Especially, since future climate changes are predicted to enhance severity of the sediment transport issues, particularly in catchments with dammed reservoirs, which capacity and water quality can be extremely altered. In the current study we tracked, with a monthly step, two mineral and one mineral/organic sediment fraction delivered from the Carpathian Mts. catchment (Raba River) to the drinking water reservoir (Dobczyce). This was possible by combining SWAT and AdH/PTM models on the digital platform - Macromodel DNS. Moreover, we have applied a variant scenario analysis including RCP 4.5 and 8.5, and land use change forecasts. The results highlighted the differences between the two analyzed hydrological units and showed large variability of the sediment load between months. The predicted climate changes will cause a significant increase of mineral fraction loads (silt and clay) during months with high flows. Due to the location and natural arrangement of the reservoir, silt particles will mainly affect faster loss of the first two reservoir zones capacities, which is consistent with their intended use as traps for larger fractions. The increased mobility of the finer particles (clay) in the reservoir may be more problematic in the future. Mainly due to their binding pollutant properties, and the possible negative impact on drinking water abstraction from the last reservoir zone.
It has been claimed that COVID-19 public stimulus packages could be sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 °C target of the Paris Agreement. Here we provide complementary perspectives to reiterate that substantial, broad, and sustained engagements beyond recovery packages will be needed for achieving the Paris Agreement long-term targets. Low-carbon investments will need to scale up and persist over the next several decades following short-term stimulus packages. The required total energy investments in the real world can be larger than the currently available estimates from Integrated Assessment Models (IAMs). Existing databases from IAMs are not sufficient for analyzing the effect of public spending on emission reduction. To inform what role COVID-19 stimulus packages and public investments may play for reaching the Paris Agreement targets, explicit modelling of such policies is required.
Research in hydrological sciences is constantly evolving to provide adequate answers to water-related issues. Methodological approaches inspired by mathematical sciences and physical sciences have shaped hydrological sciences from its beginnings to the present day. But nowadays with the increasing complexity of hydrological phenomena, hydrological sciences have integrated approaches from the social sciences which provide missing information for the study of complex hydrological objects which is the observation and perception of water resources by users. A methodological approach: the mixed methods with their different research designs make it possible to combine the quantitative approaches of the physical and mathematical sciences and the qualitative approaches of the social sciences to understand the object of study and propose adequate solutions for its management. We detail here, the use of mixed methods in research in flood hydrology, in research on low flow conditions and on the management of these hydrological extremes. Mixed methods contributions to these studies are diverse and pragmatically relevant for hydrology. They range from the densification of data on extreme flood events to reduce forecasting uncertainties, to the production of knowledge on low-flow hydrological states that are insufficiently documented and finally to support participatory management decision-making about extreme hydrological events and water management.
Since 2007, the global mole fraction of atmospheric methane (CH4) has steadily increased meanwhile the 13C/12C isotopic ratio of CH4 (expressed as δ13C-CH4) has shifted to more negative values. This suggests that CH4 emissions are primarily driven by biogenic sources. However, more in situ isotopic measurements of CH4 are needed at the local scales to identify which biogenic sources dominate CH4 emissions regionally. In California, dairies contribute a substantial amount of CH4 emissions from enteric fermentation and manure management. In this study, we present seasonal atmospheric measurements of δ13C-CH4 from dairy farms in the San Joaquin Valley of California. We used δ13C-CH4 to characterize emissions from enteric fermentation by measuring downwind of cattle housing (e.g., freestall barns, corrals) and from manure management areas (e.g., anaerobic manure lagoons) with a mobile platform equipped with cavity ring-down spectrometers. Across seasons, the δ13C-CH4 from enteric fermentation source areas ranged from -69.7 ± 0.6 per mil (‰) to -51.6 ± 0.1‰ while the δ13C-CH4 from manure lagoons ranged from -49.5 ± 0.1‰ to -40.5 ± 0.2‰. Measurements of δ13C-CH4 of enteric CH4 suggest a greater than 10‰ difference between cattle production groups in accordance with diet. Isotopic signatures of CH4 were used to characterize enteric and manure CH4 from downwind plume sampling of dairies. Our findings show that δ13C-CH4 measurements could improve the attribution of CH4 emissions from dairy sources at scales ranging from individual facilities to regions and help constrain the relative contributions from these different sources of emissions to the CH4 budget.
We show a positive vertical correlation between ozone and water ice using a vertical cross-correlation analysis with observations from the ExoMars Trace Gas Orbiter’s NOMAD instrument. We find this is particularly apparent during the first half of Mars Year 35 (LS=0-180) at high southern latitudes, when the water vapour abundance is low. This contradicts the current understanding that ozone and water are, in general, anti-correlated. However, our simulations with gas-phase-only chemistry using a 1-D model show that ozone concentration is not influenced by water ice. Heterogeneous chemistry has been proposed as a mechanism to explain the underprediction of ozone in global climate models (GCMs) through the removal of HOX. We find improving the heterogeneous chemical scheme causes ozone abundance to increase when water ice is present, better matching observed trends. When water vapour abundance is high, there is no consistent vertical correlation between observed ozone and water ice and, in simulated scenarios, the heterogeneous chemistry does not have a large influence on ozone. HOX, which are by-products of water vapour, dominate ozone abundance and mask the effects of heterogeneous chemistry on ozone. This is consistent with gas-phase-only modelled ozone, showing good agreement with observations when water vapour is abundant. High water vapour abundance masks the effect of heterogeneous reactions on ozone abundance and makes adsorption of HOX have a negligible impact on ozone. Overall, the inclusion of heterogeneous chemistry improves the ozone vertical structure in regions of low water vapour abundance, which may partially explain GCM ozone deficits.
Nitrogen oxides (NOx) are markers of combustion contributing to ozone, secondary aerosol, and acid rain, and are required to run models focusing on atmospheric environmental protection. This work presents a new model free inversion estimation framework using daily TROPOMI NO2 columns and observed fluxes from the continuous emissions monitoring systems (CEMS) to quantify emissions of NOx at 0.05°×0.05°. The average emission is 0.72±0.11Tg/yr from 2019 through 2021 over Shanxi, a major energy producing and consuming province in Northern China. The resulting emissions demonstrates significant spatial and temporal differences with bottom-up emissions databases, with 54% of the emissions concentrated in 25% of the total area. Two major forcing factors are horizontal advective transport (352.0±51.2km) and first order chemical loss (13.1±1.1hours), consistent with a non-insignificant amount of NOxadvected into the free troposphere. The third forcing factor, the computed ratio of NOx/NO2, on a pixel-by-pixel basis has a significant correlation with the combustion temperature and energy efficiency of large energy consuming sources. Specifically, thermal power plants, cement, and iron and steel companies have high NOx/NO2 ratios, while coking, industrial boilers, and aluminum show low ratios. Variance maximization applied to the daily TROPOMI NO2 columns identifies three modes dominate the variance and attributes them to this work’s computed emissions, remotely sensedTROPOMI UVAI, and transport based on TROPOMI CO. Using satellite observations for emission estimates in connection with CEMS allows the rapid update of emissions, while also providing scientific support for the identification and attribution of anthropogenic sources.