Ancient lake deposits in the Mojave Desert indicate that the water cycle in this currently dry place was radically different under past climates. Here we revisit a 700 m core drilled 55 years ago from Searles Valley, California, that recovered evidence for a lacustrine phase during the late Pliocene. We update the paleomagnetic age model and extract new biomarker evidence for climatic conditions from lacustrine deposits (3.373–2.706 Ma). The MBT5Me′ temperature proxy, based on bacterial membrane lipids, detects present-day conditions (21 ± 3 ºC, 1s, n = 2) initially, followed by warmer-than-present conditions (25 ± 3 ºC, n = 17) starting at 3.268 and ending at 2.734 Ma. This is supported by salinity indicators from bacterial and archaeal biomarkers that reveal lake salinity increased after 3.268 Ma. The δ13C values of plant waxes (-30.7 ± 1.4‰, n = 28) are consistent with local C3 taxa, likely expanded conifer woodlands during the pluvial with less C4 than the Pleistocene. dD values (-174 ± 5‰, n = 25) of plant waxes indicate precipitation dD values (‑89 ± 5‰, n = 25) in the late Pliocene are within the same range as the late Pleistocene precipitation dD. Microbial biomarkers identify a deep, freshwater lake and a cooling that corresponds to the onset of major Northern Hemisphere glaciation at marine isotope stage MIS M2. A more saline lake persisted for ~0.6 Ma across the subsequent warmth of the late Pliocene before the lake desiccated at the Pleistocene intensification of Northern Hemisphere Glaciation.
Understanding global space weather effects is of great importance to the international scientific community, but more localised space weather predictions are important on a national level. In this study, data from a ground magnetometer at Valentia Observatory is used to characterise space weather effects on the island of Ireland. The horizontal component of magnetometer observations and its time derivative are considered, and extreme values of these are identified. These extremes are fit to a generalised extreme value distribution, and from this model return values (the expected magnitude of an observation within a given time window) are predicted. The causes of extreme values are investigated both in a case study, and also statistically by looking at contributions from geomagnetic storms, substorms, and sudden commencements. This work characterises the extreme part of the distribution of space weather effects on Ireland (and at similar latitudes), and hence examines those space weather observations which are likely to have the greatest impact on susceptible technologies.
In this study, we examine the accuracy of global geospace simulations by analyzing the relationship between the solar wind and its propagation parameters and the errors in auroral electrojet index AU and AL, ring current index SYM-H and the cross-polar cap potential (CPCP) in simulations. We show that generally the error distributions are wider for higher level of solar wind driving. Our results also show that the observing solar wind monitor distance from the Sun-Earth line and the phase front normal angle produce only minor effects on the error distributions, however, for oblique angles (<0.4) of the phase front normal there are noticable effects on the error distributions. Furthermore, we show that the results hold true also when using two magnetometer station recordings, one at subauroral and the other at auroral latitudes, which speak to the similarity of the error sources in local and global activity measures. These results are important elements in assessing the accuracy of the timing and magnitude of space weather events recorded on ground.
Physically-based soil erosion models are valuable tools for the understanding and efficient management of soil erosion related problems at the basin and river reach scales, as soil loss, muddy floods, freshwater pollution or reservoir siltation, among others. We present the implementation of a new fully distributed multiclass soil erosion module. The model is based on a 2D finite volume solver (Iber+) for the 2D shallow water equations that computes the overland flow water depths and velocities. From these, the model evaluates the transport of sediment particles due to bed load and suspended load, including rainfall-driven and runoff-driven erosion processes, and using well-established physically-based formulations. The evolution of the mass of sediment particles in the soil layer is computed from a mass conservation equation for each sediment class. The solver is implemented using High Performance Computing techniques that take advantage of the computational capabilities of standard Graphical Processing Units, achieving speed-ups of two orders of magnitude relative to a sequential implementation on the CPU. We show the application and validation of the model at different spatial scales, ranging from laboratory experiments to meso-scale catchments.
Stratospheric aerosol injection (SAI) has been proposed as a possible complementary solution to limit global warming and its societal consequences. However, the climate impacts of such intervention remain unclear. Here, we introduce an explainable artificial intelligence (XAI) framework to quantify how distinguishable an SAI climate might be from a pre-deployment climate. A suite of neural networks is trained on Earth system model data to learn to distinguish between pre- and post-deployment periods across a variety of climate variables. The network accuracy is analogous to the “climate distinguishability” between the periods, and the corresponding distinctive patterns are identified using XAI methods to gain insights into the emerging signals from SAI. For many variables, the two periods are less distinguishable under SAI than under a no-SAI scenario, suggesting that the specific intervention modeled decelerates future climatic changes. Other climate variables for which the intervention has negligible effect are also highlighted.
Forestation is a major component of future long-term emissions reduction and CO$_2$ removal strategies, but the viability of carbon stored in vegetation under future climates is highly uncertain. We analyze the results from seven CMIP6 models for a combined scenario with high fossil fuel emissions (from SSP5-8.5) and moderate forest expansion (from SSP1-2.6). This scenario aims to demonstrate the ability of forestation strategies to mitigate climate change under continued increasing CO$_2$ emissions and includes the potential impacts of increased CO$_2$ concentration and a warming climate on vegetation growth. The model intercomparison shows that moderate forestation as a CO$_2$ removal strategy has limited impact on global climate under a high global warming scenario, despite generating a substantial cumulative carbon sink of 10–60 Pg C over the period 2015–2100. Using a single model ensemble, we show that there are local increases in warm extremes in response to forestation associated with decreases in the number of cool days. Furthermore, we find evidence of a shift in the global carbon balance, whereby increased carbon storage on land of $\sim$25 Pg C by 2100 associated with forestation has a concomitant decrease in the carbon uptake by the ocean due to reduced atmospheric CO$_2$ concentrations.
These Earth Energy Budgets (EEBs) came to prominence in 1997 when Kiehl and Trenberth produced their EEB known commonly as KT97. They have regularly come under attack. Primarily they show the Earth emitting 300% more radiation than it receives from the Sun. This energy is being generated out of nothing and violates the 1 st Law of Thermodynamics. They also show the Sun shining on the dark side of the Earth, something that just doesn't happen. All the radiation data in these EEBs, with the exception of Long Wave Down LWD and Long Wave Up LWU infrared IR radiation at the surface, have been divided by 4. This shows the Sun shining equally on all 4 quadrants of the Earth. This has the effect of having the Earth emitting 300% more radiation than it receives from the Sun. This 300% extra radiation is supposedly being generated out of nothing by a greenhouse effect GHE in the atmosphere. It seems apparent that this divide by 4 system is being used as a means of justifying the GHE theory. IR radiation is 100 times less energetic than visible radiation. That means the 322 W/m 2 of IR LWD is the equivalent of 3.22 W/m 2 of visible or Short Wave Down SWD radiation from the Sun. Since it appears these EEBs are being used to calibrate climate models, it has become necessary to review these EEBs and that in turn led to it becoming necessary to generate a new Earth Energy Budget to bring some realism back into them. This paper produces a new Earth Energy budget based on measured data. The Earth receives 1,361 W/m 2 of Short Wave Down SWD solar radiation at the top of atmosphere TOA and 1,361 W/m 2 of Short Wave Up SWU and LWU arrive back at the TOA. 589 W/m 2 of solar radiation is absorbed in the surface and 589 W/m 2 of LWU, latent heat and thermals is emitted by the surface. There is no mystery radiation being generated in the atmosphere and the budget is in balance.
The process of calibrating hydraulic models for water distribution systems (WDS) is crucial during the model-building process, particularly when determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, this study proposes a single-objective procedure that utilizes Genetic Algorithm (GA) for optimizing roughness coefficients in the EPANET hydraulic model. EPANET-GA incorporates an automated calibration process and a User Graphic Interface (GUI) to analyze the water head pressures of WDS nodes. Notably, the proposed method not only optimizes roughness coefficients based on pipe material but also spatial characteristics of pipes. To demonstrate the effectiveness of this method, the study builds a hydraulic analysis model for the Zhonghe and Yonghe district of the Taipei Water Department, integrating graph theory’s connectivity and the GIS database. The model was optimized with 34,783 node items, 30,940 pipes, and 140 field measurements. Results show that the optimized roughness coefficient produces a high correlation coefficient (0.9) with the measured data in a certain time slot. Furthermore, a low standard error (8.93%) was acheived compared to 24-hour monitoring data. The proposed method was further compared to WaterGEMs, and the study concludes that the proposed model provides a reliable reference for the design and routing scenario of WDS.
According to the principle of thermal expansion and cold shrinking, it is found that there is a magic one-to-one correspondence between the existing eight ancient plates of the earth and the eight planets of the solar system. It is found with further studying that there is a one-to-one correspondence between the nine celestial bodies of the solar system with the sun as the core and the nine ancient plates splitting from the unique prehistoric supercontinent. Therefore, in order to restore the prehistoric unique supercontinent, it is not to simply pile up the existing residual dominant ancient plates, but to restore the hidden ancient plate sunk into the ocean floor and take it as the core to restore the prehistoric unique supercontinent. The unique prehistoric supercontinent reconstructed by such a jigsaw recovery is similar to the shape of the solar system or an egg, presenting with a clear core and circle structure. The core ancient plate, which is recovered from the occult ancient plate on the ocean floor, determines the uniqueness, irreversibility and unrepeatability of the prehistoric supercontinent. The sudden extinction of the dinosaur family at the end of the cretaceous about 66 million years ago provides the easiest reasonable guess as to the time point when the Earth's unique supercontinent broke-up. The clam shape distribution of the geological age on the Northwestern Pacific Ocean floor gives us a hint meaning for the symbolization of the black hole in the universe which always dominant the spiral galaxy as the core of it.
The global average temperature has increased significantly since the preindustrial era. Translating global warming into regional scales is crucial to formulate effective environmental and climate policies. A realistic assessment of regional climate change requires high-resolution datasets. We present a new high-resolution (9 km) analysis of historical and future regional warming over the Middle East and North Africa (MENA) using observations, reanalysis products, and statistically downscaled global climate models from the Coupled Model Intercomparison Project (CMIP) Phase 5 and 6. The observed regional temperature change over the MENA subregions appears to be up to three times faster than the global average. Regional warming has already surpassed the 1.5 ℃ and is at the brink of exceeding 2 ℃. By the end of the 21st century, the Arabian Peninsula will warm from 2.66 ± 0.57 to 7.61 ± 1.53 ℃ under the low (SSP1–2.6) and high-end (SSP5–8.5) emission scenarios, respectively. We identify spatially distinct summer and winter warming hotspots. The most prominent spots in summer are the Arabian Peninsula Hotspot Region (APHR) and Algerian Hotspot Region. Major winter hotspots appear over Mauritania in West Arica and the Elburz Mountains. Moreover, APHR has already exceeded 2 °C of warming and will warm by about 9 °C under the high-end emission scenario by the end of the century. The 1.5, 2, 3, and 4 ℃ global warming levels are associated with substantial regional warming of 2.1 ± 0.2, 2.76 ± 0.2, 4.19 ± 0.25, and 5.49 ± 0.38 ℃, respectively, over the Arabian Peninsula.
Sea-level rise (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much land area as inundation from SLR, and 5,282 additional state-managed sites of contamination may be vulnerable to inundation from GWR in a 1.0 m SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the saltwater interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are disproportionately exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area.
Forest fires darken snow albedo and degrade forest structure altering snowpack energy balance, peak snow volume and snowmelt timing for up to 15 years following burn. To date, three-dimensional volumetric estimates of postfire effects on snow hydrology over the course of postfire recovery have not been quantified at the watershed scale. Here we present an improved parameterization of recovery of forest fire effects on snow hydrology. Using a spatially-distributed snow mass and energy balance model called SnowModel, we estimate volumetric shifts in snow-water storage and snowmelt timing across a chrono-sequence of eight burned forests occurring between 2000 and 2019. One to three years following fire, postfire effects reduced peak snow-water storage by 8.42% on average (sd = 9.38%) and advanced snow disappearance date by 34 days on average (sd = 7 days). Magnitudes of snow disappearance date advances tended to decline over recovery relative to the losses observed immediately following fire. Postfire reductions in peak snow-water equivalent (SWE) tended to decrease immediately following fire, and generally recovered over 15 years postfire, but then increased again 4 to 9 years later. Postfire reductions on peak SWE summed over the 15-year postfire recovery period were up to eighteen times greater than the losses incurred in the first winter following fire alone. Beyond 15 years following fire, postfire effects on snow persisted due to the postfire shift from forest to open meadow.
Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models — particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process can be improved via coupled NNs. We present a novel differentiable routing model that mimics the classical Muskingum-Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning’s roughness (n) and channel geometries from raw reach-scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real-world data, the trained differentiable routing model produced more accurate long-term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework’s potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national-scale flood simulations.
The elastic property of asteroids is one of the paramount parameters for understanding their physical nature. For example, the rigidity enables us to discuss the asteroid’s shape and surface features such as craters and boulders, leading to a better understanding of geomorphological and geological features on small celestial bodies. The sound velocity allows us to construct an equation of state that is the most fundamental step to simulate the formation of small bodies numerically. Moreover, seismic wave velocities and attenuation factors are useful to account for resurfacing caused by impact-induced seismic shaking. The elastic property of asteroids thus plays an important role in elucidating the asteroid’s evolution and current geological processes. The Hayabusa2 spacecraft brought back the rock samples from C-type asteroid (162173) Ryugu in December 2020. As a part of the initial analysis of returned samples, we measured the seismic wave velocity of the Ryugu samples using the pulse transmission method. We found that P- and S-wave velocities of the Ryugu samples were about 2.1 km/s and 1.2 km/s, respectively. We also estimated Young’s modulus of 6.0 – 8.0 GPa. A comparison of the derived parameters with those of carbonaceous chondrites showed that the Ryugu samples have a similar elastic property to the Tagish Lake meteorite, which may have come from a D-type asteroid. Both Ryugu and Tagish Lake show a high degree of aqueous alteration and few high-temperature components such as chondrules, indicating that they formed in the outer region of the solar system.
The aim of this work is to understand the formation of primary evaporites—sulfates, borates, and chlorides—in Gale crater using thermochemical modeling to determine constraints on their formation. We test the hypothesis that primary evaporites required multiple wet-dry cycles to form, akin to how evaporite assemblages form on Earth. Starting with a basalt-equilibrated Mars fluid, Mars-relevant concentrations of B and Li were added, and then equilibrated with Gale lacustrine bedrock. We simulated cycles of evaporation followed by groundwater recharge/dilution to establish an approximate minimum number of wet-dry cycles required to form primary evaporites. We determine that a minimum of 250 wet-dry cycles may be required to start forming primary evaporites that consist of borates and Ca-sulfates. We estimate that ~14,250 annual cycles (~25.6 k Earth years) of wet and dry periods may form primary borates and Ca-sulfates in Gale crater. These primary evaporites could have been remobilized during secondary diagenesis to form the veins that the Curiosity rover observes in Gale crater. No LiCl salts form after 14,250 cycles modeled for the Gale-relevant scenario (approximately 106 cycles would be needed) which implies Li may be leftover in a groundwater brine after the time of the lake. No major deposits of borates are observed to date in Gale crater which also implies that B may be leftover in the subsequent groundwater brine that formed after evaporites were remobilized into Ca-sulfate veins.