Feed aggregator

Abstract

Accurate initialization of CONUS convective-scale forecasting requires a proper estimate of all resolved scales. This study further develops and examines a simultaneous multiscale data assimilation (MDA) approach in EnVar with modulated cross-scale and cross-variable covariances. The method is examined using 10 retrospective cases with the assimilation of both in situ and radar reflectivity observations (hereafter, SimMDA). The necessity of the modulated and therefore weakened cross-covariances in simultaneous MDA for CONUS convective-scale forecasting is first demonstrated. The relative benefits of increasing the decomposed-scale number with increased computational cost in SimMDA are also discussed. The impact of the further developed simultaneous MDA method is revealed by comparing it with a commonly adopted DA approach (Baseline), which separately assimilates in situ and reflectivity observations using individual single-scale localization. During DA cycling, SimMDA improves analysis accuracy for temperature and reflectivity and reduces biases in all variables compared to Baseline. SimMDA yields significantly better forecasts than Baseline for most lead times. Additional experiments are conducted to attribute such improvements in a case study. Specifically, an experiment the same as Baseline except using simultaneous MDA for reflectivity assimilation enhances cold pools and inflows and thus improves storms by making larger-scale increments. An experiment the same as Baseline except using simultaneous MDA for in situ assimilation more properly constrains small-scale covariances, leading to more reasonable correlations along the front and more accurate moisture near the dryline and consequently improved analyses and forecasts. Both effects together largely contribute to the overall improvements of SimMDA compared to Baseline.

Abstract

El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific events are typically weaker than Eastern Pacific events. SSTA pattern and intensity undergo low-frequency modulations, affecting ENSO prediction skill and remote impacts, and resulting in low-frequency changes in ENSO variance. Yet, how different ENSO types contribute to these decadal variance changes remains unclear. Here, we decompose the low-frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non-binary event category memberships, where each event can belong to different clusters. Our approach identifies two La Niña and three El Niño categories and shows that the major shift of ENSO variance in the mid-1970s was associated with an increasing likelihood of strong La Niña and extreme El Niño events.

Abstract

In early June 2023, New York City (NYC) and other cities in the northeastern US experienced a severe air pollution event. Although reports associated this hazardous pollution event with the smoke from Canadian wildfires, the factors triggering the southward waft of the smoke remain unclear. We found the northerly anomaly that transported the smoke was linked to the Rossby wave train excited by the Madden–Julian Oscillation (MJO) over the Philippine Sea, which led to the formation of an enhanced northerly at the western edge of the cyclonic anomaly over the East Coast–North Atlantic. When the MJO convection left the western Pacific, the disorganized teleconnection caused the pollution to dissipate. Observational findings were further supported by model simulations and predictions. These results suggest that monitoring and predictions of MJO activity may help mitigate air pollution events over the northeastern US during Canadian wildfire seasons.

Abstract

Aged hydrocarbon wells, if proper care is not ensured, can crack, get corroded, and leak subsurface fluids. Permian Basin in Texas, home to thousands of such wells, has seen numerous blowouts and wastewater leaks. Our study employs surface deformation derived from satellite observations, and injection well records to investigate these events. The results reveal an over-pressurized wastewater aquifer producing a surface uplift of 20 cm/yr, likely due to wastewater being injected tens of kilometers away. Focusing on a January 2022 blowout resulting in 3 cm subsidence in 2 weeks, our geophysical model suggests aquifer over-pressurization as the cause. With an excess pressure of over 3 MPa in the aquifer, several more such blowouts are possible in the near future. This research highlights the urgent need to better understand the impact of subsurface fluid injection and calls for prompt action to mitigate the environmental effects of oil and gas production.

Abstract

Precipitation changes in full response to CO2 increase are widely studied but confidence in future projections remains low. Mechanistic understanding of the direct radiative effect of CO2 on precipitation changes, independent from CO2-induced SST changes, is therefore necessary. Utilizing global atmospheric models, we identify robust summer precipitation decreases across North America in response to direct CO2 forcing. We find that spatial distribution of CO2 forcing at land surface is likely shaped by climatological distribution of water vapor and clouds. This, coupled with local feedback processes, changes in convection, and moisture supply resulting from CO2-induced circulation changes, could determine North American hydroclimate changes. In central North America, increasing CO2 may decrease summertime precipitation by warming the surface and inducing dry advection into the region to reduce moisture supply. Meanwhile, for the southwest and the east, CO2-induced shift of subtropical highs generates wet advection, which might mitigate the drying effect from warming.

Abstract

The formation and evolution of the South Pole-Aitken (SPA) basin is critical to relating large impact basin formation and modification to lunar geophysical evolution. Most prior models of the SPA impact were conducted in 2D, making it difficult to compare model output to the 3D crustal structure and ejecta distribution. In order to better constrain the parameters of the SPA impactor and the expected post impact distribution of crust and ejecta, we conducted numerical simulations of the SPA impact in 3D. We tested a wide range of impact parameters and constrained model results with recent geophysical data. We found the crustal structure of the SPA basin is best fit by an oblique impact (30–45°) of a 350–400 km diameter projectile impacting at 12–16 km/s. The impact excavated material from as deep as 80–120 km, and ejecta was deposited in a butterfly pattern with a forbidden region uprange of the impact.

Brief Communication: Stay local or go global? On the construction of plausible counterfactual scenarios to assess flash flood hazards
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-119,2024
Preprint under review for NHESS (discussion: open, 1 comment)
Floods have caused significant damage in the past. To prepare for such events, we rely on historical data, but face issues due to rare rainfall events, lack of data, and climate change. Counterfactuals, or "what if" scenarios, simulate historical rainfall in different locations to estimate flood levels. Our new study refines this by deriving more plausible local scenarios, using the June 2024 Bavaria flood as a case study. This method could improve future flood preparation.

Abstract

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision-making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence-based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non-Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest.

Abstract

In this study, we revisit the shear-wave velocity structure of the lithosphere and asthenosphere surrounding the Hawaiian hotspot and Hawaiian swell using Rayleigh wave data spanning periods of 20–125 s from the PLUME project. A primary goal of this investigation is to probe the origin of the Hawaiian swell and the mechanism that elevates the topography, providing insights into mantle dynamics beneath hotspot swells. In the shear velocity model, the 30–70 km depth range is largely featureless with weak and local anomalies, indicating that the elevation of the Hawaiian swell cannot be attributed to upper lithospheric reheating or replacement. In contrast, at 80–150 km depth, a pronounced region of anomalously low velocities is well-resolved, with the lowest velocities found beneath the Hawaii-Maui-Molokai part of the island chain. Minimum shear velocities are approximately 4.0 km/s at 100–120 km depth, which is an ∼8%-10% velocity decrease relative to the surrounding velocities away from the swell. This pattern suggests that hot, buoyant mantle from deeper plume sources laterally spread out near the top of the normal oceanic asthenosphere. We find that the low-velocity pattern in the asthenosphere exhibits a strong correlation with the overall shape of the Hawaiian swell topography. Assuming that density anomalies are proportional to shear velocity anomalies, we demonstrate that the anomalous elevation of the swell can be explained by the uplift of a 30-km-thick elastic plate loaded from below by this buoyant, low-seismic-velocity layer in the asthenosphere.

TorchClim v1.0: a deep-learning plugin for climate model physics
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, 2024
Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.

Abstract

The space weather effects at the Earth's magnetosphere are mostly driven by the solar wind that carries the interplanetary magnetic field (IMF). In this paper, we use 2 years of data in the solar wind from lunar orbiting ARTEMIS and MMS spacecraft upstream of the Earth's bow shock to study the structure of the IMF. We determine the lag times of IMF structures and their dependence on spacecraft positions by conducting an information theory analysis and comparing it with two traditional analysis methods: cross-correlation (CC) analysis and minimum variance of magnetic field analysis (MVAB). For the events with long time intervals (i.e., >4 hr) and with small-spatial separation between the MMS and ARTEMIS along the y GSM -direction (i.e., <40R e , where R e is the Earth's radius), the lag times based on the CC and the mutual information (MI) analyses statistically agree with each other, with p-values of 1.675 × 10−7 and 4.833 × 10−9, with the confidence of 95%. Both the results based on MI and CC have a large deviation from the results from MVAB. For some of the events, such a deviation could be improved by taking the fast mode speed into account; however, p-tests showed that they were not statistically significant to the 95% confidence level.

Abstract

Geoelectric fields produced by time-varying magnetic fields during geomagnetic storms can result in potentially damaging geomagnetically induced currents (GICs) in long conductors at the Earth's surface. GICs can pose a significant risk to the integrity of grounded electrical infrastructure, particularly high-voltage transformers. In this study, an inferred GIC is calculated using an augmented differential magnetometer measurement (DMM) technique on a 500 kV transmission line in central Alberta and is validated using a proximal transformer neutral-to-ground (TNG) current measurement by AltaLink L.P. using a Hall probe at a transformer substation. This research outlines a custom-built and innovative DMM design by which both DMM sensors deployed around a power line measure the background geomagnetic disturbance (GMD) field and the magnetic field generated locally by the GIC. We show how this modified approach provides two independent estimates for GIC derived using only ΔB y or ΔB z , the magnetic field components perpendicular to the line carrying GIC. Results for a geomagnetic storm on 12 Oct 2021 show contemporaneous peaks in the TNG current and the DMM-inferred GIC. The two data sets have similar waveforms and are within the same order of magnitude. The background GMD is reconstructed using DMM and shows excellent correlation to the measured GMD at the permanent Canadian Array for Real-time Investigations of Magnetic Activity magnetic station at Ministik Lake, approximately 48.5 km away. Based on the results presented here, we verify the added utility value of DMM for temporary deployments for assessing GIC risk in electrical power grids.

Abstract

Atmospheric prediction at 2–6 weeks lead time (so-called subseasonal-to-seasonal timescales) entails large forecast uncertainty. Here we investigate the flow-dependence of this uncertainty during Boreal winter. We categorize the large-scale flow using North Atlantic-European weather regimes. First, we show that forecast uncertainty of near-surface geopotential height (Z1000) and temperature (T2m) are strongly sensitive to the prevailing regime. Specifically, forecast uncertainty of Z1000 reduces over northern Europe following Greenland Blocking (enhanced predictability) due to a southward shifting eddy-driven jet. However, due to strong temperature gradients and variable flow patterns, Greenland blocking is linked to increased forecast uncertainty of T2m over Europe (reduced predictability). Second, we show that forecast uncertainty of weather regimes is modulated via the stratospheric polar vortex. Weak polar vortex states tend to reduce regime-uncertainty, for example, due to more frequent predicted occurrence of Greenland blocking. These regime changes are associated with increased T2m uncertainty over Europe.

Abstract

Reconstruction of the seawater carbonate system is essential for an improved understanding of glacial-interglacial oceanic carbon cycling and climate change. However, continuous high-resolution ocean carbonate chemistry data are generally lacking for the Plio-Pleistocene. Here, we present a deep Pacific carbonate ion saturation state (Δ[CO3 2−]) record spanning the last 5.1 Myr, reconstructed from the size-normalized shell weight of planktonic foraminifer in the western tropical Pacific. Deep Pacific Δ[CO3 2−] has been modulated primarily by orbital obliquity since 5.1 Ma, during which it has exhibited in-phase behavior with the 40-Kyr obliquity cycle. Significantly, the amplitude of the 40-Kyr Δ[CO3 2−] cycles has responded linearly to obliquity forcing throughout the Plio-Pleistocene, independent of the late Pliocene intensification of Northern Hemisphere glaciation. We speculate that the obliquity signal in the deep Pacific Δ[CO3 2−] record reflects an ocean-atmosphere circulation feedback mediated by migration of the Southern Hemisphere Westerlies.

Abstract

Using model projections to study the emergence of observable climate signals presumes omniscient knowledge about the climate system. In reality, observational knowledge suffers from data quality and availability issues, for instance data gaps, changes in instrumentation, issues due to gridding and retrieval algorithms. Overlooking such deficiencies leads to misrepresentations of the time of emergence (ToE). We introduce a new definition of ToE that accounts for observational limitations, and show that significant corrections to the ToE may be necessary to achieve the same statistical confidence as would be afforded by omniscient knowledge. We also show how our method can inform future observational needs and observing systems design.

Abstract

Grid- or pixel-based models, used across various scientific disciplines from microscopic to planetary scales, contain an unquantified error that bias our interpretation of the data. The error is produced by projecting 3D data onto a 2D grid. For Digital Terrain Models (DTMs) the projection error affects all slope-dependent topographic metrics, like surface area or slope angle. Due to the proportionality of the error to the cosine of the slope, we can correct for it. We quantify the error and test the correction using synthetic landscapes for which we have analytical solutions of their metrics. Application to real-world landscapes in California, reveal the systematic underestimation of surface area by up to a third, and mean slope angles by up to 10° in steep topography in current DTMs. Correcting projection errors allow for true estimates of surface areas and slope distributions enabling physics-based models of surface processes at any spatial scale.

Abstract

The geopotential height observations from the Aura Microwave Limb Sounder show that the quasi-6-day wave (Q6DW) events with westward zonal wavenumber 1 (W1) in the Northern Hemisphere (NH) mesosphere and lower thermosphere (MLT) during September 2013 and 2014 had a prolonged lifetime of ∼45–50 days and reached their maximum amplitudes after the September equinox, while the climatological Q6DW-W1 is completely dissipated in the background atmosphere before the equinoxes. The Eliassen-Palm flux diagnostic results indicate that Q6DW-W1 during September 2013 and 2014 obtained an additional source at ∼60°-80°S and ∼40–50 km as the September equinox approached, which is related to the baroclinic/barotropic instability in this region. Further investigation on the background atmosphere reveals that several stratospheric minor warming (SMW) events occurred in the Antarctic region during September 2013 and 2014 due to the enhancement of wavenumber 1 activities, accompanied by the increase in the stratospheric temperature, changes in the shape of the polar vortex and the reversal in the zonal mean circulation. The strong planetary wave breaking during the September 2013 and 2014 Antarctic SMW events significantly weakened the strength of the polar night jet (PNJ) and made its peak height descend below ∼35 km, which generated the baroclinic/barotropic instability at the new upper boundary of the PNJ (∼60°-80°S and ∼40–50 km) by an anomalous double-jet configuration in the background winds. This unusual instability provided additional wave source and energy for the trans-equatorial propagation of Q6DW-W1, which finally led to prolonged wave activities in the NH MLT region.

Abstract

Aerosol single-scattering albedo (SSA) is the most critical factor for the accurately calculating of aerosol radiative effects, however, the observation of vertical profiles of SSA is difficult to realize. Current assessments of aerosol radiative effects remain uncertain because of the lack of long-term, high-resolution vertical profiles of SSA observations. High-resolution SSA vertical profiles were observed in a semi-arid region of Northwest China during winter using a tethered balloon. The observed SSA vertical profiles were used to calculate the aerosol direct radiative forcing and radiative heating rates. Significant differences in the calculated radiative forcing were found (e.g., a 48.3% relative difference for the heating effect in the atmosphere at 14:00) between the observed SSA profiles and the constant assumption with SSA = 0.90. Diurnal variations in the vertical distribution of SSA decisively influenced direct radiative forcing of aerosols. Furthermore, high-resolution vertical profiles of absorbing aerosols and meteorological parameters provide robust observational evidence of the heating effect of an elevated absorbing aerosol layer. This study provides a more accurate calculation of aerosol radiative forcing using observed aerosol SSA profiles.

Abstract

Emission sources influencing high particulate air pollution levels and related mortality in India have been studied earlier on country-wide and sub-national scales. Here, we use novel data sets of emissions (for 2019) and observations created under the Carbonaceous Aerosol Emissions, Source Apportionment, and Climate Impacts network in India (Venkataraman et al., 2020, https://doi.org/10.1175/bams-d-19-0030.1) in WRF-Chem simulations to evaluate drivers of high PM2.5 levels during episodes and in airsheds with different pollution levels. We identify airsheds in “extreme” (110–140 μg/m3), “severe” (80–110 μg/m3) and “significant” (40–80 μg/m3) exceedance of the Indian annual ambient air quality standard (National Ambient Air Quality Standards [NAAQS]) of 40 μg/m3 for PM2.5. We find that primary organic matter and anthropogenic mineral matter (largely coal fly-ash) drive high PM2.5 levels, both annually and during high PM2.5 episodes. PM2.5 episodes are driven by organic aerosol in north India (Mohali) in wintertime but are additionally influenced by mineral matter and secondary inorganics in central (Bhopal), south India (Mysuru) and eastern India (Shyamnagar). Across airsheds in exceedance of the NAAQS and during high PM2.5 episodes, primary PM2.5 emissions arise largely from the residential sector (50%–75%). Formal sector emissions (industry, thermal power and transport; 40%–55%) drive airshed and episode scale PM2.5 exceedance in northern and eastern India. Agricultural residue burning emissions predominate (50%–75%) on episode scales, both in northern and central India, but not on annual scales. Interestingly, residential sector emissions strongly influence (60%–90%) airsheds in compliance with the NAAQS (annual mean PM2.5 < 40 μg/m3), implying the need for modern residential energy transitions for the reduction of ambient air pollution across India.

Abstract

Regional weather variability and extremes over Europe are strongly linked to variations in the North Atlantic jet stream, especially during the winter season. Projections of the evolution of the North Atlantic jet are essential for estimating the regional impacts of climate change. Therefore, separating forced trends in the North Atlantic jet from its natural variability is an extremely relevant task. Here, a deep learning based method, the Latent Linear Adjustment Autoencoder (LLAE), is used for this purpose on an ensemble of fully-coupled climate simulations. The LLAE is based on a variational autoencoder and an additional linear component. The model uses detrended temperature and geopotential to predict the component of the zonal wind associated with natural variability. The residual between this prediction and the original wind field is interpreted as the forced component of the jet. The method is first tested for the geostrophic wind for which the forced trend can be obtained analytically from the difference between geostrophic wind computed from detrended and full geopotential. Despite the large variability of the total trends, the LLAE is shown to be effective in extracting the forced component of the trend for each individual ensemble member in both geostrophic and full wind fields. The LLAE-derived forced trend shows an increase in the upper-level zonal wind speed along a southwest–northeast oriented band over the ocean and a jet extension toward Europe. These are common characteristics over different periods and show some similarities to the upper-level zonal wind speed trend obtained from the ERA5 reanalysis.