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The Chalmers Cloud Ice Climatology: retrieval implementation and validation
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, 2024
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.

Sensitivity analysis of a Martian atmospheric column model with data from the Mars Science Laboratory
Joonas Leino, Ari-Matti Harri, Mark Paton, Jouni Polkko, Maria Hieta, and Hannu Savijärvi
Ann. Geophys., 42, 331–348, https://doi.org/10.5194/angeo-42-331-2024, 2024
The 1-D column model has been used extensively in studying the Martian atmosphere. In this study, we investigated the sensitivity of the column model to its initialization. The results of the model were compared with Curiosity rover measurements. The initial value of airborne dust and surface temperature had the greatest influence on the temperature prediction, while the initial atmospheric moisture content and the shape of the initial moisture profile modified the humidity prediction the most.

Abstract

The Equatorial Electrojet (EEJ) is one of the important near-earth space weather phenomena which exhibits significant diurnal, seasonal and solar activity variations. This paper investigates the EEJ variations at diurnal, seasonal and solar cycle time scales from the Indian sector and portrays a new empirical EEJ field model developed using the observations spanning over nearly two solar cycles. The Method of Naturally Orthogonal Components (MNOC), also known as Principal Component Analysis (PCA), was employed to extract the dominant patterns of principal diurnal, semi-diurnal, and ter-diurnal components contributing to the EEJ variation. The amplitudes of these diurnal, semi-diurnal, and ter-diurnal components in EEJ are found to vary significantly with the season and solar activity. The seasonal and solar activity dependencies of these principal components are modeled using suitable bimodal distribution functions. Finally, the empirical model for EEJ field was built by combining the principal components with their corresponding modeled amplitudes. This model accurately reproduces the diurnal, seasonal and solar activity variations of EEJ. The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error <5 nT. It also successfully captures the seasonal and solar activity variations of Counter Electrojet (CEJ). Finally, this model named “Indian Equatorial Electrojet (IEEJ) Model” is made publicly available for interested scientific users (https://iigm.res.in/system/files/IEEJ_model.html).

Abstract

This paper investigates the impact of a powerful gamma ray burst (GRB) that occurred on 9 October 2022, on the Earth's environment using a very low frequency receiver (VLF) to probe the lower ionospheric region (the D region). In addition to the VLF data analysis, we employ numerical simulation through the Long Wavelength Propagation Capability code (LWPC) to derive the increase in the D− region electron density. Our results revealed discernible perturbations in amplitude and phase across all transmitter paths (NAA, DHO, ICV, and NSC) to the Algiers receiver persisting for 40 min. At the maximum of the signal perturbation, the LWPC simulation results showed a decrease in the mean new reference height h′ from 74 to 65.71 km, along with an increase in the sharpness factor β from 0.3 to 0.4875 km−1. Under these new conditions, the electron density increased from its ambient value (216.10 cm−3) to 33.7 103 cm−3.

Abstract

The ability to understand and model ionospheric plasma flow on all spatial scales has important implications for operational space weather models. This study exploits a recently developed method to statistically separate large-scale and meso-scale contributions to probability density functions (PDFs) of ionospheric flow vorticity measured by the Super Dual Auroral Radar Network (SuperDARN). The SuperDARN vorticity data are first sub-divided depending on the Interplanetary Magnetic Field (IMF) direction, and the separation method is applied to PDFs of vorticity compiled in spatial regions of size 1° of geomagnetic latitude by 1 hr of magnetic local time, covering much of the high-latitude ionosphere in the northern hemisphere. The resulting PDFs are fit by model functions using maximum likelihood estimation (MLE) and the spatial variations of the MLE estimators for both the large-scale and meso-scale components are presented. The spatial variations of the large-scale vorticity estimators are ordered by the average ionospheric convection flow, which is highly dependent on the IMF direction. The spatial variations of the meso-scale vorticity estimators appear independent of the senses of vorticity and IMF direction, but have a different character in the polar cap, the cusp, the auroral region, and the sub-auroral region. The paper concludes by discussing the sources of the vorticity components in the different regions, and the consequences for the fidelity of ionospheric plasma flow models.

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy
Nicola Loglisci, Giorgio Boni, Arianna Cauteruccio, Francesco Faccini, Massimo Milelli, Guido Paliaga, and Antonio Parodi
Nat. Hazards Earth Syst. Sci., 24, 2495–2510, https://doi.org/10.5194/nhess-24-2495-2024, 2024
We analyse the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in Genoa. Rainfall observations were made using rain gauge networks based on either official networks or citizen science networks. The merged analysis stresses the spatial variability in the precipitation, which cannot be captured by the current spatial density of authoritative stations. Results show that at minimal distances the variations in cumulated rainfall over a sub-hourly duration are significant.

The Record-Breaking Precipitation Event of December 2022 in Portugal
Tiago M. Ferreira, Ricardo M. Trigo, Tomás H. Gaspar, Joaquim G. Pinto, and Alexandre M. Ramos
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-130,2024
Preprint under review for NHESS (discussion: open, 0 comments)
Here we investigate the synoptic evolution associated with the occurrence of an atmospheric river leading to a 24 h record-breaking extreme precipitation event (120.3 mm) in Lisbon, Portugal, on 13 December 2022. The synoptic background allowed the formation, on 10 December, of an atmospheric river associated with a deep extratropical cyclone and with a high moisture content and an inflow of moisture, due to the warm conveyor belt, throughout its life cycle. The system made landfall on day 12.

Abstract

This study investigates the specific circulation anomalies that have sustained the low Antarctic sea ice state since 2016. Firstly, we find a significant strengthening and southward shift in the Ferrel Cell (FC) during 2016–2022, resulting in a marked increase in southward transport of heat and moisture. Secondly, this enhanced FC is closely associated with a stronger mid-latitude wave pattern. This pattern is zonally asymmetric and greatly amplifies the poleward advections of heat and moisture, leading to the increased downward longwave radiation, more liquid precipitation and sea ice retreat in specific regions, including the western Pacific and Indian Ocean sectors, Ross and northern Weddell Seas. The mechanism deduced from the short-term period is further supported by the results of 40 ensemble members of simulations. The southward expansion of the FC and sea ice decline are closely linked to La Niña-like conditions but may also be driven by anthropogenic global warming.

Abstract

Probabilities of coastal extreme water levels (EWLs) are increasing as sea levels rise. Using a time-dependent statistical model on tide gauge data along U.S. and Pacific Basin coastlines, we show that EWL probability distributions also shift on an annual basis from climate forcing and long-period tidal cycles. In some regions, combined variability (>15 cm) can be as large or larger than the amount of sea level rise (SLR) experienced over the past 30 years and projected over the next 30 years. Considering SLR and variability by 2050 at a location like La Jolla, California suggests a moderate-level (damaging) flood today with a 50-year return level (2% annual chance) would occur about 3–4 times a year during an El Nino nearing the peak of the nodal tide cycle. If interannual variability is overlooked, SLR related impacts could be more severe than anticipated based solely upon decadal-scale projections.

Abstract

Black carbon in snow (BCS) is a crucial parameter in Earth System modeling, as it influences global radiative balance. Here, simulated BCS from Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) that provided BCS as a model output were evaluated. In comparison with global BCS observations, CMIP5/6 models successfully reproduced long-term historical trends linked to human activities, but struggled capturing decadal variability caused by natural climate variability. CMIP6 models NorESM2-MM, NorESM2-LM, and TaiESM1 yielded the most accurate simulations of BCS concentration with modest overestimation of <50%, while the four CESM2 models underestimated concentrations by up to ∼80%. These errors effectively balanced for the CMIP6 multi-model ensemble mean (MME), which had a relative error (RE) of −37%. However, the CMIP5 MME was less reliable due to extreme overestimation by up to 8,000% in the three MIROC models. The significant BCS concentration errors in the MIROC and CESM2 models were linked mainly to errors in handling of BC in snow processes. Conversely, marked improvements in NorESM, the only common to both CMIP5 and CMIP6, were due to improved simulation of black carbon deposition. BCS errors significantly impacted radiative forcing estimates, particularly at the poles, where model errors reached several thousandfold. CMIP6 exhibited superior results compared to CMIP5, achieving global MME RE of −33% in radiative forcing estimates. However, it's worth noting BCS output is currently limited, with only seven models available for each of CMIP5 and CMIP6 here. Additional models simulating BCS are desirable in the next CMIP generations.

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.