Extensive loss of salt marshes in back-barrier tidal embayments is undergoing worldwide as a consequence of land-use changes, wave-driven lateral marsh erosion, and relative sea-level rise compounded by mineral sediment starvation. However, how salt-marsh loss affects the hydrodynamics of back-barrier systems and feeds back into their morphodynamic evolution is still poorly understood. Here we use a depth-averaged numerical hydrodynamic model to investigate the feedback between salt-marsh erosion and hydrodynamic changes in the Venice Lagoon, a large microtidal back-barrier system in northeastern Italy. Numerical simulations are carried out for past morphological configurations of the lagoon dating back up to 1887, as well as for hypothetical scenarios involving additional marsh erosion relative to the present-day conditions. We demonstrate that the progressive loss of salt marshes significantly impacted the Lagoon hydrodynamics, both directly and indirectly, by amplifying high-tide water levels, promoting the formation of higher and more powerful wind waves, and critically affecting tidal asymmetries across the lagoon. We also argue that further losses of salt marshes, partially prevented by restoration projects and manmade protection of salt-marsh margins against wave erosion, which have been put in place over the past few decades, limited the detrimental effects of marsh loss on the lagoon hydrodynamics, while not substantially changing the risk of flooding in urban lagoon settlements. Compared to previous studies, our analyses suggest that the hydrodynamic response of back-barrier systems to salt-marsh erosion is extremely site-specific, depending closely on the morphological characteristics of the embayment as well as on the external climatic forcings.

First-ever measurements of the turbulent kinetic energy (TKE) dissipation rate in the northeastern Strait of Magellan (Segunda Angostura region) taken in March 2019 are reported here. At the time of microstructure measurements, the magnitude of the reversing tidal current ranged between 0.8 and 1.2 ms-1. The probability distribution of the TKE dissipation rate in the water interior above the bottom boundary layer was lognormal with a high median value εmed =1.2x10-6 Wkg-1. Strong vertical shear, (1-2)x10-2 s-1 in the weakly stratified water interior ensued a sub-critical gradient Richardson number, Ri<10-1-10-2. In the bottom boundary layer (BBL), the vertical shear and the TKE dissipation rate both decreased exponentially with the distance from the seafloor ζ, leading to a turbulent regime with the eddy viscosity KM~10-3 m2/s, which varied with the time and location, while being independent of the vertical coordinate in the upper part of BBL (for ζ>~2 meters above the bottom).

The Brazilian National Institute for Space Research (INPE) produces research that helps to understand climate and weather dynamics in Brazil and in the world, with significant impacts on national public and private strategic planning. Among the essential information for the studies are the rainfall data. In this context, ensuring the quality of this data has a direct impact on the reliability of the forecasts and analysis generated from them. Thus, this study, which is a partnership between INPE, the Laboratory of Atmospheric Physics and Polytechnic School of USP and the ARM-DoE (Atmospheric Radiation Measurement Climate Research Facility), aimed to establish computational tools that could deal with the quality of data from rain in accordance with the main international directives. Thus, it was proposed for this study the development of a specific toolkit for data from the Micro Rain Radar (MRR), disdrometers PARSIVEL2 and RD80, and rain gauge that would help researchers from INPE, USP and partners to: standardize the preparation of raw data for internationally accepted formats; processing figures to support quick analyses; analyze and process data quality and, finally, record metadata and quality analysis for publication in international data repositories.

A Variable-Resolution, global configuration of the Community Earth System Model (VR-CESM) in which the atmosphere and land are the only active components is employed to investigate the climate of the Euro-Mediterranean region. Two variable-resolution grids with regionally-refined resolutions of 0.25° and 0.125° over the study domain, respectively, are used. The fidelity of these VR-CESM simulations is evaluated considering the near-surface air temperature and precipitation fields for the 2000-2014 period in comparison to available observation-based datasets and those of a coarse resolution (quasi-uniform 1°) control simulation. Our analysis shows that, as a global model, VR-CESM is a promising alternative to regional climate models to advance our understanding of the Euro-Mediterranean climate. The improvements obtained are mainly related to a better representation of the complex topography of the region with higher resolution. Increasing the regional resolution to 0.25° generally yields considerable improvements over the control simulation, however some persistent biases remain. Doubling the highest resolution to 0.125° leads to only modest improvements, primarily in the representation of small-scale processes including representation of extreme events that are of substantial relevance for the present and future of the regional climate.

Convective cold pools (CPs) are known to mediate the interaction between convective rain cells and thereby help organize thunderstorm clusters, in particular mesoscale convective systems and extreme rainfall events. Unfortunately, the observational detection of CPs on a large scale has so far been hampered by the lack of relevant large-scale nearsurface data. Unlike numerical studies, where high-resolution near-surface fields of relevant quantities such as virtual temperature and winds are available and frequently used to detect cold pools, observational studies mainly identify CPs based on surface time series. Since research vessels or weather stations measure these time series locally, the characterization of cold pools from observations is limited to regional or station-based studies. To eventually enable studies on a global scale, we here develop and evaluate a methodology for the detection of CPs that relies only on data that (i) is globally available and (ii) has high spatio-temporal resolution. We trained convolutional neural networks to segment CPs in cloud and rainfall fields from high-resolution cloud resolving simulation output. Such data is not only available from simulations, but also from geostationary satellites that fulfill both (i) and (ii). The networks make use of a U-Net architecture, a common choice for image segmentation due to its strength in learning spatial correlations at different scales. Based on cloud and rainfall fields only, the trained networks systematically identify CP pixels in the simulation output. Our methodology may thus open for reliable global CP detection from space-borne sensors. As it also provides information on the spatial extent and the relative positioning of CPs over time, our method may offer new insight into the role of CPs in convective organization.

Parameterizations for bottom shear stress are required to predict sediment resuspension from field observations and within numerical models that do not resolve flow within the viscous sublayer. This study assessed three observation-based bottom shear stress (τb) parameterizations, including (1) the sum of surface wave stress and mean current (quadratic) stress (τb= τw +τc); (2) the log-law (τb= τL); and (3) the turbulent kinetic energy (τb= τTKE); using two years of observations from a large shallow lake. For this system, the parameterization τb= τw +τc was sufficient to qualitatively predict resuspension, since bottom currents and surface wave orbitals were the two major processes found to resuspend bottom sediments. However, the τL and τTKE parameterizations also captured the development of a nepheloid layer within the hypolimnion associated with high-frequency internal waves. Reynolds-averaged Navier-Stokes (RANS) equation models parameterize τb as the summation of modeled current-induced bottom stress (τc,m) and modelled surface wave-induced bottom stress (τw,m). The performance of different parameterizations for τc,m and τw,m in RANS models was assessed against the observations. The optimal parameterizations yielded root-mean-square errors of 0.031 and 0.025 Pa, respectively, when τc,m, and τw,m were set using a constant canonical drag coefficient. A RANS-based τL parameterization was developed; however, the grid-averaged modelled dissipation did not always match local observations, leading to O(10) errors in prediction of bottom stress. Turbulence-based parameterizations should be further developed for application to flows with mean shear-free boundary turbulence.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) collects thermal observations from the International Space Station to support evapotranspiration (ET) research at fine spatial resolutions (70 m x 70 m). Initial ET estimates from ECOSTRESS Collection 1 have been used in a wide range of scientific studies and applications, though subsequent analyses identified areas for improvement. This study provides an overview of updates to ECOSTRESS Collection 2 ET and presents an accuracy assessment of ET and auxiliary variables against in situ data from AmeriFlux. Key updates in Collection 2 include: four independent model estimates of ET and improved auxiliary forcing data. We find the multi-model ensemble ET estimate achieves a root mean square error (RMSE) of 109 Wm-2 for instantaneous observations and 1.5 mm/day for daily retrievals. When considering uncertainty in energy balance closure approaches for site-level data, the RMSE improves to 48 Wm-2 for instantaneous ET. We observe variable performance based on time of day of ECOSTRESS image acquisition, climate and vegetation type. Evaluation of auxiliary data highlight limitations in down-scaled net radiation and relative humidity, contributing to a diurnal hysteresis in ET estimates. We provide accuracy metrics and model sensitivity to auxiliary data to facilitate user confidence, data adoption, interpretation, and applications. ECOSTRESS is the only instrument capable of providing ET at different times of day at high spatial scales; thus, this work is an important step toward enhancing the capabilities of satellite-driven ET models in resolving diurnal ET variations and guiding directions for future improvements.

A document by Sakib Imtiaz. Click on the document to view its contents.

This paper investigates the local and global ionospheric responses to the 2022 Tonga volcano eruption, using ground-based Global Navigation Satellite System (GNSS) total electron content (TEC), Swarm in-situ plasma density measurements, the Ionospheric Connection Explorer (ICON) Ion Velocity Meter (IVM) data, and ionosonde measurements. The main results are as follows: (1) A significant local ionospheric hole of more than 10 TECU depletion was observed near the epicenter ~45~min after the eruption, comprising of several cascading TEC decreases and quasi-periodic oscillations. Such a deep local plasma hole was also observed by space-borne in-situ measurements, with an estimated horizontal radius of 10-15 deg and persisted for more than 10 hours in ICON-IVM ion density profiles until local sunrise. (2) Pronounced post-volcanic evening equatorial plasma bubbles (EPBs) were continuously observed across the wide Asia-Oceania area after the arrival of volcano-induced waves; these caused a Ne decrease of 2-3 orders of magnitude at Swarm/ICON altitude between 450-575~km, covered wide longitudinal ranges of more than 140 deg and lasted around 12 hours. (3) Various acoustic-gravity wave modes due to volcano eruption were observed by accurate Beidou geostationary orbit (GEO) TEC, and the huge ionospheric hole was mainly caused by intense shock-acoustic impulses. TEC rate of change index revealed globally propagating ionospheric disturbances at a prevailing Lamb-wave mode of ~315 m/s; the large-scale EPBs could be seeded by acoustic-gravity resonance and coupling to less-damped Lamb waves, under a favorable condition of volcano-induced enhancement of dusktime plasma upward ExB drift and postsunset rise of the equatorial ionospheric F-layer.

Climate change has altered the frequency, intensity, and timing of mean and extreme precipitation events. Extreme precipitation has caused tremendous socio-economic losses and displays strong regional variability. Although many previous studies have addressed daily extreme precipitation, hourly extreme rainfall still needs to be thoroughly investigated. In this study, we investigated the trends, spatio-temporal variability, and long-term variations in mean and extreme precipitation over South Korea using daily and hourly observational data. During the past 50 years (1973–2022), there has been a notable escalation in maximum hourly precipitation, although the boreal summer mean precipitation has increased only marginally. Regionally, an increase in mean and extreme rainfall occurred in the northern part of the central region. Moreover, increased intensity and frequency of extreme precipitation have contributed more to the total summer precipitation in recent years. Our findings provide scientific insights into the progression of extreme summer precipitation events in South Korea.

The aim of this work is to present a global ionospheric prediction model based on deep learning (DL) to forecast Total Electron Content 24 hours in advance under different space weather conditions. Three different DL techniques have been compared to select the most suitable for the purpose of an operational service: Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The modeling approach inherits and extends what has been proposed by Cesaroni and co-authors (2020). We use TEC on 18 selected grid points of Global Ionospheric Maps (GIMs) as the target parameter and Kp index as the external input. We use a dataset from 2005-2016 for training and testing, we also analyze case studies from 2017 under different geomagnetic conditions. Results show that CNN models have better predictive capabilities than the other two DL models, even under geomagnetically disturbed conditions. Considering the first 24 hours of forecasting, CNN exhibits errors between 0.5 and 2 TECu, while LSTM and GRU errors can reach 3 TECu. We also show how all the proposed models outperform the two naive models: the so-called “frozen ionosphere” and a 27 days averaged model. Moreover, we implemented the models using incremental training to update them as new data arrives and thus the trained model is able to adapt to rapid changes within the previous 24 hs to the forecasting. Thus, the proposed model can be implemented in an operative manner for Space Weather applications and services.

A weather station in Nukuʻalofa (NUKU), Tonga, ~68km away from the epicenter of the 2022 Tonga eruption, recorded exceptional pressure, temperature, and wind data representative of the eruption source hydrodynamics. These high-quality data are available for further source and propagation studies. In contrast to other barometers and infrasound sensors at greater ranges, the NUKU barometer recorded a decrease in pressure during the climactic stage of the eruption. A simple fluid dynamic explanation of the depressurization is provided, with a commentary on near- vs far-field pressure observations of very large eruptions.

The ability to rapidly simulate the climate implications of a large number of CO2 emissions trajectories is helpful for implementing mitigation and adaptation policies. A key variable of interest is near-surface air temperature, which is approximately proportional to cumulative CO2 emissions. We take advantage of this relationship, diagnosing Green’s Functions for the spatial temperature response to CO2 emissions based on CMIP6 experiment data, creating an emulator that can be used across emissions scenarios to estimate local temperature responses. As compared to CMIP6 experiments, this approach captures the spatial temperature response with some limited accuracy in polar regions. It incorporates emissions path dependency and is useful for evaluating large ensembles of policy scenarios that are otherwise prohibitively expensive to simulate using earth system models. We apply this emulator to show differing local temperature responses when a global mean of 2ºC is reached and to varying trajectories with the same cumulative emissions.

Lakes and reservoirs are an integral part of the terrestrial water cycle. In this work, we present the implementation of water balance models of lakes and reservoirs into mizuRoute, a vector-based routing model. The developments described here are termed mizuRoute-Lakes. The capabilities of mizuRoute-Lake in simulating the water balance of lakes and reservoirs are demonstrated. The main advantage of mizuRoute-Lake is flexibility in testing alternative lake water balance models within a given river and lake network topology. Users can choose between various types of parametric models that are already implemented in mizuRoute-Lake or data-driven models that provide time-series of the target volume and abstraction from a lake or reservoir from an external source such as historic observation or water management models. The parametric models for lake and reservoir water balance implemented in mizuRoute-Lake are Hanasaki, HYPE, and D{\"o}ll formulations. In general, the parametric models relate the outflow from lakes or reservoirs to the storage and various parameters including inflow, demand, volume of storage, etc. Additionally, this flexibility allows to easily evaluate and compare the effect of various water balance models for a lake or reservoir without needing to reconfigure the routing model. We show the flexibility of mizuRoute-Lake by presenting global, regional and local scale applications. The development of mizuRoute-Lake paves the way for better integration of water management models with existing and future observations such as the Surface Water and Ocean Topography (SWOT) mission, in the context of Earth system modeling.

Continuous observations from geostationary satellites have been utilized to understand land surface seasonal dynamics and fill data gaps caused by clouds. However, the limited spatial resolution of geostationary satellite products constrained the processes of detecting the terrestrial changes in landscape scale, particularly over heterogeneous areas. Moreover, the variation of sun-target-sensor geometry in geostationary satellites results in diurnal changes in surface reflectance products. To overcome the limitations of geostationary satellite products over heterogeneous areas, we conducted the series of processes: 1) characterizing the effect of the solar and viewing geometry in surface reflectance using the bidirectional reflectance distribution function (BRDF), 2) harmonizing the satellite products from different platforms into a seamless product, and 3) fusing the different satellite products to enhance the spatial resolution of geostationary satellite products. Finally, we adopted spatial and temporal gap-filling methods to achieve daily gap-filled surface reflectance products. For robust application, the results from the integrated process were evaluated in both space and time using hyperspectral maps derived from unmanned aerial vehicle (UAV) and in situ tower-based continuous spectral measurements. We expect the geostationary satellite products with high spatial resolution to uncover the cloud-bound areas towards sensing the changes of the Earth in space and time.

A document by smetzger. Click on the document to view its contents.

The number of orbiting satellites has increased significantly and in an unrestricted and unregulated manner in recent years. This trend is expected to continue with ongoing plans from the commercial space sector to build mega-constellations of microsatellites. However, the effect of satellite demise during reentry on Earth’s atmosphere has only been lightly studied, and the long-term impact remains unknown.  This poster presents, to our knowledge, the first Reactive Force Field (ReaxFF) Molecular Dynamics (MD) simulation study on the atmospheric chemical mechanisms and byproducts generated by satellite reentry. Simulations are carried out to resolve chemical reactions and byproducts for Aluminum – a typical satellite structure constituent – under reentry conditions. MD simulation results are used to estimate the presence of oxides and predict the accumulated increase of reentry byproducts in the mesosphere when compared with that from meteoroids entering atmosphere and other natural sources. A methodology to estimate the residence time of these substances in the atmosphere is also presented so as to evaluate the polluting potential of mega-constellations reentry events.

The observed retreat and anticipated further decline in Arctic sea ice hold strong climate, environmental, and societal implications. In predicting climate evolution, ensembles of coupled climate models have demonstrated appreciable accuracy in simulating sea ice area and volume trends throughout the historical period. However, individual climate models still show significant differences in simulating the sea ice thickness distribution. To better understand individual model performance in sea ice simulation, nine climate models previously identified to provide plausible sea ice decline and global temperature change were evaluated in comparison with Arctic satellite and reanalysis derived sea ice thickness data, sea ice extent records, and atmospheric reanalysis data of surface wind and air temperature. Assessment found that the simulated spatial distribution of historical sea ice thickness varies greatly between models and that several key limitations persist among models. Primarily, most models do not capture the thickest regimes of multi-year ice present in the Wandel and Lincoln Seas; those that do, often possess erroneous positive bias in other regions such as the Laptev Sea or along the Eurasian Arctic Shelf. From analysis, no model could be identified as performing best overall in simulating historic sea ice, as model bias varies regionally and seasonally. Nonetheless, the bias maps and statistical measures derived from this analysis should enhance understanding of the limitations of each climate model. This research is motivated in-part to inform future usage of coupled climate model projection for regional modeling efforts and enhance climate change preparedness and resilience in the Arctic.