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The synoptic scale variability of the Indian summer monsoon (ISM) is contributed by the weak cyclonic vortices known as low-pressure systems (LPSs). LPSs are the primary mechanism by which central Indian plains receive rainfall. Traditionally, synoptic variability is considered to have a low predictability. In the present study, we developed a framework, namely, LPS Neural Operator (LPSNO), using the neural operator-based deep learning to predict the spatial structure of daily mean sea level pressure anomalies over the Bay of Bengal at a resolution of 1°x1°. The proposed neural operator extends the Fourier neural operator framework by employing convolutional LSTMs in the operator backbone. Further, the mean sea level pressure is reconstructed using the predicted anomaly and the climatology, which is then used to track the LPSs using a Lagrangian tracking algorithm. The median pattern correlation between the predicted and actual mean sea-level pressure anomalies over the BoB is about 88%, 60%, and 50% for 24, 48, and 72-hour forecasts, respectively. The proposed model improves the accuracy of predictions compared with the earlier ConvLSTM models. The pattern correlation between the observed and predicted synoptic activity index (SAI) is 0.94, 0.9, and 0.87 for 1, 2, and 3-day ahead predictions, respectively. A well-trained model of LPSNO takes only ~3.2 s to generate a one-day forecast on a single GPU node of Nvidia V100, which is computationally extremely cheap compared to the conventional numerical weather prediction models. The proposed LPSNO can advance operational weather forecasting substantially.

As oceanic moisture evaporates, it leaves a signature on sea surface salinity. Roughly 10% of the moisture that evaporates over the ocean is transported over land, allowing the salinity fields to be a predictor of terrestrial precipitation. This research is among the first in published literature to assess the role of sea surface salinity for improved predictions on low-skill summertime subseasonal timescales for terrestrial precipitation predictions. Neural networks are trained with the CESM2 Large Ensemble using North Atlantic salinity anomalies to quantify predictability of U.S. Midwest summertime heavy rainfall events at 0 to 56-day leads. Using explainable artificial intelligence, salinity anomalies in the Caribbean Sea and Gulf of Mexico are found to provide skill for subseasonal forecasts of opportunity, e.g. confident and correct predictions. Further, a moisture-tracking algorithm applied to reanalysis data demonstrates that the regions of evaporation identified by neural networks directly provide moisture that precipitates in the Midwest.

Mesoscale-to-microscale coupling is an important tool to conduct turbulence-resolving multiscale simulations of realistic atmospheric flows, which are crucial for applications ranging from wind energy to wildfire spread studies. Different techniques are used to facilitate the development of realistic turbulence in the large-eddy simulation (LES) domain while minimizing computational cost. Here, we explore the impact of a simple and computationally efficient Stochastic Cell Perturbation method using momentum perturbation (SCPM-M) to accelerate turbulence generation in boundary-coupled LES simulations using the Weather Research and Forecasting (WRF) model. We simulate a convective boundary layer (CBL) to characterize the production and dissipation of turbulent kinetic energy (TKE) and the variation of TKE budget terms. Furthermore, we evaluate the impact of applying momentum perturbations of three magnitudes below, up to, and above the CBL on the TKE budget terms. Momentum perturbations greatly reduce the fetch associated with turbulence generation. When applied to half the vertical extent of the boundary layer, momentum 1 perturbations produce an adequate amount of turbulence. However, when applied above the CBL, additional structures are generated at the top of the CBL, near the inversion layer. The magnitudes of the TKE budgets produced by SCPM-M when applied at varying heights and with different perturbation amplitudes are always higher near the surface and inversion layer than those produced by No-SCPM, as are their contributions to the TKE. This study provides a better understanding of how SCPM-M reduces computational costs and how different budget terms contribute to TKE in a boundary-coupled LES simulation.

This study presents a novel analysis of a hailstone collected near Cordoba, Argentina, quantifying the composition, size distribution, and potential sources of non-soluble particles contained within. The hailstone contained diverse particles, with sizes ranging from 1.9 to 150.3 µm, primarily carbonaceous, including in the center, suggesting a possible biological and geological influence on hail formation. Silicate particles were distributed throughout the hailstone, likely from eroded soil and agricultural activities. Finally, salts were detected in the outer layers of the hailstone and may have originated from the nearby salt lake. This study highlights the regional influence of various land use types on hail formation and growth and points to the potential impacts of natural and anthropogenic factors on hailstone composition.

Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modelling. Here, atmospheric nitrous oxide (N2O) measurements made in the United Kingdom (U.K.) and Republic of Ireland are used to derive monthly N2O emissions for 2013-2022 using two different inverse methods. We find mean U.K. emissions of 90.5±23.0 (1\(\sigma\)) and 111.7±32.1 (1\(\sigma\)) Gg N2O yr-1 for 2013-2022, and corresponding trends of -0.68±0.48 (1\(\sigma\)) Gg N2O yr-2 and -2.10±0.72 (1\(\sigma\)) Gg N2O yr-2, respectively for the two inverse methods. The U.K. National Atmospheric Emissions Inventory (NAEI) reported mean N2O emissions of 73.9 Gg N2O yr-1 across this period, which is 14-33% smaller than the emissions derived from atmospheric data. We infer a pronounced seasonal cycle in N2O emissions, with a peak occurring in the spring and a second smaller peak in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom-up U.K. Emissions Model (UKEM). Bayesian inference is used to minimize the seasonal cycle mismatch between the average top-down (atmospheric data-based) and bottom-up (process model and inventory-based) seasonal emissions at a sub-sector level. Increasing agricultural manure management and decreasing synthetic fertilizer N2O emissions reduces some of the discrepancy between the average top-down and bottom-up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation.

A document by Gareth Dorrian. Click on the document to view its contents.

Research shows that complex terrain can affect the spatial distribution of solar radiation and atmospheric physical processes. Based on the high-resolution topographic data, there are already several parameterization schemes available to couple the terrain effects on solar radiation with atmospheric models. However, to reduce the amount of calculation, some methods that can lead to errors are used in the sub-grid parameterization schemes for clear-sky direct solar radiation (SPS-CSDSR). In addition, the common finite difference slope algorithms and the assumption of consistent sub-grid atmospheric transparency can also result in errors. This renders existing SPS-CSDSRs unsuitable for complex terrain in middle and high latitudes and in turbid weather. In this study, these three problems have been effectively solved. The most accurate geometric algorithms for direct solar radiation so far, a high-precision and fast terrain occlusion algorithm and the triangulated sub-grid algorithm, are proposed. On Taiwan Island, the accuracy of the two methods is verified in the virtual vacuum atmosphere. Based on the fact that atmospheric transparency actually increases with altitude, a correction term based on sub-grid anomaly altitude is proposed for converting the sub-grid terrain effect factors into the atmospheric model. Overall improvements constitute a high-precision SPS-CSDSR in complex terrain. Eleven reduced calculation methods and common finite difference slope algorithms can no longer be used. In further study, atmospheric models need improvement in coupling the terrain effects on solar radiation to accurately describe vertical distributions. In this case, the high-precision scheme proposed in this study can play a key role.

Fluxes of carbon dioxide (CO2) to and from vegetation can be significant on a regional scale. It is therefore important to understand biogenic CO2 fluxes in order to quantify local carbon budgets. However, these fluxes are often difficult to estimate in urban emission studies. This work uses the Solar Induced Fluorescence (SIF) for Modelling Urban biogenic Fluxes (SMUrF) model and the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) to estimate biogenic CO2 fluxes in and around the Greater Toronto and Hamilton Area, the most populous region in Canada. We have made several modifications to both vegetation models to improve the agreement with eddy-covariance flux towers in the region and improve estimates over urban areas. In our presentation, we will describe these improvements and our application of these modified models. In particular, we investigate biogenic CO2 fluxes in the Greenbelt of Ontario; a region surrounding the Greater Toronto and Hamilton Area designed to protect the region's croplands and natural landscape from urban sprawl. We find that this region absorbs significant amounts of CO2 annually and the recently proposed changes to the Greenbelt will result in reduced sequestration by the Greenbelt. We also investigate the amount of CO2 absorbed by vegetation estimated by SMUrF and UrbanVPRM in the city of Toronto, Canada. Lastly, we compare the results from this study to anthropogenic CO2 emission inventories. This work will help constrain biogenic fluxes for use in urban emission studies and may help to inform policy makers and city planners on how vegetation in and around the city affects CO2 concentrations, and thus carbon budgets.

Machine learning (ML) is used to build a bulk microphysical parameterization including ice processes. Simulations of the Lagrangian super-particle model McSnow are used as training data. The machine learning performs a coarse-graining of the particle-resolved microphysics to multi-category two-moment bulk equations. Besides mass and number, prognostic particle properties (P3) like melt water, rime mass, and rime volume are predicted by the ML-based bulk model. The ML-based scheme is tested with simulations of increasing complexity. As a box model, the ML-based bulk scheme can reproduce the simulations of McSnow quite accurately. In 3d idealized squall line simulations, the ML-based P3-like scheme provides a more realistic extended stratiform region when compared to the standard two-moment bulk scheme in ICON. In a realistic case study, the ML-based scheme runs stably, but can not significantly improve the results. This shows that machine learning can be used to coarse-grain super-particle simulations to a bulk scheme of arbitrary complexity.

Surface anticyclones connected to the ridge of an upper-tropospheric Rossby wave are the dynamical drivers of mid-latitude summer heatwaves. It is, however, unclear to which extent an anomalously low zonal phase speed of the wave in the upper troposphere is necessary for persistent temperature extremes at the surface. Here, we use spectral analysis to estimate a categorical phase speed for synoptic-scale waves. A composite analysis of ERA5 reanalysis data reveals how a meridional shift in the Rossby wave packet envelope associated with a change in phase speed alters the geographically phase-locked stationary wave pattern. In both composites for amplified low or high phase speed waves, respectively, the ridges and troughs of these temporal-mean wave trains show enhanced and reduced heatwave frequency. The phase speed of synoptic-scale waves is, hence, crucial for where, but less important for whether heatwaves occur.

Polar stratospheric chemistry is highly sensitive even to minor disruptions in water vapor or temperature. Unusual behavior in temperature and water vapor has been identified in the southern polar winter stratosphere in 2023. The potential correlation between the post-Hunga-Tonga eruption elevation of water vapor (detected in the tropics), temperature changes, and ozone anomalies is under discussion, as these parameters play a crucial role in stratospheric chemistry and dynamics. In the winter of 2023 in the Southern Hemisphere, an unexpected decrease in ozone levels and the emergence of a substantial ozone hole were observed. This event marked one of the most significant ozone decreases in the past 15 years, with an unusually large ozone hole occurring during this period, and it appears to be at least partly associated with the Hunga Tonga eruption.

Stable water isotopes in inland Antarctic ice cores are powerful paleoclimate proxies; however, their relationship with dynamical atmospheric circulations remains controversial. Using a water isotope climate model (MIROC5-iso), we assessed the influence of the Last Glacial Maximum (LGM; ~21,000 years ago) sea surface temperatures (SST) and sea ice (SIC) on Antarctic precipitation isotopes (δ18Op) through atmospheric circulation. The results revealed that the synoptic circulation mostly maintained southward moisture transport, reaching inland Antarctica. The steepened meridional SST gradient in the mid-latitudes increased δ18Op in inland Antarctica by enhancing the baroclinic instability and synoptic moisture transport. In contrast, enhanced SIC reduced the atmospheric humidity around Antarctica and lowered δ18Op through extensive surface cooling and transport from the ocean. These findings elucidate the isotopic proxies and enable us to constrain the southern hemisphere atmospheric circulation, including the westerlies, using ice cores during past climates, including the LGM.

This study introduces a tornado perturbation model utilizing the cyclostrophic wind model, implemented through a shallow-water equation framework. We conducted numerical simulations to examine development of perturbations within a static atmosphere background. Four numerical experiments were conducted: a single cyclonic wind perturbation (EXP1), a single low-geopotential height perturbation (EXP2), a cyclonic wind perturbation with a 0 Coriolis parameter (EXP3), and a single anticyclonic wind perturbation (EXP4). The outputs of these experiments were analyzed using comparative methods. In a static atmosphere setting, EXP1 generated a tornado-like pressure structure under a small-scale cyclonic wind perturbation. The centrifugal force in the central area exceeded the pressure gradient force, causing air particles to flow outward, leading to a pressure drop and strong pressure gradient. EXP2 induced a purely radial wind field; upon initiation, the central area exhibited convergence, and the geopotential height increased rapidly, indicating that a small-scale depression is insufficient to generate a tornado’s vortex flow field. EXP3’s results, with a 0 Coriolis parameter, are marginally different from EXP1, suggesting the Coriolis force’s negligible impact on small-scale movements. EXP4 demonstrates that a small-scale anticyclonic wind field perturbation can also trigger tornado-like phenomena akin to EXP1. The results indicate that a robust cyclonic and an anticyclonic wind field can potentially generate a pair of cyclonic and anticyclonic tornadoes, when the horizontal vortex tubes in an atmosphere with strong vertical wind shear tilt, forming a pair of positive and negative vorticities. These tornadoes are similar but have different rotation directions.

Convective Quasi-Equilibrium (CQE) is often adopted as a useful closure assumption to summarize the effects of unresolved convection on large-scale thermodynamics, while existing efforts to observationally validate CQE largely rely on specific spatial domains or sites rather than the source of CQE constraints—deep convection. This study employs a Lagrangian framework to investigate leading temperature perturbation patterns near deep convection, of which the centers are located by use of an ensemble of satellite measurements. Temperature perturbations near deep convection with high peak precipitation are rapidly adjusted towards the CQE structure within the two hours centered on peak precipitation. The top 1% precipitating deep convection constrains the neighboring free-tropospheric leading perturbations up to 8 degrees. Notable CQE validity beyond a 1-degree radius is observed when peak precipitation exceeds the 95th percentile. These findings suggest that only a small fraction of deep convection with extreme precipitation shapes tropical free-tropospheric temperature patterns dominantly.

Characterizing the abundance of atmospheric hydrogen (H) at Mars is critical for determining the current and, subsequently, the primordial water content on the planet. At present, the atmospheric abundance of Martian H is not directly measured but is simulated using proprietary models that are constrained with observations of H Lyman-a emission brightness, as well as with observations of other atmospheric parameters, such as temperature and solar UV irradiance. To make the data needed to model H abundances and escape rates more accessible to the community, this work utilizes over nine years of observations of H Lyman-a emissions made with the Mars Atmosphere and Volatile Evolution (MAVEN) mission. The H brightness in the upper atmosphere of Mars is analyzed for statistical variability across multiple variables and found to be dependent on solar illumination, solar cycle, and season. The resulting data trends are used to derive empirical fits to build a predictive framework for future observations or an extrapolative tool for primordial estimates. Data that was intentionally not included in the empirical derivations are used to validate the predictions and found to reproduce the H Lyman-a brightness to within 18% accuracy, on average. This first of its kind predictive model for H brightness is presented to the community and can be used with atmospheric models to further derive and interpret the abundances and escape rate of H atoms at Mars.

Synthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long-term hazard of rare high-impact storm events. Existing downscaling approaches rely on statistical or statistical-deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two-way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post-processing to introduce asymmetries in the simulated surface wind. In this study, we present an innovative data-driven approach for TC synthetic downscaling. Using a machine learning-based high-resolution global weather model (ML-GWM), our approach is able to simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two-way interactions between the storm and its environment. This approach consists of multiple components: a data-driven model for generating synthetic TC seeds, a blending method that seamlessly integrate storm seeds into the surrounding while maintain the seed structure, and a recurrent neural network-based model for correcting the biases in maximum wind speed. Compared to observations and synthetic storms simulated using existing statistical-deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Taking advantage of the computational efficiency of ML-GWM, our approach shows substantial potential for TC regional hazard and risk assessment.

Unprecedented warming of Arctic–boreal regions (ABR) has poorly understood consequences on carbon cycle processes. Uncertainties in annual methane (CH4) budgets partly arise because of limited data availability during winter. In this study, winter CH4 flux measurements were conducted using the snowpack diffusion gradient method over five ABR ecosystem types in Canada and Finland: closed–crown and open–crown coniferous boreal forest, boreal wetland and erect–shrub and prostrate–shrub tundra. Boreal forest uplands acted as net CH4 sinks, while the boreal wetland acted as net CH4 source during winter. We identified several wetland tundra CH4 emission hotspots and large spatial variability in boreal wetland CH4 emissions. In the boreal forest uplands, soil liquid water content was identified as an important environmental control of winter CH4 fluxes. Our results indicate non–negligible winter CH4 flux, which must be accounted for in annual carbon balance and terrestrial biosphere models over ABR.

This study presents an overview of the Late Cenozoic evolution of the West African Monsoon (WAM), and the associated changes in atmospheric dynamics and oxygen isotopic composition of precipitation (δ18Op). This evolution is established by using the high-resolution isotope-enabled GCM ECHAM5-wiso to simulate the climatic responses to paleoenvironmental changes during the Mid-Holocene (MH), Last Glacial Maximum (LGM), and Mid-Pliocene (MP). The simulated responses are compared to a set of GCM outputs from Paleoclimate Model Intercomparison Project phase 4 (PMIP4) to assess the added value of a high resolution and model consistency across different time periods. Results show WAM magnitudes and pattern changes that are consistent with PMIP4 models and proxy reconstructions. ECHAM5-wiso estimates the highest WAM intensification in the MH, with a precipitation increase of up to 150 mm/month reaching 25°N during the monsoon season. The WAM intensification in the MP estimated by ECHAM5-wiso (up to 80 mm/month) aligns with the mid-range of the PMIP4 estimates, while the LGM dryness magnitude matches most of the models. Despite an enhanced hydrological cycle in MP, MH simulations indicate a ~50% precipitation increase and a greater northward extent of WAM than the MP simulations. Strengthened conditions of the WAM in the MH and MP result from a pronounced meridional temperature gradient driving low-level westerly, Sahel-Sahara vegetation expansion, and a northward shift of the Africa Easterly Jet. The simulated δ18Op values patterns and their relationship with temperature and precipitation are non-stationarity over time, emphasising the implications of assuming stationarity in proxy reconstruction transfer functions.

A document by Cassandra D W Rogers. Click on the document to view its contents.

A document by Yuan-Jen Lin. Click on the document to view its contents.

Earth system models are a powerful tool to simulate the response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement tools from control theory, called “controllers”, to determine the quantity of aerosol to inject into the stratosphere to reach or maintain specified global temperature targets, such as limiting global warming to 1.5\textdegree C above pre-industrial temperatures. This work explores how internal (unforced) climate variability can impact controller-determined injection amounts using the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) simulations. Since the ARISE-SAI controller determines injection amounts by comparing global annual-mean surface temperature to predetermined temperature targets, internal variability that impacts temperature can impact the total injection amount as well. Using an offline version of the ARISE-SAI controller and data from CESM2 earth system model simulations, we quantify how internal climate variability and volcanic eruptions impact injection amounts. While idealized, this approach allows for the investigation of a large variety of climate states without additional simulations and can be used to attribute controller sensitivities to specific modes of internal variability.

Methane (CH4) is a prominent greenhouse gas responsible for about 20% of all atmospheric radiative forcing. As we notice trends in increasing global temperatures, understanding and detecting these emissions has become increasingly important. This requires the creation of robust greenhouse gas plume detectors. Previous work at the NASA Jet Propulsion Laboratory has shown Convolutional Neural Networks (CNN) to be an appropriate solution to map methane sources from future imaging spectrometer missions, such as Carbon Mapper. However, current models suffer from a high rate of false positives due to false enhancements in the detected images.We have compiled datasets from two Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) California campaigns. We then trained a GoogleNet CNN Classifier model on each campaign. The baseline current model uses a Unimodal column-wise matched filter (CMF). This results in a model known to be sensitive to false enhancements, such as water/water vapor, bright/dark surfaces, or confuser materials with similar absorption wavelengths to methane. We first note improvements between the Unimodal CMF model and a new Surface-Controlled CMF model, whose dataset matches that of the Unimodal CMF model, but removes enhancements not matching the absorption wavelength of methane. From this, we note minimal improvement (1% increase in F1 score). We then experiment with various auxiliary products measuring albedo (rgbmu, SWALB), vegetation (NDVI, ENDVI), and water (h2o, NDWI) indices designed to combat issues known to produce false enhancements. After training on these new input representations for both campaigns, we noticed a significant improvement in the multi-channel model’s results. We observe an increase in the F1 score for classifying positive tiles from 0.78 to 0.86 when trained using auxiliary albedo indices, showing promise for future use of auxiliary products in improving methane plume detectors.

A document by Camden Opfer. Click on the document to view its contents.