The International Arctic Buoy Programme (IABP) maintains fundamental in situ components of the Arctic Observing Network. Automated Drifting Stations (ADS) consisting of sea ice, meteorological, and oceanographic buoys are collectively deployed at many sites with webcams to help understand the intricate and complex interactions between sea ice, the atmosphere, and the ocean.While passive microwave satellites provide substantial information about the Arctic, remote sensing still has resolution limitations despite broad spatial coverage. Climate modeling and atmospheric reanalysis help surmount these limitations, but traditional observational methods of in situ data collection still have many advantages. Buoys and webcams can monitor Arctic sea ice changes above and below, allowing for more direct observations of localized ice floes when deployed in close proximity.Using data from webcams in the Arctic, we have stitched together images into time-lapse animations that provide insight into physical sea ice processes. Coupled with buoy data, we compare physical measurements (like temperature) with webcam observations (like cloud cover) to explain trends and anomalies. For example, isothermal periods in the buoy temperature data match time-lapse images with cloudy skies, while the opposite is also true: high variability correlates with sunny skies. Hence, these instruments allow for the verification of Arctic observations both visually and statistically.Although significant challenges like camera lifetimes and temporal resolution still persist, we argue that buoys and time-lapse videos can help validate satellite data and offer cheaper solutions to collecting vital information that increases our understanding of geophysical processes. We’ve compiled these datasets and present case studies showing the use of time-lapse videos to help monitor and understand the interplay and processes of the Arctic environment.

• Time-frequency analysis of the the total solar irradiance (TSI) dataset recoreded by the FY3E/JTSIM/DARA • DARA observations closest to the TIM/TSIS measurements in terms of mean value comparison (0.07 W/m 2) • Analysis of the integration of the new JTSIM-DARA dataset into the 43 year long TSI composite time series

Volcanic aerosols reduce global mean precipitation in the years after major eruptions, yet the mechanisms that produce this response have not been rigorously identified. Volcanic aerosols alter the atmosphere's energy balance, with precipitation changes being one pathway by which the atmosphere acts to return towards equilibrium. By assessing the atmosphere's energy budget in climate model simulations, we here show that global precipitation reduction is largely a consequence of Earth's surface cooling in response to volcanic aerosols reflecting incoming sunlight. In addition, these aerosols also directly add energy to the atmosphere by absorbing outgoing longwave radiation, and this is a major cause of precipitation decline in the first post-eruption year. We also identify mechanisms that oppose the post-eruption precipitation decline, and provide evidence that our results are robust across climate models.

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 an autoencoder and an additional linear component. The model predicts the wind component affected by natural variability by using detrended temperature and geopotential as inputs. 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 original 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 towards 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.

A document by Dhruv Balwada. Click on the document to view its contents.

Large uncertainties persist in modeling shallow, low clouds because there are many interacting nonlinear processes and multiple cloud-controlling environmental factors. In addition, sharp changes in behavior can occur when environmental thresholds are met. Model studies that follow a traditional approach of exploring the effects of factors "one-at-a-time" are unable to capture interactions between factors. We simulate a stratocumulus cloud based on the Second Dynamics and Chemistry of Marine Stratocumulus field study using a large-eddy simulation model coupled with a two-moment cloud microphysics scheme. The simulations are used to train a Gaussian process emulator, which we then use to visualize the relationships between two cloud-controlling factors and domain-averaged cloud properties. Only 29 model simulations were required to train the emulators, which then predicted cloud properties at thousands of new combinations of the two factors. Emulator response surfaces of cloud liquid water path and cloud fraction show two behavioral regimes, one of thin and patchy yet steady stratocumulus and one of thick, growing stratocumulus with cloud fraction near 1. Natural variability (initial-condition uncertainty) creates unrealistic "bumpy" response surfaces. However, we show that the variability causing the bumpiness can be characterized in an emulator "nugget term" that is adjusted to match the distribution of a small number of initial-condition ensemble simulations at various points on the surface, thereby allowing a smoother, deterministic response surface to be constructed. Accounting for variability leads to the firm conclusion that there is a smooth but steep change in cloud behavior between regimes, but not a sharp transition.

The escalating impact of climate change underscores the need for precise and timely forecasts of meteorological phenomena, particularly droughts, due to their extensive effects on agriculture, water resources, and ecosystems. Addressing this, we introduce a deep learning framework that merges Computer Vision with modified Transformer networks, tailored to predict future drought conditions leveraging historical global climate data. Our model inputs are stacked monthly global maps of Sea Surface Temperature, Temperature 2m above ground, and Total Precipitation, each spanning a year, thus creating a 36-channel input to capture seasonal variability.This study extends conventional Vision Transformers (ViT) by adapting them for sequence processing, enabling the model to learn the intricate temporal dynamics and spatial interdependencies inherent in climate data. By employing a sliding window approach, the model assimilates a sequence length of 12 months for each variable, and the target variables are stacks of Standardized Precipitation & Evapotranspiration Index (SPEI). Our modified ViT architecture successfully integrates the temporal sequencing by adjusting convolutional patch embeddings and positional embeddings, rendering the model sensitive to both the chronological progression and spatial distribution of climatic factors. Preliminary evaluations indicate the model's robust capability in forecasting drought conditions on a global scale. We substantiate these findings with performance metrics that illustrate the model's efficacy in interpreting and predicting the complex, non-linear, and non-stationary patterns of drought phenomena.

Abstract: Globally, more people are impacted by extreme hydrologic events such as flooding than all other types of natural disasters combined, and the effects can be devastating. Two examples are the 2012 and 2022 floods along the Niger and Benue Rivers within the Lower Niger River Basin (LNRB) in Nigeria. Flooding within the LNRB typically occurs annually during the rainy season, however, the 2012 and 2022 flood events were of similar magnitude, had catastrophic socioenvironmental impacts, and occurred one decade apart. Limited historical gage data along the Niger and Benue Rivers precludes traditional flood frequency analysis in the LNRB. Hence, this study seeks to utilize globally available observations from satellite remote sensing to compute flood depths using the Floodwater Depth Estimation Tool (FwDETv2.1 version) implemented in Google Earth Engine. Other hydrological and physiographic characteristics of LNRB in 2012 and 2022 are also evaluated using remote sensing observations. Since the FwDET requires only globally available input data (flood inundation map and Digital Elevation Model) which favors data-sparse regions such as Nigeria, the potential for the FwDET tool to automatically quantify flood water depths, an important variable in flood frequency estimation and damage assessment, can be analyzed even when historical observations are lacking. The utility of the FwDETv2.1 for flood management and mitigation studies along global rivers with limited historical data is discussed. ReferenceIdowu, Dorcas, and Wendy Zhou. "Performance evaluation of a potential component of an early flood warning system—A case study of the 2012 flood, Lower Niger River Basin, Nigeria." Remote Sensing 11.17 (2019): 1970.Brakenridge, G. R., Kettner, A. J., Paris, S., Cohen, S., Nghiem, S. V. , River and Reservoir Watch Version 4.5, Satellite-based river discharge and reservoir area measurements, DFO Flood Observatory, University of Colorado, USA. http://floodobservatory.colorado.edu/ SiteDisplays/ 20.htm (Accessed 6 December 2023).Cohen, S.; Peter, B.G.; Haag, A.; Munasinghe, D.; Moragoda, N.; Narayanan, A.; May, S. Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections. Remote Sens. 2022, 14, 5313. https://doi.org/10.3390/rs14215313.B. G. Peter, S. Cohen, R. Lucey, D. Munasinghe, A. Raney and G. R. Brakenridge, "Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for Rapid and Large Scale Flood Analysis," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 1501005, doi: 10.1109/LGRS.2020.3031190.Brakenridge, G. Robert, Son V. Nghiem, and Zsofia Kugler. "Passive microwave radiometry at different frequency bands for river discharge retrievals." Earth and Space Science 10.8 (2023): e2023EA002859.Idowu, Dorcas. Assessing the Utilization of Remote Sensing and GIS Techniques for Flood Studies and Land Use/Land Cover Analysis Through Case Studies in Nigeria and the USA. Diss. Colorado School of Mines, 2021.Idowu, Dorcas, and Wendy Zhou. "Global Megacities and Frequent Floods: Correlation between Urban Expansion Patterns and Urban Flood Hazards." Sustainability 15.3 (2023): 2514.

A document by HUANG Yuanyuan. Click on the document to view its contents.

The intra-seasonal CO2 flux (FCO2) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned Waveglider-Seaglider experiments were used to investigate how storms influence FCO2 through both the gas transfer velocity (kw) and the air-sea gradient in partial pressure of CO2 (ΔpCO2) in the sub-Antarctic zone. Winter-spring storms caused ΔpCO2 to weaken (by 15-55 μatm) due to mixing/entrainment and weaker stratification. This response in ΔpCO2 was in phase with kw resulting in a counteractive weakening in FCO2 (by 6.6 - 26.5% per storm), despite the wind-driven increase in kw. Stronger stratification during summer explained the weaker sensitivity of ΔpCO2 to storms, instead its thermal drivers dominated the ΔpCO2 variability. These results highlight the importance of observing synoptic-scale variability in ΔpCO2, the absence of which may propagate significant biases to the mean annual FCO2 estimates from large-scale observing programmes and reconstructions.

A document by Nicholas Lutsko. Click on the document to view its contents.

Understanding the complexities of cloud-sky radiative fluxes is crucial for improving numerical predictions of climate change. NASA's upcoming Atmosphere Observing System (AOS) mission promises unprecedented observations that will present an opportunity to enhance our understanding of the role of clouds in modulating both Earth’s radiation budget and climate sensitivity. AOS will utilize a series of active (Radar, Lidar) and passive (Imaging multi-angle polarimeter) instruments. The active instruments will provide vertically resolved cloud and aerosol information over a narrow ground-track (shown in red in the Figure below), while the passive instruments will cover a much wider swath. The high spatial resolution of AOS (~1km) will allow us to study clouds at the process level. While this will give us the opportunity to gain new insights, it also provides an unprecedented challenge to deliver satellite products at such a high resolution at which horizontal photon transport cannot be neglected, leading to biases in traditional cloud retrieval algorithms. To estimate radiative fluxes over the whole swath, we propose to extrapolate the vertical information from the active instruments to the across-track passive observations using a scene construction algorithm. This algorithm is evaluated using data from Large-Eddy Simulations and synthetic imagery computed by radiative transfer models.

A document by Poulomi Ganguli. Click on the document to view its contents.

A document by Poulomi Ganguli. Click on the document to view its contents.

Breaking atmospheric gravity waves in the tropical stratosphere are essential in driving the roughly two year oscillation of zonal winds in this region known as the Quasi-Biennial Oscillation (QBO). As Global Climate Models (GCM)s are not typically able to directly resolve the spectrum of waves required to drive the QBO, parameterizations are necessary. Such parameterizations often require knowledge of poorly constrained physical parameters. In the case of the spectral gravity parameterization used in this work, these parameters are the total equatorial gravity wave stress and the half width of phase speed distribution. Radiosonde observations are used to obtain the period and amplitude of the QBO, which are compared against values obtained from a GCM. We utilize two established calibration techniques to obtain estimates of the range of plausible parameter values: History Matching & Ensemble Kalman Inversion (EKI). History Matching is found to reduce the size of the initial range of plausible parameters by a factor of 98%, requiring only 60 model integrations. EKI cannot natively provide any uncertainty quantification but is able to produce a single best estimate of the calibrated values in 25 integrations. When directly comparing the approaches using the Calibrate, Emulate, Sample method to produce a posterior estimate from EKI, History Matching produces more compact posteriors with fewer model integrations at lower ensemble sizes compared to EKI; however, these differences become less apparent at higher ensemble sizes.

A document by Yusuf A. Bhatti. Click on the document to view its contents.

The increasing incidence and severity of biomass burning (BB) events pose a multidimensional threat impacting economic resilience, national security, and public health. Consequently, growing attention has been devoted to the radiative and chemical effects of BB gas and aerosol emission into the atmosphere, as well as the accompanying effects on human health. The emphasis of the work reported here concerns the efficacy of airborne spectral imaging in the longwave-infrared (LWIR) regime for visualizing and tracking trace gas content of the outflow from BB events, explicitly addressing the need for spatially resolved measurements.

This study was supported by NOAA grant NA19NES4320002 (Cooperative Institute for Satellite Earth System Studies -CISESS) at the University of Maryland/ESSIC.https://agu23.ipostersessions.com/default.aspx?s=E2-70-EC-CE-44-B4-7A-A0-A6-6E-79-AF-07-01-31-81&guestview=true

A document by Timothy E Dowling. Click on the document to view its contents.

A document by Timothy E Dowling. Click on the document to view its contents.

During marine cold-air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well-recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) provided a comprehensive set of ground-based in-situ and remote sensing observations of MCAOs at a coastal location in northern Norway. We determine MCAO periods that unambiguously exhibit open or closed MCC. Individual cells observed with a profiling Ka-band radar are identified using a water segmentation method. Using self-organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM-based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectral width, larger amounts of liquid water, lower precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation-induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. Such convection, when weakening, becomes virtually indistinguishable from the more intense stratiform precipitation cores in closed MCC. Non-precipitating stratiform cores have weak vertical drafts and are almost exclusively found during closed MCC periods. Convection is observed only occasionally in the closed MCC environment.

Speculations extend the opportunity space of possible future climates by increasing the potential to provide plausible estimated qualities and quantities to further scientific research and aid engineering solutions. This novel work outlines the first steps to achieving an Anthropocene reversal that completes in Zoomers’ lifetimes — by 2100. The novel experimental high-scale carbon removal pathway, which was studied in MAGICC 6.8, required CDR to counterbalance all accumulated anthropogenic emissions since 1750 to return to preindustrial temperature (0.07ºC over the 1720-1800 and 0.14ºC over the 1850-1900) means by 2100 and complete GHG phaseouts by 2077, excluding Ammonia. The experimental pathway set extreme front loading of emissions reductions to reach net zero, and avoid tipping points, then achieve scaled removal to reach 300 ppm of CO2 concentration by roughly mid-century. This work’s findings recommend exploring carbon removal of cumulative anthropogenic emissions totaling 600 GtC to 775 GtC on a recent model ensemble with 1.55 to 1.7 times preindustrial CO2 concentration driven by forcings from emissions and calibrate to reproduce present-day temperatures to provide more detailed projections of temperature, holding below 1.5ºC, regional temperatures, below ground CO2 mineralization, sea-level rise, ENSO, AMOC, and jet-stream turnover, evolve. Continued fossil-fuel use is unable to yield complete emissions phaseouts or deep removals necessary to match a preindustrial climate. The findings support the utmost urgency to attain a maximally scaled sustainable zero-carbon intensity green growth development. And reinforce the increased global commitment to achieve net zero sooner and to avoid setting off more climate tipping points. The possibility of reaching a preindustrial climate should help inform the debate of maximally scaled sustainable green growth development for the fastest path to net zero, phase out of anthropogenic emissions sources, and scaled carbon removals with zero-carbon intensity to develop a more equal future world.

Stratospheric Aerosol Injection (SAI) is a proposed method of climate intervention aiming to reduce the impacts of human-induced global warming by reflecting a portion of incoming solar radiation. Many studies have demonstrated that SAI would successfully reduce global-mean surface air temperatures, however the vast array of potential scenarios and strategies for deployment result in a diverse range of climate impacts. Here we compare two SAI strategies - a quasi- equatorial injection and a multi-latitude off-equatorial injection - simulated with the UK Earth System Model (UKESM1), both aiming to reduce the global-mean surface temperature from that of a high-end emissions scenario to that of a moderate emissions scenario. Both strategies effectively reduce global mean surface air temperatures by around 3°C by the end of the century; however, there are significant differences in the resulting regional temperature and precipitation patterns. We compare changes in the surface and stratospheric climate under each strategy to determine how the climate response depends on the injection location. In agreement with previous studies, an equatorial injection results in a tropospheric overcooling in the tropics and a residual warming in the polar regions, with substantial changes to stratospheric temperatures, water vapour and circulation. However, we demonstrate that by utilising a feedback controller in an off-equatorial injection strategy, regional surface temperature and precipitation changes relative to the target can be minimised. We conclude that moving the injection away from the equator minimises unfavourable changes to the climate, calling for a new series of inter-model SAI comparisons using an off-equatorial strategy.

Current MMRV solutions have the potential to quickly survey entire oilfields or detect methane leaks down to the component level, but also carry high price tags or, indirectly, high implementation costs. The Stanford/EDF Mobile Monitoring Challenge (MMC) conducted in 2018 was the first study to systematically evaluate methane mitigation technologies for incorporation into LDAR programs at the operator level. Three vehicle-based solutions tested in the MMC utilized a fence-line screening pattern that encompassed a production site and equipment, which we refer to as a “drive-around survey,” and showed promising results of greater than or equal to 88% true positive source identification rates for controlled releases in the 0-26 kg CH4/hr range. In this work, we evaluate a similar on-site drive-around survey as an alternative methane leak detection method under the EPA’s recent update to the Standards of Performance for New, Reconstructed, and Modified Sources and Emissions: Oil and Natural Gas Sector (NSPS). We find that a simple methane enhancement threshold binary classification system performs well with true positive rates > 0.8, though the precision of this classifier is inversely related to the magnitude of the emission rates for each class. We also describe a heuristic approach to estimating dispersion without source distance information. Incorporating this information into a linear model of emission rates regressed on survey data, we improve the model fit to R^2 > 0.9.