We combine lidar temperature observations onboard a research aircraft with numerical simulations in the framework of the SOUTHTRAC (Southern Hemisphere Transport, Dynamics, and Chemistry) Campaign. Deep propagation of gravity waves (GW) from the troposphere to the lower mesosphere is studied above the Southern Andes during two flights in September 2019. We use the Weather Research and Forecasting (WRF) model with a configuration for the simulations that has been validated in a previous study of this campaign. Strong orographic GW were detected during both flights, that were conceived for different latitudes. The observational and numerical data reveal the presence of significant GW attenuation, breaking and secondary wave generation above the stratopause due to the development of convective and dynamic instability as well as conditions for wave evanescence. The GW generated by topography were not able to alter the stable structure of the stratosphere, but the scenario was quite different in the lower mesosphere. The disturbed zones in that layer were produced by the combined effect on lapse rate of the background temperature variation and the perturbations associated with GW, which together may induce large vertical gradients. As a consequence, areas of reduced stability (with low or even negative buoyancy parameter) emerge above the stratopause. The existence of these GW self-induced attenuation layers in the mesosphere where temperature perturbations produce large negative gradients may lead to an amplitude growth control mechanism for the upward propagating waves.

A document by Ray Nassar. Click on the document to view its contents.

Hydrologic processes in snowmelt dominated systems are traditionally measured and understood at the point scale. However, snowmelt, sublimation, and snowfall rates exhibit significant spatial variability across larger landscapes. These variations are influenced by local atmospheric and terrain characteristics, which control the deviations between small-scale measurements and areally averaged responses to spatially heterogeneous mass and energy inputs. Appropriate upscaling relations are needed to translate small-scale descriptions of snow processes into constitutive relationships that are applicable at larger scales. As a proof of concept, this study examines the temporal and spatial variability of sublimation and snowmelt fluxes in a drainage basin in the Canadian Rockies using machine learning methods. Raven, a hydrological model, generates high-resolution fluxes and state variables used to train a random forest algorithm. The random forest (RF) algorithm is then applied to estimate coarse resolution fluxes. This study involves estimating spatially averaged results from discretized fine-scaled models without explicit knowledge of detailed local response, both with and without low-order statistics of state (e.g., the standard deviation of snow water equivalent). A series of experiments are used to verify that the upscaling methodology can successfully represent the impact of heterogeneity within the system. Spatially averaged forcing estimated from the scale-appropriate machine learning model is then incorporated into a mass balance equation at a coarse resolution to demonstrate the efficacy of this upscaling methodology.

Annual North Atlantic tropical cyclone (TC) counts are frequently modeled as a Poisson process with a state-dependent rate. We provide a lower bound on the forecasting error of this class of models. Remarkably we find this bound is already saturated by a simple linear model that explains roughly 50\% of the annual variance using three climate indices: El Ni\ no/Southern Oscillation (ENSO), average sea surface temperature (SST) in the main development region (MDR) of the North Atlantic and the North Atlantic oscillation (NAO) atmospheric circulation index \cite{Kozar2012}. As expected under the bound, increased model complexity does not help: we demonstrate that allowing for quadratic and interaction terms, or using an Elastic Net to forecast TC counts using global SST maps, produces no detectable increase in skill. We provide evidence that observed TC counts are consistent with a Poisson process, limiting possible improvements in TC modeling by relaxing the Poisson assumption.

Clouds are crucial to Earth’s climate system, influencing radiation and contributing to climate projection uncertainties. Here, the simulated cloud fraction by the sixth version of the Canadian Regional Climate Model (CRCM6/GEM5) was evaluated using CALIPSO lidar retrievals and the second version of the Cloud Feedback Intercomparison Project (CFMIP) Observation Simulator Package (COSP2) for the years 2014 and 2015. Horizontal and vertical distributions of clouds in the CRCM6/GEM5 model were evaluated using cloud profiles and four cloud categories (total, high-, mid- and low-level clouds) derived directly from the CRCM6/GEM5 model and treated using the COSP2 satellite simulator. A seasonal analysis was conducted across specific regions in North America. Results showed that the use of COSP2 is essential for comparing CRCM6/GEM5 outputs against satellite data to account for variable definitions and signal attenuation of active instruments (e.g., Cloud-Aerosol Lidar with Orthogonal Polarization: CALIOP). Spatial and vertical cloud distributions and seasonal patterns were generally well represented by the CRCM6/GEM5 for both winter (December-February) and summer (June-August). High- and low-level clouds were particularly well-represented, especially in winter. The CRCM6/GEM5 model demonstrated some difficulty producing enough clouds to accurately represent those at mid-level. Cloud fraction representation was systematically better during winter than summer. The CRCM6/GEM5 generally performed well over the whole North American domain for the four cloud categories and COSP2 was confirmed to help mitigate discrepancies in variable definitions. These results contribute to a better understanding of the CRCM6/GEM5 cloud representations and the use of COSP2 with high-resolution models.

Extreme climate events (ECEs) like heavy rainfall and heatwaves significantly impact society, and climate change is altering their frequency. Generalised Extreme Value (GEV) distributions help quantify these ECEs and guide human system design (e.g., return value of extreme wind gust sets construction codes at specific locations). We train a machine learning (ML) model using GEV distributions to determine the sample size required to estimate return values with specific uncertainties. For ECEs like heatwaves (with negative GEV shape parameters), fewer samples are needed to estimate the return value with specific uncertainty than rainfall extremes (positive shape parameters). For the heatwave example, a sample size of more than 20 times the annual recurrence interval is typically required to estimate the return value to ±10% uncertainty. A 1-in-20-year heatwave requires 400 samples, equating to 20 different 20-year simulations. Achieving such quantities will require extensive climate downscaling efforts, potentially aided by ML-based downscaling methods.

State estimation is a critical task in practice as it is the prerequisite for effective parameter estimation (PE), skillful prediction, optimal control, and the development of robust and complete datasets. Of essence is the probabilistic state estimation of the unobserved variables in high-dimensional complex nonlinear turbulent dynamical systems with intermittent instability, given a partially observed time series. Data assimilation (DA) through Bayesian inference combines the partial observations and the model-induced likelihood, potentially plagued by a small error in model structure or initial condition, to form the posterior distribution for optimal state estimation. In this work, an efficient algorithm with closed-form explicit expressions is developed for the optimal (in the mean-squared sense) online smoother and sampling of a rich class of nonlinear turbulent dynamical systems widely appearing in modeling natural phenomena like physics-constrained stochastic models (noisy Lorenz models, low-order models of Charney-DeVore flows, paradigm models for topographic mean flow interaction), stochastically coupled reaction-diffusion models in neuroscience and ecology (stochastically coupled FitzHugh-Nagumo models, stochastically coupled SIR epidemic models), multiscale models for geophysical flows (noisy Boussinesq equations, stochastically forced rotating shallow water equation), and to develop realistic systems for the Madden-Julian oscillation and Arctic sea ice. The conditional Gaussian nonlinear system framework is highly flexible since it encompasses the linear Kalman filter and the Rauch-Tung-Striebel smoother and facilitates studying extreme events, stochastic parameterisation, DA, and online PE. The method particularly handles systems with hidden intermittency and extreme events along with the associated highly non-Gaussian features, such as heavy tails in the probability density functions, by using a computationally effective and systematic method to determine the adaptive lag for the online procedure. A rigorous analysis of the performance of the framework will be illustrated through numerical simulations of high-dimensional Lagrangian DA and online model identification of highly intermittent time series via an expectation-maximization procedure.

The effect of warming-induced changes in different marine physical, chemical, and biological processes on ocean acidification has been largely overlooked. This study uses an Earth system model to investigate the impacts of warming-induced changes in marine dynamical and thermodynamical processes, CO2 solubility, and biological processes on ocean acidification. Under the representative concentration pathway RCP8.5 and its extension, relative to the simulation without CO2-induced warming effects on the ocean carbon cyle, by year 2500, warming increases global ocean mean [CO32-] by 11.6 μmol kg-1, of which 53% is associated with warming-induced changes in dynamical and thermodynamical processes, 20% is associated with warming-induced changes in CO2 solubility, and 27% is associated with warming-induced changes in ocean biological rates. In the Arc-Atlantic Ocean, mainly due to the greater temperature-induced changes in dynamical and thermodynamical processes, the total warming effect on the ocean carbon cycle acts to increase ocean mean [CO32-] by a considerable 60%, in particular, up to 109% in the depth of ~2500 m, by year 2500. In our simulated results, warming-induced changes in individual biological processes, including phytoplankton growth and mortality rates, and detritus remineralization rate, as well as total biological effects are found to affect ocean acidification by an amount comparable to or greater than that caused by warming-induced changes in dynamical and thermodynamical processes and CO2 solubility. Our study highlights the potentially important role of CO2-induced warming in ocean acidification and the ocean carbon cycle.

Haoran Liu1, Fa Li2, Hamid Dashti1, Fujiang Ji1, Min Chen1 *1 Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, WI, USA2 Department of Earth System Science, Stanford University, CA, USA*Corresponding author: Min Chen (mchen392@wisc.edu)

The martian tropical water ice spatial and temporal distribution was characterized using impact crater ejecta type, location, size, and age in one of two epochs, <=3.4 Ga and $>3.4 Ga, using statistical models designed for spatial and temporal correlation structures. The indicator thought to identify the presence of ice is craters with layered ejecta, while the indicator thought to identify no ice is craters with radial ejecta. These indicators imply the location (longitude and latitude) and, potentially, depth (crater diameter as a proxy) of ice, and when the ice was present. The spatial and temporal distribution of layered ejecta versus radial ejecta may inform on the geography and evolution of ice. A statistical spatial point analysis was conducted on a 54-sample data set (craters with diameters 2.77 km to 10.00 km) for an equatorial region (0o to -30o S, and 10o E to 340o W. The analysis shows there is insufficient evidence to support a non-random spatial and temporal distribution of tropical ice.

Early Late Cretaceous Sea surface temperatures (SST) records suggest extremely warm Southern Hemisphere high latitudes (SHLs) and a meridional gradient as low as 5°C, attributed to elevated atmospheric CO2. Climate models have been unable to reproduce such extreme warmth, questioning model performance and/or the validity of SSTs reconstructions. Indeed, the latter partly rely on the measurement of oxygen isotopic composition of marine organisms (δ18Oc), a proxy that requires knowledge of the δ18O of past seawater (δ18Osw). Here we use the water isotope-enabled Community Earth System Model (iCESM1.2) to investigate how paleogeography and pCO2 affect δ18Osw distribution and our understanding of Cenomanian-Turonian SSTs. Our simulations suggest that the semi-isolation of southern South Atlantic-Indian Ocean resulted in locally highly depleted δ18Osw explaining low δ18Oc measured on planktonic foraminifera. Accounting for this δ18Osw spatio-temporal variability increases the estimated meridional temperature gradient by 5°C and narrows the gap between model and proxy-based reconstructions.

Accurate prediction of the solar wind speed and arrival of solar transient events are among the most important but still open problems in space weather research. This article implements a Convolutional Neural Network (CNN) coupled Long-Short Term Memory cell (LSTM) deep-learning model for the prediction of solar wind speed at Sun-Earth L1 on a daily and 6-hourly basis. The model is trained and validated using Solar-wind speed observations at Sun-Earth L1. The proposed CNN-LSTM models are developed and trained using data for the period from 01 August 1996 to 31 December 2019 and tested with the data from 01 January 2020 to 30 June 2024. The daily prediction model can forecast solar wind speed with a RMSE of 52.12 km/s and an overall correlation coefficient of 0.79, while the 6-hourly model can forecast solar wind speed with a RMSE of 33.72 km/s and an overall correlation coefficient of 0.93. This shows that without explicitly building in physics based relationships, the Deep Learning based models are able to perform reasonably well compared to existing approaches.

We evaluated the ability of a simple ecosystem carbon dioxide (CO2) flux model, the Vegetation Photosynthesis and Respiration Model (VPRM), to capture complex CO2 background conditions observed in Indianapolis, IN. Using simulated biogenic CO2 fluxes and mole fraction tower influence functions, we estimated biogenic CO2 mole fractions at three background towers in the Indianapolis Flux Experiment (INFLUX) network from April 2017 to March 2020. The model captures afternoon average CO2 enhancements, the difference between the background towers and a common reference tower, at a monthly time scale with no significant bias, with monthly mean residuals rarely differing significantly from zero. Although not central to our application, the model could not capture day-to-day variations of observed afternoon average CO2 enhancements. Random errors, when averaged over monthly to yearly time scales, were an order of magnitude smaller than typical urban enhancements. VPRM captured site-to-site differences in the average observed daily cycle of CO2 fluxes at agricultural eddy covariance flux sites well. For 13 of 14 site-months, the modeled peak afternoon NEE was within 30% of that observed despite the observed peaks ranging from about -7 to -70 µmol m-2s-1. VPRM can be effectively used in CO2 inversions to represent complex seasonal variations in background conditions observed in Indianapolis. Indianapolis, a modest-size city surrounded by strong ecosystem fluxes, represents a rigorous test for the VPRM system. Further, this study presents an evaluation system that can be applied to assess the performance of other ecosystem CO2 flux models in cities with similar monitoring networks.

Local vortex-structured auroral spiral and a large-scale transpolar arc (TPA) were contemporaneously observed by the Polar ultraviolet imager (UVI), when a substorm almost recovered. The TPA grew along the dawnside auroral oval from the nightside to the dayside (oval-aligned TPA), and a chain of multiple auroral spots and spiral were located azimuthally near the poleward edge of the nightside auroral oval. Contemporaneous appearances of the TPA and the auroral spiral can be seen after the spiral appeared alone. Polar also detected the oval-aligned TPA and another dawnside TPA with the nightside end distorted toward the premidnight sector (J-shaped TPA) before and after the spiral’s formation, respectively. To examine these associated magnetotail structures, we performed global magnetohydrodynamic (MHD) simulations, based on two different types of code, BAT-S-RUS and improved REPPU, and examined how the field-aligned current (FAC) profiles varied in association with changes of the auroral form to TPA and/or auroral spiral. Global MHD simulations with the two different types of code can reproduce the TPAs and the associated FAC structures in the magnetotail. The auroral spiral and its nightside FAC profile, however, were not formed in both simulations, suggesting that its formation process cannot be treated within an MHD framework but is closely related to some kinetic process. When the J-shaped TPA and the auroral spiral contemporaneously appeared, the two MHD simulations could not reproduce the TPA, spiral and their associated magnetotail FAC structures, also advocating that a kinetic effect related to the spiral formation might prevent the TPA occurrence.

Marginal seas significantly impact the global carbon cycle. However, current knowledge on the role of marginal seas is limited, and only a few in situ datasets on air-sea CO2 exchange are available. This study presents the first direct measurements of air-sea CO2 flux carried out in the central Mediterranean region. Measurements used in this study were made at the Lampedusa Oceanographic Observatory (35.49°N, 12.47°E) and cover the period from December 2021 to June 2023. The daily air-sea flux is calculated based on high-resolution measurements of sea temperature, salinity, CO2 partial pressure in the ocean and in the atmosphere, and wind, using a wind-dependent parameterization for gas transfer velocity. The data show that the Central Mediterranean currently acts as a CO2 sink, with both absorption and emission phases over the course of a year. However, large differences exist between the two autumn-winter seasons comprised in the dataset, with a 30% lower air-sea flux during early 2023 than in early 2022. The possible impact on the CO2 flux of the intense and long-lasting marine heatwave which took place in the period from May 2022 to April 2023 has been investigated by analyzing the dependency of the calculated air-sea CO2 flux on SST and temperature. The reduced incidence of intense wind episodes during the heatwave period appears to be the main driver for the reduced air-sea CO2 exchange taking place during winter 2023, while the higher temperatures appear to produce a small effect on the calculated flux.

The Coral Sea houses expansive coral reefs. Reef health is inextricably linked to water temperatures, which are regulated by the hydrodynamic environment. The current system in the Coral Sea is dominated by jets of the South Equatorial Current (SEC): the North Vanuatu Jet (NVJ), the North Caledonian Jet (NCJ) and the South Caledonian Jet (SCJ). We investigated the projected near-future (2050) changes in the temperature and transport structure of the Coral Sea. We utilized the three highest resolution climate models from the CMIP6 HighResMIP experiment as these models broadly captured the historical temperature and transport structure of the SEC jets, and their El Niño Southern Oscillation (ENSO) related variability. Surface warming of 0.78°C and 1.12°C was projected in the Coral Sea under 1.5°C and 2°C global warming, respectively. The warming signal deepened by 30 m per decade, penetrating to 400 m by 2050. This indicates the additional thermal stress that could be experienced by Coral Sea ecosystems with future global warming. Decreases in the transports of the NVJ and NCJ, and an intensification of the SCJ were projected in the HighResMIP models. While the magnitudes of the changes were relatively small (2% to 7% of historical means), they were similar to the variability in NVJ and NCJ transport associated with ENSO. Our analysis further shows that the transport projections of the NVJ and NCJ varied with depth, where surface intensifications coincided with the areas of greatest warming. These changes could modify upwelling dynamics on the Great Barrier Reef shelf.

In spite of recent development, state-of-the-art debris-flow incision models (DFIM) remain under-constrained, limiting their integration in landscape evolution model (LEM), and thus our understanding of catchment erosion dynamics, including their altitude distribution, their response to climatic and tectonic changes and drainage network reorganization. Here, we use a novel method to extract channel heads on high-resolution topographies of 38 catchments spanning two orders of magnitude of erosion rates, and constrain the gradient and concavity of hundreds of first order-channels. We show that this is consistent with a novel, refined analytical DFIM prediction and constrain the exponents relating incision, slope gradient and debris-flow thickness. We also provide constraints on the range of possible value for debris-flow erodibility, as a function of debris-flow frequency, and suggest further analysis on channel junctions and concavity. These results pave the way to an implementation of a DFIM in 2D-LEMs.

The source of dust in the global atmosphere is an important factor to better understand the role of dust aerosols in the climate system. However, it is a difficult task to attribute the airborne dust over the remote land and ocean regions to their origins since dust from various sources are mixed during long-range transport. Recently, a multi-model experiment, namely the AeroCom-III Dust Source Attribution (DUSA), has been conducted to estimate the relative contribution of dust in various locations from different sources with tagged simulations from 7 participating global models. The BASE run and a series of runs with 9 tagged regions were made to estimate the contribution of dust emitted in East- and West-Africa, Middle East, Central- and East-Asia, North America, the Southern Hemisphere, and the prominent dust hot spots of the Bodele and Taklimakan Deserts. Using the multi-model simulations, the present study has found common features among models on large scales, however models show large diversity in dust source attribution. The multi-model analysis estimates that North Africa contributes 60 % of global atmospheric dust loading, followed by Middle East and Central Asia sources (24 %). Southern hemispheric sources account for 10 % of global dust loading, however it contributes more than 70 % of dust over the Southern Hemisphere. The study provides quantitative estimates of the impact of dust emitted from different source regions on the globe and various receptor regions including remote land, ocean, and the polar regions synthesized from the 7 models.