Realistically approximating the basal melting of ice shelves is critical for reliable climate model projections and the process representations in ice-ocean interaction. In this regard, extensive research attributes the massive thinning of vulnerable ice shelves to basal melting enhancement driven by ocean water warming, focusing mainly on oceanic warm water intrusion into the sub-shelf basins. However, climate models mainly underestimated the impacts of probable small-scale processes at the ice-ocean interface on basal melting by using smooth ice base topographies. This paper provides new insights into how small-scale features on the ice-ocean interface contribute to basal melting enhancement and spatial distribution. We developed a time-dependent, two-dimensional ice-shelf plume model as an optimal tool that allows a high-resolution representation of basal topography and with the unique ability to provide valuable information from the mixed boundary layer between ocean and ice shelves. In an exemplary case study for the floating ice tongue of the 79◦ North Glacier, systematic sensitive analyses were performed with the developed model. Our results show that the sub-km-scale basal channels with realistic dimensions increase the mean basal melt rate and generate extreme and sizeable lateral variability of melting at the grounding line. This mechanism is not reproducible with the tuning of drag coefficient. Besides, it reveals that the subglacial discharge in the channels has contradicting effects of reducing the melt rate by refreshing the sea water and increasing the freezing point while increasing the melt rate due to high water speed. However, the latter was dominant in our experiments.

Drought-busting events in the Colorado River Basin, such as the “Miracle May” of 2015 that greatly alleviated an unprecedented water shortage, have been observed for more than a century. But while such events are much prayed for in times of drought, they have not been well researched or even characterized. In this study, conducted in collaboration with water managers from across the basin, we propose a definition for “miracle events” that reflects real-world, actionable relevance. The resulting characterization offers a framework by which to quantify the frequency and strength of extreme dry-to-wet springtime transitions. While limited by uncertainties in model simulations and the myriad hydrological futures these simulations seek to project, and thus requiring cautious interpretation, this study finds that such transitions may become less frequent and less powerful under climate warming.

Here, we present new radio interferometer beamforming observations of lightning initiation using data from the Low Frequency Array (LOFAR). We show that the first lightning source in the flash increases exponentially in intensity by two orders of magnitude in 15 microseconds, while propagating 88 meters away from the initiation location at a constant speed of 4.8 ± 0.1 x 10^6 m/s. A second source replaces the first source at the initiation location, and subsequent propagation of the lightning leader follows. We interpret the first source to be a rapidly propagating and intensifying positive streamer discharge that subsequently produces a hot leader channel near the initiation point. How lightning initiates is one of the greatest unsolved problems in the atmospheric sciences, and these results shed light on this longstanding mystery.

Trans–ionospheric high frequency (HF) signals experience a strong attenuation following a solar flare, commonly referred to as Short–Wave Fadeout (SWF). Although solar flare-driven HF absorption is a well-known impact of SWF, the occurrence of a frequency shift on radio wave signal traversing the lower ionosphere in the early stages of SWF, also known as “Doppler Flash”, is newly reported and not well understood. Some prior investigations have suggested two possible sources that might contribute to the manifestation of Doppler Flash: first, enhancements of plasma density in the D and lower E regions; second, the lowering of the reflection point in the F region. Observations and modeling evidence regarding the manifestation and evolution of Doppler Flash in the ionosphere are limited. This study seeks to advance our understanding of the initial impacts of solar flare-driven SWF. We use WACCM-X to estimate flare-driven enhanced ionization in D, E, and F-regions and a ray-tracing code (Pharlap) to simulate a 1-hop HF communication through the modified ionosphere. Once the ray traveling path has been identified, the model estimates the Doppler frequency shift along the ray path. Finally, the outputs are validated against observations of SWF made with SuperDARN HF radars. We find that changes in refractive index in the D and lower E regions due to plasma density enhancement are the primary cause of Doppler Flash.

A document by Fabiha Rahman. Click on the document to view its contents.

As Earth warms, the tropopause is expected to rise, but predictions of its temperature change are less certain. One theory ties tropopause temperature to outgoing longwave radiation (OLR), but this contradicts simulations that exhibit a Fixed Tropopause Temperature (FiTT) even as OLR increases. Another theory ties tropopause temperature to upper tropospheric moisture, but is not precise enough to make quantitative predictions. Here, we argue that tropopause temperature, defined by where radiative cooling becomes negligible, is set by water vapor’s maximum spectroscopic absorption and Clausius-Clapeyron scaling. This “thermospectric constraint’ makes quantitative predictions for tropopause temperature that are borne out in single column and general circulation model experiments where the spectroscopy is modified and the tropopause changes in response. This constraint underpins the FiTT hypothesis, shows how tropopause temperature can decouple from OLR, suggests a way to relate the temperatures of anvil clouds and the tropopause, and shows how spectroscopy manifests in Earth’s general circulation.

Increasing anthropogenic CO2 emissions to the atmosphere are partially sequestered into the global oceans through the air-sea exchange of CO2 and its subsequent movement to depth, and this collective large-scale absorption is commonly referred to as the global ocean carbon sink. Quantifying this ocean carbon sink provides a key component for closing the global carbon budget which is used to inform and guide policy decisions. These estimates are typically accompanied by an uncertainty budget built by selecting what are perceived as critical uncertainty components based on selective experimentation. However, there is a growing realisation that these budgets are incomplete and may be underestimated, which limits their power as a constraint within global budgets. In this study, we present a methodology for quantifying spatially and temporally varying uncertainties in the air-sea CO2 flux calculations and data that allows an exhaustive assessment of all known sources of uncertainties, including decorrelation length scales between gridded measurements, and the approach follows standard uncertainty propagation methodologies. The resulting standard uncertainties are higher than previously suggested budgets, but the components are consistent with previous work, and they identify how the significance and importance of key uncertainty components change in space and time. For an exemplar method (the UEP-FNN-U method) the work identifies that we can currently estimate the annual ocean carbon sink to an accuracy of ±0.72PgCyr-1 (1 standard deviation uncertainty). Due to this method having been built on established uncertainty propagation and approaches, it appears applicable to all data-product assessments of the ocean carbon sink.

Changes in atmospheric iron (Fe) deposition to the open ocean affect net primary productivity, nitrogen fixation, and carbon uptake rates. We investigate the changes in soluble Fe (SFe) deposition from the pre-industrial period to the late 21st century using the EC-Earth3-Iron Earth System model, which stands out for its comprehensive representation of the atmospheric oxalate, sulfate, and Fe cycles. We show how anthropogenic activity has modified the magnitude and spatial distribution of SFe deposition by increasing combustion Fe emissions along with atmospheric acidity and oxalate levels. We find that SFe deposition has doubled since the early Industrial Era using the Coupled Model Intercomparison Project Phase 6 (CMIP6) emission inventory, with acidity being the main solubilization pathway for dust Fe, and ligand-promoted (oxalate) processing dominating the solubilization of combustion Fe. We project a global SFe deposition increase of 40% by the late 21st century relative to present day under Shared Socioeconomic Pathway (SSP) 3-7.0, which assumes weak climate change mitigation policies. In contrast, sustainable and business-as-usual SSPs (1-2.6 and 2-4.5) result in 35% and 10% global decreases, respectively. Despite these differences, SFe deposition consistently increases and decreases across SSPs over the (high nutrient low chlorophyl) equatorial Pacific and Southern Ocean (SO), respectively. Future changes in dust and wildfires with climate remains a key challenge for constraining SFe projections. We show that the equatorial Pacific and the SO would be sensitive not only to changes in Australian or South American dust emissions, but also to those in North Africa.

Continuous measurement and monitoring of river or creek surface water coverage is crucial for studying the exchange fluxes between the surface and subsurface water. These fluxes directly impact carbon and nitrogen exchange and cycles, which are related to organic matter transport and reactions. While satellite and related techniques have been widely used for large-scale monitoring, they may not be accurate, sensitive, or cost-efficient for monitoring and tracking of surface water at fine-scale spatial (i.e., sub-meter) and temporal (i.e., daily) variations. This is especially true for small creeks with large plant canopy coverage. On-site in-situ sensors monitoring methods primarily yield point data, often insufficient in capturing the entire spatial distribution. Wildlife cameras have proven a cost-efficient way to continuously monitor surface water coverage of rivers and creeks. To efficiently analyze the images and/or videos from the wildlife cameras, in this study, two machine learning approaches, YOLOv8 and Mask2Former, have been applied. Both models were trained by images obtained from the public dataset ADE20k along with a small dataset from wildlife camera photos collected at the current study area. Once surface water coverage is segmented, the width of the surface water in real world can be approximated according to the wildlife camera, lens, and positioning parameters. In this study, surface water was detected and monitored by applying the proposed approaches for the six wildlife cameras in the Yakima River Basin in 2023 to 2024 in United States of America. Though Mask2Former model provides slightly better transferability, both models can accurately capture the surface water from the wildlife cameras, which are installed in significantly different environments, such as the different brightness, contrast, and varying front scene object blockages. The proposed approach enables long-term continuous monitoring and quantification of river intermittency and water availability with high accuracy and low-cost, which will benefit river ecosystem research and management.

We show that the return-point memory of cyclic macroscopic trajectories enables the derivation of a thermodynamic framework for quasistatically driven dissipative systems with multiple metastable states. We use this framework to sort out and quantify the energy dissipated in quasistatic fluid-fluid displacements in disordered media. Numerical computations of imbibition--drainage cycles in a quasi-2D medium with gap thickness modulations (imperfect Hele-Shaw cell) show that energy dissipation in quasistatic displacements is due to abrupt changes in the fluid-fluid configuration between consecutive metastable states (Haines jumps), and its dependence on microstructure and gravity. The relative importance of viscous dissipation is deduced from comparison with quasistatic experiments.

\justifying The atmospheric radiative transfer calculations are among the most time-consuming components of the numerical weather prediction (NWP) models. Deep learning (DL) models have recently been increasingly applied to accelerate radiative transfer modeling. Besides, a physical relationship exists between the output variables, including fluxes and heating rate profiles. Integration of such physical laws in DL models is crucial for the consistency and credibility of the DL-based parameterizations. Therefore, we propose a physics-incorporated framework for the radiative transfer DL model, in which the physical relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. It is also found that the prediction accuracy was improved with the physic-incorporated layer. In addition, we trained and compared various types of deep learning model architectures, including fully connected (FC) neural networks (NNs), convolutional-based NNs (CNNs), bidirectional recurrent-based NNs (RNNs), transformer-based NNs, and neural operator networks, respectively. The offline evaluation demonstrates that bidirectional RNNs, transformer-based NNs, and neural operator networks significantly outperform the FC NNs and CNNs due to their capability of global perception. A global perspective of an entire atmospheric column is essential and suitable for radiative transfer modeling as the changes in atmospheric components of one layer/level have both local and global impacts on radiation along the entire vertical column. Furthermore, the bidirectional RNNs achieve the best performance as they can extract information from both upward and downward directions, similar to the radiative transfer processes in the atmosphere.

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.

As climate modellers prepare their code for kilometre-scale global simulations, the computationally demanding radiative transfer parameterization is a prime candidate for machine learning (ML) emulation. Because of the computational demands, many weather centres use a reduced spatial grid and reduced temporal frequency for radiative transfer calculations in their forecast models. This strategy is known to affect forecast quality, which further motivates the use of ML-based radiative transfer parameterizations. This paper contributes to the discussion on how to incorporate physical constraints into an ML-based radiative parameterization, and how different neural network (NN) designs and output normalisation affect prediction performance. A random forest (RF) is used as a baseline method, with the European Centre for Medium-Range Weather Forecasts (ECMWF) model ecRad, the operational radiation scheme in the Icosahedral Nonhydrostatic Weather and Climate Model (ICON), used for training. Surprisingly, the RF is not affected by the top-of-atmosphere (TOA) bias found in all NNs tested (e.g., MLP, CNN, UNet, RNN) in this and previously published studies. At lower atmospheric levels, the RF is able to compete with all NNs tested, but its memory requirements quickly become prohibitive. For a fixed memory size, most NNs outperform the RF except at TOA. For the best emulator, we use a recurrent neural network architecture which closely imitates the physical process it emulates. We additionally normalize the shortwave and longwave fluxes to reduce their dependence from the solar angle and surface temperature respectively. Finally, we train the model with an additional heating rates penalty in the loss function.

The geostationary Himawari-8 satellite offers a unique opportunity to monitor sub-daily thermal dynamics over Asia and Oceania, and several operational land surface temperature (LST) retrieval algorithms have been developed for this purpose. However, studies have reported inconsistency between LST data obtained from geostationary and polar-orbiting platforms, particularly for daytime LST, which usually shows directional artefacts and can be strongly impacted by viewing and illumination geometries and shadowing effects. To overcome this challenge, Solar Zenith Angle (SZA) serves as an ideal physical variable to quantify systematic differences between platforms. Here we presented an SZA-based Calibration (SZAC) method to operationally calibrate the daytime component of a split-window retrieved Himawari-8 LST (referred to here as the baseline). SZAC describes the spatial heterogeneity and magnitude of diurnal LST discrepancies from different products. The SZAC coefficient was spatiotemporally optimised against highest-quality assured (error < 1 K) pixels from the MODerate-resolution Imaging Spectroradiometer (MODIS) daytime LST between 01/Jan/2016 and 31/Dec/2020. We evaluated the calibrated LST data, referred to as the Australian National University LST with SZAC (ANUSZAC), against MODIS LST and the Visible Infrared Imaging Radiometer Suite (VIIRS) LST, as well as in-situ LST from the OzFlux network. Two peer Himawari-8 LST products from Chiba University and the Copernicus Global Land Service were also collected for comparisons. The median daytime bias of ANUSZAC LST against Terra-MODIS LST, Aqua-MODIS LST and VIIRS LST was 1.52 K, 0.98 K and -0.63 K, respectively, which demonstrated improved performance compared to baseline (5.37 K, 4.85 K and 3.02 K, respectively) and Chiba LST (3.71 K, 2.90 K and 1.07 K, respectively). All four Himawari-8 LST products showed comparable performance of unbiased root mean squared error (ubRMSE), ranging from 2.47 to 3.07 K, compared to LST from polar-orbiting platforms. In the evaluation against in-situ LST, the overall mean values of bias (ubRMSE) of baseline, Chiba, Copernicus and ANUSZAC LST during daytime were 4.23 K (3.74 K), 2.16 K (3.62 K), 1.73 K (3.31 K) and 1.41 K (3.24 K), respectively, based on 171,289 hourly samples from 20 OzFlux sites across Australia between 01/Jan/2016 and 31/Dec/2020. In summary, the SZAC method offers a promising approach to enhance the reliability of geostationary LST retrievals by incorporating the spatiotemporal characteristics observed by accurate polar-orbiting LST data. Furthermore, it is possible to extend SZAC for LST estimation by using data acquired by geostationary satellites in other regions, e.g., Europe, Africa and Americas, as this could improve our understanding of the error characteristics of overlapped geostationary imageries, allowing for targeted refinements and calibrations to further enhance applicability.KeywordsLand surface temperature; Geostationary; Himawari-8; Diurnal temperature cycle; Calibration; Solar zenith angle; MODIS; VIIRS

Stable water isotope signals in inland Antarctic ice cores have provided wealth of information about past climates. This study investigated atmospheric circulation processes that influence precipitation isotopes in inland Antarctica associated with atmospheric circulations in the southern mid-latitudes during the Last Glacial Maximum (LGM, ~21 000 year ago). We simulated this climate period using circulation model (MIROC5-iso) forced with different sea surface boundary conditions. Our results showed a steepened meridional sea surface temperature gradient in the southern mid-latitudes associated with a strengthening of the southern westerlies. This change in the atmospheric circulation enhanced the intrusion of warm and humid air from low latitudes that contributes to precipitation events, inducing heavy isotope precipitation inland East Antarctica. Our results suggest that the representation of past southern westerlies can be constrained using water isotopic signals in Antarctic ice cores.

Slovakia is located within the Central Western Carpathians (CWC), one of many connected curved mountain belts prominent throughout the Mediterranean area and Europe. It is divided into tectonic domains considered “superunits,” termed the Gemeric, Veporic, and Tatric that correlate to the lower, middle, and upper Austoalpine nappes. For example, granite bodies exposed in the unit (termed apophyses) yield a wide range of zircon ages from 310±21 Ma to 87±4 Ma. This range of ages leads to problems in deciphering where the Gemeric unit was located in global plate reconstructions of eastern Europe and the western Carpathians specifically. This case study involves U-Pb dating of magmatic and detrital zircons from the Gemeric tectonic unit. This area records the Variscan orogeny that formed the CWC, rifting, and opening of the Meliata Ocean. This ocean was created due to the formation of a back-arc basin during closing/subduction of the Paleo-Tethys Ocean. We aim to constrain the timing of rifting and identify the provenance of Meliata Ocean radiolarian sediments collected from an obducted Meliata ophiolite suite (Dobsina, Slovakia). The relative age of the Variscan orogeny extends from the late Devonian to early Permian and was followed by rifting throughout the Mesozoic within the CWC. Eventually, the Meliata Ocean closed during the Cretaceous. Zircons from several S-type granites were collected throughout the Gemeric tectonic unit; they were dated using Laser Ablation Inductively Coupled Plasma Mass Spectrometry and imaged using cathodoluminescence. Rim crystallization ages from the granites are 295.8±3.4 Ma (2σ, 238U-206Pb) to 213.1±4.4 Ma. Ages from the detrital zircons are 346.4±4.5 Ma to 263.9±2.7 Ma, indicating that sediments overlying the Meliata Ocean ophiolite contain remnants of both the Variscan orogeny and Gemeric granites.

Toward the achievement of reliable global kilometer-scale (k-scale) climate simulations, we improve the Nonhydrostatic ICosaherdral Atmospheric Model (NICAM) by focusing on moist physical processes. A goal of the model improvement is to establish a configuration that can simulate realistic fields seamlessly from the daily-scale variability to the climatological statistics. Referring to the two representative configurations of the present NICAM, of which each has been used for climate-scale and sub-seasonal-scale experiments, we try to find the appropriate partitioning of fast/local and slow/global-scale circulations. In a series of sensitivity experiments at 14-km horizontal mesh, (1) the tuning of terminal velocities of rain, snow, and cloud ice, (2) the implementation of turbulent diffusion by the Leonard term, and (3) enhanced vertical resolution are tested. These tests yield reasonable convection triggering and convection-induced tropospheric moistening, and result in better performance than in previous NICAM climate simulations. In the mean state, double Intertropical Convergence Zone bias disappears, and the zonal contrast of equatorial precipitation, top-of-atmosphere radiation balance, vertical temperature profile, and position/strength of subtropical jet are dramatically better reproduced. Variability such as equatorial waves and the Madden–Julian oscillation (MJO) is spontaneously realized with appropriate spectral power balance, and the Asian summer monsoon, boreal-summer MJO, and tropical cyclone (TC) activities are more realistically simulated especially around the western Pacific. Meanwhile, biases still exist in the representation of low-cloud fraction, TC intensity, and precipitation diurnal cycle, suggesting that both finer spatial resolutions and the further model development are warranted.

The impact of climate change on power demand in Japan is a matter of concern for the Japanese authorities and power companies as it may have consequences on the power grid. We trained random forest models against daily power data in ten Japanese regions and for different types of power generation to project changes in future power production and its carbon intensity. To do so, we used twelve predictors: six climate variables, five variables accounting for human exposure to climate, and one variable for the level of human activities. We then used the models trained from the present-day period to estimate the future power demand, carbon intensity, and pertaining CO2 emissions over the period 2020-2100 under three SSPs scenarios (Shared Socioeconomic Pathways: SSP126, SSP370, and SSP585). The impact of climate change on CO2 emissions via power generation shows seasonal and regional disparities. In cold regions, a decrease in power demand during winter under future warming leads to an overall decrease in power demand over the year. In contrast, the decrease in winter power demand in hot regions can be overcompensated by an increase in summer power demand because of more frequent hot days, leading to an overall annual increase. From our regional models, the power demand should increase the most in most Japanese regions in May, June, September, and October and not in the middle of summer, as has been found in older studies. Such an increase could result in regular power outages during those months if not considered, as the power grid could be particularly tense. Overall, we observed that power demand in regions with extreme climates is more sensitive to global warming than in temperate regions. The impact of climate change on power demand induces a net annual decrease in CO2 emissions in all regions except for Okinawa, in which power demand strongly increases during the summer, resulting in a net annual increase in CO2 emissions. However, climate change's impact on carbon intensity may reverse the trend in some regions (Shikoku, Tohoku). We also assessed the relative impacts of socioeconomic factors such as population, GDP, and environmental policies on CO2 emissions. When combined with these factors, we found that the climate change effect is more important than when considered individually and significantly impacts total CO2 emissions under SSP585.