Wildfires are natural disasters that impact ecosystems and economies many countries around the world. In Ukraine, wildfires occur in all agroclimatic zones, from steppes and forest-steppes to mixed forests. The country belongs to the regions in Europe experiencing the most significant increase in average annual temperature in recent decades, while precipitation levels remain unchanged, leading to soil drying due to increased atmospheric evaporative demand (AED) and more frequent droughts. The purpose of this study is to define the dynamics of ignition in Ukraine from 2001 to 2020 and to assess the relationship of their number with wet and dry conditions during the warm season. For the analysis, the MODIS active fire/hotspot database from the FIRMS (NASA) archive was used. Assessment of wet and dry conditions was conducted using the drought index SPEI on a 3-month time scale. Dynamics of both parameters were examined for regional time series tailored for the territory of Ukraine. Time course of the annual number of hotspots (HSN) showed strong interannual variability with two maximums during the study period: in 2008 and 2014-2015. A non-significant decrease trend in HSN was observed.In the seasonal distribution, two maximums of HSN were identified: a primary one in August and a secondary one in April. The number of ignitions increases rapidly in spring from March to April, but there is a sharp decrease in May, remaining low in June. In July, the number of ignitions increases, reaching a maximum in August, followed by a sharp decrease in September-October. In November and winter, the number of active fires is very low.Correlation analysis between HSN and SPEI showed that in spring, the closest relationship is observed with SPEI taken in April (r = -0.48), indicating that drier conditions in late winter and early spring contribute to ignition. In summer, the significant positive correlation is observed with SPEI taken May (r = 0.51), indicating that a wet spring apparently leads to the accumulation of more vegetation fuels. In autumn, wet-dry conditions of the previous summer months do not have a statistically significant relationship with HSN. In general, preceding drought may increase the number and areas of fires, however further research is needed to establish consecutive relationships between these compound phenomena.Keywords: wildfire, drought, SPEI, hotspot

The Arabian Gulf represents a distinctive marine environment characterized by complex oceanographic dynamics. Using Level-3 MODIS Aqua data on the Google Earth Engine (GEE) platform, this study investigated the spatiotemporal variations on annual, interannual and seasonal basis in physical variables such a daytime and nighttime sea surface temperature (SST), sea surface salinity, turbidity, and chlorophyll-a (Chl-a) concentration, Aerosol Optical Thickness (AOT), Photosynthetically Active Radiation (PAR) in the Arabian Gulf from 2003 to 2023. The data clearly indicates fluctuating patterns of different parameters analyzed. It was perceived that daytime and nighttime SST is increasing while as other parameters such as Chl-a, salinity and turbidity is showing decreasing trend. In addition, it was observed that Chl-a is strongly correlated to turbidity and salinity in Gulf waters while as negatively correlates with SST which could be attributed to the upwelling process. The spatiotemporal distribution shows that turbidity is one of the factors contributing to phytoplankton blooms in the shallow region of the Gulf. The correlation analysis showed that Chl-a is not significantly related to AOT and PAR both on annual as well as on inter-annual scale. The high temperatures are more prominent in the Eastern region of the Gulf than Western. The year 2010 marked highest daytime SST and Chl-a concentrations ranging from 19.1°C to 38.02°C and 0.83 mg/m-³ and 56.36 mg/m-³ respectively, which were considered as the record breaking El Nino year at that point of time. Likewise, the highest salinity levels ranging from 25.8 PSU to 46.31 PSU were observed in year 2009, while in 2021, turbidity values ranged from 0.08 m⁻¹ to 3.44 m⁻¹. The analysis highlights the annual variability of oceanographic variables and showed that GEE can be useful method for large-scale environmental monitoring in the Arabian Gulf and can offer important data for the sustainable management and protection of the marine resources.

The global rise of sea level has made developing predictions of coastal flooding risk on subseasonal timescales (2-6 weeks in advance) an emerging priority for the National Oceanic and Atmospheric Administration (NOAA). In this study, we assess the ability of two current operational forecast systems, the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) and the Centre National de Recherches Météorologiques climate model (CNRM), to make subseasonal ensemble predictions of the non-tidal residual component of coastal water levels at United States coastal gauge stations for the period 2000-2019. These models were chosen because they assimilate satellite altimetry at forecast initialization and attempt to predict the mean sea level, including a global mean component whose absence in other forecast systems complicates assessment of tide gauge reforecast skill. Both forecast systems have skill that exceeds damped persistence for forecast leads through 2-3 weeks, with IFS skill exceeding damped persistence for leads up to six weeks. Post-processing forecasts to include the inverse barometer effect, derived from mean sea level pressure forecasts, improves skill for relatively short forecast leads (1-3 weeks). Accounting for vertical land motion of each gauge primarily improves skill for longer leads (3-6 weeks), especially for the Alaskan and Gulf Coasts; sea-level trends contribute to reforecast skill for both model and persistence forecasts, primarily for the East and Gulf Coasts. Overall, we find that current forecast systems have sufficiently high levels of deterministic and probabilistic skill to be used in support of operational coastal flood guidance on subseasonal timescales.

Bias-adjusted climate simulations are increasingly disseminated through online platforms to support adaptation actions. However, there exists no agreed upon operational framework for producing these ”decision-ready” ensembles and for communicating the related uncertainty. In this paper, we use a systematic approach to assess the uncertainty related to bias-adjusted climate simulations across five dimensions: internal variability, shared socioeconomic pathway, global climate model, observational reference and bias-adjustment method. We calculate the fraction of uncertainty associated with each dimension for precipitation-based, temperature-based and multivariate indicators over Quebec, Canada and focus particularly on three locations: Montréal, Gaspé and Kawawachikamach. The results show that the uncertainty associated with the reference dataset can be very large and in some instances can become the first or second largest source of uncertainty. Using simple examples, we show that the resulting differences could lead to different conclusions with respect to some adaptation solutions or possibly create confusion with users. These results raise questions on the robustness of climate projections distributed through these web platforms and the ethical responsibility of data providers to adequately evaluate and communicate the underlying uncertainty.

Simulating fluid transport in microporous media is challenging due to their inherent heterogeneity and multiscale nature. This study presents a multi-scale domain discretization approach coupled with a dual porosity numerical solver to address these challenges and determine representative elementary volumes (REVs) within the media. A hybrid formulation combining two-phase Darcy equation, single-phase compressible Navier-Stokes and continuity equations, and volume of fluid advection scheme is integrated. This enables accurate simulation of complex multiphase dynamics across both micro- and macro-porosity domains. Additionally, a dedicated domain discretization technique caters to the specific demands of microporous regions, ensuring tailored treatment of distinct porosity scales. This methodology was implemented on two distinct carbonate samples: Savonnieres carbonate and Mount-Gambier limestone. Prior studies had successfully determined an REV for Savonnieres carbonate. However, incorporating dual porosity made REV determination elusive due to sample size limitations stemming from the presence of clustered heterogeneity regions. Conversely, for Mount-Gambier limestone, with its more evenly distributed heterogeneity, an REV at 1200 cubic voxels was successfully established. The results closely align with existing core-scale estimates, further validating the proposed approach’s effectiveness. Notably, this success also underscores the method’s potential for broader application to diverse microporous media with varying heterogeneity distributions.

The magnetosphere of Jupiter is an excellent natural laboratory for plasma dynamics due to its strength and internal plasma source. However, a key factor complicating the flow of energy through the system is the closure of the driving current systems in the chaotic high-latitude ionosphere of Jupiter, a region that has been poorly surveyed. The polar orbit of the Juno spacecraft permits for the first time multiple observations of the high- and mid-latitude ionosphere through the radio occultation technique. This paper presents derived electron density profiles from four such occultations. Seven profiles, four ingress and three egress, all sample the dawn limb but at a range of magnetic latitudes, including an ingress profile within 5$^o$ of the main oval that is consistent with a response to high-energy electron influx. These profiles demonstrate variability in peak densities, peak altitude, and even the number of layers present. This high level of variability in Jupiter’s high-latitude ionosphere has complex consequences for MI coupling efficiencies.

Europa is characterized by a thin ice Ih shell overlying a subsurface ocean and a large solid core. Estimates of the outer ice shell’s thickness range from a few kilometers to several tens of kilometers, with strong implications for Europa’s thermal and geological history. Here, we model the thermal evolution of Europa’s ice shell using a parameterized convection approach that explicitly accounts for internal heat release. We explore changes in the thickness of this ice shell depending on several parameters, including the bulk ice viscosity, tidal heating, and ocean composition, and we further consider possible cyclical variations in tidal heating in response to changes in the eccentricity of Europa’s orbit. Our calculations show that ice shell thickness is mostly influenced by both the ice bulk viscosity and tidal heating. While significant in absence of tidal heating, the ocean composition has no or few influence when tidal heating is accounted for. Interestingly, for dissipated tidal heat and viscosity around 1 TW and 1014 Pa·s, respectively, which are within the expected range of values for these parameters, our calculations predict an ice shell thickness in the range 15-45 km and, at the top of this shell, a stagnant lid around 10 km in thickness, in agreement with recent estimates from impact basin morphology. Our calculations further indicate that a 10% change in orbital eccentricity may trigger variations in the ice shell thickness of approximately 15 km, which further helps to reconcile estimates based on geological features and modelled thermal history.

In recent years, solar-induced chlorophyll fluorescence (SIF) has shown great potential for monitoring terrestrial photosynthesis, but existing SIF products retrieved from atmospheric sensors typically feature coarse spatial resolutions from several to hundreds of kilometers. Recently, the Chinese Terrestrial Ecosystem Carbon Inventory Satellite, Goumang, launched in August 2022, carries a unique SIF Imaging Spectrometer (SIFIS), the first spaceborne sensor specifically designed for global SIF retrieval. SIFIS provides a substantially improved spatial resolution (along track: 370 m; across track: 800 m) and high spectral resolution (0.24 nm, 664–786 nm), representing an important advance in SIF remote sensing capabilities and enabling accurate SIF retrieval using a data-driven approach. SIFIS SIF showed excellent spatial and temporal agreement with airborne AisaIBIS data and independent datasets (TROPOMI SIF, OCO-2 SIF, SMAP GPP). This new SIF product opens new avenues for studying fine-scale photosynthesis from space.

The possibility of undetected geomagnetic polarity reversals has been suggested in statistical studies. However, the fine structure of the geomagnetic reversal frequency remains unknown because the previously reported reversal frequency modeled by adaptive-bandwidth kernel density estimation (KDE) can be too smooth. In this study, we modeled the reversal frequency using fixed-bandwidth KDE to investigate how the reversal frequency changed with the addition of four recently discovered geomagnetic reversals at ~31 Ma from the Ethiopian flood basalts (named the Lima-Limo reversals) to the geomagnetic polarity timescale (GPTS). One of the concavities close to Chron C12r in our reversal frequency model was suppressed after adding the Lima-Limo reversals to the GPTS. Based on this change, we conclude that undetected geomagnetic reversals may be hidden in periods with concavity in our reversal frequency model, spanning relatively long chrons of ~0.8 Myr or more.

Antarctic Winter Water (WW) forms in the surface mixed layer south of the Antarctic Polar Front and moves subsurface on seasonal timescales. Here, we use biogeochemical-Argo float data to investigate the circumpolar patterns of the biogeochemical properties of subsurface Winter Water (sWW). Biogeochemical and physical properties of sWW have a seasonal cycle as well as geographic variability shaped by large-scale circulation features of the Southern Ocean. Advective processes associated with mesoscale and submesoscale dynamics fueled by the Polar Front shape the biogeochemical properties of sWW, particularly in regions of high eddy kinetic energy. This is in contrast to the traditional view the WW evolution is controlled solely by 1D restratification. WW plays an important role in the climate system because it connects air-sea fluxes to biogeochemical properties in the interior in the mid- and high-latitudes across the Southern Hemisphere.

This study explores the microphysical and thermodynamic influences on rain evaporation in the sub-cloud layer over Barbados from January to November 2020. The findings reveal that microphysical properties, namely geometric mean diameter (Dg,cb) and raindrop concentration, dominate evaporation processes, while thermodynamic factors like cloud base height and surface relative humidity have smaller impact due to their limited variability. A radar and a 1-D model analysis shows that smaller Dg,cb leads to rapid increases in rain evaporation fraction (REF), as smaller drops evaporate completely. In contrast, larger Dg,cb results in slower evaporation and more gradual REF changes. Weak rain cases, occurring more frequently, reduces the mean evaporation flux (Fe), while intense rain, despite being less common, significantly enhances Fe due to the greater mass of evaporated rain. These results emphasize the pivotal role of microphysical variability in driving rain evaporation and shaping its vertical structure in the trade wind cumulus environment.

Reanalysis products and numerical weather prediction (NWP) models are widely used for wind resource predictions. However, their differences—particularly for metrics beyond wind speeds—remain underexplored, especially for environments with limited observations such as offshore. This study compares the performance of two datasets – the 2-km, NWP-based National Offshore Wind dataset (NOW23) and the global reanalysis dataset ERA5—for wind resource predictions across offshore, coastal, and terrestrial regions. Using near-surface wind observations across the northeastern United States and lidar-based offshore wind measurements, we evaluate key wind energy metrics, including wind speed, wind direction, and their variations (shear and veer) across the rotor layer under different seasonal, diurnal, and atmospheric stability conditions. Results show that NOW23 outperforms ERA5 in simulating near-surface winds for elevated and coastal locations and provides better estimates of mean hub-height and rotor-equivalent wind speeds, accounting for veer, offshore. However, NOW23 exhibits higher absolute errors and an amplified diurnal cycle due to overestimated land-sea temperature contrast. ERA5, on the other hand, systematically underestimates turbine-height wind speeds and overestimates the duration of prolonged wind energy shortages (droughts), which is critical for energy system planning. Both models underestimate wind shear and veer across the rotor layer, particularly under stable atmospheric conditions, though NOW23 performs better than ERA5 under neutral and unstable conditions. By linking these differences between reanalysis and NWP datasets to model resolution, parameterizations, and underlying physics, this study provides valuable insights for advancing wind resource modeling. It also offers guidance on the effective use of numerical models in wind energy development.

Achieving the UN Sustainable Development Goals (SDGs) by 2030 necessitates a comprehensive understanding of how environmental, social, and governance (ESG) indicators influence sustainability performances globally. This study presents an innovative spatial correlation analysis across over 200 countries between Env-SDGs and ESG indicators, revealing intricate relationships between environmental sustainability and various social and economic conditions. By visualising with a heatmap from these correlations, we demonstrate that environmental sustainability is significantly affected by a range of socio-economic factors in different regions. The future work will be based on the developed causality model on ESG and carbon markets to understand better about the synergy between carbon markets and countries' ESG performances. This work offers critical insights for policy leaders, highlighting the potential of an integrated ESG evaluation system to more effectively monitor and manage SDG progress. Furthermore, our findings underscore the transformative impact of integrating EO with ESG indicators on sustainability governance, offering a novel approach to strategic resource management and policy formulation.

The mineralogy of the planetary interiors is critical in controlling the dynamics and evolution of super-Earths. For large super-Earths, the post-post-perovskite (post-pPv) phase Mg2SiO4, one of the major mantle phases, may undergo the order-disorder transition (ODT) at high temperatures. Yet, the ODT phase boundary of Mg2SiO4 has not been rigorously constrained. Additionally, fundamental thermodynamic properties of the disordered Mg2SiO4 remain to be determined. Here, we develop a unified machine-learning potential (MLP) for Mg2SiO4 of ab initio quality across super-Earth mantle conditions. This efficient MLP enables us to extensively calculate the free energy of post-pPv Mg2SiO4 using the thermodynamic integration method. The results are used to construct the ODT phase boundary. Furthermore, we report the equation of state and Grüneisen parameters for post-pPv Mg2SiO4 with various degrees of disorder. These thermodynamic properties are used to update the adiabatic thermal profiles and the mass-radius relation of super-Earths.

The Lagrangian particle method is widely used to understand scalar tracer concentra- tion fields in models of the atmosphere and oceans. Simulating virtual particles provides an alternative description of advection to the Eulerian representation in models and aids in identifying pathways, timescales, and connectivity. Atmospheric and oceanic models solve advection-diffusion-reaction equations to simulate tracers, in which only the ad- vective component is captured by traditional Lagrangian approaches. In this work, we report a novel method that closes tracer budgets on Lagrangian trajectories in a man- ner consistent with Eulerian budgets in finite-volume models. The scalar tracer concen- trations on grid cell walls are derived from the model advection scheme and then inter- polated inside grid boxes along streamlines. The divergence of the diffusive flux and re- action terms are interpolated based on velocity and tracer concentration, ensuring the tracer budget closes in terms of both trajectory and volume integrals. We demonstrate the method using a case study of Southern Ocean biogeochemistry. Another case study involves analyzing the heat budget of the 2011 Western Australian marine heat wave. The method bridges the gap between Eulerian budget and Lagrangian particle analy- ses by representing the advective processes with particle movements and interpolating the diffusive and reactive processes onto trajectories in a way consistent with the finite- volume description.

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

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

Located in the ancient and deeply weathered mid-Atlantic Piedmont physiographic province, Minebank Run is an outlier, with unusually high bed sediment loads. The combined effects of steep slopes and urbanization produce a flashy hydrology and high flows that have resulted in significant channel incision. Multiple stream restoration attempts have met with limited success, and infrastructure has failed due to the ongoing channel instability. Therefore, addressing the instability in Minebank Run requires a thorough investigation of sediment sources and the implementation of targeted management strategies to mitigate their impact. Identifying potential sediment sources and sinks in the watershed, quantifying source contributions and understanding transport pathways are crucial to effectively manage sediment at the watershed level. This research systematically examines historical changes in the watershed to understand how sediment transport in Minebank Run has changed over time. A field survey of the tributaries and main stem revealed system-wide knickpoints in the tributaries. Using LiDAR images, digital elevation models, existing sediment data inventories, and field data, the volume of sediment influx to the stream was calculated. Historical aerial images were analyzed to observe land use changes and to identify historical sediment sources and sinks. A sediment budget was developed, with a relative ranking of sediment sources. Comparison of historical and contemporary data of channel incision and migration of knickpoints will provide insights into the speed and scale of morphological changes. The anticipated results will aid in designing targeted management strategies to reduce sediment in the watershed, enhance the stability and quality of Minebank Run, and guide future efforts. This study will provide critical insights into managing unique and challenging cases like Minebank Run in urban environments.