Rapid expansion of geological storage of CO2, at the giga-tonne scale, is required to mitigate CO2 emissions into the atmosphere. Potential storage basins need to be characterized rapidly to determine the best sites for injection and long-term security. The hydrocarbon industry has developed the common risk segment mapping approach over many years, for assessing exploration potential based on static reservoir properties (geological seals, permeability, porosity etc). More recently this has also been applied to evaluating storage potential for CCS by Bump et al. (2021).

Classically, routing in Land Surface Models (LSMs) has been achieved using simple schemes with relatively coarse spatial resolution, such the 0.5-degree TRIP model and its derivatives. However, moving to higher resolution requires us to re-think this approach, but at the expense of increasing complexity and computational cost. Some clues to what these new methodologies might be can potentially be found in the field of global flood inundation modelling. Here, over the last ten years, a set of methods have been developed to map areas at risk of flood inundation at high (now 1 arcsecond) resolution over the entire land surface using full 2D hydrodynamic methods. Such approaches are likely still too costly to implement directly in Land Surface Models, but a consideration of findings from these studies can indicate the essential elements that would need to be included in more a lightweight approach.

The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility deploys a mix of long-term fixed atmospheric observatories and short to mid-term mobile facilities with a complex array of instrumentation from surface meteorology and flux stations to lidars, radars, and more. Over 30 years and 4 petabytes of data are publically available in ARM’s data discovery tool. ARM has long developed code to support quality control, data product development, and various other tools for working with the data but these have mostly been developed internally in silos across the various groups of ARM infrastructure. Twenty-one years into existence, ARM released its first open-source community software and has been engaging with the open-science community more and more as the years pass.

A document by MILUSKA ANTHUANNET ROSAS BARTUREN. Click on the document to view its contents.

We have developed a novel method for retrieving the single-point lower ionospheric density profile, utilizing observations from Loran-C and a ray-theory-based Earth-Ionosphere WaveGuide (RT-EIWG) model. Compared to other signals probing the D layer, such as VLF transmitters which provide path-averaged information on the upper boundary, and lightning sferics, which are less stable and dependent on the unpredictable occurrence of lightning, Loran-C offers significant advantages. The reflections of the Loran-C signal are clearly resolved over distances of 300-600 km. Additionally, the 1-hop reflection does not overlap with subsequent hops, and the signal is consistently emitted along a fixed path (interval ranges from 40 to 100 milliseconds). The RT-EIWG model effectively simulates ionospheric reflections at distances less than 1000 km. By combining the RT-EIWG model with Loran-C observations, we have established a new method for determining the electron density profiles of the D layer. Initial results indicate that the nighttime lower ionosphere varies with a period of a few minutes consistently. The method would be a powerful tool for the research of lower ionosphere interaction in the future.

A document by Christine Olson. Click on the document to view its contents.

The Relative Difference (%) between Solar Maximum and Minimum is highest in the UV range. We calculated the shortwave spectral files used in our models with the SOCRATES radiative transfer code. To account for stellar spectral features sensitive to activity, we designed a new band scheme (see the tables) (https://www.met.reading.ac.uk/~yt910424/home/socrates.php) We used SSI data obtained by the LASP TSIS-1 Spectral Irradiance Monitor (SIM) onboard the International Space Station. The data includes measurements taken during at Solar Minimum (averaged from December 1-7/2019, proposed as the reference spectrum for the upcoming CMIP7, as suggested by Funke et al. (2024)) and the Solar Maximum (averaged June 3-20/2024). For wavelengths beyond 2399 nm, both spectra incorporate the same data as the reference spectrum constructed by Coddington et al. (2023).

Decreased water quality in coastal environments due to land alterations by human activities has caused stressed and degraded coral reefs worldwide. The consequences of reduced water quality are not limited to coral reefs but also affect the quality of people’s lives by increasing the incidence of diseases, so areas highly impacted have been prioritized for management. The Guánica Bay Watershed Management Plan was developed to reduce the non-point sources of pollution that arrive at the bay and to protect adjacent coral reefs, however, 15 years have passed since its creation, and management actions have not been evaluated. This study aimed to assess the effectiveness of the management actions implemented in the Guánica Bay watershed. We described temporal trends (2002–2008 and 2016–2022) of remotely sensed diffuse attenuation coefficient at 490 nm (Kd490), a water clarity indicator, in one managed (Guánica Bay) and three non-managed (Guayanilla Bay, Descalabrado River, and Guanajibo River) estuaries in south and southwest Puerto Rico. This was achieved by integrating ocean-color satellite imagery from MERIS-Envisat and OLCI-Sentinel-3 sensors sampled using a beyond-Before-After-Control-Impact (beyond-BACI) approach. The analysis for the beyond-BACI found significant differences between periods (before and after) but the changes were unique to each location within the estuaries. The southern estuaries showed similar temporal trends, all having a peak in 2018 related to the passage of Hurricanes Irma and Maria and a trough in 2020 related to the Covid-19 pandemic. Kd490 did not decrease in Guánica after implementing management actions, suggesting that the impact of the hurricanes on water clarity probably outweighed the efforts made in the watershed to improve water quality. Further analysis should be done as new data is available and after the implementation of the last management actions suggested in the plan.

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

In the ongoing effort to improve Earth orientation parameter (EOP) prediction accuracy, the effect of various atmospheric models on EOP forecasts is explored. The first derivative of UT1-UTC, or length of day (LOD), is proportional to the axial component of the dimensionless effective atmospheric angular momentum (AAM) functions, given conservation of angular momentum in the Earth-atmosphere system. Previous work showed that using a robust multiple regression algorithm to pre-process AAM data improves 0 and 7-day predictions of UT1-UTC by 47% and 20%, respectively, even when significant outliers are present in other UT1 or LOD measurement sources during combination. AAM data are produced by various atmospheric modeling centers, and previous work used inputs from multiple AAM models weighted by their regression fit alone after applying effects from estimated systematic errors. This present work compares different AAM models to the derived geodetic AAM excitation functions to better characterize the systematic effects of the individual models. Various techniques to compare model accuracies are explored, and the effects of different AAM models on UT1-UTC forecasts are estimated

Tipping elements are critical components of the Earth system. These are small perturbations that can cause large, potentially cascading, and irreversible impacts on the climate system, including the biosphere and human societies. However, our current knowledge of the scale and feedbacks of Earth system components that might exhibit tipping behavior is still limited. Advancing our understanding requires large ensembles of Earth System Model (ESM) simulations. In the Tipping Point Model Intercomparison Project (TIPMIP), a “ramp up – stabilization – ramp down” scenario is suggested. This approach involves adapting either CO2 emissions or CO2 concentrations to drive the global mean surface temperature (GMST) to increase towards a certain prescribed global warming level (GWL), keeping it stable at the GWL for a certain period, and then allowing the temperature to decrease again.

Estimating evapotranspiration (ET) is crucial for understanding changes in the water cycle, yet it remains a challenge due to its dynamic nature. Numerous gridded global ET products exist, but no single product serves as a definitive benchmark. Validation of these large-scale ET products against point data is difficult since ET can vary significantly over space unless atmospheric conditions are stable and land-surface cover is uniform. In this study, six widely used global gridded ET products were first cross-compared and then validated using selected Eddy-covariance towers. The products included: ERA5 (Reanalysis based), GLEAM (Remote sensing based), WGHM (Hydrological model based), Fluxcom (Machine learning based) and KF-ET, WA-ET (Water balance based). The comparison is conducted at a 1˚ spatial and monthly temporal resolution from 2003-2016. Significant differences were observed among the ET datasets, with products performing similarly in some regions but varying considerably in others. Specifically, the Amazon, Southern Africa, Eastern India, and Southern China exhibited large discrepancies, characterized by low correlation (R) and high RMSE values. KF-ET closely resembled Fluxcom in R and RMSE, except in the Amazon and Eastern India, regions where the Fluxcom network is sparse. A dedicated network of Eddy-covariance towers is essential for optimal validation, covering at least a few 1˚ grid cells. To ensure fair comparison, 24 Fluxnet sites were selected from 267 global sites of Fluxnet 2015 dataset based on similarity between the site’s land-use/land-cover (LULC) class and the dominant LULC class within the corresponding 1˚ grid. Further the sites with atleast 30% usable data from 2003-2016 were chosen. Analysis revealed that Fluxcom, ERA5, and KF-ET performed similarly, with Fluxcom outperforming at some sites. However, as Fluxcom is directly derived from Fluxnet sites, its superior performance is expected. WGHM, incorporating various irrigation scenarios, proved reliable among hydrological models. The dominant LULC class percentage within a grid influenced the NSE value between Fluxnet sites and gridded products. The linear relationship between NSE and the dominant LULC class percentage underscores the challenge of comparing site-scale and 1˚ grid-scale data.

Methodology Conclusions and Future work Results This study investigates the impacts of climate change and increasing water demand on water stress levels in the Apalachicola-Chattahoochee-Flint (ACF) River Basin, a critical transboundary water system shared by Georgia, Alabama, and Florida. Recognized as a compound natural hazard, drought in this region is exacerbated by climatic shifts and human-driven policy changes. By analyzing compound drought indices such as SPI, MSSRI, and PDSI, the study explores the interplay between drought events and water management policies, highlighting the influence of state and federal decisions. Results emphasize the need for integrated policy frameworks to address compounded water deficits and mitigate transboundary conflicts.

Heatwave (HW) events are expected to worsen due to climate change, resulting in increased societal impacts such as human illness, crop failures, wildfires, power outages, infrastructure disruption, and damage. The southwest region of Bangladesh, particularly the Khulna division, is anticipated to experience severe consequences of climate change, with increased frequency and intensity of HWs causing numerous deaths due to heatstroke. To understand and mitigate the severity of HWs in Khulna, various aspects were analyzed, such as frequency, duration, and magnitude. This involved calculating the yearly number of HW events (HWN), the yearly sum of HW days (HWF), the length of the longest yearly event (HWD), the average size of all yearly events (HWL), the hottest day of the hottest yearly event (HWA), and the average magnitude of all yearly events (HWM). This analysis used daily maximum temperature records from four observation stations during the summer months (March to May or MAM) from 1990 to 2014, as well as data from thirteen CMIP6 models for three time periods: historical (1990-2014), near future (2026-2050), and far future (2076-2100) for two emission scenarios SSP2-4.5 and SSP3-7.0. Observation data were used to correct the bias of CMIP6 models using empirical quantile mapping (EQM) and to evaluate these models to characterize HW characteristics. HW events were identified using the 90th and 75th percentiles of the distribution of daily maximum temperature values for the reference period. Based on the Pearson Correlation Coefficient (PCC), the values of indices from GCMs are not strongly correlated with the values from the observed dataset. Additionally, indices like HWA and HWM are calculated from GCMs, which are significantly less correlated with the results from the observed dataset, that denotes GCMs cannot project extreme values for particular HW events . The frequency, duration, and magnitude of HWs showed abrupt changes for both emission scenarios compared to the historical period. The historical pattern of HW indices of the Khulna Division has not changed much over 25 years except HWF. HWs are proving to be the deadliest disaster in modern existence. Therefore, a comprehensive understanding of the characteristics of HWs in a particular area is crucial for raising awareness and potentially saving many lives.

In agricultural science, researchers face difficulties publishing and sharing their data-driven crop yield forecast models. The lack of direct and public access to the resulting models impedes collaboration among stakeholders, obscures relevant context and hinders continuous use of the contributions. In addition, researchers have to spend a significant amount of time on accessing, aggregating and pre-processing feature data from various sources. To address both issues, we present a platform that enables (i) public access to standardized and high-quality data by providing (ii) a uniform interface and SDK for data retrieval, as well as (iii) seamless hosting of data-driven crop yield prediction models. Our platform standardizes datasets, sourced from canonical providers such as Copernicus and ISRIC in the areas of observed and forecast weather, soil, and vegetation indices. We facilitate access to this data at different spatial and temporal resolutions through a uniform interface. Thereby, researchers can immediately focus on model development without having to manually gather data from heterogeneous sources. We further allow researchers to directly upload and deploy their crop yield prediction models by submitting their own code written in the Python programming language. The upload process is flexible, minimizing the integration effort on the researcher’s side. The uploaded models are deployed on the platform; historical as well as future prediction values are computed automatically and continuously. The predictions are visualized in an intuitive way, enabling direct comparison to other models and actual historical yield values as well as a direct assessment of models’ forecast capabilities. By publishing their models on our platform, researchers can increase their work’s visibility to actors from academia, media, politics, and business. This enables direct communication between the scientific community and the public, cultivating engagement, feedback and knowledge sharing. In summary, our platform revolutionizes the development and deployment of yield forecast models by providing a comprehensive suite of standardized data sets and the functionality for direct deployment of researchers’ models. This makes yield modeling seamless, collaborative and visible.

Satellite surface temperatures of São Paulo city show heat islands and coolness islands due urban form. Some planned neighborhoods have street trees, green areas, appropriate urban drainage to prevent flooding in floodplain areas, and adequate thermal comfort and ventilation conditions for urban health and human well-being. Other neighborhoods that originated irregularly within the urban grid or on the city’s outskirts suffer from extreme weather problems, such as urban heat islands and localized rainfall that cause flooding and landslides. This contrast in quality of life and urban vulnerability is very present in large cities. This work investigates urban heat at the pedestrian scale in different urban forms in Paraisópolis-Morumbi region. Methods: a) Study area selection; c) Field climate data (air temperature and humidity), every 10 min (08/25/2024 to 09/14/2024); d)Quantify heat and thermal comfort by the PET (Physiological Equivalent Temperature) and UTCI (Universal Thermal Climate Index); e)Analysis of current and future scenarios. The maximum air temperature difference between the Morumbi (area 1)  and Paraisópolis (area 3) was 7.8º C in 2023 and 11,77 ºC in 2024.  In therms of PET, difference betwenn Favela and 12.5ºC in PET and 10ºC in UTCI. The maximum relative humidity values showed similar values, around 79%, but the minimum values differed by 14.4%. Although data collection was conducted in winter season, heat conditions can worsen during the rainy season, when temperatures in PET and UTCI can exceed 45°C, in conditions of extreme thermal stress. The computer simulations were able to present the current and future scenario in which temperatures can be cooled with the implementation of a park in the Antonico Valley (area 3) and the increase in trees in the Fazendinha Community region. Keywords: Outdoor Thermal Comfort; Urban Heat Stress, Favelas, Urban Form

Variations in stable oxygen isotopic compositions in sea ice provide information on environmental conditions during sea ice formation, and also are important in understanding regional and temporal aspects of the freshwater budget of the Arctic Ocean. We analyzed the oxygen isotope fractionation between sea ice and seawater using ice core and surface ocean samples obtained in a field study in the Lincoln Sea/Switchyard region of the Arctic Ocean. Using the Sea Ice Tracking Utility (SITU) we track the sea ice backward in time along drift trajectories, and use a simple model to calculate ice growth rates. Our results indicate that sea ice at the bottom of the floes that we sampled in the Switchyard Region grew within the past winter along a trajectory extending back to the North Pole. The effective fractionation coefficients from the bottom ice layers and the parent water mass are close to 2.11 ‰ with a standard error of ± 0.06 ‰. Knowing this sea ice oxygen isotope fractionation coefficient for high Arctic drifting ice is critical for use of equations for mass balance, salinity, oxygen isotopes and nutrients to calculate water mass fractions and sources to understand freshwater balance.

With increasing climate variability, natural disaster frequency, and populations there is an emerging need for digital awareness of the built environment. Knowing who and what is where is critical to better allocating resources in emergencies, planning for the future, and translating the outcomes of different environmental models into tangible impacts that help bridge the policy-science gap. To date, a range of open source projects have sought to represent the built environment, but each has focused on either spatial completeness (e.g. AI methods), accuracy (hand digitization in OpenStreetMap), or attribute richness (OpenAddresses). The reality is that all of these are needed for different uses, and integrating these datasets offer a unique path towards a spatial and attribute rich data product. This work describes a method for developing a best available Built Environment Asset Registry (BEAR) from a suite of open - yet disparate - data sources. To date, BEAR conflates commonly utilized, continental-scale footprint and address databases covering the contiguous US. The resulting product is developed as a cloud-native database that conforms to an extensible schema. Using this core representation, we highlight efficient access patterns for retrieving subsets of BEAR from the Lynker Spatial data portal, and examples of applying BEAR to hazard modeling and social vulnerability assessment. We show how this product can increase the skill and utility of forecasts related to flooding and fires.