Tech expert Kurt'CyberGuy' Knutsson joins'Fox & Friends' to discuss the future of AI development in the United States. Vice President JD Vance is poised to kick off a trip to Italy and India on Friday – marking his third international trip with the Trump administration. Vance and the second family are poised to meet with and "discuss shared economic and geopolitical priorities with leaders in each country," according to a statement from Vance's office. When in Rome, Vance is scheduled to meet with Italy's Prime Minister Giorgia Meloni and Vatican Secretary of State Cardinal Pietro Parolin. He will meet with India's Prime Minister Narendra Modi while visiting New Delhi, Jaipur and Agra.

Extreme events are evolving as a direct consequence of climate change, leading to the emergence of new, previously unobserved phenomena [1, 2]. In remote regions like the Arctic, where in-situ measurements are sparse, scientists must increasingly depend on analytical and numerical models to explore these events. However, modeling in such regions presents significant challenges due to the uncertainties in the data required to calibrate and validate these models [3]. Consequently, large simplifications are often necessary, resulting in substantial discrepancies between observed and modeled phenomena. The mysterious 10.88 mHz very-long-period (VLP) seismic signal, which appeared following a tsunamigenic landslide in the Dickson Fjord, Greenland, on September 16th, 2023, and the subsequent interdisciplinary scientific efforts to determine its origin, underscore these challenges. Two independent studies [4, 5] have hypothesized that the signal was driven by a standing wave, or seiche, which formed in the aftermath of the tsunami. While it is well-documented that seiches can form in resonant enclosed and semi-enclosed basins [6], the loading-induced tilt they produce has only been observed locally (< 30 km) and for short durations (< 1 hour)[5, 7]. Moreover, no prior evidence exists of persistent fluid sloshing (lasting several days) without an external driver.

The spatiotemporally continuous data of normalized difference snow index (NDSI) are key to understanding the mechanisms of snow occurrence and development as well as the patterns of snow distribution changes. However, the presence of clouds, particularly prevalent in polar regions such as the Greenland Ice Sheet (GrIS), introduces a significant number of missing pixels in the MODIS NDSI daily data. To address this issue, this study proposes the utilization of a spatiotemporal extreme gradient boosting (STXGBoost) model generate a comprehensive NDSI dataset. In the proposed model, various input variables are carefully selected, encompassing terrain features, geometry-related parameters, and surface property variables. Moreover, the model incorporates spatiotemporal variation information, enhancing its capacity for reconstructing the NDSI dataset. Verification results demonstrate the efficacy of the STXGBoost model, with a coefficient of determination of 0.962, root mean square error of 0.030, mean absolute error of 0.011, and negligible bias (0.0001). Furthermore, simulation comparisons involving missing data and cross-validation with Landsat NDSI data illustrate the model's capability to accurately reconstruct the spatial distribution of NDSI data. Notably, the proposed model surpasses the performance of traditional machine learning models, showcasing superior NDSI predictive capabilities. This study highlights the potential of leveraging auxiliary data to reconstruct NDSI in GrIS, with implications for broader applications in other regions. The findings offer valuable insights for the reconstruction of NDSI remote sensing data, contributing to the further understanding of spatiotemporal dynamics in snow-covered regions.

The Greenland Ice Sheet (GrIS) has emerged as a significant contributor to global sea level rise, primarily due to increased meltwater runoff. Supraglacial lakes, which form on the ice sheet surface during the summer months, can impact ice sheet dynamics and mass loss; thus, better understanding these lakes' seasonal evolution and dynamics is an important task. This study presents a computationally efficient time series classification approach that uses Gaussian Mixture Models (GMMs) of the Reconstructed Phase Spaces (RPSs) to identify supraglacial lakes based on their seasonal evolution: 1) those that refreeze at the end of the melt season, 2) those that drain during the melt season, and 3) those that become buried, remaining liquid insulated a few meters beneath the surface. Our approach uses time series data from the Sentinel-1 and Sentinel-2 satellites, which utilize microwave and visible radiation, respectively. Evaluated on a GrIS-wide dataset, the RPS-GMM model, trained on a single representative sample per class, achieves 85.46% accuracy with Sentinel-1 data alone and 89.70% with combined Sentinel-1 and Sentinel-2 data. This performance significantly surpasses existing machine learning and deep learning models which require a large training data. The results demonstrate the robustness of the RPS-GMM model in capturing the complex temporal dynamics of supraglacial lakes with minimal training data.

The Advanced Research and Invention Agency (ARIA) will announce today that it's seeking proposals to work on systems for two related climate tipping points. One is the accelerating melting of the Greenland Ice Sheet, which could raise sea levels dramatically. The other is the weakening of the North Atlantic Subpolar Gyre, a huge current rotating counterclockwise south of Greenland that may have played a role in triggering the Little Ice Age around the 14th century. The goal of the five-year program will be to reduce scientific uncertainty about when these events could occur, how they would affect the planet and the species on it, and over what period those effects might develop and persist. In the end, ARIA hopes to deliver a proof of concept demonstrating that early warning systems can be "affordable, sustainable, and justified." No such dedicated system exists today, though there's considerable research being done to better understand the likelihood and consequences of surpassing these and other climate tipping points.

As LLMs become ubiquitous and integrated into numerous digital applications, it's an increasingly pressing research problem to understand the internal mechanisms that underlie their behaviour - this is the problem of mechanistic interpretability. A fundamental subproblem is to causally attribute particular behaviours to individual parts of the transformer forward pass, corresponding to specific components (such as attention heads, neurons, layer contributions, or residual streams), often at specific positions in the input token sequence. This is important because in numerous case studies of complex behaviours, they are found to be driven by sparse subgraphs within the model (Meng et al., 2023; Olsson et al., 2022; Wang et al., 2022). A classic form of causal attribution uses zero-ablation, or knock-out, where a component is deleted and we see if this negatively affects a model's output - a negative effect implies the component was causally important. More recent work has generalised this to replacing a component's activations with samples from some baseline distribution (with zero-ablation being a special case where activations are resampled to be zero). We focus on the popular and widely used method of Activation Patching (also known as causal mediation analysis) (Chan et al., 2022; Geiger et al., 2022; Meng et al., 2023) where the baseline distribution is a component's activations on some corrupted input, such as an alternate string with a different answer (Pearl, 2001; Robins and Greenland, 1992). Given a causal attribution method, it is common to sweep across all model components, directly evaluating the effect of intervening on each of them via resampling (Meng et al., 2023). However, when working with SoTA models it can be expensive to attribute behaviour especially to small components (e.g.

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale.

They might be the front line of climate change; however, we still don't know much about what's going on at the underwater front of Greenland's glaciers. A planned robotic dive there could change that and expose some of the mysteries, hopefully revealing just how much these ice rivers will contribute to sea-level rise as a result of human-caused global warming. The new mission, led by researchers at The University of Texas, is set to launch in midsummer 2023 and will deploy a submersible robot to study three of Greenland's glaciers: Kangilliup Sermia, Umiammakku Sermiat, and Kangerlussuup Sermia, which are all located on the island's west coast. This is going to be the first time scientists will have a close-up look beneath Greenland's glaciers. The researchers will send a remotely operated submarine called Nereid Under Ice (NUI) to the glaciers' undersides, where they meet the ocean.

Extreme waves in the Arctic typically occur every 20 years, but as climate change continues to plague the region these events could happen every two to five years, a new study reveals. Much of this area is frozen for a majority of the year, but rising temperatures have increased periods of open water that could result in catastrophic waves. Using computer models, researchers found the area hit the hardest was in the Greenland Sea, which could experience maximum annual wave heights of more than 19 feet. The team also warns coastal flooding might increase by a factor of four to 10 by the end of this century. Extreme waves in the Arctic typically occur every 20 years, but as climate change continues to plague the region these events could happen every two to five years, a new study reveals.

Since 1980, the temperature of the planet has risen by 0.8 degrees Celsius, resulting in unprecedented melting of the Greenland ice sheet and the acidification of oceans. In 2015, 175 million more people were exposed to heat waves compared with the average for 1986 to 2008, and the number of weather-related disasters from 2007 to 2016 was up by 46 percent compared with the average from 1990 to 1999. This is nothing in comparison to the horrors that await us as temperatures continue to rise. According to recent projections, global temperatures are set to increase by 3.2 degrees by the end of century. This will lock in sea level rises that will mean that the cities, towns, and villages currently occupied by 175 million people--including Hong Kong and Miami--will eventually be underwater. There is overwhelming scientific evidence that warming is largely caused by the actions of human beings.