The 2024 global mean temperature exceeded the values for all previous years in the instrumental record. Four of five commonly used datasets also exceeded the 1.5 degrees C warming level for that 12 month period relative to the 19th-century temperature record. The 2025 global temperature was lower in all datasets and below 1.5 degrees C, due to the transition from El Ni & ntilde;o to La Ni & ntilde;a. In this study, we ask: when will the 31 year centered global mean surface temperature anomaly relative to 1850-1900 first reach 1.5 and 2.0 degrees C? Examining available analyses of the temperature records, near-term future global temperatures are evaluated using an observation-based approach, extrapolating historical trends using both 20 year and 30 year linear and a 55 year quadratic method, and a LOESS-based nonlinear sensitivity test. These analyses provide a unique look at the temperature record, indicating that global temperature has not yet exceeded the 1.5 degrees C threshold for climate time scales but that this will likely occur sooner than prior estimates, perhaps as early as 2026 and likely between 2028 and 2032. Estimated exceedance of the 2.0 degrees C threshold exhibits a larger range, from the early 2040s to the mid 2050s. This is primarily dependent on whether the recent apparent acceleration of warming persists or is a transient phenomenon. The quadratic method is preferred for projecting temperature because it incorporates both steady quasi-linear multi-decadal trends and the recent apparent accelerated warming through the additional degree of freedom. The LOESS sensitivity analysis yields threshold crossing years very similar to those from 55 year quadratic method. The simplicity and robustness of the method used to project exceedances promote effective communication to diverse audiences.

For the conterminous United States, we compare two statistically downscaled climate datasets derived from the Coupled Model Intercomparison Project phase 6 (CMIP6) multimodel ensembles: the Localized Constructed Analogs, version 2 (LOCA2), and the Seasonal Trends and Analysis of Residuals-Empirical Statistical Downscaling Model (STAR-ESDM). Evaluating daily maximum (Tmax) and minimum temperature (Tmin), diurnal temperature range (DTR), temperature variability, annual extremes, and projection changes, we find that 1) both datasets demonstrate broad consistency with observations for the historical period and yield similar magnitudes and spatial patterns of projected climate change; 2) each enhances local-scale features relative to raw CMIP6 outputs and effectively corrects large-scale biases, such as the historically underestimated DTR common in CMIP6 simulations; and 3) both reduce intermodel spread in future climate projections compared to the original CMIP6 ensemble. Despite these common strengths, notable differences are observed: 1) Both datasets reflect uncertainties stemming from the observational products used in their training, particularly for Tmin and DTR; 2) LOCA2 produces a stronger fine-scale signal than STAR-ESDM; and 3) the difference between LOCA2 and STAR-ESDM is more pronounced for Tmin than for Tmax. These results underscore that training data and methodology influence downscaled outcomes, with LOCA2 generally better suited for fine-scale impact studies and STAR-ESDM for applications prioritizing regional coherence. SIGNIFICANCE STATEMENT: Localized Constructed Analogs, version 2 (LOCA2), and Seasonal Trends and Analysis of Residuals-Empirical Statistical Downscaling Model (STAR-ESDM) are widely used statistically down-scaled Coupled Model Intercomparison Project phase 6 (CMIP6) datasets that provide high-resolution surface temperature projections for regional impact assessments. Although both have been broadly validated, their relative skill in capturing temperature variability, extremes, and diurnal temperature range, as well as differences in projected changes, remain unexamined. This study conducts a focused comparison across the conterminous United States, revealing key differences in spatial patterns, variability, and future change signals that can guide dataset selection and interpretation in climate impact studies.

As climate attribution studies have become more common, routine processes are now being established for attribution analysis following extreme events. This study describes the prototype process being developed through a collaboration across National Oceanic and Atmospheric Administration (NOAA), including monitoring tools as well as observational and model-based analysis of causal factors. The prolonged period of extreme heat in summer 2023 over Texas, Louisiana and adjacent areas provided a proving ground for this emerging capability. This event posed unique challenges to the initial process. The extreme heat lasted for most of the summer while most heat wave metrics have been designed for 3-7 d events. The eastern portion of the affected area also occurred within the so-called summer-time daytime warming hole where the warming trend in maximum temperatures has been mitigated wholly or in part by increased precipitation. The extreme temperature coincided with a strong-but not record-precipitation deficit over the region. Both observations and climate model simulations illustrate that the temperatures for a given precipitation deficit have warmed in recent decades. In other words, meteorological droughts today are hotter than their historical analogs providing a stronger attribution to anthropogenic forcing than for temperature alone. These findings were summarized in a prototype plain language report that was distributed to key stakeholders. Based on their feedback, the monitoring and assessment tools will continue to be refined, and the project is exploring other climate model large ensembles to increase the robustness of attribution for future events.

Earth's climate system is composed of the oceans, atmosphere, and land surface, including snow and ice cover. Human activities, including the burning of fossil fuels, deforestation, and changing land use have led to increasing atmospheric concentrations of greenhouse gases (GHGs) such as carbon dioxide, nitrous oxides, and methane. The changes in GHGs have resulted in large changes to earth's climate system, changes that are occurring more rapidly now than at any point in human history. Multiple lines of evidence continue to support the conclusion that the climate is changing due to influences beyond natural variability. The impacts of these changes are being felt in all parts of the globe, on land, in estuaries, oceans, and the cryosphere. Impacts in the coastal zone include increased coastal erosion and flooding as well as large variations in freshwater input due to drought and enhanced wetness. This chapter provides an overview of how the climate is changing, the forcing factors causing the changes, including both natural and anthropogenic factors, and how the climate is expected to change through the 21st century.

In January 2021 work began on a NOAA Climate Program Office funded project “that develops and tests a potential rapid event analysis and assessment capability” (NOAA Climate Program Office 2020). This 3.5–yr effort brings together scientists from four NOAA Laboratories/Centers and university scientists at two of NOAA’s Cooperative Institutes. This funded project has two high-level goals: 1) to address outstanding dataset, model, and methodological gaps in explaining extreme events within a changing climate, and 2) to build a prototype rapid event attribution system for temperature-related and drought extremes that could eventually serve routine climate information needs at local, state, and regional levels. The focus on temperature-related extremes derives from the conclusions of the U.S. National Academy of Sciences report that confidence in attribution findings is greatest for this class of extremes (National Academies of Sciences Engineering and Medicine 2016). The project will leverage additional research projects that were funded under the same call that focus on the underlying mechanisms for these types of extreme events. Several climate trends in the United States present challenges for the attribution of temperature-related extremes (Fig. 1). The first is the lack of appreciable Joseph J. Barsugli, David R. Easterling, Derek S. Arndt, David A. Coates, Thomas L. Delworth, Martin P. Hoerling, Nathaniel Johnson, Sarah B. Kapnick, Arun Kumar, Kenneth E. Kunkel,Carl J. Schreck, Russell S. Vose, and Tao Zhang

Intensity-duration-frequency (IDF) curves - sometimes also called precipitation frequency estimates - are used to design urban drainage systems for stormwater control and, when combined with hydrologic modeling, to design flood control infrastructure and other structures. However, common approaches to estimating IDF curves need to be revised to account for non-stationarity from global climate change. This review synthesizes the current knowledge and on-going research regarding IDF curves under non-stationarity. In particular, this review briefly summarizes known and projected changes in extreme precipitation at the global scale, describes approaches for IDF curve estimation under non-stationarity (focusing on the covariate-based approach and including a brief overview of the specific challenges facing snow-dominated regions), addresses the topic of regionalization under non-stationarity (which has been overlooked in previous reviews), explores the challenges of design values and uncertainty in the context of non-stationarity, provides details on these topics in the context of the United States, and finishes by enumerating needed avenues of future research.

The utilization of optimization algorithms to allow interference direction finding with nondirectional sensor power reception in global positioning system (GPS) navigation applications is explored, and experimentation with simulated interference setups is conducted. Dimension reduction techniques are employed and evaluated to increase the efficiency of the algorithms.

The National Oceanic and Atmospheric Administration (NOAA) maintains an operational analysis for monitoring trends in global surface temperature. Because of limited polar coverage, the analysis does not fully capture the rapid warming in the Arctic over recent decades. Given the impact of coverage biases on trend assessments, we introduce a new analysis that is spatially complete for 1850–2018. The new analysis uses air temperature data in the Arctic Ocean and applies climate reanalysis fields in spatial interpolation. Both the operational analysis and the new analysis show statistically significant warming across the globe and the Arctic for all periods examined. The analyses have comparable global trends, but the new analysis exhibits significantly more warming in the Arctic since 1980 (0.598°C dec −1 vs. 0.478°C dec −1 ), and its trend falls outside the 95% confidence interval of its operational counterpart. Trend differences primarily result from coverage gaps in the operational analysis.

QNSTOP consists of serial and parallel (OpenMP) Fortran 2003 codes for the quasi-Newton stochastic optimization method of Castle and Trosset for stochastic search problems. A complete description of QNSTOP for both local search with stochastic objective and global search with “noisy” deterministic objective is given here, to the best of our knowledge, for the first time. For stochastic search problems, some convergence theory exists for particular algorithmic choices and parameter values. Both the parallel driver subroutine, which offers several parallel decomposition strategies, and the serial driver subroutine can be used for local stochastic search or global deterministic search, based on an input switch. Some performance data for computational systems biology problems is given.

This paper describes the Arachne expansion to the Hermes cross-language remote proce- dure call framework. The original Hermes framework is a powerful and flexible tool developed for an evolving software suite for multidisciplinary design as an intermediary between direct processor communications and larger distributed frameworks. Given the differences in proces- sor utilization across disciplines, this creates an easily utilized way to decouple the client and server to preserve implementation agnosticism on either side. While this default configuration is easily incorporated for anyone wishing to decouple multiprocessor servers and clients, it lacks an easy way to map different numbers of processors together from one side of the connection to the other, relying on direct processor-to-processor communications. As a result, any attempt to reorder the domain for use by differing numbers of processors relies on the development of an additional application-dependent interface to handle this communication. Arachne adds additional functionality to allows agnostic requests that map client and server processors. This enables more efficient computing as the data environment shifts between disciplines. This paper demonstrates the utility of this approach with an application to an aeroelastic problem couplings independent aerodynamic and structural solvers.

Trends of extreme precipitation (EP) using various combinations of average return intervals (ARIs) of 1, 2, 5, 10, and 20 years with durations of 1, 2, 5, 10, 20, and 30 days were calculated regionally across the contiguous United States. Changes in the sign of the trend of EP vary by region as well as by ARI and duration, despite the statistically significant upward trends for all combinations of EP thresholds when area averaged across the contiguous United States. Spatially, there is a pronounced east-to-west gradient in the trends of the EP with strong upward trends east of the Rocky Mountains. In general, upward trends are larger and more significant for longer ARIs, but the contribution to the trend in total seasonal and annual precipitation is significantly larger for shorter ARIs because they occur more frequently. Across much of the contiguous United States, upward trends of warm-season EP are substantially larger than those for the cold season and have a substantially greater effect on the annual trend in total precipitation. This result occurs even in areas where the total precipitation is nearly evenly divided between the cold and warm seasons. When compared with short-duration events, long-duration events-for example, 30 days-contribute the most to annual trends. Coincident statistically significant upward trends of EP and precipitable water (PW) occur in many regions, especially during the warm season. Increases in PW are likely to be one of several factors responsible for the increase in EP (and average total precipitation) observed in many areas across the contiguous United States.

Large economic losses from weather and climate hazards primarily arise from two long standing socio-economic factors: degree of urbanization and value at risk. More recently, cascading and compounding nature of recent climate events across global, regional, national and local scales are increasingly seen as fundamental characteristics of climate-related risks. These challenges illustrate the need not only to design systems "for" change but to design robust evidence-based systems that inform planning "through" changes as new risks emerge. At the same time, governments at every level are increasingly asked to balance the need to protect consumers from high insurance rates with the need to keep insurance companies from going out of business, and thus the public ends up acting as the insurer of last resort. There is a strong need for these actors to collectively view systematic and reliable historic and forward-looking risk exposure data as investments across the risk to resilience continuum rather than as costs. This chapter describes the drivers, economic impact assessment approaches, and financial services including flood and drought insurance, that have been employed for managing weather and climate risks at the national level in the United States, and outlines the analytical and decision making challenges and opportunities therein.