Paul Aaron Ullrich

Winter fire weather is shaped by interactions between anthropogenic climate change and natural climate variability, yet how the combination of human and natural forcings influences winter fire weather conditions remains poorly quantified. This study uses a multiple linear regression framework that removes long-term CO 2 -related trends to examine the influence of natural modes of variability on the December–February monthly fire weather index over the western United States (WUS), with the January 2025 Los Angeles (LA) fire risk as a motivating case study. Results show that the El Niño Southern Oscillation (ENSO), Pacific North American pattern (PNA) and Atlantic Meridional Mode (AMM) significantly influence winter fire weather across the WUS, whereas the influence of CO 2 is more pronounced and generally significant over the interior regions, but less evident along the coastal areas of the WUS. Positive phases of the PNA and AMM, combined with La Niña–like conditions during December 2024 and January 2025, significantly elevated fire weather risk across southern California, including LA County. The elevated fire-weather conditions preceding the January 2025 fires in LA and surrounding regions were aggravated by the combined influence of the natural and anthropogenic modes. Our study also shows that anthropogenic CO 2 is associated with an estimated 10%–20% increase in the chances of the 2024–25 fire weather over the WUS coastal states, although this contribution is not statistically significant, while La Niña and positive PNA significantly increase these chances by at least 20% across California and by more than twice across much of the WUS, including southern California and neighboring states. We find that large-scale natural climate modes combined with increases in greenhouse gas concentration provide valuable information for understanding and predicting WUS winter fire weather.

Extreme atmospheric river (AR) events are a dominant driver of flood risk in Northern California. Traditional flood studies and operational forecasts often rely on a single model; however, substantial structural differences persist across hydrologic modelling frameworks due to variations in process representations. Here, we present a multi-model comparison framework to evaluate differences in streamflow and flood inundation during an AR-driven flood event in the Russian River Basin (Sonoma County, California) in late February 2019. Three physical-based modelling systems, each forced with a common observation-based precipitation dataset, are compared: (1) Torrent, a computationally efficient overland flow model operating directly on digital elevation models (DEMs); (2) HEC-RAS, implemented using a two-dimensional shallow-water (Saint-Venant) solver in rain-on-grid mode; and (3) WRF-Hydro-HAND, a multi-scale hydrologic routing framework that combines distributed runoff generation with terrain-based inundation mapping. These systems differ fundamentally in their treatments of runoff generation, infiltration, and flow routing, enabling an explicit comparison of model behavior across frameworks. Model performance for channel water levels is evaluated against observations from U.S. Geological Survey stream gauges using the modified Kling–Gupta Efficiency (KGE′) and its components, including correlation, bias ratio, and variability ratio, together with diagnostics of peak timing. In addition, inundation extent is evaluated using the Landsat-derived Dynamic Surface Water Extent (DSWE) product, which provides spatially distributed observations of flood extent. Analysis of channel dynamics shows that variability and bias dominate model performance differences, while timing errors play a secondary role. Results indicate that all models capture the primary inundation along the mainstem Russian River, but substantial differences emerge in tributary and floodplain regions. Torrent and HEC-RAS tend to overestimate flood extent, while WRF-Hydro-HAND tends to underestimate it. These findings highlight the importance of model structure in flood simulation and demonstrate the value of multi-model frameworks for improving understanding of flood behavior in complex watershed systems.

Climate adaptation practitioners, planners, and policymakers rely on historical reconstructions and future projections of regional to local climate for adaptation, resilience, and risk management. To serve the many needs of these users, climate data must be relevant for informing the decision-making process (“salient”), consistent with our physical understanding of the global Earth system (“credible”), backed by expert judgment (“authoritative”), and publicly available and ready for use (“accessible”). As more decision-relevant climate data products are being developed to address outstanding needs, important challenges have emerged around the selection, evaluation, use, and standardization of these data products. This article examines opportunities and challenges associated with regional climate data for the United States and describes nascent efforts to build a coordinated strategy among communities of producers and users to maximize the utility of decision-relevant datasets. A community of practice (CoP) is now emerging to benefit both end users and data producers. The CoP will enable enhanced evaluation protocols, improved uncertainty quantification, and eased data distribution and usage, informed by ongoing input on data needs and feedback on quality and usability of data from a wide range of user communities. Broad coordination among interested parties and periodic reviews of the state of the field are vital to the success of this community of practice. Significance Statement: The need for actionable information on climate risks continues to grow across the United States, as communities work to address the localized risks faced by people, infrastructure, and ecosystems. While the climate science community advances the scientific understanding of global and regional Earth system change along with the processes relevant to those specific climate risks, two persistent gaps remain: developing usable data products and sharing of knowledge about them. This paper describes the state of decision-relevant climate data products and suggests near-term directions to cultivate a community of practice to support improved decision-making given a wide range of potential climate futures.

Scientific assessments play a critical role in synthesizing evidence to support evidence‐based decision‐making in many sectors and topics. For climate change, large‐scale assessments like the Intergovernmental Panel on Climate Change (IPCC) and the United States (U.S.) National Climate Assessment (NCA) reports help many different users—from national government to local governments, Indigenous Peoples and communities, the private sector, the social sector, and the scientific community itself—navigate the complex field of climate risks, impacts, and integrated solutions. While co‐creation with users is essential to developing actionable assessments, this paper lays the groundwork for future processes by identifying many advancing and emerging areas of relevant knowledge ready for assessment or additional near‐term research. Drawing on the expertise of many scholars who were formerly tasked with producing the Sixth NCA and authors of other national or sub‐national climate assessment reports, this paper highlights select U.S.‐relevant topics in which research advances since the 2023 Fifth NCA make new syntheses particularly valuable for future national, sub‐national, and sectoral climate assessments. It also identifies critical research topics in those select areas that would be useful for future climate assessment efforts. Overall, this research priorities roadmap aims to catalyze collaborations, research, and a network of climate assessments to synthesize the state of climate impacts, risks, and responses and to provide widely accessible and foundational information for sectoral, regional, and cross‐cutting climate action plans and decisions in the U.S.

Scientific assessments play a critical role in synthesizing evidence to support evidence-based decision-making in many sectors and topics. For climate change, large-scale assessments like the Intergovernmental Panel on Climate Change (IPCC) and the U.S. National Climate Assessment (NCA) reports help many different users—from national government to local governments, Indigenous Peoples and communities, the private sector, the social sector, and the scientific community itself—navigate the complex field of climate risks, impacts, and integrated solutions. While cocreation with users is essential to developing actionable assessments, this paper lays the groundwork for future processes by identifying many advancing and emerging areas of relevant knowledge ready for assessment or additional near-term research. Drawing on the expertise of many scholars who were formerly tasked with producing the Sixth NCA and authors of other national or subnational climate assessment reports, this paper highlights select U.S.-relevant topics in which research advances since the 2023 Fifth NCA make new syntheses particularly valuable for future national, subnational, and sectoral climate assessments. It also identifies critical research topics in those select areas that would be useful for future climate assessment efforts. Overall, this research priorities road map aims to catalyze collaborations, research, and a network of climate assessments to synthesize the state of climate impacts, risks, and responses and to provide widely accessible and foundational information for sectoral, regional, and cross-cutting climate action plans and decisions in the United States. PLAIN LANGUAGE SUMMARY: Scientific assessments are critical in informing households, communities, businesses, institutions, and governments as they respond to intensifying climate hazards. As climate change impacts accelerate, the pace of assessment and integration of science with decision-making and action must accelerate as well. A network of U.S. climate assessment efforts could rapidly synthesize actionable science and case studies to inform climate response strategies. To support future processes, this paper presents a selection of foundational topics needing assessment and near-term research across climate impacts, risks, and actions from the perspective of the United States and some of its key regions, environments, and economic sectors. For example, assessment is needed for the increasing threats posed by compound extremes and cascading climate risks, which can overwhelm systems developed for independent events. The effectiveness of climate responses likewise requires continuous evaluation and improvement, with consideration of the broader impacts (e.g., economic, environmental, health) associated with those strategies. System-level understanding of integrated climate response strategies can be advanced by combining observations and modeling with mitigation, adaptation, climate interventions, disaster management, and resilience decision scenarios. Overall, these research priorities aim to encourage new research and future national, regional, local, and sectoral climate assessments that support climate-informed decision-making in the United States.

Extreme atmospheric river (AR) events are a dominant driver of flood risk in Northern California. Traditional flood studies and operational forecasts often rely on a single model; however, substantial structural differences persist across hydrologic modelling frameworks due to variations in process representations. Here, we present a multi-model comparison framework to evaluate differences in streamflow and flood inundation during an AR-driven flood event in the Russian River Basin (Sonoma County, California) in late February 2019. Three physical-based modelling systems, each forced with a common observation-based precipitation dataset, are compared: (1) Torrent, a computationally efficient overland flow model operating directly on digital elevation models (DEMs); (2) HEC-RAS, implemented using a two-dimensional shallow-water (Saint-Venant) solver in rain-on-grid mode; and (3) WRF-Hydro-HAND, a multi-scale hydrologic routing framework that combines distributed runoff generation with terrain-based inundation mapping. These systems differ fundamentally in their treatments of runoff generation, infiltration, and flow routing, enabling an explicit comparison of model behavior across frameworks. Model performance for channel water levels is evaluated against observations from U.S. Geological Survey stream gauges using the modified Kling–Gupta Efficiency (KGE′) and its components, including correlation, bias ratio, and variability ratio, together with diagnostics of peak timing. In addition, inundation extent is evaluated using the Landsat-derived Dynamic Surface Water Extent (DSWE) product, which provides spatially distributed observations of flood extent. Analysis of channel dynamics shows that variability and bias dominate model performance differences, while timing errors play a secondary role. Results indicate that all models capture the primary inundation along the mainstem Russian River, but substantial differences emerge in tributary and floodplain regions. Torrent and HEC-RAS tend to overestimate flood extent, while WRF-Hydro-HAND tends to underestimate it. These findings highlight the importance of model structure in flood simulation and demonstrate the value of multi-model frameworks for improving understanding of flood behavior in complex watershed systems.

The destructive potential of a tropical cyclone (TC) depends on both its intensity and size, yet the coupled evolution of these properties remains insufficiently explored. This study investigates TC intensity–size relationships of TCs in observations and historical simulations from 12 HighResMIP models in the North Atlantic (NA) and western North Pacific (WNP). We apply the System for Classification of Low‐Pressure Systems (SyCLoPS) to track TC life cycle and a novel quadrant wind radii calculation mirroring the IBTrACS format to enable consistent model–observation comparisons. SyCLoPS effectively removes weak tropical and extratropical systems misidentified as TCs in a conventional tracker, leading to improved TC tracking performance in ERA5 and models. TC track types are classified by their precursors and posterior scenarios based on SyCLoPS and IBTrACS labels. They are also grouped by intensity–size evolutionary pathways defined by peak intensity, size at peak intensity, and terminal size. Tropical depressions are more likely to intensify into intense but compact TCs, whereas monsoon depressions in the WNP and TCs undergoing extratropical transition tend to grow substantially in size as they evolve. In observations, the largest TCs are not the most intense and generally occur at higher latitudes. In contrast, most models have the strongest TCs reach peak size at lower latitudes. Only the highest‐resolution (∼20 km) model assessed captures intense, compact TCs that are common in observations. Despite this, the model ensemble mean performs comparably to ERA5 regarding TC track density, TC track type compositions, and differences in intensity and size characteristics between ENSO phases.

As Earth system models (ESMs) grow in complexity and in volume of output data, there is an increasing need for rapid, comprehensive evaluation of their scientific performance. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require expeditious response for model analyses designed to inform and drive integrated Earth system assessments. To meet this challenge, the Rapid Evaluation Framework (REF), a community-driven platform for benchmarking and performance assessment of ESMs, was designed and developed. The initial implementation of the REF, constructed to meet the near-term needs of the CMIP7 Assessment Fast Track, builds upon four disparate community evaluation and benchmarking tools that are coupled together using the Coordinated Model Evaluation Capabilities (CMEC) framework. The REF runs within a containerized workflow for portability and reproducibility and is aimed at generating and organizing diagnostics covering a variety of model variables. The REF leverages well documented observational datasets to provide assessments of model fidelity across a collection of diagnostics. All diagnostics were identified and selected with community involvement and consultation. Operational integration with the Earth System Grid Federation (ESGF) will permit automated execution of the REF for selected diagnostics as soon as model output data are published on ESGF by the originating modeling centers. The REF is designed to be portable across a range of current computational platforms to facilitate use by modeling centers for assessing the evolution of model versions or gauging the relative performance of CMIP simulations before being published on ESGF. When integrated into production simulation workflows, results from the REF provide immediate quantitative feedback that allows model developers and scientists to quickly identify model biases and performance issues. After the REF is released to the community, its subsequent development and support will be prioritized by an international consortium of scientists and engineers, enabling a broader impact across Earth science disciplines. For instance, the REF will facilitate improvements to models and will enhance confidence in model projections through process-based selection of models based on their performance with respect to observations. Production of reproducible diagnostics and community-based assessments are key features of the REF. Furthermore, providing interoperability with existing evaluation packages assures that contributions from previous community efforts will be available for use in future model intercomparison projects.

Regridding is one of the most common operations in geoscientific modeling and data analysis. There are many types of regridding, each drawing from a common set of fundamental computational geometry algorithms. However, these algorithms are rarely documented together or systematically compared in a manner that elucidates their relative strengths and appropriate use. In particular, several recent studies have highlighted the importance of careful treatment of floating point operations in the implementation of these algorithms to ensure numerical robustness and stability. In this work, we organize non-conservative and conservative regridding operations end-to-end, from spatial indexing, great-circle and constant latitude geometry, and spherical predicates to spherical clipping, triangulation, and area calculation with constant latitude corrections, into a coherent set of geometric kernels on the sphere. When known, we present numerically stable floating-point formulas and characterize their error behavior. We also indicate where higher-precision techniques, such as Error-Free Transformations, can be incorporated when additional accuracy is needed. The resulting framework establishes a practical and performance-portable baseline for accurate and robust regridding on the sphere.