John Callahan

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.

Coastal ocean reanalyses provide spatially continuous water-level information that supports coastal science and hazard assessment beyond tide-gauge locations. Physics-based models provide the foundation for these reanalyses, particularly when data assimilation constrains the low-frequency response, but the tidal band still offers room for improvement. Here, we examine the extent to which errors in the NOAA Coastal Ocean Reanalysis (CORA) can be diagnosed and mitigated using sparse tide-gauge observations across contrasting coastal regimes. CORA mean-water-level differences are generally centered near zero, consistent with its assimilation of low-frequency water-level residuals. However, analysis of the tidal component shows that CORA predominantly underpredicts mean higher high water (MHHW), while errors in great diurnal range (GT) vary spatially in sign and magnitude. Coherent regional constituent-amplitude and phase errors are identified, and bias in the locally dominant constituent is strongly associated with spatial variation in MHHW bias.We develop a regime-aware hybrid machine-learning framework that combines geographic, tidal, and topobathymetric characteristics with nearby tide-gauge information and residual spatial covariance. Independent and spatially separated validation shows that the framework reduces MHHW and GT discrepancies at stations excluded from model development. Information-ablation experiments indicate that tidal characteristics provide key transferable information, with nearby observations and residual spatial dependence providing additional, datum-specific refinement. These results demonstrate that sparse observational constraints and data-science methods can enhance physics-based coastal reanalysis by diagnosing and mitigating spatially structured tidal-band errors.

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.

Coastal regions are complex environments. They lie at the confluence of physical oceanic, atmospheric, and land-based processes, and continue to undergo significant change due to both natural and human-driven factors. Although it is well known that ocean sea surface temperatures (SSTs) have increased over the past several decades, extending these trends and patterns to coastal waters is nontrivial. We evaluated a new quality-controlled dataset of coastal water temperature collected at NOAA Center for Operational Oceanographic Products and Services (CO-OPS) water level monitoring stations. Water temperature (WT) records were analyzed at 95 stations extending from the mid-1990s to 2023 and representing all the major US coastal regions. Seasonal cycle metrics, linear trends, daily distributions, and extreme percentile thresholds were computed at each site. Seasonal cycles and peak times of year tracked well with solar heating and consistently tracked behind air temperature by a few days to a few weeks. US East Coast and Great Lakes regions have a much stronger seasonal cycle and larger range (up to 25℃) than West Coast and Island regions (as low as 3℃). The great majority of stations experience warming over this time period, with statistically significant trends at 56 of 95 stations, averaging approximately 0.5 ℃/decade. Many of these locations are also experiencing sea level rise, most notably in the Southeast and Gulf regions. Although geographic patterns exist, variations are found in extreme threshold temperatures and trends station to station and between bay systems and the open coast, highlighting the need for continuous in situ monitoring along the coast. This study provides baseline statistics of long-term near shore coastal WT data and supports informed future planning and decision-making in this dynamic environment.

High Tide Flooding (HTF) is a present and increasing hazard for coastal communities across the United States. NOAA provides HTF outlooks at U.S. tide gauges, however, many coastal communities lie relatively far from a tide gauge and therefore currently lack localized HTF guidance. In this study, we demonstrate an approach to generate spatially-continuous daily predictions of HTF at 400–500 m resolution out to a year into the future, by combining NOAA’s monthly HTF outlook framework with the newly-released Coastal Ocean Reanalysis (CORA). Using CORA to derive daily HTF predictions at tide gauges, as compared to using gauge observations, results in average HTF model skill reduction of ≤5% using three different statistical metrics at one month lead time. Further, stations which obtain statistically skillful HTF predictions using gauge data also do so using CORA for 94% of cases. The results suggest that CORA could enable skillful HTF predictions away from tide gauges, supporting the possibility of providing high resolution HTF outlooks for much of the U.S. coastline. The potential value of these spatially continuous HTF predictions is illustrated by identifying communities near Charleston S.C. with different CORA-derived local HTF risk than that provided by the closest tide gauge. Finally, we describe outstanding questions and needs for the scaling of these results to an operational national-scale monthly HTF outlook.

Communities globally are experiencing an increase in high tide flood (HTF) frequency. The present-day impact of HTF for communities is expansive and recurrent, ranging from disrupted activities for infrastructure, inundated stormwater and wastewater systems, and increased public health hazards. Accurate estimates of the probability density functions (PDFs), especially for extreme water levels, are essential for quantifying risks of coastal flooding. In this work, we decompose still water levels measured at 148 tide gauge stations along the United States’ coasts and evaluate the characteristics of the nontidal residual (NTR) distributions. We compare the distribution of high-pass filtered water levels (hourly anomalies) to PDFs of a first-order autoregressive (AR1) process resulting in a Gaussian (normal) distribution and a non-Gaussian (skewed and heavy tailed) ‘Stochastically Generated Skewed’ (SGS) distribution that includes correlated additive and multiplicative noise (CAM noise). We find that the overall error computed between the PDFs and the observed anomalies is reduced at most stations when using the non-Gaussian PDF compared to the AR1 for both the bulk of the distribution and extreme values. We also show that the non-Gaussian SGS distribution is more robust at capturing extreme values in the case of sparse observations, compared to other distributions (kernel density) and extreme value analysis methods (i.e., Generalized Extreme Value and Generalized Pareto Distribution). Our non-Gaussian PDF allows us to diagnose how the shape of the distribution may evolve with climate change. Findings from this work will be implemented in the National Oceanic and Atmospheric Administration’s HTF monthly predictions and used to evaluate changes in forecast skill. This work has relevance for high tide flooding forecasts along the coast and inundation mitigation strategies, as well as estimating PDFs for other physical variables that exhibit heavy-tailed skewed distributions.

In the United States, the National Oceanic and Atmospheric Administration’s National Ocean Service (NOAA/NOS) has developed a statistical model to predict the daily risks of high tide flooding (HTF), for forecast leads of up to one year, at 98 tide gauge locations along the US coastline. NOAA/NOS predicts the daily probability of exceedance of hourly water levels above a specified flood threshold by combining the tide prediction with the (extrapolated) linear trend of mean sea level and a probabilistic prediction of the anomalous hourly non-tidal residual (NTR). In turn, the NTR anomaly prediction is made up of two components: (1) a prediction of monthly mean NTR, currently based upon the observed autocorrelation function of linearly detrended NTR, with uncertainty based upon the observed standard deviation of monthly NTR; and (2) a prediction of the probability distribution function (PDF) of hourly NTR anomalies, which uses observed historical dependence upon the total water level and is assumed to be Gaussian. These forecasts are available at https://tidesandcurrents.noaa.gov/high-tide-flooding/monthly-outlook.html. In this presentation, we introduce an updated version of the NOAA/NOS HTF framework, with three key improvements: (1) the trend estimate is determined empirically and is allowed to be nonlinear; (2) monthly mean SLA is predicted by either an empirical or dynamical climate forecast model, and includes an ensemble spread; and (3) the PDF of hourly NTR anomalies is non-Gaussian and determined separately for each month from past observations using a “stochastically-generated skewed” (SGS) distribution. Skill of the updated version is compared to the original (currently operational) version at all tide gauge locations, and the impact of each of the improvements on skill is diagnosed. Further prospects for improvement of the HTF framework are also discussed.