The observed retreat and anticipated further decline in Arctic sea ice hold strong climate, environmental, and societal implications. In predicting climate evolution, ensembles of coupled climate models have demonstrated appreciable accuracy in simulating sea ice area and volume trends throughout the historical period. However, individual climate models still show significant differences in simulating the sea ice thickness distribution. To better understand individual model performance in sea ice simulation, nine climate models previously identified to provide plausible sea ice decline and global temperature change were evaluated in comparison with Arctic satellite and reanalysis derived sea ice thickness data, sea ice extent records, and atmospheric reanalysis data of surface wind and air temperature. Assessment found that the simulated spatial distribution of historical sea ice thickness varies greatly between models and that several key limitations persist among models. Primarily, most models do not capture the thickest regimes of multi-year ice present in the Wandel and Lincoln Seas; those that do, often possess erroneous positive bias in other regions such as the Laptev Sea or along the Eurasian Arctic Shelf. From analysis, no model could be identified as performing best overall in simulating historic sea ice, as model bias varies regionally and seasonally. Nonetheless, the bias maps and statistical measures derived from this analysis should enhance understanding of the limitations of each climate model. This research is motivated in-part to inform future usage of coupled climate model projection for regional modeling efforts and enhance climate change preparedness and resilience in the Arctic.

Accurate forecasts of total water level (i.e., a combination of tides, surge, wave and freshwater components) is imperative for stakeholders and federal agencies to adopt strategies for potential flooding hazards in a timely-manner. In that regard, the National Water Center in partnership with several federal agencies have been providing forecast services to the United States since 2017. However, the complex interaction of dynamical forcing conditions among other factors (e.g., anthropogenic activities, land cover change, etc.) reduce the National Water Model’s (NWM) ability to provide accurate Total Water Level (TWL) prediction in Coastal Transition Zones (CTZs). In this study, we use an existing inland to coastal model coupling framework (i.e., NWM, HWRF, Delft3D-FM and ADCIRC) to analyze the influence of dynamical forcing conditions (e.g., local wind, surge and river discharge) on TWL prediction in Delaware Bay, USA. In addition, we quantify the contribution of each component in TWL for Hurricanes Isabel and Sandy based on a systematic set of scenarios generated in Delft3D-FM. It is revealed that in both hurricanes, storm surge-induced water level is the main contributor to TWL followed by astronomical tides. River discharge induced-water level is rather small compared to the other components. Analyses of spatial variation of TWL as well as temporal variation of error in prediction suggest that wind forcing plays a key role in TWL prediction followed by river discharge. Moreover, our results suggest that the wind module of Delft3D-FM greatly improves the model performance at TWL peak when compared to the other forcing.

This paper reports a new and significantly enhanced analysis of US flood hazard at 30m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level and inclusion of the impact of local interventions in the flooding system. For the first time we consider pluvial, fluvial and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centred on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high quality local models and the entire catalogue of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69-0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near future time horizon we project clear patterns of changing flood hazard (-3.8 to +16% changes in 100yr inundated area at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defence protection may become compromised by this time.

Existing real-time coastal flooding guidance systems in the US tend to underestimate total water level (TWL) predictions in upstream tidal areas of the Chesapeake Bay rivers, impacting flood forecasts for highly vulnerable areas, such as the National Capital Region. These under-predictions are mostly due to missing physical processes, lack of integration between hydrological and hydrodynamic models, and an oversimplification of the model setups used to predict TWL. In this study, an integrated TWL forecast system was introduced, where a high-resolution two-dimensional coastal storm surge model (ADCIRC) was implemented to simulate the combined influence of various flood drivers (storm tide, river flows, urban runoff, and local wind forcing) in the Potomac River. In this framework, the downstream boundaries of storm tide predictions are provided by existing coastal guidance systems, whereas, streamflow forecasts at upstream rivers and local urban runoff are provided by the National Weather Service and the National Water Model. Additionally, high-resolution wind fields from the North American Mesoscale and the National Blend of Models are added to account for local wind effects on TWL. This model setup was successfully validated with a range of historical events and it also demonstrated improved forecast performance against the existing large-scale coastal guidance systems in a reforecast evaluation during 2020. Unlike other studies, we provided a comprehensive evaluation on the influence of individual flood drivers on TWL modeling and clearly demonstrated that the absence of one or more flood drivers in the model framework can underestimate simulated TWL in the National Capital Region.