Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009–2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In midlatitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation–snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty, and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the midlatitudes.

Shugong (2021) Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling. The Cryosphere, 15 (2). pp. 771-791.

The intensification of extreme precipitation in a warming climate is expected to increase flood risk. In order to support flood resilience efforts, it is important to anticipate and quantify potential changes in design standards under future climate conditions. This study assessed how extreme precipitation is expected to change over the 21st century in relation to current National Oceanic and Atmospheric Administration (NOAA) Atlas 14 design standards over the contiguous United States (CONUS). We used the Community Earth System Model Version 2 large ensemble (CESM2‐LE) simulations from the Coupled Model Intercomparison Project Phase 6 and incorporated future changes into flood engineering design standard with a spatially distributed quantile delta mapping method. Relative changes in extreme daily precipitation were computed for multiple average recurrence intervals (ARIs) up to 100‐year and different planning horizons (2020, 2040, 2060, 2080, and 2100). The results indicated an intensification of extreme precipitation by approximately 10%–40% in northern regions and 20%–80% in southern regions by 2100. The current 100‐year ARI with 24‐hr duration from NOAA Atlas 14 is projected to become the 50‐year ARI in the Northern Great Plains, less than the 25‐year ARI in Southwest areas, and approximately the 25‐year ARI in the other regions by 2100. While a nationwide consensus is still needed, this work presents a possible methodology for incorporating climate uncertainty in engineering design. A comparison across major metropolitan areas also illustrates regional variability in projected changes relative to NOAA Atlas 14, suggesting a need for varied local‐scale responses.

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Much of the United States Atlantic coastline continues to undergo subsidence due to post glacial settlement and ground water depletion. Combined with eustatic sea level rise (SLR), this contributes to an increased rate of relative SLR. In this work, we utilize the ADvanced CIRCulation model to project storm surges across coastal North Carolina. Recent hurricanes Irene and Matthew are simulated considering SLR and subsidence estimates for 2100. Relative to present day conditions, storm surge susceptible regions increase by 27% (Irene) to 40% (Matthew) due to subsidence. Combined with SLR (+ 74 cm), results suggest more than a doubling of areal flood extent for Irene and more than a three-fold increase for Hurricane Matthew. Considering current regional population distributions, this translates to an increase in at-risk populations of 18% to 61% due to subsidence. Even further, exposed populations are projected to swell relative to Matthew and Irene baseline simulations (8200 and 28,500) by more than 70,000 in all SLR scenarios (79,400 to 133,600). While increases in surge inundation are driven primarily by SLR in the region, there remains a substantial contribution due to vertical land movement. This outlines the importance of exploring spatially variable land movement in surge prediction, independent of SLR.