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Atmospheric rivers (ARs) can be a boon and bane to water resource managers as they have the ability to replenish water reserves, but they can also generate million‐to‐billion‐dollar flood damages. To investigate how anthropogenic climate change may influence AR characteristics in the coastal western United States by end century, we employ a suite of novel tools such as variable resolution in the Community Earth System Model (VR‐CESM), the TempestExtremes AR detection algorithm, and the Ralph, Rutz, et al. (2019, https://doi.org/10.1175/BAMS-D-18-0023.1) AR category scale. We show that end‐century ARs primarily shift from being “mostly or primarily beneficial” to “mostly or primarily hazardous” with a concomitant sharpening and intensification of winter season precipitation totals. Changes in precipitation totals are due to a significant increase in AR (+260%) rather than non‐AR (+7%) precipitation, largely through increases in the most intense category of AR events and a decrease in the interval between landfalling ARs.

The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

This paper shows that the most predictable components of internal variability in coupled atmosphere-ocean models are remarkably similar to the most predictable components of climate models without interactive ocean dynamics (i.e., models whose ocean is represented by a 50-m-deep slab ocean mixed layer with no interactive currents). Furthermore, a linear regression model derived solely from dynamical model output can skillfully predict observed anomalies in these components at least a year or two in advance, indicating that these model-derived components and associated linear dynamics are realistic. These results suggest that interactive ocean circulation is not essential for the existence of multiyear predictability previously identified in coupled models and observations. decadal prediction | decadal predictability | average predictability time | CMIP I n recent years, climate prediction on decadal time scales has gained increased attention because of its growing scientific, geopolitical, and societal importance (1-5). Sources of decadal predictability often are divided into two kinds: predictability caused by external forcing, such as changes in solar insolation, volcanic aerosols, and anthropogenic greenhouse gases, and predictability due to internal variability arising naturally from the coupled atmosphere-ocean-land-ice climate system (6). Climate models suggest that certain structures of internal variability in sea surface temperatures (SST), such as the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), are predictable on decadal/multidecadal time scales (5, 7-9). The precise mechanisms for this decadal predictability are not clear, although the dynamics of ocean circulation are widely believed to play a major role (10-17).Recently, a few studies have challenged the notion that interactive ocean dynamics play a dominant role in decadal predictability (18,19). These studies are based on integrations of atmospheric global circulation models coupled to a slab ocean mixed layer, in which ocean circulation is a prescribed function of time and the ocean interacts with the atmosphere only thermodynamically, through radiative, sensible, and latent heat fluxes. Despite the absence of interactive ocean dynamics, these models can produce realistic variability that is predictable on interannual and longer time scales. For example, El Niño-Southern Oscillation (ENSO)-like variability can arise on interannual and decadal time scales from such models (20)(21)(22). Also, the main features of the observed AMO (e.g., spatial pattern, power spectra, and associated atmospheric circulation) have been reproduced in models without interactive ocean dynamics (23). However, not all details of these simulations are perfect: In some locations, the lag correlation between AMO and surface heat flux has the opposite sign relative to coupled models and observations (14,15). On the other hand, these heat fluxes tend to be nearly canceled by the ocean heat transport convergence on long time scales (24), raisin...

Traditional multimodel methods for estimating future changes in precipitation intensity, duration, frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process – i.e. the Earth system, we propose a novel extension of current methods through pooling model data that follows: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a non-stationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the “true” underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-hr precipitation intensity-frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-hr precipitation for 2-100 year return periods.