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Jiali Wang and Veerabhadra R. Kotamarthi

Abstract

Dynamic downscaling with regional-scale climate models is used widely for increasing the spatial resolution of global-scale climate model projections. One uncertainty in generating these projections is the choice of boundary forcing applied. In this study the Nested Regional Climate Model (NRCM) is used with a grid spacing of 12 km over the United States (excluding Hawaii) to dynamically downscale 2.5° National Centers for Environmental Prediction–U.S. Department of Energy Reanalysis-2 data, with different applications of spectral nudging (SN) for the boundary conditions. Nine numerical experiments for July 2005—each with different wavenumbers and nudging duration periods, applied to different model layers—evaluated the performance of SN in downscaling near-surface fields. The calculations were compared with the North America Regional Reanalysis dataset over four subregions of the contiguous 48 states. Results show significant differences with different wavenumbers, nudging duration periods, and nudging altitudes. The short-period SN with three waves, applied above 850 hPa, showed the highest skill in simulating precipitation, whereas whole-period SN produced a higher skill level and performed slightly better than short-period SN for surface temperature and 10-m wind, respectively. Differences in the performance of SN applied at different altitudes were not significant. On the basis of the comparisons for precipitation, surface temperature, and wind fields over entire contiguous states, whole-period nudging with six waves starting above 850 hPa for downscaling calculations for climate-related variables is recommended. This method improved the performance of the NRCM in predicting near-surface fields by more than 30.5% relative to a case with no nudging.

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Won Chang, Michael L. Stein, Jiali Wang, V. Rao Kotamarthi, and Elisabeth J. Moyer

Abstract

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%–7% K−1, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%–2% K−1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors develop a new methodology for identifying individual rainstorms and studying their physical characteristics—including starting location, intensity, spatial extent, duration, and trajectory—that allows identifying that compensating mechanism. This technique is applied to precipitation over the contiguous United States from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In the model study the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. However, these projected changes are smaller than model–observation biases, implying that the best means of incorporating them into impact assessments is via “data-driven simulations” that apply model-projected changes to observational data. The authors therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and they show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.

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Kevin Schwarzwald, Andrew Poppick, Maria Rugenstein, Jonah Bloch-Johnson, Jiali Wang, David McInerney, and Elisabeth J. Moyer

Abstract

Changes in precipitation variability can have large societal consequences, whether at the short timescales of flash floods or the longer timescales of multi-year droughts. Recent studies have suggested that in future climate projections, precipitation variability rises more steeply than does its mean, leading to concerns about societal impacts. This work evaluates changes in mean precipitation over a broad range of spatial and temporal scales using a range of models from high-resolution regional simulations to millennial-scale global simulations. Results show that changes depend on the scale of aggregation and involve strong regional differences. On local scales that resolve individual rainfall events (hours and tens of kilometers), changes in precipitation distributions are complex and variances rise substantially more than means, as is required given the well-known disproportionate rise in precipitation intensity. On scales that aggregate across many events, distributional changes become simpler and variability changes smaller. At regional scale, future precipitation distributions can be largely reproduced by a simple transformation of present-day precipitation involving a multiplicative shift and a small additive term. The “extra” broadening is negatively correlated with changes in mean precipitation: in strongly “wetting” areas, distributions broaden less than expected from a simple multiplicative mean change; in “drying” areas, distributions narrow less. Precipitation variability changes are therefore of especial concern in the subtropics, which tend to dry under climate change. Outside the tropics, variability changes are similar on timescales from days to decades, i.e. show little frequency dependence. This behavior is highly robust across models, suggesting it may stem from some fundamental constraint.

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