Eli Dennis

Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Predicting future climate change over a region of complex terrain, such as the western United States (U.S.), remains challenging due to the low resolution of global climate models (GCMs). Yet climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality predictions. Here, we present an ensemble of dynamically downscaled simulations over the western U.S. from 1980–2100 at 9-km grid spacing, driven by sixteen latest-generation GCMs. This dataset is titled the Western U.S. Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations’ realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model’s more realistic coastlines and topography: (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven intensified local atmospheric updrafts tied to topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western U.S. climate.

The 1997 New Year's flood event was the most costly in California's history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM‐E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events informs practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California‐focused RRM grids, with horizontal resolution refinement of 14 km down to 3.5 km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are better represented at finer horizontal resolutions resulting in better estimates of storm total precipitation and storm duration snowpack changes. Traditional time‐series and causal analysis frameworks are used to examine runoff sensitivities state‐wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time‐series shape, than forecast lead time, 2‐to‐4 days prior to the 1997 flood onset.

Extreme precipitation is expected to intensify as the climate warms, but the magnitude of the increase will vary regionally. In many cases, global climate models (GCMs) are not well-suited to project the changes in extreme precipitation due to their coarse resolution, particularly over complex terrain. Here, we analyze an unprecedented suite of eight bias-corrected dynamically downscaled GCMs over the western U.S., which allow us to assess extreme precipitation changes at high resolution. We pool data across the downscaled ensemble to adequately sample extreme events and characterize 99.99th percentile precipitation in Los Angeles County, home to 10M people. This high-resolution data allows us to advise a county government agency on expected changes in local extreme precipitation so that they may consider the suitability of their urban design standards in the coming decades. We find that the 99.99th percentile precipitation event is expected to increase by about 6.5% per degree Celsius global warming on average over Los Angeles County. However, Los Angeles County contains numerous micro-climates associated with, e.g., high mountains, marine ecosystems, and urban centers, whose future changes the downscaled projections are uniquely suited to predict. The absolute increases in extreme precipitation are shown to be magnified in the mountains and minimized in the desert regions. The agency will use this data to become more resilient to climate change. This project underscores the importance of stakeholder engagement with scientists for translating climate data into actionable guidance.

The 1997 New Year’s flood event was the most costly in California’s history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events inform practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California-focused RRM grids, with horizontal resolution refinement of 14km down to 3.5km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are prominently influenced by horizontal resolution. The finest resolution RRM-E3SM simulation best represents storm total precipitation and storm duration snowpack changes. Traditional time-series and causal analysis frameworks are used to examine runoff sensitivities state-wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time-series shape, than forecast lead time, 2-to-4 days prior to the 1997 flood onset.