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In this study, we investigated the urbanization‐induced summer rainfall changes in the Yangtze River Delta (YRD) by analyzing long‐term observations and numerical simulations. The observation‐based analysis showed that long‐term urbanization increased the region's summer rainfall, particularly through the intensification of heavy rainfall, which is noted as the urban rain island (URI) effect. A series of numerical sensitivity experiments with three historical land use and land cover scenarios (1990, 2000, and 2010) were designed to further understand the urbanization impacts on rainfall. The observed URI effect was well reproduced by the numerical simulations, and on average, urban expansion during 1990–2010 increased summer rainfall over urban areas by 51.91 mm. The URI effect slightly weakened in the late stage of urbanization (2000–2010) compared to the early stage (1990–2000). We conclude that the strengthening of precipitation‐inhibiting effects during the late period offset the precipitation‐enhancing effects, which led to the weakening of the URI effect. Plain Language Summary: In this study, urbanization‐induced summertime precipitation changes over the Yangtze River Delta (YRD) region were investigated using both long‐term observational data and numerical model experiments. Observations showed that over the past decades, the summer precipitation over the YRD region has generally experienced a significant increasing trend, which was mainly caused by the intensification of strong‐intensity rainfalls. We further showed that urbanization in the region has played key roles in the trend by an urban rain island (URI) effect. The URI effect was found to have slightly weakened in the late stage of the study period (2000–2010) compared to the early stage (1990–2000). We also found that precipitation‐inhibiting effects (e.g., atmospheric humidity loss), overshadowed the precipitation‐enhancing effects (e.g., the urban heat island effect), which resulted in the weakened URI effect in the late stage. Key Points: The urban modification on summertime rainfall in YRD was investigated based on both long‐term observations and numerical experimentsLong‐term urbanization increased summer rainfall, yet the urban rain island (URI) effect slightly weakened in the late stage of urbanizationPrecipitation‐inhibiting effects strengthened in the late stage of urbanization and led to a weakened URI effect [ABSTRACT FROM AUTHOR]

This study uses dynamical and statistical methods to understand end‐of‐century mean changes to Sierra Nevada snowpack. Dynamical results reveal that middle‐elevation watersheds experience considerably more rain than snow during winter, leading to substantial snowpack declines by spring. Despite some high‐elevation watersheds receiving slightly more snow in January and February, the warming signal still dominates across the wet season and leads to notable declines by springtime. A statistical model is created to mimic dynamical results for 1 April snowpack, allowing for an efficient downscaling of all available general circulation models from the Coupled Model Intercomparison Project phase 5. For all general circulation models and emission scenarios, dramatic 1 April snowpack loss occurs at elevations below 2,500 m, despite increased precipitation in many general circulation models. Only 36% (±12%) of historical 1 April total snow water equivalent volume remains at the century's end under a "business‐as‐usual" emission scenario, with 70% (±12%) remaining under a realistic "mitigation" scenario. Plain Language Summary: The Sierra Nevada is one of California's most beloved natural treasures, and mountain snowpack snow is an important water resource. As climate change continues, scientists and water managers have become increasingly concerned about the future of the frozen reservoir Californian depend on. Global climate models are the best tools we have for projecting future climate change. But they are too coarse in spatial resolution to accurately simulate future climate in topographically complex areas like the Sierra Nevada, where different elevations experience different climatic conditions. This study utilizes an innovative hybrid high‐resolution downscaling method to understand spatial and temporal patterns of snowpack changes for certain watersheds and different elevations in the Sierra Nevada. A full range of global climate models and future greenhouse emission scenarios are investigated to quantify the uncertainties. Dramatic decreases in total Sierra Nevada snowpack are projected by century's end, even under a realistic mitigation emission scenario. The results are intended to provide water resource and management agencies information to help plan for the impacts of future climate change on the reliability and inhomogeneity of water supplies. Key Points: Dynamically downscaled projections are used to understand spatial and temporal patterns of snowpack changes for Sierra Nevada watershedsA hybrid dynamical‐statistical downscaling technique is developed to project 1 April snowpack change by century's end for all GCMs and RCPsDramatic decreases in total snowpack volume are projected by century's end, even under a realistic mitigation emission scenario [ABSTRACT FROM AUTHOR]

California's Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical-statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981-2000 period and then to project the future climate of the 2081-2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems. [ABSTRACT FROM AUTHOR]

In this study (Part I), the mid-twenty-first-century surface air temperature increase in the entire CMIP5 ensemble is downscaled to very high resolution (2 km) over the Los Angeles region, using a new hybrid dynamical-statistical technique. This technique combines the ability of dynamical downscaling to capture finescale dynamics with the computational savings of a statistical model to downscale multiple GCMs. First, dynamical downscaling is applied to five GCMs. Guided by an understanding of the underlying local dynamics, a simple statistical model is built relating the GCM input and the dynamically downscaled output. This statistical model is used to approximate the warming patterns of the remaining GCMs, as if they had been dynamically downscaled. The full 32-member ensemble allows for robust estimates of the most likely warming and uncertainty resulting from intermodel differences. The warming averaged over the region has an ensemble mean of 2.3°C, with a 95% confidence interval ranging from 1.0° to 3.6°C. Inland and high elevation areas warm more than coastal areas year round, and by as much as 60% in the summer months. A comparison to other common statistical downscaling techniques shows that the hybrid method produces similar regional-mean warming outcomes but demonstrates considerable improvement in capturing the spatial details. Additionally, this hybrid technique incorporates an understanding of the physical mechanisms shaping the region's warming patterns, enhancing the credibility of the final results. [ABSTRACT FROM AUTHOR]

The Community Climate System Model, version 3 (CCSM3), is known to produce many aspects of El Niño–Southern Oscillation (ENSO) realistically, but the simulated ENSO exhibits an overly strong biennial periodicity. Hypotheses on the cause of this excessive biennial tendency have thus far focused primarily on the model's biases within the tropical Pacific. This study conducts CCSM3 experiments to show that the model's biases in simulating the Indian Ocean mean sea surface temperatures (SSTs) and the Indian and Australian monsoon variability also contribute to the biennial ENSO tendency. Two CCSM3 simulations are contrasted: a control run that includes global ocean–atmosphere coupling and an experiment in which the air–sea coupling in the tropical Indian Ocean is turned off by replacing simulated SSTs with an observed monthly climatology. The decoupling experiment removes CCSM3's warm bias in the tropical Indian Ocean and reduces the biennial variability in Indian and Australian monsoons by about 40% and 60%, respectively. The excessive biennial ENSO is found to reduce dramatically by about 75% in the decoupled experiment. It is shown that the biennial monsoon variability in CCSM3 excites an anomalous surface wind pattern in the western Pacific that projects well into the wind pattern associated with the onset phase of the simulated biennial ENSO. Therefore, the biennial monsoon variability is very effective in exciting biennial ENSO variability in CCSM3. The warm SST bias in the tropical Indian Ocean also increases ENSO variability by inducing stronger mean surface easterlies along the equatorial Pacific, which strengthen the Pacific ocean–atmosphere coupling and enhance the ENSO intensity. [ABSTRACT FROM AUTHOR]