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California’s coastal observations and global model projections indicate that California’s open coast and estuaries will experience rising sea levels over the next century. During the last several decades, the upward historical trends, quantified from a small set of California tide gages, have been approximately 20 cm/century, quite similar to that estimated for global mean sea level. In the next several decades, warming produced by climate model simulations indicates that sea level rise (SLR) could substantially exceed the rate experienced during modern human development along the California coast and estuaries. A range of future SLR is estimated from a set of climate simulations governed by lower (B1), middle–upper...
To investigate possible future climate changes in California, a set of climate change model simulations was selected and evaluated. From the IPCC Fourth Assessment, simulations of twenty-first century climates under a B1 (low emissions) and an A2 (a medium-high emissions) emissions scenarios were evaluated, along with occasional comparisons to the A1fi (high emissions) scenario. The climate models whose simulations were the focus of the present study were from the Parallel Climate Model (PCM1) from NCAR and DOE, and the NOAA Geophysical Fluid Dynamics Laboratory CM2.1 model (GFDL). These emission scenarios and attendant climate simulations are not “predictions,” but rather are a purposely diverse set of examples...
This paper introduces the analyses of the potential impacts of climate change on the city of Chicago and the Great Lakes region and potential response options that provide the basis for this special issue. Covering projected changes in climate and hydrology, this collection of studies first estimates the potential impacts of climate change on human health, natural ecosystems, water resources, energy, and infrastructure in the city of Chicago and the surrounding Great Lakes region. A consistent set of future climate projections have been used as the basis for each analysis, which together provide a vivid impression of the consequences likely to result under the SRES higher (A1FI) as compared to the lower (B1) emission...
* 1Streams collect runoff, heat, and sediment from their watersheds, making them highly vulnerable to anthropogenic disturbances such as urbanization and climate change. Forecasting the effects of these disturbances using process-based models is critical to identifying the form and magnitude of likely impacts. Here, we integrate a new biotic model with four previously developed physical models (downscaled climate projections, stream hydrology, geomorphology, and water temperature) to predict how stream fish growth and reproduction will most probably respond to shifts in climate and urbanization over the next several decades. * 2The biotic submodel couples dynamics in fish populations and habitat suitability to predict...
The magnitude of future climate change depends substantially on the greenhouse gas emission pathways we choose. Here we explore the implications of the highest and lowest Intergovernmental Panel on Climate Change emissions pathways for climate change and associated impacts in California. Based on climate projections from two state-of-the-art climate models with low and medium sensitivity (Parallel Climate Model and Hadley Centre Climate Model, version 3, respectively), we find that annual temperature increases nearly double from the lower B1 to the higher A1fi emissions scenario before 2100. Three of four simulations also show greater increases in summer temperatures as compared with winter. Extreme heat and the...
Categories: Publication; Types: Citation
The asynchronous regional regression model (ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere–ocean general circulation models (AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions...