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.
Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical–statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041–60) and end (2081–2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081–2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1–3 weeks earlier than in the baseline period.
Using hybrid dynamical–statistical downscaling, 3-km-resolution end-of-twenty-first-century runoff timing changes over California’s Sierra Nevada for all available global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are projected. All four representative concentration pathways (RCPs) adopted by the Intergovernmental Panel on Climate Change’s Fifth Assessment Report are examined. These multimodel, multiscenario projections allow for quantification of ensemble-mean runoff timing changes and an associated range of possible outcomes due to both intermodel variability and choice of forcing scenario. Under a “business as usual” forcing scenario (RCP8.5), warming leads to a shift toward much earlier snowmelt-driven surface runoff in 2091–2100 compared to 1991–2000, with advances of as much as 80 days projected in the 35-model ensemble mean. For a realistic “mitigation” scenario (RCP4.5), the ensemble-mean change is smaller but still large (up to 30 days). For all plausible forcing scenarios and all GCMs, the simulated changes are statistically significant, so that a detectable change in runoff timing is inevitable. Even for the mitigation scenario, the ensemble-mean change is approximately equivalent to one standard deviation of the natural variability at most elevations. Thus, even when greenhouse gas emissions are curtailed, the runoff change is climatically significant. For the business-as-usual scenario, the ensemble-mean change is approximately two standard deviations of the natural variability at most elevations, portending a truly dramatic change in surface hydrology by the century’s end if greenhouse gas emissions continue unabated.
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.
Climate change can significantly affect water quality, in addition contributing non-stationarity and deep uncertainty that complicates water quality management. But most of the total maximum daily load (TMDL) implementation plans crafted to meet water quality standards in the USA are developed assuming stationary climate and at best a small number of land use futures, although neither assumption seems reliably justified. To address this challenge, this study employs robust decision making (RDM) methods, commonly used to help develop water supply plans, to stress test the proposed Enhanced Watershed Management Plan (EWMP), a TMDL implementation plan, for the Tujunga Wash, the largest subwatershed of the Los Angeles River, over a wide range of climate and land use futures. We find that climate change could significantly reduce the ability of the Tujunga EWMP to meet water quality goals; however, meeting the city's goals for increasing permeable land cover offsets the risk of non-compliance in the face of climate change uncertainties. This study also introduces innovations in RDM analyses, including: treatment of the deeply uncertain incidence of extreme precipitation events, an explicit link between RDM scenario discovery methods and the specification of signposts for adaptive policy pathways, and the use of (imprecise) probabilistic climate projections to inform the choice among robust adaptive policy pathways. The paper also contributes to a larger debate over how to address climate and other uncertainties in regulatory processes involving water quality.
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