Uncertainty in projections of streamflow changes due to climate change in California

Understanding the uncertainty in the projected impacts of climate change on hydrology will help decision-makers interpret the confidence in different projected future hydrologic impacts. We focus on California, which is vulnerable to hydrologic impacts of climate change. We statistically bias correct and downscale temperature and precipitation projections from 10 GCMs participating in the Coupled Model Intercomparison Project. These GCM simulations include a control period (unchanging CO2 and other forcing) and perturbed period (1%/year CO2 increase). We force a hydrologic model with the downscaled GCM data to generate streamflow at strategic points. While the different GCMs predict significantly different regional climate responses to increasing atmospheric CO2, hydrological responses are robust across models: decreases in summer low flows and increases in winter flows, and a shift of flow to earlier in the year. Summer flow decreases become consistent across models at lower levels of greenhouse gases than increases in winter flows do

Guidelines for constructing climate scenarios

Scientists and others from academia, government, and the private sector increasingly are using climate model outputs in research and decision support. For the most recent assessment report of the Intergovernmental Panel on Climate Change, 18 global modeling centers contributed outputs from hundreds of simulations, coordinated through the Coupled Model Intercomparison Project Phase 3 (CMIP3), to the archive at the Program for Climate Model Diagnostics and Intercomparison (PCMDI; http://pcmdi3.llnl.gov) [Meehl et al., 2007]. Many users of climate model outputs prefer downscaled data—i.e., data at higher spatial resolution—to direct global climate model (GCM) outputs; downscaling can be statistical [e.g., Meehl et al., 2007] or dynamical [e.g., Mearns et al., 2009]. More than 800 users have obtained downscaled CMIP3 results from one such Web site alone (see http://gdo-dcp.ucllnl.org/downscaled cmip3_projections/, described by Meehl et al., [2007])

Detection, attribution, and sensitivity of trends toward earlier streamflow in the Sierra Nevada

Observed changes in the timing of snowmelt dominated streamflow in the western United States are often linked to anthropogenic or other external causes. We assess whether observed streamflow timing changes can be statistically attributed to external forcing, or whether they still lie within the bounds of natural (internal) variability for four large Sierra Nevada (CA) basins, at inflow points to major reservoirs. Streamflow timing is measured by “center timing” (CT), the day when half the annual flow has passed a given point. We use a physically based hydrology model driven by meteorological input from a global climate model to quantify the natural variability in CT trends. Estimated 50-year trends in CT due to natural climate variability often exceed estimated actual CT trends from 1950 to 1999. Thus, although observed trends in CT to date may be statistically significant, they cannot yet be statistically attributed to external influences on climate. We estimate that projected CT changes at the four major reservoir inflows will, with 90% confidence, exceed those from natural variability within 1–4 decades or 4–8 decades, depending on rates of future greenhouse gas emissions. To identify areas most likely to exhibit CT changes in response to rising temperatures, we calculate changes in CT under temperature increases from 1 to 5°. We find that areas with average winter temperatures between −2°C and −4°C are most likely to respond with significant CT shifts. Correspondingly, elevations from 2000 to 2800 m are most sensitive to temperature increases, with CT changes exceeding 45 days (earlier) relative to 1961–1990

Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diuWhen applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.rnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values

An Enhanced Archive Facilitating Climate Impacts and Adaptation Analysis

We describe the expansion of a publicly available archive of downscaled climate and hydrology projections for the United States. Those studying or planning to adapt to future climate impacts demand downscaled climate model output for local or regional use. The archive we describe attempts to fulfill this need by providing data in several formats, selectable to meet user needs. Our archive has served as a resource for climate impacts modelers, water managers, educators, and others. Over 1,400 individuals have transferred more than 50 TB of data from the archive. In response to user demands, the archive has expanded from monthly downscaled data to include daily data to facilitate investigations of phenomena sensitive to daily to monthly temperature and precipitation, including extremes in these quantities. New developments include downscaled output from the new Coupled Model Intercomparison Project phase 5 (CMIP5) climate model simulations at both the monthly and daily time scales, as well as simulations of surface hydrologi- cal variables. The web interface allows the extraction of individual projections or ensemble statistics for user-defined regions, promoting the rapid assessment of model consensus and uncertainty for future projections of precipitation, temperature, and hydrology. The archive is accessible online (http://gdo-dcp.ucllnl.org/downscaled_ cmip_projections)

Guidelines for Constructing Climate Scenarios

No abstract available

Making Climate Data Relevant to Decision Making: The important details of Spatial and Temporal Downscaling

This paper examines potential regional-scale impacts of climate change on sustainability of irrigated agriculture, focusing on the western San Joaquin Valley in California. We consider potential changes in irrigation water demand and supply, and quantify impacts on the hydrologic system, soil and groundwater salinity with associated crop yield reductions. Our analysis is based on archived output from General Circulation Model (GCM) climate projections through 2100, which were downscaled to the 1,400 km2 study area. We account for uncertainty in GCM climate projections by considering two different GCM\u27s, each using three greenhouse gas emission scenarios. Significant uncertainty in projected precipitation creates large uncertainty in surface water supply, ranging from a decrease of 26% to an increase of 14% in 2080-2099. Changes in projected irrigation water demand ranged from a decrease of 13% to an increase of 3% at the end of the 21st century. Greatest demand reductions were computed for the dry and warm scenarios, because of increased land fallowing with corresponding decreased total crop water requirements. A decrease in seasonal crop ET by climate warming, despite an increase in evaporative demand, was attributed to faster crop development with increasing temperatures. Simulations of hydrologic response to climate-induced changes suggest that the salt-affected area will be slightly expanded. However, irrespective of climate change, salinity is expected to increase in downslope areas, thereby limiting crop production to mostly upslope areas of the simulation domain. Results show that increasing irrigation efficiency may be effective in controlling salinization, by reducing groundwater recharge and improving soil drainage, and in mitigating climate warming effects, by reducing the need for groundwater pumping to satisfy crop water requirements

Climate scenarios for California

Possible future climate changes in California are investigated from a varied set of climate change model simulations. These simulations, conducted by three state-of-the-art global climate models, provide trajectories from three greenhouse gas (GHG) emission scenarios. These scenarios and the resulting climate simulations are not “predictions,” but rather are a limited sample from among the many plausible pathways that may affect California’s climate. Future GHG concentrations are uncertain because they depend on future social, political, and technological pathways, and thus the IPCC has produced four “families” of emission scenarios. To explore some of these uncertainties, emissions scenarios A2 (a medium-high emissions) and B1 (low emissions) were selected from the current IPCC Fourth climate assessment, which provides several recent model simulations driven by A2 and B1 emissions. The global climate model simulations addressed here were from PCM1, the Parallel Climate Model from the National Center for Atmospheric Research (NCAR) and U.S. Department of Energy (DOE) group, and CM2.1 from the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluids Dynamics Laboratory (GFDL). As part of the scenarios assessment, a statistical technique using properties of historical weather data was employed to correct model biases and “downscale” the global-model simulation of future climates to a finer level of detail, onto a grid of approximately 7 miles (12 kilometers), which is more suitable for impact studies at the scales needed by California decision makers. In current climate-change simulations, temperatures over California warm significantly during the twenty-first century, with temperature increases from approximately +3ºF (1.5ºC) in the lower emissions scenario (B1) within the less responsive model (PCM1) to +8ºF (4.5ºC) in the higher emissions scenario (A2) within the more responsive model (CM2.1). Three of the simulations (all except the low-emission scenario run of the low-response model) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter, with virtually all derived from North Pacific winter storms. Relatively little change in overall precipitation is projected. Climate warming has a profound influence in diminishing snow accumulations, because there is more rain and less snow, and earlier snowmelt. These snow losses increase as the warming increases, so that they are most severe under climate changes projected by the more sensitive model with the higher GHG emissions

A Search for Wolf-Rayet Stars in the Small Magellanic Cloud

We conducted an extensive search for Wolf-Rayet stars (W-Rs) in the SMC, using the same interference filter imaging techniques that have proved successful in finding W-Rs in more distant members of the Local Group. Photometry of some 1.6 million stellar images resulted in some 20 good candidates, which we then examined spectroscopically. Two of these indeed proved to be newly found W-Rs, bringing the total known in the SMC from 9 to 11. Other finds included previously unknown Of-type stars (one as early as O5f?p)),the recovery of the Luminous Blue Variable S18, and the discovery of a previously unknown SMC symbiotic star. More important, however, is the fact that there does not exist a significant number of W-Rs waiting to be discovered in the SMC. The number of W-Rs in the SMC is a factor of 3 lower than in the LMC (per unit luminosity), and we argue this is the result of the SMC's low metallicity on the evolution of the most massive stars.Comment: Accepted by Astrophysical Journal. Postscript version available via ftp.lowell.edu/pub/massey/smcwr.ps.gz Revised version contains slightly revised spectral types for the Of stars but is otherwise unchange