Norman L. Miller

Development of a Coupled Land Surface and Groundwater Model

Abstract Traditional land surface models (LSMs) used for numerical weather simulation, climate projection, and as inputs to water management decision support systems, do not treat the LSM lower boundary in a fully process-based fashion. LSMs have evolved from a leaky-bucket approximation to more sophisticated land surface water and energy budget models that typically have a specified bottom layer flux to depict the lowest model layer exchange with deeper aquifers. The LSM lower boundary is often assumed zero flux or the soil moisture content is set to a constant value; an approach that while mass conservative, ignores processes that can alter surface fluxes, runoff, and water quantity and quality. Conversely, groundwater models (GWMs) for saturated and unsaturated water flow, while addressing important features such as subsurface heterogeneity and three-dimensional flow, often have overly simplified upper boundary conditions that ignore soil heating, runoff, snow, and root-zone uptake. In the present stud...

Climate, Extreme Heat, and Electricity Demand in California

Climate, Extreme Heat, and Electricity Demand in California Norman L. Miller 1* , Katharine Hayhoe 2 , Jiming Jin 1 , Maximilian Auffhammer 3 Earth Sciences Division, Berkeley National Laboratory, University of California, Berkeley, CA 94720 Department of Geosciences, Texas Tech University, Lubbock, TX 79409 Agricultural and Resource Economics Department, University of California, Berkeley * Atmosphere and Ocean Sciences Group, 1 Cyclotron Road, Berkeley, CA 94720 phone: 510.495.2374, fax: 510.486.5686, email: nlmiller@lbl.gov Submitted to the Journal of Applied Meteorology and Climatology on 17 April 2006 Revised and resubmitted on 25 October 2006

Geostatistical Mapping of Precipitation from Rain Gauge Data Using Atmospheric and Terrain Characteristics

Abstract A geostatistical framework for integrating lower-atmosphere state variables and terrain characteristics into the spatial interpolation of rainfall is presented. Lower-atmosphere state variables considered are specific humidity and wind, derived from an assimilated data product from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP–NCAR reanalysis). These variables, along with terrain elevation and its gradient from a 1-km-resolution digital elevation model, are used for constructing additional rainfall predictors, such as the amount of moisture subject to orographic lifting; these latter predictors quantify the interaction of lower-atmosphere characteristics with local terrain. A “first-guess” field of precipitation estimates is constructed via a multiple regression model using collocated rain gauge observations and rainfall predictors. The final map of rainfall estimates is derived by adding to this initial field a field of spatially interpo...

Impacts of Increased Atmospheric CO 2 on the Hydroclimate of the Western United States

Abstract Regional-scale projections of climate change signals due to increases in atmospheric CO2 are generated for the western United States using a regional climate model (RCM) nested within two global scenarios from a GCM. The downscaled control climate improved the local accuracy of the GCM results substantially. The downscaled control climate is reasonably close to the results of an 8-yr regional climate hindcast using the same RCM nested within the NCEP–NCAR reanalysis, despite wet biases in high-elevation regions along the Pacific coast. The downscaled near-surface temperature signal ranges from 3 to 5 K in the western United States. The projected warming signals generally increase with increasing elevation, consistent with earlier studies for the Swiss Alps and the northwestern United States. In addition to the snow–albedo feedback, seasonal variations of the low-level flow and soil moisture appear to play important roles in the spatial pattern of warming signals. Projected changes in precipitatio...

Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.

Sensitivity Study of Four Land Surface Schemes in the WRF Model

The Weather Research and Forecasting (WRF) model version 3.0 developed by the National Center for Atmospheric Research (NCAR) includes three land surface schemes: the simple soil thermal diffusion (STD) scheme, the Noah scheme, and the Rapid Update Cycle (RUC) scheme. We have recently coupled the sophisticated NCAR Community Land Model version 3 (CLM3) into WRF to better characterize land surface processes. Among these four land surface schemes, the STD scheme is the simplest in both structure and process physics. The Noah and RUC schemes are at the intermediate level of complexity. CLM3 includes the most sophisticated snow, soil, and vegetation physics among these land surface schemes. WRF simulations with all four land surface schemes over the western United States (WUS) were carried out for the 1 October 1995 through 30 September 1996. The results show that land surface processes strongly affect temperature simulations over the (WUS). As compared to observations, WRF-CLM3 with the highest complexity level significantly improves temperature simulations, except for the wintertime maximum temperature. Precipitation is dramatically overestimated by WRF with all four land surface schemes over the (WUS) analyzed in this study and does not show a close relationship with land surface processes.

Observed 1970-2005 cooling of summer daytime temperatures in coastal California.

Abstract This study evaluated 1950–2005 summer [June–August (JJA)] mean monthly air temperatures for two California air basins: the South Coast Air Basin (SoCAB) and the San Francisco Bay Area (SFBA). The study focuses on the more rapid post-1970 warming period, and its daily minima temperature Tmin and maxima temperature Tmax values were used to produce average monthly values and spatial distributions of trends for each air basin. Additional analyses included concurrent SSTs, 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) sea level coastal pressure gradients, and GCM-downscaled average temperature Tave values. Results for all 253 California National Weather Service (NWS) Cooperative Observer Program (COOP) sites together showed increased Tave values (0.23°C decade−1); asymmetric warming, as Tmin values increase faster than Tmax values (0.27° versus 0.04°C decade−1) and thus decreased daily temperature range (DTR) values (0.15°C decade−1). The spatial distribution of...

A Seasonal Precipitation and Stream Flow Hindcast and Prediction Study in the Western United States during the 1997/98 Winter Season Using a Dynamic Downscaling System

Abstract The authors present a seasonal hindcast and prediction of precipitation in the western United States and stream flow in a northern California coastal basin for December 1997–February 1998 (DJF) using the Regional Climate System Model (RCSM). In the seasonal hindcast simulation, in which the twice-daily National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis was used for the initial conditions and time-dependent boundary forcing, RCSM has simulated realistically the temporal and spatial variations of precipitation in California and stream flow in a northern California coastal basin. For the headwater basin of the Russian River in the northern California Coast Ranges, the Topography-Based Hydrologic Model (TOPMODEL) forced by observed daily precipitation resulted in a correlation coefficient of 0.88 between observed and simulated DJF stream flow. In the coupled stream flow hindcast, the authors obtained a correlation coefficient of 0.7 between simulated and...

The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California

AbstractClimate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (>60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces Californias mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projecti...

Model integration for assessing future hydroclimate impacts on water resources, agricultural production and environmental quality in the San Joaquin Basin, California

The US National Assessment of the Potential Consequences of Climate Variability and Change provides compelling arguments for action and adaptive measures to help mitigate water resource, agricultural production and environmental quality impacts of future climate change. National resource planning at this scale can benefit by the development of integrated impact analysis toolboxes that allow linkage and integration of hydroclimate models, surface and groundwater hydrologic models, economic and environmental impact models and techniques for social impact assessment. Simulation models used in an assessment of climate change impacts on water resources, agriculture and environmental quality in the San Joaquin Basin of California are described in this paper as well as the challenges faced in linking the component models within an impacts assessment toolbox. Results from simulations performed with several of the tools in the impacts assessment toolbox are presented and discussed. After initially attempting model integration with the public domain, GIS-based modeling framework Modular Modeling System/Object User Interface (MMS/OUI), frustration with the framework’s lack of flexibility to handle monthly timestep models prompted development of a common geodatabase to allow linkage of model input and output for the linked simulation models. A GIS-based data browser was also developed that works with both network flow models and makes calls to a model post-processor that shows model output for each selected node in each model network. This data and output browser system is flexible and can readily accommodate future changes in the model network configuration and in the model database.  2003 Elsevier Ltd. All rights reserved.