Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a base set and a 10 yr independent projected set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales

William LaBounty

William LaBounty, Class of 1952, majored in Business Administration before attending ISU, where he was a Director of Printing. He continues to volunteer at the McLean County Museum of History today

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

Climate change and the Delta, San Francisco Estuary and Watershed Science

Anthropogenic climate change amounts to a rapidly approaching, “new” stressor in the Sacramento–San Joaquin Delta system. In response to California’s extreme natural hydroclimatic variability, complex water-management systems have been developed, even as the Delta’s natural ecosystems have been largely devastated. Climate change is projected to challenge these management and ecological systems in different ways that are characterized by different levels of uncertainty. For example, there is high certainty that climate will warm by about 2°C more (than late-20th-century averages) by mid-century and about 4°C by end of century, if greenhouse-gas emissions continue their current rates of acceleration. Future precipitation changes are much less certain, with as many climate models projecting wetter conditions as drier. However, the same projections agree that precipitation will be more intense when storms do arrive, even as more dry days will separate storms. Warmer temperatures will likely enhance evaporative demands and raise water temperatures. Consequently, climate change is projected to yield both more extreme flood risks and greater drought risks. Sea level rise (SLR) during the 20th century was about 22cm, and is projected to increase by at least 3-fold this century. SLR together with land subsidence threatens the Delta with greater vulnerabilities to inundation and salinity intrusion. Effects on the Delta ecosystem that are traceable to warming include SLR, reduced snowpack, earlier snowmelt and larger storm-driven streamflows, warmer and longer summers, warmer summer water temperatures, and water-quality changes. These changes and their uncertainties will challenge the operations of water projects and uses throughout the Delta’s watershed and delivery areas. Although the effects of climate change on Delta ecosystems may be profound, the end results are difficult to predict, except that native species will fare worse than invaders. Successful preparation for the coming changes will require greater integration of monitoring, modeling, and decision making across time, variables, and space than has been historically normal

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)

Climate change scenarios for the California region

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 from among the many plausible climate sequences that might affect California in the next century. Temperatures over California warm significantly during the twenty-first century in each simulation, with end-of-century temperature increases from approximately +1.5°C under the lower emissions B1 scenario in the less responsive PCM1 to +4.5°C in the higher emissions A2 scenario within the more responsive GFDL model. Three of the simulations (all except the B1 scenario in PCM1) exhibit more warming in summer than in winter. In all of the simulations, most precipitation continues to occur in winter. Relatively small (less than ~10%) changes in overall precipitation are projected. The California landscape is complex and requires that model information be parsed out onto finer scales than GCMs presently offer. When downscaled to its mountainous terrain, warming has a profound influence on California snow accumulations, with snow losses that increase with warming. Consequently, snow losses are most severe in projections by the more responsive model in response to the highest emissions

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

Climate 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 (\u3e60 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 California\u27s 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 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (\u3e60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions

Improved Bias Correction Techniques for Hydrological Simulations of Climate Change

Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM’s mean climate change signal, with differences of up to 2°C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models’ simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season’s values at once

Probabilistic estimates of future changes in California temperature and precipitation usingstatistical 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

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