Trends in Snowfall versus Rainfall in the Western United States

American Meteorological Society (AMS) | September 15th, 2006

The water resources of the western United States depend heavily on snowpack to store part of the wintertime precipitation into the drier summer months. A well-documented

The water resources of the western United States depend heavily on snowpack to store part of the wintertime precipitation into the drier summer months. A well-documented shift toward earlier runoff in recent decades has been attributed to 1) more precipitation falling as rain instead of snow and 2) earlier snowmelt. The present study addresses the former, documenting a regional trend toward smaller ratios of winter-total snowfall water equivalent (SFE) to winter-total precipitation (P) during the period 1949–2004. The trends toward reduced SFE are a response to warming across the region, with the most significant reductions occurring where winter wet-day minimum temperatures, averaged over the study period, were warmer than 5°C. Most SFE reductions were associated with winter wet-day temperature increases between 0° and 3°C over the study period. Warmings larger than this occurred mainly at sites where the mean temperatures were cool enough that the precipitation form was less susceptible to warming trends. The trends toward reduced SFE/P ratios were most pronounced in March regionwide and in January near the West Coast, corresponding to widespread warming in these months. While mean temperatures in March were sufficiently high to allow the warming trend to produce SFE/P declines across the study region, mean January temperatures were cooler, with the result that January SFE/P impacts were restricted to the lower elevations near the West Coast. Extending the analysis back to 1920 shows that although the trends presented here may be partially attributable to interdecadal climate variability associated with the Pacific decadal oscillation, they also appear to result from still longer-term climate shifts.

Uncertainty in El Niño-like warming and California precipitation changes linked by the Interdecadal Pacific Oscillation

Nature Portfolio (Springer Nature) | November 10th, 2021

Marked uncertainty in California (CA) precipitation projections challenges their use in adaptation planning in the region already experiencing severe water stress. Under

Marked uncertainty in California (CA) precipitation projections challenges their use in adaptation planning in the region already experiencing severe water stress. Under global warming, a westerly jet extension in the North Pacific analogous to the El Niño-like teleconnection has been suggested as a key mechanism for CA winter precipitation changes. However, this teleconnection has not been reconciled with the well-known El Niño-like warming response or the controversial role of internal variability in the precipitation uncertainty. Here we find that internal variability contributes > 70% and > 50% of uncertainty in the CA precipitation changes and the El Niño-like warming, respectively, based on analysis of 318 climate simulations from several multi-model and large ensembles. The Interdecadal Pacific Oscillation plays a key role in each contribution and in connecting the two via the westerly jet extension. This unifying understanding of the role of internal variability in CA precipitation provides critical guidance for reducing and communicating uncertainty to inform adaptation planning.

Understanding the Contributions of Paleo-Informed Natural Variability and Climate Changes to Hydroclimate Extremes in the San Joaquin Valley of California

American Geophysical Union (AGU) | November 13th, 2023

Understanding the recent increase in multiyear La Niñas

Nature Portfolio (Springer Nature) | September 18th, 2023

Understanding Uncertainties in Future Colorado River Streamflow

American Meteorological Society (AMS) | January 1st, 2014

The Colorado River is the primary water source for more than 30 million people in the United States and Mexico. Recent studies that project streamf low changes in the Col

The Colorado River is the primary water source for more than 30 million people in the United States and Mexico. Recent studies that project streamf low changes in the Colorado River all project annual declines, but the magnitude of the projected decreases range from less than 10% to 45% by the mid-twenty-first century. To understand these differences, we address the questions the management community has raised: Why is there such a wide range of projections of impacts of future climate change on Colorado River streamflow, and how should this uncertainty be interpreted? We identify four major sources of disparities among studies that arise from both methodological and model differences. In order of importance, these are differences in 1) the global climate models (GCMs) and emission scenarios used; 2) the ability of land surface and atmospheric models to simulate properly the high-elevation runoff source areas; 3) the sensitivities of land surface hydrology models to precipitation and temperature changes; and 4) the methods used to statistically downscale GCM scenarios. In accounting for these differences, there is substantial evidence across studies that future Colorado River streamflow will be reduced under the current trajectories of anthropogenic greenhouse gas emissions because of a combination of strong temperature-induced runoff curtailment and reduced annual precipitation. Reconstructions of preinstrumental streamflows provide additional insights; the greatest risk to Colorado River streamf lows is a multidecadal drought, like that observed in paleoreconstructions, exacerbated by a steady reduction in flows due to climate change. This could result in decades of sustained streamflows much lower than have been observed in the ~100 years of instrumental record.

User’s Manual for the Draper Climate-Distribution Software Suite with Data-Evaluation Tools

U.S. Geological Survey (USGS) | March 20th, 2019

Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiri

Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiring distributed inputs of climate data beyond what is available from observations collected at climate stations. To prepare inputs required for model-mapping units across the study area, climate data (temperature and precipitation) are distributed by combining data from gridded surfaces of mean-monthly climate-data values with (often) widely spaced daily pointobservations. Examples of climate-data files used to develop PRMS-formatted input files for the Merced River Basin Precipitation-Runoff Modeling System (PRMS) are includedin this manual. The Draper Climate-Distribution Software Suite (Draper Suite) consists of the Draper climate-distribution program (Draper) and several supporting pre- and post-processing applications. Draper combines spatially distributed input in the form of monthly averaged values for precipitation, maximum temperature, and minimum temperature with daily observed data from climate stations to estimate distributed climate-data values at predefined locations across a study area (typically adrainage basin) on a daily time step. Alternative methods are used when station data are limited or missing for a particular day. Draper uses a set of required and optional input andoutput files with defined formats and naming conventions. Ashell application also is available to manage multiple runs of the Draper application. Other applications in the Draper Suite include (1) a toolto find and interactively remove outliers in the input data, (2) atool to check and enforce a minimum daily temperature range, and (3) a tool to view output diagnostic information as time- series graphs. These tools can be used iteratively to evaluate and improve the results from Draper as part of a work flow involving physically-based environmental models, such as the Precipitation-Runoff Modeling System (PRMS).

Variability and Trends in Precipitation, Temperature and Drought Indices in the State of California

Hydrology (MDPI) | March 28th, 2016

This study presents a comprehensive assessment of the variability and trends of the precipitation and temperature along with the trends in drought indices over the State

This study presents a comprehensive assessment of the variability and trends of the precipitation and temperature along with the trends in drought indices over the State of California. The non-parametric Mann–Kendall trend test is applied with a trend-free pre-whitening procedure in trend identification. A dataset containing 120-year (water years 1896–2015) monthly precipitation, average temperature, maximum temperature, minimum temperature and the Palmer Index for seven climatic regions of the state is used for this purpose. The results confirm previous work indicating that no clear trends are observed in precipitation, while a distinct warming trend is evident in temperature over the state. New findings of this study include: (1) in general, the variability of annual, winter (December–February) and spring (March–May) precipitation shows an increasing tendency, implying intensified frequency of the occurrence of dry or wet extremes; (2) on the annual scale and in the summer, statewide meteorological, hydrological and agricultural drought indices all have decreasing trends, indicating the more frequent occurrence of drought events; and (3) among seven regions, the South Coast Drainage region generally has the most significant warming trend, as well as the most significant declining trends in drought indices. Overall, these findings are highly meaningful from both theoretical and practical perspectives, in the context of providing critical information in developing prediction models and guiding water resources management practices, respectively.

Water Supply Forecasting

U.S. Department of Agriculture (USDA) | March 9th, 2011

Chapter 7: Part 622 Snow Survey and Water Supply Forecasting from the National Engineering Handbook The U.S. Department of Agriculture (USDA) Natural Resources Conser

Chapter 7: Part 622 Snow Survey and Water Supply Forecasting from the National Engineering Handbook The U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Snow Survey and Water Supply Forecasting Program issues water supply forecasts for hundreds of points in the Western United States. The primary forecast product is a seasonal volume for a specified period of months (e.g., April–July). These forecasts are issued near the first of the month between January and June. Mid-month updates are also issued for selected forecast points. In addition, daily guidance forecasts are issued for a subset of forecast points using an automated procedure. This chapter describes the essential features of water supply forecasting as practiced in the NRCS. The topics are presented in a roughly chronological sequence of activities, from forecasting point establishment, to data gathering, to model development, to operations, and finally to verification. Citations of foundational professional literature and NRCS technical notes are given to provide sources of details on mathematical procedures and other technical information not explicitly described in this chapter.

Water Year 2023: Weather Whiplash, from Drought to Deluge

California Department of Water Resources (DWR) | October 4th, 2023

Water Year 2023 demonstrated California’s high climate variability, ending the state’s driest consecutive three-year period with one of the snowiest years of record.

Water Year 2023 demonstrated California’s high climate variability, ending the state’s driest consecutive three-year period with one of the snowiest years of record. Even though California has been experiencing a theme of aridity in the 21st century (the statewide droughts of 2007-2009, 2012-2019, and 2020-2022), Water Year 2017 was the state’s second wettest in terms of statewide precipitation. Climate change is expected to amplify naturally occurring variability in the long term, potentially result in a shorter wet season for California but one with more extreme atmospheric river storms and hence potentially greater flood damage risk.

Why Seasonal Prediction of California Winter Precipitation is Challenging

American Meteorological Society (AMS) | December 7th, 2022

Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive sk

Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.