scholarly journals Cool season precipitation projections for California and the Western United States in NA-CORDEX models

2021 ◽  
Author(s):  
Kelly Mahoney ◽  
James D. Scott ◽  
Michael Alexander ◽  
Rachel McCrary ◽  
Mimi Hughes ◽  
...  
Keyword(s):  
United States ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Western United States ◽  
Monthly Precipitation ◽  
Wet Season ◽  
Cool Season ◽  
Projected Change

AbstractUnderstanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.

scholarly journals Will Future Climate Favor More Erratic Wildfires in the Western United States?

2013 ◽  
Vol 52 (11) ◽  
pp. 2410-2417 ◽  
Author(s):  
Lifeng Luo ◽  
Ying Tang ◽  
Shiyuan Zhong ◽  
Xindi Bian ◽  
Warren E. Heilman
Keyword(s):  
United States ◽  
Climate Models ◽  
Regional Climate ◽  
Fire Behavior ◽  
Regional Climate Models ◽  
Future Climate ◽  
Lower Atmosphere ◽  
Western United States ◽  
Mountainous Regions ◽  
Increased Risk

AbstractWildfires that occurred over the western United States during August 2012 were fewer in number but larger in size when compared with all other Augusts in the twenty-first century. This unique characteristic, along with the tremendous property damage and potential loss of life that occur with large wildfires with erratic behavior, raised the question of whether future climate will favor rapid wildfire growth so that similar wildfire activity may become more frequent as climate changes. This study addresses this question by examining differences in the climatological distribution of the Haines index (HI) between the current and projected future climate over the western United States. The HI, ranging from 2 to 6, was designed to characterize dry, unstable air in the lower atmosphere that may contribute to erratic or extreme fire behavior. A shift in HI distribution from low values (2 and 3) to higher values (5 and 6) would indicate an increased risk for rapid wildfire growth and spread. Distributions of Haines index are calculated from simulations of current (1971–2000) and future (2041–70) climate using multiple regional climate models in the North American Regional Climate Change Assessment Program. Despite some differences among the projections, the simulations indicate that there may be not only more days but also more consecutive days with HI ≥ 5 during August in the future. This result suggests that future atmospheric environments will be more conducive to erratic wildfires in the mountainous regions of the western United States.

Characterizing Predictability of Precipitation Means and Extremes over the Conterminous United States, 1949–2010*

2016 ◽  
Vol 29 (7) ◽  
pp. 2621-2633 ◽  
Author(s):  
Mingkai Jiang ◽  
Benjamin S. Felzer ◽  
Dork Sahagian
Keyword(s):  
United States ◽  
Pacific Northwest ◽  
West Coast ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
The United States ◽  
Monthly Precipitation ◽  
The West ◽  
Precipitation Seasonality

Abstract The proper understanding of precipitation variability, seasonality, and predictability are important for effective environmental management. Precipitation and its associated extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate parameters to predict on the basis of global and regional climate models. Using information theory, an improved understanding of precipitation predictability in the conterminous United States over the period of 1949–2010 is sought based on a gridded monthly precipitation dataset. Predictability is defined as the recurrent likelihood of patterns described by the metrics of magnitude variability and seasonality. It is shown that monthly mean precipitation and duration-based dry and wet extremes are generally highly variable in the east compared to those in the west, while the reversed spatial pattern is observed for intensity-based wetness indices except along the Pacific Northwest coast. It is thus inferred that, over much of the U.S. landscape, variations of monthly mean precipitation are driven by the variations in precipitation occurrences rather than the intensity of infrequent heavy rainfall. It is further demonstrated that precipitation seasonality for means and extremes is homogeneously invariant within the United States, with the exceptions of the West Coast, Florida, and parts of the Midwest, where stronger seasonality is identified. A proportionally higher role of variability in regulating precipitation predictability is demonstrated. Seasonality surpasses variability only in parts of the West Coast. The quantified patterns of predictability for precipitation means and extremes have direct applications to those phenomena influenced by climate periodicity, such as biodiversity and ecosystem management.

Simulations of Present and Future Climates in the Western United States with Four Nested Regional Climate Models

2006 ◽  
Vol 19 (6) ◽  
pp. 873-895 ◽  
Author(s):  
P. B. Duffy ◽  
R. W. Arritt ◽  
J. Coquard ◽  
W. Gutowski ◽  
J. Han ◽  
...  
Keyword(s):  
United States ◽  
Boundary Conditions ◽  
Water Content ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Western United States ◽  
Lateral Boundary ◽  
Present Climate ◽  
Lateral Boundary Conditions

Abstract In this paper, the authors analyze simulations of present and future climates in the western United States performed with four regional climate models (RCMs) nested within two global ocean–atmosphere climate models. The primary goal here is to assess the range of regional climate responses to increased greenhouse gases in available RCM simulations. The four RCMs used different geographical domains, different increased greenhouse gas scenarios for future-climate simulations, and (in some cases) different lateral boundary conditions. For simulations of the present climate, RCM results are compared to observations and to results of the GCM that provided lateral boundary conditions to the RCM. For future-climate (increased greenhouse gas) simulations, RCM results are compared to each other and to results of the driving GCMs. When results are spatially averaged over the western United States, it is found that the results of each RCM closely follow those of the driving GCM in the same region in both present and future climates. This is true even though the study area is in some cases a small fraction of the RCM domain. Precipitation responses predicted by the RCMs in many regions are not statistically significant compared to interannual variability. Where the predicted precipitation responses are statistically significant, they are positive. The models agree that near-surface temperatures will increase, but do not agree on the spatial pattern of this increase. The four RCMs produce very different estimates of water content of snow in the present climate, and of the change in this water content in response to increased greenhouse gases.

scholarly journals A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yun Xu ◽  
Andrew Jones ◽  
Alan Rhoades
Keyword(s):  
Sierra Nevada ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Driving Forces ◽  
Regional Climate ◽  
Model Development ◽  
Regional Climate Models ◽  
The North ◽  
Snow Water ◽  
Regional Downscaling

Abstract The simulation of snow water equivalent (SWE) remains difficult for regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWE simulation.

scholarly journals Predictive Contributions of Snowmelt and Rainfall to Streamflow Variations in the Western United States

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaohui Zheng ◽  
Qiguang Wang ◽  
Lihua Zhou ◽  
Qing Sun ◽  
Qi Li
Keyword(s):  
United States ◽  
Pacific Northwest ◽  
Climate Models ◽  
Coastal Areas ◽  
Snow Accumulation ◽  
Western United States ◽  
Lead Times ◽  
Wet Season ◽  
Mountain Areas ◽  
Southeastern Pacific

This study used long-term in situ rainfall, snow, and streamflow data to explore the predictive contributions of snowmelt and rainfall to streamflow in six watersheds over the Western United States. Analysis showed that peak snow accumulation, snow-free day, and snowmelt slope all had strong correlation with peak streamflow, particularly in inland basins. Further analysis revealed that the variation of snow accumulation anomaly had strong lead correlation with the variation of streamflow anomaly. Over the entire Western United States, inner mountain areas had lead times of 4–10 pentads. However, in coastal areas, nearly all sites had lead times of less than one pentad. The relative contributions of rainfall and snowmelt to streamflow in different watersheds were calculated based on the snow lead time. The geographic distribution of annual relative contributions revealed that interior areas were dominated by snowmelt contribution, whereas the rainfall contribution dominated coastal areas. In the wet season, the snowmelt contribution increased in the western Pacific Northwest, whereas the rainfall contribution increased in the southeastern Pacific Northwest, southern Upper Colorado, and northern Rio Grande regions. The derived results demonstrated the predictive contributions of snowmelt and rainfall to streamflow. These findings could be considered a reference both for seasonal predictions of streamflow and for prevention of hydrological disasters. Furthermore, they will be helpful in the evaluation and improvement of hydrological and climate models.

scholarly journals A Maieutic Exploration of Nudging Strategies for Regional Climate Applications Using the WRF Model

2018 ◽  
Vol 57 (8) ◽  
pp. 1883-1906 ◽  
Author(s):  
Tanya L. Spero ◽  
Christopher G. Nolte ◽  
Megan S. Mallard ◽  
Jared H. Bowden
Keyword(s):  
United States ◽  
Climate Models ◽  
Weather Prediction ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Models ◽  
The United States ◽  
Spectral Nudging ◽  
Temperature And Precipitation ◽  
Quality Health

AbstractThe use of nudging in the Weather Research and Forecasting (WRF) Model to constrain regional climate downscaling simulations is gaining in popularity because it can reduce error and improve consistency with the driving data. While some attention has been paid to whether nudging is beneficial for downscaling, very little research has been performed to determine best practices. In fact, many published papers use the default nudging configuration (which was designed for numerical weather prediction), follow practices used by colleagues, or adapt methods developed for other regional climate models. Here, a suite of 45 three-year simulations is conducted with WRF over the continental United States to systematically and comprehensively examine a variety of nudging strategies. The simulations here use a longer test period than did previously published works to better evaluate the robustness of each strategy through all four seasons, through multiple years, and across nine regions of the United States. The analysis focuses on the evaluation of 2-m temperature and precipitation, which are two of the most commonly required downscaled output fields for air quality, health, and ecosystems applications. Several specific recommendations are provided to effectively use nudging in WRF for regional climate applications. In particular, spectral nudging is preferred over analysis nudging. Spectral nudging performs best in WRF when it is used toward wind above the planetary boundary layer (through the stratosphere) and temperature and moisture only within the free troposphere. Furthermore, the nudging toward moisture is very sensitive to the nudging coefficient, and the default nudging coefficient in WRF is too high to be used effectively for moisture.

Empirical Downscaling of High-Resolution Regional Precipitation from Large-Scale Reanalysis Fields

2012 ◽  
Vol 51 (1) ◽  
pp. 100-114 ◽  
Author(s):  
Robert E. Nicholas ◽  
David S. Battisti
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Jiangxi Province ◽  
Monthly Precipitation ◽  
Upper Colorado River Basin ◽  
Precipitation Anomalies ◽  
Empirical Downscaling ◽  
High Skill

AbstractThis study describes an EOF-based technique for statistical downscaling of high-spatial-resolution monthly-mean precipitation from large-scale reanalysis circulation fields. The method is demonstrated and evaluated for four widely separated locations: the southeastern United States, the upper Colorado River basin, China’s Jiangxi Province, and central Europe. For each location, the EOF-based downscaling models successfully reproduce the observed annual cycle while eliminating the biases seen in NCEP–NCAR reanalysis precipitation. They also frequently reproduce the monthly precipitation anomalies with greater fidelity than is seen in the precipitation field derived directly from reanalysis, and they outperform a suite of regional climate models over the two U.S. locations. With the relatively high skill achieved over a range of climate regimes, this technique may be a viable alternative to numerical downscaling of monthly-mean precipitation for many locations.

Future Snow Water Equivalent and Snowmelt Extremes from NA-CORDEX Ensembles

2020 ◽  
Author(s):  
Eunsang Cho ◽  
Rachel R. McCrary ◽  
Jennifer M. Jacobs
Keyword(s):  
21St Century ◽  
Extreme Events ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Natural Environments ◽  
The North ◽  
Snow Water ◽  
Late 21St Century

<p>Snowpack and snowmelt driven extreme events can have large societal and economic consequences. Extreme snow can damage infrastructure and buildings. Snow meltwater is a dominant driver of severe spring flooding in the north-central and -eastern U.S. and southern Canada with impacts to the built and natural environments. However, the currently there is very limited guidance regarding the magnitude of “future” snow-driven extremes in a changing climate as needed to plan, design, and manage potentially vulnerable infrastructure and ecosystems. Regional climate models (RCMs) are commonly used to study and quantify regional climate changes, even though the ability of these models to accurately represent snow varies. In this study, trends and designs of extreme 25- and 100-year snowpack (snow water equivalent; SWE) and snowmelt events are estimated in the mid and late 21st century using the North America - Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) ensemble of RCMs under Representative Con-centration Pathways 8.5 (RCP 8.5). This study aims to answer the following three research questions:</p><ol><li>How much will snow-driven extreme events be changed in the mid and late 21st century?</li> <li>Which regions have the largest differences among models?</li> <li>Which RCM models are the source of these regional uncertainties?</li> </ol>

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