scholarly journals Impact of Atmospheric Moisture Storage on Precipitation Recycling

2006 ◽  
Vol 19 (8) ◽  
pp. 1513-1530 ◽  
Author(s):  
Francina Dominguez ◽  
Praveen Kumar ◽  
Xin-Zhong Liang ◽  
Mingfang Ting
Keyword(s):  
United States ◽  
Time Scales ◽  
Analytical Models ◽  
Spatiotemporal Variability ◽  
Spatial And Temporal Variability ◽  
Monthly Precipitation ◽  
Computationally Efficient ◽  
Precipitation Recycling ◽  
Moisture Storage ◽  
Storage Term

Abstract Computations of precipitation recycling using analytical models are generally performed under the assumption of negligible change in moisture storage in the atmospheric column. Because the moisture storage term is nonnegligible at smaller time scales, most recycling studies using analytical models are done at monthly or longer time scales. A dynamic precipitation recycling model, which incorporates the change in moisture storage, is developed. It is derived formally from the conservation of mass equation and is presented in a simple and computationally efficient form. This model allows for recycling analysis at a range of temporal scales, from daily to monthly and longer. In comparison to the traditional models that do not include the storage term, the new model presents almost identical spatial and temporal variability, but predicts recycling ratios that are 12%–33% larger at a monthly level. The dynamic model is used to study the variability of monthly precipitation recycling over the conterminous United States using Reanalysis-II data from 1979 to 2000. On average, the southeastern and southwestern parts of the country exhibit high summer recycling ratios, contrasting with the low values in the northeastern and northwestern United States. The Colorado region also presents high recycling ratios. Dominant modes of spatiotemporal variability in recycling are identified using EOF analysis. The first mode captures strong recycling ratios over the western United States during the summers of 1986, 1992, and 1998. The second mode captures anomalous high recycling ratios during 1988 and 1989 over the central part of the country, and anomalous low ratios during 1980 and 1993.

scholarly journals Verification and Intercomparison of Multimodel Simulated Land Surface Hydrological Datasets over the United States

2011 ◽  
Vol 12 (4) ◽  
pp. 531-555 ◽  
Author(s):  
Yun Fan ◽  
Huug M. van den Dool ◽  
Wanru Wu
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Surface Water ◽  
Water Balance ◽  
Time Scales ◽  
Land Surface ◽  
The United States ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Water Balance Components

Abstract Several land surface datasets, such as the observed Illinois soil moisture dataset; three retrospective offline run datasets from the Noah land surface model (LSM), Variable Infiltration Capacity (VIC) LSM, and Climate Prediction Center leaky bucket soil model; and three reanalysis datasets (North American Regional Reanalysis, NCEP/Department of Energy Global Reanalysis, and 40-yr ECMWF Re-Analysis), are used to study the spatial and temporal variability of soil moisture and its response to the major components of land surface hydrologic cycles: precipitation, evaporation, and runoff. Detailed analysis was performed on the evolution of the soil moisture vertical profile. Over Illinois, model simulations are compared to observations, but for the United States as a whole some impressions can be gained by comparing the multiple soil moisture–precipitation–evaporation–runoff datasets to one another. The magnitudes and partitioning of major land surface water balance components on seasonal–interannual time scales have been explored. It appears that evaporation has the most prominent annual cycle but its interannual variability is relatively small. For other water balance components, such as precipitation, runoff, and surface water storage change, the amplitudes of their annual cycles and interannual variations are comparable. This study indicates that all models have a certain capability to reproduce observed soil moisture variability on seasonal–interannual time scales, but offline runs are decidedly better than reanalyses (in terms of validation against observations) and more highly correlated to one another (in terms of intercomparison) in general. However, noticeable differences are also observed, such as the degree of simulated drought severity and the locations affected—this is due to the uncertainty in model physics, input forcing, and mode of running (interactive or offline), which continue to be major issues for land surface modeling.

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 The Evaporative Demand Drought Index. Part II: CONUS-Wide Assessment against Common Drought Indicators

2016 ◽  
Vol 17 (6) ◽  
pp. 1763-1779 ◽  
Author(s):  
Daniel J. McEvoy ◽  
Justin L. Huntington ◽  
Michael T. Hobbins ◽  
Andrew Wood ◽  
Charles Morton ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Time Scales ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Stress Index ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Evaporative Demand ◽  
Hydrologic Drought

Abstract Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.

scholarly journals Variability of Cloudiness over Mountain Terrain in the Western United States

2017 ◽  
Vol 18 (5) ◽  
pp. 1227-1245 ◽  
Author(s):  
Edwin Sumargo ◽  
Daniel R. Cayan
Keyword(s):  
United States ◽  
Large Scale ◽  
Spatial Scales ◽  
High Elevation ◽  
Empirical Orthogonal Functions ◽  
Western United States ◽  
Spatial And Temporal Variability ◽  
Regional Patterns ◽  
Cloud Albedo

Abstract This study investigates the spatial and temporal variability of cloudiness across mountain zones in the western United States. Daily average cloud albedo is derived from a 19-yr series (1996–2014) of half-hourly Geostationary Operational Environmental Satellite (GOES) images. During springtime when incident radiation is active in driving snowmelt–runoff processes, the magnitude of daily cloud variations can exceed 50% of long-term averages. Even when aggregated over 3-month periods, cloud albedo varies by ±10% of long-term averages in many locations. Rotated empirical orthogonal functions (REOFs) of daily cloud albedo anomalies over high-elevation regions of the western conterminous United States identify distinct regional patterns, wherein the first five REOFs account for ~67% of the total variance. REOF1 is centered over Northern California and Oregon and is pronounced between November and March. REOF2 is centered over the interior northwest and is accentuated between March and July. Each of the REOF/rotated principal components (RPC) modes associates with anomalous large-scale atmospheric circulation patterns and one or more large-scale teleconnection indices (Arctic Oscillation, Niño-3.4, and Pacific–North American), which helps to explain why anomalous cloudiness patterns take on regional spatial scales and contain substantial variability over seasonal time scales.

scholarly journals Investigation of spatiotemporal variability of melt pond fraction and its relationship with sea ice extent during 2000–2017 using a new data

2019 ◽  
Author(s):  
Yifan Ding ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui ◽  
Zhenzhan Wang
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
Spatiotemporal Variability ◽  
First Year ◽  
Spatial And Temporal Variability ◽  
Sea Ice Extent ◽  
Melt Pond ◽  
Melt Ponds

Abstract. The accurate knowledge of variations of melt ponds is important for understanding Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) from the MODIS surface reflectance. We construct an ensemble-based deep neural network and use in-situ observations of MPF from multi-sources to train the network. The results show that our derived MPF is in good agreement with the observations, and relatively outperforms the MPF retrieved by University of Hamburg. Built on this, we create a new MPF data from 2000 to 2017 (the longest data in our knowledge), and analyze the spatial and temporal variability of MPF. It is found that the MPF has significant increasing trends from late July to early September, which is largely contributed by the MPF over the first-year sea ice. The analysis based on our MPF during 2000–2017 confirms that the integrated MPF to late June does promise to improve the prediction skill of seasonal Arctic sea ice minimum. However, our MPF data shows concentrated significant correlations first appear in a band, extending from the eastern Beaufort Sea, through the central Arctic, to the northern East Siberian and Laptev Seas in early-mid June, and then shifts towards large areas of the Beaufort Sea, Canadian Arctic, the northern Greenland Sea and the central Arctic basin.

scholarly journals Global Evaluation of Seasonal Precipitation and Temperature Forecasts from NMME

2020 ◽  
Vol 21 (11) ◽  
pp. 2473-2486
Author(s):  
Tirthankar Roy ◽  
Xiaogang He ◽  
Peirong Lin ◽  
Hylke E. Beck ◽  
Christopher Castro ◽  
...  
Keyword(s):  
Phase 1 ◽  
Research Unit ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Spatiotemporal Variability ◽  
Lead Times ◽  
Global Evaluation ◽  
Monthly Precipitation ◽  
Tropical Regions ◽  
Precipitation And Temperature

AbstractWe present a comprehensive global evaluation of monthly precipitation and temperature forecasts from 16 seasonal forecasting models within the NMME Phase-1 system, using Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP-V2; precipitation) and Climate Research Unit TS4.01 (CRU-TS4.01; temperature) data as reference. We first assessed the forecast skill for lead times of 1–8 months using Kling–Gupta efficiency (KGE), an objective performance metric combining correlation, bias, and variability. Next, we carried out an empirical orthogonal function (EOF) analysis to compare the spatiotemporal variability structures of the forecasts. We found that, in most cases, precipitation skill was highest during the first lead time (i.e., forecast in the month of initialization) and rapidly dropped thereafter, while temperature skill was much higher overall and better retained at higher lead times, which is indicative of stronger temporal persistence. Based on a comprehensive assessment over 21 regions and four seasons, we found that the skill showed strong regional and seasonal dependencies. Some tropical regions, such as the Amazon and Southeast Asia, showed high skill even at longer lead times for both precipitation and temperature. Rainy seasons were generally associated with high precipitation skill, while during winter, temperature skill was low. Overall, precipitation forecast skill was highest for the NASA, NCEP, CMC, and GFDL models, and for temperature, the NASA, CFSv2, COLA, and CMC models performed the best. The spatiotemporal variability structures were better captured for precipitation than temperature. The simple forecast averaging did not produce noticeably better results, emphasizing the need for more advanced weight-based averaging schemes.

scholarly journals Relations between Temperature and Residential Natural Gas Consumption in the Central and Eastern United States

2007 ◽  
Vol 46 (11) ◽  
pp. 1993-2013 ◽  
Author(s):  
Reed P. Timmer ◽  
Peter J. Lamb
Keyword(s):  
United States ◽  
Natural Gas ◽  
Time Scales ◽  
The United States ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
The South ◽  
Northern Regions ◽  
Gas Consumption

Abstract The increased U.S. natural gas price volatility since the mid-to-late-1980s deregulation generally is attributed to the deregulated market being more sensitive to temperature-related residential demand. This study therefore quantifies relations between winter (November–February; December–February) temperature and residential gas consumption for the United States east of the Rocky Mountains for 1989–2000, by region and on monthly and seasonal time scales. State-level monthly gas consumption data are aggregated for nine multistate subregions of three Petroleum Administration for Defense Districts of the U.S. Department of Energy. Two temperature indices [days below percentile (DBP) and heating degree-days (HDD)] are developed using the Richman–Lamb fine-resolution (∼1° latitude–longitude) set of daily maximum and minimum temperatures for 1949–2000. Temperature parameters/values that maximize DBP/HDD correlations with gas consumption are identified. Maximum DBP and HDD correlations with gas consumption consistently are largest in the Great Lakes–Ohio Valley region on both monthly (from +0.89 to +0.91) and seasonal (from +0.93 to +0.97) time scales, for which they are based on daily maximum temperature. Such correlations are markedly lower on both time scales (from +0.62 to +0.80) in New England, where gas is less important than heating oil, and on the monthly scale (from +0.55 to +0.75) across the South because of low January correlations. For the South, maximum correlations are for daily DBP and HDD indices based on mean or minimum temperature. The percentiles having the highest DBP index correlations with gas consumption are slightly higher for northern regions than across the South. This is because lower (higher) relative (absolute) temperature thresholds are reached in warmer regions before home heating occurs. However, these optimum percentiles for all regions are bordered broadly by surrounding percentiles for which the correlations are almost as high as the maximum. This consistency establishes the robustness of the temperature–gas consumption relations obtained. The reference temperatures giving the highest HDD correlations with gas consumption are lower for the colder northern regions than farther south where the temperature range is truncated. However, all HDD reference temperatures greater than +10°C (+15°C) yield similar such correlations for northern (southern) regions, further confirming the robustness of the findings. This robustness, coupled with the very high correlation magnitudes obtained, suggests that potentially strong gas consumption predictability would follow from accurate seasonal temperature forecasts.

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