scholarly journals Influence of snow water equivalent on droughts and their prediction in the USA

2018 ◽  
Author(s):  
Daniel Abel ◽  
Felix Pollinger ◽  
Heiko Paeth
Keyword(s):  
Sierra Nevada ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Original Data ◽  
The United States ◽  
Early Summer ◽  
Drought Severity ◽  
Southern Us ◽  
Snow Water

Abstract. Droughts can result in enormous impacts for environment, societies, and economy. In arid or semiarid regions with bordering high mountains, snow is the major source of water supply due to its role as natural water storage. The goal of this study is to examine the influence of snow water equivalent (SWE) on droughts in the United States and find large-scale climatic predictors for SWE and drought. For this, a Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition, is performed with snow data from the ERA–Interim reanalysis and the self-calibrating Palmer Drought Severity Index (sc–PDSI) as drought index. Furthermore, the relationship of resulting principal components and original data with atmospheric patterns is investigated. The leading mode shows the spatial connection between SWE and drought via downstream water/moisture transport. Especially the Rocky Mountains in Colorado (CR) play a key role for the central and western South, but the Sierra Nevada and even the Appalachian Mountains are relevant, too. The temperature and precipitation based sc–PDSI is able to capture this link because increased soil moisture results in higher evapotranspiration with lower sensible heat and vice versa. A time shifted MCA indicates a prediction skill for drought conditions in spring and early summer for the downstream regions of CR on the basis of SWE in March. Furthermore, the phase of the El Niño–Southern Oscillation is a good predictor for drought in the southern US and SWE around Colorado. The influence of the North Atlantic Oscillation and Pacific North American Pattern is not that clear.

scholarly journals Variability Common to First Leaf Dates and Snowpack in the Western Conterminous United States

2013 ◽  
Vol 17 (26) ◽  
pp. 1-18 ◽  
Author(s):  
Gregory J. McCabe ◽  
Julio L. Betancourt ◽  
Gregory T. Pederson ◽  
Mark D. Schwartz
Keyword(s):  
United States ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Late Winter ◽  
Modes Of Variability ◽  
Temperature And Precipitation ◽  
The Pacific ◽  
Snow Water ◽  
Value Decomposition

Abstract Singular value decomposition is used to identify the common variability in first leaf dates (FLDs) and 1 April snow water equivalent (SWE) for the western United States during the period 1900–2012. Results indicate two modes of joint variability that explain 57% of the variability in FLD and 69% of the variability in SWE. The first mode of joint variability is related to widespread late winter–spring warming or cooling across the entire west. The second mode can be described as a north–south dipole in temperature for FLD, as well as in cool season temperature and precipitation for SWE, that is closely correlated to the El Niño–Southern Oscillation. Additionally, both modes of variability indicate a relation with the Pacific–North American atmospheric pattern. These results indicate that there is a substantial amount of common variance in FLD and SWE that is related to large-scale modes of climate variability.

scholarly journals Effect of ENSO Phase on Large-Scale Snow Water Equivalent Distribution in a GCM

2009 ◽  
Vol 22 (23) ◽  
pp. 6153-6167 ◽  
Author(s):  
Debbie Clifford ◽  
Robert Gurney ◽  
Keith Haines
Keyword(s):  
Large Scale ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Skewed Distribution ◽  
Enso Events ◽  
Long Run ◽  
Snow Water ◽  
The Mean ◽  
June July ◽  
The Impact

Abstract Understanding links between the El Niño–Southern Oscillation (ENSO) and snow would be useful for seasonal forecasting, as well as for understanding natural variability and interpreting climate change predictions. Here, a 545-yr run of the third climate configuration of the Met Office Unified Model (HadCM3), with prescribed external forcings and fixed greenhouse gas concentrations, is used to explore the impact of ENSO on snow water equivalent (SWE) anomalies. In North America, positive ENSO events reduce the mean SWE and skew the distribution toward lower values, and vice versa during negative ENSO events. This is associated with a dipole SWE anomaly structure, with anomalies of opposite sign centered in western Canada and the central United States. In Eurasia, warm episodes lead to a more positively skewed distribution and the mean SWE is raised. Again, the opposite effect is seen during cold episodes. In Eurasia the largest anomalies are concentrated in the Himalayas. These correlations with February SWE distribution are seen to exist from the previous June–July–August (JJA) ENSO index onward, and are weakly detected in 50-yr subsections of the control run, but only a shifted North American response can be detected in the analysis of the 40-yr ECMWF Re-Analysis (ERA-40). The ENSO signal in SWE from the long run could still contribute to regional predictions, although it would only be a weak indicator.

scholarly journals Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niloufar Nouri ◽  
Naresh Devineni ◽  
Valerie Were ◽  
Reza Khanbilvardi
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Southern Oscillation ◽  
The United States ◽  
Anthropogenic Factors ◽  
Climate Variables ◽  
Term Trend ◽  
Cyclical Variability ◽  
Long Term Trend

AbstractThe annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

scholarly journals Juniper Tree-Ring Data from the Kuramin Range (Northern Tajikistan) Reveals Changing Summer Drought Signals in Western Central Asia

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 505 ◽  
Author(s):  
Feng Chen ◽  
Tongwen Zhang ◽  
Andrea Seim ◽  
Shulong Yu ◽  
Ruibo Zhang ◽  
...  
Keyword(s):  
Central Asia ◽  
Tree Ring ◽  
Large Scale ◽  
Southern Oscillation ◽  
Severity Index ◽  
Drought Severity ◽  
Summer Drought ◽  
Tree Ring Chronology ◽  
Tree Ring Data ◽  
June July

Coniferous forests cover the mountains in many parts of Central Asia and provide large potentials for dendroclimatic studies of past climate variability. However, to date, only a few tree-ring based climate reconstructions exist from this region. Here, we present a regional tree-ring chronology from the moisture-sensitive Zeravshan juniper (Juniperus seravschanica Kom.) from the Kuramin Range (Tajikistan) in western Central Asia, which is used to reveal past summer drought variability from 1650 to 2015 Common Era (CE). The chronology accounts for 40.5% of the variance of the June–July self-calibrating Palmer Drought Severity Index (scPDSI) during the instrumental period (1901 to 2012). Seven dry periods, including 1659–1696, 1705–1722, 1731–1741, 1758–1790, 1800–1842, 1860–1875, and 1931–1987, and five wet periods, including 1742–1752, 1843–1859, 1876–1913, 1921–1930, and 1988–2015, were identified. Good agreements between drought records from western and eastern Central Asia suggest that the PDSI records retain common drought signals and capture the regional dry/wet periods of Central Asia. Moreover, the spectral analysis indicates the existence of centennial (128 years), decadal (24.3 and 11.4 years), and interannual (8.0, 3.6, 2.9, and 2.0 years) cycles, which may be linked with climate forces, such as solar activity and El Niño-Southern Oscillation (ENSO). The analysis between the scPDSI reconstruction and large-scale atmospheric circulations during the reconstructed extreme dry and wet years can provide information about the linkages of extremes in our scPDSI record with the large-scale ocean–atmosphere–land circulation systems.

Automated Quality Control Procedure for the "Water Equivalent of Snow on the Ground" Measurement

1995 ◽  
Vol 34 (1) ◽  
pp. 143-151 ◽  
Author(s):  
Thomas W. Schmidlin ◽  
Daniel S. Wilks ◽  
Megan McKay ◽  
Richard P. Cember
Keyword(s):  
Quality Control ◽  
Snow Water Equivalent ◽  
Data Entry ◽  
The United States ◽  
Control Procedure ◽  
Quality Control Procedure ◽  
Ground Measurement ◽  
Snow Water ◽  
Automated Quality Control ◽  
High Winds

Abstract Snow water equivalent (SWE) has been measured daily by the United States National Weather Service since 1952, whenever snow depth is 2 in. (5 cm) or greater. These data are used to develop design snow loads for buildings, for hydrological forecasting, and as an indicator of climate change. To date they have not been subjected comprehensively to quality control. An automated quality control procedure for these data is developed here, which checks daily SWE values for common data entry errors, values beyond reasonable limits, and consistency with daily precipitation and estimated melt. Potential effects of drifting in high winds and of the intrinsic microscale variability of SWE are also considered. An SWE measurement is declared suspicious if a sufficient discrepancy is found with respect to the expected SWE. Data values flagged as potential errors are checked manually. Results of applying the procedure to available SWE data from the northeastern UnitedStates are also summarized.

scholarly journals Measuring changes in snowpack SWE continuously on a landscape scale using lake water pressure 

2021 ◽  
Author(s):  
Hamish Pritchard ◽  
Daniel Farinotti ◽  
Steven Colwell
Keyword(s):  
Lake Water ◽  
Large Scale ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Water Pressure ◽  
High Mountain ◽  
Alpine Valley ◽  
Weather And Climate ◽  
Forecast Models ◽  
Snow Water

<p>The seasonal snowpack is a globally important water resource that is notoriously difficult to measure. Existing instruments make measurements of falling or accumulating snow water equivalent (SWE) that are susceptible to bias, and most can represent only a point in the landscape. Furthermore the global array of SWE sensors is too sparse and too poorly distributed to be an adequate constraint on snow in weather and climate models. We present a new approach to monitoring snowpack SWE from time series of lake water pressure. We tested our method in the lowland Finnish Arctic and in an alpine valley and high-mountain cirque in Switzerland, and found that we could measure changes in SWE and their uncertainty through snowfalls with little bias and with an uncertainty comparable to or better than that achievable by other instruments. More importantly, our method inherently senses change over the whole lake surface which can be several square kilometres, or hundreds of million of times larger than the aperture of a pluviometer. This large scale makes our measurements directly comparable to the grid cells of weather and climate models. We find, for example, snowfall biases of up to 100% in operational forecast models AROME-Arctic and COSMO-1. Seasonally-frozen lakes are widely distributed at high latitudes and are particularly common in mountain ranges, hence our new method is particularly well suited to the widespread, autonomous monitoring of snow-water resources in remote areas that are largely unmonitored today. This is potentially transformative in reducing uncertainty in regional precipitation and runoff in seasonally-cold climates.</p>

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 Systematic Patterns of the Inconsistency between Snow Water Equivalent and Accumulated Precipitation as Reported by the Snowpack Telemetry Network

2012 ◽  
Vol 13 (6) ◽  
pp. 1970-1976 ◽  
Author(s):  
Jonathan D. D. Meyer ◽  
Jiming Jin ◽  
Shih-Yu Wang
Keyword(s):  
Sierra Nevada ◽  
Rocky Mountains ◽  
Snow Water Equivalent ◽  
Systematic Bias ◽  
Mountain Ranges ◽  
Wind Speeds ◽  
Drifting Snow ◽  
Snow Water ◽  
Interior West ◽  
One Year

Abstract The authors investigated the accuracy of snow water equivalent (SWE) observations compiled by 748 Snowpack Telemetry stations and attributed the systematic bias introduced to SWE measurements to drifting snow. Often observed, SWE outpaces accumulated precipitation (AP), which can be statistically and physically explained through 1) precipitation undercatchment and/or 2) drifting snow. Forty-four percent of the 748 stations reported at least one year where the maximum SWE was greater than AP, while 16% of the stations showed this inconsistency for at least 20% of the observed years. Regions with a higher likelihood of inconsistency contained drier snow and are exposed to higher winds speeds, both of which are positively correlated to drifting snow potential as well as gauge undercatch. Differentiating between gauge undercatch and potential drifting scenarios, days when SWE increased but AP remained zero were used. These drift days occurred on an average of 13.3 days per year for all stations, with 31% greater wind speeds at 10 m for such days (using reanalysis winds). Findings suggest marked consistency between SWE and AP throughout the Cascade Mountains and lower elevations of the interior west while indicating notable inconsistency between these two variables throughout the higher elevations of the Rocky Mountains, Utah mountain ranges, and the Sierra Nevada.

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