scholarly journals Quantifying the impact of synoptic weather types, patterns, and trends on energy fluxes of a marginal snowpack

2019 ◽  
Author(s):  
Andrew Schwartz ◽  
Hamish McGowan ◽  
Alison Theobald ◽  
Nik Callow
Keyword(s):  
Energy Flux ◽  
Southern Oscillation ◽  
Sensible Heat Flux ◽  
Strong Relationship ◽  
Energy Fluxes ◽  
Weather Patterns ◽  
Surface Energy Flux ◽  
Cold Fronts ◽  
Synoptic Weather ◽  
The Impact

Abstract. Synoptic weather patterns and teleconnection relationships across a 39 year climatology are investigated for their impact on energy fluxes driving ablation of a marginal snowpack in the Snowy Mountains, southeast Australia. K-means clustering applied to ECMWF ERA-Interim data identified common synoptic types and patterns that were then associated with in-situ snowpack energy flux measurements. The analysis showed that the largest contribution of energy to the snowpack occurred immediately prior to the passage of cold fronts through increased sensible heat flux as a result of warm air advection (WAA) ahead of the front. Indian Ocean Dipole and Southern Oscillation Index phase combination had a strong relationship with energy flux, with eight of the ten highest annual snowpack energy fluxes occurring during a negative IOD phase and positive SOI phase. Overall, seasonal snowpack energy flux over the 39 year period had a decreasing trend that is likely due to a reduction in the number of precipitation generating cold fronts and associated preceding WAA ahead of precipitation. This research is an important step towards understanding changes in surface energy flux as a result of shifts to the global atmospheric circulation as anthropogenic climate change continues.

scholarly journals Quantifying the impact of synoptic weather types and patterns on energy fluxes of a marginal snowpack

2020 ◽  
Author(s):  
Andrew Jonathan Schwartz ◽  
Hamish Andrew McGowan ◽  
Alison Theobald ◽  
Nik Callow
Keyword(s):  
Energy Flux ◽  
Sensible Heat Flux ◽  
Shortwave Radiation ◽  
Positive Energy ◽  
Energy Fluxes ◽  
Weather Types ◽  
Synoptic Type ◽  
Surface Energy Flux ◽  
Synoptic Weather ◽  
The Impact

Abstract. Synoptic weather patterns are investigated for their impact on energy fluxes driving melt of a marginal snowpack in the Snowy Mountains, southeast Australia. K-means clustering applied to ECMWF ERA-Interim data identified common synoptic types and patterns that were then associated with in-situ snowpack energy flux measurements. The analysis showed that the largest contribution of energy to the snowpack occurred immediately prior to the passage of cold fronts through increased sensible heat flux as a result of warm air advection (WAA) ahead of the front. Shortwave radiation was found to be the dominant control on positive energy fluxes when individual synoptic weather types were examined. As a result, cloud cover related to each synoptic type was shown to be highly influential on the energy fluxes to the snowpack through its reduction of shortwave radiation and reflection/emission of longwave fluxes. This research is an important step towards understanding changes in surface energy flux as a result of shifts to the global atmospheric circulation as anthropogenic climate change continues to impact marginal winter snowpacks.

scholarly journals Quantifying the impact of synoptic weather types and patterns on energy fluxes of a marginal snowpack

2020 ◽  
Vol 14 (8) ◽  
pp. 2755-2774
Author(s):  
Andrew J. Schwartz ◽  
Hamish A. McGowan ◽  
Alison Theobald ◽  
Nik Callow
Keyword(s):  
Energy Flux ◽  
Sensible Heat Flux ◽  
Shortwave Radiation ◽  
Positive Energy ◽  
Energy Fluxes ◽  
Weather Types ◽  
Synoptic Type ◽  
Synoptic Weather ◽  
Australian Alps ◽  
The Impact

Abstract. Synoptic weather patterns are investigated for their impact on energy fluxes driving melt of a marginal snowpack in the Snowy Mountains, southeast Australia. K-means clustering applied to ECMWF ERA-Interim data identified common synoptic types and patterns that were then associated with in situ snowpack energy flux measurements. The analysis showed that the largest contribution of energy to the snowpack occurred immediately prior to the passage of cold fronts through increased sensible heat flux as a result of warm air advection (WAA) ahead of the front. Shortwave radiation was found to be the dominant control on positive energy fluxes when individual synoptic weather types were examined. As a result, cloud cover related to each synoptic type was shown to be highly influential on the energy fluxes to the snowpack through its reduction of shortwave radiation and reflection/emission of longwave fluxes. As single-site energy balance measurements of the snowpack were used for this study, caution should be exercised before applying the results to the broader Australian Alps region. However, this research is an important step towards understanding changes in surface energy flux as a result of shifts to the global atmospheric circulation as anthropogenic climate change continues to impact marginal winter snowpacks.

Intercomparison of Spatially Distributed Models for Predicting Surface Energy Flux Patterns during SMACEX

2005 ◽  
Vol 6 (6) ◽  
pp. 941-953 ◽  
Author(s):  
Wade T. Crow ◽  
Fuqin Li ◽  
William P. Kustas
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Temperature ◽  
Energy Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Energy Fluxes ◽  
Spatially Distributed

Abstract The treatment of aerodynamic surface temperature in soil–vegetation–atmosphere transfer (SVAT) models can be used to classify approaches into two broad categories. The first category contains models utilizing remote sensing (RS) observations of surface radiometric temperature to estimate aerodynamic surface temperature and solve the terrestrial energy balance. The second category contains combined water and energy balance (WEB) approaches that simultaneously solve for surface temperature and energy fluxes based on observations of incoming radiation, precipitation, and micrometeorological variables. To date, few studies have focused on cross comparing model predictions from each category. Land surface and remote sensing datasets collected during the 2002 Soil Moisture–Atmosphere Coupling Experiment (SMACEX) provide an opportunity to evaluate and intercompare spatially distributed surface energy balance models. Intercomparison results presented here focus on the ability of a WEB-SVAT approach [the TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS)] and an RS-SVAT approach [the Two-Source Energy Balance (TSEB) model] to accurately predict patterns of turbulent energy fluxes observed during SMACEX. During the experiment, TOPLATS and TSEB latent heat flux predictions match flux tower observations with root-mean-square (rms) accuracies of 67 and 63 W m−2, respectively. TSEB predictions of sensible heat flux are significantly more accurate with an rms accuracy of 22 versus 46 W m−2 for TOPLATS. The intercomparison of flux predictions from each model suggests that modeling errors for each approach are sufficiently independent and that opportunities exist for improving the performance of both models via data assimilation and model calibration techniques that integrate RS- and WEB-SVAT energy flux predictions.

scholarly journals A Climatology of Heavy Rain and Major Flood Events in Victoria 1876-2019 and the Effect of the 1976 Climate Shift

2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Jeff Callaghan
Keyword(s):  
Heavy Rainfall ◽  
Southern Oscillation ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Rainfall Events ◽  
Climate Shift ◽  
Weather Patterns ◽  
Synoptic Weather ◽  
Extensive Search ◽  
Heavy Rainfall Events

An extensive search has been carried out to find all major flood and very heavy rainfall events in Victoria since 1876 when Southern Oscillation (SOI) data became available. The synoptic weather patterns were analysed and of the 319 events studied,121 events were found to be East Coast Lows (ECLs) and 82 were other types of low-pressure systems. Tropical influences also played a large role with 105 events being associated with tropical air advecting down to Victoria into weather systems. Examples are presented of all the major synoptic patterns identified. The SOI was found to be an important climate driver with positive SOIs being associated with many events over the 144 years studied. The 1976 Climate Shift and its influence on significant Victorian rainfall events is studied and negative SOI monthly values were shown to dominate following the Shift.However,one of the most active periods in 144 years of Victorian heavy rain occurred after the shift with a sustained period of positive SOI events from 2007 to 2014. Therefore, it is critical for forecasting future Victorian heavy rainfall is to understand if sequences of these positive SOI events continue like those preceding the Shift. Possible relationships between the Shift and Global Temperature rises are also explored. Upper wind data available from some of the heaviest rainfall events showed the presence of anticyclonic turning of the winds between 850hPa and 500hPa levels which has been found to be linked with extreme rainfall around the Globe. 

scholarly journals Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China

2021 ◽  
Vol 9 ◽  
Author(s):  
Min Guo ◽  
Minxuan Zhang ◽  
Hong Wang ◽  
Linlin Wang ◽  
Shuhong Liu ◽  
...  
Keyword(s):  
Diurnal Temperature Range ◽  
Eastern China ◽  
Urban Agglomeration ◽  
Anthropogenic Heat ◽  
Diurnal Temperature ◽  
Temperature Range ◽  
Weather Patterns ◽  
Synoptic Weather ◽  
Anthropogenic Heat Flux ◽  
The Impact

Previous studies on the impact of urbanization on the diurnal temperature range (DTR) have mainly concentrated on the intra-seasonal and interannual–decadal scales, while relatively fewer studies have considered synoptic scales. In particular, the modulation of DTR by different synoptic weather patterns (SWPs) is not yet fully understood. Taking the urban agglomeration of the Yangtze River Delta region (YRDUA) in eastern China as an example, and by using random forest machine learning and objective weather classification methods, this paper analyzes the characteristics of DTR and its urban–rural differences (DTRU–R) in summer from 2013 to 2016, based on surface meteorological observations, satellite remote sensing, and reanalysis data. Ultimately, the influences of urbanization-related factors and different large-scale SWPs on DTR and DTRU–R are explored. Results show that YRDUA is controlled by four SWPs in the 850-hPa geopotential height field in summer, and the DTRs in three sub-regions are significantly different under the four SWPs, indicating that they play a role in regulating the DTR in YRDUA. In terms of the average DTR for each SWP, the southern sub-region of the YRDUA is the highest, followed by the northern sub-region, and the middle sub-region is the lowest, which is most significantly affected by high-level urbanization and high anthropogenic heat emission. The DTRU–R is negative and differs under the four different SWPs with variation in sunshine and rainfall. The difference in anthropogenic heat flux between urban and rural areas is one of the potentially important urbanization-related drivers for DTRU–R. Our findings help towards furthering our understanding of the response of DTR in urban agglomerations to different SWPs via the modulation of local meteorological conditions.

scholarly journals A dual-pass data assimilation scheme for estimating surface energy fluxes with FY3A-VIRR land surface temperature

2013 ◽  
Vol 10 (3) ◽  
pp. 3927-3972
Author(s):  
T. R. Xu ◽  
S. M. Liu ◽  
Z. W. Xu ◽  
S. Liang ◽  
L. Xu
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Surface Energy ◽  
Energy Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Surface Energy Flux ◽  
Data Assimilation Scheme ◽  
Dual Pass ◽  
Assimilation Scheme

Abstract. A dual-pass data assimilation scheme is developed to improve predictions of surface energy fluxes. Pass 1 of the dual-pass data assimilation scheme optimizes model vegetation parameters at the weekly temporal scale and pass 2 optimizes soil moisture at the daily temporal scale. Based on the ensemble Kalman filter (EnKF), land surface temperature (LST) data derived from the new generation of Chinese meteorology satellite (FY3A-VIRR) is assimilated into common land model (CoLM) for the first time. Four sites are selected for the data assimilation experiments, namely Arou, BJ, Guantao, and Miyun that include alpine meadow, grass, crop, and orchard land cover types. The results are compared with data set generated by a multi-scale surface energy flux observation system that includes an automatic weather station (AWS), an eddy covariance (EC) and a large aperture scintillometer (LAS). Results indicate that the CoLM can simulate the diurnal variations of surface energy flux, but usually overestimates sensible heat flux and underestimates latent heat flux and evaporation fraction (EF). With FY3A-VIRR LST data, the dual-pass data assimilation scheme can reduce model uncertainties and improve predictions of surface energy flux. Compared with EC measurements, the average model biases (BIAS) values change from 37.8 to 7.7 W m−2 and from −27.6 to 18.8 W m−2; the root mean square error (RMSE) values drop from 74.7 to 39.1 W m−2 and from 95.1 to 62.7 W m−2 for sensible and latent heat fluxes respectively. For evaporation fraction (EF), the average BIAS values change from −0.29 to 0.0 and the average RMSE values drop from 0.38 to 0.12. To compare the results with LAS-measured sensible heat flux, the source areas are calculated using a footprint model and overlaid with FY3A pixels. The four sites averaged BIAS values drop from 63.7 to −8.5 W m−2 and RMSE values drop from 118.2 to 69.8 W m−2. Ultimately, the error sources in surface energy flux predictions are investigated, and the results show that both soil moisture and vegetation parameters caused the big model biases in surface energy flux predictions. With Pass 1 and Pass 2, the dual-pass data assimilation scheme can cut down the surface energy flux prediction biases (BIAS) to nearly zero.

scholarly journals Interannual Variability and Trends in Sea Surface Temperature, Lower and Middle Atmosphere Temperature at Different Latitudes for 1980–2019

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 454
Author(s):  
Andrew R. Jakovlev ◽  
Sergei P. Smyshlyaev ◽  
Vener Y. Galin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Lower Stratosphere ◽  
Middle Atmosphere ◽  
Southern Oscillation ◽  
Lower Troposphere ◽  
Reanalysis Data ◽  
Sea Surface ◽  
The Arctic ◽  
The Impact

The influence of sea-surface temperature (SST) on the lower troposphere and lower stratosphere temperature in the tropical, middle, and polar latitudes is studied for 1980–2019 based on the MERRA2, ERA5, and Met Office reanalysis data, and numerical modeling with a chemistry-climate model (CCM) of the lower and middle atmosphere. The variability of SST is analyzed according to Met Office and ERA5 data, while the variability of atmospheric temperature is investigated according to MERRA2 and ERA5 data. Analysis of sea surface temperature trends based on reanalysis data revealed that a significant positive SST trend of about 0.1 degrees per decade is observed over the globe. In the middle latitudes of the Northern Hemisphere, the trend (about 0.2 degrees per decade) is 2 times higher than the global average, and 5 times higher than in the Southern Hemisphere (about 0.04 degrees per decade). At polar latitudes, opposite SST trends are observed in the Arctic (positive) and Antarctic (negative). The impact of the El Niño Southern Oscillation phenomenon on the temperature of the lower and middle atmosphere in the middle and polar latitudes of the Northern and Southern Hemispheres is discussed. To assess the relative influence of SST, CO2, and other greenhouse gases’ variability on the temperature of the lower troposphere and lower stratosphere, numerical calculations with a CCM were performed for several scenarios of accounting for the SST and carbon dioxide variability. The results of numerical experiments with a CCM demonstrated that the influence of SST prevails in the troposphere, while for the stratosphere, an increase in the CO2 content plays the most important role.

Marketing China to U.S. travelers through electronic word-of-mouth and destination image: Taking Beijing as an example

2021 ◽  
pp. 135676672098786
Author(s):  
Li Ran ◽  
Luo Zhenpeng ◽  
Anil Bilgihan ◽  
Fevzi Okumus
Keyword(s):  
Quantitative Research ◽  
Behavioral Control ◽  
Strong Relationship ◽  
Tourism Industry ◽  
Destination Image ◽  
Perceived Behavioral Control ◽  
Online Data ◽  
Mediating Role ◽  
Study Results ◽  
The Impact

The tourism industry in China has grown significantly over the last two decades. Most of the growth, however, is fueled by domestic tourism. As one of the biggest tourism markets in the world, U.S. tourists might be reluctant to travel to China due to reasons such as unfamiliarity, cultural differences, visa requirements, and long flights. Building on the Theory of Planned Behavior (TPB) with relevant constructs, this research proposes that building a strong destination image via eWOM may influence the attitude and intention of U.S. travelers to visit Beijing. More specifically, the current research aims to examine the impact of eWOM and destination image on travel intention of tourists. This study used a quantitative research method and online data collection was conducted through Qualtrics. A total of 413 valid responses from U.S. residents were collected. The statistical software SPSS 21.0 and Mplus 7.0 were used to analyze the data. Study results show a strong relationship between eWOM utilitarian function and eWOM credibility, and eWOM credibility has a significant influence on destination image. Although there was no direct impact of destination image on tourists’ future travel intention, destination image plays a mediating role between eWOM credibility and perceived behavioral control (and tourists’ attitudes as well). Finally, perceived behavioral control and tourists’ attitudes mediate the impact of destination image on travel intention.

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