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.

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.

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 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 The Impact of the Temperature Inversion Breakup on the Exchange of Heat and Mass in an Idealized Valley: Sensitivity to the Radiative Forcing

2015 ◽  
Vol 54 (11) ◽  
pp. 2199-2216 ◽  
Author(s):  
Daniel Leukauf ◽  
Alexander Gohm ◽  
Mathias W. Rotach ◽  
Johannes S. Wagner
Keyword(s):  
Radiative Forcing ◽  
Sensible Heat Flux ◽  
Temperature Inversion ◽  
Shortwave Radiation ◽  
Weather Research And Forecasting ◽  
Sensible Heat ◽  
Vertical Fluxes ◽  
Nocturnal Inversion ◽  
Surface Sensible Heat Flux ◽  
The Impact

Abstract The breakup of a nocturnal temperature inversion during daytime is studied in an idealized valley by means of high-resolution numerical simulations. Vertical fluxes of heat and mass are strongly reduced as long as an inversion is present; hence it is important to understand the mechanisms leading to its removal. In this study breakup times are determined as a function of the radiative forcing. Further, the effect of the nocturnal inversion on the vertical exchange of heat and mass is quantified. The Weather Research and Forecasting Model is applied to an idealized quasi-two-dimensional valley. The net shortwave radiation is specified by a sine function with amplitudes between 150 and 850 W m−2 during daytime and at zero during the night. The valley inversion is eroded within 5 h for the strongest forcing. A minimal amplitude of 450 W m−2 is required to reach the breakup, in which case the inversion is removed after 11 h. Depending on the forcing amplitude, between 10% and 57% of the energy provided by the surface sensible heat flux is exported out of the valley during the whole day. The ratio of exported energy to provided energy is approximately 1.6 times as large after the inversion is removed as before. More than 5 times the valley air mass is turned over in 12 h for the strongest forcing, whereas the mass is turned over only 1.3 times for 400 W m−2.

scholarly journals The interaction between urbanization and aerosols during a typical winter haze event in Beijing

2020 ◽  
Vol 20 (16) ◽  
pp. 9855-9870 ◽  
Author(s):  
Miao Yu ◽  
Guiqian Tang ◽  
Yang Yang ◽  
Qingchun Li ◽  
Yonghong Wang ◽  
...  
Keyword(s):  
Heat Flux ◽  
Fine Particulate Matter ◽  
Sensible Heat Flux ◽  
Mixing Layer ◽  
Anthropogenic Heat ◽  
Shortwave Radiation ◽  
Haze Event ◽  
Haze Formation ◽  
Impact Of Urbanization ◽  
The Impact

Abstract. Aerosols cause cooling at the surface by reducing shortwave radiation, while urbanization causes warming by altering the surface albedo and releasing anthropogenic heat. The combined effect of the two phenomena needs to be studied in depth. The effects of urbanization and aerosols were investigated during a typical winter haze event. The event, which occurred in Beijing from 15 to 22 December 2016, was studied via the Rapid-Refresh Multiscale Analysis and Prediction System – Short Term (RMAPS-ST) model. The mechanisms of the impacts of aerosols and urbanization were analyzed and quantified. Aerosols reduced urban-related warming during the daytime by 20 % (from 30 % to 50 %) as concentrations of fine particulate matter (PM2.5) increased from 200 to 400 µg m−3. Conversely, aerosols also enhanced urban-related warming at dawn, and the increment was approximately 28 %, which contributed to haze formation. Urbanization reduced the aerosol-related cooling effect by approximately 54 % during the haze event, and the strength of the impact changed little with increasing aerosol content. The impact of aerosols on urban-related warming was more significant than the impact of urbanization on aerosol-related cooling. Aerosols decreased the urban impact on the mixing-layer height by 148 % and on the sensible heat flux by 156 %. Furthermore, aerosols decreased the latent heat flux; however, this reduction decreased by 48.8 % due to urbanization. The impact of urbanization on the transport of pollutants was more important than that of aerosols. The interaction between urbanization and aerosols may enhance the accumulation of pollution and weigh against diffusion.

scholarly journals Variability of Observed Energy Fluxes during Rain-on-Snow and Clear Sky Snowmelt in a Midlatitude Mountain Environment

2014 ◽  
Vol 15 (3) ◽  
pp. 1220-1237 ◽  
Author(s):  
J. Garvelmann ◽  
S. Pohl ◽  
M. Weiler
Keyword(s):  
Energy Balance ◽  
Longwave Radiation ◽  
Negative Energy ◽  
Spatiotemporal Variability ◽  
Shortwave Radiation ◽  
Positive Energy ◽  
Energy Fluxes ◽  
Area Index ◽  
Clear Sky ◽  
The Individual

Abstract Hourly observations of 65 snow monitoring stations were used to investigate the spatiotemporal variability of the surface energy balance during snowmelt in the Black Forest region of southwestern Germany. The study focuses on two rain-on-snow (ROS) events in December 2012 and a clear sky period at the beginning of March 2013 using the same study locations. ROS and clear sky were chosen since they are completely different snowmelt conditions in terms of energy exchanges and dynamics. The results show that snowmelt was dominated by turbulent exchanges at the open field sites and by both turbulent exchanges and net longwave radiation in the forest during ROS. The energy available for snowmelt can be almost identical at open and forest locations during ROS, and a constant energy flux even during night was directed toward the snowpack. During the clear sky conditions, net shortwave radiation was the dominating term in the open, whereas net shortwave and net longwave radiation were most important in the forest. A diurnal signal with positive energy balance during daylight and negative energy balance in the night was observed, with considerably reduced energy available for snowmelt in the forest. Furthermore, the stratified sampling design revealed the strong influence of the canopy and the topography at the locations on the observed energy fluxes. Elevation, aspect, and leaf area index (LAI) were the most important predictor variables during ROS, whereas aspect and LAI were most influential during the clear sky period. The study highlights the distinct spatial variability of the individual energy balance terms over a relatively small area during the differing snowmelt conditions.

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