The role of soil hydro-physical properties in the atmospheric water cycle

2021 ◽  
Author(s):  
Eli Dennis ◽  
Hugo Berbery
Keyword(s):  
Heat Flux ◽  
Grain Size ◽  
Physical Properties ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Water Cycle ◽  
The United States ◽  
Atmospheric Water ◽  
Community Land Model ◽  
Soil Grain Size

<p>Soil hydro-physical properties are necessary components in regional climate simulation; yet, the parameter inaccuracies introduce uncertainty in the representation of surface water and energy fluxes leading to differences in land-atmosphere interactions, and precipitation. This study examines the uncertainties in the North American atmospheric water cycle that result from the use of different soil datasets. Two soil datasets are considered: STATSGO from the United States Department of Agriculture and GSDE from Beijing Normal University.  Each dataset's dominant soil category allocations differ significantly at the model's resolution. Large regional discrepancies are found in the assignments of soil category, such that, for instance, in the Midwestern US (hereafter, Midwest), there is a systematic reduction in soil grain size allowing the impacts of the differing assignments to project onto regional scales.</p> <p>The two simulations are conducted from June 1–August 31, 2016–2018 using the Weather Research and Forecasting Model coupled with the Community Land Model version 4. In the Midwest, where soil grain size decreases from STATSGO to GSDE, the GSDE simulation experiences reduced mean latent heat flux (–15 W m<sup>-2</sup>), and increased sensible heat flux (+15 W m<sup>-2</sup>).  The boundary layer thermodynamic structure responds to these changes resulting in differences in mean CAPE and CIN. In the GSDE simulation, there is more energy available for convection (CAPE: +200 J kg<sup>-1</sup>) in the Midwest, but it is more difficult to access that energy (CIN: +75 J kg<sup>-1</sup>). Differences arise in dynamic quantities, as well: the vertically-integrated moisture fluxes suggest a reduction in continental cyclonic rotation co-located with the decrease in latent heat flux and, the vertically-integrated moisture flux convergence is also affected. This combination of thermodynamic and dynamic responses culminate in a reduction of precipitation in the Midwest, which can be related to changes in the placement of soil hydro-physical properties.</p>

The role of soil hydrophysical properties in the atmospheric water cycle

2021 ◽  
Author(s):  
Eli Dennis ◽  
Ernesto Berbery
Keyword(s):  
United States ◽  
Boundary Layer ◽  
Heat Flux ◽  
Grain Size ◽  
Soil Texture ◽  
Water Cycle ◽  
Surface Fluxes ◽  
Atmospheric Water ◽  
Moisture Fluxes ◽  
Soil Grain Size

<p>Soil hydrophysical properties are necessary components in weather and climate simulation; yet, the parameter inaccuracies may introduce considerable uncertainty in the representation of surface water and energy fluxes. The surface fluxes not only affect the terrestrial water and energy budgets, but through land-atmosphere interactions, they can influence the boundary layer, atmospheric stability, moisture transports, and regional precipitation characteristics. This study uses seasonal coupled simulations to examine the uncertainties in the North American atmospheric water cycle that result from the use of different soil datasets. Two soil datasets are considered: State Soil Geographic dataset (STATSGO) from the United States Department of Agriculture and Global Soil Dataset for Earth System Modeling (GSDE) from Beijing Normal University.  Each dataset's dominant soil category allocations differ significantly at the model's resolution (15 km). It is found that large coherent regional discrepancies exist in the assignments of soil category, such that, for instance, in the Midwestern United States (hereafter, Midwest), there is a systematic reduction in soil grain size. Because the soil grain size is regionally biased, it allows for analysis of the impact of soil hydrophysical properties projected onto regional scales.</p><p>The two simulations are conducted from June 1–August 31, 2016–2018 using the Weather Research and Forecasting Model (WRF) coupled with the Community Land Model (CLM) version 4. It is found that in the Midwest, where the soil grain size decreases from STATSGO to GSDE, the GSDE simulation experiences reduced mean latent heat flux (–15 W m<sup>-2</sup>), and increased sensible heat flux (+15 W m<sup>-2</sup>).  The differences in fluxes lead to differences in low-level specific humidity and 2-m temperature. The boundary layer thermodynamic structure responds to these changes resulting in differences in mean CAPE and CIN. In the GSDE simulation, there is more energy available for convection (CAPE: +200 J kg<sup>-1</sup>) in the Midwest, but it is more difficult to access that energy (CIN: +75 J kg<sup>-1</sup>). Furthermore, a reduction in low-level moisture generates a similar reduction in column-integrated moisture (i.e., precipitable water), resulting in conditions that are less conducive for precipitation.</p><p>Interestingly, the soil-texture-related surface fluxes are not confined to thermodynamic influence, but their influence extends to dynamic fields as well. Differences in the vertically-integrated wind field suggest a weakening of the continental low-pressure system (i.e., denoted by a reduction in cyclonic rotation) co-located with the decrease in latent heat flux in the Midwest. The associated vertically-integrated moisture fluxes mirror the dissimilarities in the wind fields. Consequently, the moisture fluxes yield differences in vertically-integrated moisture flux convergence in the same region, as well. This combination of thermodynamic and dynamic variable differences culminates in a reduction of average precipitation in the Midwest, which can be related to changes in the placement of soil hydrophysical properties via soil texture. Through land-atmosphere interactions, it is shown that soil parameters can affect each component of the atmospheric water budget.</p>

scholarly journals Numerical simulation of extreme snow melt observed at the SIGMA-A site, northwest Greenland, during summer 2012

2015 ◽  
Vol 9 (1) ◽  
pp. 495-539
Author(s):  
M. Niwano ◽  
T. Aoki ◽  
S. Matoba ◽  
S. Yamaguchi ◽  
T. Tanikawa ◽  
...  
Keyword(s):  
Heat Flux ◽  
Grain Size ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
In Situ Measurements ◽  
The Arctic ◽  
Snow Surface ◽  
Near Surface ◽  
Surface Snow

Abstract. The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78°03' N, 67°38' W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements, as well as numerical modeling with a one-dimensional, multi-layered, physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. However, comparison of the SMAP-calculated surface snow grain size with in situ measurements during the period when surface hoar with small grain size was observed on-site revealed that it was necessary to input air temperature, relative humidity, and wind speed data from two heights to simulate the latent heat flux into the snow surface and subsequent surface hoar formation. The calculated latent heat flux was always directed away from the surface if data from only one height were input to the SMAP model, even if the value for roughness length of momentum was perturbed between the possible maximum and minimum values in numerical sensitivity tests. This result highlights the need to use two-level atmospheric profiles to obtain realistic latent heat flux. Using such profiles, we calculated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region.

scholarly journals Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

2020 ◽  
Author(s):  
Theresa Boas ◽  
Heye Bogena ◽  
Thomas Grünwald ◽  
Bernard Heinesch ◽  
Dongryeol Ryu ◽  
...  
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Winter Wheat ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Field Data ◽  
Energy Fluxes ◽  
Cash Crop ◽  
Cover Cropping ◽  
Community Land Model

Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding these crop functional types (CFT) to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is a common agricultural management technique in humid and sub-humid regions. We compared simulation results with field data and found that both the parameterization of the CFTs, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes, leaf area index (LAI), net ecosystem exchange (RMSE reduction for latent and sensible heat by up to 57 % and 59 % respectively) and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI curve and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). We anticipate that our model modifications offer opportunities to improve yield predictions, to study the effects of large-scale cover cropping on energy fluxes, soil carbon and nitrogen pools, and soil water storage in future studies with CLM5.

scholarly journals Advances in the Estimation of Global Surface Net Heat Flux Based on Satellite Observation: J-OFURO3 V1.1

2021 ◽  
Vol 8 ◽  
Author(s):  
Hiroyuki Tomita ◽  
Kunio Kutsuwada ◽  
Masahisa Kubota ◽  
Tsutomu Hihara
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Water Cycle ◽  
High Latitudes ◽  
Global Water Cycle ◽  
Net Heat Flux ◽  
Global Surface ◽  
Global Water

The reliability of surface net heat flux data obtained from the latest satellite-based estimation [the third-generation Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations (J-OFURO3, V1.1)] was investigated. Three metrics were utilized: (1) the global long-term (30 years) mean for 1988–2017, (2) the local accuracy evaluation based on comparison with observations recorded at buoys located at 11 global oceanic points with varying climatological characteristics, and (3) the physical consistency with the freshwater balance related to the global water cycle. The globally averaged value of the surface net heat flux of J-OFURO3 was −22.2 W m−2, which is largely imbalanced to heat the ocean surface. This imbalance was due to the turbulent heat flux being smaller than the net downward surface radiation. On the other hand, compared with the local buoy observations, the average difference was −5.8 W m−2, indicating good agreement. These results indicate a paradox of the global surface net heat flux. In relation to the global water cycle, the balance between surface latent heat flux (ocean evaporation) and precipitation was estimated to be almost 0 when river runoff from the land was taken into consideration. The reliability of the estimation of the latent heat flux was reconciled by two different methods. Systematic ocean-heating biases by surface sensible heat flux (SHF) and long wave radiation were identified. The bias in the SHF was globally persistent and especially large in the mid- and high latitudes. The correction of the bias has an impact on improving the global mean net heat flux by +5.5 W m−2. Furthermore, since J-OFURO3 SHF has low data coverage in high-latitudes areas containing sea ice, its impact on global net heat flux was assessed using the latest atmospheric reanalysis product. When including the sea ice region, the globally averaged value of SHF was approximately 1.4 times larger. In addition to the bias correction mentioned above, when assuming that the global ocean average of J3 SHF is 1.4 times larger, the net heat flux value changes to the improved value (−11.3 W m−2), which is approximately half the original value (−22.2 W m−2).

scholarly journals Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

2021 ◽  
Author(s):  
Theresa Boas ◽  
Heye Bogena ◽  
Thomas Grünwald ◽  
Bernard Heinesch ◽  
Dongryeol Ryu ◽  
...  
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Sugar Beet ◽  
Winter Wheat ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Field Data ◽  
Cash Crop ◽  
Cover Cropping ◽  
Community Land Model

<p>The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding the two crop functional types (CFT) for sugar beet and potatoes to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity to reduce erosion and improve soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop specific parameterization, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes (RMSE reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes and seasonal cycle of LAI, and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water and energy fluxes.</p>

scholarly journals ANN-Based Estimation of Low-Latitude Monthly Ocean Latent Heat Flux by Ensemble Satellite and Reanalysis Products

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4773
Author(s):  
Xiaowei Chen ◽  
Yunjun Yao ◽  
Yufu Li ◽  
Yuhu Zhang ◽  
Kun Jia ◽  
...  
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Water Cycle ◽  
Global Climate ◽  
Great Influence ◽  
Global Ocean ◽  
Relative Uncertainty ◽  
Low Latitude ◽  
Essential Variable

Ocean latent heat flux (LHF) is an essential variable for air–sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R2 increasing by 3.7–46.4%. Among them, the LHF estimation using the ANN method increased the R2 of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m−2. Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003–2007. The ocean LHF fusion products estimated from ANN methods were 10–30 W m−2 lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations.

scholarly journals Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

2021 ◽  
Vol 14 (1) ◽  
pp. 573-601
Author(s):  
Theresa Boas ◽  
Heye Bogena ◽  
Thomas Grünwald ◽  
Bernard Heinesch ◽  
Dongryeol Ryu ◽  
...  
Keyword(s):  
Land Use ◽  
Heat Flux ◽  
Sugar Beet ◽  
Winter Wheat ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Field Data ◽  
Cash Crop ◽  
Cover Cropping ◽  
Community Land Model

Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5; (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5; and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.

Mixtures of Gaussians for Uncertainty Description in Bivariate Latent Heat Flux Proxies

2006 ◽  
Vol 7 (3) ◽  
pp. 330-345 ◽  
Author(s):  
R. Wójcik ◽  
Peter A. Troch ◽  
H. Stricker ◽  
P. Torfs ◽  
E. Wood ◽  
...  
Keyword(s):  
Heat Flux ◽  
Probability Density ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Probabilistic Approach ◽  
Bowen Ratio ◽  
Gaussian Mixture ◽  
The United States ◽  
Mixture Density ◽  
Vegetation Fraction

Abstract This paper proposes a new probabilistic approach for describing uncertainty in the ensembles of latent heat flux proxies. The proxies are obtained from hourly Bowen ratio and satellite-derived measurements, respectively, at several locations in the southern Great Plains region in the United States. The novelty of the presented approach is that the proxies are not considered separately, but as bivariate samples from an underlying probability density function. To describe the latter, the use of Gaussian mixture density models—a class of nonparametric, data-adaptive probability density functions—is proposed. In this way any subjective assumptions (e.g., Gaussianity) on the form of bivariate latent heat flux ensembles are avoided. This makes the estimated mixtures potentially useful in nonlinear interpolation and nonlinear probabilistic data assimilation of noisy latent heat flux measurements. The results in this study show that both of these applications are feasible through regionalization of estimated mixture densities. The regionalization scheme investigated here utilizes land cover and vegetation fraction as discriminatory variables.

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