The Local Biophysical Response to Land-Use Change in HadGEM2-ES

2019 ◽  
Vol 32 (22) ◽  
pp. 7611-7627 ◽  
Author(s):  
E. Robertson

Abstract The biophysical response to a local change in land use is calculated using the HadGEM2-ES Earth system model. The biophysical temperature response is found to be a small residual of three large opposing flux responses: available energy, sensible heat, and latent heat. Deforestation reduces available energy, which is balanced by a reduction in heat lost via turbulent fluxes. However, the changes in turbulent heat fluxes are not simply a response to the reduction in available energy; rather, they are a direct response to land-use change, caused by reduced roughness length and, in the tropics, an increase in the Bowen ratio. Evaluation against satellite-derived observational datasets shows that in response to deforestation, the model has too much albedo-driven cooling and too little latent-heat-driven warming, leading to a large cooling bias.

2021 ◽  
Vol 22 (10) ◽  
pp. 2547-2564
Author(s):  
Georg Lackner ◽  
Daniel F. Nadeau ◽  
Florent Domine ◽  
Annie-Claude Parent ◽  
Gonzalo Leonardini ◽  
...  

AbstractRising temperatures in the southern Arctic region are leading to shrub expansion and permafrost degradation. The objective of this study is to analyze the surface energy budget (SEB) of a subarctic shrub tundra site that is subject to these changes, on the east coast of Hudson Bay in eastern Canada. We focus on the turbulent heat fluxes, as they have been poorly quantified in this region. This study is based on data collected by a flux tower using the eddy covariance approach and focused on snow-free periods. Furthermore, we compare our results with those from six Fluxnet sites in the Arctic region and analyze the performance of two land surface models, SVS and ISBA, in simulating soil moisture and turbulent heat fluxes. We found that 23% of the net radiation was converted into latent heat flux at our site, 35% was used for sensible heat flux, and about 15% for ground heat flux. These results were surprising considering our site was by far the wettest site among those studied, and most of the net radiation at the other Arctic sites was consumed by the latent heat flux. We attribute this behavior to the high hydraulic conductivity of the soil (littoral and intertidal sediments), typical of what is found in the coastal regions of the eastern Canadian Arctic. Land surface models overestimated the surface water content of those soils but were able to accurately simulate the turbulent heat flux, particularly the sensible heat flux and, to a lesser extent, the latent heat flux.


2020 ◽  
Vol 12 (7) ◽  
pp. 1065
Author(s):  
Elahe Tajfar ◽  
Sayed M. Bateni ◽  
Essam Heggy ◽  
Tongren Xu

This study investigated the feasibility of partitioning the available energy between sensible (H) and latent (LE) heat fluxes via variational assimilation of reference-level air temperature and specific humidity. For this purpose, sequences of reference-level air temperature and specific humidity were assimilated into an atmospheric boundary layer model (ABL) within a variational data assimilation (VDA) framework to estimate H and LE. The VDA approach was tested at six sites (namely, Arou, Audubon, Bondville, Brookings, Desert, and Willow Creek) with contrasting climatic and vegetative conditions. The unknowns of the VDA system were the neutral bulk heat transfer coefficient (CHN) and evaporative fraction (EF). EF estimates were found to agree well with observations in terms of magnitude and day-to-day fluctuations in wet/densely vegetated sites but degraded in dry/sparsely vegetated sites. Similarly, in wet/densely vegetated sites, the variations in the CHN estimates were found to be consistent with those of the leaf area index (LAI) while this consistency deteriorated in dry/sparely vegetated sites. The root mean square errors (RMSEs) of daily H and LE estimates at the Arou site (wet) were 25.43 (Wm−2) and 55.81 (Wm−2), which are respectively 57.6% and 45.4% smaller than those of 60.00 (Wm−2) and 102.21 (Wm−2) at the Desert site (dry). Overall, the results show that the VDA system performs well at wet/densely vegetated sites (e.g., Arou and Willow Creek), but its performance degrades at dry/slightly vegetated sites (e.g., Desert and Audubon). These outcomes show that the sequences of reference-level air temperature and specific humidity have more information on the partitioning of available energy between the sensible and latent heat fluxes in wet/densely vegetated sites than dry/slightly vegetated sites.


2014 ◽  
Vol 11 (24) ◽  
pp. 7369-7382 ◽  
Author(s):  
K. Mallick ◽  
A. Jarvis ◽  
G. Wohlfahrt ◽  
G. Kiely ◽  
T. Hirano ◽  
...  

Abstract. This paper introduces a relatively simple method for recovering global fields of latent heat flux. The method focuses on specifying Bowen ratio estimates through exploiting air temperature and vapour pressure measurements obtained from infrared soundings of the AIRS (Atmospheric Infrared Sounder) sensor onboard NASA's Aqua platform. Through combining these Bowen ratio retrievals with satellite surface net available energy data, we have specified estimates of global noontime surface latent heat flux at the 1°×1° scale. These estimates were provisionally evaluated against data from 30 terrestrial tower flux sites covering a broad spectrum of biomes. Taking monthly average 13:30 data for 2003, this revealed promising agreement between the satellite and tower measurements of latent heat flux, with a pooled root-mean-square deviation of 79 W m−2, and no significant bias. However, this success partly arose as a product of the underspecification of the AIRS Bowen ratio compensating for the underspecification of the AIRS net available energy, suggesting further refinement of the approach is required. The error analysis suggested that the landscape level variability in enhanced vegetation index (EVI) and land surface temperature contributed significantly to the statistical metric of the predicted latent heat fluxes.


2021 ◽  
Author(s):  
Adrien Pierre ◽  
Daniel Nadeau ◽  
Pierre-Érik Isabelle ◽  
Antoine Thiboult ◽  
Alain Rousseau ◽  
...  

<p>Observations of sensible and latent heat fluxes over inland water bodies are unfortunately scarce and, yet, critical to the development of adequate lake parameterization for numerical weather forecast and climate models. When available, they usually consist of eddy covariance (EC) or lysimeter measurements, both representative of a relatively small footprint area, typically of a few hectares in the case of the EC approach. Over the past decades, we have seen the emergence of bichromatic scintillometry (SC), which allows for a ‘regional’ (~km<sup>2</sup>) estimation of turbulent heat fluxes. In brief, two beams travelling from a set of transmitters to a set of receivers scintillate in the turbulent air above the surface of interest and enable, using the Monin-Obukhov Similarity Theory, the computation of sensible and latent heat fluxes at the land-atmosphere interface. While a handful of studies have looked at the performance of this approach over land surfaces, very few have assessed it over water bodies. This study presents an evaluation of scintillometry-derived turbulent heat fluxes over an 85-km<sup>2</sup> boreal hydropower reservoir of eastern Canada (50.69°N, 63.24°W) with respect to those obtained with EC measurements collected on a nearby floating platform. The scintillometer beam path travelled for 1.7 km over a surface of the reservoir that reached depths of ~100m, from 14 August to 9 October 2019. Results indicate positive, day-and-night, latent heat fluxes throughout the whole period; highlighting that the reservoir steadily released heat over the second half of the open water period, from mid-august until freeze-up. Sensible heat fluxes peaked at night due to the near-surface air temperature vertical gradient reaching its daily maximum. For sensible heat fluxes, the SC approach corroborates well with the EC approach, while for latent heat fluxes, the agreement between EC and SC decreases. This suggests that the larger footprint of the SC system might be affected by heterogeneous surface flux characteristics in the reservoir, which encapsulates the need for large-scale measurements. Grouping results by atmospheric stability regimes does not improve comparison results. These results provide an opportunity to validate an innovative approach for measuring turbulent fluxes at a regional scale and, hence, improving our understanding of turbulent fluxes over large reservoirs and lakes.</p>


1999 ◽  
Vol 11 (1) ◽  
pp. 93-99 ◽  
Author(s):  
S. Argentini ◽  
G. Mastrantonio ◽  
A. Viola

Simultaneous acoustic Doppler sodar and tethersonde measurements were used to study some of the characteristics of the unstable boundary layer at Dumont d'Urville, Adélie Land, East Antarctica during the summer 1993–94. A description of the convective boundary layer and its behaviour in connection with the wind regime is given along with the frequency distribution of free convection episodes. The surface heat flux has been evaluated using the vertical velocity variance derived from sodar measurements. The turbulent exchange coefficients, estimated by coupling sodar and tethered balloon measurements, are in strong agreement with those present in literature for the Antarctic regions.


2021 ◽  
Author(s):  
Andrew Bennett ◽  
Bart Nijssen

<p>Machine learning (ML), and particularly deep learning (DL), for geophysical research has shown dramatic successes in recent years. However, these models are primarily geared towards better predictive capabilities, and are generally treated as black box models, limiting researchers’ ability to interpret and understand how these predictions are made. As these models are incorporated into larger models and pushed to be used in more areas it will be important to build methods that allow us to reason about how these models operate. This will have implications for scientific discovery that will ensure that these models are robust and reliable for their respective applications. Recent work in explainable artificial intelligence (XAI) has been used to interpret and explain the behavior of machine learned models.</p><p>Here, we apply new tools from the field of XAI to provide physical interpretations of a system that couples a deep-learning based parameterization for turbulent heat fluxes to a process based hydrologic model. To develop this coupling we have trained a neural network to predict turbulent heat fluxes using FluxNet data from a large number of hydroclimatically diverse sites. This neural network is coupled to the SUMMA hydrologic model, taking imodel derived states as additional inputs to improve predictions. We have shown that this coupled system provides highly accurate simulations of turbulent heat fluxes at 30 minute timesteps, accurately predicts the long-term observed water balance, and reproduces other signatures such as the phase lag with shortwave radiation. Because of these features, it seems this coupled system is learning physically accurate relationships between inputs and outputs. </p><p>We probe the relative importance of which input features are used to make predictions during wet and dry conditions to better understand what the neural network has learned. Further, we conduct controlled experiments to understand how the neural networks are able to learn to regionalize between different hydroclimates. By understanding how these neural networks make their predictions as well as how they learn to make predictions we can gain scientific insights and use them to further improve our models of the Earth system.</p>


2018 ◽  
Vol 19 (10) ◽  
pp. 1599-1616 ◽  
Author(s):  
Jonathan P. Conway ◽  
John W. Pomeroy ◽  
Warren D. Helgason ◽  
Nicholas J. Kinar

Abstract Forest clearings are common features of evergreen forests and produce snowpack accumulation and melt differing from that in adjacent forests and open terrain. This study has investigated the challenges in specifying the turbulent fluxes of sensible and latent heat to snowpacks in forest clearings. The snowpack in two forest clearings in the Canadian Rockies was simulated using a one-dimensional (1D) snowpack model. A trade-off was found between optimizing against measured snow surface temperature or snowmelt when choosing how to specify the turbulent fluxes. Schemes using the Monin–Obukhov similarity theory tended to produce negatively biased surface temperature, while schemes that enhanced turbulent fluxes, to reduce the surface temperature bias, resulted in too much melt. Uncertainty estimates from Monte Carlo experiments showed that no realistic parameter set could successfully remove biases in both surface temperature and melt. A simple scheme that excludes atmospheric stability correction was required to successfully simulate surface temperature under low wind speed conditions. Nonturbulent advective fluxes and/or nonlocal sources of turbulence are thought to account for the maintenance of heat exchange in low-wind conditions. The simulation of snowmelt was improved by allowing enhanced latent heat fluxes during low-wind conditions. Caution is warranted when snowpack models are optimized on surface temperature, as model tuning may compensate for deficiencies in conceptual and numerical models of radiative, conductive, and turbulent heat exchange at the snow surface and within the snowpack. Such model tuning could have large impacts on the melt rate and timing of the snow-free transition in simulations of forest clearings within hydrological and meteorological models.


2015 ◽  
Vol 42 (6) ◽  
pp. 1856-1862 ◽  
Author(s):  
A. B. Villas Bôas ◽  
O. T. Sato ◽  
A. Chaigneau ◽  
G. P. Castelão

2005 ◽  
Vol 36 (4-5) ◽  
pp. 381-396 ◽  
Author(s):  
A. Rutgersson ◽  
A. Omstedt ◽  
Y. Chen

In this paper, which reports on part of the BALTEX project, various components of the heat balance over the Baltic Sea are calculated using a number of gridded meteorological databases. It is the heat exchange between the Baltic Sea surface and the atmosphere that is of interest. The databases have different origins, comprising synoptic data, data re-analysed with a 3D assimilation system, an ocean model forced with gridded synoptic data, ship data and satellite data. We compared the databases and found that the greatest variation between them is in the long- and short-wave radiation values. However, considerable upward long-wave radiation is followed by considerable downward short-wave radiation, so the total radiation component is partly compensated for in the total budget. The variation in the total heat transport in the databases therefore appears smaller (1.5±3 W m−2) as the average and one standard deviation. The turbulent heat fluxes estimated from satellite data have very low values; this can largely be explained by the method of calculating air temperature, which also produces an unrealistic stratification over the Baltic Sea. The ERA40 data was compared with measured values: there, we found a certain land influence even in the centre of the Baltic proper. The indicated turbulent heat fluxes were too large, mainly in the fall and winter, and the sensible heat flux was too large in a downward direction in spring and summer.


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