scholarly journals Calculating the turbulent fluxes in the atmospheric surface layer with neural networks

2018 ◽  
Author(s):  
Lukas Hubert Leufen ◽  
Gerd Schädler
Keyword(s):  
Land Surface ◽  
Field Experiments ◽  
Climate Models ◽  
Similarity Theory ◽  
Turbulent Fluxes ◽  
Global Climate Models ◽  
Surface Model ◽  
Time Step ◽  
One Dimensional ◽  
Stability Functions

Abstract. The turbulent fluxes of momentum, heat and water vapour link the Earth's surface with the atmosphere. The correct modelling of the flux interactions between these two systems with very different time scales is therefore vital for climate (resp. Earth system) models. Conventionally, these fluxes are modelled using Monin–Obukhov similarity theory (MOST) with stability functions derived from a small number of field experiments; this results in a range of formulations of these functions and thus also in the flux calculations; furthermore, the underlying equations are non-linear and have to be solved iteratively at each time step of the model. For these reasons, we tried here a different approach, namely using an artificial neural network (ANN) to calculate the fluxes resp. the scaling quantities u* and θ*, thus avoiding explicit formulas for the stability functions. The network was trained and validated with multi-year datasets from seven grassland, forest and wetland sites worldwide using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithm and six-fold cross validation. Extensive sensitivity tests showed that an ANN with six input variables and one hidden layer gave results comparable to (and in some cases even slightly better than) the standard method. Similar satisfying results were obtained when the ANN routine was implemented in a one-dimensional stand alone land surface model (LSM), opening the way to implementation in three-dimensional climate models. In case of the one-dimensional LSM, no CPU time was saved when using the ANN version, since the small time step of the standard version required only one iteration in most cases. This could be different in models with longer time steps, e.g. global climate models.

scholarly journals Calculating the turbulent fluxes in the atmospheric surface layer with neural networks

2019 ◽  
Vol 12 (5) ◽  
pp. 2033-2047
Author(s):  
Lukas Hubert Leufen ◽  
Gerd Schädler
Keyword(s):  
Land Surface ◽  
Field Experiments ◽  
Climate Models ◽  
Similarity Theory ◽  
Turbulent Fluxes ◽  
Global Climate Models ◽  
Surface Model ◽  
Time Step ◽  
One Dimensional ◽  
Better Than

Abstract. The turbulent fluxes of momentum, heat and water vapour link the Earth's surface with the atmosphere. Therefore, the correct modelling of the flux interactions between these two systems with very different timescales is vital for climate and weather forecast models. Conventionally, these fluxes are modelled using Monin–Obukhov similarity theory (MOST) with stability functions derived from a small number of field experiments. This results in a range of formulations of these functions and thus also in differences in the flux calculations; furthermore, the underlying equations are non-linear and have to be solved iteratively at each time step of the model. In this study, we tried a different and more flexible approach, namely using an artificial neural network (ANN) to calculate the scaling quantities u* and θ* (used to parameterise the fluxes), thereby avoiding function fitting and iteration. The network was trained and validated with multi-year data sets from seven grassland, forest and wetland sites worldwide using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithm and six-fold cross validation. Extensive sensitivity tests showed that an ANN with six input variables and one hidden layer gave results comparable to (and in some cases even slightly better than) the standard method; moreover, this ANN performed considerably better than a multivariate linear regression model. Similar satisfying results were obtained when the ANN routine was implemented in a one-dimensional stand-alone land surface model (LSM), paving the way for implementation in three-dimensional climate models. In the case of the one-dimensional LSM, no CPU time was saved when using the ANN version, as the small time step of the standard version required only one iteration in most cases. This may be different in models with longer time steps, e.g. global climate models.

scholarly journals Exploring how groundwater buffers the influence of heatwaves on vegetation function during multi-year droughts

2021 ◽  
Vol 12 (3) ◽  
pp. 919-938
Author(s):  
Mengyuan Mu ◽  
Martin G. De Kauwe ◽  
Anna M. Ukkola ◽  
Andy J. Pitman ◽  
Weidong Guo ◽  
...  
Keyword(s):  
Land Surface ◽  
Forest Canopy ◽  
Climate Models ◽  
Root Zone ◽  
Stomatal Closure ◽  
Capillary Rise ◽  
Vapour Pressure Deficit ◽  
Global Climate Models ◽  
Surface Model ◽  
The Impact

Abstract. The co-occurrence of droughts and heatwaves can have significant impacts on many socioeconomic and environmental systems. Groundwater has the potential to moderate the impact of droughts and heatwaves by moistening the soil and enabling vegetation to maintain higher evaporation, thereby cooling the canopy. We use the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model, coupled to a groundwater scheme, to examine how groundwater influences ecosystems under conditions of co-occurring droughts and heatwaves. We focus specifically on south-east Australia for the period 2000–2019, when two significant droughts and multiple extreme heatwave events occurred. We found groundwater plays an important role in helping vegetation maintain transpiration, particularly in the first 1–2 years of a multi-year drought. Groundwater impedes gravity-driven drainage and moistens the root zone via capillary rise. These mechanisms reduced forest canopy temperatures by up to 5 ∘C during individual heatwaves, particularly where the water table depth is shallow. The role of groundwater diminishes as the drought lengthens beyond 2 years and soil water reserves are depleted. Further, the lack of deep roots or stomatal closure caused by high vapour pressure deficit or high temperatures can reduce the additional transpiration induced by groundwater. The capacity of groundwater to moderate both water and heat stress on ecosystems during simultaneous droughts and heatwaves is not represented in most global climate models, suggesting that model projections may overestimate the risk of these events in the future.

scholarly journals A Modeling and Observational Framework for Diagnosing Local Land–Atmosphere Coupling on Diurnal Time Scales

2009 ◽  
Vol 10 (3) ◽  
pp. 577-599 ◽  
Author(s):  
Joseph A. Santanello ◽  
Christa D. Peters-Lidard ◽  
Sujay V. Kumar ◽  
Charles Alonge ◽  
Wei-Kuo Tao
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Field Experiments ◽  
Climate Models ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Critical Role ◽  
Land Surface Model ◽  
Surface Model ◽  
Test Bed

Abstract Land–atmosphere interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture states. The degree of coupling between the land surface and PBL in numerical weather prediction and climate models remains largely unexplored and undiagnosed because of the complex interactions and feedbacks present across a range of scales. Furthermore, uncoupled systems or experiments [e.g., the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS)] may lead to inaccurate water and energy cycle process understanding by neglecting feedback processes such as PBL-top entrainment. In this study, a framework for diagnosing local land–atmosphere coupling is presented using a coupled mesoscale model with a suite of PBL and land surface model (LSM) options along with observations during field experiments in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting Model (WRF) has been coupled to the Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. Within this framework, the coupling established by each pairing of the available PBL schemes in WRF with the LSMs in LIS is evaluated in terms of the diurnal temperature and humidity evolution in the mixed layer. The coevolution of these variables and the convective PBL are sensitive to and, in fact, integrative of the dominant processes that govern the PBL budget, which are synthesized through the use of mixing diagrams. Results show how the sensitivity of land–atmosphere interactions to the specific choice of PBL scheme and LSM varies across surface moisture regimes and can be quantified and evaluated against observations. As such, this methodology provides a potential pathway to study factors controlling local land–atmosphere coupling (LoCo) using the LIS–WRF system, which will serve as a test bed for future experiments to evaluate coupling diagnostics within the community.

scholarly journals Model improvement and future projection of permafrost processes in a global land surface model

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tokuta Yokohata ◽  
Kazuyuki Saito ◽  
Kumiko Takata ◽  
Tomoko Nitta ◽  
Yusuke Satoh ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Global Climate ◽  
Land Surface Model ◽  
Global Climate Models ◽  
Soil Freezing ◽  
Surface Model ◽  
Southern Limit ◽  
Future Projections ◽  
Global Land

AbstractTo date, the treatment of permafrost in global climate models has been simplified due to the prevailing uncertainties in the processes involving frozen ground. In this study, we improved the modeling of permafrost processes in a state-of-the-art climate model by taking into account some of the relevant physical properties of soil such as changes in the thermophysical properties due to soil freezing. As a result, the improved version of the global land surface model was able to reproduce a more realistic permafrost distribution at the southern limit of the permafrost area by increasing the freezing of soil moisture in winter. The improved modeling of permafrost processes also had a significant effect on future projections. Using the conventional formulation, the predicted cumulative reduction of the permafrost area by year 2100 was approximately 60% (40–80% range of uncertainty from a multi-model ensemble) in the RCP8.5 scenario, while with the improved formulation, the reduction was approximately 35% (20–50%). Our results indicate that the improved treatment of permafrost processes in global climate models is important to ensuring more reliable future projections.

Examining the vulnerability of Australian eucalypts to future drought-induced tree mortality

2021 ◽  
Author(s):  
Martin De Kauwe ◽  
Manon Sabot ◽  
Andrew Pitman ◽  
Sami Rifai ◽  
Patrick Meir ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Tree Mortality ◽  
Climate Models ◽  
Global Climate Models ◽  
Annual Rainfall ◽  
Surface Model ◽  
Eucalyptus Species ◽  
Drought Risk ◽  
The Impact

<p>Australia is the driest inhabited continent. Annual rainfall is low and is accompanied by marked inter-annual variability, leading to multi-year droughts. Climate change is expected to alter the frequency, magnitude, and intensity of future droughts, with potentially major environmental and socio-economic consequences for Australia. However, Australian vegetation is well adapted to extended dry periods, thus, the likelihood of drought-induced mortality in the future depends both on the severity of future drought events and inherent vegetation resilience. Here, we used the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model, coupled with a stomatal optimisation scheme, to examine the projected impact of future drought for 24 Eucalyptus species. We forced CABLE with future climate from four global climate models (MIROC, ECHAM, CCCMA, and CSIRO) dynamically downscaled by three regional climate models. We separated the impact of climate change (e.g. increasing VPD, precipitation variability) from rising CO<sub>2</sub> (increasing water use-efficiency) to provide the first assessment of future drought risk to Australian trees.</p>

scholarly journals Predicting the response of the Amazon rainforest to persistent drought conditions under current and future climates: a major challenge for global land surface models

2014 ◽  
Vol 7 (6) ◽  
pp. 2933-2950 ◽  
Author(s):  
E. Joetzjer ◽  
C. Delire ◽  
H. Douville ◽  
P. Ciais ◽  
B. Decharme ◽  
...  
Keyword(s):  
Land Surface ◽  
Tree Mortality ◽  
Climate Models ◽  
Global Climate ◽  
Land Surface Model ◽  
Co2 Concentration ◽  
Accurate Estimate ◽  
Global Climate Models ◽  
Surface Model ◽  
Vegetation Response

Abstract. While a majority of global climate models project drier and longer dry seasons over the Amazon under higher CO2 levels, large uncertainties surround the response of vegetation to persistent droughts in both present-day and future climates. We propose a detailed evaluation of the ability of the ISBACC (Interaction Soil–Biosphere–Atmosphere Carbon Cycle) land surface model to capture drought effects on both water and carbon budgets, comparing fluxes and stocks at two recent throughfall exclusion (TFE) experiments performed in the Amazon. We also explore the model sensitivity to different water stress functions (WSFs) and to an idealized increase in CO2 concentration and/or temperature. In spite of a reasonable soil moisture simulation, ISBACC struggles to correctly simulate the vegetation response to TFE whose amplitude and timing is highly sensitive to the WSF. Under higher CO2 concentrations, the increased water-use efficiency (WUE) mitigates the sensitivity of ISBACC to drought. While one of the proposed WSF formulations improves the response of most ISBACC fluxes, except respiration, a parameterization of drought-induced tree mortality is missing for an accurate estimate of the vegetation response. Also, a better mechanistic understanding of the forest responses to drought under a warmer climate and higher CO2 concentration is clearly needed.

scholarly journals Predicting the response of the Amazon rainforest to persistent drought conditions under current and future climates: a major challenge for global land surface models

2014 ◽  
Vol 7 (4) ◽  
pp. 5295-5340 ◽  
Author(s):  
E. Joetzjer ◽  
C. Delire ◽  
H. Douville ◽  
P. Ciais ◽  
B. Decharme ◽  
...  
Keyword(s):  
Land Surface ◽  
Tree Mortality ◽  
Climate Models ◽  
Global Climate ◽  
Land Surface Model ◽  
Co2 Concentration ◽  
Accurate Estimate ◽  
Global Climate Models ◽  
Surface Model ◽  
Vegetation Response

Abstract. While a majority of Global Climate Models project dryer and longer dry seasons over the Amazon under higher CO2 levels, large uncertainties surround the response of vegetation to persistent droughts in both present-day and future climates. We propose a detailed evaluation of the ability of the ISBACC Land Surface Model to capture drought effects on both water and carbon budgets, comparing fluxes and stocks at two recent ThroughFall Exclusion (TFE) experiments performed in the Amazon. We also explore the model sensitivity to different Water Stress Function (WSF) and to an idealized increase in CO2 concentration and/or temperature. In spite of a reasonable soil moisture simulation, ISBACC struggles to correctly simulate the vegetation response to TFE whose amplitude and timing is highly sensitive to the WSF. Under higher CO2 concentration, the increased Water Use Efficiency (WUE) mitigates the ISBACC's sensitivity to drought. While one of the proposed WSF formulation improves the response of most ISBACC fluxes, except respiration, a parameterization of drought-induced tree mortality is missing for an accurate estimate of the vegetation response. Also, a better mechanistic understanding of the forest responses to drought under a warmer climate and higher CO2 concentration is clearly needed.

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

scholarly journals Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim

2017 ◽  
Vol 21 (4) ◽  
pp. 2187-2201 ◽  
Author(s):  
Pere Quintana-Seguí ◽  
Marco Turco ◽  
Sixto Herrera ◽  
Gonzalo Miguez-Macho
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Interpolation Method ◽  
Rain Gauge ◽  
Surface Model ◽  
Low Resolution ◽  
Precipitation Events

Abstract. Offline land surface model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/1980–2013/2014). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate regional climate models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The validation of SAFRAN with independent data shows that this is a robust product. SAFRAN and Spain02 have very similar scores, although the latter slightly surpasses the former. The scores are robust with altitude and throughout the year, save perhaps in summer when a diminished skill is observed. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model.

scholarly journals The Impact of Error Accounting in a Bayesian Approach to Calibrating Modeled Turbulent Fluxes in an Open-Canopy Forest

2017 ◽  
Vol 18 (7) ◽  
pp. 2029-2042
Author(s):  
Tony E. Wong ◽  
William Kleiber ◽  
David C. Noone
Keyword(s):  
Markov Chain ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Soil Layer ◽  
Turbulent Fluxes ◽  
Surface Model ◽  
Model Parameters ◽  
Top Soil ◽  
The Impact ◽  
Calibration Approach

Abstract Land surface models are notorious for containing many parameters that control the exchange of heat and moisture between land and atmosphere. Properly modeling the partitioning of total evapotranspiration (ET) between transpiration and evaporation is critical for accurate hydrological modeling, but depends heavily on the treatment of turbulence within and above canopies. Previous work has constrained estimates of evapotranspiration and its partitioning using statistical approaches that calibrate land surface model parameters by assimilating in situ measurements. These studies, however, are silent on the impacts of the accounting of uncertainty within the statistical calibration framework. The present study calibrates the aerodynamic, leaf boundary layer, and stomatal resistance parameters, which partially control canopy turbulent exchange and thus the evapotranspiration flux partitioning. Using an adaptive Metropolis–Hastings algorithm to construct a Markov chain of draws from the joint posterior distribution of these resistance parameters, an ensemble of model realizations is generated, in which latent and sensible heat fluxes and top soil layer temperature are optimized. A set of five calibration experiments demonstrate that model performance is sensitive to the accounting of various sources of uncertainty in the field observations and model output and that it is critical to account for model structural uncertainty. After calibration, the modeled fluxes and top soil layer temperature are largely free from bias, and this calibration approach successfully informs and characterizes uncertainty in these parameters, which is essential for model improvement and development. The key points of this paper are 1) a Markov chain Monte Carlo calibration approach successfully improves modeled turbulent fluxes; 2) ET partitioning estimates hinge on the representation of uncertainties in the model and data; and 3) despite these inherent uncertainties, constrained posterior estimates of ET partitioning emerge.

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