scholarly journals Modeling of land-surface interactions in the PALM model system 6.0: Land surface model description, first evaluation, and sensitivity to model parameters

2020 ◽  
Author(s):  
Katrin Frieda Gehrke ◽  
Matthias Sühring ◽  
Björn Maronga
Keyword(s):  
Boundary Layer ◽  
Energy Balance ◽  
Surface Potential ◽  
Land Surface ◽  
Potential Temperature ◽  
Land Surface Model ◽  
Model System ◽  
Horizontal Wind ◽  
Surface Model ◽  
Near Surface

Abstract. In this paper the land-surface model embedded in the PALM model system is described and evaluated against in-situ measurement data in Cabauw. For this, two consecutive clear-sky days are simulated and the components of surface energy balance, as well as near-surface potential temperature, humidity and horizontal wind speed are compared against observation data. For the simulated period, components of the energy balance agree well during day- and nighttime, and also the daytime Bowen ratio agrees fairly well compared to the observations. Although the model simulates a significantly more stably-stratified nocturnal boundary layer compared to the observation, near-surface potential temperature and humidity agree fairly well during day. Moreover, we performed a sensitivity study in order to investigate how much the model results depend on land-surface and soil specifications, as well as atmospheric initial conditions. By this, we find that a false estimation of the leaf area index, the albedo, or the initial humidity causes a serious misrepresentation of the daytime turbulent sensible and latent heat fluxes. During night, the boundary-layer characteristics are mostly affected by grid size, surface roughness, and the applied radiation schemes.

scholarly journals Modeling of land–surface interactions in the PALM model system 6.0: land surface model description, first evaluation, and sensitivity to model parameters

2021 ◽  
Vol 14 (8) ◽  
pp. 5307-5329
Author(s):  
Katrin Frieda Gehrke ◽  
Matthias Sühring ◽  
Björn Maronga
Keyword(s):  
Boundary Layer ◽  
Energy Balance ◽  
Land Surface ◽  
Potential Temperature ◽  
Land Surface Model ◽  
Model System ◽  
Horizontal Wind ◽  
Model Description ◽  
Surface Model ◽  
Model Parameters

Abstract. In this paper the land surface model embedded in the PALM model system is described and evaluated against in situ measurements at Cabauw, Netherlands. A total of 2 consecutive clear-sky days are simulated, and the components of surface energy balance, potential temperature, humidity, and horizontal wind speed are compared to observations. For the simulated period, components of the energy balance are consistent with daytime and nighttime observations, and the daytime Bowen ratio also agrees fairly well with observations. The model simulates a more stably stratified nocturnal boundary layer than the observations, and near-surface potential temperature and humidity agree fairly well during the day. Moreover, a sensitivity analysis is performed to investigate dependence of the model on land surface and soil specifications, as well as atmospheric initial conditions, because they represent a major source of uncertainty in the simulation setup. It is found that an inaccurate estimation of leaf area index, albedo, or initial humidity causes a significant misrepresentation of the daytime turbulent sensible and latent heat fluxes. During the night, the boundary-layer characteristics are primarily affected by surface roughness and the applied radiation schemes.

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

Development of stochastically perturbed parameterization scheme for the Noah Land Surface Model with the optimized random forcing parameters using the micro-genetic algorithm

2021 ◽  
Author(s):  
Sujeong Lim ◽  
Claudio Cassardo ◽  
Seon Ki Park
Keyword(s):  
Genetic Algorithm ◽  
Land Surface ◽  
Weather Forecasting ◽  
Land Surface Model ◽  
Surface Model ◽  
Ensemble Spread ◽  
Near Surface ◽  
Random Forcing ◽  
Micro Genetic Algorithm ◽  
Stochastically Perturbed

<p>The ensemble data assimilation system is beneficial to represent the initial uncertainties and flow-dependent background error covariance (BEC). In particular, the inevitable model uncertainties can be expressed by ensemble spread, that is the standard deviation of ensemble BEC. However, the ensemble spread generally suffers from under-estimated problems. To alleviate this problem, recent studies employed stochastic perturbation schemes to increases the ensemble spreads by adding the random forcing in the model tendencies (i.e., physical or dynamical tendencies) or parameterization schemes (i.e., PBL, convective scheme, etc.). In this study, we focus on the near-surface uncertainties which are affected by the interactions between the land and atmosphere process. The land surface model (LSM) provides various fluxes as the lower boundary condition to the atmosphere, influencing the accuracy of hourly-to-seasonal scale weather forecasting, but the surface uncertainties were not much addressed yet. In this study, we developed the stochastically perturbed parameterization (SPP) scheme for the Noah LSM. The Weather Research and Forecasting (WRF) ensemble system is used for regional weather forecasting over East Asia, especially over the Korean Peninsula. As a testbed experiment with the newly-developed Noah LSM-SPP system, we first perturbed the soil temperature — a crucial variable for the near-surface forecasts by affecting sensible heat fluxes, land surface skin temperature and surface air temperature, and hence lower-tropospheric temperature. Here, the random forcing used in perturbation is made by the tuning parameters for amplitude, length scale, and time scales: they are commonly determined empirically by trial and error. In order to find optimal tuning parameter values, we applied a global optimization algorithm — the micro-genetic algorithm (micro-GA) — to achieve the smallest root-mean-squared errors. Our results indicate that optimization of the random forcing parameters contributes to an increase in the ensemble spread and a decrease in the ensemble mean errors in the near-surface and lower-troposphere uncertainties. Further experiments will be conducted by including soil moisture in the testbed.</p>

scholarly journals High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation

2020 ◽  
Author(s):  
Benjamin Fersch ◽  
Alfonso Senatore ◽  
Bianca Adler ◽  
Joël Arnault ◽  
Matthias Mauder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Weather Prediction ◽  
Surface Model ◽  
Near Surface ◽  
Ground Heat Flux ◽  
The Impact ◽  
Fully Coupled ◽  
Regional Water

<p>The land surface and the atmospheric boundary layer are closely intertwined with respect to the exchange of water, trace gases and energy. Nonlinear feedback and scale dependent mechanisms are obvious by observations and theories. Modeling instead is often narrowed to single compartments of the terrestrial system or bound to traditional viewpoints of definite scientific disciplines. Coupled terrestrial hydrometeorological modeling systems attempt to overcome these limitations to achieve a better integration of the processes relevant for regional climate studies and local area weather prediction. We examine the ability of the hydrologically enhanced version of the Weather Research and Forecasting Model (WRF-Hydro) to reproduce the regional water cycle by means of a two-way coupled approach and assess the impact of hydrological coupling with respect to a traditional regional atmospheric model setting. It includes the observation-based calibration of the hydrological model component (offline WRF-Hydro) and a comparison of the classic WRF and the fully coupled WRF-Hydro models both with identical calibrated parameter settings for the land surface model (Noah-MP). The simulations are evaluated based on extensive observations at the pre-Alpine Terrestrial Environmental Observatory (TERENO Pre-Alpine) for the Ammer (600 km²) and Rott (55 km²) river catchments in southern Germany, covering a five month period (Jun–Oct 2016).</p><p>The sensitivity of 7 land surface parameters is tested using the <em>Latin-Hypercube One-factor-At-a-Time</em> (LH-OAT) method and 6 sensitive parameters are subsequently optimized for 6 different subcatchments, using the Model-Independent <em>Parameter Estimation and Uncertainty Analysis software</em> (PEST).</p><p>The calibration of the offline WRF-Hydro leads to Nash-Sutcliffe efficiencies between 0.56 and 0.64 and volumetric efficiencies between 0.46 and 0.81 for the six subcatchments. The comparison of classic WRF and fully coupled WRF-Hydro shows only tiny alterations for radiation and precipitation but considerable changes for moisture- and energy fluxes. By comparison with TERENO Pre-Alpine observations, the fully coupled model slightly outperforms the classic WRF with respect to evapotranspiration, sensible and ground heat flux, near surface mixing ratio, temperature, and boundary layer profiles of air temperature. The subcatchment-based water budgets show uniformly directed variations for evapotranspiration, infiltration excess and percolation whereas soil moisture and precipitation change randomly.</p>

scholarly journals Effects of Historical Urbanization in the Brussels Capital Region on Surface Air Temperature Time Series: A Model Study

2009 ◽  
Vol 48 (10) ◽  
pp. 2181-2196 ◽  
Author(s):  
R. Hamdi ◽  
A. Deckmyn ◽  
P. Termonia ◽  
G. R. Demarée ◽  
P. Baguis ◽  
...  
Keyword(s):  
Land Surface ◽  
Minimum Temperature ◽  
Computational Cost ◽  
Land Surface Model ◽  
Maximum Temperature ◽  
Surface Model ◽  
Impervious Surfaces ◽  
Urban Bias ◽  
Near Surface ◽  
Capital Region

Abstract The authors examine the local impact of change in impervious surfaces in the Brussels capital region (BCR), Belgium, on trends in maximum, minimum, and mean temperatures between 1960 and 1999. Specifically, data are combined from remote sensing imagery and a land surface model including state-of-the-art urban parameterization—the Town Energy Balance scheme. To (i) isolate effects of urban growth on near-surface temperature independent of atmospheric circulations and (ii) be able to run the model over a very long period without any computational cost restrictions, the land surface model is run in a stand-alone mode coupled to downscaled 40-yr ECMWF reanalysis data. BCR was considered a lumped urban volume and the rate of urbanization was assessed by estimating the percentage of impervious surfaces from Landsat images acquired for various years. Model simulations show that (i) the annual mean urban bias (AMUB) on minimum temperature is rising at a higher rate (almost 3 times more) than on maximum temperature, with a linear trend of 0.14° and 0.05°C (10 yr)−1, respectively, (ii) the 40-yr AMUB on mean temperature is estimated to be 0.62°C, (iii) 45% of the overall warming trend is attributed to intensifying urban heat island effects rather than to changes in local–regional climate, and (iv) during summertime, a stronger dependence between the increase of urban bias on minimum temperature and the change in percentage of impervious surfaces is found.

Homogenized Variability of Radiosonde-Derived Atmospheric Boundary Layer Height over the Global Land Surface from 1973 to 2014

2016 ◽  
Vol 29 (19) ◽  
pp. 6893-6908 ◽  
Author(s):  
Xiaoyan Wang ◽  
Kaicun Wang
Keyword(s):  
United States ◽  
Boundary Layer ◽  
Time Series ◽  
Land Surface ◽  
Statistical Significance ◽  
Potential Temperature ◽  
The United States ◽  
Near Surface ◽  
Layer Height ◽  
Boundary Layer Height

Abstract Boundary layer height (BLH) significantly impacts near-surface air quality, and its determination is important for climate change studies. Integrated Global Radiosonde Archive data from 1973 to 2014 were used to estimate the long-term variability of the BLH based on profiles of potential temperature, relative humidity, and atmospheric refractivity. However, this study found that there was an obvious inhomogeneity in the radiosonde-derived BLH time series because of the presence of discontinuities in the raw radiosonde dataset. The penalized maximal F test and quantile-matching adjustment were used to detect the changepoints and to adjust the raw BLH series. The most significant inhomogeneity of the BLH time series was found over the United States from 1986 to 1992, which was mainly due to progress made in sonde models and processing procedures. The homogenization did not obviously change the magnitude of the daytime convective BLH (CBLH) tendency, but it improved the statistical significance of its linear trend. The trend of nighttime stable BLH (SBLH) is more dependent on the homogenization because the magnitude of SBLH is small, and SBLH is sensitive to the observational biases. The global daytime CBLH increased by about 1.6% decade−1 before and after homogenization from 1973 to 2014, and the nighttime homogenized SBLH decreased by −4.2% decade−1 compared to a decrease of −7.1% decade−1 based on the raw series. Regionally, the daytime CBLH increased by 2.8%, 0.9%, 1.6%, and 2.7% decade−1 and the nighttime SBLH decreased significantly by −2.7%, −6.9%, −7.7%, and −3.5% decade−1 over Europe, the United States, Japan, and Australia, respectively.

scholarly journals The CSIRO Mk3L climate system model v1.0 coupled to the CABLE land surface scheme v1.4b: evaluation of the control climatology

2011 ◽  
Vol 4 (4) ◽  
pp. 1115-1131 ◽  
Author(s):  
J. Mao ◽  
S. J. Phipps ◽  
A. J. Pitman ◽  
Y. P. Wang ◽  
G. Abramowitz ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Climate System ◽  
System Model ◽  
Surface Model ◽  
Climate System Model ◽  
Near Surface ◽  
Land Surface Scheme ◽  
Surface Scheme ◽  
The Impact

Abstract. The CSIRO Mk3L climate system model, a reduced-resolution coupled general circulation model, has previously been described in this journal. The model is configured for millennium scale or multiple century scale simulations. This paper reports the impact of replacing the relatively simple land surface scheme that is the default parameterisation in Mk3L with a sophisticated land surface model that simulates the terrestrial energy, water and carbon balance in a physically and biologically consistent way. An evaluation of the new model's near-surface climatology highlights strengths and weaknesses, but overall the atmospheric variables, including the near-surface air temperature and precipitation, are simulated well. The impact of the more sophisticated land surface model on existing variables is relatively small, but generally positive. More significantly, the new land surface scheme allows an examination of surface carbon-related quantities including net primary productivity which adds significantly to the capacity of Mk3L. Overall, results demonstrate that this reduced-resolution climate model is a good foundation for exploring long time scale phenomena. The addition of the more sophisticated land surface model enables an exploration of important Earth System questions including land cover change and abrupt changes in terrestrial carbon storage.

scholarly journals Near-Surface and Land Surface Forecast System of the Vancouver 2010 Winter Olympic and Paralympic Games

2011 ◽  
Vol 12 (4) ◽  
pp. 508-530 ◽  
Author(s):  
Natacha B. Bernier ◽  
Stéphane Bélair ◽  
Bernard Bilodeau ◽  
Linying Tong
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Near Surface ◽  
Elevation Changes ◽  
Forecast Models ◽  
Operational Models ◽  
Paralympic Games

Abstract A high-resolution 2D near-surface and land surface model was developed to produce snow and temperature forecasts over the complex alpine region of the Vancouver 2010 Winter Olympic and Paralympic Games. The model is driven by downscaled operational outputs from the Meteorological Service of Canada’s regional and global forecast models. Downscaling is applied to correct forcings for elevation differences between the operational forecast models and the high-resolution surface model. The high-resolution near-surface and land surface model is then used to further refine the forecasts. The model was validated against temperature and snow depth observations. The largest improvements were found in regions where low-resolution (i.e., on the order of 10 km or more) operational models typically lack the spatial resolution to capture rapid elevation changes. The model was found to better reproduce the intermittent snow cover at low-lying stations and to reduce snow depth error by as much as 3 m at alpine stations.

scholarly journals On the use of the post-closure methods uncertainty band to evaluate the performance of land surface models against eddy covariance flux data

2015 ◽  
Vol 12 (8) ◽  
pp. 2311-2326 ◽  
Author(s):  
J. Ingwersen ◽  
K. Imukova ◽  
P. Högy ◽  
T. Streck
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Eddy Covariance ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Land Surface Model ◽  
Bowen Ratio ◽  
Surface Model ◽  
Flux Data ◽  
Uncertainty Band

Abstract. The energy balance of eddy covariance (EC) flux data is normally not closed. Therefore, at least if used for modelling, EC flux data are usually post-closed, i.e. the measured turbulent fluxes are adjusted so as to close the energy balance. At the current state of knowledge, however, it is not clear how to partition the missing energy in the right way. Eddy flux data therefore contain some uncertainty due to the unknown nature of the energy balance gap, which should be considered in model evaluation and the interpretation of simulation results. We propose to construct the post-closure methods uncertainty band (PUB), which essentially designates the differences between non-adjusted flux data and flux data adjusted with the three post-closure methods (Bowen ratio, latent heat flux (LE) and sensible heat flux (H) method). To demonstrate this approach, simulations with the NOAH-MP land surface model were evaluated based on EC measurements conducted at a winter wheat stand in southwest Germany in 2011, and the performance of the Jarvis and Ball–Berry stomatal resistance scheme was compared. The width of the PUB of the LE was up to 110 W m−2 (21% of net radiation). Our study shows that it is crucial to account for the uncertainty in EC flux data originating from lacking energy balance closure. Working with only a single post-closing method might result in severe misinterpretations in model–data comparisons.

scholarly journals The ERA5-Land soil temperature bias in permafrost regions

2020 ◽  
Vol 14 (8) ◽  
pp. 2581-2595 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Donghai Zheng ◽  
Xin Li
Keyword(s):  
Soil Temperature ◽  
Land Surface ◽  
Soil Column ◽  
Land Surface Model ◽  
Surface Model ◽  
Active Layer Thickness ◽  
Near Surface ◽  
Warm Bias ◽  
Temperature Bias ◽  
Permafrost Regions

Abstract. ERA5-Land (ERA5L) is a reanalysis product derived by running the land component of ERA5 at increased resolution. This study evaluates ERA5L soil temperature in permafrost regions based on observations and published permafrost products. We find that ERA5L overestimates soil temperature in northern Canada and Alaska but underestimates it in mid–low latitudes, leading to an average bias of −0.08 ∘C. The warm bias of ERA5L soil is stronger in winter than in other seasons. As calculated from its soil temperature, ERA5L overestimates active-layer thickness and underestimates near-surface (<1.89 m) permafrost area. This is thought to be due in part to the shallow soil column and coarse vertical discretization of the land surface model and to warmer simulated soil. The soil temperature bias in permafrost regions correlates well with the bias in air temperature and with maximum snow height. A review of the ERA5L snow parameterization and a simulation example both point to a low bias in ERA5L snow density as a possible cause for the warm bias in soil temperature. The apparent disagreement of station-based and areal evaluation techniques highlights challenges in our ability to test permafrost simulation models. While global reanalyses are important drivers for permafrost simulation, we conclude that ERA5L soil data are not well suited for informing permafrost research and decision making directly. To address this, future soil temperature products in reanalyses will require permafrost-specific alterations to their land surface models.

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