scholarly journals Coupling the high complexity land surface model ACASA to the mesoscale model WRF

2014 ◽  
Vol 7 (3) ◽  
pp. 2829-2875 ◽  
Author(s):  
L. Xu ◽  
R. D. Pyles ◽  
K. T. Paw U ◽  
S. H. Chen ◽  
E. Monier
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Mesoscale Model ◽  
Land Surface Model ◽  
Carbon Dioxide Concentration ◽  
Atmospheric Model ◽  
Dew Point ◽  
Surface Model ◽  
High Complexity ◽  
Surface Conditions

Abstract. In this study, the Weather Research and Forecasting Model (WRF) is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF are simple and lack the capability to simulate carbon dioxide, for example, the popular NOAH LSM. ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. Simulations of surface conditions such as air temperature, dew point temperature, and relative humidity from WRF–ACASA and WRF–NOAH are compared with surface observation from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF–ACASA model not only maintains model accuracy, it also properly accounts for the dominant biological and physical processes describing ecosystem-atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF–ACASA and WRF–NOAH models also highlight the impacts of different land surface and model components on atmospheric and surface conditions.

scholarly journals Coupling the high-complexity land surface model ACASA to the mesoscale model WRF

2014 ◽  
Vol 7 (6) ◽  
pp. 2917-2932 ◽  
Author(s):  
L. Xu ◽  
R. D. Pyles ◽  
K. T. Paw U ◽  
S. H. Chen ◽  
E. Monier
Keyword(s):  
Carbon Dioxide ◽  
Air Temperature ◽  
Land Surface ◽  
Mesoscale Model ◽  
Land Surface Model ◽  
Carbon Dioxide Concentration ◽  
Dew Point ◽  
Surface Model ◽  
High Complexity ◽  
The Impact

Abstract. In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF, such as the popular NOAH model, are simple and lack the capability of representing the canopy structure. In contrast, ACASA is a complex multilayer land surface model with interactive canopy physiology and high-order turbulence closure that allows for an accurate representation of heat, momentum, water, and carbon dioxide fluxes between the land surface and the atmosphere. It allows for microenvironmental variables such as surface air temperature, wind speed, humidity, and carbon dioxide concentration to vary vertically within and above the canopy. Surface meteorological conditions, including air temperature, dew point temperature, and relative humidity, simulated by WRF-ACASA and WRF-NOAH are compared and evaluated with observations from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy but also properly accounts for the dominant biological and physical processes describing ecosystem–atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impact of different land surface models on atmospheric and surface conditions.

scholarly journals Simulating carbon exchange using a regional atmospheric model coupled to an advanced land-surface model

2010 ◽  
Vol 7 (8) ◽  
pp. 2397-2417 ◽  
Author(s):  
H. W. Ter Maat ◽  
R. W. A. Hutjes ◽  
F. Miglietta ◽  
B. Gioli ◽  
F. C. Bosveld ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Land Surface Model ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Large Variability ◽  
Regional Atmospheric Modelling System

Abstract. This paper is a case study to investigate what the main controlling factors are that determine atmospheric carbon dioxide content for a region in the centre of The Netherlands. We use the Regional Atmospheric Modelling System (RAMS), coupled with a land surface scheme simulating carbon, heat and momentum fluxes (SWAPS-C), and including also submodels for urban and marine fluxes, which in principle should include the dominant mechanisms and should be able to capture the relevant dynamics of the system. To validate the model, observations are used that were taken during an intensive observational campaign in central Netherlands in summer 2002. These include flux-tower observations and aircraft observations of vertical profiles and spatial fluxes of various variables. The simulations performed with the coupled regional model (RAMS-SWAPS-C) are in good qualitative agreement with the observations. The station validation of the model demonstrates that the incoming shortwave radiation and surface fluxes of water and CO2 are well simulated. The comparison against aircraft data shows that the regional meteorology (i.e. wind, temperature) is captured well by the model. Comparing spatially explicitly simulated fluxes with aircraft observed fluxes we conclude that in general latent heat fluxes are underestimated by the model compared to the observations but that the latter exhibit large variability within all flights. Sensitivity experiments demonstrate the relevance of the urban emissions of carbon dioxide for the carbon balance in this particular region. The same tests also show the relation between uncertainties in surface fluxes and those in atmospheric concentrations.

scholarly journals Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals

2014 ◽  
Vol 11 (2) ◽  
pp. 217-235 ◽  
Author(s):  
T. W. Hilton ◽  
K. J. Davis ◽  
K. Keller
Keyword(s):  
Carbon Dioxide ◽  
Parameter Estimation ◽  
North American ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Ecosystem Respiration ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Considerable Uncertainty

Abstract. Global terrestrial atmosphere–ecosystem carbon dioxide fluxes are well constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency land surface model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

scholarly journals Simulating carbon exchange using a regional atmospheric model coupled to an advanced land-surface model

2008 ◽  
Vol 5 (5) ◽  
pp. 4161-4207 ◽  
Author(s):  
H. W. Ter Maat ◽  
R. W. A. Hutjes
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Large Scale ◽  
Regional Scale ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Surface Model ◽  
Carbon Exchange ◽  
Regional Atmospheric Modelling System

Abstract. A large scale mismatch exists between our understanding and quantification of ecosystem atmosphere exchange of carbon dioxide at local scale and continental scales. This paper will focus on the carbon exchange on the regional scale to address the following question: What are the main controlling factors determining atmospheric carbon dioxide content at a regional scale? We use the Regional Atmospheric Modelling System (RAMS), coupled with a land surface scheme simulating carbon, heat and momentum fluxes (SWAPS-C), and including also sub models for urban and marine fluxes, which in principle include the main controlling mechanisms and capture the relevant dynamics of the system. To validate the model, observations are used which were taken during an intensive observational campaign in the central Netherlands in summer 2002. These included flux-site observations, vertical profiles at tall towers and spatial fluxes of various variables taken by aircraft. The coupled regional model (RAMS-SWAPS-C) generally does a good job in simulating results close to reality. The validation of the model demonstrates that surface fluxes of heat, water and CO2 are reasonably well simulated. The comparison against aircraft data shows that the regional meteorology is captured by the model. Comparing spatially explicit simulated and observed fluxes we conclude that in general simulated latent heat fluxes are underestimated by the model to the observations which exhibit large standard deviation for all flights. Sensitivity experiments demonstrated the relevance of the urban emissions of carbon dioxide for the carbon balance in this particular region. The same test also show the relation between uncertainties in surface fluxes and those in atmospheric concentrations.

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Atmospheric Model ◽  
Surface Model ◽  
Soil Moisture Data ◽  
The Impact ◽  
Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

scholarly journals Land–Atmosphere Coupling Strength in the Global Forecast System

2011 ◽  
Vol 12 (1) ◽  
pp. 147-156 ◽  
Author(s):  
Li Zhang ◽  
Paul A. Dirmeyer ◽  
Jiangfeng Wei ◽  
Zhichang Guo ◽  
Cheng-Hsuan Lu
Keyword(s):  
Coupling Strength ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Atmospheric Model ◽  
Surface Model ◽  
Global Forecast System ◽  
Forecast System ◽  
Subsurface Soil ◽  
Soil Wetness

Abstract The operational coupled land–atmosphere forecast model from the National Centers for Environmental Prediction (NCEP) is evaluated for the strength and characteristics of its coupling in the water cycle between land and atmosphere. Following the protocols of the Global Land–Atmosphere Coupling Experiment (GLACE) it is found that the Global Forecast System (GFS) atmospheric model coupled to the Noah land surface model exhibits extraordinarily weak land–atmosphere coupling, much as its predecessor, the GFS–Oregon State University (OSU) coupled system. The coupling strength is evaluated by the ability of subsurface soil wetness to affect locally the time series of precipitation. The surface fluxes in Noah are also found to be rather insensitive to subsurface soil wetness. Comparison to another atmospheric model coupled to Noah as well as a different land surface model show that Noah is responsible for some of the lack of sensitivity, primarily because its thick (10 cm) surface layer dominates the variability in surface latent heat fluxes. Noah is found to be as responsive as other land surface models to surface soil wetness and temperature variations, suggesting the design of the GLACE sensitivity experiment (based only on subsurface soil wetness) handicapped the Noah model. Additional experiments, in which the parameterization of evapotranspiration is altered, as well as experiments where surface soil wetness is also constrained, isolate the GFS atmospheric model as the principal source of the weak sensitivity of precipitation to land surface states.

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 Effects of Trade-Wind Strength and Direction on the Leeside Circulations and Rainfall of the Island of Hawaii

2008 ◽  
Vol 136 (12) ◽  
pp. 4799-4818 ◽  
Author(s):  
Yang Yang ◽  
Yi-Leng Chen ◽  
Francis M. Fujioka
Keyword(s):  
Potential Vorticity ◽  
Land Surface ◽  
Mesoscale Model ◽  
Land Surface Model ◽  
Coastal Region ◽  
Trade Wind ◽  
Surface Model ◽  
Katabatic Flow ◽  
Reversed Flow ◽  
Trade Wind Inversion

Abstract The leeside circulations and weather of the island of Hawaii were studied from the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) land surface model simulations for eight strong (∼7.9 m s−1) and eight weak (∼5.2 m s−1) trade-wind days and for five days with southeasterly trades (∼7.1 m s−1) during summer 2004. The objective is to investigate the effects of trade-wind strength and directions on the leeside circulations and rainfall and the modification of these effects by the land surface thermal forcing. For the small wake on the lee side of the Kohala Mountains (1700 m, lower than the trade-wind inversion at 2000 m) over northern Hawaii, the hydraulic jump is present with stronger downslope winds and warmer and drier conditions on the lee side and a weaker westerly reversed flow offshore when trades are stronger. In contrast, the westerly reversed flow along the large wake axis off the central Kona leeside coast (behind massive mountains with tops &gt;4000 m) is 1–1.5 m s−1 stronger and 200–300 m deeper with higher moisture content when trades are stronger. Over the Kona slopes, the daytime thermally direct circulation cell is more significant when trades are stronger because of descending airflow aloft with less cloudiness. In the evening, the convergence between the westerly reversed flow offshore along the wake axis and the offshore/katabatic flow in the Kona coastal region is more significant with higher evening rainfall when trades are stronger. During the day, the lee side of the Kohala Mountains is characterized by a reversed flow (∼4 m s−1) merging with sea-breeze circulations along the coast. When trades are stronger, the convergence between the anabatic winds and the descending flow from the upper slopes is greater. However, the simulated cloud water there is less under strong trades because of warmer and drier conditions due to significant adiabatic descent in the lee. At night, when trades are stronger, the combined downslope/katabatic flow prevails without a reversed flow offshore. Under a southeasterly trade-wind flow with a lower trade-wind inversion (1.5 km), the westerly reversed flow is shallower; the adiabatic descent aloft on the southwestern leeside areas is more significant with warmer temperatures (0.5 K), a larger negative potential vorticity maximum [0.2 potential vorticity units (PVU), 1 PVU = 10−6 K m2 s−1 kg−1], and a more pronounced anticyclonic vortex offshore. The westerly reversed flow off the Kona coast shifts northward.

scholarly journals Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals

2013 ◽  
Vol 10 (8) ◽  
pp. 13753-13802
Author(s):  
T. W. Hilton ◽  
K. J. Davis ◽  
K. Keller
Keyword(s):  
Carbon Dioxide ◽  
Parameter Estimation ◽  
North American ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Ecosystem Respiration ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Considerable Uncertainty

Abstract. Global terrestrial atmosphere-ecosystem carbon dioxide fluxes are well-constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency ecosystem model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely-sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all of the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

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