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 Diagnosing the Nature of Land–Atmosphere Coupling: A Case Study of Dry/Wet Extremes in the U.S. Southern Great Plains

2013 ◽  
Vol 14 (1) ◽  
pp. 3-24 ◽  
Author(s):  
Joseph A. Santanello ◽  
Christa D. Peters-Lidard ◽  
Aaron Kennedy ◽  
Sujay V. Kumar
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Critical Role ◽  
Land Surface Model ◽  
Coupled System ◽  
Surface Model ◽  
Test Bed ◽  
Southern Great Plains ◽  
Pbl Scheme ◽  
The U.S

Abstract Land–atmosphere (L–A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address model deficiencies, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, a diagnosis of the nature and impacts of local land–atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of 2006 and 2007 in the U.S. southern Great Plains. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation is applied to the dry/wet regimes exhibited in this region, and in the process, a thorough evaluation of nine different land–PBL scheme couplings is conducted under the umbrella of a high-resolution regional modeling test bed. Results show that the sign and magnitude of errors in land surface energy balance components are sensitive to the choice of land surface model, regime type, and running mode. In addition, LoCo diagnostics show that the sensitivity of L–A coupling is stronger toward the land during dry conditions, while the PBL scheme coupling becomes more important during the wet regime. Results also demonstrate how LoCo diagnostics can be applied to any modeling system (e.g., reanalysis products) in the context of their integrated impacts on the process chain connecting the land surface to the PBL and in support of hydrological anomalies.

scholarly journals ERA-Interim/Land: a global land water resources dataset

2013 ◽  
Vol 10 (12) ◽  
pp. 14705-14745 ◽  
Author(s):  
G. Balsamo ◽  
C. Albergel ◽  
A. Beljaars ◽  
S. Boussetta ◽  
H. Cloke ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
Land Surface ◽  
Climate Models ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Forcing ◽  
Global Land ◽  
Land Water

Abstract. The ERA-Interim/Land is a global land-surface dataset covering the period 1979–2010 and describing the evolution of the soil (moisture and temperature) and snowpack. ERA-Interim/Land is the result of a single 32 yr simulation with the latest ECMWF land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on GPCP v2.1. ERA-Interim/Land preserves closure of the water balance and includes a number of parameterisations improvements in the land surface scheme with respect to the original ERA-Interim dataset, which makes it suitable for climate studies involving land water resources. The quality of ERA-Interim/Land, assessed by comparing with ground-based and remote sensing observations is discussed. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of sites measurements. ERA-Interim/Land provides a global integrated and coherent water resources estimate that is used also for the initialization of numerical weather prediction and climate models.

scholarly journals The Flexible Ocean and Climate Infrastructure Version 1 (FOCI1): Mean State and Variability

2020 ◽  
Author(s):  
Katja Matthes ◽  
Arne Biastoch ◽  
Sebastian Wahl ◽  
Jan Harlaß ◽  
Torge Martin ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Ocean Model ◽  
Meridional Overturning Circulation ◽  
Climate System ◽  
Surface Model ◽  
Model Components

Abstract. A new Earth system model, the Flexible Ocean and Climate Infrastructure (FOCI), is introduced. A first version of FOCI consists of a global high-top atmosphere (ECHAM6.3) and an ocean model (NEMO3.6) as well as sea ice (LIM2) and land surface model components (JSBACH), which are coupled through the OASIS3-MCT software package. FOCI includes a number of optional modules which can be activated depending on the scientific question of interest. In the atmosphere, interactive stratospheric chemistry can be used (ECHAM6-HAMMOZ) to study, for example, the effects of the ozone hole on the climate system. In the ocean, a biogeochemistry model (MOPS) is available to study the global carbon cycle. A unique feature of FOCI is the ability to explicitly resolve mesoscale ocean eddies in specific regions. This is realized in the ocean through nesting; first examples for the Agulhas Current and the Gulf Stream systems are described here. FOCI therefore bridges the gap between coarse-resolution climate models and global high-resolution weather prediction and ocean-only models. It allows to study the evolution of the climate system on regional and seasonal to (multi-) decadal scales. The development of FOCI resulted from a combination of the long-standing expertise in ocean and climate modeling in several research units and divisions at GEOMAR. FOCI will thus be used to complement and interpret long-term observations in the Atlantic, enhance the process understanding of the role of mesoscale oceanic eddies for large-scale oceanic and atmospheric circulation patterns, study feedback mechanisms with stratospheric processes, estimate future ocean acidification, improve the simulation of the Atlantic Meridional Overturning Circulation changes and their influence on climate, ocean chemistry and biology. In this paper we present both the scientific vision for the development of FOCI as well as some technical details. This includes a first validation of the different model components using several configurations of FOCI. Results show that the model in its basic configuration runs stably under pre-industrial control as well as under historical forcing, and produces a mean climate and variability which compares well with observations, reanalysis products and other climate models. The nested configurations reduce some long-standing biases in climate models and are an important step forward to include the atmospheric response in multi-decadal eddy-rich configurations.

scholarly journals The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations

2014 ◽  
Vol 7 (1) ◽  
pp. 361-386 ◽  
Author(s):  
D. N. Walters ◽  
K. D. Williams ◽  
I. A. Boutle ◽  
A. C. Bushell ◽  
J. M. Edwards ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Regional Climate ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Development Cycle ◽  
Global Land ◽  
Overall Performance ◽  
Ongoing Development

Abstract. We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. Overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations include improvements to the science of several parametrisation schemes, however, and will form a baseline for further ongoing development.

scholarly journals Integration of nitrogen dynamics into the Noah-MP land model v1.1 for climate and environmental predictions

2015 ◽  
Vol 8 (5) ◽  
pp. 4113-4153 ◽  
Author(s):  
X. Cai ◽  
Z.-L. Yang ◽  
J. B. Fisher ◽  
X. Zhang ◽  
M. Barlage ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Net Primary Productivity ◽  
Assessment Tool ◽  
Land Surface Model ◽  
Coupled Model ◽  
Nitrogen Dynamics ◽  
Surface Model ◽  
Meteorological Model ◽  
Environmental Models

Abstract. Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long-term Ecological Research site within the U.S. Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of the carbon and water cycles (e.g., net primary productivity and evapotranspiration). The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.

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.

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

2018 ◽  
Vol 19 (12) ◽  
pp. 1917-1933 ◽  
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jifu Yin ◽  
Jicheng Liu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Vegetation Fraction ◽  
Data Products ◽  
Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

scholarly journals Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 815
Author(s):  
Marcelo Somos-Valenzuela ◽  
Francisco Manquehual-Cheuque
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Short Wave ◽  
Reanalysis Data ◽  
Wave Radiation ◽  
Surface Model ◽  
Nwp Model

The use of numerical weather prediction (NWP) model to dynamically downscale coarse climate reanalysis data allows for the capture of processes that are influenced by land cover and topographic features. Climate reanalysis downscaling is useful for hydrology modeling, where catchment processes happen on a spatial scale that is not represented in reanalysis models. Selecting proper parameterization in the NWP for downscaling is crucial to downscale the climate variables of interest. In this work, we are interested in identifying at least one combination of physics in the Weather Research Forecast (WRF) model that performs well in our area of study that covers the Baker River Basin and the Northern Patagonian Icecap (NPI) in the south of Chile. We used ERA-Interim reanalysis data to run WRF in twenty-four different combinations of physics for three years in a nested domain of 22.5 and 4.5 km with 34 vertical levels. From more to less confident, we found that, for the planetary boundary layer (PBL), the best option is to use YSU; for the land surface model (LSM), the best option is the five-Layer Thermal, RRTM for longwave, Dudhia for short wave radiation, and Thompson for the microphysics. In general, the model did well for temperature (average, minimum, maximum) for most of the observation points and configurations. Precipitation was good, but just a few configurations stood out (i.e., conf-9 and conf-10). Surface pressure and Relative Humidity results were not good or bad, and it depends on the statistics with which we evaluate the time series (i.e., KGE or NSE). The results for wind speed were inferior; there was a warm bias in all of the stations. Once we identify the best configuration in our experiment, we run WRF for one year using ERA5 and FNL0832 climate reanalysis. Our results indicate that Era-interim provided better results for precipitation. In the case of temperature, FNL0832 gave better results; however, all of the models’ performances were good. Therefore, working with ERA-Interim seems the best option in this region with the physics selected. We did not experiment with changes in resolution, which may have improved results with ERA5 that has a better spatial and temporal resolution.

Intercomparison of terrestrial water budgets in EURO-CORDEX and TSMP evaluation runs

2020 ◽  
Author(s):  
Mohamed Eltahan ◽  
Klaus Goergen ◽  
Carina Furusho-Percot ◽  
Stefan Kollet
Keyword(s):  
Land Surface ◽  
Water Budget ◽  
Climate Models ◽  
Water Cycle ◽  
Weather Prediction ◽  
Regional Climate ◽  
Shallow Groundwater ◽  
Surface Model ◽  
Frequency Distributions ◽  
Terrestrial Water

<p>Water is one of Earth’s most important geo-ecosystem components. Here we present an evaluation of water cycle components using 12 EURO-CORDEX Regional Climate Models (RCMs) and the Terrestrial Systems Modeling Platform (TSMP) from ERA-Interim driven evaluation runs. Unlike the other RCMs, TSMP provides an <span>integrated</span> representation of the terrestrial water cycle by coupling the numerical weather prediction model COSMO, the land surface model CLM and the surface-subsurface hydrological model ParFlow, which simulates shallow groundwater states and fluxes. The study analyses precipitation (P), evapotranspiration (E), runoff (R), and terrestrial water storage (TWS=P-E-R) at a 0.11degree spatial resolution (about 12km) on EUR-11 CORDEX grid from 1996 to 2008. As reference datasets, we use ERA5 reanalysis to <span>represent</span> the complete terrestrial water budget, <span>as well as </span>the E-OBS, GLEAM and E-Run datasets for precipitation, evapotranspiration and runoff, respectively. The terrestrial water budget is investigated for twenty catchments over Europe (Guadalquivir, Guadiana, Tagus, Douro, Ebro, Garonne, Rhone, Po, Seine, Rhine, Loire, Maas, Weser, Elbe, Oder, Vistuala, Danube, Dniester, Dnieper, and Neman). Annual cycles, seasonal variations, empirical frequency distributions, spatial distributions for the water cycle components and budgets over the catchments are assessed. The analysis <span>demonstrates</span> the capability of the RCMs and TSMP to reproduce the overall <span>characteristics of the</span> water cycle over the EURO-CORDEX domain<span>, which is a prerequisite if, e.g., climate change projections with the CORDEX RCMs or TSMP are to be used for vulnerability, impacts, and adaptation studies.</span></p>

scholarly journals The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations

2017 ◽  
Vol 10 (4) ◽  
pp. 1487-1520 ◽  
Author(s):  
David Walters ◽  
Ian Boutle ◽  
Malcolm Brooks ◽  
Thomas Melvin ◽  
Rachel Stratton ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Model Performance ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Main Trunk ◽  
Global Land ◽  
Prediction Systems ◽  
The Tropics

Abstract. We describe Global Atmosphere 6.0 and Global Land 6.0 (GA6.0/GL6.0): the latest science configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) land surface model developed for use across all timescales. Global Atmosphere 6.0 includes the ENDGame (Even Newer Dynamics for General atmospheric modelling of the environment) dynamical core, which significantly increases mid-latitude variability improving a known model bias. Alongside developments of the model's physical parametrisations, ENDGame also increases variability in the tropics, which leads to an improved representation of tropical cyclones and other tropical phenomena. Further developments of the atmospheric and land surface parametrisations improve other aspects of model performance, including the forecasting of surface weather phenomena. We also describe GA6.1/GL6.1, which includes a small number of long-standing differences from our main trunk configurations that we continue to require for operational global weather prediction. Since July 2014, GA6.1/GL6.1 has been used by the Met Office for operational global numerical weather prediction, whilst GA6.0/GL6.0 was implemented in its remaining global prediction systems over the following year.

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