Impact of land surface processes on the simulation of sea breeze circulation and tritium dispersion over the kaiga complex terrain region near west coast of India using the Weather Research and Forecasting (WRF) model

The Weather Research and Forecasting (WRF) model version 3.0 developed by the National Center for Atmospheric Research (NCAR) includes three land surface schemes: the simple soil thermal diffusion (STD) scheme, the Noah scheme, and the Rapid Update Cycle (RUC) scheme. We have recently coupled the sophisticated NCAR Community Land Model version 3 (CLM3) into WRF to better characterize land surface processes. Among these four land surface schemes, the STD scheme is the simplest in both structure and process physics. The Noah and RUC schemes are at the intermediate level of complexity. CLM3 includes the most sophisticated snow, soil, and vegetation physics among these land surface schemes. WRF simulations with all four land surface schemes over the western United States (WUS) were carried out for the 1 October 1995 through 30 September 1996. The results show that land surface processes strongly affect temperature simulations over the (WUS). As compared to observations, WRF-CLM3 with the highest complexity level significantly improves temperature simulations, except for the wintertime maximum temperature. Precipitation is dramatically overestimated by WRF with all four land surface schemes over the (WUS) analyzed in this study and does not show a close relationship with land surface processes.

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The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

Hydrology Research ◽

10.2166/nh.2011.071 ◽

2011 ◽

Vol 42 (2-3) ◽

pp. 95-112 ◽

Cited By ~ 31

Author(s):

Venkat Lakshmi ◽

Seungbum Hong ◽

Eric E. Small ◽

Fei Chen

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Wrf Model ◽

Land Surface Model ◽

Surface Processes ◽

Surface Model ◽

Land Surface Processes ◽

Area Index ◽

Vegetation Fraction ◽

Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

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Response of the surface climate to different land surface models: WRF sensitivity to surface model options

10.5194/egusphere-egu21-15663 ◽

2021 ◽

Author(s):

Daniela C.A. Lima ◽

Rita M. Cardoso ◽

Pedro M.M. Soares

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Wrf Model ◽

Land Surface Model ◽

Heat Fluxes ◽

Surface Processes ◽

Surface Model ◽

Land Surface Models ◽

Land Surface Processes ◽

Surface Models

The Weather Research and Forecasting (WRF) model version 4.2 includes different land surface schemes, allowing a better representation of the land surface processes. Four simulations with the WRF model differing in land surface models and options were investigated as a sensitivity study over the European domain. These experiments span from 2004-2006 with a one-month spin-up and were performed at 0.11o horizontal resolution with 50 vertical levels, following the CORDEX guidelines. The lateral boundary conditions were driven by ERA5 reanalysis from European Centre for Medium-Range Weather Forecasts. For the first experiment, the Noah land surface model was used. For the remaining simulations, the Noah-MP (multi-physics) land surface model was used with different runoff and groundwater options: (1) original surface and subsurface runoff (free drainage), (2) TOPMODEL with groundwater and (3) Miguez-Macho & Fan groundwater scheme. The physical parameterizations options are the same for all simulations. These experiments allow the analysis of the sensitivity of different land surface options and to understand how the representation of land surface processes impacts on the atmosphere properties. This study focusses on the investigation of land-atmosphere feedbacks trough the analysis of the soil moisture &#8211; temperature and soil moisture &#8211; precipitation interactions, latent and sensible heat fluxes, and moisture fluxes. The influence of different surface model options on atmospheric boundary layer is also explored.Acknowledgements. The authors wish to acknowledge the LEADING (PTDC/CTA-MET/28914/2017) project funded by FCT. The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 &#8211; Instituto Dom Luiz.

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A Review on Land Surface Processes Modelling over Complex Terrain

Advances in Meteorology ◽

10.1155/2015/607181 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17 ◽

Cited By ~ 15

Author(s):

Wei Zhao ◽

Ainong Li

Keyword(s):

Land Surface ◽

Complex Terrain ◽

Water Cycle ◽

Vertical Direction ◽

Good Alternative ◽

Surface Processes ◽

Surface Model ◽

Atmospheric Forcing ◽

Land Surface Processes ◽

Rapid Variation

Complex terrain, commonly represented by mountainous region, occupies nearly one-quarter of the Earth’s continental areas. An accurate understanding of water cycle, energy exchange, carbon cycle, and many other biogeophysical or biogeochemical processes in this area has become more and more important for climate change study. Due to the influences from complex topography and rapid variation in elevation, it is usually difficult for field measurements to capture the land-atmosphere interactions well, whereas land surface model (LSM) simulation provides a good alternative. A systematic review is introduced by pointing out the key issues for land surface processes simulation over complex terrain: (1) high spatial heterogeneity for land surface parameters in horizontal direction, (2) big variation of atmospheric forcing data in vertical direction related to elevation change, (3) scale effect on land surface parameterization in LSM, and (4) two-dimensional modelling which considers the gravity influence. Regarding these issues, it is promising for better simulation at this special region by involving higher spatial resolution atmospheric forcing data which can reflect the influences from topographic changes and making necessary improvements on model structure related to topographic factors. In addition, the incorporation of remote sensing techniques will significantly help to reduce uncertainties in model initialization, simulation, and validation.

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Land surface processes and Sahel climate

Reviews of Geophysics ◽

10.1029/1999rg000014 ◽

2000 ◽

Vol 38 (1) ◽

pp. 117-140 ◽

Cited By ~ 8

Author(s):

Sharon Nicholson

Keyword(s):

Land Surface ◽

Surface Processes ◽

Land Surface Processes

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Land Surface Processes Relevant to Sub-seasonal to Seasonal (S2S) Prediction

Sub-Seasonal to Seasonal Prediction ◽

10.1016/b978-0-12-811714-9.00008-5 ◽

2019 ◽

pp. 165-181 ◽

Cited By ~ 7

Author(s):

Paul A. Dirmeyer ◽

Pierre Gentine ◽

Michael B. Ek ◽

Gianpaolo Balsamo

Keyword(s):

Land Surface ◽

Surface Processes ◽

Land Surface Processes

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Combinational Optimization of the WRF Physical Parameterization Schemes to Improve Numerical Sea Breeze Prediction Using Micro-Genetic Algorithm

Applied Sciences ◽

10.3390/app112311221 ◽

2021 ◽

Vol 11 (23) ◽

pp. 11221

Author(s):

Ji Won Yoon ◽

Sujeong Lim ◽

Seon Ki Park

Keyword(s):

Genetic Algorithm ◽

Wind Speed ◽

Land Surface ◽

Wrf Model ◽

Longwave Radiation ◽

Sea Breeze ◽

Meteorological Variables ◽

Parameterization Schemes ◽

Micro Genetic Algorithm ◽

Physical Parameterization

This study aims to improve the performance of the Weather Research and Forecasting (WRF) model in the sea breeze circulation using the micro-Genetic Algorithm (micro-GA). We found the optimal combination of four physical parameterization schemes related to the sea breeze system, including planetary boundary layer (PBL), land surface, shortwave radiation, and longwave radiation, in the WRF model coupled with the micro-GA (WRF-μGA system). The optimization was performed with respect to surface meteorological variables (2 m temperature, 2 m relative humidity, 10 m wind speed and direction) and a vertical wind profile (wind speed and direction), simultaneously for three sea breeze cases over the northeastern coast of South Korea. The optimized set of parameterization schemes out of the WRF-μGA system includes the Mellor–Yamada–Nakanishi–Niino level-2.5 (MYNN2) for PBL, the Noah land surface model with multiple parameterization options (Noah-MP) for land surface, and the Rapid Radiative Transfer Model for GCMs (RRTMG) for both shortwave and longwave radiation. The optimized set compared with the various other sets of parameterization schemes for the sea breeze circulations showed up to 29 % for the improvement ratio in terms of the normalized RMSE considering all meteorological variables.

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Land Surface Processes in Hydrology

10.1007/978-3-642-60567-3 ◽

1997 ◽

Cited By ~ 2

Keyword(s):

Land Surface ◽

Surface Processes ◽

Land Surface Processes

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Impact of land–surface processes on the interannual variability of tropical climate in the LMD GCM

Climate Dynamics ◽

10.1007/s00382-002-0297-x ◽

2003 ◽

Vol 20 (6) ◽

pp. 613-633 ◽

Cited By ~ 4

Author(s):

K. Maynard ◽

J. Polcher

Keyword(s):

Interannual Variability ◽

Land Surface ◽

Tropical Climate ◽

Surface Processes ◽

Land Surface Processes

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The use of multi-tracer flask-measurements to understand changes in the land-surface processes.

10.5194/egusphere-egu21-15179 ◽

2021 ◽

Author(s):

Theertha Kariyathan ◽

Wouter Peters ◽

Julia Marshall ◽

Ana Bastos ◽

Markus Reichstein

Keyword(s):

Northern Hemisphere ◽

Land Surface ◽

Seasonal Cycle ◽

Growing Season ◽

Plant Productivity ◽

Surface Processes ◽

Terrestrial Biosphere ◽

Land Surface Processes ◽

Species Analysis

Carbon dioxide (CO2)&#160;is an important greenhouse gas, and it accounts for about 20% of the present-day anthropogenic greenhouse effect. Atmospheric CO2 is cycled between the terrestrial biosphere and the atmosphere through various land-surface processes and thus links the atmosphere and terrestrial biosphere through positive and negative feedback. Since multiple trace gas elements are linked by common biogeochemical processes, multi-species analysis is useful for reinforcing our understanding and can help in partitioning CO2 fluxes. For example,&#160;in the northern hemisphere, CO2 has a distinct seasonal cycle mainly regulated by plant photosynthesis and respiration and it&#160;has a distinct negative correlation with the&#160;seasonal cycle of the &#948;13C isotope of CO2,&#160;due to a stronger isotopic fractionation&#160;associated with terrestrial photosynthesis.&#160;Therefore, multi-species flask-data measurements are useful for the long-term analysis of various green-house gases. Here we try to&#160;infer the complex interaction between the atmosphere and the terrestrial biosphere by&#160;multi-species analysis using&#160;atmospheric flask measurement data from different NOAA flask measurement sites across the northern hemisphere.This study focuses on the long-term changes in the seasonal cycle of CO2 over the northern hemisphere and tries to attribute the observed changes to driving land-surface processes through a combined analysis of the &#948;13C seasonal cycle. For this we generate metrics of different parameters of the CO2&#160;and &#948;13C&#160;seasonal cycle like the seasonal cycle amplitude given by the peak-to-peak difference of the cycle (indicative of the amount of CO2 taken up by terrestrial uptake),&#160; the intensity of plant productivity inferred from the slope of the seasonal cycle during the growing season , length of growing season and the start of the growing season. We analyze the inter-relation between these metrics and how they change across latitude and over time.&#160;We hypothesize that the CO2 seasonal cycle amplitude is controlled both by the intensity of plant productivity and period of the active growing season and that the timing of the growing season can affect the intensity of plant productivity. We then quantify these relationships, including their variation over time and latitudes and describe the effects of an earlier start of the growing season on the intensity of plant productivity and the CO2&#160;uptake by plants.

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