scholarly journals The Role of an Advanced Land Model in Seasonal Dynamical Downscaling for Crop Model Application

2006 ◽  
Vol 45 (5) ◽  
pp. 686-701 ◽  
Author(s):  
D. W. Shin ◽  
J. G. Bellow ◽  
T. E. LaRow ◽  
S. Cocke ◽  
James J. O'Brien
Keyword(s):  
Land Surface ◽  
Crop Yields ◽  
Dynamical Downscaling ◽  
Warm Season ◽  
Regional Model ◽  
Crop Model ◽  
Shortwave Radiation ◽  
Seasonal Climate ◽  
Community Land Model ◽  
Surface Scheme

Abstract An advanced land model [the National Center for Atmospheric Research (NCAR) Community Land Model, version 2 (CLM2)] is coupled to the Florida State University (FSU) regional spectral model to improve seasonal surface climate outlooks at very high spatial and temporal resolution and to examine its potential for crop yield estimation. The regional model domain is over the southeast United States and is run at 20-km resolution, roughly resolving the county level. Warm-season (March–September) simulations from the regional model coupled to the CLM2 are compared with those from the model with a simple land surface scheme (i.e., the original FSU model). In this comparison, two convective schemes are also used to evaluate their roles in simulating seasonal climate, primarily for rainfall. It is shown that the inclusion of the CLM2 produces consistently better seasonal climate scenarios of surface maximum and minimum temperatures, precipitation, and shortwave radiation, and hence provides superior inputs to a site-based crop model to simulate crop yields. The FSU regional model with the CLM2 exhibits some capability in the simulation of peanut (Arachis hypogaea L.) yields, depending upon the convective scheme employed and the site selected.

scholarly journals Interactive coupling of regional atmosphere with biosphere in the new generation regional climate system model REMO-iMOVE

2014 ◽  
Vol 7 (3) ◽  
pp. 1093-1114 ◽  
Author(s):  
C. Wilhelm ◽  
D. Rechid ◽  
D. Jacob
Keyword(s):  
Land Surface ◽  
Regional Climate ◽  
Regional Model ◽  
Horizontal Resolution ◽  
Soil Conditions ◽  
Data Sets ◽  
Vegetation Phenology ◽  
Land Surface Scheme ◽  
New Model ◽  
Surface Scheme

Abstract. The main objective of this study is the coupling of the regional climate model REMO with a new land surface scheme including dynamic vegetation phenology, and the evaluation of the new model version called REMO with interactive MOsaic-based VEgetation: REMO-iMOVE. First, we focus on the documentation of the technical aspects of the new model constituents and the coupling mechanism. The representation of vegetation in iMOVE is based on plant functional types (PFTs). Their geographical distribution is prescribed to the model which can be derived from different land surface data sets. Here, the PFT distribution is derived from the GLOBCOVER 2000 data set which is available on 1 km × 1 km horizontal resolution. Plant physiological processes like photosynthesis, respiration and transpiration are incorporated into the model. The vegetation modules are fully coupled to atmosphere and soil. In this way, plant physiological activity is directly driven by atmospheric and soil conditions at the model time step (two minutes to some seconds). In turn, the vegetation processes and properties influence the exchange of substances, energy and momentum between land and atmosphere. With the new coupled regional model system, dynamic feedbacks between vegetation, soil and atmosphere are represented at regional to local scale. In the evaluation part, we compare simulation results of REMO-iMOVE and of the reference version REMO2009 to multiple observation data sets of temperature, precipitation, latent heat flux, leaf area index and net primary production, in order to investigate the sensitivity of the regional model to the new land surface scheme and to evaluate the performance of both model versions. Simulations for the regional model domain Europe on a horizontal resolution of 0.44° had been carried out for the time period 1995–2005, forced with ECMWF ERA-Interim reanalyses data as lateral boundary conditions. REMO-iMOVE is able to simulate the European climate with the same quality as the parent model REMO2009. Differences in near-surface climate parameters can be restricted to some regions and are mainly related to the new representation of vegetation phenology. The seasonal and interannual variations in growth and senescence of vegetation are captured by the model. The net primary productivity lies in the range of observed values for most European regions. This study reveals the need for implementing vertical soil water dynamics in order to differentiate the access of plants to water due to different rooting depths. This gets especially important if the model will be used in dynamic vegetation studies.

Can a Regional Climate Model Improve the Ability to Forecast the North American Monsoon?

2012 ◽  
Vol 25 (23) ◽  
pp. 8212-8237 ◽  
Author(s):  
Christopher L. Castro ◽  
Hsin-I Chang ◽  
Francina Dominguez ◽  
Carlos Carrillo ◽  
Jae-Kyung Schemm ◽  
...  
Keyword(s):  
North American ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Warm Season ◽  
Regional Model ◽  
Precipitation Anomaly ◽  
Early Summer ◽  
North American Monsoon ◽  
The North ◽  
Temperature And Precipitation

Abstract Global climate models are challenged to represent the North American monsoon, in terms of its climatology and interannual variability. To investigate whether a regional atmospheric model can improve warm season forecasts in North America, a retrospective Climate Forecast System (CFS) model reforecast (1982–2000) and the corresponding NCEP–NCAR reanalysis are dynamically downscaled with the Weather Research and Forecasting model (WRF), with similar parameterization options as used for high-resolution numerical weather prediction and a new spectral nudging capability. The regional model improves the climatological representation of monsoon precipitation because of its more realistic representation of the diurnal cycle of convection. However, it is challenged to capture organized, propagating convection at a distance from terrain, regardless of the boundary forcing data used. Dynamical downscaling of CFS generally yields modest improvement in surface temperature and precipitation anomaly correlations in those regions where it is already positive in the global model. For the North American monsoon region, WRF adds value to the seasonally forecast temperature only in early summer and does not add value to the seasonally forecast precipitation. CFS has a greater ability to represent the large-scale atmospheric circulation in early summer because of the influence of Pacific SST forcing. The temperature and precipitation anomaly correlations in both the global and regional model are thus relatively higher in early summer than late summer. As the dominant modes of early warm season precipitation are better represented in the regional model, given reasonable large-scale atmospheric forcing, dynamical downscaling will add value to warm season seasonal forecasts. CFS performance appears to be inconsistent in this regard.

scholarly journals Modeling agriculture in the Community Land Model

2013 ◽  
Vol 6 (2) ◽  
pp. 495-515 ◽  
Author(s):  
B. Drewniak ◽  
J. Song ◽  
J. Prell ◽  
V. R. Kotamarthi ◽  
R. Jacob
Keyword(s):  
United States ◽  
Crop Yield ◽  
Crop Yields ◽  
The United States ◽  
Crop Model ◽  
Planting Date ◽  
Climate Impacts ◽  
Residue Management ◽  
Temperature Threshold ◽  
Community Land Model

Abstract. The potential impact of climate change on agriculture is uncertain. In addition, agriculture could influence above- and below-ground carbon storage. Development of models that represent agriculture is necessary to address these impacts. We have developed an approach to integrate agriculture representations for three crop types – maize, soybean, and spring wheat – into the coupled carbon–nitrogen version of the Community Land Model (CLM), to help address these questions. Here we present the new model, CLM-Crop, validated against observations from two AmeriFlux sites in the United States, planted with maize and soybean. Seasonal carbon fluxes compared well with field measurements for soybean, but not as well for maize. CLM-Crop yields were comparable with observations in countries such as the United States, Argentina, and China, although the generality of the crop model and its lack of technology and irrigation made direct comparison difficult. CLM-Crop was compared against the standard CLM3.5, which simulates crops as grass. The comparison showed improvement in gross primary productivity in regions where crops are the dominant vegetation cover. Crop yields and productivity were negatively correlated with temperature and positively correlated with precipitation, in agreement with other modeling studies. In case studies with the new crop model looking at impacts of residue management and planting date on crop yield, we found that increased residue returned to the litter pool increased crop yield, while reduced residue returns resulted in yield decreases. Using climate controls to signal planting date caused different responses in different crops. Maize and soybean had opposite reactions: when low temperature threshold resulted in early planting, maize responded with a loss of yield, but soybean yields increased. Our improvements in CLM demonstrate a new capability in the model – simulating agriculture in a realistic way, complete with fertilizer and residue management practices. Results are encouraging, with improved representation of human influences on the land surface and the potentially resulting climate impacts.

scholarly journals Reconstruction of droughts in India using multiple land-surface models (1951–2015)

2018 ◽  
Vol 22 (4) ◽  
pp. 2269-2284 ◽  
Author(s):  
Vimal Mishra ◽  
Reepal Shah ◽  
Syed Azhar ◽  
Harsh Shah ◽  
Parth Modi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Crop Yields ◽  
Soil Depth ◽  
Monsoon Season ◽  
Agricultural Drought ◽  
Drought Severity ◽  
Rabi Season ◽  
Community Land Model ◽  
Spatial Coverage

Abstract. India has witnessed some of the most severe historical droughts in the current decade, and severity, frequency, and areal extent of droughts have been increasing. As a large part of the population of India is dependent on agriculture, soil moisture drought affecting agricultural activities (crop yields) has significant impacts on socio-economic conditions. Due to limited observations, soil moisture is generally simulated using land-surface hydrological models (LSMs); however, these LSM outputs have uncertainty due to many factors, including errors in forcing data and model parameterization. Here we reconstruct agricultural drought events over India during the period of 1951–2015 based on simulated soil moisture from three LSMs, the Variable Infiltration Capacity (VIC), the Noah, and the Community Land Model (CLM). Based on simulations from the three LSMs, we find that major drought events occurred in 1987, 2002, and 2015 during the monsoon season (June through September). During the Rabi season (November through February), major soil moisture droughts occurred in 1966, 1973, 2001, and 2003. Soil moisture droughts estimated from the three LSMs are comparable in terms of their spatial coverage; however, differences are found in drought severity. Moreover, we find a higher uncertainty in simulated drought characteristics over a large part of India during the major crop-growing season (Rabi season, November to February: NDJF) compared to those of the monsoon season (June to September: JJAS). Furthermore, uncertainty in drought estimates is higher for severe and localized droughts. Higher uncertainty in the soil moisture droughts is largely due to the difference in model parameterizations (especially soil depth), resulting in different persistence of soil moisture simulated by the three LSMs. Our study highlights the importance of accounting for the LSMs' uncertainty and consideration of the multi-model ensemble system for the real-time monitoring and prediction of drought over India.

scholarly journals Relative Roles of Large-Scale Orography and Land Surface Processes in the Global Hydroclimate. Part II: Impacts on Hydroclimate over Eurasia

2006 ◽  
Vol 7 (4) ◽  
pp. 642-659 ◽  
Author(s):  
Kazuyuki Saito ◽  
Tetsuzo Yasunari ◽  
Kumiko Takata
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Warm Season ◽  
Summer Precipitation ◽  
Winter Precipitation ◽  
Bare Rock ◽  
Surface Scheme ◽  
Water Holding ◽  
East West ◽  
Higher Sensitivity

Abstract A series of simplistic simulations from an AGCM coupled to a simple land surface scheme and water vapor tracers was performed to explore the relative roles of basic factors in land surface conditions, with regard to the seasonal evolution of the hydroclimate over Eurasia. Large-scale orography in Asia and vegetation (further decomposed to soil and vegetation skin) were evaluated, with orography represented in the model by surface altitude, soil represented by water-holding capacity, and vegetation skin represented by surface albedo and roughness. The percentage of global annual precipitation over land (occupying 25.6% of the total surface) was 14.8%, 15.0%, and 21.7% for the mountainless “bare rock” (i.e., vegetationless) surface, and the bare-rock and vegetated surface, respectively. The result for evaporation was 8.9%, 9.0%, and 16.2%, respectively, showing higher sensitivity to the land surface changes than precipitation. The orography and vegetation (i.e., soil and vegetation skin) showed different impacts on Eurasian hydroclimate on the seasonal and regional scales. Thermodynamical forcings to the atmosphere increased over the continent with the inclusion of both. Large-scale orography in Asia exerted east–west contrast in the surface energy exchange in summer in eastern Eurasia. An increase in extratropical winter precipitation with mountains was also noticed because of the atmospheric vapor transport changes. Impact of soil and vegetation skin was clearly found in the warm season in the extratropics; soil impacts extratropical summer precipitation due to enhanced recycling of water and the resultant increased water supply.

scholarly journals Modeling agriculture in the Community Land Model

2012 ◽  
Vol 5 (4) ◽  
pp. 4137-4185 ◽  
Author(s):  
B. Drewniak ◽  
J. Song ◽  
J. Prell ◽  
V. R. Kotamarthi ◽  
R. Jacob
Keyword(s):  
Crop Yield ◽  
Management Practices ◽  
Crop Yields ◽  
The United States ◽  
Crop Model ◽  
Planting Date ◽  
Climate Impacts ◽  
Residue Management ◽  
Temperature Threshold ◽  
Community Land Model

Abstract. The potential impact of climate change on agriculture is uncertain. In addition, agriculture could influence above- and below-ground carbon storage. Development of models that represent agriculture is necessary to address these impacts. We have developed an approach to integrate agriculture representations for three crop types – maize, soybean, and spring wheat – into the coupled carbon-nitrogen version of the Community Land Model (CLM), to help address these questions. Here we present the new model, CLM-Crop, validated against observations from two AmeriFlux sites in the United States, planted with maize and soybean. Seasonal carbon fluxes compared well with field measurements. CLM-Crop yields were comparable with observations in some regions, although the generality of the crop model and its lack of technology and irrigation made direct comparison difficult. CLM-Crop was compared against the standard CLM3.5, which simulates crops as grass. The comparison showed improvement in gross primary productivity in regions where crops are the dominant vegetation cover. Crop yields and productivity were negatively correlated with temperature and positively correlated with precipitation. In case studies with the new crop model looking at impacts of residue management and planting date on crop yield, we found that increased residue returned to the litter pool increased crop yield, while reduced residue returns resulted in yield decreases. Using climate controls to signal planting date caused different responses in different crops. Maize and soybean had opposite reactions: when low temperature threshold resulted in early planting, maize responded with a loss of yield, but soybean yields increased. Our improvements in CLM demonstrate a new capability in the model – simulating agriculture in a realistic way, complete with fertilizer and residue management practices. Results are encouraging, with improved representation of human influences on the land surface and the potentially resulting climate impacts.

Warm Season Evaluation of Soil Moisture Prediction in the Soil, Vegetation, and Snow (SVS) Scheme

2016 ◽  
Vol 17 (8) ◽  
pp. 2315-2332 ◽  
Author(s):  
Nasim Alavi ◽  
Stéphane Bélair ◽  
Vincent Fortin ◽  
Shunli Zhang ◽  
Syed Z. Husain ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Land Surface ◽  
Root Zone ◽  
Weather Prediction ◽  
Warm Season ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Large Set ◽  
Surface Scheme

Abstract A new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in higher correlations with observations and reduced errors. The sensitivity tests carried out during this study revealed that the SVS soil moisture results are not affected significantly by the soil texture data from different sources. The vegetation data source, however, has a major impact on the soil moisture results predicted by SVS, and accurate specification of vegetation characteristics is therefore crucial for accurate soil moisture prediction.

scholarly journals A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

2020 ◽  
Vol 12 (10) ◽  
pp. 1641
Author(s):  
Yunfei Zhang ◽  
Yunhao Chen ◽  
Jing Li ◽  
Xi Chen
Keyword(s):  
Land Surface ◽  
Missing Values ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Shortwave Radiation ◽  
Simple Method ◽  
The Real ◽  
Independent Variables ◽  
Clear Sky ◽  
Reconstruction Methods

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

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