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Divyansh Chug and Francina Dominguez

1. Introduction Changes in the land surface, specifically vegetation, can modulate climate at different spatiotemporal scales (e.g., Bonan 1997 ; Dickinson and Henderson–Seller 1988 ; Bounoua et al. 2000 ). The atmosphere, through variations in precipitation, temperature, wind, CO 2 concentration, and radiation, dominates this complex two-way interaction ( Budyko 1974 ; Woodward, 1987 ; Nemani et al. 2003 ; Woodward et al. 2004 ). As a response, the vegetation exerts a feedback on the

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M. Decker, A. J. Pitman, and J. P. Evans

goal is to determine whether including groundwater is necessary in simulations of land–atmosphere interactions over SE Australia by evaluating the simulated transpiration response to drought using the observed vegetation response over various different vegetation types. 2. Methodology a. Model configuration and experiments We use the Community Land Model version 4 (CLM4) to simulate the land surface ( Oleson et al. 2010 ). CLM4, a one-dimensional water and energy balance land surface model, is the

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Kshitij Parajuli, Scott B. Jones, David G. Tarboton, Lawrence E. Hipps, Lin Zhao, Morteza Sadeghi, Mark L. Rockhold, Alfonso Torres-Rua, and Gerald N. Flerchinger

1. Introduction Land surface models (LSMs) have been used widely in studying interactions within the soil, vegetation and atmosphere continuum, in addition to predicting water and energy fluxes. Improved understanding of land–atmosphere interactions potentially enhances the ability of weather and climate models to predict future conditions ( Barlage et al. 2015 ; Chen and Dudhia 2001 ; Gao et al. 2015 ; Kumar et al. 2014 ; Sadeghi et al. 2019 ). Detailed land–atmosphere processes and

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Zhaosheng Wang, Mei Huang, Rong Wang, Shaoqiang Wang, Xiaodong Liu, Xiaoning Xie, Zhengjia Liu, He Gong, and Man Hao

, precipitation covaries with atmospheric water changes via ocean–atmosphere–land interactions, thus resulting in changes in vegetation growth. In brief, VIWV IPWP has a larger direct impact on the spatiotemporal distribution of global atmospheric water vapor and precipitation, and therefore indirectly affects TVG patterns through precipitation. However, the mechanism of indirect influence of ocean water vapor on TVG through atmospheric transmission is poorly understood. Notably, relatively little attention

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Celeste Saulo, Lorena Ferreira, Julia Nogués-Paegle, Marcelo Seluchi, and Juan Ruiz

1. Introduction The crucial role of land–atmosphere feedbacks on climate has long been recognized in the climate modeling community. Nevertheless, large uncertainties in the representation of surface processes continue to lead to poor understanding of land–atmosphere interactions. More recent, significant improvements of land surface process modeling have been made. These improvements are related to development of more sophisticated land surface models that, combined with available observations

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Brian J. Butterworth, Ankur R. Desai, Stefan Metzger, Philip A. Townsend, Mark D. Schwartz, Grant W. Petty, Matthias Mauder, Hannes Vogelmann, Christian G. Andresen, Travis J. Augustine, Timothy H. Bertram, William O. J. Brown, Michael Buban, Patricia Cleary, David J. Durden, Christopher R. Florian, Trevor J. Iglinski, Eric L. Kruger, Kathleen Lantz, Temple R. Lee, Tilden P. Meyers, James K. Mineau, Erik R. Olson, Steven P. Oncley, Sreenath Paleri, Rosalyn A. Pertzborn, Claire Pettersen, David M. Plummer, Laura D. Riihimaki, Eliceo Ruiz Guzman, Joseph Sedlar, Elizabeth N. Smith, Johannes Speidel, Paul C. Stoy, Matthias Sühring, Jonathan E. Thom, David D. Turner, Michael P. Vermeuel, Timothy J. Wagner, Zhien Wang, Luise Wanner, Loren D. White, James M. Wilczak, Daniel B. Wright, and Ting Zheng

Land–atmosphere exchanges of energy, water, and carbon influence weather and climate. The biological processes that mediate these exchanges with the atmosphere occur at multiple spatial and temporal scales, necessitating cross-scale observational platforms. Accurate accounting of land–atmosphere interactions is critical for improving the performance of numerical weather and climate models. Unfortunately, there is a persistent mismatch between the scales of observations and models. This scale

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Shuzhou Wang and Yaoming Ma

1. Introduction As the largest and highest plateau in the world, the Tibetan Plateau affects the general circulation of the atmosphere and plays a very important role in the Asian monsoon system. The land–atmosphere interactions not only affect the development of the local atmospheric boundary layer, but also change the horizontal gradient of temperature and moisture at a continent scale ( Yang et al. 2003 ). What is more, the land surface heterogeneity leads to differences in the surface

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T. J. Lyons, P. Schwerdtfeger, J. M. Hacker, I. J. Foster, R. C. G. Smith, and Huang Xinmei

Southwestern Australia, with a semiarid Mediterranean climate, has been extensively cleared of native vegetation for winter-growing agricultural species. The resultant reduction in evapotranspiration has increased land salinisation. Through detailed meteorological and vegetation measurements over both agricultural and native vegetation, the bunny fence experiment is addressing the impact on the climate of replacing native perennial vegetation with wintergrowing annual species. Such measurements will give a better understanding of the interaction between the land surface and the atmosphere and are important for improved parameterization of the boundary layer in climate models.

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Samuel Levis, Gordon B. Bonan, Erik Kluzek, Peter E. Thornton, Andrew Jones, William J. Sacks, and Christopher J. Kucharik

, they found increased net radiation and evapotranspiration, mainly in response to reduced surface albedo. Again, such studies do not account for possible two-way climate–crop interactions. Only Osborne et al. (2007 , 2009 ) performed global coupled atmosphere–land simulations with an interactive crop model. Osborne et al. used the crop model GLAM (a groundnut, i.e., warm climate crop, model) in the land component of the Hadley Centre Atmosphere Model, version 3 (HadAM3). Osborne et al. (2009

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M. Breil, D. Rechid, E. L. Davin, N. de Noblet-Ducoudré, E. Katragkou, R. M. Cardoso, P. Hoffmann, L. L. Jach, P. M. M. Soares, G. Sofiadis, S. Strada, G. Strandberg, M. H. Tölle, and K. Warrach-Sagi

simulated diurnal temperature cycles among the different LUCAS-Ensemble members, the treatment of the land–atmosphere exchange in the different LSMs is essential. Figure 3 shows a schematic structure of the interactions between the snow-free land surface and the atmosphere as described in a dual source LSM ( Fig. 3a ; CCLM-CLM4.5, CCLM-VEG3D, WRF-NoahMP, and WRF-CLM4.0) and a bulk LSM ( Fig. 3b ; CCLM-TERRA and REMO-iMOVE). For both LSM types, the surface is defined as the area at which the energy

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