Impact of a physically based soil water flow and soil‐plant interaction representation for modeling large‐scale land surface processes

Rosnay, Patricia de; Polcher, Jan; Bruen, Michael; Laval, K.

doi:10.1029/2001jd000634

Cited by 120 publications

(111 citation statements)

References 38 publications

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“…Future pertinent developments of the LPJ dynamic global vegetation model will have to be included in ORCHIDEE. The inclusion of a more detailed soil hydrology [de Rosnay et al, 2002], which hopefully will improve the model behavior in arid regions, will also be an important future development. Further developments will include, among others, a distinction between direct and diffuse solar radiation in the photosynthesis parameterizations.…”

Section: Discussionmentioning

confidence: 99%

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

Krinner

Viovy

Noblet‐Ducoudré

et al. 2005

Global Biogeochemical Cycles

1,996

2,063

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[1] This work presents a new dynamic global vegetation model designed as an extension of an existing surface-vegetation-atmosphere transfer scheme which is included in a coupled ocean-atmosphere general circulation model. The new dynamic global vegetation model simulates the principal processes of the continental biosphere influencing the global carbon cycle (photosynthesis, autotrophic and heterotrophic respiration of plants and in soils, fire, etc.) as well as latent, sensible, and kinetic energy exchanges at the surface of soils and plants. As a dynamic vegetation model, it explicitly represents competitive processes such as light competition, sapling establishment, etc. It can thus be used in simulations for the study of feedbacks between transient climate and vegetation cover changes, but it can also be used with a prescribed vegetation distribution. The whole seasonal phenological cycle is prognostically calculated without any prescribed dates or use of satellite data. The model is coupled to the IPSL-CM4 coupled atmosphereocean-vegetation model. Carbon and surface energy fluxes from the coupled hydrologyvegetation model compare well with observations at FluxNet sites. Simulated vegetation distribution and leaf density in a global simulation are evaluated against observations, and carbon stocks and fluxes are compared to available estimates, with satisfying results.

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Section: Discussionmentioning

confidence: 99%

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

Krinner

Viovy

Noblet‐Ducoudré

et al. 2005

Global Biogeochemical Cycles

1,996

2,063

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“…As documented in Table 2, most models use one-dimensional Richards' equation to simulate the time evolution of volumetric liquid water content [Chen and Dudhia, 2001;de Rosnay et al, 2002;Milly et al, 2014;Clark et al, 2015b] Water Resources Research where K5f h ð Þ is the hydraulic conductivity (m s 21 ) and w5f h ð Þ is the liquid water matric potential (m). The boundary conditions for equation (A1) are the fluxes at the soil surface (i.e., the infiltration of water into the soil profile, see section A1.1) and the fluxes at the bottom of the soil profile (section A1.2).…”

Section: A1 Storage and Transmission Through Soilsmentioning

confidence: 99%

Improving the representation of hydrologic processes in Earth System Models

Clark

Fan

Lawrence

et al. 2015

Water Resources Research

417

404

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Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large-scale land models (as a component of Earth System Models, or ESMs) do not yet reflect the best hydrologic process understanding or utilize the large amount of hydrologic observations for model testing. This paper discusses the opportunities and key challenges to improve hydrologic process representations and benchmarking in ESM land models, suggesting that (1) land model development can benefit from recent advances in hydrology, both through incorporating key processes (e.g., groundwater-surface water interactions) and new approaches to describe multiscale spatial variability and hydrologic connectivity; (2) accelerating model advances requires comprehensive hydrologic benchmarking in order to systematically evaluate competing alternatives, understand model weaknesses, and prioritize model development needs, and (3) stronger collaboration is needed between the hydrology and ESM modeling communities, both through greater engagement of hydrologists in ESM land model development, and through rigorous evaluation of ESM hydrology performance in research watersheds or Critical Zone Observatories. Such coordinated efforts in advancing hydrology in ESMs have the potential to substantially impact energy, carbon, and nutrient cycle prediction capabilities through the fundamental role hydrologic processes play in regulating these cycles.

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“…The Nemo ocean circulation model was also upgraded from OPA8 to OPA9 configuration, with in particular an improved vertical mixing scheme and an improved representation of solar absorption, as well as the Orchidee surface-vegetation-atmosphere transfer and dynamic vegetation model (de Rosnay et al 2002;Krinner et al 2005), with modified root profiles and an interactive Leaf Area Index computation. Those changes are detailed by Dufresne et al (this issue).…”

Section: Ipsl-cm5a Versus Ipsl-cm4mentioning

confidence: 99%

Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model

et al. 2012

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The IPSL-CM5A climate model was used to perform a large number of control, historical and climate change simulations in the frame of CMIP5. The refined horizontal and vertical grid of the atmospheric component, LMDZ, constitutes a major difference compared to the previous IPSL-CM4 version used for CMIP3. From imposed-SST (Sea Surface Temperature) and coupled numerical experiments, we systematically analyze the impact of the horizontal and vertical grid resolution on the simulated climate. The refinement of the horizontal grid results in a systematic reduction of major biases in the mean tropospheric structures and SST. The mid-latitude jets, located too close to the equator with the coarsest grids, move poleward. This robust feature, is accompanied by a drying at mid-latitudes and a reduction of cold biases in mid-latitudes relative to the equator. The model was also extended to the stratosphere by increasing the number of layers on the vertical from 19 to 39 (15 in the stratosphere) and adding relevant parameterizations. The 39-layer version captures the dominant modes of the stratospheric variability and exhibits stratospheric sudden warmings. Changing either the vertical or horizontal resolution modifies the global energy balance in imposed-SST simulations by typically several W/m 2 which translates in the coupled atmosphere-ocean simulations into a different global-mean SST. The sensitivity is of about 1.2 K per 1 W/m 2 when varying the horizontal grid. A re-tuning of model parameters was thus required to restore this energy balance in the imposed-SST simulations and reduce the biases in the simulated mean surface temperature and, to some extent, latitudinal SST variations in the coupled experiments for the modern climate. The tuning hardly compensates, however, for robust biases of the coupled model. Despite the wide range of grid configurations explored and their significant impact on the present-day climate, the climate sensitivity remains essentially unchanged.

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Impact of a physically based soil water flow and soil‐plant interaction representation for modeling large‐scale land surface processes

Cited by 120 publications

References 38 publications

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system

Improving the representation of hydrologic processes in Earth System Models

Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model

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