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

Krinner, Gerhard; Viovy, Nicolas; Noblet‐Ducoudré, Nathalie de; Ogée, Jérôme; Polcher, Jan; Friedlingstein, Pierre; Ciais, Philippe; Sitch, Stephen; Prentice, I. Colin

doi:10.1029/2003gb002199

Cited by 2,049 publications

(2,193 citation statements)

References 102 publications

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“…The ORCHIDEE (Organising Carbon and Hydrology in Dynamic Ecosystems) model (Krinner et al, 2005) has been implemented with a wetland CH 4 emissions scheme. This version of ORCHIDEE has been previously used to simulate the evolution of wetland CH 4 emissions under future climate change and to study the feedback between climate, atmospheric CH 4 , and CO 2 .…”

Section: Orchideementioning

confidence: 99%

“…Approaches to simulate wetland distribution in order to study the interaction between climate and free water bodies were developed by Coe (1997Coe ( , 1998 and Krinner (2003). The earliest attempt at wetland modelling for the purpose of estimating wetland CH 4 emissions was designed to estimate wetland emissions during the Last Glacial Maximum (LGM, ∼ 21 ka; Kaplan, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Wania

Melton²,

Hodson³

et al. 2013

Geosci. Model Dev.

187

180

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Abstract. The Wetland and Wetland CH 4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH 4 ) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO 2 ) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO 2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH 4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH 4 fluxes from wetland systems. Even though modelling of wetland extent and CH 4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH 4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

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Section: Orchideementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Wania

Melton²,

Hodson³

et al. 2013

Geosci. Model Dev.

187

180

View full text Add to dashboard Cite

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“…It has a low horizontal resolution of 2.58 in latitude and 3.758 in longitude, but it is a high-top model with 39 vertical levels up to 4 Pa. The land surface model is ORCHIDEE (Krinner et al 2005) with the same resolution as the atmosphere. The ocean model component is NEMO (Madec 2008) with the ORCA2 grid configurationhorizontal resolution of 28 increasing to 0.58 at the equator-coupled to the LIM version 2 (LIM2) sea ice model (Fichefet and Morales Maqueda 1999).…”

Section: Introductionmentioning

confidence: 99%

Mechanisms Determining the Winter Atmospheric Response to the Atlantic Overturning Circulation

et al. 2016

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In climate models, an intensification of the Atlantic meridional overturning circulation (AMOC) precedes a warming in the North Atlantic subpolar basin by a few years. In the IPSL-CM5A-LR model, this warming may explain the atmospheric response to the AMOC observed in winter, which resembles a negative phase of the North Atlantic Oscillation (NAO). To firmly establish the causality links between the ocean and the atmosphere and illustrate the underlying mechanisms in this model, ensembles of atmosphere-only simulations are conducted, prescribing the SST and sea ice anomalies that follow an AMOC intensification. In late winter, the North Atlantic SST and sea ice anomalies drive atmospheric circulation anomalies similar to those found in the coupled model. Simulations only driven by the SST anomalies related to the AMOC show that the largest oceanic influence is due to the warm subpolar SST anomaly, which enhances the oceanic heat release and decreases the lower-tropospheric baroclinicity in the region of maximum eddy growth, resulting in a weaker meridional eddy heat flux in the atmosphere. The transient eddy feedback leads to a negative NAO-like response. An AMOC intensification is also followed by less sea ice over the Labrador Sea and more sea ice over the Nordic seas. The simulations with full boundary forcing suggest that such anomalies act to strengthen both the poleward momentum flux and the upward heat flux into the polar stratosphere and lead to a stratospheric warming, which then reinforces the negative NAO signal in late winter.

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“…This ensemble includes HTESSEL-CaMa (Balsamo et al, 2009), JULES Clark et al, 2011), LISFLOOD (van der Knijff et al, 2010), ORCHIDEE (Krinner et al, 2005;Ngo-Duc et al, 2007;d'Orgeval et al, 2008), SURFEX-TRIP (Alkama et al, 2010;Decharme et al, 2013), W3RA (van Dijk andWarren, 2010;van Dijk et al, 2014), WaterGAP3 (Flörke et al, 2013;Döll et al, 2009), PCR-GLOBWB (van Beek et al, 2011Wada et al, 2014), and SWBM (Orth et al, 2013). For consistency, we processed the model estimates in the same manner as our model simulations to directly compare modelled SWE and TWS to observations from GlobSnow and GRACE, respectively.…”

Section: Evaluation Of Model Performancementioning

confidence: 99%

Understanding terrestrial water storage variations in northern latitudes across scales

Trautmann

Koirala

Eicker

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The GRACE satellites provide signals of total terrestrial water storage (TWS) variations over large spatial domains at seasonal to inter-annual timescales. While the GRACE data have been extensively and successfully used to assess spatio-temporal changes in TWS, little effort has been made to quantify the relative contributions of snowpacks, soil moisture, and other components to the integrated TWS signal across northern latitudes, which is essential to gain a better insight into the underlying hydrological processes. Therefore, this study aims to assess which storage component dominates the spatio-temporal patterns of TWS variations in the humid regions of northern mid-to high latitudes.To do so, we constrained a rather parsimonious hydrological model with multiple state-of-the-art Earth observation products including GRACE TWS anomalies, estimates of snow water equivalent, evapotranspiration fluxes, and gridded runoff estimates. The optimized model demonstrates good agreement with observed hydrological spatio-temporal patterns and was used to assess the relative contributions of solid (snowpack) versus liquid (soil moisture, retained water) storage components to total TWS variations. In particular, we analysed whether the same storage component dominates TWS variations at seasonal and inter-annual temporal scales, and whether the dominating component is consistent across small to large spatial scales.Consistent with previous studies, we show that snow dynamics control seasonal TWS variations across all spatial scales in the northern mid-to high latitudes. In contrast, we find that inter-annual variations of TWS are dominated by liquid water storages at all spatial scales. The relative contribution of snow to inter-annual TWS variations, though, increases when the spatial domain over which the storages are averaged becomes larger. This is due to a stronger spatial coherence of snow dynamics that are mainly driven by temperature, as opposed to spatially more heterogeneous liquid water anomalies, that cancel out when averaged over a larger spatial domain. The findings first highlight the effectiveness of our model-data fusion approach that jointly interprets multiple Earth observation data streams with a simple model. Secondly, they reveal that the determinants of TWS variations in snow-affected northern latitudes are scale-dependent. In particular, they seem to be not merely driven by snow variability, but rather are determined by liquid water storages on interannual timescales. We conclude that inferred driving mechanisms of TWS cannot simply be transferred from one scale to another, which is of particular relevance for understanding the short-and long-term variability of water resources.

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

Cited by 2,049 publications

References 102 publications

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Mechanisms Determining the Winter Atmospheric Response to the Atlantic Overturning Circulation

Understanding terrestrial water storage variations in northern latitudes across scales

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