scholarly journals Assessing methane emissions for northern peatlands in ORCHIDEE-PEAT revision 7020

2021 ◽  
Author(s):  
Elodie Salmon ◽  
Fabrice Jégou ◽  
Bertrand Guenet ◽  
Line Jourdain ◽  
Chunjing Qiu ◽  
...  
Keyword(s):  
Methane Production ◽  
Land Surface ◽  
Soil Depth ◽  
Land Surface Model ◽  
Methane Emissions ◽  
Surface Model ◽  
Model Parameters ◽  
Methane Budget ◽  
Northern Peatlands ◽  
Over The Top

Abstract. In the global methane budget, the largest natural source is attributed to wetlands that encompass all ecosystems composed of waterlogged or inundated ground, capable of methane production. Among them, northern peatlands that store large amounts of soil organic carbon have been functioning, since the end of the last glaciation period, as long-term sources of methane (CH4) and are one of the most significant methane sources among wetlands. To reduce global methane budget uncertainties, it is of significance to understand processes driving methane production and fluxes in northern peatlands. A methane model that features methane production and transport by plants, ebullition process and diffusion in soil, oxidation to CO2 and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4 was calibrated and evaluated on 14 peatland sites distributed on both Eurasian and American continents in the northern boreal and temperate regions. Data assimilation approaches were employed to optimized parameters at each site and at all sites simultaneously. Results show that, in ORCHIDEE-PCH4, methanogenesis is sensitive to temperature and substrate availability over the top 75 cm of soil depth. Methane emissions estimated using single site optimization (SSO) of model parameters are underestimated by 9 g CH4 m−2 year−1 on average (i.e. 50 % higher than the site average of yearly methane emissions). While using the multi-sites optimization (MSO), methane emissions are overestimated by 5 g CH4 m−2 year−1 on average across all investigated sites (i.e. 37 % lower than the site average of yearly methane emissions).

L'apport de la télédétection spatiale à la modélisation des surfaces continentales

2020 ◽  
pp. 052
Author(s):  
Jean-Christophe Calvet ◽  
Jean-Louis Champeaux
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Land Surface Model ◽  
Static Model ◽  
Geographic Information ◽  
Surface Model ◽  
Model Parameters

Cet article présente les différentes étapes des développements réalisés au CNRM des années 1990 à nos jours pour spatialiser à diverses échelles les simulations du modèle Isba des surfaces terrestres. Une attention particulière est portée sur l'intégration, dans le modèle, de données satellitaires permettant de caractériser la végétation. Deux façons complémentaires d'introduire de l'information géographique dans Isba sont présentées : cartographie de paramètres statiques et intégration au fil de l'eau dans le modèle de variables observables depuis l'espace. This paper presents successive steps in developments made at CNRM from the 1990s to the present-day in order to spatialize the simulations of the Isba land surface model at various scales. The focus is on the integration in the model of satellite data informative about vegetation. Two complementary ways to integrate geographic information in Isba are presented: mapping of static model parameters and sequential assimilation of variables observable from space.

scholarly journals Modelling northern peatlands area and carbon dynamics since the Holocene with the ORCHIDEE-PEAT land surface model (SVN r5488)

2018 ◽  
Author(s):  
Chunjing Qiu ◽  
Dan Zhu ◽  
Philippe Ciais ◽  
Bertrand Guenet ◽  
Shushi Peng ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Land Surface ◽  
Net Primary Production ◽  
Soil Column ◽  
Land Surface Model ◽  
Surface Model ◽  
Past Century ◽  
Positive Trend ◽  
Peat Core ◽  
Northern Peatlands

Abstract. The importance of northern peatlands in the global carbon cycle has recently been recognized, especially for long-term changes. Yet, the complex interactions between climate and peatland hydrology, carbon storage and area dynamics make it challenging to represent these systems in land surface models. This study describes how peatland are included as an independent sub-grid hydrological soil unit (HSU) into the ORCHIDEE-MICT land surface model. The peatland soil column in this tile is characterized by multi-layered vertical water and carbon transport, and peat-specific hydrological properties. A cost-efficient TOPMODEL approach is implemented to simulate the dynamics of peatland area, calibrated by present-day wetland areas that are regularly inundated or subject to shallow water tables. The model is tested across a range of northern peatland sites and for gridded simulations over the Northern Hemisphere (> 30° N). Simulated northern peatland area (3.9 million km2), peat carbon stock (463 PgC) and peat depth are generally consistent with observed estimates of peatland area (3.4–4.0 million km2), peat carbon (270–540 PgC) and data compilations of peat core depths. Our results show that both net primary production (NPP) and heterotrophic respiration (HR) of northern peatlands increased over the past century in response to CO2 and climate change. NPP increased more rapidly than HR, and thus net ecosystem production (NEP) exhibited a positive trend, contributing a cumulative carbon storage of 11.13 Pg C since 1901, most of it being realized after the 1950s.

The Role of Different Sources of Uncertainty in Data Assimilation with Coupled Land Surface - Subsurface Models

2021 ◽  
Author(s):  
Eduardo Emilio Sanchez-Leon ◽  
Natascha Brandhorst ◽  
Bastian Waldowski ◽  
Ching Pui Hung ◽  
Insa Neuweiler ◽  
...  
Keyword(s):  
Plant Physiology ◽  
Data Assimilation ◽  
Land Surface ◽  
Reference Model ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Sources Of Uncertainty

<p>The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1×5km<sup>2</sup>, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.</p>

scholarly journals Detectability of Arctic methane sources at six sites performing continuous atmospheric measurements

2017 ◽  
Author(s):  
Thibaud Thonat ◽  
Marielle Saunois ◽  
Philippe Bousquet ◽  
Isabelle Pison ◽  
Zeli Tan ◽  
...  
Keyword(s):  
Land Surface ◽  
Transport Model ◽  
Land Surface Model ◽  
Arctic Region ◽  
The Arctic ◽  
Surface Model ◽  
Multiple Sources ◽  
Atmospheric Measurements ◽  
Methane Cycle ◽  
Methane Budget

Abstract. Understanding the recent evolution of methane emissions in the Arctic is necessary to interpret the global methane cycle. Emissions are affected by significant uncertainties and are sensitive to climate change, leading to potential feedbacks. A polar version of the CHIMERE chemistry-transport model is used to simulate the evolution of tropospheric methane in the Arctic during 2012, including all known regional anthropogenic and natural sources. CHIMERE simulations are compared to atmospheric continuous observations at six measurement sites in the Arctic region. In winter, the Arctic is dominated by anthropogenic emissions; emissions from continental seepages and oceans, including from the East Siberian Arctic Shelf, can contribute significantly in more limited areas. In summer, emissions from wetland and freshwater sources dominate across the whole region. The model is able to reproduce the seasonality and synoptic variations of methane measured at the different sites. We find that all methane sources significantly affect the measurements at all stations at least at the synoptic scale, except for biomass burning; this indicates the relevance of continuous observations to gain a mechanistic understanding of Arctic methane sources. Sensitivity tests reveal that the choice of the land surface model used to prescribe wetland emissions can be critical in correctly representing methane concentrations. Also testing different freshwater emission inventories leads to large differences in modelled methane. Attempts to include methane sinks (OH oxidation and soil uptake) reduced the model bias relative to observed atmospheric CH4. The study illustrates how multiple sources, having different spatiotemporal dynamics and magnitudes, jointly influence the overall Arctic methane budget, and highlights ways towards further improved assessments.

Improving soil moisture estimation through a dual-cycle assimilation strategy

2020 ◽  
Author(s):  
Jiaxin Tian ◽  
Jun Qin ◽  
Kun Yang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Significant Implication ◽  
Large Spatial Scale ◽  
Dual Cycle ◽  
Moisture Estimation

<p>Soil moisture plays a key role in land surface processes. Both remote sensing and model simulation have their respective limitations in the estimation of soil moisture on a large spatial scale. Data assimilation is a promising way to merge remote sensing observation and land surface model (LSM), thus having a potential to acquire more accurate soil moisture. Two mainstream assimilation algorithms (variational-based and sequential-based) both need model and observation uncertainties due to their great impact on assimilation results. Besides, as far as land surface models are concerned, model parameters have a significant implication for simulation. However, how to specify these two uncertainties and parameters has been confusing for a long time. A dual-cycle assimilation algorithm, which consists of two cycles, is proposed for addressing the above issue. In the outer cycle, a cost function is constructed and minimized to estimate model parameters and uncertainties in both model and observation. In the inner cycle, a sequentially based filtering method is implemented to estimate soil moisture with the parameters and uncertainties estimated in the outer cycle. For the illustration of the effectiveness of the proposed algorithm, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures are assimilated into land surface model with a radiative transfer model as the observation operator in three experimental fields, including Naqu and Ngari on the Tibetan Plateau, and Coordinate Enhanced Observing (CEOP) reference site on Mongolia. The results indicate that the assimilation algorithm can significantly improve soil moisture estimation.</p>

scholarly journals Modelling river runoff and estimating its weather-related uncertainty for 11 large-scale rivers located in different regions of the globe

2017 ◽  
Vol 49 (4) ◽  
pp. 1072-1087 ◽  
Author(s):  
Yeugeniy M. Gusev ◽  
Olga N. Nasonova ◽  
Evgeny E. Kovalev ◽  
Georgii V. Aizel
Keyword(s):  
River Runoff ◽  
Land Surface ◽  
Input Data ◽  
Large Scale ◽  
Land Surface Model ◽  
Grid Cell ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Natural Conditions

Abstract In order to study the possibility of reproducing river runoff with making use of the land surface model Soil Water–Atmosphere–Plants (SWAP) and information based on global data sets 11 river basins suggested within the framework of the Inter-Sectoral Impact Model Intercomparison Project and located in various regions of the globe under a wide variety of natural conditions were used. Schematization of each basin as a set of 0.5° × 0.5° computational grid cells connected by a river network was carried out. Input data including atmospheric forcing data and land surface parameters based, respectively, on the global WATCH and ECOCLIMAP data sets were prepared for each grid cell. Simulations of river runoff performed by SWAP with a priori input data showed poor agreement with observations. Optimization of a number of model parameters substantially improved the results. The obtained results confirm the universal character of SWAP. Natural uncertainty of river runoff caused by weather noise was estimated and analysed. It can be treated as the lowest limit of predictability of river runoff. It was shown that differences in runoff uncertainties obtained for different rivers depend greatly on natural conditions of a river basin, in particular, on the ratio of deterministic and random components of the river runoff.

scholarly journals Multi-site evaluation of the JULES land surface model using global and local data

2015 ◽  
Vol 8 (2) ◽  
pp. 295-316 ◽  
Author(s):  
D. Slevin ◽  
S. F. B. Tett ◽  
M. Williams
Keyword(s):  
Land Surface ◽  
Meteorological Data ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Local Data ◽  
Area Index ◽  
Phenology Model ◽  
Global Data

Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, gross primary productivity (GPP) estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16% across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so) were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30% across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7% reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI). Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model.

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