scholarly journals Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system

2018 ◽  
Author(s):  
Efrén López-Blanco ◽  
Jean-François Exbrayat ◽  
Magnus Lund ◽  
Torben R. Christensen ◽  
Mikkel P. Tamstorf ◽  
...  
Keyword(s):  
Data Assimilation ◽  
The Arctic ◽  
Soil Organic C ◽  
Area Index ◽  
Organic C ◽  
C Cycle ◽  
C Stocks ◽  
Vegetation Models ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. The Arctic accounts for approximately 50 % of the global soil organic C stock, emphasizing the important role of Arctic regions in the global C cycle. Recent studies have pointed to the poor understanding of C pools turnover, although remain unclear as to whether productivity or biomass dominate the biases. Here, we use an improved version of the CARDAMOM data-assimilation system, to produce pan-Arctic terrestrial C-related variables without using traditional plant functional type or steady-state assumptions. Our approach integrates a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely state of the high latitude C cycle at a 1° × 1° resolution for the first 15 years of the 21st century, but also to provide general guidance about the controlling biases in the turnover dynamics. As average, CARDAMOM estimates 513 (456, 579), 245 (208, 290) and 204 (109, 427) g C m−2 yr−1 (90 % confidence interval) from photosynthesis, autotrophic and heterotrophic respiration respectively, suggesting that the pan-Arctic region acted as a likely sink −55 (−152, 157) g C m−2 yr−1, weaker in tundra and stronger in taiga, but our confidence intervals remain large (and so the region could be a source of C). In general, we find a good agreement between CARDAMOM and different sources of assimilated and independent data at both pan-Arctic and local scale. Using CARDAMOM as a benchmarking tool for global vegetation models (GVM), we also conclude that turnover time of vegetation C is weakly simulated in vegetation models and is a major component of error in their forecasts. Our findings highlight that GVM modellers need to focus on the vegetation C stocks dynamics, but also their respiratory losses, to improve our process-based understanding of internal C cycle dynamics in the Arctic.

scholarly journals Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system

2019 ◽  
Vol 10 (2) ◽  
pp. 233-255 ◽  
Author(s):  
Efrén López-Blanco ◽  
Jean-François Exbrayat ◽  
Magnus Lund ◽  
Torben R. Christensen ◽  
Mikkel P. Tamstorf ◽  
...  
Keyword(s):  
The Arctic ◽  
Soil Organic C ◽  
Area Index ◽  
Organic C ◽  
Soil C ◽  
Transit Times ◽  
C Cycle ◽  
C Stocks ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. Recent studies have highlighted a poor understanding particularly of C pool transit times and of whether productivity or biomass dominate these biases. The Arctic, accounting for approximately 50 % of the global soil organic C stocks, has an important role in the global C cycle. Here, we use the CARbon DAta MOdel (CARDAMOM) data-assimilation system to produce pan-Arctic terrestrial C cycle analyses for 2000–2015. This approach avoids using traditional plant functional type or steady-state assumptions. We integrate a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely state of the high-latitude C cycle at a 1∘ × 1∘ resolution and also to provide general guidance about the controlling biases in transit times. On average, CARDAMOM estimates regional mean rates of photosynthesis of 565 g C m−2 yr−1 (90 % confidence interval between the 5th and 95th percentiles: 428, 741), autotrophic respiration of 270 g C m−2 yr−1 (182, 397) and heterotrophic respiration of 219 g C m−2 yr−1 (31, 1458), suggesting a pan-Arctic sink of −67 (−287, 1160) g Cm−2 yr−1, weaker in tundra and stronger in taiga. However, our confidence intervals remain large (and so the region could be a source of C), reflecting uncertainty assigned to the regional data products. We show a clear spatial and temporal agreement between CARDAMOM analyses and different sources of assimilated and independent data at both pan-Arctic and local scales but also identify consistent biases between CARDAMOM and validation data. The assimilation process requires clearer error quantification for leaf area index (LAI) and biomass products to resolve these biases. Mapping of vegetation C stocks and change over time and soil C ages linked to soil C stocks is required for better analytical constraint. Comparing CARDAMOM analyses to global vegetation models (GVMs) for the same period, we conclude that transit times of vegetation C are inconsistently simulated in GVMs due to a combination of uncertainties from productivity and biomass calculations. Our findings highlight that GVMs need to focus on constraining both current vegetation C stocks and net primary production to improve a process-based understanding of C cycle dynamics in the Arctic.

Enriched nutrient availability strengthens the net C uptake of the northernmost ecosystem station in Greenland

2020 ◽  
Author(s):  
Efrén López-Blanco ◽  
Marcin Jackowicz-Korczynski ◽  
Mikhail Mastepanov ◽  
Kirstine Skov ◽  
Andreas Westergaard-Nielsen ◽  
...  
Keyword(s):  
The Arctic ◽  
Leaf Mass Per Area ◽  
Sink Strength ◽  
Sampling Point ◽  
Arctic Ecosystems ◽  
Ancillary Data ◽  
Area Index ◽  
Leaf Mass ◽  
C Cycle ◽  
C Stocks

<p>Although the Arctic tundra is an essential contributor to the global carbon (C) cycle, there is a lack of reference sites from where full C exchange dynamics can be characterized under harsh conditions and remoteness. The Greenland Ecosystem Monitoring (GEM) programme efforts have envisioned integrated and long-term activities to contribute to the basic scientific understanding of the Arctic and their responses to climate changes. Here we present 20+ years across the 2008-2018 period of C flux and ancillary data from two twin ecosystem stations in Greenland: Zackenberg (74°N) and Kobbefjord (64°N). In this project we show that Zackenberg fen has a significant higher C sink strength in a higher latitude during regularly shorter growing seasons compared to Kobbefjord fen. This ecosystem acted as a sink of CO<sub>2</sub> uptaking on average -50 g C m<sup>-2</sup> (range of +21 to -90 g C m<sup>-2</sup>), more than twice compared to Kobbefjord (-18 g C m<sup>-2 </sup>as average and range of +41 to -41 g C m<sup>-2</sup>). We found that Zackenberg is a nutrient richer fen - the increased C uptake strength is associated with 3 times higher levels in soils of dissolved organic carbon and 5 times more plant nutrients, including dissolved organic nitrogen, nitrates. Additional evidences from in-situ sampling point to higher leaf area index (140%), foliar nitrogen (71%), and leaf mass per area (5%) in the northernmost site supporting the nutrient richer hypothesis. To test this overarching hypothesis, we further used the Soil-Plant-Atmosphere (SPA) model. We can explain ~68%, ~80% and ~67% of the variability of daily net ecosystem exchange of CO<sub>2</sub>, photosynthesis and respiration respectively applying the model parameterization previously used in Kobbefjord but with increases in initial C stocks, leaf mass per area, N content and Q<sub>10 </sub>of foliar and root respiration rates. Therefore, we conclude that the limitations of plant phenology timing in Zackenberg regarding net C uptake have not only been counterbalanced but also intensified due to richer compositions of nutrients and minerals. <span>More high-temporal monitoring activities in Arctic ecosystems are needed not only to allow straightforward comparisons of key biogeochemical processes but also to help us understand the underlying differences in sensitive and rapidly changing ecosystems. </span></p>

scholarly journals Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France

2010 ◽  
Vol 7 (2) ◽  
pp. 1705-1744 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
C. Rüdiger ◽  
A. L. Barbu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Fluxes ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Atmospheric Variables ◽  
Assimilation System

Abstract. A Land Data Assimilation System (LDAS) able to ingest surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested at local scale to increase prediction accuracy for water and carbon fluxes. The ISBA-A-gs Land Surface Model (LSM) is used together with LAI and the soil water content observations of a grassland at the SMOSREX experimental site in southwestern France for a seven-year period (2001–2007). Three configurations corresponding to contrasted model errors are considered: (1) best case (BC) simulation with locally observed atmospheric variables and model parameters, and locally observed SSM and LAI used in the assimilation, (2) same as (1) but with the precipitation forcing set to zero, (3) real case (RC) simulation with atmospheric variables and model parameters derived from regional atmospheric analyses and from climatological soil and vegetation properties, respectively. In configuration (3) two SSM time series are considered: the observed SSM using Thetaprobes, and SSM derived from the LEWIS L-band radiometer located 15 m above the ground. Performance of the LDAS is examined in the three configurations described above with either one variable (either SSM or LAI) or two variables (both SSM and LAI) assimilated. The joint assimilation of SSM and LAI has a positive impact on the carbon, water, and heat fluxes. It represents a greater impact than assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to counterbalance large errors in the precipitation forcing given as input to the model.

The effects of assimilating a sub-grid scale sea ice thickness distribution in a new Arctic sea ice data assimilation system

2021 ◽  
Author(s):  
Nicholas Williams ◽  
Nicholas Byrne ◽  
Daniel Feltham ◽  
Peter Jan Van Leeuwen ◽  
Ross Bannister ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Data Assimilation System ◽  
Assimilation System

<div><span>A modified, standalone version of the Los Alamos Sea Ice Model (CICE) has been coupled to the Parallelized Data Assimilation Framework (PDAF) to produce a new Arctic sea ice data assimilation system CICE-PDAF, with routines for assimilating many types of recently developed sea ice observations. In this study we explore the effects of assimilating a sub-grid scale sea ice thickness distribution derived from Cryosat-2 Arctic sea ice estimates into CICE-PDAF. The true state of the sub-grid scale ice thickness distribution is not well established, and yet it plays a key role in large scale sea ice models and is vital to the dynamical and thermodynamical processes necessary to produce a good representation of the Arctic sea ice state. We examine how assimilating sub-grid scale sea ice thickness distributions can affect the evolution of the sea ice state in CICE-PDAF and better our understanding of the Arctic sea ice system.</span></div>

Impact study of scatterometer observations with improved representation error in an Arctic data assimilation system

2020 ◽  
Author(s):  
Máté Mile ◽  
Roger Randriamampianina ◽  
Gert-Jan Marseille
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Satellite Observations ◽  
The Arctic ◽  
Limited Area ◽  
Observation Operator ◽  
Representation Error ◽  
Data Assimilation System ◽  
Assimilation System

<p align="justify">Nowadays, satellite observations are providing primary information for initial conditions of state-of-the-art numerical weather prediction (NWP) systems and the amount of remote sensing data in the Global Observing System increases rapidly. However, the way such data are assimilated is usually conservative and sub-optimal especially in high resolution limited-area models. Our objective is to improve the use of scatterometer observations from polar-orbiting satellites by taking into account the observation footprint and reducing the observation representation error through the observation operator.</p><p align="justify"> </p><p align="justify">The variational assimilation system (including 3D- and 4D-Var) of HARMONIE-AROME is widely used for research and operational NWP purposes by many European countries. In most cases, the HARMONIE-AROME model and its data assimilation are run on higher resolution (corresponding to around 2.5km grid size or smaller) than the effective resolution of some satellite observations (e.g. the effective resolution of scatterometer instruments). The use of ASCAT scatterometer observations is studied in an Arctic data assimilation system (AROME-Arctic) and a new observation operator (called supermodding) is evaluated in terms of scatterometer representation error. The results are demonstrated through data assimilation diagnostics, observing system experiments and case studies focusing on the challenges of the Arctic weather forecasting as well.</p>

scholarly journals TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 633-656 ◽  
Author(s):  
P. Sakov ◽  
F. Counillon ◽  
L. Bertino ◽  
K. A. Lisæter ◽  
P. R. Oke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
North Atlantic ◽  
The Arctic ◽  
Ensemble Spread ◽  
The North ◽  
State Dependent ◽  
The North Atlantic ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.

scholarly journals TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

2012 ◽  
Vol 9 (2) ◽  
pp. 1519-1575 ◽  
Author(s):  
P. Sakov ◽  
F. Counillon ◽  
L. Bertino ◽  
K. A. Lisæter ◽  
P. R. Oke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
North Atlantic ◽  
The Arctic ◽  
Ensemble Spread ◽  
The North ◽  
State Dependent ◽  
The North Atlantic ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation and the sea ice. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.

scholarly journals Monitoring of water and carbon fluxes using a land data assimilation system: a case study for southwestern France

2010 ◽  
Vol 14 (6) ◽  
pp. 1109-1124 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
C. Rüdiger ◽  
A. L. Barbu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Fluxes ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Atmospheric Variables ◽  
Assimilation System

Abstract. A Land Data Assimilation System (LDAS) able to ingest surface soil moisture (SSM) and Leaf Area Index (LAI) observations is tested at local scale to increase prediction accuracy for water and carbon fluxes. The ISBA-A-gs Land Surface Model (LSM) is used together with LAI and the soil water content observations of a grassland at the SMOSREX experimental site in southwestern France for a seven-year period (2001–2007). Three configurations corresponding to contrasted model errors are considered: (1) best case (BC) simulation with locally observed atmospheric variables and model parameters, and locally observed SSM and LAI used in the assimilation, (2) same as (1) but with the precipitation forcing set to zero, (3) real case (RC) simulation with atmospheric variables and model parameters derived from regional atmospheric analyses and from climatological soil and vegetation properties, respectively. In configuration (3) two SSM time series are considered: the observed SSM using Thetaprobes, and SSM derived from the LEWIS L-band radiometer located 15m above the ground. Performance of the LDAS is examined in the three configurations described above with either one variable (either SSM or LAI) or two variables (both SSM and LAI) assimilated. The joint assimilation of SSM and LAI has a positive impact on the carbon, water, and heat fluxes. It represents a greater impact than assimilating one variable (either LAI or SSM). Moreover, the LDAS is able to counterbalance large errors in the precipitation forcing given as input to the model.

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