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Geology topics

M. Chen

Publications and source records attributed to M. Chen.

6 recordsLinked to original sources
USGS2010

An analysis of the carbon balance of the Arctic Basin from 1997 to 2006

This study used several model-based tools to analyse the dynamics of the Arctic Basin between 1997 and 2006 as a linked system of land-ocean-atmosphere C exchange. The analysis estimates that terrestrial areas of the Arctic Basin lost 62.9 Tg C yr -1 and that the Arctic Ocean gained 94.1 Tg C yr -1 . Arctic lands and oceans were a net CO 2 sink of 108.9 Tg C yr -1 , which is within the range of uncertainty in estimates from atmospheric inversions. Although both lands and oceans of the Arctic were estimated to be CO 2 sinks, the land sink diminished in strength because of increased fire disturbance compared to previous decades, while the ocean sink increased in strength because of increased biological pump activity associated with reduced sea ice cover. Terrestrial areas of the Arctic were a net source of 41.5 Tg CH 4 yr -1 that increased by 0.6 Tg CH 4 yr -1 during the decade of analysis, a magnitude that is comparable with an atmospheric inversion of CH 4 . Because the radiative forcing of the estimated CH 4 emissions is much greater than the CO 2 sink, the analysis suggests that the Arctic Basin is a substantial net source of green house gas forcing to the climate system.

Tellus, Series B: Chemical and Physical Meteorolog↗
USGS2008

Model-data fusion in studies of the terrestrial carbon sink

Current uncertainty in quantifying the global carbon budget remains a major contributing source of uncertainty in reliably projecting future climate change. Furthermore, quantifying the global carbon budget and characterising uncertainties have emerged as critical to a successful implementation of the United Nations Framework Convention on Climate Change and its Kyoto Protocol. Beyond fundamental quantification, attribution of the processes responsible for the so-called ‘residual terrestrial uptake’ is important to the carbon cycle communities' ability to simulate the future response of the terrestrial biosphere to climate change and intentional sequestration activities. The objective of this chapter is to describe the approaches to model-data fusion enabling continued advances in quantifying carbon cycling and the terrestrial mechanisms at work. The major impediments to advances in this field include accounting for climate variability and uncertainties in model outcomes. One proposed solution to overcome these obstacles is the use of data from the FLUXNET network to characterise the relative strength of climate impact on plant productivity and respiration. Other solutions involve the use of atmospheric CO 2 concentration measurements for model validation and the use of remote sensing data.

Book chapter↗
USGS2007

Optimization of an ecosystem model through the assimilation of eddy flux observations using a smoothed ensemble Kalman filter

The parameters of ecosystem models are conventionally optimized through nonsequential inversion methods, which treat observations as a whole and lack the flexibility to investigate possible temporal evolution of the model parameters. This research developed a smoothed ensemble Kalman filter (SEnKF) to assess to what extent the parameters and state variables of an ecosystem model can be simultaneously optimized through the assimilation of eddy flux observations. The performance of the SEnKF was demonstrated in one case study: the assimilation of measurements of carbon exchange between a mixed forest and the atmosphere at Niwot Ridge Forest (Colorado, USA) from 2000 to 2004 into a carbon flux partition model. Our analyses demonstrated that some model parameters, such as light use efficiency and respiration coefficients, were highly constrained by eddy flux data at daily to seasonal time scales. Light use efficiency was strongly seasonal. Model predictions based on parameters modified by the SEnKF were much improved, compared to predictions made without progressive data assimilation. The SEnKF reduced the variance of state variables that is caused by uncertainties of parameters and driving variables. The analysis of net ecosystem exchange of carbon between the forest and the atmosphere was improved.

Conference Paper↗
USGS2006

Model-data fusion in the studies of terrestrial carbon sink

Current uncertainty in quantifying the global carbon budget remains a major contributing source of uncertainty in reliably projecting future climate change. Furthermore, quantifying the global carbon budget and characterizing uncertainties have emerged as critical to a successful implementation of United National Framework Convention on Climate Change and its Kyoto Protocol. Beyond fundamental quantification, attribution of the processes responsible for the so-called ‘residual terrestrial uptake’ is important to the carbon cycle communities’ ability to simulated the future response of the terrestrial biosphere to climate change and intentional sequestration activities. This paper’s objective is to describe the efforts of the workshop participants and their approaches to model-data fusion enabling continued advances in the solution of quantifying carbon cycling and the terrestrial mechanisms at work.

Conference Paper↗
USGS2006

State-parameter estimation of ecosystem models using a smoothed ensemble Kalman filter

Much of the effort in data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the other hand, the accuracy of estimating state variables may be reduced if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) to estimate simultaneously the system states and model parameters of an eddy flux partition model. The approach is used to assimilate observed fluxes of carbon and major driving forces at an AmeriFlux forest station: Howland, Maine, USA. The aim of applying a kernel-smoothing algorithm to an ensemble Kalman filter is to overcome the dramatic, sudden change of parameter values in time and the loss of continuity between two consecutive points in time. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and so on, are highly constrained by eddy flux data at daily-to-seasonal time scales. The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Potential ecosystem light use efficiency demonstrates a strong seasonality. Results show that the simultaneous parameter estimation procedure significantly improves model predictions. Results also show that the SEnKF can dramatically reduce variance in state variables stemming from the uncertainty of parameters and driving variables. The SEnKF is a robust and effective algorithm in evaluating and developing ecosystem models and in improving understanding and quantification of carbon cycle parameters and processes.

Conference Paper↗