Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics.
نویسندگان
چکیده
The ensemble Kalman filter (EnKF) has been used in weather forecasting to assimilate observations into weather models. In this study, we examine how effectively forecasts of a forest carbon cycle can be improved by assimilating observations with the EnKF. We used the EnKF to assimilate into the terrestrial ecosystem (TECO) model eight data sets collected at the Duke Forest between 1996 and 2004 (foliage biomass, fine root biomass, woody biomass, litterfall, microbial biomass, forest floor carbon, soil carbon, and soil respiration). We then used the trained model to forecast changes in carbon pools from 2004 to 2012. Our daily analysis of parameters indicated that all the exit rates were well constrained by the EnKF, with the exception of the exit rates controlling the loss of metabolic litter and passive soil organic matter. The poor constraint of these two parameters resulted from the low sensitivity of TECO predictions to their values and the poor correlation between these parameters and the observed variables. Using the estimated parameters, the model predictions and observations were in agreement. Model forecasts indicate 15 380-15 660 g C/ m2 stored in Duke Forest by 2012 (a 27% increase since 2004). Parameter uncertainties decreased as data were sequentially assimilated into the model using the EnKF. Uncertainties in forecast carbon sinks increased over time for the long-term carbon pools (woody biomass, structure litter, slow and passive SOM) but remained constant over time for the short-term carbon pools (foliage, fine root, metabolic litter, and microbial carbon). Overall, EnKF can effectively assimilate multiple data sets into an ecosystem model to constrain parameters, forecast dynamics of state variables, and evaluate uncertainty.
منابع مشابه
Enhanced Predictions of Tides and Surges through Data Assimilation (TECHNICAL NOTE)
The regional waters in Singapore Strait are characterized by complex hydrodynamic phenomena as a result of the combined effect of three large water bodies viz. the South China Sea, the Andaman Sea, and the Java Sea. This leads to anomalies in water levels and generates residual currents. Numerical hydrodynamic models are generally used for predicting water levels in the ocean and seas. But thei...
متن کاملA Comparison Study of Data Assimilation Algorithms for Ozone Forecasts
The objective of this report is to evaluate the performances of different data assimilation schemes with the aim of designing suitable assimilation algorithms for short-range ozone forecasts in realistic applications. The underlying atmospheric chemistry-transport models are stiff but stable systems with high uncertainties (e.g., over 20% for ozone daily peaks, Hanna et al. [1998]; Mallet and S...
متن کاملAn Ensemble Ocean Data Assimilation System for Seasonal Prediction
A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS, the POAMA Ensemble Ocean Data Assimilation System. PEODAS is an approximate form of an ensemble Kalman filter system. For a given assimilation cycle, a central forecast is integrated, along with a small ensemble of forecasts tha...
متن کاملA data assimilation method of the Ensemble Kalman Filter for use in severe dust storm forecasts over China
متن کامل
Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)
In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two vers...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Ecological applications : a publication of the Ecological Society of America
دوره 21 5 شماره
صفحات -
تاریخ انتشار 2011