Efficient Estimation of Climate State and Its Uncertainty Using Kalman Filtering and Application to Policy Thresholds
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Herein we present the Energy Balance Model – Kalman Filter (EBM-KF), a hybrid model of the global mean surface temperature (GMST) and ocean heat content anomaly (OHCA), which combines a theoretical energy balance equation based in Earth science literature and a statistical extended Kalman Filter incorporating observed and/or climate model simulated GMST & OHCA data. Our motivation is to create an efficient and natural estimator of the climate state and its uncertainty.  This synthesis is straightforward because climate models and historical records of GMST follow easily representable normal distributions. Our emulator is tied to the immediate physical rationale of an energy budget, benefits from the mathematical similarities between an energy balance model and a Kalman filter and allows access to proven methodologies for parameter estim' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Climate model ensembles","Internal Variability","Kalman filters","Time series"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Herein we present the Energy Balance Model – Kalman Filter (EBM-KF), a hybrid model of the global mean surface temperature (GMST) and ocean heat content anomaly (OHCA), which combines a theoretical energy balance equation based in Earth science literature and a statistical extended Kalman Filter incorporating observed and/or climate model simulated GMST & OHCA data. Our motivation is to create an efficient and natural estimator of the climate state and its uncertainty.  This synthesis is straightforward because climate models and historical records of GMST follow easily representable normal distributions. Our emulator is tied to the immediate physical rationale of an energy budget, benefits from the mathematical similarities between an energy balance model and a Kalman filter and allows access to proven methodologies for parameter estim' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Herein we present the Energy Balance Model – Kalman Filter (EBM-KF), a hybrid model of the global mean surface temperature (GMST) and ocean heat content anomaly (OHCA), which combines a theoretical energy balance equation based in Earth science literature and a statistical extended Kalman Filter incorporating observed and/or climate model simulated GMST & OHCA data. Our motivation is to create an efficient and natural estimator of the climate state and its uncertainty.  This synthesis is straightforward because climate models and historical records of GMST follow easily representable normal distributions. Our emulator is tied to the immediate physical rationale of an energy budget, benefits from the mathematical similarities between an energy balance model and a Kalman filter and allows access to proven methodologies for parameter estim
Climate model ensembles; Internal Variability; Kalman filters; Time series