Geosat Observations of Sea Level Response to Barometric Pressure Forcing

Altimeter and sea level pressure data from the Geosat mission are analyzed for evidence of inverted barometer response of sea level to atmospheric pressure forcing. Estimates of the inverted barometer coefficient are given for a variety of geographic regions and time scales using various orbit error removal strategies. There is some sensitivity to the orbit error removal method, but the estimated coefficients show a clear latitudinal dependence and are generally between-0.5 cm/mbar and-0.9 cm/mbar. The southern oceans respond slightly more like an inverted barometer than the northern oceans for similar latitudes. The regression exhibits significant geographic variability, particularly near major circulation features and in the northern hemisphere. The results suggest that the inverted barometer approximation is reasonable over much of the oceans, but that some sea level variability may be correlated with barometric pressure by means other than the inverted barometer effect. 1 Now at N..

Ionospheric data assimilation and forecasting during storms

Ionospheric storms can have important effects on radio communications and navigation systems. Storm time ionospheric predictions have the potential to form part of effective mitigation strategies to these problems. Ionospheric storms are caused by strong forcing from the solar wind. Electron density enhancements are driven by penetration electric fields, as well as by thermosphere-ionosphere behavior including Traveling Atmospheric Disturbances and Traveling Ionospheric Disturbances and changes to the neutral composition. This study assesses the effect on 1 h predictions of specifying initial ionospheric and thermospheric conditions using total electron content (TEC) observations under a fixed set of solar and high-latitude drivers. Prediction performance is assessed against TEC observations, incoherent scatter radar, and in situ electron density observations. Corotated TEC data provide a benchmark of forecast accuracy. The primary case study is the storm of 10 September 2005, while the anomalous storm of 21 January 2005 provides a secondary comparison. The study uses an ensemble Kalman filter constructed with the Data Assimilation Research Testbed and the Thermosphere Ionosphere Electrodynamics General Circulation Model. Maps of preprocessed, verticalized GPS TEC are assimilated, while high-latitude specifications from the Assimilative Mapping of Ionospheric Electrodynamics and solar flux observations from the Solar Extreme Ultraviolet Experiment are used to drive the model. The filter adjusts ionospheric and thermospheric parameters, making use of time-evolving covariance estimates. The approach is effective in correcting model biases but does not capture all the behavior of the storms. In particular, a ridge-like enhancement over the continental USA is not predicted, indicating the importance of predicting storm time electric field behavior to the problem of ionospheric forecasting.</p

Evaluation of a Data Assimilation System for Land Surface Models using CLM4. 5

The magnitude and persistence of land carbon (C) pools influence long‐term climate feedbacks. Interactive ecological processes influence land C pools and our understanding of these processes is imperfect so land surface models have errors and biases when compared to each other and to real observations. Here we implement an Ensemble Adjustment Kalman Filter (EAKF), a sequential state data assimilation technique to reduce these errors and biases. We implement the EAKF using the Data Assimilation Research Testbed coupled with the Community Land Model (CLM 4.5 in CESM 1.2). We assimilated simulated and real satellite observations for a site in central New Mexico, United States. A series of observing system simulation experiments allowed assessment of the data assimilation system without model error. This showed that assimilating biomass and leaf area index observations decreased model error in C dynamics forecasts (29% using biomass observations and 40% using leaf area index observations) and that assimilation in combination shows greater improvement (51% using both observation streams). Assimilating real observations highlighted likely model structural errors and we implemented an adaptive model‐variance‐inflation technique to allow the model to track the observations. Monthly and longer model forecasts using real observations were improved relative to forecasts without data assimilation. The reliable forecast lead‐time varied by model pool and is dependent on how tightly the C pool is coupled to meteorologically driven processes. The EAKF and similar state data assimilation techniques could reduce errors in projections of the land C sink and provide more robust forecasts of C pools and land‐atmosphere exchanges