Timothy J. Hoar

The 1990–1995 El Niño‐Southern Oscillation Event: Longest on Record

The tendency for more frequent E1 Nifio events America to the International Dateline. It is the basin-scale and fewer La Nifia events since the late 1970s has been phenomenon, however, that is linked to globaJ atmospheric linked to decadl changes in climate throughout the Pacific circulation and associated weather anomalies. The primary basin. Aspects of the most recent warming in the tropical response in the atmosphere coupled to EN is the SO and, Pacific from 1990 to 1995, which are connected to but not together, the tropical Pacific warm events are often referred synonymous with E1 Nifio, are unprecedented in the climate to as ENSO events. record of the past 113 years. There is a distinction between E1 Nifio (EN), the Southern Oscillation (SO)in the atmo- sphere, and ENSO, where the two are strongly linked, that emerges clearly on decadal time scaJes. In the traditional E1 Nifio region, sea surface temperature anomalies (SSTAs) have waxed and waned, while SSTAs in the centraequatorial Pa- cific, which are better linked to the SO, remained positive from 1990 to June 1995. We carry out several statisticaJ tests to assess the likelihood that the recent behavior of the SO is part of a natural decadal-timescae variation. One test fits an autoregressive-moving average (ARMA) model to a measure of the SO given by the first hundred years of the pressures at Darwin, Australia, beginning in 1882. Both the recent trend for more ENSO events since 1976 and the prolonged 1990- 1995 ENSO event are unexpected given the previous record, with a probability of occurrence about once in 2,000 years. This opens up the possibility that the ENSO changes may be partly caused by the observed increases in greenhouse gases.

El Niño and climate change

A comprehensive statistical analysis of how an index of the Southern Oscillation changed from 1882 to 1995 was given by Trenberth and Hoar [1996], with a focus on the unusual nature of the 1990–1995 El Nino-Southern Oscillation (ENSO) warm event in the context of an observed trend for more El Nino and fewer La Nina events after the late 1970s. The conclusions of that study have been challenged by two studies which deal with only the part of our results pertaining to the length of runs of anomalies of one sign in the Southern Oscillation Index. They therefore neglect the essence of Trenberth and Hoar, which focussed on the magnitude of anomalies for certain periods and showed that anomalies during both the post-1976 and 1990-mid-1995 periods were highly unlikely given the previous record. With updated data through mid 1997, we have performed additional tests using a regression model with autoregressive-moving average (ARMA) errors that simultaneously estimates the appropriate ARMA model to fit the data and assesses the statistical significance of how unusual the two periods of interest are. The mean SOI for the post-1976 period is statistically different from the overall mean at <0.05% and so is the 1990-mid-1995 period. The recent evolution of ENSO, with a major new El Nino event underway in 1997, reinforces the evidence that the tendency for more El Nino and fewer La Nina events since the late 1970s is highly unusual and very unlikely to be accounted for solely by natural variability.

The Data Assimilation Research Testbed: A Community Facility

Abstract The Data Assimilation Research Testbed (DART) is an open-source community facility for data assimilation education, research, and development. DARTs ensemble data assimilation algorithms, careful software engineering, and diagnostic tools allow atmospheric scientists, oceanographers, hydrologists, chemists, and other geophysicists to build state-of-the-art data assimilation systems with unprecedented ease. For global numerical weather prediction, DART produces ensemble-mean analyses comparable to analyses from major centers while also providing initial conditions for ensemble predictions. In addition, DART supports more novel assimilation applications like parameter estimation, sensitivity analysis, observing system design, and smoothing. Implementing basic systems for large models requires only a few person-weeks; comprehensive systems have been built in a few months. Incorporating new observation types is also straightforward, requiring only a forward operator mapping between a models state a...

Assimilation of Surface Pressure Observations Using an Ensemble Filter in an Idealized Global Atmospheric Prediction System

An ensemble filter data assimilation system is tested in a perfect model setting using a low resolution Held–Suarez configuration of an atmospheric GCM. The assimilation system is able to reconstruct details of the model’s state at all levels when only observations of surface pressure (PS) are available. The impacts of varying the spatial density and temporal frequency of PS observations are examined. The error of the ensemble mean assimilation prior estimate appears to saturate at some point as the number of PS observations available once every 24 h is increased. However, increasing the frequency with which PS observations are available from a fixed network of 1800 randomly located stations results in an apparently unbounded decrease in the assimilation’s prior error for both PS and all other model state variables. The error reduces smoothly as a function of observation frequency except for a band with observation periods around 4 h. Assimilated states are found to display enhanced amplitude high-frequency gravity wave oscillations when observations are taken once every few hours, and this adversely impacts the assimilation quality. Assimilations of only surface temperature and only surface wind components are also examined. The results indicate that, in a perfect model context, ensemble filters are able to extract surprising amounts of information from observations of only a small portion of a model’s spatial domain. This suggests that most of the remaining challenges for ensemble filter assimilation are confined to problems such as model error, observation representativeness error, and unknown instrument error characteristics that are outside the scope of perfect model experiments. While it is dangerous to extrapolate from these simple experiments to operational atmospheric assimilation, the results also suggest that exploring the frequency with which observations are used for assimilation may lead to significant enhancements to assimilated state estimates.

DART/CAM: An Ensemble Data Assimilation System for CESM Atmospheric Models

AbstractThe Community Atmosphere Model (CAM) has been interfaced to the Data Assimilation Research Testbed (DART), a community facility for ensemble data assimilation. This provides a large set of data assimilation tools for climate model research and development. Aspects of the interface to the Community Earth System Model (CESM) software are discussed and a variety of applications are illustrated, ranging from model development to the production of long series of analyses. CAM output is compared directly to real observations from platforms ranging from radiosondes to global positioning system satellites. Such comparisons use the temporally and spatially heterogeneous analysis error estimates available from the ensemble to provide very specific forecast quality evaluations. The ability to start forecasts from analyses, which were generated by CAM on its native grid and have no foreign model bias, contributed to the detection of a code error involving Arctic sea ice and cloud cover. The potential of param...

An investigation into the application of an ensemble Kalman smoother to high-dimensional geophysical systems

We examine the application of ensemble Kalman filter algorithms to the smoothing problem in high-dimensional geophysical prediction systems. The goal of smoothing is to make optimal estimates of the geophysical system state making best use of observations taken before, at, and after the analysis time. We begin by reviewing the underlying probabilistic theory, along with a discussion how to implement a smoother using an ensemble Kalman filter algorithm. The novel contribution of this paper is the investigation of various key issues regarding the application of ensemble Kalman filters to smoothing using a series of Observing System Simulation Experiments in both a Lorenz 1996 model and an Atmospheric General Circulation Model. The results demonstrate the impacts of non-linearities, ensemble size, observational network configuration and covariance localization. The Atmospheric General Circulation model results demonstrate that the ensemble Kalman smoother (EnKS) can be successfully applied to high-dimensional estimation problems and that covariance localization plays a critical role in its success. The results of this paper provide a foundation of understanding which will be useful in future applications of EnKS algorithms.

Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (23°–45°N). Only minimal modifications are made in the higher-middle (45°–66°N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100%. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snow move poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

An Ensemble Adjustment Kalman Filter for the CCSM4 Ocean Component

AbstractThe authors report on the implementation and evaluation of a 48-member ensemble adjustment Kalman filter (EAKF) for the ocean component of the Community Climate System Model, version 4 (CCSM4). The ocean assimilation system described was developed to support the eventual generation of historical ocean-state estimates and ocean-initialized climate predictions with the CCSM4 and its next generation, the Community Earth System Model (CESM). In this initial configuration of the system, daily subsurface temperature and salinity data from the 2009 World Ocean Database are assimilated into the ocean model from 1 January 1998 to 31 December 2005. Each ensemble member of the ocean is forced by a member of an independently generated CCSM4 atmospheric EAKF analysis, making this a loosely coupled framework. Over most of the globe, the time-mean temperature and salinity fields are improved relative to an identically forced ocean model simulation without assimilation. This improvement is especially notable in s...

Quasi‐stationary wave variability in NSCAT winds

Planetary and interseasonal properties of the NASA scatterometer (NSCAT) winds are explored in the context of the quasi-stationary waves (QSWs) of the southern hemisphere. The QSWs are examined by means of zonal asymmetries of 3-month running averages of the meridional velocity derived from NSCAT. The study period spans the entire NSCAT record from September 15, 1996, through June 29, 1997. Meridional winds from the European Space Agency ERS 2 scatterometer are used to augment 3-month averages centered on August 1996 through May 1997. The time period corresponds to the transition from year −1 to 0 of the 1997 warm event in the Southern Oscillation. Comparisons are made with QSW signals in geopotential height anomalies from the National Centers for Environmental Prediction/National Center for Atmospheric Research Climate Data Assimilation System. The zonal anomalies of meridional wind from NSCAT are shown to be in approximate geostrophic balance with zonal gradients in the zonal anomalies of geopotential height at 500 and 1000 hPa.

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.