Ma, Hsi-Yen, A Cheska Siongco, Stephen A Klein, Shaocheng Xie, Alicia R Karspeck, Kevin Raeder, Jeffrey L Anderson, Jiwoo Lee, Ben P Kirtman, William J Merryfield, Hiroyuki Murakami, and Joseph J Tribbia, January 2021: On the correspondence between seasonal forecast biases and long-term climate biases in sea surface temperature. Journal of Climate, 34(1), DOI:10.1175/JCLI-D-20-0338.1. Abstract
The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.
We
present
a
mechanism
for
exchange
of
quantities
between
components
of
a
coupled
Earth
system
model,
where
each
component
is
independently
discretized.
The
exchange
grid
is
formed
by
overlaying
two
grids,
such
that
each
exchange
grid
cell
has
a
unique
parent
cell
on
each
of
its
antecedent
grids.
In
Earth
System
models
in
particular,
processes
occurring
near
component
surfaces
require
special
surface
boundary
layer
physical
processes
to
be
represented
on
the
exchange
grid.
The
exchange
grid
is
thus
more
than
just
a
stage
in
a
sequence
of
regrid-
ding
between
component
grids.
We
present
the
design
and
use
of
a
2-dimensional
exchange
grid
on
a
horizontal
planetary
surface
in
the
GFDL
Flexible
Modeling
System
(FMS),
highlighting
issues
of
parallelism
and
performance
Anderson, Jeffrey L., Bruce Wyman, Shaoqing Zhang, and T Hoar, August 2005: Assimilation of surface pressure observations using an ensemble filter in an idealized global atmospheric prediction system. Journal of the Atmospheric Sciences, 62(8), DOI:10.1175/JAS3510.1. Abstract
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 resulrts also suggest that exploring the frequency with which observations are used for assimilation may lead to significant enhancements to assimilated state estimates.
As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing.
A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980-2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF's utilization of anisotropic background error covariances that may vary in time.
for climate research developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are presented. The atmosphere model, known as AM2, includes a new gridpoint dynamical core, a prognostic cloud scheme, and a multispecies aerosol climatology, as well as components from previous models used at GFDL. The land model, known as LM2, includes soil sensible and latent heat storage, groundwater storage, and stomatal resistance. The performance of the coupled model AM2–LM2 is evaluated with a series of prescribed sea surface temperature (SST) simulations. Particular focus is given to the model's climatology and the characteristics of interannual variability related to E1 Niño– Southern Oscillation (ENSO).
One AM2–LM2 integration was performed according to the prescriptions of the second Atmospheric Model Intercomparison Project (AMIP II) and data were submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI). Particular strengths of AM2–LM2, as judged by comparison to other models participating in AMIP II, include its circulation and distributions of precipitation. Prominent problems of AM2– LM2 include a cold bias to surface and tropospheric temperatures, weak tropical cyclone activity, and weak tropical intraseasonal activity associated with the Madden–Julian oscillation.
An ensemble of 10 AM2–LM2 integrations with observed SSTs for the second half of the twentieth century permits a statistically reliable assessment of the model's response to ENSO. In general, AM2–LM2 produces a realistic simulation of the anomalies in tropical precipitation and extratropical circulation that are associated with ENSO.
Time-stepping schemes in ocean-atmosphere models can involve multiple time levels. Traditional data assimilation implementation considers only the adjustment of the current state using observations available, i.e. the one time level adjustment. However, one time level adjustment introduces an inconsistency between the adjusted and unadjusted states into the model time integration, which can produce extra assimilation errors. For time-dependent assimilation approaches such as ensemble-based filtering algorithms, the persistent introduction of this inconsistency can give rise to computational instability and requires extra time filtering to maintain the assimilation.
A multiple time level adjustment assimilation scheme is thus proposed, in which the states at times t and t- 1, t- 2, ... , if applicable, are adjusted using observations at time t. Given a leap frog time-stepping scheme, a low-order (Lorenz-63) model and a simple atmospheric (global barotropic) model are used to demonstrate the impact of the two time level adjustment on assimilation results in a perfect model framework with observing/assimilation simulation experiments. The assimilation algorithms include an ensemble-based filter (the ensemble adjustment Kalman filter, EAKF) and a strong constraint four-dimensional variational (4D-Var) assimilation method. Results show that the two time level adjustment always reduces the assimilation errors for both filtering and variational algorithms due to the consistency of the adjusted states at times t and t- 1 that are used to produce the future state in the leap frog time-stepping. The magnitude of the error reduction made by the two time level adjustment varies according to the availability of observations, the nonlinearity of the assimilation model and the strength of the time filter used in the model. Generally the sparser the observations in time, the larger the error reduction. In particular, for the EAKF when the model uses a weak time filter and for the 4D-Var method when the model is strongly nonlinear, two time level adjustment can significantly improve the performance of these assimilation algorithms.
Anderson, Jeffrey L., April 2003: A local least squares framework for ensemble filtering. Monthly Weather Review, 131(4), 634-642. Abstract PDF
Many methods using ensemble integrations of prediction models as integral parts of data assimilation have appeared in the atmospheric and oceanic literature. In general, these methods have been derived from the Kalman filter and have been known as ensemble Kalman filters. A more general class of methods including these ensemble Kalman filter methods is derived starting from the nonlinear filtering problem. When working in a joint state-observation space, many features of ensemble filtering algorithms are easier to derive and compare. The ensemble filter methods derived here make a (local) least squares assumption about the relation between prior distributions of an observation variable and model state variables. In this context, the update procedure applied when a new observation becomes available can be described in two parts. First, an update increment is computed for each prior ensemble estimate of the observation variable by applying a scalar ensemble filter. Second, a linear regression of the prior ensemble sample of each state variable on the observation variable is performed to compute update increments for each state variable ensemble member from corresponding observation variable increments. The regression can be applied globally or locally using Gaussian kernel methods.
Several previously documented ensemble Kalman filter methods, the perturbed observation ensemble Kalman filter and ensemble adjustment Kalman filter, are developed in this context. Some new ensemble filters that extend beyond the Kalman filter context are also discussed. The two-part method can provide a computationally efficient implementation of ensemble filters and allows more straightforward comparison of methods since they differ only in the solution of a scalar filtering problem.
Chin, Mian, Paul Ginoux, R Lucchesi, B Huebert, R J Weber, Jeffrey L Anderson, S Masonis, B Blomquist, A Bandy, and D Thornton, 2003: A global aerosol model forecast for the ACE-Asia field experiment. Journal of Geophysical Research, 108(D23), 8654, DOI:10.1029/2003JD003642. Abstract PDF
We present the results of aerosol forecast during the ACE-Asia field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The model provides direct information on aerosol optical thickness and concentrations for effective flight planning, while feedbacks from measurements constantly evaluate the model for successful model improvements. We verify the model forecast skill by comparing model-predicted aerosol quantities and meteorological variables with those measured by the C-130 aircraft. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature, with skill scores usually in the range of 0.7-0.99. The model is also skillful in forecasting pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this "missing" dust source to desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed boundary layer high dust concentrations over the Yellow Sea. We demonstrate that our global model can not only account for the large-scale intercontinental transport but also produce the small-scale spatial and temporal variations that are adequate for aircraft measurements planning.
Ensemble data assimilation methods assimilate observations using state-space estimation methods and low-rank representation of forecast and analysis error covariances. A key element of such methods is the transformation of the forecast ensemble into an analysis ensemble with appropriate statistics. This transformation may be performed stochastically by treating observations as random variables, or deterministically by requiring that the updated analysis perturbations satisfy the Kalman filter analysis error covariance equation. Deterministic analysis ensemble updates are implementations of Kalman square root filters. The nonuniqueness of the deterministic transformation used in square root Kalman filters provides a framework to compare three recently proposed ensemble data assimilation methods.
Zhang, Shaoqing, and Jeffrey L Anderson, 2003: Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model. Tellus A, 55A(2), 126-147. Abstract PDF
The background error covariance (correlation) between model state variables is of central importance for implementing data assimilation and understanding model dynamics. Traditional approaches for estimating the background error covariance involve many heuristic approximations, and often the estimated covariance is flow-independent, i.e., only reflecting statistics of the climatological background. This study examines temporally and spatially varying estimates of error covariance in a spectral barotropic model using a Monte Carlo approach, an implementation of an ensemble square root filter called the ensemble adjustment Kalman filter (EAKF). The EAKF is designed to maintain as much information about the distribution of the prior state variables as possible, and results show that this method can produce reasonable estimates of error correlation structure with an affordable sample (ensemble) size. The impact of using temporally and spatially varying estimates of error covariance in the EAKF is examined by using the time and spatial mean error covariances derived from the EAKF in an ensemble optimal interpolation (OI) assimilation scheme. Three key results are: (1) for the same ensemble size, an ensemble filter such as the EAKF produces better assimilations since its flow-dependent error covariance estimates are able to reflect more about the synoptic-scale wave structure in the simulated flows; (2) an ensemble OI scheme can also produce reasonably good assimilation results if the time-invariate covariance matrix is chosen appropriately; (3) when using the EAKF to estimate the error covariance matrix for improving traditional assimilation algorithms such as variational analysis and OI, a relatively small ensemble size may be used to estimate correlation structure although larger ensembles produce progressively better results.
Ploshay, Jeff J., and Jeffrey L Anderson, 2002: Large sensitivity to initial conditions in seasonal predictions with a coupled ocean-atmosphere general circulation model. Geophysical Research Letters, 29(8), DOI:10.1029/2000GL012710. Abstract PDF
An ensemble of one-year forecasts differing only in details of the atmospheric initial conditions was produced with a coupled ocean-atmosphere general circulation model (GCM) in order to investigate the predictability of the coupled system. For some ocean initial conditions, the evolution of the tropical Pacific ocean thermal structure seems to be relatively deterministic for lead times out to one year. However, there are other ocean initial conditions, mostly in the mid 1990's for which coupled model forecasts of the tropical Pacific are much more sensitive to details of the atmosphere initial conditions. In some cases, the ensemble forecasts appear to split, with some ensemble members predicting El Niño-like conditions, and others predicting La Niña. Very large ensembles were run for several of these cases. Very slight perturbations added to the atmospheric initial conditions led to large spread in predicted SST anomalies in some years. These are model results; however, they do suggest the possibility that seasonal predictions of the coupled tropical system may be highly non-deterministic in some years.
Anderson, Jeffrey L., 2001: An ensemble adjustment Kalman filter for data assimilation. Monthly Weather Review, 129(12), 2884-2903. Abstract PDF
A theory for estimating the probability distribution of the state of a model given a set of observations exists. This nonlinear filtering theory unifies the data assimilation and ensemble generation problem that have been key foci of prediction and predictability research for numerical weather and ocean prediction applications. A new algorithm, referred to as an ensemble adjustment Kalman filter, and the more traditional implementation of the ensemble Kalman filter in which “perturbed observations” are used, are derived as Monte Carlo approximations to the nonlinear filter. Both ensemble Kalman filter methods produce assimilations with small ensemble mean errors while providing reasonable measures of uncertainty in the assimilated variables. The ensemble methods can assimilate observations with a nonlinear relation to model state variables and can also use observations to estimate the value of imprecisely known model parameters. These ensemble filter methods are shown to have significant advantages over four-dimensional variational assimilation in low-order models and scale easily to much larger applications. Heuristic modifications to the filtering algorithms allow them to be applied efficiently to very large models by sequentially processing observations and computing the impact of each observation on each state variable in an independent calculation. The ensemble adjustment Kalman filter is applied to a nondivergent barotropic model on the sphere to demonstrate the capabilities of the filters in models with state spaces that are much larger than the ensemble size.
When observations are assimilated in the traditional ensemble Kalman filter, the resulting updated ensemble has a mean that is consistent with the value given by filtering theory, but only the expected value of the covariance of the updated ensemble is consistent with the theory. The ensemble adjustment Kalman filter computes a linear operator that is applied to the prior ensemble estimate of the state, resulting in an updated ensemble whose mean and also covariance are consistent with the theory. In the cases compared here, the ensemble adjustment Kalman filter performs significantly better than the traditional ensemble Kalman filter, apparently because noise introduced into the assimilated ensemble through perturbed observations in the traditional filter limits its relative performance. This superior performance may not occur for all problems and is expected to be most notable for small ensembles. Still, the results suggest that careful study of the capabilities of different varieties of ensemble Kalman filters is appropriate when exploring new applications.
Stensrud, D J., and Jeffrey L Anderson, 2001: Is midlatitude convection an active or a passive player in producing global circulation patterns?Journal of Climate, 14(10), 2222-2237. Abstract PDF
The ability of persistent midlatitude convective regions to influence hemispheric circulation patterns during the Northern Hemisphere summer is investigated. Global rainfall data over a 15-yr period indicate anomalously large July total rainfalls occurred over mesoscale-sized, midlatitude regions of North America and/or Southeast Asia during the years of 1987, 1991, 1992, and 1993. The anomalous 200-hPa vorticity patterns for these same years are suggestive of Rossby wave trains emanating from the regions of anomalous rainfall in the midlatitudes. #Results from an analysis of an 11-yr mean monthly 200-hPa July wind field indicate that, in the climatological mean, Rossby waveguides are present that could assist in developing a large-scale response from mesoscale-sized regions of persistent convection in the midlatitudes. This hypothesis is tested using a barotropic model linearized about the 200-hPa July time-mean flow and forced by the observed divergence anomalies. The model results are in qualitative agreement in the observed July vorticity anomalies for the four years investigated. Model results forced by observed tropical forcings for the same years do not demonstrate any significant influence on the midlatitude circulation. It is argued that persistent midlatitude convective regions may play a role in the development, maintenance, and dissipation of the large-scale circulations that help to support the convective regions.
Vitart, Frederic, and Jeffrey L Anderson, 2001: Sensitivity of Atlantic tropical storm frequency to ENSO and interdecadal variability of SSTs in an ensemble of AGCM integrations. Journal of Climate, 14(4), 533-545. Abstract PDF
A significant reduction (increase) of tropical storm activity over the Atlantic basin is observed during El Niño (La Niño) events. Furthermore, the number of Atlantic tropical storms displays an interdecadal variability with more storms in the 1950s and 1960s than in the 1970s and 1980s. Ensembles of simulations with an atmospheric general circulation model (AGCM) are used to explore the mechanisms responsible for this observed variability.
The interannual variability is investigated using a 10-member ensemble of AGCM simulations forced by climatological SSTs of the 1980s everywhere except over the tropical Pacific and Indian Oceans. Significantly fewer tropical storms are simulated with El Niño SSTs imposed over the tropical Pacific and Indian Oceans than with La Niño conditions. Increased simulated vertical wind shear over the Atlantic is the most likely explanation for the reduction of simulated tropical storms during El Niño years. SST forcing from different El Niño events has distinct impacts on Atlantic tropical storms in the simulation: simulated tropical storms are significantly less numerous with 1982 SSTs imposed over the tropical Pacific and Indian Oceans than with 1 986 SSTs.
The interdecadal variability of tropical storm activity seems to coincide with an interdecadal variability of the North Atlantic SSTs with colder SSTs in the 1970s than in the 1950s. Ensembles of AGCM simulations produce significantly more tropical storms when forced by observed SSTs of the 1950s than when forced by SSTs of the 1970s. This supports the theory that the interdecadal variability of SSTs has a significant impact on the expected number of Atlantic tropical storms and suggests that Atlantic tropical storms may be more numerous in coming years if North Atlantic SSTs are getting warmer. A significant increase of vertical wind shear and a significant decrease in the convective available potential energy over the tropical Atlantic in the 1970s may explain the simulated interdecadal variability of Atlantic tropical storms.
Vitart, Frederic, Jeffrey L Anderson, Joseph J Sirutis, and Robert E Tuleya, 2001: Sensitivity of tropical storms simulated by a general circulation model to changes in cumulus parameterization. Quarterly Journal of the Royal Meteorological Society, 127(571), 25-51. Abstract PDF
A number of recent studies have examined the statistics of tropical storms simulated by general circulation models (GCMs) forced by observed sea surface temperatures. Many GCMs have demonstrated an ability to simulate some aspects of the observed interannual variability of tropical storms, in particular, variability in storm frequency. This has led to nascent attempts to use GCMs as part of programs to produce operational seasonal forecasts of tropical-storm numbers.
In this study, the sensitivity of the statistics of GCM-simulated tropical storms to changes in the model's physical parameterization is examined. After preliminary results indicated that these statistics were most sensitive to details of the convective parameterization, GCM simulations with identical dynamical cores but different convective parameterizations were created. The parameterizations examined included moist convective adjustment, two variants of the Arakawa-Schubert scheme, and several variants of the relaxed Arakawa-Schubert (RAS) scheme; the impact of including a shallow-convection parameterization was also examined.
The simulated tropical -storm frequency, intensity, structure, and interannual variability were all found to exhibit significant sensitivities to changes in convective parameterization. A particularly large sensitivity was found when the RAS and Arakawa-Schubert parameterizations were modified to place restrictions on the production of deep convection.
Climatologies of the GCM tropical atmosphere and composites of tropical storms were examined to address the question of whether the tropical-storm statistics were directly impacted on by changes in convection associated with tropical storms, or if they were indirectly affected by parameterization-induced changes in the tropical mean atmosphere. A number of results point to the latter being the primary cause. A regional hurricane model , initialized with mean states from the GCM simulation climatologies, is used to further investigate this point. Particularly compelling is the fact that versions of the RAS scheme that produce significantly less realistic simulations of tropical storms nevertheless produce a much more realistic interannual variability of storms, apparently due to an improved tropical mean climate.
A careful analysis of the background convective available potential energy (CAPE) is used to suggest that this quantity is particularly relevant to the occurrence of tropical storms in the low-resolution GCMs, although this may not be the case with observations. If the tropical CAPE is too low, tropical storms in the low-resolution GCMs cannot form with realistic frequency.
Anderson, Jeffrey L., and Jeff J Ploshay, 2000: Impact of initial conditions on seasonal simulations with an atmospheric general circulation model. Quarterly Journal of the Royal Meteorological Society, 126(567), 2241-2264. Abstract PDF
Many previous studies have examined the use of very long integrations of atmospheric general circulation models (AGCMs) forced by observed sea surface temperatures (SSTs) as proxies for seasonal atmospheric predictions. These long simulations explore a boundary-value problem in which significant deviations from the model's long-term climatology must be a result of the SST forcing. Seasonal lead simulations starting with observed initial conditions (ICs) for the atmosphere and land surface while retaining observed SST forcing are an intermediate step between the pure boundary-value problem and the pure initial-value forecast problem in which SSTs are also predicted. As part of the Dynamical Seasonal Prediction (DSP) experiment, an ensemble of AGCM integrations with observed atmospheric ICs and model climatology land surface ICs was integrated from mid-December through March for 16 years. These DSP simulation ensembles are compared to ensembles of long boundary-value simulations from the same AGCM in a perfect-model setting (no comparisons of simulations to observations are attempted). Significant differences must be due to the impact of the DSP ICs. Surprisingly large and long-lived differences are found in both the mean and the variance of the ensembles. Many appear to occur because the ICs of the DSP runs are inconsistent with the AGCM climatology; an extended period of model 'spinup' is the result. Some differences are related to local impacts of the land surface ICs while others, like shifts in the distribution of tropical precipitation and a cooling of the northern hemisphere, are less obviously related to the ICs. The results suggest that care will be needed when inserting observed ICs into seasonal predictions in order to avoid the long-term effects of model spin-up.
Shukla, J, Jeffrey L Anderson, D Baumhefner, Y Chang, E Kalnay, L Marx, T N Palmer, D Paolino, and Jeff J Ploshay, et al., November 2000: Dynamical Seasonal Prediction. Bulletin of the American Meteorological Society, 81(11), DOI:10.1175/1520-0477(2000)081<2593:DSP>2.3.CO;2. Abstract
Dynamical Seasonal Prediction (DSP) is an informally coordinated multi-institution research project to investigate the predictability of seasonal mean atmospheric circulation and rainfall. The basic idea is to test the feasibility of extending the technology of routine numerical weather prediction beyond the inherent limit of deterministic predictability of weather to produce numerical climate predictions using state-of-the-art global atmospheric models. Atmospheric general circulation models (AGCMs) either forced by predicted sea surface temperature (SST) or as part of a coupled forecast system have shown in the past that certain regions of the extratropics, in particular, the Pacific–North America (PNA) region during Northern Hemisphere winter, can be predicted with significant skill especially during years of large tropical SST anomalies. However, there is still a great deal of uncertainty about how much the details of various AGCMs impact conclusions about extratropical seasonal prediction and predictability.
DSP is designed to compare seasonal simulation and prediction results from five state-of-the-art U.S. modeling groups (NCAR, COLA, GSFC, GFDL, NCEP) in order to assess which aspects of the results are robust and which are model dependent. The initial emphasis is on the predictability of seasonal anomalies over the PNA region. This paper also includes results from the ECMWF model, and historical forecast skill over both the PNA region and the European region is presented for all six models.
It is found that with specified SST boundary conditions, all models show that the winter season mean circulation anomalies over the Pacific–North American region are highly predictable during years of large tropical sea surface temperature anomalies. The influence of large anomalous boundary conditions is so strong and so reproducible that the seasonal mean forecasts can be given with a high degree of confidence. However, the degree of reproducibility is highly variable from one model to the other, and quantities such as the PNA region signal to noise ratio are found to vary significantly between the different AGCMs. It would not be possible to make reliable estimates of predictability of the seasonal mean atmosphere circulation unless causes for such large differences among models are understood.
Yang, X-Q, and Jeffrey L Anderson, 2000: Correction of systematic errors in coupled GCM forecasts. Journal of Climate, 13(12), 2072-2085. Abstract PDF
The prognostic tendency (PT) correction method is applied in an attempt to reduce systematic errors in coupled GCM seasonal forecasts. The PT method computes the systematic initial tendency error (SITE) of the coupled model and subtracts it from the discrete prognostic equations. In this study, the PT correction is applied only to the three-dimensional ocean temperature. The SITE is computed by calculating a climatologically averaged difference between coupled model initial conditions and resulting forecasts at very short lead times and removing the observed mean seasonal tendency.
Two sets of coupled GCM forecasts, one using an annual mean SITE correction and the other using a SITE correction that is a function of season, are compared with a control set of uncorrected forecasts. Each set consists of 17 12-month forecasts starting on 1 January from 1980 through 1996. The PT correction is found to be an effective method for maintaining a more realistic forecast climatology by reducing systematic ocean temperature errors that lead to a relaxation of the tropical Pacific thermocline slope and a weak tropical SST annual cycle in the control set. The annual mean PT correction, which allows the model to freely generate its own seasonal cycle, leads to increased prediction skill for tropical Pacific SSTs while the seasonally varying PT correction has no impact on this skill.
Physical mechanisms responsible for improvements in the coupled model's annual cycle and forecast skill are investigated. The annual mean structure of the tropical Pacific thermocline is found to be essential for producing a realistic SST annual cycle. The annual mean PT correction helps to maintain a realistic thermocline slope that allows surface winds to impact the annual cycle of SST in the eastern Pacific. Forecast skill is increased if the coupled model correctly captures dynamical modes related to ENSO. The annual mean correction leads to a model ENSO that is best characterized as a delayed oscillator mode while the control model appears to have a more stationary ENSO mode; this apparently has a positive impact on ENSO forecast skill in the PT corrected model.
Anderson, Jeffrey L., 1999: Why are statistical models for seasonal prediction competitive with current generation GCM predictions? In Proceedings of the Twenty-Fourth Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 176-178.
Anderson, Jeffrey L., and S L Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Monthly Weather Review, 127(12), 2741-2758. Abstract PDF
Knowledge of the probability distribution of initial conditions is central to almost all practical studies of predictability and to improvements in stochastic prediction of the atmosphere. Traditionally, data assimilation for atmospheric predictability or prediction experiments has attempted to find a single "best" estimate of the initial state. Additional information about the initial condition probability distribution is then obtained primarily through heuristic techniques that attempt to generate representative perturbations around the best estimate. However, a classical theory for generating an estimate of he complete probability distribution of an initial state given a set of observations exists. This nonlinear filtering theory can be applied to unify the data assimilation and ensemble generation problem and to produce superior estimates of the probability distribution of the initial state of the atmosphere (or ocean) on regional or global scales. A Monte Carlo implementation of the fully nonlinear filter has been developed and applied to several low-order models. The method is able to produce assimilations with small ensemble mean errors while also providing random samples of the initial conditions probability distribution. The Monte Carlo method can be applied in models that traditionally require the application of initialization techniques without any explicit initialization. Initial applications to larger models is promising, but a number of challenges remain before the method can be extended to large realistic forecast models.
Anderson, Jeffrey L., H van den Dool, A Barnston, W Chen, William F Stern, and Jeff J Ploshay, 1999: Present-day capabilities of numerical and statistical models for atmospheric extratropical seasonal simulation and prediction. Bulletin of the American Meteorological Society, 80(7), 1349-1361. Abstract PDF
A statistical model and extended ensemble integrations of two atmospheric general circulation models (GCMs) are used to simulate the extratropical atmospheric response to forcing by observed SSTs for the years 1980 through 1988. The simulations are compared to observations using the anomaly correlation and root-mean-square error of the 700-hPa height field over a region encompassing the extratropical North Pacific Ocean and most of North America. On average, the statistical model is found to produce considerably better simulations than either numerical model, even when simple statistical corrections are used to remove systematic errors from the numerical model simulations. In the mean, the simulation skill is low, but there are some individual seasons for which all three models produce simulations with good skill.
An approximate upper bound to the simulation skill that could be expected from a GCM ensemble, if the model's response to SST forcing is assumed to be perfect, is computed. This perfect model predictability allows one to make some rough extrapolations about the skill that could be expected if one could greatly improve the mean response of the GCMs without significantly impacting the variance of the ensemble. These perfect model predictability skills are better than the statistical model simulations during the summer, but for the winter, present-day statistical forecasts already have skill that is as high as the upper bound for the GCMs. Simultaneous improvements to the GCM mean response and reduction in the GCM ensemble variance would be required for these GCMs to do significantly better than the statistical model in winter. This does not preclude the possibility that, as is presently the case, a statistical blend of GCM and statistical predictions could produce a simulation better than either alone.
Because of the primitive state of coupled ocean-atmosphere GCMs, the vast majority of seasonal predictions currently produced by GCMs are performed using a two-tiered approach in which SSTs are first predicted and then used to force an atmospheric model; this motivates the examination of the simulation problem. However, it is straightforward to use the statistical model to produce true forecasts by changing its predictors from simultaneous to precursor SSTs. An examination of the decrease in skill of the statistical model when changed from simulation to prediction mode is extrapolated to draw conclusions about the skill to be expected from good coupled GCM predictions.
Anderson, Jeffrey L., and Jeff J Ploshay, 1999: Impacts of land surface initial conditions on seasonal lead GCM simulations In Proceedings of the Twenty-Fourth Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 319-322.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1999: Impact of large-scale circulation on tropical storm frequency, intensity, and location, simulated by an ensemble of GCM integrations. Journal of Climate, 12(11), 3237-3254. Abstract PDF
Tropical storms simulated by a nine-member ensemble of GCM integrations forced by observed SSTs have been tracked by an objective procedure for the period 1980-88. Statistics on tropical storm frequency, intensity, and first location have been produced. Statistical tools such as the chi-square and the Kolmogorov-Smirnov test indicate that there is significant potential predictability of interannual variability of simulated tropical storm frequency, intensity, and first location over most of the ocean basins. The only common point between the nine members of the ensemble is the SST forcing. This implies that SSTs play a fundamental role in model tropical storm frequency, intensity, and first location interannual variability. Although the interannual variability of tropical storm statistics is clearly affected by SST forcing in the GCM, there is also a considerable amount of noise related to internal variability of the model. An ensemble of atmospheric model simulations allows one to filter this noise and gain a better understanding of the mechanisms leading to interannual tropical storm variability.
An EOF analysis of local SSTs over each ocean basin and a combined EOF analysis of vertical wind shear, 850-mb vorticity, and 200-mb vorticity have been performed. Over some ocean basins such as the western North Atlantic, the interannual frequency of simulated tropical storms is highly correlated to the first combined EOF, but it is not significantly correlated to the first EOF of local SSTs. This suggests that over these basins the SSTs have an impact on the simulated tropical storm statistics from a remote area through the large-scale circulation as in observations. Simulated and observed tropical storm statistics have been compared. The interannual variability of simulated tropical storm statistics is consistent with observations over the ocean basins where the model simulates a realistic interannual variability of the large-scale circulation.
Anderson, Jeffrey L., H van den Dool, A Barnston, W Chen, William F Stern, and Jeff J Ploshay, 1998: Capabilities of dynamical and statistical methods for atmospheric extratropical seasonal prediction In Proceedings of the Twenty-Second Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 46-49.
Anderson, Jeffrey L., Richard G Gudgel, and Jeff J Ploshay, 1998: Seasonal-interannual predictions from an ensemble of fully-coupled ocean-atmosphere GCM integrations. In Proceedings of the Twenty-Second Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 18-20.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1998: Evaluation of the skill of an ensemble of GCM integrations in simulating seasonal tropical storm frequency, intensity and location In Proceedings of the Twenty-Second Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 38-41.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1998: Simulation of the internally variability of tropical storm frequency, intensity and location in an ensemble of GCM integrations In Research Activities in Atmospheric and Oceanic Modelling, WMO/TD No. 865, Geneva, Switzerland, World Meteorological Organization, 6.28-6.29.
Wittenberg, Andrew T., and Jeffrey L Anderson, 1998: Dynamical implications of prescribing part of a coupled system: Results from a low-order model. Nonlinear Processes in Geophysics, 5(3), 167-179. Abstract PDF
It is a common procedure in climate modeling to specify dynamical system components from an external source; a prominent example is the forcing of an atmospheric model with observed sea surface temperatures. In this paper, we examine the dynamics of such forced models using a simple prototype climate system. A particular fully-coupled run of the model is designated the "true" solution, and an ensemble of perturbed initial states is generated by adding small errors to the "true" initial state. The perturbed ensemble is then integrated for the same period as the true solution, using both the fully-coupled model and a model in which the ocean is prescribed exactly from the true solution at every time step. Although the prescribed forcing is error-free, the forced-atmosphere ensemble is shown to converge to spurious solutions. Statistical tests show that neither the time-mean state nor the variability of the forced ensemble is consistent with the fully-coupled system. A stability analysis reveals the source of the inconsistency, and suggests that such behavior may be a more general feature of models with prescribed subsystems. Possible implications for model validation and predictability are discussed.
Yang, X-Q, Jeffrey L Anderson, and William F Stern, 1998: Reproducible forced modes in AGCM ensemble integrations and potential predictability of atmospheric seasonal variations in the extratropics In Proceedings of the Twenty-Second Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 50-53.
Yang, X-Q, Jeffrey L Anderson, and William F Stern, 1998: Reproducible forced modes in AGCM ensemble integrations and potential predictability of atmospheric seasonal variations in the extratropics. Journal of Climate, 11(11), 2942-2959. Abstract PDF
An approach to assess the potential predictability of the extratropical atmospheric seasonal variations in an ensemble of atmospheric general circulation model (AGCM) integrations has been proposed in this study by isolating reproducible forced modes and examining their contributions to the local ensemble mean. The analyses are based on the monthly mean output of an eight-member ensemble of 10-yr Atmospheric Model Intercomparison Project integrations with a T42L18 AGCM.
An EOF decomposition applied to the ensemble anomalies shows that there exist some forced modes that are less affected by the internal process and thus appear to be highly reproducible. By reconstructing the ensemble in terms of the more reproducible forced modes and by developing a quantitative measure, the potential predictability index (PPI), which combines the reproducibility with the local variance contribution, the local ensemble mean over some selective geographic areas in the extratropics was shown to result primarily from reproducible forced modes rather than internal chaotic fluctuations. Over those regions the ensemble mean is potentially predictable. Extratropical potentially predictable regions are found mainly over North America and part of the Asian monsoon regions. Interestingly, the potential predictability over some preferred areas such as Indian monsoon areas and central Africa occasionally results primarily from non-ENSO-related boundary forcing, although ENSO forcing generally dominates over most of the preferred areas.
The quantitative analysis of the extratropical potential predictability with PPI has shown that the preferred geographic areas have obvious seasonality. For the 850-hPa temperature, for example, potentially predictable regions during spring and winter are confined to Alaska, northwest Canada, and the southeast United States, the traditional PNA region, while during summer and fall they are favored over the middle part of North America. It has also been shown that the boreal summer season (June-August) possesses the largest potentially predictable area, which seems to indicate that it is a favored season for the extratropical potential predictability. On the contrary, boreal winter (December-February) appears to have a minimum area of extratropical potential predictability.
The results have been compared with the more traditional statistical tests for potential predictability and with observations from the National Centers for Environmental Prediction reanalysis, which indicates that the PPI analysis proposed here is successful in revealing extratropical potential predictability determined by the external forcing.
Anderson, Jeffrey L., 1997: The impact of dynamical constraints on the selection of initial conditions for ensemble predictions: Low-order perfect model results. Monthly Weather Review, 125(11), 2969-2983. Abstract PDF
A number of operational atmospheric prediction centers now produce ensemble forecasts of the atmosphere. Because of the high-dimensional phase spaces associated with operational forecast models, many centers use constraints derived from the dynamics of the forecast model to define a greatly reduced subspace from which ensemble initial conditions are chosen. For instance, the European Centre for Medium-Range Weather Forecasts uses singular vectors of the forecast model and the National Centers for Environmental Prediction use the "breeding cycle" to determine a limited set of directions in phase space that are sampled by the ensemble forecast.
The use of dynamical constraints on the selection of initial conditions for ensemble forecasts is examined in a perfect model study using a pair of three-variable dynamical systems and a prescribed observational error distribution. For these systems, one can establish that the direct use of dynamical constraints has no impact on the error of the ensemble mean forecast and a negative impact on forecasts of higher-moment quantities such as forecast spread. Simple examples are presented to show that this is not a result of the use of low-order dynamical systems but is instead related to the fundamental nature of the dynamics of these particular low-order systems themselves. Unless operational prediction models have fundamentally different dynamics, this study suggests that the use of dynamically constrained ensembles may not be justified. Further studies with more realistic prediction models are needed to evaluate this possibility.
Anderson, Jeffrey L., and Richard G Gudgel, 1997: Impact of atmospheric initial conditions on seasonal predictions with a coupled ocean-atmosphere model In Proceedings of the Twenty-First Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 61-66.
Anderson, Jeffrey L., and V Hubeny, 1997: A reexamination of methods for evaluating the predictability of the atmosphere. Nonlinear Processes in Geophysics, 4(3), 157-165. Abstract PDF
Pioneering work by Lorenz (1965, 1968, 1969) developed a number of methods for exploring the limits of predictability of the atmosphere. One method uses an integration of a realistic numerical model as a surrogate for the atmosphere. The evolution of small perturbations to the integration are used to estimate how quickly errors resulting from a given observational error distribution would grow in this perfect model context.
In reality, an additional constraint must be applied to this predictability problem. In the real atmosphere, only states that belong to the atmosphere's climate occur and one is only interested in how such realizable states diverge in time. Similarly, in a perfect model study, only states on the model's attractor occur. However, a prescribed observational error distribution may project on states that are off the attractor, resulting in unrepresentative error growth. The 'correct' error growth problem examines growth for the projection of the observational error distribution onto the model's attractor.
Simple dynamical systems are used to demonstrate that this additional constraint is vital in order to correctly assess the rate of error growth. A naive approach in which this information about the model's 'climate' is not used can lead to significant errors. Depending on the dynamical system, error doubling times may be either underestimated or overestimated although the latter seems more likely for more realistic models. While the magnitude of these errors is not large in the simple dynamical systems examined, the impact could be much larger in more realistic forecast models.
Anderson, Jeffrey L., Anthony Rosati, and Richard G Gudgel, 1997: Potential predictability in an ensemble of coupled atmosphere-ocean general circulation model seasonal forecasts In Proceedings of the Twenty-First Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 18-21.
Nakamura, H, M Nakamura, and Jeffrey L Anderson, 1997: The role of high- and low-frequency dynamics in blocking formation. Monthly Weather Review, 125(9), 2074-2093. Abstract PDF
Time evolutions of prominent blocking flow configurations over the North Pacific and Europe are compared based upon composites for the 30 strongest events observed during 27 recent winter seasons. Fluctuations associated with synoptic-scale migratory eddies have been filtered out before the compositing. A quasi-stationary wave train across the Atlantic is evident during the blocking amplification over Europe, while no counterpart is found to the west of the amplifying blocking over the North Pacific. Correlation between the tropopause-level potential vorticity (PV) and meridional wind velocity associated with the amplifying blocking is found to be negative over Europe in association with the anticyclonic evolution of the low-PV center, but it is almost zero over the North Pacific. Feedback from the synoptic-scale eddies, as evaluated in the form of 250-mb geopotential height tendency due to the eddy vorticity flux convergence, accounts for more than 75% of the observed amplification for the Pacific blocking and less than 45% for the European blocking. This difference is highlighted in two types of "contour advection with surgery" experiments. In one of them PV contours observed four days before the peak blocking time were advected by composite time series of the low-pass-filtered observational wind, and in the other experiment they were advected by the low-pass-filtered wind from which the transient eddy feedback evaluated as above had been removed at every time step. Hence, the latter data should be dominated by low-frequency dynamics. For the European blocking both experiments can reproduce the anticyclonic evolution of low-PV air within a blocking ridge as observed. For the Pacific blocking, in contrast, the observed intrusion of low-PV air into the higher latitudes cannot be reproduced without the transient feedback. Furthermore, in a barotropic model initialized with the composite 250-mb flow observed three days before the peak time, a simulated blocking development over the North Pacific is more sensitive to the insertion of the observed transient feedback than that over Europe. These results suggest that the incoming wave activity flux associated with a quasi-stationary Rossby wave train is of primary importance in the blocking formation over Europe, whereas the forcing by the synoptic-scale transients is indispensable to that over the North Pacific.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1997: Simulation of interannual variability of tropical storm frequency in an ensemble of GCM integrations. Journal of Climate, 10(4), 745-760. Abstract PDF
The present study examines the simulation of the number of tropical storms produced in GCM integrations with a prescribed SST. A 9-member ensemble of 10-yr integrations (1979-88) of a T42 atmospheric model forced by observed SSTs has been produced; each ensemble member differs only in the initial atmospheric conditions. An objective procedure for tracking model-generated tropical storms is applied to this ensemble during the last 9 yrs of the integrations (1980-88). The seasonal and monthly variations of tropical storm numbers are compared with observations for each ocean basin.
Statistical tools such as the Chi-square test, the F test, and the t test are applied to the ensemble number of tropical storms, leading to the conclusion that the potential predictability is particularly strong over the western North Pacific and the eastern North Pacific, and to a lesser extent over the western North Atlantic. A set of tools including the joint probability distribution and the ranked probability score are used to evaluate the simulation skill of this ensemble simulation. The simulation skill over the western North Atlantic basin appears to be exceptionally high, particularly during years of strong potential predictability.
Anderson, Jeffrey L., 1996: Impacts of dynamically constrained initial conditions on ensemble forecasts In 11th Conference on Numerical Weather Prediction, Boston, MA, American Meteorological Society, 56-57.
Anderson, Jeffrey L., 1996: A method of producing and evaluating probabilistic forecasts from ensemble model integrations. Journal of Climate, 9(7), 1518-1530. Abstract PDF
The binned probability ensemble (BPE) technique is presented as a method for producing forecasts of the probability distribution of a variable using an ensemble of numerical model integrations. The ensemble forecasts are used to partition the real line into a number of bins, each of which has an equal probability of containing the "true" forecast. The method is tested for both a simple low-order dynamical system and a general circulation model (GCM) forced with observed sea surface temperatures (an ensemble of Atmospheric Model Intercomparison Project integrations). The BPE method can also be used to calculate the probability that probabilistic ensemble forecasts are consistent with the verifying observations. The method is not sensitive to the fact that the characteristics of the forecast probability distribution may change drastically for different initial condition (or boundary condition) probability distributions. For example, the method is capable of evaluating whether the variance of a set of ensemble forecasts is consistent with the verifying observed variance. Applying the method to the ensemble of boundary-forced GCM integrations demonstrates that the GCM produces probabilistic forecasts with too little variability for upper-level dynamical fields. Operational weather prediction centers including the U.K. Meteorological Office, the European Centre for Medium-Range Forecasts, and the National Centers for Environmental Prediction have been applying this method, referred to by them as Talagrand diagrams, to the verification of operational ensemble predictions. The BPE method only evaluates the consistency of ensemble predictions and observations and should be used in conjunction with additional verification tools to provide a complete assessment of a set of probabilistic forecasts.
Anderson, Jeffrey L., 1996: Selection of initial conditions for ensemble forecasts in a simple perfect model framework. Journal of the Atmospheric Sciences, 53(1), 22-36. Abstract PDF
An extremely simple chaotic model, the three-variable Lorenz convective model, is used in a perfect model setting to study the selection of initial conditions for ensemble forecasts. Observations with a known distribution of error are sampled from the "climate" of the simple model. Initial condition distributions that use only information about the observation and the observational error distribution (i.e., traditional Monte Carlo methods) are shown to differ from the correct initial condition distributions, which make use of additional information about the local structure of the model's attractor. Three relatively inexpensive algorithms for finding the local attractor structure in a simple model are examined; these make use of singular vectors, normal modes, and perturbed integrations. All of these are related to heuristic algorithms that have been applied to select ensemble members in operational forecast models. The method of perturbed integrations, which is somewhat similar to the "breeding" method used at the National Meteorological Center, is shown to be the most effective in this context. Validating the extension of such methods to realistic models is expected to be extremely difficult; however, it seems reasonable that utilizing all available information about the attractor structure of real forecast models when selecting ensemble initial conditions could improve the success of operational ensemble forecasts.
Anderson, Jeffrey L., 1996: Verification of seasonal 'forecasts' from ensemble GCM integrations In Proceedings of the 20th Annual Climate Diagnostics Workshop, U.S. Dept. of Commerce/NOAA/NWS, 433-436.
Anderson, Jeffrey L., and William F Stern, 1996: Evaluating the potential predictive utility of ensemble forecasts. Journal of Climate, 9(2), 260-269. Abstract PDF
A method is presented for determining when an ensemble of model forecasts has the potential to provide some useful information. An ensemble forecast of a particular scalar quantity is said to have potential predictive utility when the ensemble forecast distribution is significantly different from an appropriate climatological distribution. Here, the potential predictive utility is measured using Kuiper's statistical test for comparing two discrete distributions. More traditional measures of the potential usefulness of an ensemble forecast based on ensemble mean or variance discard possibly valuable information by making implicit assumptions about the distributions being compared
Application of the potential predictive utility to long integrations of an atmospheric general circulation model in a boundary value problem (an ensemble of Atmospheric Model Intercomparison Project integrations) reveals a number of features about the response of a GCM to observed sea surface temperatures. In particular, the ensemble of forecasts is found to have potential predictive utility over large geographic areas for a number of atmospheric fields during strong El Niño-Southern Oscillation anomalous events. Unfortunately, there are only limited areas of potential predictive utility for near-surface fields and precipitation outside the regions of the tropical oceans. Nevertheless, the method presented here can identify all areas where the GCM ensemble may provide useful information, whereas methods that make assumptions about the distribution of the ensemble forecast variables may not be able to do so.
Harrison, Matthew J., Anthony Rosati, Richard G Gudgel, and Jeffrey L Anderson, 1996: Initialization of coupled model forecasts using an improved ocean data assimilation system In 11th Conference on Numerical Weather Prediction, Boston, MA, American Meteorological Society, 7.
Lee, S, and Jeffrey L Anderson, 1996: A simulation of atmospheric storm tracks with a forced barotropic model. Journal of the Atmospheric Sciences, 53(15), 2113-2128. Abstract PDF
A forced, nonlinear barotropic model on the sphere is shown to simulate some of the structure of the observed Northern Hemisphere midlatitude storm tracks with reasonable accuracy. For the parameter range chosen, the model has no unstable modes with significant amplitude in the storm track regions; however, several decaying modes with structures similar to the storm track are discovered. The model's midlatitude storm tracks also coincide with the location of a waveguide that is obtained by assuming that the horizontal variation of the time-mean flow is small compared with the scale of the transient eddies. Since the model is able to mimic the behavior of the observed storm tracks without any baroclinic dynamics, it is argued that the barotropic waveguide effects of the time-mean background flow acting on individual eddies are partially responsible for the observed storm track structure.
Stern, William F., and Jeffrey L Anderson, 1996: Interannual variability of tropical intraseasonal oscillations in the GFDL/DERF GCM inferred from an ensemble of AMIP integrations In 11th Conference on Numerical Weather Prediction, Boston, MA, American Meteorological Society, 15-16.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1996: Potential predictability of tropical storms in an ensemble of forecasts In Proceedings of the 20th Annual Climate Diagnostics Workshop, U.S. Dept. of Commerce/NOAA/NWS, 263-266.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1996: Potential predictability of tropical storms in ensemble GCM simulations In Research Activities in Atmospheric and Oceanic Modelling, CAS/JSC Working Group on Numerical Experimentation, Report No. 23 WMO/TD No. 734, World Meteorological Organization, 6.32.
Anderson, Jeffrey L., 1995: A simulation of atmospheric blocking with a forced barotropic model. Journal of the Atmospheric Sciences, 52(15), 2593-2608. Abstract PDF
observed atmospheric blocking patterns have recently been derived. Examining the way such NSSs affect integrations of the BVE is of interest. Unfortunately, the BVE rapidly evolves away from the neighborhood of blocking NSSs due to instability and never again generates suffucient amplitude to return to the vicinity of the blocking NSSs. However, forced versions of the BVE with both a high amplitude blocking NSS and more zonal low amplitude NSSs can be constructed. For certain parameter ranges, extended integrations of these forced BVEs exhibit two "regimes," one strongly blocked and the other relatively zonal. Somewhat realistic simulations of low and high frequency variability and individual blocking event life cycles are also produced by these forced barotropic models. It is argued here that these regimes are related to "attractor-like" behavior of the NSSs of the forced BVE. Strong barotropic short waves apparently provide the push needed to cause a transition to or from the blocked regime. In the purely barotropic model used here, there is a rather delicate balance required between the forcing strength for different spatial scales in order to produce regimelike behavior. However, the mechanism proposed appears to be a viable candidate for explaining the observed behavior of blocking events in the atmosphere.
Anderson, Jeffrey L., and William F Stern, 1995: A method of evaluating the predictive ability of ensemble forecasts In Proceedings of the 19th Annual Climte Diagnostics Workshop, Springfield, VA, NTIS, 472-475.
Miyakoda, Kikuro, Joseph J Sirutis, Anthony Rosati, C Tony Gordon, Richard G Gudgel, William F Stern, Jeffrey L Anderson, and A Navarra, 1995: Atmospheric parameterizations in coupled air-sea models used for forecasts of ENSO In Proceedings of the International Scientific Conference on the Tropical Ocean Global Atmosphere (TOGA) Programme, WCRP-91, WMO/TD No. 717, Geneva, Switzerland, World Meteorological Organization, 802-806. Abstract
In order to investigate the feasibility of seasonal forecasts, a prediction system is developed. Here the main theme is the study of atmospheric physics parameterization for coupled air-sea modeling. The oceanic GCM uses 1 degree global grid with a finer resolution in the equatorial belt. The atmospheric GCM has the spectral T30 representation, which includes all of the usual physics parameterizations. Using a first version of the model (Coupled Model I) and a set of appropriate initial conditions, the capability of El Niño and La Niña forecasting with a 13 month lead time was tested, resulting in successful forecasts of the 1982/83 and 1988/89 events (Rosati et al., 1995b). However, longer runs of this system have revealed a sizable systematic error in simulations with a tendency to cool most of the world ocean, particularly the western tropical Pacific, and also without an adequate annual cycle of the SST in the eastern tropical Pacific.
In order to improve some of these features, particularly the ENSO phenomena, various versions of the atmospheric parameterizations and mountain representation are incorporated into the atmospheric GCM, and the model simulations are examined. The experiments are divided into two steps: one is with the uncoupled atmospheric model, and the other is with the coupled model. In the first step, five year simulations are carried out with the observed SST prescribed, and the results are compared with observations, which enables one to make the critical validation of the model. The second step is to couple the atmospheric and oceanic models, and integrate them from a January 1982 initial condition for 7 years, and also for another initial condition, i.e., January, 1988 for 13 months.
Compared with the boundary forced simulation, the coupling process introduces more degree of freedom, with increase of the sensitivity as well as the complexity considerably. In particular, the El Niño simulation is sensitive to any change of physics. For this reason, the objective of the simulation is focused only on the equatorial Pacific process and secondly the Indian monsoon, as opposed to the overall improvement of the general circulation. In other words, the approach is close to that of mechanistic modeling with specific targets rather than that of a GCM with broader objectives. The research is proceeding in two directions. One is: investigating the model's sensitivity for El Niño and La Niña processes to variation in a coupling parameter. The second is: after a number of trial-and-error experiments on various combinations of the parameterizations, the second atmospheric model, i.e., Model II, is selected. It is shown that Coupled Model II performs substantially better in some aspects but worse in other aspects than Coupled Model I. The improvement is found in the SST: warming occurs not only over the equatorial Pacific but also over the whole globe. The SST increase is achieved by the strong effect of the cumulus convection. On the other hand, some deficiencies remain the same in both models, i.e., the large positive errors of the SST in the eastern oceans, the lack of an annual cycle of the SST in the eastern equatorial Pacific, and the failure in forecast of the second El Niño. In summary, the prediction of the Southern Oscillation has been achieved by the two models for a full first cycle but not for the second cycle .
Anderson, Jeffrey L., 1994: Ensemble forecasting and non-linear dynamics In Proceedings of the 18th Annual Climate Diagnostics Workshop, U.S. Dept. of Commerce/NOAA/NWS, 366-369.
Anderson, Jeffrey L., and H van den Dool, 1994: Skill and return of skill in dynamic extended-range forecasts. Monthly Weather Review, 122(3), 507-516. Abstract PDF
The skill of a set of extended-range dynamical forecasts made with a modern numerical forecast model is examined. A forecast is said to be skillful if it produces a high quality forecast by correctly modeling some aspects of the dynamics of the real atmosphere; high quality forecasts may also occur by chance. The dangers of making a conclusion about model skill by verifying a single long-range forecast are pointed out by examples of apparently high "skill" verifications between extended-range forecasts and observed fields from entirely different years
To avoid these problems, the entire distribution of forecast quality for a large set of forecasts as a function of lead time is examined. A set of control forecasts that clearly have no skill is presented. The quality distribution for the extended-range forecasts is compared to the distributions of quality for the no-skill control forecast set
The extended-range forecast quality distributions are found to be essentially indistinguishable from those for the no-skill control at leads somewhat greater than 12 days. A search for individual forecasts with a "return of skill" at extended ranges is also made. Although it is possible to find individual forecasts that have a return of quality, a comparison to the no-skill controls demonstrates that these return of skill forecasts occur only as often as is expected by chance.
Anderson, Jeffrey L., 1993: The climatology of blocking in a numerical forecast model. Journal of Climate, 6(6), 1041-1056. Abstract PDF
An objective criterion for identifying blocking events is applied to a ten-year climate run of the National Meteorological Center's Medium-Range Forecast Model (MRF) and to observations. The climatology of blocking in the ten-year run is found to be somewhat realistic in the Northern Hemisphere, although when averaged over all longitudes and seasons a general lack of blocking is found. Previous studies have suggested that numerical models are incapable of producing realistic numbers of blocks, however, the ten-year model run is able to produce realistic numbers of blocks for selected geographic regions and seasons. In these regions, blocks are found to persist longer than observed blocking events. The ten-year run of the model is also able to reproduce the average longitudinal extent and motion of the observed blocks. These results suggest that the MRF is able to generate and persist realistic blocks, but only at longitudes and seasons for which the underlying model climate is conductive. In the Southern Hemisphere, the ten-year run blocking climatology is considerably less realistic. The appearance of "transient" blocking events in the model distinguishes it from the Southern Hemisphere observations and from the Northern Hemisphere. A set of 60-day forecasts by the MRF is used to evaluate the evolution of the model blocking climatology with lead time (blocking climate drift) for a 90-day period in autumn of 1990. Although the ten-year run and observed blocking climates are quite similar at most longitudes at this time of year, it is found that blocking almost entirely disappears from the model forecasts at lead times of approximately 10 days before reappearing at leads greater than 15 days. It is argued that this lack of a direct transition between observed and model blocking climates is the result of a drift in the underlying climate (for example, the positions of the jet streams) in the MRF forecasts. If so, the climate drift of the MRF must be further reduced in order to produce more accurate medium-range forecasts of blocking events.
Anderson, Jeffrey L., 1993: Return of skill in extended range forecasts In Proceedings of the 17th Annual Climate Diagnostics Workshop, Springfield, VA, NTIS, 416-418.
