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H. Frey, M. Latif, and T. Stockdale

Abstract

In this paper the performance of the global coupled general circulation model (CGCM) ECHO-2, which was integrated for 10 years without the application of flux correction, is described. Although the integration is rather short, strong and weak points of this CGCM can be clearly identified, especially in view of the model’s performance of the annual cycle in the tropical Pacific. The latter is simulated with more success relative to the earlier version, ECHO-1. A better representation of the low-level stratus clouds in the atmosphere model associated with a reduction in the shortwave radiative flux at the air–sea interface improved the coupled model’s performance in the southeastern tropical oceans, with a strongly reduced warm bias in these regions. Modifications in the atmospheric convection scheme also eliminated the AGCM’s tendency to simulate a double ITCZ, and this behavior is maintained in the CGCM simulation. Finally, a new numerical scheme for active tracer advection in the ocean model strongly reduced the numerical mixing, which seems to enhance considerably the level of interannual variability in the equatorial Pacific.

One weak point is an overall cold bias in the Tropics and midlatitudes, which typically amounts to 1°C in open ocean regions. Another weak point is the still too strong equatorial cold tongue, which penetrates too far into the western equatorial Pacific. Although this model deficiency is not as pronounced as in ECHO-1, the too strong cold tongue reduces the level of interannual rainfall variability in the western and central equatorial Pacific. Finally, the interannual fluctuations in equatorial Pacific sea surface temperatures (SSTs) are too equatorially trapped, a problem that is also found in “ocean-only” simulations. Overall, however, the authors believe that the ECHO-2 CGCM has been considerably improved relative to ECHO-1.

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J. Segschneider, D. L. T. Anderson, and T. N. Stockdale

Abstract

The TOPEX/Poseidon and ERS-1/2 satellites have now been observing sea level anomalies for a continuous time span of more than 6 yr. These sea level observations are first compared with tide gauge data and then assimilated into an ocean model that is used to initialize coupled ocean–atmosphere forecasts with a lead time of 6 months. Ocean analyses in which altimeter data are assimilated are compared with those from a no-assimilation experiment and with analyses in which subsurface temperature observations are assimilated. Analyses with altimeter data show variations of upper-ocean heat content similar to analyses using subsurface observations, whereas the ocean model has large errors when no data are assimilated. However, obtaining good results from the assimilation of altimeter data is not straightforward: it is essential to add a good mean sea level to the observed anomalies, to filter the sea level observations appropriately, to start the analyses from realistic initial temperature and salinity fields, and to assign appropriate weights for the analyzed increments.

To assess the impact of altimeter data assimilation on the coupled system, ensemble hindcasts are initialized from ocean analyses in which either no data, subsurface temperatures, or sea level observations were assimilated. For each kind of ocean analysis, a five-member ensemble is started every 3 months from January 1993 to October 1997, adding up to 100 forecasts for each type. The predicted SST anomalies for the equatorial Pacific are intercompared between the experiments and against observations. The predicted anomalies are on average closer to observed values when forecasts are initialized from the ocean analysis using altimeter data than when initialized from the no-assimilation ocean analysis, and forecast errors appear to be only slightly larger than for forecasts initialized from ocean analyses using subsurface temperatures. However, even based on 100 coupled forecasts, the distinction between the two experiments that benefit from data assimilation is barely statistically significant. The verification should still be considered preliminary, because the period covered by the forecasts is only 5 yr, which is too short properly to sample ENSO variability. It is, nonetheless, encouraging that altimeter assimilation can improve the forecast skill to a level comparable to that obtained from using Tropical Ocean Atmosphere–expendable bathythermograph data.

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Jérôme Vialard, Frédéric Vitart, Magdalena A. Balmaseda, Timothy N. Stockdale, and David L. T. Anderson

Abstract

Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.

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Geert Jan van Oldenborgh, Magdalena A. Balmaseda, Laura Ferranti, Timothy N. Stockdale, and David L. T. Anderson

Abstract

The European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts since 1997 with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.

Seasonal predictability is to a large extent due to the El Niño–Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of statistical models, some of which are used operationally. The relative skill depends strongly on the season. The dynamical models are better at forecasting the onset of El Niño or La Niña in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.

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Geert Jan van Oldenborgh, Magdalena A. Balmaseda, Laura Ferranti, Timothy N. Stockdale, and David L. T. Anderson

Abstract

Since 1997, the European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.

The main cause of seasonal predictability is El Niño and La Niña perturbing the average weather in many regions and seasons throughout the world. As a baseline to compare the dynamical models with, a set of simple statistical models (STAT) is constructed. These are based on persistence and a lagged regression with the first few EOFs of SST from 1901 to 1986 wherever the correlations are significant. The first EOF corresponds to ENSO, and the second corresponds to decadal ENSO. The temperature model uses one EOF, the sea level pressure (SLP) model uses five EOFs, and the precipitation model uses two EOFs but excludes persistence.

As the number of verification data points is very low (15), the simplest measure of skill is used: the correlation coefficient of the ensemble mean. To further reduce the sampling uncertainties, we restrict ourselves to areas and seasons of known ENSO teleconnections.

The dynamical ECMWF models show better skill in 2-m temperature forecasts over sea and the tropical land areas than STAT, but the modeled ENSO teleconnection pattern to North America is shifted relative to observations, leading to little pointwise skill. Precipitation forecasts of the ECMWF models are very good, better than those of the statistical model, in southeast Asia, the equatorial Pacific, and the Americas in December–February. In March–May the skill is lower. Overall, S1 (S2) shows better skill than STAT at lead time of 2 months in 29 (32) out of 40 regions and seasons of known ENSO teleconnections.

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J. Segschneider, D. L. T. Anderson, J. Vialard, M. Balmaseda, T. N. Stockdale, A. Troccoli, and K. Haines

Abstract

In this paper, the combined assimilation of satellite observed sea level anomalies and in situ temperature data into a global ocean model, which is used to initialize a coupled ocean–atmosphere forecast system, is described. The altimeter data are first used to create synthetic temperature observations, which are then combined with the directly observed temperature profiles in an optimum interpolation scheme. In addition to temperature, salinity is corrected based on a preservation of the model's local temperature–salinity relationship. Coupled forecasts with a lead time of up to 6 months are initialized from the ocean analyses and the impact of the data assimilation on both the ocean analysis and the coupled forecasts is investigated. It is shown that forecasts of sea surface temperature anomalies in the Niño-3 area can be improved by initializing the coupled forecast model with the ocean analysis in which temperature and altimeter data are assimilated in combination. The results further imply that a good simulation of the salinity field is required to make optimum use of the altimeter data.

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Alberto Troccoli, Magdalena Alonso Balmaseda, Joachim Segschneider, Jerome Vialard, David L. T. Anderson, Keith Haines, Tim Stockdale, Frederic Vitart, and Alan D. Fox

Abstract

This paper is an evaluation of the role of salinity in the framework of temperature data assimilation in a global ocean model that is used to initialize seasonal climate forecasts. It is shown that the univariate assimilation of temperature profiles, without attempting to correct salinity, can induce first-order errors in the subsurface temperature and salinity fields. A recently developed scheme by A. Troccoli and K. Haines is used to improve the salinity field. In this scheme, salinity increments are derived from the observed temperature, by using the model temperature and salinity profiles, assuming that the temperature–salinity relationship in the model profiles is preserved. In addition, the temperature and salinity fields are matched below the observed temperature profile by vertically displacing the original model profiles.

Two data assimilation experiments were performed for the 6-yr period 1993–98. These show that the salinity scheme is effective at maintaining the haline and thermal structures at and below thermocline level, especially in tropical regions, by avoiding spurious convection. In addition to improvements in the mean state, the scheme allows more temporal variability than simply controlling the salinity field by relaxation to climatological data. Some comparisons with sparse salinity observations are also made, which suggest that the subsurface salinity variability in the western Pacific is better reproduced in the experiment in which the salinity scheme is used. The salinity analyses might be improved further by use of altimeter sea level or sea surface salinity observations from satellite.

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C.R. Mechoso, A.W. Robertson, N. Barth, M.K. Davey, P. Delecluse, P.R. Gent, S. Ineson, B. Kirtman, M. Latif, H. Le Treut, T. Nagai, J.D. Neelin, S.G.H. Philander, J. Polcher, P.S. Schopf, T. Stockdale, M.J. Suarez, L. Terray, O. Thual, and J.J. Tribbia

Abstract

The seasonal cycle over the tropical Pacific simulated by 11 coupled ocean–atmosphere general circulation models (GCMs) is examined. Each model consists of a high-resolution ocean GCM of either the tropical Pacific or near-global means coupled to a moderate- or high-resolution atmospheric GCM, without the use of flux correction. The seasonal behavior of sea surface temperature (SST) and eastern Pacific rainfall is presented for each model.

The results show that current state-of-the-art coupled GCMs share important successes and troublesome systematic errors. All 11 models are able to simulate the mean zonal gradient in SST at the equator over the central Pacific. The simulated equatorial cold tongue generally tends to be too strong, too narrow, and extend too far west. SSTs are generally too warm in a broad region west of Peru and in a band near 10°S. This is accompanied in some models by a double intertropical convergence zone (ITCZ) straddling the equator over the eastern Pacific, and in others by an ITCZ that migrates across the equator with the seasons; neither behavior is realistic. There is considerable spread in the simulated seasonal cycles of equatorial SST in the eastern Pacific. Some simulations do capture the annual harmonic quite realistically, although the seasonal cold tongue tends to appear prematurely. Others overestimate the amplitude of the semiannual harmonic. Nonetheless, the results constitute a marked improvement over the simulations of only a few years ago when serious climate drift was still widespread and simulated zonal gradients of SST along the equator were often very weak.

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