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Michael Mayer, Leopold Haimberger, and Magdalena A. Balmaseda

Abstract

Vast amounts of energy are exchanged between the ocean, atmosphere, and space in association with El Niño–Southern Oscillation (ENSO). This study examines energy budgets of all tropical (30°S–30°N) ocean basins and the atmosphere separately using different, largely independent oceanic and atmospheric reanalyses to depict anomalous energy flows associated with ENSO in a consistent framework. It is found that variability of area-averaged ocean heat content (OHC) in the tropical Pacific to a large extent is modulated by energy flow through the ocean surface. While redistribution of OHC within the tropical Pacific is an integral part of ENSO dynamics, variability of ocean heat transport out of the tropical Pacific region is found to be mostly small. Noteworthy contributions arise from the Indonesian Throughflow (ITF), which is anticorrelated with ENSO at a few months lag, and from anomalous oceanic poleward heat export during the La Niña events in 1999 and 2008. Regression analysis reveals that atmospheric energy transport and radiation at the top of the atmosphere (RadTOA) almost perfectly balance the OHC changes and ITF variability associated with ENSO. Only a small fraction of El Niño–related heat lost by the Pacific Ocean through anomalous air–sea fluxes is radiated to space immediately, whereas the major part of the energy is transported away by the atmosphere. Ample changes in tropical atmospheric circulation lead to enhanced surface fluxes and, consequently, to an increase of OHC in the tropical Atlantic and Indian Ocean that almost fully compensates for tropical Pacific OHC loss. This signature of energy redistribution is robust across the employed datasets for all three tropical ocean basins and explains the small ENSO signal in global mean RadTOA.

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Kevin E. Trenberth, John T. Fasullo, and Magdalena A. Balmaseda

Abstract

Climate change from increased greenhouse gases arises from a global energy imbalance at the top of the atmosphere (TOA). TOA measurements of radiation from space can track changes over time but lack absolute accuracy. An inventory of energy storage changes shows that over 90% of the imbalance is manifested as a rise in ocean heat content (OHC). Data from the Ocean Reanalysis System, version 4 (ORAS4), and other OHC-estimated rates of change are used to compare with model-based estimates of TOA energy imbalance [from the Community Climate System Model, version 4 (CCSM4)] and with TOA satellite measurements for the year 2000 onward. Most ocean-only OHC analyses extend to only 700-m depth, have large discrepancies among the rates of change of OHC, and do not resolve interannual variability adequately to capture ENSO and volcanic eruption effects, all aspects that are improved with assimilation of multivariate data. ORAS4 rates of change of OHC quantitatively agree with the radiative forcing estimates of impacts of the three major volcanic eruptions since 1960 (Mt. Agung, 1963; El Chichón, 1982; and Mt. Pinatubo, 1991). The natural variability of the energy imbalance is substantial from month to month, associated with cloud and weather variations, and interannually mainly associated with ENSO, while the sun affects 15% of the climate change signal on decadal time scales. All estimates (OHC and TOA) show that over the past decade the energy imbalance ranges between about 0.5 and 1 W m−2. By using the full-depth ocean, there is a better overall accounting for energy, but discrepancies remain at interannual time scales between OHC- and TOA-based estimates, notably in 2008/09.

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Magdalena A. Balmaseda, Arthur Vidard, and David L. T. Anderson

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A new operational ocean analysis/reanalysis system (ORA-S3) has been implemented at ECMWF. The reanalysis, started from 1 January 1959, is continuously maintained up to 11 days behind real time and is used to initialize seasonal forecasts as well as to provide a historical representation of the ocean for climate studies. It has several innovative features, including an online bias-correction algorithm, the assimilation of salinity data on temperature surfaces, and the assimilation of altimeter-derived sea level anomalies and global sea level trends. It is designed to reduce spurious climate variability in the resulting ocean reanalysis due to the nonstationary nature of the observing system, while still taking advantage of the observation information. The new analysis system is compared with the previous operational version; the equatorial temperature biases are reduced and equatorial currents are improved. The impact of assimilation in the ocean state is discussed by diagnosis of the assimilation increment and bias correction terms. The resulting analysis not only improves the fit to the data, but also improves the representation of the interannual variability. In addition to the basic analysis, a real-time analysis is produced (RT-S3). This is needed for monthly forecasts and in the future may be needed for shorter-range forecasts. It is initialized from the near-real-time ORA-S3 and run each day from it.

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Magdalena A. Balmaseda, Michael K. Davey, and David L. T. Anderson

Abstract

When forecasting sea surface temperature (SST) in the Equatorial Pacific on a timescale of several seasons, most prediction schemes have a spring barrier; that is, they have skill scores that are substantially lower when predicting northern spring and summer conditions compared to autumn and winter. This feature is investigated by examining predictions during the 1970s and the 1980s, using a dynamic ocean model of intermediate complexity coupled to a statistical atmosphere. Results show that predictions initialized during the 1970s exhibit the typical prominent skill decay in spring, whereas the seasonal dependence in those predictions initialized during the 1980s is rather small. Similar changes in seasonal dependence are also found in predictions based on simple persistence of observed SST anomalies.

This decadal change in the spring barrier is related to decadal variations found in the seasonal phase locking of the SST anomalies, which is largely determined by the timing of El Niño events. The spring barrier was strong in the 1970s, when El Niño was strongly phaselocked to the annual cycle. An analysis of observed SST anomalies from 1900 to 1990 shows several changes in behavior on a decadal scale, with the largest change being from the 1970s to the 1980s.

The seasonal dependence of model heat content predictions is investigated and found to be similar to that for SST, except that it shows a winter barrier rather than the spring barrier evident in SST.

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Timothy N. Stockdale, Magdalena A. Balmaseda, and Arthur Vidard

Abstract

Variations in tropical Atlantic SST are an important factor in seasonal forecasts in the region and beyond. An analysis is given of the capabilities of the latest generation of coupled GCM seasonal forecast systems to predict tropical Atlantic SST anomalies. Skill above that of persistence is demonstrated in both the northern tropical and equatorial Atlantic, but not farther south. The inability of the coupled models to correctly represent the mean seasonal cycle is a major problem in attempts to forecast equatorial SST anomalies in the boreal summer. Even when forced with observed SST, atmosphere models have significant failings in this area. The quality of ocean initial conditions for coupled model forecasts is also a cause for concern, and the adequacy of the near-equatorial ocean observing system is in doubt. A multimodel approach improves forecast skill only modestly, and large errors remain in the southern tropical Atlantic. There is still much scope for improving forecasts of tropical Atlantic SST.

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Michael J. McPhaden, Axel Timmermann, Matthew J. Widlansky, Magdalena A. Balmaseda, and Timothy N. Stockdale

Abstract

Forty years ago, Klaus Wyrtki of the University of Hawaii launched an “El Niño Watch” expedition to the eastern equatorial Pacific to document oceanographic changes that were expected to develop during the onset of an El Niño event in early 1975. He and his colleagues used a very simple atmospheric pressure index to predict the event and convinced the National Science Foundation and Office of Naval Research to support an expedition to the eastern Pacific on relatively short notice. An anomalous warming was detected during the first half of the expedition, but it quickly dissipated. Given the state of the art in El Niño research at the time, Wyrtki and colleagues could offer no explanation for why the initial warming failed to amplify, nor could they connect what they observed to what was happening in other parts of the basin prior to and during the expedition. With the benefit of hindsight, the authors provide a basin-scale context for what the expedition observed, elucidate the dynamical processes that gave rise to the abbreviated warming that was detected, and present retrospective forecasts of the event using modern coupled ocean–atmosphere dynamical model prediction systems. Reviewing this history highlights how early pioneers in El Niño research, despite the obstacles they faced, were able to make significant progress through bold initiatives that advanced the frontiers of our knowledge. It is also evident that, even though the scientific community today has a much deeper understanding of climate variability, more advanced observational capabilities, and sophisticated seasonal forecasting tools, skillful predictions of El Niño and its cold counterpart La Niña remain a major challenge.

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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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Gerrit Burgers, Magdalena A. Balmaseda, Femke C. Vossepoel, Geert Jan van Oldenborgh, and Peter Jan van Leeuwen

Abstract

The question is addressed whether using unbalanced updates in ocean-data assimilation schemes for seasonal forecasting systems can result in a relatively poor simulation of zonal currents. An assimilation scheme, where temperature observations are used for updating only the density field, is compared to a scheme where updates of density field and zonal velocities are related by geostrophic balance. This is done for an equatorial linear shallow-water model. It is found that equatorial zonal velocities can be detoriated if velocity is not updated in the assimilation procedure. Adding balanced updates to the zonal velocity is shown to be a simple remedy for the shallow-water model. Next, optimal interpolation (OI) schemes with balanced updates of the zonal velocity are implemented in two ocean general circulation models. First tests indicate a beneficial impact on equatorial upper-ocean zonal currents.

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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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