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  • Author or Editor: Eugenia Kalnay x
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Ming Cai
and
Eugenia Kalnay

Abstract

This paper shows analytically that a reanalysis made with a frozen model can detect the warming trend due to an increase of greenhouse gases within the atmosphere at its full strength (at least 95% level) after a short transient (less than 100 analysis cycles). The analytical proof is obtained by taking into consideration the following three possible deficiencies in the model used to create first-guess fields: (i) the physical processes responsible for the observed trend (e.g., an increase of greenhouse gases) are completely absent from the model, (ii) the first-guess fields are affected by an initial drift caused by the imbalance between the model equilibrium and the analysis that contains trends due to the observations, and (iii) the model used in the reanalysis has a constant model bias. The imbalance contributes to a systematic reduction in the reanalysis trend compared to the observations. The analytic derivation herein shows that this systematic reduction can be very small (less than 5%) when the observations are available for twice-daily assimilation. Moreover, the frequent analysis cycle is essential to compensate for the impact due to relatively poor space coverage of the observational network, which effectively yields smaller weights assigned to observations in a global data assimilation system.

Other major issues about using reanalysis for a long-term trend analysis, particularly the impact of the major changes in the global observing system that took place in the 1950s and in 1979, are not addressed. Here it is merely proven mathematically that using a frozen model in a reanalysis does not cause significant harm to the fidelity of the long-term trend in the reanalysis.

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Malaquías Peña
,
Ming Cai
, and
Eugenia Kalnay

Abstract

The impact of the local phase relationship between the low-level atmospheric circulation and the sea surface temperature (SST) on the duration of atmospheric anomalies is statistically evaluated. Using 5-day-average data from the NCEP–NCAR reanalysis, it is found that most of the long-lasting atmospheric anomalies are locally coupled with SST anomalies, with their number decreasing from the equator to the extratropics. The longer-lasting anomalies tend to have relationships of cyclonic-over-cold or anticyclonic-over-warm phase in the extratropics, and cyclonic-over-warm or anticyclonic-over-cold in the Tropics. This preferential phase relationship of the long-lasting anomalies is consistent with a predominant “atmosphere-driving” situation in the extratropics and an “ocean-driving” one in the Tropics.

A similar analysis using data from a one-way interaction model, with the ocean always forcing the atmosphere is carried out to compare the results with those from the reanalysis. The results show that the one-way interaction produces fewer (more) long-lasting anomalies in the extratropics (Tropics). These differences arise mostly in atmosphere-driving situations, namely, the cyclonic-over-cold or anticyclonic-over-warm phase relation. This suggests that ignoring the atmosphere's feedback effect on the ocean can lead to erroneous damping (lengthening) of atmospheric anomalies in the extratropics (Tropics).

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Song-You Hong
and
Eugenia Kalnay

Abstract

This study presents results from mechanistic experiments to clarify the origin and maintenance of the Oklahoma–Texas (OK–TX) drought of the 1998 summer, using the National Centers for Environmental Prediction (NCEP) global and regional models. In association with this unprecedented drought, three major mechanisms that can produce extended atmospheric anomalies have been identified: (i) sea surface temperature (SST) anomalies, (ii) soil moisture anomalies, and (iii) atmospheric initial conditions favorable to such a climate extreme even in the absence of surface forcing (i.e., internal forcing).

The authors found that the SST anomalies during April–May 1998 established the large-scale conditions for the drought. However, the warm El Niño–Southern Oscillation (ENSO) SST anomalies over the central and eastern tropical Pacific alone did not play a major role in initiating the drought. The internal structure of atmospheric conditions played as significant a role as the SST anomalies over the globe. In June 1998, soil moisture anomalies started to play an important role in maintaining the drought, and the regional positive feedback associated with lower evaporation/lower precipitation explained most of the water deficit in July. After July, synoptic-scale disturbances overwhelmed the impact of dry soil moisture near the Gulf of Mexico states where above-normal precipitation occurred, but the regional feedback was still prominent over the OK–TX region, where the drought persisted until early October.

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Ming Cai
,
Eugenia Kalnay
, and
Zoltan Toth

Abstract

The breeding method is used to obtain the bred vectors (BV) of the Zebiak–Cane (ZC) atmosphere–ocean coupled model. Bred vectors represent a nonlinear, finite-time extension of the leading local Lyapunov vectors of the ZC model. The spatial structure and growth rate of bred vectors are strongly related to the background ENSO evolution of the ZC model. It is equally probable for the BVs to have a positive or negative sign (defined using the Niño-3 index of the BV), though often there is a sign change just before or after an El Niño event. The growth rate (and therefore the spatial coherence) of the BVs peaks several months prior to and after an El Niño event and it is nearly neutral at the mature stage.

Potential applications of bred vectors for ENSO predictions are explored in the context of data assimilation and ensemble forecasting under a perfect model scenario. It is shown that when bred vectors are removed from random initial error fields, forecast errors can be reduced by up to 30%. This suggests that minimizing the projection of the bred vectors on the observation-minus-analysis field may be a beneficial factor to an operational forecast system. The ensemble mean of a pair of forecasts perturbed with positive/negative bred vectors improves the forecast skill, particularly for lead times longer than 6 months, substantially reducing the “spring barrier” for ENSO prediction.

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Shu-Chih Yang
,
Christian Keppenne
,
Michele Rienecker
, and
Eugenia Kalnay

Abstract

Coupled bred vectors (BVs) generated from the NASA Global Modeling and Assimilation Office (GMAO) coupled general circulation model are designed to capture the uncertainties related to slowly varying coupled instabilities. Two applications of the BVs are investigated in this study.

First, the coupled BVs are used as initial perturbations for ensemble-forecasting purposes. Results show that the seasonal-to-interannual variability forecast skill can be improved when the oceanic and atmospheric perturbations are initialized with coupled BVs. The impact is particularly significant when the forecasts are initialized from the cold phase of tropical Pacific SST (e.g., August and November), because at these times the early coupled model errors, not accounted for in the BVs, are small.

Second, the structure of the BVs is applied to construct hybrid background error covariances carrying flow-dependent information for the ocean data assimilation. Results show that the accuracy of the ocean analyses is improved when Gaussian background covariances are supplemented with a term obtained from the BVs. The improvement is especially noticeable for the salinity field.

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Eviatar Bach
,
Safa Motesharrei
,
Eugenia Kalnay
, and
Alfredo Ruiz-Barradas

Abstract

Due to the physical coupling between atmosphere and ocean, information about the ocean helps to better predict the future of the atmosphere, and in turn, information about the atmosphere helps to better predict the ocean. Here, we investigate the spatial and temporal nature of this predictability: where, for how long, and at what frequencies does the ocean significantly improve prediction of the atmosphere, and vice versa? We apply Granger causality, a statistical test to measure whether a variable improves prediction of another, to local time series of sea surface temperature (SST) and low-level atmospheric variables. We calculate the detailed spatial structure of the atmosphere-to-ocean and ocean-to-atmosphere predictability. We find that the atmosphere improves prediction of the ocean most in the extratropics, especially in regions of large SST gradients. This atmosphere-to-ocean predictability is weaker but longer-lived in the tropics, where it can last for several months in some regions. On the other hand, the ocean improves prediction of the atmosphere most significantly in the tropics, where this predictability lasts for months to over a year. However, we find a robust signature of the ocean on the atmosphere almost everywhere in the extratropics, an influence that has been difficult to demonstrate with model studies. We find that both the atmosphere-to-ocean and ocean-to-atmosphere predictability are maximal at low frequencies, and both are larger in the summer hemisphere. The patterns we observe generally agree with dynamical understanding and the results of the Kalnay dynamical rule, which diagnoses the direction of forcing between the atmosphere and ocean by considering the local phase relationship between simultaneous sea surface temperature and vorticity anomaly signals. We discuss applications to coupled data assimilation.

Open access
James A. Carton
,
Stephen G. Penny
, and
Eugenia Kalnay

Abstract

This study extends recent ocean reanalysis comparisons to explore improvements to several next-generation products, the Simple Ocean Data Assimilation, version 3 (SODA3); the Estimating the Circulation and Climate of the Ocean, version 4, release 3 (ECCO4r3); and the Ocean Reanalysis System 5 (ORAS5), during their 23-yr period of overlap (1993–2015). The three reanalyses share similar historical hydrographic data, but the forcings, forward models, estimation algorithms, and bias correction methods are different. The study begins by comparing the reanalyses to independent analyses of historical SST, heat, and salt content, as well as examining the analysis-minus-observation misfits. While the misfits are generally small, they still reveal some systematic biases that are not present in the reference Hadley Center EN4 objective analysis. We next explore global trends in temperature averaged into three depth intervals: 0–300, 300–1000, and 1000–2000 m. We find considerable similarity in the spatial structure of the trends and their distribution among different ocean basins; however, the trends in global averages do differ by 30%–40%, which implies an equivalent level of disagreement in net surface heating rates. ECCO4r3 is distinct in having quite weak warming trends while ORAS5 has stronger trends that are noticeable in the deeper layers. To examine the performance of the reanalyses in the Arctic we explore representation of Atlantic Water variability on the Atlantic side of the Arctic and upper-halocline freshwater storage on the Pacific side of the Arctic. These comparisons are encouraging for the application of ocean reanalyses to track ocean climate variability and change at high northern latitudes.

Open access
Eviatar Bach
,
Safa Mote
,
V. Krishnamurthy
,
A. Surjalal Sharma
,
Michael Ghil
, and
Eugenia Kalnay

Abstract

Oscillatory modes of the climate system are among its most predictable features, especially at intraseasonal time scales. These oscillations can be predicted well with data-driven methods, often with better skill than dynamical models. However, since the oscillations only represent a portion of the total variance, a method for beneficially combining oscillation forecasts with dynamical forecasts of the full system was not previously known. We introduce Ensemble Oscillation Correction (EnOC), a general method to correct oscillatory modes in ensemble forecasts from dynamical models. We compute the ensemble mean—or the ensemble probability distribution—with only the best ensemble members, as determined by their discrepancy from a data-driven forecast of the oscillatory modes. We also present an alternate method that uses ensemble data assimilation to combine the oscillation forecasts with an ensemble of dynamical forecasts of the system (EnOC-DA). The oscillatory modes are extracted with a time series analysis method called multichannel singular spectrum analysis (M-SSA), and forecast using an analog method. We test these two methods using chaotic toy models with significant oscillatory components and show that they robustly reduce error compared to the uncorrected ensemble. We discuss the applications of this method to improve prediction of monsoons as well as other parts of the climate system. We also discuss possible extensions of the method to other data-driven forecasts, including machine learning.

Open access