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Gilbert P. Compo
and
Prashant D. Sardeshmukh

Abstract

An important question in assessing twentieth-century climate change is to what extent have ENSO-related variations contributed to the observed trends. Isolating such contributions is challenging for several reasons, including ambiguities arising from how ENSO itself is defined. In particular, defining ENSO in terms of a single index and ENSO-related variations in terms of regressions on that index, as done in many previous studies, can lead to wrong conclusions. This paper argues that ENSO is best viewed not as a number but as an evolving dynamical process for this purpose. Specifically, ENSO is identified with the four dynamical eigenvectors of tropical SST evolution that are most important in the observed evolution of ENSO events. This definition is used to isolate the ENSO-related component of global SST variations on a month-by-month basis in the 136-yr (1871–2006) Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST). The analysis shows that previously identified multidecadal variations in the Pacific, Indian, and Atlantic Oceans all have substantial ENSO components. The long-term warming trends over these oceans are also found to have appreciable ENSO components, in some instances up to 40% of the total trend. The ENSO-unrelated component of 5-yr average SST variations, obtained by removing the ENSO-related component, is interpreted as a combination of anthropogenic, naturally forced, and internally generated coherent multidecadal variations. The following two surprising aspects of these ENSO-unrelated variations are emphasized: 1) a strong cooling trend in the eastern equatorial Pacific Ocean and 2) a nearly zonally symmetric multidecadal tropical–extratropical seesaw that has amplified in recent decades. The latter has played a major role in modulating SSTs over the Indian Ocean.

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Gilbert P. Compo
and
Prashant D. Sardeshmukh

Abstract

This paper is concerned with estimating the predictable variation of extratropical daily weather statistics (“storm tracks”) associated with global sea surface temperature (SST) changes on interannual to interdecadal scales, and its magnitude relative to the unpredictable noise. The SST-forced storm track signal in each northern winter in 1950–99 is estimated as the mean storm track anomaly in an ensemble of atmospheric general circulation model (AGCM) integrations for that winter with prescribed observed SSTs. Two sets of ensembles available from two modeling centers, with anomalous SSTs prescribed either globally or only in the Tropics, are used. Since the storm track signals cannot be derived directly from the archived monthly AGCM output, they are diagnosed from the SST-forced winter-mean 200-mb height signals using an empirical linear storm track model (STM). For two particular winters, the El Niño of January–February–March (JFM) 1987 and the La Niña of JFM 1989, the storm track signals and noise are estimated directly, and more accurately, from additional large ensembles of AGCM integrations. The linear STM is remarkably successful at capturing the AGCM's storm track signal in these two winters, and is thus also suitable for estimating the signal in other winters.

The principal conclusions from this analysis are as follows. A predictable SST-forced storm track signal exists in many winters, but its strength and pattern can change substantially from winter to winter. The correlation of the SST-forced and observed storm track anomalies is high enough in the Pacific–North America (PNA) sector to be of practical use. Most of the SST-forced signal is associated with tropical Pacific SST forcing; the central Pacific (Niño-4) is somewhat more important than the eastern Pacific (Niño-3) in this regard. Variations of the pattern correlation of the SST-forced and observed storm track anomaly fields from winter to winter, and among five-winter averages, are generally consistent with variations of the signal strength, and to that extent are identifiable a priori. Larger pattern correlations for the five-winter averages found in the second half of the 50-yr record are consistent with the stronger El Niño SST forcing in the second half. None of these conclusions, however, apply in the Euro-Atlantic sector, where the correlations of the SST-forced and observed storm track anomalies are found to be much smaller. Given also that they are inconsistent with the estimated signal-to-noise ratios in this region, substantial AGCM error in representing the regional response to tropical SST forcing, rather than intrinsically lower Euro-Atlantic storm track predictability, is argued to be behind these lower correlations.

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Jeffrey S. Whitaker
,
Gilbert P. Compo
, and
Jean-Noël Thépaut

Abstract

An observing system experiment, simulating a surface-only observing network representative of the 1930s, is carried out with three- and four-dimensional variational data assimilation systems (3D-VAR and 4D-VAR) and an ensemble-based data assimilation system (EnsDA). It is found that 4D-VAR and EnsDA systems produce analyses of comparable quality and that both are much more accurate than the analyses produced by the 3D-VAR system. The EnsDA system also produces useful estimates of analysis error, which are not directly available from the variational systems.

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Prashant D. Sardeshmukh
,
Gilbert P. Compo
, and
Cécile Penland

Abstract

Given the reality of anthropogenic global warming, it is tempting to seek an anthropogenic component in any recent change in the statistics of extreme weather. This paper cautions that such efforts may, however, lead to wrong conclusions if the distinctively skewed and heavy-tailed aspects of the probability distributions of daily weather anomalies are ignored or misrepresented. Departures of several standard deviations from the mean, although rare, are far more common in such a distinctively non-Gaussian world than they are in a Gaussian world. This further complicates the problem of detecting changes in tail probabilities from historical records of limited length and accuracy.

A possible solution is to exploit the fact that the salient non-Gaussian features of the observed distributions are captured by so-called stochastically generated skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise and as such represent the simplest physically based prototypes of the observed distributions. The tails of SGS distributions can also be directly linked to generalized extreme value (GEV) and generalized Pareto (GP) distributions. The Markov process model can be used to provide rigorous confidence intervals and to investigate temporal persistence statistics. The procedure is illustrated for assessing changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the period 1872–2011. No significant changes in these indices are found from the first to the second half of the period.

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Gilbert P. Compo
,
Jeffrey S. Whitaker
, and
Prashant D. Sardeshmukh

Climate variability and global change studies are increasingly focused on understanding and predicting regional changes of daily weather statistics. Assessing the evidence for such variations over the last 100 yr requires a daily tropospheric circulation dataset. The only dataset available for the early twentieth century consists of error-ridden hand-drawn analyses of the mean sea level pressure field over the Northern Hemisphere. Modern data assimilation systems have the potential to improve upon these maps, but prior to 1948, few digitized upper-air sounding observations are available for such a “reanalysis.” We investigate the possibility that the additional number of newly recovered surface pressure observations is sufficient to generate useful weather maps of the lower-tropospheric extratropical circulation back to 1890 over the Northern Hemisphere, and back to 1930 over the Southern Hemisphere. Surprisingly, we find that by using an advanced data assimilation system based on an ensemble Kalman filter, it would be feasible to produce high-quality maps of even the upper troposphere using only surface pressure observations. For the beginning of the twentieth century, the errors of such upper-air circulation maps over the Northern Hemisphere in winter would be comparable to the 2-3-day errors of modern weather forecasts.

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Prashant D. Sardeshmukh
,
Gilbert P. Compo
, and
Cécile Penland

Abstract

Away from the tropical Pacific Ocean, an ENSO event is associated with relatively minor changes of the probability distributions of atmospheric variables. It is nonetheless important to estimate the changes accurately for each ENSO event, because even small changes of means and variances can imply large changes of the likelihood of extreme values. The mean signals are not strictly symmetric with respect to El Niño and La Niña. They also depend upon the unique aspects of the SST anomaly patterns for each event. As for changes of variance and higher moments, little is known at present. This is a concern especially for precipitation, whose distribution is strongly skewed in areas of mean tropospheric descent.

These issues are examined here in observations and GCM simulations of the northern winter (January–March, JFM). For the observational analysis, the 42-yr (1958–99) reanalysis data generated at NCEP are stratified into neutral, El Niño, and La Niña winters. The GCM analysis is based on NCEP atmospheric GCM runs made with prescribed seasonally evolving SSTs for neutral, warm, and cold ENSO conditions. A large number (180) of seasonal integrations, differing only in initial atmospheric states, are made each for observed climatological mean JFM SSTs, the SSTs for an observed warm event (JFM 1987), and the SSTs for an observed cold event (JFM 1989). With such a large ensemble, the changes of probability even in regions not usually associated with strong ENSO signals are ascertained.

The results suggest a substantial asymmetry in the remote response to El Niño and La Niña, not only in the mean but also the variability. In general the remote seasonal mean geopotential height response in the El Niño experiment is stronger, but also more variable, than in the La Niña experiment. One implication of this result is that seasonal extratropical anomalies may not necessarily be more predictable during El Niño than La Niña. The stronger seasonal extratropical variability during El Niño is suggested to arise partly in response to stronger variability of rainfall over the central equatorial Pacific Ocean. The changes of extratropical variability in these experiments are large enough to affect substantially the risks of extreme seasonal anomalies in many regions. These and other results confirm that the remote impacts of individual tropical ENSO events can deviate substantially from historical composite El Niño and La Niña signals. They also highlight the necessity of generating much larger GCM ensembles than has traditionally been done to estimate reliably the changes to the full probability distribution, and especially the altered risks of extreme anomalies, during those events.

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Jeffrey S. Whitaker
,
Gilbert P. Compo
,
Xue Wei
, and
Thomas M. Hamill

Abstract

Studies using idealized ensemble data assimilation systems have shown that flow-dependent background-error covariances are most beneficial when the observing network is sparse. The computational cost of recently proposed ensemble data assimilation algorithms is directly proportional to the number of observations being assimilated. Therefore, ensemble-based data assimilation should both be more computationally feasible and provide the greatest benefit over current operational schemes in situations when observations are sparse. Reanalysis before the radiosonde era (pre-1931) is just such a situation.

The feasibility of reanalysis before radiosondes using an ensemble square root filter (EnSRF) is examined. Real surface pressure observations for 2001 are used, subsampled to resemble the density of observations we estimate to be available for 1915. Analysis errors are defined relative to a three-dimensional variational data assimilation (3DVAR) analysis using several orders of magnitude more observations, both at the surface and aloft. We find that the EnSRF is computationally tractable and considerably more accurate than other candidate analysis schemes that use static background-error covariance estimates. We conclude that a Northern Hemisphere reanalysis of the middle and lower troposphere during the first half of the twentieth century is feasible using only surface pressure observations. Expected Northern Hemisphere analysis errors at 500 hPa for the 1915 observation network are similar to current 2.5-day forecast errors.

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Prashant D. Sardeshmukh
,
Jih-Wang Aaron Wang
,
Gilbert P. Compo
, and
Cécile Penland

Abstract

It is well known that randomly perturbing an atmospheric model’s diabatic tendencies can increase its probabilistic forecast skill, mainly by increasing the spread of ensemble forecasts and making it more consistent with the errors of ensemble-mean forecasts. Less obvious and less well established is that such perturbations can also reduce the errors of the ensemble-mean forecasts and improve the model’s mean climate, variability, and sensitivity to forcing. A clear reduction in ensemble-mean forecast errors is demonstrated here in large ensembles of 15-day forecasts made with NOAA’s Global Forecast System model. The nearly ubiquitous reduction around the globe, obtained throughout the forecast range, is interpreted as arising in effect from a modification of the model’s deterministic evolution operator by a stochastic noise-induced drift. The effect is general in systems with state-dependent noise, and occurs even if the noise is not white. In the atmospheric context considered here, the effect is suggested to arise largely from noise-induced reductions of mechanical and thermal damping by chaotic boundary layer and cloud-radiative processes, which also tend to increase model sensitivity to forcing. The results presented here are consistent with many previous studies performed with models ranging from simple stochastically forced models to comprehensive global weather and climate models. They suggest that the diabatic interactions in most current global atmospheric models may not be sufficiently chaotic and this deficiency could be partly remedied by specifying additional stochastic terms. Using some empirical guidance in such specifications may be unavoidable, given the generally intractable complexity of the diabatic interactions.

Open access
Marco Rohrer
,
Stefan Brönnimann
,
Olivia Martius
,
Christoph C. Raible
,
Martin Wild
, and
Gilbert P. Compo

Abstract

Atmospheric circulation types, blockings, and cyclones are central features of the extratropical flow and key to understanding the climate system. This study intercompares the representation of these features in 10 reanalyses and in an ensemble of 30 climate model simulations between 1980 and 2005. Both modern, full-input reanalyses and century-long, surface-input reanalyses are examined. Modern full-input reanalyses agree well on key statistics of blockings, cyclones, and circulation types. However, the intensity and depth of cyclones vary among them. Reanalyses with higher horizontal resolution show higher cyclone center densities and more intense cyclones. For blockings, no strict relationship is found between frequency or intensity and horizontal resolution. Full-input reanalyses contain more intense blocking, compared to surface-input reanalyses. Circulation-type classifications over central Europe show that both versions of the Twentieth Century Reanalysis dataset contain more easterlies and fewer westerlies than any other reanalysis, owing to their high pressure bias over northeast Europe. The temporal correlation of annual circulation types over central Europe and blocking frequencies over the North Atlantic–European domain between reanalyses is high (around 0.8). The ensemble simulations capture the main characteristics of midlatitudinal atmospheric circulation. Circulation types of westerlies to northerlies over central Europe are overrepresented. There are too few blockings in the higher latitudes and an excess of cyclones in the midlatitudes. Other characteristics, such as blocking amplitude and cyclone intensity, are realistically represented, making the ensemble simulations a rich dataset to assess changes in climate variability.

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Johannes Loschnigg
,
Gerald A. Meehl
,
Peter J. Webster
,
Julie M. Arblaster
, and
Gilbert P. Compo

Abstract

The interaction of the Indian Ocean dynamics and the tropospheric biennial oscillation (TBO) is analyzed in the 300-yr control run of the National Center for Atmospheric Research (NCAR) Climate System Model (CSM). Sea surface temperature (SST) anomalies and equatorial ocean dynamics in the Indian Ocean are associated with the TBO and interannual variability of Asian–Australian monsoons in observations. The air–sea interactions involved in these processes in the coupled ocean–atmosphere model are analyzed, so as to diagnose the causes of the SST anomalies and their role in the development of a biennial cycle in the Indian–Pacific Ocean region.

By using singular value decomposition (SVD) analysis, it is found that the model reproduces the dominant mechanisms that are involved in the development of the TBO's influence on the south Asian monsoon: large-scale forcing from the tropical Pacific and regional forcing associated with both the meridional temperature gradient between the Asian continent and the Indian Ocean, as well as Indian Ocean SST anomalies. Using cumulative anomaly pattern correlation, the strength of each of these processes in affecting the interannual variability of both Asian and Australian monsoon rainfall is assessed.

In analyzing the role of the Indian Ocean dynamics in the TBO, it is found that the Indian Ocean zonal mode (IOZM) is an inherent feature of the Asian summer monsoon and the TBO. The IOZM is thus a part of the biennial nature of the Indian–Pacific Ocean region. The coupled ocean–atmosphere dynamics and cross-equatorial heat transport contribute to the interannual variability and biennial nature of the ENSO–monsoon system, by affecting the heat content of the Indian Ocean and resulting SST anomalies over multiple seasons, which is a key factor in the TBO.

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