Although there in general are no significant long‐term correlations between the quasi‐biennial oscillation (QBO) and the El Niño–Southern Oscillation (ENSO) in observations, we find that the QBO and ...the ENSO were aligned in the 3 to 4 years after the three warm ENSO events in 1982, 1997, and 2015. We investigate this indicated relationship with a version of the EC‐Earth climate model which includes nonorographic gravity waves. We analyze the modeled QBO in ensembles forced with climatological sea surface temperatures (SSTs) and observed SSTs. In the ensemble with observed SSTs we find a strong and significant alignment of the ensemble members in the equatorial stratospheric winds in the 2 to 4 years after the strong ENSO event in 1997. This alignment also includes the observed QBO. No such alignment is found in the ensemble with climatological SSTs. These results indicate that strong warm ENSO events can lock the phase of the QBO.
Key Points
In the years after the warm ENSO events, 1982, 1997, and 2015, the QBO and the ENSO were aligned
In climate model ensemble with observed SSTs the QBOs align after a strong warm ENSO event
The climate model with observed SSTs reproduces observed relation between QBO phase propagation and ENSO
The existence of multiple regimes in the extratropical tropospheric circulation is a hypothesis of theoretical importance with potential practical consequences. It is also a controversial hypothesis, ...and an abundance of conflicting results regarding both the existence and the number of regimes can be found in the literature.
Studies of atmospheric regime behavior are often based on clustering methods such ask-means and mixture models. In the basic implementation of these methods the number of clusters has to be specified a priori and “How many clusters?” is a highly nontrivial question. For the mixture model a procedure to assess the number of clusters by cross validation has recently been introduced. For thek-means model a Monte Carlo test is introduced that compares the clustering of the original data with the clustering of Gaussian distributed surrogate data. The robustness of these methods and their ability to produce the right number of clusters is critically assessed. The study is based on both idealized data and atmospheric data.
It is shown that applying the clustering methods to the Northern Hemisphere winter tropospheric geopotential heights gives conflicting and fragile results. In particular the number of clusters depends both on the clustering algorithm and on the period considered. Furthermore, the clustering methods find multiple clusters when applied to data similar to the atmospheric data but drawn from a unimodal, skewed distribution.
It is also shown that both clustering methods report multiple clusters for idealized data drawn from distributions that are skewed or platykurtic but otherwise smooth and without bumps or shoulders. In these cases the number of clusters found depends on the sample size. In particular, for the mixture model the number of clusters increases without bounds with increasing sample size.
It is concluded that in the atmospheric dataset studied the clustering methods provide only weak evidence for multiple regimes although the data is non-Gaussian with high statistical significance. It is also concluded that statistical models with basically unknown properties should be approached with utmost care or avoided completely.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Stratospheric Bimodality Christiansen, Bo
Journal of climate,
07/2010, Letnik:
23, Številka:
14
Journal Article
Recenzirano
Odprti dostop
The Northern Hemisphere extended winter mean stratospheric vortex alternates between a strong and a weak state, which is manifested in a statistically significant bimodal distribution. In the end of ...the 1970s a regime change took place, increasing the frequency of the strong phase relative to the weak phase. This paper investigates the connection between the regime behavior of the vortex and the equatorial quasi-biennial oscillation (QBO) in three different datasets. Although there are some differences between the datasets, they agree regarding the general picture. It is found that stratospheric equatorial wind between 70 and 8 hPa shows a bimodal structure in the Northern Hemisphere winter. Such bimodality is nontrivial as it requires only weak variability in the amplitude. Unimodality is found above 8 hPa, where the semiannual oscillation dominates. A strong coincidence is found between strong (weak) vortex winters and winter in the westerly (easterly) QBO regime. Furthermore, the change of the vortex in the late 1970s can be related to a change in the QBO from a period with strong bimodality to a period with weak bimodality. Careful consideration of the statistical significance shows that this change in the QBO can be a random process simply related to the annual sampling of the QBO. Finally, previous findings of phase locking between the QBO and the annual cycle are considered; it is shown that the phase locking is related to the seasonal variations in the bimodality of the QBO.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The author analyzes the impact of 13 major stratospheric aerosol producing volcanic eruptions since 1870 on the large-scale variability modes of sea level pressure in the Northern Hemisphere winter. ...The paper focuses on the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO) to address the question about the physical nature of these modes. The hypothesis that the phase of the El Niño–Southern Oscillation (ENSO) may control the geographical extent of the dominant mode in the Northern Hemisphere is also investigated, as well as the related possibility that the impact of the eruptions may be different according to the phase of ENSO.
The author finds that both the AO and the NAO are excited in the first winter after the eruptions with statistical significance at the 95% level. Both the signal and the significance are larger for the NAO than for the AO. The excitation of the AO and the NAO is connected with the excitation of a secondary mode, which resembles an augmented Pacific–North American pattern. This mode has opposite polarity in the Atlantic and the Pacific and interferes negatively with the AO in the Pacific and positively in the Atlantic in the first winter after the eruptions, giving the superposition a strong NAO resemblance.
Some evidence is found that the correlations between the Atlantic and the Pacific are stronger in the negative ENSO phase than in the positive phase, although this difference is not statistically significant when all data since 1870 are considered. The author does not find any evidence that the impact of the volcanic eruptions is more hemispheric in the negative than in the positive ENSO phase.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Quasi-Biennial Oscillation (QBO) and the El Niño–Southern Oscillation (ENSO) are two dominant modes of climate variability at the Equator. There exist observational evidences of mutual ...interactions between these two phenomena, but this possibility has not been widely studied using climate model simulations. In this work we assess how current models represent the ENSO/QBO relationship, in terms of the response of the amplitude and descent rate of stratospheric wind regimes, by analyzing atmosphere-only and ocean–atmosphere coupled simulations from a large multi-model ensemble. The annual cycle of the QBO descent rate is well represented in both coupled and uncoupled models. Previous results regarding the phase alignment of the QBO after the 1997/98 strong warm ENSO event are confirmed in a larger ensemble of uncoupled experiments. However, in general we find that a relatively high horizontal resolution is necessary to reproduce the observed modulation of the QBO descent rate under strong ENSO events, while the amplitude response is generally weak at any horizontal resolution. We argue that biases in the mean state and over-dependence on parameterized wave forcing undermine the realism of the simulated coupling between the ocean and the stratosphere in the tropics in current climate models. The modulation of the QBO by the ENSO in a high emission scenario consistently differs from that in the historical period, suggesting that this relationship is sensitive to changes in the large-scale circulation.
Abstract Model ensembles may provide estimates of uncertainties arising from unknown initial conditions and model deficiencies. Often, the ensemble mean is taken as the best estimate, and quantities ...such as the mean‐squared error between model mean and observations decrease with the number of ensemble members. But the ensemble size is often limited by available resources, and so some idea of how many ensemble members that are needed before the error has saturated would be advantageous. The behaviour with ensemble size is often estimated by producing subsamples from a large ensemble. But this strategy requires that this large ensemble is already available. Fortunately, in many situations, the dependence on ensemble size follows simple analytical relations when the quantity under interest (such as the mean‐squared error between ensemble mean and observations) is calculated over many grid points or time points. This holds both for ensemble means and the related sampling variance. Here, we present such relations and demonstrate how they can be used to estimate the gain of increasing the ensemble. Whereas previous work has mainly focused on the size of the model ensemble, we recognize that uncertainties in observations play a role. We therefore also study the effect of using the mean of an ensemble of reanalyses. We show how the analytical relations can be used to estimate the point where the gain of increasing the size of the model ensemble is dwarfed by the gain of increasing the number of reanalyses. We demonstrate these points using two climate model ensembles: a large multimodel ensemble and a large single‐model initial‐condition ensemble.
When analyzing multimodel climate ensembles it is often assumed that the ensemble is either truth centered or that models and observations are drawn from the same distribution. Here we analyze CMIP5 ...ensembles focusing on three measures that separate the two interpretations: the error of the ensemble mean relative to the error of individual models, the decay of the ensemble mean error for increasing ensemble size, and the correlations of the model errors. The measures are analyzed using a simple statistical model that includes the two interpretations as different limits and for which analytical results for the three measures can be obtained in high dimensions. We find that the simple statistical model describes the behavior of the three measures in the CMIP5 ensembles remarkably well. Except for the large-scale means we find that the indistinguishable interpretation is a better assumption than the truth centered interpretation. Furthermore, the indistinguishable interpretation becomes an increasingly better assumption when the errors are based on smaller temporal and spatial scales. Building on this, we present a simple conceptual mechanism for the indistinguishable interpretation based on the assumption that the climate models are calibrated on large-scale features such as, e.g., annual means or global averages.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Reconstruction of the earth’s surface temperature from proxy data is an important task because of the need to compare recent changes with past variability. However, the statistical properties and ...robustness of climate reconstruction methods are not well known, which has led to a heated discussion about the quality of published reconstructions. In this paper a systematic study of the properties of reconstruction methods is presented. The methods include both direct hemispheric-mean reconstructions and field reconstructions, including reconstructions based on canonical regression and regularized expectation maximization algorithms. The study will be based on temperature fields where the target of the reconstructions is known. In particular, the focus will be on how well the reconstructions reproduce low-frequency variability, biases, and trends.
A climate simulation from an ocean–atmosphere general circulation model of the period A.D. 1500–1999, including both natural and anthropogenic forcings, is used. However, reconstructions include a large element of stochasticity, and to draw robust statistical interferences, reconstructions of a large ensemble of realistic temperature fields are needed. To this end a novel technique has been developed to generate surrogate fields with the same temporal and spatial characteristics as the original surface temperature field from the climate model. Pseudoproxies are generated by degrading a number of gridbox time series. The number of pseudoproxies and the relation between the pseudoproxies and the underlying temperature field are determined realistically from Mann et al.
It is found that all reconstruction methods contain a large element of stochasticity, and it is not possible to compare the methods and draw conclusions from a single or a few realizations. This means that very different results can be obtained using the same reconstruction method on different surrogate fields. This might explain some of the recently published divergent results.
Also found is that the amplitude of the low-frequency variability in general is underestimated. All methods systematically give large biases and underestimate both trends and the amplitude of the low-frequency variability. The underestimation is typically 20%–50%. The shape of the low-frequency variability, however, is well reconstructed in general.
Some potential in validating the methods on independent data is found. However, to gain information about the reconstructions’ ability to capture the preindustrial level it is necessary to consider the average level in the validation period and not the year-to-year correlations. The influence on the reconstructions of the number of proxies, the type of noise used to generate the proxies, the strength of the variability, as well as the effect of detrending the data prior to the calibration is also reported.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Even in the simple case of univariate linear regression and prediction there are important choices to be made regarding the origins of the noise terms and regarding which of the two variables under ...consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. A unified probabilistic (i.e., Bayesian with flat priors) treatment of univariate linear regression and prediction is given by taking, as starting point, the general errors-in-variables model. Other versions of linear regression can be obtained as limits of this model. The likelihood of the model parameters and predictands of the general errors-in-variables model is derived by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well-known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference cannot be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. A probabilistic version of classical calibration is also included and it is shown how it is related to the errors-in-variables model. The results are illustrated by an example fromthe coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The connection between the Arctic Oscillation and the stratosphere is investigated on intra‐annual timescales. Both the National Centers for Environmental Prediction reanalysis data and a general ...circulation model simulation are used. In the winter half year November–April the dominant variability in the stratosphere in middle and high latitudes has the form of downward propagation of zonal mean zonal wind anomalies. The strength of the anomalies decays below 10 hPa, but often the anomalies reach the surface. The time for the propagation from 10 hPa to the surface is ∼15 days. When positive anomalies reach the surface, the phase of the Arctic Oscillation tends to be positive. The stratospheric variability and the downward propagation is found to be driven by the vertical component of the Eliassen‐Palm flux. This flux propagates from the lower troposphere to the tropopause on a timescale of 5 days. Model and reanalysis compare well in the structure of the stratospheric variability and the connection between the stratosphere and troposphere. However, the strength of the stratospheric variability is ∼25% weaker in the model.
PDF
