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Nathaniel C. Johnson

Abstract

It is now widely recognized that El Niño–Southern Oscillation (ENSO) occurs in more than one form, with the canonical eastern Pacific (EP) and more recently recognized central Pacific (CP) ENSO types receiving the most focus. Given that these various ENSO “flavors” may contribute to climate variability and long-term trends in unique ways, and that ENSO variability is not limited to these two types, this study presents a framework that treats ENSO as a continuum but determines a finite maximum number of statistically distinguishable representative ENSO patterns. A neural network–based cluster analysis called self-organizing map (SOM) analysis paired with a statistical distinguishability test determines nine unique patterns that characterize the September–February tropical Pacific SST anomaly fields for the period from 1950 through 2011. These nine patterns represent the flavors of ENSO, which include EP, CP, and mixed ENSO patterns. Over the 1950–2011 period, the most significant trends reflect changes in La Niña patterns, with a shift in dominance of La Niña–like patterns with weak or negative western Pacific warm pool SST anomalies until the mid-1970s, followed by a dominance of La Niña–like patterns with positive western Pacific warm pool SST anomalies, particularly after the mid-1990s. Both an EP and especially a CP El Niño pattern experienced positive frequency trends, but these trends are indistinguishable from natural variability. Overall, changes in frequency within the ENSO continuum contributed to the pattern of tropical Pacific warming, particularly in the equatorial eastern Pacific and especially in relation to changes of La Niña–like rather than El Niño–like patterns.

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Chueh-Hsin Chang and Nathaniel C. Johnson

Abstract

This study uses the method of self-organizing maps (SOMs) to categorize the June–August atmospheric teleconnections in the 500-hPa geopotential height field of the Southern Hemisphere (SH) extratropics. This approach yields 12 SOM patterns that provide a discretized representation of the continuum of SH teleconnection patterns from 1979 to 2012. These 12 patterns are large in spatial scale, exhibiting a mix of annular mode characteristics and wave trains of zonal wavenumber varying from 2 to 4. All patterns vary with intrinsic time scales of about 5–10 days, but some patterns exhibit quasi-oscillatory behavior over a period of 20–30 days, whereas still others exhibit statistically significant enhanced and suppressed frequencies up to about four weeks in association with the Madden–Julian oscillation. Two patterns are significantly influenced by El Niño–Southern Oscillation (ENSO) on interannual time scales. All 12 patterns have strong influences on surface air temperature and sea ice concentrations, with the sea ice response occurring over a time scale of about 2–4 weeks. The austral winter has featured a positive frequency trend in patterns that project onto the negative phase of the southern annular mode (SAM) and a negative frequency trend in positive SAM-like patterns. Such atmospheric circulation trends over 34 yr may arise through atmospheric internal variability alone, and, unlike other seasons in the SH, it is not necessary to invoke external forcing as a dominant source of circulation trends.

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Nathaniel C. Johnson and Steven B. Feldstein

Abstract

This study combines k-means cluster analysis with linear unidimensional scaling to illustrate the spatial and temporal variability of the wintertime North Pacific sea level pressure (SLP) field. Daily wintertime SLP data derived from the NCEP–NCAR reanalysis are used to produce 16 SLP anomaly patterns that represent a discretized approximation of the continuum of North Pacific SLP patterns. This study adopts the continuum perspective for teleconnection patterns, which provides a much simpler framework for understanding North Pacific variability than the more commonly used discrete modal approach.

The primary focus of this research is to show that variability in the North Pacific—on intraseasonal, interannual, and interdecadal time scales—can be understood in terms of changes in the frequency distribution of the cluster patterns that compose the continuum, each of which has a time scale of about 10 days. This analysis reveals 5–6 Pacific–North American–like (PNA-like) patterns for each phase, as well as dipoles and wave trains. A self-organizing map (SOM) analysis of coupled SLP and outgoing longwave radiation data shows that many of these patterns are associated with convection in the tropical Indo-Pacific region. On intraseasonal time scales, the frequency distribution of these patterns, in particular the PNA-like patterns, is strongly influenced by the Madden–Julian oscillation (MJO). On interannual time scales, the El Niño–Southern Oscillation (ENSO) impacts the North Pacific continuum, with warm ENSO episodes resulting in the increased frequency of easterly displaced Aleutian low pressure anomaly patterns and cold ENSO episodes resulting in the increased frequency of southerly displaced Aleutian high pressure anomaly patterns. In addition, the results of this analysis suggest that the interdecadal variability of the North Pacific SLP field, including the well-known “regime shift” of 1976/77, also results from changes in the frequency distribution within the continuum of SLP patterns.

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Hiroyuki Ito, Nathaniel C. Johnson, and Shang-Ping Xie

Abstract

This study investigates interannual variability in the frequency of occurrence of daily surface air temperature (SAT) extremes over East Asia in summer and winter between 1979 and 2009. In particular, this study examines the dominant seasonal SAT patterns, as obtained through empirical orthogonal function (EOF) analysis, and the associated variability in SAT extreme occurrence. Overall, the authors find that changes in extreme temperature occurrence associated with these dominant patterns are impacted by both shifts and narrowing/broadening of the subseasonal SAT probability distribution functions (PDFs). In summer, the leading pattern features large SAT anomalies in midlatitude East Asia centered over Mongolia. Over this center of action, positive SAT anomalies are accompanied by decreased precipitation and soil moisture, which increases the ratio of sensible to latent heat flux. Consequently, subseasonal SAT variance increases, resulting in an enhanced occurrence of positive SAT extremes relative to a simple SAT PDF shift. In winter, the leading pattern, which is highly correlated with the Arctic Oscillation, features large loadings in high-latitude Siberia that decay southward. In contrast with summer, large-scale dynamics play a larger role in the leading pattern: positive SAT anomalies are accompanied by a weakened and northward-shifted storm track, reduced subseasonal SAT variance, and a more pronounced decrease of cold extreme occurrence relative to a simple PDF shift. Finally, a brief look at the secular trends suggests that both shifts and narrowing/broadening of the PDF may also impact long-term trends in SAT extreme occurrence over some regions of East Asia.

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Nathaniel C. Johnson, Steven B. Feldstein, and Bruno Tremblay

Abstract

In this study, the method of self-organizing maps (SOMs) is used with NCEP–NCAR reanalysis data to advance the continuum perspective of Northern Hemisphere teleconnection patterns and to shed light on the secular eastward shift of the North Atlantic Oscillation (NAO) that began in the late 1970s. A 20-pattern SOM analysis of daily, wintertime, Northern Hemisphere sea level pressure reveals a continuum of patterns that correspond closely with well-known teleconnection patterns. This analysis also reveals that interdecadal variability of the hemispheric sea level pressure field may be understood in terms of changes in the frequency distribution within the continuum of sea level pressure patterns described by the SOM. Based on the continuum perspective illustrated with the SOM, the above secular shift of the NAO may be understood as a change in dominance from westward-displaced, negative NAO-like patterns to eastward-displaced, positive NAO-like patterns, though westward- and eastward-displaced NAO-like patterns existed during all time periods and for both phases.

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Nathaniel C. Johnson, Dan C. Collins, Steven B. Feldstein, Michelle L. L’Heureux, and Emily E. Riddle

Abstract

Previous work has shown that the combined influence of El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) significantly impacts the wintertime circulation over North America for lead times up to at least 4 weeks. These findings suggest that both the MJO and ENSO may prove beneficial for generating a seamless prediction link between short-range deterministic forecasts and longer-range seasonal forecasts. To test the feasibility of this link, wintertime (December–March) probabilistic 2-m temperature (T2m) forecasts over North America are generated solely on the basis of the linear trend and statistical relationships with the initial state of the MJO and ENSO. Overall, such forecasts exhibit substantial skill for some regions and some initial states of the MJO and ENSO out to a lead time of approximately 4 weeks. In addition, the primary ENSO T2m regions of influence are nearly orthogonal to those of the MJO, which suggests that the MJO and ENSO generally excite different patterns within the continuum of large-scale atmospheric teleconnections. The strong forecast skill scores for some regions and initial states confirm the promise that information from the MJO and ENSO may offer forecasts of opportunity in weeks 3 and 4, which extend beyond the current 2-week extended-range outlooks of the National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Center (CPC), and an intraseasonal link to longer-range probabilistic forecasts.

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Arthur A. Small III, Jason B. Stefik, Johannes Verlinde, and Nathaniel C. Johnson

Abstract

A decision algorithm is presented that improves the productivity of data collection activities in stochastic environments. The algorithm was developed in the context of an aircraft field campaign organized to collect data in situ from boundary layer clouds. Required lead times implied that aircraft deployments had to be scheduled in advance, based on imperfect forecasts regarding the presence of conditions meeting specified requirements. Given an overall cap on the number of flights, daily fly/no-fly decisions were taken traditionally using a discussion-intensive process involving heuristic analysis of weather forecasts by a group of skilled human investigators. An alternative automated decision process uses self-organizing maps to convert weather forecasts into quantified probabilities of suitable conditions, together with a dynamic programming procedure to compute the opportunity costs of using up scarce flights from the limited budget. Applied to conditions prevailing during the 2009 Routine ARM Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) campaign of the U.S. Department of Energy’s Atmospheric Radiation Measurement Program, the algorithm shows a 21% increase in data yield and a 66% improvement in skill over the heuristic decision process used traditionally. The algorithmic approach promises to free up investigators’ cognitive resources, reduce stress on flight crews, and increase productivity in a range of data collection applications.

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Jie He, Nathaniel C. Johnson, Gabriel A. Vecchi, Ben Kirtman, Andrew T. Wittenberg, and Stephan Sturm

Abstract

The driving of tropical precipitation by the variability of the underlying sea surface temperature (SST) plays a critical role in the atmospheric general circulation. To assess the precipitation sensitivity to SST variability, it is necessary to observe and understand the relationship between precipitation and SST. However, the precipitation–SST relationships from any coupled atmosphere–ocean system can be difficult to interpret given the challenge of disentangling the SST-forced atmospheric response and the atmospheric intrinsic variability. This study demonstrates that the two components can be isolated using uncoupled atmosphere-only simulations, which extract the former when driven by time-varying SSTs and the latter when driven by climatological SSTs. With a simple framework that linearly combines the two types of uncoupled simulations, the coupled precipitation–SST relationships are successfully reproduced. Such a framework can be a useful tool for quantitatively diagnosing tropical air–sea interactions. The precipitation sensitivity to SST variability is investigated with the use of uncoupled simulations with prescribed SST anomalies, where the influence of atmospheric intrinsic variability on SST is deactivated. Through a focus on local precipitation–SST relationships, the precipitation sensitivity to local SST variability is determined to be predominantly controlled by the local background SST. In addition, the strength of the precipitation response increases monotonically with the local background SST, with a very sharp growth at high SSTs. These findings are supported by basic principles of moist static stability, from which a simple formula for precipitation sensitivity to local SST variability is derived.

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Changhyun Yoo, Nathaniel C. Johnson, Chueh-Hsin Chang, Steven B. Feldstein, and Young-Ha Kim

Abstract

A composite-based statistical model utilizing Northern Hemisphere teleconnection patterns is developed to predict East Asian wintertime surface air temperature for lead times out to 6 weeks. The level of prediction is determined by using the Heidke skill score. The prediction skill of the statistical model is compared with that of hindcast simulations by a climate model, Global Seasonal Forecast System, version 5. When employed individually, three teleconnections (i.e., the east Atlantic/western Russian, Scandinavian, and polar/Eurasian teleconnection patterns) are found to provide skillful predictions for lead times beyond 4–5 weeks. When information from the teleconnections and the long-term linear trend are combined, the statistical model outperforms the climate model for lead times beyond 3 weeks, especially during those times when the teleconnections are in their active phases.

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Kai-Chih Tseng, Nathaniel C. Johnson, Eric D. Maloney, Elizabeth A. Barnes, and Sarah B. Kapnick

Abstract

The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads.

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