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.

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.

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.

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.

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.

Abstract

This study presents mechanisms for the polar amplification of surface air temperature that occurred in the Northern Hemisphere (NH) between the periods of 1958–77 (P1) and 1982–2001 (P2). Using European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) reanalysis data, it is found that over the ice-covered Arctic Ocean, the winter surface warming arises from dynamic warming (stationary eddy heat flux and adiabatic warming). Over the ice-free Arctic Ocean between the Greenland and the Barents Seas, downward infrared radiative (IR) flux is found to dominate the warming.

To investigate whether the difference in the flow between P1 and P2 is due to changes in the frequency of occurrence of a small number of teleconnection patterns, a coupled self-organizing map (SOM) analysis of the 250-hPa streamfunction and tropical convective precipitation is performed. The latter field was specified to lead the former by 5 days. The results of the analysis showed that the P2 − P1 trend arises from a decrease in the frequency of negative phase PNA-like and circumglobal streamfunction patterns and a corresponding increase in the frequency of positive PNA-like and circumglobal streamfunction patterns. The occurrence of the two strong 1982–83 and 1997–98 El Niño events also contributes toward this trend. The corresponding trend in the convective precipitation is from below average to above average values in the tropical Indo-western Pacific region. Each of the above patterns was found to have an e-folding time scale from 6 to 8 days, which implies that the P2 − P1 trend can be understood as arising from the change in the frequency of occurrence of teleconnection patterns that fluctuate on intraseasonal time scales.

The link between intraseasonal and interannual variability was further examined by linearly regressing various quantities against trend patterns with interannual variability subtracted. It was found that enhanced convective precipitation is followed 3–6 days later by the occurrence of the P2 − P1 circulation trend pattern, and then 1–2 days later by the corresponding trend pattern in the downward IR flux. This finding suggests that an increased frequency of the above sequence of events, which occurs on intraseasonal time scales, can account for the NH winter polar amplification of the surface air temperature via increased dynamic warming and downward IR flux.

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.

Abstract

The continuing decline of the summertime sea ice cover has reduced the sea ice path that must be traversed to Arctic destinations and through the Arctic between the Atlantic and Pacific Oceans, stimulating interest in trans–Arctic Ocean routes. Seasonal prediction of the sea ice cover along these routes could support the increasing summertime ship traffic taking advantage of recent low ice conditions. We introduce the minimum Arctic sea ice path (MIP) between Atlantic and Pacific Oceans as a shipping-relevant metric that is amenable to multidecadal hindcast evaluation. We show, using 1992–2017 retrospective predictions, that bias correction is necessary for the GFDL Seamless System for Prediction and Earth System Research (SPEAR) forecast system to improve upon damped persistence seasonal forecasts of summertime daily MIP between the Atlantic and Pacific Oceans both east and west of Greenland, corresponding roughly to the Northeast and Northwest Passages. Without bias correction, only the Northwest Passage MIP forecasts have lower error than a damped persistence forecast. Using the forecast ensemble spread to estimate a lower bound on forecast error, we find large opportunities for forecast error reduction, especially at lead times of less than 2 months. Most of the potential improvement remains after linear removal of climatological and trend biases, suggesting that significant error reduction might come from improved initialization and simulation of subannual variability. Using a different passive microwave sea ice dataset for calculating error than was used for data assimilation increases the raw forecast errors but not the trend anomaly forecast errors.

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.

Abstract

Eastern North America contains densely populated, highly developed areas, making winter storms with strong winds and high snowfall among the costliest storm types. For this reason, it is important to determine how the frequency of high-impact winter storms, specifically, those combining significant snowfall and winds, will change in this region under increasing greenhouse gas concentrations. This study uses a high-resolution coupled global climate model to simulate the changes in extreme winter conditions from the present climate to a future scenario with doubled CO₂ concentrations (2XC). In particular, this study focuses on changes in high-snowfall, extreme-wind (HSEW) events, which are defined as the occurrence of 2-day snowfall and high winds exceeding thresholds based on extreme values from the control simulation, where greenhouse gas concentrations remain fixed. Mean snowfall consistently decreases across the entire region, but extreme snowfall shows a more inconsistent pattern, with some areas experiencing increases in the frequency of extreme-snowfall events. Extreme-wind events show relatively small changes in frequency with 2XC, with the exception of high-elevation areas where there are large decreases in frequency. As a result of combined changes in wind and snowfall, HSEW events decrease in frequency in the 2XC simulation for much of eastern North America. Changes in the number of HSEW events in the 2XC environment are driven mainly by changes in the frequency of extreme-snowfall events, with most of the region experiencing decreases in event frequency, except for certain inland areas at higher latitudes.