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Justin E. Jones
and
Judah Cohen

Abstract

Strong anticyclones have a significant impact on the cool season climate over mid- and high-latitude landmasses as they are typically accompanied by arctic air masses that can eventually move into populated midlatitude regions. Composite analyses of Alaskan and Siberian strong anticyclones based on sea level pressure (SLP) thresholds of 1050 and 1060 hPa, respectively, were performed to diagnose large-scale dynamical and thermodynamical parameters associated with the formation of strong anticyclones over these two climatologically favorable regions. The anticyclone composite analyses indicate the presence of moderate-to-high-amplitude ridge–trough patterns associated with anticyclogenesis. These ridge–trough patterns are critical as they lead to dynamically favorable circumstances for rapid anticyclogenesis.

The strong Alaskan anticyclone develops downstream of a highly amplified upper-tropospheric ridge and is associated with a region of strong tropospheric subsidence due to differential anticyclonic vorticity advection and cold-air advection over the anticyclone center. The strong Siberian anticyclone is associated with an upper-tropospheric pattern of lesser amplitude, suggesting that these dynamical factors, while still important, are less critical to its development. The relative location of elevated terrain features also appears to contribute greatly to the overall evolution of each of these anticyclones.

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Gavin Gong
,
Dara Entekhabi
, and
Judah Cohen

Abstract

Previous modeling studies have identified a teleconnection pathway linking observation-based early season Siberian snow perturbations to a modulation of the winter Arctic Oscillation (AO) mode. In this study, the key role of orography in producing this modeled teleconnection is explicitly investigated using numerical experiments analogous to the previous studies. The climatic response to the same snow perturbation is investigated under modified orographic barriers in southern and eastern Siberia. Reducing these barriers results in a weakening of the prevailing orographically forced region of stationary wave activity centered over Siberia, as well as the snow-forced upward wave flux anomaly that initiates the teleconnection. This diminished anomaly propagates upward, but does not extend into the stratosphere to weaken the polar vortex. Consequently, poleward refraction of upper-tropospheric waves and downward propagation of coupled wave–mean flow anomalies, which ultimately produce the negative winter AO response, fail to develop. Thus, the mountains represent an orographic constraint on the snow–AO teleconnection pathway. By reducing the orographic barrier, the snow-forced influx of wave energy remains in the troposphere and, instead, produces a hemispheric-scale equatorward wave refraction.

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Hengchun Ye
,
Judah Cohen
, and
Michael Rawlins

Abstract

Daily synoptic observations were examined to determine the critical air temperatures and dewpoints that separate solid versus liquid precipitation for the fall and spring seasons at 547 stations over northern Eurasia. The authors found that critical air temperatures are highly geographically dependent, ranging from −1.0° to 2.5°C, with the majority of stations over European Russia ranging from 0.5° to 1.0°C and those over south-central Siberia ranging from 1.5° to 2.5°C. The fall season has a 0.5°–1.0°C lower value than the spring season at 42% stations. Relative humidity, elevation, the station's air pressure, and climate regime were found to have varying degrees of influences on the distribution of critical air temperature, although the relationships are very complex and cannot be formulated into a simple rule that can be applied universally. Although the critical dewpoint temperatures have a spread of −1.5° to 1.5°C, 92% of stations have critical values of 0.5°–1.0°C. The critical dewpoint is less dependent on environmental factors and seasons. A combination of three critical dewpoints and three air temperatures is developed for each station for spring and fall separately that has improved snow event predictability when the dewpoint is in the range of −0.5°–1.5°C and has improved rainfall event predictability when the dewpoint is higher than or equal to 0°C based on the statistics of all 537 stations. Results suggest that application of site-specific critical values of air temperature and dewpoint to discriminate between solid and liquid precipitation is needed to improve snow and hydrological modeling at local and regional scales.

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Leonard M. Druyan
,
Patrick Lonergan
, and
Judah Cohen

Abstract

African wave disturbances (AWDs), an important trigger of Sahel summer rainfall, are studied using ECMWF gridded datasets for July and August 1987 and 1988. Power spectra of time series of 700-mb meridional winds near Niamey taken from analyses at both 2° × 2.5° and 4° × 5° horizontal resolution are compared to spectra based on Niamey station data. In addition, spatial distributions of meteorological fields at both resolutions are discussed for three case studies, including the synoptic features of several AWDs. Additional examples are presented from GCM simulations at comparable horizontal resolutions. While vertical motion and divergence centers were more extreme at 2° × 2.5°, many of the deduced characteristics of an AWD were similar at both resolutions. The higher-resolution analyses and simulation show a sharp transition across wave troughs between lower-tropospheric convergence (uplift) on the west and divergence (subsidence) on the east for several AWDs. For the two more southerly AWDs analyzed here, uplift associated with the convergence ahead of the trough appears to be displaced to the southwest at midtropospheric altitudes. Twice-daily July–September precipitation at Niamey is weakly, but significantly, correlated with corresponding time series of ECMWF analyzed vertical motion at nearby grid points.

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Judah Cohen
,
Allan Frei
, and
Richard D. Rosen

Abstract

The simulated North Atlantic Oscillation (NAO) teleconnection patterns and their interannual variability are evaluated from a suite of atmospheric models participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2). In general the models simulate the observed spatial pattern well, although there are important differences among models. The NAO response to interannual variations in sea surface temperature (SST) and snow-cover boundary forcings are also evaluated. The simulated NAO indices are not correlated with the observed NAO index, despite being forced with observed SSTs, indicating that SSTs are not driving NAO variability in the models. Similarly, although a number of studies have identified a link between Eurasian snow extent and the phase of the NAO, no such link is apparent in the AMIP-2 results. It appears that, within the framework of the AMIP-2 experiments, the NAO is an internal mode of atmospheric variability and that impacts of SSTs and Eurasian snow cover on the phase of the NAO are not discernable. However, these conclusions do not necessarily apply to decadal-scale and longer variability or to coupled atmosphere–ocean models.

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Judah Cohen
,
Dara Entekhabi
,
Kazuyuki Saito
,
Gavin Gong
, and
David Salstein
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Judah Cohen
,
Mathew Barlow
,
Paul J. Kushner
, and
Kazuyuki Saito

Abstract

A diagnostic of Northern Hemisphere winter extratropical stratosphere–troposphere interactions is presented to facilitate the study of stratosphere–troposphere coupling and to examine what might influence these interactions. The diagnostic is a multivariate EOF combining lower-stratospheric planetary wave activity flux in December with sea level pressure in January. This EOF analysis captures a strong linkage between the vertical component of lower-stratospheric wave activity over Eurasia and the subsequent development of hemisphere-wide surface circulation anomalies, which are strongly related to the Arctic Oscillation. Wintertime stratosphere–troposphere events picked out by this diagnostic often have a precursor in autumn: years with large October snow extent over Eurasia feature strong wintertime upward-propagating planetary wave pulses, a weaker wintertime polar vortex, and high geopotential heights in the wintertime polar troposphere. This provides further evidence for predictability of wintertime circulation based on autumnal snow extent over Eurasia. These results also raise the question of how the atmosphere will respond to a modified snow cover in a changing climate.

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Karen L. Smith
,
Paul J. Kushner
, and
Judah Cohen

Abstract

One of the outstanding questions regarding the observed relationship between October Eurasian snow cover anomalies and the boreal winter northern annular mode (NAM) is what causes the multiple-week lag between positive Eurasian snow cover anomalies in October and the associated peak in Rossby wave activity flux from the troposphere to the stratosphere in December. This study explores the following hypothesis about this lag: in order to achieve amplification of the wave activity, the vertically propagating Rossby wave train associated with the snow cover anomaly must reinforce the climatological stationary wave, which corresponds to constructive linear interference between the anomalous wave and the climatological wave. It is shown that the lag in peak wave activity flux arises because the Rossby wave train associated with the snow cover is in quadrature or out of phase with the climatological stationary wave from October to mid-November. Beginning in mid-November the associated wave anomaly migrates into a position that is in phase with the climatological wave, leading to constructive interference and anomalously positive upward wave activity fluxes until mid-January. Climate models from the Coupled Model Intercomparison Project 3 (CMIP3) do not capture this behavior. This linear interference effect is not only associated with stratospheric variability related to Eurasian snow cover anomalies but is a general feature of Northern Hemisphere troposphere–stratosphere interactions and, in particular, dominated the negative NAM events of the fall–winter of 2009/10.

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Judah L. Cohen
,
David A. Salstein
, and
Richard D. Rosen

Abstract

The zonal-mean meridional transport of water vapor across the globe is evaluated using the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis for 1948–97. The shape of the meridional profile of the climatological mean transport closely resembles that of previous mean climate descriptions, but values tend to be notably larger than in climatologies derived from radiosonde-only-based analyses. The unprecedented length of the NCEP–NCAR dataset invites a focus on interannual variations in the zonal-mean moisture transport, and these results for northern winter are highlighted here. Although interannual variability in the transport is typically small at most latitudes, a significant ENSO signal is present, marked by a strengthening of water vapor transports over much of the winter hemisphere during warm events. Because of an increase in tropical sea surface temperatures and in the frequency of warm events relative to cold events in the latter half of the 50-yr record, this interannual signal projects onto an overall trend toward enhanced meridional moisture transports in the global hydrological cycle.

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Elizabeth Weirich-Benet
,
Maria Pyrina
,
Bernat Jiménez-Esteve
,
Ernest Fraenkel
,
Judah Cohen
, and
Daniela I. V. Domeisen

Abstract

Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves.

Significance Statement

Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.

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