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  • Author or Editor: Brian V. Smoliak x
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Brian V. Smoliak
and
John M. Wallace

Abstract

The leading patterns of variability of the monthly mean Northern Hemisphere (NH) sea level pressure (SLP) field, as derived from empirical orthogonal teleconnection (EOT) analysis of a 93-yr (1920–2012) record of NOAA–CIRES 20th Century Reanalyses, are presented and discussed, with emphasis on wintertime patterns. The analysis yields nine or more highly reproducible wintertime hemispheric EOTs, the first six of which closely resemble EOF1 or EOF2 in their respective sectors of the hemisphere. Collectively, the first nine wintertime patterns account for 70% of the variance of NH SLP, 40% of the variance of NH surface air temperature (SAT), and 52% of the variance of the time series of NH-mean SAT poleward of 20°N. Wintertime EOT1 corresponds to the NH annular mode (NAM) and EOT2 corresponds to the SLP expression of the Pacific–North America pattern. The remaining wintertime EOT patterns are monopoles arranged like the links of a chain wrapped around the primary center of action of the annular mode. The NH summertime and Southern Hemisphere patterns are arranged in a similar manner. The continental NH wintertime patterns exhibit strong temperature anomalies of reversed polarity to their respective SLP monopoles. The interannual variability of wintertime EOTs 3–9 and summertime EOTs 2–9 is dominated by sampling fluctuations. Over the 93-yr record, the more prominent continental wintertime patterns exhibit weak trends toward falling SLP and rising SAT, particularly over Russia and Alaska. The interpretation of shorter-term trends is more ambiguous.

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Brian V. Smoliak
,
John M. Wallace
,
Pu Lin
, and
Qiang Fu

Abstract

The influence of atmospheric circulation changes reflected in spontaneously occurring sea level pressure (SLP) anomalies upon surface air temperature (SAT) variability and trends is investigated using partial least squares (PLS) regression, a statistical method that seeks to maximally explain covariance between a predictand time series or field and a predictor field. Applying PLS regression in any one of the three variants described in this study (pointwise, PC-wise, and fieldwise), the method yields a dynamical adjustment to the observed NH SAT field that accounts for approximately 50% of the variance in monthly mean, cold season data. It is shown that PLS regression provides a more parsimonious and statistically robust dynamical adjustment than an adjustment method based on the leading principal components of the extratropical SLP field. The usefulness of dynamical adjustment is demonstrated by applying it to the attribution of cold season SAT trends in two reference intervals: 1965–2000 and 1920–2011. The adjustment is shown to reconcile much of the spatial structure and seasonal differences in the observed SAT trends. The dynamically adjusted SAT fields obtained from this analysis provide datasets capable of being analyzed for residual variability and trends associated with thermodynamic and radiative processes.

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Clara Deser
,
Adam S. Phillips
,
Michael A. Alexander
, and
Brian V. Smoliak

Abstract

This study highlights the relative importance of internally generated versus externally forced climate trends over the next 50 yr (2010–60) at local and regional scales over North America in two global coupled model ensembles. Both ensembles contain large numbers of integrations (17 and 40): each of which is subject to identical anthropogenic radiative forcing (e.g., greenhouse gas increase) but begins from a slightly different initial atmospheric state. Thus, the diversity of projected climate trends within each model ensemble is due solely to intrinsic, unpredictable variability of the climate system. Both model ensembles show that natural climate variability superimposed upon forced climate change will result in a range of possible future trends for surface air temperature and precipitation over the next 50 yr. Precipitation trends are particularly subject to uncertainty as a result of internal variability, with signal-to-noise ratios less than 2. Intrinsic atmospheric circulation variability is mainly responsible for the spread in future climate trends, imparting regional coherence to the internally driven air temperature and precipitation trends. The results underscore the importance of conducting a large number of climate change projections with a given model, as each realization will contain a different superposition of unforced and forced trends. Such initial-condition ensembles are also needed to determine the anthropogenic climate response at local and regional scales and provide a new perspective on how to usefully compare climate change projections across models.

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Brian V. Smoliak
,
Peter K. Snyder
,
Tracy E. Twine
,
Phillip M. Mykleby
, and
William F. Hertel

Abstract

Data from a dense urban meteorological network (UMN) are analyzed, revealing the spatial heterogeneity and temporal variability of the Twin Cities (Minneapolis–St. Paul, Minnesota) canopy-layer urban heat island (UHI). Data from individual sensors represent surface air temperature (SAT) across a variety of local climate zones within a 5000-km2 area and span the 3-yr period from 1 August 2011 to 1 August 2014. Irregularly spaced data are interpolated to a uniform 1 km × 1 km grid using two statistical methods: 1) kriging and 2) cokriging with impervious surface area data. The cokriged SAT field exhibits lower bias and lower RMSE than does the kriged SAT field when evaluated against an independent set of observations. Maps, time series, and statistics that are based on the cokriged field are presented to describe the spatial structure and magnitude of the Twin Cities metropolitan area (TCMA) UHI on hourly, daily, and seasonal time scales. The average diurnal variation of the TCMA UHI exhibits distinct seasonal modulation wherein the daily maximum occurs by night during summer and by day during winter. Daily variations in the UHI magnitude are linked to changes in weather patterns. Seasonal variations in the UHI magnitude are discussed in terms of land–atmosphere interactions. To the extent that they more fully resolve the spatial structure of the UHI, dense UMNs are advantageous relative to limited collections of existing urban meteorological observations. Dense UMNs are thus capable of providing valuable information for UHI monitoring and for implementing and evaluating UHI mitigation efforts.

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Rebecca Bendick
,
Kyla M. Dahlin
,
Brian V. Smoliak
,
Lori Kumler
,
Sierra J. Jones
,
Athena Aktipis
,
Ezekiel Fugate
,
Rachel Hertog
,
Claus Moberg
, and
Dane Scott

Abstract

Anthropogenic greenhouse gas emissions change earth’s climate by altering the planet’s radiative balance. An important first step in mitigation of climate change is to reduce annual increases in these emissions. However, the many suggested means of limiting emissions rates have led to few actual changes in policy or behavior. This disconnection can be attributed in part to the difficulty of convening groups of stakeholders with diverse values, the polarizing nature of current political systems, poor communication across disciplines, and a lack of clear, usable information about emission mitigation strategies. Here, electronically facilitated ethical deliberation, a method of determining courses of action on common goals by collaborative discussion, is used to evaluate Pacala and Socolow’s climate change stabilization strategies based on economic, technological, social, and ecological impacts across a wide range of spatial and temporal scales. Few previous analyses of climate mitigation strategies include all of these factors; rather, short-term technological feasibility studies and economic cost–benefit analyses predominate. After accounting for tradeoffs among disparate criteria, strategies involving end-user efficiency (e.g., efficient buildings and vehicles), wind, and solar power rank highest, while carbon capture and storage, hydrogen fuel cells, and biofuels options rank lowest. This electronically facilitated deliberation method offers an alternative to oppositional debate or cost–benefit analysis for assessing strategies where both quantitative and qualitative factors are important, information from disparate disciplines is relevant, and stakeholders are geographically dispersed.

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