Climate Science Special Report

USGCRP

5.1: Introduction

The causes of regional climate trends cannot be understood without considering the impact of variations in large-scale atmospheric circulation and an assessment of the role of internally generated climate variability. There are contributions to regional climate trends from changes in large-scale latitudinal circulation, which is generally organized into three cells in each hemisphere—Hadley cell, Ferrell cell and Polar cell—and which determines the location of subtropical dry zones and midlatitude jet streams (Figure 5.1). These circulation cells are expected to shift poleward during warmer periods,¹ ^,² ^,³ ^,⁴ which could result in poleward shifts in precipitation patterns, affecting natural ecosystems, agriculture, and water resources.⁵ ^,⁶

Figure 5.1

(top) Plan and (bottom) cross-section schematic view representations of the general circulation of the atmosphere. Three main circulations exist between the equator and poles due to solar heating and Earth’s rotation: 1) Hadley cell – Low-latitude air moves toward the equator. Due to solar heating, air near the equator rises vertically and moves poleward in the upper atmosphere. 2) Ferrel cell – A midlatitude mean atmospheric circulation cell. In this cell, the air flows poleward and eastward near the surface and equatorward and westward at higher levels. 3) Polar cell – Air rises, diverges, and travels toward the poles. Once over the poles, the air sinks, forming the polar highs. At the surface, air diverges outward from the polar highs. Surface winds in the polar cell are easterly (polar easterlies). A high pressure band is located at about 30° N/S latitude, leading to dry/hot weather due to descending air motion (subtropical dry zones are indicated in orange in the schematic views). Expanding tropics (indicted by orange arrows) are associated with a poleward shift of the subtropical dry zones. A low pressure band is found at 50°–60° N/S, with rainy and stormy weather in relation to the polar jet stream bands of strong westerly wind in the upper levels of the atmosphere. (Figure source: adapted from NWS 2016¹⁷⁷ ).

In addition, regional climate can be strongly affected by non-local responses to recurring patterns (or modes) of variability of the atmospheric circulation or the coupled atmosphere–ocean system. These modes of variability represent preferred spatial patterns and their temporal variation. They account for gross features in variance and for teleconnections which describe climate links between geographically separated regions. Modes of variability are often described as a product of a spatial climate pattern and an associated climate index time series that are identified based on statistical methods like Principal Component Analysis (PC analysis), which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster analysis.

On intraseasonal to interannual time scales, the climate of the United States is strongly affected by modes of atmospheric circulation variability like the North Atlantic Oscillation (NAO)/Northern Annular Mode (NAM), North Pacific Oscillation (NPO), and Pacific/North American Pattern (PNA).⁷ ^,⁸ ^,⁹ These modes are closely linked to other atmospheric circulation phenomena like blocking and quasi-stationary wave patterns and jet streams that can lead to weather and climate extremes.¹⁰ On an interannual time scale, coupled atmosphere–ocean phenomena like El Niño–Southern Oscillation (ENSO) have a prominent effect.¹¹ On longer time scales, U.S. climate anomalies are linked to slow variations of sea surface temperature related to the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO).¹² ^,¹³ ^,¹⁴

These modes of variability can affect the local-to-regional climate response to external forcing in various ways. The climate response may be altered by the forced response of these existing, recurring modes of variability.¹⁵ Further, the structure and strength of regional temperature and precipitation impacts of these recurring modes of variability may be modified due to a change in the background climate.¹⁶ Modes of internal variability of the climate system also contribute to observed decadal and multidecadal temperature and precipitation trends on local to regional scales, masking possible systematic changes due to an anthropogenic influence.¹⁷ However, there are still large uncertainties in our understanding of the impact of human-induced climate change on atmospheric circulation.⁴ ^,¹⁸ Furthermore, the confidence in any specific projected change in ENSO variability in the 21st century remains low.¹⁹

5.2: Modes of Variability: Past and Projected Changes

5.2.1 Width of the Tropics and Global Circulation

Evidence continues to mount for an expansion of the tropics over the past several decades, with a poleward expansion of the Hadley cell and an associated poleward shift of the subtropical dry zones and storm tracks in each hemisphere.⁵ ^,²⁰ ^,²¹ ^,²² ^,²³ ^,²⁴ ^,²⁵ ^,²⁶ ^,²⁷ ^,²⁸ ^,²⁹ The rate of expansion is uncertain and depends on the metrics and data sources that are used. Recent estimates of the widening of the global tropics for the period 1979–2009 range between 1° and 3° latitude (between about 70 and 200 miles) in each hemisphere, an average trend of between approximately 0.5° and 1.0° per decade.²⁶ While the roles of increasing greenhouse gases in both hemispheres,⁴ ^,³⁰ stratospheric ozone depletion in the Southern Hemisphere,³¹ and anthropogenic aerosols in the Northern Hemisphere³² ^,³³ have been implicated as contributors to the observed expansion, there is uncertainty in the relative contributions of natural and anthropogenic factors, and natural variability may currently be dominating.²³ ^,³⁴ ^,³⁵

Most of the previous work on tropical expansion to date has focused on zonally averaged changes. There are only a few recent studies that diagnose regional characteristics of tropical expansion. The findings depend on analysis methods and datasets. For example, a northward expansion of the tropics in most regions of the Northern Hemisphere, including the Eastern Pacific with impact on drying in the American Southwest, is found based on diagnosing outgoing longwave radiation.³⁶ However, other studies do not find a significant poleward expansion of the tropics over the Eastern Pacific and North America.³⁷ ^,³⁸ Thus, while some studies associate the observed drying of the U.S. Southwest with the poleward expansion of the tropics,⁵ ^,³⁹ regional impacts of the observed zonally averaged changes in the width of the tropics are not understood.

Due to human-induced greenhouse gas increases, the Hadley cell is likely to widen in the future, with an accompanying poleward shift in the subtropical dry zones, midlatitude jets, and storm tracks.² ^,⁴ ^,⁵ ^,⁴⁰ ^,⁴¹ ^,⁴² ^,⁴³ Large uncertainties remain in projected changes in non-zonal to regional circulation components and related changes in precipitation patterns.¹⁸ ^,⁴⁰ ^,⁴⁴ ^,⁴⁵ Uncertainties in projected changes in midlatitude jets are also related to the projected rate of arctic amplification and variations in the stratospheric polar vortex. Both factors could shift the midlatitude jet equatorward, especially in the North Atlantic region.⁴⁶ ^,⁴⁷ ^,⁴⁸ ^,⁴⁹

5.2.2 El Niño–Southern Oscillation

El Niño–Southern Oscillation (ENSO) is a main source of climate variability, with a two- to seven-year timescale, originating from coupled ocean–atmosphere interactions in the tropical Pacific. Major ENSO events affect weather patterns over many parts of the globe through atmospheric teleconnections. ENSO strongly affects precipitation and temperature in the United States with impacts being most pronounced during the cold season (Figure 5.2).¹¹ ^,⁵⁰ ^,⁵¹ ^,⁵² ^,⁵³ A cooling trend of the tropical Pacific Ocean that resembles La Niña conditions contributed to drying in southwestern North America from 1979 to 2006⁵⁴ and is found to explain most of the decrease in heavy daily precipitation events in the southern United States from 1979 to 2013.⁵⁵

Figure 5.2

El Niño- and La Niña-related winter features over North America. Shown are typical January to March weather anomalies and atmospheric circulation during moderate to strong El Niño and La Niña conditions: (top) During El Niño, there is a tendency for a strong jet stream and storm track across the southern part of the United States. The southern tier of Alaska and the U.S. Pacific Northwest tend to be warmer than average, whereas the southern tier of United States tends to be cooler and wetter than average. (bottom) During La Niña, there is a tendency of a very wave-like jet stream flow over the United States and Canada, with colder and stormier than average conditions across the North and warmer and less stormy conditions across the South. (Figure source: adapted from Lindsey 2016¹⁷⁸ ).

El Niño teleconnections are modulated by the location of maximum anomalous tropical Pacific sea surface temperatures (SST). Eastern Pacific (EP) El Niño events affect winter temperatures primarily over the Great Lakes, Northeast, and Southwest, while Central Pacific (CP) events influence temperatures primarily over the northwestern and southeastern United States.⁵⁶ The CP El Niño also enhances the drying effect, but weakens the wetting effect, typically produced by traditional EP El Niño events on U.S. winter precipitation.⁵⁷ It is not clear whether observed decadal-scale modulations of ENSO properties, including an increase in ENSO amplitude⁵⁸ and an increase in frequency of CP El Niño events,⁵⁹ ^,⁶⁰ are due to internal variability or anthropogenic forcing. Uncertainties in both the diagnosed distinct U.S. climate effects of EP and CP events and causes for the decadal scale changes result from the limited sample size of observed ENSO events in each category⁶¹ ^,⁶² and the relatively short record of the comprehensive observations (since late 1970s) that would allow the investigation of ENSO-related coupled atmosphere–ocean feedbacks.¹⁹ Furthermore, unforced global climate model simulations show that decadal to centennial modulations of ENSO can be generated without any change in external forcing.⁶³ A model study based on large, single-model ensembles of atmospheric and coupled atmosphere–ocean models finds that external radiative forcing resulted in an atmospheric teleconnection pattern that is independent of ENSO-like variations during the 1979–2014 period and is characterized by a hemisphere-scale increasing trend in heights.⁵³

The representation of ENSO in climate models has improved from CMIP3 to CMIP5 models, especially in relation to ENSO amplitude.⁶⁴ ^,⁶⁵ However, CMIP5 models still cannot capture the seasonal timing of ENSO events.⁶⁶ Furthermore, they still exhibit errors in simulating key atmospheric feedbacks, and the improvement in ENSO amplitudes might therefore result from error compensations.⁶⁴ Limited observational records and the nonstationarity of tropical Pacific teleconnections to North America on multidecadal time scales pose challenges for evaluating teleconnections between ENSO and U.S. climate in coupled atmosphere–ocean models.⁶¹ ^,⁶⁷ For a given SST forcing, however, the atmospheric component of CMIP5 models simulate the sign of the precipitation change over the southern section of North America.⁶⁸

Climate projections suggest that ENSO will remain a primary mode of natural climate variability in the 21st century.¹⁹ Climate models do not agree, however, on projected changes in the intensity or spatial pattern of ENSO.¹⁹ This uncertainty is related to a model dependence of simulated changes in the zonal gradient of tropical Pacific sea surface temperature in a warming climate.¹⁹ Model studies suggest an eastward shift of ENSO-induced teleconnection patterns due to greenhouse gas-induced climate change.⁶⁹ ^,⁷⁰ ^,⁷¹ ^,⁷² However, the impact of such a shift on ENSO-induced climate anomalies in the United States is not well understood.⁷² ^,⁷³

In summary, there is high confidence that, in the 21st century, ENSO will remain a main source of climate variability over the United States on seasonal to interannual timescales. There is low confidence for a specific projected change in ENSO variability.

5.2.3 Extra-tropical Modes of Variability and Phenomena

NORTH ATLANTIC OSCILLATION AND NORTHERN ANNULAR MODE

The North Atlantic Oscillation (NAO), the leading recurring mode of variability in the extratropical North Atlantic region, describes an opposing pattern of sea level pressure between the Atlantic subtropical high and the Iceland/Arctic low. Variations in the NAO are accompanied by changes in the location and intensity of the Atlantic midlatitude storm track and blocking activity that affect climate over the North Atlantic and surrounding continents. A negative NAO phase is related to anomalously cold conditions and an enhanced number of cold outbreaks in the eastern United States, while a strong positive phase of the NAO tends to be associated with above-normal temperatures in this region.⁷ ^,⁷⁴ The positive phase of the NAO is associated with increased precipitation frequency and positive daily rainfall anomalies, including extreme daily precipitation anomalies in the northeastern United States.⁷⁵ ^,⁷⁶

The Northern Annular Mode/Arctic Oscillation (NAM/AO) is closely related to the NAO. It describes a similar out-of-phase pressure variation between mid- and high latitudes but on a hemispheric rather than regional scale.⁷⁷ ^,⁷⁸ The time series of the NAO and NAM/AO are highly correlated, with persistent NAO and NAM/AO events being indistinguishable.⁷⁹ ^,⁸⁰

The wintertime NAO/NAM index exhibits pronounced variability on multidecadal time scales, with an increase from the 1960s to the 1990s, a shift to a more negative phase since the 1990s due to a series of winters like 2009–2010 and 2010–2011 (which had exceptionally low index values), and a return to more positive values after 2011.³⁰ Decadal scale temperature trends in the eastern United States, including occurrences of cold outbreaks during recent years, are linked to these changes in the NAO/NAM.⁸¹ ^,⁸² ^,⁸³ ^,⁸⁴

The NAO’s influence on the ocean occurs through changes in heat content, gyre circulations, mixed layer depth, salinity, high-latitude deep water formation, and sea ice cover.⁷ ^,⁸⁵ Climate model simulations show that multidecadal variations in the NAO induce multidecadal variations in the strength of the Atlantic Meridional Overturning Circulation (AMOC) and poleward ocean heat transport in the Atlantic, extending to the Arctic, with potential impacts on recent arctic sea ice loss and Northern Hemisphere warming.⁸⁵ However, other model simulations suggest that the NAO and recent changes in Northern Hemisphere climate were affected by recent variations in the AMOC,⁸⁶ for which enhanced freshwater discharge from the Greenland Ice Sheet (GrIS) may have been a contributing cause.⁸⁷

Climate models are widely analyzed for their ability to simulate the spatial patterns of the NAO/NAM and their relationship to temperature and precipitation anomalies over the United States.⁹ ^,⁶⁵ ^,⁸⁸ Climate models reproduce the broad spatial and temporal features of the NAO, although there are large differences among the individual models in the location of the NAO centers of action and their average magnitude. These differences affect the agreement between observed and simulated climate anomalies related to the NAO.⁹ ^,⁶⁵ Climate models tend to have a NAM pattern that is more annular than observed,⁶⁵ ^,⁸⁸ resulting in a strong bias in the Pacific center of the NAM. As a result, temperature anomalies over the northwestern United States associated with the NAM in most models are of opposite sign compared to observation.⁸⁸ Biases in the model representation of NAO/NAM features are linked to limited abilities of general circulation models to reproduce dynamical processes, including atmospheric blocking,⁸⁹ troposphere–stratosphere coupling,⁹⁰ and climatological stationary waves.⁹⁰ ^,⁹¹

The CMIP5 models on average simulate a progressive shift of the NAO/NAM towards the positive phase due to human-induced climate change.⁹² However, the spread between model simulations is larger than the projected multimodel increase,¹⁹ and there are uncertainties related to future scenarios.⁹ Furthermore, it is found that shifts between preferred periods of positive and negative NAO phase will continue to occur similar to those observed in the past.¹⁹ ^,⁹³ There is no consensus on the location of changes of NAO centers among the global climate models under future warming scenarios.⁹ Uncertainties in future projections of the NAO/NAM in some seasons are linked to model spread in projected future arctic warming⁴⁶ ^,⁴⁷ (Ch. 11: Arctic Changes) and to how models resolve stratospheric processes.¹⁹ ^,⁹⁴

In summary, while it is likely that the NAO/NAM index will become slightly more positive (on average) due to increases in GHGs, there is low confidence in temperature and precipitation changes over the United States related to such variations in the NAO/NAM.

NORTH PACIFIC OSCILLATION/WEST PACIFIC OSCILLATION

The North Pacific Oscillation (NPO) is a recurring mode of variability in the extratropical North Pacific region and is characterized by a north-south seesaw in sea level pressure. Effects of NPO on U.S. hydroclimate and marginal ice zone extent in the arctic seas have been reported.⁸

The NPO is linked to tropical sea surface temperature variability. Specifically, NPO contributes to the excitation of ENSO events via the “Seasonal Footprinting Mechanism”.⁹⁵ ^,⁹⁶ In turn, warm events in the central tropical Pacific Ocean are suggested to force an NPO-like circulation pattern.⁹⁷ There is low confidence in future projections of the NPO due to the small number of modeling studies as well as the finding that many climate models do not properly simulate the observed linkages between the NPO and tropical sea surface temperature variability.¹⁹ ^,⁹⁸

PACIFIC/NORTH AMERICAN PATTERN

The Pacific/North American (PNA) pattern is the leading recurring mode of internal atmospheric variability over the North Pacific and the North American continent, especially during the cold season. It describes a quadripole pattern of mid-tropospheric height anomalies, with anomalies of similar sign located over the subtropical northeastern Pacific and northwestern North America and of the opposite sign centered over the Gulf of Alaska and the southeastern United States. The PNA pattern is associated with strong fluctuations in the strength and location of the East Asian jet stream. The positive phase of the PNA pattern is associated with above average temperatures over the western and northwestern United States, and below average temperatures across the south-central and southeastern United States, including an enhanced occurrence of extreme cold temperatures.⁹ ^,⁹⁹ ^,¹⁰⁰ Significant negative correlation between the PNA and winter precipitation over the Ohio River Valley has been documented.⁹ ^,⁹⁹ ^,¹⁰¹ The PNA is related to ENSO events¹⁰² and also serves as a bridge linking ENSO and NAO variability.¹⁰³

Climate models are able to reasonably represent the atmospheric circulation and climate anomalies associated with the PNA pattern. However, individual models exhibit differences compared to the observed relationship, due to displacements of the simulated PNA centers of action and offsets in their magnitudes.⁹ Climate models do not show consistent location changes of the PNA centers due to increases in GHGs.⁹ ^,⁷² Therefore, there is low confidence for projected changes in the PNA and the association with temperature and precipitation variations over the United States.

BLOCKING AND QUASI-STATIONARY WAVES

Anomalous atmospheric flow patterns in the extratropics that remain in place for an extended period of time (for example, blocking and quasi-stationary Rossby waves)—and thus affect a region with similar weather conditions like rain or clear sky for several days to weeks—can lead to flooding, drought, heat waves, and cold waves.¹⁰ ^,¹⁰⁴ ^,¹⁰⁵ Specifically, blocking describes large-scale, persistent high pressure systems that interrupt the typical westerly flow, while planetary waves (Rossby waves) describe large-scale meandering of the atmospheric jet stream.

A persistent pattern of high pressure in the circulation off the West Coast of the United States has been associated with the recent multiyear California drought¹⁰⁶ ^,¹⁰⁷ ^,¹⁰⁸ (Ch. 8: Droughts, Floods, and Wildfire). Blocking in the Alaskan region, which is enhanced during La Niña winters (Figure 5.2),¹⁰⁹ is associated with higher temperatures in western Alaska but shift to lower mean and extreme surface temperatures from the Yukon southward to the southern Plains.¹¹⁰ The anomalously cold winters of 2009–2010 and 2010–2011 in the United States are linked to the blocked (or negative) phase of the NAO.¹¹¹ Stationary Rossby wave patterns may have contributed to the North American temperature extremes during summers like 2011.¹¹² It has been suggested that arctic amplification has already led to weakened westerly winds and hence more slowly moving and amplified wave patterns and enhanced occurrence of blocking¹¹³ ^,¹¹⁴ (Ch. 11: Arctic Changes). While some studies suggest an observed increase in the metrics of these persistent circulation patterns,¹¹³ ^,¹¹⁵ other studies suggest that observed changes are small compared to atmospheric internal variability.¹¹⁶ ^,¹¹⁷ ^,¹¹⁸

A decrease of blocking frequency with climate change is found in CMIP3, CMIP5, and higher-resolution models.¹⁹ ^,¹¹⁹ ^,¹²⁰ Climate models robustly project a change in Northern Hemisphere winter quasi-stationary wave fields that are linked to a wetting of the North American West Coast,⁴⁵ ^,¹²¹ ^,¹²² due to a strengthening of the zonal mean westerlies in the subtropical upper troposphere. However, CMIP5 models still underestimate observed blocking activity in the North Atlantic sector while they tend to overestimate activity in the North Pacific, although with a large intermodel spread.¹⁹ Most climate models also exhibit biases in the representation of relevant stationary waves.⁴⁴

In summary, there is low confidence in projected changes in atmospheric blocking and wintertime quasi-stationary waves. Therefore, our confidence is low on the association between observed and projected changes in weather and climate extremes over the United States and variations in these persistent atmospheric circulation patterns.

Modes of Variability on Decadal to Multidecadal Time Scales

PACIFIC DECADAL OSCILLATION (PDO) / INTERDECADAL PACIFIC OSCILLATION (IPO)

The Pacific Decadal Oscillation (PDO) was first introduced by Mantua et al. 1997¹²³ as the leading empirical orthogonal function of North Pacific (20°–70°N) monthly averaged sea surface temperature anomalies.¹⁴ Interdecadal Pacific Oscillation (IPO) refers to the same phenomenon and is based on Pacific-wide sea surface temperatures. PDO/IPO lacks a characteristic timescale and represents a combination of physical processes that span the tropics and extratropics, including both remote tropical forcing and local North Pacific atmosphere–ocean interactions.¹⁴ Consequently, PDO-related variations in temperature and precipitation in the United States are very similar to (and indeed may be caused by) variations associated with ENSO and the strength of the Aleutian low (North Pacific Index, NPI), as shown in Figure 5.3. A PDO-related temperature variation in Alaska is also apparent.¹²⁴ ^,¹²⁵

Figure 5.3

Cold season relationship between climate indices and U.S. precipitation and temperature anomalies determined from U.S. climate division data,¹⁷⁹

for the years 1901–2014. November–March mean U.S. precipitation anomalies correlated with (a) the Pacific Decadal Oscillation (PDO) index, (b) the El Niño–Southern Oscillation (ENSO) index, and (c) the North Pacific Index (NPI). November–March U.S. temperature anomalies correlated with (d) the PDO index, (e) the ENSO index, and (f) the NPI. United States temperature and precipitation related to the Pacific Decadal Oscillation are very similar to (and indeed may be caused by) variations associated with ENSO and the Aleutian low strength (North Pacific Index). (Figure source: Newman et al. 2016¹⁴ ; © American Meteorological Society, used with permission).

The PDO does not show a long-term trend either in SST reconstructions or in the ensemble mean of historical CMIP3 and CMIP5 simulations.¹⁴ Emerging science suggests that externally forced natural and anthropogenic factors have contributed to the observed PDO-like variability. For example, a model study finds that the observed PDO phase is affected by large volcanic events and the variability in incoming solar radiation.¹²⁶ Aerosols from anthropogenic sources could change the temporal variability of the North Pacific SST through modifications of the atmospheric circulation.¹²⁷ ^,¹²⁸ Furthermore, some studies show that periods with near-zero warming trends of global mean temperature and periods of accelerated temperatures could result from the interplay between internally generated PDO/IPO-like temperature variations in the tropical Pacific Ocean and greenhouse gas-induced ocean warming.¹²⁹ ^,¹³⁰

Future changes in the spatial and temporal characteristics of PDO/IPO are uncertain. Based on CMIP3 models, one study finds that most of these models do not exhibit significant changes,⁹⁸ while another study points out that the PDO/IPO becomes weaker and more frequent by the end of the 21st century in some models.¹³¹ Furthermore, future changes in ENSO variability, which strongly contributes to the PDO/IPO,¹³² are also uncertain (Section 5.2.2). Therefore, there is low confidence in projected future changes in the PDO/IPO.

ATLANTIC MULTIDECADAL VARIABILITY (AMV) / ATLANTIC MULTIDECADAL OSCILLATION (AMO)

The North Atlantic Ocean region exhibits coherent multidecadal variability that exerts measurable impacts on regional climate for variables such as U.S. precipitation¹² ^,¹³³ ^,¹³⁴ ^,¹³⁵ and Atlantic hurricane activity.¹³ ^,¹³⁶ ^,¹³⁷ ^,¹³⁸ ^,¹³⁹ ^,¹⁴⁰ This observed Atlantic multidecadal variability, or AMV, is generally understood to be driven by a combination of internal and external factors.¹² ^,¹⁴¹ ^,¹⁴² ^,¹⁴³ ^,¹⁴⁴ ^,¹⁴⁵ ^,¹⁴⁶ ^,¹⁴⁷ ^,¹⁴⁸ The AMV manifests in SST variability and patterns as well as synoptic-scale variability of atmospheric conditions. The internal part of the observed AMV is often referred to as the Atlantic Multidecadal Oscillation (AMO) and is putatively driven by changes in the strength of the Atlantic Meridional Overturning Circulation (AMOC).¹⁴² ^,¹⁴³ ^,¹⁴⁹ ^,¹⁵⁰ It is important to understand the distinction between the AMO, which is often assumed to be natural (because of its putative relationship with natural AMOC variability), and AMV, which simply represents the observed multidecadal variability as a whole.

The relationship between observed AMV and the AMOC has recently been called into question and arguments have been made that AMV can occur in the absence of the AMOC via stochastic forcing of the ocean by coherent atmospheric circulation variability, but this is presently a topic of debate.¹⁵¹ ^,¹⁵² ^,¹⁵³ ^,¹⁵⁴ Despite the ongoing debates, it is generally acknowledged that observed AMV, as a whole, represents a complex conflation of natural internal variability of the AMOC, natural red-noise stochastic forcing of the ocean by the atmosphere,¹⁴⁶ natural external variability from volcanic events¹⁵⁵ ^,¹⁵⁶ and mineral aerosols,¹⁵⁷ and anthropogenic forcing from greenhouse gases and pollution aerosols.¹⁵⁸ ^,¹⁵⁹ ^,¹⁶⁰ ^,¹⁶¹

As also discussed in Chapter 9: Extreme Storms (in the context of Atlantic hurricanes), determining the relative contributions of each mechanism to the observed multidecadal variability in the Atlantic is presently an active area of research and debate, and no consensus has yet been reached.¹⁴⁶ ^,¹⁶¹ ^,¹⁶² ^,¹⁶³ ^,¹⁶⁴ ^,¹⁶⁵ ^,¹⁶⁶ Still, despite the level of disagreement about the relative magnitude of human influences (particularly whether natural or anthropogenic factors are dominating), there is broad agreement in the literature of the past decade or so that human factors have had a measurable impact on the observed AMV. Furthermore, the AMO, as measured by indices constructed from environmental data (e.g., Enfield et al. 2001¹² ), is generally based on detrended SST data and is then, by construction, segregated from the century-scale linear SST trends that are likely forced by increasing greenhouse gas concentrations. In particular, removal of a linear trend is not expected to account for all of the variability forced by changes in sulfate aerosol concentrations that have occurred over the past century. In this case, increasing sulfate aerosols are argued to cause cooling of Atlantic SST, thus offsetting the warming caused by increasing greenhouse gas concentration. After the Clean Air Act and Amendments of the 1970s, however, a steady reduction of sulfate aerosols is argued to have caused SST warming that compounds the warming from the ongoing increases in greenhouse gas concentrations.¹⁶⁰ ^,¹⁶¹ This combination of greenhouse gas and sulfate aerosol forcing, by itself, can lead to Atlantic multidecadal SST variability that would not be removed by removing a linear trend.¹⁵⁵

In summary, it is unclear what the statistically derived AMO indices represent, and it is not readily supportable to treat AMO index variability as tacitly representing natural variability, nor is it clear that the observed AMV is truly oscillatory in nature.¹⁶⁷ There is a physical basis for treating the AMOC as oscillatory (via thermohaline circulation arguments),¹⁶⁸ but there is no expectation of true oscillatory behavior in the hypothesized external forcing agents for the remaining variability. Detrending the SST data used to construct the AMO indices may partially remove the century-scale trends forced by increasing greenhouse gas concentrations, but it is not adequate for removing multidecadal variability forced by aerosol concentration variability. There is evidence that natural AMOC variability has been occurring for hundreds of years,¹⁴⁹ ^,¹⁶⁹ ^,¹⁷⁰ ^,¹⁷¹ ^,¹⁷² and this has apparently played some role in the observed AMV as a whole, but a growing body of evidence shows that external factors, both natural and anthropogenic, have played a substantial additional role in the past century.

5.3: Quantifying the Role of Internal Variability on Past and Future U.S. Climate Trends

The role of internal variability in masking trends is substantially increased on regional and local scales relative to the global scale, and in the extratropics relative to the tropics (Ch. 4: Projections). Approaches have been developed to better quantify the externally forced and internally driven contributions to observed and future climate trends and variability and further separate these contributions into thermodynamically and dynamically driven factors.¹⁷ Specifically, large “initial condition” climate model ensembles with 30 ensemble members and more⁹³ ^,¹⁷³ ^,¹⁷⁴ and long control runs¹⁷⁵ have been shown to be useful tools to characterize uncertainties in climate change projections at local/regional scales.

North American temperature and precipitation trends on timescales of up to a few decades are strongly affected by intrinsic atmospheric circulation variability.¹⁷ ^,¹⁷³ For example, it is estimated that internal circulation trends account for approximately one-third of the observed wintertime warming over North America during the past 50 years. In a few areas, such as the central Rocky Mountains and far western Alaska, internal dynamics have offset the warming trend by 10%–30%.¹⁷ Natural climate variability superimposed upon forced climate change will result in a large range of possible trends for surface air temperature and precipitation in the United States over the next 50 years (Figure 5.4).¹⁷³

Figure 5.4

(left) Total 2010–2060 winter trends decomposed into (center) internal and (right) forced components for two contrasting CCSM3 ensemble members (runs 29 and 6) for (a) surface air temperature [color shading; °F/(51 years)] and sea level pressure (SLP; contours) and (b) precipitation [color shading; inches per day/(51 years)] and SLP (contours). SLP contour interval is 1 hPa/(51 years), with solid (dashed) contours for positive (negative) values; the zero contour is thickened. The same climate model (CCSM3) simulates a large range of possible trends in North American climate over the 2010–2060 period because of the influence of internal climate variability superposed upon forced climate trends. (Figure source: adapted from Deser et al. 2014;¹⁷³ © American Meteorological Society, used with permission).

Climate models are evaluated with respect to their proper simulation of internal decadal variability. Comparing observed and simulated variability estimates at timescales longer than 10 years suggest that models tend to overestimate the internal variability in the northern extratropics, including over the continental United States, but underestimate it over much of the tropics and subtropical ocean regions.⁹³ ^,¹⁷⁶ Such biases affect signal-to-noise estimates of regional scale climate change response and thus assessment of internally driven contributions to regional/local trends.

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Chapter 5: Large-Scale Circulation and Climate Variability

Key Findings

Key Finding 1

Key Finding 2