After identifying what an interglacial is, this review aims to summarize the common features and differences between them. This allows us to highlight particular interglacials that may enlighten us about the climate system and to describe current knowledge of the factors that lead into and out of an interglacial. The review ends with a discussion about the likely extent of the present interglacial, both in the absence and presence of human influence on the climate system.

Finally the entries into interglacials (and exits from them) are fascinating examples of nonlinear behavior of the Earth system that need to be understood. For these reasons—importance of warm periods, need to understand how our current climate arose, fascinating climate dynamics, and recent availability of significant amounts of data and model simulations—it is timely to review and synthesize existing knowledge about the warm periods (interglacials) of the last 800 ka.

Our understanding of the climate of our current interglacial is good in a static sense: given the boundary conditions (solar irradiance, astronomical characteristics, geography, ice cover, and concentrations of long‐lived greenhouse gases such as CO 2 ) and the resulting radiation balance of the planet. However, we cannot currently give a satisfactory explanation of how the climate system evolved to this state: why we live in an interglacial at this time, or indeed why we live in an interglacial embedded in 100 ka rather than 40 ka cycles.

Interglacials are the warm, low ice extent (high sea level) end‐members of the glacial cycles – although as we shall discuss, the exact definition of an interglacial is not simply stated. We live in such an interglacial. Human actions, increasing the concentration of greenhouse gases in the atmosphere, ensure that our climate will become warmer in the next century and remain warm for many millennia to come [ Intergovernmental Panel on Climate Change ( IPCC ), 2013 ]. This makes particularly pertinent the study of periods in which at least sectors of the Earth system may have been “warmer” than they are currently.

Somewhere between 1.2 and 0.6 Ma ago, weaker cycles with a period of ~40 ka gave way to stronger (greater isotopic amplitude) cycles with a recurrence period closer to 100 ka. This change is known as the Mid‐Pleistocene Transition or Revolution. Its exact date is debated, and it is likely that different aspects of climate shifted into their new mode of operation at different times [ Mudelsee and Schulz , 1997 ; Rutherford and D ' Hondt , 2000 ; Clark et al. , 2006 ; Elderfield et al. , 2012 ]. By 800 ka ago, the change in amplitude was complete in most records, and glacial cycles with sea level amplitudes of more than 100 m were occurring, mostly with lengths of the order of 100 ka. The last 800 ka time period also benefits from the enormous progress over recent decades in collecting and analyzing a wide range of continuous climate records from terrestrial sites, marine sediment cores, and the oldest Antarctic ice cores. It is approximately the most recent interval associated with the orientation of the Earth's magnetic field in the “normal” polarity with the inclination vector pointing north [ Shackleton et al. , 1990 ], providing a useful sedimentary demarcation in settings where magnetic reversals are preserved. As an additional motivation, it has also become possible in recent years to run Earth models of intermediate complexity (EMICs) through several glacial cycles, and full Earth system models (ESMs) for a significant range of boundary conditions, including those appropriate for some of the recent interglacials.

Benthic oxygen isotope stack for the last 5 Ma []. Oxygen isotopes in benthic foraminifera represent a combination of changes in the oxygen isotope content of the water (dominated by ice volume) and deepwater temperature. This plot shows the onset of cyclicity, when Northern Hemisphere ice started to wax and wane, at ~2.6 Ma ago (arrow marks the official start of the Quaternary), and the strengthening and lengthening of cycles across the mid‐Pleistocene transition (horizontal bar).

Earth's climate of the last 800 ka (1 ka = 1000 years) is the latest stage in a slow cooling that has been in progress for the last ~50 Ma (1 Ma = 1 million years) [ Zachos et al. , 2008 ]. During this cooling, ice sheets formed on the Antarctic continent ~40 Ma ago, while the first signs of Northern Hemisphere (NH) glaciation appeared much more recently. Only at the start of the Quaternary Period and the Pleistocene Epoch, ~2.6 Ma ago, did alternations between cold glacial periods with ice on the NH continents, and warmer intervals with little or no NH continental ice, first appear, reflected in the appearance of ice‐rafted debris [ Shackleton et al. , 1984 ; Kleiven et al. , 2002 ] and in enhanced amplitude of cyclicity in benthic oxygen isotopes in marine sediment records (Figure 1 ) [ Lisiecki and Raymo , 2005 ].

The scheme we have used allows for the occurrence of more than one interglacial within the same complex and does not require an interglacial to follow a traditionally numbered glacial termination. Only a definition that insists on a minimum temporal spacing (of order 100 ka) between interglacials could force a more traditional list of interglacials. While we recognize that the definition applied here will lead to some inconsistency in relation to traditional nomenclature, we feel it is justified by its objectivity and robustness against a range of definitions and thresholds; the traditional assumptions on the other hand will lead to misleading conclusions about the spacing and timing of interglacials. It is important to reiterate that this is a working definition based on a global metric and as such it has global stratigraphic significance. Interglacial stages may continue to be defined in local contexts using local signals (e.g., local temperature or forest development), but they do not carry general applicability. One cautionary note to add is that our definition is clearly dependent on the context of the last 800 ka and would not necessarily be suitable for the early Quaternary, even though we recognize from benthic isotopes that periods with interglacial character existed then, nor for earlier periods containing alternations of icy and ice‐free intervals.

As a final sensitivity study, we investigate the ice core CO 2 record [ Lüthi et al. , 2008 ; Bereiter et al. , 2015 ]; because of its importance as a radiative forcer at global scale, CO 2 may plausibly be one of the controlling factors on interglacial strength in a range of other records (see section 6.7 ). If the threshold is 260 ppm or higher, only one interglacial (MIS 19) is recognized before MIS 11. However, as the threshold is lowered other interglacials qualify: by 250 ppm, all the interglacials in our roster (including 7c and 15a) have qualified, with the exception of MIS 17c. Periods of clearly interstadial nature in other records get drawn in for lower thresholds, starting with MIS 5a. The low values of CO 2 in MIS 17c were already noted [ Lüthi et al. , 2008 ] and persist even in the revised data set recently published [ Bereiter et al. , 2015 ]. Perhaps most importantly for this sensitivity study, there is again no reasonable criterion using CO 2 that qualifies MIS 13a as an interglacial without also qualifying MIS 7ac and 15a as separate interglacials.

We need also to assess the criterion for how deep the surrounding glacials must be to decide that a peak qualifies as a separate interglacial. Using LR04, it would in theory be possible to set a second threshold for the depth of the valley between interglacials, such that MIS 13 and 15 are separate interglacials, while MIS 7a‐7e and MIS 15a‐15e each form single very long interglacials. However, the range of values for such a second threshold is very narrow (~0.1‰), suggesting that it has no robust validity. In addition, it would imply inclusion in an interglacial of isotopic values (during MIS 7d and 15b) that are generally characteristic of periods that are clearly part of the glacial world. A final argument against this solution is that two of the derived sea level records [ Elderfield et al. , 2012 ; Shakun et al. , 2015 ] place sea level lower in MIS 7d than in MIS 14. Thus, unless we are willing to describe MIS 13–15 as a very long continuous interglacial, we are forced to set the threshold such that MIS 7ac and 7e (similarly 15a and 15e) are separate interglacials.

Our sensitivity studies with the LR04 benthic isotope stack [ Lisiecki and Raymo , 2005 ] would produce the same set of interglacials for any threshold between 3.5 and 3.73‰ (to qualify as an interglacial, values lower than the threshold must be achieved). Any threshold set lower than 3.5‰ would exclude first MIS 17c and very quickly all other stages except MIS 1, 5e, 9e, and 11c. At thresholds higher than 3.73‰, several other stages, starting with MIS 5c, would become interglacials. The very wide range of thresholds (3.5–3.73‰) that lead to the same answer suggest that this solution is robust, i.e., that new data are unlikely to make it obsolete.

Although a definition based on sea level, as we have suggested, is attractive in its simplicity, its application is hampered by uncertainties that remain in the values of sea level provided by indirect but continuous methods. In particular, direct sea level evidence suggests [ Dutton and Lambeck , 2012 ; Kopp et al. , 2013 ] that sea level during MIS 5e was higher than present. However, the (ODP 1123) derived sea level record [ Elderfield et al. , 2012 ] places the MIS 5e highstand at the edge of the lowest range of our proposed threshold (−20 m), and at a similar level in MIS 5a, with MIS 5c higher than both (Figure 2 ). Had we only the ODP1123 data [ Elderfield et al. , 2012 ], then MIS 5a and MIS 5c would have been identified as interglacials on a par with MIS 5e, but we use knowledge from other sea level data sets [ Dutton and Lambeck , 2012 ; Grant et al. , 2012 ; Kopp et al. , 2013 ] to distinguish MIS 5e as an interglacial and MIS 5a and 5c as interstadials. This, however, casts doubt on the reliability of the reconstruction in earlier periods (especially before 450 ka ago) when alternative sea level evidence is weaker. Given this uncertainty, we also test the sensitivity of our interglacial assignments by using simple thresholds in the uncorrected benthic oxygen isotope record (in this case the LR04 stack [ Lisiecki and Raymo , 2005 ]), and in the ice core record of CO 2 (chosen as a global‐scale indicator which, together with ice volume/extent, affects the global radiative forcing).

Tzedakis et al . [ 2009 ] assigned an interglacial status to the most prominent temperate (low δ 18 O) interval within each odd‐numbered marine isotope stage of the LR04 benthic δ 18 O stack [ Lisiecki and Raymo , 2005 ] (with the exception of the historical artifact of MIS 3). On this basis, MIS 5e, 9e, 11c, 13a, 17c, and 19c (and by default MIS 1) are unambiguously the most prominent intervals within each MIS (Figure 2 ), but choosing the most prominent interval in MIS 7 and MIS 15 is difficult. Yin and Berger [ 2010 , 2012 ] chose a similar set of interglacials to simulate, using the expectation of a 100 ka cycle to justify including MIS 7e but not 7a‐7c, and including MIS 15a but not 15e. Using our sea level definition, MIS 7a‐7c and 7e, and also MIS 15a and 15e are all equally prominent within their respective stages, and as we do not assume a particular periodicity, they are all included in our interglacial roster, which therefore comprises MIS 1, 5e, 7a‐7c (as a single interglacial), 7e, 9e, 11c, 13a, 15a, 15e, 17c, and 19c.

Definition of interglacials based on sea level. (top) δO of benthic foraminifera in the LR04 stack []; (bottom) probability maximum Red Sea relative sea level [] (orange line); and deconvolved δO of seawater [] (grey line). Marine Isotopic substages of interglacial status are indicated (as well as interstadials MIS 5a and 5c). Note that there is some confusion in the literature about the lettering of substages in MIS 9 and 15. Here we use the substage numbering recommended in a recent paper [], such that the oldest substages in MIS 9 and 15 are 9e and 15e. This contrasts with the usage in some recent papers []. Dashed lines mark values discussed in the text that might be used in the definition of interglacials.

Given that the LR04 stack is affected by deepwater temperature and hydrographic effects of variable influence through time, we first examine those records where the sea level component has been (mostly) isolated. The ODP Site 1123 [ Elderfield et al. , 2012 ], the Red Sea/Mediterranean [ Rohling et al. , 2009 , 2014 ], and the detrended surface seawater stack [ Shakun et al. , 2015 ] records all suggest that within the uncertainties of the reconstructions (±20 m), highstands during the most prominent isotopic substages of the last 800 ka reached a level similar to present (Figure 2 ). Assuming that the East Antarctic ice sheet was not significantly smaller than today, this observation points to a more specific definition of an interglacial as an interval within which the distribution of Northern Hemisphere ice resembled the present (0 ± 20 m), i.e., there was little Northern Hemisphere ice outside Greenland, with periods of significantly greater ice volume (sea level passing below approximately −50 m compared to present) before and after the interglacial period. The second half of the definition is required in order to separate periods of interglacial character that are interrupted by clearly glacial conditions. However, it has consequences for the number of interglacials in MIS 15 and 7. The distinction between an interglacial and an interstadial under this definition lies in the absence/presence of significant Northern Hemisphere ice outside Greenland. An example of this distinction is MIS 5. Evidence from the Norwegian Sea and western Scandinavia indicates ice‐free conditions during the Eemian (interglacial), but expansion of mountain glaciers during the Amersfoort/Brørup (MIS 5c) and Odderade (MIS 5a) interstadials leading to increased background values in the relative abundance of ice‐rafted detritus in the Norwegian Sea during these intervals [ Baumann et al. , 1995 ].

A eustatic, or global mean, sea level curve would therefore provide a useful measure to address this issue, but in practice direct sea level determinations are often complicated by local isostatic and gravitational effects and dating uncertainties, are discontinuous, and only cover the most recent part of the record [e.g., Thompson and Goldstein , 2006 ]. The δ 18 O from benthic foraminifera is sometimes used as a sea level proxy, but it is overprinted by local deepwater temperature and hydrographic effects, which can obscure the glacioeustatic signal [ Chappell and Shackleton , 1986 ; Skinner and Shackleton , 2005 ]; averaging many globally distributed δ 18 O benthic records as in the stacked record of Lisiecki and Raymo [ 2005 ] (hereafter referred to as LR04) does however remove some of the regional variability. Various approaches to isolate the sea level component of δ 18 O benthic [ Shackleton , 1987 ; McManus et al. , 1999 ; Shackleton , 2000 ; Waelbroeck et al. , 2002 ] have been attempted, but until recently none of them have been extended to cover the last 800 ka. A modeled sea level curve by Bintanja and van de Wal [ 2008 ] extends over the last 3 Ma but depends on uncertain assumptions about deepwater temperatures and their coupling with atmospheric temperatures. A record using shallow‐infaunal benthic foraminifera to deconvolve deepwater temperatures (using Mg/Ca ratios) and the δ 18 O of seawater (δ 18 O sw ) over the last 1.5 Ma has recently become available from Ocean Drilling Program (ODP) site 1123 on the Chatham Rise, east of New Zealand [ Elderfield et al. , 2012 ]. With hydrographic effects considered minimal at this site, the δ 18 O sw provides an important insight into ice volume changes over this interval, although the propagated error of ±0.2‰ in calculated δ 18 O sw estimated by Elderfield et al . [ 2012 ] means that the uncertainty is ±20 m sea level equivalent. A continuous sea level reconstruction based on planktonic δ 18 O records and a hydrological model for the Red Sea is available for the last 520 ka [ Rohling et al. , 2009 ]. A related method covering a longer time period, using a hydrological model for the Mediterranean [ Rohling et al. , 2014 ], gives a further useful overview, but with a large propagated uncertainty in sea level, similar to that of Elderfield et al . [ 2012 ]. Finally, recent work [ Shakun et al. , 2015 ] has used a global compilation of paired planktonic δ 18 O and sea surface temperatures in an attempt to deduce δ 18 O of surface seawater, interpreted to reflect mainly ice volume and sea level. However, it is hard to use this record to compare the strength of interglacials because the authors had to make a correction for a long‐term trend, believed to be unrelated to sea level.

As discussed earlier, interglacials have been defined as episodes during which global climate was incompatible with the wide extent of glaciers [ American Commission on Stratigraphic Nomenclature , 1961 ]. While remembering that there is still a large volume of ice in Antarctica during every interglacial, this nonetheless emphasizes that the fundamental concept underlying the terminology of an interglacial is that of low continental ice volume, a consequence of integrated global climate effects, which would also be observed as high sea level or less positive seawater δ 18 O. This is in line with early definitions of interglacials based on the marine transgression [e.g., Harting , 1874 ]. The slow response of ice sheets would mean that the sea level highstand or isotopic minimum may be attained relatively late compared to increases in warmth or climate forcing. Here we clearly distinguish the use of such values to define whether an interglacial is occurring, from any measure used to define when the interglacial starts and ends. As an example, for the current interglacial, sea level close to present, a value we might use to define MIS 1 as an interglacial, occurs as late as ~ 7–8 ka ago. However, having determined that an interglacial is occurring, its start can be defined in other ways [ Walker et al. , 2009 ].

We might also be tempted to define interglacials on the basis of the external forcing. At the multimillennial timescale, the dominant external forcing of Earth's climate is the astronomical forcing. This drives the climate system dynamics which, lead, for example, to variations in atmospheric CO 2 concentration and in the mass of the ice sheets. These in turn may be viewed as feedbacks, or as “internal” forcing factors of the atmospheric and oceanic conditions all over the globe. Increased concentrations of CO 2 , in particular, are a characteristic of interglacials. However, the relationships between astronomical forcing, CO 2 and the mass of continental ice are nonlinear and complex. In particular, eccentricity, often cited as a possible origin of the 100 ka periodicity characteristic of glacial‐interglacial cycles does not appear in the spectrum of regional insolation changes; it merely modulates the amplitude of insolation changes. Combinations of obliquity and precession predict the occurrence of some interglacials [ Kukla et al. , 1981 ], but as longer paleoclimate records have appeared, it has become clear that such simple formulae do not correctly predict the full roster observed in the data. It is therefore not currently practical to use astronomical forcing to define interglacials.

Central to any definition of an interglacial is the choice of an appropriate criterion that can be applied consistently through time. A number of approaches are possible, each with inherent advantages and disadvantages (Table 1 ). A recurring issue is that distinctions between interglacials and interstadials depend on the metric used. It is desirable that whatever metric is chosen has global significance, such that interglacials are phenomena that can be considered widespread (probably of global extent), even if their regional expression is neither globally uniform or synchronous. Some definitions that have been used are based on temperature. Temperature records exist at many sites but are essentially local, and therefore, the definition of an interglacial based on temperature would vary according to the location. This will remain the case until there are sufficient data with accurate chronologies, such that some kind of global average temperature can be constructed across multiple glacial/interglacial cycles. Only recently has a first such reconstruction [ Shakun et al. , 2012 ; Marcott et al. , 2013 ] become available for the last 22 ka and none is available for earlier interglacials.

The population of interglacials must be defined before any attempt is made to discuss their individual characteristics, including the statistics of their timing and spacing. Given that interglacial complexes (odd‐numbered Marine Isotope Stages, such as MIS 5, but excluding MIS3 which is not now considered to include an interglacial period) contain a number of temperate intervals within them, a crucial question is how to categorize interglacials and distinguish them from interstadials. In some conceptual models [ Parrenin and Paillard , 2003 ], the climate system occupies (and jumps between) a glacial and deglacial (or interglacial) state, with different governing equations in each. While a bifurcation structure may indeed be relevant for describing the dynamics of glacial‐interglacial cycles, identifying it from the available paleo‐observations is complex [ Imbrie et al. , 2011 ]. Distinguishing interglacial from interstadial climates is nonetheless important, not only on formal stratigraphic grounds but also in climatic context. Though temperate in climatic signature, interstadials are still part of a glacial world with excess ice in the Northern Hemisphere outside Greenland. Thus, for example, the Amersfoort/Brörup and Odderade interstadials in northwestern European nomenclature (roughly MIS 5a and 5c) are part of the Early Glacial (MIS 5d‐5a) in this region, which reflects not only their stratigraphic position but also their overall climatic character compared to the Eemian.

A major development in the understanding of the correspondence between marine and terrestrial stages was the realization that the Eemian (Last) Interglacial of northwest Europe is not equivalent to the whole of MIS 5, but rather approximates to a substage (MIS 5e) within it [ Shackleton , 1969 ]. In line with the terrestrial evidence, other isotopically depleted marine substages corresponded to interstadials associated with the same interglacial complex (so that MIS 5c and 5a would broadly correspond to the Amersfoort/Brörup and Odderade interstadials, respectively, of the northwest European stratigraphy). Joint foraminiferal isotopic and pollen analyses from marine sequences have not only confirmed such correlations but also showed that the marine and terrestrial stage boundaries are not necessarily time parallel, reflecting geographical asynchroneity and differences in response times of, e.g., vegetation and ice [ Sánchez Goñi et al. , 1999 ; Shackleton et al. , 2002 , 2003 ; Tzedakis et al. , 2004 ].

In deep‐sea sediments, the terms glacial and interglacial were first applied to even‐ and odd‐numbered oxygen isotope stages, respectively, defined by variations in the oxygen isotope ratio (δ 18 O) in planktonic foraminifera and first thought to reflect changes in sea surface temperature [ Emiliani , 1955 ]. However, the concept of a marine isotopic stratigraphy is based on the principle that changes in the oxygen isotopic composition of the oceans primarily arises from changes in the volume of ice stored on the continents [ Shackleton , 1967 ] and that given the slow mixing of the deep ocean, such a signal would be global on timescales of thousands of years. Shackleton and Opdyke [ 1973 ] proposed the formalization of Marine Isotope Stages (MIS) 1–22, using the δ 18 O in foraminifera in core V28‐238 from the Eastern Equatorial Pacific, a practice which has been gradually extended over the entire Pleistocene, Pliocene, and beyond. Marine isotope stages may be further divided into substages (e.g., MIS 5 into 5a‐5e [ Shackleton , 1969 ]), characterized by local minima and maxima in δ 18 O values known as marine isotopic events [ Prell et al. , 1986 ], such that MIS 5a contains event 5.1, MIS 5b contains event 5.2, and so on. The decimal event notation has occasionally been used to denote substages, but the two systems are not interchangeable. An event refers to a point in time rather than an interval; in a detailed record there will be space between successive events, whereas substages have boundaries and together fill the stratigraphic record and the time that it represents [ Shackleton , 2006 ]. To avoid confusion, please note that we use a recently recommended substage nomenclature [ Railsback et al. , 2015 ], which contrasts with some literature usage, particularly for MIS 9 and MIS 15.

The term “interglacial” derives from the position of temperate‐climate sediments interbedded between glacial sediments [ Heer , 1865 ; Geikie , 1874 ]. In the higher midlatitudes of Europe and North America the concept of an interglacial as an interval of time during which conditions were at least as warm as the present interglacial (Holocene) was traditionally based on floral evidence, indicating the development of temperate deciduous forest [e.g., Jessen and Milthers , 1928 ; Fairbridge , 1972 ; Woillard , 1978 ; West , 1984 ; Gibbard and West , 2000 ]. By comparison, the term interstadial has been used to describe a period that was either too short or too cold to allow the development of temperate deciduous forest. Similar distinctions were also adopted for faunal evidence [e.g., Coope , 1977 ].

Terrestrial, marine, and ice core sequences deposited since the intensification of NH glaciation, ~2.6 Ma ago have traditionally been divided on the basis of climate changes inferred from their lithological, biological, and geochemical signature. The fundamental units of this geologic‐climate classification [ American Commission on Stratigraphic Nomenclature , 1961 ] are (i) glaciations (or glacials), representing episodes during which extensive glaciers developed, and (ii) interglaciations (or interglacials), representing episodes during which the climate was incompatible with the wide extent of glaciers. Further subdivisions include stades (stadials) and interstades (interstadials), reflecting secondary advances and recessions or standstills of glaciers, respectively.

A final class of models that should be mentioned seeks to understand the dynamics of glacial‐interglacial cycles through simplified mathematical concepts [ Paillard , 1998 ; Crucifix , 2013 , and references therein]. Although these conceptual models are addressing an issue much wider than just interglacial climate, we include them here because they form part of the discussion in sections 7 and 8 .

At least two EMICs have been run across the full 800 ka period we are considering in this paper, one with prescribed [ Holden et al. , 2010 , 2011 ], the other with interactive [ Ganopolski and Calov , 2011 ], ice sheets, and both with prescribed CO 2 concentration. While these models necessarily have extremely simplified dynamics and other simplifications, they do allow the exploration of the influence of different factors (such as GHG concentrations, insolation, and dust) on the timing and occurrence of interglacials. An alternative way to look at what influences glacial cycles is to drive a state of the art ice sheet model with parameterized climates, which are themselves developed from GCM simulations [ Abe‐Ouchi et al. , 2013 ]. This latter study covered a period of the last 400 ka. A range of studies have investigated the loss and growth of ice sheets across one or more glacial cycles using a variety of simplified forcings [e.g., Berger et al. , 1998b ].

To evaluate the mechanisms at the start and the end of interglacials, and eventually to understand the interactions during interglacials between ice sheet history, climate, vegetation, and greenhouse gases, transient simulations are essential. However, due to limitations of computer resources, it remains challenging to run even simplified GCMs for many thousands of years (remember that a full simulation of the last interglacial would need to run at least from 135 to 110 ka). A few EMICs, and GCMs running at low resolution (and in some cases with accelerated astronomical forcing), have been deployed across the last interglacial (130–115 ka) [ Bakker et al. , 2013 ], and transient simulations of earlier interglacials (in this case 9e, 11c, and 19c) are starting to appear [ Yin and Berger , 2015 ]. Another development that may allow a detailed analysis of the sensitivity of climate (including that of interglacials) to different boundary conditions (such as orbital parameters, CO 2 , and ice sheet size) is the use of an emulator that is based on, and attempts to mimic, a GCM or EMIC [ Araya‐Melo et al. , 2015 ; Bounceur et al. , 2015 ].

By far the most common approach is to carry out snapshot simulations of particular time slices, using models ranging from full ESMs and atmosphere–ocean general circulation models (GCMs), in some cases identical to those used to study present and future climate, through to EMICs. The majority of such simulations have been conducted for specific time periods in the last interglacial (LIG), in particular under the Paleoclimate Model Intercomparison Project (PMIP)3 exercise [ Lunt et al. , 2013 ], which focused on time slices at 130, 128, 125, and 115 ka. Such simulations are driven by boundary conditions distinct from those of the Holocene. For most interglacial simulations, ice sheet extent is assumed similar to that of today, as are other components such as vegetation and aerosol concentration. Therefore, the main changes are in astronomical parameters (which can be calculated precisely) and GHG concentrations (which are derived from ice core data); these can be modified as appropriate for the time period under study. A very few studies have carried out snapshots for earlier interglacials [ Yin and Berger , 2010 ; Herold et al. , 2012 ; Yin and Berger , 2012 ; Milker et al. , 2013 ; Muri et al. , 2013 ; Yin and Berger , 2015 ]. The evaluation of snapshot simulations is severely hampered by the scarcity of data compilations for previous interglacials. As an example, the PMIP3 LIG intercomparison was evaluated against a compilation that consisted of a single value of temperature for each site [ Turney and Jones , 2010 ] for the whole interglacial, which clearly makes the separate evaluation of snapshots at 125 and 130 ka impossible. Work is underway to improve this situation for a number of interglacials, including MIS 5e [ Capron et al. , 2014 ] and MIS 11 [ Milker et al. , 2013 ; Kleinen et al. , 2014 ].

A range of different types of model has been used to study past interglacials. Experiments can be carried out with a range of underlying purposes, for example: testing models that are used to simulate the present and future climate, understanding the dynamics of termination and inception of glacials (i.e., the start and end of interglacials), or understanding the whole dynamics of glacial‐interglacial cycles.

Estimates of relative sea level change at individual sites and dates can be made using a range of methods (already partly discussed in section 2.2 ). While dated corals are of particular importance, to obtain a continuous (over multiple glacial cycles) and global, eustatic, sea level record, marine sediments remain important. The δ 18 O of benthic foraminifera is affected not only by changes in continental ice volume but also by deepwater temperature and local hydrographic (water mass) changes. On timescales significantly greater than the mixing time of the oceans (10 3 years), the δ 18 O calcite signal represents changes in the mean state of the “global ocean” as local/regional hydrographic changes that exist in the ocean are diminished [ Shackleton , 1967 ; Imbrie et al. , 1984 ; Skinner and Shackleton , 2006 ]. If an independent estimate of temperature is derived, the seawater δ 18 O component (i.e., δ 18 O seawater ) of the benthic δ 18 O signal can be calculated. As discussed in section 2.2 , one approach has been to stack benthic δ 18 O records assuming that local and regional hydrographic signals are averaged out, thereby leaving the common global δ 18 O signal [ Imbrie et al. , 1984 ; Prell et al. , 1986 ; Lisiecki and Raymo , 2005 ]. In reality, however, the relative contributions of temperature and ice volume still remain unconstrained in a stacked record. Another approach has been to measure benthic δ 18 O in cores from regions where deepwater temperature is close to freezing [e.g., Labeyrie et al. , 1987 ]. Others have attempted to model δ 18 O seawater and sea level from the benthic δ 18 O stack [ Bintanja et al. , 2005 ; Bintanja and van de Wal , 2008 ] or use a sea level transfer function to convert benthic δ 18 O to relative sea level equivalent [ Waelbroeck et al. , 2002 ]. Mg/Ca ratios of foraminifera have the potential to reconstruct temperature and, when combined with foraminiferal δ 18 O measurements, provide an estimate of changes in the δ 18 O of seawater [ Elderfield et al. , 2010 , 2012 ]. This approach was taken to deconvolve δ 18 O seawater and deepwater temperature at Site ODP1123 in the deep SW Pacific. A completely separate approach for obtaining a continuous (albeit local, and therefore relative) sea level record has been used in a series of papers using marine cores from the Red Sea. This relies on an assumption that δ 18 O in foraminifera in these cores is controlled mainly by changes in salinity induced by evaporation, whose influence varies depending on the depth of seawater above a shallow sill that controls exchange with the open ocean. The Red Sea records so far extend only to just over 500 ka ago [ Rohling et al. , 2009 ], although a longer record using a similar method for the Mediterranean has been published recently [ Rohling et al. , 2014 ]. Different sea level estimates are compared in Figure 2 .

GHG concentrations (particularly, CO 2 and CH 4 ) for the last 800 ka can be determined directly from measurements in the air within ice cores [e.g., Loulergue et al. , 2008 ; Lüthi et al. , 2008 ]. The uncertainty in the concentrations measured in this way is very small (a few parts per million for CO 2 and about 10 parts per billion for CH 4 ), although methodology is important for obtaining correct values, especially in deep ice [ Bereiter et al. , 2015 ]. However, in the cores for which old ice is available, there is an inherent limit to the resolution (of several centuries), and an uncertainty in the age relative to the temperature proxy in the same core, because of changes in delta age, the age difference between the ice and the younger air that diffuses from the surface and is trapped within the ice matrix only at depth [e.g., Parrenin et al. , 2013 ]. CH 4 changes result from changes in wetlands in the tropics and high northern latitudes and thus are often used as an indicator in the gas phase of the timing of Northern Hemisphere and tropical climate change relative to other signals.

In the polar regions, temperature is derived from the ratios of water isotopes (δ 18 O, δD) in ice cores. This has a strong physical basis as a temperature proxy [ Jouzel et al. , 1997 ], but there remains uncertainty about its calibration, particularly for higher temperatures [ Sime et al. , 2009 ]. Additional methods to constrain temperature in ice cores have been used [e.g., Buizert et al. , 2014 ] in Greenland ice cores, but so far the water isotopes are the only method used to reconstruct temperatures in the long Antarctic ice cores that extend through several glacial cycles. SSTs are estimated from marine sediment cores using a range of geochemical and biological proxies: fossil assemblages (using foraminifera, coccolithophores, radiolarians, diatoms, or dinocysts), alkenone, and other biomarkers (U k 37 , U k′ 37 , TEX 86 ), Mg/Ca ratios and “clumped” isotopologs of carbon and oxygen. Each of these methods comes with uncertainty about calibration and about the precise interpretation of the proxy (for example, whether it indicates an annual or seasonal signal). Where methods have been compared, discrepancies are found in some regions [ Nurnberg et al. , 2000 ; de Vernal et al. , 2006 ; de Garidel‐Thoron et al. , 2007 ]. This means that it is difficult to construct consistent spatial time slices of absolute temperature. However, the relative signal within a single proxy at a single site should be much more reliable, so that anomaly data can be used with some confidence to assess the relative strength of different interglacials, or the timing of maxima and transitions at a given location. For terrestrial sites, pollen assemblages may sometimes be used to estimate temperature; many other commonly described terrestrial proxies are influenced by factors other than temperature.

Temperature, greenhouse gas (GHG) concentrations, and ice volume (sea level) have been the three reconstructed climate parameters used most often for studying past interglacial periods, although changes in the hydrologic cycle are also important especially at low latitudes and changes in ocean circulation are also highly relevant. Clearly, it is not appropriate here to review all the methods used in paleoclimate reconstruction; however, we briefly summarize the main methods used to derive the records that appear most often in this paper.

The accuracy of timescales directly influences our interpretations of the causes of interglacial periods. For example, the errors associated with the ages for the onset and end of an interglacial period (regardless of defining criteria) are large relative to the accuracy of astronomical solutions, which we take to have no error for the last 800 ka. The absolute chronological error (of order ±5 ka for ice cores and marine records) is also large relative to the duration of interglacial periods, which ranges from ~10 to 30 ka. The age uncertainty (about one quarter of a precession cycle) provides considerable leeway in interpretation of how the onset and end of interglacials align with astronomical forcing, underscoring the importance of reducing chronological error in the future.

While speleothem records can be radiometrically dated, the age models of other long terrestrial records have been derived by tuning lithostratigraphical or biostratigraphical variations to an astronomical target (which may be a local insolation target) or to marine records [ Prokopenko et al. , 2006 ; Tzedakis et al. , 2006 ; Melles et al. , 2012 ; Torres et al. , 2013 ]. While this can be done rather precisely, it involves assumptions about synchroneity that could be in error by several thousand years. Unless these assumptions can be tested (for example, through the use of pollen in marine cores, of tephra layers that can be shown to have the same origin, or through the use of dated paleomagnetic boundaries), it is impossible to assess the phasing of change between different processes during relatively rapid changes that occur at terminations in particular.

Speleothems and corals provide an opportunity for absolute dating of the timing of terminations [e.g., Cheng et al. , 2009 ] and the duration of interglacials by radiometric U series dating, and to determine the phasing of climate change relative to insolation forcing. If age markers from speleothems can be identified as unequivocally synchronous to features in ice cores and/or marine sediment cores, then the speleothem chronology can be exported to other archives [e.g., Barker et al. , 2011 ; Grant et al. , 2012 ] and used to evaluate the accuracy of independent chronologies. Full use of such methods awaits firm proof that rapid jumps in local speleothem δ 18 O are synchronous with rapid jumps in ice core temperature or atmospheric methane and marine sea surface temperature (SST) records. Assumptions of this nature have been disputed [e.g., Blaauw et al. , 2010 ]. Barker et al . [ 2011 ] placed the EPICA Dome C (EDC) record on a “Speleo‐Age” timescale for the last 400 ka by first differentiating the Antarctic δD record, according to a conceptual model of interhemispheric millennial temperature change, to produce a synthetic Greenland record, and then correlating cold events in Greenland with “weak monsoon events” in the detrended speleothem δ 18 O record. The ages agree to within ±2–3 ka with EDC3 from present to MIS 11 where the difference increases to 5 ka. A similar approach has been applied to Iberian Margin sediments where planktonic δ 18 O and SST have been used to synchronize millennial‐scale events among marine sediment core, Antarctic ice core, and speleothem records [ Barker et al. , 2011 ; Hodell et al. , 2013 ]. Differences between the speleo‐synchronized and LR04 age models from the Iberian Margin are less than ±5 ka for the last 400 ka.

LR04 and EDC3 or AICC2012 are well aligned with each other although Antarctic temperature changes consistently lead LR04 δ 18 O by an average of 3 ka. The deep‐sea temperature component of the benthic δ 18 O signal, as estimated by Mg/Ca, significantly leads the δ 18 O seawater signal [ Elderfield et al. , 2012 ] (see discussion below) and agrees closely with the timing of Antarctic temperature change (Figure 3 ). This likely reflects the late response of ice volume compared to temperature in the benthic δ 18 O signal. The lead of warming Antarctic and deep‐sea temperatures over (NH) ice volume change underscores the problem of how to define the start of interglacial periods because the early warming may not be considered to be part of the interglacial period if defined by ice volume reduction.

Most deep‐sea records are dated by correlating benthic δ 18 O signals to the LR04 stack [ Lisiecki and Raymo , 2005 ]. The LR04 stack is tuned to the output of an ice volume model [ Imbrie and Imbrie , 1980 ] that, in turn, is driven by insolation, assuming fixed response times. The estimated error for LR04 is ±4 ka for the past million years. One of the caveats associated with the LR04 timescale is its reliance on assumptions associated with astronomical forcing of climate. The depth‐derived ages of Huybers and Wunsch [ 2004 ] are almost free of such assumptions but are estimated to be accurate only to within ±9 ka. The differences between LR04 and other age models fall within the uncertainties of these error estimates (Figure 3 ).

The long ice core records have relied mostly on the Antarctic Ice Core Chronology 2012 (AICC2012) based on information from several ice cores [ Bazin et al. , 2013 ], and its predecessor, the European Project for Ice Coring in Antarctica (EPICA) Dome C, third generation (EDC3) chronology that was derived by inversion of a snow accumulation and mechanical ice flow model using a set of independently dated horizons (age markers) along the core [ Parrenin et al. , 2007 ]. Note that all ages shown in figures in this paper are nominally relative to 1950 as present. The uncertainty of the EDC3 age model is estimated to be ±3 ka at 100 ka ago and increasing to ±6 ka from 130 ka to the base of the core. AICC2012 [ Bazin et al. , 2013 ] shows only small differences from EDC3 and is well within the quoted age uncertainty. The duration of the last four interglacial periods is not affected by more than 5% using AICC2012 instead of EDC3 [ Bazin et al. , 2013 ]. One powerful alternative method for developing ice core chronologies is orbital tuning of the O 2 /N 2 ratio to local insolation forcing [ Bender , 2002 ; Kawamura et al. , 2007 ]. This method has the advantage that it dates the ice (which holds the climate signal), rather than the enclosed air, which can be considerably younger because the air bubbles close only at depths of typically 60–100 m). With an assumption that O 2 /N 2 is in phase with local summer insolation, this method provides a link to absolute ages; however, the precision of this assumption has been questioned [ Landais et al. , 2012 ]. Vostok O 2 /N 2 data are included in AICC2012 as one component of the age model, but the Dome Fuji (DFO2006) age model relies mainly on this tuning, and a smaller estimated uncertainty (up to 2.5 ka) is quoted. Comparison of EDC3 and Dome Fuji chronologies reveals reasonable agreement for the last 350 ka (within the estimated uncertainty of EDC3, but not always within the uncertainty quoted for DFO2006).

Regardless of what definition is adopted for an interglacial, the study of past interglacials must be underpinned by a robust stratigraphy and chronology. Comparing climate signals in different archives is challenging and requires precise stratigraphic correlation of marine, ice, and terrestrial records, including those providing information about sea level and atmospheric composition. Study of the initiation, termination, and duration of interglacials as well as rates of processes is entirely dependent on the accuracy of the timescale. The age model controls the rate, shape, and duration of interglacial periods and absolute ages are needed to discuss the phasing of proxies relative to astronomical forcing. Sampling frequency as well as chronological precision can affect the degree of variability and peak values (amplitude) of the interglacial signal.

Before attempting to assess the nature, intensity, and timing of interglacials, it is necessary to establish and summarize the main methods used to measure interglacial climate characteristics, to place them on an age scale, and to model the processes involved.

The GHG concentrations reached during different interglacials show considerable variation (Figure 6 ). In particular, the interglacials between 450 and 800 ka ago have significantly lower concentrations of both CO 2 and CH 4 than do the later interglacials. The radiative forcing from greenhouse gases between an interglacial with relatively high GHG concentrations (280 ppm, 700 ppb) and the ones with lower values is around 1 W m −2 . This radiative forcing is sufficient, all other things being equal, to drive a global temperature drop of just under 1°C (for typical values of climate sensitivity [ IPCC , 2013 ]).

In general, and as discussed earlier, our definition assumes that the Northern Hemisphere ice sheets were similar to today for at least part of each interglacial, i.e., with significant ice only in Greenland. Most studies have also assumed that the Antarctic Ice Sheet was at a similar size to today in each previous interglacial. Thus, changes in ice sheet forcing are generally ignored when comparing interglacials. However, given the high sea levels [ Dutton et al. , 2015 ] that have been inferred for MIS 5e [ Kopp et al. , 2009 ] and MIS 11 [ Raymo and Mitrovica , 2012 ], a significant reduction in these interglacials of either or both of the Greenland Ice Sheet [ Otto‐Bliesner et al. , 2006 ; Overpeck et al. , 2006 ; de Vernal and Hillaire‐Marcel , 2008 ] or West Antarctic Ice Sheet [ Scherer et al. , 1998 ] cannot be discounted and might impose a significant additional forcing to regional climate [ Holden et al. , 2011 ].

In addition to the insolation distributions discussed above, other combinations of insolation factors might also deserve attention for the determination of the differences between the interglacials. The mean annual insolation, and its gradient, might be important for moisture transport and ocean physics [ Loutre et al. , 2004 ], while the latitudinal insolation gradient may induce a systematic difference between the pre‐MB and the post‐MB interglacials in the Southern Ocean ventilation and deep‐sea temperature [ Yin , 2013 ].

The values in Figures 5 a– 5 c are controlled mainly by precession, while the total annual irradiation (Figure 5 d) is only a function of obliquity [ Berger et al. , 2010 ]. As a result of the falling obliquity at the interglacial peak, the time evolution of total annual irradiation exhibits also a more or less similar pattern for all interglacials, except MIS 7c. The interglacial peak (in LR04) (Figure 5 d) is preceded by a positive anomaly in the high latitudes of both hemispheres centered over the poles, lasting more than 10 ka, and followed by a negative anomaly. The differences between the interglacials and today and between the interglacials themselves are mainly in the high latitudes and are very small between 60°N and 60°S. Therefore, the high latitudes are considered critical regions for explaining the differences between the interglacials. An interesting feature is that the 10 ka long positive anomalies of the high latitudes occurring before the interglacial peaks tend on average to be larger since the mid‐Brunhes (MB) than before, related to the larger amplitude of obliquity after the MB associated with the 1.3 Ma periodicity in the amplitude modulation of obliquity [ Berger et al. , 1998a ]. To what degree this difference in obliquity and therefore in the total energy received at high latitudes explains any systematic differences between the pre‐MB and post‐MB interglacials needs further investigation.

A seasonal maximum in insolation at 65°N is seen before the interglacial peaks in LR04 (Figure 5 b), shifting from the month of April to July over a period of more than 10 ka. The same pattern occurs, with some differences in timing, for all the interglacials with rising precession. After the peaks of the interglacials (in LR04), the maximum insolation occurs in boreal fall and winter, and the minimum in boreal spring and summer.

Time evolution of the deviation from today for four types of insolation parameters []. Theaxis is latitude in Figures 5 a, 5 c, and 5 d and month in Figure 5 b; theaxis is age in ka. (a) The insolation at the time of Northern Hemisphere summer solstice. (b) The seasonal pattern of insolation at 65°N. (c) The mean insolation during boreal summer (total irradiation during astronomical summer divided by the number of days). (d) The total annual irradiation; for comparison, note that 31.6 MJ mover a year averages to 1 W m. MIS 5e is used as an example showing the common features of most interglacials. Exceptions for Figures 5 a– 5 c are MIS 13a, 15e, and 17c; for Figure 5 d (total irradiation) MIS 7c has a different temporal pattern. The vertical line marks the time when LR04 δO has its minimum value.

A full analysis must consider the insolation distribution as a function of time, as illustrated in Figure 5 for MIS 5e for an example. This interglacial shows the common features of many others [ Yin and Berger , 2015 ] (the exceptions are those where the interglacial peak does not occur during rising precession (MIS 13a, 15e, and 17c) and falling obliquity (MIS 7c)). For the eight interglacials with rising precession index, a positive anomaly in insolation at the NH summer solstice (Figure 5 a) occurs before the interglacial peaks (based on LR04), covers the whole Earth and lasts more than 10 ka. It is followed by a negative anomaly a few thousands of years later. The amplitude of the negative and positive anomalies varies with interglacial and is controlled by the amplitude of precession variation (which in turn is determined by eccentricity), and at high latitude also by obliquity. The time evolution of the mean summer irradiance is very similar to that of the insolation at the summer solstice except that its maximum occurs 1 or 2 ka later and is shifted toward lower latitudes (Figure 5 c). This underlines the problem raised by the choice of the insolation parameter that is used to “tune” or to compare with proxy records, the timing being different from one parameter to the other by a non‐negligible amount.

The latitudinal‐seasonal distribution of daily insolation for a single time slice is usually used in model snapshot simulations. Compared to the average of recent interglacials, MIS 1, MIS 11c, and MIS 19c are characterized by less (more) insolation over the Earth during boreal (austral) summer because of weak precession caused by low eccentricity. In contrast, MIS 5e, MIS 7c, MIS 15a, MIS 15e, and MIS 17c are characterized by more (less) insolation over the Earth during boreal (austral) summer due to their high eccentricity. Due to its low obliquity, MIS 7e shows low insolation over the summer hemispheres with a deep minimum over the poles, and higher than average insolation over the winter hemispheres with a maximum in the midlatitudes. MIS 9e has high obliquity and a unique insolation pattern with high insolation centered over the poles in both summer hemispheres. For MIS 13a, the precession peak (506 ka) occurs so long before the δ 18 O peak (491 ka) that it does not make sense to consider it in the same way [ Yin and Berger , 2012 ].

It is not obvious that the astronomical factors controlling the timing of interglacials should also control their strength. Of the two factors governing the benthic δ 18 O signal, ice volume (and hence boreal insolation) may have a limited effect on interglacial strength, if most NH ice disappears in every interglacial [ Elderfield et al. , 2012 ]. The insolation control on deepwater temperature is not clear. It is therefore unsurprising that (Figure 4 ) there is no strict general relationship valid for all the interglacials of the last 800 ka between the strength of the interglacials and either the strength of the nearest precessional peak or the phase of obliquity maximum and precession minimum [ Yin and Berger , 2010 ].

The structure of the latitudinal and seasonal distribution of insolation depends upon the precessional parameter and the obliquity [ Berger , 1978 ; Berger and Loutre , 1991 ]. When precession minima (i.e., NH summer occurs at perihelion (when Earth is closest to the Sun in its elliptical orbit)) and obliquity maxima are in phase, they strengthen the NH insolation during boreal summer, especially at high latitudes. According to Milanković theory ( Milankovitch [ 1941 ] and Milanković [ 1998 ] (translation)), boreal summer insolation controls the growth and shrinkage of ice sheets, so one might expect the interglacial minimum in ice volume to occur sometime after the insolation maximum. This timing issue will be discussed further in the sections about glacial terminations and inceptions. Yin and Berger [ 2010 ] noted that the δ 18 O minima of many interglacials occur while precession is rising (NH summer moving from perihelion toward aphelion), and in six cases (of 11) the isotopic minimum occurs about one fourth of a precessional cycle after the precessional minimum (NH summer at perihelion) (Figure 4 ). This timing would be consistent with the expected response time of the melting ice sheets to the peak forcing. However, such a relationship should be treated cautiously as it is partly related to the astronomical tuning of the LR04 stack, and the phase relationship of the second control on LR04 (i.e., deepwater temperature) is not directly addressed by Milanković theory. Furthermore, there are also several interglacials where this timing is not seen. The minimum in δ 18 O also always occurs within or just later than a quarter of an obliquity cycle after the obliquity maximum, with the notable exception of MIS 7c and 7e.

Yin and Berger [ 2010 ] discussed the insolation patterns at the precession minimum (NH summer insolation maximum) near to what they considered to be the “peak” of each interglacial. In the absence of robust data sets for global temperature or ice volume, they identified the period of interest using the LR04 benthic δ 18 O stack [ Lisiecki and Raymo , 2005 ]. They chose this data set because it has some global character, and peak values (isotopic minima) are easily identified. Within the chronological uncertainty, the LR04 dates for the interglacial peaks (shown on Figure 4 ) are generally similar to those for the Antarctic temperature proxy from the EDC3 or AICC2012 Antarctic ice core age models; however, we note that all these age models are partly dependent on astronomical tuning, so that the exact phasing between astronomical parameters and peak interglacial should be treated cautiously.

The largest difference in forcing between and during different interglacials lies in the latitudinal and seasonal pattern of incoming solar radiation [ Yin and Berger , 2012 , 2015 ], which is controlled by the three astronomical parameters precession, obliquity, and eccentricity (Figure 4 ). Here we consider first the instantaneous forcing at a characteristic time in each interglacial, and then the trends and patterns of astronomical forcing through the interglacial.

Because of the effect of components of the climate system with a long response time, the state of the climate at any given time depends not only on the instantaneous forcing but also the forcing history. This section focuses on the instantaneous forcing during interglacials, while the impact of forcing history during the preceding glacial is considered in later sections. This will provide a context for discussions about the relative strength of interglacials, their timing, and the trends that occur during them.

Presentation of key data sets for each interglacial discussed in this paper. In each case after this first expanded exemplar, theaxis (time) andaxis for each interglacial have approximately the same scaling to allow comparison between interglacials. Each plot runs (right to left) from the preceding glacial period through the termination, across the interglacial peak, and into the inception of the following glacial period (except MIS 1 which stops at the present). (a) Obliquity and precession []. (b) The LR04 benthic oxygen isotope stack [], and the sea level estimate based on separating deepwater temperature and water isotopic content from a record at site ODP1123 (South Pacific) []. (c) The ice core record of CO] and CH]. (d) The temperature estimated from δD at EPICA Dome C, Antarctica [], and SSTs at the North Atlantic site ODP982 [].

Presentation of key data sets for each interglacial discussed in this paper. In each case after this first expanded exemplar, theaxis (time) andaxis for each interglacial have approximately the same scaling to allow comparison between interglacials. Each plot runs (right to left) from the preceding glacial period through the termination, across the interglacial peak, and into the inception of the following glacial period (except MIS 1 which stops at the present). (a) Obliquity and precession []. (b) The LR04 benthic oxygen isotope stack [], and the sea level estimate based on separating deepwater temperature and water isotopic content from a record at site ODP1123 (South Pacific) []. (c) The ice core record of CO] and CH]. (d) The temperature estimated from δD at EPICA Dome C, Antarctica [], and SSTs at the North Atlantic site ODP982 [].

Figure 8 shows, for each interglacial, a range of key climate data sets. Each data set is plotted on its own age scale (i.e., there is no attempt at synchronizing), so spurious phasings may result. Each is presented with the same scales on each x and y axis, so that it is immediately possible to recognize long interglacials as opposed to short ones, and ones that are strong in a particular property. In each figure, the four panels relate to (Figure 8 a) astronomical parameters (obliquity and precession), (Figure 8 b) indicators related to sea level (LR04 benthic oxygen isotope stack, and ODP1123 sea level estimate based on derived isotopic content of bottom water), (Figure 8 c) GHG (CO 2 and CH 4 ), and (Figure 8 d) temperature (high‐latitude North Atlantic SST (ODP982) and Antarctic air temperature (EPICA Dome C)).

Moving forward through time, we first observe the end of the preceding glacial (MIS 6). In this case, as in many but not all other glacials, the glacial maximum (lowest sea level/coldest temperatures) was reached just before the termination. The glacial termination is characterized by several thousand years of rising sea level and temperature. A brief early maximum is seen in some parameters, notably Antarctic deuterium (representing temperature), and the transient peak is sometimes referred to as an “overshoot.” Caution is needed in the use of this latter term, as it has been used to refer to brief centennial‐scale rises of greenhouse gas concentrations above the interglacial value that followed, and to the much longer millennial‐scale peak in temperatures seen for Antarctic δD at the start of the interglacial in Figure 7 . We will discuss the likely origin of this early maximum in the section on terminations. However since it clearly provides a particularly warm climate at least in Antarctica, we consider it a part of the local interglacial when assessing maximum amplitudes. Methane clearly displays a different pattern of change, and this reflects the fact that maximum values for a given interglacial are achieved at different times in each record. After the initial maximum, a period of relatively stable interglacial climate is seen in MIS 5e (but not in all interglacials) and then values start to descend (generally more slowly than they rise during the termination): the first period of descent is the glacial inception. We also note that full glacial conditions are achieved only slowly, with cooling continuing well beyond the period shown in Figure 7 . In this case, the interstadials MIS 5c and 5a intervene before strong glacial conditions arrive only in MIS 4, ~74 ka ago.

Climate and sea level records across marine isotope stage 5e. (top) Benthic δO [] (blue, on LR04 age scale) and relative sea level derived from Red Sea data [] (red, on speleothem age scale). (bottom) EPICA Dome C δD [] (representing Antarctic temperature, blue) and CH] (red) on the AICC2012 age scale. Note that although there are not large age offsets, this plot should not be used to determine phasing between parameters as no attempt at synchronizing the three age scales used has been made.

To illustrate the terminology, we use the last interglacial (MIS 5e, identified with the Eemian in NW Europe and the Sangamonian in North America) as an example (Figure 7 ). The figure shows benthic δ 18 O, relative sea level as estimated by the “Red Sea method,” and Antarctic deuterium (temperature proxy, representing an example of a Southern Hemisphere (SH) signal) and methane concentration (representing a signal based in the NH).

The following sections will consider key aspects of interglacials: their strength, timing, shape, variability, and length. However, it is first important to have an overview of the collection of interglacials occurring in the last 800 ka, and of their anatomy and characteristics, in order to introduce the terminology we use later.

6 The Intensity of Interglacials

Numerous records from the marine and terrestrial (including ice core) realm show a pattern in which the basic glacial‐interglacial sequence is seen and in which peaks and troughs can easily be aligned (but not necessarily synchronized) with marine isotope stages (MIS) identified in marine benthic isotope records, such as the LR04 stack [Lisiecki and Raymo, 2005]. This indicates that, however, we define them, interglacial states are global phenomena, expressed in almost every type of paleoenvironmental signal at every location. (Some tropical records are an exception to this: in particular, the Chinese speleothem record [Cheng et al., 2012] expresses strong precessional variability, but the interglacials (as we have defined them) do not stand out as unusual.) However, the intensity and precise timing of that expression varies between records and the different spatial and environmental patterns seen in each of them are important evidence to test against our understanding of the mechanisms behind them. In this section, we focus on the intensity or strength of interglacials.

In some cases we can relate the proxy record we use with a single climate parameter (for example, the ice core measurements of CO 2 or Mg/Ca in marine records representing near surface temperature); in others (for example, the biosilica content of sediments in Lake Baikal) the association is more complex but still the glacial‐interglacial pattern is seen. In each record one can define the intensity of the interglacial based on the extent to which the measured parameter changes in the interglacial direction (higher CO 2 , more arboreal pollen, less dust, etc.): this allows us to use a range of proxies without necessarily having a clear or unique interpretation of its meaning. In assessing the intensity of each interglacial, we therefore have a range of measures representing different aspects of the Earth system and different geographical regions.

Comparing the identical time period in each record would allow us to build the kind of snapshot climate reconstructions that are useful for comparison with most climate model outputs. However, the accuracy of the chronology in many records is variable, and our ability to synchronize records in any of the earlier interglacials is too low to allow such an approach in practice. An average over the entire interglacial would probably be the most intuitive approach, allowing us to assess the integrated strength of an interglacial without assuming that the strength occurred simultaneously at every location. However, the period of averaging, and hence the result, is very dependent on the definitions used to determine the timing of the start and end of an interglacial, as discussed in section 7.5. In addition, this method carries an underlying assumption that there is a stable interglacial period over which it is possible to define stationary statistics, whereas in some parameters and interglacials no such period exists: rather, values approach a peak and immediately start to descend toward glacial values again. Using the maximum value obtained during a particular interglacial puts focus on what may be a short anomalous period caused by local factors. This approach also suffers from the fact that we have records of varying resolution available, implying that we could find a maximum that is resolved in one record and miss it because it is unresolved in another. We must also be very aware that the maximum value may occur at different dates in different records, so that comparisons must not be used incorrectly. Nonetheless, it is very easy to define the maximum value objectively, it represents the value that the human eye is drawn to and is of environmental importance because it is the most intense condition that other aspects of the system (including ecological ones) have had to endure. Finally, we could assess the amplitude of change over the termination. This has the advantage that it can be defined statistically and pinned to a particular time period in which the forcing may be determined. It shares with the maximum value criterion the disadvantage that termination may not be simultaneous in all records. Additionally, it is sensitive to the magnitude of the preceding glaciation and places emphasis on only one part of the interglacial, reducing its relevance for aspects such as sea level and ecological impact. A further issue to consider is whether one defines the intensity based on the maximum value achieved, or on the average over the entire interglacial or over a specified period. There are advantages and disadvantages to each approach:

6.1 Methodology In this paper we will follow and build on the approach and compilation used in earlier work [Lang and Wolff, 2011]. To ensure that we are comparing the same marine isotopic substage (but with no expectation that we have synchronized records to within a few kiloannum), marine records are all aligned to the LR04 age model [Lisiecki and Raymo, 2005]; ice core records are aligned to the AICC2012 age scale [Bazin et al., 2013]; terrestrial records are on their own age scale, which often implicitly uses a similar tuning target as LR04. We then take the maximum (or minimum, as appropriate) value that occurs within the interglacial (i.e., for practical reasons we use method 3 above): the only exception is that where resolution is better than 1000 years, we take the maximum value after applying a 1000 year smoothing, to avoid the influence of noisy outliers. More complex methods were attempted in previous work [Lang and Wolff, 2011], but as they gave similar results, we have decided to take the simplest approach here. It is important to note that the resulting indices or maps of interglacial intensity do not represent snapshots of a single time slice, since the maximum value does not always occur at the same date in different records. In some cases we will note where maxima occur particularly and noticeably asynchronously in different records, but will leave a more general discussion of the trends during interglacials to section 7.2. Records must cover the entire 800 ka period to allow a consistent comparison to be made between interglacials. We have relaxed this criterion for two 700 ka marine records that supply information in a region (western South Pacific) that is otherwise poorly represented. Records must be continuous, with no hiatuses or data gaps, at least during interglacials. The resolution must be better than 3 ka. However, in order to include a reasonable geographical range of SSTs, we relaxed this rule to just over 4 ka in some cases for such records. Data have been included only if they meet the following criteria:

6.2 Data Compilation There are a few differences (Figures 9 and 10) from a recent compilation [Lang and Wolff, 2011], on which we have built. First, a few additional marine records of SST have been included, allowing better, geographical coverage. There are now 17 SST records (of which two fall short of the full 800 ka period). Antarctic temperature is still represented by only one record (EPICA Dome C): although a second long ice core record, covering 720 ka from Dome F, distant from Dome C on the East Antarctic plateau, exists, the data are as yet unpublished. However, we note that the shorter published record [Kawamura et al., 2007; Sime et al., 2009] suggests that the relative intensity of different interglacials at Dome F is similar (though not identical) to that at Dome C and that this likely represents an Antarctic‐wide pattern. Figure 9 Open in figure viewer PowerPoint Maximum values for each interglacial for global and terrestrial indicators. Figure 10 Open in figure viewer PowerPoint Maximum values for each interglacial for temperature records. A second difference is that in the present discussion we have more strongly emphasized temperature (and especially SST) records; the earlier paper [Lang and Wolff, 2011] devoted a lot of attention to benthic and planktonic marine isotope records. While that study did show some minor differences in pattern between sites, it was clear that the common overprint (due to ice volume and for benthic records, deepwater temperature) on such records is sufficiently strong that they basically tell a single story. Here we therefore only display the LR04 stack but now supplemented with a new record that splits the isotopic signal at one site into a deepwater temperature (based on Mg/Ca) and seawater δ18O (ice volume) component [Elderfield et al., 2012]. In the terrestrial realm, the previous compilation [Lang and Wolff, 2011] included stacked loess records from China, biogenic silica from Lake Baikal and arboreal tree pollen from Tenaghi Philippon. Here we have added data for the lacustrine sediments of Funza in South America and for Lake El'gygytgyn in the Russian Arctic. For Lake El'gygytgyn [Melles et al., 2012], temperature data have been deduced from pollen for some periods in the record but are not yet available through 800 ka, so we are forced to use the Si/Ti data as an indicator of interglacial strength without attaching any particular climatic interpretation to them. For Funza, a new age model [Torres et al., 2013] has facilitated use of the data, but it remains difficult to know which data provide a climate signal. While a relationship is observed between arboreal pollen fraction, tree line altitude and temperature [Hooghiemstra and Ran, 1994], total arboreal pollen percentages are certainly biased by the fact that Quercus arrived in the region only at the mid‐Brunhes. It is not obvious how to compare data before and after this arrival: we have calculated arboreal pollen percentage after removing Quercus but urge caution in interpreting the resulting record, particularly across the MB. The pollen record from Lake Van (Turkey) offers a chance to discuss conditions in south western Asia but only extends to MIS 15b. We therefore discuss it but do not include it in Figure 9. For the Chinese loess record, the earlier compilation [Lang and Wolff, 2011] used magnetic susceptibility and mass accumulation rate: both parameters showed a pattern of glacial‐interglacial variability that became indistinct in parts of the last 800 ka, and it is not clear that they gave a meaningful indication of interglacial intensity. It has been argued that indices of chemical weathering may be more useful, being controlled largely by the summer monsoon related moisture and temperature [Guo et al., 2009]. Here we have therefore used the ratio of free to total Fe 2 O 3 (Fed/Fet, representing weathering) and the redness index (believed to reflect soil temperature) at Xifeng in northern China [Guo et al., 2009]. However, we have additionally used a new frequency‐dependent record of magnetic susceptibility [Hao et al., 2012] that does show a glacial‐interglacial pattern and that is interpreted as a record of East Asian summer monsoon strength. The same study also presents a grain size parameter that is interpreted as a record of winter monsoon strength. We refer to this record but do not include it in our tabular compilation because the minimum values of the parameter (indicating weakest winter monsoon) generally occur after the end of the interglacial (as judged in other records) and are therefore not directly indicative of interglacial amplitude. A further long, continuous record is available in data from lake sediments at Heqing Basin, China, where an Indian summer monsoon index was compiled [An et al., 2011]. This record shows glacial‐interglacial variability in the part of the record from 900 to 350 ka, but little pattern after that, and does not include MIS 5e or MIS 1. We therefore discuss it but have not included it in our table. A final continuous and well‐resolved 800 ka record is available from the sapropel record obtained in the eastern Mediterranean, at sites ODP 967 and 968 [Ziegler et al., 2010; Konijnendijk et al., 2014]. Following the authors, we interpret the Ti/Al ratio as an indicator for monsoon strength in central to north Africa, and we therefore treat it as a terrestrial record. Like many speleothem records, it is dominated by precessional rather than glacial‐interglacial variability. Finally, there is a small change in methodology. In the earlier work, the maximum immediately after the glacial termination was used as the intensity value. Here we simply take the highest (or lowest) value that occurs during the interglacial, making no judgment about which part of the interglacial it occurs in.

6.3 Findings—Large‐Scale Measures (Ice Volume, Deep Ocean Temperature, and CO 2 ) We start by considering some measures (Figure 9) that have a wide significance as an indicator, forcing and/or feedback at global scale: benthic oxygen isotopes, deepwater temperature (using site ODP1123, east of New Zealand), seawater oxygen isotope content (which is equivalent to global ice volume or sea level), and atmospheric CO 2 . In the LR04 benthic oxygen isotope stack, MIS 5e, 11c, 1, and 9e (in that order) are considerably stronger than the remaining interglacials. MIS 7e and 7c are of comparable strength to all the interglacials that occurred before 450 ka ago, with MIS 17 as the weakest. Although there are small variations in rank order, this general pattern is confirmed in each of the numerous benthic and planktonic isotope records that were examined earlier [Lang and Wolff, 2011]: MIS 5e and 11c are consistently the strongest interglacials, with MIS 1 and 9e just behind, and MIS 17 and 13 are most commonly the weakest interglacial in the last 800 ka. Note that in the earlier compilation the values for MIS 13 were taken from MIS 13c, making it stand out as a particularly cold stage; in the new compilation, the inclusion of stronger values in MIS 13a, which is considered here as part of the continuing MIS 13 interglacial, means that it is not always the weakest. Although the benthic isotope record from ODP Site 1123 has very similar intensities to that of the LR04 stack, the pattern becomes more complex when the benthic isotopes are split into their two components, deepwater temperature and seawater δ18O [Elderfield et al., 2012]. For seawater δ18O [Elderfield et al., 2012], MIS 11 stands out as having the lowest value (implying lowest ice volume), with MIS 9e and, surprisingly, MIS 17 next in rank. Most other interglacials (whether before or after 450 ka) show similar minimum values, comparable to those of the Holocene. One interpretation is that this is a threshold effect: all interglacials were warm enough to lose most NH ice volume outside Greenland, and higher values (for example, in MIS 11) imply further loss from Antarctica and/or Greenland. However, the imprecision of the ice volume calculation (using Mg/Ca values to correct the benthic δ18O) means that the latter inference is not robust; this is illustrated by the relatively low inferred sea level for MIS 5e, for which abundant other evidence suggests a level higher than today [Thompson and Goldstein, 2006; Dutton and Lambeck, 2012; Kopp et al., 2013]. Other attempts to partition benthic δ18O, such that sea level/ice volume can be derived from it [Bintanja et al., 2005; Siddall et al., 2010], used only a single measurement series to infer two outputs and therefore had to make assumptions that were not necessary in the work of Elderfield et al. [2012]. However, the latter suffers from the fact that the joint precision of the derived seawater δ18O is poor (±20 m for single data points). To test how robust the derived interglacials strengths are, we can look at independent estimates of sea level, albeit not covering 800 ka continuously. Continuous measurements over the last 500 ka using the “Red Sea” method [Rohling et al., 2009] suggest that MIS 5e has the highest sea level in that period; MIS 9e does not show high values, and even MIS 11c is not unusual compared to the other interglacials. Similar results emerge from globally distributed studies of foraminifera δ18O [McManus et al., 1999; Hodell et al., 2000; King and Howard, 2000; McManus et al., 2003]. Estimates using coral terraces and other indicators certainly suggest that sea level was higher than today in MIS 5e [Kopp et al., 2009; Dutton and Lambeck, 2012]. Some less direct evidence suggests the same for MIS 11 [Raymo and Mitrovica, 2012], although there continues to be more debate about sea level in this earlier interglacial [e.g., Hearty et al., 1999; Olson and Hearty, 2009; Bowen, 2010; Raymo and Mitrovica, 2012]. One way to explain particularly low ice volume/high sea level during interglacials would be through melting of all or part of the Greenland ice sheet. There is only indirect evidence about this: measurements of pollen in a marine core off south Greenland [de Vernal and Hillaire‐Marcel, 2008] suggest a much reduced ice sheet particularly in MIS 11 and, more surprisingly, in MIS 13a. The result for MIS 11 is supported by geochemical provenancing of silt deposited south of the Greenland margin, suggesting near complete deglaciation of southern Greenland [Reyes et al., 2014]. Relatively, pollen concentrations in MIS 5e were only slightly raised, and other interglacials showed low values similar to those of today. The MIS 5e result is also supported by the geochemistry of silt, which in this case suggests that only limited retreat occurred in southern Greenland [Colville et al., 2011]. In summary, ice volume was not very different in each interglacial of the last 800 ka, and there is no particular evidence for a change at the MB. However, there appears to be relatively strong evidence for somewhat higher sea level and lower ice volume during MIS 5e and some evidence suggesting the same for MIS 11. The low ice volume deduced for MIS 9 [Elderfield et al., 2012] is not yet supported by other studies, while the reduced Greenland ice sheet inferred in MIS 13 [de Vernal and Hillaire‐Marcel, 2008] does not appear to have led to high sea level or low overall ice volume. For deepwater temperature, where there are values deduced directly from Mg/Ca ratios at a South Pacific site [Elderfield et al., 2012] MIS 11c, 7e, 5e, 1, and 9e have significantly warmer values than MIS 7c or any of the interglacials before 450 ka ago. We note that a different ranking would be derived for the only other site where bottom water temperature has been measured, DSDP 607 [Sosdian and Rosenthal, 2009] in the North Atlantic. As well as representing a different water mass, the latter study used epifaunal rather than infaunal benthic foraminifera, and the reliability of deepwater temperature estimates in this case has been questioned [Yu and Broecker, 2010] and defended [Sosdian and Rosenthal, 2010]. The same interglacials emerge as the highest in atmospheric CO 2 , although the exact rank order is different, with MIS 9e showing the highest value at nearly 300 ppm. All of MIS 1, 5e, 7e, 9e, and 11c exceed 275 ppm, while none of MIS 7c, 13, 15a, 15e, or 17c exceed 260 ppm. MIS 19c stands out as an interglacial in the earlier part of the record that has a moderately high concentration in the newly corrected compilation [Bereiter et al., 2015], while MIS 17c stands out in the case of CO 2 for its particularly low values, reaching a maximum of 244 ppm.

6.4 Findings—SST and Surface Air Temperatures We now consider temperature estimates from around the world (Figures 10 and 11), considering SSTs derived from Mg/Ca, alkenones or foraminifer assemblages from marine sediments, and surface air temperatures (SAT) derived from water isotopes for Antarctic ice. A pattern that seems consistent across most of the world is of particular warmth during MIS 5e and to a lesser extent MIS 11c, and of particularly cold conditions during MIS 13. There are apparent exceptions to this first‐order rule; for example, at ODP1123 in the SW Pacific, MIS 5e is relatively cool and MIS 13a not unusually so. Nonetheless high temperatures are observed, e.g., for MIS 5e in Antarctica (EDC), the South Atlantic (ODP1090), SE Pacific (GeoB3388‐1 and PS75/034‐2), Eastern equatorial Pacific (ODP846), western equatorial Pacific (ODP806B, ODP1146, and MD97‐2140), NE Pacific (ODP1020), and the North Atlantic (ODP982 and ODP607). Only at site ODP552 (which is close to ODP982 in the Atlantic), and at site ODP1123 mentioned above, does MIS 5e rank lower. MIS 5e is also an exceptionally warm interglacial in records from regions that are not represented in our compilation because they do not meet our criteria: for example, MIS 5e has the warmest SSTs of any interglacial of the last 600 ka [Martrat et al., 2007; Rodrigues et al., 2011] in records from the Portuguese Margin on the east of the North Atlantic, while MIS 11c, 5e, and 9e stand out as considerably warmer than other interglacials [Caley et al., 2011] in a record from the east coast of South Africa (MD96‐2048, Indian Ocean) that lacks resolution for a quantitative analysis. Figure 11 Open in figure viewer PowerPoint Maps showing peak interglacial SSTs and EDC temperature for MIS 5e, 7e, 11c, and 13a. For each location the colors follow those in Figure 10 , i.e., the site has a dark red dot in the warmest interglacial, dark blue in the coldest. A similar global intensity seems to be observed for MIS 11c, but there are some notable exceptions in the table as well as in SST records that do not reach back 800 ka [e.g., McManus et al., 1999; Martrat et al., 2007] and in some isotopic records [Helmke and Bauch, 2003]. The only exceptions to the cold conditions of MIS 13 in the continuous records we used occur at a couple of tropical sites where the differences between interglacials are anyway small. However we note that some shorter SST records show a relatively warm MIS 13 [e.g., McManus et al., 1999], and some areas of the world have little or no data. Other interglacials show warmth at only some sites. MIS 9e is the warmest interglacial of all in Antarctica, mirroring the high CO 2 concentration. Similar warmth is also seen at some other sites (such as site ODP1123 in the SW Pacific, site ODP1146 in the eastern equatorial Pacific, and site ODP982 in the north Atlantic). However, there is no consistent geographical pattern to warmth in this interglacial and because this interglacial has an exceptionally short peak, we must exercise caution in comparing records with low resolution. MIS 7e, though warm in Antarctica and at site ODP1123 in the SW Pacific, is otherwise cool, comparable to MIS 15a and 15e, 17c, and 19c. At least in the Northern Hemisphere, MIS 7c is generally warmer than MIS 7e.

6.5 Findings—Terrestrial Records There are few terrestrial records we can use in this synthesis. Ice core CH 4 may be viewed as a signal integrating parts of the NH terrestrial biosphere. It shows a pattern in which MIS 9e is strongest, while MIS 11c, 19c, 1, and 5e stand significantly (more than 25 ppb higher concentration) stronger than MIS 7e, 15e, and 17c. MIS 13a again emerges as the weakest interglacial (Figure 6a). For arboreal pollen at Tenaghi Philippon in Greece [Tzedakis et al., 2006], MIS 11c and 1 are strongest and 17c and 19c are weakest. Going eastward, arboreal pollen at Lake Van (Turkey, not included in Figure 9) is not especially prominent in MIS 11, and instead MIS 5e (followed by MIS 9 and MIS 7c) appears particularly strong. For biogenic silica in Lake Baikal [Prokopenko et al., 2006], MIS 11c and 15a are the strongest interglacials and MIS 13a and 7e the weakest. For Si/Ti at Lake El'gygytgyn in the Russian Arctic [Melles et al., 2012], MIS 11c is hugely stronger than any other interglacial. We counsel caution in interpreting the Funza [Torres et al., 2013] (arboreal pollen minus Quercus) data because of the changing structure of the tree population but have presented the data for completeness. With the Xifeng loess indices used here, MIS 13a, 11c, and 5e are particularly intense [Guo et al., 2009]. The unusual strength of MIS 13 in the East Asian monsoon region is supported by the magnetic susceptibility stack (summer monsoon proxy, strong in interglacials, strongest in MIS 13) [Hao et al., 2012]; the winter monsoon proxy (not shown in the table; weakest winter monsoon in interglacials and inceptions) also reaches its most extreme (weak winter monsoon) values during MIS 13. The intensity of MIS 13a in these records contrasts with its weakness in the methane record, also assumed to have a strong East Asian control. The Indian monsoon index reported from lake sediments at Heqing [An et al., 2011] is hard to interpret in terms of interglacial strength. The Indian summer monsoon index was, at face value, strongest in MIS 15e, and very weak in all interglacials since MIS 10. However, the authors of this study interpreted the variability in terms of a combination of Northern and Southern Hemisphere controls that lead to a complex pattern that is not expected to relate to other climate indices in any simple way. Interestingly, the authors also present [An et al., 2011] a winter temperature proxy (percent Tsuga) for the site; in this proxy, MIS 11c is the warmest interglacial, and MIS 13a, MIS 7e, and MIS 7c are the coolest, more in line with the pattern seen in marine temperatures. The Mediterranean sapropel record (Ti/Al at ODP sites 967/968) suggests that rainfall in the African monsoonal belt was particularly intense in early MIS 13a, and particularly weak in MIS 19c. However, we emphasize that the record is dominated by precession and also shows intense monsoonal peaks in glacial periods, so this record cannot really be considered to be providing a measure of interglacial intensity.

6.6 Synthesis of Interglacial Intensity The main conclusions do not differ from those of an earlier synthesis. Across the whole range of records, there is a tendency for more intense interglacials in the period after 450 ka compared to that from 800 to 450 ka. However, as others have emphasized [Candy et al., 2010; Candy and McClymont, 2013] this is by no means a general rule: MIS 7c and in most cases 7e is often of similar strength to earlier interglacials and each of MIS 13a, 15a and 15e, 17c, and 19c show strength comparable to the later interglacials in one or more important records (MIS 13a shows a strong loess weathering signal, MIS 15a and 15e are particularly strong at Baikal, MIS 17c is particularly strong in seawater δ18O and in two North Atlantic SST records, and MIS 19c shows high CH 4 values). For the midlatitude Atlantic, it has been argued, using a range of shorter SST records along with British terrestrial evidence that there is no significant shift even in mean interglacial strength at 450 ka [Candy and McClymont, 2013], though more data are needed to confirm whether this is a true regional finding. In any case, it can be agreed that there is not an exclusive step change for most properties at the MB (mid‐Brunhes), as MIS 7e (and certainly MIS 7c) show a widespread weakness that is more characteristic of the pre‐MB events. If we exclude MIS 7c, then a step change to persistently stronger values after 450 ka can be inferred only for CO 2 , Antarctic temperature and deepwater temperature. As an example, for CO 2 , the new interglacial range after the MB (279–299 ppm), excluding MIS 7 ac, is about 30 ppm higher than that seen before it (244–269 ppm). The general strength of MIS 5e and the widespread weakness of MIS 13a are of almost global nature, extending to temperatures nearly everywhere. A very strong MIS 5e and very weak MIS 13a are also observed in the first attempt at a global SST stack [Shakun et al., 2015]. The one characteristic in which MIS 13 shows unusual strength is the summer monsoon recorded in Chinese loess. The patchy nature of warmth in MIS 9e may arise in part from the short length of this interglacial compared to the resolution of many records. Its overwhelming strength in CO 2 , CH 4 , Antarctic temperature, and in the low δ18O seawater values suggest that this interglacial may warrant further detailed study. A final point is that MIS 15a and 7c, which we newly introduce as interglacials, stand comparison in all respects with their peers. MIS 15a and 15e are of comparable strength in most records. MIS 7c is weaker than 7e in many but not all records, but still as strong as MIS 13a.

6.7 Understanding Interglacial Intensity The mechanisms for the observed pattern of interglacial intensities can be explored in more detail by model simulations. Conceptual models are able to some extent to simulate the sea level amplitude of interglacials [Parrenin and Paillard, 2003], but more physically based models must be used to understand the mechanisms. However, few transient simulations designed to explore this issue have not yet been carried out, except for MIS 5e (discussed later), so that studies to date have generally simply used peak forcing in snapshot experiments. The individual contributions of insolation and CO 2 to the interglacial climates of the past 800 ka were quantified by Yin and Berger [2012] through snapshot simulations with the model LOVECLIM (and did not include MIS 7a‐7c and 15e). They carried out simulations using astronomical parameters at the times when NH summer occurs at perihelion just preceding the interglacial peaks of the LR04 stack, combined with the maximum GHG concentrations occurring around the isotopic peak. Their simulations show that, globally, MIS 9e, 5e, and 11c are indeed expected to be the warmest interglacials, MIS 1 and 19c are very similar and slightly above the average of the last nine interglacials, MIS 7e and 15a should be cool interglacials, and MIS 13a and 17c the coolest. This is in broad agreement with the qualitative conclusions of the data. However, the differences in their intensity result from different contributions of insolation and CO 2 . According to the model [Yin and Berger, 2012], the insolation and CO 2 of MIS 5e and 9e both contribute to a warming, but MIS 11 is a warm interglacial only because of its high CO 2 concentration. The insolation and CO 2 of MIS 13a and 17c both contribute to a cooling. MIS 7e is a cool interglacial because its insolation‐induced cooling beats its CO 2 ‐induced warming, and MIS 15a is due to the reverse situation. The relative contributions of insolation and CO 2 to the modeled climate are different from one region to another and from one climatic variable to another [Yin and Berger, 2012]. The modeled anomalies of the annual mean temperatures averaged over the globe and over the southern high latitudes are mainly controlled by CO 2 and perhaps obliquity, whereas over the northern high latitudes, regional insolation plays a dominant role. In low latitudes, insolation (and precession) definitely dominates changes in monsoon precipitation and therefore in vegetation. For example, due to high eccentricity, MIS 15a is the simulated wettest and most vegetated interglacial at least at low latitudes, followed by MIS 5e and MIS 17. The relative roles of eccentricity, precession, and obliquity on temperature, precipitation, sea ice, and vegetation at different latitudes have been assessed and quantified using snapshot [Yin and Berger, 2012] and transient simulations [Yin and Berger, 2015] and is shown to vary between climatic variables and latitudes. The varying importance of insolation, CO 2 , and the three astronomical parameters among variables and latitudes may explain at least partly the regional and temporal diversity of the interglacials. Herold et al., 2012 2 for five warm interglacials (MIS 1, 5e, 9e, 11c, and 19c) used the ESM CCSM3. Their results show that the greatest variation between these interglacials occurs in sea ice margins and in the regions where the insolation difference is the largest. At northern middle latitudes, large insolation variations and extensive land masses lead to large temperature differences between the interglacials during boreal summer. However, the mean annual temperature exhibits relatively little change over most of the continents but shows large variation in far North Pacific, Arctic, and around Wilkes Land. These model results reveal the complex interplay between insolation and CO 2 in different regions and for different variables. However, when comparing them with proxy reconstructions, one should keep in mind that Ice sheets were fixed in these simulations, and therefore, the possible impact of ice sheet changes were not taken into account. Although the difference in minimum ice sheet size between the interglacials might be small according to the sea water δ18O [Elderfield et al., 2012 7) might still be strongly influenced by both ice sheets and freshwater inputs. All the experiments described are snapshot simulations; therefore, the possible impact of interglacial duration and deglacial history on intensity is not taken into account, and they do not represent the peak insolation forcing (Figure 5) at particular latitudes. The climate response to insolation change has strong seasonal, as well as regional, dependence. It is therefore essential to consider the seasonality of the signals recorded in proxies before comparing them with modeling results. In addition to EMIC studies, a similar intercomparison [] of the climate response to the combined effect of insolation and COfor five warm interglacials (MIS 1, 5e, 9e, 11c, and 19c) used the ESM CCSM3. Their results show that the greatest variation between these interglacials occurs in sea ice margins and in the regions where the insolation difference is the largest. At northern middle latitudes, large insolation variations and extensive land masses lead to large temperature differences between the interglacials during boreal summer. However, the mean annual temperature exhibits relatively little change over most of the continents but shows large variation in far North Pacific, Arctic, and around Wilkes Land. These model results reveal the complex interplay between insolation and COin different regions and for different variables. However, when comparing them with proxy reconstructions, one should keep in mind that As far as the MB is concerned, one modeling study [Yin and Berger, 2012] shows that an MB clearly appears in the modeled variables dominated by CO 2 (such as the global annual mean temperature and the southern high‐latitude temperature), acting particularly in boreal winter and the SH [Yin and Berger, 2010]. There is no clear MB in modeled precipitation, vegetation, and the northern high‐latitude temperature which are all dominated by insolation [Yin and Berger, 2012]. A related study [Yin, 2013] suggests that insolation alone can induce stronger southern westerly winds and weaker stratification during several of the pre‐MB interglacials (MIS 13a, 15a, and 17c but also 7e). These lead to stronger Southern Ocean ventilation and a cooler deep ocean. A cooler pre‐MB deep ocean is observed in the data [Elderfield et al., 2012]; the model simulations [Yin, 2013] suggest that this cooling results from a combination of insolation and lower CO 2 concentration. This work implies that the apparent shift at 450 ka resulted from a series of individual interglacial responses to various combinations of insolation conditions. To the extent that there is a change in behavior at 450 ka, it is essential mainly to understand the change in peak CO 2 concentrations. While these model experiments are a promising start, the one interglacial (MIS 5e) for which multiple experiments are available suggests that we should interpret the results with caution. Within the Paleoclimate Model Intercomparison Project (PMIP3), time slice climate simulations have been run by 13 modeling groups with a hierarchy of climate models (AOGCMs and EMICs, different resolutions) forced with the astronomical and GHG changes of 130–125 ka [Lunt et al., 2013]. These simulations can be compared to global quantitative data syntheses of maximum annual surface temperatures for MIS 5e [Turney and Jones, 2010; McKay et al., 2011]. A caveat is that these data syntheses implicitly treat the warmest phases as globally synchronous [Cortese et al., 2013], and this emphasizes the need to reconstruct individual time slices within the interglacial [Capron et al., 2014]. Much higher mean annual temperatures than preindustrial are reconstructed for Europe, the Arctic coastal regions of Alaska and Siberia, Greenland, and the Atlantic Ocean north of 40°N. The multimodel mean simulates the reconstructed pattern of NH annual warming [Bakker et al., 2013]. The magnitude