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Editor's Introduction

A reconstruction of regional and global temperature for the past 11,300 years

annotated by
Rich Fiorella

Today’s fossil fuel use drives a global climate experiment: how much will global temperatures rise as a result of carbon emissions? To answer this question, scientists need to understand processes that cause global temperatures to vary in the absence of emissions. However, measured temperature records have short histories. To extend these records further into the past, we must use proxies that record local temperatures indirectly. Scientists in this study compile proxy records to provide a global picture of temperatures for the past 11,300 years. Global cooling of ~0.7°C between 5000 and 200 years ago follows early Holocene warmth (10,000 to 5000 years ago) due to subtle, slow changes in Earth's orbit. Yet last century's temperatures increased from close to their lowest Holocene values to close to their highest. Many other studies show that this recent warming results from fossil fuel combustion and industrialization. Finally, they estimate temperatures for the end of the 21st century will be greater than those of any period from the Holocene.

Paper Details

Original title
A reconstruction of regional and global temperature for the past 11,300 years
Authors
Shaun Marcott
Original publication date
Reference
Vol. 339 no. 6124 pp. 1198-1201
Issue name
Science
DOI
10.1126/science.1228026
Topics
Earth and environmental science

Abstract

Surface temperature reconstructions of the past 1500 years suggest that recent warming is unprecedented in that time. Here we provide a broader perspective by reconstructing regional and global temperature anomalies for the past 11,300 years from 73 globally distributed records. Early Holocene (10,000 to 5000 years ago) warmth is followed by ~0.7°C cooling through the middle to late Holocene (<5000 years ago), culminating in the coolest temperatures of the Holocene during the Little Ice Age, about 200 years ago. This cooling is largely associated with ~2°C change in the North Atlantic. Current global temperatures of the past decade have not yet exceeded peak interglacial values but are warmer than during ~75% of the Holocene temperature history. Intergovernmental Panel on Climate Change model projections for 2100 exceed the full distribution of Holocene temperature under all plausible greenhouse gas emission scenarios.

Report

Placing present climate into a historical perspective beyond the instrumental record is important for distinguishing anthropogenic influences on climate from natural variability (1). Proxy-based temperature reconstructions of the past 1500 years suggest that the warming of the past few decades is unusual relative to pre-anthropogenic variations (2, 3), but whether recent warming is anomalous relative to variability over the entirety of the Holocene interglaciation (the past 11,500 years) (4) has yet to be established.

The 73 globally distributed temperature records used in our analysis are based on a variety of paleotemperature proxies and have sampling resolutions ranging from 20 to 500 years, with a median resolution of 120 years (5). We account for chronologic and proxy calibration uncertainties with a Monte Carlo–based randomization scheme (6). Our data set exhibits several important strengths, as well as limitations, as compared to global and hemispheric reconstructions of the past 1500 years (2, 3, 7, 8). For example, whereas reconstructions of the past millennium rapidly lose data coverage with age, our coverage increases with age (Fig. 1, G and H). Published reconstructions of the past millennium are largely based on tree rings and may underestimate low-frequency (multicentury-to-millennial) variability because of uncertainty in detrending (9) [although progress is being made on this front (10)], whereas our lower-resolution records are well suited for reconstructing longer-term changes. Terrestrial records dominate reconstructions of the past millennium, whereas our stack is largely derived from marine archives (~80%). Unlike the reconstructions of the past millennium, our proxy data are converted quantitatively to temperature before stacking, using independent core-top or laboratory-culture calibrations with no post-hoc adjustments in variability.

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Fig. 1.  Comparison of different methods and reconstructions of global and hemispheric temperature anomalies. (A and B) Globally stacked temperature anomalies for the 5° × 5° area-weighted mean calculation (purple line) with its 1σ uncertainty (blue band) and Mann et al.'s global CRU-EIV composite mean temperature (dark gray line) with their uncertainty (light gray band). (C and D) Global temperature anomalies stacked using several methods (Standard and Standard5x5Grid; 30x30Grid; 10-lat: Arithmetic mean calculation, area-weighted with a 5° × 5° grid, area-weighted with a 30° × 30° grid, and area-weighted using 10° latitude bins, respectively; RegEM and RegEM5x5Grid: Regularized expectation maximization algorithm-infilled arithmetic mean and 5° × 5° area-weighted). The gray shading [50% Jackknife (Jack50)] represents the 1σ envelope when randomly leaving 50% of the records out during each Monte Carlo mean calculation. Uncertainties shown are 1σ for each of the methods. (E and F) Published temperature anomaly reconstructions that have been smoothed with a 100-year centered running mean, Mann08Global (2), Mann08NH (2), Moberg05 (3), WA07 (8), Huange04 (36), and plotted with our global temperature stacks [blue band as in (A)]. The temperature anomalies for all the records are referenced to the 1961–1990 instrumental mean. (G and H) Number of records used to construct the Holocene global temperature stack through time (orange line) and Mann et al.'s (2) reconstruction (gold vertical bars). Note they axis break at 100. The latitudinal distribution of Holocene records (gray horizontal bars) through time is shown. (I and J) Number of age control points (e.g., 14C dates) that constrain the time series through time.

Holocene temperature reconstructions

Panels A to F show the researchers’ Holocene temperature reconstructions in different ways.

The left column shows only the last 2000 years, whereas the right column shows the full 11,300-year reconstruction presented in this paper.

The temperatures are shown as the difference between average temperatures from 1961 to 1990 and the reconstructed time periods. 

The researchers’ stacked record shows temperatures that were warmer than this reference period between ~11,000 years ago and ~2000 years ago, and cooler than this reference period prior to ~11,000 years ago and after ~2000 years ago (prior to the Industrial Revolution).

In panels C and D, the researchers show how their results vary based on different methods of calculating the global temperature anomaly—the different methods result in different magnitude of temperature changes, particularly at the most recent end of the data set, but generally preserve the overall temperature trend.

Comparison to prior temperature reconstructions 

In A and B, the researchers compare their results with a previous study—Mann et al. (2008)—that reconstructed the past 1500 years.

Their record broadly captures the trend of the Mann et al. record, but appears to be significantly less variable as the stacked temperature record of this study is unable to preserve climate variability occurring on timescales of less than 300 years.

In panels E and F, the researchers compare their results with an additional four temperature reconstructions ranging in length from the past ~400 years to the past ~1900 years.

Again, though the magnitudes of the temperatures vary between the different reconstructions, they tend to show the same general pattern of relatively stable temperatures for the past 2000 years, with a comparatively cool period lasting from ~750 years ago until the Industrial Revolution.

Record Density

Panels G and H show the density and total number of records used to reconstruct the stacked temperature record of this paper, as well as the number of records used in the Mann et al. (2008) temperature reconstruction for the past 2000 years.

The number of records from each latitude is variable through time, and high latitudes, particularly the South Pole, have the lowest number of records (grey bars).

In general, though, the number of records in the Marcott et al. study increases with time for the first 2000 years and remains relatively stable throughout the Holocene (orange line), whereas the number of records in the Mann et al. study decreases through time (gold bars, and note the break in the y-axis relevant to these records).

This trend indicates that the statistical uncertainty resulting from the size of the data set is relatively constant for the Marcott et al. study, but increases with the length of the reconstruction in the Mann et al. study.

Age Control Points

In figures I and J, the researchers show the number of “age control points” from the proxy records they used to build the temperature stack.

Researchers must use tracers to constrain how old their samples are, and therefore the number of age control points here is related to how well the researchers know the time period that their temperature reconstructions correspond to.

For periods with a greater number of age control points, they have more confidence that those samples correspond to the proper time period, whereas periods with fewer age control points are more uncertain.

We took the 5° × 5° area-weighted mean of the 73 records to develop a global temperature stack for the Holocene (referred to as the Standard5×5 reconstruction) (Fig. 1, A and B). To compare our Standard 5×5 reconstruction with modern climatology, we aligned the stack's mean for the interval 510 to 1450 yr B.P. (where yr B.P. is years before 1950 CE) with the same interval's mean of the global Climate Research Unit error-in-variables (CRU-EIV) composite temperature record (2), which is, in turn, referenced to the 1961–1990 CE instrumental mean (Fig. 1A). We then assessed the sensitivity of the temperature reconstruction to several averaging schemes, including an arithmetic mean of the data sets, a 30° × 30° area-weighted mean, a 10° latitudinal weighted mean, and a calculation of 1000 jackknifed stacks that randomly exclude 50% of the records in each realization (Fig. 1, C and D, and fig. S4). Although some differences exist at the centennial scale among the various methods (Fig. 1, C and D), they are small (<0.2°C) for most of the reconstructions, well within the uncertainties of our Standard5x5reconstruction, and do not affect the long-term trend in the reconstruction.

In addition to the previously mentioned averaging schemes, we also implemented the RegEM algorithm (11) to statistically infill data gaps in records not spanning the entire Holocene, which is particularly important over the past several centuries (Fig. 1G). Without filling data gaps, our Standard5×5 reconstruction (Fig. 1A) exhibits 0.6°C greater warming over the past ~60 yr B.P. (1890 to 1950 CE) than our equivalent infilled 5° × 5° area-weighted mean stack (Fig. 1, C and D). However, considering the temporal resolution of our data set and the small number of records that cover this interval (Fig. 1G), this difference is probably not robust. Before this interval, the gap-filled and unfilled methods of calculating the stacks are nearly identical (Fig. 1D).

Because the relatively low resolution and time-uncertainty of our data sets should generally suppress higher-frequency temperature variability, an important question is whether the Holocene stack adequately represents centennial- or millennial-scale variability. We evaluated this question in two ways. First, we generated a single mean zero, unit variance white-noise time series and used it in place of our 73 records. The white-noise records were then perturbed through Monte Carlo simulations using the resolution and chronological uncertainty specific to each proxy record as well as a common 1°C proxy uncertainty. We composited a Standard5x5 global stack from these synthetic records and calculated the ratio between the variances of the stack and the input white noise as a function of frequency to derive a gain function. The results suggest that at longer periods, more variability is preserved, with essentially no variability preserved at periods shorter than 300 years, ~50% preserved at 1000-year periods, and nearly all of the variability preserved for periods longer than 2000 years (figs. S17 and S18). Second, spectral analysis indicates that the variance of the Holocene proxy stack approaches that of the global CRU-EIV reconstruction of the past 1500 years (2) at millennial time scales and longer (figs. S20 and S23).

Our global temperature reconstruction for the past 1500 years is indistinguishable within uncertainty from the Mann et al. (2) reconstruction; both reconstructions document a cooling trend from a warm interval (~1500 to 1000 yr B.P.) to a cold interval (~500 to 100 yr B.P.), which is approximately equivalent to the Little Ice Age (Fig. 1A). This similarity confirms that published temperature reconstructions of the past two millennia capture long-term variability, despite their short time span (3, 12, 13). Our median estimate of this long-term cooling trend is somewhat smaller than in Mann et al. (2) though, which may reflect our bias toward marine and lower-latitude records.

The Standard5x5 reconstruction exhibits ~0.6°C of warming from the early Holocene (11,300 yr B.P.) to a temperature plateau extending from 9500 to 5500 yr B.P.. This warm interval is followed by a long-term 0.7°C cooling from 5500 to ~100 yr B.P. (Fig. 1B). Extratropical Northern Hemisphere sites (30° to 90°N), in particular from the North Atlantic sector, contribute most of the variance to the global signal; temperatures in this region decrease by ~2°C from 7000 yr B.P. to ~100 yr B.P. (Fig. 2H). By comparison, the low latitudes (30°N to 30°S) exhibit a slight warming of ~0.4°C from 11,000 to 5000 yr B.P., with temperature leveling off thereafter (Fig. 2I), whereas the extratropical Southern Hemisphere (30°S to 90°S) cooled ~0.4°C from about 11,000 to 7000 yr B.P., followed by relatively constant temperatures except for some possible strong multicentennial variability in the past 2500 years (Fig. 2J). The Southern Hemisphere is represented by fewer data sets (n = 11) than the equatorial (n= 33) and Northern Hemisphere (n = 29) regions, providing fewer constraints on characterizing the variability in our reconstruction for this region.

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Fig. 2.  Holocene climate forcings and paleoclimate records. Contour plots of (A) December, (B) June, and (C) annual mean latitudinal insolation anomalies relative to present for the past 11,500 years (36). (D) Calculated radiative forcing (28) derived from ice-core greenhouse gases (GHG) (CO2 + N2O + CH4). (E) Total solar irradiance anomalies (ΔTSI) relative to 1944–1988 CE derived from cosmogenic isotopes (31). (F and G) Proxies for the strength of the Atlantic meridional overturning circulation (37,38). (H) Volcanic sulfate flux (in kg/km2) from Antarctica (32) and volcanic sulfate concentration (in parts per billion) from Greenland (33) in 100-year bins. Both records are normalized relative to the volcanic sulfate flux/concentration associated with the Krakatoa eruption. (I to K) Zonal mean temperature reconstructions for 60° latitude bands from this study compared to speleothem (14, 15, 17–20) and Ti data (16), which are proxies for precipitation and local temperature. Speleothem data sets were smoothed with a seven-point running mean for clarity. ITCZ, Intertropical Convergence Zone; EASM, East Asian Summer Monsoon; AISM, Australian-Indonesian Summer Monsoon.

What controls Earth's temperature?

The surface temperature on Earth that we experience reflects the balance between energy arriving at Earth from the sun (light, or “shortwave radiation”) and the emission of this energy back into space from Earth through heat (or “longwave radiation”).

Normally, the energy inputs almost exactly balance the outputs; however, when these fluxes are not in balance, Earth’s temperature changes in response.

The energy balance of Earth can change in several ways. First, the amount of solar radiation arriving at the planet can change through changes in the sun’s energy inputs, or in changes in Earth’s orbit that alter how far Earth is from the sun or how light is distributed across the planet.

Second, a class of gases in the atmosphere, the greenhouse gases, can absorb longwave radiation and alter the radiation of energy to space. Increases in greenhouse gas concentrations, such as carbon dioxide and methane, increase Earth’s temperature primarily by affecting the radiation of energy to space.

Third, volcanoes can alter the radiative balance of Earth by releasing greenhouse gases as well as tiny solid particles called aerosols. Depending on their chemical composition, aerosols can either raise or lower Earth’s temperature, but the aerosols associated with large volcanic eruptions tend to lead to short-term climate cooling.

Finally, changes in ice area in high latitudes can affect Earth’s albedo and change the amount of shortwave radiation absorbed at Earth’s surface. For example, during glacial periods when ice sheets and sea ice expand toward the equator, this ice reflects more sunlight than the ground they cover, and thus this promotes further climate cooling.

How does Earth's orbit change on long time scales?

Earth’s orbit varies over thousands of years in three major ways. First, Earth’s path around the Sun varies from nearly circular to slightly elliptical in a cycle with a mean period of 100,000 years. When Earth’s orbit is circular, Earth is the same distance from the Sun the entire year while when Earth’s orbit is slightly elliptical, Earth is closer to the sun for part of the year and further from it in the other half of its orbit. The degree that Earth’s orbit diverges from a perfectly circular orbit is known as eccentricity.

Second, the axis of Earth’s rotation wobbles around in a circle with a period of ~26,000 years. This process is known as precession. In the Northern Hemisphere, summer occurs when Earth is tilted toward the sun and winter occurs when Earth is tilted away from the sun. If Earth’s orbit is not perfectly circular (i.e., eccentricity is not zero), then the portion of the orbit where Northern Hemisphere summer occurs can be at aphelion (the place in Earth’s orbit where it is closest to the sun) or perihelion (the place in Earth’s orbit where it is furthest from the sun), and thus Northern Hemisphere summers can be warmer or cooler depending on this relationship. The warmest summers occur when Earth’s orbit is highly eccentric and the Northern Hemisphere is tilted toward the sun at aphelion, while the coldest winters occur when Earth’s orbit is highly eccentric but the Northern Hemisphere is tilted away from the sun at perihelion. When Earth’s orbit is more circular, precessional changes have little influence on Earth’s radiation balance since the distance between the sun and Earth changes less during the year.

Finally, the degree that Earth points toward or away from the sun during winter or summer changes in a process known as orbital obliquity. When Earth tilts toward the sun more and obliquity is high, seasons are more extreme, with winters being colder and summers being warmer than average. Conversely, when obliquity is low, Earth tilts toward the sun less and seasonal variations are lower.

These three changes alter the solar radiation input to Earth on timescales of thousands to hundreds of thousands of years, and are thought to be a primary driver to the pacing of glacial and interglacial time periods over the past few million years. The authors show the predicted changes in top-of-atmosphere insolation as functions of time and latitude in panels A to C of this figure for December (Northern Hemisphere winter, Southern Hemisphere summer), July (Northern Hemisphere summer, Southern Hemisphere winter), and an annual average change.

Theory behind proxies used in this figure

The researchers show data from several different proxies in this figure to understand what other changes in Earth’s climate are associated with changes in temperature and insolation, and whether most of the temperature changes can be associated with orbital changes. In this panel, the rationale behind these proxies is briefly described.

Panel D—calculation of the change in radiative forcing through changes in greenhouse gas concentrations. Greenhouse gas concentrations are measured from air bubbles trapped in ice cores and are then converted to radiative forcings based on the changes in these gas concentrations.

Panel E—proxy reconstruction of the changes in solar energy input through the Holocene using cosmogenic nuclides. Cosmogenic nuclides are radioactive atoms generated at Earth’s surface by radiation from the sun, and the rate of production of cosmogenic nuclides depends on the intensity of sunlight reaching the surface.

Panels F and G are two different proxies for the strength of the Atlantic meridional overturning circulation. The 231Pa/230Th ratio is a measure of specific isotopes of protactinium and thorium found in sea water that provides an indirect measurement of how quickly water in the deep Atlantic Ocean flows from north to south. A weaker, slower meridional overturning circulation is associated with high 231Pa/230Th ratios. Likewise, the mean sortable silt size is higher when Atlantic meridional overturning circulation is stronger, as stronger currents can carry larger particles.

Panel H shows a reconstruction of volcanic activity from sulfate aerosol concentrations in ice cores in Antarctica (for the Southern Hemisphere reconstruction) and Greenland (for the Northern Hemisphere reconstruction). Sulfates are released during volcanic eruptions and have a short-term but often dramatic cooling effect. Independent reconstructions from both hemispheres are required as the lifetime of sulfate aerosols in the atmosphere is short and therefore, eruptions in one hemisphere may be not be recorded in the record from the opposite hemisphere.

Panel I shows the reconstructed temperature stack built in this study for the northern mid- and high-latitudes (30 to 90°S, blue) compared to proxies for the position of the intertropical convergence zone (ITCZ) (brown curve), a band of intense convection and precipitation in the tropics, and the intensity of the East Asian Summer Monsoon (EASM) (red curve). Researchers reconstructed the position of the ITCZ by measuring the concentration of titanium in ocean sediments off the coast of northern South America. Titanium in ocean sediments is primarily from the weathering of terrestrial rocks, and thus, titanium concentration is related to the amount and intensity of precipitation on continental South America near the core site. The strength of the EASM is estimated using the isotopic ratio of oxygen in carbonates in ocean sediments. More negative oxygen isotope ratios are interpreted as a stronger, more intense EASM.

Panel J shows the temperature record from the proxy stack for low latitudes (30°N to 30°S, black) along with oxygen isotopic records from cave deposits (speleothems) from Borneo (blue) and Indonesia (green). In both oxygen isotope records, more negative values are interpreted as increased precipitation in the vicinity of the cave deposit. In addition, the Borneo record is interpreted to reflect the location of the ITCZ while the Indonesian record is interpreted to reflect the strength of the Australian-Indonesian summer monsoon.

Finally, panel K shows the temperature record from the proxy stack for southern mid- and high latitudes (30°S   to 90°S, red) along with oxygen isotope records from cave deposits from South America (black) and South Africa (purple). The South African record is interpreted to reflect local temperature and precipitation amount while the South American record is interpreted to reflect precipitation amount.

Putting it all together

Based on all of these subpanels, what are the authors trying to illustrate? 

These different panels attempt to constrain different possible reasons for the reconstructed temperature changes in their data set stack.

Panels A to E and  panel H show different ways by which the energy balance of Earth may have changed over the Holocene, including changes in solar insolation (globally in panel E, by latitude and due to orbital changes in A to C), changes in greenhouse gas concentrations (panel D), and volcanic aerosol flux (panel H).

The remainder of the panels explore various aspects of climate features across the globe. Also, we can see from comparing the temperature stack in panel I with the orbital changes observed in panels A to C, that northern hemisphere temperatures match summer insolation anomalies quite well (Fig. B).

Trends in regional temperature reconstructions show strong similarities with high-resolution precipitation records, consistently associating greater warmth with greater wetness (Fig. 2, H to J). For example, extratropical Northern Hemisphere mid-to-high–latitude temperature correlates well with records of Asian monsoon intensity (14, 15) and the position of the Atlantic intertropical convergence zone (16) (Fig. 2H), tropical temperatures track precipitation proxies from speleothems in Borneo (17) and Indonesia (18) (Fig. 2I), and extratropical Southern Hemisphere temperatures parallel speleothem proxies of precipitation and temperature from South Africa (19) and South America (20) that are independent of our reconstruction.

The general pattern of high-latitude cooling in both hemispheres opposed by warming at low latitudes is consistent with local mean annual insolation forcing associated with decreasing orbital obliquity since 9000 years ago (Fig. 2C). The especially pronounced cooling of the Northern Hemisphere extratropics, however, suggests an important role for summer insolation in this region, perhaps through snow-ice albedo and vegetation feedbacks (21, 22). Such a mechanism that mediates seasonal insolation is plausible at these latitudes, where the fraction of continental landmasses relative to the ocean is high (~50% land from 30° to 90°N; 25% land from 30°N to 30°S; 15% land from 30° to 90°S).

We cannot fully exclude the possibility of a seasonal proxy bias in our temperature reconstructions (23), but a sensitivity experiment with an intermediate-complexity model (fig. S8) suggests that the effects of such a bias would probably be modest in the global reconstruction. The dominance of the northern signal in our global stack is consistent with Milankovitch theory, in which summer insolation would drive the planet toward eventual future glacial inception in the Northern Hemisphere (24), excluding any anthropogenic influence. Models support our finding of a global mean cooling in response to an obliquity decrease, though of lesser magnitude (25), and also support the idea about the sensitivity of the northern high latitudes to summer insolation (21).

Additional effects probably further influenced the evolution of climate through the Holocene. In the early-to-middle Holocene, the deglaciating Northern Hemisphere ice sheets would have modulated warming of the northern high latitudes relative to peak seasonal insolation (26, 27). Radiative forcing by greenhouse gases (primarily CO2) rose 0.5 W/m2 during the mid-to-late Holocene (Fig. 2D), which would be expected to yield ~0.4°C warming for a mid-range climate sensitivity (28). Response to such forcing may have been offset by opposing orbital insolation forcing that was greater than greenhouse gas forcing by up to one (annual) to two (seasonal) orders of magnitude over the course of the Holocene (Fig. 2, A to C). Northward heat transport in the Atlantic basin by the meridional overturning circulation (MOC) may have weakened since the middle Holocene (29), contributing to the strong cooling in the North Atlantic while dampening cooling in the mid-to-high latitude Southern Hemisphere due to the bipolar seesaw (30). Insofar as winter conditions influence the sources and strength of deepwater formation, a weakening MOC may partly reflect the increase in high northern-latitude winter insolation over the Holocene (Fig. 2A). Total solar irradiance reconstructed from cosmogenic isotopes (31) also varied by 0.5 to 1 W/m2, and volcanic eruptions occurred throughout the duration of the Holocene (32, 33), although most of this variance is at higher frequencies than those resolved by our stacked temperature records, and the scaling of both is poorly constrained.

Although our temperature stack does not fully resolve variability at periods shorter than 2000 years, such high-frequency changes would only modestly broaden the statistical distribution of Holocene temperatures (Fig. 3 and fig. S22). Moreover, we suggest that accounting for any spatial or seasonal biases in the stack would tend to reduce its variability because of the cancellation of noise in a large-scale mean and the opposing nature of seasonal insolation forcing over the Holocene, causing the Holocene temperature distribution to contract.

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Fig. 3.  Holocene temperature distribution compared to modern temperature and future projections. Shown are relative frequency plots of Holocene temperature anomalies in 0.05°C bins using multiple data subsets and reconstructions (colored lines), instrumental means for 1900–1909 and 2000–2009 CE (vertical black lines), 2100 CE projections based on various emissions scenarios (35) (black squares and gray bars give the best estimate and 66% confidence interval), and the Holocene median and 66% range from Standard5×5 + high-frequency stack (black square and blue bar). Projections in (35) were referenced to 1980–1999 CE, whereas we reference them to 1961–1990 CE here. Data sets are divided by proxy type: UK'37, Mg/Ca, and the remainder (Other); method: arithmetic mean (Standard) and RegEM; weighting: equal Northern and Southern Hemisphere weighting (0.5NH + 0.5SH), 5° × 5°grid, and 30° × 30°grid; exclusion of data sets: no North Atlantic and Jack50; and high-frequency addition: red noise with the same power spectrum as Mann et al. (2) added to the global stack (supplementary materials).

Question

In this figure, the researchers are trying to compare the mean temperature from their Holocene stacked record and its variability to projected temperatures for the end of the 21st century based on climate model simulations.

In addition, they show probability distributions for several different ways of calculating Holocene mean temperature and its variability to investigate how their calculation depends on the method used.

Key results

The researchers note that predicted warming in the next century based on the SRES emissions scenarios are likely to exceed the temperatures from their proxy reconstruction of the Holocene.

Though higher emissions scenarios are more likely to exceed their reconstructed Holocene temperatures, this result holds regardless of the emissions scenario used.

Emissions scenarios

The grey bars in this figure refer to different greenhouse gas emission scenarios that have been generated for the next century.

Each scenario is based on a different set of assumptions about how human societies will grow, what energy sources we will use, and what technologies we might deploy to reduce our energy use. In general, the A scenarios tend to be assume a greater use of fossil fuels and faster population growth, and tend to predict more warming compared to B scenarios.

More information about the scenarios can be found here: http://www.ipcc.ch/pdf/special-reports/spm/sres-en.pdf.

Unresolved questions

One major question arising from this figure is how well the proxy stack here captures the full range of Holocene temperatures.

The long averaging window of the proxy stack tends to remove the most extreme temperature values, though the researchers have attempted to account for this by artificially simulating high-frequency variability (i.e., Standard 5 by 5 + high-frequency curve).

The researchers note that progress on this question is likely to come from improved accuracy of dating proxy materials (how well do we know when this tree lived, for example) and obtaining higher frequency records that better elucidate high-frequency variability.

Our results indicate that global mean temperature for the decade 2000–2009 (34) has not yet exceeded the warmest temperatures of the early Holocene (5000 to 10,000 yr B.P.). These temperatures are, however, warmer than 82% of the Holocene distribution as represented by the Standard5×5 stack, or 72% after making plausible corrections for inherent smoothing of the high frequencies in the stack (6) (Fig. 3). In contrast, the decadal mean global temperature of the early 20th century (1900–1909) was cooler than >95% of the Holocene distribution under both the Standard5×5 and high-frequency corrected scenarios. Global temperature, therefore, has risen from near the coldest to the warmest levels of the Holocene within the past century, reversing the long-term cooling trend that began ~5000 yr B.P. Climate models project that temperatures are likely to exceed the full distribution of Holocene warmth by 2100 for all versions of the temperature stack (35) (Fig. 3), regardless of the greenhouse gas emission scenario considered (excluding the year 2000 constant composition scenario, which has already been exceeded). By 2100, global average temperatures will probably be 5 to 12 standard deviations above the Holocene temperature mean for the A1B scenario (35) based on our Standard5×5 plus high-frequency addition stack (Fig. 3).

Strategies to better resolve the full range of global temperature variability during the Holocene, particularly with regard to decadal to centennial time scales, will require better chronologic constraints through increased dating control. Higher-resolution sampling and improvements in proxy calibration also play an important role, but our analysis (fig. S18) suggests that improvements in chronology are most important. Better constraints on regional patterns will require more data sets from terrestrial archives and both marine and terrestrial records representing the mid-latitudes of the Southern Hemisphere and central Pacific.

Supplementary Materials

www.sciencemag.org/cgi/content/full/339/6124/1198/DC1

Supplementary Text

Figs. S1 to S26

References

References and Notes 

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  2. M. E. Mann et al., Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia. Proc. Natl. Acad. Sci. U.S.A. 105, 13252 (2008).

  3. Moberg, D. M. Sonechkin, K. Holmgren, N. M. Datsenko, W. Karlén, Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data. Nature 433, 613 (2005).

  4. H. Wanner et al., Mid- to late Holocene climate change: An overview.. Quat. Sci. Rev. 27, 1791 (2008).

  5. The majority of the data sets can be found at NOAA National Climate Data Center (www.ncdc.noaa.gov/paleo/paleo.html) and the PANGAEA data repository (www.pangaea.de/). Sources for all data sets are available online (6).

  6. Materials, methods, and supporting data are available as supplementary materials on Science Online.

  7. J. Esper, E. R. Cook, F. H. Schweingruber, Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability. Science 295, 2250 (2002).

  8. C. M. Ammann, E. R. Wahl, The importance of the geophysical context in statistical evaluations of climate reconstruction procedures. Clim. Change 85, 71 (2007).

  9. J. Esper, D. C. Frank, R. J. S. Wilson, Low-frequency ambition and high-frequency ratifications. EOS 85, 113, 120 (2004).

  10. J. Esper et al., Orbital forcing of tree-ring data. Nat. Clim. Change 10.1038/nclimate1589 (2012).

  11. T. Schneider, Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. J. Clim. 14, 853 (2001).

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  39. Acknowledgments: We thank J. Alder, T. Bauska, E. Brook, C. Buizert, V. Ersek, S. Hostetler, P. Huybers, N. Pisias, J. Rosen, G. Schmidt, A. Schmittner, M. Tingley, and our reviewers for invaluable insight and helpful discussion. The data sets provided by T. Barrows, J.-H. Kim, Y. Kubota, I. Larocque, G. Leduc, M. McGlone, T. Rodrigues, C. Rühlemann, J. Sachs, R. Schneider, H. Seppä, J. Tierney, M. Yamamoto, B. Vinther, and C. Waelbrock, as well as the data sets compiled from the National Oceanic and Atmospheric Administration Climate Data Center and PANGAEA databases, made this research possible. Funding for this work was provided by the NSF Paleoclimate Program for the Paleovar Project.