globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2174
论文题名:
Inter-hemispheric temperature variability over the past millennium
作者: Raphael Neukom
刊名: Nature Climate Change
ISSN: 1758-1353X
EISSN: 1758-7473
出版年: 2014-03-30
卷: Volume:4, 页码:Pages:362;367 (2014)
语种: 英语
英文关键词: Palaeoclimate ; Climate and Earth system modelling ; Projection and prediction
英文摘要:

The Earth’s climate system is driven by a complex interplay of internal chaotic dynamics and natural and anthropogenic external forcing. Recent instrumental data have shown a remarkable degree of asynchronicity between Northern Hemisphere and Southern Hemisphere temperature fluctuations, thereby questioning the relative importance of internal versus external drivers of past as well as future climate variability1, 2, 3. However, large-scale temperature reconstructions for the past millennium have focused on the Northern Hemisphere4, 5, limiting empirical assessments of inter-hemispheric variability on multi-decadal to centennial timescales. Here, we introduce a new millennial ensemble reconstruction of annually resolved temperature variations for the Southern Hemisphere based on an unprecedented network of terrestrial and oceanic palaeoclimate proxy records. In conjunction with an independent Northern Hemisphere temperature reconstruction ensemble5, this record reveals an extended cold period (1594–1677) in both hemispheres but no globally coherent warm phase during the pre-industrial (1000–1850) era. The current (post-1974) warm phase is the only period of the past millennium where both hemispheres are likely to have experienced contemporaneous warm extremes. Our analysis of inter-hemispheric temperature variability in an ensemble of climate model simulations for the past millennium suggests that models tend to overemphasize Northern Hemisphere–Southern Hemisphere synchronicity by underestimating the role of internal ocean–atmosphere dynamics, particularly in the ocean-dominated Southern Hemisphere. Our results imply that climate system predictability on decadal to century timescales may be lower than expected based on assessments of external climate forcing and Northern Hemisphere temperature variations5, 6 alone.

From over 25 hemispheric-scale temperature reconstructions published in recent decades, only three cover the ocean-dominated Southern Hemisphere7. These Southern Hemisphere temperature reconstructions include only seven8 or fewer9 proxy datasets for the entire Southern Hemisphere, or were provided as peripheral components of Northern Hemisphere and global reconstruction efforts4 with the caveat that ‘more confident statements about long-term temperature variations in the Southern Hemisphere and globe on the whole must await additional proxy data collection’4. Consequently, attribution of temperature changes to external forcings10, 11 and investigations of the coupling between temperature and greenhouse gas concentrations5, 6 have focused on the Northern Hemisphere.

Data spanning inter-annual to multi-millennial timescales suggest limited temperature coherence between the two hemispheres. The degree of independence in Northern Hemisphere and Southern Hemisphere temperature trends over the past 150 years2 indicates that responses to external forcing may be modulated by ocean–atmosphere variability, reducing predictability of the climate system in twenty-first century model projections1, 3. Patterns of late Quaternary deglaciation have also demonstrated high inter-hemispheric variability, attributed to a coupling of orbital forcing, ice-albedo feedbacks and the Atlantic Meridional Overturning Circulation12, 13. Finally, a recent evaluation of multi-centennial reconstructions from seven continents also suggests stronger regional temperature coherence within the hemispheres than between them14. Yet, the preliminary nature of existing annually resolved Southern Hemisphere temperature reconstructions has hindered knowledge of the existence and driving mechanisms of inter-hemispheric climate variability on the societally relevant multi-decadal to centennial timescales.

Here, we introduce a Southern Hemisphere temperature reconstruction ensemble and assess inter-hemispheric temperature variability over the past millennium in both empirical reconstructions and state-of-the-art climate model simulations. We use an extensive Southern Hemisphere palaeoclimate data network from more than 300 individual sites15 yielding 111 temperature predictors (Supplementary Section 1). This proxy collection nearly doubles the number of records considered in the most advanced previous reconstruction attempt4, now allowing the development of an annually resolved and well-verified Southern Hemisphere temperature reconstruction for the past millennium (Fig. 1 and Supplementary Section 2) which is insensitive to moderate changes in reconstruction methodology or proxy network composition (Supplementary Section 3).

Figure 1: Proxy data and calibration performance.
Proxy data and calibration performance.

a, Southern Hemisphere temperature reconstruction proxies. Shading represents GISS instrumental grid-cell temperature28 correlations in the period 1911–1990, with the Southern Hemisphere field mean used as reconstruction target (all data linearly detrended). Cells with less than 30 years of data are blank. b, Temporal evolution of the number of proxy time series used in the reconstruction, with colours indicating the relative contribution of each archive and calibration (red) and verification (green) RE skill metric for the period 1000–2000. c, Instrumental target temperatures (with respect to 1961–1990) over the 1911–1990 calibration/verification period (black) and reconstruction ensemble means of the years used for calibration (red) and verification (green) for the most replicated proxy nest. Details in Methods and Supplementary Section 3.1.3.

Southern Hemisphere reconstruction ensemble.

We use the Southern Hemisphere spatial mean of the Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP) temperature grid28 as the instrumental predictand for the reconstruction. The palaeoclimate data network15 consists of 48 marine (46 coral and 2 sediment time series) and 277 terrestrial (206 tree-ring sites, 42 ice core, 19 documentary, 8 lake sediment and 2 speleothem) records (details in Supplementary Section 1). Although proxy records are preferentially located towards land areas, the network represents a considerable improvement of both geographical coverage and proxy quantity and quality (for example, resolution, length) since the last Southern Hemisphere reconstruction effort4. Proxies are screened with local grid-cell temperatures28 yielding 111 temperature predictors (Fig. 1) for the nested multivariate principal component regression procedure23. A 3,000-member ensemble reconstruction of annual Southern Hemisphere temperatures over the period 1000–2000 was generated with the spread of ensemble members considered a measure of uncertainty.

For each ensemble member we use different reconstruction parameters by randomly selecting a subset of proxies, as well as varying the calibration/verification intervals within 1911–1990, and other reconstruction parameters (details in Supplementary Section 2.2). The perturbation of calibration/verification periods allows a ‘verification ensemble mean’ to be calculated over the 1911–1990 period by averaging all members where a given year was used for verification (and not for calibration). Analogously, a ‘calibration ensemble mean’ was calculated. These time series and their corresponding Reduction of Error (RE) skills are shown in Fig. 1c, b, respectively. These statistics along with additional verification based on the sparse early Southern Hemisphere instrumental data (RE = 0.41–0.90; Supplementary Fig. 10) point to reconstructive skill over the past millennium. In addition to traditional reconstruction uncertainty estimates based on regression residuals, we assess the influence of the ensemble perturbations on the reconstruction outcome. Uncertainty envelopes in Fig. 2a represent combined calibration and ensemble uncertainties (details in Supplementary Section 2.4).

Although we have taken steps to provide robust results considering the challenges of proxy-based reconstructions (for example, potential underestimation of past climate amplitudes) discussed in the literature, we note that all reconstruction approaches contain uncertainties. The fact that our reconstruction verifies well and captures interannual and decadal-scale temperature fluctuations during the instrumental period (Fig. 1 and Supplementary Section 3) indicates reduced probability of such artefacts. An extensive assessment of reconstruction robustness is provided in Supplementary Section 3.2 and Supplementary Figs 13–26, with tests demonstrating that the potential bias introduced by the proxy-screening and reconstruction methods or by single dominant records or proxy archives is small.

Northern Hemisphere reconstruction ensemble.

Details concerning the Northern Hemisphere reconstructions are provided in ref. 5 and Supplementary Section 5. The most important difference from our Southern Hemisphere reconstruction is that it is not based on a single predictor matrix but uses nine published Northern Hemisphere reconstructions based on different (but not independent) proxy sets and various reconstruction methodologies. In ref. 5, the individual single-member reconstructions were recalibrated to instrumental temperature data using different calibration periods as ensemble parameters, resulting in a total of 521 ensemble members. The Northern Hemisphere ensemble spread is larger than in the Southern Hemisphere owing to the relatively large differences between some of the original sub-reconstructions and the composite-plus-scaling approach over a range of time windows in ref. 5. To best illustrate these two approaches, the ensemble means of the nine sub-reconstructions are shown for the Northern Hemisphere in Fig. 2a. As a consequence of these methodological differences and the larger ensemble spread in the Northern Hemisphere, one would expect generally reduced probabilities for extreme periods in the Northern Hemisphere. However, Fig. 3a, b shows similar fractions of periods with high probabilities for extremes, indicating a similar consistency between ensemble members in the timing of extreme periods in both hemispheres.

Extreme periods (Fig. 3).

To quantify extreme periods, we calculate 10-year running averages relative to the 1000–2000 mean. All years exceeding the ±1 standard deviation range (calculated over the unfiltered 1000–2000 period) are considered extreme. We quantify the probability of extreme periods as the fraction of ensemble members exceeding this threshold for each year (Fig. 3a, b). Probabilities for simultaneous extreme periods are calculated by multiplying the probabilities of each hemisphere (Fig. 3c).

Northern Hemisphere–Southern Hemisphere difference (Fig. 4).

To evaluate the decadal to centennial coherence, reconstruction and climate model data are detrended using a 200-year loess-filter before the analysis. Instrumental data are linearly detrended. Ten-year running averages are then calculated and divided by the standard deviation over 1000–2000 to allow relative comparison of hemispheric fluctuations. Each of the standardized filtered Southern Hemisphere reconstructions is subtracted from a randomly selected Northern Hemisphere reconstruction, with shaded probabilities in Fig. 4a indicating the fraction of ensemble members enclosed. Modelled Northern Hemisphere–Southern Hemisphere differences are calculated individually for each simulation (alternative calculations in Supplementary Section 10). The distributions in Fig. 4b, c are calculated from the average absolute Northern Hemisphere–Southern Hemisphere difference over the respective time interval for each ensemble member. Boxes, whiskers and circles of the boxplots in Fig. 4b, c represent interquartile range, 5th/95th percentiles and extremes, respectively; bold line indicates the median.

  1. Deser, C., Knutti, R., Solomon, S. & Phillips, A. S. Communication of the role of natural variability in future North American climate. Nature Clim. Change 2, 775779 (2012).
  2. Thompson, D. W. J., Wallace, J. M., Kennedy, J. J. & Jones, P. D. An abrupt drop in Northern Hemisphere sea surface temperature around 1970. Nature 467, 444447 (2010).
  3. Friedmann, A., Hwang, Y., Chiang, J. & Frierson, D. Interhemispheric temperature asymmetry over the 20th century and in future projections. J. Clim. 26, 54195433 (2013).
  4. Mann, M. et al. Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia. Proc. Natl Acad. Sci. USA 105, 1325213257 (2008).
  5. Frank, D. C. et al. Ensemble reconstruction constraints on the global carbon cycle sensitivity to climate. Nature 463, 527532 (2010).
  6. Hegerl, G. C., Crowley, T. J., Hyde, W. T. & Frame, D. J. Climate sensitivity constrained by temperature reconstructions over the past seven centuries.
URL: http://www.nature.com/nclimate/journal/v4/n5/full/nclimate2174.html
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/5174
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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Raphael Neukom. Inter-hemispheric temperature variability over the past millennium[J]. Nature Climate Change,2014-03-30,Volume:4:Pages:362;367 (2014).
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