Global Signatures and Dynamical Origins of the Little Ice Age and Medieval Climate Anomaly

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Science  27 Nov 2009:
Vol. 326, Issue 5957, pp. 1256-1260
DOI: 10.1126/science.1177303

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  1. Fig. 1

    Decadal surface temperature reconstructions. Surface temperature reconstructions have been averaged over (A) the entire Northern Hemisphere (NH), (B) North Atlantic AMO region [sea surface temperature (SST) averaged over the North Atlantic ocean as defined by (30)], (C) North Pacific PDO (Pacific Decadal Oscillation) region (SST averaged over the central North Pacific region 22.5°N–57.5°N, 152.5°E–132.5°W as defined by (31)], and (D) Niño3 region (2.5°S–2.5°N, 92.5°W–147.5°W). Shading indicates 95% confidence intervals, based on uncertainty estimates discussed in the text. The intervals best defining the MCA and LIA based on the NH hemispheric mean series are shown by red and blue boxes, respectively. For comparison, results are also shown for parallel (“screened”) reconstructions that are based on a subset of the proxy data that pass screening for a local temperature signal [see (13) for details]. The Northern Hemisphere mean Errors in Variables (EIV) reconstruction (13) is also shown for comparison.

  2. Fig. 2

    Reconstructed surface temperature pattern for MCA (950 to 1250 C.E.) and LIA (1400 to 1700 C.E.). Shown are the mean surface temperature anomaly (left) and associated relative weightings of various proxy records used (indicated by size of symbols) for the low-frequency component of the reconstruction (right). Anomalies are defined relative to the 1961–1990 reference period mean. Statistical skill is indicated by hatching [regions that pass validation tests at the P = 0.05 level with respect to RE (CE) are denoted by / (\) hatching]. Gray mask indicates regions for which inadequate long-term modern observational surface temperature data are available for the purposes of calibration and validation.

  3. Fig. 3

    Spatial pattern of MCA-LIA surface temperature difference in reconstructions and model simulations. (A) Proxy-based temperature reconstructions, (B) GISS-ER (using the same solar forcing difference used in the NCAR simulation—shown is the ensemble mean; see the SOM for example results from one of six realizations), and (C) NCAR CSM 1.4 simulation (using the same MCA and LIA time intervals as defined above). The observational mask has been applied to both model patterns for ease of comparison. Statistical skill for (A) is indicated with the same conventions as in Fig. 2 (statistical significance here indicates that the particular test statistic independently passed during both the MCA and LIA intervals).

  4. Fig. 4

    Spatial pattern of MCA-LIA sea-level pressure difference in model simulations. (A) NCAR CSM 1.4 and (B) GISS-ER. For the NCAR model, a single run was available (23). For the GISS-ER coupled model, we show the ensemble mean of six realizations; see SOM section 5 for further details.

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