A multi-proxy reconstruction of spatial and temporal variations in Asian summer temperatures over the last millennium

Abstract

To investigate climate variability in Asia during the last millennium, the spatial and temporal evolution of summer (June-July-August; JJA) temperature in eastern and south-central Asia is reconstructed using multi-proxy records and the regularized expectation maximization (RegEM) algorithm with truncated total least squares (TTLS), under a point-by-point regression (PPR) framework. The temperature index reconstructions show that the late 20th century was the Institute of Geography, Russian Academy of Sciences, Moscow, Russia warmest period in Asia over the past millennium. The temperature field reconstructions illustrate that temperatures in central, eastern, and southern China during the 11th and 13th centuries, and in western Asia during the 12th century, were significantly higher than those in other regions, and comparable to levels in the 20th century. Except for the most recent warming, all identified warm events showed distinct regional expressions and none were uniform over the entire reconstruction area. The main finding of the study is that spatial temperature patterns have, on centennial timescales, varied greatly over the last millennium. Moreover, seven climate model simulations, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), over the same region of Asia, are all consistent with the temperature index reconstruction at the 99 % confidence level. Only spatial temperature patterns extracted as the first empirical orthogonal function (EOF) from the GISS-E2-R and MPI-ESM-P model simulations are significant and consistent with the temperature field reconstruction over the past millennium in Asia at the 90 % confidence level. This indicates that both the reconstruction and the simulations depict the temporal climate variability well over the past millennium. However, the spatial simulation or reconstruction capability of climate variability over the past millennium could be still limited. For reconstruction, some grid points do not pass validation tests and reveal the need for more proxies with high temporal resolution, accurate dating, and sensitive temperature signals, especially in central Asia and before AD 1400.

Multi-proxy reconstructions of May–September precipitation field in China over the past 500 years

“…8a) displays a main loading in eastern China, and a general (monopole) variation over most of China, with the exception of the northeastern and western margins of the Tibetan Plateau. The explained variance in this mode accounts for 16.6 % of the total variance, and lower than that in the leading mode of temperature field (Shi et al, 2015a), but this is normal in precipitation analysis (Day et al, 2015). A main loading in eastern China also appears in the EOF1 of the reconstructed MJJAS precipitation anomalies during the interval AD 1850-2000, the EOF1 of the reconstructed data during the interval AD 1961-2000, and the EOF1 of the instrumental data during the same interval (AD 1961(AD -2000.…”

“…These are BCC-CSM1.1 (T. , CCSM4 (Landrum et al, 2012), FGOALS-s2 (Man et al, 2014), GISS-E2-R (Schmidt et al, 2014), IPSL-CM5A-LR (Dufresne et al, 2013), and MPI-ESM-P (Jungclaus et al, 2010). A description of the six models, sponsoring institutions, and main references is given in Table S3 of Shi et al (2015a). For details and data of the past 1000 experiments, see the websites of the Paleoclimate Modelling Intercomparison Project Phase 3 (PMIP3) and the fifth phase of the Coupled Model Intercomparison Project (CMIP5).…”

“…Consequently, climate field reconstructions for the past millennium have focused mainly on regions for which abundant multi-proxy records are avail-able, including Europe (Luterbacher et al, 2004), North America (Cook et al, 2004) and East Asia (Shi et al, 2015a). The primary types of proxy records are often treering records and historical documents, mainly because of the data availability and their accurate dating, annual resolution, and demonstrable relationships with instrumental climate data (Fritts, 1976;Zhang, 1991).…”

“…PCR (Cook et al, 1999), RegEM regression (Shi et al, 2015a) or the OIE method -by searching adequate proxy records among the ones available near the grid point. The goal of the PPR-based method is to maximize the retention of spatial information, but this method requires a sufficient number of suitable proxy records.…”

Decreasing Indian summer monsoon on the northern Indian sub-continent during the last 180 years: evidence from five tree-ring cellulose oxygen isotope chronologies

“…However, the tree-ring δ 18 O record in northwestern India, influenced significantly by the Arabian Sea branch of the ISM, exhibits a drying trend since 1950 CE , which does not support the idea of a strengthening Arabian Sea branch of the ISM (Anderson et al, 2002). Moreover, there are no calibrated radiocarbon dates for the (c) Shi et al, 2015a, b vs. Tierney et al, 2015a (low frequency) Wang et al, 2015a, b vs. Tierney et al, 2015a (low frequency) Cook et al, 2013a, b vs. Tierney et al, 2015a 2 1 0 -1 Shi et al, 2015a, b vs. Tierney et al, 2015a, b Wang et al, 2015a, b vs. Tierney et al, 2015a, b Cook et al, 2013a, b vs. Tierney et al, 2015a (a) last 300 years for the two records from the Arabian Sea (Anderson et al, 2002;Chauhan et al, 2010). We suggest that further high-resolution and well-dated ISM records from western India are needed to improve our understanding of the behavior of the ISM.…”