Are Karakoram Temperatures out of Phase Compared to Hemispheric Trends?

Clim Dyn DOI 10.1007/s00382-016-3273-6 Are Karakoram temperatures out of phase compared to hemispheric trends? Fayaz Asad1,2 · Haifeng Zhu1,3 · Hui Zhang1 · Eryuan Liang1,3 · Sher Muhammad1,2 · Suhaib Bin Farhan4 · Iqtidar Hussain1,2 · Muhammad Atif Wazir1,2 · Moinuddin Ahmed5,6 · Jan Esper7 Received: 12 March 2016 / Accepted: 10 July 2016 © Springer-Verlag Berlin Heidelberg 2016 Abstract In contrast to a global retreating trend, glaciers the past 438 years (AD 1575–2012). The reconstruction in the Karakoram showed stability and/or mass gaining dur- passes all statistical calibration and validation tests and ing the past decades. This “Karakoram Anomaly” has been represents 49 % of the temperature variance recorded over assumed to result from an out-of-phase temperature trend the 1955–2012 instrumental period. It shows a substantial compared to hemispheric scales. However, the short instru- warming accounting to about 1.12 °C since the mid-twen- mental observations from the Karakoram valley bottoms tieth century, and 1.94 °C since the mid-nineteenth century, do not support a quantitative assessment of long-term tem- and agrees well with the Northern Hemisphere temperature perature trends in this high mountain area. Here, we pre- reconstructions. These findings provide evidence that the sented a new April–July temperature reconstruction from Karakoram temperatures are in-phase, rather than out-of- the Karakoram region in northern Pakistan based on a high phase, compared to hemispheric scales since the AD 1575. elevation (~3600 m a.s.l.) tree-ring chronology covering The synchronous temperature trends imply that the anomalous glacier behavior reported from the Karakoram may need further explanations beyond basic regional thermal Electronic supplementary material The online version of this anomaly. article (doi:10.1007/s00382-016-3273-6) contains supplementary material, which is available to authorized users. Keywords Paleoclimate · Climate variability · Tree rings · Dendrochronology · Karakoram Anomaly * Haifeng Zhu [email protected] 1 Key Laboratory of Alpine Ecology and Biodiversity, Institute 1 Introduction of Tibetan Plateau Research, Chinese Academy of Sciences, No. 16 Lincui Road, Chaoyang District, Beijing 100101, 2 China With 7820 glaciers covering an area of 17,946 km , the Karakoram region in northern Pakistan comprises the most 2 University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China glaciated area outside the polar regions. Unlike the retreating status of most glaciers elsewhere, the Karakoram gla- 3 CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100100, China ciers are stable or re-advance over the past 20 years (Bolch et al. 2012), which has been coined as the “Karakoram 4 Pakistan Space and Upper Atmosphere Research Commission, SUPARCO Headquarters, SUPARCO Road, Anomaly” (Hewitt 2005; Kumar et al. 2015). Anomalous P.O. Box No. 8402, Karachi 75270, Pakistan decreasing summer temperatures, compared to globally 5 Botany Department, Federal Urdu University of Arts, Science warming trends, as well as increasing winter precipitation and Technology, Gulshan‑e‑Iqbal, Karachi 75300, Pakistan were assumed to be the main climatic drivers of the Kara- 6 Department of Earth and Environmental Systems, Indiana koram Anomaly (Hewitt 2005; Kapnick et al. 2014). How- State University, Science 159E, Terre Haute, IN 47809, USA ever, most assessments were based on the relatively short 7 Department of Geography, Johannes Gutenberg University, meteorological observations (typically <50 years), record- 55099 Mainz, Germany ing conditions in the deep and semi-arid Karakoram valleys 1 3 F. Asad et al. rather than in the high, glaciated regions (Hewitt 2011, region based on a positive response of tree growth to tem- 2014), limiting our understanding of mountainous climate perature, and to compare the retained temperature trends variability on longer timescales. with previous attempts based on inverse growth/tempera- Tree rings, as an annually resolved and quantitative cli- ture correlations. We expect that the Karakoram tempera- mate proxy, have been used to investigate past temperature ture variability is synchronous with the trends at larger variability over the Tibetan Plateau, Himalaya, and neighbor- spatial scales and assess this by comparison with recon- ing regions (Cook et al. 2003; Deng et al. 2014; Liang et al. structions of Northern Hemisphere and Northern Hemi- 2010; Thapa et al. 2014; Lv and Zhang 2013; Wang et al. sphere extra-tropical temperature variability. 2014; Yadav et al. 2011; Zhang et al. 2015). In the Karako- ram area, several studies revealed tree rings to be sensitive to changing temperatures (Ahmed et al. 2011; Esper 2000; 2 Materials and methods Esper et al. 2002b, 2007; Zafar et al. 2015). Recent work by Zafar et al. (2015) presented a rigorously calibrated summer 2.1 Study area (June–August, JJA) temperature reconstruction extending over the past 500 years based on Picea smithiana and Pinus Our study area is situated in the Bagrot valley in the Kara- gerardiana tree-ring data. This reconstruction indicated that koram region of northern Pakistan (Fig. 1). It covers an area temperature variations in the Karakoram were out-of-phase of 452 km2, characterized by extreme relief from 1500 to compared to the hemispheric trends. In contrast, the North- 7788 m a.s.l. at the Rakaposhi summit (Mayer et al. 2010). ern Hemisphere shows general warming trends during the Precipitation in this region changes gradually with altitude, twentieth century (Esper et al. 2002a; IPCC 2007; Wilson semi-arid in lower elevations (<1500 m) and wet in higher et al. 2016). However, the Zafar et al. (2015) reconstruction elevations (>4000 m) (Mayer et al. 2010; Winiger et al. is based on a negative association between tree-ring data and 2005). The precipitation in the study region is estimated to summer temperatures, indicating that moisture stress is the increase to 1000 mm at the upper treeline in 4000 m a.s.l. + key driver of tree growth at the study sites. (Cramer 2000). In summer, the monsoon is contributing to The objective of this study is to present a new high- the precipitation in this region, while westerlies dominate elevation temperature reconstruction for the Karakoram during the cold season (Hewitt 2005; Kapnick et al. 2014; Fig. 1 Location of the tree-ring sampling site and the CRU TS 3.32 grid point in the Karakoram, northern Pakistan 1 3 Are Karakoram temperatures out of phase compared to hemispheric trends? Table 1 Site information and statistics for the tree-ring chronology Study site Bagrot (Karakoram) Species of tree Pinus wallichiana Latitude 36° 01 Longitude 74° 39 Elevation range (m) 3550–3710 Cores/trees 60/33 Time span 1194–2013 Percent of missing rings (%) 0.17 Mean sensitivity 0.13 Standard deviation 0.22 Mean correlation between all series 0.45 Mean correlation between trees 0.44 Mean correlation within a tree 0.65 Fig. 2 Climate diagram of the gridded monthly climate data sets of First order autocorrelation AC (1) 0.68 CRU TS 3.23, covering the period of 1955–2013 Signal to noise ratio (SNR) 42.5 Expressed population signal (EPS) 0.97 Kumar et al. 2015). The steep precipitation gradient, from the semi-arid valley bottoms to the humid summits, results from an inner-mountainous circulation system generat- To preserve low-frequency climate signals in the final ing clouds and downpour at the slopes and summits, and TRW chronology, we used ARSTAN program (Cook 1985) persisting clear sky conditions above the valley centers to calculate ratios between the raw measurement series and (Weiers 1998). According to the gridded monthly climate negative exponential functions (Cook and Kairiukstis 1990). data sets (36° 25′N, 74° 75′E from 1955 to 2013) of CRU For 18 cores we used 90-year cubic smoothing splines for TS 3.23 (Mitchell and Jones 2005), the mean annual pre- standardization to avoid artificially increasing trends in the cipitation, typically measured by stations situated in the index series. We averaged the detrended series to produce valley bottoms, is 301 mm. Annual mean temperature is a standard mean chronology (STD; Fig. 3). The statistical 1.2 °C, ranging from 11.4 °C in January to 12.5 °C in characteristics of this chronology over the period common − July (Fig. 2). to all trees (1800–2013) are shown in Table 1. To investigate whether there is a trend bias in the STD chronology calcu- 2.2 Tree-ring sampling and chronology development lated using ratios, we also produced a chronology (using the same negative exponential and spline fits) by calculating Pinus wallichiana is a naturally distributed evergreen spe- residuals subsequent to the application of a data-adaptive cies in Pakistan, Bhutan, Afghanistan, China, Nepal and power transformation (Cook and Peters 1997). India (Orwa et al. 2015). It usually grows at an altitudinal The mean inter-series correlation is used to estimate coher- range from 1800 to 3900 meters a.s.l (Singh and Yadav ence inherent to the chronology, and the Expressed Popula- 2007). P. wallichiana is a dominant species at the timber- tion Signal (EPS) is used to evaluate temporal changes of line in the Karakoram reaching ages up to about 700 years the reconstruction signal strength (Wigley et al. 1984). Both (e.g. Astore-Rama; Ahmed et al. 2011). We here selected metrics were calculated over 50-year intervals lagged by healthy P. wallichiana trees between 3550 and 3710 m a.s.l. 25 years. We considered an EPS > 0.85 threshold to define near the upper treeline in the Bagrot valley, with no evi- the period over which the chronology likely reflects the signal dence of human disturbance or fire injuries (Fig. 1). A total expressed by a theoretically infinite population. According of 60 increment cores were extracted from 33 living trees to the 50-year running EPS values, the period after AD 1575 in May 2014 (Table 1).

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