Dendroclimatic Reconstruction of Mean Annual Temperatures over Treeline Regions of Northern Bhutan Himalayas

Abstract

The Himalayan region is likely particularly exposed to climate change indicated by the high regional rate of change. The number of high-resolution, well-calibrated, and long-term paleoclimate reconstructions are however regrettably few, to set this change in a longer-term context. The dendroclimatic reconstructions over Himalaya that do exist have only reconstructed summer season temperatures, and rarely or never attempted to reconstruct mean annual temperatures. The paucity of long meteorological records is a matter of concern when developing chronologies of climate sensitive tree-ring data in Bhutan, but the chronologies would theoretically be of high potential for extending short meteorological records back in time using trees in high-elevation ecotones. The objectives of this study were to explore dendroclimatic signals in tree-ring width chronologies of Abies densa growing in these extreme ecotones and to reconstruct, if possible, annual temperatures over Northern Bhutan. A point-by-point regression analysis revealed that the regional composite chronology was significantly and positively correlated with temperatures of all months of the current year, i.e., January to December. The chronology was highly correlated with annual temperatures (calibration period R = 0.67 and validation period R = 0.50; p < 0.001) allowing a reconstruction of temperature over Northern Bhutan (NB-TEMR). The NB-TEMR reveals some common variations with summer temperature reconstructions of the Northern Hemisphere as well as the Himalayan region, particularly w.r.t to the recent warming trend. The reconstruction covers the period of 1765 to 2017. This reconstruction reveals a warming trend since 1850 with higher rates of warming 1935 to 2017, but with a pause around 1940–1970. The warming is consistent with reduced volcanic activity and increase of greenhouse gases. We anticipate that our new reconstruction of annual mean temperature could be an important contribution for future climate change studies and assessments of climate models.