A new method for reconstructing past-climate trends using tree-ring data and kernel smoothing

Abstract

Mediterranean high-relief karst areas are very vulnerable to changes in temporal patterns of precipitation and temperature. Understanding climate change in these areas requires current climate trends to be assessed within the context of the variability of rainfall and temperature trends in the recent past. A major difficulty is that the instrumental record in these high-relief areas is very limited and the use of data from paleoclimatic proxies, such as tree-ring data, is required to infer past climate variability. Furthermore, for complex relationships between tree-ring data and climatic variables, it is almost impossible to infer past inter-annual variations in temperature or precipitation, and the inference is limited to the reconstruction of low-frequency variability (i.e., the trend). To do so, in this work, we propose a new method based on detecting trends (by kernel smoothing) in tree variables that show maximum correlation with the trends (also estimated by kernel smoothing) of climate variables. This enables a standard regression framework to be established to reconstruct past climate. We have used tree-ring proxy data from Abies pinsapo to evaluate past climate trends in the Sierra de las Nieves karst massif in Southern Spain. Our analysis has found that during the last three hundred years the smoothed mean annual rainfall steadily decreased until the beginning of the 20th century and thereafter it remained more or less constant until the end of the century. On the other hand, the smoothed mean annual temperature has steadily increased since the beginning of the 18th century until recent times. These trends are also suggested by the climate projections for the latter part of the current 21st century. As the study area is a high-relief karst massif of significant hydrologic and ecologic interest, the implications of these trends should be taken into account when formulating effective action plans to mitigate the impact of climate change.