Leaf unfolding of Tibetan alpine meadows captures the arrival of monsoon rainfall.

The alpine meadow on the Tibetan Plateau is the highest and largest pasture in the world, and its formation and distribution are mainly controlled by Indian summer monsoon effects. However, little is known about how monsoon-related cues may trigger spring phenology of the vast alpine vegetation. Based on the 7-year observations with fenced and transplanted experiments across lower to upper limits of Kobresia meadows in the central plateau (4400-5200 m), we found that leaf unfolding dates of dominant sedge and grass species synchronized with monsoon onset, regardless of air temperature. We also found similar patterns in a 22-year data set from the northeast plateau. In the monsoon-related cues for leaf unfolding, the arrival of monsoon rainfall is crucial, while seasonal air temperatures are already continuously above 0 °C. In contrast, the early-emerging cushion species generally leafed out earlier in warmer years regardless of precipitation. Our data provide evidence that leaf unfolding of dominant species in the alpine meadows senses the arrival of monsoon-season rainfall. These findings also provide a basis for interpreting the spatially variable greening responses to warming detected in the world's highest pasture, and suggest a phenological strategy for avoiding damages of pre-monsoon drought and frost to alpine plants.

Comparing the skill of different reanalyses and their ensembles as predictors for daily air temperature on a glaciated mountain (Peru).

It is well known from previous research that significant differences exist amongst reanalysis products from different institutions. Here, we compare the skill of NCEP-R (reanalyses by the National Centers for Environmental Prediction, NCEP), ERA-int (the European Centre of Medium-range Weather Forecasts Interim), JCDAS (the Japanese Meteorological Agency Climate Data Assimilation System reanalyses), MERRA (the Modern Era Retrospective-Analysis for Research and Applications by the National Aeronautics and Space Administration), CFSR (the Climate Forecast System Reanalysis by the NCEP), and ensembles thereof as predictors for daily air temperature on a high-altitude glaciated mountain site in Peru. We employ a skill estimation method especially suited for short-term, high-resolution time series. First, the predictors are preprocessed using simple linear regression models calibrated individually for each calendar month. Then, cross-validation under consideration of persistence in the time series is performed. This way, the skill of the reanalyses with focus on intra-seasonal and inter-annual variability is quantified. The most important findings are: (1) ERA-int, CFSR, and MERRA show considerably higher skill than NCEP-R and JCDAS; (2) differences in skill appear especially during dry and intermediate seasons in the Cordillera Blanca; (3) the optimum horizontal scales largely vary between the different reanalyses, and horizontal grid resolutions of the reanalyses are poor indicators of this optimum scale; and (4) using reanalysis ensembles efficiently improves the performance of individual reanalyses.