## 3 Results
### Altitudinal Patterns of Changes in \(SIF_{yield}\) Temperature Sensitivity  
#### 3.2.1 Seasonal and Interannual Temperature Sensitivity of \(SIF_{yield}\)  
In addition to the altitudinal variation in \(SIF_{yield}\) itself, its temperature sensitivity also varies along the altitudinal gradient. The altitudinal variation in seasonal temperature sensitivity is shown in Figure 6a. In spite of increased uncertainty, the temperature sensitivity rises along the altitudinal gradient. When the altitudinal rises above 5000 m, temperature sensitivity begins to decrease, probably because the plants there are already subnival plants, and the extreme environment there make them different from other alpine plants. Taking the difference between temperature sensitivity at 4900-5000 m and 3200-3300 m as an example, the Wilcoxon rank sum test was conducted to examine the altitudinal differences. Figure 5: (**a**) Variations in R\({}^{2}\) between \(SIF_{yield}\) and environmental variables (PAR and temperature). Observations from DOY 133–277 are used. Color of bars indicates direction of correlation (red indicates positive and blue indicates negative); deeper color indicates higher R\({}^{2}\). (**b**) Relationship between \(SIF_{yield}\) and PAR at different elevations on DOY 205 (July, the peak of summer growth period). According to Figure 6b, there is indeed a significant difference between the averages of temperature sensitivity at high and low altitudes (\(p<0.001\)). The figure also shows a flatter shape of the temperature sensitivity distribution at 4900-5000 m than that at 3200-3300 m, which results from a larger standard deviation (for 3200-3300 m, stdev = 0.001028; for 4900-5000 m, stdev = 0.000278) at higher altitudes. With regard to interannual variance, the coefficient of determination between temperature sensitivity and altitude is not so large (R\({}^{2}\) = 0.41) (Figure 6a), but that of the relationship between multi-year average temperature sensitivity and elevation is fairly high (R\({}^{2}\) = 0.93). These results show that elevation itself is enough to explain the latitudinal variation in temperature sensitivity, although there are still latent variables driving the multi-year changes of variation. When we look into the altitudinal variation in temperature sensitivity in different years (Figure 6d), it can be discovered easily that the interannual differences are significantly enhanced for places above 4200 m. Except for 2018, it seems that years with large seasonal temperature sensitivity in high altitude regions, for example 2008, 2011, and 2013, usually had relatively high \(SIF_{yield}\), and years with small seasonal temperature sensitivity, such as 2015, had low \(SIF_{yield}\). Figure 6: (**a**) Seasonal temperature sensitivity along altitudinal gradient, derived from annual cycle. (**b**) Distribution of temperature sensitivity at 4900–5000 m (in blue) and 3200–3300 m (in gray), showing significant differences between them. (**c**) Interannual temperature sensitivity along altitudinal gradient; different from results in Figure 5a, temperature sensitivity calculated here does not distinguish differences in DOYs, and shows a generally positive relationship between \(SIF_{yield}\) and temperature (positive temperature sensitivity) at interannual scale in most regions. (**d**) Altitudinal variation in temperature sensitivity in different years. Shaded areas represent the 95% confidence interval and the unit of temperature sensitivity is nm\({}^{-1}\) sr\({}^{-1}\) K\({}^{-1}\). Besides the seasonal scale, temperature sensitivity calculated at the interannual scale also increases as the elevation rises (Figure 6c). Although the coefficient of determination of interannual temperature sensitivity is smaller than that of seasonal temperature sensitivity, its slope is much greater (seasonal: slope = 0.000188 nm\({}^{-1}\) sr\({}^{-1}\) K\({}^{-1}\) m\({}^{-1}\); interannual: slope = 0.000096 nm\({}^{-1}\) sr\({}^{-1}\) K\({}^{-1}\) m\({}^{-1}\)).