There are expectations that increasing temperatures will lead to significant changes in structure and function of montane meadows, including greater water stress on vegetation and lowered vegetation production and productivity. We evaluated spatio-temporal dynamics in production and productivity in meadows within the Sierra Nevada mountain range of North America by: (1) compiling Landsat satellite data for the Normalized Difference Vegetation Index (NDVI) across a 37-year period (1985–2021) for 8,095 meadows >2,500 m elevation; then, (2) used state-space models, changepoint analysis, geographically-weighted regression (GWR), and distance-decay analysis (DDA) to: (a) identify meadows with decreasing, increasing or no trends for NDVI; (b) detect meadows with abrupt changes (changepoints) in NDVI; and (c) evaluate variation along gradients of latitude, longitude, and elevation for eight indices of temporal dynamics in annual production (mean growing season NDVI; MGS) and productivity (rate of spring greenup; RSP). Meadows with no long-term change or evidence of increasing NDVI were 2.6x more frequent as those with decreasing NDVI (72% vs. 28%). Abrupt changes in NDVI were detected in 48% of the meadows; they occurred in every year of the study and with no indication that their frequency had changed over time. The intermixing of meadows with different temporal dynamics was a consistent pattern for monthly NDVI and, especially, the eight annual indices of MGS and RSP. The DDA showed temporal dynamics in pairs of meadow within a few 100 m of each other were often as different as those hundreds of kilometers apart. Our findings point strongly toward a great diversity of temporal dynamics in meadow production and productivity in the SNV. The heterogeneity in spatial patterns indicated that production and productivity of meadow vegetation is being driven by interplay among climatic, physiographic and biotic factors at basin and meadow scales. Thus, when evaluating spatio-temporal dynamics in condition for many high elevation meadow systems, what might often be considered “noise” may provide greater insight than a “signal” embedded within a large amount of variability.