The high temporal variability of the soil-to-atmosphere CO2 flux (soil respiration, RS) has been studied at hourly to multiannual timescales, but remains less well understood than RS spatial variability. How RS fluxes vary and are auto-correlated at various time lags has practical implications for sampling, and more fundamentally for our understanding of its abiotic and biotic underlying mechanisms. We examined the variability, correlation, and sampling requirements of RS over a wide range of temporal scales in a temperate deciduous forest in eastern Maryland, USA, using both automated (temporally continuous, N = 30,036 over ten months) and survey (spatially diverse, temporally sparse, N = 1,912 over 17 months) data. Data from a global RS database were also used to examine interannual variability in comparable forests. The coefficient of variability of successive measurements generally varied from the minute (median CV 16%) to hourly and daily (11-12%) timescales. Successive RS values measured at a given collar exhibited a strong hour-to-hour correlation (r = 0.931), and a moderate correlation at a two-hour lag (0.289); day-to-day (i.e., 24 hour lag) hourly observations were uncorrelated. Daily RS means were well correlated at a one-day lag (r = 0.856), but not at any further time lag. In a linear mixed-effects model predicting RS, soil temperature and moisture exerted consistently strong effects regardless of timescale, and model coefficient of variability was generally high (>80%). These results provide new opportunities to explore the drivers and variability of RS fluxes, quantify sampling requirements, and improve error propagation.