How can uncertainty about the position of an object decrease over time?

The Near-Earth Objects Coordination Centre predicts future close approaches with Earth, for example these approaches for 2010RF12. For example, the time uncertainty of the 2022-09-23 closest approach is 276.7 minutes, with a distance of 3.834E-4 AU, in 2024-03-01 uncertainty of 21.3 minutes and 4.456E-4 AU, and in 2034-07-16 uncertainty of 119.3 minutes and 3.82E-5 AU.

My question is why do those numbers not monotonically increase? If their precision is such that they only state with an uncertainty of 4 hours for 2022, how do they get that down to 20 minutes for a prediction two years away?

Another interesting possibility is that when treated carefully, some errors sometimes subtract rather than add. This specific object has only been observed on five consecutive days in 2010. That is a very small fraction of a complete orbit, so the program that solved for the closest-fit orbit probably reported significant correlations among the fit parameter estimates. In such a case, the simple "root sum square" (RSS) error propagation can be very misleading. For example, if we are interested in a quantity $$Q$$ which equals some function $$f$$ of the three variables $$u$$, $$v$$, and $$w$$, then the square of the standard error of estimating $$Q$$ is $$\newcommand{\d}{\partial} \newcommand{\s}{\sigma} \left( \frac{\d f}{\d u} \right)^{\!2} \!\s_u^2 + \left( \frac{\d f}{\d v} \right)^{\!2} \!\s_v^2 + \left( \frac{\d f}{\d w} \right)^{\!2} \!\s_w^2 \\ + 2 \frac{\d f}{\d u} \frac{\d f}{\d v} \rho_{uv}\s_u\s_v + 2 \frac{\d f}{\d u} \frac{\d f}{\d w} \rho_{uw}\s_u\s_w + 2 \frac{\d f}{\d v} \frac{\d f}{\d w} \rho_{vw}\s_v\s_w$$ The first three terms are the familiar RSS, and are necessarily positive, but the other three each contain three things (two derivatives and a correlation) that might be negative, and might partly cancel each other or the squared parts. Further, even without correlation, the exact contribution of each of three supposedly independent errors is scaled by the derivatives of the estimated state with respect to that variable, so for example a slowly increasing $$\s_u$$ might be paired with a large $$\d f/\d u$$ in two months but a much smaller $$\d f/\d u$$ in two years.