Climate Signal Detection Times and Constraints on Climate Benchmark Accuracy Requirements

Long term trends in the climate system are always partly obscured by naturally occurring interannual variability. All else being equal, the larger the natural variability is, the less precisely one can estimate a trend in a timeseries of data. Measurement uncertainty, though, also obscures long term trends. We derive how measurement uncertainty and natural interannual variability interact in inhibiting the detection of climate trends using simple linear regression and show how the interaction between the two can be used to formulate accuracy requirements for satellite climate benchmark missions. We find that measurement uncertainty increases detection times but only when considered in direct proportion to natural variability. We also find that detection times depend critically on the correlation time of natural variability and satellite lifetime. As a consequence, requirements on satellite climate benchmark accuracy and mission lifetime must be directly related to natural variability of the climate system and its associated correlation times.