Data Fusion of Total Solar Irradiance Composite Time Series Using 41
years of Satellite Measurements
Abstract
Since the late 70’s, successive satellite missions have been monitoring
the sun’s activity, recording the total solar irradiance (TSI). Some
of these measurements last for more than a decade. It is then
mandatory to merge them to obtain a seamless record whose duration
exceeds that of the individual instruments. Climate models can be better
validated using such long TSI records which can also help provide
stronger constraints on past climate reconstructions (e.g.,back to the
Maunder minimum). We propose a 3-stepmethod based on data fusion,
including a stochastic noise model to take into account short and
long-term correlations. Compared with previous products, the difference
in terms of mean value over the whole time series and at the various
solar minima are below 0.2W/m2. Next, we model the frequency
spectrum of this 41-year TSI composite time series with a Generalized
Gauss-Markov model to help describing an observed flattening at high
frequencies. It allows us to fit a linear trend into these TSI time
series by joint inversion with the stochastic noise model via a
maximum-likelihood estimator. Our results show that the amplitude of
such trend is∼−0.009±0.010 W/(m2yr) for the
period 1980-2021. These results are compared with the difference of
irradiance values estimated from two consecutive solar minima. We
conclude that the trend in these composite time series is mostly an
artefact due to the coloured noise.