Temporal analysis
We calculated temporal trends for all the above datasets for each TMCF
using observations from 1997 to 2020. For this purpose, hourly
observations were aggregated to an annual average. Linear regressions
were then performed to calculate trends (i.e., slope or rate of change)
(Δ, year-1).
Regional studies tend to evaluate
climatic trends from ERA5 using monthly average (i.e., Lei et al., 2020;
Yilmaz, 2023), here we use annual average instead to provide a global
perspective of temporal changes in low-clouds and others ECVs. The
latter was performed using the IBM PAIRS Geoscope platform using a
pixel-based approach. This cloud-based platform enables the deployment
of user-defined functions on ERA-5 and CHIRP datasets without
downloading raw data but obtains the regression coefficients directly
(Lu et al., 2016). Trends were
extracted from 1997 and not previous decades (e.g., 1940) given the
availability of data in IBM PAIRS Geoscope and the reliability of
products from recent decades that leverage on new available remote
sensing and meteorological observations (Yilmaz, 2023). After extracting
the trends, we performed Bayes one-sample t-tests (Kruschke,
2013) to compute mean estimates of trends and evaluate the probability
of these differing from zero. In addition, we performed an analysis at
the realm level to determine how trends in low-clouds and ECV depend on
the biogeography of these ecosystems. Bayes one-sample t-testanalyses were performed on R (R Core Team, 2023) using 30000 Markov
chain Monte Carlo iterations in the BayesianFirstAid package (Bååth,
2014). All statistical analyses (including those in the following
section) were weighed by the pixel projected area according to the TMCFs
location to account for the latitudinal variation of the pixel area.