DEVELOPMENT OF A GLOBAL LONG TERM SURFACE ALBEDO DATA RECORD FROM NOAA AVHRR FOR THE ESTIMATION OF 38 YEAR TRENDS (1982-2020)

Abstract

Long-term consistent data records and their analyses are crucial in the prediction of global climate and the associated environmental changes happening around the globe. In particular, surface albedo is of critical importance, since it is a key forcing parameter controlling the Earth’s radiative energy budget and the energy exchange between surface and atmosphere. Given its significance, the Global Climate Observing System (GCOS) set a list of requirements that would aid the scientific community in climate model predictions of climate change.The requirements for a dataset length of 30+ years and a daily temporal resolution can only be satisfied using data from the Advanced Very High Resolution Radiometer (AVHRR) aboard the North Oceanic and Atmospheric Administration (NOAA) satellites. The goal of this dissertation is to create a long-term surface albedo dataset from the Long Term Data Record (LTDR) product, spanning from 1982-2018, that can provide surface albedo estimates at 0.05⁰ spatial resolution and a daily temporal resolution. To do this, the original LTDR product goes through several pre-processing steps to tackle some of its weaknesses and limitations. First, the data from the different AVHRR sensors aboard all NOAA satellites that comprise the dataset are harmonized, using a novel spectral adjustment method. Second, an algorithm is derived, to discriminate cloud and snow surfaces, which were previously only reported as the same class. Third, the clear land surface albedo was retrieved by improving upon a model optimized for the MODerate resolution Imaging Spectrometer (MODIS). The snow albedo, on the other hand, was obtained through a random forest approach, using MODIS-derived albedo as a reference. These steps allowed the computation of the Satellite AVHRR Land Surface Albedo (SALSA) product, which was cross-compared with the well-validated MCD43C3 product, based on MODIS data. This comparison revealed the main strengths and limitations of the product, but an overall acceptable behavior, with uncertainties below 0.03 in average. The product was then used to estimate long-term surface albedo trends. The results revealed that the overall surface albedo has not significantly changed through the period 1982-2018, highlighting the importance of computing long-term trends using 30+ years of observations.