Investigation of Antarctic Precipitable Water Vapor Variability and Trend from 18 Year (2001 to 2018) Data of Four Reanalyses Based on Radiosonde and GNSS Observations

Abstract

Precipitable water vapor (PWV) plays a vital role in climate research, especially for Antarctica in which meteorological observations are insufficient due to the adverse climate and topography therein. Reanalysis data sets provide a great opportunity for Antarctic water vapor research. This study investigates the climatological PWV means, variability and trends over Antarctica from four reanalyses, including the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2), Japanese 55-year Reanalysis (JRA-55) and National Centers for Environmental Prediction/Department of Energy (NCEP/DOE), in the period of 2001–2018 based on radiosonde and GNSS observations. PWV data from the ERA5, MERRA-2, JRA-55 and NCEP/DOE have been evaluated by radiosonde and GNSS observations, showing that ERA5 and MERRA-2 perform better than JRA-55 and NCEP/DOE with mean root mean square (RMS) errors below 1.2 mm. The climatological PWV mean distribution over Antarctica roughly shows a decreasing trend from west to east, with the highest content in summer and the lowest content in winter. The PWV variability is generally small over Antarctica, showing a seasonal dependence that is larger in the cold season and smaller in the warm season. PWV trends for all reanalyses at most Antarctic regions are insignificant and most reanalyses present overall drying trends from 2001 to 2018, except for ERA5 exhibiting a moistening trend. PWV trends also show seasonal and regional dependence. All reanalyses are generally consistent with radiosonde and GNSS observations in reproducing the PWV means (mean differences within 1.1 mm), variability (mean differences within 3%) and trends (mean differences within 6.4% decade−1) over Antarctica, except for NCEP/DOE showing spurious variability and trends in East Antarctica. Results can help us further understand these four reanalysis PWV products and promote climate research in Antarctica.

Comprehensive Evaluation and Comparison of AIRS, VASS, and VIRR Water Vapor Products Over Antarctica

“…where 𝐴𝐴 PWVℎ 1 represents the PWV corresponding to the height h 1 (unit: km), 𝐴𝐴 𝐴𝐴 𝐴𝐴 𝐴𝐴ℎ 2 represents the PWV corresponding to the height 2 (unit: km), and β (unit: mm/km) represents the empirical attenuation coefficient. Considering the unique climate of Antarctica, the Antarctic empirical attenuation coefficient model is used for calculation (Mo et al, 2021),…”

“…Currently, the observation methods for Antarctic water vapor mainly include meteorological stations, radiosondes, Global Navigation Satellite Systems (GNSS), and satellite remote sensing (Bevis et al., 1994; Connolley & King, 1996; Gettelman et al., 2006; Lazzara et al., 2012; Mo et al., 2021; Negusini et al., 2016; Ricaud et al., 2013; Thomas et al., 2011). Among them, radiosondes are considered a reliable water vapor detection technology (Antón et al., 2015).…”

“…Polar‐orbiting satellites in low Earth orbit can pass over the Antarctic region more than 10 times daily, providing extensive coverage and excellent temporal resolution. However, several variables, such as the performance of the instruments themselves, weather, cloud cover, and ground conditions, can influence the precision of satellite water vapor products (Alraddawi et al., 2018; Boylan et al., 2015; Mo et al., 2021). Furthermore, reanalysis data, another type of product that can provide water vapor information for the entire Antarctic continent, heavily relies on in situ observations and the input of great‐quality satellite data (Dee et al., 2011; Laloyaux et al., 2018).…”

Snow Accumulation Variability at the South Pole From 1983 to 2020, Associated With Central Tropical Pacific Forcing

“…ERA5 is the newest‐generation global climate and weather reanalysis provided by ECMWF. Benefiting from improvements in model physics and data assimilation methodology, ERA5 assimilated near‐surface and upper‐air observations, based on a hybrid incremental four‐dimensional variational analysis (4D‐Var) scheme in the Integrated Forecasting System (IFS) Cy41r2 model (Bonavita et al., 2016; Hersbach et al., 2020; Mo et al., 2021).…”

“…One of the largest sources of the uncertainties is poor constraint of snow accumulation due to the difficult measurements and the complicated data interpretation. To decrease these gaps, many observational and modeling attempts have been made (e.g., Mo et al, 2021). However, because the processes governing snow accumulation changes have not been fully understood, the ability of modeling is very limited, which constrains better projections of future ice sheet changes and the corresponding contribution to global sea level.The main methods available for snow accumulation measurements include snow stake/stake farms, snow pits, firn/ice cores, snow radar altimetry and satellite-based techniques (…”