On the interchangeability of sea-surface and near-surface air temperature anomalies in climatologies

Abstract

on global and hemispheric scales, sea-surface temperature (SSt) anomalies are assumed to be good surrogates for near-surface marine air temperature (MAt) anomalies. in fact, global gridded temperature datasets commonly blend SSt and near-surface air temperature anomalies to overcome the lack of geographically homogeneous and reliable MAt observations. Here, we show that SSt and MAT anomalies differ regarding crucial statistical properties such as multiannual trends and probabilistic distributions of daily and monthly averages. We provide evidence of the lack of interchangeability from an array of moored buoys in the tropical Pacific Ocean. We identify statistically significant discrepancies between SSt and MAt anomalies for single as well as groups of such buoys. thus, caution is required when characterizing and interpreting MAt variability through SSt observations, especially at shorter than decadal timescale. Near-surface air temperature observations over the oceans-the large majority of the Earth's surface area-are relatively scarce. Until a few decades ago, they were limited to coastal areas and major shipping routes 1. Nowadays, several monitoring programs are active in different oceanic regions. Among them, the Tropical Atmosphere Ocean (TAO) project was initiate in the 1980s to meet the growing needs of monitoring, understanding and predicting El Niño events and related phenomena 2. The TAO array (now referred to as TAO/TRITON) consists of about 70 moorings covering the Tropical Pacific Ocean that currently provide multidecadal time series of surface and near-surface oceanographic and meteorological variables, including sea-surface temperature (SST) and near-surface marine air temperature (MAT). Several attempts have been undertaken by the climate research community to merge information from the poorly observed early decades to the better observed recent decades and generate spatially homogeneous global gridded temperature datasets covering the full instrumental period 3-8. In such datasets, near-surface air temperature anomalies over land and SST anomalies are commonly blended, assuming that at the hemispheric and larger scales SST variations are good surrogates of MAT variations 9,10. In this sense, many studies have reported on the similarity between MAT and SST anomalies on large (global and hemispheric) spatial scales 9-20. A comparative analysis of SST and satellite-measured MAT shows that, notwithstanding a distinct difference between both variables, about 80% of the variance of one is captured by the other 21. Then, night-time MAT (nMAT) estimates are used to identify and remove SST biases to construct climate data records of SSTs in SST datasets 22. Accordingly, during the past few decades, global gridded surface temperature datasets over the ocean have been extensively used to put oceanic climate variability in the context of global climate change 23. Still, some studies point to issues potentially affecting the comparability between SST and MAT at large (even global) scales. Among them, MAT-SST ...

Comparing CMIP6 Climate Model Simulations of Annual Global Mean Temperatures to a New Combined Data Product

“…However, when the measurements are replaced by anomalies, this difference disappears, so the anomalies from these two quantities can be considered exchangeable on an annual‐global spatio‐temporal scale. Recent work, using relatively sparse data from the TAO buoys, indicates that the difference may recently have been increasing in the equatorial Pacific (Rubino et al., 2020). We are currently investigating this issue in other research.…”

How much has the Sun influenced Northern Hemisphere temperature trends? An ongoing debate

“…For this reason, most of the analysis of ocean surface temperature trends has focused on the SST data. For brevity, our ocean surface temperature analysis will be confined to the SST data, but we encourage further research examining both types of dataset and note that they can yield different results (Rubino et al 2020). Yet, even with the SST data, it is widely acknowledged that nonclimatic biases are likely to exist in the data, especially for the pre-1950s period (Jones 2016;Kennedy et al 2019;Kent et al 2017;Cowtan et al 2018;Kennedy 2014;Davis et al 2019).…”

Supplemental Material: Differences between soil and air temperatures: Implications for geological reconstructions of past climate

“…Correspondingly from Equations 12 and 13, B commonly is small over oceans and lakes (Table 4) (Oke, 1987, p. 70); ΔT in the numerator and q*(T a ) in the denominator of Equation 12 govern values of B. The difference between sea-surface temperatures (SSTs) and overlying air temperatures can be several degrees Celsius in winter, but annual averages of them commonly are only ~1 °C (Cayan, 1980) and in the tropics only 0.1 °C (Rubino et al, 2020). It follows that B increases as SSTs decrease.…”