Inferring the Linkage of Sea Surface Height Anomalies, Surface Wind Stress and Sea Surface Temperature with the Falling Ice Radiative Effects Using AVIOS and Global Climate Models

AbstractThis study attempts to infer the linkage of sea surface height anomaly (SSHA), oceanic mixed-layer depth (MLD), surface wind stress and sea surface temperature with the falling ice (snow) radiative effects (FIREs) over the Pacific Ocean using CESM1-CAM5 sensitivity experiment with FIREs-off (NOS) and on (SON) under CMIP5 historical run. The obs4MIPs monthly SSH data based upon satellite measurements are used as a reference. The seasonal and annual mean spatial patterns of SSHA difference between NOS and SON are tightly linked to the spatial pattern differences in SST and TAU over the whole domain, in particular, over south Pacific oceans. Compared with NOS, SON simulates significant improved seasonal and annual mean SSHA associated with improved sea surface temperature (SST), surface wind stress (TAU) over the trade-wind areas. In SON, the simulated mean absolute bias of SSHA over the subtropical and tropical Pacific is reduced (up to 35%) against NOS relative to observations. Compared with CMIP5 models, their ensemble mean absolute biases of SSHA show similarities to NOS mainly over the south Pacific Ocean. Despite the biases of SST and SSHA over the south and north flanks of the equator in SON, the seasonal variations of improved SSHA are closely related to those of TAU and SST resulting from the FIREs; that is, higher SSHA and deeper MLD are associated with weaker TAU and warmer SST changes and vice versa. Keywords: Sea surface height (SSH), Mixed-Layer depth (MLD), falling ice radiative effects (FIREs), Surface wind stress (TAU) 1. Introduction The variations of sea surface height (SSH) play a vital role in the climate system by affecting the fluctuations of thermocline depth and ocean currents and affecting model simulations of climate variability, such as El Niño–Southern Oscillation (ENSO) (Milne et al., 2009; Rebert et al., 1985; Wyrtki, 1985) and Interdecadal Pacific Oscillation (IPO) (Ham and Kug 2015; Lyu et al. 2016) and the links of upwelling or downwelling associated with the lower or higher SSH (Liu and Weisberg, 2007; Umaroh et al., 2017). It is found that the preceding thermocline anomalies (sea surface height anomalies) in the western (eastern) tropical Pacific have a positive (negative) contribution to the discharge phase of ENSO mainly due to the asymmetric wind stress anomalies associated with El Niño and La Niña (Li et al., 2022). Observational studies showed that there is a close relationship between spatial distributions of SSH, surface wind stress (TAU) and sea surface temperature (SST), especially in the Tropics (Zhang et al., 1997; Casey et al., 2002). The prevailing trade winds force the near-surface ocean water westward and further accumulate over the western Pacific warm pool region with higher SSH, accompanied by lower SSH over eastern Pacific Ocean. The changes in SSH that are controlled by the changes in the prevailing trade winds, in turn, leading to the changes in SST (Casey and Adamec, 2002; Flato et al., 2013; Yang et al., 2022). Anthropogenic climate change in SSH (i.e., sea level) has been steadily rising over the past century and the rate of SSH rise will be expected increasing with continued global warming. Thus, robust projections are needed to assess mitigation options and guide adaptation measures (Mengel et al., 2016). The reliability of future projections of SSH depends on the fidelity of the states-of-the-art general circulation models (GCMs) in simulating a realistic present-day SSH mean state. Moreover, the imprecisely reproducing ability of realistic SSH would further improve model simulations of projected SSH climate variability. However, most GCMs exhibited considerable differences in their simulations of present-day SSH mean states (Flato et al. (2013). Landerer et al. (2014) demonstrated that Coupled Model Intercomparison Project phase 5 (CMIP5) models (Taylor et al., 2012) simulated non-trivial SSHA bias against the observation over the tropical region. They found that the SSH biases were generally associated with the TAU biases in GCMs (Landerer et al., 2014; Lee et al., 2013). It is reported that the changes in SSH are highly related to the changes in SST under greenhouse warming projections (Dhage and Widlansky, 2022). SSH is an important climate indicator and has been rising for decades in response to a warming climate, and that rise appears to be accelerating and is of interest to scientists [Church et al., 2011; 2013]. The variations of SSH reveal the ocean heat content with warm water is less dense than cold water, so higher areas tend to be warmer than lower areas affecting the sea level influencing the coast lines human activities (Ezer and Atkinson, 2014; Sweet et al., 2017) and islandic countries (Domingues, et al., 2018). Since the simulated SSHs are affected by the modeled SST and TAU, Li et al. (2014; 2015) found that most CMIP5 GCMs do not consider the falling ice radiative effects (FIREs), producing too weak TAU and too warm SST over subtropical and Tropical oceans (Li et al., 2012, 2013). The inclusion of FIREs improves model simulated tropical climate states of radiation fields (Figure S1), TAU and SST in CMIP5 (Li et al., 2014; 2015; Supplementary information). Li et al. [2015] pointed out that CMIP5 models tend to have too strong convection, producing anomalous low-level outflows (See SI, Figure S2b) over the Intertropical Convergence Zone (ITCZ), South Pacific Convergence Zone (SPCZ), and Maritime Continent (See SI, Figure S3). This leads to weakening surface wind stress (Figure S4), and upper ocean mixing, resulting in warmer SSTs (See SI, Figure S5) over the trade-wind regions [Li et al. 2014a; 2016; 2018]. Motivated by the aforementioned studies, this study will address: to what extent do the responses of SSH simulations linked to weaker surface winds, warmer SSTs resulting from the lack of FIREs? In other words, what are the impacts on local SSH through the remote influence of changes in surface wind stress and SST that are linked to the FIREs? To address these questions, annual-mean spatial patterns and seasonal cycles of mean bias (MBs) and mean absolute biases (MABs) over the study domain (240oW – 0o, 40oS – 40oN) and the south Pacific trade-wind regions (160 oW – 120 oW, 30 oS – 0 oS) will be examined to understand the biases of the SSHA related to the lack of FIREs and their linkage to TAU and SST differences between NOS and SON using obs4MIP Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) SSH data as a reference. This paper is structured as follows: Section 2 includes a brief description of the data and methodology and model simulations used. Section 3 illustrates the simulation assessment and shows the influence of FIREs on SSHA performance and their links to SST and TAU changes. Section 4 summarizes and discusses the major findings of this study.