Assessing Ocean Forcing on Sea Surface Temperature Variability from Surface Heat Budget

Statistical Noise and Missing Forcing Limit Estimates of Earth's Feedback from Prescribed Sea-Surface Temperature Simulations

Earth's feedback parameter measures how the Earth system responds to forcing and is inversely proportional to climate sensitivity. Sea-surface temperature (SST) patterns can modulate the value of the feedback parameter. Differences between observed and simulated SSTs have raised the question how the observed SSTs evolution impacts the global feedback. The standard method for estimating this effect uses observed SSTs prescribed to an atmospheric model with fixed pre-industrial atmospheric forcing. This method makes two assumptions: first, that the observed SSTs capture all relevant effects from the forcing, so that prescribing a time-varying forcing is unnecessary; second, that the temporal variations in the feedback parameter are driven by the evolving SST pattern and can be estimated via moving-window regressions. We test these assumptions by running controlled experiments in which SSTs from fully-coupled historical simulations are prescribed to an atmospheric model. We find that the prescribed-SST experiments fail to capture the coupled feedback evolution. This is explained by two effects: First, the absence of prescribed atmospheric forcing, and second, statistical noise arising from the computation of moving-window regressions. We find no evidence of any significant relationship between evolving SST patterns and changes in the feedback time series in a 4000-year pre-industrial control simulation. Any trends in the feedback parameter detected on timescales shorter than ~100 years are indistinguishable from statistical noise, making their attribution to the evolving SST pattern extremely difficult. Our results imply that prescribed SST simulations offer limited potential for inferring temporal changes in Earth's parameter over the observational period.

Scale dependence of the marine atmospheric boundary‐layer response to ocean surface‐temperature variability in the EUREC4A region

A large share of energy within the global oceans lies within the mesoscale range, (100 km), where very large dynamic and thermodynamic variability has been observed. We address how the marine atmospheric boundary layer (MABL) in a trade‐wind region adapts to fast and spatially varying sea‐surface temperature (SST) structures, with a focus on changes in behaviour as a function of spatial scales. High‐resolution atmospheric simulation data indicate that, at scales smaller than 1000 km, the effects of enhanced entrainment of dry free tropospheric air overcome those of surface evaporation over warm SST anomalies. This is supported by computations from a conceptual bulk model, which confirm two different responses in MABL temperature and specific humidity: an increase in the forcing SST warms the MABL and reduces its humidity content slightly. Locally, this behaviour suppresses the surface sensible heat flux (SHF) and enhances the surface latent heat flux (LHF), as observed recently with in situ, satellite, and numerical modelling data. At larger scales, instead, the MABL is more in equilibrium with the ocean surface and the sensitivity of turbulent fluxes to the underlying SST anomalies is significantly smaller. The scale dependence of the LHF variability is analysed with a linear scale decomposition method. SST is found to be the primary driver of LHF variability when scales smaller than about 1000 km are resolved, whereas atmospheric variability takes the lead for larger scales. Despite the effects on the mean LHF being small, the link between SST and LHF variability potentially has important implications for atmospheric shallow mesoscale circulations, which remain to be explored.

Improving ocean bottom pressure fields using space gravity data in state estimation

Validation of ECMWF Ensemble Forecasts of Sea Surface Latent and Sensible Heat Fluxes in the Pacific Arctic Against Saildrone Observations

Estimates of sea‐surface latent and sensible heat fluxes using in situ observations are very rare in the Arctic Ocean. Saildrone Explorer uncrewed surface vehicles (saildrones) were deployed in the Bering, Chukchi, and Beaufort Seas from May–October 2019. Sea‐surface fluxes are estimated using surface state variables (temperature, humidity, and wind) observed by the saildrones applied to a bulk algorithm. In this study, the observationally based estimates of sea‐surface fluxes and the surface state variables are compared with those in ECMWF ensemble forecasts. Errors in the forecasts are mostly random and within the observed standard deviation. There are, however, sporadic and large error spikes (>3 standard deviations). Sea‐surface temperature and surface air temperature and humidity are systematically underestimated in the forecasts. From the perspective of bulk flux calculations, errors in latent heat fluxes are mainly due to errors in air‐sea differences in humidity, and errors in sensible heat fluxes are mainly due to those in air‐sea differences in temperature. Contributions from errors in wind speed are secondary. Differences in the flux algorithms used in the forecast and observations contribute only slightly. This study illustrates that even limited in situ observations from uncrewed mobile platforms may yield useful information on the accuracy of ensemble forecasts that otherwise is unavailable in regions without other in situ observations. Issues related to statistical treatment of data from moving sources are addressed through a simple application of an ergodicity test to the saildrone data.

Improved Global Ocean Heat Content Estimation by Modeling Vertical Spatio-Temporal Dependence

Estimating ocean heat content (OHC) with reliable uncertainties is critical for understanding and monitoring the evolution of Earth's climate, as the ocean has stored most of the energy accumulated in the climate system due to Earth Energy Imbalance. Here, we use Argo profiling float data from 2004-2022 to map OHC. As fewer Argo observations are available deeper in the water column, previous studies have partitioned the ocean into at least two pressure layers and mapped each separately, which complicates the estimation of uncertainties when the maps are summed to get the total OHC. In this work, we consider the case of two pressure layers and propose an improved mapping and uncertainty quantification method using bivariate locally stationary Gaussian processes and conditional simulations to map the two sections jointly while accounting for the correlation between them. We find that modeling this correlation results in improved OHC anomaly mapping and up to a 15 percent reduction of global OHC anomaly uncertainties in comparison to mapping the two layers separately without accounting for their dependence. These estimated uncertainties are essential to analyze the statistical significance of OHC anomalies on both regional and global scales, which we demonstrate using several climatological case studies.

The sensitivity of a coupled climate model to numerical mixing in its ocean component

An ensemble of seven integrations of the HadGEM3-GC5 coupled model is used to investigate the sensitivity of the simulated climate system to settings in the ocean component that have been shown to affect the level of numerical mixing in forced simulations. This configuration is closely related to the UK contribution to CMIP7. The ensemble is integrated for 60 years with constant year 2000 greenhouse forcing. The ocean surface temperature has a strong and consistent response to numerical mixing, with increased mixing leading consistently to warming over the global ocean by up to 0.5°C, while reducing mixing cools the surface by a similar degree. The response of the surface air temperature is very similar to that of the SST, but is seasonally amplified at high latitudes in the respective winter. Robust and strong sensitivities are also found for sea ice cover, rainfall, and the surface shortwave and latent heat fluxes, the latter two showing opposing changes in their global means of over 2 Wm −2 across the ensemble. We present large-scale ocean and atmospheric metrics and discuss mechanisms for the sensitivity of surface temperatures in these simulations to numerical mixing in the ocean, in which more mixing warms the surface and vice versa. The magnitude of this sensitivity of the surface temperature is significant, since it is comparable with the changes found in CMIP simulations with historical or future greenhouse scenario forcings, and we speculate on the implications for modelling future climates.