The Earth’s energy imbalance (EEI) that develops at the top of the atmosphere is accommodated by gains or losses of energy in Earth’s heat reservoirs, leading to temperature and sea level change. The global ocean has stored about 90% of the EEI from anthropogenic forcing, but the attribution of past changes of EEI remains largely unknown, thus obscuring our understanding of past climate change. Here, we reconstruct changes in mean ocean temperature over the past 150,000 years that, with a reconstruction of ice sheet–volume changes, allow us to isolate the contributions of the dominant ocean and ice sheet heat reservoirs to the global energy inventory as well as to derive their associated contributions to EEI. We attribute orbital-scale EEI variability to joint precessional and CO2 forcing that caused changes in rates of ice sheet energy storage. In contrast, millennial-scale EEI variability can be attributed to radiative responses to decreases in the Atlantic meridional overturning circulation and increasing CO2 during Heinrich stadials that caused changes in rates of ocean energy storage.
Sara C Sanchez, P. U. Clark, Chenyu Zhu et al.· Science Advances· 0 citations
This paper compares three methods for quantifying stochastic ocean forcing to low frequency sea surface temperature (SST) variability from the surface heat budget in the framework of a simple stochastic climate model, especially in the case of red noise ocean forcing. The three methods are: PT21 (Patrizio and Thompson, 2021), PT22 (Patrizio and Thompson, 2022) and LGD23 (Liu et al., 2023). PT21 estimates the ratio of ocean over atmosphere forcing as the ratio of the covariance of SST tendency with ocean heat transport over the covariance with surface heat flux, while PT22 and LGD23 first derive the time series of oceanic and atmospheric forcing before estimating their ratio. The three methods are first applied to synthetic data of the stochastic climate model and then to the mid-latitude North Atlantic in observations. It is found that the LGD23 method provides an unbiased estimation of oceanic forcing with a modest sampling error at low frequency, if the persistence time of the sea surface salinity can be treated as a good approximation of that of SST associated with ocean heat transport. The PT22 method has the smallest sampling error, but tends to underestimate the ocean forcing modestly when the ocean forcing is a red noise process. The PT21 method gives the correct ratio of oceanic over atmospheric forcing in spectral density in theory, but suffers from a very large sampling error for practical application to a data set of a finite length of decades. We recommend the use of both LGD23 and PT22 as two complimentary methods for the estimation of ocean forcing, with the PT22 providing likely a lower bound for red noise ocean forcing.
Zhengyu Liu, Peng Gu· Journal of Climate· 0 citations
