Ocean heat content

Summary

Ocean heat content (OHC) is the energy absorbed and stored by oceans. To calculate the ocean heat content, measurements of ocean temperature at many different locations and depths are required. Integrating the areal density of ocean heat over an ocean basin, or entire ocean, gives the total ocean heat content. Between 1971 and 2018, the rise in OHC accounted for over 90% of Earth’s excess thermal energy from global heating. The main driver of this OHC increase was anthropogenic forcing via rising greenhouse gas emissions. By 2020, about one third of the added energy had propagated to depths below 700 meters. In 2022, the world’s oceans, as given by OHC, were again the hottest in the historical record and exceeded the previous 2021 record maximum. The four highest ocean heat observations occurred in the period 2019–2022 with the North Pacific, North Atlantic, the Mediterrane

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Spatial variability of multi-annual seasonal surface heat flux patterns of Lake Geneva

David Andrew Barry, Andrea Cimatoribus, Abolfazl Irani Rahaghi, Ulrich Lemmin

The dynamics of the spatiotemporal surface heat flux (SurHF) of Lake Geneva (Switzerland/France) was estimated for a 7-y period. Data sources included hourly maps of over-the-lake assimilated meteorological data from a numerical weather model and Lake Surface Water Temperatures (LSWT) from satellite imagery. Analysis results indicate an average spatial SurHF range of > 40 W/m^2, mainly due to wind sheltering over parts of the lake. The difference between the time variation of the heat content in western and eastern parts of the lake derived from the SurHF estimates was consistent with the spatial heat content variation obtained from long-term temperature profile measurements in those parts. Our analyses also indicate a noticeable temporal change of the main controlling forcing when comparing heat fluxes in spring to the rest of the year. Such a regime change can be explained by the atmospheric thermal boundary layer dynamics that were unstable except in spring (March to early June). This resulted in much less spatial variability during springtime. The results emphasize that spatial variability in the meteorological and LSWT patterns will cause spatiotemporal SurHF variability that should be taken into consideration when assessing the time evolution of the heat budget of large water bodies.

2018

Improving surface heat flux estimation for a large lake through model optimization and two-point calibration: The case of Lake Geneva

David Andrew Barry, Damien Bouffard, Andrea Cimatoribus, Abolfazl Irani Rahaghi, Ulrich Lemmin

Net Surface Heat Flux (SurHF) was estimated from 2008 to 2014 for Lake Geneva (Switzerland/France), using long‐term temperature depth profiles at two locations, hourly maps of reanalysis meteorological data from a numerical weather model and lake surface water temperatures from calibrated satellite imagery. Existing formulas for different heat flux components were combined into 54 different total SurHF models. The coefficients in these models were calibrated based on SurHF optimization. Four calibration factors characterizing the incoming long‐wave radiation, sensible, and latent heat fluxes were further investigated for the six best performing models. The combination of the modified parameterization of the Brutsaert equation for incoming atmospheric radiation and of similarity theory‐based bulk parameterization algorithms for latent and sensible surface heat fluxes provided the most accurate SurHF estimates. When optimized for one lake temperature profile location, SurHF models failed to predict the temperature profile at the other location due to the spatial variability of meteorological parameters between the two locations. Consequently, the optimal SurHF models were calibrated using two profile locations. The results emphasize that even relatively small changes in calibration factors, particularly in the atmospheric emissivity, significantly modify the estimated long‐term heat content. The lack of calibration can produce changes in the calculated heat content that are much higher than the observed annual climate change‐induced trend. The calibration improved parameterization of bulk transfer coefficients, mainly under low wind regimes.

2018

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In many lakes, surface heat flux (SHF) is the most important component controlling the lake’s energy content. Accurate methods for the determination of SHF are valuable for water management, and for use in hydrological and meteorological models. Large lakes, not surprisingly, are subject to spatially and temporally varying meteorological conditions, and hence SHF. Here, we report on an investigation for estimating the SHF of a large European lake, Lake Geneva. We evaluated several bulk formulas to estimate Lake Geneva’s SHF based on different data sources. A total of 64 different surface heat flux models were realized using existing representations for different heat flux components. Data sources to run the models included meteorological data (from an operational numerical weather prediction model, COSMO-2) and lake surface water temperature (LSWT, from satellite imagery). Models were calibrated at two points in the lake for which regular depth profiles of temperature are available, and which enabled computation of the total heat content variation. The latter, computed for 03.2008-12.2013, was the metric used to rank the different models. The best calibrated model was then selected to calculate the spatial distribution of SHF. Analysis of the model results shows that evaporative and convective heat fluxes are the dominant terms controlling the spatial pattern of SHF. The former is significant in all seasons while the latter plays a role only in fall and winter. Meteorological observations illustrate that wind-sheltering, and to some extent relative humidity variability, are the main reasons for the observed large-scale spatial variability. In addition, both modeling and satellite observations indicate that, on average, the eastern part of the lake is warmer than the western part, with a greater temperature contrast in spring and summer than in fall and winter whereas the SHF spatial splitting is stronger in fall and winter. This is mainly due to negative heat flux values (net cooling) and stronger wind forcing, and consequently stronger mixing, in cold seasons.

2015