Subtropical Mode Water (STMW), an isothermal layer that forms on the equatorward side of western boundary current (WBC) in response to wintertime cooling, is central to understanding climate variability in mid-latitude regions because it integrates anomalies in both the ocean and atmospheric to contribute to climate system memory. The STMW region has a large capacity to store heat, and its heat storage rate has been shown to depend both on air-sea fluxes and on ocean circulation. The volume of STMW is so large that several years of air-sea interaction alone cannot dissipate it; after formation, it is partially re-entrained in subsequent winters to again interact with the atmosphere.
Many processes have been identified that could affect STMW formation or its subsequent destruction. A primary goal of the NSF-funded CLIMODE (CLIVAR Mode Water Experiment) is to quantify the processes contributing to the evolution of the STMW of the western North Atlantic, commonly referred to as 18-degree-water (EDW) because of its nearly constant temperature. An extensive set of measurements has been obtained over the two-year field program; analyses and modeling of the observational period can help evaluate the relative importance of the processes contributing to EDW evolution. A primary motivation is that CLIMODE analyses will lead to improvements in climate modeling. This study aims to provide a link between the CLIMODE-specific analyses, longer period variability, and the need for metrics to evaluate and verify climate models.
Intellectual merit: The EDW region with its large heat storage and air-sea fluxes, variable poleward heat transport, and energetic ocean circulation is a prime candidate for memory in the atmosphere-ocean system. The proposed research is an examination of the interannual-to-decadal variations in EDW volume, of the processes that contribute to it, and its impact on air-sea interaction. Some competing processes in EDW evolution (warm water advection by the Gulf Stream, mixing, and oceanic heat loss through air-sea fluxes) have variability linked to climate indices such as the North Atlantic Oscillation. The investigators will examine whether important processes can be monitored using proxy variables and thus link the field program results to the longer climate record to evaluate the importance of each process, the predictability of EDW evolution, and the ability of EDW to contribute to climate memory.
Broader impacts: To be successful in predicting climate variability and change, models must be able to simulate those processes that produce interannual-to-decadal climate impact. An effective climate observing system must monitor the variables needed to characterize those fundamental processes and improve model parameterization and simulation. For example, the EDW region is a center of rapid intensification of mid-latitude storms that may be responding to changing ocean conditions, in particular, to ocean heat storage. Simulation and verification of climate predictions (such as changes in storm intensity) depend on developing a series of metrics that measure how well the model simulates important processes (heat storage). For the processes that are important to EDW evolution and its climate impact, critical measurements will be identified in order to develop simple and appropriate metrics with which to evaluate climate models.
This project is a contribution to the U.S. CLIVAR (CLImate VARiability and predictability) program.
Subtropical Mode Water (STMW), an isothermal layer that forms on the equatorward side of western boundary current in response to wintertime cooling, is central to understanding climate variability in mid-latitude regions because it integrates anomalies in both the ocean and atmosphere to contribute to climate system memory. The STMW region has a large capacity to store heat, and its heat storage rate has been shown to depend both on air-sea fluxes and on ocean circulation. The volume of STMW is so large that several years of air-sea interaction alone cannot dissipate it; after formation, it is partially re-entrained in subsequent winters to again interact with the atmosphere. Many processes have been identified that could affect STMW formation or its subsequent destruction. To be successful in predicting climate variability and change, models must be able to simulate those processes that produce interannual-to-decadal climate impact. An effective climate observing system must monitor the variables needed to characterize those fundamental processes and improve model parameterization and simulation. For example, the STMW region is a center of rapid intensification of mid-latitude storms that may be responding to changing ocean conditions, in particular, to ocean heat storage. Simulation and verification of climate predictions depend on developing a series of metrics that measure how well the model simulates important processes. Our major research goal is to characterize interannual-to-decadal variations in the North Atlantic STMW (referred to as 18-degree-water, EDW) and to provide metrics with which to evaluate the ability of climate models in simulating these impacts. Our analysis of historical data suggests that the EDW formation depends about equally on air-sea fluxes and on size of outcrop region variability, which is different from previous studies only focused on the air-sea fluxes. A simple model is developed to further hindcast observed EDW volume anomalies in two regions: one in which EDW is formed and an adjacent region of subducted EDW. Estimates of the relative contributions of heat flux anomalies, vertical mixing from Ekman advection, mixing, and circulation are examined using proxy variables derived from winds, sea surface temperature, hydrographic data and altimetric sea level. The importance of each process is evaluated by its contribution to observed EDW volume anomalies in two regions. The study produced some robust conclusions: (1) anomalies of formation by surface heat fluxes are clearly reflected in EDW volume anomalies with some contributions by Ekman advection; (2) of the newly formed EDW about 65% is lost by mixing and about 35% is transferred to the subducted region; (3) mixing losses are well parameterized by the meandering of the nearby GS and (4) transfer and losses from the subducted region can be parameterized by the geostrophic surface flow. Coupled atmosphere–ocean models are used to study climate variability on seasonal-to-centennial time scales and the predictability of the climate system. The ability of coupled climate models to capture the interactions among climate systems and to predict future climate change depends on both the representation of oceanic processes and the ability of the model atmosphere to respond to persistent ocean temperature anomalies. Thus, it is essential to assess the ability of climate models in reproducing oceanic processes. Many efforts and resources have been focused on assimilation of observations. While data assimilation may correct the variables that are readily observed, it may change the relationship between forcing and response. Therefore, we analyzed climate models both with and without data assimilation to examine to what extent the data-assimilation process alters the coupling between atmosphere and ocean. We analyzed the EDW in three coupled models, both with data assimilation (GFDL coupled data assimilation; CDA), and without data assimilation (GFDL CM2.1 and NCAR CCSM3) to evaluate how well EDW processes are simulated in those models and to examine whether data assimilation alters the model response to forcing. In comparison with estimates from observations, the data-assimilating model gives a better representation of the formation rate, the spatial distribution of EDW, and its thickness, with the largest EDW variability along the Gulf Stream (GS) path. The EDW formation rate in CM2.1 is very weak because of weak heat loss from the ocean in the model. Unlike the observed dominant southward movement of the EDW, the EDW in CM2.1 and CCSM3 moves eastward after formation in the excessively wide GS in the models. The CDA does not capture the observed thermal response of the overlying atmosphere to the ocean. Observations show a robust anticorrelation between the upper-ocean heat content and air–sea heat flux, with upper-ocean heat content leading air–sea heat flux by a few months. This anticorrelation is well captured by CM2.1 and CCSM3 but not by CDA. Only CM2.1 captures the observed anticorrelation between the upper-ocean heat content and EDW volume. This suggests that, although data assimilation corrects the readily observed variables, it degrades the model thermodynamic response to forcing.
