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Correction for Systematic Errors in the Global Dataset of Temperature Profiles from Mechanical BathythermographsAbstract
A homogeneous, consistent, high-quality in situ temperature dataset covering some decades in time is crucial for the detection of climate changes in the ocean. For the period from 1940 to the present, this study investigates the data quality of temperature profiles from mechanical bathythermographs (MBT) by comparing these data with reference data obtained from Nansen bottle casts and conductivity–temperature–depth (CTD) profilers. This comparison reveals significant systematic errors in MBT measurements. The MBT bias is as large as 0.2°C before 1980 on the global average and reduces to less than 0.1°C after 1980. A new empirical correction scheme for MBT data is derived, where the MBT correction is country, depth, and time dependent. Comparison of the new MBT correction scheme with three schemes proposed earlier in the literature suggests a better performance of the new schemes. The reduction of the biases increases the homogeneity of the global ocean database being mostly important for climate change–related studies, such as the improved estimation of the ocean heat content changes.
Abstract
A homogeneous, consistent, high-quality in situ temperature dataset covering some decades in time is crucial for the detection of climate changes in the ocean. For the period from 1940 to the present, this study investigates the data quality of temperature profiles from mechanical bathythermographs (MBT) by comparing these data with reference data obtained from Nansen bottle casts and conductivity–temperature–depth (CTD) profilers. This comparison reveals significant systematic errors in MBT measurements. The MBT bias is as large as 0.2°C before 1980 on the global average and reduces to less than 0.1°C after 1980. A new empirical correction scheme for MBT data is derived, where the MBT correction is country, depth, and time dependent. Comparison of the new MBT correction scheme with three schemes proposed earlier in the literature suggests a better performance of the new schemes. The reduction of the biases increases the homogeneity of the global ocean database being mostly important for climate change–related studies, such as the improved estimation of the ocean heat content changes.
Abstract
A complete map of the ocean subsurface temperature is essential for monitoring aspects of climate change such as the ocean heat content (OHC) and sea level changes and for understanding the dynamics of the ocean/climate variation. However, global observations have not been available in the past, so a mapping strategy is required to fill the data gaps. In this study, an advanced mapping method is proposed to reconstruct the historical ocean subsurface (0–700 m) temperature field from 1940 to 2014 by using ensemble optimal interpolation with a dynamic ensemble (EnOI-DE) approach and a multimodel ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical and representative concentration pathway 4.5 simulations. The reconstructed field is a combination of two parts: a first guess provided by the ensemble mean of CMIP5 models and an adjustment by minimizing the analysis error with the assistance of error covariance determined by the CMIP5 models. The uncertainty of the field can also be assessed. This new approach was evaluated using a series of tests, including subsample tests by using data from the Argo period, idealized tests by specifying a truth field from the models, and withdrawn-data tests by removing 20% of the observations for validation. In addition, the authors showed that the ocean mean state, long-term trends, and interannual and decadal variability are all well represented. Furthermore, the most significant benefit of this method is to provide an improved estimate of the long-term historical OHC changes since 1940, which have important implications for Earth’s energy budget.
Abstract
A complete map of the ocean subsurface temperature is essential for monitoring aspects of climate change such as the ocean heat content (OHC) and sea level changes and for understanding the dynamics of the ocean/climate variation. However, global observations have not been available in the past, so a mapping strategy is required to fill the data gaps. In this study, an advanced mapping method is proposed to reconstruct the historical ocean subsurface (0–700 m) temperature field from 1940 to 2014 by using ensemble optimal interpolation with a dynamic ensemble (EnOI-DE) approach and a multimodel ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical and representative concentration pathway 4.5 simulations. The reconstructed field is a combination of two parts: a first guess provided by the ensemble mean of CMIP5 models and an adjustment by minimizing the analysis error with the assistance of error covariance determined by the CMIP5 models. The uncertainty of the field can also be assessed. This new approach was evaluated using a series of tests, including subsample tests by using data from the Argo period, idealized tests by specifying a truth field from the models, and withdrawn-data tests by removing 20% of the observations for validation. In addition, the authors showed that the ocean mean state, long-term trends, and interannual and decadal variability are all well represented. Furthermore, the most significant benefit of this method is to provide an improved estimate of the long-term historical OHC changes since 1940, which have important implications for Earth’s energy budget.
Abstract
Assessment of the upper-ocean (0–700 m) heat content (OHC) is a key task for monitoring climate change. However, irregular spatial and temporal distribution of historical subsurface observations has induced uncertainties in OHC estimation. In this study, a new source of uncertainties in calculating OHC due to the insufficiency of vertical resolution in historical ocean subsurface temperature profile observations was diagnosed. This error was examined by sampling a high-vertical-resolution climatological ocean according to the depth intervals of in situ subsurface observations, and then the error was defined as the difference between the OHC calculated by subsampled profiles and the OHC of the climatological ocean. The obtained resolution-induced error appeared to be cold in the upper 100 m (with a peak of approximately −0.1°C), warm within 100–700 m (with a peak of ~0.1°C near 180 m), and warm when averaged over 0–700-m depths (with a global average of ~0.01°–0.025°C, ~1–2.5 × 1022 J). Geographically, it showed a warm bias within 30°S–30°N and a cold bias at higher latitudes in both hemispheres, the sign of which depended on the concave or convex shape of the vertical temperature profiles. Finally, the authors recommend maintaining an unbiased observation system in the future: a minimal vertical depth bin of 5% of the depth was needed to reduce the vertical-resolution-induced bias to less than 0.005°C on global average (equal to Argo accuracy).
Abstract
Assessment of the upper-ocean (0–700 m) heat content (OHC) is a key task for monitoring climate change. However, irregular spatial and temporal distribution of historical subsurface observations has induced uncertainties in OHC estimation. In this study, a new source of uncertainties in calculating OHC due to the insufficiency of vertical resolution in historical ocean subsurface temperature profile observations was diagnosed. This error was examined by sampling a high-vertical-resolution climatological ocean according to the depth intervals of in situ subsurface observations, and then the error was defined as the difference between the OHC calculated by subsampled profiles and the OHC of the climatological ocean. The obtained resolution-induced error appeared to be cold in the upper 100 m (with a peak of approximately −0.1°C), warm within 100–700 m (with a peak of ~0.1°C near 180 m), and warm when averaged over 0–700-m depths (with a global average of ~0.01°–0.025°C, ~1–2.5 × 1022 J). Geographically, it showed a warm bias within 30°S–30°N and a cold bias at higher latitudes in both hemispheres, the sign of which depended on the concave or convex shape of the vertical temperature profiles. Finally, the authors recommend maintaining an unbiased observation system in the future: a minimal vertical depth bin of 5% of the depth was needed to reduce the vertical-resolution-induced bias to less than 0.005°C on global average (equal to Argo accuracy).
Abstract
The choice of climatology is an essential step in calculating the key climate indicators, such as historical ocean heat content (OHC) change. The anomaly field is required during the calculation and is obtained by subtracting the climatology from the absolute field. The climatology represents the ocean spatial variability and seasonal circle. This study found a considerable weaker long-term trend when historical climatologies (constructed by using historical observations within a long time period, i.e., 45 yr) were used rather than Argo-period climatologies (i.e., constructed by using observations during the Argo period, i.e., since 2004). The change of the locations of the observations (horizontal sampling) during the past 50 yr is responsible for this divergence, because the ship-based system pre-2000 has insufficient sampling of the global ocean, for instance, in the Southern Hemisphere, whereas this area began to achieve full sampling in this century by the Argo system. The horizontal sampling change leads to the change of the reference time (and reference OHC) when the historical-period climatology is used, which weakens the long-term OHC trend. Therefore, Argo-period climatologies should be used to accurately assess the long-term trend of the climate indicators, such as OHC.
Abstract
The choice of climatology is an essential step in calculating the key climate indicators, such as historical ocean heat content (OHC) change. The anomaly field is required during the calculation and is obtained by subtracting the climatology from the absolute field. The climatology represents the ocean spatial variability and seasonal circle. This study found a considerable weaker long-term trend when historical climatologies (constructed by using historical observations within a long time period, i.e., 45 yr) were used rather than Argo-period climatologies (i.e., constructed by using observations during the Argo period, i.e., since 2004). The change of the locations of the observations (horizontal sampling) during the past 50 yr is responsible for this divergence, because the ship-based system pre-2000 has insufficient sampling of the global ocean, for instance, in the Southern Hemisphere, whereas this area began to achieve full sampling in this century by the Argo system. The horizontal sampling change leads to the change of the reference time (and reference OHC) when the historical-period climatology is used, which weakens the long-term OHC trend. Therefore, Argo-period climatologies should be used to accurately assess the long-term trend of the climate indicators, such as OHC.
On the Consistency of the Bottle and CTD Profile DataAbstract
Nansen bottle casts served as the main oceanographic instrumentation type for more than a century since the establishing of the technique in the late 1890s. Between the end of the 1960s and the end of the 1990s Nansen cast technique has been gradually replaced by electronic sensor profilers (CTD). Both instrumentation types are considered as the most accurate among other oceanographic instruments and are often used as the unbiased reference. We conducted a comprehensive investigation of the consistency of the temperature data from Nansen casts and CTD profilers analyzing the quasi-collocated bottle and CTD data between the 1960s and the 1990s when both instrumentation types overlap. We found that Nansen casts tend to overestimate the sample depth with reversing mercury-in-glass thermometer temperatures being on average slightly lower compared to CTD data. Respectively, depth and temperature corrections are provided. Further, we estimated the ocean heat content changes between 1955 and 1990 using (along with all other instrumentation types) corrected and uncorrected Nansen cast data. These calculations show that for the upper 2 km layer the global average warming trend for this time period increases from 0.20 ± 0.05 W m−2 for the uncorrected data to 0.28 ± 0.06 W m−2 for the corrected data at the 90% confidence level. Finally, we suggest that the Nansen bottle cast profiles be put into a separate instrumentation group within the World Ocean Database.
Abstract
Nansen bottle casts served as the main oceanographic instrumentation type for more than a century since the establishing of the technique in the late 1890s. Between the end of the 1960s and the end of the 1990s Nansen cast technique has been gradually replaced by electronic sensor profilers (CTD). Both instrumentation types are considered as the most accurate among other oceanographic instruments and are often used as the unbiased reference. We conducted a comprehensive investigation of the consistency of the temperature data from Nansen casts and CTD profilers analyzing the quasi-collocated bottle and CTD data between the 1960s and the 1990s when both instrumentation types overlap. We found that Nansen casts tend to overestimate the sample depth with reversing mercury-in-glass thermometer temperatures being on average slightly lower compared to CTD data. Respectively, depth and temperature corrections are provided. Further, we estimated the ocean heat content changes between 1955 and 1990 using (along with all other instrumentation types) corrected and uncorrected Nansen cast data. These calculations show that for the upper 2 km layer the global average warming trend for this time period increases from 0.20 ± 0.05 W m−2 for the uncorrected data to 0.28 ± 0.06 W m−2 for the corrected data at the 90% confidence level. Finally, we suggest that the Nansen bottle cast profiles be put into a separate instrumentation group within the World Ocean Database.
Abstract
Recent estimates of the global warming rates suggest that approximately 9% of Earth’s excess heat has been cumulated in the deep and abyssal oceans (below 2000-m depth) during the last two decades. Such estimates assume stationary trends deducted as long-term rates. To reassess the deep ocean warming and potentially shed light on its interannual variability, we formulate the balance between Earth’s energy imbalance (EEI), the steric sea level, and the ocean heat content (OHC), at yearly time scales during the 2003–18 period, as a variational problem. The solution is achieved through variational minimization, merging observational data from top-of-atmosphere EEI, inferred from Clouds and the Earth’s Radiant Energy System (CERES), steric sea level estimates from altimetry minus gravimetry, and upper-ocean heat content estimates from in situ platforms (mostly Argo floats). Global ocean reanalyses provide background-error covariances for the OHC analysis. The analysis indicates a 2000-m–bottom warming of 0.08 ± 0.04 W m−2 for the period 2003–18, equal to 13% of the total ocean warming (0.62 ± 0.08 W m−2), slightly larger than previous estimates but consistent within the error bars. The analysis provides a fully consistent optimized solution also for the steric sea level and EEI. Moreover, the simultaneous use of the different heat budget observing networks is able to decrease the analysis uncertainty with respect to the observational one, for all observation types and especially for the 0–700-m OHC and steric sea level (more than 12% reduction). The sensitivity of the analysis to the choice of the background time series proved insignificant.
Significance Statement
Several observing networks provide complementary information about the temporal evolution of the global energy budget. Here, satellite observations of Earth’s energy imbalance (EEI) and steric sea level and in situ–derived estimates of ocean heat content anomalies are combined in a variational analysis framework, with the goal of assessing the deep ocean warming. The optimized solution accounts for the uncertainty of the different observing networks. Furthermore, it provides fully consistent analyses of global ocean heat content, steric sea level, and EEI, which show smaller uncertainty than the original observed time series. The deep ocean (below 2000-m depth) exhibits a significant warming of 0.08 ± 0.04 W m−2 for the period 2003–18, equal to the 13% of the total ocean warming.
Abstract
Recent estimates of the global warming rates suggest that approximately 9% of Earth’s excess heat has been cumulated in the deep and abyssal oceans (below 2000-m depth) during the last two decades. Such estimates assume stationary trends deducted as long-term rates. To reassess the deep ocean warming and potentially shed light on its interannual variability, we formulate the balance between Earth’s energy imbalance (EEI), the steric sea level, and the ocean heat content (OHC), at yearly time scales during the 2003–18 period, as a variational problem. The solution is achieved through variational minimization, merging observational data from top-of-atmosphere EEI, inferred from Clouds and the Earth’s Radiant Energy System (CERES), steric sea level estimates from altimetry minus gravimetry, and upper-ocean heat content estimates from in situ platforms (mostly Argo floats). Global ocean reanalyses provide background-error covariances for the OHC analysis. The analysis indicates a 2000-m–bottom warming of 0.08 ± 0.04 W m−2 for the period 2003–18, equal to 13% of the total ocean warming (0.62 ± 0.08 W m−2), slightly larger than previous estimates but consistent within the error bars. The analysis provides a fully consistent optimized solution also for the steric sea level and EEI. Moreover, the simultaneous use of the different heat budget observing networks is able to decrease the analysis uncertainty with respect to the observational one, for all observation types and especially for the 0–700-m OHC and steric sea level (more than 12% reduction). The sensitivity of the analysis to the choice of the background time series proved insignificant.
Significance Statement
Several observing networks provide complementary information about the temporal evolution of the global energy budget. Here, satellite observations of Earth’s energy imbalance (EEI) and steric sea level and in situ–derived estimates of ocean heat content anomalies are combined in a variational analysis framework, with the goal of assessing the deep ocean warming. The optimized solution accounts for the uncertainty of the different observing networks. Furthermore, it provides fully consistent analyses of global ocean heat content, steric sea level, and EEI, which show smaller uncertainty than the original observed time series. The deep ocean (below 2000-m depth) exhibits a significant warming of 0.08 ± 0.04 W m−2 for the period 2003–18, equal to the 13% of the total ocean warming.
Measurement Biases in Ocean Temperature Profiles from Marine Mammal DataloggersAbstract
The study focuses on biases in ocean temperature profiles obtained by means of Satellite Relay Data Loggers (SRDL recorders) and time–depth recorder (TDR) attached to marine mammals. Quasi-collocated profiles from Argo floats and from ship-based conductivity–temperature–depth (CTD) profilers are used as reference. SRDL temperature biases depend on the sensor type and vary with depth. For the most numerous group of Valeport 3 (VP3) and conductivity–temperature–fluorescence (CTF) sensors, the bias is negative except for the layer 100–200 m. The vertical bias structure suggests a link to the upper-ocean thermal structure within the upper 200-m layer. Accounting for a time lag which might remain in the postprocessed data reduces the bias variability throughout the water column. Below 200-m depth, the bias remains negative with the overall mean of −0.027° ± 0.07°C. The suggested depth and thermal corrections for biases in SRDL data are within the uncertainty limits declared by the manufacturer. TDR recorders exhibit a different bias pattern, showing the predominantly positive bias of 0.08°–0.14°C below 100 m primarily due to the systematic error in pressure.
Significance Statement
The purpose of this work is to improve the consistency of the data from the specific instrumentation type used to measure ocean water temperature, namely, the data from miniature temperature sensors attached to marine mammals. As mammals dive during their route to and from their feeding areas, these sensors measure water temperature and dataloggers send the measured temperature data to oceanographic data centers via satellites as soon as the mammals return to the sea surface. We have shown that these data exhibit small systematic instrumental errors and suggested the respective corrections. Taking these corrections into account is important for the assessment of the ocean climate change.
Abstract
The study focuses on biases in ocean temperature profiles obtained by means of Satellite Relay Data Loggers (SRDL recorders) and time–depth recorder (TDR) attached to marine mammals. Quasi-collocated profiles from Argo floats and from ship-based conductivity–temperature–depth (CTD) profilers are used as reference. SRDL temperature biases depend on the sensor type and vary with depth. For the most numerous group of Valeport 3 (VP3) and conductivity–temperature–fluorescence (CTF) sensors, the bias is negative except for the layer 100–200 m. The vertical bias structure suggests a link to the upper-ocean thermal structure within the upper 200-m layer. Accounting for a time lag which might remain in the postprocessed data reduces the bias variability throughout the water column. Below 200-m depth, the bias remains negative with the overall mean of −0.027° ± 0.07°C. The suggested depth and thermal corrections for biases in SRDL data are within the uncertainty limits declared by the manufacturer. TDR recorders exhibit a different bias pattern, showing the predominantly positive bias of 0.08°–0.14°C below 100 m primarily due to the systematic error in pressure.
Significance Statement
The purpose of this work is to improve the consistency of the data from the specific instrumentation type used to measure ocean water temperature, namely, the data from miniature temperature sensors attached to marine mammals. As mammals dive during their route to and from their feeding areas, these sensors measure water temperature and dataloggers send the measured temperature data to oceanographic data centers via satellites as soon as the mammals return to the sea surface. We have shown that these data exhibit small systematic instrumental errors and suggested the respective corrections. Taking these corrections into account is important for the assessment of the ocean climate change.
Quantification of the Effect of Water Temperature on the Fall Rate of Expendable BathythermographsAbstract
A very large portion of the historical information on ocean temperatures has been measured using expendable bathythermograph (XBT) devices. For decades, these devices provided the majority of global information. It is, therefore, important to quantify their accuracy and identify biases in this important historical dataset. Here, calculations are made of the influence of water temperature on the rate of descent of the XBT devices into the ocean waters. In colder regions, the larger viscosity of the water is expected to cause a greater drag force on the device, which would slow the descent. It was found through computational fluid dynamic models that the impact of temperature and viscosity on the probe descent is approximately 2.2% for water temperatures that range from 0° to 27°C. Probe-specific temperature-dependent fall rate equations were applied to 269 collocated XBT/conductivity–temperature–depth (CTD) measurements from two different research cruises. It was found that the probe-specific descent equations were an improvement over the uncorrected method. Next, in an effort to automate the process, the fall rate coefficients were related to the topmost measured temperature in the water column. With this relationship, comparisons were made between the probe-specific descent calculations and 2937 high-resolution XBT–CTD pairs. It was found that again, the new methodology outperformed the standard fall rate equation. The new method was also compared with an independent correction method that was previously published. It was found that both new methods were improvements upon the industry-standard fall rate calculation. Subsequent calculations using the top-100-m water temperature were performed and were found to be statistically insignificant compared to the proposed simplified method.
Abstract
A very large portion of the historical information on ocean temperatures has been measured using expendable bathythermograph (XBT) devices. For decades, these devices provided the majority of global information. It is, therefore, important to quantify their accuracy and identify biases in this important historical dataset. Here, calculations are made of the influence of water temperature on the rate of descent of the XBT devices into the ocean waters. In colder regions, the larger viscosity of the water is expected to cause a greater drag force on the device, which would slow the descent. It was found through computational fluid dynamic models that the impact of temperature and viscosity on the probe descent is approximately 2.2% for water temperatures that range from 0° to 27°C. Probe-specific temperature-dependent fall rate equations were applied to 269 collocated XBT/conductivity–temperature–depth (CTD) measurements from two different research cruises. It was found that the probe-specific descent equations were an improvement over the uncorrected method. Next, in an effort to automate the process, the fall rate coefficients were related to the topmost measured temperature in the water column. With this relationship, comparisons were made between the probe-specific descent calculations and 2937 high-resolution XBT–CTD pairs. It was found that again, the new methodology outperformed the standard fall rate equation. The new method was also compared with an independent correction method that was previously published. It was found that both new methods were improvements upon the industry-standard fall rate calculation. Subsequent calculations using the top-100-m water temperature were performed and were found to be statistically insignificant compared to the proposed simplified method.
Abstract
A new technique to estimate three major biases of XBT probes (improper fall rate, start-up transient, and pure temperature error) has been developed. Different from the well-known and standard “temperature error free” differential method, the new method analyses temperature profiles instead of vertical gradient temperature profiles. Consequently, it seems to be more noise resistant because it uses the integral property over the entire vertical profile instead of gradients. Its validity and robustness have been checked in two ways. In the first case, the new integral technique and the standard differential method have been applied to a set of simulated XBT profiles having a known fall-rate equation to which various combinations of pure temperature errors, random errors, and spikes have been added for the sake of this simulation. Results indicated that the single pure temperature error has little impact on the fall-rate coefficients for both methods, whereas with the added random error and spikes the simulation leads to better results with the new integral technique than with the standard differential method. In the second case, two sets of profiles from actual XBT versus CTD comparisons, collected near Barbados in 1990 and in the western Mediterranean (2003–04 and 2008–09), have been used. The individual fall-rate coefficients and start-up transient for each XBT profile, along with the overall pure temperature correction, have been calculated for the XBT profiles. To standardize procedures and to improve the terms of comparison, the individual start-up transient estimated by the integral method was also assigned and included in calculations with the differential method. The new integral method significantly reduces both the temperature difference between XBT and CTD profiles and the standard deviation. Finally, the validity of the mean fall-rate coefficients and the mean start-up transient, respectively, for DB and T7 probes as precalculated equations was verified. In this case, the temperature difference is reduced to less than 0.1°C for both datasets, and it randomly distributes around the null value. In addition, the standard deviation on depth values is largely reduced, and the maximum depth error computed with the datasets near Barbados is within 1.1% of its real value. Results also indicate that the integral method has a good performance mainly when applied to profiles in regions with either a very large temperature gradient, at the thermocline or a very small one, toward the bottom.
Abstract
A new technique to estimate three major biases of XBT probes (improper fall rate, start-up transient, and pure temperature error) has been developed. Different from the well-known and standard “temperature error free” differential method, the new method analyses temperature profiles instead of vertical gradient temperature profiles. Consequently, it seems to be more noise resistant because it uses the integral property over the entire vertical profile instead of gradients. Its validity and robustness have been checked in two ways. In the first case, the new integral technique and the standard differential method have been applied to a set of simulated XBT profiles having a known fall-rate equation to which various combinations of pure temperature errors, random errors, and spikes have been added for the sake of this simulation. Results indicated that the single pure temperature error has little impact on the fall-rate coefficients for both methods, whereas with the added random error and spikes the simulation leads to better results with the new integral technique than with the standard differential method. In the second case, two sets of profiles from actual XBT versus CTD comparisons, collected near Barbados in 1990 and in the western Mediterranean (2003–04 and 2008–09), have been used. The individual fall-rate coefficients and start-up transient for each XBT profile, along with the overall pure temperature correction, have been calculated for the XBT profiles. To standardize procedures and to improve the terms of comparison, the individual start-up transient estimated by the integral method was also assigned and included in calculations with the differential method. The new integral method significantly reduces both the temperature difference between XBT and CTD profiles and the standard deviation. Finally, the validity of the mean fall-rate coefficients and the mean start-up transient, respectively, for DB and T7 probes as precalculated equations was verified. In this case, the temperature difference is reduced to less than 0.1°C for both datasets, and it randomly distributes around the null value. In addition, the standard deviation on depth values is largely reduced, and the maximum depth error computed with the datasets near Barbados is within 1.1% of its real value. Results also indicate that the integral method has a good performance mainly when applied to profiles in regions with either a very large temperature gradient, at the thermocline or a very small one, toward the bottom.
Abstract
The heat content in the Indian Ocean has been increasing owing to anthropogenic greenhouse warming. Yet, where and how the anthropogenic heat is stored in the Indian Ocean have not been comprehended. Analysis of various observational and model-based datasets since the 1950s reveals a robust spatial pattern of the 0–700 m ocean heat content trend (ΔOHC), with enhanced warming in the subtropical southern Indian Ocean (SIO) but weak to minimal warming in the tropical Indian Ocean (TIO). The meridional temperature gradient between the TIO and SIO declined by 16.4% ± 7.5% during 1958–2014. The heat redistribution driven by time-varying surface winds plays a crucial role in shaping this ΔOHC pattern. Sensitivity experiments using a simplified ocean dynamical model suggest that changes in surface winds over the Indian Ocean, particularly those of the SIO, caused a convergence trend in the upper SIO and a divergence trend in the upper TIO. These wind changes primarily include the enhancements of westerlies in the Southern Ocean and the subtropical anticyclone in the SIO. Albeit with systematic biases, the ΔOHC pattern and surface wind changes simulated by phase 6 of the Coupled Model Intercomparison Project (CMIP6) models broadly resemble the observation and highlight the essence of external forcing in causing these changes. This heat storage pattern is projected to persist in the model-projected future, potentially impacting future climate.
Abstract
The heat content in the Indian Ocean has been increasing owing to anthropogenic greenhouse warming. Yet, where and how the anthropogenic heat is stored in the Indian Ocean have not been comprehended. Analysis of various observational and model-based datasets since the 1950s reveals a robust spatial pattern of the 0–700 m ocean heat content trend (ΔOHC), with enhanced warming in the subtropical southern Indian Ocean (SIO) but weak to minimal warming in the tropical Indian Ocean (TIO). The meridional temperature gradient between the TIO and SIO declined by 16.4% ± 7.5% during 1958–2014. The heat redistribution driven by time-varying surface winds plays a crucial role in shaping this ΔOHC pattern. Sensitivity experiments using a simplified ocean dynamical model suggest that changes in surface winds over the Indian Ocean, particularly those of the SIO, caused a convergence trend in the upper SIO and a divergence trend in the upper TIO. These wind changes primarily include the enhancements of westerlies in the Southern Ocean and the subtropical anticyclone in the SIO. Albeit with systematic biases, the ΔOHC pattern and surface wind changes simulated by phase 6 of the Coupled Model Intercomparison Project (CMIP6) models broadly resemble the observation and highlight the essence of external forcing in causing these changes. This heat storage pattern is projected to persist in the model-projected future, potentially impacting future climate.
