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- Author or Editor: John M. Lyman x
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Abstract
Ocean heat content anomalies are analyzed from 1950 to 2011 in five distinct depth layers (0–100, 100–300, 300–700, 700–900, and 900–1800 m). These layers correspond to historic increases in common maximum sampling depths of ocean temperature measurements with time, as different instruments—mechanical bathythermograph (MBT), shallow expendable bathythermograph (XBT), deep XBT, early sometimes shallower Argo profiling floats, and recent Argo floats capable of worldwide sampling to 2000 m—have come into widespread use. This vertical separation of maps allows computation of annual ocean heat content anomalies and their sampling uncertainties back to 1950 while taking account of in situ sampling advances and changing sampling patterns. The 0–100-m layer is measured over 50% of the globe annually starting in 1956, the 100–300-m layer starting in 1967, the 300–700-m layer starting in 1983, and the deepest two layers considered here starting in 2003 and 2004, during the implementation of Argo. Furthermore, global ocean heat uptake estimates since 1950 depend strongly on assumptions made concerning changes in undersampled or unsampled ocean regions. If unsampled areas are assumed to have zero anomalies and are included in the global integrals, the choice of climatological reference from which anomalies are estimated can strongly influence the global integral values and their trend: the sparser the sampling and the bigger the mean difference between climatological and actual values, the larger the influence.
Abstract
Ocean heat content anomalies are analyzed from 1950 to 2011 in five distinct depth layers (0–100, 100–300, 300–700, 700–900, and 900–1800 m). These layers correspond to historic increases in common maximum sampling depths of ocean temperature measurements with time, as different instruments—mechanical bathythermograph (MBT), shallow expendable bathythermograph (XBT), deep XBT, early sometimes shallower Argo profiling floats, and recent Argo floats capable of worldwide sampling to 2000 m—have come into widespread use. This vertical separation of maps allows computation of annual ocean heat content anomalies and their sampling uncertainties back to 1950 while taking account of in situ sampling advances and changing sampling patterns. The 0–100-m layer is measured over 50% of the globe annually starting in 1956, the 100–300-m layer starting in 1967, the 300–700-m layer starting in 1983, and the deepest two layers considered here starting in 2003 and 2004, during the implementation of Argo. Furthermore, global ocean heat uptake estimates since 1950 depend strongly on assumptions made concerning changes in undersampled or unsampled ocean regions. If unsampled areas are assumed to have zero anomalies and are included in the global integrals, the choice of climatological reference from which anomalies are estimated can strongly influence the global integral values and their trend: the sparser the sampling and the bigger the mean difference between climatological and actual values, the larger the influence.
Abstract
The effects of irregular in situ ocean sampling on estimates of annual globally integrated upper ocean heat content anomalies (OHCA) are investigated for sampling patterns from 1955 to 2006. An analytical method is presented for computing the effective area covered by an objective map for any given in situ sampling distribution. To evaluate the method, appropriately scaled sea surface height (SSH) anomaly maps from Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) are used as a proxy for OHCA from 1993 to 2006. Use of these proxy data demonstrates that the simple area integral (SI) of such an objective map for sparse datasets does not agree as well with the actual integral as the weighted integral (WI), defined as the simple integral weighted by the ratio of the total area over the “observed” area. From 1955 to 1966, in situ ocean sampling is inadequate to estimate accurately annual global integrals of the proxy upper OHCA. During this period, the SI for the sampling pattern of any given year underestimates the 13-yr trend in proxy OHCA from 1993 to 2006 by around 70%, and confidence limits for the WI are often very large. From 1967 to 2003 there appear to be sufficient data to estimate annual global integrals. Limited by the constraints of this analysis, the SI for any given year’s sampling pattern still underestimates the 1993–2006 13-yr trend in the proxy by around 30%, but the WI matches the trend well with small confidence limits. For 2004 through 2006 in situ sampling, with near-global in situ Argo data coverage, the 1993–2006 13-yr trend in the proxy is equally well represented by the SI or WI.
Abstract
The effects of irregular in situ ocean sampling on estimates of annual globally integrated upper ocean heat content anomalies (OHCA) are investigated for sampling patterns from 1955 to 2006. An analytical method is presented for computing the effective area covered by an objective map for any given in situ sampling distribution. To evaluate the method, appropriately scaled sea surface height (SSH) anomaly maps from Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) are used as a proxy for OHCA from 1993 to 2006. Use of these proxy data demonstrates that the simple area integral (SI) of such an objective map for sparse datasets does not agree as well with the actual integral as the weighted integral (WI), defined as the simple integral weighted by the ratio of the total area over the “observed” area. From 1955 to 1966, in situ ocean sampling is inadequate to estimate accurately annual global integrals of the proxy upper OHCA. During this period, the SI for the sampling pattern of any given year underestimates the 13-yr trend in proxy OHCA from 1993 to 2006 by around 70%, and confidence limits for the WI are often very large. From 1967 to 2003 there appear to be sufficient data to estimate annual global integrals. Limited by the constraints of this analysis, the SI for any given year’s sampling pattern still underestimates the 1993–2006 13-yr trend in the proxy by around 30%, but the WI matches the trend well with small confidence limits. For 2004 through 2006 in situ sampling, with near-global in situ Argo data coverage, the 1993–2006 13-yr trend in the proxy is equally well represented by the SI or WI.
Abstract
The ocean, with its low albedo and vast thermal inertia, plays key roles in the climate system, including absorbing massive amounts of heat as atmospheric greenhouse gas concentrations rise. While the Argo array of profiling floats has vastly improved sampling of ocean temperature in the upper half of the global ocean volume since the mid-2000s, they are not sufficient in number to resolve eddy scales in the oceans. However, satellite sea surface temperature (SST) and sea surface height (SSH) measurements do resolve these scales. Here we use random forest regressions to map ocean heat content anomalies (OHCA) using in situ training data from Argo and other sources on a 7-day × 1/4° × 1/4° grid with latitude, longitude, time, SSH, and SST as predictors. The maps display substantial patterns on eddy scales, resolving variations of ocean currents and fronts. During the well-sampled Argo period, global integrals of these maps reduce noise relative to estimates based on objective mapping of in situ data alone by roughly a factor of 3 when compared to time series of CERES (satellite data) top-of-the-atmosphere energy flux measurements and improve correlations of anomalies with CERES on annual time scales. Prior to and early on in the Argo period, when in situ data were sparser, global integrals of these maps retain low variance, and do not relax back to a climatological mean, avoiding potential deficiencies of various methods for infilling data-sparse regions with objective maps by exploiting temporal and spatial patterns of OHCA and its correlations with SST and SSH.
Significance Statement
We use a simple machine learning technique to improve maps of subsurface ocean warming by exploiting the relationships between subsurface ocean temperature both sea surface temperature and sea level. Mapping ocean warming is important because it contributes to sea level rise through thermal expansion; impacts marine life through marine heatwaves and changes in mixing, oxygen, and carbon dioxide levels; increases energy available to tropical cyclones; and stores most of the energy building up in Earth’s climate system owing to the accumulation of anthropogenic greenhouse gases in the atmosphere. Our new estimates generally have lower noise energy and higher correlations than other products when compared with global energy fluxes at the top of the atmosphere measured by satellite.
Abstract
The ocean, with its low albedo and vast thermal inertia, plays key roles in the climate system, including absorbing massive amounts of heat as atmospheric greenhouse gas concentrations rise. While the Argo array of profiling floats has vastly improved sampling of ocean temperature in the upper half of the global ocean volume since the mid-2000s, they are not sufficient in number to resolve eddy scales in the oceans. However, satellite sea surface temperature (SST) and sea surface height (SSH) measurements do resolve these scales. Here we use random forest regressions to map ocean heat content anomalies (OHCA) using in situ training data from Argo and other sources on a 7-day × 1/4° × 1/4° grid with latitude, longitude, time, SSH, and SST as predictors. The maps display substantial patterns on eddy scales, resolving variations of ocean currents and fronts. During the well-sampled Argo period, global integrals of these maps reduce noise relative to estimates based on objective mapping of in situ data alone by roughly a factor of 3 when compared to time series of CERES (satellite data) top-of-the-atmosphere energy flux measurements and improve correlations of anomalies with CERES on annual time scales. Prior to and early on in the Argo period, when in situ data were sparser, global integrals of these maps retain low variance, and do not relax back to a climatological mean, avoiding potential deficiencies of various methods for infilling data-sparse regions with objective maps by exploiting temporal and spatial patterns of OHCA and its correlations with SST and SSH.
Significance Statement
We use a simple machine learning technique to improve maps of subsurface ocean warming by exploiting the relationships between subsurface ocean temperature both sea surface temperature and sea level. Mapping ocean warming is important because it contributes to sea level rise through thermal expansion; impacts marine life through marine heatwaves and changes in mixing, oxygen, and carbon dioxide levels; increases energy available to tropical cyclones; and stores most of the energy building up in Earth’s climate system owing to the accumulation of anthropogenic greenhouse gases in the atmosphere. Our new estimates generally have lower noise energy and higher correlations than other products when compared with global energy fluxes at the top of the atmosphere measured by satellite.
Abstract
Tropical instability waves (TIWs) within a half-degree of the equator in the Pacific Ocean have been consistently observed in meridional velocity with periods of around 20 days. On the other hand, near 5°N, TIWs have been observed in sea surface height (SSH), thermocline depth, and velocity to have periods near 30 days. Tropical Atmosphere–Ocean (TAO) Project moored equatorial velocity and temperature time series are used to investigate the spatial and temporal structure of TIWs during 3 years of La Niña conditions from 1998 through 2001. Along 140°W, where the TIW temperature and velocity variabilities are at their maxima, these variabilities include two distinct TIWs with periods of 17 and 33 days, rather than one broadbanded process. As predicted by modeling studies, the 17-day TIW variability is shown to occur not only in meridional velocity at the equator, but also in subsurface temperature at 2°N and 2°S, while the 33-day TIW variability is observed primarily in subsurface temperature at 5°N. These two TIWs, respectively, are shown to have characteristics similar to a Yanai wave/surface-trapped instability and an unstable first meridional mode Rossby wave. One implication of such a description is that the velocity variability on the equator is not directly associated with the dominant 33-day variability along 5°N.
Abstract
Tropical instability waves (TIWs) within a half-degree of the equator in the Pacific Ocean have been consistently observed in meridional velocity with periods of around 20 days. On the other hand, near 5°N, TIWs have been observed in sea surface height (SSH), thermocline depth, and velocity to have periods near 30 days. Tropical Atmosphere–Ocean (TAO) Project moored equatorial velocity and temperature time series are used to investigate the spatial and temporal structure of TIWs during 3 years of La Niña conditions from 1998 through 2001. Along 140°W, where the TIW temperature and velocity variabilities are at their maxima, these variabilities include two distinct TIWs with periods of 17 and 33 days, rather than one broadbanded process. As predicted by modeling studies, the 17-day TIW variability is shown to occur not only in meridional velocity at the equator, but also in subsurface temperature at 2°N and 2°S, while the 33-day TIW variability is observed primarily in subsurface temperature at 5°N. These two TIWs, respectively, are shown to have characteristics similar to a Yanai wave/surface-trapped instability and an unstable first meridional mode Rossby wave. One implication of such a description is that the velocity variability on the equator is not directly associated with the dominant 33-day variability along 5°N.
Abstract
Data from full-depth closely sampled hydrographic sections and Argo floats are analyzed to inform the design of a future Deep Argo array. Here standard errors of local decadal temperature trends and global decadal trends of ocean heat content and thermosteric sea level anomalies integrated from 2000 to 6000 dbar are estimated for a hypothetical 5° latitude × 5° longitude × 15-day cycle Deep Argo array. These estimates are made using temperature variances from closely spaced full-depth CTD profiles taken during hydrographic sections. The temperature data along each section are high passed laterally at a 500-km scale, and the resulting variances are averaged in 5° × 5° bins to assess temperature noise levels as a function of pressure and geographic location. A mean global decorrelation time scale of 62 days is estimated using temperature time series at 1800 dbar from Argo floats. The hypothetical Deep Argo array would be capable of resolving, at one standard error, local trends from <1 m °C decade−1 in the quiescent abyssal North Pacific to about 26 m °C decade−1 below 2000 dbar along 50°S in the energetic Southern Ocean. Larger decadal temperature trends have been reported previously in these regions using repeat hydrographic section data, but those very sparse data required substantial spatial averaging to obtain statistically significant results. Furthermore, the array would provide decadal global ocean heat content trend estimates from 2000 to 6000 dbar with a standard error of ±3 TW, compared to a trend standard error of ±17 TW from a previous analysis of repeat hydrographic data.
Abstract
Data from full-depth closely sampled hydrographic sections and Argo floats are analyzed to inform the design of a future Deep Argo array. Here standard errors of local decadal temperature trends and global decadal trends of ocean heat content and thermosteric sea level anomalies integrated from 2000 to 6000 dbar are estimated for a hypothetical 5° latitude × 5° longitude × 15-day cycle Deep Argo array. These estimates are made using temperature variances from closely spaced full-depth CTD profiles taken during hydrographic sections. The temperature data along each section are high passed laterally at a 500-km scale, and the resulting variances are averaged in 5° × 5° bins to assess temperature noise levels as a function of pressure and geographic location. A mean global decorrelation time scale of 62 days is estimated using temperature time series at 1800 dbar from Argo floats. The hypothetical Deep Argo array would be capable of resolving, at one standard error, local trends from <1 m °C decade−1 in the quiescent abyssal North Pacific to about 26 m °C decade−1 below 2000 dbar along 50°S in the energetic Southern Ocean. Larger decadal temperature trends have been reported previously in these regions using repeat hydrographic section data, but those very sparse data required substantial spatial averaging to obtain statistically significant results. Furthermore, the array would provide decadal global ocean heat content trend estimates from 2000 to 6000 dbar with a standard error of ±3 TW, compared to a trend standard error of ±17 TW from a previous analysis of repeat hydrographic data.
Abstract
Two significant instrument biases have been identified in the in situ profile data used to estimate globally integrated upper-ocean heat content. A large cold bias was discovered in a small fraction of Argo floats along with a smaller but more prevalent warm bias in expendable bathythermograph (XBT) data. These biases appear to have caused the bulk of the upper-ocean cooling signal reported by Lyman et al. between 2003 and 2005. These systematic data errors are significantly larger than sampling errors in recent years and are the dominant sources of error in recent estimates of globally integrated upper-ocean heat content variability. The bias in the XBT data is found to be consistent with errors in the fall-rate equations, suggesting a physical explanation for that bias. With biased profiles discarded, no significant warming or cooling is observed in upper-ocean heat content between 2003 and 2006.
Abstract
Two significant instrument biases have been identified in the in situ profile data used to estimate globally integrated upper-ocean heat content. A large cold bias was discovered in a small fraction of Argo floats along with a smaller but more prevalent warm bias in expendable bathythermograph (XBT) data. These biases appear to have caused the bulk of the upper-ocean cooling signal reported by Lyman et al. between 2003 and 2005. These systematic data errors are significantly larger than sampling errors in recent years and are the dominant sources of error in recent estimates of globally integrated upper-ocean heat content variability. The bias in the XBT data is found to be consistent with errors in the fall-rate equations, suggesting a physical explanation for that bias. With biased profiles discarded, no significant warming or cooling is observed in upper-ocean heat content between 2003 and 2006.
Abstract
To understand the characteristics of sea surface height signatures of tropical instability waves (TIWs), a linearized model of the central Pacific Ocean was developed in which the vertical structures of the state variables are projected onto a set of orthogonal baroclinic eigenvectors. In lieu of in situ current measurements with adequate spatial and temporal resolution, the mean current structure used in the model was obtained from the Parallel Ocean Climate Model (POCM). The TIWs in the linear model have cross-equatorial structure and wavenumber–frequency content similar to the TIWs in POCM, even when the vertical structures of the state variables are projected onto only the first two orthogonal baroclinic eigenvectors. Because this model is able to reproduce TIWs with relatively simple vertical structure, it is possible to examine the mechanism for the formation of TIWs. TIWs are shown to form from a resonance between two equatorial Rossby waves as the strength of the background currents is slowly increased.
Abstract
To understand the characteristics of sea surface height signatures of tropical instability waves (TIWs), a linearized model of the central Pacific Ocean was developed in which the vertical structures of the state variables are projected onto a set of orthogonal baroclinic eigenvectors. In lieu of in situ current measurements with adequate spatial and temporal resolution, the mean current structure used in the model was obtained from the Parallel Ocean Climate Model (POCM). The TIWs in the linear model have cross-equatorial structure and wavenumber–frequency content similar to the TIWs in POCM, even when the vertical structures of the state variables are projected onto only the first two orthogonal baroclinic eigenvectors. Because this model is able to reproduce TIWs with relatively simple vertical structure, it is possible to examine the mechanism for the formation of TIWs. TIWs are shown to form from a resonance between two equatorial Rossby waves as the strength of the background currents is slowly increased.
Abstract
Ocean warming accounts for the majority of the earth’s recent energy imbalance. Historic ocean heat content (OHC) changes are important for understanding changing climate. Calculations of OHC anomalies (OHCA) from in situ measurements provide estimates of these changes. Uncertainties in OHCA estimates arise from calculating global fields from temporally and spatially irregular data (mapping method), instrument bias corrections, and the definitions of a baseline climatology from which anomalies are calculated. To investigate sensitivity of OHCA estimates for the upper 700 m to these different factors, the same quality-controlled dataset is used by seven groups and comparisons are made. Two time periods (1970–2008 and 1993–2008) are examined. Uncertainty due to the mapping method is 16.5 ZJ for 1970–2008 and 17.1 ZJ for 1993–2008 (1 ZJ = 1 × 1021 J). Uncertainty due to instrument bias correction varied from 8.0 to 17.9 ZJ for 1970–2008 and from 10.9 to 22.4 ZJ for 1993–2008, depending on mapping method. Uncertainty due to baseline mean varied from 3.5 to 14.5 ZJ for 1970–2008 and from 2.7 to 9.8 ZJ for 1993–2008, depending on mapping method and offsets. On average mapping method is the largest source of uncertainty. The linear trend varied from 1.3 to 5.0 ZJ yr−1 (0.08–0.31 W m−2) for 1970–2008 and from 1.5 to 9.4 ZJ yr−1 (0.09–0.58 W m−2) for 1993–2008, depending on method, instrument bias correction, and baseline mean. Despite these complications, a statistically robust upper-ocean warming was found in all cases for the full time period.
Abstract
Ocean warming accounts for the majority of the earth’s recent energy imbalance. Historic ocean heat content (OHC) changes are important for understanding changing climate. Calculations of OHC anomalies (OHCA) from in situ measurements provide estimates of these changes. Uncertainties in OHCA estimates arise from calculating global fields from temporally and spatially irregular data (mapping method), instrument bias corrections, and the definitions of a baseline climatology from which anomalies are calculated. To investigate sensitivity of OHCA estimates for the upper 700 m to these different factors, the same quality-controlled dataset is used by seven groups and comparisons are made. Two time periods (1970–2008 and 1993–2008) are examined. Uncertainty due to the mapping method is 16.5 ZJ for 1970–2008 and 17.1 ZJ for 1993–2008 (1 ZJ = 1 × 1021 J). Uncertainty due to instrument bias correction varied from 8.0 to 17.9 ZJ for 1970–2008 and from 10.9 to 22.4 ZJ for 1993–2008, depending on mapping method. Uncertainty due to baseline mean varied from 3.5 to 14.5 ZJ for 1970–2008 and from 2.7 to 9.8 ZJ for 1993–2008, depending on mapping method and offsets. On average mapping method is the largest source of uncertainty. The linear trend varied from 1.3 to 5.0 ZJ yr−1 (0.08–0.31 W m−2) for 1970–2008 and from 1.5 to 9.4 ZJ yr−1 (0.09–0.58 W m−2) for 1993–2008, depending on method, instrument bias correction, and baseline mean. Despite these complications, a statistically robust upper-ocean warming was found in all cases for the full time period.
Abstract
The Earth system is accumulating energy due to human-induced activities. More than 90% of this energy has been stored in the ocean as heat since 1970, with ∼60% of that in the upper 700 m. Differences in upper-ocean heat content anomaly (OHCA) estimates, however, exist. Here, we use a dataset protocol for 1970–2008—with six instrumental bias adjustments applied to expendable bathythermograph (XBT) data, and mapped by six research groups—to evaluate the spatiotemporal spread in upper OHCA estimates arising from two choices: 1) those arising from instrumental bias adjustments and 2) those arising from mathematical (i.e., mapping) techniques to interpolate and extrapolate data in space and time. We also examined the effect of a common ocean mask, which reveals that exclusion of shallow seas can reduce global OHCA estimates up to 13%. Spread due to mapping method is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Spread due to XBT bias adjustment is largest in the Pacific Ocean within 30°N–30°S. In both mapping and XBT cases, spread is higher for 1990–2004. Statistically different trends among mapping methods are found not only in the poorly observed Southern Ocean but also in the well-observed northwest Atlantic. Our results cannot determine the best mapping or bias adjustment schemes, but they identify where important sensitivities exist, and thus where further understanding will help to refine OHCA estimates. These results highlight the need for further coordinated OHCA studies to evaluate the performance of existing mapping methods along with comprehensive assessment of uncertainty estimates.
Abstract
The Earth system is accumulating energy due to human-induced activities. More than 90% of this energy has been stored in the ocean as heat since 1970, with ∼60% of that in the upper 700 m. Differences in upper-ocean heat content anomaly (OHCA) estimates, however, exist. Here, we use a dataset protocol for 1970–2008—with six instrumental bias adjustments applied to expendable bathythermograph (XBT) data, and mapped by six research groups—to evaluate the spatiotemporal spread in upper OHCA estimates arising from two choices: 1) those arising from instrumental bias adjustments and 2) those arising from mathematical (i.e., mapping) techniques to interpolate and extrapolate data in space and time. We also examined the effect of a common ocean mask, which reveals that exclusion of shallow seas can reduce global OHCA estimates up to 13%. Spread due to mapping method is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Spread due to XBT bias adjustment is largest in the Pacific Ocean within 30°N–30°S. In both mapping and XBT cases, spread is higher for 1990–2004. Statistically different trends among mapping methods are found not only in the poorly observed Southern Ocean but also in the well-observed northwest Atlantic. Our results cannot determine the best mapping or bias adjustment schemes, but they identify where important sensitivities exist, and thus where further understanding will help to refine OHCA estimates. These results highlight the need for further coordinated OHCA studies to evaluate the performance of existing mapping methods along with comprehensive assessment of uncertainty estimates.
