Nature: Large-scale increases in upper-ocean temperatures are evident in observational records1. Several studies have used well-established detection and attribution methods to demonstrate that the observed basin-scale temperature changes are consistent with model responses to anthropogenic forcing and inconsistent with model-based estimates of natural variability2, 3, 4, 5.
These studies relied on a single observational data set and employed results from only one or two models. Recent identification of systematic instrumental biases6 in expendable bathythermograph data has led to improved estimates of ocean temperature variability and trends7, 8, 9 and provide motivation to revisit earlier detection and attribution studies. We examine the causes of ocean warming using these improved observational estimates, together with results from a large multimodel archive of externally forced and unforced simulations. The time evolution of upper ocean temperature changes in the newer observational estimates is similar to that of the multimodel average of simulations that include the effects of volcanic eruptions.
Our detection and attribution analysis systematically examines the sensitivity of results to a variety of model and data-processing choices. When global mean changes are included, we consistently obtain a positive identification (at the 1% significance level) of an anthropogenic fingerprint in observed upper-ocean temperature changes, thereby substantially strengthening existing detection and attribution evidence.
We examine volume average temperature anomalies (ΔT) for the upper 700 m of the global ocean (see Methods). Figure 1a compares uncorrected observational ΔT estimates ISH-UNCOR (ref. 10) and LEV-UNCOR (ref. 11) with improved versions, ISH (ref. 8) and LEV (ref. 9), which incorporate corrections for expendable bathythermograph (XBT) biases. The bias-corrected temperature analysis7 from a third group (DOM) is also shown. Bias corrections have a substantial impact on the time evolution of ΔT, particularly during the 1970s–1980s, when they markedly reduce spurious decadal variability.
As shown below, these bias adjustments have important implications for detection and attribution (D&A) studies. Although there are no significant differences between the ΔT trends (which range from 0.022 to 0.028 °C per decade) in the three improved observational data sets, Fig. 1a illustrates that substantial structural uncertainties remain. The impact of different XBT bias corrections is a major source of this uncertainty12.
Another important component of observational uncertainty relates to the sparseness of ocean temperature measurements and to the different methods used to objectively infill data where and when measurements are not available13, 14, 15. ISH and LEV use objective mapping techniques to carry out infilling, generating anomalies that are biased towards zero in data-sparse regions. The infilling method of DOM employs statistics of observed ocean variability estimated from altimeter data. We compare the spatially complete infilled estimates (ΔTIF) with subsampled ΔT data (ΔTSS) restricted to available in situ measurements (see Methods). Not surprisingly, the ΔTSS variability in Fig. 1b is greater than that of ΔTIF, particularly at the times/locations of the sparsest sampling (early in the record and in the southern oceans; Supplementary Fig. S1).
We use results from phase 3 of the Coupled Model Intercomparison Project (CMIP3; see Methods and Supplementary Information) to obtain information on the behaviour of ΔT in unforced (control) simulations and in externally forced twentieth-century runs (20CEN). External forcing is by a variety of anthropogenic factors (primarily greenhouse gases and sulphate aerosols). In some models, the applied forcing also includes natural changes in volcanic aerosols and solar irradiance. The seven CMIP3 models (with the data required for our analysis) incorporating the effects of volcanic eruptions (VOL) in the 20CEN simulations uptake less heat than the six that do not (NoV)16.
Accounting for residual simulation drift (see Methods), the multimodel VOL global mean ΔT time series are within the spread of observational estimates over the entire observational record, whereas the warming in the NoV multimodel average is larger than observed in the most recent decades (Fig. 1c). Twenty-first-century ΔT changes in CMIP3 future projections are also shown17, and are based on the SRES A1B scenario from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES). The small discontinuity between the 20CEN and SRES A1B results arises because fewer simulations are available for the scenario runs and forcing discontinuities are known to exist in some simulations18 (see Supplementary Information). Note that inclusion of volcanic forcing increases the simulated variability7, 15, 19.
Figure 2 shows linear trends over 1960–1999 in observed and simulated ΔTSS and ΔTIF data. Results are for global averages and each of the six ocean basins. Observed ΔTIFtrends are generally smaller than their ΔTSScounterparts, probably because the ΔTIF results are biased low in data-sparse regions. Note that in both models and observations, the Atlantic warming is larger than in the Pacific.
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