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- Author or Editor: Rachel E. Killick x
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Abstract
Ocean heat content (OHC) is one of the most relevant metrics tracking the current global heating. Therefore, simulated OHC time series are a cornerstone for assessing the scientific performance of Earth system models and global climate models. Here we present a detailed analysis of OHC change in simulations of the historical climate (1850–2014) performed with two pairs of CMIP6 models: U.K. Earth System Model 1 (UKESM1.0) and HadGEM3-GC3.1-LL, and CNRM-ESM2-1 and CNRM-CM6-1. The small number of models enables us to analyze OHC change globally and for individual ocean basins, making use of a novel ensemble of observational products. For the top 700 m of the global ocean, the two CNRM models reproduce the observed OHC change since the 1960s closely. The two U.K. models (UKESM1.0-LL and HadGEM3-GC3.1-LL) compensate a lack of warming in the 0–700 m layer in the 1970s and 1980s with warming below 2000 m. The observed warming between 700 and 2000 m is substantially underestimated by all models. An increased relevance for ocean heat uptake in the Atlantic after 1991—suggested by observations—is picked up by the U.K. models but less so by the CNRM models, probably related to an AMOC strengthening in the U.K. models. The regional ocean heat uptake characteristics differ even though all four models share the same ocean component (NEMO ORCA1). Differences in the simulated global, full-depth OHC time series can be attributed to differences in the model’s total effective radiative forcing.
Abstract
Ocean heat content (OHC) is one of the most relevant metrics tracking the current global heating. Therefore, simulated OHC time series are a cornerstone for assessing the scientific performance of Earth system models and global climate models. Here we present a detailed analysis of OHC change in simulations of the historical climate (1850–2014) performed with two pairs of CMIP6 models: U.K. Earth System Model 1 (UKESM1.0) and HadGEM3-GC3.1-LL, and CNRM-ESM2-1 and CNRM-CM6-1. The small number of models enables us to analyze OHC change globally and for individual ocean basins, making use of a novel ensemble of observational products. For the top 700 m of the global ocean, the two CNRM models reproduce the observed OHC change since the 1960s closely. The two U.K. models (UKESM1.0-LL and HadGEM3-GC3.1-LL) compensate a lack of warming in the 0–700 m layer in the 1970s and 1980s with warming below 2000 m. The observed warming between 700 and 2000 m is substantially underestimated by all models. An increased relevance for ocean heat uptake in the Atlantic after 1991—suggested by observations—is picked up by the U.K. models but less so by the CNRM models, probably related to an AMOC strengthening in the U.K. models. The regional ocean heat uptake characteristics differ even though all four models share the same ocean component (NEMO ORCA1). Differences in the simulated global, full-depth OHC time series can be attributed to differences in the model’s total effective radiative forcing.
Improved Infilling of Missing Metadata from Expendable Bathythermographs (XBTs) Using Multiple Machine Learning MethodsAbstract
Historical in situ ocean temperature profile measurements are important for a wide range of ocean and climate research activities. A large proportion of the profile observations have been recorded using expendable bathythermographs (XBTs), and required bias corrections for use in climate change studies. It is generally accepted that the bias, and therefore bias correction, depends on the type of XBT used. However, poor historical metadata collection practices mean the XBT probe type information is often missing, for 59% of profiles between 1967 and 2000, limiting the development of reliable bias corrections. We develop a process of estimating missing instrument type metadata (the combination of both model and manufacturer) systematically, constructing a machine learning pipeline based on thorough data exploration to inform these choices. The predicted instrument type, where missing, will facilitate improved XBT bias corrections. The new approach improves the accuracy of the XBT type classification compared to previous approaches from a recall value of 0.75–0.94. We also develop an approach to account for the uncertainty associated with metadata assignments using ensembles of decision trees, which could feed into an ensemble approach to creating ocean temperature datasets. We describe the challenges arising from the nature of the dataset in applying standard machine learning techniques to the problem. We have implemented this in a portable, reproducible way using standard data science tools, with a view to these techniques being applied to other similar problems in climate science.
Abstract
Historical in situ ocean temperature profile measurements are important for a wide range of ocean and climate research activities. A large proportion of the profile observations have been recorded using expendable bathythermographs (XBTs), and required bias corrections for use in climate change studies. It is generally accepted that the bias, and therefore bias correction, depends on the type of XBT used. However, poor historical metadata collection practices mean the XBT probe type information is often missing, for 59% of profiles between 1967 and 2000, limiting the development of reliable bias corrections. We develop a process of estimating missing instrument type metadata (the combination of both model and manufacturer) systematically, constructing a machine learning pipeline based on thorough data exploration to inform these choices. The predicted instrument type, where missing, will facilitate improved XBT bias corrections. The new approach improves the accuracy of the XBT type classification compared to previous approaches from a recall value of 0.75–0.94. We also develop an approach to account for the uncertainty associated with metadata assignments using ensembles of decision trees, which could feed into an ensemble approach to creating ocean temperature datasets. We describe the challenges arising from the nature of the dataset in applying standard machine learning techniques to the problem. We have implemented this in a portable, reproducible way using standard data science tools, with a view to these techniques being applied to other similar problems in climate science.
Abstract
Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.
Abstract
Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.
