The absorption of energy by water vapour keeps the average temperature on Earth at a level that makes life possible. However, the average temperature increased in recent decades due to increased greenhouse gas emissions. This anthropogenic increase is amplified by the water vapour feedback. Water vapour is further an important component of the water cycle and indirectly of the energy cycle. The role of water vapour in these cycles in a changing climate needs to be fully understood and quantified. Thus, high-quality observations of water vapour and the characterisation and validation of associated uncertainties are of high importance for climate applications. Topics covered around atmospheric water vapour include, among others, retrieval development, data records, uncertainty characterisation and validation, inter-comparisons, climate applications, and model validation.

This special issue originates from the GEWEX Water Vapor Assessment (G-VAP, http://gewex-vap.org) and is open for all submissions within scope, not just from G-VAP.

A large number of machine learning studies are currently performed in Earth System science to extract information from observations or model data, to learn the dynamics of components of the Earth system, or to build tools to improve weather and climate predictions (for example in model post-processing). These studies require both the domain knowledge of Earth system scientists and the expertise of machine learning experts. The astounding success of machine learning in other scientific disciplines – such as computer vision, speech recognition, robotics, and autonomous driving – is not least a result of the free and open sharing of benchmark datasets and software, which allows easy reproducibility by the community as well as the quantitative comparison of the quality of machine learning solutions. Contrary to many other machine learning disciplines, Earth system datasets and validation methods require thorough documentation to achieve the goal of providing a fair and firm basis for model comparisons according to the accepted community standards. Therefore, the Earth system science community has expressed a need for dedicated benchmark datasets and machine learning tools to process Earth system data.

With this special issue, we offer a platform to develop and openly share these benchmark datasets together with specific machine learning tasks and evaluation metrics as well as innovative solutions for machine learning applications in Earth system science with the wider community of machine learners and environmental scientists.

The special issue is managed jointly by ESSD and GMD and invites manuscripts focusing either on the description of new benchmark datasets (ESSD) or new machine learning model developments (GMD). Dataset papers can reference model papers and vice versa, but this is not a requirement as long as both the datasets and model codes are made freely available and easily accessible. To be accepted as special issue contribution a manuscript must define a specific machine learning task including a loss function and evaluation metric and the authors must provide executable code which demonstrates the machine learning problem with a vanilla solution. The usual guidelines of both journals apply.

The special issue will present results from the MOSES/REEBUS Eddy Study on the mesoscale and sub-mesoscale dynamics associated with ocean eddies of West Africa which was carried out on the German R/V Meteor cruises M156 and M160 in 2019.

The scope of the study included physical, chemical, biological and geological research questions around the physical-chemical-biological coupling and the overall role of ocean eddies in an ocean area in the vicinity of the Canary Current System, a major eastern boundary upwelling system. The observational concept featured a wide range of platforms (research vessel, glider airplane, Saildrones, wave gliders, gliders, BGC Argo floats, drifters, moorings, AUVs, bottom landers, bottom crawler), observation and sampling approaches (towed, winched, free-falling and drifting instruments, multinet, rhodamine dye release experiment), instruments, and observed variables (in situ, underway, discrete samples) and specifically addressed the mesoscale and sub-mesoscale variability associated with ocean eddies.

The glacial–interglacial cyclicity of the climate system varied in the past, most notably during the transition from a 40 ka to a 100 ka world in the mid-Pleistocene. Gases trapped in Antarctic ice are the most direct access available to investigate the composition of the paleo-atmosphere of that age and processes related to climate variability. The International Partnerships in Ice Core Sciences (IPICS) Oldest Ice endeavour aims at obtaining an undisturbed ice-core record older than 1 Ma. In addition to ice cores, time slices of paleo-records, available, for example, in Antarctic blue-ice fields, provide further valuable information, which complements continuous time series based on ice cores. This special issue will assemble contributions dedicated to the preparatory phase of this global effort to obtain ice samples and time series older than 700 000 years. This includes a consideration of glaciogical and geophysical settings which allow the presence of old ice, results from pre-site surveys and modelling studies, aspects of ice-core and other sampling techniques and analyses, and requirements for drilling and core handling.

The climate research community uses reanalyses widely to understand atmospheric processes and variability in the middle atmosphere, yet different reanalyses may give very different results for the same diagnostics. For example, the global energy budget and hydrological cycle, the Brewer–Dobson circulation, stratospheric vortex weakening and intensification events, and large-scale wave activity at the tropical tropopause are known to differ among reanalyses.

The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets with respect to a variety of key diagnostics. The objectives of this project are

1. to understand the causes of differences among reanalyses;
2. to provide guidance on the appropriate usage of various reanalysis products in scientific studies;
3. to contribute to future improvements in the reanalysis products by establishing collaborative links between the reanalysis centres and the SPARC community.

The project focuses predominantly on differences among reanalyses (although studies that include operational analyses are welcome and studies comparing reanalyses with observations are encouraged), with an emphasis on diagnostics in the upper troposphere, stratosphere and mesosphere. This special issue serves to collect research with relevance to S-RIP in preparation for the publication of the S-RIP report in 2018. Although participation in S-RIP is not a prerequisite for submission to this special issue, authors contributing to this collection are encouraged to consider contributing to the preparation of the S-RIP report.

The special issue will present results from the MOSES/REEBUS Eddy Study on the mesoscale and sub-mesoscale dynamics associated with ocean eddies of West Africa which was carried out on the German R/V Meteor cruises M156 and M160 in 2019.

The scope of the study included physical, chemical, biological and geological research questions around the physical-chemical-biological coupling and the overall role of ocean eddies in an ocean area in the vicinity of the Canary Current System, a major eastern boundary upwelling system. The observational concept featured a wide range of platforms (research vessel, glider airplane, Saildrones, wave gliders, gliders, BGC Argo floats, drifters, moorings, AUVs, bottom landers, bottom crawler), observation and sampling approaches (towed, winched, free-falling and drifting instruments, multinet, rhodamine dye release experiment), instruments, and observed variables (in situ, underway, discrete samples) and specifically addressed the mesoscale and sub-mesoscale variability associated with ocean eddies.

A large number of machine learning studies are currently performed in Earth System science to extract information from observations or model data, to learn the dynamics of components of the Earth system, or to build tools to improve weather and climate predictions (for example in model post-processing). These studies require both the domain knowledge of Earth system scientists and the expertise of machine learning experts. The astounding success of machine learning in other scientific disciplines – such as computer vision, speech recognition, robotics, and autonomous driving – is not least a result of the free and open sharing of benchmark datasets and software, which allows easy reproducibility by the community as well as the quantitative comparison of the quality of machine learning solutions. Contrary to many other machine learning disciplines, Earth system datasets and validation methods require thorough documentation to achieve the goal of providing a fair and firm basis for model comparisons according to the accepted community standards. Therefore, the Earth system science community has expressed a need for dedicated benchmark datasets and machine learning tools to process Earth system data.

With this special issue, we offer a platform to develop and openly share these benchmark datasets together with specific machine learning tasks and evaluation metrics as well as innovative solutions for machine learning applications in Earth system science with the wider community of machine learners and environmental scientists.

The special issue is managed jointly by ESSD and GMD and invites manuscripts focusing either on the description of new benchmark datasets (ESSD) or new machine learning model developments (GMD). Dataset papers can reference model papers and vice versa, but this is not a requirement as long as both the datasets and model codes are made freely available and easily accessible. To be accepted as special issue contribution a manuscript must define a specific machine learning task including a loss function and evaluation metric and the authors must provide executable code which demonstrates the machine learning problem with a vanilla solution. The usual guidelines of both journals apply.

The absorption of energy by water vapour keeps the average temperature on Earth at a level that makes life possible. However, the average temperature increased in recent decades due to increased greenhouse gas emissions. This anthropogenic increase is amplified by the water vapour feedback. Water vapour is further an important component of the water cycle and indirectly of the energy cycle. The role of water vapour in these cycles in a changing climate needs to be fully understood and quantified. Thus, high-quality observations of water vapour and the characterisation and validation of associated uncertainties are of high importance for climate applications. Topics covered around atmospheric water vapour include, among others, retrieval development, data records, uncertainty characterisation and validation, inter-comparisons, climate applications, and model validation.

This special issue originates from the GEWEX Water Vapor Assessment (G-VAP, http://gewex-vap.org) and is open for all submissions within scope, not just from G-VAP.

The climate research community uses reanalyses widely to understand atmospheric processes and variability in the middle atmosphere, yet different reanalyses may give very different results for the same diagnostics. For example, the global energy budget and hydrological cycle, the Brewer–Dobson circulation, stratospheric vortex weakening and intensification events, and large-scale wave activity at the tropical tropopause are known to differ among reanalyses.

The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets with respect to a variety of key diagnostics. The objectives of this project are

1. to understand the causes of differences among reanalyses;
2. to provide guidance on the appropriate usage of various reanalysis products in scientific studies;
3. to contribute to future improvements in the reanalysis products by establishing collaborative links between the reanalysis centres and the SPARC community.

The project focuses predominantly on differences among reanalyses (although studies that include operational analyses are welcome and studies comparing reanalyses with observations are encouraged), with an emphasis on diagnostics in the upper troposphere, stratosphere and mesosphere. This special issue serves to collect research with relevance to S-RIP in preparation for the publication of the S-RIP report in 2018. Although participation in S-RIP is not a prerequisite for submission to this special issue, authors contributing to this collection are encouraged to consider contributing to the preparation of the S-RIP report.

The glacial–interglacial cyclicity of the climate system varied in the past, most notably during the transition from a 40 ka to a 100 ka world in the mid-Pleistocene. Gases trapped in Antarctic ice are the most direct access available to investigate the composition of the paleo-atmosphere of that age and processes related to climate variability. The International Partnerships in Ice Core Sciences (IPICS) Oldest Ice endeavour aims at obtaining an undisturbed ice-core record older than 1 Ma. In addition to ice cores, time slices of paleo-records, available, for example, in Antarctic blue-ice fields, provide further valuable information, which complements continuous time series based on ice cores. This special issue will assemble contributions dedicated to the preparatory phase of this global effort to obtain ice samples and time series older than 700 000 years. This includes a consideration of glaciogical and geophysical settings which allow the presence of old ice, results from pre-site surveys and modelling studies, aspects of ice-core and other sampling techniques and analyses, and requirements for drilling and core handling.