Earth's global “climate sensitivity” is a fundamental quantitative measure of the susceptibility of Earth's climate to human influence. A landmark report in 1979 concluded that it probably lies between 1.5°C and 4.5°C per doubling of atmospheric carbon dioxide, assuming that other influences on climate remain unchanged. In the 40 years since, it has appeared difficult to reduce this uncertainty range. In this report we thoroughly assess all lines of evidence including some new developments. We find that a large volume of consistent evidence now points to a more confident view of a climate sensitivity near the middle or upper part of this range. In particular, it now appears extremely unlikely that the climate sensitivity could be low enough to avoid substantial climate change (well in excess of 2°C warming) under a high-emission future scenario. We remain unable to rule out that the sensitivity could be above 4.5°C per doubling of carbon dioxide levels, although this is not likely. Continued research is needed to further reduce the uncertainty, and we identify some of the more promising possibilities in this regard.

With the rapid development of artificial intelligence (AI), students and researchers in the geophysical community would like to know what AI can bring to geophysical discoveries. We present a review of deep learning (DL), a popular AI technique, for geophysical readers to understand recent advances, open problems, and future trends. This review aims to pave the way for more geophysical researchers, students, and teachers to understand and use DL techniques.

Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio-temporal fusion are two common and low-cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio-temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with synthetic aperture radar data and digital elevation model data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.

The ocean plays a major role in the global carbon cycle and the storage of anthropogenic carbon dioxide. This key function of the ocean is related to the reaction of dissolved carbon dioxide with water to form bicarbonate (and minor quantities of carbonic acid and carbonate). Alkalinity, the excess of bases, governs the efficiency at which this occurs and provides buffering capacity toward acidification. Here we discuss ocean alkalinity, buffering, and biogeochemical processes and provide quantitative tools that may help to better understand the role of the ocean in carbon cycling during times of global change.