Decarbonisation plans across the globe require zero-carbon energy sources to be widely deployed by 2050 or 2060. Solar energy is the most widely available energy resource on Earth, and its economic attractiveness is improving fast in a cycle of increasing investments. Here we use data-driven conditional technology and economic forecasting modelling to establish which zero carbon power sources could become dominant worldwide. We find that, due to technological trajectories set in motion by past policy, a global irreversible solar tipping point may have passed where solar energy gradually comes to dominate global electricity markets, without any further climate policies. Uncertainties arise, however, over grid stability in a renewables-dominated power system, the availability of sufficient finance in underdeveloped economies, the capacity of supply chains and political resistance from regions that lose employment. Policies resolving these barriers may be more effective than price instruments to accelerate the transition to clean energy.

Climate change and political polarization are two of the twenty-first century’s critical socio-political issues. Here we investigate their intersection by studying the discussion around the United Nations Conference of the Parties on Climate Change (COP) using Twitter data from 2014 to 2021. First, we reveal a large increase in ideological polarization during COP26, following low polarization between COP20 and COP25. Second, we show that this increase is driven by growing right-wing activity, a fourfold increase since COP21 relative to pro-climate groups. Finally, we identify a broad range of ‘climate contrarian’ views during COP26, emphasizing the theme of political hypocrisy as a topic of cross-ideological appeal; contrarian views and accusations of hypocrisy have become key themes in the Twitter climate discussion since 2019. With future climate action reliant on negotiations at COP27 and beyond, our results highlight the importance of monitoring polarization and its impacts in the public climate discourse.

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets.

Climate services are technology-intensive, science-based and user-tailored tools providing timely climate information to a wide set of users. They accelerate innovation, while contributing to societal adaptation. Research has explored the advancements of climate services in multiple fields, producing a wealth of interdisciplinary knowledge ranging from climatology to the social sciences. The aim of this paper is to map the global landscape of research on climate services and to identify patterns at individual, affiliation and country level and the structural properties of each community. We use a sample of 358 records published between 1974 and 2018 and quantitatively analyze them. We provide insights into the main characteristics of the community of climate services through Bibliometrics and complement these findings with Network Science. We have computed the centrality of each actor as derived from a Principal Component Analysis of 42 different measures. By exploring the structural properties of the networks of individuals, institutions and countries we derive implications on the most central agents. Furthermore, we detect brokers in the network, capable of facilitating the information flow and increasing the cohesion of the community. We finally analyze the abstracts of the sample via Content Analysis. We find a progressive shift towards climate adaptation and user-centric visions. Agriculture and Energy are the top mentioned sectors. Anglophone countries and institutions are quantitatively dominant, and they are also important in connecting different discipline of the network of scholars, by building on established partnerships. Finding that nodes facilitating the diffusion of information flows (the brokers) are not necessarily the most central, but have a high degree of interdisciplinarity facilitating interactions of different communities.Social media abstract. #WhoisWho in #climateservices? A comprehensive map of research in #Europe and beyond