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Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth's energy imbalance, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.8000192, Smith et al., 2023a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human-induced warming reached 1.14 [0.9 to 1.4] ∘C averaged over the 2013–2022 decade and 1.26 [1.0 to 1.6] ∘C in 2022. Over the 2013–2022 period, human-induced warming has been increasing at an unprecedented rate of over 0.2 ∘C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 54 ± 5.3 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that increases in greenhouse gas emissions have slowed, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for human influence on climate.

Neuromodulatory signalingviaG protein-coupled receptor (GPCRs) plays a pivotal role in regulating neural network function and animal behavior. Recent efforts have led to the development of optogenetic tools to induce G protein-mediated signaling, with the promise of acute and cell type-specific manipulation of neuromodulatory signals. However, designing and deploying optogenetically functionalized GPCRs (optoXRs) with accurate specificity and activity to mimic endogenous signalingin vivoremains challenging. Here we optimized the design of optoXRs by considering evolutionary conserved GPCR-G protein interactions and demonstrate the feasibility of this approach using twoDrosophilaDopamine receptors (optoDopRs). We validated these optoDopRs showing that they exhibit high signaling specificity and light sensitivityin vitro.In vivowe detected receptor and cell type-specific effects of dopaminergic signaling in various behaviors including the ability of optoDopRs to rescue loss of the endogenous receptors. This work demonstrates that OptoXRs can enable optical control of neuromodulatory receptor specific signaling in functional and behavioral studies.

Animals must maintain physiological processes within an optimal temperature range despite changes in their environment. While the preoptic area of the hypothalamus (PoA) acts as a thermostat in mammals through autonomic and behavioral adaptations, its role in temperature regulation of animals lacking internal homeostatic mechanisms is not known. Through novel behavioral assays, wholebrain functional imaging and neural ablations, we show that larval zebrafish achieve thermoregulation through movement and a neural network connecting the PoA to brain areas enabling spatial navigation. PoA drives reorientation when thermal conditions are worsening and conveys this information for instructing future motor actions to the navigation-controlling habenula (Hb) - interpeduncular nucleus (IPN) circuit. These results suggest a conserved function of the PoA in thermoregulation acting through species- specific neural networks. We propose that homeostatic navigation arose from an ancient chemotaxis navigation circuit that was subsequently extended to serve in other sensory modalities.

GSK878 is a newly described HIV-1 inhibitor that binds to the mature capsid (CA) hexamer in a pocket originally identified as the binding site of the well-studied CA inhibitor PF-74. Here, we show that GSK878 is highly potent, inhibiting an HIV-1 reporter virus in MT-2 cells with a mean 50% effective concentration (EC 50 ) of 39 pM and inhibiting a panel of 48 chimeric viruses containing diverse CA sequences with a mean EC 50 of 94 pM.

Some disciplines, e.g. Astrophysics or Earth system sciences, work with large to very large amounts of data. Storing this data, but also processing it, is a challenge for researchers because novel concepts for processing data and workflows have not developed as quickly. This problem will only become more pronounced with the ever increasing performance of High Performance Computing (HPC) – systems.At the German Climate Computing Center, we analysed the users, their goals and working methods. DKRZ provides the climate science community with resources such as high-performance computing (HPC), data storage and specialised services and hosts the World Data Center for Climate (WDCC). In analysing users, we distinguish between two main groups: those who need the HPC system to run resource-intensive simulations and then analyse them, and those who reuse, build on and analyse existing data. Each group subdivides into subgroups. We have analysed the workflows for each identified user and found identical parts in an abstracted form and derived Canonical Workflow Modules. In the process, we critically examined the possible use of so-called FAIR Digital Objects (FDOs) and checked to what extent the derived workflows and workflow modules are actually future-proof.We will show the analysis of the different users, the Canonical workflow and the vision of the FDOs. Furthermore, we will present the framework Freva and further developments and implementations at DKRZ with respect to the reproducibility of simulation-based research in the ESS.

Accurately interpreting past climate variability can be a challenging task, particularly when it comes to distinguishing between forced and unforced changes. In the case of large volcanic eruptions, ice core records are a very valuable tool but still often not sufficient to link reconstructed anomaly patterns to a volcanic eruption at all or to its geographical location. In this study, we developed a convolutional neural network (CNN) that is able to classify whether a volcanic eruption occurred and its location (northern hemisphere extratropical, southern hemisphere extratropical, or tropics) with an accuracy of 92%.To train the CNN, we used 100 member ensembles of the MPI-ESM-LR global climate model, generated using the easy volcanic aerosol (EVA) model, which provides the radiative forcing of idealized volcanic eruptions of different strengths and locations. The model considered global sea surface temperature and precipitation patterns 12 months after the eruption over a time period of 3 months.In addition to demonstrating the high accuracy of the CNN, we also applied layer-wise relevance propagation (LRP) to the model to understand its decision-making process and identify the input data that influenced its predictions. Our study demonstrates the potential of using CNNs and interpretability techniques for identifying and locating past volcanic eruptions as well as improving the accuracy and understanding of volcanic climate signals.

Slowly varying ocean heat content is one of the most important variables when describing cli-mate variability on interannual to decadal time scales. Since observation-based estimates ofocean heat content require extensive observational coverage, incomplete observations are oftencombined with numerical models via data assimilation to simulate the evolution of oceanic heat.However, incomplete observations, particularly in the subsurface ocean, lead to large uncertain-ties in the resulting model-based estimate. As an alternative approach, Kadow et al (2020) haveproven that artificial intelligence can successfully be utilized to reconstruct missing climate in-formation for surface temperatures. In the following, we investigate the possibility to train theirthree-dimensional convolutional neural network to reconstruct missing subsurface temperaturesto obtain ocean heat content estimates with a focus on the North Atlantic ocean.The network is trained and tested to reconstruct a 16 member Ensemble Kalman Filter assimi-lation ensemble constructed with the Max-Planck Institute Earth System Model for the periodfrom 1958 to 2020. Specifically, we examine whether the partial convolutional U-net representsa valid alternative to the Ensemble Kalman Filter assimilation to estimate North Atlantic sub-polar gyre ocean heat content.The neural network is capable of reproducing the assimilation reduced to datapoints with ob-servational coverages within its ensemble spread with a correlation coefficient of 0.93 over theentire time period and of 0.99 over 2004 – 2020 (the Argo-Era). Additionally, the network isable to reconstruct the observed ocean heat content directly from observations for 12 additionalmonths with a correlation of 0.97, essentially replacing the assimilation experiment by an extrap-olation. When reconstructing the pre-Argo-Era, the network is only trained with assimilationsfrom the Argo-Era. The lower correlation in the resulting reconstruction indicates higher un-certainties in the assimilation outside of its ensemble spread at times with low observationaldensity. These uncertainties are highlighted by inconsistencies in the assimilation’s represen-tations of the North Atlantic Current at times and grid points without observations detectedby the neural network. Our results demonstrate that a neural network is not only capable ofreproducing the observed ocean heat content over the training period, but also before and aftermaking the neural network a suitable candidate to step-wise extend or replace data assimilation.

The number of damaging events caused by natural disasters is increasing because of climate change. Projects of public interest such as ClimXtreme (Climate Change and Extreme Events [1, 2]), aim to improve our knowledge of extreme events, the influence of environmental changes and their societal impacts.ClimXtreme focuses on an integral evaluation through a three-pronged approach, namely: the physical processes behind the extremes, the statistical assessment of them, and their impact. The success of such a project depends on a coordinate effort from many interdisciplinary groups down to the management of computational and data storage resources. The ever-growing amount of available data at the researcher’s disposal is a two-sided blade that craves for greater resources to host, access, and evaluate them efficiently through High Performance Computing (HPC) infrastructures. Additionally, these last years the community is demanding an easier reproducibility of evaluation workflows and data FAIRness [3]. Frameworks like Freva (Free Evaluation System Framework [4, 5]) offer an efficient solution to handle customizable evaluation systems of large research projects, institutes or universities in the Earth system community [6-8] over the HPC environment and in a centralized manner. Mainly written on python, Freva offers:A centralized access. Freva can be accessed via command line interface, via web, and via python module (e.g. for jupyter notebooks) offering similar features. A standardized data search. Freva allows for a quick and intuitive incorporation and search of several datasets stored centrally. Flexible analysis. Freva provides a common interface for user defined data evaluation routines to plug them in to the system irrespective of the programming language. These plugins are able to search from and integrate own results back to Freva. This environment enables an ecosystem of plugins that fosters the interchange of results and ideas between researchers, and facilitates the portability to any other research project that uses a Freva instance. Transparent and reproducible results. Every analysis run through Freva (including parameter configuration and plugin version information) is stored in a central database and can be consulted, shared, modified and re-run by anyone within the project. Freva optimizes the usage of computational and storage resources and paves the way of traceability in line with FAIR data principles. Hosted at the DKRZ, ClimXtreme’s Freva instance (XCES [7]) offers quick access to more than 9 million datafiles of models (e.g. CMIP, CORDEX), observations (stations, gridded) and evaluation outputs. The ClimXtreme community has been actively contributing with plugins to XCES, its biggest asset, with close to 20 plugins of different disciplines at the disposal of everyone within the project, and more than 20,000 analysis run through the system. At present, any researcher can focus on a past, present or future period and a geographical region and run a series of evaluations ranging from coocurrence probabilities of extreme events, their impact on crops to wind tracking algorithms among many others. Freva facilitates comprehensive and exhaustive analysis of extreme events in an easy way. References:[1] https://www.fona.de/de/massnahmen/foerdermassnahmen/climxtreme.php[2] https://www.climxtreme.net/index.php/en/[3] https://www.go-fair.org/fair-principles/[4] http://doi.org/10.5334/jors.253[5] https://github.com/FREVA-CLINT/freva-deployment[6] freva.met.fu-berlin.de[7] https://www.xces.dkrz.de/[8] www-regiklim.dkrz.de

Evaluating the trends of extreme indices (EI) is crucial to detect and attribute extreme events (EE) and establish adaptation and mitigation strategies to the current and future climate conditions. However, the observational climate data used for the calculation of these indices often contains many missing values and leads to incomplete and inaccurate EI. This problem is even greater as we go back in time due to the scarcity of the older measurements.To tackle this problem, interpolation techniques such as the kriging method are often used to fill in the gaps. However, it has been shown that such techniques are inadequate to reconstruct specific climatic patterns [1]. Deep-learning based technologies give the possibility to surpass standard statistical methods by learning complex patterns and features in climate data.In this work, we are using an inpainting technique based on a U-Net neural network made of partial convolutional layers and a loss function designed to produce semantically meaningful predictions [1]. Models are trained using vast amounts of climate model data and can be used to reconstruct large and irregular regions of missing data with few computational resources.The efficiency of the method is well demonstrated through its application to the HadEX3 dataset [2]. This dataset contains gridded land surface EI, among which the TX90p index that measures the monthly (or annual) frequency of warm days (defined as a percentage of days where daily maximum temperature is above the 90th percentile). As for other EI, there is a lack of TX90p values in many regions of the world, even in recent years. It is particularly true when looking at an intermediate product of HadEX3 where the station-based indices have been combined without interpolation. This is illustrated by the left map of the figure where the gray pixels correspond to missing values. By training our model using data from the CMIP6 archive, we have been able to reconstruct the missing TX90p values for all the time steps of HadEX3 (see right map in the figure) and detect EE that were not included in the original dataset. The reconstructed dataset is being prepared for the community in the framework of the H2020 CLINT project [3] for further detection and attribution studies.[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)[2] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)[3] https://climateintelligence.eu/

Weather radars are a significant component of modern precipitation recordings,as they provide information with high spatial and temporal resolution. However, radars as a tool for weather applications emerged only after the 1950s. AI/ML methods have proven to be successful when it comes to determining patterns and connections between related fields in space and time. Moreover, AI/ML methods have exhibited remarkable skill in infilling missing climate information (see Kadow et al. 2020). Desired outcomes of the project include using these AI/ML techniques to build a spatial precipitation field by combining station and radar data. We will use data from two well-known datasets: RADOLAN and COSMO-REA2. The validity of this digital twin will be investigated by comparing its output with other reanalysis data (e.g. ERA5). Further evaluation can be carried out by testing the radar field’s accuracy in detecting extreme precipitation events in the past (e.g. heavy rain events in the summer of 2021 in Western Germany). We aim for the creation of a radar field that will be successfully projected in the past. Moreover, it will uncover new information on regional climatology, especially in areas where station data is sparse.

We present the CMIP6 version of the Max Planck Institute-Grand Ensemble (MPI-GE CMIP6) with 30 realisations for the historical period and five emission scenarios. The power of MPI-GE CMIP6 goes beyond its predecessor ensemble MPI-GE by providing high-frequency model output, the full range of emission scenarios including the highly policy relevant scenarios SSP1-1.9 and SSP1-2.6, and the opportunity to compare the ensemble to high resolution simulations of the same model version. We demonstrate with six novel application examples how to use the power of MPI-GE CMIP6 to better quantify and understand present and future extreme events in the Earth system, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI-GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heat waves are projected to occur every year in 2071-2100 in high-emission scenarios, that the storm activity in most tropical to mid-latitude oceans is projected to decrease, and that the ensemble is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Freva (the Free Evaluation System Framework [1; 2]) is a platform developed by the earth science community for the earth science community. Designed to work over HPC environments, it efficiently handles the data search and analysis of large projects, institutes or universities. Written on python, the framework has undergone a major update of the core. Freva offers:A centralized access. Freva comes in three different flavours with similar functionalities: a command line interface, a web user interface, and a python module that allows the usage of Freva in python environments, like jupyter notebooks. A standardized data search. Freva allows for a quick and intuitive search of several datasets stored centrally. The datasets are internally indexed in a SOLR server with an implemented metadata system that satisfies the international standards provided by the Earth System Grid Federation. Flexible analysis. Freva provides a common interface for user defined data analysis tools to plug them in to the system irrespective of the used language. Each plugin can be encapsulated in a personalized conda environment, facilitating the reproducibility and portability to any other Freva instance. These plugins are able to search from and integrate own results back to the database, enabling an ecosystem of different tools. This environment fosters the interchange of results and ideas between researchers, and the collaboration between users and plugin developers alike. Transparent and reproducible results. The analysis history and parameter configuration (including tool and system Git versioning) of every plugin run is stored in a MariaDB database. Any analysis configuration and result can be consulted and shared among the scientists, offering traceability in line with FAIR data principles, and optimizing the usage of computational and storage resources. Freva has also experienced an upgrade on the sysadmin side:Painless deployment via Ansible, with a highly customizable configuration of the services via Docker. Secure system configuration via Vault integration. Straightforward migration from old Freva database servers or between Freva instances. Improvements in the dataset incorporation. Automatic backup of database and SOLR services. [1] https://www.freva.dkrz.de/[2] https://github.com/FREVA-CLINT/freva-deployment

Heatwave episodes have severe consequences in the forms of excess mortality in many regions around the world, shortage of agricultural products, drastic changes in ecosystem function and health risks. Due to the global mean temperature rising, the acceleration of extreme temperature disturbing highly at the local scale level, particularly in urban areas. From an economic growth point of view, Major cities are contributing in terms of GDP more. Heatwaves have impacted European GDP significantly in recent years. Our work is to find the number of frequent heat wave days affecting cities which are contributing to the growth of the economy in terms of GDP and density of population wise in Europe over the near future, mid future and long future using the Apriori algorithm. The features of the heat wave and their attributes have been defined according to the criteria explained in ETCCDI. The dataset that contains heat wave days in Europe derived from EURO-CORDEX climate projections is used in this work.ReferencesCopernicus Climate Change Service (C3S): Heat waves and cold spells in Europe derived from climate projections, Climate Change Service Climate Data Store (CDS), DOI:10.24381/cds.9e7ca677 David García-León.et.al, Current and projected regional economic impacts of heatwaves in Europe, Nature Communications, https://doi.org/10.1038/s41467-021-26050-z Christophe Lavaysse.et.al, Towards a monitoring system of temperature extremes in Europe, Nat. Hazards Earth Syst. Sci,doi:10.5194/nhess-2017-181, 2017 Chloé Prodhomme. et.al, Seasonal prediction of European summer heatwaves,https://doi.org/10.1007/s00382-021-05828-3 S. E. Perkins and L.V.Alexander, On the Measurement of Heat Waves, DOI: https://doi.org/10.1175/JCLI-D-12-00383.1 S. E. Perkins-Kirkpatrick.et.al, Changes in regional heatwave characteristics as a function increasing global temperature, DOI:10.1038/s41598-017-12520-2 Agrawal, R. and Srikant, Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago de Chile, 12-15 September 1994, 487-499.

High resolution in weather and climate was always a common and ongoing goal of the community. In this regards, machine learning techniques accompanied numerical and statistical methods in recent years. Here we demonstrate that artificial intelligence can skilfully downscale low resolution climate model data when combined with numerical climate model data. We show that recently developed image inpainting technique perform accurate super-resolution via transfer learning using the HighResMIP of CMIP6 (Coupled Model Intercomparison Project Phase 6) experiments. Its huge data base offers a unique training opportunity for machine learning approaches. The transfer learning purpose allows also to downscale other CMIP6 experiments and models, as well as observational data like HadCRUT5. Combined with the technology of Kadow et al. 2020 of infilling missing climate data, we gain a neural network which reconstructs and downscales the important observational data set (IPCC AR6) at the same time. We further investigate the application of our method to downscale quantities predicted from a numerical ocean model (ICON-O) to improve computation times. In this process we focus on the ability of the model to predict eddies from low-resolution data.An extension to:Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nature Geoscience 13, 408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We base this update on the assessment methods used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report, updating the monitoring datasets and to produce updated estimates for key climate indicators including emissions, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth’s energy imbalance, warming attributed to human activities, the remaining carbon budget and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.7883758, Smith et al., 2023). As they are traceable and consistent with IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human induced warming reached 1.14 [0.9 to 1.4] °C over the 2013–2022 period and 1.26 [1.0 to 1.6] °C in 2022. Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 57 ± 5.6 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there are signs that emission levels are starting to stabilise, and we can hope that a continued series of these annual updates might track a real-world change of direction for the climate over this critical decade.