Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project.

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.

10The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different 15 methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is 20 required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the 25 variety of methodologies in the current literature. We also present results from our 1-month "proof of concept" trial run designed to illustrate the utility and feasibility of the ARTMIP project.Geosci. Model Dev. Discuss., https://doi

Sea level anomaly (SLA) data spanning 1992-2012 were analyzed to study the statistical properties of eddies in the Red Sea. An algorithm that identifies winding angles was employed to detect 4998 eddies propagating along 938 unique eddy tracks. Statistics suggest that eddies are generated across the entire Red Sea but that they are prevalent in certain regions. A high number of eddies is found in the central basin between 18 N and 24 N. More than 87% of the detected eddies have a radius ranging from 50 to 135 km. Both the intensity and relative vorticity scale of these eddies decrease as the eddy radii increase. The averaged eddy lifespan is approximately 6 weeks. AEs and cyclonic eddies (CEs) have different deformation features, and those with stronger intensities are less deformed and more circular. Analysis of long-lived eddies suggests that they are likely to appear in the central basin with AEs tending to move northward. In addition, their eddy kinetic energy (EKE) increases gradually throughout their lifespans. The annual cycles of CEs and AEs differ, although both exhibit significant seasonal cycles of intensity with the winter and summer peaks appearing in February and August, respectively. The seasonal cycle of EKE is negatively correlated with stratification but positively correlated with vertical shear of horizontal velocity and eddy growth rate, suggesting that the generation of baroclinic instability is responsible for the activities of eddies in the Red Sea.

Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some existing stochastic parameterizations utilize data‐driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and subgrid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate time scales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both time scales, and the networks closely reproduce the spatiotemporal correlations and regimes of the Lorenz '96 system. We also find that, in general, those models which produce skillful forecasts are also associated with the best climate simulations.

The early twentieth-century warming (EW; 1910–45) and the mid-twentieth-century cooling (MC; 1950–80) have been linked to both internal variability of the climate system and changes in external radiative forcing. The degree to which either of the two factors contributed to EW and MC, or both, is still debated. Using a two-box impulse response model, we demonstrate that multidecadal ocean variability was unlikely to be the driver of observed changes in global mean surface temperature (GMST) after AD 1850. Instead, virtually all (97%–98%) of the global low-frequency variability (>30 years) can be explained by external forcing. We find similarly high percentages of explained variance for interhemispheric and land–ocean temperature evolution. Three key aspects are identified that underpin the conclusion of this new study: inhomogeneous anthropogenic aerosol forcing (AER), biases in the instrumental sea surface temperature (SST) datasets, and inadequate representation of the response to varying forcing factors. Once the spatially heterogeneous nature of AER is accounted for, the MC period is reconcilable with external drivers. SST biases and imprecise forcing responses explain the putative disagreement between models and observations during the EW period. As a consequence, Atlantic multidecadal variability (AMV) is found to be primarily controlled by external forcing too. Future attribution studies should account for these important factors when discriminating between externally forced and internally generated influences on climate. We argue that AMV must not be used as a regressor and suggest a revised AMV index instead [the North Atlantic Variability Index (NAVI)]. Our associated best estimate for the transient climate response (TCR) is 1.57 K (±0.70 at the 5%–95% confidence level).

The latest generation of coupled models, the sixth Coupled Models Intercomparison Project (CMIP6), is used to study the changes in the El Niño-Southern Oscillation (ENSO) in a warming climate. For the four future scenarios studied, the sea surface temperature variability increases in most CMIP6 models, but to varying degrees. This increase is linked to a weakening of the east-west temperature gradient in the tropical Pacific Ocean, which is evident across all models. Just as in previous generations of climate models, we find that many characteristics of future ENSO remain uncertain. This includes changes in dominant time scale, extratropical teleconnection patterns, and amplitude of El Niño and La Niña events. For models with the strongest increase in future variability, the majority of the increase happens in the Eastern Pacific, where the strongest El Niño events usually occur. Plain Language Summary The El Niño-Southern Oscillation (ENSO) is a naturally occurring irregular oscillation in the tropical Pacific Ocean alternating between warm (El Niño) and cold (La Niña) phases every 2-7 years. The sea surface temperature anomalies associated with ENSO are linked to variability in key climate quantities, such as temperature, winds, and precipitation over many parts of the globe. Hence, it is of great scientific and societal interest to determine how ENSO may change in a warming climate. We find that the latest generation of climate models shows changes in ENSO in a warmer world. The future variability is increasing, especially in the eastern equatorial Pacific, where the extreme warm events usually occur. This increase appears to be related to a reduced temperature difference between the eastern and western equatorial Pacific. The global weather patterns influenced by both the warm and cold events will also change, but models disagree on how large these changes will be.

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation.