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The 1960s was an exciting era for atmospheric predictability research: a finite predictability of the atmosphere was uncovered using Lorenz’s models and the well-acknowledged predictability limit of two weeks was estimated using a general circulation model (GCM). Here, we delve into details regarding how a correlation between the two-week predictability limit and a doubling time of five days was established, recognize Lorenz’s pioneering work, and suggest non-impossibility for predictability beyond two weeks. We reevaluate the outcomes of three different approaches—dynamical, empirical, and dynamical-empirical—presented in Lorenz’s and Charney et al.’s papers from the 1960s. Using the intrinsic characteristics of the irregular solutions found in Lorenz’s studies and the dynamical approach, a doubling time of five days was estimated using the Mintz–Arakawa model and extrapolated to propose a predictability limit of approximately two weeks. This limit is now termed “Predictability Limit Hypothesis”, drawing a parallel to Moore’s Law, to recognize the combined direct and indirect influences of Lorenz, Mintz, and Arakawa under Charney’s leadership. The concept serves as a bridge between the hypothetical predictability limit and practical model capabilities, suggesting that long-range simulations are not entirely constrained by the two-week predictability hypothesis. These clarifications provide further support to the exploration of extended-range predictions using both partial differential equation (PDE)-physics-based and Artificial Intelligence (AI)—powered approaches.

Abstract This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ∼6× the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.

Abstract This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO2 is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO2 radiative forcing and stronger positive feedbacks.

Temporal change in rainfall erosivity varies due to the rainfall characteristic (amount, intensity, frequency, duration), which affects the conservation of soil and water. This study illustrates the variation of rainfall erosivity due to changing rainfall in the past and the future. The projected rainfall is generated by SDSM (Statistical DownScaling Model) after calibration and validation using two GCMs (general circulation model) data of HadCM3 (A2 and B2 scenario) and CGCM3 (A1B and A2 scenario). The selected study area is mainly a cultivable area with an agricultural based economy. This economy depends on rainfall and is located in a part of the Narmada river basin in central India. Nine rainfall locations are selected that are distributed throughout the study area and surrounding. The results indicate gradually increasing projected rainfall while the past rainfall has shown a declined pattern by Mann–Kendall test with statistical 95% confidence level. Rainfall erosivity has increased due to the projected increase in the future rainfall (2080 s) in comparison to the past. Rainfall erosivity varies from −32.91% to 24.12% in the 2020s, −18.82 to 75.48% in 2050 s and 20.95–202.40% in 2080s. The outputs of this paper can be helpful for the decision makers to manage the soil water conservation in this study area.

The Brazil-Malvinas Confluence (BMC) region constitutes one of the most important ocean features in the South Atlantic Ocean basin. Understanding the BMC upper ocean temperature variability could represent one of the key factors to understanding its connection to local and global ocean circulation. Some important local and remote ocean processes are related to the annual and interannual variability of its thermal/salt front position. This paper investigates the upper ocean variability in the BMC region under the influence of an El Niño/Southern Oscillation-type wind stress field using an oceanic general circulation model (Modular Ocean Model version 4.0). The results revealed the occurrence of positive anomalies, with values ranging from 0.5ºC to 1.0ºC (centered at 40.5ºS and 55.0ºW), near the BMC region associated with upper ocean velocity anomalies during the peak of the ocean kinetic energy phase. This mechanism was a response of a volume transport balance between the Brazil Current (BC) and the Malvinas Current (MC), the latter being accelerated by the Antarctic Circumpolar Current (ACC). The occurrence of positive anomalies was always associated with an enhanced BC meridional surface volume transport, together with a decrease of the ACC zonal volume transport, vertically integrated across the Drake Passage, and a correspondent decrease in the upper layer MC volume transport. It was possible to see negative upper layer temperature anomaly predominance just before the peak ocean kinetic energy month and positive anomaly predominance immediately following this month. The occurrence of upper layer temperature anomalies in the BMC region was not restricted to just the peak ocean kinetic energy month since they occurred throughout the perturbed experiment integration time.