Abstract. This paper describes the experimental protocol for a set of coordinated simulations involving oceanic surface freshwater flux perturbations, conducted as part of the international Tipping Points Modelling Intercomparison Project (TIPMIP). These simulations constitute the first phase of the TIPMIP-OCEAN domain. We propose this protocol for inclusion in the Coupled Model Intercomparison Project Phase 7 (CMIP7), although it can also be implemented within CMIP6+ or other types of coupled or ocean standalone models. This initial phase focuses primarily on the dynamics of the North Atlantic Ocean, particularly the Atlantic Meridional Overturning Circulation (AMOC). The different experiments are designed to (i) evaluate the impacts of a potential major AMOC weakening under a 2 °C global warming scenario, (ii) assess the sensitivity of the AMOC to combined global warming and freshwater forcing, (iii) investigate the potential recovery of the AMOC following the reversal of forcings, and (iv) compare past AMOC variations with available climate observations and reconstructions. Four categories of experiments are included. Experiment group A examines the effect of freshwater release around Greenland under ramp-up, stabilization, and ramp-down scenarios in both CO2 emissions and freshwater input. Experiment group B complements this idealized set by using historical climate simulations and projections for 1850–2100, incorporating realistic estimates of Greenland Ice Sheet melt based on observations for the historical period and ice-sheet model projections for the future. Experiment group C extends the existing North Atlantic Hosing Model Intercomparison Project (NAHosMIP) by applying large freshwater perturbations to both control and 2 °C-warming simulations to assess how global warming influences AMOC reversibility. Finally, experiment group D imposes freshwater inputs, consistent with those inferred for the 8.2 kyr before present event, under pre-industrial conditions, in order to constrain model sensitivity to freshwater forcing using paleoclimate reconstructions. Together, these coordinated experiments will allow systematic evaluation of how different climate models respond to identical freshwater perturbations—an essential step toward better understanding the wide inter-model spread in North Atlantic dynamics and projected future AMOC changes.

Climate models are essential tools for understanding the Earth’s climate system, and undergo continual development to improve their skill across a range of metrics. Here we evaluate the new cloud representations and their radiative feedbacks in the NASA Goddard Institute for Space Studies (GISS)-E3 model relative to its predecessor GISS-E2.1. The new model now incorporates a considerable reworking of the cloud micro-and macrophysics, higher vertical resolution, and changes to moist turbulence. The results show substantial improvements in cloud climatology, primarily through a more realistic simulation of low-level clouds, especially stratocumulus, leading to improved shortwave and longwave cloud radiative effects. A machine learning (ML)-and observation-constrained tuning framework further improves tropical low-cloud regimes and Arctic cloud seasonality, making GISS-E3 competitive with CMIP6 models. However, persistent biases remain, including overly reflective tropical low clouds, excess high-cloud cover, and underestimated cloud cover over the Southern Ocean, highlighting limitations that parametric tuning alone cannot address. GISS-E3 exhibits a weakly positive net cloud feedback and an equilibrium climate sensitivity (ECS; 2.77◦C) comparable to GISS-E2.1. This behavior is linked to a weak non-low cloud feedback that lies near the lower bound of both the GISS-E3 calibrated-physics ensemble (CPE) and CMIP6 models, and outside observational constraints. In contrast, the CPE members that are more consistent with observations show higher ECS values (∼3.8◦C). These results highlight the importance of incorporating additional process-level observational constraints during both model development and tuning, particularly for high-and extratropical-cloud regimes, to better capture the mean state and cloud feedbacks.

Abstract. Parties to the 2015 Paris Agreement agreed to limit the long-term increase in global average temperature to well below 2 °C and pursue efforts to keep temperatures below 1.5 °C relative to pre-industrial levels. As the world is fast approaching the 1.5 °C warming level on a sustained basis, and with 2024 likely the first year that was over 1.5 °C warmer than 1850-1900, there is ever increasing interest in how we will know whether and when 1.5 °C warming since pre-industrial has been reached or exceeded with respect to a long-term average. This paper represents a comprehensive community methodological overview, building on the IPCC 6th assessment. It explains why there is no straightforward answer and proposes clear and reasoned ways forward. Existing challenges are as follows. Firstly, the Paris Agreement text contains definitional ambiguities around 'pre-industrial', 'global average temperature', whether the assessment should be on realised or long-term human-induced warming, and over what time frame the long-term temperature goal applies. Then, there are intrinsic limitations of observational records which get more uncertain further back in time due to data sparsity and measurement heterogeneity. Finally, in a non-stationary climate, multidecadal mean indicators of global temperature change will either lag behind the change or must rely on expected future temperature changes (based on extrapolation, initialized predictions, or scenario-based and constrained projections). Our analysis shows that knowing 'whether we are there yet' is a multifaceted and inherently probabilistic problem that includes information on the definition of a specific level of global warming, temperature changes over multiple timescales, and also potentially includes unpacking the attribution of human-caused changes from observed variations. Given the policy relevance of understanding where the world stands relative to 1.5 °C, or any other level of global warming since pre-industrial, there are a number of practical steps which could be taken to increase specificity in answering this critical question in a timely manner, and inform future monitoring and assessment activities. This paper reviews a broad range of approaches, identifies the most pragmatic, robust and transparent, and clarifies requirements for use in real time including how to handle and represent remaining uncertainties. We show that it is possible by combining lines of evidence and several methodologies to estimate the present long-term warming level without delay in a manner that is robust both in retrospective validation of crossing past warming levels and, critically, to divergent warming futures including potential wildcard impacts of large volcanoes which can mask underlying warming for several years. Results are benchmarked against historical exceedances of 0.5 °C and 1 °C warming. Long-term warming as assessed using the approaches developed herein and data up to and including 2024 stands at 1.40 [1.23–1.58] °C, and underlying human-caused warming stands at 1.34 [1.18–1.50] °C. In IPCC quantified likelihood language this means that it was unlikely that long-term realised warming had exceeded 1.5 °C by the end of 2024 and very unlikely that human-induced warming had exceeded 1.5 °C.

We present the first results of the Water Isotope Model Intercomparison Project (WisoMIP), with Phase 1 focused on modern simulations (1979-2023) from a suite of isotope-enabled atmospheric general circulation models nudged to ERA5 reanalyzes. Water sources, mixing, and rainout history influence the isotopic composition of vapor and precipitation, making these simulations powerful tools for tracing the global water cycle. By prescribing identical winds, sea surface temperatures, and sea ice conditions, we isolate differences in water isotope behavior across models, controlling for variability in atmospheric dynamics and mean climate. Our analyses show that the ensemble mean best matches observations, as individual model errors cancel out to yield a more accurate representation of Earth's isotope distributions. We also evaluate trends and responses to major climate modes during the recent warming period, highlighting regional and temporal sensitivities in the isotope signals. These diagnostics extend beyond traditional model evaluation metrics (e.g., temperature, precipitation) to reveal uncertainties in physical processes and guide improvements in model parameterizations. The resulting modern nudged ensemble data set serves as a benchmark for isotope-enabled model development, satellite product comparison, and understanding of water cycle changes in a warming climate. Given its standardized design and broad participation, WisoMIP provides a valuable "isotope reanalysis" product for applications ranging from paleoclimate reconstruction to model tuning. Our work demonstrates the importance of coordinated isotope model evaluation in advancing the use of water isotopes as a diagnostic tool in climate science.

We present simulations from two Earth system models in which liquefied natural gas (LNG) rocket fuel burning injects gaseous and particulate material into the stratosphere during liftoff. We considered two heavy rocket launch scenarios representative for the year 2050, one with 1000 launches per year, a scenario equivalent with present-day emissions using kerosene propellant, and another with 10 times more. Injected species that include gaseous carbon monoxide, nitrogen oxides, and water vapor showed marginal or insignificant changes in atmospheric composition, while particulate black carbon was found to be the most influential of all injected material, via feedbacks that involve tropopause and stratospheric warming, followed by a moistening of the stratosphere via tropospheric water vapor intrusions. The fact that the LNG is still a concern via aerosol pollution in our simulations, despite burning more efficiently than conventional fuels, underscores the need to better understand the future climate impacts of space flight on atmospheric composition and climate.

A model is a living portfolio of conceptual, formal and implemented versions of a physical description of ‘something’ - including possibly different mathematical and computational implementations in modeling software - in relation to a collection of documentations of analyses and applications of these different versions. As the culture in modeling software shifts towards Open Science, many in our science community imagine a future infrastructure designed so that anyone in science can fully participate in research and development in an open way, where significant progress in our understanding of physical phenomena and accuracy of forecasting can be realized on faster timescales, and next generation of experts can receive quality training regardless of their backgrounds. However, we must acknowledge that funders cannot fund every software execution, develop every model, or archive every output desired by the research community, and that Open Science is not a magical solution to every problem or a gold standard to be required of every effort. However, we can purposefully design a prioritization system and the necessary supporting infrastructure to better enable research, make access to relevant education more equitable, accelerate, support collaboration, and make modeling software and their outputs more FAIR 1 (Wilkinson et al. 2016). Accomplishing these goals promises to increase the return on investment funders, scientists, and research software engineers put into these computational models by increasing community trust in results through increased transparency, discoverability through increased interlinking, and interoperability and reusability through more standardized approaches to documentation and file formats. This paper focuses on improvements to the infrastructure in United States for computational models in natural sciences, with the understanding that several aspects of the envisioned structure will also benefit other disciplines that deal with computationally intensive modeling software and simulations, both in US and other regions of the world.

Abstract Stratospheric ozone depletion strongly influences Southern Ocean climate change. Using coupled climate model simulations, we quantify the transient effect of stratospheric ozone depletion on sea surface temperature (SST) over the Southern Ocean from 1982 to 2005. We find that stratospheric ozone depletion intensifies surface zonal winds south of 46°S, which increases northward Ekman transports and promotes cold water advection, resulting in SST cooling there. In addition, meridional SST gradients are enhanced across the Southern Ocean, which, in turn, prompt colder water advection driven by climatological surface winds to exacerbate the SST cooling between 46°S and 60°S. Because of the increase in stratospheric ozone depletion from 1982 to the early 2000s, the sustained Ekman transport induced horizontal cold‐water advection—though partially compensated by changes in surface heat flux and vertical advection below the mixed layer—plays a central role in maintaining Southern Ocean SST cooling and regional Antarctic sea ice expansion.

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a "calibrated physics ensemble," or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many "unimportant" parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

Precipitation isotopes are valuable tracers for understanding the hydrologic cycle and climate variations. Distinct from d‐excess, 17 O‐excess has recently emerged as a promising new tracer of precipitation processes because of its insensitivity to moisture source temperature. However, the control mechanisms on precipitation 17 O‐excess remain poorly understood. In this study, we evaluated the performance of the GISS‐E2.1 climate model in simulating the precipitation isotopes, focusing on 17 O‐excess. Through comprehensive analysis, we explored how variations in seawater isotopes, rain evaporation, kinetic isotope fractionation parameters, and supersaturation factors influence the simulated precipitation d‐excess and 17 O‐excess. Our findings reveal that GISS‐E2.1 accurately captures the spatial distribution and temporal variations of precipitation δ 18 O. Moreover, it reasonably reproduces the spatial patterns of precipitation d‐excess, though slightly underestimating the mean value in the low latitudes. Although most simulated 17 O‐excess values fall within the observed range, evaluating the accuracy of 17 O‐excess simulations is challenging due to the limited availability of observational data. Notably, in tropical regions, the spatiotemporal distributions of d‐excess and 17 O‐excess are sensitive to convective processes, such as rain evaporation. The model's limitations in 17 O‐excess simulation suggest that current formulations are inadequate to fully capture the variability of 17 O‐excess. This underscores the complexity of the processes influencing 17 O‐excess and highlights the need for additional data and further research to comprehensively understand its controlling factors. Our findings contribute to our understanding of the mechanisms driving the observed variation in precipitation triple oxygen isotopes and to the validation and improvement of climate models.

Anomalous freshwater fluxes from the Greenland and Antarctic ice sheets and ice shelves are impacting the surrounding oceans, and we need to be able to account for these effects in climate model simulations over the historical period and in future projections. In previous phases of the Coupled Model Intercomparison Project (CMIP), models mostly either assumed that the ice sheets were in mass balance, or that discharge from the ice sheets was constant, but in neither case was the observed increasing discharge over the historical period properly represented. In this paper, we present data products of absolute and anomalous freshwater mass fluxes from both major ice sheets, and recommendations for their use in historical simulations. These fluxes can be implemented in climate simulations as a forcing for models that do not (yet) include interactive ice sheets, or used to evaluate models that do. We also make recommendations for how climatological and anomalous fluxes can be implemented in climate models that may have different approaches to interactions with the ice sheets. These forcings are available for CMIP7 simulations and should lead to more robust and coherent simulation of sea surface temperature, sea ice and regional sea level trends in the recent historical period and, as these data are extended, improve the credibility of projections.

The historical global temperature record is an essential data product for quantifying the variability and change of the Earth system. In recent years, better characterization of observational uncertainty in global and hemispheric trends has become available, but the methodologies are not necessarily applicable to analyses at smaller regional areas, or monthly or seasonal means, where station sparsity and other systematic issues contribute to greater uncertainty. This study presents a gridded uncertainty ensemble of historical surface temperature anomalies from the Goddard Institute for Space Studies (GISS) Surface Temperature (GISTEMP) product. This ensemble characterizes the complex spatial and temporal correlation structure of uncertainty, enabling better uncertainty propagation for climate and applied science in applications of historical temperature products at spatial scales from global to regional and temporal scales from centennial to monthly. This work details the methodology for generating the uncertainty ensemble, presents key statistics of the uncertainty evolution over space and time, and provides best practices for using the uncertainty ensemble in future studies. Summary statistics from the uncertainty ensemble agree well with the previous GISTEMP global uncertainty assessment, providing confidence in both.

Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed. Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed.

Observational studies have shown that the El Niño–Southern Oscillation (ENSO) exerts an influence on the Quasi-Biennial Oscillation (QBO). The downward propagation of the QBO tends to speed up and slow down during El Niño and La Niña, respectively. Recent results from general circulation models have indicated that the ENSO modulation of the QBO requires a relatively high horizontal resolution, and that it does not show up in the climate models with parameterized but temporally constant gravity wave sources. Here, we demonstrate that the NASA Goddard Institute for Space Studies (GISS) E2.2 models can capture the observed ENSO modulation of the QBO period with a horizontal resolution of 2∘ latitude by 2.5∘ longitude but with its gravity wave sources being parameterized interactively. This is because El Niño events lead to more vigorous gravity wave sources generating more absolute momentum fluxes over the equatorial belt, as well as less filtering of these waves into the tropical lower stratosphere through a weakening of the Walker circulation. Various components of the ENSO system, such as the sea surface temperatures, the convective activities, and the Walker circulation, are intimately involved in the generation and propagation of parameterized gravity waves, through which ENSO modulates the QBO period in GISS E2.2 models.

We present simulations of an Earth system model in which launch vehicles inject gaseous and particulate combustion products into the stratosphere. We considered two plausible scenarios representative for the year 2050, one with 1000 launches per year and another with 10 times more, all of which assuming heavy lift (80 tons to low Earth orbit; LEO) launches using methane as a proxy for liquefied natural gas (LNG) fuel. An industry-standard plume flowfield model was used to predict emission of gaseous species including carbon monoxide, nitrogen oxides, and water vapor. Black carbon was introduced assuming vacuum emission equal 25% of equivalent kerosene engine. The gas emissions showed marginal or insignificant changes in atmospheric composition, while the black carbon emission was found to be the most influential component. Feedbacks involve tropopause and stratospheric warming, followed by a moistening of the stratosphere via tropospheric water vapor intrusions, together with a reduction in global albedo. The fact that LNG-fueled rockets could be a concern from aerosol emissions in our simulations, despite burning more efficiently than conventional fuels, underscores the need to better understand actual black carbon emissions from rocket engines in order to accurately predict the global impacts of launch vehicles on future climate.

Anthropogenic methane (CH4) emissions increases from the period 1850–1900 until 2019 are responsible for around 65% as much warming as carbon dioxide (CO2) has caused to date, and large reductions in methane emissions are required to limit global warming to 1.5°C or 2°C. However, methane emissions have been increasing rapidly since ~2006. This study shows that emissions are expected to continue to increase over the remainder of the 2020s if no greater action is taken and that increases in atmospheric methane are thus far outpacing projected growth rates. This increase has important implications for reaching net zero CO2 targets: every 50 Mt CH4 of the sustained large cuts envisioned under low-warming scenarios that are not realized would eliminate about 150 Gt of the remaining CO2 budget. Targeted methane reductions are therefore a critical component alongside decarbonization to minimize global warming. We describe additional linkages between methane mitigation options and CO2, especially via land use, as well as their respective climate impacts and associated metrics. We explain why a net zero target specifically for methane is neither necessary nor plausible. Analyses show where reductions are most feasible at the national and sectoral levels given limited resources, for example, to meet the Global Methane Pledge target, but they also reveal large uncertainties. Despite these uncertainties, many mitigation costs are clearly low relative to real-world financial instruments and very low compared with methane damage estimates, but legally binding regulations and methane pricing are needed to meet climate goals.

In 2023, global full-depth ocean heat content (OHC) reached a record increase of 464 +/- 55 ZJ since 1960, with strong heat gain observed in the Southern and Atlantic Oceans. OHC was 16 +/- 10 ZJ higher than in 2022, continuing the long-term increasing trend that started in 1960. Full-depth OHC peaked in 2023, with 40%, 24%, 28%, and 8% of heat accumulated within the 0-300 m, 300-700 m, 700-2000 m and below-2000 m layers, respectively, since 1960.In situ and satellite approaches indicate an increasing - and accelerating - global heating rate, with a trend of 0.16 +/- 0.02 W m-2 dec-1 estimated from 1960-2023.Observed global ocean warming in 2023 is consistent with the projection of the CMIP6 multi-model-median.

Stratospheric ozone, and its response to anthropogenic forcings, provides an important pathway for the coupling between atmospheric composition and climate. In addition to stratospheric ozone ' s radiative impacts, recent studies have shown that changes in the ozone layer due to 4xCO 2 have a considerable impact on the Northern Hemisphere (NH) tropospheric circulation, inducing an equatorward shift of the North Atlantic jet during boreal winter. Using simulations produced with the NASA Goddard Institute for Space Studies (GISS) high -top climate model (E2.2), we show that this equatorward shift of the Atlantic jet can induce a more rapid weakening of the Atlantic meridional overturning circulation (AMOC). The weaker AMOC, in turn, results in an eastward acceleration and poleward shift of the Atlantic and Paci fi c jets, respectively, on longer time scales. As such, coupled feedbacks from both stratospheric ozone and the AMOC result in a two -time -scale response of the NH midlatitude jet to abrupt 4xCO 2 forcing: a " fast " response (5 - 20 years) during which it shifts equatorward and a " total " response ( ; 100 - 150 years) during which the jet accelerates and shifts poleward. The latter is driven by a weakening of the AMOC that develops in response to weaker surface zonal winds that result in reduced heat fl uxes out of the subpolar gyre and reduced North Atlantic Deep Water formation. Our results suggest that stratospheric ozone changes in the lower stratosphere can have a surprisingly powerful effect on the AMOC, independent of other aspects of climate change.

Trawling the seafloor can disturb carbon that took millennia to accumulate, but the fate of that carbon and its impact on climate and ecosystems remains unknown. Using satellite-inferred fishing events and carbon cycle models, we find that 55-60% of trawling-induced aqueous CO2 is released to the atmosphere over 7-9 years. Using recent estimates of bottom trawling’s impact on sedimentary carbon, we found that between 1996-2020 trawling could have released, at the global scale, up to 0.34-0.37 Pg CO2 yr-1 to the atmosphere, and locally altered water pH in some semi-enclosed and heavy trawled seas. Our results suggest that the management of bottom-trawling efforts could be an important climate solution.