CMIP7 data request: ocean and sea ice priorities and opportunities

Energy and CO2 Footprint of Climate Model Intercomparison Projects

Earth System Models (ESMs) rely heavily on High-Performance Computing (HPC) resources to simulate global climate. As these models evolve, their computational demands continue to grow, driven by three factors: (1) finer spatial grid resolutions, (2) the integration of complex biogeochemical processes (e.g., atmospheric chemistry, interactive vegetation, land use, and ice sheets), and (3) larger climate ensembles to manage uncertainty. Historically, growth in peak computing performance (FLOP/s) has outpaced improvements in energy efficiency (FLOP/Watt), increasing total HPC power consumption. Despite the central role of Model Intercomparison Projects (MIPs) in climate research, quantifying their computational and environmental costs has received limited systematic attention. This paper examines the evolution of climate model carbon accounting from voluntary post-hoc estimation in the Coupled Model Intercomparison Project phase 6 (CMIP6) to standardized accounting under the newly established CMIP7 Task Team on Energy Consumption. Using high-resolution Destination Earth simulations on MareNostrum 5, we empirically evaluate how different accounting boundaries (operational, active-only, and embodied carbon) impact reported energy, carbon emissions, and financial costs. Finally, we outline key methodological considerations for standardizing energy and carbon accounting for Model Intercomparison Projects (MIPs).

Evaluation of subseasonal sea ice changes in the Community Earth System Model version two

Top-of-atmosphere radiation over the last millennium reconstructed from proxies

Earth’s energy imbalance at the top of the atmosphere is a key climate system metric, but its natural variability is poorly constrained by the short observational record and large uncertainty in coupled climate models. While existing ocean heat content reconstructions offer a longer perspective, they cannot separate the contributions of shortwave and longwave radiation, obscuring the underlying processes. We extend the energy-budget record into the pre-industrial period by reconstructing the top-of-atmosphere radiation and related surface variables over the last millennium (850–2000 CE) using data assimilation, combining proxy data and dynamics from a coupled climate emulator. Validation reveals skill in the reconstructed radiation fields, especially in the global mean and the tropics. We find that the well-documented last-millennium cooling trend coincides with persistent energy loss, largest early in the millennium, and a reduction in upper-ocean heat content. The cooling trend differs by season and latitude, and is associated with anomalies in outgoing longwave radiation suggestive of an eastward shift in Indo–Pacific convection. Following large volcanic eruptions, ocean heat content anomalies persist for 10–20 years on average, supporting previous evidence that multidecadal cooling was forced by decadally paced eruptions. The reconstruction also reveals that the current rate of energy gain is unprecedented relative to the period before 1850.

Changing Dynamics of the Arctic Ocean–Ice System: Drivers, Feedbacks, and Future Trajectories

The Arctic is undergoing rapid and disproportionate climate change, driven by tightly coupled interactions among the ocean, sea ice, and atmosphere. This review synthesizes current understanding of historical and projected changes in the Arctic ocean–ice system, emphasizing the role of variability across timescales—from seasonal to multidecadal—in shaping observed trends. We highlight the interconnected nature of Arctic system components, focusing on feedbacks—including emerging coupled ocean–ice processes—pathways, and mechanisms that link variability and long‐term change. Particular attention is given to state‐dependent and potentially nonlinear responses, as well as to the roles of remote forcing and increasing connectivity with sub‐Arctic regions. We assess the relative contributions of internal variability and anthropogenic forcing, highlighting key challenges in attribution, and identify priorities for improving future projections. This synthesis provides a process‐based framework for understanding Arctic change and its growing influence on the global climate system.

Historical Climate and Future Projection in the North Atlantic and Arctic: Insights from EC-Earth3 High-Resolution Simulations

Abstract. This study presents a new set of high-resolution global climate simulations conducted with the EC-Earth3 model, including a 350 year pre-industrial, followed by historical (1850–2014) and future (2015–2100, SSP2-4.5) simulations. The model features a horizontal resolution of ∼ 40 km in the atmosphere and 0.25° in the ocean. The high-resolution EC-Earth3 (EC-Earth3-HR) is compared to the standard-resolution version used in CMIP6 to assess the impact of increased resolution on the representation of key climate variables, focusing particularly on the Arctic and North Atlantic regions. The high-resolution model aligns more closely with reanalysis data, particularly for global mean surface temperature and sea surface temperature. Both model resolutions exhibit similar biases in North Atlantic sea surface temperature and salinity, and in Arctic sea ice concentration, although the higher-resolution version shows regional improvements. The EC-Earth3-HR model captures the observed AMOC variability in the early 2000s, along with the trend and rapid loss event in Arctic sea ice. For future projection under SSP2-4.5, the high-resolution model projects a nearly ice-free Arctic by 2040 – earlier than the standard-resolution model – while simulating less Arctic warming and a more pronounced weakening of the AMOC. We also introduce a framework to diagnose deep-water formation (DWF) in the Labrador, Irminger, and Greenland Seas and to quantify their regional contributions to the AMOC. Applying this framework, we find that projected DWF weakens across all regions, with the largest reduction in the Labrador Sea, making it the dominant contributor to long-term AMOC weakening. By 2100, diagnosed DWF ceases in the Labrador Sea, compared with declines of 62 % in the Greenland Sea and 13 % in the Irminger Sea.

The CMIP6-downscaled CORDEX-Southeast Asia (SEA) ensemble: evaluation and benchmarking for megacities of SEA

Abstract. A 21-member ensemble of regional climate simulations has been produced for Southeast Asia (SEA) by dynamically downscaling Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) under the World Climate Research Programme's Coordinated Regional Climate Downscaling Experiment (CORDEX). The ensemble was generated by several modelling institutes using three regional climate models (RCMs) with eight distinct model configurations, resulting in a total of 62 simulations spanning the historical period and multiple future emissions scenarios. Model performance for mean, daily maximum/minimum temperature, and precipitation was evaluated against multiple observations at annual, seasonal, and daily time scales over SEA and its two subregions: Mainland and Maritime Continent (MC). Despite large observational uncertainties in precipitation intensity, the CMIP6 CORDEX-SEA ensemble captures the spatial and seasonal rainfall distribution reasonably well but tends to substantially overestimate observed rainfall. Wet biases, evident in about two-thirds of the models, are regionally and seasonally heterogeneous and larger over monsoon-dominated regions and seasons (e.g., MC during November–April and the Mainland during May–October). All RCMs showed widespread, statistically significant cold biases in daily mean temperature, which were largest during boreal winter, over the Mainland, and in simulations that have significant wet biases. These cold biases primarily arise from the models' underestimation of daily maximum temperature. The MC remains a challenging region since models struggle to accurately capture the spatial variability of rainfall and the internal variability of temperature. A standardised benchmarking framework was applied to precipitation and temperature, which ultimately identified 15 historical simulations that met our a priori model performance expectations. Analysing the range of future projections and model independence shows that simulations from the same RCM family exhibit similar bias structures, highlighting the importance of RCM setup and the selection of statistically independent models. From this process, eight simulations spanning three RCM configurations were selected for further kilometre-scale dynamical downscaling over megacities of SEA.