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Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, \"what are the origins and consequences of systematic model biases?\", but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Climate change is affecting ocean temperature, acidity, currents, and primary production, causing shifts in species distributions, marine ecosystems, and ultimately fisheries. Earth system models simulate climate change impacts on physical and biogeochemical properties of future oceans under varying emissions scenarios. Coupling these simulations with an ensemble of global marine ecosystem models has indicated broad decreases of fish biomass with warming. However, regional details of these impacts remain much more uncertain. Here, we employ CMIP5 and CMIP6 climate change impact projections using two Earth system models coupled with four regional and nine global marine ecosystem models in 10 ocean regions to evaluate model agreement at regional scales. We find that models developed at different scales can lead to stark differences in biomass projections. On average, global models projected greater biomass declines by the end of the 21st century than regional models. For both global and regional models, greater biomass declines were projected using CMIP6 than CMIP5 simulations. Global models projected biomass declines in 86% of CMIP5 simulations for ocean regions compared to 50% for regional models in the same ocean regions. In CMIP6 simulations, all global model simulations projected biomass declines in ocean regions by 2100, while regional models projected biomass declines in 67% of the ocean region simulations. Our analysis suggests that improved understanding of the causes of differences between global and regional marine ecosystem model climate change projections is needed, alongside observational evaluation of modeled responses. Plain Language Summary Climate change is affecting the world's oceans, marine ecosystems, biodiversity, and the ecosystem services that they support, including fisheries that feed millions of people worldwide. Anticipating the impacts of climate change can help society and managers to prepare for, and adapt to, changes ahead. Present understanding of climate change impacts on the world's oceans based on global models indicates a 5% loss in animal biomass with every 1°C that the planet warms. Here, we compare potential future biomass on regional scales that are most relevant for management decisions about sustainable resource use. We used regional scale ecosystem models tailored to the species and fisheries they represent. We compared climate change projections of ocean biomass changes from these regional models to corresponding areas from global models to see how well they agreed. We found key differences in climate change projections of ocean biomass between global and regional models. In some cases, both global and regional models projected biomass declines, while in others global models suggested a decline and regional models an increase. Our study highlights that we need further exploration and understanding of the differences in ocean biomass change between global and regional marine ecosystem models. Key Points Global marine ecosystem models projected greater biomass declines with climate change than regional marine ecosystem models for many regions For both global and regional models, greater biomass declines were projected in CMIP6 than CMIP5 and in IPSL versus GFDL simulations Projected impacts of climate change on marine ecosystems at regional scales are currently less certain than at global scale

Climate change is expected to result in more frequent and intense heat waves (HWs) in South Asia (SA). The simultaneous increases in temperature and population will exacerbate the population exposure to future HWs. Here we estimate the future population exposure to daytime and nighttime HWs in SA using the Coupled Model Intercomparison Project 6 (CMIP6) models under four Shared Socioeconomic Pathways (SSPs) during 2061–2100, relative to 1975–2014. The results show that the projected frequency and spatial extent of the daytime (nighttime) HWs will be higher under scenario SSP5‐8.5, followed by SSP2‐4.5, SSP3‐7.0, and SSP1‐2.6 (SSP5‐8.5, followed by SSP3‐7.0, SSP2‐4.5, and SSP1‐2.6), relative to the historical period. The approach presented here allows decomposing the effects of climate change and future population on the overall exposure. The results reveal that the compounding effects of projected trends in population and HWs will significantly escalate the population exposure to HWs. Under the selected SSPs, the total population exposure to daytime and nighttime HWs ranges from 185 to 492 and 204–555 million people‐event, respectively, with the maximum exposure occurring in the Indo‐Gigantic Plain. The wide range of exposed populations highlights the sensitivity of the overall exposure to our future socioeconomic pathway decisions, emphasizing the importance of curbing anthropogenic greenhouse gas emissions and adopting sustainable urban planning solutions to minimize the potential socioeconomic and health impacts of HWs. Plain Language Summary Climate change will intensify the occurrence and intensity of heatwaves (HWs) in South Asia, with severe impacts on the population. Here we estimate the population exposure to daytime and nighttime HWs using the Coupled Model Intercomparison Project 6 (CMIP6) models under four Shared Socioeconomic Pathways (SSPs). The results show that the projected daytime and nighttime HWs will impact around 185–492 and 204–555 million people under the selected SSPs, respectively, with the maximum exposure occurring in the Indo‐Gigantic Plain. The wide range of populations under different SSPs highlights the sensitivity of the population to future SSP decisions. This emphasizes the importance of reducing greenhouse gas emissions and adopting sustainable urban planning solutions to minimize the potential socioeconomic and health impacts of HWs. Key Points The number and extent of heat waves (HWs) are projected to increase under all the Shared Socioeconomic Pathways (SSPs) The population exposure to HWs ranges from 185 to 555 million people‐event with the highest exposure in the Indo‐Gigantic Plain Compounding effects of climate change and population increase substantially aggravate exposure to HWs

We utilize a global warming level (GWL) lens to evaluate global and regional patterns of agricultural impacts as global surface temperature increases, providing a unique perspective on the experience of stakeholders with continued warming in the 21st century. We analyze crop productivity outputs from 11 crop models simulating 5 climate models under 3 emissions scenarios across 4 crops within the AgMIP/ISIMIP Phase 3 ensemble. We categorize regional productivity changes (without adaptation) into 9 characteristic climate change response patterns, identifying consistent increases and decreases as well as non‐linear (peak or dip) responses indicative of inflection points reversing trends as GWLs increase. Many maize regions and pockets of wheat, rice and soybean show peak decrease patterns where initial increases may lull stakeholders into complacency or maladaptation before productivity shifts to losses at higher GWLs. Although the GWL perspective has proven useful in connecting diverse climate models and emissions scenarios, we identify multiple pitfalls that recommend proceeding with caution when applying this approach to climate impacts. Chief among these is that carbon dioxide (CO2) concentrations at any GWL depend on a climate model's transient climate response (TCR). Higher CO2 concentrations generally benefit crop productivity, so this leads to more pessimistic agricultural projections for so‐called “hot” models and can skew multi‐model ensemble results as models with high TCR are disproportionately likely to reach higher GWLs. While there are strong connections between many climatic impact‐drivers and GWLs, vulnerability and exposure components of food system risk are strongly dependent on development pathways. Plain Language Summary This study uses the latest ensemble of crop and climate models with a framework to identify cropping systems and regions where non‐linear aspects of agricultural system response to climate change could lead to complacency or maladaptation. Results will help readers understand the different experiences and contextual adaptation needs for food systems in the coming decades. This framework could be widely applied across all impacts sectors. Analysis also highlights that climate models with particularly strong climate sensitivity lead more pessimistic projections by crop impacts, underscoring the need for special care in the use of climate model ensembles for impact assessments. Key Points Climate change response patterns show regional farming systems where initial positive changes give way to long‐term detrimental impacts Regional and species‐dependent patterns of agricultural impacts on benchmark global warming levels prioritize risk management strategies Climate models with high climate sensitivity have less CO2 benefit on a given global warming level (GWL) and are over‐represented in ensembles of higher GWLs

In simulations of radiative‐convective equilibrium (RCE), and with sufficiently large domains, organized convection enhances top of atmosphere outgoing longwave radiation due to the reduced cloud coverage and drying of the mean climate state. As a consequence, estimates of climate sensitivity and cloud feedbacks may be affected. Here, we use a multi‐model ensemble configured in RCE to study the dependence of explicitly calculated cloud feedbacks on the existence of organized convection, the degree to which convection within a domain organizes, and the change in organized convection with warming sea surface temperature. We find that, when RCE simulations with organized convection are compared to RCE simulations without organized convection, the propensity for convection to organize in RCE causes cloud feedbacks to have larger magnitudes due to the inclusion of low clouds, accompanied by a much larger inter‐model spread. While we find no dependence of the cloud feedback on changes in organization with warming, models that are, on average, more organized have less positive, or even negative, cloud feedbacks. This is primarily due to changes in cloud optical depth in the shortwave, specifically high clouds thickening with warming in strongly organized domains. The shortwave cloud optical depth feedback also plays an important role in causing the tropical anvil cloud area feedback to be positive which is directly opposed to the expected negative or near zero cloud feedback found in prior work. Plain Language Summary Tropical clouds play an important role in the uncertainty associated with understanding how the Earth's climate responds to an imposed warming. Here, we look at how organized cloud systems associated with the tropics affect the processes that govern the climate response to warming. We find that strongly organized cloud systems reduce how strongly the Earth responds to warming. This is primarily associated with the changes in the optical properties of high clouds. These cloud properties impact the overall understanding of the role tropical clouds play in modulating the Earth's temperature and calls into question prior assumptions of its behavior. Key Points Domains with organized convection have cloud feedbacks with the same sign but larger inter‐model spread than domains without organization The cloud feedback parameter is less positive, or even negative, for more strongly organized domains In radiative‐convective equilibrium, the tropical anvil cloud area feedback is positive due to the shortwave cloud optical depth feedback

Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the “FluidFlower”), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations.

Operational oceanography can be described as the provision of routine oceanographic information needed for decision-making purposes. It is dependent upon sustained research and development through the end-to-end framework of an operational service, from observation collection to delivery mechanisms. The core components of operational oceanographic systems are a multi-platform observation network, a data management system, a data assimilative prediction system, and a dissemination/accessibility system. These are interdependent, necessitating communication and exchange between them, and together provide the mechanism through which a clear picture of ocean conditions, in the past, present, and future, can be seen. Ocean observations play a critical role in all aspects of operational oceanography, not only for assimilation but as part of the research cycle, and for verification and validation of products. Data assimilative prediction systems are advancing at a fast pace, in tandem with improved science and the growth in computing power. To make best use of the system capability these advances would be matched by equivalent advances in operational observation coverage. This synergy between the prediction and observation systems underpins the quality of products available to stakeholders, and justifies the need for sustained ocean observations. In this white paper, the components of an operational oceanographic system are described, highlighting the critical role of ocean observations, and how the operational systems will evolve over the next decade to improve the characterization of ocean conditions, including at finer spatial and temporal scales.

Projections of regional changes in surface-air temperature and precipitation, in response to unconstrained emissions as well as a climate mitigation policy, for the Zambezi River Basin (ZRB) are presented. These projections are cast in a probabilistic context through a hybrid technique that combines the projections of the MIT Integrated Global System Model (IGSM) to pattern-change kernels from climate-model results of the Coupled Model Intercomparison Project (CMIP). Distributional changes of precipitation and surface-air temperature averaged over the western and eastern ZRB are considered. Overall, the most significant response to climate policy is seen in the spring. Frequency distributions of precipitation change for the unconstrained emission scenario indicate a majority of the outcomes to be drier by 2050, although the distribution spans both increased and decreased precipitation. Through climate policy, the distributions’ total range of outcomes collapses considerably, and perhaps more importantly, the mode of the distribution aligns with zero precipitation change. For surface-air temperature, climate policy consistently reduces the modal value of warming, and this reduction is strongest for the western ZRB. Climate policy also considerably abates the occurrence of the most extreme temperature increases, but the minimum warming in the distributions is less affected.

Climate change can affect the distribution and abundance of marine life, with consequences for goods and services provided to people. Because different models can lead to divergent conclusions about marine futures, we present an integrated global ocean assessment of climate change impacts using an ensemble of multiple climate and ecosystem models. It reveals that global marine animal biomass will decline under all emission scenarios, driven by increasing temperature and decreasing primary production. Notably, climate change impacts are amplified at higher food web levels compared with phytoplankton. Our ensemble projections provide the most comprehensive outlook on potential climate-driven ecological changes in the global ocean to date and can inform adaptive management and conservation of marine resources under climate change.While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends.