Alejandro Bodas-Salcedo

Cloud processes constitute one of the key uncertainties for climate change projections. The fourth iteration of the Cloud Feedback Model Intercomparison Project, CFMIP4, contributes to the Coupled Model Intercomparison Project phase 7 (CMIP7), by providing a set of global climate model experiments aiming to enhance our understanding of clouds, circulation and climate sensitivity, thereby informing improved projections of future climate change. CFMIP4 targets four knowledge gaps: (1) Physical mechanisms of cloud feedback and adjustment; (2) Dependence of cloud feedback and adjustment on climate base state and on the nature of the forcing; (3) Coupled mechanisms of the sea-surface temperature “pattern effect”; and (4) Coupling of clouds with circulation and precipitation. CFMIP4 contributes four CMIP7 Assessment Fast Track experiments that are central to the quantification of climate feedback and sensitivity in past, present and future climates, essential for process understanding and model evaluation. Furthermore, CFMIP4 supports the joint analysis of models and observations through a data request that includes process and satellite simulator output.

GC5 is the latest global coupled configuration of the Met Office Unified Model (MetUM), which is a combination of Global Atmosphere and Land (GAL9) and Global Ocean and Sea Ice (GOSI9). GAL9 includes more than 26 science tickets that cover changes in almost all areas of global model science, including several enhancements to the 6A convection scheme, latent heating in the gravity wave drag scheme, and the ’fountain buster’ scheme, which adds improvements to the advection conservation under conditions of near-grid-scale convergence, bimodal cloud initiation, and dust-dependent ice-nucleation temperature. GOSI9 is based on the NEMO 4.0.4 ocean model and a new sea ice (SI) model SI3. This includes a new equation of state (TEOS-10), 4th-order tracer advection, adaptive-implicit vertical advection and Southern Ocean tuning. GC5 is evaluated against Global Coupled Model version 4 (GC4) and ERA5 reanalysis and observations in terms of the global mean state, variability and NWP performance. Important model improvements in global temperature and winds, subtropical jet, monsoon processes over India and the Maritime continent, and their diurnal cycle in convection were noted. Most aspects of processes over Africa were also notably improved. Southern Ocean biases continue to decrease in GC5 as a result of the Southern Ocean package including the Antarctic Sea ice area. On NWP (Numerical Weather Prediction) scales, significant improvements in the tropical temperature at 850 hPa and precipitation SEEPS (Stable Equitable Error in Probability Space) score at all lead times were achieved. In addition, the tropical cyclone track and intensity are significantly better in GC5 compared to GC4. There are aspects where the model has slightly degraded such as the MJO, Indian Ocean SSTs and winds, biases over northeastern parts of South America, western Pacific high in northern hemisphere summer and European blocking in northern hemisphere summer. These degradations require more detailed investigations. Degradations in NWP performance at higher resolution also need to be investigated before operational implementation, but will require minimal adjustments. Although this paper documents the performance of the GC5 model at N216 resolution (6~0 km at mid-latitudes) as the physical model configuration for various applications such as seasonal forecasting, more work is being done in the climate science community to address the cold bias in global mean temperatures in the GC5 N96ORCA1 (1~30 km atmospheric and 1 degree ocean resolution) configuration. This configuration will form the basis for the UK Earth System Model (UKESM). The resulting tuned GC5 configurations for a wider range of climate applications will be documented separately.

The Global Coupled model version 5 (GC5) of the Met Office Unified Model incorporates substantial developments across its model components and shows improved performance for a range of applications. However, it exhibits a very high effective climate sensitivity (EffCS) of 6.7 K and an excessive rise in recent global-mean surface air temperatures (GMSAT), limiting its suitability for some climate applications. This motivated the development of an alternative GC5-based configuration with EffCS constrained to lie within the IPCC Sixth Assessment Report “very likely” range, improved simulation of historical temperatures, and acceptable climatological performance. This new configuration, called UKCM2-LL, will form part of the UK’s submission to CMIP7. We describe a two-stage methodology used to develop UKCM2-LL. First, a 503-member atmosphere-only perturbed parameter ensemble (PPE) of GC5 variants was used to train statistical emulators that predict climatological performance metrics and atmosphere-only feedbacks. These emulators were then used to generate 41 candidate configurations predicted to have substantially reduced EffCS relative to GC5. Second, coupled preindustrial control and abrupt 4×CO₂ experiments were used to evaluate the candidates against large-scale climatological metrics and to diagnose EffCS, progressively narrowing the set of viable configurations through an expert-led evaluation process. Final fine-tuning of a single candidate was performed manually using the coupled experiments and was informed by parameter sensitivity information derived from the PPE. The resulting UKCM2-LL configuration has an EffCS of 3.6 K and exhibits an improved simulation of historical temperatures relative to GC5. However, achieving this lower EffCS required a degradation in climatological performance, reflecting a structural constraint of the GC5 model. This work demonstrates the value of PPE-based approaches for systematically exploring such structural constraints, and for parameter tuning during model development. We discuss potential improvements to the methodology and consider the implications of explicitly constraining climate sensitivity for future model development and multi-model ensemble diversity.

Cloud processes constitute one of the key uncertainties for climate change projections. The fourth iteration of the Cloud Feedback Model Intercomparison Project, CFMIP4, contributes to the Coupled Model Intercomparison Project phase 7 (CMIP7), by providing a set of global climate model experiments aiming to enhance our understanding of clouds, circulation and climate sensitivity, thereby informing improved projections of future climate change. CFMIP4 targets four knowledge gaps: (1) Physical mechanisms of cloud feedback and adjustment; (2) Dependence of cloud feedback and adjustment on climate base state and on the nature of the forcing; (3) Coupled mechanisms of the sea-surface temperature "pattern effect"; and (4) Coupling of clouds with circulation and precipitation. CFMIP4 contributes four CMIP7 Assessment Fast Track experiments that are central to the quantification of climate feedback and sensitivity in past, present and future climates, essential for process understanding and model evaluation. Furthermore, CFMIP4 supports the joint analysis of models and observations through a data request that includes process and satellite simulator output.

Global warming results from anthropogenic greenhouse gas emissions which upset the delicate balance between the incoming sunlight, and the reflected and emitted radiation from Earth. The imbalance leads to energy accumulation in the atmosphere, oceans and land, and melting of the cryosphere, resulting in increasing temperatures, rising sea levels, and more extreme weather around the globe. Despite the fundamental role of the energy imbalance in regulating the climate system, as known to humanity for more than two centuries, our capacity to observe it is rapidly deteriorating as satellites are being decommissioned.

Arctic clouds play a key role in Arctic climate variability and change; however, contemporary climate models struggle to simulate cloud properties accurately. Model‐simulated cloud properties are determined by the physical parameterizations and their interactions within the model configuration. Quantifying effects of individual parameterization changes on model‐simulated clouds informs efforts to improve models and provides insights on climate system behavior. This study quantities the influence of parameterization scheme changes on Arctic low cloud properties within the Hadley Center Global Environmental Model 3 atmospheric model using a suite of experiments where individual parameterization packages are changed between the two configurations. The results indicate, surprisingly, that single parameterization changes explain most of the cloud property changes, whereas multiple parameterizations, including non‐cloud schemes, contribute to cloud radiative effect differences. Non‐cloud parameterizations are those not used to compute time step cloud properties. We employ a three‐term decomposition to quantify contributions from (a) regime independent, (b) regime dependent, and (c) the regime frequency of occurrence changes. Decomposition results indicate that cloud property changes vary by meteorological regime, each term contributes differently to each cloud property change, and non‐cloud parameterization changes make substantial contributions to the LW and SW cloud radiative effects by affecting clear‐sky fluxes. The analysis provides insights on the role of non‐cloud parameterizations for setting cloud radiative effects, a pathway for cloud‐atmosphere circulation interactions, raises questions on the importance of infrequently occurring regimes to climate simulations, and on useful observational approaches for improving models.

A newly available radiative flux dataset specifically designed to enable the evaluation of the diurnal cycle in top-of-atmosphere (TOA) fluxes as captured by climate and Earth system models is presented. Observations over the period 2007–2012 made by the Geostationary Earth Radiation Budget (GERB) instrument are used to derive monthly hourly mean outgoing longwave radiation (OLR) and reflected shortwave (RSW) fluxes on a regular 1° latitude–longitude grid covering approximately 60° N–60° S and 60° E–60° W. The impact of missing data is evaluated in detail, and a data-filling solution is implemented using estimates of broadband fluxes from the Spinning Enhanced Visible and Infrared Imager flying on the same Meteosat platform, scaled to the GERB observations. This relatively simple approach is shown to deliver an approximate improvement by a factor of 10 in both the bias caused by missing data and the associated variability in the error. To demonstrate the utility of this V1.1 filled GERB Observations for Climate Model Intercomparison Projects (Obs4MIPs) dataset, comparisons are made to radiative fluxes from two climate configurations of the Hadley Centre's Global Environmental Model: HadGEM3-GC3.1 and HadGEM3-GC5.0. Focusing on marine stratocumulus and deep convective cloud regimes, diurnally resolved comparisons between the models and observations highlight discrepancies between the model configurations in terms of their ability to capture the diurnal amplitude and the phase in TOA fluxes, details that cannot be diagnosed by comparisons at lower temporal resolutions. For these cloud regimes the GC5.0 configuration shows improved fidelity to the observations relative to GC3.1, although notable differences remain. The V1.1 filled GERB Obs4MIPs monthly hourly TOA fluxes are available from the Centre for Environmental Data Analysis, with the OLR fluxes accessible at https://doi.org/10.5285/90148d9b1f1c40f1ac40152957e25467 (Bantges et al., 2023a) and the RSW fluxes accessible at https://doi.org/10.5285/57821b58804945deaf4cdde278563ec2 (Bantges et al., 2023b).

Arctic clouds play a key role in Arctic climate variability and change; however, contemporary climate models struggle to simulate cloud properties accurately. Model-simulated cloud properties are determined by the physical parameterizations and their interactions within the model configuration. Quantifying effects of individual parameterization changes on model-simulated clouds informs efforts to improve cloud properties in models and provides insights on climate system behavior. This study quantities the influence of individual parameterization schemes on Arctic low cloud properties within the Hadley Centre Global Environmental Model 3 atmospheric model using a suite of experiments where individual parameterization packages are changed one-at-a-time between two configurations: GA6 and GA7.1. The results indicate that individual parameterization changes explain most of the cloud property differences, whereas multiple parameterizations, including non-cloud schemes, contribute to cloud radiative effect differences. The influence of a parameterization change on cloud properties is found to vary by meteorological regime. We employ a three-term decomposition to quantify contributions from (1) regime independent, (2) regime dependent, and (3) the regime frequency of occurrence changes. Decomposition results indicate that each term contributes differently to each cloud property change and that non-cloud parameterization changes make a substantial contribution to the LW and SW cloud radiative effects by modifying clear-sky fluxes differently across regimes. The analysis provides insights on the role of non-cloud parameterizations for setting cloud radiative effects, a model pathway for cloud-atmosphere circulation interactions, and raises questions on the most useful observational approaches for improving models.

A new radiative flux dataset, specifically designed to enable the evaluation of the diurnal cycle in top of the atmosphere fluxes, as captured by climate and Earth-system models is presented. Observations over the period 2007–2012 made by the Geostationary Earth Radiation Budget (GERB) instrument are used to derive monthly hourly mean reflected shortwave (RSW) and outgoing longwave fluxes (OLR) on a regular 1°x1° latitude-longitude grid covering 60° N–60° S and 60° E–60° W. The impact of missing data is evaluated in detail, and a data-filling solution is implemented using estimates of the broadband fluxes from the Spinning Enhanced Visible and Infrared Imager, flying on the same Meteosat platform, scaled to the GERB observations. This relatively simple approach is shown to deliver an approximate factor of ten improvement in both the bias caused by missing data and the associated variability in the error. To demonstrate the utility of this GERB ‘obs4MIPs’ dataset, comparisons are made to radiative fluxes from two climate configurations of the Hadley Centre Global Environmental model: HadGEM3-GC3.1 and HadGEM3-GC5.0. Focusing on marine stratocumulus and deep convective cloud regimes, diurnally resolved comparisons between the model and observations highlight discrepancies between the model configurations in terms of their ability to capture the diurnal amplitude and phase of the top of atmosphere fluxes: details that cannot be diagnosed by comparisons at lower temporal resolution. For these cloud regimes the GC5.0 configuration shows improved fidelity with the observations although notable differences remain. The GERB Obs4MIPs monthly hourly TOA fluxes are available from the Centre for Environmental Data Analysis with the OLR fluxes accessible at https://doi.org/10.5285/90148d9b1f1c40f1ac40152957e25467 (Bantges et al. 2023a) and the RSW fluxes at https://doi.org/10.5285/57821b58804945deaf4cdde278563ec2 (Bantges et al. 2023b).

NASA’s upcoming Libera mission is predominantly a continuity mission, aiming at the seamless continuation of the Earth Radiation Budget record maintained by CER