We investigate the dependence of radiative feedback on the pattern of sea-surface temperature (SST) change in fourteen Atmospheric General Circulation Models (AGCMs) forced with observed variations in SST and sea-ice over the historical record from 1871 to near-present. We find that over 1871-1980, the Earth warmed with feedbacks largely consistent and strongly correlated with long-term climate sensitivity feedbacks (diagnosed from corresponding atmosphere-ocean GCM abrupt-4xCO2 simulations). Post 1980 however, the Earth warmed with unusual trends in tropical Pacific SSTs (enhanced warming in the west, cooling in the east) that drove climate feedback to be uncorrelated with – and indicating much lower climate sensitivity than – that expected for long-term CO2 increase. We show that these conclusions are not strongly dependent on the AMIP II SST dataset used to force the AGCMs, though the magnitude of feedback post 1980 is generally smaller in eight AGCMs forced with alternative HadISST1 SST boundary conditions. We quantify a ‘pattern effect’ (defined as the difference between historical and long-term CO2 feedback) equal to 0.44 ± 0.47 [5-95%] W m-2 K-1 for the time-period 1871-2010, which increases by 0.05 ± 0.04 W m-2 K-1 if calculated over 1871-2014. Assessed changes in the Earth’s historical energy budget are in agreement with the AGCM feedback estimates. Furthermore satellite observations of changes in top-of-atmosphere radiative fluxes since 1985 suggest that the pattern effect was particularly strong over recent decades, though this may be waning post 2014 due to a warming of the eastern Pacific.

We demonstrate a new approach for climate model tuning in a realistic situation. Our approach, described in detail in Part I, systematically uses a single-column configuration of a global atmospheric model on a series of test cases for which reference large-eddy-simulations are available. The space of free parameters is sampled running the single-column model from which metrics are estimated in the full parameter space using emulators. The parameter space is then reduced by retaining only the values that are consistent with the metrics computed on large eddy simulations within a given tolerance to error. The approach is applied to the recently designed 6A version of the LMDZ model, itself the result of a long investment in the development of physics parameterizations and by-hand tuning. The boundary layer is revisited by increasing the vertical resolution and varying parameters that were kept fixed so far. The approach allows us to automatically reach a tuning as good as that of the 6A version, after some improvements are done at process scale. This approach helps accelerate the introduction of new parameterizations, by avoiding a tedious manual tuning process and preventing some of the error compensations that could occur if calibration was carried out directly with the full atmospheric model. This way of using machine learning techniques allows us to maintain the physical foundations of the model and to ensure that the improvement of global metrics is obtained for a reasonable behavior at process level. That is, we get things right for the right reasons.

This study investigates the spontaneous self-aggregation of convection in non-rotating Radiative-Convective Equilibrium (RCE) simulations performed by the CNRM-CM6-1 General Circulation Model within the framework of the RCE Model Intercomparison Project (RCEMIP). In this model, the level of convection self-aggregation at equilibrium, as quantified by metrics based on moisture or moist static energy, strongly increases with sea surface temperature (SST). As it gets warmer, the troposphere gets drier, high cloud cover diminishes in dry regions, the top of high cloud rises and their thickness increases in moist regions, and low cloud cover increases. At high SSTs, the large-scale circulation exhibits a shallow component, stronger than its deep counterpart. The transition towards self-aggregation has a similar first 20-day phase for all SSTs within the 295-305-K range. It primarily involves radiative positive feedback processes. Then, for SSTs above approximately 300 K, a new, slower, transition towards higher levels of self-aggregation occurs. It is concomitant with a shift from a top-heavy to a more bottom-heavy large-scale circulation, a strengthening of the shallow circulation and a reduced mobility of convective aggregates. This second transition is mostly driven by the dry regions, still involves longwave radiative positive feedbacks, but also advective positive feedbacks in the driest regions. It is argued that boundary-layer radiative cooling difference between moist and dry regions, which is stronger at high SSTs, is instrumental in this second phase of self-aggregation. The sensitivity of deep convection to environmental dry air also likely acts as a positive feedback on the system.

Convection is usually parameterized in global climate models, and there are often large discrepancies between results obtained with different convection schemes. Conventional methods of comparing convection schemes using observational cases or directly in 3D models do not always clearly identify parameterization strengths and weaknesses. In this paper we evaluate the response of parameterizations to various perturbations rather than their behavior under particular strong forcing. We use the linear response function method proposed by Kuang (2010) to compare twelve physical packages in five atmospheric models using single-column model (SCM) simulations under idealized radiative-convective equilibrium conditions. The models are forced with anomalous temperature and moisture tendencies. The temperature and moisture departures from equilibrium are compared with published results from a cloud-resolving model (CRM). Results show that the procedure is capable of isolating the behavior of a convection scheme from other physics schemes. We identify areas of agreement but also substantial differences between convection schemes, some of which can be related to scheme design. Some aspects of the model linear responses are related to their RCE profiles (the relative humidity profile in particular), while others constitute independent diagnostics. All the SCMs show irregularities or discontinuities in behavior that are likely related to switches or thresholds built into the convection schemes, and which do not appear in the CRM. Our results highlight potential flaws in convection schemes and suggest possible new directions to explore for parameterization evaluation.

The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or â\euro˜tuningâ\euro™ the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up the development process. While some studies propose to replace parameterizations by data-driven neural networks, we rather advocate that keeping physical parameterizations is key for the reliability of climate projections. In this paper we propose to harness machine learning to improve physical parameterizations. In particular we use Gaussian process-based methods from uncertainty quantification to calibrate the model free parameters at a process level. To achieve this, we focus on the comparison of single-column simulations and reference large-eddy simulations over multiple boundary-layer cases. Our method returns all values of the free parameters consistent with the references and any structural uncertainties, allowing a reduced domain of acceptable values to be considered when tuning the 3D global model. This tool allows to disentangle deficiencies due to poor parameter calibration from intrinsic limits rooted in the parameterization formulations. This paper describes the tool and the philosophy of tuning in single-column mode. Part 2 shows how the results from our process-based tuning can help in the 3D global model tuning.

The representation of stable boundary layers (SBLs) still challenges turbulence parameterizations implemented in current weather or climate models. The present work assesses whether these model deficiencies reflect calibration choices or intrinsic limits in currently-used turbulence parameterization formulations and implementations. This question is addressed for the ARPEGE-Climat 6.3 CNRM atmospheric model in a single-column model/large-eddy simulation (SCM/LES) comparison framework, using the history matching with iterative refocusing statistical approach. The GABLS4 case, which samples a nocturnal strong SBL observed at Dome C, Antarctic Plateau, is used. The standard calibration of the ARPEGE-Climat 6.3 turbulence parameterization leads to a too deep SBL, a too high low-level jet and misses the nocturnal wind rotation. This behavior is found for low and high vertical resolution model configurations. The statistical tool then proves that these model deficiencies reflect a poor parameterization calibration rather than intrinsic limits of the parameterization formulation itself. In particular, the role of two lower bounds that were heuristically introduced during the parameterization implementation to increase mixing in the free troposphere and to avoid runaway cooling in snow- or ice-covered region is emphasized. The statistical tool identifies the space of the parameterization free parameters compatible with the LES reference, accounting for the various sources of uncertainty. This space is non-empty, thus proving that the ARPEGE-Climat 6.3 turbulence parameterization contains the required physics to capture the GABLS4 SBL. The SCM framework is also used to validate the statistical framework and a few guidelines for its use in parameterization development and calibration are discussed.