Cloud Feedback Model Intercomparison Project Research Articles

Comparing the atmospheric responses to reduced Arctic sea ice, a warmer ocean, and increased CO2 and their contributions to projected change at 2 °C global warming

Abstract We consider the combined and individual influences of Arctic sea-ice loss, sea surface temperature (SST) warming, and the direct radiative effect of increased CO2 on the Northern Hemispheric climate. The surface climate (e.g., temperature, precipitation) and atmospheric circulation responses (e.g., sea level pressure, wind) to these drivers are quantified using simulations from the Polar Amplification Model Intercomparison Project (for sea-ice loss and SST warming) and the Cloud Feedback Model Intercomparison Project (for increased CO2). We verify the linear additivity of the PAMIP-derived winter responses to sea-ice loss and SST change. The responses to SST change are of greater magnitude than that due to sea-ice loss or due to CO2 direct radiative forcing in most seasons and regions, excluding the Arctic. Notably however, sea-ice loss is at least as important as SST change for the winter atmospheric circulation response over the North Atlantic and Siberia. The dynamical responses to sea-ice loss and SST change oppose each other in many regions in winter, while the responses to SST and CO2 direct radiative forcing are often opposing in summer. Such opposing responses are less evident for the thermodynamical response. The sum of all three responses reproduces well the spatial patterns of change at 2 °C global warming in winter and autumn in the Coupled Model Intercomparison Project phase 6 projections, but overestimates their magnitude.

Understanding changes in cloud simulations from E3SM version 1 to version 2

Abstract. This study documents clouds simulated by the Energy Exascale Earth System Model (E3SM) version 2 (E3SMv2) and attempts to understand what causes the model behavior change in clouds relative to E3SMv1. This is done by analyzing the last 30-year (1985–2014) data from the 165-year historical simulations using E3SMv1 and v2 and four sensitivity tests to isolate the impact of changes in model parameter choices in its turbulence, shallow convection, and cloud macrophysics parameterization (Cloud Layers Unified By Binormals, CLUBB); microphysical parameterization (MG2); and deep-convection scheme (ZM), as well as model physics changes in convective triggering. It is shown that E3SMv2 significantly improves the simulation of subtropical coastal stratocumulus clouds and clouds with optical depth larger than 3.6 over the stratocumulus-to-cumulus transition regimes, where the shortwave cloud radiative effect (SWCRE) is also improved, and the Southern Ocean (SO) while seeing an overall slight degradation in low clouds over other tropical and subtropical oceans. The better performance in E3SMv1 over those regions is partially due to error compensation between its simulated optically thin and intermediate low clouds for which E3SMv2 actually improves simulation of optically intermediate low clouds. Sensitivity tests indicate that the changes in low clouds are primarily due to the tuning done in CLUBB. The impact of the ZM tuning is mainly on optically intermediate and thick high clouds, contributing to an improved SWCRE and longwave cloud radiative effect (LWCRE). The impact of the MG2 tuning and the new convective trigger is primarily on the high latitudes and the SO. They have a relatively smaller impact on clouds than CLUBB tuning and ZM tuning do. This study offers additional insights into clouds simulated in E3SMv2 by utilizing multiple data sets and the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) diagnostic tool as well as sensitivity tests. The improved understanding will benefit future E3SM developments.

On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions

Abstract. The long-term comparison between simulated and observed spectrally resolved outgoing longwave radiation (OLR) can represent a stringent test for the direct verification and improvement of general circulation models (GCMs), which are regularly tuned by adjusting parameters related to subgrid processes not explicitly represented in the model to constrain the integrated OLR energy fluxes to observed values. However, a good agreement between simulated and observed integrated OLR fluxes may be obtained from the cancellation of opposite-in-sign systematic errors localized in specific spectral ranges. Since the mid-2000s, stable hyperspectral observations of the mid-infrared region (667 to 2750 cm−1) of the Earth emission spectrum have been provided by different sensors (e.g. AIRS, IASI and CrIS). Furthermore, the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission, selected to be the ninth ESA Earth Explorer, will measure, starting from 2027, the terrestrial radiation emitted to space at the top of the atmosphere (TOA) from 100 to 1600 cm−1, filling the observational gap in the far-infrared (FIR) region, from 100 to 667 cm−1. In this work, in anticipation of FORUM measurements, we compare Infrared Atmospheric Sounding Interferometer (IASI) Metop-A observations to radiances simulated on the basis of the atmospheric fields predicted by the EC-Earth Global Climate Model (version 3.3.3) in clear-sky conditions. To simulate spectra based on the atmospheric and surface state provided by the climate model, the radiative transfer model σ-IASI has been integrated in the Cloud Feedback Model Intercomparison Project (COSP) package. Therefore, online simulations, provided by the EC-Earth model equipped with the new COSP–σ-IASI module, have been performed in clear-sky conditions with prescribed sea surface temperature and sea ice concentration, every 6 h, over a time frame consistent with the availability of IASI data. Systematic comparisons between observed and simulated brightness temperature (BT) have been performed in 10 cm−1 spectral intervals, on a global scale over the ocean, with a specific focus on the latitudinal belt between 30∘ S and 30∘ N. The analysis has shown a warm BT bias of about 3.5 K in the core of the CO2 absorption band and a cold BT bias of approximately 1 K in the wing of the CO2 band, due to a positive temperature bias in the stratosphere and a negative temperature bias in the middle troposphere of the climate model, respectively. Finally, considering a warm BT bias in the rotational–vibrational water vapour band, we have highlighted a dry bias of the water vapour concentration in the upper troposphere of the model.

Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation

Abstract. Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

Role of Cloud Subgrid‐Scale Structure in Modulating Clouds Viewed by ISCCP, MODIS, and MISR Simulators

AbstractThe role of cloud subgrid‐scale structure in modulating satellite views of clouds was investigated. This was realized by implementing a stochastic cloud generator into the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package together with surrogate clouds produced by a cloud‐resolving model (CRM). The subgrid‐scale structural parameters are decorrelation length for overlapping cloud fraction (Lcf), decorrelation length for overlapping cloud condensate (Lcw), and the shape parameter v for measuring cloud inhomogeneity. With the use of median values of Lcf, Lcw, and v derived from CRM, the simulated satellite views bear close resemblance to those using CRM‐inherent clouds. Varying these parameters in the range of lower and upper quartiles leads to differences that are about one‐fifth of those caused by changing cloud microphysics in CRM. While Lcf influences clouds throughout the whole troposphere, Lcw and v result in changes mostly within the upper layers. Increasing (decreasing) cloud inhomogeneity or overlapping degree leads to decreased (increased) occurrence of clouds, except for high‐topped clouds viewed by Multiangle Imaging Spectroradiometer. Care must then be exercised when interpreting model biases in comparison with different instruments. Sensitivity tests show changing condensate distribution from gamma to lognormal makes little impact on final results. Although the differences induced by any of the parameters alone are much limited, they are getting comparable to those seen between models and observations when all parameters are synergistically altered. This brings encouraging results to the modeling community that simulator‐diagnosed clouds can be potentially improved by tuning cloud subgrid‐scale parameters.

Investigating Parametric Dependence of Climate Feedbacks in the Atmospheric Component of CNRM‐CM6‐1

AbstractWe sample 30 calibration parameters of the CNRM‐CM6‐1 atmospheric component to produce a perturbed parameter ensemble. An error score aggregating four annual‐averaged metrics is used here as an example to represent the model performance. We propose a method using statistical emulators of climatic fields and feedback parameters to produce model candidates spanning the maximal range of net feedback while minimizing the error score. Optimal candidates are found for a range of climate sensitivity chosen to span 3.8–10.1 K. The candidates show large errors in the extremes of the climate sensitivity range and none of them have better scores than the released version of CNRM‐CM6. However, 65% of the optimal candidates have errors within the inter‐model standard deviation of the seven models participating in the Cloud Feedback Model Intercomparison Project, spanning and ECS range of (4.14–7.22 K). These optimal calibrations will be used to assess feedback diversity in coupled experiments.

The Earth Model Column Collaboratory (EMC<sup>2</sup>) v1.1: an open-source ground-based lidar and radar instrument simulator and subcolumn generator for large-scale models

Abstract. Climate models are essential for our comprehensive understanding of Earth's atmosphere and can provide critical insights on future changes decades ahead. Because of these critical roles, today's climate models are continuously being developed and evaluated using constraining observations and measurements obtained by satellites, airborne, and ground-based instruments. Instrument simulators can provide a bridge between the measured or retrieved quantities and their sampling in models and field observations while considering instrument sensitivity limitations. Here we present the Earth Model Column Collaboratory (EMC2), an open-source ground-based lidar and radar instrument simulator and subcolumn generator, specifically designed for large-scale models, in particular climate models, but also applicable to high-resolution model output. EMC2 provides a flexible framework enabling direct comparison of model output with ground-based observations, including generation of subcolumns that may statistically represent finer model spatial resolutions. In addition, EMC2 emulates ground-based (and air- or space-borne) measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. The simulator uses either single particle or bulk particle size distribution lookup tables, depending on the selected scheme approach, to perform the forward calculations. To facilitate model evaluation, EMC2 also includes three hydrometeor classification methods, namely, radar- and sounding-based cloud and precipitation detection and classification, lidar-based phase classification, and a Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) lidar simulator emulator. The software is written in Python, is easy to use, and can be straightforwardly customized for different models, radars, and lidars. Following the description of the logic, functionality, features, and software structure of EMC2, we present a case study of highly supercooled mixed-phase cloud based on measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE). We compare observations with the application of EMC2 to outputs from four configurations of the NASA Goddard Institute for Space Studies (GISS) climate model (ModelE3) in single-column model (SCM) mode and from a large-eddy simulation (LES) model. We show that two of the four ModelE3 configurations can form and maintain highly supercooled precipitating cloud for several hours, consistent with observations and LES. While our focus is on one of these ModelE3 configurations, which performed slightly better in this case study, both of these configurations and the LES results post-processed with EMC2 generally provide reasonable agreement with observed lidar and radar variables. As briefly demonstrated here, EMC2 can provide a lightweight and flexible framework for comparing the results of both large-scale and high-resolution models directly with observations, with relatively little overhead and multiple options for achieving consistency with model microphysical or radiation scheme physics.

The influence of direct radiative forcing versus indirect sea surface temperature warming on southern hemisphere subtropical anticyclones under global warming

Southern hemisphere subtropical anticyclones are projected to change in a warmer climate during both austral summer and winter. A recent study of CMIP 5 & 6 projections found a combination of local diabatic heating changes and static-stability-induced changes in baroclinic eddy growth as the dominant drivers. Yet the underlying mechanisms forcing these changes still remain uninvestigated. This study aims to enhance our mechanistic understanding of what drives these Southern Hemisphere anticyclones changes during both seasons. Using an AGCM, we decompose the response to CO2-induced warming into two components: (1) the fast atmospheric response to direct CO2 radiative forcing, and (2) the slow atmospheric response due to indirect sea surface temperature warming. Additionally, we isolate the influence of tropical diabatic heating with AGCM added heating experiments. As a complement to our numerical AGCM experiments, we analyze the Atmospheric and Cloud Feedback Model Intercomparison Project experiments. Results from sensitivity experiments show that slow subtropical sea surface temperature warming primarily forces the projected changes in subtropical anticyclones through baroclinicity change. Fast CO2 atmospheric radiative forcing on the other hand plays a secondary role, with the most notable exception being the South Atlantic subtropical anticyclone in austral winter, where it opposes the forcing by sea surface temperature changes resulting in a muted net response. Lastly, we find that tropical diabatic heating changes only significantly influence Southern Hemisphere subtropical anticyclone changes through tropospheric wind shear changes during austral winter.

Adapting the COSP Radar Simulator to Compare GCM Output and GPM Precipitation Radar Observations

AbstractComparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolutions. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the subgrid scale. This work adapts Quickbeam to the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate to heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land–ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP subgrid algorithm by applying a more realistic, nondeterministic approach to assigning GCM gridbox convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated subgrid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud-cover assumption.

Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

Abstract. The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

Decomposition of projected summer rainfall change over East Asia based on timeslice experiments

The summer rainfall change over East Asia in response to CO2 forcing and the associated processes are investigated via a set of pilot timeslice piSST experiments from the Cloud Feedback Model Intercomparison Project Phase 3 (CFMIP-3). The total response of rainfall to 4 $$\times $$ CO2 in coupled models is decomposed into components associated with uniform SST warming, SST pattern change, the direct radiative effect of increased CO2, and the plant physiological response. In general, the contributions of the individual responses of summer rainfall to different forcings are subject to a regional dependence. The uniform SST warming reduces rainfall in many land regions in East Asia, but increases rainfall over the northern East Asia. The spatial patterns of the rainfall change as the result of SST pattern change are nearly opposite to those of the uniform SST warming, which account for most inter-model uncertainty in the simulations. The direct radiative effect of increasing CO2 is largely responsible for an increase of rainfall across the East Asian continent, especially over the central China and southern Tibetan Plateau, and the plant physiological effect appears to increase rainfall over the eastern and southern China. Also discussed are the atmospheric circulation changes that are driven by the distinct aspects of CO2 forcing and directly tied to the summer rainfall changes.

Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0)

Abstract. Automatic lidars and ceilometers (ALCs) provide valuable information on cloud and aerosols but have not been systematically used in the evaluation of general circulation models (GCMs) and numerical weather prediction (NWP) models. Obstacles associated with the diversity of instruments, a lack of standardisation of data products and open processing tools mean that the value of large ALC networks worldwide is not being realised. We discuss a tool, called the Automatic Lidar and Ceilometer Framework (ALCF), that overcomes these problems and also includes a ground-based lidar simulator, which calculates the radiative transfer of laser radiation and allows one-to-one comparison with models. Our ground-based lidar simulator is based on the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP), which has been extensively used for spaceborne lidar intercomparisons. The ALCF implements all steps needed to transform and calibrate raw ALC data and create simulated attenuated volume backscattering coefficient profiles for one-to-one comparison and complete statistical analysis of clouds. The framework supports multiple common commercial ALCs (Vaisala CL31, CL51, Lufft CHM 15k and Droplet Measurement Technologies MiniMPL), reanalyses (JRA-55, ERA5 and MERRA-2) and models (the Unified Model and AMPS – the Antarctic Mesoscale Prediction System). To demonstrate its capabilities, we present case studies evaluating cloud in the supported reanalyses and models using CL31, CL51, CHM 15k and MiniMPL observations at three sites in New Zealand. We show that the reanalyses and models generally underestimate cloud fraction. If sufficiently high-temporal-resolution model output is available (better than 6-hourly), a direct comparison of individual clouds is also possible. We demonstrate that the ALCF can be used as a generic evaluation tool to examine cloud occurrence and cloud properties in reanalyses, NWP models, and GCMs, potentially utilising the large amounts of ALC data already available. This tool is likely to be particularly useful for the analysis and improvement of low-level cloud simulations which are not well monitored from space. This has previously been identified as a critical deficiency in contemporary models, limiting the accuracy of weather forecasts and future climate projections. While the current focus of the framework is on clouds, support for aerosol in the lidar simulator is planned in the future.

An evaluation of the Arctic clouds and surface radiative fluxes in CMIP6 models

To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance, the 2001–2014 Arctic Basin surface radiation budget, clouds, and the cloud radiative effects (CREs) in 22 coupled model intercomparison project 6 (CMIP6) models are evaluated against satellite observations. For the results from CMIP6 multi-model mean, cloud fraction (CF) peaks in autumn and is lowest in winter and spring, consistent with that from three satellite observation products (CloudSat-CALIPSO, CERES-MODIS, and APP-x). Simulated CF also shows consistent spatial patterns with those in observations. However, almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x. On average, clouds warm the surface of the Arctic Basin mainly via the longwave (LW) radiation cloud warming effect in winter. Simulated surface energy loss of LW is less than that in CERES-EBAF observation, while the net surface shortwave (SW) flux is underestimated. The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models. These two biases compensate each other, yielding similar net surface radiation flux between model output (3.0 W/m2) and CERES-EBAF observation (6.1 W/m2). During 2001–2014, significant increasing trend of spring CF is found in the multi-model mean, consistent with previous studies based on surface and satellite observations. Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path (LWP/IWP), large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year, indicating the influences of different cloud parameterization schemes used in different models. Cloud Feedback Model Intercomparison Project (CFMIP) observation simulator package (COSP) is a great tool to accurately assess the performance of climate models on simulating clouds. More intuitive and credible evaluation results can be obtained based on the COSP model output. In the future, with the release of more COSP output of CMIP6 models, it is expected that those inter-model spreads and the model-observation biases can be substantially reduced. Longer term active satellite observations are also necessary to evaluate models’ cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.

Fast adjustment versus slow SST-mediated response of daily precipitation statistics to abrupt 4xCO2

Projected changes in global to regional precipitation remain highly model-dependent and are driven by both fast atmospheric adjustments and slower ocean-mediated responses to increasing concentrations of greenhouse gases. Understanding the relative influence of these multiple drivers is one of the main objectives of the Cloud Feedback Model Intercomparison Project. Here the focus is on the daily precipitation response to an abrupt quadrupling of atmospheric CO2, as simulated by the CNRM-CM6-1 global climate model. Extended atmosphere-only experiments with prescribed sea surface temperature (SST) are used to decompose the precipitation changes into separate responses to uniform SST warming, the pattern of SST anomalies, as well as fast radiative and physiological CO2 effects. The uniform SST warming dominates the global and regional changes in annual mean and annual maximum daily precipitation intensity. In contrast, the annual mean number of wet days and the annual maximum of consecutive dry days show a strong fast adjustment. They are also sensitive to the SST warming pattern that strongly influences changes in large-scale circulation. The increase in daily precipitation intensity drives the global-mean magnitude of the annual precipitation change. In contrast, the response of wet day frequency shapes the geographical distribution and interannual variability of the annual mean precipitation, especially in the subtropics, and is more sensitive to changes in near-surface relative humidity than in the total water column over land. Although the annual precipitation response does not seem highly sensitive to the base state, these results deserve further investigation and model intercomparison within CFMIP.

Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations

Abstract. Southern Ocean (SO) shortwave (SW) radiation biases are a common problem in contemporary general circulation models (GCMs), with most models exhibiting a tendency to absorb too much incoming SW radiation. These biases have been attributed to deficiencies in the representation of clouds during the austral summer months, either due to cloud cover or cloud albedo being too low. The problem has been the focus of many studies, most of which utilised satellite datasets for model evaluation. We use multi-year ship-based observations and the CERES spaceborne radiation budget measurements to contrast cloud representation and SW radiation in the atmospheric component Global Atmosphere (GA) version 7.1 of the HadGEM3 GCM and the MERRA-2 reanalysis. We find that the prevailing bias is negative in GA7.1 and positive in MERRA-2. GA7.1 performs better than MERRA-2 in terms of absolute SW bias. Significant errors of up to 21 W m−2 (GA7.1) and 39 W m−2 (MERRA-2) are present in both models in the austral summer. Using ship-based ceilometer observations, we find low cloud below 2 km to be predominant in the Ross Sea and the Indian Ocean sectors of the SO. Utilising a novel surface lidar simulator developed for this study, derived from an existing Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) – active remote sensing simulator (ACTSIM) spaceborne lidar simulator, we find that GA7.1 and MERRA-2 both underestimate low cloud and fog occurrence relative to the ship observations on average by 4 %–9 % (GA7.1) and 18 % (MERRA-2). Based on radiosonde observations, we also find the low cloud to be strongly linked to boundary layer atmospheric stability and the sea surface temperature. GA7.1 and MERRA-2 do not represent the observed relationship between boundary layer stability and clouds well. We find that MERRA-2 has a much greater proportion of cloud liquid water in the SO in austral summer than GA7.1, a likely key contributor to the difference in the SW radiation bias. Our results suggest that subgrid-scale processes (cloud and boundary layer parameterisations) are responsible for the bias and that in GA7.1 a major part of the SW radiation bias can be explained by cloud cover underestimation, relative to underestimation of cloud albedo.

Evaluation on the Vertical Distribution of Liquid and Ice Phase Cloud Fraction in Community Atmosphere Model Version 5.3 using Spaceborne Lidar Observations

AbstractCloud partition between liquid and ice phases and their vertical distributions are crucial to energy budget and global climate. Liquid and ice cloud fractions simulated by Community Atmosphere Model version 5.3 and Cloud Feedback Model Intercomparison Project Observational Simulator Package version 1.4 are evaluated by comparing to satellite retrieval data from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation. Model underestimates liquid cloud by 3.3%, 5.5%, and 3.1% and overestimates ice cloud by 1.5%, 6.3%, and 4.6% in high, medium, and low levels, respectively. The misclassification of liquid cloud to ice cloud occurs in all model levels, leading to an overall underestimation of supercooled liquid fraction (SLF) globally and a shift of 50% SLF line from about −20°C in observation to −5°C in model. Specifically, model produces excessive ice cloud in extratropics and insufficient liquid cloud in tropics at mixed‐phase levels with temperature between 0 and −40°C.

A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness

Abstract. This paper describes a new satellite simulator for the CLARA-A2 climate data record (CDR). This simulator takes into account the variable skill in cloud detection in the CLARA-A2 CDR by using a different approach to other similar satellite simulators to emulate the ability to detect clouds. In particular, the paper describes three methods to filter out clouds from climate models undetectable by observations. The first method is comparable to the current simulators in the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP), since it relies on a single visible cloud optical depth at 550 nm (τc) threshold applied globally to delineate cloudy and cloud-free conditions. Methods two and three apply long/lat-gridded values separated by daytime and nighttime conditions. Method two uses gridded varying τc as opposed to method one, which uses just a τc threshold, and method three uses a cloud probability of detection (POD) depending on the model τc. The gridded POD values are from the CLARA-A2 validation study by Karlsson and Håkansson (2018). Methods two and three replicate the relative ease or difficulty for cloud retrievals depending on the region and illumination. They increase the cloud sensitivity where the cloud retrievals are relatively straightforward, such as over midlatitude oceans, and they decrease the sensitivity where cloud retrievals are notoriously tricky, such as where thick clouds may be inseparable from cold snow-covered surfaces, as well as in areas with an abundance of broken and small-scale cumulus clouds such as the atmospheric subsidence regions over the ocean. The simulator, together with the International Satellite Cloud Climatology Project (ISCCP) simulator of the COSP, is used to assess Arctic clouds in the EC-Earth climate model compared to the CLARA-A2 and ISCCP H-Series (ISCCP-H) CDRs. Compared to CLARA-A2, EC-Earth generally underestimates cloudiness in the Arctic. However, compared to ISCCP and its simulator, the opposite conclusion is reached. Based on EC-Earth, this paper shows that the simulated cloud mask of CLARA-A2, using method three, is more representative of the CDR than method one used for the ISCCP simulator. The simulator substantially improves the simulation of the CLARA-A2-detected clouds, especially in the polar regions, by accounting for the variable cloud detection skill over the year. The approach to cloud simulation based on the POD of clouds depending on their τc, location, and illumination is the preferred one as it reduces cloudiness over a range of cloud optical depths. Climate model comparisons with satellite-derived information can be significantly improved by this approach, mainly by reducing the risk of misinterpreting problems with satellite retrievals as cloudiness features. Since previous studies found that the CLARA-A2 CDR performs well in the Arctic during the summer months, and that method three is more representative than method one, the conclusion is that EC-Earth likely underestimates clouds in the Arctic summer.

Decomposing East-Asian winter temperature and monsoonal circulation changes using timeslice experiments

Based on a set of pilot atmosphere-only experiments from the Cloud Feedback Model Intercomparison Project Phase 3 (CFMIP-3), in this study, the winter surface air temperature (SAT) and monsoonal circulation changes in East Asia as a response to the 4 × CO2 forcing in coupled model are decomposed into the four parts in terms of the responses to the uniform SST warming, 4 × CO2 radiative effect, SST pattern changes, and plant physiological effect. The uniform SST warming presents the most significant influence on the increase of SAT change, which strengthens the East Asian winter monsoon (EAWM) circulation. The CO2 radiative effect can also induce the SAT increase over East Asia but with a magnitude smaller compared to the uniform SST warming, in which more warming is in land than ocean and the EAWM circulation could be weakened consequently due to the decreased land–sea thermal contrast in response to the CO2 radiative effect. The SAT changes in response to the SST pattern change show inconsistencies over the eastern and southern parts of East Asia between the two models, associated with the large difference for EAWM circulation changes, indicating that the SST pattern change could be the primary source of inter-model uncertainties in the East-Asian SAT change. As for the influence of plant physiological effect, it could generate a SAT rise in many highly vegetated regions. Further analyses for different areas show that both the uniform SST warming and CO2 radiative effects could induce more intense SAT increase in northern East Asia, while the plant physiological effect has a more significant influence on that in southern/eastern part of East Asia.

Radiative effects of daily cycle of cloud frequency in past and future climates

The daily cloud cycle (DCC) and its response to global warming are critical to the Earth's energy budget, but their radiative effects have not been systematically quantified. Toward this goal, here we analyze the radiation at the top of the atmosphere and propose a measure of the DCC radiative effect (DCCRE) as the difference between the total radiative fluxes with the full cloud cycle and its uniformly distributed cloud counterpart. We apply it to the cloud fraction from four climate models that participated in the second phase of the Cloud Feedback Model Inter-comparison Project. The results allow us to objectively compare inter-model differences in the daily cycle of cloud fraction and their influences on the global energy balance. In particular, the spatial patterns of the DCC response to global warming obtained in our analysis suggest possible impacts on large-scale circulation. Such a framework can be used for conducting a more systematic evaluation of the DCC in climate models and observations with the goal to reduce uncertainty in climate projections.

Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

Abstract. The Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) is used to diagnose model performance and physical processes via an apple-to-apple comparison to satellite measurements. Although the COSP provides useful information about clouds and their climatic impact, outputs that have a subcolumn dimension require large amounts of data. This can cause a bottleneck when conducting sets of sensitivity experiments or multiple model intercomparisons. Here, we incorporate two diagnostics for warm rain microphysical processes into the latest version of the simulator (COSP2). The first one is the occurrence frequency of warm rain regimes (i.e., non-precipitating, drizzling, and precipitating) classified according to CloudSat radar reflectivity, putting the warm rain process diagnostics into the context of the geographical distributions of precipitation. The second diagnostic is the probability density function of radar reflectivity profiles normalized by the in-cloud optical depth, the so-called contoured frequency by optical depth diagram (CFODD), which illustrates how the warm rain processes occur in the vertical dimension using statistics constructed from CloudSat and MODIS simulators. The new diagnostics are designed to produce statistics online along with subcolumn information during the COSP execution, eliminating the need to output subcolumn variables. Users can also readily conduct regional analysis tailored to their particular research interest (e.g., land–ocean differences) using an auxiliary post-process package after the COSP calculation. The inline diagnostics are applied to the MIROC6 general circulation model (GCM) to demonstrate how known biases common among multiple GCMs relative to satellite observations are revealed. The inline multi-sensor diagnostics are intended to serve as a tool that facilitates process-oriented model evaluations in a manner that reduces the burden on modelers for their diagnostics effort.