Abstract

We calculate the implied horizontal heat transport due to the spatial anomalies of radiative fluxes at the top of the atmosphere (TOA). The regional patterns of implied heat transport for different components of the TOA fluxes are calculated by solving the Poisson equation with the flux components as source terms. The shortwave (SW) part of the spectrum governs the spatial patterns of the total implied heat transport. Using the cloud radiative effect (CRE) as source term, we show that the direct effect of clouds is to reduce the poleward heat transport in the majority of the Northern Hemisphere and at high southern latitudes. Clouds flatten the gradients of the clear-sky energy flux potential and hence reduce the implied heat transport with respect to clear skies. Clouds reduce the implied cross-equatorial heat transport with respect to clear sky through changes in the SW part of the spectrum. It changes from 0.83 PW in clear sky to −0.01 PW in all sky, equivalent to the hemispheric albedo symmetry reported in previous studies. We investigate hemispheric symmetry by introducing a metric that measures the symmetry of implied meridional heat transports at all latitudes. The direct effect of clouds is to increase the symmetry in the implied heat transport, and this is achieved through an increase in symmetry in the SW part of the spectrum in the tropics. Whether this is trivial or the result of a fundamental control in the climate system is still an open question.

Abstract

The partitioning of the earth radiation budget (ERB) between its atmosphere and surface components is of crucial interest in climate studies as it has a significant role in the oceanic and atmospheric general circulation. An analysis of the present-day climate simulation of the surface radiation budget in the atmospheric component of the new Hadley Centre Global Environmental Model version 1 (HadGEM1) is presented, and the simulations are assessed by comparing the results with fluxes derived from satellite data from the International Satellite Cloud Climatology Project (ISCCP) and ground measurements from the Baseline Surface Radiation Network (BSRN).

Comparisons against radiative fluxes from satellite and ground observations show that the model tends to overestimate the surface incoming solar radiation (S_s , _d ). The model simulates S_s , _d very well over the polar regions. Consistency in the comparisons against BSRN and ISCCP-FD suggests that the ISCCP-FD database is a good test for the performance of the surface downwelling solar radiation in climate model simulations. Overall, the simulation of downward longwave radiation is closer to observations than its shortwave counterpart. The model underestimates the downward longwave radiation with respect to BSRN measurements by 6.0 W m⁻².

Comparisons of land surface albedo from the model and estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) show that HadGEM1 overestimates the land surface albedo over deserts and over midlatitude landmasses in the Northern Hemisphere in January. Analysis of the seasonal cycle of the land surface albedo in different regions shows that the amplitude and phase of the seasonal cycle are not well represented in the model, although a more extensive validation needs to be carried out.

Two decades of coupled model simulations of the twentieth-century climate are used to look into the model’s simulation of global dimming/brightening. The model results are in line with the conclusions of the studies that suggest that global dimming is far from being a uniform phenomenon across the globe.

Abstract

The authors study the role of clouds in the persistent bias of surface downwelling shortwave radiation (SDSR) in the Southern Ocean in the atmosphere-only version of the Met Office model. The reduction of this bias in the atmosphere-only version is important to minimize sea surface temperature biases when the atmosphere model is coupled to a dynamic ocean. The authors use cloud properties and radiative fluxes estimates from the International Satellite Cloud Climatology Project (ISCCP) and apply a clustering technique to classify clouds into different regimes over the Southern Ocean. Then, they composite the cloud regimes around cyclone centers, which allows them to study the role of each cloud regime in a mean composite cyclone. Low- and midlevel clouds in the cold-air sector of the cyclones are responsible for most of the bias. Based on this analysis, the authors develop and test a new diagnosis of shear-dominated boundary layers. This change improves the simulation of the SDSR through a better simulation of the frequency of occurrence of the cloud regimes in the cyclone composite. Substantial biases in the radiative properties of the midtop and stratocumulus regimes are still present, which suggests the need to increase the optical depth of the low-level cloud with moderate optical depth and cloud with tops at midlevels.

Abstract

We develop a statistical method to assess CMIP6 simulations of large-scale surface temperature change during the historical period (1850–2014), considering all time scales, allowing for the different unforced variability of each model and the observations, observational uncertainty, and variable ensemble size. The generality of this method, and the fact that it incorporates information about the unforced variability, makes it a useful model assessment tool. We apply this method to the historical simulations of the CMIP6 multimodel ensemble. We use three indices that measure different aspects of large-scale surface air temperature change: global mean, hemispheric gradient, and a recently developed index that captures the sea surface temperature (SST) pattern in the tropics (SST^#; see Fueglistaler and Silvers). We use the following observations: HadCRUT5 for the first two indices, and AMIPII and ERSSTv5 for SST^#. In each case, we test the hypothesis that the model’s forced response is compatible with the observations, accounting for unforced variability in both models and observations as well as measurement uncertainty. This hypothesis is accepted more often (75% of the models) for the hemispheric gradient than for the global mean, for which half of the models fail the test. The tropical SST pattern is poorly simulated in all models. Given that the tropical SST pattern can strongly modulate the relationship between energy imbalance and global-mean surface temperature anomalies on annual to decadal time scales (short-term feedback parameter), we suggest this should be a focus area for future improvements due to its potential implications for the global-mean temperature evolution in decadal time scales.

The launch of the CloudSat cloud radar has provided some of the first near-global views of the threedimensional structure of clouds from space. To evaluate clouds in numerical models and compare them to the observations made by CloudSat, it is useful to have a tool that converts modeled clouds to radar returns that might be viewed by a radar system on a satellite passing over the model domain. QuickBeam is a user-friendly radar simulation package that performs this function and is freely available to the meteorological community. The workings of the simulator are briefly described and several applications of the simulator to numerical models are demonstrated.

Abstract

The Transpose-Atmospheric Model Intercomparison Project (AMIP) is an international model intercomparison project in which climate models are run in “weather forecast mode.” The Transpose-AMIP II experiment is run alongside phase 5 of the Coupled Model Intercomparison Project (CMIP5) and allows processes operating in climate models to be evaluated, and the origin of climatological biases to be explored, by examining the evolution of the model from a state in which the large-scale dynamics, temperature, and humidity structures are constrained through use of common analyses.

The Transpose-AMIP II experimental design is presented. The project requests participants to submit a comprehensive set of diagnostics to enable detailed investigation of the models to be performed. An example of the type of analysis that may be undertaken using these diagnostics is illustrated through a study of the development of cloud biases over the Southern Ocean, a region that is problematic for many models. Several models share a climatological bias for too little reflected shortwave radiation from cloud across the region. This is found to mainly occur behind cold fronts and/or on the leading side of transient ridges and to be associated with more stable lower-tropospheric profiles. Investigation of a case study that is typical of the bias and associated meteorological conditions reveals the models to typically simulate cloud that is too optically and physically thin with an inversion that is too low. The evolution of the models within the first few hours suggests that these conditions are particularly sensitive and a positive feedback can develop between the thinning of the cloud layer and boundary layer structure.

Abstract

Since 2016, the South African Weather Service (SAWS) has been running convective-scale simulations to assist with forecast operations across southern Africa. These simulations are run with a tropical configuration of the Met Office Unified Model (UM), nested in the Met Office global model, but without data assimilation. For November 2016, convection-permitting simulations at 4.4- and 1.5-km grid lengths are compared against a simulation at 10-km grid length with convection parameterization (the current UM global atmosphere configuration) to identify the benefits of increasing model resolution for forecasting convection across southern Africa. The simulations are evaluated against satellite rainfall estimates, CloudSat vertical cloud profiles, and SAWS radar data. In line with previous studies using the UM, on a monthly time scale, the diurnal cycle of convection and the distribution of rainfall rates compare better against observations when convection-permitting model configurations are used. The SAWS radar network provides a three-dimensional composite of radar reflectivity for northeast South Africa at 6-min intervals, allowing the evaluation of the vertical development of precipitating clouds and of the timing of the onset of deep convection. Analysis of four case study days indicates that the 4.4-km simulations have a later onset of convection than the 1.5-km simulations, but there is no consistent bias of the simulations against the radar observations across the case studies.

Abstract

The Southern Ocean is a critical region for global climate, yet large cloud and solar radiation biases over the Southern Ocean are a long-standing problem in climate models and are poorly understood, leading to biases in simulated sea surface temperatures. This study shows that supercooled liquid clouds are central to understanding and simulating the Southern Ocean environment. A combination of satellite observational data and detailed radiative transfer calculations is used to quantify the impact of cloud phase and cloud vertical structure on the reflected solar radiation in the Southern Hemisphere summer. It is found that clouds with supercooled liquid tops dominate the population of liquid clouds. The observations show that clouds with supercooled liquid tops contribute between 27% and 38% to the total reflected solar radiation between 40° and 70°S, and climate models are found to poorly simulate these clouds. The results quantify the importance of supercooled liquid clouds in the Southern Ocean environment and highlight the need to improve understanding of the physical processes that control these clouds in order to improve their simulation in numerical models. This is not only important for improving the simulation of present-day climate and climate variability, but also relevant for increasing confidence in climate feedback processes and future climate projections.

Errors in the simulation of clouds in general circulation models (GCMs) remain a long-standing issue in climate projections, as discussed in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. This highlights the need for developing new analysis techniques to improve our knowledge of the physical processes at the root of these errors. The Cloud Feedback Model Intercomparison Project (CFMIP) pursues this objective, and under that framework the CFMIP Observation Simulator Package (COSP) has been developed. COSP is a flexible software tool that enables the simulation of several satellite-borne active and passive sensor observations from model variables. The flexibility of COSP and a common interface for all sensors facilitates its use in any type of numerical model, from high-resolution cloud-resolving models to the coarser-resolution GCMs assessed by the IPCC, and the scales in between used in weather forecast and regional models. The diversity of model parameterization techniques makes the comparison between model and observations difficult, as some parameterized variables (e.g., cloud fraction) do not have the same meaning in all models. The approach followed in COSP permits models to be evaluated against observations and compared against each other in a more consistent manner. This permits a more detailed diagnosis of the physical processes that govern the behavior of clouds and precipitation in numerical models. The World Climate Research Programme (WCRP) Working Group on Coupled Modelling has recommended the use of COSP in a subset of climate experiments that will be assessed by the next IPCC report. In this article we describe COSP, present some results from its application to numerical models, and discuss future work that will expand its capabilities.

Abstract

Current climate models generally reflect too little solar radiation over the Southern Ocean, which may be the leading cause of the prevalent sea surface temperature biases in climate models. The authors study the role of clouds on the radiation biases in atmosphere-only simulations of the Cloud Feedback Model Intercomparison Project phase 2 (CFMIP2), as clouds have a leading role in controlling the solar radiation absorbed at those latitudes. The authors composite daily data around cyclone centers in the latitude band between 40° and 70°S during the summer. They use cloud property estimates from satellite to classify clouds into different regimes, which allow them to relate the cloud regimes and their associated radiative biases to the meteorological conditions in which they occur. The cloud regimes are defined using cloud properties retrieved using passive sensors and may suffer from the errors associated with this type of retrievals. The authors use information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to investigate in more detail the properties of the “midlevel” cloud regime. Most of the model biases occur in the cold-air side of the cyclone composite, and the cyclone composite accounts for most of the climatological error in that latitudinal band. The midlevel regime is the main contributor to reflected shortwave radiation biases. CALIPSO data show that the midlevel cloud regime is dominated by two main cloud types: cloud with tops actually at midlevel and low-level cloud. Improving the simulation of these cloud types should help reduce the biases in the simulation of the solar radiation budget in the Southern Ocean in climate models.