Conventional Global Climate Models Research Articles

Intensification of daily tropical precipitation extremes from more organized convection.

Tropical precipitation extremes and their changes with surface warming are investigated using global storm resolving simulations and high-resolution observations. The simulations demonstrate that the mesoscale organization of convection, a process that cannot be physically represented by conventional global climate models, is important for the variations of tropical daily accumulated precipitation extremes. In both the simulations and observations, daily precipitation extremes increase in a more organized state, in association with larger, but less frequent, storms. Repeating the simulations for a warmer climate results in a robust increase in monthly-mean daily precipitation extremes. Higher precipitation percentiles have a greater sensitivity to convective organization, which is predicted to increase with warming. Without changes in organization, the strongest daily precipitation extremes over the tropical oceans increase at a rate close to Clausius-Clapeyron (CC) scaling. Thus, in a future warmer state with increased organization, the strongest daily precipitation extremes over oceans increase at a faster rate than CC scaling.

Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations

Abstract. Heavy rainfall is the main driver of soil erosion by water, which is a threat to soil and water resources across the globe. As a consequence of climate change, precipitation – especially extreme precipitation – is increasing in a warmer world, leading to an increase in rainfall erosivity. However, conventional global climate models struggle to represent extreme rain events and cannot provide precipitation data at the high spatiotemporal resolution that is needed for an accurate estimation of future rainfall erosivity. Convection-permitting simulations (CPSs), on the other hand, provide high-resolution precipitation data and a better representation of extreme rain events, but they are mostly limited to relatively small spatial extents and short time periods. Here, we present, for the first time, rainfall erosivity in a large modeling domain such as central Europe based on high-resolution CPS climate data generated with the regional climate model COSMO-CLM using the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario. We calculated rainfall erosivity for the past (1971–2000), present (2001–2019), near future (2031–2060) and far future (2071–2100). Our results showed that future increases in rainfall erosivity in central Europe can be up to 84 % in the region's river basins. These increases are much higher than previously estimated based on regression with mean annual precipitation. We conclude that despite remaining limitations, CPSs have an enormous and currently unexploited potential for climate impact studies on soil erosion. Thus, the soil erosion modeling community should closely follow the recent and future advances in climate modeling to take advantage of new CPSs for climate impact studies.

Tropical Cirrus in Global Storm‐Resolving Models: 2. Cirrus Life Cycle and Top‐of‐Atmosphere Radiative Fluxes

AbstractCirrus clouds of various thicknesses and radiative characteristics extend over much of the tropics, especially around deep convection. They are difficult to observe due to their high altitude and sometimes small optical depths. They are also difficult to simulate in conventional global climate models, which have coarse grid spacings and simplified parameterizations of deep convection and cirrus formation. We investigate the representation of tropical cirrus in global storm‐resolving models (GSRMs), which have higher spatial resolution and explicit convection and could more accurately represent cirrus cloud processes. This study uses GSRMs from the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) project. The aggregate life cycle of tropical cirrus is analyzed using joint albedo and outgoing longwave radiation (OLR) histograms to assess the fidelity of models in capturing the observed cirrus cloud populations over representative tropical ocean and land regions. The proportions of optically thick deep convection, anvils, and cirrus vary across models and are portrayed in the vertical distribution of cloud cover and top‐of‐atmosphere radiative fluxes. Model differences in cirrus populations, likely driven by subgrid processes such as ice microphysics, dominate over regional differences between convectively active tropical land and ocean locations.

Arctic Clouds Simulated by a Multiscale Modeling Framework and Comparisons With Observations and Conventional GCMs

AbstractClouds are an important component of the Arctic climate system through their regulation of the surface energy budget; however, Arctic clouds are poorly simulated in global climate models (GCMs). In this study, we evaluate the Arctic clouds simulated by a multiscale modeling framework (MMF). The results are compared against a merged CloudSat‐CALIPSO radar‐lidar cloud product and contrasted with an atmospheric reanalysis and conventional GCMs. The comparisons focus on the annual cycle of cloud covers, vertical structures of cloud fraction, and condensate mixing ratio, as well as the relationships between low‐cloud cover and atmospheric static stability. The MMF is found to represent Arctic boundary layer clouds slightly more realistically than the reanalysis and GCMs in both the annual cycle and vertical distribution except that middle‐ and high‐cloud covers are underestimated and the amplitude of annual cycle of total cloud cover is larger. The relationship between low‐cloud cover and near‐surface atmospheric stability produced by MMF is remarkably similar to the satellite observation during autumn, winter, and early spring, as low‐cloud cover decreases with colder surface and stronger stability. Such relationships over the annual cycle are not reproduced by other modeling approaches. Lastly, MMF yields a positive correlation between low‐cloud cover and atmospheric stability over the Arctic ocean from May to August, opposite to the satellite observation, implying stronger control of horizontal advection on low‐cloud formation. This modeled relationship is contributed by cloud fraction near the surface, which is known to be underestimated due to radar's surface clutter.

The Impact of Hybrid Usage of a Cumulus Parameterization Scheme on Tropical Convection and Large‐Scale Circulations in a Global Cloud‐System Resolving Model

AbstractThe impact of activating a cumulus parameterization scheme in the global nonhydrostatic icosahedral atmospheric model (NICAM) coupled with a one‐dimensional (1‐D) mixed‐layer ocean model is assessed using a cloud‐system‐resolving, 14‐km mesh. The Chikira‐Sugiyama (CS) scheme, which employs an entrainment rate sensitive to the humidity of the environment, can consider congestus clouds in the tropics when used in conventional global climate models. Congestus clouds are underresolved in the default 14‐km mesh NICAM. In the present study, boreal summer NICAM simulations are performed with and without the CS scheme, and several different scheme parameters are evaluated. The results show that the horizontal scale of convection and precipitable water increased in the tropics when using the CS scheme. Model adjustments were apparent at two different timescales: a rapid adjustment within the first week and a slower adjustment at 1 to 2 months. Both effects were magnified in the simulations that employed smaller values for the parameter that defines the fractional of loss of buoyancy‐generated energy in parameterized convection. The upward branch of the Hadley circulation shifted northward, and the Walker circulation was enhanced when the CS scheme was activated. These large‐scale adjustments suggested that increased midtropospheric moisture in the tropics tends to favor larger organized convective activities, which require an abundant supply of moisture, which, in this case, is available to the north of the equatorial West Pacific Ocean.

Changes in clouds and atmospheric circulation associated with rapid adjustment induced by increased atmospheric CO2: a\xa0multiscale modeling framework study

The radiative heating increase due to increased CO2 concentration is the primary source for the rapid adjustment of atmospheric circulation and clouds. In this study, we investigate the rapid adjustment resulting from an instantaneous doubling of CO2 and its physical mechanism using a multiscale modeling framework (MMF). The cloud-resolving model component of this MMF includes a sophisticated third-order turbulence closure and the MMF simulates realistic shallow and deep cloud climatology and boundary layer turbulence. Although the simulated cloud adjustment and its mechanism generally agree with earlier studies with conventional global climate models and another MMF with a lower-order turbulence closure, this MMF simulates an increase in the global-mean shortwave and net cloud radiative cooling and a negative cloud radiative effect change due to cloud adjustment. This result is related to the large increase in low-level clouds over the extratropical and subtropical oceans, resulting from reduced cloud-top entrainment implied from strengthened inversion. The downshift of planetary boundary layer and low-level clouds is generally weaker than that simulated by other models, which is due to reduction of shallow cumulus in the ascending and weak subsidence circulation regimes but to increase of stratocumulus in the strongest subsidence regime. Optically thicker stratocumulus compensates for reduced cooling by shallow cumulus. The reduced strength of all oceanic circulation regimes, which may be contributed by weakened energy transport resulting from water vapor and cloud CO2 masking effects, not only reduces optical depth of convective clouds but also shifts cloud coverage to lands where deep convection is enhanced.

Differences in the Hydrological Cycle and Sensitivity Between Multiscale Modeling Frameworks with and without a Higher-order Turbulence Closure.

Current conventional global climate models (GCMs) produce a weak increase in global mean precipitation with anthropogenic warming in comparison with the lower-tropospheric moisture increases. The motive of this study is to understand the differences in the hydrological sensitivity between two multiscale modeling frameworks (MMFs) that arise from the different treatments of turbulence and low clouds in order to aid to the understanding of the model spread among conventional GCMs. We compare the hydrological sensitivity and its energetic constraint from MMFs with (SPCAM-IPHOC) or without (SPCAM) an advanced higher-order turbulence closure. SPCAM-IPHOC simulates higher global hydrological sensitivity for the slow response but lower sensitivity for the fast response than SPCAM. Their differences are comparable to the spreads of conventional GCMs. The higher sensitivity in SPCAM-IPHOC is associated with the higher ratio of the changes in latent heating to those in net atmospheric radiative cooling, which is further related to a stronger decrease in the Bowen ratio with warming than in SPCAM. The higher sensitivity of cloud radiative cooling resulting from the lack of low clouds in SPCAM is another major factor in contributing to the lower precipitation sensitivity. The two MMFs differ greatly in the hydrological sensitivity over the tropical lands, where the simulated sensitivity of surface sensible heat fluxes to surface warming and CO2 increase in SPCAM-IPHOC is weaker than in SPCAM. The difference in divergences of dry static energy flux simulated by the two MMFs also contributes to the difference in land precipitation sensitivity between the two models.

A parametrization of 3‐D subgrid‐scale clouds for conventional GCMs: Assessment using A‐Train satellite data and solar radiative transfer characteristics

ABSTRACTA stochastic algorithm for generating 3‐D cloud fields based on profiles of cloud fraction and mean cloud water content is presented and assessed using cloud properties inferred from A‐Train satellite data. The ultimate intention is to employ the algorithm, along with 3‐D radiative transfer (RT) models, in Global Climate Models (GCMs). The algorithm approaches cloud fields as whole objects demarcated by contiguous layers with . This contrasts with conventional GCM radiation routines that deal with clouds on a per‐(arbitrary) layer basis. A‐Train cloud data for August 2007 were partitioned into ∼29,000 domains, each ∼280 km long, to represent nominal GCM columns. For each A‐Train/stochastic pair of domains, profiles of domain‐averaged fluxes were computed by a 1‐D broadband solar RT model in Independent Column Approximation mode. Globally averaged, mean bias error for upwelling radiation at top‐of‐atmosphere (TOA) is 6.8 W m−2. Upon advancing the RT model to 2‐D, differences between 1‐D and 2‐D upwelling fluxes at TOA for A‐Train domains differed from corresponding differences for model‐generated domains by ∼1 W m−2, on average, with differences for the model domains exhibiting stronger dependence on solar zenith angle . Moving on to 3‐D RT for model domains, 1‐D–3‐D differences became slightly stronger functions of thanks mostly to accentuated 3‐D effects at small . Simple parametrizations for the stochastic algorithm's variables that govern horizontal and vertical structure of clouds should be adequate to capture the ramifications of systematic neglect of 3‐D solar RT in GCMs.

Cloud feedbacks on greenhouse warming in the superparameterized climate model SP‐CCSM4

AbstractCloud feedbacks on greenhouse warming are studied in a superparameterized version of the Community Climate System Model (SP‐CCSM4) in an atmospheric component SP‐CAM4 that explicitly simulates cumulus convection. A 150 year simulation in an abrupt quadrupling of CO2 is branched from a control run. It develops moderate positive global cloud feedback and an implied climate sensitivity of 2.8 K comparable to the conventionally parameterized CCSM4 and the median of other modern climate models. All of SP‐CCSM4's positive shortwave cloud feedback is due to a striking decrease in low cloud over land, which is much more pronounced than in most other climate models, including CCSM4. Four other cloud responses – decreased midlevel cloud, more Arctic water and ice cloud, a slight poleward shift of midlatitude storm track cloud, and an upward shift of high clouds – are also typical of conventional global climate models. SP‐CCSM4 does not simulate the large warming‐induced decrease in Southern Ocean cloud found in CCSM4. Two companion uncoupled SP‐CAM4 simulations, one with a uniform 4 K sea‐surface temperature increase and one with quadrupled CO2 but fixed SST, suggest that SP‐CCSM4's global‐scale cloud changes are primarily mediated by the warming, rather than by rapid adjustments to increased CO2. SP‐CAM4 show spatial patterns of cloud response qualitatively similar to the previous‐generation superparameterized SP‐CAM3, but with systematically more positive low cloud feedbacks over low‐latitude land and ocean.

Effects of explicit atmospheric convection at high CO2.

The effect of clouds on climate remains the largest uncertainty in climate change predictions, due to the inability of global climate models (GCMs) to resolve essential small-scale cloud and convection processes. We compare preindustrial and quadrupled CO2 simulations between a conventional GCM in which convection is parameterized and a "superparameterized" model in which convection is explicitly simulated with a cloud-permitting model in each grid cell. We find that the global responses of the two models to increased CO2 are broadly similar: both simulate ice-free Arctic summers, wintertime Arctic convection, and enhanced Madden-Julian oscillation (MJO) activity. Superparameterization produces significant differences at both CO2 levels, including greater Arctic cloud cover, further reduced sea ice area at high CO2, and a stronger increase with CO2 of the MJO.

A case study of subdaily simulated and observed continental convective precipitation: CMIP5 and multiscale global climate models comparison

[1] We analyze subdaily continental convective precipitation data relative to the Southeastern U.S. from gridded rain gauge measurements, conventional global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive, and a multiscale GCM. GCMs react too quickly to local convective instability and, therefore, overestimate the incidence of middle rainfall events and underestimate the incidence of no, little, and heavy rainfall events. Moreover, GCMs overestimate the persistence of heavy precipitation and underestimate the persistence of no and light precipitation. In general, GCMs with suppression mechanisms in the treatments of convective precipitation compare best with rain gauge derived data and should be trusted more than the others when assessing the risk from extreme precipitation events. The multiscale GCM has the best estimate of the diurnal cycle and a good estimate of heavy rainfall persistence.

Orogenic Propagating Precipitation Systems over the United States in a Global Climate Model with Embedded Explicit Convection

Abstract In the lee of major mountain chains worldwide, diurnal physics of organized propagating convection project onto seasonal and climate time scales of the hydrologic cycle, but this phenomenon is not represented in conventional global climate models (GCMs). Analysis of an experimental version of the superparameterized (SP) Community Atmosphere Model (CAM) demonstrates that propagating orogenic nocturnal convection in the central U.S. warm season is, however, representable in GCMs that use the embedded explicit convection model approach [i.e., multiscale modeling frameworks (MMFs)]. SP-CAM admits propagating organized convective systems in the lee of the Rockies during synoptic conditions similar to those that generate mesoscale convective systems in nature. The simulated convective systems exhibit spatial scales, phase speeds, and propagation speeds comparable to radar observations, and the genesis mechanism in the model agrees qualitatively with established conceptual models. Convective heating and condensate structures are examined on both resolved scales in SP-CAM, and coherently propagating cloud “metastructures” are shown to transcend individual cloud-resolving model arrays. In reconciling how this new mode of diurnal convective variability is admitted in SP-CAM despite the severe idealizations in the cloud-resolving model configuration, an updated discussion is presented of what physics may transcend the re-engineered scale interface in MMFs. The authors suggest that the improved diurnal propagation physics in SP-CAM are mediated by large-scale first-baroclinic gravity wave interactions with a prognostic organization life cycle, emphasizing the physical importance of preserving “memory” at the inner resolved scale.

Aerosol indirect effects in a multi-scale aerosol-climate model PNNL-MMF

Abstract. Much of the large uncertainty in estimates of anthropogenic aerosol effects on climate arises from the multi-scale nature of the interactions between aerosols, clouds and dynamics, which are difficult to represent in conventional general circulation models (GCMs). In this study, we use a multi-scale aerosol-climate model that treats aerosols and clouds across multiple scales to study aerosol indirect effects. This multi-scale aerosol-climate model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each vertical column of a GCM grid. The extension allows a more physically-based treatment of aerosol-cloud interactions in both stratiform and convective clouds on the global scale in a computationally feasible way. Simulated model fields, including liquid water path (LWP), ice water path, cloud fraction, shortwave and longwave cloud forcing, precipitation, water vapor, and cloud droplet number concentration are in reasonable agreement with observations. The new model performs quantitatively similar to the previous version of the MMF model in terms of simulated cloud fraction and precipitation. The simulated change in shortwave cloud forcing from anthropogenic aerosols is −0.77 W m−2, which is less than half of that (−1.79 W m−2) calculated by the host GCM (NCAR CAM5) with traditional cloud parameterizations and is also at the low end of the estimates of other conventional global aerosol-climate models. The smaller forcing in the MMF model is attributed to a smaller (3.9 %) increase in LWP from preindustrial conditions (PI) to present day (PD) compared with 15.6 % increase in LWP in stratiform clouds in CAM5. The difference is caused by a much smaller response in LWP to a given perturbation in cloud condensation nuclei (CCN) concentrations from PI to PD in the MMF (about one-third of that in CAM5), and, to a lesser extent, by a smaller relative increase in CCN concentrations from PI to PD in the MMF (about 26 % smaller than that in CAM5). The smaller relative increase in CCN concentrations in the MMF is caused in part by a smaller increase in aerosol lifetime from PI to PD in the MMF, a positive feedback in aerosol indirect effects induced by cloud lifetime effects from aerosols. The smaller response in LWP to anthropogenic aerosols in the MMF model is consistent with observations and with high resolution model studies, which may indicate that aerosol indirect effects simulated in conventional global climate models are overestimated and point to the need to use global high resolution models, such as MMF models or global CRMs, to study aerosol indirect effects. The simulated total anthropogenic aerosol effect in the MMF is −1.05 W m−2, which is close to the Murphy et al. (2009) inverse estimate of −1.1±0.4 W m−2 (1σ) based on the examination of the Earth's energy balance. Further improvements in the representation of ice nucleation and low clouds in MMF are needed to refine the aerosol indirect effect estimate.

The Monte Carlo Independent Column Approximation: an assessment using several global atmospheric models

AbstractThe Monte Carlo Independent Column Approximation (McICA) computes domain‐average, broadband radiative flux profiles within conventional global climate models (GCMs). While McICA is unbiased with respect to the full ICA, it generates, as a by‐product, random noise. If this by‐product leads to statistically significant impacts on GCM simulations, it could limit the usefulness of McICA. This paper assesses the impact of McICA's random noise on six GCMs. To this end, the GCMs performed ensembles of 14‐day long simulations for various renditions of McICA, each with differing amounts of random noise. As seen in the past, low‐cloud fraction and surface temperature were affected most by noise. However, all GCM simulations using operationally viable renditions of McICA showed no statistically significant impacts, even for precipitation ‐ a highly intermittent variable that one might expect to be sensitive to random fluctuations. Two GCMs showed statistically significant responses using an academic version of McICA that generates overly large sampling noise. Time series analyses of high‐resolution (i.e. typically 2‐hourly) data revealed that fluctuations associated with most variables and GCMs are immune to McICA noise. Moreover, the nature of these fluctuations can vary substantially among GCMs and most often they overwhelm any noise impacts. Overall, the results presented here corroborate a range of previous studies done on one GCM at a time: random noise produced by recommended versions of McICA has statistically insignificant effects on GCM simulations. Copyright © 2008 Royal Meteorological Society and Her Majesty in Right of Canada.

Radiative sensitivities for cloud structural properties that are unresolved by conventional GCMs

AbstractThe Independent Column Approximation (ICA), a stochastic subgrid‐scale cloudy column generator, and a dataset produced by an array of two‐dimensional cloud system‐resolving models are used to estimate radiative sensitivities and uncertainties for parameters that describe the macrostructure of layered cloud at scales unresolved by conventional global climate models (GCMs). Three cloud structural parameters are considered: vertical overlap of fractional cloud, vertical overlap of cloud condensate, and horizontal variability of condensate. Radiative sensitivity is the derivative of top of atmosphere (TOA) fluxes with respect to a cloud structural parameter. Radiative uncertainty is defined as the product of radiative sensitivity and corresponding uncertainty in structural parameter. It is argued that radiative sensitivities and uncertainties can help direct and prioritize development of subgrid‐scale parametrizations.Using estimates of sensitivities and uncertainties for structural parameters, it is shown that uncertainties in TOA short‐wave fluxes for cloud fraction overlap and horizontal variability are of similar magnitude averaged over the globe but display distinct spatial differences. Corresponding values for overlap of condensate are smaller and thus less important. For long‐wave radiation, cloud horizontal variability is generally the most important of the three parameters considered here, but sensitivities, and thus uncertainties, are smaller than those for short‐wave radiation. It is estimated that uncertainties associated with overlap of fractional cloud and horizontal variability are approximately as large as those associated with effective size of cloud particles. These results reinforce prior claims that GCM parametrizations for cloud overlap and horizontal variability should be addressed together, especially at solar wavelengths. These results are echoed in the appendix which examines TOA forcings that would arise in a GCM if the maximum‐random overlap of homogeneous clouds model was replaced by a model that addresses the cloud parameters mentioned above. Copyright © 2005 Royal Meteorological Society.