PC2: A prognostic cloud fraction and condensation scheme. II: Climate model simulations

Wilson, Damian R.; Bushell, Andrew C.; Kerr-Munslow, Amanda M.; Price, J. D.; Morcrette, C. J.; Bodas‐Salcedo, Alejandro

doi:10.1002/qj.332

Cited by 84 publications

(77 citation statements)

References 26 publications

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“…As was found by Wilson et al (2008b) the average mass flux is reduced in the PC2 simulation compared with the diagnostic cloud-scheme simulation (see Figure 5(a)). However, when averaged over only the times when the deep convection scheme is active, the updraft mass flux from the PC2 simulation is larger between the boundary layer and 13 km during the active monsoon phase (Figure 5(c)).…”

Section: The Active Monsoon Period (Julian Days 19-24)supporting

confidence: 76%

“…The two model versions used are based on the UK Met Office Unified Model (MetUM), whereby one version uses a new generation prognostic cloud scheme (Wilson et al, 2008a) while the other employs the current (or control) diagnostic scheme used routinely in the model (Smith, 1990). The results presented here complement the study of Wilson et al (2008b) who compared the same two representations of clouds using climate simulations. The next section will describe the model and the forcing and validation data used in this study.…”

Section: Introductionmentioning

confidence: 83%

“…The second change is a reduction of the phasechange temperature between liquid and ice condensate in the convective updrafts. In the PC2 simulation this temperature is reduced from the control value of 0 • C to −10 • C. These changes have been found to be necessary to produce realistic anvil clouds due to the direct interaction of the large-scale cloud variables in PC2 with the convection scheme (see Wilson et al (2008b) for more details on these changes).…”

Section: Description Of the Access/metum Single Column Modelmentioning

confidence: 99%

“…This can be seen by the larger evaporation rates from the large-scale microphysics in the PC2 simulation compared with that from the diagnostic scheme simulation (Figure 6(b)). As discussed by Wilson et al (2008b), the large in-cloud condensate amounts from convective detrainment in shallow convection allow for the efficient removal of cloud water through the generation of precipitation in the PC2 simulation. They found that with increased vertical resolution the reduction of liquid water by precipitation was decreased and instead it was the erosion term, which models the entrainment of dry air, that reduced the cloud water.…”

Section: The Suppressed Monsoon Period (Julian Days 25-35)mentioning

confidence: 99%

“…The large incloud ice-water contents allow large particles to form that quickly fall to the surface and limit the evaporation in the cloud and sub-cloud layers. Note that the tuning applied to the convective precipitation function occurs only deep in the convective plumes (Wilson et al, 2008b) and is not having an effect for these suppressed monsoon phase convective clouds with tops up to 8 km. Typically the convective processes during suppressed convection act to moisten the atmosphere, preconditioning the environment for subsequent deep convection (e.g.…”

Section: The Suppressed Monsoon Period (Julian Days 25-35)mentioning

confidence: 99%

See 4 more Smart Citations

Assessing the performance of a prognostic and a diagnostic cloud scheme using single column model simulations of TWP–ICE

Franklin

Jakob

Protat

et al. 2011

Quart J Royal Meteoro Soc

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Single column model simulations using the UK Met Office Unified Model, as used in the Australian Community Climate Earth System Simulator, are presented for the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) field study. Two formulations for the representation of clouds are compared with the extensive observations taken during the campaign, giving insight into the ability of the model to simulate tropical cloud systems. During the active monsoon phase the modelled cloud cover has a stronger dependence on relative humidity than the observations. Observed ice cloud properties during the suppressed monsoon period show that the ice water content is significantly underestimated in the simulations. The profiles of modelled ice fall speeds are faster than those observed in the levels above 12 km, implying that the observations have smaller sized particles in larger concentrations than the models. Both simulations show similar errors in the diurnal cycle of relative humidity during the active monsoon phase, suggesting that the error is less sensitive to the choice of cloud scheme and rather is driven by the convection scheme. However, during the times of suppressed convection the relative humidity error is different between the simulations, with congestus convection drying the environment too much, particularly in the prognostic cloud-scheme simulation. This result shows that the choice of cloud scheme and the way that the cloud and convection schemes interact plays a role in the temperature and moisture errors during the suppressed monsoon phase, which will impact the three-dimensional model simulations of tropical variability.

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Section: The Active Monsoon Period (Julian Days 19-24)supporting

confidence: 76%

Section: Introductionmentioning

confidence: 83%

Section: Description Of the Access/metum Single Column Modelmentioning

confidence: 99%

Section: The Suppressed Monsoon Period (Julian Days 25-35)mentioning

confidence: 99%

Section: The Suppressed Monsoon Period (Julian Days 25-35)mentioning

confidence: 99%

See 3 more Smart Citations

Assessing the performance of a prognostic and a diagnostic cloud scheme using single column model simulations of TWP–ICE

Franklin

Jakob

Protat

et al. 2011

Quart J Royal Meteoro Soc

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Short‐term time step convergence in a climate model

Wan

Rasch

Taylor

et al. 2015

J Adv Model Earth Syst

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This paper evaluates the numerical convergence of very short (1 h) simulations carried out with a spectral-element (SE) configuration of the Community Atmosphere Model version 5 (CAM5). While the horizontal grid spacing is fixed at approximately 110 km, the process-coupling time step is varied between 1800 and 1 s to reveal the convergence rate with respect to the temporal resolution. Special attention is paid to the behavior of the parameterized subgrid-scale physics. First, a dynamical core test with reduced dynamics time steps is presented. The results demonstrate that the experimental setup is able to correctly assess the convergence rate of the discrete solutions to the adiabatic equations of atmospheric motion. Second, results from full-physics CAM5 simulations with reduced physics and dynamics time steps are discussed. It is shown that the convergence rate is 0.4-considerably slower than the expected rate of 1.0. Sensitivity experiments indicate that, among the various subgrid-scale physical parameterizations, the stratiform cloud schemes are associated with the largest time-stepping errors, and are the primary cause of slow time step convergence. While the details of our findings are model specific, the general test procedure is applicable to any atmospheric general circulation model. The need for more accurate numerical treatments of physical parameterizations, especially the representation of stratiform clouds, is likely common in many models. The suggested test technique can help quantify the time-stepping errors and identify the related model sensitivities.

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Idealized climate change simulations with a high‐resolution physical model: HadGEM3‐GC2

Senior

Andrews

Burton

et al. 2016

J Adv Model Earth Syst

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Idealized climate change simulations with a new physical climate model, HadGEM3-GC2 fromThe Met Office Hadley Centre are presented and contrasted with the earlier MOHC model, HadGEM2-ES. The role of atmospheric resolution is also investigated. The Transient Climate Response (TCR) is 1.9 K/2.1 K at N216/N96 and Effective Climate Sensitivity (ECS) is 3.1 K/3.2 K at N216/N96. These are substantially lower than HadGEM2-ES (TCR: 2.5 K; ECS: 4.6 K) arising from a combination of changes in the size of climate feedbacks. While the change in the net cloud feedback between HadGEM3 and HadGEM2 is relatively small, there is a change in sign of its longwave and a strengthening of its shortwave components. At a global scale, there is little impact of the increase in atmospheric resolution on the future climate change signal and even at a broad regional scale, many features are robust including tropical rainfall changes, however, there are some significant exceptions. For the North Atlantic and western Europe, the tripolar pattern of winter storm changes found in most CMIP5 models is little impacted by resolution but for the most intense storms, there is a larger percentage increase in number at higher resolution than at lower resolution. Arctic sea-ice sensitivity shows a larger dependence on resolution than on atmospheric physics.

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PC2: A prognostic cloud fraction and condensation scheme. II: Climate model simulations

Cited by 84 publications

References 26 publications

Assessing the performance of a prognostic and a diagnostic cloud scheme using single column model simulations of TWP–ICE

Assessing the performance of a prognostic and a diagnostic cloud scheme using single column model simulations of TWP–ICE

Short‐term time step convergence in a climate model

Idealized climate change simulations with a high‐resolution physical model: HadGEM3‐GC2

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