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Webb M.J.,UK Met Office | Lock A.P.,UK Met Office | Bretherton C.S.,Seattle University | Bony S.,Institute Pierre Simon Laplace IPSL | And 14 more authors.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences | Year: 2015

We investigate the sensitivity of cloud feedbacks to the use of convective parametrizations by repeating the CMIP5/CFMIP-2 AMIP/AMIP + 4K uniform sea surface temperature perturbation experiments with 10 climate models which have had their convective parametrizations turned off. Previous studies have suggested that differences between parametrized convection schemes are a leading source of inter-model spread in cloud feedbacks. We find however that 'ConvOff' models with convection switched off have a similar overall range of cloud feedbacks compared with the standard configurations. Furthermore, applying a simple bias correction method to allow for differences in present-day global cloud radiative effects substantially reduces the differences between the cloud feedbacks with and without parametrized convection in the individual models. We conclude that, while parametrized convection influences the strength of the cloud feedbacks substantially in some models, other processes must also contribute substantially to the overall inter-model spread. The positive shortwave cloud feedbacks seen in the models in subtropical regimes associated with shallow clouds are still present in the ConvOff experiments. Inter-model spread in shortwave cloud feedback increases slightly in regimes associated with trade cumulus in the ConvOff experiments but is quite similar in the most stable subtropical regimes associated with stratocumulus clouds. Inter-model spread in longwave cloud feedbacks in strongly precipitating regions of the tropics is substantially reduced in the ConvOff experiments however, indicating a considerable local contribution from differences in the details of convective parametrizations. In both standard and ConvOff experiments, models with less mid-level cloud and less moist static energy near the top of the boundary layer tend to have more positive tropical cloud feedbacks. The role of non-convective processes in contributing to inter-model spread in cloud feedback is discussed. © 2015 The Authors. Source

Jonsson B.F.,Princeton University | Doney S.C.,Woods Hole Oceanographic Institution | Dunne J.,Geophysical Fluid Dynamics Laboratory GFDL | Bender M.,Princeton University
Journal of Geophysical Research: Biogeosciences | Year: 2013

The sea-air biological O2 flux assessed from measurements of surface O2 supersaturation in excess of Ar supersaturation ("O2 bioflux") is increasingly being used to constrain net community production (NCP) in the upper ocean mixed layer. In making these calculations, one generally assumes that NCP is at steady state, mixed layer depth is constant, and there is no O2 exchange across the base of the mixed layer. The object of this paper is to evaluate the magnitude of errors introduced by violations of these assumptions. Therefore, we examine the differences between the sea-air biological O2 flux and NCP in the Southern Ocean mixed layer as calculated using two ocean biogeochemistry general circulation models. In this approach, NCP is considered a known entity in the prognostic model, whereas O2 bioflux is estimated using the model-predicted O2/Ar ratio to compute the mixed layer biological O2 saturation and the gas transfer velocity to calculate flux. We find that the simulated biological O2 flux gives an accurate picture of the regional-scale patterns and trends in model NCP. However, on local scales, violations of the assumptions behind the O2/Ar method lead to significant, non-uniform differences between model NCP and biological O 2 flux. These errors arise from two main sources. First, venting of biological O2 to the atmosphere can be misaligned from NCP in both time and space. Second, vertical fluxes of oxygen across the base of the mixed layer complicate the relationship between NCP and the biological O2 flux. Our calculations show that low values of O2 bioflux correctly register that NCP is also low (<10 mmol m-2 day-1), but fractional errors are large when rates are this low. Values between 10 and 40 mmol m-2 day-1 in areas with intermediate mixed layer depths of 30 to 50 m have the smallest absolute and relative errors. Areas with O2 bioflux higher than 30 mmol m-2 day-1 and mixed layers deeper than 40 m tend to underestimate NCP by up to 20 mmol m -2 day-1. Excluding time periods when mixed layer biological O2 is undersaturated, O2 bioflux underestimates time-averaged NCP by 5%-15%. If these time periods are included, O2 bioflux underestimates mixed layer NCP by 20%-35% in the Southern Ocean. The higher error estimate is relevant if one wants to estimate seasonal NCP since a significant amount of biological production takes place when mixed layer biological O2 is undersaturated. Key Points The O2/Ar method show significant non-uniform errors when evaluated in a GCMThere are lags between biological production and O2 outgassing in the oceanThe O2 flux from the mixed layer downwards is significant ©2013. American Geophysical Union. All Rights Reserved. Source

Su H.,Jet Propulsion Laboratory | Jiang J.H.,Jet Propulsion Laboratory | Zhai C.,Jet Propulsion Laboratory | Perun V.S.,Jet Propulsion Laboratory | And 25 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2013

The vertical distributions of cloud water content (CWC) and cloud fraction (CF) over the tropical oceans, produced by 13 coupled atmosphere-ocean models submitted to the Phase 5 of Coupled Model Intercomparison Project (CMIP5), are evaluated against CloudSat/CALIPSO observations as a function of large-scale parameters. Available CALIPSO simulator CF outputs are also examined. A diagnostic framework is developed to decompose the cloud simulation errors into large-scale errors, cloud parameterization errors and covariation errors. We find that the cloud parameterization errors contribute predominantly to the total errors for allmodels. The errors associated with large-scale temperature and moisture structures are relatively greater than those associated with large-scale midtropospheric vertical velocity and lower-level divergence. All models capture the separation of deep and shallow clouds in distinct large-scale regimes; however, the vertical structures of high/low clouds and their variations with large-scale parameters differ significantly from the observations. The CWCs associated with deep convective clouds simulated in most models do not reach as high in altitude as observed, and their magnitudes are generally weaker than CloudSat total CWC, which includes the contribution of precipitating condensates, but are close to CloudSat nonprecipitating CWC. All models reproduce maximum CF associated with convective detrainment, but CALIPSO simulator CFs generally agree better with CloudSat/CALIPSO combined retrieval than the model CFs, especially in the midtroposphere. Model simulated low clouds tend to have little variation with large-scale parameters except lower-troposphere stability, while the observed low cloud CWC, CF, and cloud top height vary consistently in all large-scale regimes. © 2012. American Geophysical Union. All Rights Reserved. Source

He C.,Peking University | He C.,University of California at Los Angeles | Liu J.,Peking University | Carlton A.G.,Rutgers University | And 4 more authors.
Atmospheric Chemistry and Physics | Year: 2013

Secondary organic aerosols (SOA) exert a significant influence on ambient air quality and regional climate. Recent field, laboratorial and modeling studies have confirmed that in-cloud processes contribute to a large fraction of SOA production with large space-time heterogeneity. This study evaluates the key factors that govern the production of cloud-process SOA (SOAcld) on a global scale based on the GFDL coupled chemistry-climate model AM3 in which full cloud chemistry is employed. The association between SOAcld production rate and six factors (i.e., liquid water content (LWC), total carbon chemical loss rate (TCloss), temperature, VOC/NOx, OH, and O 3) is examined. We find that LWC alone determines the spatial pattern of SOAcld production, particularly over the tropical, subtropical and temperate forest regions, and is strongly correlated with SOAcld production. TCloss ranks the second and mainly represents the seasonal variability of vegetation growth. Other individual factors are essentially uncorrelated spatiotemporally to SOAcld production. We find that the rate of SOAcld production is simultaneously determined by both LWC and TCloss, but responds linearly to LWC and nonlinearly (or concavely) to TCloss. A parameterization based on LWC and TCloss can capture well the spatial and temporal variability of the process-based SOAcld formation (R 2 Combining double low line 0.5) and can be easily applied to global three dimensional models to represent the SOA production from cloud processes. © Author(s) 2013. Source

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