The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection

The LMDZ4 general circulation model is the atmospheric component of the IPSL–CM4 coupled model which has been used to perform climate change simulations for the 4th IPCC assessment report. The main aspects of the model climatology (forced by observed sea surface temperature) are documented here, as well as the major improvements with respect to the previous versions, which mainly come form the parametrization of tropical convection. A methodology is proposed to help analyse the sensitivity of the tropical Hadley–Walker circulation to the parametrization of cumulus convection and clouds. The tropical circulation is characterized using scalar potentials associated with the horizontal wind and horizontal transport of geopotential (the Laplacian of which is proportional to the total vertical momentum in the atmospheric column). The effect of parametrized physics is analysed in a regime sorted framework using the vertical velocity at 500 hPa as a proxy for large scale vertical motion. Compared to Tiedtke’s convection scheme, used in previous versions, the Emanuel’s scheme improves the representation of the Hadley–Walker circulation, with a relatively stronger and deeper large scale vertical ascent over tropical continents, and suppresses the marked patterns of concentrated rainfall over oceans. Thanks to the regime sorted analyses, these differences are attributed to intrinsic differences in the vertical distribution of convective heating, and to the lack of self-inhibition by precipitating downdraughts in Tiedtke’s parametrization. Both the convection and cloud schemes are shown to control the relative importance of large scale convection over land and ocean, an important point for the behaviour of the coupled model.     

LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection

Based on a decade of research on cloud processes, a new version of the LMDZ atmospheric general circulation model has been developed that corresponds to a complete recasting of the parameterization of turbulence, convection and clouds. This LMDZ5B version includes a mass-flux representation of the thermal plumes or rolls of the convective boundary layer, coupled to a bi-Gaussian statistical cloud scheme, as well as a parameterization of the cold pools generated below cumulonimbus by re-evaporation of convective precipitation. The triggering and closure of deep convection are now controlled by lifting processes in the sub-cloud layer. An available lifting energy and lifting power are provided both by the thermal plumes and by the spread of cold pools. The individual parameterizations were carefully validated against the results of explicit high resolution simulations. Here we present the work done to go from those new concepts and developments to a full 3D atmospheric model, used in particular for climate change projections with the IPSL-CM5B coupled model. Based on a series of sensitivity experiments, we document the differences with the previous LMDZ5A version distinguishing the role of parameterization changes from that of model tuning. Improvements found previously in single-column simulations of case studies are confirmed in the 3D model: (1) the convective boundary layer and cumulus clouds are better represented and (2) the diurnal cycle of convective rainfall over continents is delayed by several hours, solving a longstanding problem in climate modeling. The variability of tropical rainfall is also larger in LMDZ5B at intraseasonal time-scales. Significant biases of the LMDZ5A model however remain, or are even sometimes amplified. The paper emphasizes the importance of parameterization improvements and model tuning in the frame of climate change studies as well as the new paradigm that represents the improvement of 3D climate models under the control of single-column case studies simulations.     

Structure of microbial communities and hydrocarbon-dependent sulfate reduction in the anoxic layer of a polluted microbial mat

The bacterial communities in the anoxic layer of a heavily polluted microbial mat and their growth on hydrocarbons under sulfate-reducing conditions were investigated. Microbial communities were dominated by members of Alphaproteobacteria (27% of the total rRNA), Planctomycetes (21.1%) and sulfate-reducing bacteria (SRB: 17.5%). 16S rRNA cloning revealed sequences beloning to the same bacterial groups with SRB affiliated to the genera Desulfobulbus, Desulfocapsa, Desulfomicrobium, Desulfobacterium and Desulfosarcina/Desulfococcus. The derived enrichment cultures on crude oil, hexadecane and toluene were dominated by SRB. While most SRB sequences of the toluene and hexadecane cultures were related to the sequence of Desulfotignum toluolica, the crude oil enrichment showed a more diverse bacterial community with sequences from the genera Desulfotignum, Desulfobacter, Desulfatibacillus, Desulfosalina, and Desulfococcus. We conclude that the anoxic layer of the studied mats contains a diverse community of anaerobic bacteria, dominated by SRB, some of which are able to grow on hydrocarbons.     

On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO₂ doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.     

Calculating snow-avalanche return period from tree-ring data

The return period is a key element used for snow-avalanche characterization. To calculate the return period, historical data regarding past snow-avalanche activity are required. In mountain areas where past snow avalanches are poorly documented, dendrogeomorphic approaches constitute a reliable method for the reconstruction of past snow avalanches at the temporal scale of living trees. This paper presents an automated method for calculating the snow-avalanche return period using a digital elevation model and the location of the trees disturbed by every reconstructed snow-avalanche occurrence. Unlike the existing method, the method we propose requires neither the calculation of return period for every sampled tree nor the use of interpolation. This new method is based on the determination of spatial extent for every past snow-avalanche occurrence using the upslope area algorithm. The number of past snow-avalanche occurrences is calculated for every pixel of the path. The chronology length is divided by the number of past snow-avalanche occurrences to obtain the return period. In the present paper, both the proposed method and the existing method are applied to calculate the return period for three confined snow-avalanche paths located in Parâng Mountains, part of the Romanian Carpathians. Results are compared and discussed.     

Key features of the IPSL ocean atmosphere model and its sensitivity to atmospheric resolution

This paper presents the major characteristics of the Institut Pierre Simon Laplace (IPSL) coupled ocean–atmosphere general circulation model. The model components and the coupling methodology are described, as well as the main characteristics of the climatology and interannual variability. The model results of the standard version used for IPCC climate projections, and for intercomparison projects like the Paleoclimate Modeling Intercomparison Project (PMIP 2) are compared to those with a higher resolution in the atmosphere. A focus on the North Atlantic and on the tropics is used to address the impact of the atmosphere resolution on processes and feedbacks. In the North Atlantic, the resolution change leads to an improved representation of the storm-tracks and the North Atlantic oscillation. The better representation of the wind structure increases the northward salt transports, the deep-water formation and the Atlantic meridional overturning circulation. In the tropics, the ocean–atmosphere dynamical coupling, or Bjerknes feedback, improves with the resolution. The amplitude of ENSO (El Niño-Southern oscillation) consequently increases, as the damping processes are left unchanged.     

Evaluation of a component of the cloud response to climate change in an intercomparison of climate models

Most of the uncertainty in the climate sensitivity of contemporary general circulation models (GCMs) is believed to be connected with differences in the simulated radiative feedback from clouds. Traditional methods of evaluating clouds in GCMs compare time–mean geographical cloud fields or aspects of present-day cloud variability, with observational data. In both cases a hypothetical assumption is made that the quantity evaluated is relevant for the mean climate change response. Nine GCMs (atmosphere models coupled to mixed-layer ocean models) from the CFMIP and CMIP model comparison projects are used in this study to demonstrate a common relationship between the mean cloud response to climate change and present-day variability. Although atmosphere–mixed-layer ocean models are used here, the results are found to be equally applicable to transient coupled model simulations. When changes in cloud radiative forcing (CRF) are composited by changes in vertical velocity and saturated lower tropospheric stability, a component of the local mean climate change response can be related to present-day variability in all of the GCMs. This suggests that the relationship is not model specific and might be relevant in the real world. In this case, evaluation within the proposed compositing framework is a direct evaluation of a component of the cloud response to climate change. None of the models studied are found to be clearly superior or deficient when evaluated, but a couple appear to perform well on several relevant metrics. Whilst some broad similarities can be identified between the 60°N–60°S mean change in CRF to increased CO₂ and that predicted from present-day variability, the two cannot be quantitatively constrained based on changes in vertical velocity and stability alone. Hence other processes also contribute to the global mean cloud response to climate change.