Overview of the Coupled Model Intercomparison Project (CMIP)

The Coupled Model Intercomparison Project (CMIP) involves study and intercomparison of multimodel simulations of present and future climate. The simulations of the future use idealized forcing in which CO, increase is compounded 1% yr(-1) until it doubles (near year 70) with global coupled models that contain, typically, components representing atmosphere, ocean, sea ice, and land surface. Results from CMIP diagnostic sub-projects were presented at the Second CMIP Workshop held at the Max Planck Institute for Meteorology in Hamburg, Germany, in September 2003. Significant progress in diagnosing and understanding results from global coupled models has been made since the time of the First CMIP Workshop in Melbourne, Australia, in 1998. For example, the issue of flux adjustment is slowly fading as more and more models obtain stable multicentury surface climates without them. El Nino variability, usually about half the observed amplitude in the previous generation of coupled models, is now more accurately simulated in the present generation of global coupled models, though there are still biases in simulating the patterns of maximum variability. Typical resolutions of atmospheric component models contained in coupled models are now usually around 2.5degrees latitude-longitude, with the ocean components often having about twice the atmospheric model resolution, with even higher resolution in the equatorial Tropics. Some new-generation coupled models have atmospheric resolutions of around 1.5degrees latitude - longitude. Modeling groups now routinely run the CMIP control and 1% CO2 simulations in addition to twentieth- and twenty-first-century climate simulations with a variety of forcings e.g., volcanoes, solar variability, anthropogenic sulfate aerosols, ozone, and greenhouse gases, with the anthropogenic forcings for future climate as well. However, persistent systematic errors noted in previous generations of global coupled models are still present in the current generation (e.g., overextensive equatorial Pacific cold tongue, double ITCZ). This points to the next challenge for the global coupled climate modeling community. Planning and commencement of the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) has prompted rapid coupled model development, which is leading to an expanded CMIP-like activity to collect and analyze results for the control, 1% CO2, and twentieth-, twenty-first, and twenty-second-century simulations performed for the AR4. The international climate community is encouraged to become involved in this analysis effort

The WCRP CMIP3 multi-model dataset: a new era in climate change research

A coordinated set of global coupled climate model [atmosphere–ocean general circulation model (AOGCM)] experiments for twentieth- and twenty-first-century climate, as well as several climate change commitment and other experiments, was run by 16 modeling groups from 11 countries with 23 models for assessment in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Since the assessment was completed, output from another model has been added to the dataset, so the participation is now 17 groups from 12 countries with 24 models. This effort, as well as the subsequent analysis phase, was organized by the World Climate Research Programme (WCRP) Climate Variability and Predictability (CLIVAR) Working Group on Coupled Models (WGCM) Climate Simulation Panel, and constitutes the third phase of the Coupled Model Intercomparison Project (CMIP3). The dataset is called the WCRP CMIP3 multimodel dataset, and represents the largest and most comprehensive international global coupled climate model experiment and multimodel analysis effort ever attempted. As of March 2007, the Program for Climate Model Diagnostics and Intercomparison (PCMDI) has collected, archived, and served roughly 32 TB of model data. With oversight from the panel, the multimodel data were made openly available from PCMDI for analysis and academic applications. Over 171 TB of data had been downloaded among the more than 1000 registered users to date. Over 200 journal articles, based in part on the dataset, have been published so far. Though initially aimed at the IPCC AR4, this unique and valuable resource will continue to be maintained for at least the next several years. Never before has such an extensive set of climate model simulations been made available to the international climate science community for study. The ready access to the multimodel dataset opens up these types of model analyses to researchers, including students, who previously could not obtain state-of-the-art climate model output, and thus represents a new era in climate change research. As a direct consequence, these ongoing studies are increasing the body of knowledge regarding our understanding of how the climate system currently works, and how it may change in the future

Global mean cloud feedbacks in idealized climate change experiments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94978/1/grl21109.pd

Cloud feedback in atmospheric general circulation models: An update

Six years ago, we compared the climate sensitivity of 19 atmospheric general circulation models and found a roughly threefold variation among the models; most of this variation was attributed to differences in the models' depictions of cloud feedback. In an update of this comparison, current models showed considerably smaller differences in net cloud feedback, with most producing modest values. There are, however, substantial differences in the feedback components, indicating that the models still have physical disagreements

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

ABSTRACT: BACKGROUND: Simulation models of influenza spread play an important role for pandemic preparedness. However, as the world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread. METHODS: We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns. RESULTS: The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east. CONCLUSIONS: We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficia

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 CO 2 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%.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45863/1/382_2006_Article_111.pd

On a class of Hamiltonian laceable 3-regular graphs

Discrete Mathematics1511-319-38DSMH

The Role of surface energy balance complexity in land surface models' sensitivity to increasing carbon dioxide

Global simulations with the Bureau of Meteorology Research Centre climate model coupled to the CHAmeleon Surface Model (CHASM) are used to explore the sensitivity of simulated changes in evaporation, precipitation, air temperature and soil moisture resulting from a doubling of carbon dioxide in the atmosphere. Five simulations, using prescribed sea surface temperatures, are conducted which are identical except in the level of complexity used to represent the surface energy balance. The simulation of air temperature, precipitation, evaporation and soil moisture at 1 × CO₂ and at 2 × CO₂ are generally sensitive at statistically significant levels to the complexity of the surface energy balance representation (i.e. the level of complexity used to represent these processes affects the simulated climate). However, changes in mean quantities, resulting from a doubling of atmospheric CO₂, are generally insensitive to the surface energy balance complexity. Conversely, changes in the spatial and temporal variance of evaporation and soil moisture are sensitive to the surface energy balance complexity. The addition of explicit canopy interception to the simplest model examined here enables that model to capture the change in the variance of evaporation simulated by the more complex models. In order to simulate changes in the variability of soil moisture, an explicit parameterization of bare soil evaporation is required. Overall, our results increase confidence that the simulation by climate models of the mean impact of increasing CO₂ on climate are reliable. Changes in the variability resulting from increased CO₂ on air temperature, precipitation or evaporation are also likely to be reliable since climate models typically use sufficiently complex land surface schemes. However, if the impact of increased CO₂ on soil moisture is required, then a more complex surface energy balance representation may be needed in order to capture changes in variability. Overall, our results imply that the level of complexity used by most climate models to represent the surface energy balance is appropriate and does not contribute significant uncertainty in the simulation of changes resulting from increasing CO₂. Our results only relate to surface energy balance complexity, and major uncertainties remain in how to model the surface hydrology and changes in the physiology, structural characteristics and distribution of vegetation. Future developments of land surface models should therefore focus on improving the representation of these processes.10 page(s

Impact of varying land surface energy balance complexity on the sensitivity of Australian climate to increasing carbon dioxide

This study explores whether regional climate model scenarios are sensitive to uncertainty in the representation of surface energy balance (SEB) complexity. Simulations with the Bureau of Meteorology Research Centre climate model and the CHAmeleon Surface Model (CHASM) were used to explore the sensitivity of changes in air temperature (T), evaporation (E), precipitation (P) and soil moisture (W) over Australia resulting from a doubling of atmospheric carbon dioxide (ΔCO₂). The 1 × CO₂ and the 2 × CO₂ simulations of T, E, P and W were sensitive to the complexity of the SEB, even though the grand mean of these quantities was almost always insensitive to SEB complexity. Seasonal variations in T, E, P and W at 1 × CO₂ and 2 × CO₂ were sensitive in terms of the point-by-point temporal mean and temporal variance. The overall spatial and temporal variances of T and P were insensitive to SEB complexity, but E and W were sensitive during periods of drying. The simulated seasonal change in T, E, P and W was insensitive to the SEB, and uncertainty in SEB parameterisation does not limit the reliability of existing climate change scenarios for Australia. However, the temporal variance of E, P and W was sensitive to the SEB complexity during periods of drying. Use of temporal variances of these quantities in future impact assessments are therefore likely to be very limited until uncertainty in the representation of SEB in climate models is reduced. To simulate the climate over Australia at either 1 × CO₂ or 2 × CO₂, a reasonably complex representation of the SEB, including a temporally and spatially variable surface resistance and an explicit representation of canopy interception, is required. Finally, our results say nothing about the importance of the land surface in general, since our analysis is restricted to a consideration of the SEB alone.13 page(s