AGW Observer

Observations of anthropogenic global warming

New research – climate models and projections (August 16, 2016)

Posted by Ari Jokimäki on August 16, 2016

Some of the latest papers on climate models and projections are shown below. First a few highlighted papers with abstracts and then a list of some other papers. If this subject interests you, be sure to check also the other papers – they are by no means less interesting than the highlighted ones.

Highlights

CMIP5 scientific gaps and recommendations for CMIP6 (Stouffer et al. 2016)http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00013.1

Abstract: The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent fifth phase, CMIP5, has created nearly two petabytes of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP, i.e. CMIP6:

1.How does the Earth System respond to changes in forcing?

2.What are the origins and consequences of systematic model biases?

3.How can we assess future climate changes given internal climate variability, predictability and uncertainties in scenarios?

CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor and requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP which remain a major climate modeling challenge. These biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.

Climate change in the next 30 years: What can a convection-permitting model tell us that we did not already know? (Fosser et al. 2016) http://rd.springer.com/article/10.1007%2Fs00382-016-3186-4

Abstract: To investigate the climate change in the next 30 years over a complex terrain in southwestern Germany, simulations performed with the regional climate model COSMO-CLM at convection-permitting resolution are compared to simulations at 7 km resolution with parameterised convection. An earlier study has shown the main benefits of convection-permitting resolution in the hourly statistics and the diurnal cycle of precipitation intensities. Here, we investigate whether the improved simulation of precipitation in the convection-permitting model is affecting future climate projections in summer. Overall, the future scenario (ECHAM5 with A1B forcing) brings weak changes in mean precipitation, but stronger hourly intensities in the morning and less frequent but more intense daily precipitation. The two model simulations produce similar changes in climate, despite differences in their physical characteristics linked to the formation of convective precipitation. A significant increase in the morning precipitation probably due to large-scale forced convection is found when considering only the most extreme events (above 50 mm/day). In this case, even the diurnal cycles of precipitation and convection-related indices are similar between resolutions, leading to the conclusion that the 7 km model sufficiently resolves the most extreme convective events. In this region and time periods, the 7 km resolution is deemed sufficient for most assessments of near future precipitation change. However, conclusions could be dependent on the characteristics of the region of investigation.

Evaluating Arctic warming mechanisms in CMIP5 models (Franzke et al. 2016) http://link.springer.com/article/10.1007%2Fs00382-016-3262-9

Abstract: Arctic warming is one of the most striking signals of global warming. The Arctic is one of the fastest warming regions on Earth and constitutes, thus, a good test bed to evaluate the ability of climate models to reproduce the physics and dynamics involved in Arctic warming. Different physical and dynamical mechanisms have been proposed to explain Arctic amplification. These mechanisms include the surface albedo feedback and poleward sensible and latent heat transport processes. During the winter season when Arctic amplification is most pronounced, the first mechanism relies on an enhancement in upward surface heat flux, while the second mechanism does not. In these mechanisms, it has been proposed that downward infrared radiation (IR) plays a role to a varying degree. Here, we show that the current generation of CMIP5 climate models all reproduce Arctic warming and there are high pattern correlations—typically greater than 0.9—between the surface air temperature (SAT) trend and the downward IR trend. However, we find that there are two groups of CMIP5 models: one with small pattern correlations between the Arctic SAT trend and the surface vertical heat flux trend (Group 1), and the other with large correlations (Group 2) between the same two variables. The Group 1 models exhibit higher pattern correlations between Arctic SAT and 500 hPa geopotential height trends, than do the Group 2 models. These findings suggest that Arctic warming in Group 1 models is more closely related to changes in the large-scale atmospheric circulation, whereas in Group 2, the albedo feedback effect plays a more important role. Interestingly, while Group 1 models have a warm or weak bias in their Arctic SAT, Group 2 models show large cold biases. This stark difference in model bias leads us to hypothesize that for a given model, the dominant Arctic warming mechanism and trend may be dependent on the bias of the model mean state.

The Impact of SST Biases on Projections of Anthropogenic Climate Change: A Greater Role for Atmosphere-only Models? (He & Soden, 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016GL069803/abstract

Abstract: There is large uncertainty in the model simulation of regional climate change from anthropogenic forcing. Recent studies have tried to link such uncertainty to intermodel differences in the pattern of sea surface temperature (SST) change. On the other hand, coupled climate models also contain systematic biases in their climatology, largely due to drift in SSTs. To the extent that the projected changes depend on the mean state, biases in the present-day climatology also contribute to the intermodel spread in climate change projections. By comparing atmospheric general circulation model (AGCM) simulations using the climatological SSTs from different coupled models, we show that biases in the climatological SST generally have a larger impact on regional projections over land than do intermodel differences in the pattern of SST change. These results advocate for a greater application of AGCM simulations with observed SSTs or flux-adjusted coupled models to improve regional projections of anthropogenic climate change.

The art and science of climate model tuning (Hourdin et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00135.1

Abstract: We survey the rationale and diversity of approaches for tuning, a fundamental aspect of climate modeling which should be more systematically documented and taken into account in multi-model analysis.

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate sub-models. Most sub-models depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called ‘objective‘ methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

Other papers

High-resolution ensemble projections of near-term regional climate over the continental United States (Ashfaq et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016JD025285/abstract

Twentieth century temperature trends in CMIP3, CMIP5, and CESM-LE climate simulations – spatial-temporal uncertainties, differences and their potential sources (Kumar et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2015JD024382/abstract

Assessing the robustness and uncertainties of projected changes in temperature and precipitation in AR4 Global Climate Models over the Arabian Peninsula (Almazroui et al. 2016) http://www.sciencedirect.com/science/article/pii/S0169809516302058

The influence of model resolution on temperature variability (Klavans et al. 2016) http://link.springer.com/article/10.1007%2Fs00382-016-3249-6

Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA (Slater et al. 2016) http://link.springer.com/article/10.1007%2Fs00382-016-3286-1

Assessing uncertainties in land cover projections (Alexander et al. 2016) http://onlinelibrary.wiley.com/doi/10.1111/gcb.13447/abstract

Effects of southeastern Pacific sea surface temperature on the double-ITCZ bias in NCAR CESM1 (Song & Zhang, 2016) http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-15-0852.1

Stochastic Parameterization: Towards a new view of Weather and Climate Models (Berner et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00268.1

Do convection-permitting regional climate models improve projections of future precipitation change? (Kendon et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-0004.1

MiKlip – a National Research Project on Decadal Climate Prediction (Marotzke et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-15-00184.1

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