Models

 

Understanding Global Warming

Climate Models

CMIP

The CMIP5 (Coupled Model Intercomparison Project Phase 5) is a standard experimental framework for studying the output of coupled atmosphere-ocean general circulation models. It is the most current and extensive of the CMIPs organized by the Working Group on Coupled Modelling (WGCM) of the World Climate Research Programme’s (WCRP).

Table 1. The tropical region receives the most solar energy. There are important discrepancies between models and observations regarding tropical tropospheric temperature trends. The most likely cause of the discrepancies between models and observations is that the feedbacks in the models are too strongly positive. Source: December 12th, 2019, Roy W. Spencer.
Abbreviations: IPSL-CM5A-LR (Institut Pierre-Simon Laplace - CM5A-LR: Low resolution). GFDL-CM3 (Geophysical Fluid Dynamics Laboratory – CM3). NorESM1-M (The Norwegian Earth System Model). FIO-ESM (First Institute of Oceanography-Earth System Model, China). MRI CGCM3 (Meteorological Research Institute CGCM Version 3, Japan). INMCM4 (Institute for Numerical Mathematics, Moscow). RSS (Remote Sensing Systems, California). 4 Reanalyses (All available observations every 6-12 hours). UAH (University of Alabama in Huntsville).

In the nineteen-sixties Syukuro Manabe was as the first running global climate models in Princeton. His purpose when he ran computer models was not to predict climate but to understand it. Acc. to his friend Freeman Dyson, also Princeton, May 1991: “If we persevere patiently with observing the real world and improving the models, the time will come when we are able both to understand and to predict. Until then, we must continue to warn the politicians and the public: don't believe the numbers just because they come out of a supercomputer”.

The IPCC is pretty well aware of the difficulty of predicting the climate. This is stated in the Third Assessment Report: "The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future exact climate states is not possible. Rather the focus must be upon the prediction of the probability distribution of the system’s future possible states by the generation of ensembles of model solutions" – TAR page 78.

Institute for Numerical Mathematics, Moscow Oct. 2018 published “Simulation of observed climate changes in 1850–2014 with climate model INM-CM5” by Evgeny Volodin and Andrey Gritsun – paper. In all runs global mean surface temperature rises by 0.8 K at the end of the experiment (2014) in agreement with the observations. Periods of fast warming in 1920–1940 and 1980–2000 as well as its slowdown in 1950–1975 and 2000–2014 are correctly reproduced by the ensemble mean. Predictions for the coming Grand Solar Minimum are not carried out - personal communication.

Also, models developed by First Institute of Oceanography, Qingdao City, P.R China and Meteorological Research Institute, Tsukuba, Ibaraki, Japan has managed to reproduce a temperature trend rather close to the observed.

Reliable reconstruction of the past is the first condition for using models for predictions.

Oleg M Pokrovsky, Russian State Hydrometeorological University (RSHU), Saint-Petersburg publishes 2019 “Cloud changes in the period of global warming: the results of the international satellite project.” - article. Pokrovsky concludes: “Cloud coverage changes over three decades during global warming not only explain the linear trend of global temperature but also some interannual variation. But the inclusion of a block describing the temporal evolution of clouds in climate models remains an issue due to the stochastic nature of cloud variability. However, climate models are deterministic and cannot be directly combined with stochastic cloud blocks. Nevertheless, the factor of cloud cover on climate change cannot be ignored due to the significant contribution of this climate-forming parameter and should be investigated more closely to improve climate forecasts.”

Cloud cover is an inverse proxy for sunshine. The problems and unwillingness to take into account the cloud cover and thus the influence of the sun on the climate here on Earth weaken the models' value as tools for realistic predictions.


Understanding Global Warming, Oversigt - LINK



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