Review Of Climate Models Show They Are Unreliable For Forecasting

Before getting to the subject of climate models and why they aren’t reliable as forecasting tools, there are two small points worth bringing up:

Eco-Trumpism spreading

Firstly, it appears that Trump’s policies are sending powerful political impulses worldwide. For example, ultra-alarmist German climate and energy site Klimaretter.info bemoans that leading socialist Sigmar Gabriel seems to be turning into an “Eco-Trump.”

Gabriel actually had the audacity to remind Germany that economics need have as great as or greater priority than climate change does, something causing a bit of political indigestion at klimaretter.info.

Fears of German companies moving to the USA

Secondly, German business daily Handelsblatt here cites a study that tells us Germany will likely see jobs lost due to Trump’s tax reforms. It is feared that a number of German companies may opt to flock over to the USA to take advantage of lower taxes, cheaper energy, and less stringent regulation. Germany helping MAGA! Climate models totally fail in practice: Can atmospheric circulation be simulated at all?

By Dr. Sebastian Lüning and Prof. Fritz Vahrenholt
(German text translated/edited by P Gosselin)

Much of international climate policy is based on the predictions of climate models. Here the key players act as if they are highly robust and thus serve as a good basis for policy decision-making. But what rarely makes it through the media filter is the rather hectic discussion of the climate modelers taking place behind the scenes.

In September 2014, Theodore Shepherd of the University of Reading summarized the full extent of the problems in an article in Nature Geoscience. The models just can’t grasp atmospheric circulation. And Shepherd fears this will remain the case in the future:

Atmospheric circulation as a source of uncertainty in climate change projections
The evidence for anthropogenic climate change continues to strengthen, and concerns about severe weather events are increasing. As a result, scientific interest is rapidly shifting from detection and attribution of global climate change to a prediction of its impacts at the regional scale. However, nearly everything we have any confidence in when it comes to climate change is related to global patterns of surface temperature, which are primarily controlled by thermodynamics. In contrast, we have much less confidence in atmospheric circulation aspects of climate change, which are primarily controlled by dynamics and exert a strong control over the regional climate. Model projections of circulation-related fields, including precipitation, show a wide range of possible outcomes, even on centennial timescales. Sources of uncertainty include low-frequency chaotic variability and the sensitivity to model error of the circulation response to climate forcing. As the circulation response to external forcing appears to project strongly onto existing patterns of variability, knowledge of errors in the dynamics of variability may provide some constraints on model projections. Nevertheless, higher scientific confidence in circulation-related aspects of climate change will be difficult to obtain. For effective decision-making, it is necessary to move to a more explicitly probabilistic, risk-based approach.”

There are also major problems in the modeling of solar radiation, as Zhou et al (2015) to consider:

On the incident solar radiation in CMIP5 models
Annual incident solar radiation at the top of atmosphere should be independent of longitudes. However, in many Coupled Model Intercomparison Project phase 5 (CMIP5) models, we find that the incident radiation exhibited zonal oscillations, with up to 30 W/m² of spurious variations. This feature can affect the interpretation of regional climate and diurnal variation of CMIP5 results. This oscillation is also found in the Community Earth System Model. We show that this feature is caused by temporal sampling errors in the calculation of the solar zenith angle. The sampling error can cause zonal oscillations of surface clear-sky net shortwave radiation of about 3 W/m² when an hourly radiation time step is used and 24 W/m² when a 3 h radiation time step is used.”

Currently, the author teams are gathering for the planned sixth IPCC climate report. Are the serious problems with the climate models finally eliminated? Not a trace. On October 11, 2017, Stony Brook University set off the alarms: The models still are not running properly! And the German press has been silent. The Stony Brook press release follows:

Study Reveals Need for Better Modeling of Weather Systems for Climate Prediction
Computer-generated models are essential for or scientists to predict the nature and magnitude of weather systems, including their changes and patterns. Using 19 climate models, a team of researchers led by Professor Minghua Zhang of the School of Marine and Atmospheric Sciences at Stony Brook University, discovered persistent dry and warm biases of simulated climate over the region of the Southern Great Plain in the central U.S. that was caused by poor modeling of atmospheric convective systems – the vertical transport of heat and moisture in the atmosphere. Their findings, to be published in Nature Communications, call for better calculations in global climate models.

The climate models analyzed in the paper “Causes of model dry and warm bias over central U.S. and impact on climate projections,” included a precipitation deficit that is associated with widespread failure of the models in capturing actual strong rainfall events in summer over the region. By correcting for the biases, the authors found that future changes of precipitation over the US Southern Great Plain by the end of the 21^st Century would be nearly neutral. This projection is unlike what has been predicted as a drying period by the majority of current climate models. The correction also reduces the projected warming of the region by 20 percent relative to projections of previous climate models.

“Current climate models are limited by available computing powers even when cutting-edge supercomputers are used,” said Professor Zhang. “As a result, some atmospheric circulations systems cannot be resolved by these models, and this clearly impacts the accuracy of climate change predictions as shown in our study.” Professor Zhang and colleagues believe climate models will become more accurate in the coming years with the use of exascale supercomputing, now in development worldwide.”

As early as 2014, Mauri et al complained of enormous discrepancies between the real and simulated developments for precipitation and temperature in Europe 5,000 years ago. Modelling of the past, i.e. the calibration, didn’t work at all. With so much disappointment, one has to ask where all the confidence came from surrounding the models as being reliable forecasters.

The paper’s abstract follows:

The influence of atmospheric circulation on the mid-Holocene climate of Europe: a data–model comparison
The atmospheric circulation is a key area of uncertainty in climate model simulations of future climate change, especially in mid-latitude regions such as Europe where atmospheric dynamics have a significant role in climate variability. It has been proposed that the mid-Holocene was characterized in Europe by a stronger westerly circulation in winter compared with a more positive AO/NAO, and a weaker westerly circulation in summer caused by anti-cyclonic blocking near Scandinavia. Model simulations indicate at best only a weakly positive AO/NAO, whilst changes in summer atmospheric circulation have not been widely investigated. Here we use a new pollen-based reconstruction of European mid-Holocene climate to investigate the role of atmospheric circulation in explaining the spatial pattern of seasonal temperature and precipitation anomalies. We find that the footprint of the anomalies is entirely consistent with those from modern analog atmospheric circulation patterns associated with a strong westerly circulation in winter (positive AO/NAO) and a weak westerly circulation in summer associated with anti-cyclonic blocking (positive SCAND). We find little agreement between the reconstructed anomalies and those from 14 GCMs that performed mid-Holocene experiments as part of the PMIP3/CMIP5 project, which show a much greater sensitivity to top-of-the-atmosphere changes in solar insolation.Our findings are consistent with data–model comparisons on contemporary timescales that indicate that models underestimate the role of atmospheric circulation in recent climate change, whilst also highlighting the importance of atmospheric dynamics in explaining the interglacial warming.”

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