Category Archives: Climate Models

Ocean cycles synchronous with global temperature fluctuations

“Now isn’t it a bit odd that the authors made absolutely no mention of the ocean cycles in the abstract? As our regular readers know, the ocean cycles run surprisingly synchronous with the fluctuations in global temperatures, i.e. the key factors here are the AMO and PDO.” click here

A spirited discussion over global temperature rise and IPCC models.

“No matter how hard climate-catastrophe obsessed alarmists attempt to beat out a little doom from the data, their results still fall far way short of their projections. Moreover, the modest warming the planet has seen over the recent decades is tied more to natural cycles.” click here

James Hansen 1988 global warming predictions not based on reality

“To theorize that 15 ten-millionths of an overall system will control the temperature of that interrelated system, or of any large part of it, is prima facie nonsense. What should surprise us is not that the idea has turned out wrong, but that anyone embraced it in the first place and that it’s taking so long for so many people, including many highly intelligent scientists, to abandon it in the face of the clear empirical evidence of its falsehood.” click here

‘Climate science’ in its current state is not self-correcting

“Many problems plagued climatology since climate science took over in the 1980s. Each specialist in a different area suddenly became an expert in climate and climate change. They brought their different perspectives, sometimes helpful, but usually unhelpful and even distorting. Most came for funding opportunities, but many for the political objectives. They all lack awareness that climatology is a generalist discipline. It involves putting together, in a systems approach manner, all the studies from specialists who, because they get involved in climate studies, call themselves climate scientists. This piecemeal approach reflects the problems of creating computer climate models. Modellers assemble as many facts as they think apply, or will achieve their result and then, with improper or inadequate connecting mechanisms, put together what they think represents global climate.” click here

State-of-the-art computer models fail to reproduce recent ocean cooling

Jing-Jia Luo, Gang Wang, Dietmar Dommenget. Many common model biases reduce CMIP5’s ability to simulate the recent Pacific La Niña-like cooling? Climate Dynamics, February 2018, Volume 50, Issue 3–4, pp 1335–1351

Over the recent three decades sea surface temperate (SST) in the eastern equatorial Pacific has decreased, which helps reduce the rate of global warming. However, most CMIP5 model simulations with historical radiative forcing do not reproduce this Pacific La Niña-like cooling. Based on the assumption of “perfect” models, previous studies have suggested that errors in simulated internal climate variations and/or external radiative forcing may cause the discrepancy between the multi-model simulations and the observation. But the exact causes remain unclear. Recent studies have suggested that observed SST warming in the other two ocean basins in past decades and the thermostat mechanism in the Pacific in response to increased radiative forcing may also play an important role in driving this La Niña-like cooling. Here, we investigate an alternative hypothesis that common biases of current state-of-the-art climate models may deteriorate the models’ ability and can also contribute to this multi-model simulations-observation discrepancy. Our results suggest that underestimated inter-basin warming contrast across the three tropical oceans, overestimated surface net heat flux and underestimated local SST-cloud negative feedback in the equatorial Pacific may favor an El Niño-like warming bias in the models. Effects of the three common model biases do not cancel one another and jointly explain ~50% of the total variance of the discrepancies between the observation and individual models’ ensemble mean simulations of the Pacific SST trend. Further efforts on reducing common model biases could help improve simulations of the externally forced climate trends and the multi-decadal climate fluctuations.

Paris agreement is not based on the best available science

Antero Ollila. Challenging the scientific basis of the Paris climate agreement. International Journal of Climate Change Strategies and Management. https://doi.org/10.1108/IJCCSM-05-2017-0107

Purpose
The purpose of this paper is to analyze the scientific basis of the Paris climate agreement.

Design/methodology/approach
The analyses are based on the IPCC’s own reports, the observed temperatures versus the IPCC model-calculated temperatures and the warming effects of greenhouse gases based on the critical studies of climate sensitivity (CS).

Findings
The future emission and temperature trends are calculated according to a baseline scenario by the IPCC, which is the worst-case scenario RCP8.5. The selection of RCP8.5 can be criticized because the present CO2 growth rate 2.2 ppmy−1 should be 2.8 times greater, meaning a CO2 increase from 402 to 936 ppm. The emission target scenario of COP21 is 40 GtCO2equivalent, and the results of this study confirm that the temperature increase stays below 2°C by 2100 per the IPCC calculations. The IPCC-calculated temperature for 2016 is 1.27°C, 49 per cent higher than the observed average of 0.85°C in 2000.

Originality/value
Two explanations have been identified for this significant difference in the IPCC’s calculations: The model is too sensitive for CO2 increase, and the positive water feedback does not exist. The CS of 0.6°C found in some critical research studies means that the temperature increase would stay below the 2°C target, even though the emissions would follow the baseline scenario. This is highly unlikely because the estimated conventional oil and gas reserves would be exhausted around the 2060s if the present consumption rate continues.

Global models underestimate decadal water storage trends

Bridget R. Scanlon, Zizhan Zhang, Himanshu Save, Alexander Y. Sun, Hannes Müller Schmied, Ludovicus P. H. van Beek, David N. Wiese, Yoshihide Wada, Di Long, Robert C. Reedy, Laurent Longuevergne, Petra Döll, and Marc F. P. Bierkens. Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. PNAS January 22, 2018. 201704665; https://doi.org/10.1073/pnas.1704665115.

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002–2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤−0.5 km3/y) and increasing (≥0.5 km3/y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km3/y, whereas most models estimate decreasing trends (−71 to 11 km3/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71–82 km3/y) but negative for models (−450 to −12 km3/y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated.