Category Archives: Climate Models

‘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.

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

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).

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.

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;

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.

Observed warming a factor of two less than model projections

John R. Christy, Roy W. Spencer, William D. Braswell & Robert Junod. Examination of space-based bulk atmospheric temperatures used in climate research. International Journal of Remote Sensing
Volume 39, 2018 – Issue 11

The Intergovernmental Panel on Climate Change Assessment Report 5 (IPCC AR5, 2013) discussed bulk atmospheric temperatures as indicators of climate variability and change. We examine four satellite datasets producing bulk tropospheric temperatures, based on microwave sounding units (MSUs), all updated since IPCC AR5. All datasets produce high correlations of anomalies versus independent observations from radiosondes (balloons), but differ somewhat in the metric of most interest, the linear trend beginning in 1979. The trend is an indicator of the response of the climate system to rising greenhouse gas concentrations and other forcings, and so is critical to understanding the climate. The satellite results indicate a range of near-global (+0.07 to +0.13°C decade−1) and tropical (+0.08 to +0.17°C decade−1) trends (1979–2016), and suggestions are presented to account for these differences. We show evidence that MSUs on National Oceanic and Atmospheric Administration’s satellites (NOAA-12 and −14, 1990–2001+) contain spurious warming, especially noticeable in three of the four satellite datasets.

Comparisons with radiosonde datasets independently adjusted for inhomogeneities and Reanalyses suggest the actual tropical (20°S-20°N) trend is +0.10 ± 0.03°C decade−1. This tropical result is over a factor of two less than the trend projected from the average of the IPCC climate model simulations for this same period (+0.27°C decade−1).

Climate models are theoretical; Underlying mechanisms are poorly understood

Matthew Collins, Shoshiro Minobe, Marcelo Barreiro, Simona Bordoni,
Yohai Kaspi, Akira Kuwano-Yoshida, Noel Keenlyside, Elisa Manzini, Christopher H. O’Reilly, Rowan Sutton, Shang-Ping Xie, Olga Zolina. Challenges and opportunities for improved understanding of regional climate dynamics. Nature Climate Change, volume 8, pages 101–108 (2018).

Dynamical processes in the atmosphere and ocean are central to determining the large-scale drivers of regional climate change, yet their predictive understanding is poor. Here, we identify three frontline challenges in climate dynamics where significant progress can be made to inform adaptation: response of storms, blocks and jet streams to external forcing; basin-to-basin and tropical–extratropical teleconnections; and the development of non-linear predictive theory. We highlight opportunities and techniques for making immediate progress in these areas, which critically involve the development of high-resolution coupled model simulations, partial coupling or pacemaker experiments, as well as the development and use of dynamical metrics and exploitation of hierarchies of models.

Regional Greenhouse Gas Initiative (RGGI) Program has not reduced emissions

“Positive RGGI program reviews have been from RGGI, Inc. (the program administrator) and the Acadia Center, which advocates for reduced emissions (see Stutt, Shattuck, and Kumar 2015). In this article, I investigate whether reported reductions in CO2 emissions from electric power plants, along with associated gains in health benefits and other claims, were actually achieved by the RGGI program. Based on my findings, any form of carbon tax is not the policy to accomplish emission reductions. The key results are:

• There were no added emissions reductions or associated health benefits from the RGGI program.

• Spending of RGGI revenue on energy efficiency, wind, solar power, and low-income fuel assistance had minimal impact.

• RGGI allowance costs added to already high regional electric bills. The combined pricing impact resulted in a 12 percent drop in goods production and a 34 percent drop in the production of energy-intensive goods. Comparison states increased goods production by 20 percent and lost only 5 percent of energy-intensive manufacturing. Power imports from other states increased from 8 percent to 17 percent.” click here