The Problem with Computer Models

Below are studies dealing with the problems of computer modeling and figuring climate change.

Collins et al., 2018     Here there is a dynamical gap in our understanding. While we have conceptual models of how weather systems form and can predict their evolution over days to weeks, we do not have theories that can adequately explain the reasons for an extreme cold or warm, or wet or dry, winter at continental scales. More importantly, we do not have the ability to credibly predict such states. Likewise, we can build and run complex models of the Earth system, but we do not have adequate enough understanding of the processes and mechanisms to be able to quantitatively evaluate the predictions and projections they produce, or to understand why different models give different answers. … The global warming ‘hiatus’ provides an example of a climate event potentially related to inter-basin teleconnections. While decadal climate variations are expected, the magnitude of the recent event was unforeseen. A decadal period of intensified trade winds in the Pacific and cooler sea surface temperatures (SSTs) has been identified as a leading candidate mechanism for the global slowdown in warming.

Christy et al., 2018     [A]s new versions of the datasets are produced, trend magnitudes have changed markedly, for example the central estimate of the global trend of the mid-troposphere in Remote Sensing System’s increased 60% from +0.078 to +0.125°C decade−1, between consecutive versions 3.3 and 4.0 (Mears and Wentz 2016). … As an experiment, Mears et al. recalculated the RSS overall trend by simply truncating NOAA-14 data after 1999 (which reduced their long-term trend by 0.02 K decade−1). However, this does not address the problem that the trends of the entire NOAA-12 and −14 time series (i.e. pre-2000) are likely too positive and thus still affect the entire time series. Additionally, the evidence from the Australian and U.S. VIZ comparisons support the hypothesis that RSS contains extra warming (due to NOAA-12, −14 warming.) Overall then, this analysis suggests spurious warming in the central estimate trend of RSS of at least +0.04°C decade−1, which is consistent with results shown later based on other independent constructions for the tropical belt. … When examining all of the evidence presented here, i.e. the correlations, magnitude of errors and trend comparisons, the general conclusion is that UAH data tend to agree with (a) both unadjusted and adjusted IGRA radiosondes, (b) independently homogenized radiosonde datasets and (c) Reanalyses at a higher level, sometimes significantly so, than the other three [NOAA, RSS, UW]. … One key result here is that substantial evidence exists to show that the processed data from NOAA-12 and −14 (operating in the 1990s) were affected by spurious warming that impacted the four datasets, with UAH the least affected due to its unique merging process. RSS, NOAA and UW show considerably more warming in this period than UAH and more than the US VIZ and Australian radiosondes for the period in which the radiosonde instrumentation did not change. … [W]e estimate the global TMT trend is +0.10 ± 0.03°C decade−1. … The rate of observed warming since 1979 for the tropical atmospheric TMT layer, which we calculate also as +0.10 ± 0.03°C decade−1, is significantly less than the average of that generated by the IPCC AR5 climate model simulations. Because the model trends are on average highly significantly more positive and with a pattern in which their warmest feature appears in the latent-heat release region of the atmosphere, we would hypothesize that a misrepresentation of the basic model physics of the tropical hydrologic cycle (i.e. water vapour, precipitation physics and cloud feedbacks) is a likely candidate.

Abbott and Marohasy, 2018     While general circulation models are used by meteorological agencies around the world for rainfall forecasting, they do not generally perform well at forecasting medium-term rainfall, despite substantial efforts to enhance performance over many years. These are the same models used by the Intergovernmental Panel on Climate Change (IPCC) to forecast climate change over decades. Though recent studies suggest ANNs [artificial neural networks] have considerable application here, including to evaluate natural versus climate change over millennia, and also to better understand equilibrium climate sensitivity.

Guo et al., 2018    The snow‐albedo feedback is a crucial component in high‐altitude cryospheric change but is poorly quantified over the Third Pole, encompassing the Karakoram and Tibetan Plateau. … [I]t is noteworthy that the magnitude of the constrained strength is only half of the unconstrained model estimate for the Third Pole, suggesting that current climate models generally overestimate the feedback of spring snow change to temperature changebased on the unmitigated scenario.

Scafetta et al., 2018     The period from 2000 to 2016 shows a modest warming trend that the advocates of the anthropogenic global warming theory have labeled as the “pause” or “hiatus.” These labels were chosen to indicate that the observed temperature standstill period results from an unforced internal fluctuation of the climate (e.g. by heat uptake of the deep ocean) that the computer climate models are claimed to occasionally reproduce without contradicting the anthropogenic global warming theory (AGWT) paradigm. In part 1 of this work, it was shown that the statistical analysis rejects such labels with a 95% confidence because the standstill period has lasted more than the 15 year period limit provided by the AGWT advocates themselves. Anyhow, the strong warming peak observed in 2015-2016, the “hottest year on record,” gave the impression that the temperature standstill stopped in 2014.Herein, the authors show that such a temperature peak is unrelated to anthropogenic forcing: it simply emerged from the natural fast fluctuations of the climate associated to the El Niño-Southern Oscillation (ENSO) phenomenon. By removing the ENSO signature, the authors show that the temperature trend from 2000 to 2016 clearly diverges from the general circulation model (GCM) simulations. Thus, the GCMs models used to support the AGWT [anthropogenic global warming theory] are very likely flawed. By contrast, the semi-empirical climate models proposed in 2011 and 2013 by Scafetta, which are based on a specific set of natural climatic oscillations believed to be astronomically induced plus a significantly reduced anthropogenic contribution, agree far better with the latest observations.

Kundzewicz et al., 2018     Climate models need to be improved before they can be effectively used for adaptation planning and design. Substantial reduction of the uncertainty range would require improvement of our understanding of processes implemented in models and using finer resolution of GCMs and RCMs. However, important uncertainties are unlikely to be eliminated or substantially reduced in near future (cf. Buytaert et al., 2010). Uncertainty in estimation of climate sensitivity (change of global mean temperature, corresponding to doubling atmospheric CO2 concentration) has not decreased considerably over last decades. Higher resolution of climate input for impact models requires downscaling (statistical or dynamic) of GCM outputs, adding further uncertainty. … [C]limate models do not currently simulate the water cycle at sufficiently fine resolution for attribution of catchment-scale hydrological impacts to anthropogenic climate change. It is expected that climate models and impact models will become better integrated in the future. … Calibration and validation of a hydrological model should be done before applying it for climate change impact assessment, to reduce the uncertainty of results. Yet, typically, global hydrological models are not calibrated and validated. … Model-based projections of climate change impact on water resources can largely differ. If this is the case, water managers cannot have confidence in an individual scenario or projection for the future. Then, no robust, quantitative, information can be delivered and adaptation procedures need to be developed which use identified projection ranges and uncertainty estimates. Moreover, there are important, nonclimatic, factors affecting future water resources. … As noted by Funtowicz and Ravetz (1990), in the past, science was assumed to provide “hard” results in quantitative form, in contrast to “soft” determinants of politics, that were interest-driven and value-laden. Yet, the traditional assumption of the certainty of scientific information is now recognized as unrealistic and counterproductive. Policy-makers have to make “hard” decisions, choosing between conflicting options (with commitments and stakes being the primary focus), using “soft” scientific information that is bound with considerable uncertainty. Uncertainty has been policitized in that policy-makers have their own agendas that can include the manipulation of uncertainty. Parties in a policy debate may invoke uncertainty in their arguments selectively, for their own advantage.

Lean, 2018     Climate change detection and attribution have proven unexpectedly challenging during the 21st century. Earth’s global surface temperature increased less rapidly from 2000 to 2015 than during the last half of the 20th century, even though greenhouse gas concentrations continued to increase. A probable explanation is the mitigation of anthropogenic warming by La Niña cooling and declining solar irradiance. Physical climate models overestimated recent global warming because they did not generate the observed phase of La Niña cooling and may also have underestimated cooling by declining solar irradiance. Ongoing scientific investigations continue to seek alternative explanations to account for the divergence of simulated and observed climate change in the early 21st century, which IPCC termed a “global warming hiatus.” … Understanding and communicating the causes of climate change in the next 20 years may be equally challenging. Predictions of the modulation of projected anthropogenic warming by natural processes have limited skill. The rapid warming at the end of 2015, for example, is not a resumption of anthropogenic warming but rather an amplification of ongoing warming by El Niño. Furthermore, emerging feedbacks and tipping points precipitated by, for example, melting summer Arctic sea ice may alter Earth’s global temperature in ways that even the most sophisticated physical climate models do not yet replicate.

Hunziker et al., 2018     About 40% of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.
Roach et al., 2018     Consistent biases in Antarctic sea ice concentration simulated by climate models … The simulation of Antarctic sea ice in global climate models often does not agree with observations. [M]odels simulate too much loose, low-concentration sea ice cover throughout the year, and too little compact, high-concentration cover in the summer. [C]urrent sea ice thermodynamics contribute to the inadequate simulation of the low-concentration regime in many models.
Scanlon et al., 2018     The models underestimate the large decadal (2002–2014) trends in water storage relative to GRACE satellites, both decreasing trends related to human intervention and climate and increasing trends related primarily to climate variations. The poor agreement between models and GRACE underscores the challenges remaining for global models to capture human or climate impacts on global water storage trends. … Increasing TWSA [total water storage anomalies] trends are found primarily in nonirrigated basins, mostly in humid regions, and may be related to climate variations. Models also underestimate median GRACE increasing trends (1.6–2.1 km3/y) by up to a factor of 8 in GHWRMs [global hydrological and water resource models] (0.3–0.6 km3/y). Underestimation of GRACE-derived TWSA increasing trends is much greater for LSMs [global land surface models], with four of the five LSMs [global land surface models] yielding opposite trends (i.e., median negative rather than positive trends) … Increasing GRACE trends are also found in surrounding basins, with most models yielding negative trends. Models greatly underestimate the increasing trends in Africa, particularly in southern Africa. .. TWSA trends from GRACE in northeast Asia are generally increasing, but many models show decreasing trends, particularly in the Yenisei. … Subtracting the modeled human intervention contribution from the total land water storage contribution from GRACE results in an estimated climate-driven contribution of −0.44 to −0.38 mm/y. Therefore, the magnitude of the estimated climate contribution to GMSL [global mean sea level] is twice that of the human contribution and opposite in sign.While many previous studies emphasize the large contribution of human intervention to GMSL [global mean sea level], it has been more than counteracted by climate-driven storage increase on land over the past decade. … GRACE-positive TWSA trends (71 km3/y) contribute negatively (−0.2 mm/y) to GMSL, slowing the rate of rise of GMSL, whereas models contribute positively to GMSL, increasing the rate of rise of GMSL.

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