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The last-generation CMIP6 global circulation models (GCMs) are currently used to interpret past and future climatic changes and to guide policymakers, but they are very different from each other; for example, their equilibrium climate sensitivity (ECS) varies from 1.83 to 5.67 °C (IPCC AR6, 2021). Even assuming that some of them are sufficiently re...
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... ECS of the CMIP5 models varied from 2.1 to 4.5 °C; and in 2013 the IPCC [2] estimated that it likely ranges from 1.5 to 4.5 °C, as already proposed by Jule Charney in 1979 [2,22]. Paradoxically, the ECS of the novel CMIP6 GCMs present even a larger range: from 1.83 to 5.67 °C (see Figure 1). The issue is of great concern because the ECS of many of these new models (at least 13 of them are shown in the figure) even exceeds 4.5 °C, which was the previously accepted upper-limit value [2,23]. ...
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... 1963, Möller [51] showed that the ECS could vary greatly, up to one order of magnitude, according to how water vapor and/or cloudiness responded to the CO 2 perturbation; the author concluded that such a large uncertainty implied that "the theory that climatic variations are affected by variations in the CO 2 content becomes very questionable". Manabe and Wetherald [52] developed a one-dimensional model of radiative-convective equilibrium and concluded that the ECS had to be around 2 °C (which is compatible only with the very low ECS end predicted by the modern CMIP5 and CMIP6 GCMs, see Figure 1). In 1974, the same authors [53] used early computer facilities, upgraded their model into a theoretical circulation climate model, and estimated ECS = 2.93 °C. ...
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... CMIP6 models, such as the previous generation models, predict that nearly 100% of the warming observed since the pre-industrial period (1850-1900) is anthropogenic. The proposed argument is that using only the natural (solar and volcanic) forcing, they produce nearly no warming from 1850 to 2020 [1][2][3]: see, for example, Figure SPM.1 (b) in the Summary for Policymakers of the IPCC AR6 WGI. However, a significant portion of the observed 20th century warming could also have been induced by natural oscillations. ...
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... halving of the GCMs' ECS could also be justified by observing that the volcano cooling spikes produced by several models appear too deep relative to the observations: see, for example, the simulated climatic effect of the eruption of Mt. Pinatubo between 1991 and 1992 produced by the E3SMv1 GCM (ECS = 5.3 °C) shown in Figure 23 in Golaz et al. [33]: cf. also with Figure 2. Figure 10 shows the semi-empirical models proposed by Scafetta [5] and [6] against the ERA5-T2m, ERA5-850mb, and UAH MSU v.6.0 Tlt records since 1950. ...
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... semi-empirical models ( Figure 10 A and C) agree with the data significantly better-in particular after 2000-than how the currently adopted GCMs do ( Figure 10 B and D), although in the period 2015-2020, two warming peaks due to oceanic oscillations were observed. These two peaks were not supposed to be captured in the model proposed in Reference [5] and were partially predicted by the model proposed in Reference [6], which was calibrated using the temperature data up to 2014. ...
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... semi-empirical models ( Figure 10 A and C) agree with the data significantly better-in particular after 2000-than how the currently adopted GCMs do ( Figure 10 B and D), although in the period 2015-2020, two warming peaks due to oceanic oscillations were observed. These two peaks were not supposed to be captured in the model proposed in Reference [5] and were partially predicted by the model proposed in Reference [6], which was calibrated using the temperature data up to 2014. ...
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... result suggests that halving the ECS of the CMIP5 or CMIP6-that is, assuming it ranges between 0.9 and 2.8 °C-could be sufficient in modeling the observed climatic changes under the conditions that there are natural oscillations that are not modeled by the GCMs because of missing or erroneous astronomical forcings or other internal mechanisms. Figure 10. ERA5-T2m, ERA5-850mb, and UAH MSU v.6.0 ...
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... RMSE values imply a better agreement between the two records. Figure 10 also shows that the semi-empirical models predict for the future a significantly more moderate warming trend than those predicted by both the CMIP5 or CMIP6 models, that, on the contrary, appear to be increasingly diverging from the data. ...
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... above results suggest that the CMIP6 models present some serious problems in modeling the atmospheric and oceanic circulations, the albedo feedback related to glaciers and sea ice formation and melting, and the cloudiness between the temperate and subpolar regions. Serious differences among the 38 CMIP6 GCMs herein analyzed are also highlighted by a simple visual comparison among the images depicted in the Appendix A. Therefore, the CMIP6 models are very different from each other, as also demonstrated by their large ECS variability range spanning from 1.83 to 5.67 °C (Table 1, Figure 1), and a major scientific challenge is to narrow such a large uncertainty range. ...
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... panel also contains the latitudinal temperature profile for the ocean, land, and ocean+land areas. The statistical analysis referring to each model is reported in Table 1 and summarized in Figures 8 and 9. Figure A1. Warming patterns from 1980Warming patterns from -1990Warming patterns from to 2011Warming patterns from -2021 for the indicated CMIP6-tas GCM (left) and its comparison against the ERA5-T2m record (right). Figure A3. ...
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... The representation concentration pathway (RCP) 4.5, which is an optimistic scenario shortly after 2100, a lower long-run radiative forcing target level was used in the models [27]. The study acknowledges the difference in the equilibrium climate sensitivity(ECS) between the two models, where CCSM2 and HadGEM2 have ECSs of 5.16 and 5.55, respectively [32]. This difference in climate sensiyivity between the two models would be a merit to this study as it is based on only the optimistic RCP. ...
The coffee sector in Ethiopia is the livelihood of more than 20% of the population and accounts more than 25% of the country’s foreign exchange earnings. Climate change is expected to affect the climatic suitability of coffee in Ethiopia, and this would have implications for global coffee output, the national economy, and farmers’ livelihoods in Ethiopia. The objective of this paper is to assess the current and future impacts of climate change on bioclimatic suitability to C.arbica production in Ethiopia. Based on the current distribution of coffee production areas and climate change predictions from HadGEM2 and CCSM2 models and using the Maximum Entropy (MaxEnt) bioclimatic modeling approach, future changes in climatic suitability for C. arabica were predicted. Coffee production sites in Ethiopia were geo-referenced and used as input in the MAXENT model. The findings indicated that climate change will increase the suitable growing area for coffee by about 44.2% and 30.37% under HadGEM2 and CCSM2 models, respectively, by 2080 in Ethiopia. The study also revealed a westward and northwestward shift in the climatic suitability to C. arabica production in Ethiopia. This indicates that the suitability of some areas will continue with some adaptation practice, whilst others currently suitable will be unsuitable, yet others that are unsuitable will be suitable for arabica coffee production. These findings are intended to support stakeholders in the coffee sector in developing strategies for reducing the vulnerability of coffee production to climate change. Site-specific strategies should be developed to build a more climate resilient coffee livelihood in the changing climate.
... While ERA5 has been used to evaluate the performance of GCMs land surface temperature simulation [84][85][86], our study would represent, to our understanding, the first use of ERA5 Land with that specific objective in mind. The monthly averaged temperature dataset from ERA5 Land was acquired through the Copernicus Climate Change Service (C3S) website (https://cds.climate.copernicus.eu/, ...
The European Mediterranean Basin (Euro-Med), a region particularly vulnerable to global warming, notably lacks research aimed at assessing and enhancing the widely used remote climate detection products known as General Circulation Models (GCMs). In this study, the proficiency of GCMs in replicating reanalyzed 1981–2010 temperature data sourced from the ERA5 Land was assessed. Initially, the least data-modifying interpolation method for achieving a resolution match of 0.1° was ascertained. Subsequently, a pixel-by-pixel evaluation was conducted, employing five goodness-of-fit metrics. From these metrics, we compiled a Comprehensive Rating Index (CRI). A Multi-Model Ensemble using Random Forest was constructed and projected across three emission scenarios (SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5) and timeframes (2026–2050, 2051–2075, and 2076–2100). Empirical Bayesian Kriging, selected for its minimal data alteration, supersedes the commonly employed Bilinear Interpolation. The evaluation results underscore MPI-ESM1-2-HR, GFDL-ESM4, CNRM-CM6-1, MRI-ESM2-0, CNRM-ESM2-1, and IPSL-CM6A-LR as top-performing models. Noteworthy geospatial disparities in model performance were observed. The projection outcomes, notably divergent from IPCC forecasts, revealed a warming trend of 1 to over 2 °C less than anticipated for spring and winter over the medium–long term, juxtaposed with heightened warming in mountainous/elevated regions. These findings could substantially refine temperature projections for the Euro-Med, facilitating the implementation of policy strategies to mitigate the effects of global warming in vulnerable regions worldwide.
... ECS and TCR values, critical metrics for comparing model responses to greenhouse gas changes, can be found in Forster et al. (2021), IPCC (2021) or in Meehl et al. (2020) or in Appendix A of Scafetta (2023). Concerns about a subset of CMIP6 models with higher climate sensitivities, potentially leading to misleading temperature projections, have been raised in previous studies (e.g., Scafetta, 2021;Hausfather et al., 2022;Scafetta, 2023). Among the 22 models used, 7 of them (BCC-CSM2-MR, CAMS-CSM1-0, CNRM-ESM2-1, FGOALS-g3, MIROC6, MRI-ESM2-0, NorESM2-LM) belong to the low ECS (1.83 − 3.02 • C) or low TCR (1.22 − 1.83 • C) GCM sub-ensemble recommended for effective use in climate change policy Scafetta (2023). ...
... Notably, this cooling feedback is less pronounced in CMIP6 compared to CMIP5, thereby contributing to intensified greenhouse warming [28,29]. A review of the existing literature indicates that CMIP6 still encounters challenges in accurately replicating climate change and its inherent variability [30,31]. ...
... The evaluation of model accuracy revealed that the CMIP5 model outperformed the CMIP6 model in simulating and predicting T mean and rrr24, across all time periods and scenarios, this confirms the weakness of the CMIP6 model [25,31]. The climate projections by global climate models (GCMs) are subject to multi-source and considerable uncertainties. ...
Iran is highly vulnerable to climate change, particularly evident in shifting precipitation and temperature patterns, especially in its southern coastal region. With these changing climate conditions, there is an urgent need for practical and adaptive management of water resources and energy supply to address the challenges posed by future climate change. Over the next two to three decades, the effects of climate change, such as precipitation and temperature, are expected to worsen, posing greater risks to water resources, agriculture, and infrastructure stability. Therefore, this study aims to evaluate the alterations in mean daily temperature (Tmean) and total daily rainfall (rrr24) utilizing climate change scenarios from both phases 5 and 6 of the Coupled Model Inter-comparison Project (CMIP5 and CMIP6, respectively) in the southern coastal regions of Iran (Hormozgan province), specifically north of the Strait of Hormuz. The predictions were generated using the Statistical Downscaling Model (SDSM) and National Centre for Environmental Prediction (NCEP) predictors, incorporating climate change scenarios from CMIP5 with Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 and CMIP6 with Shared Socioeconomic Pathways (SSPs) 1, 2, and 5. The analysis was conducted for three distinct time periods: the early 21st century (2021–2045), middle 21st century (2046–2071), and late 21st century (2071–2095). The results indicated that the CMIP5 model outperformed the CMIP6 model in simulating and predicting Tmean and rrr24. In addition, a significant increase in Tmean was observed across all the scenarios and time periods, with the most pronounced trend occurring in the middle and late 21st century future periods. This increase was already evident during the base period of 2021–2045 across all scenarios. Moreover, the fluctuations in precipitation throughout the region and across all scenarios were significant in the three examined future periods. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of Tmean (+1.22 °C) in Bandar Lengeh station in 2071–2095 period. The lowest change magnitude of Tmean among CMIP5 scenarios was found in RCP4.5 (−1.94 °C) in Ch station in 2046–2070 period. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of rrr24 (+150.2 mm) in Chabahar station in 2071–2095 period. The lowest change magnitude of rrr24 among CMIP5 scenarios was found in RCP8.5 (−25.8 mm) in Bandar Abbas station in 2046–2070 period. In conclusion, the study reveals that the coastal area of Hormozgan province will experience rising temperatures and changing rainfall patterns in the future. These changes may lead to challenges such as increased water and energy consumption, heightened risks of droughts or floods, and potential damage to agriculture and infrastructure. These findings offer valuable insights for implementing local mitigation policies and strategies and adapting to emerging climate changes in Hormozgan's coastal areas. For example, utilizing water harvesting technologies, implementing watershed management practices, and adopting new irrigation systems can address challenges like water consumption, agricultural impacts, and infrastructure vulnerability. Future research should accurately assess the effect of these changes in precipitation and temperature on water resources, forest ecosystems, agriculture, and other infrastructures in the study area to implement effective management measures.
... However, simulations obtained with different models with the same forcing scenario have different global temperature responses (so-called climate sensitivity, see e.g. Mauritzen et al, 2017) so that a warming level corresponds to different time windows according to the GCM (Scafetta, 2021). To account for the climate sensitivity of the climate model, a simple solution is to choose a different time slice for each model (Vautard et al, 2014;Schleussner et al, 2016;Nikulin et al, 2018). ...
IPCC reports and climate change impact studies generally exploit ensembles of climate projections based on different socio-economic pathways and climate models, which provide the temporal evolution of plausible future climates. However, The Paris Agreement and many national and international commitments consider adaptation and mitigation plans targeting future global warming levels. Model uncertainty and scenario uncertainty typically affect both the crossing-time of future warming levels and the climate features at a given global warming level. In this study, we assess the uncertainties in a multi-model multi-member CMIP6 ensemble (MME) of seasonal and regional temperature and precipitation projections. In particular, we show that the uncertainties of regional temperature projections are considerably reduced if considered at a specific global warming level, with a limited effect of the emission scenarios and a reduced influence of GCM sensitivity. We also describe in detail the large uncertainties related to the different behavior of the GCMs in some regions.
... Problems with the estimation of E, ET, and surface temperature persist in the newer generation of land-atmosphere interaction models (e.g., CMIP5, CMIP6). Though significantly better at representing TFVC and the land-atmosphere interaction, these models still bear testament to significant deviations between modeled and observed results(Baker & Spracklen, 2022;Berg & Sheffield, 2019;Lejeune et al., 2020;Scafetta, 2021). As IPCC authors highlight, the role and impact of clouds and water vapor on global temperatures, "has long been the biggest source of uncertainty in climate projections" (IPCC AR6 WGI Ch7). ...
Scientific innovation is overturning conventional paradigms of forest, water, and energy cycle interactions. This has implications for our understanding of the principal causal pathways by which tree, forest, and vegetation cover (TFVC) influence local and global warming/cooling. Many identify surface albedo and carbon sequestration as the principal causal pathways by which TFVC affects global warming/cooling. Moving toward the outer latitudes, in particular, where snow cover is more important, surface albedo effects are perceived to overpower carbon sequestration. By raising surface albedo, deforestation is thus predicted to lead to surface cooling, while increasing forest cover is assumed to result in warming. Observational data, however, generally support the opposite conclusion, suggesting surface albedo is poorly understood. Most accept that surface temperatures are influenced by the interplay of surface albedo, incoming shortwave (SW) radiation, and the partitioning of the remaining, post‐albedo , SW radiation into latent and sensible heat. However, the extent to which the avoidance of sensible heat formation is first and foremost mediated by the presence (absence) of water and TFVC is not well understood. TFVC both mediates the availability of water on the land surface and drives the potential for latent heat production (evapotranspiration, ET). While latent heat is more directly linked to local than global cooling/warming, it is driven by photosynthesis and carbon sequestration and powers additional cloud formation and top‐of‐cloud reflectivity, both of which drive global cooling. TFVC loss reduces water storage, precipitation recycling, and downwind rainfall potential, thus driving the reduction of both ET (latent heat) and cloud formation. By reducing latent heat, cloud formation, and precipitation, deforestation thus powers warming (sensible heat formation), which further diminishes TFVC growth (carbon sequestration). Large‐scale tree and forest restoration could, therefore, contribute significantly to both global and surface temperature cooling through the principal causal pathways of carbon sequestration and cloud formation.
... However, given that CanESM5 is identified as a 'hot model' (Scafetta 2022) its projections must be taken carefully unless model weighting or rescaling the ensemble is applied to avoid highly biased projections (Tokarska et al. 2020). Also, choosing the ensemble with the more reliable models has been proposed (Scafetta 2021). Similarly, the 'hot model' ACCESS-CM2 was the best performing model for Northern Chile, and therefore the same care must be applied in using its raw projections. . ...
Precipitation and near-surface temperature from an ensemble of 36 new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) are evaluated over Chile’s climate. The analysis is focused on four distinct climatic subregions: Northern Chile, Central Chile, Northern Patagonia, and Southern Patagonia. Over each of the subregions, first, we evaluate the performance of individual global climate models (GCMs) against a suit of precipitation and temperature observation-based gridded datasets over the historical period (1986–2014) and then we analyze the models’ projections for the end of the century (2080–2099) for four different shared socioeconomic pathways scenarios (SSP). Although the models are characterized by general wet and warm mean bias, they reproduce realistically the main spatiotemporal climatic variability over different subregions. However, none of the models is best across all subregions for both precipitation and temperature. Moreover, among the best performing models defined based on the Taylor skill score, one finds the so-called “hot models” likely exhibiting an overestimated climate sensitivity, which suggests caution in using these models for accessing future climate change in Chile. We found robust (90% of models agree in the direction of change) projected end-of-the-century reductions in mean annual precipitation for Central Chile (~ − 20 to ~ − 40%) and Northern Patagonia (~ − 10 to ~ − 30%) under scenario SSP585, but changes are strong from scenario SSP245 onwards, where precipitation is reduced by 10–20%. Northern Chile and Southern Patagonia show non-robust changes in precipitation across the models. Yet, future near-surface temperature warming presented high inter-model agreement across subregions, where the greatest increments occurred along the Andes Mountains. Northern Chile displays the strongest increment of up to ~ 6 °C in SSP585, followed by Central Chile (up to ~ 5 °C). Both Northern and Southern Patagonia show a corresponding increment by up to ~ 4 °C. We also briefly discuss about the environmental and socio-economic implications of these future changes for Chile.
... This estimate from AR6 is used as the target distribution for ECS in our study and is shown as the black curve in Fig. 1c. Individual CMIP6 models are expected to simulate a similar climate sensitivity, yet some models are below, and other models are well above this range 41 . Table 1 lists a set of 16 models and their ECS values (also shown in Fig. 1a) from the CMIP6 archive that are common to two forthcoming statistically downscaled datasets to be used in scientific impact and assessment activities across North America. ...
... However, other metrics can be useful in this regard as well, such as the TCR, which is the mean global warming predicted to occur around the time of doubling CO 2 in ESM runs for which atmospheric CO 2 concentration is prescribed to increase at 1% per year. Based on multiple lines of evidence 41 , TCR has an assessed likely range of 1.4-2.2°C (c.f. the IPCC AR6 WG1 technical summary). ...
Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections. Various methods have been proposed to ameliorate this ‘hot model’ problem, such as model emulators or model culling. Here, we utilize Bayesian Model Averaging as a framework to address this problem without resorting to outright rejection of models from the ensemble. Taking advantage of multiple lines of evidence used to construct the best estimate of the earth’s climate sensitivity, the Bayesian Model Averaging framework produces an unbiased posterior probability distribution of model weights. The updated multi-model ensemble projects end-of-century global mean surface temperature increases of 2 oC for a low emissions scenario (SSP1-2.6) and 5 oC for a high emissions scenario (SSP5-8.5). These estimates are lower than those produced using a simple multi-model mean for the CMIP6 ensemble. The results are also similar to results from a model culling approach, but retain some weight on low-probability models, allowing for consideration of the possibility that the true value could lie at the extremes of the assessed distribution. Our results showcase Bayesian Model Averaging as a path forward to project future climate change that is commensurate with the available scientific evidence.
... We note also that concerns about the reliability of both the CMIP5 and CMIP6 hindcasts in replicating observed climate changes have been expressed [31,[33][34][35][36][37][38][39][40][41]. However, we suggest that most of the differences can be effectively distilled down to different views on these two specific issues. ...
... Much of this might be explained by an additional contribution from anthropogenic forcings [1, 2,17,106,107,111]. Although, if so, it probably would involve a much lower climate sensitivity to greenhouse gases than the CMIP6 models imply-as several studies have suggested, e.g., [17,18,31,33,34,[138][139][140][141][142][143][144][145][146][147][148]. There may also be additional non-climatic biases remaining in the data [5,7,10,76]. ...
A statistical analysis was applied to Northern Hemisphere land surface temperatures (1850–2018) to try to identify the main drivers of the observed warming since the mid-19th century. Two different temperature estimates were considered—a rural and urban blend (that matches almost exactly with most current estimates) and a rural-only estimate. The rural and urban blend indicates a long-term warming of 0.89 °C/century since 1850, while the rural-only indicates 0.55 °C/century.
This contradicts a common assumption that current thermometer-based global temperature indices are relatively unaffected by urban warming biases. Three main climatic drivers were considered, following the approaches adopted by the Intergovernmental Panel on Climate Change (IPCC)’s recent 6th Assessment Report (AR6): two natural forcings (solar and volcanic) and the composite “all anthropogenic forcings combined” time series recommended by IPCC AR6. The volcanic time series was that recommended by IPCC AR6. Two alternative solar forcing datasets were contrasted.
One was the Total Solar Irradiance (TSI) time series that was recommended by IPCC AR6. The other TSI time series was apparently overlooked by IPCC AR6. It was found that altering the temperature estimate and/or the choice of solar forcing dataset resulted in very different conclusions as to the primary drivers of the observed warming. Our analysis focused on the Northern Hemispheric land
component of global surface temperatures since this is the most data-rich component. It reveals that important challenges remain for the broader detection and attribution problem of global warming:
(1) urbanization bias remains a substantial problem for the global land temperature data; (2) it is still unclear which (if any) of the many TSI time series in the literature are accurate estimates of past TSI; (3) the scientific community is not yet in a position to confidently establish whether the warming since 1850 is mostly human-caused, mostly natural, or some combination. Suggestions for how these scientific challenges might be resolved are offered.
... With a temperature increase of almost 1.7°C over the Industrial Era it reveals an even larger discrepancy than the CMIP5 simulation. A recent study of Scafetta (2021) [49] comes to a similar conclusion that CMIP6 ESMs are significantly overestimating global warming over the last 40 years. ...
The Intergovernmental Panel on Climate Change classifies the human influence on our climate as extremely likely to be the main reason of global warming over the last decades. Particularly anthropogenic emissions of carbon dioxide are made responsible for the observed temperature changes, while any natural forcings are almost completely excluded. However, detailed own calculations with an advanced energy-radiation-balance model indicate that the temperature increase and its variations over the last 140 years can much better be explained by additionally including solar radiative forcing and its amplification by induced cloud cover changes. We present simulations based on different time series of the total solar irradiance and compare them with composed land-ocean-surface temperature measurements of the Northern Hemisphere. From these simulations we follow that CO2 should not have contributed more than about one third to global warming over the last century, while solar variations over this period can well explain two thirds of the increase.
