Intergovernmental Panel On Climate Change Models Research Articles

Mathematical Modeling of The Estimate of Methane Gas Generation, of The Organic Fraction Of Msw From The Agreste Alagoano Sanitary Landfill, From Standard Data And Experimental Data

Objective: The objective of this study is to carry out mathematical modeling in the generation of methane gas, through two mathematical models, using standard data and data obtained in the laboratory. Theoretical framework: The emission of methane gas, generated by the decomposition of solid waste in landfills, is one of the serious problems of air pollution and one way to minimize the impacts of methane on the environment is through its use to generate energy. To this end, there are ways to estimate the generation of CH4 during the useful life of the landfill. Method: For modeling, the computer programs of LandGEM and the Intergovernmental Panel on Climate Change (IPCC) were used to carry out the theoretical estimate of CH4 production during the useful life of the Agreste Alagoano landfill in two scenarios: one with standard program values for the local climate region and another with information obtained from experimental data from research by Santana (2022). Results and Discussion: When standard input data was adopted, the LandGEM model presented a 38% higher value in CH4 production compared to the IPCC model. When considering input data obtained experimentally, the IPCC showed CH4 generation 32.66% higher than the LandGEM, this was possible due to the COD value that was high as a result of the L0 of the organic fraction, exceeding the theoretical value of 0.15 to the experimental value of 0.27 in the new scenario. Implications of the research: Determining parameters for modeling methane gas generation will help in managing landfill waste and its energy recovery potential, in addition to the application of mathematical models for estimating biogas production potential as tools essential for both energy and environmental studies. Originality/value: This research presents as originality the determination of experimental parameters applied to the mathematical modeling of methane gas generation in landfills in the rural region, since studies on modeling in landfills with the climatic conditions of the rural region of Brazil are not common in the literature.

The social construction of sustainable futures: how models and scenarios limit climate mitigation possibilities

ABSTRACT This article shines a light on the act of social construction of climate change mitigation as a policy issue at the hands of expert bodies enjoying epistemic power, notably the Intergovernmental Panel on Climate Change (IPCC), using Integrated Assessment Models (IAMs) as key tools. This is crucial to the politics of tackling climate change as IPCC models and scenarios profoundly shape what are seen as viable futures and mitigation policy options. Analysing how technocratic governance bodies broach climate change, its mitigation, and the associated economic costs and implications, reveals contestations within the technocratic politics of climate change. The particular social construction of mitigation as a policy issue through IAMs and IPCC scenarios has important and real socio-ecological consequences. This engagement with the technocratic politics of mitigation problematises five key assumptions that are fed into modelling, and reveals why and how they matter politically. We also highlight ways in which contestable assumptions built into IPCC IAMs undermine their credibility and usefulness for planning mitigation strategies.

Greenhouse gas mitigation scenarios in the solid waste sector for compliance with the Brazilian NDC: Case study of the Recife metropolitan area, Brazil.

The Intergovernmental Panel on Climate Change (IPCC) indicates that the waste sector is a potential emitter of methane gas (CH4), which has a greenhouse effect up to 28 times greater than that of carbon dioxide (CO2). The management of municipal solid waste (MSW) generates greenhouse gases (GHG) directly through emissions from the process itself as well as indirectly through transportation and energy consumption. The objective of this study was to evaluate the GHG emissions contributed by the waste sector in the Recife metropolitan region (RMR) and to define mitigation scenarios to comply with the Brazilian Nationally Determined Contribution (NDC), a result of the Paris Agreement. To achieve this, an exploratory study was carried out, including a literature review, collection of data, estimation of emissions using the IPCC model (2006), and comparison between the values assumed by the country in 2015 and those estimated in the adopted mitigation scenarios. The RMR is composed of 15 municipalities, has an area of 3,216,262 km2 and a population of 4,054,866 inhabitants (2018), generating approximality 1.4 million t-year of MSW. It was estimated that, in the period from 2006 to 2018, 25.4 million tCO2e were emitted. The comparative analysis between the absolute values defined in the Brazilian NDC and the results from the mitigation scenarios showed that approximately 36 million tCO2e could be avoided through the disposal of MSW in the RMR, equivalent to a 52% reduction in emissions estimated for 2030, a percentage greater than the 47% reduction assumed in the Paris Agreement.

Limited Skill of Projected Land Precipitation by IPCC Models During 2002−2020

AbstractClimate models are widely used to project future climate changes and analyze underlying mechanisms. However, it remains unknown whether the earlier precipitation projections match the subsequent observations in recent decades, which is the scientific basis for the confidence of precipitation projections and associated impacts. Here, we find models in the Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) skillfully project the subsequent climatological changes in global mean land precipitation for 2002–2020, 2008–2020, and 2014–2020 (relative to 1980–1999 climatology) from several to 10 years ahead, respectively. Skillful regional projections are mainly found in the northern mid to high latitudes. IPCC models are less skillful in projecting subsequent changes in land precipitation at regional scales than at global scales, which is likely to be at least partly explained by the lower signal‐to‐noise ratio.

Accuracy of enteric methane emission models for cattle in sub-Saharan Africa: status quo and the way forward.

Cattle emit over 65% of enteric methane (CH4) in sub-Saharan Africa (SSA), making them the focus of many mitigation strategies targeting livestock emissions. Since measured feed intake data are sparse, emission factors for enteric CH4 (EFCH4) are mainly estimated indirectly from gross energy intake (GEI) using the net energy (NE) requirements for different metabolic processes in cattle. However, all NE requirement systems commonly used for cattle in SSA were developed for cattle in temperate regions. Therefore, we assessed the suitability of different enteric CH4 models for estimating the GEI of cattle in SSA. The Intergovernmental Panel on Climate Change (IPCC) and South African models were identified as the main tier 2-based methods used to estimate enteric CH4 emissions from cattle in SSA. In the IPCC model, EFCH4 was estimated as (GEI * [Ym/100])/55.65, where Ym is the conversion factor (%) of gross energy in feed to CH4 and 55.65 the energy content of CH4 (MJ/kg). The GEI was estimated based on NE requirements for different metabolic processes in cattle as per the American National Research Council. In the South African model, EFCH4 was estimated as (Y/100 * GEI/55.22), where Y is the CH4 yield and 55.22 is the energy content of CH4; Y was calculated from the dry matter (DM) digestibility while GEI was calculated by predicting DM intake and multiplying it by 18.4 MJ (gross energy per kilogram DM). Also, the suitability of the British and German NE requirement systems was assessed as alternatives used for cattle nutrition in SSA. These NE systems were implemented in the IPCC model to yield the "AFRC" and "GfE" models, respectively. The four models were then evaluated using an evaluation dataset summarizing feed quality and DM intake results from 21 studies conducted in SSA, with 125 dietary treatments, and 822 cattle observations. The relative prediction error (RPE) and concordance correlation coefficient (CCC) were used to evaluate the models' accuracy. Only the South African model estimated the GEI in dairy cattle with an acceptable RPE (18.9%) and highest CCC (0.87), while the other three models yielded estimates with RPE > 20%. None of the four models we assessed estimated GEI for other cattle (i.e., nondairy) with an RPE < 20% or CCC > 0.30. The inaccuracy in GEI estimates suggests an error of the same magnitude in EFCH4 estimates. Therefore, a concerted effort is needed to improve the accuracy of enteric CH4 estimation models for cattle in SSA.

The importance of particularising the model to estimate landfill GHG emissions

Methane generation in landfills can be estimated using mathematical models. One of the most widespread estimation models is that developed by the Intergovernmental Panel on Climate Change (IPCC). Despite its popularity, the simplicity that characterises this model markedly limits the possibility of representing operation alternatives, which can strongly impact surface emissions and hinder the introduction of local data that are sometimes available. In this study, the IPCC model was applied to a case study from which field data on gas emissions were available. To fit the model to the studied landfill conditions, a series of modifications were made, including changes in Degradable Organic Carbon (DOC) and methane generation rate constant (k) values, and degradation times for some waste fractions, and by considering leachate carbon and the inclusion of gas lateral migration phenomena or changes in the methane oxidation factor. The model's Final Version improved the fit of its Initial Version to the experimentally estimated values in the case study by more than 65%. Some modifications, such as considering the carbon dragged by leachate or the contour migration of gas, have a minor impact on the model's fit. However, changes in the degradation time of some fractions according to their particular pretreatment or the modification of parameter k in accordance with the moisture conditions in each landfill phase, strongly influence the model's results. This highlights the importance of particularising estimation models to achieve more accurate results, which allow better estimates of the efficiency of mitigation measures for landfill gas emissions in each facility.

Prediction of Climate Change using SVM and Naïve Bayes Machine Learning Algorithms

Various reasons are there in failures of Intergovernmental Panel on Climate Change (IPCC) simulation model for prediction of climate change. For the better understanding of IPCC model’s failures by researchers, an improvement is qualitative and quantitative analysis is required and to be implemented. We come across a continuous crashes in simulation of Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4), while measuring the impact of ocean model parameter uncertainties on weather simulations, during the period of uncertainty quantification (UQ) ensemble. This manuscript analyse the different machine learning algorithms, such as, Random forest, Linear Regression, k-means and naïve-bayes algorithms. From machine learning, a quality classifier called support vector machine (SVM) classification is used to predict and quantify the failures probability as a function of the values of POP2 parameters. Apart from quantification and prediction, this method performs a better understanding in simulation crashes in other complex geo-scientific models.

Prediction of Greenhouse Gas Emissions in Municipal Solid Waste Landfills Using LandGEM and IPCC Methods in Yazd, Iran

Introduction: The increase in greenhouse gas (GHG) emissions has changed the global temperature and had a negative impact on global climate conditions. Landfill gas is one of the major GHG contributors. With the knowledge of GHG inventory, it is possible to carry out disaster prevention measures.&#x0D; Materials and Methods: In this study, tow Landfill Gas Emissions Modeling (LandGEM) and Intergovernmental Panel on Climate Change (IPCC), were used to determine the GHG quantity of the Yazd county landfill sector using from 2000 to 2020.&#x0D; Results: During this period, by the IPCC model, the total level of methane emissions from the Yazd county landfill was 23.17 Giga gram/y (Gg/y), while based on the LandGEM model, the total value of methane emissions from the Yazd county landfill was 5.74 Gg/y. The total amount of CO2 in the Yazd county landfill of the years 2000–2020 is estimated to be 15.75 Gg/y in the LandGEM model. There is the potential to generate 11.88 MWh/year electricity for the Yazd county landfill in 2020.&#x0D; Conclusion: The results of the present study can be employed to plan and implement a system for collecting methane gas and control the emission of GHG to landfills.

Does nature of livelihood regulate the urban community's vulnerability to climate change? Guwahati city, a case study from North East India

There is dearth of studies on climate change based vulnerabilities of the urban people. It has been a matter of widespread debate whether nature of livelihood has any role to play in regulating the vulnerabilities of an individual. To find an answer, in a first ever attempt, this study tested three different approaches viz. Livelihood Vulnerability Index (LVI), LVI IPCC (Intergovernmental Panel on Climate Change) models and Climate Vulnerability Index (CVI) to estimate climate change vulnerability of urban communities from various livelihoods in Guwahati city, Assam, India. Guwahati is considered to be the gateway to the seven North Eastern states of India and therefore strategically very important for this region. A structured survey was conducted involving 200 stakeholders from various livelihood sectors viz. construction workers, perishable item sellers, farmers, taxi/auto driver/rickshaw puller/coolie, tea stall/fast food seller, gas cylinder deliverymen, street vendors/salespersons, traffic police/police, doctors and boatmen. Data was systematically aggregated and examined using the above-mentioned composite indices. The differential vulnerabilities were compared and results suggested that the farmers were the most vulnerable community by virtue of their high sensitivity towards health, economic losses, exacerbated by their poor adaptive capacity toward unpredictable climatic variations. Doctors were the least vulnerable owing to their higher levels of awareness and adaptive capacity. These results reiterated the importance of awareness and access to resources in regulating vulnerability. The vulnerability scores also revealed that LVI and Model II (M II) of LVI IPCC approaches were the two most suitable indices and could be used for comparative vulnerability analysis. These pragmatic approaches can be used to assess the community vulnerabilities and could stimulate robust Climate Smart Urban Planning (CSUP).

Human CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Emissions Have Little Effect on Atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;

The United Nations Intergovernmental Panel on Climate Change (IPCC) agrees human CO2 is only 5 percent and natural CO2 is 95 percent of the CO2 inflow into the atmosphere. The ratio of human to natural CO2 in the atmosphere must equal the ratio of the inflows. Yet IPCC claims human CO2 has caused all the rise in atmospheric CO2 above 280 ppm, which is now 130 ppm or 32 percent of today’s atmospheric CO2. To cause the human 5 percent to become 32 percent in the atmosphere, the IPCC model treats human and natural CO2 differently, which is impossible because the molecules are identical. IPCC’s Bern model artificially traps human CO2 in the atmosphere while it lets natural CO2 flow freely out of the atmosphere. By contrast, a simple Physics Model treats all CO2 molecules the same, as it should, and shows how CO2 flows through the atmosphere and produces a balance level where outflow equals inflow. Thereafter, if inflow is constant, level remains constant. The Physics Model has only one hypothesis, that outflow is proportional to level. The Physics Model exactly replicates the 14C data from 1970 to 2014 with only two physical parameters: balance level and e-time. The 14C data trace how CO2 flows out of the atmosphere. The Physics Model shows the 14 CO2 e-time is a constant 16.5 years. Other data show e-time for 12CO2 is about 4 to 5 years. IPCC claims human CO2 reduces ocean buffer capacity. But that would increase e-time. The constant e-time proves IPCC’s claim is false. IPCC argues that the human-caused reduction of 14C and 13C in the atmosphere prove human CO2 causes all the increase in atmospheric CO2. However, numbers show these isotope data support the Physics Model and reject the IPCC model. The Physics Model shows how inflows of human and natural CO2 into the atmosphere set balance levels proportional to their inflows. Each balance level remains constant if its inflow remains constant. Continued constant CO2 emissions do not add more CO2 to the atmosphere. No CO2 accumulates in the atmosphere. Present human CO2 inflow produces a balance level of about 18 ppm. Present natural CO2 inflow produces a balance level of about 392 ppm. Human CO2 is insignificant to the increase of CO2 in the atmosphere. Increased natural CO2 inflow has increased the level of CO2 in the atmosphere.

Methane Flux Estimation in MSW Open Dump Site by In-Situ Measurement and IPCC (DM and FOD) Model

This paper deals with the use of different methods to predict methane generation on an open dump site. Methane recovery data obtained on site as part of a research program being carried out at the Devguradia dumpsite, Indore, is analyzed and used to obtain field methane emission in three seasons. Field data from Municipal solid waste (MSW) dump site are presented and discussed measured in-situ technique i.e. using close chamber methods. The Intergovernmental Panel on Climate Change (IPCC) Default Method (DM) and First order Decay (FOD) method is used to estimate methane generation. It is found that despite the assumptions and the simplicity of the adopted procedures, the values methane obtained are slightly varying to those measured in the field. The values obtained were 4.5, 2.9 and 0.35 Gg/y respectively.

Climate Change Detection with the Global GPS Information System

The Intergovernmental Panel on Climate Change (IPCC) has predicted a persistent and wide-spread warming of temperatures close to the surface over the entire globe in the 21st century, due to the continuously increases in the concentrations of anthropogenic greenhouse gases in the atmosphere. How realistic are the predictions made by the IPCC models, especially over the polar region where the highest increases in temperatures were predicted? Due to the lack of accurate temperature measurements over polar region, the estimate of temperature trends over the polar region remains to be determined. The polar region is also one of the regions where temperature trends remain uncertain. In this work we use profiles of temperature measurements from the FORMOSAT-3/COSMIC (F3C) to determine global temperature trends during the period 2006-2016. The radio occultation (RO) technique gives the F3C a unique and accurate temperature measurement when verified against radiosonde measurements, and when compared with other passive-based temperature measurements using the thermal infra-red emission method. The RO methods use known sources from the radio waves provided by the Global Positioning System (GPS). With a good global coverage by the F3C, we can work out temperature trends on a global scale. Our analysis shows following main results. The distinctive warming over the northern hemisphere polar regions. This warming in the polar region, identified by the F3C observations, confirms the predictions made by the IPCC models. As warming trends in the polar region are the most worried concerns for the global warming, which is likely to lead to progressive reduction in sea ice content, melting glaciers, etc. Our work with the F3C data proves this is terrifying picture is actually happening now. Our analysis also shows distinctive warming over the southern hemisphere polar regions. Again, this proves significant warming during the cold season in the polar region which is consistent with what we have found in the northern hemisphere polar regions. In conclusion, the F3C data demonstrates that significant warming has occurred during the period 2007-2013, and most pronounced warming areas have occurred over the polar region. The pronounced warming over the polar region likely to have worsen effects on sea ice lost, and melting glaciers.

Global warming projections to 2100 using simple CO2 greenhouse gas modeling and comments on CO2 climate sensitivity factor

As global climate change auspiciously transcends national boundaries, it is imperative to make effective treaties to reduce greenhouse gas (GHG) emissions (e.g., CO2 in particular) while improving energy usage efficiency. The scientific community through the Intergovernmental Panel on Climate Change (IPCC) has made reasonable anthropogenic global warming (AGW) predictions. However, the IPCC models have failed to predict the global warming pause since ca. 2000 to 2014. The 1880–2015 global temperature anomaly (GTA) can be modelled by the equation proposed in part by Loehle and Scafetta, 2011, Open Atmos. Sci. J. 5, 74–86 with the incorporation of the CO2 sensitivity factor (c) in this work: GTA=a∗cos(ω∗(y−1880)+ϕ)+c∗ln([CO2]y/[CO2]1958)+d∗(y−1958)+e where a (amplitude) = 0.15 °C, ω (angular frequency) = 2.π/58 rad y−1, ϕ (phase shift) = −0.05 rad, c (climate sensitivity factor) = 2.52 °C, y = year, d = 0.0013 °C y−1 (global warming since the Little Ice Age), [CO2] is the atmospheric CO2 concentration (in ppm), and e (constant) = −0.02 °C. The extracted model CSF (2.52 °C (CSF)) is in excellent agreement with an earlier value of 2.52 °C (Callendar, 1938, Quart. J. Roy. Meteorol. Soc. 64, 223–240 – corresponds to 283 K, a water vapor pressure of 1,000 Pa, and a [CO2] range of 100–600 ppm), but is significantly smaller than the average IPCC AR5 CSF (4.33 °C). Hence, a more reasonable CSF would be 2.52 °C based on actual GTA data in the present work modeling. From that equation, the 2100 GTA is projected to be −0.3, 0.7, and 1.8 °C under 3 representative 100.Δ[CO2]/([CO2].Δyear change scenarios of −0.5, 0.0, and 0.5%.y−1, respectively, compared to the 2015 GTA of 0.9 °C. The COP21 agreement calls for the GTA to be less than 1.4 °C (preferrably 0.9 °C) by 2100, and this can only be achieved if the increase in atmospheric CO2 level after 2015 is maintained at 0.0% per year.

Evaluation of the AR4 CMIP3 and the AR5 CMIP5 Model and Projections for Precipitation in Northeast Brazil

With the simulations of the models used in the latest reports from the Intergovernmental Panel on Climate Change (IPCC), comparative studies are necessary between observations and the so-called historical run (C20) and future projections of the AR4 (A2) and AR5 (RCP8.5) experiments, in order to assess whether the AR5 models had a better performance in the representation of physical processes. This article compares the sensitivity of IPCC models (AR4 and AR5) in representing the anuall average and seasonal rainfall variation (summer and autumn) in three regions of the Northeast of Brazil between 1979 and 2000, using the CMAP - CPC (Merged Analysis of Precipitation) data as reference. The projections made by these models for the period 2040-2070 were also analyzed.

Comparing climate projections to observations up to 2011

We analyse global temperature and sea-level data for the past few decades and compare them to projections published in the third and fourth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). The results show that global temperature continues to increase in good agreement with the best estimates of the IPCC, especially if we account for the effects of short-term variability due to the El Niño/Southern Oscillation, volcanic activity and solar variability. The rate of sea-level rise of the past few decades, on the other hand, is greater than projected by the IPCC models. This suggests that IPCC sea-level projections for the future may also be biased low.

Atlantic Warm-Pool Variability in the IPCC AR4 CGCM Simulations

Abstract This study investigates Atlantic warm pool (AWP) variability in the twentieth century and preindustrial simulations of coupled GCMs submitted to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In the twentieth-century simulations, most coupled models show very weak AWP variability, represented by an AWP area index, because of the cold SST bias in the AWP. Among the IPCC models, a higher AWP SST index corresponds to increased net downward shortwave radiation and decreased low-level cloud fraction during the AWP peak season. This suggests that the cold SST bias in the AWP region is at least partly caused by an excessive amount of simulated low-level cloud, which blocks shortwave radiation from reaching the sea surface. AWP natural variability is examined in preindustrial simulations. Spectral analysis reveals that only multidecadal band variability of the AWP is significant in observations. All models successfully capture the multidecadal band, but they show that interannual and/or decadal variability is also significant. On the multidecadal time scale, the global SST difference pattern between large AWP years and small AWP years resembles the geographic pattern of the AMO for most coupled models. Observational analysis indicates that both positive ENSO phase and negative NAO phase in winter correspond to reduced trade winds in the AWP region. The westerly anomalies induced by positive ENSO and negative NAO lead to local heating and warm SST from March to May and February to April, respectively. This behavior as a known feature of anomalous AWP growth is well captured by only five models.

The Roles of Greenhouse Gases in Global Warming

Scientists are still debating the reasons for “global warming”. The author questions the validity of the calculations for the models published by the Intergovernmental Panel on Climate Change (IPCC) and especially the future scenarios. Through spectral calculations, the author finds that water vapour accounts for approximately 87% of the greenhouse (GH) effect and only 10% of CO2. A doubling of the present level of CO2 would increase the global temperature by only 0.51 °C without water feedback. The IPCC claims that a temperature increase of 0.76 °C for 2005 was caused in part by water (about 50%), because relative humidity (RH) stays constant in their model. The calculations prove that CO2 would have increased the temperature by only 0.2 °C since 1750 and that the measured decrease in water since 1948 has compensated for this increase. This study has also produced results indicating a negative feedback for relative humidity. The simulations of this study propose that the IPCC's model atmospheres could be approximately 50% too dry.

Will Amazonia Dry Out? Magnitude and Causes of Change from IPCC Climate Model Projections

Abstract The Amazon rain forest may undergo significant change in response to future climate change. To determine the likelihood and causes of such changes, the authors analyzed the output of 24 models from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and a dynamic vegetation model, Vegetation–Global–Atmosphere–Soil (VEGAS), driven by these climate output. Their results suggest that the core of the Amazon rain forest should remain largely stable because rainfall in the core of the basin is projected to increase in nearly all models. However, the periphery, notably the southern edge of Amazonia and farther south into central Brazil (SAB), is in danger of drying out, driven by two main processes. First, a decline in precipitation of 11% during the southern Amazonia’s dry season (May–September) reduces soil moisture. Two dynamical mechanisms may explain the forecast reduction in dry season rainfall: 1) a general subtropical drying under global warming when the dry season southern Amazon basin is under the control of subtropical high pressure and 2) a stronger north–south tropical Atlantic sea surface temperature gradient and, to a lesser degree, a warmer eastern equatorial Pacific. The drying corresponds to a lengthening of the dry season by approximately 10 days. The decline in soil moisture occurs despite an increase in precipitation during the wet season, because of nonlinear responses in hydrology associated with the decline in dry season precipitation, ecosystem dynamics, and an increase in evaporative demand due to the general warming. In terms of ecosystem response, higher maintenance cost and reduced productivity under warming may also have additional adverse impact. Although the IPCC models have substantial intermodel variation in precipitation change, these latter two hydroecological effects are highly robust because of the general warming simulated by all models. As a result, when forced by these climate projections, a dynamic vegetation model VEGAS projects an enhancement of fire risk by 20%–30% in the SAB region. Fire danger reaches its peak in Amazonia during the dry season, and this danger is expected to increase primarily because of the reduction in soil moisture and the decrease in dry season rainfall. VEGAS also projects a reduction of about 0.77 in leaf area index (LAI) over the SAB region. The vegetation response may be partially mediated by the CO2 fertilization effect, because a sensitivity experiment without CO2 fertilization shows a higher 0.89 decrease in LAI. Southern Amazonia is currently under intense human influence as a result of deforestation and land-use change. Should this direct human impact continue at present rates, added pressure to the region’s ecosystems from climate change may subject the region to profound changes in the twenty-first century.

Research on Forecasting for the Manmade Global Warming Alarm

The validity of the manmade global warming alarm requires the support of scientific forecasts of (1) a substantive long-term rise in global mean temperatures in the absence of regulations, (2) serious net harmful effects due to global warming, and (3) cost-effective regulations that would produce net beneficial effects versus alternatives policies, including doing nothing. Without scientific forecasts for all three aspects of the alarm, there is no scientific basis to enact regulations. In effect, the warming alarm is like a three-legged stool: Each leg needs to be strong. Despite repeated appeals to global warming alarmists, we have been unable to find scientific forecasts for any of the three legs. We drew upon scientific (evidence-based) forecasting principles to audit the forecasting procedures used to forecast global mean temperatures by the Intergovernmental Panel on Climate Change (IPCC) — leg “1” of the stool. This audit found that the IPCC procedures violated 81% of the 89 relevant forecasting principles. We also audited forecasting procedures, used in two papers, that were written to support regulation regarding the protection of polar bears from global warming — leg “3” of the stool. On average, the forecasting procedures violated 85% of the 90 relevant principles. The warming alarmists have not demonstrated the predictive validity of their procedures. Instead, their argument for predictive validity is based on their claim that nearly all scientists agree with the forecasts. This count of “votes” by scientists is not only an incorrect tally of scientific opinion, it is also, and most importantly, contrary to the scientific method. We conducted a validation test of the IPCC forecasts that were based on the assumption that there would be no regulations. The errors for the IPCC model long-term forecasts (for 91 to 100 years in the future) were 12.6 times larger than those from an evidence-based “no change” model. Based on our own analyses and the documented unscientific behavior of global warming alarmists, we concluded that the global warming alarm is the product of an anti-scientific political movement. Having come to this conclusion, we turned to the “structured analogies” method to forecast the likely outcomes of the warming alarmist movement. In our ongoing study we have, to date, identified 26 similar historical alarmist movements. None of the forecasts behind the analogous alarms proved correct. Twenty-five alarms involved calls for government intervention and the government imposed regulations in 23. None of the 23 interventions was effective and harm was caused by 20 of them. Our findings on the scientific evidence related to global warming forecasts lead to the following recommendations: End government funding for climate change research. End government funding for research predicated on global warming (e.g., alternative energy; CO2 reduction; habitat loss). End government programs and repeal regulations predicated on global warming. End government support for organizations that lobby or campaign predicated on global warming.

Models fail to capture Arctic ice thinning, drift trends

Observations have shown that Arctic sea ice extent has declined over the past several decades and that this decline has accelerated in recent years. However, climate models used in the Intergovernmental Panel on Climate Change (IPCC) fourth assessment report underestimate the observed decrease in ice extent. Furthermore, Rampal et al. studied 13 IPCC models and found that the models underestimate Arctic sea ice thinning by about a factor of 4 on average. In addition to thinning, observations show that Arctic sea ice is drifting more rapidly, but the authors found that the climate models do not capture this drift acceleration. The authors note that in most of the models, sea ice drifts faster when it is thicker, contrary to observations, which show thinner ice drifting faster. Faster drift increases the rate at which ice flows out of the Arctic. The models' weak coupling between ice thickness and drift velocity partly explains the models' underestimation of ice area, thickness, and velocity trends.