CLIMATE CHANGE: Health Scenarios for a Warming World

Abstract. Pattern scaling is a well-established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is better in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO2-/non-CO2-forcing decomposition is applied. The methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change.

Background and objectivesHeat related mortality is of great concern for public health, and estimates of future mortality under a warming climate are important for planning of resources and possible adaptation measures. Papers providing projections of future heat-related mortality were critically reviewed with a focus on the use of climate model data. Some best practice guidelines are proposed for future research.MethodsThe electronic databases Web of Science and PubMed/Medline were searched for papers containing a quantitative estimate of future heat-related mortality. The search was limited to papers published in English in peer-reviewed journals up to the end of March 2017. Reference lists of relevant papers and the citing literature were also examined. The wide range of locations studied and climate data used prevented a meta-analysis.ResultsA total of 608 articles were identified after removal of duplicate entries, of which 63 were found to contain a quantitative estimate of future mortality from hot days or heat waves. A wide range of mortality models and climate model data have been used to estimate future mortality. Temperatures in the climate simulations used in these studies were projected to increase. Consequently, all the papers indicated that mortality from high temperatures would increase under a warming climate. The spread in projections of future climate by models adds substantial uncertainty to estimates of future heat-related mortality. However, many studies either did not consider this source of uncertainty, or only used results from a small number of climate models. Other studies showed that uncertainty from changes in populations and demographics, and the methods for adaptation to warmer temperatures were at least as important as climate model uncertainty. Some inconsistencies in the use of climate data (for example, using global mean temperature changes instead of changes for specific locations) and interpretation of the effects on mortality were apparent. Some factors which have not been considered when estimating future mortality are summarised.ConclusionsMost studies have used climate data generated using scenarios with medium and high emissions of greenhouse gases. More estimates of future mortality using climate information from the mitigation scenario RCP2.6 are needed, as this scenario is the only one under which the Paris Agreement to limit global warming to 2°C or less could be realised. Many of the methods used to combine modelled data with local climate observations are simplistic. Quantile-based methods might offer an improved approach, especially for temperatures at the ends of the distributions. The modelling of adaptation to warmer temperatures in mortality models is generally arbitrary and simplistic, and more research is needed to better quantify adaptation. Only a small number of studies included possible changes in population and demographics in their estimates of future mortality, meaning many estimates of mortality could be biased low. Uncertainty originating from establishing a mortality baseline, climate projections, adaptation and population changes is important and should be considered when estimating future mortality.

This paper is the second of a two-part series analysing details of regional, hemispheric and global temperature change during the twentieth century. Based on the grid box data described in Part 1 we present global maps of the strength of regional temperature coherence measured in terms of the correlation decay length for both annual and seasonal mean data. Correlation decay lengths are generally higher for annual rather than seasonal data; higher in the Southern compared to the Northern Hemisphere; and consistently higher over the oceans, particularly the Indian and central north Pacific oceans. Spatial coherence is relatively low in all seasons over the mid to high latitudes of the Northern Hemisphere and especially low in summer over the northern North Atlantic region. We also describe selected regional temperature series and examine the similarities between these and hemispheric mean data, placing emphasis on the nature of the relationships in different seasons. The Equatorial Indian and Atlantic oceans appear to be the best regional proxies for representing global mean temperatures. Temperature change in regions bordering the northern North Atlantic are less representative of hemisphere mean data, particularly in summer. The implications of these results for the large-scale interpretation of high-frequency palaeoclimate proxies are discussed. Strictly, given the length of the instrumental data, our results apply to temperature variability on timescales up to, at most, 50 years. Traditional inferences on global mean temperature change based on regional proxies originating around the margins of the northern North Atlantic must be viewed with some caution. Information on temperature change over the oceans, especially the tropical oceans, is an important prerequisite for accurate portrayal of global mean change. Care must be taken when integrating the evidence of different climate proxies to respect the seasonality of the response. Warm-season temperatures are most atypical of the various seasonal averages, especially in the Northern Hemisphere. Inferring annual temperature change on the basis of summer-responsive data is highly questionable.

The realistic physical functioning of the greenhouse effect is reviewed, and the role of dynamic transport and water vapor is identified. Model errors and uncertainties are quantitatively compared with the forcing due to doubling CO2, and they are shown to be too large for reliable model evaluations of climate sensitivities. The possibility of directly measuring climate sensitivity is reviewed. A direct approach using satellite data to relate changes in globally averaged radiative flux changes at the top of the atmosphere to naturally occurring changes in global mean temperature is described. Indirect approaches to evaluating climate sensitivity involving the response to volcanic eruptions and Eocene climate change are also described. Finally, it is explained how, in principle, a climate that is insensitive to gross radiative forcing as produced by doubling CO2 might still be able to undergo major changes of the sort associated with ice ages and equable climates.

The participating member nations in Paris at the 2015 convention of the United Nations Framework Convention on Climate Change (UNFCCC) resolved to maintain the rise in global average temperature to a level much less than 2.0 °C compared to pre-industrial levels. It was also committed that the parties would continue with all-out endeavor to limit warming to 1.5 °C. For a country like India with a primarily agrarian economy this leads to two key questions. Firstly, what does the global rise of mean annual temperature (1.5 °C and 2.0 °C) mean at the regional scale? Secondly, what are the implications of keeping warming at or below 1.5 °C for different sectors and in particular on agriculture and water resources? To address these questions we have examined the annual and seasonal impacts of 1.5 °C and 2 °C global temperature rise (GTR) on temperature and rainfall change over all the states of India under two Representative concentration pathways, RCP 8.5 and RCP 4.5, using all Coupled Model Inter Comparison Project CMIP5 Models. Rainfall is projected to increase over all the states with very low change in the western part of the country and highest change in the North eastern and southern region of the country under RCP 8.5. 35% of the country is projected to witness a temperature change equal to or lesser than global mean temperature of 1.5 °C and 2.0 °C whereas 65% is expected to show a greater rise in temperature. The most severe temperature change is expected to be witnessed by the presently colder Northern most states of India such as Jammu and Kashmir, Himachal Pradesh and Uttaranchal (2.0 °C to 2.2 °C at 1.5 °C and 2.5 °C to 2.8 °C at 2.0 °C) in both RCPs. There are opportunities and threats due to climate change and it is imperative for researchers and policy makers to recognize these in the context of the scenarios of 1.5 °C and 2.0 °C global temperature changes. It is essential for the current national and state action plan on climate change and adaptation to be more sensitive in strategizing an efficient response to the different scenarios at the global level (3 °C, 2 °C and 1.5 °C) in order to take more informed policy decisions at global level in synergy with the regional analysis to be able to develop strategies that benefit the local populace.

The assessment of future flood risk presented considers three climate change scenarios (a 2°C and 4°C change in Global Mean Temperature by the 2050s and 2080s and a more extreme, but plausible future, the so-called H++ future), and three population growth projections (low, high and no growth). The analysis covers the whole of the UK (England, Wales, Scotland and Northern Ireland) and the risks associated with coastal, fluvial, surface water and groundwater flooding. Eight individual Adaptation Measures (including spatial planning, flood defence, catchment storage) are used to construct five Adaptation Scenarios (including enhanced and reduced levels of adaptation ambition in comparison to present day). Future flood risks for a range of climate, population and adaptation combinations are assessed using the UK Future Flood Explorer. The analysis highlights that significant increases in flood risk are projected to occur as early as the 2020s; a finding that reinforces the need for urgent action. The analysis also highlights that to manage risk effectively under a 2 or 4°C future an enhanced whole system approach to adaptation is needed. This will require action by a broad range of stakeholders, from national level down to individual households and businesses.

CO2 over the past 5 million years: Continuous simulation and new δ11B-based proxy data

Integrated assessment models (IAMs) have commonly been used to understand the relationship between the economy, the earth’s climate system and climate impacts. We compare the IPCC simulations of CO2 concentration, radiative forcing, and global mean temperature changes associated with five SRES ‘marker’ emissions scenarios with the responses of three IAMs—DICE, FUND and PAGE—to these same emission scenarios. We also compare differences in simulated temperature increase resulting from moving from a high to a low emissions scenario. These IAMs offer a range of climate outcomes, some of which are inconsistent with those of IPCC, due to differing treatments of the carbon cycle and of the temperature response to radiative forcing. In particular, in FUND temperatures up until 2100 are relatively similar for the four emissions scenarios, and temperature reductions upon switching to lower emissions scenarios are small. PAGE incorporates strong carbon cycle feedbacks, leading to higher CO2 concentrations in the twenty-second century than other models. Such IAMs are frequently applied to determine ‘optimal’ climate policy in a cost–benefit approach. Models such as FUND which show smaller temperature responses to reducing emissions than IPCC simulations on comparable timescales will underestimate the benefits of emission reductions and hence the calculated ‘optimal’ level of investment in mitigation.

There are large uncertainties in the estimation of greenhouse-gas climate feedback. Recent observations do not provide strong constraints because they are short and complicated by human interventions, while model-based estimates differ considerably. Rapid climate changes during the last glacial period (Dansgaard-Oeschger events), observed near-globally, were comparable in both rate and magnitude to current and projected 21st century climate warming and therefore provide a relevant constraint on feedback strength. Here we use these events to quantify the centennial-scale feedback strength of CO2, CH4 and N2O by relating global mean temperature changes, simulated by an appropriately forced low-resolution climate model, to the radiative forcing of these greenhouse gases derived from their concentration changes in ice-core records. We derive feedback estimates (95% CI) of 0.155 ± 0.035 W m−2 K−1 for CO2, 0.114 ± 0.013 W m−2 K−1 for CH4 and 0.106 ± 0.026 W m−2 K−1 for N2O. This indicates that much lower or higher estimates, particularly some previously published values for CO2, are unrealistic.

In recent years, the earth is burning, whether global warming and its influencing factors, based on time series and gray prediction model, put forward the hypothesis and conclusion, provide the data of various factors, to evaluate the past temperature level, to predict the future global temperature level and its trend and analyze the influencing factors. For problem 1, first, based on the analysis of the global average air temperature data from March 2012-February 2022 and March 2022-October 2022, it is unscientific that the global temperature rise in March 2022 led to a larger rise than in the past 10 years. Second, the ARIMA model and the gray prediction model are used to describe the past global temperature levels and predict the future global temperature levels, respectively. The ARIMA model and the grey prediction model show that the global average annual temperature change has been stable in the past, while the prediction that the average temperature of 20.00°C in 2050 or 2100 is inaccurate. The grey prediction model shows that the global temperature levels will continue to rise in the future, and shows that the average temperature in 2,289 observations is expected to reach 20.00°C. According to the forecast, the global average temperature will not, as long as natural resources are properly used, reach 20.00°C. For problem 2, We first divide the Earth into the southern and northern hemisphere, and second into tropical and southern temperate and northern temperate zones. The linear relationship between global average temperature and the two conditions is discussed separately. According to the results, it significantly shows a strong correlation between the global average temperature and time. If unchecked, the global average temperature will grow over time. Second, using the principal component analysis method shows that may affect the global average temperature in the Middle East energy consumption, global hydropower consumption, world oil consumption, global niobium minerals, the world gas proven reserves, global crude oil production, global cadmium production, and to the results are tested, concluded that the principal component analysis method to influence the main indicators of the global average temperature method is more accurate. Therefore, the suggestions for global temperature change are made: to strive to reduce methane emissions; to further strengthen the strategic deployment and practical actions for energy revolution and low-carbon development, while increasing the intensity of energy low-carbon transformation. For problem 3, We propose the feasibility of this model evaluation and prediction results on slowing down or curbing global warming trends. One is direct emissions reduction, with countries signing contracts to force their carbon emissions and unite against global warming. Reduce heat emissions should be put on everyone's daily living habits, with a limited use of daily necessities, to achieve limited production, limited processing, so as to curb industrial flooding, reduce heat emissions. Second, the indirect emission reduction. With the development of industry, carbon dioxide emissions are constantly increasing, leading to a series of problems such as global warming and sea level rise. Afforestation can absorb the carbon dioxide contained in the atmosphere and reduce the greenhouse gas content in the atmosphere. Therefore, we must realize the importance of afforestation, take appropriate measures to improve the effect of afforestation, and better alleviate global warming.

In 2015, the 21st Conference of the Parties reaffirmed the target of keeping the global mean temperature rise below 2 °C or 1.5 °C by 2100 while finding no consensus on how to decarbonize the global economy. In this regard, the speed of decarbonization reflects the (in)flexibility of transforming the energy sector due to engineering, political, or societal constraints. Using economy–energy–climate-integrated assessment models (IAMs), the maximum absolute rate of change in carbon emission allowed from each time step to the next, so-called carbon emission inertia (CEI), governs the magnitude of emission change, affecting investment decisions and economic welfare. Employing the model of investment and endogenous technological development (MIND), we conduct a cost-effectiveness analysis and examine anthropogenic global carbon emission scenarios in line with decarbonizing the global economy while measuring the global mean temperature. We examine the role of CEI as a crucial assumption, where the CEI can vary in four scenarios from 3.7% to 12.6% p.a. We provide what-if studies on global carbon emissions, global mean temperature change, and investments in renewable energy production and show that decarbonizing the global economy might still be possible before 2100 only if the CEI is high enough. In addition, we show that climate policy scenarios with early decarbonization and without negative emissions may still comply with the 2 °C target. However, our results indicate that the 1.5 °C target is not likely to be reached without negative emission technologies. Hence, the window of opportunity is beginning to close. This work can also assist to better interpret existing publications on various climate targets when altering CEI could have played a significant role.

Solar geoengineering has been suggested as a tool that might reduce damage from anthropogenic climate change. Analysis often assumes that geoengineering would be used to maintain a constant global mean temperature. Under this scenario, geoengineering would be required either indefinitely (on societal time scales) or until atmospheric CO2 concentrations were sufficiently reduced. Impacts of climate change, however, are related to the rate of change as well as its magnitude. We thus describe an alternative scenario in which solar geoengineering is used only to constrain the rate of change of global mean temperature; this leads to a finite deployment period for any emissions pathway that stabilizes global mean temperature. The length of deployment and amount of geoengineering required depends on the emissions pathway and allowable rate of change, e.g. in our simulations, reducing the maximum approximately 0.3°C per decade rate of change in an RCP 4.5 pathway to 0.1°C per decade would require geoengineering for 160 years; under RCP 6.0, the required time nearly doubles. We demonstrate that feedback control can limit rates of change in a climate model. Finally, we note that a decision to terminate use of solar geoengineering does not automatically imply rapid temperature increases: feedback could be used to limit rates of change in a gradual phase-out.

The recent publication of the summary for policy makers by Working Group I of the Intergovernmental Panel on Climate Change (IPCC) [1] has injected a renewed sense of urgency to address climate change. It is therefore timely to review the notion of preventing 'dangerous anthropogenic interference with the climate system' as put forward in the United Nations Framework Convention on Climate Change (UNFCCC). The article by Danny Harvey in this issue [2] offers a fresh perspective by rephrasing the concept of 'dangerous interference' as a problem of risk assessment. As Harvey points out, identification of 'dangerous interference' does not require us to know with certainty that future climate change will be dangerous—an impossible task given that our knowledge about future climate change includes uncertainty. Rather, it requires the assertion that interference would lead to a significant probability of dangerous climate change beyond some risk tolerance, and therefore would pose an unacceptable risk.

The recent (twentieth century) and future (twenty-first century) climate evolution in the Mediterranean region is analyzed in relation to annual mean global surface temperature change. The CMIP5 (Coupled Model Intercomparison Project, Phase 5) simulations, the CRU (Climate Research Unit) observational gridded dataset, and two twentieth century reanalyzes (ECMWF, European Center for Medium range Weather Forecasts) and NOAA ESRL (National Oceanic and Atmospheric Administration-Earth System Research Laboratory) are used. These datasets to large extent agree that in the twentieth century: (a) Mediterranean regional and global temperatures have warmed at a similar rate until the 1980s and (b) decadal variability determines a large uncertainty that prevents to identify long-term links between precipitation in the Mediterranean region and global temperature. However, in the twenty-first century, as mean global temperature increases, in the Mediterranean region, precipitation will decrease at a rate around − 20 mm/K or − 4%/K and temperature will warm 20% more than the global average. Warming will be particularly large in summer (approximately 50% larger than global warming) and for the land areas located north of the basin (locally up to 100% larger than global warming). Reduction of precipitation will affect all seasons in the central and southern Mediterranean areas, with maximum reduction for winter precipitation (− 7 mm/K or − 7%/K for the southern Mediterranean region). For areas along the northern border of the Mediterranean region, reduction will be largest in summer (− 7 mm/K or − 9%/K for the whole northern Mediterranean region), while they will not experience a significant reduction of precipitation in winter.

Solar radiation management (SRM) has been proposed as a form of geoengineering to reduce the climate effects of anthropogenic greenhouse gas emissions. Modeling studies have concluded that SRM, through a reduction in total solar irradiance by approximately 2%, roughly compensates for global mean temperature changes from a doubling of carbon dioxide concentrations. This paper examines the impact of SRM on the terrestrial hydrologic cycle using the Community Land Model, version 4, coupled to the Community Atmosphere Model, version 4, with reductions in solar radiation relative to simulations with present-day and elevated CO2 concentrations. There are significant global and regional impacts due to vegetation–climate interactions that are not compensated when reductions in total solar irradiance of 1%, 2%, and 3% are imposed on top of a doubling of present-day CO2 concentrations. Water cycling slows down under SRM, including decreases in global mean precipitation and evapotranspiration. Changes in runoff and soil moisture are spatially and temporally variable, with implications for local water availability. In the tropics, evapotranspiration decreases because of increases in vegetation water use efficiency. In northern midlatitudes, soil moisture increases when evapotranspiration decreases, with some exceptions during boreal summer. Changes in soil evaporation influence water cycling in the southern subtropics, rather than changes in transpiration. The hydrologic response to SRM is nonlinear, with global mean decreases greater than expected. These results imply that SRM may not compensate for higher greenhouse gas concentrations when one considers land–atmosphere interactions.