Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks

Final published version of article.© 2014 American Meteorological SocietyIn the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO2 concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO2 emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations.When forced by RCP8.5 CO2 emissions, models simulate a large spread in atmospheric CO2; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO2 (on average by 44 ppm, 985 ± 97ppm by 2100) and hence higher radiative forcing (by 0.25Wm-2) when driven by CO2 emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO2, with the present-day biases reasonably well correlated with future atmospheric concentrations' departure from the prescribed concentration. The uncertainty in CO2 projections is mainly attributable to uncertainties in the response of the land carbon cycle. As a result of simulated higher CO2 concentrations than in the concentration-driven simulations, temperature projections are generally higher when ESMs are driven with CO2 emissions. Global surface temperature change by 2100 (relative to present day) increased by 3.9° ± 0.9°C for the emission-driven simulations compared to 3.7° ± 0.7°C in the concentration-driven simulations. Although the lower ends are comparable in both sets of simulations, the highest climate projections are significantly warmer in the emission-driven simulations because of stronger carbon cycle feedbacks. © 2014 American Meteorological Society.Department for Environment, Food and Rural Affairs (DEFRA)Department of Energy & Climate Change (DECC

Selecting CMIP5 GCMs for downscaling over multiple regions

The unprecedented availability of 6-hourly data from a multi-model GCM ensemble in the CMIP5 data archive presents the new opportunity to dynamically downscale multiple GCMs to develop high-resolution climate projections relevant to detailed assessment of climate vulnerability and climate change impacts. This enables the development of high resolution projections derived from the same set of models that are used to characterise the range of future climate changes at the global and large-scale, and as assessed in the IPCC AR5. However, the technical and human resource required to dynamically-downscale the full CMIP5 ensemble are significant and not necessary if the aim is to develop scenarios covering a representative range of future climate conditions relevant to a climate change risk assessment. This paper illustrates a methodology for selecting from the available CMIP5 models in order to identify a set of 8–10 GCMs for use in regional climate change assessments. The selection focuses on their suitability across multiple regions—Southeast Asia, Europe and Africa. The selection (a) avoids the inclusion of the least realistic models for each region and (b) simultaneously captures the maximum possible range of changes in surface temperature and precipitation for three continental-scale regions. We find that, of the CMIP5 GCMs with 6-hourly fields available, three simulate the key regional aspects of climate sufficiently poorly that we consider the projections from those models ‘implausible’ (MIROC-ESM, MIROC-ESM-CHEM, and IPSL-CM5B-LR). From the remaining models, we demonstrate a selection methodology which avoids the poorest models by including them in the set only if their exclusion would significantly reduce the range of projections sampled. The result of this process is a set of models suitable for using to generate downscaled climate change information for a consistent multi-regional assessment of climate change impacts and adaptation

Towards a typology for constrained climate model forecasts

In recent years several methodologies have been developed to combine and interpret ensembles of climate models with the aim of quantifying uncertainties in climate projections. Constrained climate model forecasts have been generated by combining various choices of metrics used to weight individual ensemble members, with diverse approaches to sampling the ensemble. The forecasts obtained are often significantly different, even when based on the same model output. Therefore, a climate model forecast classification system can serve two roles: to provide a way for forecast producers to self-classify their forecasts; and to provide information on the methodological assumptions underlying the forecast generation and its uncertainty when forecasts are used for impacts studies. In this review we propose a possible classification system based on choices of metrics and sampling strategies. We illustrate the impact of some of the possible choices in the uncertainty quantification of large scale projections of temperature and precipitation changes, and briefly discuss possible connections between climate forecast uncertainty quantification and decision making approaches in the climate change context

The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios

This paper presents a preliminary assessment of the relative effects of rate of climate change (four Representative Concentration Pathways - RCPs), assumed future population (five Shared Socio-economic Pathways - SSPs), and pattern of climate change (19 CMIP5 climate models) on regional and global exposure to water resources stress and river flooding. Uncertainty in projected future impacts of climate change on exposure to water stress and river flooding is dominated by uncertainty in the projected spatial and seasonal pattern of change in climate. There is little clear difference in impact between RCP2.6, RCP4.5 and RCP6.0 in 2050, and between RCP4.5 and RCP6.0 in 2080. Impacts under RCP8.5 are greater than under the other RCPs in 2050 and 2080. For a given RCP, there is a difference in the absolute numbers of people exposed to increased water resources stress or increased river flood frequency between the five SSPs. With the ‘middle-of-the-road’ SSP2, climate change by 2050 would increase exposure to water resources stress for between approximately 920 and 3400 million people under the highest RCP, and increase exposure to river flood risk for between 100 and 580 million people. Under RCP2.6, exposure to increased water scarcity would be reduced in 2050 by 22-24%, compared to impacts under the RCP8.5, and exposure to increased flood frequency would be reduced by around 16%. The implications of climate change for actual future losses and adaptation depend not only on the numbers of people exposed to changes in risk, but also on the qualitative characteristics of future worlds as described in the different SSPs. The difference in ‘actual’ impact between SSPs will therefore be greater than the differences in numbers of people exposed to impact

Using the past to constrain the future: how the palaeorecord can improve estimates of global warming

Climate sensitivity is defined as the change in global mean equilibrium temperature after a doubling of atmospheric CO2 concentration and provides a simple measure of global warming. An early estimate of climate sensitivity, 1.5-4.5{\deg}C, has changed little subsequently, including the latest assessment by the Intergovernmental Panel on Climate Change. The persistence of such large uncertainties in this simple measure casts doubt on our understanding of the mechanisms of climate change and our ability to predict the response of the climate system to future perturbations. This has motivated continued attempts to constrain the range with climate data, alone or in conjunction with models. The majority of studies use data from the instrumental period (post-1850) but recent work has made use of information about the large climate changes experienced in the geological past. In this review, we first outline approaches that estimate climate sensitivity using instrumental climate observations and then summarise attempts to use the record of climate change on geological timescales. We examine the limitations of these studies and suggest ways in which the power of the palaeoclimate record could be better used to reduce uncertainties in our predictions of climate sensitivity.Comment: The final, definitive version of this paper has been published in Progress in Physical Geography, 31(5), 2007 by SAGE Publications Ltd, All rights reserved. \c{opyright} 2007 Edwards, Crucifix and Harriso

Toward a Quantitative Estimate of Future Heat Wave Mortality under Global Climate Change

Background: Climate change is anticipated to affect human health by changing the distribution of known risk factors. Heat waves have had debilitating effects on human mortality, and global climate models predict an increase in the frequency and severity of heat waves. The extent to which climate change will harm human health through changes in the distribution of heat waves and the sources of uncertainty in estimating these effects have not been studied extensively. Objectives: We estimated the future excess mortality attributable to heat waves under global climate change for a major U.S. city. Methods: We used a database comprising daily data from 1987 through 2005 on mortality from all nonaccidental causes, ambient levels of particulate matter and ozone, temperature, and dew point temperature for the city of Chicago, Illinois. We estimated the associations between heat waves and mortality in Chicago using Poisson regression models. Results: Under three different climate change scenarios for 2081–2100 and in the absence of adaptation, the city of Chicago could experience between 166 and 2,217 excess deaths per year attributable to heat waves, based on estimates from seven global climate models. We noted considerable variability in the projections of annual heat wave mortality; the largest source of variation was the choice of climate model. Conclusions: The impact of future heat waves on human health will likely be profound, and significant gains can be expected by lowering future carbon dioxide emissions

Assessing variations of extreme indices inducing weather-hazards on critical infrastructures over Europe?the INTACT framework

Extreme weather events are projected to be more frequent and severe across the globe because of global warming. This poses challenging problems for critical infrastructures, which could be dramatically affected (or disrupted), and may require adaptation plans to the changing climate conditions. The INTACT FP7-European project evaluated the resilience and vulnerability of critical infrastructures to extreme weather events in a climate change scenario. To identify changes in the hazard induced by climate change, appropriate extreme weather indicators (EWIs), as proxies of the main atmospheric features triggering events with high impact on the infrastructures, were defined for a number of case studies and different approaches were analyzed to obtain local climate projections. We considered the influence of weighting and bias correction schemes on the delta approach followed to obtain the resulting projections, considering data from the Euro-CORDEX ensemble of regional future climate scenarios over Europe. The aim is to provide practitioners, decision-makers, and administrators with appropriate methods to obtain actionable and plausible results on local/regional future climate scenarios. Our results show a small sensitivity to the weighting approach and a large sensitivity to bias correcting the future projections.This work has been carried out within the activities of INTACT project, receiving funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° FP7-SEC-2013-1-606799. The information and views set out in this paper are those of the authors and do not necessarily reflect the opinion of the European Union. We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5

The Detection and Attribution Model Intercomparison Project DAMIP v1.0) contribution to CMIP6

Detection and attribution (D&A) simulations were important components of CMIP5 and underpinned the climate change detection and attribution assessments of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The primary goals of the Detection and Attribution Model Intercomparison Project (DAMIP) are to facilitate improved estimation of the contributions of anthropogenic and natural forcing changes to observed global warming as well as to observed global and regional changes in other climate variables; to contribute to the estimation of how historical emissions have altered and are altering contemporary climate risk; and to facilitate improved observationally constrained projections of future climate change. D&A studies typically require unforced control simulations and historical simulations including all major anthropogenic and natural forcings. Such simulations will be carried out as part of the DECK and the CMIP6 historical simulation. In addition D&A studies require simulations covering the historical period driven by individual forcings or subsets of forcings only: such simulations are proposed here. Key novel features of the experimental design presented here include firstly new historical simulations with aerosols-only, stratospheric-ozone-only, CO2-only, solar-only, and volcanic-only forcing, facilitating an improved estimation of the climate response to individual forcing, secondly future single forcing experiments, allowing observationally constrained projections of future climate change, and thirdly an experimental design which allows models with and without coupled atmospheric chemistry to be compared on an equal footing

Reconciled climate response estimates from climate models and the energy budget of Earth

Climate risks increase with mean global temperature, so knowledge about the amount of future global warming should better inform risk assessments for policymakers. Expected near-term warming is encapsulated by the transient climate response (TCR), formally defined as the warming following 70 years of 1% per year increases in atmospheric CO2 concentration, by which point atmospheric CO2 has doubled. Studies based on Earth’s historical energy budget have typically estimated lower values of TCR than climate models, suggesting that some models could overestimate future warming. However, energy-budget estimates rely on historical temperature records that are geographically incomplete and blend air temperatures over land and sea ice with water temperatures over open oceans. We show that there is no evidence that climate models overestimate TCR when their output is processed in the same way as the HadCRUT4 observation-based temperature record3, 4. Models suggest that air-temperature warming is 24% greater than observed by HadCRUT4 over 1861–2009 because slower-warming regions are preferentially sampled and water warms less than air5. Correcting for these biases and accounting for wider uncertainties in radiative forcing based on recent evidence, we infer an observation-based best estimate for TCR of 1.66 °C, with a 5–95% range of 1.0–3.3 °C, consistent with the climate models considered in the IPCC 5th Assessment Report