Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5° C and 2° C global warming with a higher-resolution global climate model

We projected changes in weather extremes, hydrological impacts and vulnerability to food insecurity at global warming of 1.5°C and 2°C relative to pre-industrial, using a new global atmospheric general circulation model HadGEM3A-GA3.0 driven by patterns of sea-surface temperatures and sea ice from selected members of the 5th Coupled Model Intercomparison Project (CMIP5) ensemble, forced with the RCP8.5 concentration scenario. To provide more detailed representations of climate processes and impacts, the spatial resolution was N216 (approx. 60 km grid length in mid-latitudes), a higher resolution than the CMIP5 models. We used a set of impacts-relevant indices and a global land surface model to examine the projected changes in weather extremes and their implications for freshwater availability and vulnerability to food insecurity. Uncertainties in regional climate responses are assessed, examining ranges of outcomes in impacts to inform risk assessments. Despite some degree of inconsistency between components of the study due to the need to correct for systematic biases in some aspects, the outcomes from different ensemble members could be compared for several different indicators. The projections for weather extremes indices and biophysical impacts quantities support expectations that the magnitude of change is generally larger for 2°C global warming than 1.5°C. Hot extremes become even hotter, with increases being more intense than seen in CMIP5 projections. Precipitation-related extremes show more geographical variation with some increases and some decreases in both heavy precipitation and drought. There are substantial regional uncertainties in hydrological impacts at local scales due to different climate models producing different outcomes. Nevertheless, hydrological impacts generally point towards wetter conditions on average, with increased mean river flows, longer heavy rainfall events, particularly in South and East Asia with the most extreme projections suggesting more than a doubling of flows in the Ganges at 2°C global warming. Some areas are projected to experience shorter meteorological drought events and less severe low flows, although longer droughts and/or decreases in low flows are projected in many other areas, particularly southern Africa and South America. Flows in the Amazon are projected to decline by up to 25%. Increases in either heavy rainfall or drought events imply increased vulnerability to food insecurity, but if global warming is limited to 1.5°C, this vulnerability is projected to remain smaller than at 2°C global warming in approximately 76% of developing countries. At 2°C, four countries are projected to reach unprecedented levels of vulnerability to food insecurity. This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels’

New generation of climate models track recent unprecedented changes in Earth's radiation budget observed by CERES

We compare top‐of‐atmosphere (TOA) radiative fluxes observed by the Clouds and the Earth's Radiant Energy System (CERES) and simulated by seven general circulation models forced with observed sea‐surface temperature (SST) and sea‐ice boundary conditions. In response to increased SSTs along the equator and over the eastern Pacific (EP) following the so‐called global warming “hiatus” of the early 21st century, simulated TOA flux changes are remarkably similar to CERES. Both show outgoing shortwave and longwave TOA flux changes that largely cancel over the west and central tropical Pacific, and large reductions in shortwave flux for EP low‐cloud regions. A model's ability to represent changes in the relationship between global mean net TOA flux and surface temperature depends upon how well it represents shortwave flux changes in low‐cloud regions, with most showing too little sensitivity to EP SST changes, suggesting a “pattern effect” that may be too weak compared to observations

EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6

This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average −0.09 W m−2 with a standard deviation due to interannual variability of 0.25 W m−2, showing no significant drift. The global surface air temperature in the simulation is on average 14.08 ∘C with an interannual standard deviation of 0.17 ∘C, exhibiting a small drift of 0.015 ± 0.005 ∘C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 ∘C, and its transient climate response is estimated at 2.1 ∘C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995–2014 has an average bias of −0.86 ± 0.05 ∘C with a standard deviation across ensemble members of 0.35 ∘C in the Northern Hemisphere and 1.29 ± 0.02 ∘C with a corresponding standard deviation of 0.05 ∘C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091–2100) of 4.9 ∘C above the preindustrial mean. A 0.5 ∘C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 ∘C

Predicted chance that global warming will temporarily exceed 1.5 °C

The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5 °C above preindustrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5 °C. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events

Dynamical and statistical downscaling of a global seasonal hindcast in eastern Africa

Within the FP7 EUPORIAS project we have assessed the utility of dynamical and statistical downscaling to provide seasonal forecast for impact modelling in eastern Africa. An ensemble of seasonal hindcasts was generated by the global climate model (GCM) EC-EARTH and then downscaled by four regional climate models and by two statistical methods over eastern Africa with focus on Ethiopia. The five-month hindcast includes 15 members, initialised on May 1?st covering 1991?2012. There are two sub-regions where the global hindcast has some skill in predicting June?September rainfall (northern Ethiopia ? northeast Sudan and southern Sudan - northern Uganda). The regional models are able to reproduce the predictive signal evident in the driving EC-EARTH hindcast over Ethiopia in June?September showing about the same performance as their driving GCM. Statistical downscaling, in general, loses a part of the EC-EARTH signal at grid box scale but shows some improvement after spatial aggregation. At the same time there are no clear evidences that the dynamical and statistical downscaling provide added value compared to the driving EC-EARTH if we define the added value as a higher forecast skill in the downscaled hindcast, although there is a tendency of improved reliability through the downscaling. The use of the global and downscaled hindcasts as input for the Livelihoods, Early Assessment and Protection (LEAP) platform of the World Food Programme in Ethiopia shows that the performance of the LEAP platform in predicting humanitarian needs at the national and sub-national levels is not improved by using downscaled seasonal forecasts.This work was done in the EUPORIAS project that received funding from the European Union Seventh Framework Programme (FP7) for Research, under grant agreement 308291. The authors thank the European Centre for Medium-Range Weather Forecasts (ECMWF), the Global Precipitation Climatology Centre (GPCC), the British Atmospheric Data Centre (BADC), the University of East Anglia (UEA), the University of Delaware, the University of Reading, the University of California, the Climate Prediction Center (CPC), the US Agency for International Development’s Famine Early Warning Network (FEWS NET) and the WATCH project for providing data. For the WRF simulations, the UCAN group acknowledges Santander Supercomputacion support group at the University of Cantabria, who provided access to the Altamira Supercomputer at the Institute of Physics of Cantabria (IFCA-CSIC), member of the Spanish Supercomputing Network. DWD wants to thank ECMWF for the support during the CCLM4 simulations which have been carried out at the ECMWF computing system. The SMHI RCA4 simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at National Supercomputer Centre (NSC) and the PDC Center for High Performance Computing (PDC-HPC)

The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.Peer reviewe

Nordic regionalisation of a greenhouse-gas stabilisation scenario

The impact of a CO2 stabilisation on the Swedish climate is investigated with the regional climate model RCA3 driven by boundary conditions obtained from a global coupled climate system model (CCSM3). The global model has been forced with observed greenhouse gas concentrations from pre-industrial conditions until today’s, and with an idealised further increase until the stabilisation level is reached. After stabilisation the model integration continues for another 150+ years in order to follow the delayed response of the climate system over a period of time.Results from the global and regional climate model are compared against observations and ECMWF reanalysis for 1961-1990. For this period, the global model is found to be too cold over Europe and with a zonal flow from the North Atlantic towards Europe that is too strong. The climate of the driving global model controls the climate of the regional model and the same deviations from one are thus inherited by the other. We therefore analyse the relative climate changes differences, or ratios, of climate variables between future's and today's climate.Compared to pre-industrial conditions, the global mean temperature changes by about 1.5oC as a result of the stabilisation at 450 ppmv equivalent CO2. Averaged over Europe, the temperature change is slightly larger, and it is even larger for Sweden and Northern Europe. Annual mean precipitation for Europe is unaffected, but Sweden receives more precipitation under higher CO2 levels. The inter-annual and decadal variability of annual mean temperature and precipitation does not change with any significant degree.The changes in temperature and precipitation are not evenly distributed with the season: the largest warming and increased precipitation in Northern Europe occurs during winter months while the summer climate remains more or less unchanged. The opposite is true for the Mediterranean region where the precipitation decreases mostly during summer. This also implies higher summer temperatures, but changes in winter are smaller. No substantial change in the wind climate over Europe is found

Potential decadal predictability and its sensitivity to sea ice albedo parameterization in a global coupled model

Decadal prediction is one focus of the upcoming 5th IPCC Assessment report. To be able to interpret the results and to further improve the decadal predictions it is important to investigate the potential predictability in the participating climate models. This study analyzes the upper limit of climate predictability on decadal time scales and its dependency on sea ice albedo parameterization by performing two perfect ensemble experiments with the global coupled climate model EC-Earth. In the first experiment, the standard albedo formulation of EC-Earth is used, in the second experiment sea ice albedo is reduced. The potential prognostic predictability is analyzed for a set of oceanic and atmospheric parameters. The decadal predictability of the atmospheric circulation is small. The highest potential predictability was found in air temperature at 2 m height over the northern North Atlantic and the southern South Atlantic. Over land, only a few areas are significantly predictable. The predictability for continental size averages of air temperature is relatively good in all northern hemisphere regions. Sea ice thickness is highly predictable along the ice edges in the North Atlantic Arctic Sector. The meridional overturning circulation is highly predictable in both experiments and governs most of the decadal climate predictability in the northern hemisphere. The experiments using reduced sea ice albedo show some important differences like a generally higher predictability of atmospheric variables in the Arctic or higher predictability of air temperature in Europe. Furthermore, decadal variations are substantially smaller in the simulations with reduced ice albedo, which can be explained by reduced sea ice thickness in these simulations. © 2011 The Author(s)