Source: https://www.crescendoproject.eu/publications/
Timestamp: 2019-04-20 05:15:30+00:00

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Short summary: The Global Carbon Budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results in order to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.
Short summary: The global coastal ocean took up 0.1 Pg C yr−1 of anthropogenic carbon during 1993–2012 based on new biogeochemical simulations with an eddying 3-D global model. That is about half of the most recent estimate, an extrapolation based on surface areas. It should not be confused with the continental shelf pump, perhaps 10 times larger, which includes natural as well as anthropogenic carbon. Coastal uptake of anthropogenic carbon is limited by its offshore transport.
Short summary: The Scenario Model Intercomparison Project (ScenarioMIP) will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. The design consists of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions. Climate model projections will facilitate integrated studies of climate change as well as address targeted scientific questions.
Short summary: We argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently to enable a systematic and rapid performance assessment of the large number of models participating in CMIP, and we announce our intention to implement such a system for CMIP6. At the same time, continuous scientific research is required to develop innovative metrics and diagnostics that help narrowing the spread in climate projections.
Abstract: Carbon dioxide removal from the atmosphere (CDR)—also known as ‘negative emissions’—features prominently in most 2 °C scenarios and has been under increased scrutiny by scientists, citizens, and policymakers. Critics argue that ‘negative emission technologies’ (NETs) are insufficiently mature to rely on them for climate stabilization. Some even argue that 2 °C is no longer feasible or might have unacceptable social and environmental costs. Nonetheless, the Paris Agreement endorsed an aspirational goal of limiting global warming to even lower levels, arguing that climate impacts—especially for vulnerable nations such as small island states—will be unacceptably severe in a 2 °C world. While there are few pathways to 2 °C that do not rely on negative emissions, 1.5 °C scenarios are barely conceivable without them. Building on previous assessments of NETs, we identify some urgent research needs to provide a more complete picture for reaching ambitious climate targets, and the role that NETs can play in reaching them.
At a glance: Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales. Discernible human impacts on the Earth’s climate system have been revealed by using statistical frameworks of detection–attribution. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts.
At a glance: Uncertainties in the response of vegetation to rising atmospheric CO2 concentrations contribute to the large spread in projections of future climate change. Climate–carbon cycle models generally agree that elevated atmospheric CO2 concentrations will enhance terrestrial gross primary productivity (GPP). However, the magnitude of this CO2 fertilization effect varies from a 20 per cent to a 60 per cent increase in GPP for a doubling of atmospheric CO2 concentrations in model studies. Here we demonstrate emergent constraints on large-scale CO2 fertilization using observed changes in the amplitude of the atmospheric CO2 seasonal cycle that are thought to be the result of increasing terrestrial GPP. Our comparison of atmospheric CO2 measurements from Point Barrow in Alaska and Cape Kumukahi in Hawaii with historical simulations of the latest climate–carbon cycle models demonstrates that the increase in the amplitude of the CO2 seasonal cycle at both measurement sites is consistent with increasing annual mean GPP, driven in part by climate warming, but with differences in CO2 fertilization controlling the spread among the model trends. As a result, the relationship between the amplitude of the CO2 seasonal cycle and the magnitude of CO2 fertilization of GPP is almost linear across the entire ensemble of models. When combined with the observed trends in the seasonal CO2 amplitude, these relationships lead to consistent emergent constraints on the CO2 fertilization of GPP. Overall, we estimate a GPP increase of 37 ± 9 per cent for high-latitude ecosystems and 32 ± 9 per cent for extratropical ecosystems under a doubling of atmospheric CO2 concentrations on the basis of the Point Barrow and Cape Kumukahi records, respectively.
Short summary: How the carbon cycle interacts with climate will affect future climate change and how society plans emissions reductions to achieve climate targets. The Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) is an endorsed activity of CMIP6 and aims to quantify these interactions and feedbacks in state-of-the-art climate models. This paper lays out the experimental protocol for modelling groups to follow to contribute to C4MIP. It is a contribution to the CMIP6 GMD special issue.
Abstract: Natural carbon sinks currently absorb approximately half of the anthropogenic CO2 emitted by fossil fuel burning, cement production and land-use change. However, this airborne fraction may change in the future depending on the emissions scenario. An important issue in developing carbon budgets to achieve climate stabilisation targets is the behaviour of natural carbon sinks, particularly under low emissions mitigation scenarios as required to meet the goals of the Paris Agreement. A key requirement for low carbon pathways is to quantify the effectiveness of negative emissions technologies which will be strongly affected by carbon cycle feedbacks. Here we find that Earth system models suggest significant weakening, even potential reversal, of the ocean and land sinks under future low emission scenarios. For the RCP2.6 concentration pathway, models project land and ocean sinks to weaken to 0.8 ± 0.9 and 1.1 ± 0.3 GtC yr−1 respectively for the second half of the 21st century and to −0.4 ± 0.4 and 0.1 ± 0.2 GtC yr−1 respectively for the second half of the 23rd century. Weakening of natural carbon sinks will hinder the effectiveness of negative emissions technologies and therefore increase their required deployment to achieve a given climate stabilisation target. We introduce a new metric, the perturbation airborne fraction, to measure and assess the effectiveness of negative emissions.
Short summary: This paper explores how the large diversity in spin-up protocols used for ocean biogeochemistry in CMIP5 models contributed to inter-model differences in modeled fields. We show that a link between spin-up duration and skill-score metrics emerges from both individual IPSL-CM5A-LR’s results and an ensemble of CMIP5 models. Our study suggests that differences in spin-up protocols constitute a source of inter-model uncertainty which would require more attention in future intercomparison exercises.
Abstract: As a major CO2 sink, the North Atlantic, especially its subpolar gyre region, is essential for the global carbon cycle. Decadal fluctuations of CO2 uptake in the North Atlantic subpolar gyre region are associated with the evolution of the North Atlantic Oscillation, the Atlantic meridional overturning circulation, ocean mixing and sea surface temperature anomalies. While variations in the physical state of the ocean can be predicted several years in advance by initialization of Earth system models, predictability of CO2 uptake has remained unexplored. Here we investigate the predictability of CO2 uptake variations by initialization of the MPI-ESM decadal prediction system. We find large multi-year variability in oceanic CO2 uptake and demonstrate that its potential predictive skill in the western subpolar gyre region is up to 4–7 years. The predictive skill is mainly maintained in winter and is attributed to the improved physical state of the ocean.
Abstract: Observed seasonal cycles in atmospheric potential oxygen (APO ~ O2 + 1.1 CO2) were used to evaluate eight ocean biogeochemistry models from the Coupled Model Intercomparison Project (CMIP5). Model APO seasonal cycles were computed from the CMIP5 air-sea O2 and CO2 fluxes and compared to observations at three Southern Hemisphere monitoring sites. Four of the models captured either the observed APO seasonal amplitude or phasing relatively well, while the other four did not. Many models had an unrealistic seasonal phasing or amplitude of the CO2 flux, which in turn influenced APO. By 2100 under RCP8.5, the models projected little change in the O2 component of APO but large changes in the seasonality of the CO2 component associated with ocean acidification. The models with poorer performance on present-day APO tended to project larger net carbon uptake in the Southern Ocean, both today and in 2100.
Short summary: The present study analyzes how cold region physical soil processes, especially freezing of soil water, impact large-scale hydrology and climate over northern hemisphere high latitude land areas. For this analysis, an atmosphere/land global climate model was used. It is shown that including these processes in the model leads to improved discharge in spring and a positive land atmosphere feedback to precipitation over the high latitudes that has previously not been noted for the high latitudes.
Scott, CE, Monks, SA, Spracklen, DV et al. (7 more authors) (2017) Faraday Discussions, 200. pp. 101-120. ISSN 1359-6640. doi.org/10.1039/c7fd00028f.
Abstract: More than one quarter of natural forests have been cleared by humans to make way for other land-uses, with changes to forest cover projected to continue. The climate impact of land-use change (LUC) is dependent upon the relative strength of several biogeophysical and biogeochemical effects. In addition to affecting the surface albedo and exchanging carbon dioxide (CO2) and moisture with the atmosphere, vegetation emits biogenic volatile organic compounds (BVOCs), altering the formation of short-lived climate forcers (SLCFs) including aerosol, ozone (O3) and methane (CH4). Once emitted, BVOCs are rapidly oxidised by O3, and the hydroxyl (OH) and nitrate (NO3) radicals. These oxidation reactions yield secondary organic products which are implicated in the formation and growth of aerosol particles and are estimated to have a negative radiative effect on the climate (i.e. a cooling). These reactions also deplete OH, increasing the atmospheric lifetime of CH4, and directly affect concentrations of O3; the latter two being greenhouse gases which impose a positive radiative effect (i.e. a warming) on the climate. Our previous work assessing idealised deforestation scenarios found a positive radiative effect due to changes in SLCFs; however, since the radiative effects associated with changes to SLCFs result from a combination of non-linear processes it may not be appropriate to scale radiative effects from complete deforestation scenarios according to the deforestation extent. Here we combine a land-surface model, a chemical transport model, a global aerosol model, and a radiative transfer model to assess the net radiative effect of changes in SLCFs due to historical LUC between the years 1850 and 2000.
Sergio Marconi, Tommaso Chiti, Angelo Nolè, Riccardo Valentini, Alessio Collalti. Forests 2017, 8, 220. doi: 10.20944/preprints201703.0141.v1.
Abstract: Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (Rh) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (Reco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC . The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.
Sam S. Rabin, Joe R. Melton, Gitta Lasslop, Dominique Bachelet, Matthew Forrest, Stijn Hantson, Jed O. Kaplan, Fang Li, Stéphane Mangeon, Daniel S. Ward, Chao Yue, Vivek K. Arora, Thomas Hickler, Silvia Kloster, Wolfgang Knorr, Lars Nieradzik, Allan Spessa, Gerd A. Folberth et al. Geosci. Model Dev., 10, 1175-1197, 2017. doi.org/10.5194/gmd-10-1175-2017.
Short summary: Global vegetation models are important tools for understanding how the Earth system will change in the future, and fire is a critical process to include. A number of different methods have been developed to represent vegetation burning. This paper describes the protocol for the first systematic comparison of global fire models, which will allow the community to explore various drivers and evaluate what mechanisms are important for improving performance. It also includes equations for all models.
N. Rastak, A. Pajunoja, J. C. Acosta Navarro, J. Ma, M. Song, D. G. Partridge, A. Kirkevåg, Y. Leong, W. W. Hu, N. F. Taylor, A. Lambe, K. Cerully, A. Bougiatioti, P. Liu, R. Krejci, et al. Geophys. Res. Lett., 44, 5167–5177, 2017. doi:10.1002/2017GL073056.
Abstract: A large fraction of atmospheric organic aerosol (OA) originates from natural emissions that are oxidized in the atmosphere to form secondary organic aerosol (SOA). Isoprene (IP) and monoterpenes (MT) are the most important precursors of SOA originating from forests. The climate impacts from OA are currently estimated through parameterizations of water uptake that drastically simplify the complexity of OA. We combine laboratory experiments, thermodynamic modeling, field observations, and climate modeling to (1) explain the molecular mechanisms behind RH‐dependent SOA water‐uptake with solubility and phase separation; (2) show that laboratory data on IP‐ and MT‐SOA hygroscopicity are representative of ambient data with corresponding OA source profiles; and (3) demonstrate the sensitivity of the modeled aerosol climate effect to assumed OA water affinity. We conclude that the commonly used single‐parameter hygroscopicity framework can introduce significant error when quantifying the climate effects of organic aerosol. The results highlight the need for better constraints on the overall global OA mass loadings and its molecular composition, including currently underexplored anthropogenic and marine OA sources.
Taraka Davies-Barnard, Andy Ridgwell, Joy Singarayer, Paul Valdes. Clim. Past, 13, 1381-1401, 2017. doi.org/10.5194/cp-13-1381-2017.
Abstract: The terrestrial biosphere is thought to be a key component in the climatic variability seen in the palaeo-record. It has a direct impact on surface temperature through changes in surface albedo and evapotranspiration (so-called biogeophysical effects) and, in addition, has an important indirect effect through changes in vegetation and soil carbon storage (biogeochemical effects) and hence modulates the concentrations of greenhouse gases in the atmosphere. The biogeochemical and biogeophysical effects generally have opposite signs, meaning that the terrestrial biosphere could potentially have played only a very minor role in the dynamics of the glacial–interglacial cycles of the late Quaternary. Here we use a fully coupled dynamic atmosphere–ocean–vegetation general circulation model (GCM) to generate a set of 62 equilibrium simulations spanning the last 120 kyr. The analysis of these simulations elucidates the relative importance of the biogeophysical versus biogeochemical terrestrial biosphere interactions with climate. We find that the biogeophysical effects of vegetation account for up to an additional −0.91 °C global mean cooling, with regional cooling as large as −5 °C, but with considerable variability across the glacial–interglacial cycle. By comparison, while opposite in sign, our model estimates of the biogeochemical impacts are substantially smaller in magnitude. Offline simulations show a maximum of +0.33 °C warming due to an increase of 25 ppm above our (pre-industrial) baseline atmospheric CO2 mixing ratio. In contrast to shorter (century) timescale projections of future terrestrial biosphere response where direct and indirect responses may at times cancel out, we find that the biogeophysical effects consistently and strongly dominate the biogeochemical effect over the inter-glacial cycle. On average across the period, the terrestrial biosphere has a −0.26 °C effect on temperature, with −0.58 °C at the Last Glacial Maximum. Depending on assumptions made about the destination of terrestrial carbon under ice sheets and where sea level has changed, the average terrestrial biosphere contribution over the last 120 kyr could be as much as −50 °C and −0.83 °C at the Last Glacial Maximum.
Julia Pongratz, Han Dolman, Axel Don, Karl‐Heinz Erb, Richard Fuchs, Martin Herold, Chris Jones, Tobias Kuemmerle, Sebastiaan Luyssaert, Patrick Meyfroidt, Kim Naudts. Glob Change Biol. 2018;24:1470 –1487. doi.org/10.1111/gcb.13988.
Abstract: As the applications of Earth system models (ESMs) move from general climate projections toward questions of mitigation and adaptation, the inclusion of land management practices in these models becomes crucial. We carried out a survey among modeling groups to show an evolution from models able only to deal with land‐cover change to more sophisticated approaches that allow also for the partial integration of land management changes. For the longer term a comprehensive land management representation can be anticipated for all major models. To guide the prioritization of implementation, we evaluate ten land management practices—forestry harvest, tree species selection, grazing and mowing harvest, crop harvest, crop species selection, irrigation, wetland drainage, fertilization, tillage, and fire—for (1) their importance on the Earth system, (2) the possibility of implementing them in state‐of‐the‐art ESMs, and (3) availability of required input data. Matching these criteria, we identify “low‐hanging fruits” for the inclusion in ESMs, such as basic implementations of crop and forestry harvest and fertilization. We also identify research requirements for specific communities to address the remaining land management practices. Data availability severely hampers modeling the most extensive land management practice, grazing and mowing harvest, and is a limiting factor for a comprehensive implementation of most other practices. Inadequate process understanding hampers even a basic assessment of crop species selection and tillage effects. The need for multiple advanced model structures will be the challenge for a comprehensive implementation of most practices but considerable synergy can be gained using the same structures for different practices. A continuous and closer collaboration of the modeling, Earth observation, and land system science communities is thus required to achieve the inclusion of land management in ESMs.
Katja Frieler, Stefan Lange, Franziska Piontek, Christopher P. O. Reyer, Jacob Schewe, Lila Warszawski, Fang Zhao, Louise Chini, Sebastien Denvil et al. Geosci. Model Dev., 10, 4321-4345, 2017. doi.org/10.5194/gmd-10-4321-2017.
Short summary: This paper describes the simulation scenario design for the next phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is designed to facilitate a contribution to the scientific basis for the IPCC Special Report on the impacts of 1.5 °C global warming. ISIMIP brings together over 80 climate-impact models, covering impacts on hydrology, biomes, forests, heat-related mortality, permafrost, tropical cyclones, fisheries, agriculture, energy, and coastal infrastructure.
Elisa Palazzi, Luca Filippi, Jost von Hardenberg. Climate Dynamics 2017, Volume 48, Issue 11–12, pp 3991–4008. doi.org/10.1007/s00382-016-3316-z.
Abstract: We use the output of twenty-seven Global Climate Models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5) to investigate the temperature changes and their dependence on the elevation in the Tibetan Plateau, Himalaya and Karakoram mountains and in the surrounding areas in historical model simulations and in future projections. The aim of this study is to explore if and to what extent the CMIP5 models show elevation-dependent warming (EDW) in this part of the globe and to investigate what are the driving factors at play and their relative importance. Our results indicate that the models show enhanced rates of warming at higher elevations in the Tibetan Plateau-Himalayan region in the twentieth century, and this phenomenon is projected to strengthen by the end of the twenty-first century under a high-emission scenario. We find a nonlinear relationship between the warming rates and the elevation, for both the minimum and the maximum temperature: regions with temperatures below the freezing level of water show more warming than the regions with temperatures above, likely suggesting a key role of mechanisms involving water phase changes, the presence/absence of snow and the snow-albedo feedback. We consider the main variables simulated by the CMIP5 models whose change may be related to temperature changes at higher elevations. We find that changes in surface albedo, atmospheric humidity and downward longwave radiation are relevant factors for EDW in the Tibetan Plateau-Himalayas, with surface albedo being the leading driver.
Peter A. G. Watson, Judith Berner, Susanna Corti, Paolo Davini, Jost von Hardenberg, Claudio Sanchez, Antje Weisheimer, Tim N. Palmer. 2017 J. Geophys. Res. Atmos.,122, 5738–5762. doi.org/10.1002/2016JD026386.
Abstract: Many global atmospheric models have too little precipitation variability in the tropics on daily to weekly time scales and also a poor representation of tropical precipitation extremes associated with intense convection. Stochastic parameterizations have the potential to mitigate this problem by representing unpredictable subgrid variability that is left out of deterministic models. We evaluate the impact on the statistics of tropical rainfall of two stochastic schemes: the stochastically perturbed parameterization tendency scheme (SPPT) and stochastic kinetic energy backscatter scheme (SKEBS), in three climate models: EC‐Earth, the Met Office Unified Model, and the Community Atmosphere Model, version 4. The schemes generally improve the statistics of simulated tropical rainfall variability, particularly by increasing the frequency of heavy rainfall events, reducing its persistence and increasing the high‐frequency component of its variability. There is a large range in the size of the impact between models, with EC‐Earth showing the largest improvements. The improvements are greater than those obtained by increasing horizontal resolution to ∼20 km. Stochastic physics also strongly affects projections of future changes in the frequency of extreme tropical rainfall in EC‐Earth. This indicates that small‐scale variability that is unresolved and unpredictable in these models has an important role in determining tropical climate variability statistics. Using these schemes, and improved schemes currently under development, is therefore likely to be important for producing good simulations of tropical variability and extremes in the present day and future.
P. Davini, S. Corti, F. D’Andrea, G. Rivière, J. von Hardenberg. Journal of Advances in Modeling Earth Systems, 9, 2615–2634. doi.org/10.1002/2017MS001082.
Abstract: The numerical simulation of atmospheric blocking, in particular over the Euro‐Atlantic region, still represents a main concern for the climate modeling community. We discuss the Northern Hemisphere winter atmospheric blocking representation in a set of 30 year atmosphere‐only simulations using the EC‐Earth Earth System Model with several ensemble members at five different horizontal resolutions (from 125 to 16 km). Results show that the negative bias in blocking frequency over Europe becomes negligible at resolutions of about 40 and 25 km. A combined effect by the more resolved orography and by a change in tropical precipitation is identified as the source of an upper tropospheric planetary wave. At the same time, a weakening of the meridional temperature gradient reduces the upper level baroclinicity and the zonal mean winds. Following these changes, in the high‐resolution configurations the Atlantic eddy‐driven jet stream is weakened favoring the breaking of synoptic Rossby waves over the Atlantic ridge and thus increasing the simulated European blocking frequency. However, at high‐resolution the Atlantic jet stream is too weak and the blocking duration is still underestimated. This suggests that the optimal blocking frequencies are achieved through compensation of errors between eddies found at upper levels (too strong) and eddies at lower levels (too weak). This also implies that eddies are not necessarily better represented at higher resolutions.
Ben B. B. Booth, Glen R. Harris, James M. Murphy, Jo I. House, Chris D. Jones, David Sexton and Stephen Sitch. 2017 J. Climate, 30: 3039-3053, doi.org/10.1175/JCLI-D-16-0178.1.
Abstract: Uncertainty in the behaviour of the carbon cycle is important in driving the range in future projected climate change. Previous comparisons of model responses with historical CO2 observations have suggested a strong constraint on simulated projections that could narrow the range considered plausible. Here we use a new 57 member perturbed parameter ensemble of variants of an Earth System model for 3 future scenarios, that (a) explores a wider range of potential climate responses than before, and (b) includes the impact of past uncertainty in carbon emissions on simulated trends. These two factors represent a more complete exploration of uncertainty, although they lead to a weaker constraint on the range of future CO2 concentrations as compared to earlier studies. Nevertheless, CO2 observations are shown to be effective at narrowing the distribution, excluding 30 of 57 simulations as inconsistent with historical CO2 changes. The perturbed model variants excluded are mainly at the high end of the future projected CO2 changes, with only 8 of the 26 variants projecting RCP8.5 2100 concentrations in excess of 1100 ppm retained. Interestingly, a minority of the high-end variants were able to capture historical CO2 trends, with the large magnitude response emerging later in the century (due to either high climate sensitivities, strong carbon feedbacks, or both). Comparison with observed CO2 is effective at narrowing both the range and distribution of projections out to mid 21st century for all scenarios, and to 2100 for a scenario with low emissions.
Abstract: The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.
Vera Melinda Gálfi, Tamás Bódai, and Valerio Lucarini. Complexity Volume 2017 (2017), Article ID 5340858, 20 pages. doi.org/10.1155/2017/5340858.
Abstract: We search for the signature of universal properties of extreme events, theoretically predicted for Axiom A flows, in a chaotic and high-dimensional dynamical system. We study the convergence of GEV (Generalized Extreme Value) and GP (Generalized Pareto) shape parameter estimates to the theoretical value, which is expressed in terms of the partial information dimensions of the attractor. We consider a two-layer quasi-geostrophic atmospheric model of the mid-latitudes, adopt two levels of forcing, and analyse the extremes of different types of physical observables (local energy, zonally averaged energy, and globally averaged energy). We find good agreement in the shape parameter estimates with the theory only in the case of more intense forcing, corresponding to a strong chaotic behaviour, for some observables (the local energy at every latitude). Due to the limited (though very large) data size and to the presence of serial correlations, it is difficult to obtain robust statistics of extremes in the case of the other observables. In the case of weak forcing, which leads to weaker chaotic conditions with regime behaviour, we find, unsurprisingly, worse agreement with the theory developed for Axiom A flows.
Gábor Drótos, Tamás Bódai, Tamás Tél. Eur. Phys. J. Special Topics 226, 2031–2038 (2017). Doi: 10.1140/epjst/e2017-70045-7.
Abstract: Ensemble approaches are becoming widely used in climate research. In contrast to weather forecast, however, in the climatic context one is interested in long-time properties, those arising on the scale of several decades. The well-known strong internal variability of the climate system implies the existence of a related dynamical attractor with chaotic properties. Under the condition of climate change this should be a snapshot attractor, naturally arising in an ensemble-based framework. Although ensemble averages can be evaluated at any instant of time, results obtained during the process of convergence of the ensemble towards the attractor are not relevant from the point of view of climate. In simulations, therefore, attention should be paid to whether the convergence to the attractor has taken place. We point out that this convergence is of exponential character, therefore, in a finite amount of time after initialization relevant results can be obtained. The role of the time scale separation due to the presence of the deep ocean is discussed from the point of view of ensemble simulations.
Bódai T, Franzke C. Phys Rev E. 2017 Sep;96(3-1):032120. doi: 10.1103/PhysRevE.96.032120.
Abstract: We conjecture for a linear stochastic differential equation that the predictability of threshold exceedances (I) improves with the event magnitude when the noise is a so-called correlated additive-multiplicative noise, no matter the nature of the stochastic innovations, and also improves when (II) the noise is purely additive, obeying a distribution that decays fast, i.e., not by a power law, and (III) deteriorates only when the additive noise distribution follows a power law. The predictability is measured by a summary index of the receiver operating characteristic curve. We provide support to our conjecture—to compliment reports in the existing literature on (II)—by a set of case studies. Calculations for the prediction skill are conducted in some cases by a direct numerical time-series-data-driven approach and in other cases by an analytical or semianalytical approach developed here.
Paolo Davini, Jost von Hardenberg, Susanna Corti, Hannah M. Christensen, Stephan Juricke, Aneesh Subramanian, Peter A. G. Watson, Antje Weisheimer, and Tim N. Palmer. Geosci. Model Dev., 10, 1383-1402, 2017.
Short summary: The Climate SPHINX project is a large set of more than 120 climate simulations run with the EC-Earth global climate. It explores the sensitivity of present-day and future climate to the model horizontal resolution (from 150 km up to 16 km) and to the introduction of two stochastic physics parameterisations. Results shows that the the stochastic schemes can represent a cheaper alternative to a resolution increase, especially for the representation of the tropical climate variability.
Silvia Terzago, Jost von Hardenberg, Elisa Palazzi, and Antonello Provenzale. The Cryosphere, 11, 1625-1645, 2017.
Short summary: The estimate of the current and future conditions of snow resources in mountain areas depends on the availability of reliable fine-resolution data sets and of climate models capable of properly representing snow processes and snow–climate interactions. This work considers the snow water equivalent data sets from remote sensing, reanalyses, regional and global climate models available for the Alps and explores their ability to provide a coherent view of the snowpack features and its changes.
Daniel S. Goll, Alexander J. Winkler, Thomas Raddatz, Ning Dong, Ian Colin Prentice, Philippe Ciais, and Victor Brovkin. Geosci. Model Dev., 10, 2009-2030, 2017. doi.org/10.5194/gmd-10-2009-2017.
Short summary: The response of soil organic carbon decomposition to warming and the interactions between nitrogen and carbon cycling affect the feedbacks between the land carbon cycle and the climate. In the model JSBACH carbon–nitrogen interactions have only a small effect on the feedbacks, whereas modifications of soil organic carbon decomposition have a large effect. The carbon cycle in the improved model is more resilient to climatic changes than in previous version of the model.
Burke, E. J., Ekici, A., Huang, Y., Chadburn, S. E., Huntingford, C., Ciais, P., Friedlingstein, P., Peng, S., and Krinner, G. Biogeosciences, 14, 3051-3066, 2017. doi.org/10.5194/bg-14-3051-2017.
Short summary: There are large reserves of carbon within the permafrost which might be released to the atmosphere under global warming. Our models suggest this release may cause an additional global temperature increase of 0.005 to 0.2°C by the year 2100 and 0.01 to 0.34°C by the year 2300. Under climate mitigation scenarios this is between 1.5 and 9 % (by 2100) and between 6 and 16 % (by 2300) of the global mean temperature change. There is a large uncertainty associated with these results.
Axel Lauer, Veronika Eyring, Mattia Righi, Michael Buchwitz, Pierre Defourny, Martin Evaldsson, Pierre Friedlingstein, Richard de Jeu, Gerrit de Leeuw, Alexander Loew, et al. 2017. Remote Sensing of Environment 203, 9-39, doi.org/10.1016/j.rse.2017.01.007.
Abstract: The Coupled Model Intercomparison Project (CMIP) is now moving into its sixth phase and aims at a more routine evaluation of the models as soon as the model output is published to the Earth System Grid Federation (ESGF). To meet this goal the Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for the systematic evaluation of Earth system models (ESMs) in CMIP, has been developed and a first version (1.0) released as open source software in 2015. Here, an enhanced version of the ESMValTool is presented that exploits a subset of Essential Climate Variables (ECVs) from the European Space Agency’s Climate Change Initiative (ESA CCI) Phase 2 and this version is used to demonstrate the value of the data for model evaluation. This subset includes consistent, long-term time series of ECVs obtained from harmonized, reprocessed products from different satellite instruments for sea surface temperature, sea ice, cloud, soil moisture, land cover, aerosol, ozone, and greenhouse gases. The ESA CCI data allow extending the calculation of performance metrics as summary statistics for some variables and add an important alternative data set in other cases where observations are already available. The provision of uncertainty estimates on a per grid basis for the ESA CCI data sets is used in a new extended version of the Taylor diagram and provides important additional information for a more objective evaluation of the models. In our analysis we place a specific focus on the comparability of model and satellite data both in time and space. The ESA CCI data are well suited for an evaluation of results from global climate models across ESM compartments as well as an analysis of long-term trends, variability and change in the context of a changing climate. The enhanced version of the ESMValTool is released as open source software and ready to support routine model evaluation in CMIP6 and at individual modeling centers.
Tamas Bodai. In Nonlinear and Stochastic Climate Dynamics, Franzke and O’Kane (Eds), Cambridge University Press, 2017 pp 392-429. doi.org/10.1017/9781316339251.015.
Abstract: We give here a brief summary of classical Extreme Value Theory for random variables, followed by that for deterministic dynamical systems, which is a rapidly developing area of research. Here we would like to contribute to that by conducting a numerical analysis designed to show particular features of extreme value statistics in dynamical systems, and also to explore the validity of the theory. We find that formulae that link the extreme value statistics with geometrical properties of the attractor hold typically for high-dimensional systems – whether a so-called geometric distance observable or a physical observable is concerned. In very low-dimensional settings, however, the fractality of the attractor prevents the system from having an extreme value law, which might well render the evaluation of extreme value statistics meaningless and so ill-suited for application.
L. Bopp, L. Resplandy, A. Untersee, P. Le Mezo, M. Kageyama. Philos Trans A Math Phys Eng Sci. 2017 Sep 13;375(2102). doi: 10.1098/rsta.2016.0323.
Abstract: All Earth System models project a consistent decrease in the oxygen content of oceans for the coming decades because of ocean warming, reduced ventilation and increased stratification. But large uncertainties for these future projections of ocean deoxygenation remain for the subsurface tropical oceans where the major oxygen minimum zones are located. Here, we combine global warming projections, model-based estimates of natural short-term variability, as well as data and model estimates of the Last Glacial Maximum (LGM) ocean oxygenation to gain some insights into the major mechanisms of oxygenation changes across these different time scales. We show that the primary uncertainty on future ocean deoxygenation in the subsurface tropical oceans is in fact controlled by a robust compensation between decreasing oxygen saturation (O2sat) due to warming and decreasing apparent oxygen utilization (AOU) due to increased ventilation of the corresponding water masses. Modelled short-term natural variability in subsurface oxygen levels also reveals a compensation between O2sat and AOU, controlled by the latter. Finally, using a model simulation of the LGM, reproducing data-based reconstructions of past ocean (de)oxygenation, we show that the deoxygenation trend of the subsurface ocean during deglaciation was controlled by a combination of warming-induced decreasing O2sat and increasing AOU driven by a reduced ventilation of tropical subsurface waters.This article is part of the themed issue ‘Ocean ventilation and deoxygenation in a warming world’.
Oliver Andrews, Erik Buitenhuis, Corinne Le Quéré, Parvadha Suntharalingam. Philos Trans A Math Phys Eng Sci. 2017 Sep 13;375(2102). doi: 10.1098/rsta.2016.0328.
Abstract: Secular decreases in dissolved oxygen concentration have been observed within the tropical oxygen minimum zones (OMZs) and at mid- to high latitudes over the last approximately 50 years. Earth system model projections indicate that a reduction in the oxygen inventory of the global ocean, termed ocean deoxygenation, is a likely consequence of on-going anthropogenic warming. Current models are, however, unable to consistently reproduce the observed trends and variability of recent decades, particularly within the established tropical OMZs. Here, we conduct a series of targeted hindcast model simulations using a state-of-the-art global ocean biogeochemistry model in order to explore and review biases in model distributions of oceanic oxygen. We show that the largest magnitude of uncertainty is entrained into ocean oxygen response patterns due to model parametrization of pCO2-sensitive C : N ratios in carbon fixation and imposed atmospheric forcing data. Inclusion of a pCO2-sensitive C : N ratio drives historical oxygen depletion within the ocean interior due to increased organic carbon export and subsequent remineralization. Atmospheric forcing is shown to influence simulated interannual variability in ocean oxygen, particularly due to differences in imposed variability of wind stress and heat fluxes.This article is part of the themed issue ‘Ocean ventilation and deoxygenation in a warming world’.
Short summary: The Ocean Model Intercomparison Project (OMIP) is a model comparison effort under Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Its physical component is described elsewhere in this special issue. Here we describe its ocean biogeochemical component (OMIP-BGC), detailing simulation protocols and analysis diagnostics. Simulations focus on ocean carbon, other biogeochemical tracers, air-sea exchange of CO2 and related gases, and chemical tracers used to evaluate modeled circulation.
Abstract: Growth in terrestrial gross primary production (GPP)—the amount of carbon dioxide that is ‘fixed’ into organic material through the photosynthesis of land plants—may provide a negative feedback for climate change1,2. It remains uncertain, however, to what extent biogeochemical processes can suppress global GPP growth3. As a consequence, modelling estimates of terrestrial carbon storage, and of feedbacks between the carbon cycle and climate, remain poorly constrained4. Here we present a global, measurement-based estimate of GPP growth during the twentieth century that is based on long-term atmospheric carbonyl sulfide (COS) records, derived from ice-core, firn and ambient air samples5. We interpret these records using a model that simulates changes in COS concentration according to changes in its sources and sinks—including a large sink that is related to GPP. We find that the observation-based COS record is most consistent with simulations of climate and the carbon cycle that assume large GPP growth during the twentieth century (31% ± 5% growth; mean ± 95% confidence interval). Although this COS analysis does not directly constrain models of future GPP growth, it does provide a global-scale benchmark for historical carbon-cycle simulations.
Abstract: Deep-water benthic communities in the ocean are almost wholly dependent on near-surface pelagic ecosystems for their supply of energy and material resources. Primary production in sunlit surface waters is channelled through complex food webs that extensively recycle organic material, but lose a fraction as particulate organic carbon (POC) that sinks into the ocean interior. This exported production is further rarefied by microbial breakdown in the abyssal ocean, but a residual ultimately drives diverse assemblages of seafloor heterotrophs. Advances have led to an understanding of the importance of size (body mass) in structuring these communities. Here we force a size-resolved benthic biomass model, BORIS, using seafloor POC flux from a coupled ocean-biogeochemistry model, NEMO-MEDUSA, to investigate global patterns in benthic biomass. BORIS resolves 16 size classes of metazoans, successively doubling in mass from approximately 1 μg to 28 mg. Simulations find a wide range of seasonal responses to differing patterns of POC forcing, with both a decline in seasonal variability, and an increase in peak lag times with increasing body size. However, the dominant factor for modelled benthic communities is the integrated magnitude of POC reaching the seafloor rather than its seasonal pattern. Scenarios of POC forcing under climate change and ocean acidification are then applied to investigate how benthic communities may change under different future conditions. Against a backdrop of falling surface primary production (−6.1%), and driven by changes in pelagic remineralization with depth, results show that while benthic communities in shallow seas generally show higher biomass in a warmed world (+3.2%), deep-sea communities experience a substantial decline (−32%) under a high greenhouse gas emissions scenario. Our results underscore the importance for benthic ecology of reducing uncertainty in the magnitude and seasonality of seafloor POC fluxes, as well as the importance of studying a broader range of seafloor environments for future model development.
Reto Knutti, Maria A. A. Rugenstein and Gabriele C. Hegerl. Nature Geoscience 10, 727–736 (2017). doi:10.1038/ngeo3017.
Abstract: Equilibrium climate sensitivity characterizes the Earth’s long-term global temperature response to increased atmospheric CO2 concentration. It has reached almost iconic status as the single number that describes how severe climate change will be. The consensus on the ‘likely’ range for climate sensitivity of 1.5 °C to 4.5 °C today is the same as given by Jule Charney in 1979, but now it is based on quantitative evidence from across the climate system and throughout climate history. The quest to constrain climate sensitivity has revealed important insights into the timescales of the climate system response, natural variability and limitations in observations and climate models, but also concerns about the simple concepts underlying climate sensitivity and radiative forcing, which opens avenues to better understand and constrain the climate response to forcing. Estimates of the transient climate response are better constrained by observed warming and are more relevant for predicting warming over the next decades. Newer metrics relating global warming directly to the total emitted CO2 show that in order to keep warming to within 2 °C, future CO2 emissions have to remain strongly limited, irrespective of climate sensitivity being at the high or low end.
Abstract: We review what is known about the microphysical, chemical, and radiative properties of aerosols in the pre-industrial atmosphere and the processes that control them. Aerosol properties were controlled by a combination of natural emissions, modification of the natural emissions by human activities such as land-use change, and anthropogenic emissions from biofuel combustion and early industrial processes. Although aerosol concentrations were lower in the pre-industrial atmosphere than today, model simulations show that relatively high aerosol concentrations could have been maintained over continental regions due to biogenically controlled new particle formation and wildfires. Despite the importance of pre-industrial aerosols for historical climate change, the relevant processes and emissions are given relatively little consideration in climate models, and there have been very few attempts to evaluate them. Consequently, we have very low confidence in the ability of models to simulate the aerosol conditions that form the baseline for historical climate simulations. Nevertheless, it is clear that the 1850s should be regarded as an early industrial reference period, and the aerosol forcing calculated from this period is smaller than the forcing since 1750. Improvements in historical reconstructions of natural and early anthropogenic emissions, exploitation of new Earth system models, and a deeper understanding and evaluation of the controlling processes are key aspects to reducing uncertainties in future.
Short summary: Wetlands are one of the most significant natural sources of the strong greenhouse gas methane. We developed a model that can be used within a larger wetland carbon model to simulate the methane emissions. In this study, we present the model and results of its testing. We found that the model works well with different settings and that the results depend primarily on the rate of input anoxic soil respiration and also on factors that affect the simulated oxygen concentrations in the wetland soil.
Abstract: In this study we investigate the implications of reaching the 2 °C climate target for global woody biomass use by applying the Global Biosphere Management Model (GLOBIOM) and the recently published SSP-RCP scenario calculations. We show that the higher biomass demand for energy needed to reach the 2 °C target can be achieved without significant distortions to woody biomass material use and that it can even benefit certain forest industries and regions. This is because the higher woody biomass use for energy increases the demand for forest industry by-products, which makes forest industry final products production more profitable and compensates for the cost effect of increased competition over raw materials. The higher woody biomass use for energy is found to benefit sawnwood, plywood and chemical pulp production, which provide large amounts of by-products, and to inhibit fiberboard and mechanical pulp production, which provide small amounts of by-products. At the regional level, the higher woody biomass use for energy is found to benefit material production in regions, which use little roundwood for energy (Russia, North-America and EU28), and to inhibit material production in regions, which use large amounts of roundwood for energy (Asia, Africa and South-America). Even if the 2 °C target increases harvest volumes in the tropical regions significantly compared to the non-mitigation scenario, harvest volumes remain in these regions at a relatively low level compared to the harvest potential.
Short summary: Climate change is primarily driven by human-induced increases of greenhouse gas (GHG) concentrations. Based on ongoing community efforts (e.g. AGAGE and NOAA networks, ice cores), this study presents historical concentrations of CO2, CH4, N2O and 40 other GHGs from year 0 to year 2014. The data is recommended as input for climate models for pre-industrial, historical runs under CMIP6. Global means, but also latitudinal by monthly surface concentration fields are provided.
Abstract: Ground-level ozone and fine particulate matter (PM 2.5) are associated with premature human mortality1, 2, 3, 4; their future concentrations depend on changes in emissions, which dominate the near-term5, and on climate change6, 7. Previous global studies of the air-quality-related health effects of future climate change8, 9 used single atmospheric models. However, in related studies, mortality results differ among models10, 11, 12. Here we use an ensemble of global chemistry–climate models13 to show that premature mortality from changes in air pollution attributable to climate change, under the high greenhouse gas scenario RCP8.5 (ref. 14), is probably positive. We estimate 3,340 (−30,300 to 47,100) ozone-related deaths in 2030, relative to 2000 climate, and 43,600 (−195,000 to 237,000) in 2100 (14% of the increase in global ozone-related mortality). For PM 2.5, we estimate 55,600 (−34,300 to 164,000) deaths in 2030 and 215,000 (−76,100 to 595,000) in 2100 (countering by 16% the global decrease in PM 2.5-related mortality). Premature mortality attributable to climate change is estimated to be positive in all regions except Africa, and is greatest in India and East Asia. Most individual models yield increased mortality from climate change, but some yield decreases, suggesting caution in interpreting results from a single model. Climate change mitigation is likely to reduce air-pollution-related mortality.
Aleksandra Borodina, Erich M. Fischer, and Reto Knutti. 2017 Journal of Climate 30: 3655-3670, dx.doi.org/10.1175/JCLI-D-16-0662.s1.
Abstract: Climate projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensemble show a decrease in interannual surface temperature variability over high latitudes with a large intermodel spread, in particular over the areas of sea ice retreat. Here relationships are found between the models’ present-day performance in sea ice–related metrics and future changes in temperature variability. These relations, so-called emergent constraints, can produce ensembles of models calibrated with present-day observations with a narrower spread across their members than across the full ensemble. The underlying assumption is that models in better agreement with observations or reanalyses in a carefully selected metric probably have a more realistic representation of local processes, and therefore are more reliable for projections. Thus, the reliability of this method depends on the availability of high-quality observations or reanalyses. This work represents a step toward formalization of the emergent constraints framework, as so far there is no consensus on how the constraints should be best implemented. The authors quantify the reduction in spread from emerging constraints for various metrics and their combinations, different emission scenarios, and seasons. Some of the general features of emerging constraints are discussed, and how to effectively aggregate information across metrics and seasons to achieve the largest reduction in model spread. It is demonstrated, based on the case of temperature variability, that a robust constraint can be obtained by combining relevant metrics across all seasons. Such a constraint results in a strongly reduced spread across model projections, which is consistent with a process understanding of variability changes due to sea ice retreat.
Knutti, R., J. Sedláček, B. M. Sanderson, R. Lorenz, E. M. Fischer, and V. Eyring. 2017 Geophys. Res. Lett., 44, 1909–1918, doi:10.1002/2016GL072012.
Abstract: Uncertainties of climate projections are routinely assessed by considering simulations from different models. Observations are used to evaluate models, yet there is a debate about whether and how to explicitly weight model projections by agreement with observations. Here we present a straightforward weighting scheme that accounts both for the large differences in model performance and for model interdependencies, and we test reliability in a perfect model setup. We provide weighted multimodel projections of Arctic sea ice and temperature as a case study to demonstrate that, for some questions at least, it is meaningless to treat all models equally. The constrained ensemble shows reduced spread and a more rapid sea ice decline than the unweighted ensemble. We argue that the growing number of models with different characteristics and considerable interdependence finally justifies abandoning strict model democracy, and we provide guidance on when and how this can be achieved robustly.
Eleanor J. Burke, Sarah E. Chadburn, and Altug Ekici. 2017 Geosci. Model Dev., 10, 959-975, doi:10.5194/gmd-10-959-2017.
Short summary: There is a large amount of relatively inert organic carbon locked into permafrost soils. In a warming climate the permafrost will thaw and this organic carbon will become vulnerable to decomposition. This process is not typically included within Earth system models (ESMs). This paper describes the development of a vertically resolved soil organic carbon decomposition model which, in the future, can be included within the UKESM to quantify the response of the climate to permafrost carbon loss.
Lester Kwiatkowski, Laurent Bopp, Olivier Aumont, Philippe Ciais, Peter M. Cox, Charlotte Laufkötter, Yue Li & Roland Séférian. 2017 Nature Climate Change 7, 355–358 (2017), doi:10.1038/nclimate3265.
Abstract: Marine primary production is a fundamental component of the Earth system, providing the main source of food and energy to the marine food web, and influencing the concentration of atmospheric CO 2 (refs 1,2). Earth system model (ESM) projections of global marine primary production are highly uncertain with models projecting both increases3, 4 and declines of up to 20% by 21005, 6. This uncertainty is predominantly driven by the sensitivity of tropical ocean primary production to climate change, with the latest ESMs suggesting twenty-first-century tropical declines of between 1 and 30% (refs 5,6). Here we identify an emergent relationship7, 8, 9, 10, 11 between the long-term sensitivity of tropical ocean primary production to rising equatorial zone sea surface temperature (SST) and the interannual sensitivity of primary production to El Niño/Southern Oscillation (ENSO)-driven SST anomalies. Satellite-based observations of the ENSO sensitivity of tropical primary production are then used to constrain projections of the long-term climate impact on primary production. We estimate that tropical primary production will decline by 3 ± 1% per kelvin increase in equatorial zone SST. Under a business-as-usual emissions scenario this results in an 11 ± 6% decline in tropical marine primary production and a 6 ± 3% decline in global marine primary production by 2100.
S. E. Chadburn, E. J. Burke, P. M. Cox, P. Friedlingstein, G. Hugelius & S. Westermann. 2017 Nature Climate Change 7, 340–344 (2017), doi:10.1038/nclimate3262.
Abstract: Permafrost, which covers 15 million km2 of the land surface, is one of the components of the Earth system that is most sensitive to warming1, 2. Loss of permafrost would radically change high-latitude hydrology and biogeochemical cycling, and could therefore provide very significant feedbacks on climate change3, 4, 5, 6, 7, 8. The latest climate models all predict warming of high-latitude soils and thus thawing of permafrost under future climate change, but with widely varying magnitudes of permafrost thaw9, 10. Here we show that in each of the models, their present-day spatial distribution of permafrost and air temperature can be used to infer the sensitivity of permafrost to future global warming. Using the same approach for the observed permafrost distribution and air temperature, we estimate a sensitivity of permafrost area loss to global mean warming at stabilization of million km2 °C−1 (1σ confidence), which is around 20% higher than previous studies9. Our method facilitates an assessment for COP21 climate change targets11: if the climate is stabilized at 2 °C above pre-industrial levels, we estimate that the permafrost area would eventually be reduced by over 40%. Stabilizing at 1.5 °C rather than 2 °C would save approximately 2 million km2 of permafrost.
Olivier Aumont, Marco van Hulten, Matthieu Roy-Barman, Jean-Claude Dutay, Christian Éthé, and Marion Gehlen. 2017 Biogeosciences, 14, 2321–2341, doi:10.5194/bg-14-2321-2017 .
Short summary: The marine biological carbon pump is dominated by the vertical transfer of particulate organic carbon (POC) from the surface ocean to its interior. In this study, we explore the impacts of a variable composition of this organic matter using a global ocean biogeochemical model. We show that accounting for a variable lability of POC increases POC concentrations by up to 2 orders of magnitude in the ocean’s interior. Furthermore, the amount of carbon that reaches the sediments is twice as large.
Stephanie A. Henson, Claudie Beaulieu, Tatiana Ilyina, Jasmin G. John, Matthew Long, Roland Séférian, Jerry Tjiputra & Jorge L. Sarmiento. 2017 Nature Communications 8, Article number: 14682 (2017), doi:10.1038/ncomms14682.
Abstract: Climate change is expected to modify ecological responses in the ocean, with the potential for important effects on the ecosystem services provided to humankind. Here we address the question of how rapidly multiple drivers of marine ecosystem change develop in the future ocean. By analysing an ensemble of models we find that, within the next 15 years, the climate change-driven trends in multiple ecosystem drivers emerge from the background of natural variability in 55% of the ocean and propagate rapidly to encompass 86% of the ocean by 2050 under a ‘business-as-usual’ scenario. However, we also demonstrate that the exposure of marine ecosystems to climate change-induced stress can be drastically reduced via climate mitigation measures; with mitigation, the proportion of ocean susceptible to multiple drivers within the next 15 years is reduced to 34%. Mitigation slows the pace at which multiple drivers emerge, allowing an additional 20 years for adaptation in marine ecological and socio-economic systems alike.
Short summary: We have designed a set of climate model experiments called the Aerosol Chemistry Model Intercomparison Project (AerChemMIP). These are designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases in the climate models that are used to simulate past and future climate. We hope that many climate modelling centres will choose to run these experiments to help understand the contribution of aerosols and chemistry to climate change.
Short summary: We present a review of the make-up of 20 models participating in the Chemistry–Climate Model Initiative (CCMI). In comparison to earlier such activities, most of these models comprise a whole-atmosphere chemistry, and several of them include an interactive ocean module. This makes them suitable for studying the interactions of tropospheric air quality, stratospheric ozone, and climate. The paper lays the foundation for other studies using the CCMI simulations for scientific analysis.
Peter M. Cox, Chris Huntingford and Mark S. Williamson. Nature 553, 319–322 (18 January 2018). doi.org/10.1038/nature25450.
Abstract: Equilibrium climate sensitivity (ECS) remains one of the most important unknowns in climate change science. ECS is defined as the global mean warming that would occur if the atmospheric carbon dioxide (CO2) concentration were instantly doubled and the climate were then brought to equilibrium with that new level of CO2. Despite its rather idealized definition, ECS has continuing relevance for international climate change agreements, which are often framed in terms of stabilization of global warming relative to the pre-industrial climate. However, the ‘likely’ range of ECS as stated by the Intergovernmental Panel on Climate Change (IPCC) has remained at 1.5-4.5 degrees Celsius for more than 25 years. The possibility of a value of ECS towards the upper end of this range reduces the feasibility of avoiding 2 degrees Celsius of global warming, as required by the Paris Agreement. Here we present a new emergent constraint on ECS that yields a central estimate of 2.8 degrees Celsius with 66 per cent confidence limits (equivalent to the IPCC ‘likely’ range) of 2.2-3.4 degrees Celsius. Our approach is to focus on the variability of temperature about long-term historical warming, rather than on the warming trend itself. We use an ensemble of climate models to define an emergent relationship between ECS and a theoretically informed metric of global temperature variability. This metric of variability can also be calculated from observational records of global warming, which enables tighter constraints to be placed on ECS, reducing the probability of ECS being less than 1.5 degrees Celsius to less than 3 per cent, and the probability of ECS exceeding 4.5 degrees Celsius to less than 1 per cent.
J. P. Mulcahy C. Jones A. Sellar B. Johnson I. A. Boutle A. Jones T. Andrews S. T. Rumbold J. Mollard N. Bellouin C. E. Johnson K. D. Williams D. P. Grosvenor D. T. McCoy. Journal of Advances in Modeling Earth Systems, 10, 2786–2805, doi.org/10.1029/2018MS001464.
Abstract: Aerosol processes and, in particular, aerosol‐cloud interactions cut across the traditional physical‐Earth system boundary of coupled Earth system models and remain one of the key uncertainties in estimating anthropogenic radiative forcing of climate. Here we calculate the historical aerosol effective radiative forcing (ERF) in the HadGEM3‐GA7 climate model in order to assess the suitability of this model for inclusion in the UK Earth system model, UKESM1. The aerosol ERF, calculated for the year 2000 relative to 1850, is large and negative in the standard GA7 model leading to an unrealistic negative total anthropogenic forcing over the twentieth century. We show how underlying assumptions and missing processes in both the physical model and aerosol parameterizations lead to this large aerosol ERF. A number of model improvements are investigated to assess their impact on the aerosol ERF. These include an improved representation of cloud droplet spectral dispersion, updates to the aerosol activation scheme, and black carbon optical properties. One of the largest contributors to the aerosol forcing uncertainty is insufficient knowledge of the preindustrial aerosol climate. We evaluate the contribution of uncertainties in the natural marine emissions of dimethyl sulfide and organic aerosol to the ERF. The combination of model improvements derived from these studies weakens the aerosol ERF by up to 50% of the original value and leads to a total anthropogenic historical forcing more in line with assessed values.
Renaud Person, Olivier Aumont, Marina Lévy. Journal of Geophysical Research. Oceans, 2018, 123 (5), pp.3204-3226. doi.org/10.1029/2018JC013775.
Abstract : Iron is known to limit primary production in the Southern Ocean (SO). To cope with the lack of this micronutrient, diatoms, a dominant phytoplankton group in this oceanic region, have been shown in cultures to have developed an original adaptation strategy to maintain efficient growth rates despite very low cellular iron quotas, even in low light conditions. Using a global ocean biogeochemical model, we explored the consequences of this physiological adaptation for the biological pump and the seasonal variability of both surface chlorophyll concentrations and surface partial pressure of carbon dioxide (pCO2) in this key region for global climate. In the model, we implemented a low intracellular Fe:C requirement in the SO for diatoms uniquely. This results in an increase of 10% in the relative contribution of diatoms to total SO primary production. The biological pump is also strengthened, which increases the biological contribution to the seasonal evolution of pCO2 relative to the thermodynamic component. Therefore, the seasonal evolution of both surface chlorophyll and surface pCO2 is significantly impacted, with a marked improvement, in our model, in the SO polar zone compared to the observations. Our model study underscores the potentially important consequences that this adaptive physiological behavior of diatoms could have on marine biogeochemistry in the SO. It is thus critical to improve our understanding of the physiology of this key phytoplankton group, in particular in the SO.
Elisa Palazzi, Luca Mortarini, Silvia Terzago, Jost von Hardenberg. Clim Dyn (2019) 52: 2685. doi.org/10.1007/s00382-018-4287-z.
Abstract: The enhancement of warming rates with elevation, so-called elevation-dependent warming (EDW), is one of the regional, still not completely understood, expressions of global warming. Sentinels of climate and environmental changes, mountains have experienced more rapid and intense warming trends in the recent decades, leading to serious impacts on mountain ecosystems and downstream. In this paper we use a state-of-the-art Global Climate Model (EC-Earth) to investigate the impact of model spatial resolution on the representation of this phenomenon and to highlight possible differences in EDW and its causes in different mountain regions of the Northern Hemisphere. To this end we use EC-Earth climate simulations at five different spatial resolutions, from ∼ 125 to ∼ 16 km, to explore the existence and the driving mechanisms of EDW in the Colorado Rocky Mountains, the Greater Alpine Region and the Tibetan Plateau–Himalayas. Our results show that the more frequent EDW drivers in all regions and seasons are the changes in albedo and in downward thermal radiation and this is reflected in both daytime and nighttime warming. In the Tibetan Plateau-Himalayas and in the Greater Alpine Region, an additional driver is the change in specific humidity. We also find that, while generally the model shows no clear resolution dependence in its ability to simulate the existence of EDW in the different regions, specific EDW characteristics such as its intensity and the relative role of different driving mechanisms may be different in simulations performed at different spatial resolutions. Moreover, we find that the role of internal climate variability can be significant in modulating the EDW signal, as suggested by the spread found in the multi-member ensemble of the EC-Earth experiments which we use.
A. Rap, C. E. Scott, C. L. Reddington, L. Mercado, R. J. Ellis, S. Garraway, M. J. Evans, D. J. Beerling, A. R. MacKenzie, C. N. Hewitt & D. V. Spracklen. Nature Geoscience 11 (9). pp. 640-644 (2018), doi.org/10.1038/s41561-018-0208-3.
Abstract: Terrestrial vegetation releases large quantities of plant volatiles into the atmosphere that can then oxidize to form secondary organic aerosol. These particles affect plant productivity through the diffuse radiation fertilization effect by altering the balance between direct and diffuse radiation reaching the Earth’s surface. Here, using a suite of models describing relevant coupled components of the Earth system, we quantify the impacts of biogenic secondary organic aerosol on plant photosynthesis through this fertilization effect. We show that this leads to a net primary productivity enhancement of 1.23 Pg C yr⁻¹ (range 0.76–1.61 Pg C yr⁻¹ due to uncertainty in biogenic secondary organic aerosol formation). Notably, this productivity enhancement is twice the mass of biogenic volatile organic compound emissions (and ~30 times larger than the mass of carbon in biogenic secondary organic aerosol) causing it. Hence, our simulations indicate that there is a strong positive ecosystem feedback between biogenic volatile organic compound emissions and plant productivity through plant-canopy light-use efficiency. We estimate a gain of 1.07 in global biogenic volatile organic compound emissions resulting from this feedback.
Yohei Takano, Takamitsu Ito, Curtis Deutsch. Global Biogeochemical Cycles, 32, 1329-1349. doi:10.1029/2018GB005939.
Abstract: We explore centennial changes in tropical Pacific oxygen (O2) using numerical models to illustrate the dominant patterns and mechanisms under centennial climate change. Future projections from state-of-the-art Earth System Models exhibit significant model to model differences, but decreased solubility and weakened ventilation together deplete thermocline O2 in middle to high latitudes. In contrast, the tropical thermocline O2 undergoes much smaller changes or even a slight increase. A suite of sensitivity experiments using a coarse resolution ocean circulation and biogeochemistry model show that ocean warming is the leading cause of global deoxygenation in the thermocline across all latitudes with secondary contributions from changes in hydrological cycles and wind stress modulating regional changes in O2. The small O2 changes in the tropical Pacific thermocline reflect near-complete compensation between the solubility decrease due to warming and reduction in apparent oxygen utilization (AOU). We further quantified the changes in AOU due to contributions from changes in water mass age and biological remineralization from the sensitivity experiments. The two effects almost equally contribute to the reduction of AOU in the tropical Pacific thermocline (43 for physical circulations and 57 for biology). Our results suggest that better understanding of water mass changes in the tropical oceans is key to improving projections and reducing the uncertainties of future O2 changes.
Till Kuhlbrodt, Colin G. Jones, Alistair Sellar, Dave Storkey, Ed Blockley, Marc Stringer, Richard Hill, Tim Graham, Jeff Ridley, Adam Blaker, Daley Calvert, Dan Copsey, Richard Ellis, Helene Hewitt, et al. J Adv Model Earth Syst. 2018 Nov; 10(11): 2865–2888, doi: 10.1029/2018MS001370.
Abstract: A new climate model, HadGEM3 N96ORCA1, is presented that is part of the GC3.1 configuration of HadGEM3. N96ORCA1 has a horizontal resolution of ~135 km in the atmosphere and 1° in the ocean and requires an order of magnitude less computing power than its medium‐resolution counterpart, N216ORCA025, while retaining a high degree of performance traceability. Scientific performance is compared to both observations and the N216ORCA025 model. N96ORCA1 reproduces observed climate mean and variability almost as well as N216ORCA025. Patterns of biases are similar across the two models. In the northwest Atlantic, N96ORCA1 shows a cold surface bias of up to 6 K, typical of ocean models of this resolution. The strength of the Atlantic meridional overturning circulation (16 to 17 Sv) matches observations. In the Southern Ocean, a warm surface bias (up to 2 K) is smaller than in N216ORCA025 and linked to improved ocean circulation. Model El Niño/Southern Oscillation and Atlantic Multidecadal Variability are close to observations. Both the cold bias in the Northern Hemisphere (N96ORCA1) and the warm bias in the Southern Hemisphere (N216ORCA025) develop in the first few decades of the simulations. As in many comparable climate models, simulated interhemispheric gradients of top‐of‐atmosphere radiation are larger than observations suggest, with contributions from both hemispheres. HadGEM3 GC3.1 N96ORCA1 constitutes the physical core of the UK Earth System Model (UKESM1) and will be used extensively in the Coupled Model Intercomparison Project 6 (CMIP6), both as part of the UK Earth System Model and as a stand‐alone coupled climate model.
Corinne Le Quéré, Robbie M. Andrew, Pierre Friedlingstein, Stephen Sitch, Judith Hauck, Julia Pongratz, Penelope A. Pickers, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell, Almut Arneth, Vivek K. Arora, Leticia Barbero, Ana Bastos, Laurent Bopp et al. Earth Syst. Sci. Data, 10, 2141-2194, 2018, doi.org/10.5194/essd-10-2141-2018.
Short summary: The Global Carbon Budget 2018 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018.
Rebecca J. Oliver, Lina M. Mercado, Stephen Sitch, David Simpson, Belinda E. Medlyn, Yan-Shih Lin, and Gerd A. Folberth. Biogeosciences 15, 4245-4269, 2018, doi.org/10.5194/bg-15-4245-2018.
Short summary: Potential gains in terrestrial carbon sequestration over Europe from elevated CO2 can be partially offset by concurrent rises in tropospheric O3. The land surface model JULES was run in a factorial suite of experiments showing that by 2050 simulated GPP was reduced by 4 to 9 % due to plant O3 damage. Large regional variations exist with larger impacts identified for temperate compared to boreal regions. Plant O3 damage was greatest over the twentieth century and declined into the future.
D. S. Hamilton, S. Hantson, C. E. Scott, J. O. Kaplan, K. J. Pringle, L. P. Nieradzik, A. Rap, G. A. Folberth, D. V. Spracklen & K. S. Carslaw. Nature Communications 9: 3182 (2018), doi.org/10.1038/s41467-018-05592-9.
Abstract: Uncertainty in pre-industrial natural aerosol emissions is a major component of the overall uncertainty in the radiative forcing of climate. Improved characterisation of natural emissions and their radiative effects can therefore increase the accuracy of global climate model projections. Here we show that revised assumptions about pre-industrial fire activity result in significantly increased aerosol concentrations in the pre-industrial atmosphere. Revised global model simulations predict a 35% reduction in the calculated global mean cloud albedo forcing over the Industrial Era (1750–2000 CE) compared to estimates using emissions data from the Sixth Coupled Model Intercomparison Project. An estimated upper limit to pre-industrial fire emissions results in a much greater (91%) reduction in forcing. When compared to 26 other uncertain parameters or inputs in our model, pre-industrial fire emissions are by far the single largest source of uncertainty in pre-industrial aerosol concentrations, and hence in our understanding of the magnitude of the historical radiative forcing due to anthropogenic aerosol emissions.
de Mora, L., Yool, A., Palmieri, J., Sellar, A., Kuhlbrodt, T., Popova, E., Jones, C., and Allen, J. I. Geosci. Model Dev., 11, 4215-4240, 2018, doi.org/10.5194/gmd-11-4215-2018.
Short summary: Climate change is expected to have a significant impact on the Earth’s weather, ice caps, land surface, and ocean. Computer models of the Earth system are the only tools available to make predictions about how the climate may change in the future. However, in order to trust the model predictions, we must first demonstrate that the models have a realistic description of the past. The BGC-val toolkit was built to rapidly and simply evaluate the behaviour of models of the Earth’s oceans.
T. Gasser, M. Kechiar, P. Ciais, E. J. Burke, T. Kleinen, D. Zhu, Y. Huang, A. Ekici & M. Obersteiner. Nature geoscience, Vol. 11 (Sep. 2018) , p. 830–835, doi.org/10.1038/s41561-018-0227-0.
Abstract: Emission budgets are defined as the cumulative amount of anthropogenic CO₂ emission compatible with a global temperature-change target. The simplicity of the concept has made it attractive to policy-makers, yet it relies on a linear approximation of the global carbon–climate system’s response to anthropogenic CO2 emissions. Here we investigate how emission budgets are impacted by the inclusion of CO₂ and CH₄ emissions caused by permafrost thaw, a non-linear and tipping process of the Earth system. We use the compact Earth system model OSCAR v2. 2. 1, in which parameterizations of permafrost thaw, soil organic matter decomposition and CO₂ and CH₄ emission were introduced based on four complex land surface models that specifically represent high-latitude processes. We found that permafrost carbon release makes emission budgets path dependent (that is, budgets also depend on the pathway followed to reach the target). The median remaining budget for the 2 °C target reduces by 8% (1–25%) if the target is avoided and net negative emissions prove feasible, by 13% (2–34%) if they do not prove feasible, by 16% (3–44%) if the target is overshot by 0. 5 °C and by 25% (5–63%) if it is overshot by 1 °C. (Uncertainties are the minimum-to-maximum range across the permafrost models and scenarios. ) For the 1. 5 °C target, reductions in the median remaining budget range from ~10% to more than 100%. We conclude that the world is closer to exceeding the budget for the long-term target of the Paris Climate Agreement than previously thought.
Anna B. Harper, Andrew J. Wiltshire, Peter M. Cox, Pierre Friedlingstein, Chris D. Jones, Lina M. Mercado, Stephen Sitch, Karina Williams, and Carolina Duran-Rojas. Geosci. Model Dev., 11, 2857-2873, 2018, doi.org/10.5194/gmd-11-2857-2018.
Short summary: Dynamic global vegetation models are used for studying historical and future changes to vegetation and the terrestrial carbon cycle. JULES is a DGVM that represents the land surface in the UK Earth System Model. We compared simulated gross and net primary productivity of vegetation, vegetation distribution, and aspects of the transient carbon cycle to observational datasets. JULES was able to accurately reproduce many aspects of the terrestrial carbon cycle with the recent improvements.
Ciao-Kai Liang, J. Jason West, Raquel A. Silva, Huisheng Bian, Mian Chin, Yanko Davila, Frank J. Dentener, Louisa Emmons, Johannes Flemming, Gerd Folberth, et al. Atmos. Chem. Phys., 18, 10497-10520, 2018, doi.org/10.5194/acp-18-10497-2018.
Short summary: Emissions from one continent affect air quality and health elsewhere. Here we quantify the effects of intercontinental PM2.5 and ozone transport on human health using a new multi-model ensemble, evaluating the health effects of emissions from six world regions and three emission source sectors. Emissions from one region have significant health impacts outside of that source region; similarly, foreign emissions contribute significantly to air-pollution-related deaths in several world regions.
Mielonen, Tero; Hienola, Anca; Kühn, Thomas.; Merikanto, Joonas.; Lipponen, Antti; Bergman, Tommi; Korhonen, Hannele; Kolmonen, Pekka; Sogacheva, Larisa; Ghent, D.; Pitkänen, Mikko; Arola, Antti; de Leeuw, Gerrit; Kokkola, Harri. Atmosphere 2018, 9(5), 180; doi.org/10.3390/atmos9050180.
Abstract: Satellite data suggest that summertime aerosol optical depth (AOD) over the southeastern USA depends on the air/land surface temperature, but the magnitude of the radiative effects caused by this dependence remains unclear. To quantify these radiative effects, we utilized several remote sensing datasets and ECMWF reanalysis data for the years 2005–2011. In addition, the global aerosol–climate model ECHAM-HAMMOZ was used to identify the possible processes affecting aerosol loads and their dependence on temperature over the studied region. The satellite-based observations suggest that changes in the total summertime AOD in the southeastern USA are mainly governed by changes in anthropogenic emissions. In addition, summertime AOD exhibits a dependence on southerly wind speed and land surface temperature (LST). Transport of sea salt and Saharan dust is the likely reason for the wind speed dependence, whereas the temperature-dependent component is linked to temperature-induced changes in the emissions of biogenic volatile organic compounds (BVOCs) over forested regions. The remote sensing datasets indicate that the biogenic contribution increases AOD with increasing temperature by approximately (7 ± 6) × 10−3 K−1 over the southeastern USA. In the model simulations, the increase in summertime AOD due to temperature-enhanced BVOC emissions is of a similar magnitude, i.e., (4 ± 1) × 10−3 K−1. The largest source of BVOC emissions in this region is broadleaf trees, thus if the observed temperature dependence of AOD is caused by biogenic emissions the dependence should be the largest in the vicinity of forests. Consequently, the analysis of the remote sensing data shows that over mixed forests the biogenic contribution increases AOD by approximately (27 ± 13) × 10−3 K−1, which is over four times higher than the value for over the whole domain, while over other land cover types in the study region (woody savannas and cropland/natural mosaic) there is no clear temperature dependence. The corresponding clear-sky direct radiative effect (DRE) of the observation-based biogenic AOD is −0.33 ± 0.29 W/m2/K for the whole domain and −1.3 ± 0.7 W/m2/K over mixed forests only. The model estimate of the regional clear-sky DRE for biogenic aerosols is similar to the observational estimate for the whole domain: −0.29 ± 0.09 W/m2/K. Furthermore, the model simulations showed that biogenic emissions have a significant effective radiative forcing (ERF) in this region: −1.0 ± 0.5 W/m2/K.
Lange, Stefan. Earth Syst. Dynam., 9, 627-645, 2018. https://doi.org/10.5194/esd-9-627-2018.
Short summary: The bias correction of surface downwelling longwave and shortwave radiation using parametric quantile mapping methods is shown to be more effective (i) at the daily than at the monthly timescale, (ii) if the spatial resolution gap between the reference data and the data to be corrected is bridged in a more suitable manner than by bilinear interpolation, and (iii) if physical upper limits are taken into account during the adjustment of either radiation component.
Abstract: Scenarios that limit global warming to below 2 °C by 2100 assume significant land-use change to support large-scale carbon dioxide (CO2) removal from the atmosphere by afforestation/reforestation, avoided deforestation, and Biomass Energy with Carbon Capture and Storage (BECCS). The more ambitious mitigation scenarios require even greater land area for mitigation and/or earlier adoption of CO2 removal strategies. Here we show that additional land-use change to meet a 1.5 °C climate change target could result in net losses of carbon from the land. The effectiveness of BECCS strongly depends on several assumptions related to the choice of biomass, the fate of initial above ground biomass, and the fossil-fuel emissions offset in the energy system. Depending on these factors, carbon removed from the atmosphere through BECCS could easily be offset by losses due to land-use change. If BECCS involves replacing high-carbon content ecosystems with crops, then forest-based mitigation could be more efficient for atmospheric CO2 removal than BECCS.
Sonia I. Seneviratne, Joeri Rogelj, Roland Séférian, Richard Wartenburger, Myles R. Allen, Michelle Cain, Richard J. Millar, Kristie L. Ebi, Neville Ellis, Ove Hoegh-Guldberg, Antony J. Payne, Carl-Friedrich Schleussner, Petra Tschakert & Rachel F. Warren. Nature 558, 41–49 (2018). doi.org/10.1038/s41586-018-0181-4.
Abstract: The United Nations’ Paris Agreement includes the aim of pursuing efforts to limit global warming to only 1.5 °C above pre-industrial levels. However, it is not clear what the resulting climate would look like across the globe and over time. Here we show that trajectories towards a ‘1.5 °C warmer world’ may result in vastly different outcomes at regional scales, owing to variations in the pace and location of climate change and their interactions with society’s mitigation, adaptation and vulnerabilities to climate change. Pursuing policies that are considered to be consistent with the 1.5 °C aim will not completely remove the risk of global temperatures being much higher or of some regional extremes reaching dangerous levels for ecosystems and societies over the coming decades.
Nadja Herger, Gab Abramowitz, Reto Knutti, Oliver Angélil, Karsten Lehmann, and Benjamin M. Sanderson. Earth Syst. Dynam., 9, 135-151, 2018. doi.org/10.5194/esd-9-135-2018.
Short summary: Users presented with large multi-model ensembles commonly use the equally weighted model mean as a best estimate, ignoring the issue of near replication of some climate models. We present an efficient and flexible tool that finds a subset of models with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments.
Lester Kwiatkowski , Olivier Aumont, Laurent Bopp, Philippe Ciais. Global Biogeochemical Cycles, 32, 516–528. 2018. doi.org/10.1002/2017GB005799.
Abstract: Ocean biogeochemical models are integral components of Earth system models used to project the evolution of the ocean carbon sink, as well as potential changes in the physical and chemical environment of marine ecosystems. In such models the stoichiometry of phytoplankton C:N:P is typically fixed at the Redfield ratio. The observed stoichiometry of phytoplankton, however, has been shown to considerably vary from Redfield values due to plasticity in the expression of phytoplankton cell structures with different elemental compositions. The intrinsic structure of fixed C:N:P models therefore has the potential to bias projections of the marine response to climate change. We assess the importance of variable stoichiometry on 21st century projections of net primary production, food quality, and ocean carbon uptake using the recently developed Pelagic Interactions Scheme for Carbon and Ecosystem Studies Quota (PISCES‐QUOTA) ocean biogeochemistry model. The model simulates variable phytoplankton C:N:P stoichiometry and was run under historical and business‐as‐usual scenario forcing from 1850 to 2100. PISCES‐QUOTA projects similar 21st century global net primary production decline (7.7%) to current generation fixed stoichiometry models. Global phytoplankton N and P content or food quality is projected to decline by 1.2% and 6.4% over the 21st century, respectively. The largest reductions in food quality are in the oligotrophic subtropical gyres and Arctic Ocean where declines by the end of the century can exceed 20%. Using the change in the carbon export efficiency in PISCES‐QUOTA, we estimate that fixed stoichiometry models may be underestimating 21st century cumulative ocean carbon uptake by 0.5–3.5% (2.0–15.1 PgC).
Buitenhuis, Erik T., Suntharalingam, Parvadha and Le Quéré, Corinne. Biogeosciences, 15, 2161-2175, 2018. doi.org/10.5194/bg-15-2161-2018.
Short summary: Thanks to decreases in CFC concentrations, N2O is now the third-most important greenhouse gas, and the dominant contributor to stratospheric ozone depletion. Here we estimate the ocean–atmosphere N2O flux. We find that an estimate based on observations alone has a large uncertainty. By combining observations and a range of model simulations we find that the uncertainty is much reduced to 2.45 ± 0.8 Tg N yr−1, and better constrained and at the lower end of the estimate in the latest IPCC report.
Gidden, M.; Fujimori, S.; van den Berg, M.; Klein, D.; Smith, S.J.; van Vuuren, D.; Riahi, K. Environmental Modelling & Software 105, 187-200, 2018. doi.org/10.1016/j.envsoft.2018.04.002.
Highlights: (i) A novel methodology for the automated harmonization with a common historical data set of generic emissions trajectories of Integrated Assessment Models (IAMs) is proposed. (ii) A framework and open-source software implementation of the methodology is described. (iii) Two scenarios from the Shared Socioeconomic Pathways (SSPs) are harmonized and proof-of-concept results are presented. (iv) Over 96% of the approximately 2000 emissions trajectories are shown to be successfully harmonized without the need for further review. (v) Common situations in which additional review is required (4% of trajectories in this study) are presented and solutions are suggested.
Matthias Mengel, Alexander Nauels, Joeri Rogelj & Carl-Friedrich Schleussner. Nature Communications 9, 601 (2018). doi:10.1038/s41467-018-02985-8.
Abstract: Sea-level rise is a major consequence of climate change that will continue long after emissions of greenhouse gases have stopped. The 2015 Paris Agreement aims at reducing climate-related risks by reducing greenhouse gas emissions to net zero and limiting global-mean temperature increase. Here we quantify the effect of these constraints on global sea-level rise until 2300, including Antarctic ice-sheet instabilities. We estimate median sea-level rise between 0.7 and 1.2 m, if net-zero greenhouse gas emissions are sustained until 2300, varying with the pathway of emissions during this century. Temperature stabilization below 2 °C is insufficient to hold median sea-level rise until 2300 below 1.5 m. We find that each 5-year delay in near-term peaking of CO2 emissions increases median year 2300 sea-level rise estimates by ca. 0.2 m, and extreme sea-level rise estimates at the 95th percentile by up to 1 m. Our results underline the importance of near-term mitigation action for limiting long-term sea-level rise risks.
Hamish Gordon, Jasper Kirkby, Urs Baltensperger, Federico Bianchi, Martin Breitenlechner, Joachim Curtius, Antonio Dias, Josef Dommen, Neil M. Donahue, Eimear M. Dunne et al. J. Geophys. Res. Atmos., 122, 8739–8760, 2018. doi:10.1002/2017JD026844.
Abstract: New particle formation has been estimated to produce around half of cloud‐forming particles in the present‐day atmosphere, via gas‐to‐particle conversion. Here we assess the importance of new particle formation (NPF) for both the present‐day and the preindustrial atmospheres. We use a global aerosol model with parametrizations of NPF from previously published CLOUD chamber experiments involving sulfuric acid, ammonia, organic molecules, and ions. We find that NPF produces around 67% of cloud condensation nuclei at 0.2% supersaturation (CCN0.2%) at the level of low clouds in the preindustrial atmosphere (estimated uncertainty range 45–84%) and 54% in the present day (estimated uncertainty range 38–66%). Concerning causes, we find that the importance of biogenic volatile organic compounds (BVOCs) in NPF and CCN formation is greater than previously thought. Removing BVOCs and hence all secondary organic aerosol from our model reduces low‐cloud‐level CCN concentrations at 0.2% supersaturation by 26% in the present‐day atmosphere and 41% in the preindustrial. Around three quarters of this reduction is due to the tiny fraction of the oxidation products of BVOCs that have sufficiently low volatility to be involved in NPF and early growth. Furthermore, we estimate that 40% of preindustrial CCN0.2% are formed via ion‐induced NPF, compared with 27% in the present day, although we caution that the ion‐induced fraction of NPF involving BVOCs is poorly measured at present. Our model suggests that the effect of changes in cosmic ray intensity on CCN is small and unlikely to be comparable to the effect of large variations in natural primary aerosol emissions.
Jouni Susiluoto, Maarit Raivonen, Leif Backman, Marko Laine, Jarmo Makela, Olli Peltola, Timo Vesala, Tuula Aalto. Geosci. Model Dev., 11, 1199-1228, 2018. doi.org/10.5194/gmd-11-1199-2018.
Short summary: Methane is an important greenhouse gas and methane emissions from wetlands contribute to the warming of the climate. Wetland methane emissions are also challenging to estimate. We analyze the performance of a new wetland emission computer model utilizing mathematical methods and using data from a wetland in southern Finland. The analysis helps to explain how wetlands produce methane and how emission modeling can be improved and uncertainties in the emission estimates reduced in future studies.
Andrea Alessandri, Matteo De Felice, Franco Catalano, June-Yi Lee, Bin Wang, Doo Young Lee, Jin-Ho Yoo, Antije Weisheimer. Clim Dyn (2018) 50: 2719. doi.org/10.1007/s0038.
Abstract: Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles. Previous works suggested that the potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two MME Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation compared to previous estimates from the contributing MMEs. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models. To verify the above results for a real world application, the Grand ENSEMBLES-APCC/CliPAS MME is used to predict retrospective energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990–2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. The above results demonstrate for the first time the potential of the Grand MME to significantly contribute in obtaining useful predictions at the seasonal time-scale.
Longhui Li, Yingping Wang, Vivek K. Arora, Derek Eamus, Hao Shi, Jing Li, Lei Cheng, James Cleverly, T. Hajima, Duoying Ji, C. Jones, M. Kawamiya, Weiping Li, J. Tjiputra, A. Wiltshire, Lu Zhang, and Qiang Yu. 2018 Journal of Climate, 31: 2995-3008. doi.org/10.1175/JCLI-D-16-0177.1.
Abstract: Water and carbon fluxes simulated by 12 Earth system models (ESMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) over several recent decades were evaluated using three functional constraints that are derived from both model simulations, or four global datasets, and 736 site-year measurements. Three functional constraints are ecosystem water-use efficiency (WUE), light-use efficiency (LUE), and the partitioning of precipitation P into evapotranspiration (ET) and runoff based on the Budyko framework. Although values of these three constraints varied significantly with time scale and should be quite conservative if being averaged over multiple decades, the results showed that both WUE and LUE simulated by the ensemble mean of 12 ESMs were generally lower than the site measurements. Simulations by the ESMs were generally consistent with the broad pattern of energy-controlled ET under wet conditions and soil water-controlled ET under dry conditions, as described by the Budyko framework. However, the value of the parameter in the Budyko framework ω, obtained from fitting the Budyko curve to the ensemble model simulation (1.74), was larger than the best-fit value of ω to the observed data (1.28). Globally, the ensemble mean of multiple models, although performing better than any individual model simulations, still underestimated the observed WUE and LUE, and overestimated the ratio of ET to P, as a result of overestimation in ET and underestimation in gross primary production (GPP). The results suggest that future model development should focus on improving the algorithms of the partitioning of precipitation into ecosystem ET and runoff, and the coupling of water and carbon cycles for different land-use types.
Axel Lauer, Colin Jones, Veronika Eyring, Martin Evaldsson, Stefan Hagemann, Jarmo Mäkela, Gill Martin, Romain Roehrig, Shiyu Wang. Earth Syst. Dynam., 9, 33-67, 2018. doi.org/10.5194/esd-9-33-2018.
Abstract: The performance of updated versions of the four earth system models (ESMs) CNRM, EC-Earth, HadGEM, and MPI-ESM is assessed in comparison to their predecessor versions used in Phase 5 of the Coupled Model Intercomparison Project. The Earth System Model Evaluation Tool (ESMValTool) is applied to evaluate selected climate phenomena in the models against observations. This is the first systematic application of the ESMValTool to assess and document the progress made during an extensive model development and improvement project. This study focuses on the South Asian monsoon (SAM) and the West African monsoon (WAM), the coupled equatorial climate, and Southern Ocean clouds and radiation, which are known to exhibit systematic biases in present-day ESMs.
The analysis shows that the tropical precipitation in three out of four models is clearly improved. Two of three updated coupled models show an improved representation of tropical sea surface temperatures with one coupled model not exhibiting a double Intertropical Convergence Zone (ITCZ). Simulated cloud amounts and cloud–radiation interactions are improved over the Southern Ocean. Improvements are also seen in the simulation of the SAM and WAM, although systematic biases remain in regional details and the timing of monsoon rainfall. Analysis of simulations with EC-Earth at different horizontal resolutions from T159 up to T1279 shows that the synoptic-scale variability in precipitation over the SAM and WAM regions improves with higher model resolution. The results suggest that the reasonably good agreement of modeled and observed mean WAM and SAM rainfall in lower-resolution models may be a result of unrealistic intensity distributions.
Donatella D’Onofrio, Jost von Hardenberg and Mara Baudena. Global Ecol Biogeogr 2018; 1–12. dx.doi.org/10.1111/geb.12735.
Abstract: Aim: Although much tropical ecology generally focuses on trees, grasses are fundamental for characterizing the extensive tropical grassy biomes (TGBs) and, together with the tree functional types, for determining the contrasting functional patterns of TGBs and tropical forests (TFs). To study the factors that determine African biome distribution and the transitions between them, we performed the first continental analysis to include grass and tree functional types. Location: Sub‐Saharan Africa. Time period: 2000–2010. Major taxa studied: Savanna and forest trees and C4 grasses. Methods: We combined remote‐sensing data with a land cover map, using tree functional types to identify TGBs and TFs. We analysed the relationships of grass and tree cover with fire interval, rainfall annual average and seasonality. Results: In TGBs experiencing < 630 mm annual rainfall, grass growth was water limited. Grass cover and fire recurrence were strongly and directly related over the entire subcontinent. Some TGBs and TFs with annual rainfall > 1,200 mm had the same rainfall seasonality but displayed strongly different fire regimes. Main conclusions: Water limitation to grass growth was fundamental in the driest TGBs, acting alongside the well‐known limitation to tree growth. Marked differences in fire regimes across all biomes indicated that fire was especially relevant for maintaining mesic and humid TGBs. At high rainfall, our results support the hypothesis of TGBs and TFs being alternative stable states maintained by a vegetation–fire feedback for similar climatic conditions.
Ruth Lorenz, Nadja Herger, Jan Sedláček, Veronika Eyring, Erich M. Fischer, Reto Knutti. Journal of Geophysical Research: Atmospheres, 123. doi.org/10.1029/2017JD027992.
Abstract: Uncertainties in climate projections exist due to natural variability, scenario uncertainty, and model uncertainty. It has been argued that model uncertainty can be decreased by giving more weight to those models in multimodel ensembles that are more skillful and realistic for a specific process or application. In addition, some models in multimodel ensembles are not independent. We use a weighting approach proposed recently that takes into account both model performance and interdependence and apply it to investigate projections of summer maximum temperature climatology over North America in two regions of different sizes. We quantify the influence of predicting diagnostics included in the method, look at ways how to choose them, and assess the influence of the observational data set used. The trend in shortwave radiation, mean precipitation, sea surface temperature variability, and variability and trend in maximum temperature itself are the most promising constraints on projections of summer maximum temperature over North America. The influence of the observational data sets is large for summer temperature climatology, since the observational and reanalysis products used for absolute maximum temperatures disagree. Including multiple predicting diagnostics leads to more similar results for different data sets. We find that the weighted multimodel mean reduces the change in summer daily temperature maxima compared to the nonweighted mean slightly (0.05–0.45 °C) over the central United States. We show that it is essential to have reliable observations for key variables to be able to constrain multimodel ensembles of future projections.
David P. Keller, Andrew Lenton, Vivian Scott, Naomi E. Vaughan, Nico Bauer, Duoying Ji, Chris D. Jones, Ben Kravitz, Helene Muri, and Kirsten Zickfeld. Geosci. Model Dev., 11, 1133-1160, 2018. doi.org/10.5194/gmd-11-1133-2018.
Short summary: There is little consensus on the impacts and efficacy of proposed carbon dioxide removal (CDR) methods as a potential means of mitigating climate change. To address this need, the Carbon Dioxide Removal Model Intercomparison Project (or CDR-MIP) has been initiated. This project brings together models of the Earth system in a common framework to explore the potential, impacts, and challenges of CDR. Here, we describe the first set of CDR-MIP experiments.
Annette L. Hirsch, Benoit P. Guillod, Sonia I. Seneviratne, Urs Beyerle, Lena R. Boysen, Hyungjun Kim, Elke Stehfest, Victor Brovkin, Edouard L. Davin, Jonathan C. Doelman, Daniel M. Mitchell, Detlef P. van Vuuren, Tomoko Nitta, Hideo Shiogama, and Simon Wilson. Earths’ Journal 6 (3): 396-409. doi.org/10.1002/2017EF000744.
Abstract: The impacts of land use have been shown to have considerable influence on regional climate. With the recent international commitment to limit global warming to well below 2°C, emission reductions need to be ambitious and could involve major land‐use change (LUC). Land‐based mitigation efforts to curb emissions growth include increasing terrestrial carbon sequestration through reforestation, or the adoption of bioenergy crops. These activities influence local climate through biogeophysical feedbacks, however, it is uncertain how important they are for a 1.5° climate target. This was the motivation for HAPPI‐Land: the half a degree additional warming, prognosis, and projected impacts—land‐use scenario experiment. Using four Earth system models, we present the first multimodel results from HAPPI‐Land and demonstrate the critical role of land use for understanding the characteristics of regional climate extremes in low‐emission scenarios. In particular, our results show that changes in temperature extremes due to LUC are comparable in magnitude to changes arising from half a degree of global warming. We also demonstrate that LUC contributes to more than 20% of the change in temperature extremes for large land areas concentrated over the Northern Hemisphere. However, we also identify sources of uncertainty that influence the multimodel consensus of our results including how LUC is implemented and the corresponding biogeophysical feedbacks that perturb climate. Therefore, our results highlight the urgent need to resolve the challenges in implementing LUC across models to quantify the impacts and consider how LUC contributes to regional changes in extremes associated with sustainable development pathways.
Roland Séférian, Matthias Rocher, Celine Guivarch and Jeanne Colin. 2018 Environmental Research Letters, 13: 054011. doi.org/10.1088/1748-9326/aabcd7.
Abstract: To limit global warming to well below 2 ° most of the IPCC-WGIII future stringent mitigation pathways feature a massive global-scale deployment of negative emissions technologies (NETs) before the end of the century. The global-scale deployment of NETs like Biomass Energy with Carbon Capture and Storage (BECCS) can be hampered by climate constraints that are not taken into account by Integrated assessment models (IAMs) used to produce those pathways. Among the various climate constraints, water scarcity appears as a potential bottleneck for future land-based mitigation strategies and remains largely unexplored. Here, we assess climate constraints relative to water scarcity in response to the global deployment of BECCS. To this end, we confront results from an Earth system model (ESM) and an IAM under an array of 25 stringent mitigation pathways. These pathways are compatible with the Paris Agreement long-term temperature goal and with cumulative carbon emissions ranging from 230 Pg C and 300 Pg C from January 1st onwards. We show that all stylized mitigation pathways studied in this work limit warming below 2 °C or even 1.5 °C by 2100 but all exhibit a temperature overshoot exceeding 2 °C after 2050. According to the IAM, a subset of 17 emission pathways are feasible when evaluated in terms of socio-economic and technological constraints. The ESM however shows that water scarcity would limit the deployment of BECCS in all the mitigation pathways assessed in this work. Our findings suggest that the evolution of the water resources under climate change can exert a significant constraint on BECCS deployment before 2050. In 2100, the BECCS water needs could represent more than 30% of the total precipitation in several regions like Europe or Asia.
Séférian, R., Berthet, S., & Chevallier, M. 2018 Geophysical Research Letters, 45(5), 2455–2466. doi.org/10.1002/2017GL076092.
Abstract: The decadal predictability of carbon fluxes has been examined over continents and oceans using a “perfect model” approach based on a 400 year preindustrial simulation and five 10‐member ensembles from the Centre National de Recherches Météorologiques‐Earth System Model version 1. From these experiments, we find that the global land uptake and ocean carbon uptake are potentially predictable by up to six years, with a median predictability horizon of four years. Predictability of global carbon uptake is prominently driven by the ocean’s predictability. The difference in predictability between ocean and land carbon fluxes stems from the relative capability of ocean or land to generate low‐frequency fluctuations in carbon flux. Indeed, ocean carbon fluxes display low‐frequency variability that emerges from the year‐to‐year variability in the North Atlantic, the North Pacific, and the Southern Ocean. The Southern Ocean carbon uptake can be predicted up to six years in advance and explains most of the global carbon uptake predictability.
Séférian, R., Baek, S., Boucher, O., Dufresne, J.-L., Decharme, B., Saint-Martin, D., & Roehrig, R. 2018 Geoscientific Model Development, 11(1), 321–338. doi.org/10.5194/gmd-11-321-2018.
Short summary: This paper presents a new interactive scheme for ocean surface albedo suited for the current generation of Earth system models. This scheme computes the ocean surface albedo accounting for the spectral dependence (across a range of wavelengths between 200 and 4000 nm), the characteristics of incident solar radiation (direct of diffuse), the effects of surface winds, chlorophyll content and whitecaps in addition to the canonical solar zenith angle dependence.
Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Pongratz, J., Manning, A. C., et al. Earth System Science Data Discussions, 1–79, 2017. doi.org/10.5194/essd-10-405-2018.
Short summary: The Global Carbon Budget 2017 describes data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. It is the 12th annual update and the 6th published in this journal.
Joeri Rogelj, Alexander Popp, Katherine V. Calvin, Gunnar Luderer, Johannes Emmerling, David Gernaat, Shinichiro Fujimori, Jessica Strefler, Tomoko Hasegawa, Giacomo Marangoni, Volker Krey, Elmar Kriegler, Keywan Riahi, Detlef P. van Vuuren, et al. Nature Climate Change 8, 325–332 (2018). doi:10.1038/s41558-018-0091-3.
Abstract: The 2015 Paris Agreement calls for countries to pursue efforts to limit global-mean temperature rise to 1.5 °C. The transition pathways that can meet such a target have not, however, been extensively explored. Here we describe scenarios that limit end-of-century radiative forcing to 1.9 W m−2, and consequently restrict median warming in the year 2100 to below 1.5 °C. We use six integrated assessment models and a simple climate model, under different socio-economic, technological and resource assumptions from five Shared Socio-economic Pathways (SSPs). Some, but not all, SSPs are amenable to pathways to 1.5 °C. Successful 1.9 W m−2 scenarios are characterized by a rapid shift away from traditional fossil-fuel use towards large-scale low-carbon energy supplies, reduced energy use, and carbon-dioxide removal. However, 1.9 W m−2 scenarios could not be achieved in several models under SSPs with strong inequalities, high baseline fossil-fuel use, or scattered short-term climate policy. Further research can help policy-makers to understand the real-world implications of these scenarios.
Eleanor J Burke, Sarah E Chadburn, Chris Huntingford and Chris D Jones. 2018 Environmental Research Letters 13, 024024. doi.org/10.1088/1748-9326/aaa138.
Abstract: Large amounts of carbon are stored in the permafrost of the northern high latitude land. As permafrost degrades under a warming climate, some of this carbon will decompose and be released to the atmosphere. This positive climate-carbon feedback will reduce the natural carbon sinks and thus lower anthropogenic CO2 emissions compatible with the goals of the Paris Agreement. Simulations using an ensemble of the JULES-IMOGEN intermediate complexity climate model (including climate response and process uncertainty) and a stabilization target of 2 °C, show that including the permafrost carbon pool in the model increases the land carbon emissions at stabilization by between 0.09 and 0.19 Gt C year−1 (10th to 90th percentile). These emissions are only slightly reduced to between 0.08 and 0.16 Gt C year−1 (10th to 90th percentile) when considering 1.5 °C stabilization targets. This suggests that uncertainties caused by the differences in stabilization target are small compared with those associated with model parameterisation uncertainty. Inertia means that permafrost carbon loss may continue for many years after anthropogenic emissions have stabilized. Simulations suggest that between 225 and 345 Gt C (10th to 90th percentile) are in thawed permafrost and may eventually be released to the atmosphere for stabilization target of 2 °C. This value is 60–100 Gt C less for a 1.5 °C target. The inclusion of permafrost carbon will add to the demands on negative emission technologies which are already present in most low emissions scenarios.
Abstract: How ocean acidification will affect marine organisms depends on changes in both the long-term mean and the short-term temporal variability of carbonate chemistry. Although the decadal-to-centennial response to atmospheric CO2 and climate change is constrained by observations and models, little is known about corresponding changes in seasonality, particularly for pH. Here we assess the latter by analysing nine earth system models (ESMs) forced with a business-as-usual emissions scenario. During the twenty-first century, the seasonal cycle of surface-ocean pH was attenuated by 16 ± 7%, on average, whereas that for hydrogen ion concentration [H+] was amplified by 81 ± 16%. Simultaneously, the seasonal amplitude of the aragonite saturation state (Ωarag) was attenuated except in the subtropics, where it was amplified. These contrasting changes derive from regionally varying sensitivities of these variables to atmospheric CO2 and climate change and to diverging trends in seasonal extremes in the primary controlling variables (temperature, dissolved inorganic carbon and alkalinity). Projected seasonality changes will tend to exacerbate the impacts of increasing [H+] on marine organisms during the summer and ameliorate the impacts during the winter, although the opposite holds in the high latitudes. Similarly, over most of the ocean, impacts from declining Ωarag are likely to be intensified during the summer and dampened during the winter.
C. E. Scott, S. A. Monks, D. V. Spracklen, S. R. Arnold, P. M. Forster, A. Rap, M. Äijälä, P. Artaxo, K. S. Carslaw, M. P. Chipperfield, M. Ehn, S. Gilardoni, L. Heikkinen, M. Kulmala, T. Petäjä, C. L. S. Reddington, L. V. Rizzo, E. Swietlicki, E. Vignati & C. Wilson. Nature Communications 9, Article number: 157 (2018). doi:10.1038/s41467-017-02412-4.
Abstract: The climate impact of deforestation depends on the relative strength of several biogeochemical and biogeophysical effects. In addition to affecting the exchange of carbon dioxide (CO2) and moisture with the atmosphere and surface albedo, vegetation emits biogenic volatile organic compounds (BVOCs) that alter the formation of short-lived climate forcers (SLCFs), which include aerosol, ozone and methane. Here we show that a scenario of complete global deforestation results in a net positive radiative forcing (RF; 0.12 W m−2) from SLCFs, with the negative RF from decreases in ozone and methane concentrations partially offsetting the positive aerosol RF. Combining RFs due to CO2, surface albedo and SLCFs suggests that global deforestation could cause 0.8 K warming after 100 years, with SLCFs contributing 8% of the effect. However, deforestation as projected by the RCP8.5 scenario leads to zero net RF from SLCF, primarily due to nonlinearities in the aerosol indirect effect.
Abstract: The terrestrial biosphere is an important source of natural aerosol. Natural aerosol sources alter climate, but are also strongly controlled by climate, leading to the potential for natural aerosol–climate feedbacks. Here we use a global aerosol model to make an assessment of terrestrial natural aerosol–climate feedbacks, constrained by observations of aerosol number. We find that warmer-than-average temperatures are associated with higher-than-average number concentrations of large (>100 nm diameter) particles, particularly during the summer. This relationship is well reproduced by the model and is driven by both meteorological variability and variability in natural aerosol from biogenic and landscape fire sources. We find that the calculated extratropical annual mean aerosol radiative effect (both direct and indirect) is negatively related to the observed global temperature anomaly, and is driven by a positive relationship between temperature and the emission of natural aerosol. The extratropical aerosol–climate feedback is estimated to be −0.14 W m−2 K−1 for landscape fire aerosol, greater than the −0.03 W m−2 K−1 estimated for biogenic secondary organic aerosol. These feedbacks are comparable in magnitude to other biogeochemical feedbacks, highlighting the need for natural aerosol feedbacks to be included in climate simulations.
Florent F. Malavelle, Jim M. Haywood, Lina M. Mercado, Gerd A. Folberth, Nicolas Bellouin, Stephen Sitch, and Paulo Artaxo. Atmos. Chem. Phys., 19, 1301-1326, 2019, doi.org/10.5194/acp-19-1301-2019.
Short summary: Diffuse light can increase the efficiency of vegetation photosynthesis. Diffuse light results from scattering by either clouds or aerosols in the atmosphere. During the dry season biomass burning (BB) on the edges of the Amazon rainforest contributes significantly to the aerosol burden over the entire region. We show that despite a modest effect of change in light conditions, the overall impact of BB aerosols on the vegetation is still important when indirect climate feedbacks are considered.
Gab Abramowitz, Nadja Herger, Ethan Gutmann, Dorit Hammerling, Reto Knutti, Martin Leduc, Ruth Lorenz, Robert Pincus, and Gavin A. Schmidt. Earth Syst. Dynam., 10, 91-105, 2019, doi.org/10.5194/esd-10-91-2019.
Short summary: Best estimates of future climate projections typically rely on a range of climate models from different international research institutions. However, it is unclear how independent these different estimates are, and, for example, the degree to which their agreement implies robustness. This work presents a review of the varied and disparate attempts to quantify and address model dependence within multi-model climate projection ensembles.
Alessio Collalti, Peter E. Thornton, Alessandro Cescatti, Angelo Rita, Marco Borghetti, Angelo Nolè, Carlo Trotta, Philippe Ciais, Giorgio Matteucci. Ecological Applications, 0(0), 2019, e01837, doi.org/10.1002/eap.1837.
Abstract: The future trajectory of atmospheric CO2 concentration depends on the development of the terrestrial carbon sink, which in turn is influenced by forest dynamics under changing environmental conditions. An in‐depth understanding of model sensitivities and uncertainties in non‐steady‐state conditions is necessary for reliable and robust projections of forest development and under scenarios of global warming and CO2 enrichment. Here, we systematically assessed if a biogeochemical process‐based model (3D‐CMCC‐CNR), which embeds similarities with many other vegetation models, applied in simulating net primary productivity (NPP) and standing woody biomass (SWB), maintained a consistent sensitivity to its 55 input parameters through time, during forest ageing and structuring as well as under climate change scenarios. Overall, the model applied at three contrasting European forests showed low sensitivity to the majority of its parameters. Interestingly, model sensitivity to parameters varied through the course of >100 yr of simulations. In particular, the model showed a large responsiveness to the allometric parameters used for initialize forest carbon and nitrogen pools early in forest simulation (i.e., for NPP up to ~37%, 256 g C·m−2·yr−1 and for SWB up to ~90%, 65 Mg C/ha, when compared to standard simulation), with this sensitivity decreasing sharply during forest development. At medium to longer time scales, and under climate change scenarios, the model became increasingly more sensitive to additional and/or different parameters controlling biomass accumulation and autotrophic respiration (i.e., for NPP up to ~30%, 167 g C·m−2·yr−1 and for SWB up to ~24%, 64 Mg C/ha, when compared to standard simulation). Interestingly, model outputs were shown to be more sensitive to parameters and processes controlling stand development rather than to climate change (i.e., warming and changes in atmospheric CO2 concentration) itself although model sensitivities were generally higher under climate change scenarios. Our results suggest the need for sensitivity and uncertainty analyses that cover multiple temporal scales along forest developmental stages to better assess the potential of future forests to act as a global terrestrial carbon sink.

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