1,721,050 research outputs found
How historic simulation-observation discrepancy affects future warming projections in a very large model ensemble
Full text linkProjections of future climate made by model-ensembles have credibility because the historic simulations by these models are consistent with, or near-consistent with, historic observations. However, it is not known how small inconsistencies between the ranges of observed and simulated historic climate change affects the future projections made by a model ensemble. Here, the impact of historical simulation-observation inconsistencies on future warming projections is quantified in a 4-million member Monte Carlo ensemble from a new efficient Earth System Model (ESM). Of the 4-million ensemble members, a subset of 182,500 are consistent with historic ranges of warming, heat uptake and carbon uptake simulated by the Climate Model Intercomparison Project 5 (CMIP5) ensemble. This simulation-consistent subset projects similar future warming ranges to the CMIP5 ensemble for all four RCP scenarios, indicating the new ESM represents an efficient tool to explore parameter space for future warming projections based on historic performance. A second subset of 14,500 ensemble members are consistent with historic observations for warming, heat uptake and carbon uptake. This observation-consistent subset projects a narrower range for future warming, with the lower bounds of projected warming still similar to CMIP5, but the upper warming bounds reduced by 20 to 35%. These findings suggest that part of the upper range of 21st century CMIP5 warming projections may reflect historical simulation-observation inconsistencies. However, the agreement of lower bounds for projected warming implies that the likelihood of warming exceeding dangerous levels over the 21st century is unaffected by small discrepancies between CMIP5 models and observations
Quantifying the terrestrial carbon feedback to anthropogenic carbon emission
No full textThe surface warming response to carbon emission is dependent on feedbacks operating in both the physical climate and carbon cycle systems, with physical climate feedbacks quantified via linearly combinable climate feedback terms, λclimate in Wm-2K-1. However, land carbon feedbacks are often quantified using a two-parameter description, with separate cumulative carbon uptake responses to surface warming, γL in PgC K-1, and rising atmospheric CO2concentration, βL in PgC ppm-1. Converting the γL and βL responses to an overall terrestrial carbon feedback parameter, λcarbon in Wm-2K-1, has remained problematic, with λcarbon affected by significant non-linear interactions between carbon-climate and carbon-concentration responses and a non-linear relation between atmospheric CO2and subsequent radiative forcing. This study presents new relationships quantifying how the overall steady state terrestrial carbon feedback to anthropogenic emission, λcarbon, is dependent on the terrestrial carbon responses to rising CO2and temperature, βL, and γL, and the physical climate feedback, λclimate. Non-linear interactions between βL and γL responses to carbon emission are quantified via a three-parameter description of the land carbon sensitivities to rising CO2 and temperature. Numerical vegetation model output supports the new relationships, revealing an emerging sensitivity of land carbon feedback to climate feedback of ∂λcarbon/∂λclimate~0.3. The results highlight that terrestrial carbon feedback and physical climate feedback cannot be considered in isolation: additional surface warming from stronger climate feedback is automatically compounded by reduced cooling from terrestrial carbon feedback, meanwhile around half the uncertainty in terrestrial carbon feedback originates from uncertainty in the physical climate feedback
Probabilistic projections of future warming and climate sensitivity trajectories
No full textProjections of future global mean surface warming for a given forcing scenario remain uncertain, largely due to uncertainty in the climate sensitivity. The ensemble of Earth system models from the Climate Model Intercomparison Project phase 6 (CMIP6) represent the dominant tools for projecting future global warming. However, the distribution of climate sensitivities within the CMIP6 ensemble is not representative of recent independent probabilistic estimates, and the ensemble contains significant variation in simulated historic surface warming outside agreement with observational datasets. Here, a Bayesian approach is used to infer joint probabilistic projections of future surface warming and climate sensitivity for SSP scenarios. The projections use an efficient climate model ensemble filtered and weighted to encapsulate observational uncertainty in historic warming and ocean heat content anomalies. The probabilistic projection of climate sensitivity produces a best estimate of 2.9°C, and 5th to 95th percentile range of 1.5 to 4.6 °C, in line with previous estimates using multiple lines of evidence. The joint projection of surface warming over the period 2030 to 2040 has a 50% or greater probability of exceeding 1.5 °C above preindustrial for all SSPs considered: 119, 126, 245, 370 and 585. Average warming by the period 2050 to 2060 has a greater than 50% chance of exceeding 2°C for SSPs 245, 370 and 585. These results imply that global warming is no longer likely to remain under 1.5°C, even with drastic and immediate mitigation, and highlight the importance of urgent action to avoid exceeding 2°C warming
An Isopycnal Box Model with predictive deep-ocean structure for biogeochemical cycling applications
No full textTo simulate global ocean biogeochemical tracer budgets a model must accurately determine both the volume and surface origins of each water-mass. Water-mass volumes are dynamically linked to the ocean circulation in General Circulation Models, but at the cost of high computational load. In computationally efficient Box Models the water-mass volumes are simply prescribed and do not vary when the circulation transport rates or water mass densities are perturbed. A new computationally efficient Isopycnal Box Model is presented in which the sub-surface box volumes are internally calculated from the prescribed circulation using a diffusive conceptual model of the thermocline, in which upwelling of cold dense water is balanced by a downward diffusion of heat. The volumes of the sub-surface boxes are set so that the density stratification satisfies an assumed link between diapycnal diffusivity, ?d, and buoyancy frequency, N: ?d = c/(N?), where c and ? are user prescribed parameters. In contrast to conventional Box Models, the volumes of the sub-surface ocean boxes in the Isopycnal Box Model are dynamically linked to circulation, and automatically respond to circulation perturbations. This dynamical link allows an important facet of ocean biogeochemical cycling to be simulated in a highly computationally efficient model framework
On the time evolution of climate sensitivity and future warming
No full textThe Earth’s climate sensitivity to radiative forcing remains a key source of uncertainty in future warming projections. There is a growing realisation in recent literature that research must go beyond an equilibrium and CO2-only viewpoint, towards considering how climate sensitivity will evolve over time in response to anthropogenic and natural radiative forcing from multiple sources. Here, the transient behaviour of climate sensitivity is explored using a modified energy balance model, in which multiple climate feedbacks evolve independently over time to multiple sources of radiative forcing, combined with constraints from observations and from the Climate Model Intercomparison Project phase 5 (CMIP5). First, a large initial ensemble of 107 simulations is generated, with a distribution of climate feedback strengths from sub-annual to 102 year timescales constrained by the CMIP5 ensemble; including the Planck feedback, the combined water-vapour lapse-rate feedback, snow and sea-ice albedo feedback, fast cloud feedbacks, and the cloud response to SST-adjustment feedback. These 107 simulations are then tested against observational metrics representing decadal trends in warming, heat and carbon uptake, leaving only 4.6×103 history-matched simulations consistent with both the CMIP5 ensemble and historical observations. The results reveal an annual-timescale climate sensitivity of 2.1 °C (ranging from 1.6 to 2.8 °C at 95% uncertainty), rising to 2.9 °C (from 1.9 to 4.6 °C) on century timescales. These findings provide a link between lower estimates of climate sensitivity, based on the current transient state of the climate system, and higher estimates based on long-term behaviour of complex models and palaeoclimate evidence
State-dependence of Cenozoic thermal extremes
Full text linkOxygen isotopes in sediments reflect Earth’s past temperature, revealing a cooling over the Cenozoic punctuated by multimillenial thermal extreme events. The magnitude of these extremes and their dependency on baseline climate state is not clearly understood. Here we use global records of deep sea foraminiferal δ18O as a proxy for atmospheric temperature over the Cenezoic and investigate how closely the generalised extreme value distribution matches δ18O block maxima. We find that the distribution of these extremes is captured well by the generalized extreme value distribution. In addition, the distribution of extremes’ shape changes with baseline temperature such that large thermal extremes are more likely in warmer climates. We therefore suggest that anthropogenic warming has the potential to return the baseline climate state to one where large thermal extremes are more likely
Bayesian estimation of Earth’s climate sensitivity and transient climate response from observational warming and heat content datasets
Full text linkFuture climate change projections, impacts and mitigation targets are directly affected by how sensitive Earth’s global mean surface temperature is to anthropogenic forcing, expressed via the effective climate sensitivity (ECS) and transient climate response (TCR). However, the ECS and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate ECS and TCR by using historic observations of surface warming, since the mid-19th century, and ocean heat uptake, since the mid 20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and slow feedbacks (acting over decades). We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions are similar when using different historic datasets: from a TCR of 1.5 (1.3 to 1.7 at 5–95 % range) °C, up to 1.7 (1.4 to 2.0) °C. However, the posterior probability distribution for ECS on a 100-year response timescale varies depending on which combinations of temperature and heat content anomaly datasets are used: from ECS of 2.2 (1.5 to 4.5) °C, for datasets with less historic warming, up to 2.8 (1.8 to 6.1) °C, for datasets with more historic warming. Our results demonstrate how differences between historic climate reconstructions imply significant differences in expected future global warming
The effect of ocean ventilation on the Transient Climate Response to Emissions
No full textThe surface warming response to carbon emissions is affected by how the ocean sequesters excess heat and carbon supplied to the climate system. This ocean uptake involves the ventilation mechanism, where heat and carbon are taken up by the mixed layer and transferred to the thermocline and deep ocean. The effect of ocean ventilation on the surface warming response to carbon emissions is explored using simplified conceptual models of the atmosphereocean with and without explicit representation of the meridional overturning. Sensitivity experiments are conducted to investigate the effects of (i) mixedlayer thickness, (ii) rate of ventilation of the ocean interior, (iii) strength of the meridional overturning and (iv) extent of subduction in the Southern Ocean. Our diagnostics focus on a climate metric, the Transient Climate Response to carbon Emissions (TCRE), defined by the ratio of surface warming to the cumulative carbon emissions, which may be expressed in terms of separate thermal and carbon contributions. The variability in the thermal contribution due to changes in ocean ventilation dominates the variability in the TCRE on timescales of years to centuries, while that of the carbon contribution dominates on timescales of centuries to millennia. These ventilated controls are primarily from changes in the mixed-layer thickness on decadal timescales, and in the rate of ventilated transfer from the mixed layer to the thermocline and deep ocean on centennial and millennial timescales, which is itself affected by the strength of the meridional overturning and extent of subduction in the Southern Ocean
Carbon-cycle feedbacks operating in the climate system
No full textClimate change involves a direct response of the climate system to forcing which is amplified or damped by feedbacks operating in the climate system. Carbon-cycle feedbacks alter the land and ocean carbon inventories and so act to reduce or enhance the increase in atmospheric CO2 from carbon emissions. The prevailing framework for carbon-cycle feedbacks connect changes in land and ocean carbon inventories with a linear sum of dependencies on atmospheric CO2 and surface temperature. Carbon-cycle responses and feedbacks provide competing contributions: the dominant effect is that increasing atmospheric CO2 acts to enhance the land and ocean carbon stores, so providing a negative response and feedback to the original increase in atmospheric CO2, while rising surface temperature acts to reduce the land and ocean carbon stores, so providing a weaker positive feedback for atmospheric CO2. The carbon response and feedback of the land and ocean system may be expressed in terms of a combined carbon response and feedback parameter, λcarbon in units of W m− 2K− 1, and is linearly related to the physical climate feedback parameter, λclimate, revealing how carbon and climate responses and feedbacks are inter-connected. The magnitude and uncertainties in the carbon-cycle response and feedback parameter are comparable with the magnitude and uncertainties in the climate feedback parameter from clouds. Further mechanistic insight needs to be gained into how the carbon-cycle feedbacks are controlled for the land and ocean, particularly to separate often competing effects from changes in atmospheric CO2 and climate forcing
Carbonate ion concentrations, ocean carbon storage, and atmospheric CO2
No full textReconstructing past ocean [CO32?] allows the paleodepth of the chemical lysocline to be constrained, an important control on past atmospheric CO2. However, the causal mechanisms responsible for observed spatial and temporal variations in [CO32?] are difficult to quantify because of the complicated carbonate chemistry system. Here spatial and temporal variations in [CO32?] are quantitatively and concisely related to variations in ocean carbon storage due to different processes. The spatial variation in [CO32?] is given by ?[CO32?]?=??(?Csoft?+??Cdis?+?(?Csat/?T)?T????Ccarb), where Csoft and Ccarb are the dissolved inorganic carbon (DIC) from remineralization of marine soft tissue and CaCO3, respectively, T is seawater temperature, (?Csat/?T) is the temperature-solubility sensitivity of DIC, Cdis is the DIC from air-sea disequilibrium, and ? is a carbonate chemistry coefficient. A similar quantitative function for temporal variation in global mean ocean [CO32?] is derived in terms of atmospheric CO2, CaCO3 precipitation and dissolution, and carbon exchanges of terrestrial or fossil fuel origin. Comparing published [CO32?] reconstructions at the Last Glacial Maximum (LGM) and the late Holocene, the quantitative relationships reveal how the spatial distribution of ocean carbon storage was altered. Relative to the Intermediate North Atlantic, the rest of the ocean saw Csoft?+?Cdis?+?(?Csat/?T)T???Ccarb increase by an extra 570–970 Pg C during the LGM. Assuming that the Intermediate North Atlantic Csoft?+?Cdis?+?(?Csat/?T)T???Ccarb did not decrease during the LGM, this 570–970 Pg C increase in the rest of the ocean is enough to explain 40%–70% of the observed glacial decrease in atmospheric CO2
- …
