Learning about the ocean carbon cycle from observational constraints and model simulations of multiple tracers

A key question in studies of the potential for reducing uncertainty in climate change projections is how additional observations may be used to constrain models. We examine the case of ocean carbon cycle models. The reliability of ocean models in projecting oceanic CO₂ uptake is fundamentally dependent on their skills in simulating ocean circulation and air–sea gas exchange. In this study we demonstrate how a model simulation of multiple tracers and utilization of a variety of observational data help us to obtain additional information about the parameterization of ocean circulation and air–sea gas exchange, relative to approaches that use only a single tracer. The benefit of using multiple tracers is based on the fact that individual tracer holds unique information with regard to ocean mixing, circulation, and air–sea gas exchange. In a previous modeling study, we have shown that the simulation of radiocarbon enables us to identify the importance of parameterizing sub-grid scale ocean mixing processes in terms of diffusive mixing along constant density surface (isopycnal mixing) and the inclusion of the effect of mesoscale eddies. In this study we show that the simulation of phosphate, a major macronutrient in the ocean, helps us to detect a weak isopycnal mixing in the upper ocean that does not show up in the radiocarbon simulation. We also show that the simulation of chlorofluorocarbons (CFCs) reveals excessive upwelling in the Southern Ocean, which is also not apparent in radiocarbon simulations. Furthermore, the updated ocean inventory data of man-made radiocarbon produced by nuclear tests (bomb ¹⁴C) enable us to recalibrate the rate of air–sea gas exchange. The progressive modifications made in the model based on the simulation of additional tracers and utilization of updated observational data overall improve the model’s ability to simulate ocean circulation and air–sea gas exchange, particularly in the Southern Ocean, and has great consequence for projected CO₂ uptake. Simulated global ocean uptake of anthropogenic CO₂ from pre-industrial time to the present day by both previous and updated models are within the range of observational-based estimates, but with substantial regional difference, especially in the Southern Ocean. By year 2100, the updated model estimated CO₂ uptake are 531 and 133 PgC (1PgC?=?10¹⁵ gram carbon) for the global and Southern Ocean respectively, whereas the previous version model estimated values are 540 and 190 PgC.     

海洋碳循环模式的进展

本文综述了两类近年来国外使用的海洋碳循环数值模式.一类是国外通常使用的比较简单的箱模式;另一类是基于大洋环流模式的三维无机碳循环模式,以及在该模式的基础上引进了海洋生物群作用的海洋碳循环模式.后者是目前比较完整的模式,也是本文重点介绍的内容.     

Contributions of carbon cycle uncertainty to future climate projection spread

We have characterized the relative contributions to uncertainty in predictions of global warming amount by year 2100 in the C4MIP model ensemble ( Friedlingstein et al., 2006 ) due to both carbon cycle process uncertainty and uncertainty in the physical climate properties of the Earth system. We find carbon cycle uncertainty to be important. On average the spread in transient climate response is around 40% of that due to the more frequently debated uncertainties in equilibrium climate sensitivity and global heat capacity.
This result is derived by characterizing the influence of different parameters in a global climate-carbon cycle 'box' model that has been calibrated against the 11 General Circulation models (GCMs) and Earth system Models of Intermediate Complexity (EMICs) in the C4MIP ensemble; a collection of current state-of-the-art climate models that include an explicit representation of the global carbon cycle.     

Factors influencing anthropogenic carbon dioxide uptake in the North Atlantic in models of the ocean carbon cycle

The uptake and storage of anthropogenic carbon in the North Atlantic is investigated using different configurations of ocean general circulation/carbon cycle models. We investigate how different representations of the ocean physics in the models, which represent the range of models currently in use, affect the evolution of CO₂ uptake in the North Atlantic. The buffer effect of the ocean carbon system would be expected to reduce ocean CO₂ uptake as the ocean absorbs increasing amounts of CO₂. We find that the strength of the buffer effect is very dependent on the model ocean state, as it affects both the magnitude and timing of the changes in uptake. The timescale over which uptake of CO₂ in the North Atlantic drops to below preindustrial levels is particularly sensitive to the ocean state which sets the degree of buffering; it is less sensitive to the choice of atmospheric CO₂ forcing scenario. Neglecting physical climate change effects, North Atlantic CO₂ uptake drops below preindustrial levels between 50 and 300 years after stabilisation of atmospheric CO₂ in different model configurations. Storage of anthropogenic carbon in the North Atlantic varies much less among the different model configurations, as differences in ocean transport of dissolved inorganic carbon and uptake of CO₂ compensate each other. This supports the idea that measured inventories of anthropogenic carbon in the real ocean cannot be used to constrain the surface uptake. Including physical climate change effects reduces anthropogenic CO₂ uptake and storage in the North Atlantic further, due to the combined effects of surface warming, increased freshwater input, and a slowdown of the meridional overturning circulation. The timescale over which North Atlantic CO₂ uptake drops to below preindustrial levels is reduced by about one-third, leading to an estimate of this timescale for the real world of about 50 years after the stabilisation of atmospheric CO₂. In the climate change experiment, a shallowing of the mixed layer depths in the North Atlantic results in a significant reduction in primary production, reducing the potential role for biology in drawing down anthropogenic CO₂.     

The uncertainty about the social cost of carbon: A decomposition analysis using fund

We report the results of an uncertainty decomposition analysis of the social cost of carbon as estimated by FUND, a model that has a more detailed representation of the economic impact of climate change than any other model. Some of the parameters particularly influence impacts in the short run whereas other parameters are important in the long run. Some parameters are influential in some regions only. Some parameters are known reasonably well, but others are not. Ethical values, such as the pure rate of time preference and the rate of risk aversion, therefore affect not only the social cost of carbon, but also the importance of the parameters that determine its value. Some parameters, however, are consistently important: cooling energy demand, migration, climate sensitivity, and agriculture. The last two are subject to a large research effort, but the first two are not.     

Carbon cycle–climate feedback sensitivity to parameter changes of a zero-dimensional terrestrial carbon cycle scheme in a climate model of intermediate complexity

Summary A series of sensitivity runs have been performed with a coupled climate–carbon cycle model. The climatic component consists of the climate model of intermediate complexity IAP RAS CM. The carbon cycle component is formulated as a simple zero-dimensional model. Its terrestrial part includes gross photosynthesis, and plant and soil respirations, depending on temperature via Q ₁₀-relationships (Lenton, 2000). Oceanic uptake of anthropogenic carbon is formulated is a bi-linear function of tendencies of atmospheric concentration of CO₂ and globally averaged annual mean sea surface temperature. The model is forced by the historical industrial and land use emissions of carbon dioxide for the second half of the 19th and the whole of the 20th centuries, and by the emission scenario SRES A2 for the 21st century. For the standard set of the governing parameters, the model realistically captures the main features of the Earth’s observed carbon cycle. A large number of simulations have been performed, perturbing the governing parameters of the terrestrial carbon cycle model. In addition, the climate part is perturbed, either by zeroing or artificially increasing the climate model sensitivity to the doubling of the atmospheric CO₂ concentration. Performing the above mentioned perturbations, it is possible to mimic most of the range found in the C4MIP simulations. In this way, a wide range of the climate–carbon cycle feedback strengths is obtained, differing even in the sign of the feedback. If the performed simulations are subjected to the constraints of a maximum allowed deviation of the simulated atmospheric CO₂ concentration (pCO_2(a)) from the observed values and correspondence between simulated and observed terrestrial uptakes, it is possible to narrow the corresponding uncertainty range. Among these constraints, considering pCO_2(a) and uptakes are both important. However, the terrestrial uptakes constrain the simulations more effectively than the oceanic ones. These constraints, while useful, are still unable to rule out both extremely strong positive and modest negative climate–carbon cycle feedback.     

The energy cycle in atmospheric models

The energy cycle characterizes basic aspects of the physical behaviour of the climate system. Terms in the energy cycle involve first and second order climate statistics (means, variances, covariances) and the intercomparison of energetic quantities offers physically motivated “second order” insight into model and system behaviour. The energy cycle components of 12 models participating in AMIP2 are calculated, intercompared and assessed against results based on NCEP and ERA reanalyses. In general, models simulate a modestly too vigorous energy cycle and the contributions to and reasons for this are investigated. The results suggest that excessive generation of zonal available potential energy is an important driver of the overactive energy cycle through “generation push” while excessive dissipation of eddy kinetic energy in models is implicated through “dissipation pull‘’. The study shows that “ensemble model” results are best or among the best in the comparison of energy cycle quantities with reanalysis-based values. Thus ensemble approaches are apparently “best” not only for the simulation of 1st order climate statistics as in Lambert and Boer (Clim Dyn 17:83–106, 2001) but also for the higher order climate quantities entering the energy cycle.     

Optimal Parameter and Uncertainty Estimation of a Land Surface Model： Sensitivity to Parameter Ranges and Model Complexities

Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI) to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.     

The benefits of climate change mitigation in integrated assessment models: the role of the carbon cycle and climate component

Integrated Assessment Models (IAMs) are an important tool to compare the costs and benefits of different climate policies. Recently, attention has been given to the effect of different discounting methods and damage estimates on the results of IAMs. One aspect to which little attention has been paid is how the representation of the climate system may affect the estimated benefits of mitigation action. In that respect, we analyse several well-known IAMs, including the newest versions of FUND, DICE and PAGE. Given the role of IAMs in integrating information from different disciplines, they should ideally represent both best estimates and the ranges of anticipated climate system and carbon cycle behaviour (as e.g. synthesised in the IPCC Assessment reports). We show that in the longer term, beyond 2100, most IAM parameterisations of the carbon cycle imply lower CO₂ concentrations compared to a model that captures IPCC AR4 knowledge more closely, e.g. the carbon-cycle climate model MAGICC6. With regard to the climate component, some IAMs lead to much lower benefits of mitigation than MAGICC6. The most important reason for the underestimation of the benefits of mitigation is the failure in capturing climate dynamics correctly, which implies this could be a potential development area to focus on.     

Policy uncertainty and diffusion of carbon capture and storage in an optimal region

Abstract

The social cost of carbon (SCC) is the value of the climate change impacts from 1 tonne of carbon emitted today as CO₂, aggregated over time and discounted back to the present day. We used PAGE2002, the same probabilistic integrated assessment model as used by the Stern Review (Stern et al., 2006), to calculate the SCC and to examine how it varies with discount rate; and find that it is not sensitive to the path of emissions on which the tonne of carbon is superimposed. The mean value of the SCC is $43 per tonne under both a business-as-usual scenario, and under a scenario aimed at stabilizing CO₂ concentrations at 550 ppm. This counter-intuitive result is caused by the interplay between the logarithmic relationship between forcing and concentration, the nonlinear relationship of damage to temperature, and discounting. However, the SCC is sensitive to a number of scientific and economic inputs to the model. Two recent distributions for the sensitivity of climate to a doubling of atmospheric CO₂ (Murphy et al., 2004; Stainforth et al., 2005) increase the mean value of the SCC from $43 to $68 and $90 per tonne. Using a pure rate of time preference of 0.1% per year, as in the Stern Review, gives a mean SCC of $365 per tonne.     

地面有效辐射气候学模型评估和参数优化

基于中国19个辐射站1993-2012年的地面辐射平衡资料和气象资料,分析评估了布朗特法、彭曼法、别尔良德法、FAO24法、FAO56-PM法、邓根云法和童宏良法7种参数化方案计算中国地面有效辐射的适用性;并以均方根误差最小为目标函数,利用步长加速法和多元回归法迭代求解最优参数,建立适合于中国的最优参数化逐日有效辐射估算方法。结果表明:参与评估的7种方案都不同程度低估了中国的有效辐射;从全中国总体误差水平看,童宏良法的平均绝对百分比误差和均方根误差小于其他6种方案,分别为27.0%和24.5 W/m2,估算效果较好;其次是彭曼法和邓根云法;FAO56-PM法精度较低,不适用于中国的有效辐射估算。针对单站来说,邓根云法在东部平原地区的精度最高,童宏良法由于考虑了海拔高度的订正,适用于西部高原地区。相关分析表明水汽压是影响有效辐射估算误差的最关键因素,因此根据水汽压的地理分布规律,分东部区和西部区建立分区方案。基于观测资料建立的全中国方案和分区方案的均方根误差分别为20.8和21.4 W/m2,精度均高于已有参与评估的7种方案;而且在绝大多数站点,分区方案的误差小于全中国方案,所以划分东部区和西部区进行有效辐射模型参数化很有必要。同时发现,分区方案在西部区明显优于邓根云法,在东部区明显优于童宏良法,因此推荐其作为中国有效辐射的计算方法。      

Some results about the relation between an objective front parameter and cloud bands in satellite images and its connection to classical cold front models

Summary Two different manifestations of cold fronts are compared: an objective front parameter (the thermal front parameter =TFP) which locates a front where the maximal change of the temperature (thickness) gradient appears and the cloud band in the satellite images. TheTFP is merely a line while the cloud band is mostly several hundred kilometers broad. The relation between the position of theTFP and the cloud band is investigated statistically using all cold front events of one year. It can be shown that theTFP prefers special positions relative to the cloud band and that these positions remain constant during the next 12 hours in a high number of cases. This confirms the usefulness of its diagnosis and very short range prognosis as is done with an operationally issued satellite report (Satrep). Vertical cross sections of equivalent potential temperature and humidity with some parameters superimposed show a connection between the position of theTFP relative to the cloud band and the classical cold front models of the ana-and katafront.With 8 Figures     

The seasonal cycle in coupled ocean-atmosphere general circulation models

We examine the seasonal cycle of near-surface air temperature simulated by 17 coupled ocean-atmosphere general circulation models participating in the Coupled Model Intercomparison Project (CMIP). Nine of the models use ad hoc “flux adjustment” at the ocean surface to bring model simulations close to observations of the present-day climate. We group flux-adjusted and non-flux-adjusted models separately and examine the behavior of each class. When averaged over all of the flux-adjusted model simulations, near-surface air temperature falls within 2?K of observed values over the oceans. The corresponding average over non-flux-adjusted models shows errors up to ～6?K in extensive ocean areas. Flux adjustments are not directly applied over land, and near-surface land temperature errors are substantial in the average over flux-adjusted models, which systematically underestimates (by ～5?K) temperature in areas of elevated terrain. The corresponding average over non-flux-adjusted models forms a similar error pattern (with somewhat increased amplitude) over land. We use the temperature difference between July and January to measure seasonal cycle amplitude. Zonal means of this quantity from the individual flux-adjusted models form a fairly tight cluster (all within ～30% of the mean) centered on the observed values. The non-flux-adjusted models perform nearly as well at most latitudes. In Southern Ocean mid-latitudes, however, the non-flux-adjusted models overestimate the magnitude of January-minus-July temperature differences by ～5?K due to an overestimate of summer (January) near-surface temperature. This error is common to five of the eight non-flux-adjusted models. Also, over Northern Hemisphere mid-latitude land areas, zonal mean differences between July and January temperatures simulated by the non-flux-adjusted models show a greater spread (positive and negative) about observed values than results from the flux-adjusted models. Elsewhere, differences between the two classes of models are less obvious. At no latitude is the zonal mean difference between averages over the two classes of models greater than the standard deviation over models. The ability of coupled GCMs to simulate a reasonable seasonal cycle is a necessary condition for confidence in their prediction of long-term climatic changes (such as global warming), but it is not a sufficient condition unless the seasonal cycle and long-term changes involve similar climatic processes. To test this possible connection, we compare seasonal cycle amplitude with equilibrium warming under doubled atmospheric carbon dioxide for the models in our data base. A small but positive correlation exists between these two quantities. This result is predicted by a simple conceptual model of the climate system, and it is consistent with other modeling experience, which indicates that the seasonal cycle depends only weakly on climate sensitivity.