Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data

Aerosol indirect effects continue to constitute one of the most important uncertainties for anthropogenic climate perturbations. Within the international AEROCOM initiative, the representation of aerosol-cloud-radiation interactions in ten different general circulation models (GCMs) is evaluated using three satellite datasets. The focus is on stratiform liquid water clouds since most GCMs do not include ice nucleation effects, and none of the model explicitly parameterises aerosol effects on convective clouds. We compute statistical relationships between aerosol optical depth (τa) and various cloud and radiation quantities in a manner that is consistent between the models and the satellite data. It is found that the model-simulated influence of aerosols on cloud droplet number concentration (Nd ) compares relatively well to the satellite data at least over the ocean. The relationship between �a and liquid water path is simulated much too strongly by the models. This suggests that the implementation of the second aerosol indirect effect mainly in terms of an autoconversion parameterisation has to be revisited in the GCMs. A positive relationship between total cloud fraction (fcld) and �a as found in the satellite data is simulated by the majority of the models, albeit less strongly than that in the satellite data in most of them. In a discussion of the hypotheses proposed in the literature to explain the satellite-derived strong fcld–�a relationship, our results indicate that none can be identified as a unique explanation. Relationships similar to the ones found in satellite data between �a and cloud top temperature or outgoing long-wave radiation (OLR) are simulated by only a few GCMs. The GCMs that simulate a negative OLR - �a relationship show a strong positive correlation between �a and fcld. The short-wave total aerosol radiative forcing as simulated by the GCMs is strongly influenced by the simulated anthropogenic fraction of �a, and parameterisation assumptions such as a lower bound on Nd . Nevertheless, the strengths of the statistical relationships are good predictors for the aerosol forcings in the models. An estimate of the total short-wave aerosol forcing inferred from the combination of these predictors for the modelled forcings with the satellite-derived statistical relationships yields a global annual mean value of −1.5±0.5Wm−2. In an alternative approach, the radiative flux perturbation due to anthropogenic aerosols can be broken down into a component over the cloud-free portion of the globe (approximately the aerosol direct effect) and a component over the cloudy portion of the globe (approximately the aerosol indirect effect). An estimate obtained by scaling these simulated clearand cloudy-sky forcings with estimates of anthropogenic �a and satellite-retrieved Nd–�a regression slopes, respectively, yields a global, annual-mean aerosol direct effect estimate of −0.4±0.2Wm−2 and a cloudy-sky (aerosol indirect effect) estimate of −0.7±0.5Wm−2, with a total estimate of −1.2±0.4Wm−2.

Variability of marine aerosol fine-mode fraction and estimates of anthropogenic aerosol component over cloud-free oceans from the Moderate Resolution Imaging Spectroradiometer (MODIS)

In this study, we examine seasonal and geographical variability of marine aerosol fine-mode fraction ( fm) and its impacts on deriving the anthropogenic component of aerosol optical depth (ta) and direct radiative forcing from multispectral satellite measurements. A proxy of fm, empirically derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 data, shows large seasonal and geographical variations that are consistent with the Goddard Chemistry Aerosol Radiation Transport (GOCART) and Global Modeling Initiative (GMI) model simulations. The so-derived seasonally and spatially varying fm is then implemented into a method of estimating ta and direct radiative forcing from the MODIS measurements. It is found that the use of a constant value for fm as in previous studies would have overestimated ta by about 20% over global ocean, with the overestimation up to �45% in some regions and seasons. The 7-year (2001–2007) global ocean average ta is 0.035, with yearly average ranging from 0.031 to 0.039. Future improvement in measurements is needed to better separate anthropogenic aerosol from natural ones and to narrow down the wide range of aerosol direct radiative forcing.

An urban parameterization for a global climate model. Part I: Formulation and evaluation for two cities

Urbanization, the expansion of built-up areas, is an important yet less-studied aspect of land use/land cover change in climate science. To date, most global climate models used to evaluate effects of land use/land cover change on climate do not include an urban parameterization. Here, the authors describe the formulation and evaluation of a parameterization of urban areas that is incorporated into the Community Land Model, the land surface component of the Community Climate System Model. The model is designed to be simple enough to be compatible with structural and computational constraints of a land surface model coupled to a global climate model yet complex enough to explore physically based processes known to be important in determining urban climatology. The city representation is based upon the “urban canyon” concept, which consists of roofs, sunlit and shaded walls, and canyon floor. The canyon floor is divided into pervious (e.g., residential lawns, parks) and impervious (e.g., roads, parking lots, sidewalks) fractions. Trapping of longwave radiation by canyon surfaces and solar radiation absorption and reflection is determined by accounting for multiple reflections. Separate energy balances and surface temperatures are determined for each canyon facet. A one-dimensional heat conduction equation is solved numerically for a 10-layer column to determine conduction fluxes into and out of canyon surfaces. Model performance is evaluated against measured fluxes and temperatures from two urban sites. Results indicate the model does a reasonable job of simulating the energy balance of cities.

A generalised Bayesian instrumental variable approach under student t-distributed errors with application

Bayesian analysis is given of an instrumental variable model that allows for heteroscedasticity in both the structural equation and the instrument equation. Specifically, the approach for dealing with heteroscedastic errors in Geweke (1993) is extended to the Bayesian instrumental variable estimator outlined in Rossi et al. (2005). Heteroscedasticity is treated by modelling the variance for each error using a hierarchical prior that is Gamma distributed. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm with an augmented draw for the heteroscedastic case. An example using real data illustrates the approach and shows that ignoring heteroscedasticity in the instrument equation when it exists may lead to biased estimates.

Global runoff routing with the hydrological component of the ECMWF NWP system

A global river routing scheme coupled to the ECMWF land surface model is implemented and tested within the framework of the Global Soil Wetness Project II, to evaluate the feasibility of modelling global river runoff at a daily time scale. The exercise is designed to provide benchmark river runoff predictions needed to verify the land surface model. Ten years of daily runoff produced by the HTESSEL land surface scheme is input into the TRIP2 river routing scheme in order to generate daily river runoff. These are then compared to river runoff observations from the Global Runoff Data Centre (GRDC) in order to evaluate the potential and the limitations. A notable source of inaccuracy is bias between observed and modelled discharges which is not primarily due to the modelling system but instead of to the forcing and quality of observations and seems uncorrelated to the river catchment size. A global sensitivity analysis and Generalised Likelihood Uncertainty Estimation (GLUE) uncertainty analysis are applied to the global routing model. The ground water delay parameter is identified as being the most sensitive calibration parameter. Significant uncertainties are found in results, and those due to parameterisation of the routing model are quantified. The difficulty involved in parameterising global river discharge models is discussed. Detailed river runoff simulations are shown for the river Danube, which match well observed river runoff in upstream river transects. Results show that although there are errors in runoff predictions, model results are encouraging and certainly indicative of useful runoff predictions, particularly for the purpose of verifying the land surface scheme hydrologicly. Potential of this modelling system on future applications such as river runoff forecasting and climate impact studies is highlighted. Copyright © 2009 Royal Meteorological Society.

How large is congressional dependence in agriculture. Bayesian inference about ‘scale’ and ‘scope’ in measuring a spatial externality

The political economy literature on agriculture emphasizes influence over political outcomes via lobbying conduits in general, political action committee contributions in particular and the pervasive view that political preferences with respect to agricultural issues are inherently geographic. In this context, ‘interdependence’ in Congressional vote behaviour manifests itself in two dimensions. One dimension is the intensity by which neighboring vote propensities influence one another and the second is the geographic extent of voter influence. We estimate these facets of dependence using data on a Congressional vote on the 2001 Farm Bill using routine Markov chain Monte Carlo procedures and Bayesian model averaging, in particular. In so doing, we develop a novel procedure to examine both the reliability and the consequences of different model representations for measuring both the ‘scale’ and the ‘scope’ of spatial (geographic) co-relations in voting behaviour.

Substitutional and orientational disorder in organic crystals: a symmetry-adapted ensemble model

Modelling of disorder in organic crystals is highly desirable since it would allow thermodynamic stabilities and other disorder-sensitive properties to be estimated for such systems. Two disordered organic molecular systems are modeled using a symmetry-adapted ensemble approach, in which the disordered system is treated as an ensemble of the configurations of a supercell with respect to substitution of one disorder component for another. Computation time is kept manageable by performing calculations only on the symmetrically inequivalent configurations. Calculations are presented on a substitutionally disordered system, the dichloro/dibromobenzene solid solution, and on an orientationally disordered system, eniluracil, and the resultant free energies, disorder patterns, and system properties are discussed. The results are found to be in agreement with experiment following manual removal of physically implausible configurations from ensemble averages, highlighting the dangers of a completely automated approach to organic crystal thermodynamics which ignores the barriers to equilibration once the crystal has been formed.

Mechanisms for AMOC variability simulated by the NEMO model

We have investigated mechanisms for the Atlantic Meridional Overturning Circulation (AMOC) variability at 26.5° N (other than the Ekman component) that can be related to external forcings, in particular wind variability. Resolution dependence is studied using identical experiments with 1° and 1/4° NEMO model runs over 1960–2010. The analysis shows that much of the variability in the AMOC at 26° N can be related to the wind strength over the North Atlantic, through mechanisms lagged on different timescales. At ~ 1-year lag the January–June difference of mean sea level pressure between high and mid-latitudes in the North Atlantic explains 35–50% of the interannual AMOC variability (with negative correlation between wind strength and AMOC). At longer lead timescales ~ 4 years, strong (weak) winds over the northern North Atlantic (specifically linked to the NAO index) are followed by higher (lower) AMOC transport, but this mechanism only works in the 1/4° model. Analysis of the density correlations suggests an increase (decrease) in deep water formation in the North Atlantic subpolar gyre to be the cause. Therefore another 30% of the AMOC variability at 26° N can be related to density changes in the top 1000 m in the Labrador and Irminger seas occurring ~ 4 years earlier.

The importance of wind and buoyancy forcing for the boundary density variations and the geostrophic component of the AMOC at 26°N

It is widely thought that changes in both the surface buoyancy fluxes and wind stress drive variability in the Atlantic meridional overturning circulation (AMOC), but that they drive variability on different time scales. For example, wind forcing dominates short-term variability through its effects on Ekman currents and coastal upwelling, whereas buoyancy forcing is important for longer time scales (multiannual and decadal). However, the role of the wind forcing on multiannual to decadal time scales is less clear. Here the authors present an analysis of simulations with the Nucleus for European Modelling of the Ocean (NEMO) ocean model with the aim of explaining the important drivers of the zonal density gradient at 26°N, which is directly related to the AMOC. In the experiments, only one of either the wind stress or the buoyancy forcing is allowed to vary in time, whereas the other remains at its seasonally varying climatology. On subannual time scales, variations in the density gradient, and in the AMOC minus Ekman, are driven largely by local wind-forced coastal upwelling at both the western and eastern boundaries. On decadal time scales, buoyancy forcing related to the North Atlantic Oscillation dominates variability in the AMOC. Interestingly, however, it is found that wind forcing also plays a role at longer time scales, primarily impacting the interannual variability through the excitation of Rossby waves in the central Atlantic, which propagate westward to interact with the western boundary, but also by modulating the decadal time-scale response to buoyancy forcing.

Forecasting with Bayesian multivariate vintage-based VARs

We consider the forecasting of macroeconomic variables that are subject to revisions, using Bayesian vintage-based vector autoregressions. The prior incorporates the belief that, after the first few data releases, subsequent ones are likely to consist of revisions that are largely unpredictable. The Bayesian approach allows the joint modelling of the data revisions of more than one variable, while keeping the concomitant increase in parameter estimation uncertainty manageable. Our model provides markedly more accurate forecasts of post-revision values of inflation than do other models in the literature.

Formulation and implementation of a residual-mean ocean circulation model

A parameterization of mesoscale eddies in coarse-resolution ocean general circulation models (GCM) is formulated and implemented using a residual-mean formalism. In that framework, mean buoyancy is advected by the residual velocity (the sum of the Eulerian and eddy-induced velocities) and modified by a residual flux which accounts for the diabatic effects of mesoscale eddies. The residual velocity is obtained by stepping forward a residual-mean momentum equation in which eddy stresses appear as forcing terms. Study of the spatial distribution of eddy stresses, derived by using them as control parameters to ‘‘fit’’ the residual-mean model to observations, supports the idea that eddy stresses can be likened to a vertical down-gradient flux of momentum with a coefficient which is constant in the vertical. The residual eddy flux is set to zero in the ocean interior, where mesoscale eddies are assumed to be quasi-adiabatic, but is parameterized by a horizontal down-gradient diffusivity near the surface where eddies develop a diabatic component as they stir properties horizontally across steep isopycnals. The residual-mean model is implemented and tested in the MIT general circulation model. It is shown that the resulting model (1) has a climatology that is superior to that obtained using the Gent and McWilliams parameterization scheme with a spatially uniform diffusivity and (2) allows one to significantly reduce the (spurious) horizontal viscosity used in coarse resolution GCMs.

Simulation of the global ENSO–Tropical cyclone teleconnection by a high-resolution coupled general circulation model

This study assesses the influence of the El Niño–Southern Oscillation (ENSO) on global tropical cyclone activity using a 150-yr-long integration with a high-resolution coupled atmosphere–ocean general circulation model [High-Resolution Global Environmental Model (HiGEM); with N144 resolution: ~90 km in the atmosphere and ~40 km in the ocean]. Tropical cyclone activity is compared to an atmosphere-only simulation using the atmospheric component of HiGEM (HiGAM). Observations of tropical cyclones in the International Best Track Archive for Climate Stewardship (IBTrACS) and tropical cyclones identified in the Interim ECMWF Re-Analysis (ERA-Interim) are used to validate the models. Composite anomalies of tropical cyclone activity in El Niño and La Niña years are used. HiGEM is able to capture the shift in tropical cyclone locations to ENSO in the Pacific and Indian Oceans. However, HiGEM does not capture the expected ENSO–tropical cyclone teleconnection in the North Atlantic. HiGAM shows more skill in simulating the global ENSO–tropical cyclone teleconnection; however, variability in the Pacific is overpronounced. HiGAM is able to capture the ENSO–tropical cyclone teleconnection in the North Atlantic more accurately than HiGEM. An investigation into the large-scale environmental conditions, known to influence tropical cyclone activity, is used to further understand the response of tropical cyclone activity to ENSO in the North Atlantic and western North Pacific. The vertical wind shear response over the Caribbean is not captured in HiGEM compared to HiGAM and ERA-Interim. Biases in the mean ascent at 500 hPa in HiGEM remain in HiGAM over the western North Pacific; however, a more realistic low-level vorticity in HiGAM results in a more accurate ENSO–tropical cyclone teleconnection.

Earthworm distribution and abundance predicted by a process-based model

Earthworms are significant ecosystem engineers and are an important component of the diet of many vertebrates and invertebrates, so the ability to predict their distribution and abundance would have wide application in ecology, conservation and land management. Earthworm viability is known to be affected by the availability and quality of food resources, soil water conditions and temperature, but has not yet been modelled mechanistically to link effects on individuals to field population responses. Here we present a novel model capable of predicting the effects of land management and environmental conditions on the distribution and abundance of Aporrectodea caliginosa, the dominant earthworm species in agroecosystems. Our process-based approach uses individual based modelling (IBM), in which each individual has its own energy budget. Individual earthworm energy budgets follow established principles of physiological ecology and are parameterised for A. caliginosa from experimental measurements under optimal conditions. Under suboptimal conditions (e.g. food limitation, low soil temperatures and water contents) reproduction is prioritised over growth. Good model agreement to independent laboratory data on individual cocoon production and growth of body mass, under variable feeding and temperature conditions support our representation of A. caliginosa physiology through energy budgets. Our mechanistic model is able to accurately predict A. caliginosa distribution and abundance in spatially heterogeneous soil profiles representative of field study conditions. Essential here is the explicit modelling of earthworm behaviour in the soil profile. Local earthworm movement responds to a trade-off between food availability and soil water conditions, and this determines the spatiotemporal distribution of the population in the soil profile. Importantly, multiple environmental variables can be manipulated simultaneously in the model to explore earthworm population exposure and effects to combinations of stressors. Potential applications include prediction of the population-level effects of pesticides and changes in soil management e.g. conservation tillage and climate change.

Probabilistic parameterisation of the surface mass balance--elevation feedback in regional climate model simulations of the Greenland ice sheet

We present a new parameterisation that relates surface mass balance (SMB: the sum of surface accumulation and surface ablation) to changes in surface elevation of the Greenland ice sheet (GrIS) for the MAR (Modèle Atmosphérique Régional: Fettweis, 2007) regional climate model. The motivation is to dynamically adjust SMB as the GrIS evolves, allowing us to force ice sheet models with SMB simulated by MAR while incorporating the SMB–elevation feedback, without the substantial technical challenges of coupling ice sheet and climate models. This also allows us to assess the effect of elevation feedback uncertainty on the GrIS contribution to sea level, using multiple global climate and ice sheet models, without the need for additional, expensive MAR simulations. We estimate this relationship separately below and above the equilibrium line altitude (ELA, separating negative and positive SMB) and for regions north and south of 77� N, from a set of MAR simulations in which we alter the ice sheet surface elevation. These give four “SMB lapse rates”, gradients that relate SMB changes to elevation changes. We assess uncertainties within a Bayesian framework, estimating probability distributions for each gradient from which we present best estimates and credibility intervals (CI) that bound 95% of the probability. Below the ELA our gradient estimates are mostly positive, because SMB usually increases with elevation: 0.56 (95% CI: −0.22 to 1.33) kgm−3 a−1 for the north, and 1.91 (1.03 to 2.61) kgm−3 a−1 for the south. Above the ELA, the gradients are much smaller in magnitude: 0.09 (−0.03 to 0.23) kgm−3 a−1 in the north, and 0.07 (−0.07 to 0.59) kgm−3 a−1 in the south, because SMB can either increase or decrease in response to increased elevation. Our statistically founded approach allows us to make probabilistic assessments for the effect of elevation feedback uncertainty on sea level projections (Edwards et al., 2014).

Parametric preference functionals under risk in the gain domain: a Bayesian analysis

The performance of rank dependent preference functionals under risk is comprehensively evaluated using Bayesian model averaging. Model comparisons are made at three levels of heterogeneity plus three ways of linking deterministic and stochastic models: the differences in utilities, the differences in certainty equivalents and contextualutility. Overall, the"bestmodel", which is conditional on the form of heterogeneity is a form of Rank Dependent Utility or Prospect Theory that cap tures the majority of behaviour at both the representative agent and individual level. However, the curvature of the probability weighting function for many individuals is S-shaped, or ostensibly concave or convex rather than the inverse S-shape commonly employed. Also contextual utility is broadly supported across all levels of heterogeneity. Finally, the Priority Heuristic model, previously examined within a deterministic setting, is estimated within a stochastic framework, and allowing for endogenous thresholds does improve model performance although it does not compete well with the other specications considered.