J. C. Hargreaves

Long-term climate commitments projected with climate-carbon cycle models

Eight earth system models of intermediate complexity (EMICs) are used to project climate change commitments for the recent Intergovernmental Panel on Climate Change’s (IPCC’s) Fourth Assessment Report (AR4). Simulations are run until the year 3000 A.D. and extend substantially farther into the future than conceptually similar simulations with atmosphere–ocean general circulation models (AOGCMs) coupled to carbon cycle models. In this paper the following are investigated: 1) the climate change commitment in response to stabilized greenhouse gases and stabilized total radiative forcing, 2) the climate change commitment in response to earlier CO2 emissions, and 3) emission trajectories for profiles leading to the stabilization of atmospheric CO2 and their uncertainties due to carbon cycle processes. Results over the twenty-first century compare reasonably well with results from AOGCMs, and the suite of EMICs proves well suited to complement more complex models. Substantial climate change commitments for sea level rise and global mean surface temperature increase after a stabilization of atmospheric greenhouse gases and radiative forcing in the year 2100 are identified. The additional warming by the year 3000 is 0.6–1.6 K for the low-CO2 IPCC Special Report on Emissions Scenarios (SRES) B1 scenario and 1.3–2.2 K for the high-CO2 SRES A2 scenario. Correspondingly, the post-2100 thermal expansion commitment is 0.3–1.1 m for SRES B1 and 0.5–2.2 m for SRES A2. Sea level continues to rise due to thermal expansion for several centuries after CO2 stabilization. In contrast, surface temperature changes slow down after a century. The meridional overturning circulation is weakened in all EMICs, but recovers to nearly initial values in all but one of the models after centuries for the scenarios considered. Emissions during the twenty-first century continue to impact atmospheric CO2 and climate even at year 3000. All models find that most of the anthropogenic carbon emissions are eventually taken up by the ocean (49%–62%) in year 3000, and that a substantial fraction (15%–28%) is still airborne even 900 yr after carbon emissions have ceased. Future stabilization of atmospheric CO2 and climate change requires a substantial reduction of CO2 emissions below present levels in all EMICs. This reduction needs to be substantially larger if carbon cycle–climate feedbacks are accounted for or if terrestrial CO2 fertilization is not operating. Large differences among EMICs are identified in both the response to increasing atmospheric CO2 and the response to climate change. This highlights the need for improved representations of carbon cycle processes in these models apart from the sensitivity to climate change. Sensitivity simulations with one single EMIC indicate that both carbon cycle and climate sensitivity related uncertainties on projected allowable emissions are substantial.

Sources of multi-decadal variability in Arctic sea ice extent

The observed dramatic decrease in September sea ice extent (SIE) has been widely discussed in the scientific literature. Though there is qualitative agreement between observations and ensemble members of the Third Coupled Model Intercomparison Project (CMIP3), it is concerning that the observed trend (1979?2010) is not captured by any ensemble member. The potential sources of this discrepancy include: observational uncertainty, physical model limitations and vigorous natural climate variability. The latter has received less attention and is difficult to assess using the relatively short observational sea ice records. In this study multi-centennial pre-industrial control simulations with five CMIP3 climate models are used to investigate the role that the Arctic oscillation (AO), the Atlantic multi-decadal oscillation (AMO) and the Atlantic meridional overturning circulation (AMOC) play in decadal sea ice variability. Further, we use the models to determine the impact that these sources of variability have had on SIE over both the era of satellite observation (1979?2010) and an extended observational record (1953?2010). There is little evidence of a relationship between the AO and SIE in the models. However, we find that both the AMO and AMOC indices are significantly correlated with SIE in all the models considered. Using sensitivity statistics derived from the models, assuming a linear relationship, we attribute 0.5?3.1%/decade of the 10.1%/decade decline in September SIE (1979?2010) to AMO driven variability.

Understanding the CMIP3 Multimodel Ensemble

AbstractThe Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel ensemble has been widely utilized for climate research and prediction, but the properties and behavior of the ensemble are not yet fully understood. Here, some investigations are undertaken into various aspects of the ensemble’s behavior, in particular focusing on the performance of the multimodel mean. This study presents an explanation of this phenomenon in the context of the statistically indistinguishable paradigm and also provides a quantitative analysis of the main factors that control how likely the mean is to outperform the models in the ensemble, both individually and collectively. The analyses lend further support to the usage of the paradigm of a statistically indistinguishable ensemble and indicate that the current ensemble size is too small to adequately sample the space from which the models are drawn.

Efficient parameter estimation for a highly chaotic system

We present a practical, efficient and powerful solution to the problem of parameter estimation in highly non-linear models. The method is based on the ensemble Kalman filter, and has previously been successfully applied to a simple climate model with steady-state dynamics. We demonstrate, via application to the well-known Lorenz model, that the method can successfully perform multivariate parameter estimation even in the presence of chaotic dynamics. Traditional variational methods using an adjoint model have limited applicability to problems of this nature, and the alternative of a brute force (or randomized) search in parameter space is prohibitively expensive for high-dimensional applications. The cost of our method is comparable to that of integrating an ensemble to statistical convergence, and therefore this technique appears to be ideally suited for probabilistic climate prediction.

Structural Similarities and Differences in Climate Responses to CO2 Increase between Two Perturbed Physics Ensembles

Abstract The equilibrium climate sensitivity (ECS) of the two perturbed physics ensembles (PPE) generated using structurally different GCMs, Model for Interdisciplinary Research on Climate (MIROC3.2) and the Third Hadley Centre Atmospheric Model with slab ocean (HadSM3), is investigated. A method to quantify the shortwave (SW) cloud feedback by clouds with different cloud-top pressure is developed. It is found that the difference in the ensemble means of the ECS between the two ensembles is mainly caused by differences in the SW low-level cloud feedback. The ensemble mean SW cloud feedback and ECS of the MIROC3.2 ensemble is larger than that of the HadSM3 ensemble. This is likely related to the 1XCO2 low-level cloud albedo of the former being larger than that of the latter. It is also found that the largest contribution to the within-ensemble variation of ECS comes from the SW low-level cloud feedback in both ensembles. The mechanism that causes the within-ensemble variation is different between the two e...

The influence of binary stars on dwarf spheroidal galaxy kinematics

We have completed a Monte-Carlo simulation to estimate the effect of binary star orbits on the measured velocity dispersion in dwarf spheroidal galaxies. This paper analyses previous attempts at this calculation, and explains the simulations which were performed with mass, period and ellipticity distributions similar to that measured for the solar neighbourhood. The conclusion is that with functions such as these, the contribution of binary stars to the velocity dispersion is small. The distributions are consistent with the percentage of binaries detected by observations, although this is quite dependent on the measuring errors and on the number of years over which measurements have been taken. For binaries to be making a significant contribution to the dispersion measured in dSph galaxies, the distributions of the orbital parameters would need to be very different from those of stars in the solar neighbourhood. In particular more smaller period orbits with higher mass secondaries would be required. The shape of the velocity distribution may help to resolve this issue when more data becomes available. In general, the scenarios producing a larger apparent dispersion have a velocity distribution which deviates more clearly from Gaussian.

Reliability of multi-model and structurally different single-model ensembles

The performance of several state-of-the-art climate model ensembles, including two multi-model ensembles (MMEs) and four structurally different (perturbed parameter) single model ensembles (SMEs), are investigated for the first time using the rank histogram approach. In this method, the reliability of a model ensemble is evaluated from the point of view of whether the observations can be regarded as being sampled from the ensemble. Our analysis reveals that, in the MMEs, the climate variables we investigated are broadly reliable on the global scale, with a tendency towards overdispersion. On the other hand, in the SMEs, the reliability differs depending on the ensemble and variable field considered. In general, the mean state and historical trend of surface air temperature, and mean state of precipitation are reliable in the SMEs. However, variables such as sea level pressure or top-of-atmosphere clear-sky shortwave radiation do not cover a sufficiently wide range in some. It is not possible to assess whether this is a fundamental feature of SMEs generated with particular model, or a consequence of the algorithm used to select and perturb the values of the parameters. As under-dispersion is a potentially more serious issue when using ensembles to make projections, we recommend the application of rank histograms to assess reliability when designing and running perturbed physics SMEs.

Dependency of Feedbacks on Forcing and Climate State in Physics Parameter Ensembles

AbstractClimate sensitivity is one of the most important metrics for future climate projections. In previous studies the climate of the last glacial maximum has been used to constrain the range of climate sensitivity, and similarities and differences of temperature response to the forcing of the last glacial maximum and to idealized future forcing have been investigated. The feedback processes behind the response have not, however, been fully explored in a large model parameter space. In this study, the authors first examine the performance of various feedback analysis methods that identify important feedbacks for a physics parameter ensemble in experiments simulating both past and future climates. The selected methods are then used to reveal the relationship between the different ensemble experiments in terms of individual feedback processes. For the first time, all of the major feedback processes for an ensemble of paleoclimate simulations are evaluated. It is shown that the feedback and climate sensiti...

Efficient estimation and ensemble generation in climate modelling

In this paper, we review progress towards efficiently estimating parameters in climate models. Since the general problem is inherently intractable, a range of approximations and heuristic methods have been proposed. Simple Monte Carlo sampling methods, although easy to implement and very flexible, are rather inefficient, making implementation possible only in the very simplest models. More sophisticated methods based on random walks and gradient-descent methods can provide more efficient solutions, but it is often unclear how to extract probabilistic information from such methods and the computational costs are still generally too high for their application to state-of-the-art general circulation models (GCMs). The ensemble Kalman filter is an efficient Monte Carlo approximation which is optimal for linear problems, but we show here how its accuracy can degrade in nonlinear applications. Methods based on particle filtering may provide a solution to this problem but have yet to be studied in any detail in the realm of climate models. Statistical emulators show great promise for future research and their computational speed would eliminate much of the need for efficient sampling techniques. However, emulation of a full GCM has yet to be achieved and the construction of such represents a substantial computational task in itself.

Tide, wave and suspended sediment modelling on an open coast — Holderness

Abstract An intensive series of observations off the Holderness coast was followed by a related set of modelling applications. Observations included: aircraft and satellite remote sensing, H.F. and X-band radar, ship surveys and in situ instruments on the sea bed and at the sea surface. These observations aimed to monitor, over three successive winter periods, the dynamics and sediment distributions in the vicinity of this rapidly eroding coastline. Associated modelling applications included components simulating: (i) tides and surge currents; (ii) wave evolution; (iii) vertical distributions of turbulence and SPM (suspended particulate matter) and (iv) resulting spatial patterns of sediment transport in the region. Simulations of tidal currents confirmed the accuracy of such models, given accurate fine-resolution bathymetry and appropriate boundary conditions. New developments of WAM, the spectral wave model required for fine-resolution applications in shallow water (described by Monbaliu et al. [Monbaliu, J., Padilla-Hernandez, R., Hargreaves, J.C., Carretero Albiach, J.C., Luo, W., Sclavo, M., Gunther, H., 2000. The spectral wave model WAM adapted for applications with high spatial resolution. This volume.]) are tested here. A number of additional features pertaining to shallow water are revealed including the sensitivity to specification of wind directions and the excessive temporal spreading of short-lived distant events. Likewise, the application of the generic single-point models for vertical profiles of turbulence and SPM (described by Baumert et al. [Baumert, H., Chapalain, G., Smaoui, H., McManus, J.P., Yagi, H., Regener, M., Sundermann, J., Szilagy, B., 2000. Modelling and numerical simulation of turbulence, waves and suspended sediment for pre-operational use in coastal seas. This volume]), are tested and also shown to be appropriate for simulating localised resuspension of SPM. This simulation also illustrates how, in shallow water ( Some preliminary simulations of net sediment movement are included, involving an integration of the above effects. These simulations emphasise how, in all but the shallowest water, the mobility of coarse grain sediments is limited to occasions of extreme waves. By contrast, the movement of fine sediments follows that of the residual tidal current streamlines, i.e., primarily longshore with attendant cross-shore dispersion. However, significant variation between closely-spaced observations indicates the irregularity and complexity of such distributions. It is concluded that because of the inability to prescribe the spatial distribution of available surficial sediments (including size distributions) such simulations can only be expected to reproduce the essential statistical characteristics of SPM concentrations. The availability of extensive remote sensing or in situ data can help to circumvent this problem.