Influence of initial ocean conditions on temperature and precipitation in a coupled climate model’s solution

This is the final version. Available on open access from Copernicus Publications via the DOI in this recordCode and data availability: Model output and software code are available through a request to the authors.This paper describes results of an experiment that perturbed the initial conditions for the ocean’s temperature field of the Community Earth System Model (CESM) with a well defined design. The resulting thirty member ensemble of CESM simulations, each of ten years in length is used to create an emulator (a non-linear regression relating the initial conditions to various outcomes) from the simulators. Through the use of the emulator to expand the output distribution space, we estimate the spatial uncertainties at 10 years for surface air temperature, 25m ocean temperature, precipitation, and rain. Basin averages, outside the tropics, for the uncertainty in the ocean temperature field range between 0.48◦C (Indian Ocean) and 0.87◦C (North Pacific) (two standard deviations). The tropical Pacific uncertainty is the largest due to different phasings of the ENSO signal. Over land areas, the regional temperature uncertainty varies from 1.03◦C (South America) to 10.82◦C (Europe) (two standard deviations). Similarly, the regional average uncertainty in precipitation varies from 0.001 cm/day over Antarctica to 0.163 cm/day over Australia with the global average of 0.075 cm/day. In general, both temperature and precipitation uncertainties are larger over land than over the ocean. A maximum covariance analysis is used to examine how ocean temperatures affect both surface air temperatures and precipitation over land. The analysis shows that the tropical Pacific influences the temperature over North America, but the North America surface temperature is also moderated by the state of the North Pacific outside the tropics. It also indicates which regions show a high degree of variance between the simulations in the ensemble and are, therefore, less predictable. The calculated uncertainties are also compared to an estimate of internal variability within CESM. Finally, the importance of feedback processes on the solution of the simulation over the ten years of the experiment is quantified. These estimates of uncertainty are without the consideration of anthropogenic effect on warming of the atmosphere and ocean.National Science Foundation: NS

Environmental feature exploration with a single autonomous vehicle

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, a sliding mode based guidance strategy is proposed for the control of an autonomous vehicle. The aim of the autonomous vehicle deployment is the study of unknown environmental spatial features. The proposed approach allows the solution of both boundary tracking and source seeking problems with a single autonomous vehicle capable of sensing the value of the spatial field at its position. The movement of the vehicle is controlled through the proposed guidance strategy, which is designed on the basis of the collected measurements without the necessity of pre-planning or human intervention. Moreover, no a priori knowledge about the field and its gradient is required. The proposed strategy is based on the so-called sub-optimal sliding mode controller. The guidance strategy is demonstrated by computer based simulations and a set of boundary tracking experimental sea trials. The efficacy of the algorithm to autonomously steer the C-Enduro surface vehicle to follow a fixed depth contour in a dynamic coastal region is demonstrated by the results from the trial described in this paper.Natural Environment Research Council (NERC)Defence Science and Technology Laboratory (DSTL)Innovate UKAutonomous Surface Vehicles (ASV) Ltd., Portcheste

Precalibrating an intermediate complexity climate model

Credible climate predictions require a rational quantification of uncertainty, but full Bayesian calibration requires detailed estimates of prior probability distributions and covariances, which are difficult to obtain in practice. We describe a simplified procedure, termed precalibration, which provides an approximate quantification of uncertainty in climate prediction, and requires only that uncontroversially implausible values of certain inputs and outputs are identified. The method is applied to intermediate-complexity model simulations of the Atlantic meridional overturning circulation (AMOC) and confirms the existence of a cliff-edge catastrophe in freshwaterforcing input space. When uncertainty in 14 further parameters is taken into account, an implausible, AMOC-off, region remains as a robust feature of the model dynamics, but its location is found to depend strongly on values of the other parameters

Multi-level emulation of complex climate model responses to boundary forcing data

Climate model components involve both high-dimensional input and output fields. It is desirable to e ciently generate spatio-temporal out-puts of these models for applications in integrated assessment modelling or to assess the statistical relationship between such sets of inputs and outputs, for example, uncertainty analysis. However, the need for e ciency often compromises the fidelity of output through the use of low complexity models. Here, we develop a technique which combines statistical emulation with a dimensionality reduction technique to emulate a wide range of outputs from an atmospheric general circulation model, PLASIM, as functions of the boundary forcing prescribed by the ocean component of a lower complexity climate model, GENIE-1. Although accurate and detailed spatial information on atmospheric variables such as precipitation and wind speed is well beyond the capability of GENIE-1’s energy-moisture balance model of the atmosphere, this study demonstrates that the output of this model is useful in predicting PLASIM’s spatio-temporal fields through multi-level emulation. Meaningful information from the fast model, GENIE-1 was extracted by utilising the correlation between variables of the same type in the two models and between variables of di↵erent types in PLASIM. We present here the construction and validation of several PLASIM variable emulators and discuss their potential use in developing a hybrid model with statistical components

Sensitivity of climate response to variations in freshwater hosing location

In a recent intercomparison of the response of general circulation models (GCMs) to high-latitude freshwater forcing (Stouffer et al., J Climate 19(8):1365-1387, 2006), a number of the GCMs investigated showed a localised warming response in the high-latitude North Atlantic, as opposed to the cooling that the other models showed. We investigated the causes for this warming by testing the sensitivity of the meridional overturning circulation (MOC) to variations in freshwater forcing location, and then analysing in detail the causes of the warming. By analysing results from experiments with HadCM3, we are able to show that the high-latitude warming is independent of the exact location of the additional freshwater in the North Atlantic or Arctic Ocean basin. Instead, the addition of freshwater changes the circulation in the sub-polar gyre, which leads to enhanced advection of warm, saline, sub-surface water into the Greenland-Iceland-Norwegian Sea despite the overall slowdown of the MOC. This sub-surface water is brought to the surface by convection, where it leads to a strong warming of the surface waters and the overlying atmosphere