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Abstract This study provides an analysis of atmospheric neutrino oscillations at the ESSnuSB far detector facility. The prospects of the two cylindrical Water Cherenkov detectors with a total fiducial mass of 540 kt are investigated over 10 years of data taking in the standard three-flavor oscillation scenario. We present the confidence intervals for the determination of mass ordering, θ 23 octant as well as for the precisions on sin2 θ 23 and Δ m 31 2 $$ \left|\Delta {m}_{31}^2\right| $$ . It is shown that mass ordering can be resolved by 3σ CL (5σ CL) after 4 years (10 years) regardless of the true neutrino mass ordering. Correspondingly, the wrong θ 23 octant could be excluded by 3σ CL after 4 years (8 years) in the case where the true neutrino mass ordering is normal ordering (inverted ordering). The results presented in this work are complementary to the accelerator neutrino program in the ESSnuSB project.

Abstract Neutrino oscillation experiments provide a unique window in exploring several new physics scenarios beyond the standard three flavour. One such scenario is quantum decoherence in neutrino oscillation which tends to destroy the interference pattern of neutrinos reaching the far detector from the source. In this work, we study the decoherence in neutrino oscillation in the context of the ESSnuSB experiment. We consider the energy-independent decoherence parameter and derive the analytical expressions for P μe and P μμ probabilities in vacuum. We have computed the capability of ESSnuSB to put bounds on the decoherence parameters namely, Γ21 and Γ32 and found that the constraints on Γ21 are competitive compared to the DUNE bounds and better than the most stringent LBL ones from MINOS/MINOS+. We have also investigated the impact of decoherence on the ESSnuSB measurement of the Dirac CP phase δ CP and concluded that it remains robust in the presence of new physics.

Abstract The El Niño–Southern Oscillation (ENSO) teleconnection to Europe is projected to strengthen under global warming in most climate model simulations. However, given the current difference between recent observations and historical model simulations of tropical Pacific sea surface temperature trends, with models simulating an El Niño‐like warming in recent decades which is in disagreement with observations, it is important to understand the relative contributions of changes to the teleconnection forcing and background state to the overall teleconnection change. Using idealized climate model experiments, we show that both the eastward shift of El Niño precipitation and background state changes make contributions to the overall teleconnection change. These results suggest that the ENSO–Europe teleconnection can be expected to strengthen under global warming, even if ENSO precipitation anomalies do not shift eastwards as currently projected. However, the magnitude of the strengthening may depend on how much of an eastward shift does occur.

Abstract Precipitation projections in transient climate change scenarios have been extensively studied over multiple climate model generations. Although these simulations have also been used to make projections at specific Global Warming Levels (GWLs), dedicated simulations are more appropriate to study changes in a stabilizing climate. Here, we analyze precipitation projections in six multi‐century experiments with fixed atmospheric concentrations of greenhouse gases, conducted with the UK Earth System Model and which span a range of GWLs between 1.5 and 5°C of warming. Regions are identified where the sign of precipitation trends in high‐emission transient projections is reversed in the stabilization experiments. For example, stabilization reverses a summertime precipitation decline across Europe. This precipitation recovery occurs concurrently with changes in the pattern of Atlantic sea surface temperature trends due to a slow recovery of the Atlantic Meridional Overturning Circulation in the stabilization experiments, along with changes in humidity and atmospheric circulation.

Arctic sea ice is declining rapidly, but predictions of its future loss are made difficult by the large spread both in present-day and in future sea ice area and volume; hence, there is a need to better understand the drivers of model spread in sea ice state. Here we present a framework for understanding differences between modelled sea ice simulations based on attributing seasonal ice growth and melt differences. In the method presented, the net downward surface flux is treated as the principal driver of seasonal sea ice growth and melt. An energy balance approach is used to estimate the pointwise effect of model differences in key Arctic climate variables on this surface flux and hence on seasonal sea ice growth and melt. We compare three models with very different historical sea ice simulations: HadGEM2-ES, HadGEM3-GC3.1 and UKESM1.0. The largest driver of differences in ice growth and melt between these models is shown to be the ice area in summer (representing the surface albedo feedback) and the ice thickness distribution in winter (the thickness–growth feedback). Differences in snow and melt pond cover during the early summer exert a smaller effect on the seasonal growth and melt, hence representing the drivers of model differences in both this and in the sea ice volume. In particular, the direct impacts on sea ice growth and melt of differing model parameterisations of snow area and of melt ponds are shown to be small but non-negligible.

Abstract In this paper, we present the physics performance of the ESSnuSB experiment in the standard three flavor scenario using the updated neutrino flux calculated specifically for the ESSnuSB configuration and updated migration matrices for the far detector. Taking conservative systematic uncertainties corresponding to a normalization error of $$5\%$$ 5 % for signal and $$10\%$$ 10 % for background, we find that there is $$10\sigma $$ 10 σ $$(13\sigma )$$ ( 13 σ ) CP violation discovery sensitivity for the baseline option of 540 km (360 km) at $$\delta _\mathrm{CP} = \pm 90^\circ $$ δ CP = ± 90 ∘ . The corresponding fraction of $$\delta _\mathrm{CP}$$ δ CP for which CP violation can be discovered at more than $$5 \sigma $$ 5 σ is $$70\%$$ 70 % . Regarding CP precision measurements, the $$1\sigma $$ 1 σ error associated with $$\delta _\mathrm{CP} = 0^\circ $$ δ CP = 0 ∘ is around $$5^\circ $$ 5 ∘ and with $$\delta _\mathrm{CP} = -90^\circ $$ δ CP = - 90 ∘ is around $$14^\circ $$ 14 ∘ $$(7^\circ )$$ ( 7 ∘ ) for the baseline option of 540 km (360 km). For hierarchy sensitivity, one can have $$3\sigma $$ 3 σ sensitivity for 540 km baseline except $$\delta _\mathrm{CP} = \pm 90^\circ $$ δ CP = ± 90 ∘ and $$5\sigma $$ 5 σ sensitivity for 360 km baseline for all values of $$\delta _\mathrm{CP}$$ δ CP . The octant of $$\theta _{23}$$ θ 23 can be determined at $$3 \sigma $$ 3 σ for the values of: $$\theta _{23} > 51^\circ $$ θ 23 > 51 ∘ ( $$\theta _{23} < 42^\circ $$ θ 23 < 42 ∘ and $$\theta _{23} > 49^\circ $$ θ 23 > 49 ∘ ) for baseline of 540 km (360 km). Regarding measurement precision of the atmospheric mixing parameters, the allowed values at $$3 \sigma $$ 3 σ are: $$40^\circ< \theta _{23} < 52^\circ $$ 40 ∘ < θ 23 < 52 ∘ ( $$42^\circ< \theta _{23} < 51.5^\circ $$ 42 ∘ < θ 23 < 51 . 5 ∘ ) and $$2.485 \times 10^{-3}$$ 2.485 × 10 - 3 eV $$^2< \varDelta m^2_{31} < 2.545 \times 10^{-3}$$ 2 < Δ m 31 2 < 2.545 × 10 - 3 eV $$^2$$ 2 ( $$2.49 \times 10^{-3}$$ 2.49 × 10 - 3 eV $$^2< \varDelta m^2_{31} < 2.54 \times 10^{-3}$$ 2 < Δ m 31 2 < 2.54 × 10 - 3 eV $$^2$$ 2 ) for the baseline of 540 km (360 km).

The future of the Antarctic Ice Sheet in response to climate warming is one of the largest sources of uncertainty in estimates of future changes in global mean sea level (ΔGMSL). Mass loss is currently concentrated in regions of warm circumpolar deep water, but it is unclear how ice shelves currently surrounded by relatively cold ocean waters will respond to climatic changes in the future. Studies suggest that warm water could flush the Filchner–Ronne (FR) ice shelf cavity during the 21st century, but the inland ice sheet response to a drastic increase in ice shelf melt rates is poorly known. Here, we use an ice flow model and uncertainty quantification approach to project the GMSL contribution of the FR basin under RCP emissions scenarios, and we assess the forward propagation and proportional contribution of uncertainties in model parameters (related to ice dynamics and atmospheric/oceanic forcing) on these projections. Our probabilistic projections, derived from an extensive sample of the parameter space using a surrogate model, reveal that the FR basin is unlikely to contribute positively to sea level rise by the 23rd century. This is primarily due to the mitigating effect of increased accumulation with warming, which is capable of suppressing ice loss associated with ocean-driven increases in sub-shelf melt. Mass gain (negative ΔGMSL) from the FR basin increases with warming, but uncertainties in these projections also become larger. In the highest emission scenario RCP8.5, ΔGMSL is likely to range from −103 to 26 mm, and this large spread can be apportioned predominantly to uncertainties in parameters driving increases in precipitation (30 %) and sub-shelf melting (44 %). There is potential, within the bounds of our input parameter space, for major collapse and retreat of ice streams feeding the FR ice shelf, and a substantial positive contribution to GMSL (up to approx. 300 mm), but we consider such a scenario to be very unlikely. Adopting uncertainty quantification techniques in future studies will help to provide robust estimates of potential sea level rise and further identify target areas for constraining projections.

We describe a case of left hand extensor tenosynovitis due to histoplasmosis in a patient with dermatomyositis on chronic immunosuppression. Treatment involved surgical debridement and antifungal therapy. The patient experienced paradoxical worsening of tenosynovial inflammation during de-augmentation of immunosuppression felt to be immune reconstitution inflammatory syndrome.

ESSnuSB is a design study for an experiment to measure the CP violation in the leptonic sector at the second neutrino oscillation maximum using a neutrino beam driven by the uniquely powerful ESS linear accelerator. The reduced impact of systematic errors on sensitivity at the second maximum allows for a very precise measurement of the CP violating parameter. This review describes the fundamental advantages of measurement at the second maximum, the necessary upgrades to the ESS linac in order to produce a neutrino beam, the near and far detector complexes, and the expected physics reach of the proposed ESSnuSB experiment, concluding with the near future developments aimed at the project realization.

A simple diagnostic cloud scheme (SimCloud) for general circulation models (GCMs), which has a modest level of complexity and is transparent in describing its dependence on tunable parameters, is proposed in this study. The large-scale clouds, which form the core of the scheme, are diagnosed from relative humidity. In addition, the marine low stratus clouds, typically found off the west coast of continents over subtropical oceans, are determined largely as a function of inversion strength. A “freeze-dry” adjustment based on a simple function of specific humidity is also available to reduce an excessive cloud bias in polar regions. Other cloud properties, such as the effective radius of cloud droplet and cloud liquid water content, are specified as simple functions of temperature. All of these features are user-configurable. The cloud scheme is implemented in Isca, a modeling framework designed to enable the construction of GCMs at varying levels of complexity, but could readily be adapted to other GCMs. Simulations using the scheme with realistic continents generally capture the observed structure of cloud fraction and cloud radiative effect (CRE), as well as its seasonal variation. Specifically, the explicit low-cloud scheme improves the simulation of shortwave CREs over the eastern subtropical oceans by increasing the cloud fraction and cloud water path. The freeze-dry adjustment alleviates the longwave CRE biases in polar regions, especially in winter. However, the longwave CRE in tropical regions and shortwave CRE over the extratropics are both still too strong compared to observations. Nevertheless, this simple cloud scheme provides a suitable basis for examining the impacts of clouds on climate in idealized modeling frameworks.

A new method of sea ice model evaluation is demonstrated. Data from the network of Arctic ice mass balance buoys (IMBs) are used to estimate distributions of vertical energy fluxes over sea ice in two densely sampled regions – the North Pole and Beaufort Sea. The resulting dataset captures seasonal variability in sea ice energy fluxes well, and it captures spatial variability to a lesser extent. The dataset is used to evaluate a coupled climate model, HadGEM2-ES (Hadley Centre Global Environment Model, version 2, Earth System), in the two regions. The evaluation shows HadGEM2-ES to simulate too much top melting in summer and too much basal conduction in winter. These results are consistent with a previous study of sea ice state and surface radiation in this model, increasing confidence in the IMB-based evaluation. In addition, the IMB-based evaluation suggests an additional important cause for excessive winter ice growth in HadGEM2-ES, a lack of sea ice heat capacity, which was not detectable in the earlier study. Uncertainty in the IMB fluxes caused by imperfect knowledge of ice salinity, snow density and other physical constants is quantified (as is inaccuracy due to imperfect sampling of ice thickness) and in most cases is found to be small relative to the model biases discussed. Hence the IMB-based evaluation is shown to be a valuable tool with which to analyse sea ice models and, by extension, better understand the large spread in coupled model simulations of the present-day ice state. Reducing this spread is a key task both in understanding the current rapid decline in Arctic sea ice and in constraining projections of future Arctic sea ice change.

Background Informed consent is an integral part of clinical practice. There is widespread agreement amongst health professionals that obtaining procedural consent needs to move away from a unidirectional transfer of information to a process of supporting patients in making informed, self‐determined decisions. This review aimed to identify processes and measures that warrant consideration when engaging in consent‐based discussions with competent patients undergoing elective procedures. Methods Formal written guidance from the General Medical Council and Royal College of Surgeons of England, in addition to peer‐reviewed literature and case law, was considered in the formulation of this review. Results A framework for obtaining consent is presented that is informed by the key tenets of shared decision‐making (SDM), a model that advocates the contribution of both the clinician and patient to the decision‐making process through emphasis on patient participation, analysis of empirical evidence, and effective information exchange. Moreover, areas of contention are highlighted in which further guidance and research are necessary for improved enhancement of the consent process. Conclusion This SDM‐centric framework provides structure, detail and suggestions for achieving meaningful consent.

Preservice teachers engage in clinical experiences with in-service teacher mentors to observe and apply theories being learned in coursework in classroom settings. Among the obstacles with clinical experiences is that mentor teachers may lack knowledge of theories being practiced by preservice teachers and may struggle to provide mentorship supports. This convergent mixed-method study engaged four undergraduate student researchers in exploring how a clinical-based course supports the development of the preservice teacher and how professional learning and mentoring experience support the development of the in-service mentor teacher. Findings indicate increases in knowledge and applications of PBI for preservice teachers and mentor teachers and increases in confidence from the in-service teachers to serve as mentors.

Fieldwork (FW) education is integral to occupational therapy (OT) education and enables the transition of a student to an entry-level practitioner. Clinicians who serve as FW educators play a significant role in this process. To deliver OT education, universities must support FW educators and address their needs and concerns. This study surveyed OT FW educators who supervised entry-level OT doctoral students from a public university for Level I and/or Level II FW regarding strategies to address their concerns and needs. An anonymous survey was distributed to the FW educators (n=349) who supervised the students for FW between Spring 2021-2023. By the response deadline, the survey yielded a 32.09% (n=112) response rate. Fieldwork educators perceived themselves to be competent clinical educators, and their perception was not associated with the completion of FW educator training courses, years of experience as a practitioner, or number of students supervised in the past. However, FW educators reported difficulty in teaching soft skills (e.g., communicating with patients/caregivers, participating in Admission, Review, and Dismissal meetings, etc.) and supervising challenging students. They considered providing FW supervision as beneficial to them. Their concerns related to FW supervision centered around student readiness, student behavior, and time management. They expected universities to assess student readiness before sending them on FW. Also, they expected more clarity and guidance from universities on expectations related to FW supervision. Further, they indicated a need for FW educator training programs and access to library/scholarly resources. The implications of the findings for different stakeholders were discussed.

A new framework is presented for analysing the proximate causes of model Arctic sea ice biases, demonstrated with the CMIP5 model HadGEM2-ES (Hadley Centre Global Environment Model version 2 – Earth System). In this framework the Arctic sea ice volume is treated as a consequence of the integrated surface energy balance, via the volume balance. A simple model allows the local dependence of the surface flux on specific model variables to be described as a function of time and space. When these are combined with reference datasets, it is possible to estimate the surface flux bias induced by the model bias in each variable. The method allows the role of the surface albedo and ice thickness–growth feedbacks in sea ice volume balance biases to be quantified along with the roles of model bias in variables not directly related to the sea ice volume. It shows biases in the HadGEM2-ES sea ice volume simulation to be due to a bias in spring surface melt onset date, partly countered by a bias in winter downwelling longwave radiation. The framework is applicable in principle to any model and has the potential to greatly improve understanding of the reasons for ensemble spread in the modelled sea ice state. A secondary finding is that observational uncertainty is the largest cause of uncertainty in the induced surface flux bias calculation.

Public schools in the United States saw unprecedented reductions to in-person instruction during the 2020-2021 school year. Using the Elementary School Operating Status database, the American Community Survey, and the Current Population Survey, we show remote instruction was associated with reduced employment among mothers compared with fathers and women without children. The gender gap in employment between mothers and fathers grew as much as 5 percentage points in areas with remote instruction. Compared to women without children, mothers' employment fell by as much as 2 percentage points under remote schooling. Employment disparities among mothers deepened by race, educational attainment, and marital status. We show employment disparities endured through spring 2021, even as many school districts returned to in-person instruction.

A super-ensemble methodology is proposed to improve the quality of short-term ocean analyses for sea surface temperature (SST) in the Mediterranean Sea. The methodology consists of a multiple linear regression technique applied to a multi-physics multi-model super-ensemble (MMSE) data set. This is a collection of different operational forecasting analyses together with ad hoc simulations, created by modifying selected numerical model parameterizations. A new linear regression algorithm based on empirical orthogonal function filtering techniques is shown to be efficient in preventing overfitting problems, although the best performance is achieved when a simple spatial filter is applied after the linear regression. Our results show that the MMSE methodology improves the ocean analysis SST estimates with respect to the best ensemble member (BEM) and that the performance is dependent on the selection of an unbiased operator and the length of training. The quality of the MMSE data set has the largest impact on the MMSE analysis root mean square error (RMSE) evaluated with respect to observed satellite SST. The MMSE analysis estimates are also affected by training period length, with the longest period leading to the smoothest estimates. Finally, lower RMSE analysis estimates result from the following: a 15-day training period, an overconfident MMSE data set (a subset with the higher-quality ensemble members) and the least-squares algorithm being filtered a posteriori.

Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño–Southern Oscillation.

Objective: Decreasing participation in intervention research among college students has implications for the external validity of behavioral intervention research. We describe recruitment and retention strategies used to promote participation in intervention research across a series of four randomized experiments. Method: We report the recruitment and retention rates by school for each experiment and qualitative feedback from students about recommendations for improving research participation. Results: There was considerable variation among schools' recruitment (4.9% to 64.7%) and retention (12% to 67.8%) rates. Student feedback suggested study timing (e.g., early in the semester), communication strategies (e.g., social media), and incentive structure (e.g., guaranteed incentives) could improve research participation. The highest survey participation rate was observed at the university which mandated students to complete the intervention (but not the survey). Conclusions: Intervention scientists must consider the population and study context to make informed decisions related to recruitment and retention strategies.

Water isotope-enabled coupled atmosphere–ocean climate models allow for exploration of the relative contributions to coral stable oxygen isotope (δ18Ocoral) variability arising from sea surface temperature (SST) and the isotopic composition of seawater (δ18Osw). The unforced behaviour of the isotope-enabled HadCM3 coupled general circulation model suggests that the extent to which inter-annual δ18Osw variability contributes to that in model δ18Ocoral is strongly spatially dependent, ranging from being negligible in the eastern equatorial Pacific to accounting for 50% of δ18Ocoral variance in parts of the western Pacific. In these latter cases, a significant component of the inter-annual δ18Osw variability is correlated to that in SST, meaning that local calibrations of the effective local δ18Ocoral–SST relationships are likely to be essential. Furthermore, the relationship between δ18Osw and SST can be non-linear, such that the model interpretation of central and western equatorial Pacific δ18Ocoral in the context of a linear dependence on SST alone leads to overestimation (by up to 20%) of the SST anomalies associated with large El Niño events. Intra-model evaluation of a salinity-based pseudo-coral approach shows that such an approach captures the first-order features of the model δ18Osw behaviour. However, the utility of the pseudo-corals is limited by the extent of spatial variability seen within the modelled slopes of the temporal salinity–δ18Osw relationship.

Spatially-explicit maps of aboveground biomass are essential for calculating the losses and gains in forest carbon at a regional to national level. The production of such maps across wide areas will become increasingly necessary as international efforts to protect primary forests, such as the REDD+ (Reducing Emissions from Deforestation and forest Degradation) mechanism, come into effect, alongside their use for management and research more generally. However, mapping biomass over high-biomass tropical forest is challenging as (1) direct regressions with optical and radar data saturate, (2) much of the tropics is persistently cloud-covered, reducing the availability of optical data, (3) many regions include steep topography, making the use of radar data complex, (5) while LiDAR data does not suffer from saturation, expensive aircraft-derived data are necessary for complete coverage. We present a solution to the problems, using a combination of terrain-corrected L-band radar data (ALOS PALSAR), spaceborne LiDAR data (ICESat GLAS) and ground-based data. We map Gabon's Lopé National Park (5000 km2) because it includes a range of vegetation types from savanna to closed-canopy tropical forest, is topographically complex, has no recent contiguous cloud-free high-resolution optical data, and the dense forest is above the saturation point for radar. Our 100 m resolution biomass map is derived from fusing spaceborne LiDAR (7142 ICESat GLAS footprints), 96 ground-based plots (average size 0.8 ha) and an unsupervised classification of terrain-corrected ALOS PALSAR radar data, from which we derive the aboveground biomass stocks of the park to be 78 Tg C (173 Mg C ha−1). This value is consistent with our field data average of 181 Mg C ha−1, from the field plots measured in 2009 covering a total of 78 ha, and which are independent as they were not used for the GLAS-biomass estimation. We estimate an uncertainty of &pm;25% on our carbon stock value for the park. This error term includes uncertainties resulting from the use of a generic tropical allometric equation, the use of GLAS data to estimate Lorey's height, and the necessity of separating the landscape into distinct classes. As there is currently no spaceborne LiDAR satellite in operation (GLAS data is available for 2003–2009 only), this methodology is not suitable for change-detection. This research underlines the need for new satellite LiDAR data to provide the potential for biomass-change estimates, although this need will not be met before 2015.

We present joint probability distribution functions of future seasonal-mean changes in surface air temperature and precipitation for the European region for the SRES A1B emissions scenario. The probabilistic projections quantify uncertainties in the leading physical, chemical and biological feedbacks and combine information from perturbed physics ensembles, multi-model ensembles and observations.

It has been shown previously that one member of the Met Office Hadley Centre single-parameter perturbed physics ensemble – the so-called "low entrainment parameter" member – has a much higher climate sensitivity than other individual parameter perturbations. Here we show that the concentration of stratospheric water vapour in this member is over three times higher than observations, and, more importantly for climate sensitivity, increases significantly when climate warms. The large surface temperature response of this ensemble member is more consistent with stratospheric humidity change, rather than upper tropospheric clouds as has been previously suggested. The direct relationship between the bias in the control state (elevated stratospheric humidity) and the cause of the high climate sensitivity (a further increase in stratospheric humidity) lends further doubt as to the realism of this particular integration. This, together with other evidence, lowers the likelihood that the climate system's physical sensitivity is significantly higher than the likely upper range quoted in the Intergovernmental Panel on Climate Change's Fourth Assessment Report.

We examine the behaviour of the El Niño – Southern Oscillation (ENSO) in an ensemble of global climate model simulations with perturbations to parameters in the atmosphere and ocean components respectively. The influence of the uncertainty in these parametrisations on ENSO are investigated systematically. The ensemble exhibits a range of different ENSO behaviour in terms of the amplitude and spatial structure of the sea surface temperature (SST) variability. The nature of the individual feedbacks that operate within the ENSO system are diagnosed using an Intermediate Complexity Model (ICM), which has been used previously to examine the diverse ENSO behaviour of the CMIP3 multi-model ensemble. Unlike in that case, the ENSO in these perturbed physics experiments is not principally controlled by variations in the mean climate state. Rather the parameter perturbations influence the ENSO characteristics by modifying the coupling feedbacks within the cycle. The associated feedbacks that contribute most to the ensemble variations are the response of SST to local wind variability and damping, followed by the response of SST to thermocline anomalies and the response of the zonal wind stress to those SST anomalies. Atmospheric noise amplitudes and oceanic processes play a relatively minor role.

The sensitivity of El Niño-Southern Oscillation (ENSO) to changes in mean climate is investigated for simulations of pre-industrial and mid-Holocene (6000 years before present) climate using the Hadley Centre coupled atmosphere-ocean model, HadCM3. Orbitally-forced changes in insolation in the mid-Holocene produce changes in seasonality which may alter ENSO amplitude and frequency. The model simulations are compared with mid-Holocene fossil coral oxygen isotope records from the western Pacific Warm Pool. The coral records imply a reduction of around 60% in the amplitude of interannual variability associated with ENSO in the mid-Holocene, while the model simulates a smaller reduction in ENSO amplitude of around 10%. The model also simulates a slight shift to longer period variability and a weakening of ENSO phase-locking to the seasonal cycle in the mid-Holocene. There is little change in the pattern of ENSO tropical precipitation teleconnections in the simulated mid-Holocene climate.