Does the PMSP connectionist model of single word reading learn to read in the same way as a child?

The Plaut, McClelland, Seidenberg and Patterson (1996) connectionist model of reading was evaluated at two points early in its training against reading data collected from British children on two occasions during their first year of literacy instruction. First, the network’s non-word reading was poor relative to word reading when compared with the children. Second, the network made more non-lexical than lexical errors, the opposite pattern to the children. Three adaptations were made to the training of the network to bring it closer to the learning environment of a child: an incremental training regime was adopted; the network was trained on grapheme– phoneme correspondences; and a training corpus based on words found in children’s early reading materials was used. The modifications caused a sharp improvement in non-word reading, relative to word reading, resulting in a near perfect match to the children’s data on this measure. The modified network, however, continued to make predominantly non-lexical errors, although evidence from a small-scale implementation of the full triangle framework suggests that this limitation stems from the lack of a semantic pathway. Taken together, these results suggest that, when properly trained, connectionist models of word reading can offer insights into key aspects of reading development in children.

Incorporating toxicokinetics into an individual-based model for more realistic pesticide exposure estimates: A case study of the wood mouse

The potential risk of agricultural pesticides to mammals typically depends on internal concentrations within individuals, and these are determined by the amount ingested and by absorption, distribution, metabolism, and excretion (ADME). Pesticide residues ingested depend, amongst other things, on individual spatial choices which determine how much and when feeding sites and areas of pesticide application overlap, and can be calculated using individual-based models (IBMs). Internal concentrations can be calculated using toxicokinetic (TK) models, which are quantitative representations of ADME processes. Here we provide a population model for the wood mouse (Apodemus sylvaticus) in which TK submodels were incorporated into an IBM representation of individuals making choices about where to feed. This allows us to estimate the contribution of individual spatial choice and TK processes to risk. We compared the risk predicted by four IBMs: (i) “AllExposed-NonTK”: assuming no spatial choice so all mice have 100% exposure, no TK, (ii) “AllExposed-TK”: identical to (i) except that the TK processes are included where individuals vary because they have different temporal patterns of ingestion in the IBM, (iii) “Spatial-NonTK”: individual spatial choice, no TK, and (iv) “Spatial-TK”: individual spatial choice and with TK. The TK parameters for hypothetical pesticides used in this study were selected such that a conventional risk assessment would fail. Exposures were standardised using risk quotients (RQ; exposure divided by LD50 or LC50). We found that for the exposed sub-population including either spatial choice or TK reduced the RQ by 37–85%, and for the total population the reduction was 37–94%. However spatial choice and TK together had little further effect in reducing RQ. The reasons for this are that when the proportion of time spent in treated crop (PT) approaches 1, TK processes dominate and spatial choice has very little effect, and conversely if PT is small spatial choice dominates and TK makes little contribution to exposure reduction. The latter situation means that a short time spent in the pesticide-treated field mimics exposure from a small gavage dose, but TK only makes a substantial difference when the dose was consumed over a longer period. We concluded that a combined TK-IBM is most likely to bring added value to the risk assessment process when the temporal pattern of feeding, time spent in exposed area and TK parameters are at an intermediate level; for instance wood mice in foliar spray scenarios spending more time in crop fields because of better plant cover.

Long-term variations in the magnetic fields of the Sun and the heliosphere: their origin, effects, and implications

Recent studies of the variation of geomagnetic activity over the past 140 years have quantified the "coronal source" magnetic flux F-s that leaves the solar atmosphere and enters the heliosphere and have shown that it has risen, on average, by an estimated 34% since 1963 and by 140% since 1900. This variation of open solar flux has been reproduced by Solanki et al. [2000] using a model which demonstrates how the open flux accumulates and decays, depending on the rate of flux emergence in active regions and on the length of the solar cycle. We here use a new technique to evaluate solar cycle length and find that it does vary in association with the rate of change of F-s in the way predicted. The long-term variation of the rate of flux emergence is found to be very similar in form to that in F-s, which may offer a potential explanation of why F-s appears to be a useful proxy for extrapolating solar total irradiance back in time. We also find that most of the variation of cosmic ray fluxes incident on Earth is explained by the strength of the heliospheric field (quantified by F-s) and use observations of the abundance of the isotope Be-10 (produced by cosmic rays and deposited in ice sheets) to study the decrease in F-s during the Maunder minimum. The interior motions at the base of the convection zone, where the solar dynamo is probably located, have recently been revealed using the helioseismology technique and found to exhibit a 1.3-year oscillation. This periodicity is here reported in observations of the interplanetary magnetic field and geomagnetic activity but is only present after 1940, When present, it shows a strong 22-year variation, peaking near the maximum of even-numbered sunspot cycles and showing minima at the peaks of odd-numbered cycles. We discuss the implications of these long-term solar and heliospheric variations for Earth's environment.

The representation of the West-African Monsoon vertical cloud structure in the Met Office Unified Model: an evaluation with CloudSat

Weather and climate model simulations of the West African Monsoon (WAM) have generally poor representation of the rainfall distribution and monsoon circulation because key processes, such as clouds and convection, are poorly characterized. The vertical distribution of cloud and precipitation during the WAM are evaluated in Met Office Unified Model simulations against CloudSat observations. Simulations were run at 40-km and 12-km horizontal grid length using a convection parameterization scheme and at 12-km, 4-km, and 1.5-km grid length with the convection scheme effectively switched off, to study the impact of model resolution and convection parameterization scheme on the organisation of tropical convection. Radar reflectivity is forward-modelled from the model cloud fields using the CloudSat simulator to present a like-with-like comparison with the CloudSat radar observations. The representation of cloud and precipitation at 12-km horizontal grid length improves dramatically when the convection parameterization is switched off, primarily because of a reduction in daytime (moist) convection. Further improvement is obtained when reducing model grid length to 4 km or 1.5 km, especially in the representation of thin anvil and mid-level cloud, but three issues remain in all model configurations. Firstly, all simulations underestimate the fraction of anvils with cloud top height above 12 km, which can be attributed to too low ice water contents in the model compared to satellite retrievals. Secondly, the model consistently detrains mid-level cloud too close to the freezing level, compared to higher altitudes in CloudSat observations. Finally, there is too much low-level cloud cover in all simulations and this bias was not improved when adjusting the rainfall parameters in the microphysics scheme. To improve model simulations of the WAM, more detailed and in-situ observations of the dynamics and microphysics targeting these non-precipitating cloud types are required.

Decadal predictability of the Atlantic meridional overturning circulation and climate in the IPSL-CM5A-LR model

This study explores the decadal potential predictability of the Atlantic Meridional Overturning Circulation (AMOC) as represented in the IPSL-CM5A-LR model, along with the predictability of associated oceanic and atmospheric fields. Using a 1000-year control run, we analyze the prognostic potential predictability (PPP) of the AMOC through ensembles of simulations with perturbed initial conditions. Based on a measure of the ensemble spread, the modelled AMOC has an average predictive skill of 8 years, with some degree of dependence on the AMOC initial state. Diagnostic potential predictability of surface temperature and precipitation is also identified in the control run and compared to the PPP. Both approaches clearly bring out the same regions exhibiting the highest predictive skill. Generally, surface temperature has the highest skill up to 2 decades in the far North Atlantic ocean. There are also weak signals over a few oceanic areas in the tropics and subtropics. Predictability over land is restricted to the coastal areas bordering oceanic predictable regions. Potential predictability at interannual and longer timescales is largely absent for precipitation in spite of weak signals identified mainly in the Nordic Seas. Regions of weak signals show some dependence on AMOC initial state. All the identified regions are closely linked to decadal AMOC fluctuations suggesting that the potential predictability of climate arises from the mechanisms controlling these fluctuations. Evidence for dependence on AMOC initial state also suggests that studying skills from case studies may prove more useful to understand predictability mechanisms than computing average skill from numerous start dates.

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

Intercomparison and evaluation of the global ocean surface mixed layer depth (MLD) fields estimated from a suite of major ocean syntheses are conducted. Compared with the reference MLDs calculated from individual profiles, MLDs calculated from monthly mean and gridded profiles show negative biases of 10–20 m in early spring related to the re-stratification process of relatively deep mixed layers. Vertical resolution of profiles also influences the MLD estimation. MLDs are underestimated by approximately 5–7 (14–16) m with the vertical resolution of 25 (50) m when the criterion of potential density exceeding the 10-m value by 0.03 kg m−3 is used for the MLD estimation. Using the larger criterion (0.125 kg m−3) generally reduces the underestimations. In addition, positive biases greater than 100 m are found in wintertime subpolar regions when MLD criteria based on temperature are used. Biases of the reanalyses are due to both model errors and errors related to differences between the assimilation methods. The result shows that these errors are partially cancelled out through the ensemble averaging. Moreover, the bias in the ensemble mean field of the reanalyses is smaller than in the observation-only analyses. This is largely attributed to comparably higher resolutions of the reanalyses. The robust reproduction of both the seasonal cycle and interannual variability by the ensemble mean of the reanalyses indicates a great potential of the ensemble mean MLD field for investigating and monitoring upper ocean processes.