Enhancement of ENSO variability by a weakened Atlantic thermohaline circulation in a coupled GCM

A coupled ocean–atmosphere general circulation model is used to investigate the modulation of El Niño–Southern Oscillation (ENSO) variability due to a weakened Atlantic thermohaline circulation (THC). The THC weakening is induced by freshwater perturbations in the North Atlantic, and leads to a well-known sea surface temperature dipole and a southward shift of the intertropical convergence zone (ITCZ) in the tropical Atlantic. Through atmospheric teleconnections and local coupled air–sea feedbacks, a meridionally asymmetric mean state change is generated in the eastern equatorial Pacific, corresponding to a weakened annual cycle, and westerly anomalies develop over the central Pacific. The westerly anomalies are associated with anomalous warming of SST, causing an eastward extension of the west Pacific warm pool particularly in August–February, and enhanced precipitation. These and other changes in the mean state lead in turn to an eastward shift of the zonal wind anomalies associated with El Niño events, and a significant increase in ENSO variability. In response to a 1-Sv (1 Sv ≡ 106 m3 s−1) freshwater input in the North Atlantic, the THC slows down rapidly and it weakens by 86% over years 50–100. The Niño-3 index standard deviation increases by 36% during the first 100-yr simulation relative to the control simulation. Further analysis indicates that the weakened THC not only leads to a stronger ENSO variability, but also leads to a stronger asymmetry between El Niño and La Niña events. This study suggests a role for an atmospheric bridge that rapidly conveys the influence of the Atlantic Ocean to the tropical Pacific and indicates that fluctuations of the THC can mediate not only mean climate globally but also modulate interannual variability. The results may contribute to understanding both the multidecadal variability of ENSO activity during the twentieth century and longer time-scale variability of ENSO, as suggested by some paleoclimate records.

Sampling uncertainties in surface radiation budget calculations in RADAGAST

In the Radiative Atmospheric Divergence Using ARM Mobile Facility GERB and AMMA Stations (RADAGAST) project we calculate the divergence of radiative flux across the atmosphere by comparing fluxes measured at each end of an atmospheric column above Niamey, in the African Sahel region. The combination of broadband flux measurements from geostationary orbit and the deployment for over 12 months of a comprehensive suite of active and passive instrumentation at the surface eliminates a number of sampling issues that could otherwise affect divergence calculations of this sort. However, one sampling issue that challenges the project is the fact that the surface flux data are essentially measurements made at a point, while the top-of-atmosphere values are taken over a solid angle that corresponds to an area at the surface of some 2500 km2. Variability of cloud cover and aerosol loading in the atmosphere mean that the downwelling fluxes, even when averaged over a day, will not be an exact match to the area-averaged value over that larger area, although we might expect that it is an unbiased estimate thereof. The heterogeneity of the surface, for example, fixed variations in albedo, further means that there is a likely systematic difference in the corresponding upwelling fluxes. In this paper we characterize and quantify this spatial sampling problem. We bound the root-mean-square error in the downwelling fluxes by exploiting a second set of surface flux measurements from a site that was run in parallel with the main deployment. The differences in the two sets of fluxes lead us to an upper bound to the sampling uncertainty, and their correlation leads to another which is probably optimistic as it requires certain other conditions to be met. For the upwelling fluxes we use data products from a number of satellite instruments to characterize the relevant heterogeneities and so estimate the systematic effects that arise from the flux measurements having to be taken at a single point. The sampling uncertainties vary with the season, being higher during the monsoon period. We find that the sampling errors for the daily average flux are small for the shortwave irradiance, generally less than 5 W m−2, under relatively clear skies, but these increase to about 10 W m−2 during the monsoon. For the upwelling fluxes, again taking daily averages, systematic errors are of order 10 W m−2 as a result of albedo variability. The uncertainty on the longwave component of the surface radiation budget is smaller than that on the shortwave component, in all conditions, but a bias of 4 W m−2 is calculated to exist in the surface leaving longwave flux.

Decadal predictability of the Atlantic Ocean in a coupled GCM: forecast skill and optimal perturbations using Linear Inverse Modelling

The decadal predictability of three-dimensional Atlantic Ocean anomalies is examined in a coupled global climate model (HadCM3) using a Linear Inverse Modelling (LIM) approach. It is found that the evolution of temperature and salinity in the Atlantic, and the strength of the meridional overturning circulation (MOC), can be effectively described by a linear dynamical system forced by white noise. The forecasts produced using this linear model are more skillful than other reference forecasts for several decades. Furthermore, significant non-normal amplification is found under several different norms. The regions from which this growth occurs are found to be fairly shallow and located in the far North Atlantic. Initially, anomalies in the Nordic Seas impact the MOC, and the anomalies then grow to fill the entire Atlantic basin, especially at depth, over one to three decades. It is found that the structure of the optimal initial condition for amplification is sensitive to the norm employed, but the initial growth seems to be dominated by MOC-related basin scale changes, irrespective of the choice of norm. The consistent identification of the far North Atlantic as the most sensitive region for small perturbations suggests that additional observations in this region would be optimal for constraining decadal climate predictions.

G-protein-coupled-receptor-mediated presynaptic inhibition in the cerebellum

Throughout the central nervous system a dominant form of inhibition of neurotransmitter release from presynaptic terminals is mediated by G-protein-coupled receptors (GPCRs). Neurotransmitter release is typically induced by action potentials (APs), but can also occur spontaneously. Presynaptic inhibition by GPCRs has been associated with modulation of voltage-dependent ion channels. However, electrophysiological recordings of spontaneous, AP-independent (so-called ‘miniature’) postsynaptic events reveal an additional, important form of GPCR-mediated presynaptic inhibition, distinct from effects on ionic conductances and consistent with a direct action on the vesicle release machinery. Recent studies suggest that such miniature events might be of physiological relevance not only in signalling but also in development. In the cerebellum, neurotransmitter release onto Purkinje cells occurs by AP-dependent and AP-independent pathways. Here, I focus on inhibitory synapses between interneurons and Purkinje cells, which are subject to strong, identifiable regulation by endogenous GPCR agonists, to consider mechanisms of GPCR-mediated presynaptic inhibition.

Examining the interaction of growing crops with local climate using a coupled crop-climate model

This paper examines to what extent crops and their environment should be viewed as a coupled system. Crop impact assessments currently use climate model output offline to drive process-based crop models. However, in regions where local climate is sensitive to land surface conditions more consistent assessments may be produced with the crop model embedded within the land surface scheme of the climate model. Using a recently developed coupled crop–climate model, the sensitivity of local climate, in particular climate variability, to climatically forced variations in crop growth throughout the tropics is examined by comparing climates simulated with dynamic and prescribed seasonal growth of croplands. Interannual variations in land surface properties associated with variations in crop growth and development were found to have significant impacts on near-surface fluxes and climate; for example, growing season temperature variability was increased by up to 40% by the inclusion of dynamic crops. The impact was greatest in dry years where the response of crop growth to soil moisture deficits enhanced the associated warming via a reduction in evaporation. Parts of the Sahel, India, Brazil, and southern Africa were identified where local climate variability is sensitive to variations in crop growth, and where crop yield is sensitive to variations in surface temperature. Therefore, offline seasonal forecasting methodologies in these regions may underestimate crop yield variability. The inclusion of dynamic crops also altered the mean climate of the humid tropics, highlighting the importance of including dynamical vegetation within climate models.