Combining satellite data and models to estimate cloud radiative effect at the surface and in the atmosphere

Satellite measurements and numerical forecast model reanalysis data are used to compute an updated estimate of the cloud radiative effect on the global multi-annual mean radiative energy budget of the atmosphere and surface. The cloud radiative cooling effect through reflection of shortwave radiation dominates over the longwave heating effect, resulting in a net cooling of the climate system of –21 Wm-2. The shortwave radiative effect of cloud is primarily manifest as a reduction in the solar radiation absorbed at the surface of -53 Wm-2. Clouds impact longwave radiation by heating the moist tropical atmosphere (up to around 40 Wm-2 for global annual means) while enhancing the radiative cooling of the atmosphere over other regions, in particular higher latitudes and sub-tropical marine stratocumulus regimes. While clouds act to cool the climate system during the daytime, the cloud greenhouse effect heats the climate system at night. The influence of cloud radiative effect on determining cloud feedbacks and changes in the water cycle are discussed.

Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

Long-term acclimation of leaf production, development, longevity and quality following 3 yr exposure to free-air CO2 enrichment during canopy closure in Populus

The effects of elevated CO2 on leaf development in three genotypes of Populus were investigated during canopy closure, following exposure to elevated CO2 over 3 yr using free-air enrichment.• Leaf quality was altered such that nitrogen concentration per unit d. wt (Nmass) declined on average by 22 and 13% for sun and shade leaves, respectively, in elevated CO2. There was little evidence that this was the result of ‘dilution’ following accumulation of nonstructural carbohydrates. Most likely, this was the result of increased leaf thickness. Specific leaf area declined in elevated CO2 on average by 29 and 5% for sun and shade leaves, respectively.• Autumnal senescence was delayed in elevated CO2 with a 10% increase in the number of days at which 50% leaf loss occurred in elevated as compared with ambient CO2.• These data suggest that changes in leaf quality may be predicted following long-term acclimation of fast-growing forest trees to elevated CO2, and that canopy longevity may increase, with important implications for forest productivity.

The transcriptome of Populus in elevated CO2

The consequences of increasing atmospheric carbon dioxide for long-term adaptation of forest ecosystems remain uncertain, with virtually no studies undertaken at the genetic level. A global analysis using cDNA microarrays was conducted following 6 yr exposure of Populus × euramericana (clone I-214) to elevated [CO2] in a FACE (free-air CO2 enrichment) experiment.• Gene expression was sensitive to elevated [CO2] but the response depended on the developmental age of the leaves, and < 50 transcripts differed significantly between different CO2 environments. For young leaves most differentially expressed genes were upregulated in elevated [CO2], while in semimature leaves most were downregulated in elevated [CO2].• For transcripts related only to the small subunit of Rubisco, upregulation in LPI 3 and downregulation in LPI 6 leaves in elevated CO2 was confirmed by anova. Similar patterns of gene expression for young leaves were also confirmed independently across year 3 and year 6 microarray data, and using real-time RT–PCR.• This study provides the first clues to the long-term genetic expression changes that may occur during long-term plant response to elevated CO2.

Adaptation of tree growth to elevated CO2: quantitative trait loci for biomass in Populus

Information on the genetic variation of plant response to elevated CO2 (e[CO2]) is needed to understand plant adaptation and to pinpoint likely evolutionary response to future high atmospheric CO2 concentrations.• Here, quantitative trait loci (QTL) for above- and below-ground tree growth were determined in a pedigree – an F2 hybrid of poplar (Populus trichocarpa and Populus deltoides), following season-long exposure to either current day ambient CO2 (a[CO2]) or e[CO2] at 600 µl l−1, and genotype by environment interactions investigated.• In the F2 generation, both above- and below-ground growth showed a significant increase in e[CO2]. Three areas of the genome on linkage groups I, IX and XII were identified as important in determining above-ground growth response to e[CO2], while an additional three areas of the genome on linkage groups IV, XVI and XIX appeared important in determining root growth response to e[CO2].• These results quantify and identify genetic variation in response to e[CO2] and provide an insight into genomic response to the changing environment

Interpretation bias and anxiety in childhood: stability, specificity and longitudinal associations

Background: Biases in the interpretation of ambiguous material are central to cognitive models of anxiety; however, understanding of the association between interpretation and anxiety in childhood is limited. To address this, a prospective investigation of the stability and specificity of anxious cognitions and anxiety and the relationship between these factors was conducted. Method: Sixty-five children (10–11 years) from a community sample completed measures of self-reported anxiety, depression, and conduct problems, and responded to ambiguous stories at three time points over one-year. Results: Individual differences in biases in interpretation of ambiguity (specifically “anticipated distress” and “threat interpretation”) were stable over time. Furthermore, anticipated distress and threat interpretation were specifically associated with anxiety symptoms. Distress anticipation predicted change in anxiety symptoms over time. In contrast, anxiety scores predicted change in threat interpretation over time. Conclusions: The results suggest that different cognitive constructs may show different longitudinal links with anxiety. These preliminary findings extend research and theory on anxious cognitions and their link with anxiety in children, and suggest that these cognitive processes may be valuable targets for assessment and intervention.

Discrete time representation of continuous time ARMA processes

This paper derives exact discrete time representations for data generated by a continuous time autoregressive moving average (ARMA) system with mixed stock and flow data. The representations for systems comprised entirely of stocks or of flows are also given. In each case the discrete time representations are shown to be of ARMA form, the orders depending on those of the continuous time system. Three examples and applications are also provided, two of which concern the stationary ARMA(2, 1) model with stock variables (with applications to sunspot data and a short-term interest rate) and one concerning the nonstationary ARMA(2, 1) model with a flow variable (with an application to U.S. nondurable consumers’ expenditure). In all three examples the presence of an MA(1) component in the continuous time system has a dramatic impact on eradicating unaccounted-for serial correlation that is present in the discrete time version of the ARMA(2, 0) specification, even though the form of the discrete time model is ARMA(2, 1) for both models.