Patterns of litter production in a secondary alluvial Atlantic Rain Forest in southern Brazil

Above-ground litter production is one of the most accessible ways to estimate ecosystem productivity, nutrient fluxes and carbon transfers. Phenological patterns and climatic conditions are still not fully explained well for tropical and subtropical forests under less pronounced dry season and non-seasonal climates, as well as the interaction of these patterns with successional dynamics. Monthly litterfall was estimated for two years in a 9 to 10 year old secondary alluvial Atlantic Rain forest. Total litterfall was higher in the site with more developed vegetation (6.4 ± 1.2 ton ha-1 year-1; 95% confidence interval) as compared to the site with less developed vegetation (3.0 ± 1.0 ton ha-1 year-1). The monthly production of 11 litter fractions (eight fractions comprising the leaf litter of the seven main species of the community and other species; reproductive parts, twigs £ 2 cm diameter, and miscellaneous material) were correlated with meteorological variables making possible to identify three patterns of deposition. The main pattern, dominated by leaf-exchanging species, consisted of a cycle with the highest litterfall at the beginning of the rainy season, preceding by basically three months the peaks of the annual cycles of rainfall and temperatures. Other two patterns, dominated by brevi-deciduous species, peaked at the end of the rainy season and at the end of the non-rainy season. Tropical and subtropical dry forests that present the highest leaf fall gradually earlier than rain forests (as the studied sites) are possibly related to the start of senescence process. It seems that such process is triggered earlier by a more severe hydric stress, besides other factors linked to a minor physiological activity of plants that result in abscission.

Anàlisi d'observacions de superfície en relació al diagnòstic de tipus de precipitació observat amb radar

Màster en Meteorologia

Comparing meta-analysis and ecological-longitudinal analysis in time-series studies: a case study of the effects of air pollution on mortality in three Spanish cities

The objective of this paper is to introduce a diVerent approach, called the ecological-longitudinal, to carrying out pooled analysis in time series ecological studies. Because it gives a larger number of data points and, hence, increases the statistical power of the analysis, this approach, unlike conventional ones, allows the complementation of aspects such as accommodation of random effect models, of lags, of interaction between pollutants and between pollutants and meteorological variables, that are hardly implemented in conventional approaches. Design—The approach is illustrated by providing quantitative estimates of the short-termeVects of air pollution on mortality in three Spanish cities, Barcelona,Valencia and Vigo, for the period 1992–1994. Because the dependent variable was a count, a Poisson generalised linear model was first specified. Several modelling issues are worth mentioning. Firstly, because the relations between mortality and explanatory variables were nonlinear, cubic splines were used for covariate control, leading to a generalised additive model, GAM. Secondly, the effects of the predictors on the response were allowed to occur with some lag. Thirdly, the residual autocorrelation, because of imperfect control, was controlled for by means of an autoregressive Poisson GAM. Finally, the longitudinal design demanded the consideration of the existence of individual heterogeneity, requiring the consideration of mixed models. Main results—The estimates of the relative risks obtained from the individual analyses varied across cities, particularly those associated with sulphur dioxide. The highest relative risks corresponded to black smoke in Valencia. These estimates were higher than those obtained from the ecological-longitudinal analysis. Relative risks estimated from this latter analysis were practically identical across cities, 1.00638 (95% confidence intervals 1.0002, 1.0011) for a black smoke increase of 10 μg/m3 and 1.00415 (95% CI 1.0001, 1.0007) for a increase of 10 μg/m3 of sulphur dioxide. Because the statistical power is higher than in the individual analysis more interactions were statistically significant,especially those among air pollutants and meteorological variables. Conclusions—Air pollutant levels were related to mortality in the three cities of the study, Barcelona, Valencia and Vigo. These results were consistent with similar studies in other cities, with other multicentric studies and coherent with both, previous individual, for each city, and multicentric studies for all three cities

Layers of nocturnal insect migrants at high-altitude: the influence of atmospheric conditions on their formation

1 Radar studies of nocturnal insect migration have often found that the migrants tend to form well-defined horizontal layers at a particular altitude. 2 In previous short-term studies, nocturnal layers were usually observed to occur at the same altitude as certain meteorological features, most notably at the altitudes of temperature inversions or nocturnal wind jets. 3 Statistical analyses are presented of four years’ data that compared the presence, sharpness and duration of nocturnal layer profiles (observed using continuously-operating entomological radar) with meteorological variables at typical layer altitudes over the UK. 4 Analysis of these large datasets demonstrated that temperature was the foremost meteorological factor persistently associated with the presence and formation of longer-lasting and sharper layers of migrating insects over southern UK.

A comparison of three canopy interception models for a leafless mixed deciduous forest stand in the eastern United States

Canopy interception of incident precipitation is a critical component of the forest water balance during each of the four seasons. Models have been developed to predict precipitation interception from standard meteorological variables because of acknowledged difficulty in extrapolating direct measurements of interception loss from forest to forest. No known study has compared and validated canopy interception models for a leafless deciduous forest stand in the eastern United States. Interception measurements from an experimental plot in a leafless deciduous forest in northeastern Maryland (39°42'N, 75°5'W) for 11 rainstorms in winter and early spring 2004/05 were compared to predictions from three models. The Mulder model maintains a moist canopy between storms. The Gash model requires few input variables and is formulated for a sparse canopy. The WiMo model optimizes the canopy storage capacity for the maximum wind speed during each storm. All models showed marked underestimates and overestimates for individual storms when the measured ratio of interception to gross precipitation was far more or less, respectively, than the specified fraction of canopy cover. The models predicted the percentage of total gross precipitation (PG) intercepted to within the probable standard error (8.1%) of the measured value: the Mulder model overestimated the measured value by 0.1% of PG; the WiMo model underestimated by 0.6% of PG; and the Gash model underestimated by 1.1% of PG. The WiMo model’s advantage over the Gash model indicates that the canopy storage capacity increases logarithmically with the maximum wind speed. This study has demonstrated that dormant-season precipitation interception in a leafless deciduous forest may be satisfactorily predicted by existing canopy interception models.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

A sensitivity study for water availability in the Northern Caucasus based on climate projections

A strong climatic warming is currently observed in the Caucasus mountains, which has profound impact on runoff generation in the glaciated Glavny (Main) Range and on water availability in the whole region. To assess future changes in the hydrological cycle, the output of a general circulation model was downscaled statistically. For the 21st century, a further warming by 4–7 °C and a slight precipitation increase is predicted. Measured and simulated meteorological variables were used as input into a runoff model to transfer climate signals into a hydrological response under both present and future climate forcings. Runoff scenarios for the mid and the end of the 21st century were generated for different steps of deglaciation. The results show a satisfactory model performance for periods with observed runoff. Future water availability strongly depends on the velocity of glacier retreat. In a first phase, a surplus of water will increase flood risk in hot years and after continuing glacier reduction, annual runoff will again approximate current values. However, the seasonal distribution of streamflow will change towards runoff increase in spring and lower flows in summer.

Revisiting Asian monsoon formation and change associated with Tibetan Plateau forcing: II. Change

Data analysis based on station observations reveals that many meteorological variables averaged over the Tibetan Plateau (TP) are closely correlated, and their trends during the past decades are well correlated with the rainfall trend of the Asian summer monsoon. However, such correlation does not necessarily imply causality. Further diagnosis confirms the existence of a weakening trend in TP thermal forcing, characterized by weakened surface sensible heat flux in spring and summer during the past decades. This weakening trend is associated with decreasing summer precipitation over northern South Asia and North China and increasing precipitation over northwestern China, South China, and Korea. An atmospheric general circulation model, the HadAM3, is employed to elucidate the causality between the weakening TP forcing and the change in the Asian summer monsoon rainfall. Results demonstrate that a weakening in surface sensible heating over the TP results in reduced summer precipitation in the plateau region and a reduction in the associated latent heat release in summer. These changes in turn result in the weakening of the near-surface cyclonic circulation surrounding the plateau and the subtropical anticyclone over the subtropical western North Pacific, similar to the results obtained from the idealized TP experiment in Part I of this study. The southerly that normally dominates East Asia, ranging from the South China Sea to North China, weakens, resulting in a weaker equilibrated Sverdrup balance between positive vorticity generation and latent heat release. Consequently, the convergence of water vapor transport is confined to South China, forming a unique anomaly pattern in monsoon rainfall, the so-called “south wet and north dry.” Because the weakening trend in TP thermal forcing is associated with global warming, the present results provide an effective means for assessing projections of regional climate over Asia in the context of global warming.

Modelling climate impact on floods with ensemble climate projections

The evidence provided by modelled assessments of future climate impact on flooding is fundamental to water resources and flood risk decision making. Impact models usually rely on climate projections from global and regional climate models (GCM/RCMs). However, challenges in representing precipitation events at catchment-scale resolution mean that decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs. Here the impacts on projected high flows of differing ensemble approaches and application of Model Output Statistics to RCM precipitation are evaluated while assessing climate change impact on flood hazard in the Upper Severn catchment in the UK. Various ensemble projections are used together with the HBV hydrological model with direct forcing and also compared to a response surface technique. We consider an ensemble of single-model RCM projections from the current UK Climate Projections (UKCP09); multi-model ensemble RCM projections from the European Union's FP6 ‘ENSEMBLES’ project; and a joint probability distribution of precipitation and temperature from a GCM-based perturbed physics ensemble. The ensemble distribution of results show that flood hazard in the Upper Severn is likely to increase compared to present conditions, but the study highlights the differences between the results from different ensemble methods and the strong assumptions made in using Model Output Statistics to produce the estimates of future river discharge. The results underline the challenges in using the current generation of RCMs for local climate impact studies on flooding. Copyright © 2012 Royal Meteorological Society

The surface urban energy and water balance scheme (SUEWS): evaluation in Los Angeles and Vancouver

An urban energy and water balance model is presented which uses a small number of commonly measured meteorological variables and information about the surface cover. Rates of evaporation-interception for a single layer with multiple surface types (paved, buildings, coniferous trees and/or shrubs, deciduous trees and/or shrubs, irrigated grass, non-irrigated grass and water) are calculated. Below each surface type, except water, there is a single soil layer. At each time step the moisture state of each surface is calculated. Horizontal water movements at the surface and in the soil are incorporated. Particular attention is given to the surface conductance used to model evaporation and its parameters. The model is tested against direct flux measurements carried out over a number of years in Vancouver, Canada and Los Angeles, USA. At all measurement sites the model is able to simulate the net all-wave radiation and turbulent sensible and latent heat well (RMSE = 25–47 W m−2, 30–64 and 20–56 W m−2, respectively). The model reproduces the diurnal cycle of the turbulent fluxes but typically underestimates latent heat flux and overestimates sensible heat flux in the day time. The model tracks measured surface wetness and simulates the variations in soil moisture content. It is able to respond correctly to short-term events as well as annual changes. The largest uncertainty relates to the determination of surface conductance. The model has the potential be used for multiple applications; for example, to predict effects of regulation on urban water use, landscaping and planning scenarios, or to assess climate mitigation strategies.

High resolution simulation of the variability of surface energy balance fluxes across central London with urban zones for energy partitioning

The parameterization of surface heat-flux variability in urban areas relies on adequate representation of surface characteristics. Given the horizontal resolutions (e.g. ≈0.1–1km) currently used in numerical weather prediction (NWP) models, properties of the urban surface (e.g. vegetated/built surfaces, street-canyon geometries) often have large spatial variability. Here, a new approach based on Urban Zones to characterize Energy partitioning (UZE) is tested within a NWP model (Weather Research and Forecasting model;WRF v3.2.1) for Greater London. The urban land-surface scheme is the Noah/Single-Layer Urban Canopy Model (SLUCM). Detailed surface information (horizontal resolution 1 km)in central London shows that the UZE offers better characterization of surface properties and their variability compared to default WRF-SLUCM input parameters. In situ observations of the surface energy fluxes and near-surface meteorological variables are used to select the radiation and turbulence parameterization schemes and to evaluate the land-surface scheme

Development and evaluation of an Earth-System model – HadGEM2

We describe here the development and evaluation of an Earth system model suitable for centennial-scale climate prediction. The principal new components added to the physical climate model are the terrestrial and ocean ecosystems and gas-phase tropospheric chemistry, along with their coupled interactions. The individual Earth system components are described briefly and the relevant interactions between the components are explained. Because the multiple interactions could lead to unstable feedbacks, we go through a careful process of model spin up to ensure that all components are stable and the interactions balanced. This spun-up configuration is evaluated against observed data for the Earth system components and is generally found to perform very satisfactorily. The reason for the evaluation phase is that the model is to be used for the core climate simulations carried out by the Met Office Hadley Centre for the Coupled Model Intercomparison Project (CMIP5), so it is essential that addition of the extra complexity does not detract substantially from its climate performance. Localised changes in some specific meteorological variables can be identified, but the impacts on the overall simulation of present day climate are slight. This model is proving valuable both for climate predictions, and for investigating the strengths of biogeochemical feedbacks.

Estudo estatístico sobre eventos de precipitação intensa no nordeste do Brasil

The Northeast of Brazil (NEB) shows high climate variability, ranging from semiarid regions to a rainy regions. According to the latest report of the Intergovernmental Panel on Climate Change, the NEB is highly susceptible to climate change, and also heavy rainfall events (HRE). However, few climatology studies about these episodes were performed, thus the objective main research is to compute the climatology and trend of the episodes number and the daily rainfall rate associated with HRE in the NEB and its climatologically homogeneous sub regions; relate them to the weak rainfall events and normal rainfall events. The daily rainfall data of the hydrometeorological network managed by the Agência Nacional de Águas, from 1972 to 2002. For selection of rainfall events used the technique of quantiles and the trend was identified using the Mann-Kendall test. The sub regions were obtained by cluster analysis, using as similarity measure the Euclidean distance and Ward agglomerative hierarchical method. The results show that the seasonality of the NEB is being intensified, i.e., the dry season is becoming drier and wet season getting wet. The El Niño and La Niña influence more on the amount of events regarding the intensity, but the sub-regions this influence is less noticeable. Using daily data reanalysis ERAInterim fields of anomalies of the composites of meteorological variables were calculated for the coast of the NEB, to characterize the synoptic environment. The Upper-level cyclonic vortex and the South atlantic convergene zone were identified as the main weather systems responsible for training of EPI on the coastland

Changes in phytoplankton composition in response to tides, wind-induced mixing conditions, and freshwater outflows in an urbanised estuarine complex

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)