Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble

A statistical-dynamical downscaling method is used to estimate future changes of wind energy output (Eout) of a benchmark wind turbine across Europe at the regional scale. With this aim, 22 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble are considered. The downscaling method uses circulation weather types and regional climate modelling with the COSMO-CLM model. Future projections are computed for two time periods (2021–2060 and 2061–2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 ensemble mean response reveals a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an ensemble mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra-annual variability for most of Europe. The latter is, in particular, probable during the second half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061–2100 compared to 2021–2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter-annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an ensemble of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi-model ensembles are needed to provide a better quantification and understanding of the future changes.

Diagnosing links between atmospheric moisture and extreme daily precipitation over the UK

Atmospheric moisture characteristics associated with the heaviest 1% of daily rainfall events affecting regions of the British Isles are analysed over the period 1997–2008. A blended satellite/rain-gauge data set (GPCP-1DD) and regionally averaged daily rain-gauge observations (HadUKP) are combined with the ERA Interim reanalysis. These are compared with simulations from the HadGEM2-A climate model which applied observed sea surface temperature and realistic radiative forcings. Median extreme daily rainfall across the identified events and locations is larger for GPCP (32 mm day−1) than HadUKP and the simulations (∼25 mm day−1). The heaviest observed and simulated daily rainfall events are associated with increased specific humidity and horizontal transport of moisture (median 850 hPa specific humidity of ∼6 g kg−1 and vapour transport of ∼150 g kg−1 m s−1 for both observed and simulated events). Extreme daily rainfall events are less common during spring and summer across much of the British Isles, but in the south east region, they contribute up to 60% of the total number of distinct extreme daily rainfall events during these months. Compared to winter events, the summer events over south east Britain are associated with a greater magnitude and more southerly location of moisture maxima and less spatially extensive regions of enhanced moisture transport. This contrasting dependence of extreme daily rainfall on moisture characteristics implies a range of driving mechanisms that depend upon location and season. Higher spatial and temporal resolution data are required to explore these processes further, which is vital in assessing future projected changes in rainfall and associated flooding.

Global climatology of surface water temperatures of large lakes by remote sensing

Lake surface water temperatures (LSWTs) of 246 globally distributed large lakes were derived from Along-Track Scanning Radiometers (ATSR) for the period 1991–2011. The climatological cycles of mean LSWT derived from these data quantify on a global scale the responses of large lakes' surface temperatures to the annual cycle of forcing by solar radiation and the ambient meteorological conditions. LSWT cycles reflect the twice annual peak in net solar radiation for lakes between 1°S to 12°N. For lakes without a lake-mean seasonal ice cover, LSWT extremes exceed air temperatures by 0.5–1.7 °C for maximum and 0.7–1.9 °C for minimum temperature. The summer maximum LSWTs of lakes from 25°S to 35°N show a linear decrease with increasing altitude; −3.76 ± 0.17 °C km−1 (inline image = 0.95), marginally lower than the corresponding air temperature decrease with altitude −4.15 ± 0.24 °C km−1 (inline image = 0.95). Lake altitude of tropical lakes account for 0.78–0.83 (inline image) of the variation in the March to June LSWT–air temperature differences, with differences decreasing by 1.9 °C as the altitude increases from 500 to 1800 m above sea level (a.s.l.) We define an ‘open water phase’ as the length of time the lake-mean LSWT remains above 4 °C. There is a strong global correlation between the start and end of the lake-mean open water phase and the spring and fall 0 °C air temperature transition days, (inline image = 0.74 and 0.80, respectively), allowing for a good estimation of timing and length of the open water phase of lakes without LSWT observations. Lake depth, lake altitude and distance from coast further explain some of the inter-lake variation in the start and end of the open water phase.

Non-stationary frequency analysis of extreme daily rainfall in Sao Paulo, Brazil

This work is an assessment of frequency of extreme values (EVs) of daily rainfall in the city of Sao Paulo. Brazil, over the period 1933-2005, based on the peaks-over-threshold (POT) and Generalized Pareto Distribution (GPD) approach. Usually. a GPD model is fitted to a sample of POT Values Selected With a constant threshold. However. in this work we use time-dependent thresholds, composed of relatively large p quantities (for example p of 0.97) of daily rainfall amounts computed from all available data. Samples of POT values were extracted with several Values of p. Four different GPD models (GPD-1, GPD-2, GPD-3. and GDP-4) were fitted to each one of these samples by the maximum likelihood (ML) method. The shape parameter was assumed constant for the four models, but time-varying covariates were incorporated into scale parameter of GPD-2. GPD-3, and GPD-4, describing annual cycle in GPD-2. linear trend in GPD-3, and both annual cycle and linear trend in GPD-4. The GPD-1 with constant scale and shape parameters is the simplest model. For identification of the best model among the four models WC used rescaled Akaike Information Criterion (AIC) with second-order bias correction. This criterion isolates GPD-3 as the best model, i.e. the one with positive linear trend in the scale parameter. The slope of this trend is significant compared to the null hypothesis of no trend, for about 98% confidence level. The non-parametric Mann-Kendall test also showed presence of positive trend in the annual frequency of excess over high thresholds. with p-value being virtually zero. Therefore. there is strong evidence that high quantiles of daily rainfall in the city of Sao Paulo have been increasing in magnitude and frequency over time. For example. 0.99 quantiles of daily rainfall amount have increased by about 40 mm between 1933 and 2005. Copyright (C) 2008 Royal Meteorological Society

Climate perspective on the large-scale circulation associated with the transition of the first South Atlantic hurricane

The landfall of Cyclone Catarina on the Brazilian coast in March 2004 became known as the first documented hurricane in the South Atlantic Ocean, promoting a new view oil how large-scale features can contribute to tropical transition. The aim of this paper is to put the large-scale circulation associated with Catarina`s transition in climate perspective. This is discussed in the light of a robust pattern of spatial correlations between thermodynamic and dynamic variables of importance for hurricane formation. A discussion on how transition mechanisms respond to the present-day circulation is presented. These associations help in understanding why Catarina was formed in a region previously thought to be hurricane-free. Catarina developed over a large-scale area of thermodynamically favourable air/sea temperature contrast. This aspect explains the paradox that such a rare system developed when the sea surface temperature was slightly below average. But, although thermodynamics played an important role, it is apparent that Catarina would not have formed without the key dynamic interplay triggered by a high latitude blocking. The blocking was associated with an extreme positive phase of the Southern Annular Mode (SAM) both hemispherically and locally, and the nearby area where Catarina developed is found to be more cyclonic during the positive phase of the SAM. A conceptual model is developed and a `South Atlantic index` is introduced as a useful diagnostic of potential conditions leading to tropical transition in the area, where large-scale indices indicate trends towards more favourable atmospheric conditions for tropical cyclone formation. Copyright (c) 2008 Royal Meteorological Society

The South American Monsoon System and the 1970s climate transition

The South American Monsoon System (SAMS) is characterised by intense convective activity and precipitation during austral summer. This study investigates changes in the onset, demise and duration of SAMS during 1948-2008. The results show a significant change in these characteristics in the early 1970s. Onset becomes steadily earlier from 1948 to early 1970s and has occurred earlier than 23-27 October after 1972-1973. Demise dates have remained later than 21-25 April after the mid-to-late 1970s. SAMS duration shows a statistical changepoint in the summer of 1971-1972 such that the mean duration was similar to 170 days (1948-1972) and 195 days (1972-1982). Vertically integrated moisture flux is used to diagnose changes in mean state and reveal statistically significant increases over South America after 1971-1972. Copyright. (C) 2010 Royal Meteorological Society

Multivariate analysis of the energy cycle of the South American rainy season

The South American (SA) rainy season is studied in this paper through the application of a multivariate Empirical Orthogonal Function (EOF) analysis to a SA gridded precipitation analysis and to the components of Lorenz Energy Cycle (LEC) derived from the National Centers for Environmental Prediction (NCEP) reanalysis. The EOF analysis leads to the identification of patterns of the rainy season and the associated mechanisms in terms of their energetics. The first combined EOF represents the northwest-southeast dipole of the precipitation between South and Central America, the South American Monsoon System (SAMS). The second combined EOF represents a synoptic pattern associated with the SACZ (South Atlantic convergence zone) and the third EOF is in spatial quadrature to the second EOF. The phase relationship of the EOFs, as computed from the principal components (PCs), suggests a nonlinear transition from the SACZ to the fully developed SAMS mode by November and between both components describing the SACZ by September-October (the rainy season onset). According to the LEC, the first mode is dominated by the eddy generation term at its maximum, the second by both baroclinic and eddy generation terms and the third by barotropic instability previous to the connection to the second mode by September-October. The predominance of the different LEC components at each phase of the SAMS can be used as an indicator of the onset of the rainy season in terms of physical processes, while the existence of the outstanding spectral peaks in the time dependence of the EOFs at the intraseasonal time scale could be used for monitoring purposes. Copyright (C) 2009 Royal Meteorological Society

Frequency of extreme rainfall events in Southern Brazil modulated by interannual and interdecadal variability

The frequency of extreme rainfall events in Southern Brazil is impacted by Ell Nino - Southern Oscillation (ENSO) episodes, especially in austral spring. There are two areas in which this impact is more significant: one is on the coast, where extreme events are more frequent during El Nino (EN) and the other one extends inland, where extreme events increase during EN and decrease during La Nina (LN). Atmospheric circulation patterns associated with severe rainfall in those areas are similar (opposite) to anomalous patterns characteristic of EN (LN) episodes, indicating why increase (decrease) of extreme events in EN (LN) episodes is favoured. The most recurrent precipitation patterns during extreme rainfall events in each of these areas are disclosed by Principal Component Analysis (PCA) and evidence the separation between extreme events in these areas: a severe precipitation event generally does not occur simultaneously in the coast and inland, although they may Occur inland and in the coastal region in sequence. Although EN predominantly enhances extreme rainfall, there are EN years in which fewer severe events occur than the average of neutral years, and also the enhancement of extreme rainfall is not uniform for different EN episodes, because the interdecadal non-ENSO variability also modulates significantly the frequency of extreme events in Southern Brazil. The inland region, which is more affected, shows increase (decrease) of extreme rainfall in association with the negative (positive) phase of the Atlantic Multidecadal Variability, with the negative (positive) phase of the Pacific Multidecadal Variability and with the positive (negative) phase of the Pacific Interdecadal Variability. Copyright (C) 2008 Royal Meteorological Society

Non-stationary frequency analysis of extreme daily rainfall in São Paulo, Brazil

This work is an assessment of frequency of extreme values (EVs) of daily rainfall in the city of São Paulo. Brazil, over the period 1933-2005, based on the peaks-over-threshold (POT) and Generalized Pareto Distribution (GPD) approach. Usually. a GPD model is fitted to a sample of POT Values Selected With a constant threshold. However. in this work we use time-dependent thresholds, composed of relatively large p quantities (for example p of 0.97) of daily rainfall amounts computed from all available data. Samples of POT values were extracted with several Values of p. Four different GPD models (GPD-1, GPD-2, GPD-3. and GDP-4) were fitted to each one of these samples by the maximum likelihood (ML) method. The shape parameter was assumed constant for the four models, but time-varying covariates were incorporated into scale parameter of GPD-2. GPD-3, and GPD-4, describing annual cycle in GPD-2. linear trend in GPD-3, and both annual cycle and linear trend in GPD-4. The GPD-1 with constant scale and shape parameters is the simplest model. For identification of the best model among the four models WC used rescaled Akaike Information Criterion (AIC) with second-order bias correction. This criterion isolates GPD-3 as the best model, i.e. the one with positive linear trend in the scale parameter. The slope of this trend is significant compared to the null hypothesis of no trend, for about 98% confidence level. The non-parametric Mann-Kendall test also showed presence of positive trend in the annual frequency of excess over high thresholds. with p-value being virtually zero. Therefore. there is strong evidence that high quantiles of daily rainfall in the city of São Paulo have been increasing in magnitude and frequency over time. For example. 0.99 quantiles of daily rainfall amount have increased by about 40 mm between 1933 and 2005. Copyright (C) 2008 Royal Meteorological Society

Using phenology to assess urban heat islands in tropical and temperate regions

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau

Climate change has significantly influenced vegetation dynamics on the Tibetan Plateau (TP). Past research mainly focused on vegetation responses to temperature variation and water stress, but the influence of sunshine duration on NDVI and vegetation phenology on the TP is not well understood. In this study, NDVI time series from 1982-2008 were used to retrieve spatiotemporal vegetation dynamics on the TP. Empirical orthogonal function (EOF) analysis was conducted to understand the spatiotemporal variations of NDVI. The Start of Season (SOS) was estimated from NDVI time series with a local threshold method. The first EOF, accounting for 35.1% of NDVI variations on the TP, indicates that NDVI variations are larger in areas with shorter sunshine duration. The needle-leaved forest and shrub in the southeastern TP are more sensitive to sunshine duration anomalies (p < 0.01) than broad-leaved forest, steppe, and meadow due to spatial and altitudinal distribution of sunshine duration and vegetation types. The decrease in sunshine duration for the growing season on the TP has resulted in a decreased NDVI trend in some areas of southeastern TP (p ranging from 0.32-0.05 with threshold ranging from 0.05 to 0.25) in spite of the overall NDVI increase. SOS dynamics in most parts of the TP were mainly related to temperature variability, with precipitation and sunshine duration playing a role in a few regions. This study enhances our understanding of vegetation responses to climatic change on the TP.

Statistical detection of spurious variations in daily raingauge data caused by changes in observation practices, as applied to records from various parts of the world

In the instrumental records of daily precipitation, we often encounter one or more periods in which values below some threshold were not registered. Such periods, besides lacking small values, also have a large number of dry days. Their cumulative distribution function is shifted to the right in relation to that for other portions of the record having more reliable observations. Such problems are examined in this work, based mostly on the two-sample Kolmogorov–Smirnov (KS) test, where the portion of the series with more number of dry days is compared with the portion with less number of dry days. Another relatively common problem in daily rainfall data is the prevalence of integers either throughout the period of record or in some part of it, likely resulting from truncation during data compilation prior to archiving or by coarse rounding of daily readings by observers. This problem is identified by simple calculation of the proportion of integers in the series, taking the expected proportion as 10%. The above two procedures were applied to the daily rainfall data sets from the European Climate Assessment (ECA), Southeast Asian Climate Assessment (SACA), and Brazilian Water Resources Agency (BRA). Taking the statistic D of the KS test >0.15 and the corresponding p-value <0.001 as the condition to classify a given series as suspicious, the proportions of the ECA, SACA, and BRA series falling into this category are, respectively, 34.5%, 54.3%, and 62.5%. With relation to coarse rounding problem, the proportions of series exceeding twice the 10% reference level are 3%, 60%, and 43% for the ECA, SACA, and BRA data sets, respectively. A simple way to visualize the two problems addressed here is by plotting the time series of daily rainfall for a limited range, for instance, 0–10 mm day−1.