MODIS-based vegetation index has sufficient sensitivity to indicate stand-level intra-seasonal climatic stress in oak and beech forests

Context: Variation in photosynthetic activity of trees induced by climatic stress can be effectively evaluated using remote sensing data. Although adverse effects of climate on temperate forests have been subjected to increased scrutiny, the suitability of remote sensing imagery for identification of drought stress in such forests has not been explored fully. Aim: To evaluate the sensitivity of MODIS-based vegetation index to heat and drought stress in temperate forests, and explore the differences in stress response of oaks and beech. Methods: We identified 8 oak and 13 beech pure and mature stands, each covering between 4 and 13 MODIS pixels. For each pixel, we extracted a time series of MODIS NDVI from 2000 to 2010. We identified all sequences of continuous unseasonal NDVI decline to be used as the response variable indicative of environmental stress. Neural Networks-based regression modelling was then applied to identify the climatic variables that best explain observed NDVI declines. Results: Tested variables explained 84–97% of the variation in NDVI, whilst air temperature-related climate extremes were found to be the most influential. Beech showed a linear response to the most influential climatic predictors, while oak responded in a unimodal pattern suggesting a better coping mechanism. Conclusions: MODIS NDVI has proved sufficiently sensitive as a stand-level indicator of climatic stress acting upon temperate broadleaf forests, leading to its potential use in predicting drought stress from meteorological observations and improving parameterisation of forest stress indices.

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'.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Adaptation to climate change and variability: farmer responses to intra-seasonal precipitation trends in South Africa

We describe the nature of recent (50 year) rainfall variability in the summer rainfall zone, South Africa, and how variability is recognised and responded to on the ground by farmers. Using daily rainfall data and self-organising mapping (SOM) we identify 12 internally homogeneous rainfall regions displaying differing parameters of precipitation change. Three regions, characterised by changing onset and timing of rains, rainfall frequencies and intensities, in Limpopo, North West and KwaZulu Natal provinces, were selected to investigate farmer perceptions of, and responses to, rainfall parameter changes. Village and household level analyses demonstrate that the trends and variabilities in precipitation parameters differentiated by the SOM analysis were clearly recognised by people living in the areas in which they occurred. A range of specific coping and adaptation strategies are employed by farmers to respond to climate shifts, some generic across regions and some facilitated by specific local factors. The study has begun to understand the complexity of coping and adaptation, and the factors that influence the decisions that are taken.

Seasonal changes in olive fruit characteristics and oil accumulation during ripening process

Olive fruits of three of the most important Spanish and Italian cultivars, 'Picual', `Hojiblanca' and 'Frantoio', were harvested at bi-weekly periods during three crop seasons to study their development and ripening process. Fresh and dry weights and ripening index were determined for fruit, while dry matter, oil and moisture contents were determined in both fruit and pulp (flesh). Fruit growth rate and oil accumulation were calculated. Each olive cultivar showed a different ripening pattern, 'Hojiblanca' being the last one to maturate. Fruit weight increased, decreasing its growth rate from the middle of November. Dry matter and moisture contents decreased during ripening in pulp and fruit, 'Hojiblanca' showing the highest values for both. Oil content, when expressed on a fresh weight basis, increased in all cultivars, although for the last time period showed variations due to climatic conditions. During ripening, oil content on a dry weight basis increased in fruit, but oil biosynthesis in flesh ceased from November. Olive fruits presented lower oil and higher dry matter contents in the year of lowest rainfall. Therefore fruit harvesting should be carried out from the middle of November in order to obtain the highest oil yield and avoid natural fruit drop. (C) 2004 Society of Chemical Industry.

Assessment of projected changes in upland environments using simple climatic indices

Climate controls upland habitats, soils and their associated ecosystem services; therefore, understanding possible changes in upland climatic conditions can provide a rapid assessment of climatic vulnerability over the next century. We used 3 different climatic indices that were optimised to fit the upland area classified by the EU as a Severely Disadvantaged Area (SDA) 1961–1990. Upland areas within the SDA covered all altitudinal ranges, whereas the maximum altitude of lowland areas outside of the SDA was ca. 300 m. In general, the climatic index based on the ratio between annual accumulated temperature (as a measure of growing season length) and annual precipitation predicted 96% of the SDA mapped area, which was slightly better than those indices based on annual or seasonal water deficit. Overall, all climatic indices showed that upland environments were exposed to some degree of change by 2071–2100 under UKCIP02 climate projections for high and low emissions scenarios. The projected area declined by 13 to 51% across 3 indices for the low emissions scenario and by 24 to 84% for the high emissions scenario. Mean altitude of the upland area increased by +11 to +86 m for the low scenario and +21 to +178 m for the high scenario. Low altitude areas in eastern and southern Great Britain were most vulnerable to change. These projected climatic changes are likely to affect upland habitat composition, long-term soil carbon storage and wider ecosystem service provision, although it is not yet possible to determine the rate at which this might occur.

Agriculture modifies the seasonal decline of breeding success in a tropical wild bird population

1.Habitat conversion for agriculture is a major driver of biodiversity loss, but our understanding of the demographic processes involved remains poor. We typically investigate the impacts of agriculture in isolation even though populations are likely to experience multiple, concurrent changes in the environment (e.g. land and climate change). Drivers of environmental change may interact to affect demography but the mechanisms have yet to be explored fully in wild populations. 2.Here, we investigate the mechanisms linking agricultural land-use with breeding success using long-term data for the formerly Critically Endangered Mauritius kestrel Falco punctatus; a tropical forest specialist that also occupies agricultural habitats. We specifically focused on the relationship between breeding success, agriculture and the timing of breeding because the latter is sensitive to changes in climatic conditions (spring rainfall), and enables us to explore the interactive effects of different (land and climate) drivers of environmental change. 3.Breeding success, measured as egg survival to fledging, declines seasonally in this population, but we found that the rate of this decline became increasingly rapid as the area of agriculture around a nest site increased. If the relationship between breeding success and agriculture was used in isolation to estimate the demographic impact of agriculture it would significantly under-estimate breeding success in dry (early) springs, and over-estimate breeding success in wet (late) springs. 4.Analysis of prey delivered to nests suggests that the relationship between breeding success and agriculture might be due, in part, to spatial variation in the availability of native, arboreal geckos. 5.Synthesis and applications. Agriculture modifies the seasonal decline in breeding success in this population. As springs are becoming wetter in our study area and since the kestrels breed later in wetter springs, the impact of agriculture on breeding success will become worse over time. Our results suggest that forest restoration designed to reduce the detrimental impacts of agriculture on breeding may also help reduce the detrimental effects of breeding late due to wetter springs. Our results therefore highlight the importance of considering the interactive effects of environmental change when managing wild populations.

Seasonal variations of the physical and optical characteristics of Saharan Dust: results from the Dust outflow and Deposition to the Ocean (DODO) experiment

North African dust is important for climate through its direct radiative effect on solar and terrestrial radiation and its role in the biogeochemical system. The Dust Outflow and Deposition to the Ocean project (DODO) aimed to characterize the physical and optical properties of airborne North African dust in two seasons and to use these observations to constrain model simulations, with the ultimate aim of being able to quantify the deposition of iron to the North Atlantic Ocean. The in situ properties of dust from airborne campaigns measured during February and August 2006, based at Dakar, Senegal, are presented here. Average values of the single scattering albedo (0.99, 0.98), mass specific extinction (0.85 m^2 g^-1 , 1.14 m^2 g^-1 ), asymmetry parameter (0.68, 0.68), and refractive index (1.53--0.0005i,1.53--0.0014i) for the accumulation mode were found to differ by varying degrees between the dry and wet season, respectively. It is hypothesized that these differences are due to different source regions and transport processes which also differ between the DODO campaigns. Elemental ratios of Ca/Al were found to differ between the dry and wet season (1.1 and 0.5, respectively). Differences in vertical profiles are found between seasons and between land and ocean locations and reflect the different dynamics of the seasons. Using measurements of the coarse mode size distribution and illustrative Mie calculations, the optical properties are found to be very sensitive to the presence and amount of coarse mode of mineral dust, and the importance of accurate measurements of the coarse mode of dust is highlighted.

Stratospheric circulation in seasonal forecasting models: implications for seasonal prediction

Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.

Decadal Prediction. Can It Be Skillful?

A new field of study, “decadal prediction,” is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10–30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence time-evolving regional climate at the decadal time scale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally generated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internally generated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global time-evolving three-dimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated Coupled Model Intercomparison Model, phase 5 (CMIP5) experiments, some of which will be assessed for the IPCC Fifth Assessment Report (AR5). These experiments will likely guide work in this emerging field over the next 5 yr.

Annual textural banding in Holocene estuarine silts, Severn Estuary Levels (SW Britain): patterns, cause and implications

A high-resolution textural study has been made of laminated and banded estuarine silts exposed intertidally at representative localities and horizons in the Holocene deposits of the Severn Estuary Levels. The laminae, on a submillimetre to millimetre scale, are sharp-based, graded couplets formed of a lower silty part overlain by a finer-textured clayey element. The centimetre- to decimetre-scale banding is formed of laminae in alternating, gradually intergrading sets of relatively coarse and relative fine-grained examples. At outcrop in the field, the banding is recognizable because the coarse sets prove to be recessive to varying degrees under the influence of weathering and current action. Independent evidence at two localities points toward an annual origin for the banding; at a third it arose during part of what appears to have been a relatively short period. Quantified physical arguments suggest that the textural banding is a response of suspended fine sediment to marked seasonal changes in sea temperature and windiness. The banded silts occur in four distinct stratigraphical contexts and record high deposition rates (order 0.01-0.1 m/yr). Because physical factors determine their textures, the silts potentially afford insights in all contexts into aspects of changing Holocene climatic conditions. In one context, the thickness of the bands points to high (order 0.01-0.1 m/yr) but comparatively short-lived (order 10s-100s yrs) rates of relative water-level rise. In the others, however, the banding has no implications for sea-level behaviour, and simply records gross environmental disequilibrium, for example, the recovery of mudflats/marshes after an erosional episode. Similarly, because on account of their rapid accumulation the banded silts preserve animal and human tracks and trackways especially well, they provide an archive of animal and human behaviour in the area during the Holocene.

Seasonal deposition of Holocene banded sediments in the Severn Estuary Levels (southwest Britain): palynological and sedimentological evidence

Banded sediments outcrop widely in the intertidal zone of the Severn Estuary and have been suggested, on the basis of textural analysis, to have formed in response to seasonal variations in sea temperature and windiness (Holocene, 14 (2004) 536). Here palynological and sedimentological analyses of banded sediments of mid-Holocene date from Gold Cliff, on the Welsh side of the Severn Estuary, are combined to test and further develop the hypothesis of seasonal deposition. Pollen percentage and concentration data are presented from a short sequence of bands to establish whether textural variations in the bands coincide with variations in pollen content reflecting seasonal flowering patterns. It is shown that fine-grained band parts contain higher total pollen concentrations, and a higher proportion of pollen from late spring- to summer-flowering plants, than coarse-grained band parts. Pollen in the coarser deposits appears primarily to reflect deposition from the buffering `reservoir' of suspended pollen in the estuarine water-body and from rivers, when there is little pollen in the air in winter, while the finer sediments contain pollen deposited from the atmosphere during the flowering season, superimposed on these `background' sources. The potential of such deposits for refining chronologies and identifying seasonality of coastal processes is noted, and the results of charcoal particle analysis of the bands presented as an example of how they have the potential to shed light on seasonal and annual patterns of human activity. (C) 2004 Elsevier Ltd. All rights reserved.