Algunas características de los programas antárticos de India y China (Tema Central)

This paper states that even though the Antarctic Treaty is a remarkable instrument for peaceful collaboration and scientific research, it is, basically, a by-product of the Cold War that reaffirms a particular status quo. This paper explores whether the ATS will meet the needs of an emerging world order. Particularly, the paper evaluates the ATS in the face of new global challenges, both internal and external to the Antarctic Treaty System (ATS) itself, as well as power shifting processes on a world scale, climate change and the changing interests of new states. China is a key component in understanding this issue because its Antarctic engagement is entering a new phase one that challenges current multilateral provisions. China’s Antarctic program will be analyzed within the framework of International Relations approaches and theories.

Modeling the mechanisms that control in-stream dissolved organic carbon dynamics in upland and forested catchments

[1] We present a new, process-based model of soil and stream water dissolved organic carbon (DOC): the Integrated Catchments Model for Carbon (INCA-C). INCA-C is the first model of DOC cycling to explicitly include effects of different land cover types, hydrological flow paths, in-soil carbon biogeochemistry, and surface water processes on in-stream DOC concentrations. It can be calibrated using only routinely available monitoring data. INCA-C simulates daily DOC concentrations over a period of years to decades. Sources, sinks, and transformation of solid and dissolved organic carbon in peat and forest soils, wetlands, and streams as well as organic carbon mineralization in stream waters are modeled. INCA-C is designed to be applied to natural and seminatural forested and peat-dominated catchments in boreal and temperate regions. Simulations at two forested catchments showed that seasonal and interannual patterns of DOC concentration could be modeled using climate-related parameters alone. A sensitivity analysis showed that model predictions were dependent on the mass of organic carbon in the soil and that in-soil process rates were dependent on soil moisture status. Sensitive rate coefficients in the model included those for organic carbon sorption and desorption and DOC mineralization in the soil. The model was also sensitive to the amount of litter fall. Our results show the importance of climate variability in controlling surface water DOC concentrations and suggest the need for further research on the mechanisms controlling production and consumption of DOC in soils.

Atmospheric processes and observations

The central role of the atmosphere in abrupt climate change is proposed and discussed. This discussion is given in the context of the poleward transport of energy in the climate system and of climate variability and change. A number of examples based on observational and model data are used to illustrate the ideas.

Multidecadal modulation of El Nino-Southern Oscillation (ENSO) variance by Atlantic Ocean sea surface temperatures

Observations suggest a possible link between the Atlantic Multidecadal Oscillation (AMO) and El Nino Southern Oscillation (ENSO) variability, with the warm AMO phase being related to weaker ENSO variability. A coupled ocean-atmosphere model is used to investigate this relationship and to elucidate mechanisms responsible for it. Anomalous sea surface temperatures (SSTs) associated with the positive AMO lead to change in the basic state in the tropical Pacific Ocean. This basic state change is associated with a deepened thermocline and reduced vertical stratification of the equatorial Pacific ocean, which in turn leads to weakened ENSO variability. We suggest a role for an atmospheric bridge that rapidly conveys the influence of the Atlantic Ocean to the tropical Pacific. The results suggest a non-local mechanism for changes in ENSO statistics and imply that anomalous Atlantic ocean SSTs can modulate both mean climate and climate variability over the Pacific.

A new perspective on warming of the global oceans

Changes in ocean circulation associated with internal climate variability have a major influence on upper ocean temperatures, particularly in regions such as the North Atlantic, which are relatively well-observed and therefore over-represented in the observational record. As a result, global estimates of upper ocean heat content can give misleading estimates of the roles of natural and anthropogenic factors in causing oceanic warming. We present a method to quantify ocean warming that filters out the natural internal variability from both observations and climate simulations and better isolates externally forced air-sea heat flux changes. We obtain a much clearer picture of the drivers of oceanic temperature changes, being able to detect the effects of both anthropogenic and volcanic influences simultaneously in the observed record. Our results show that climate models are capable of capturing in remarkable detail the externally forced component of ocean temperature evolution over the last five decades.

Impact of the Atlantic Ocean on the multidecadal fluctuation of El Nino–Southern Oscillation–South Asian monsoon relationship in a coupled general circulation model

The multidecadal variability of El Niño–Southern Oscillation (ENSO)–South Asian monsoon relationship is elucidated in a 1000 year control simulation of a coupled general circulation model. The results indicate that the Atlantic Multidecadal Oscillation (AMO), resulting from the natural fluctuation of the Atlantic Meridional Overturning Circulation (AMOC), plays an important role in modulating the multidecadal variation of the ENSO-monsoon relationship. The sea surface temperature anomalies associated with the AMO induce not only significant climate impact in the Atlantic but also the coupled feedbacks in the tropical Pacific regions. The remote responses in the Pacific Ocean to a positive phase of the AMO which is resulted from enhanced AMOC in the model simulation and are characterized by statistically significant warming in the North Pacific and in the western tropical Pacific, a relaxation of tropical easterly trades in the central and eastern tropical Pacific, and a deeper thermocline in the eastern tropical Pacific. These changes in mean states lead to a reduction of ENSO variability and therefore a weakening of the ENSO-monsoon relationship. This study suggests a nonlocal mechanism for the low-frequency fluctuation of the ENSO-monsoon relationship, although the AMO explains only a fraction of the ENSO–South Asian monsoon variation on decadal-multidecadal timescale. Given the multidecadal variation of the AMOC and therefore of the AMO exhibit decadal predictability, this study highlights the possibility that a part of the change of climate variability in the Pacific Ocean and its teleconnection may be predictable.

Design and optimisation of a large-area process-based model for annual crops

The formulation of a new process-based crop model, the general large-area model (GLAM) for annual crops is presented. The model has been designed to operate on spatial scales commensurate with those of global and regional climate models. It aims to simulate the impact of climate on crop yield. Procedures for model parameter determination and optimisation are described, and demonstrated for the prediction of groundnut (i.e. peanut; Arachis hypogaea L.) yields across India for the period 1966-1989. Optimal parameters (e.g. extinction coefficient, transpiration efficiency, rate of change of harvest index) were stable over space and time, provided the estimate of the yield technology trend was based on the full 24-year period. The model has two location-specific parameters, the planting date, and the yield gap parameter. The latter varies spatially and is determined by calibration. The optimal value varies slightly when different input data are used. The model was tested using a historical data set on a 2.5degrees x 2.5degrees grid to simulate yields. Three sites are examined in detail-grid cells from Gujarat in the west, Andhra Pradesh towards the south, and Uttar Pradesh in the north. Agreement between observed and modelled yield was variable, with correlation coefficients of 0.74, 0.42 and 0, respectively. Skill was highest where the climate signal was greatest, and correlations were comparable to or greater than correlations with seasonal mean rainfall. Yields from all 35 cells were aggregated to simulate all-India yield. The correlation coefficient between observed and simulated yields was 0.76, and the root mean square error was 8.4% of the mean yield. The model can be easily extended to any annual crop for the investigation of the impacts of climate variability (or change) on crop yield over large areas. (C) 2004 Elsevier B.V. All rights reserved.

Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather-yield relationships

Reanalysis data provide an excellent test bed for impacts prediction systems. because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM. when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields "were simulated well across much of India. Correlations between observed and modeled yields, where these are significant. are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales. and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Phenotyping parents of mapping populations of rice for heat tolerance during anthesis

Seed set of rice (Oryza sativa L.) is highly sensitive to short episodes of high temperature at anthesis events that are likely to be more frequent in future climates. Breeding for tolerance is therefore an essential component of adaptation to climate variability and change. Experiments were conducted in 2003 and 2004 at optimum (30 degrees C daytime) and high (35 and 38 degrees C) air temperature using parents of some prominent mapping populations (i) to determine whether there were differences in the daily flowering pattern and hence a potential heat avoidance mechanism, and (ii) to identify rice genotypes having true heat tolerance during anthesis, that is, high seed set in spikelets exposed to high temperature. Rice cultivar CG14 (O. glaberrima) reached peak anthesis earlier in the morning (1.5 h after dawn) under both control (30 degrees C) and high (38 degrees C) temperature conditions than O. sativa genotypes (>= 3 h after dawn). Exposure to high temperature (centered on the time of peak anthesis) for 6 h reduced spikelet fertility more than exposure for 2 h, and fertility was lower at 38 degrees C than at 35 degrees C. Genotypic ranking for spikelet fertility at 35 and 38 degrees C was highly correlated in both 2003 and 2004. Fertility was also highly correlated across years, suggesting a consistent and reproducible response of spikelet fertility to temperature. The check cultivar N22 was the most heat tolerant genotype (64-86% fertility at 38 degrees C) and cultivars Azucena and Moroberekan the most susceptible (<8%).

Climatic and oceanic associations with daily rainfall extremes over southern Africa

Changes in climate variability and, in particular, changes in extreme climate events are likely to be of far more significance for environmentally vulnerable regions than changes in the mean state. It is generally accepted that sea-surface temperatures (SSTs) play an important role in modulating rainfall variability. Consequently, SSTs can be prescribed in global and regional climate modelling in order to study the physical mechanisms behind rainfall and its extremes. Using a satellite-based daily rainfall historical data set, this paper describes the main patterns of rainfall variability over southern Africa, identifies the dates when extreme rainfall occurs within these patterns, and shows the effect of resolution in trying to identify the location and intensity of SST anomalies associated with these extremes in the Atlantic and southwest Indian Ocean. Derived from a Principal Component Analysis (PCA), the results also suggest that, for the spatial pattern accounting for the highest amount of variability, extremes extracted at a higher spatial resolution do give a clearer indication regarding the location and intensity of anomalous SST regions. As the amount of variability explained by each spatial pattern defined by the PCA decreases, it would appear that extremes extracted at a lower resolution give a clearer indication of anomalous SST regions.

Can REDD+ reconcile local priorities and needs with global mitigation benefits? lessons from Angai Forest, Tanzania

The scope of the reducing emissions from deforestation and forest degradation (REDD) mechanism has broadened REDD+ to accommodate different country interests such as natural forests, protected areas, as well as forests under community-based management. In Tanzania the REDD+ mechanism is still under development and pilot projects are at an early stage. In this paper, we seek to understand how local priorities and needs could be met in REDD+ implementation and how these expectations match with global mitigation benefits. We examine the local priorities and needs in the use of land and forest resources in the Angai Villages Land Forest Reserve (AVLFR) in the Liwale District of Lindi Region in Tanzania. Primary data was collected in two villages, Mihumo and Lilombe, using semistructured key informant interviews and participatory rural appraisal methods. In addition, the key informant interviews were conducted with other village, district, and national level actors, as well as international donors. Findings show that in the two communities REDD+ is seen as something new and is generating new expectations among communities. However, the Angai villagers highlight three key priorities that have yet to be integrated into the design of REDD+: water scarcity, rural development, and food security. At the local level improved forest governance and sustainable management of forest resources have been identified as one way to achieve livelihood diversification. Although the national goals of REDD+ include poverty reduction, these goals are not necessarily conducive to the goals of these communities. There exist both structural and cultural limits to the ability of the Angai villages to implement these goals and to improve forestry governance. Given the vulnerability to current and future climate variability and change it will be important to consider how the AVLFR will be managed and for whose benefit?

Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe

New crop cultivars will be required for a changing climate characterised by increased summer drought and heat stress in Europe. However, the uncertainty in climate predictions poses a challenge to crop scientists and breeders who have limited time and resources and must select the most appropriate traits for improvement. Modelling is a powerful tool to quantify future threats to crops and hence identify targets for improvement. We have used a wheat simulation model combined with local-scale climate scenarios to predict impacts of heat stress and drought on winter wheat in Europe. Despite the lower summer precipitation projected for 2050s across Europe, relative yield losses from drought is predicted to be smaller in the future, because wheat will mature earlier avoiding severe drought. By contrast, the risk of heat stress around flowering will increase, potentially resulting in substantial yield losses for heat sensitive cultivars commonly grown in northern Europe.

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near-term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.

The atmospheric impact of uncertainties in recent Arctic Sea ice reconstructions

There are significant discrepancies between observational datasets of Arctic sea ice concentrations covering the last three decades, which result in differences of over 20% in Arctic summer sea ice extent/area and 5%–10% in winter. Previous modeling studies have shown that idealized sea ice anomalies have the potential for making a substantial impact on climate. In this paper, this theory is further developed by performing a set of simulations using the third Hadley Centre Coupled Atmospheric Model (HadAM3). The model was driven with monthly climatologies of sea ice fractions derived from three of these records to investigate potential implications of sea ice inaccuracies for climate simulations. The standard sea ice climatology from the Met Office provided a control. This study focuses on the effects of actual inaccuracies of concentration retrievals, which vary spatially and are larger in summer than winter. The smaller sea ice discrepancies in winter have a much larger influence on climate than the much greater summer sea ice differences. High sensitivity to sea ice prescription was observed, even though no SST feedbacks were included. Significant effects on surface fields were observed in the Arctic, North Atlantic, and North Pacific. Arctic average surface air temperature anomalies in winter vary by 2.5°C, and locally exceed 12°C. Arctic mean sea level pressure varies by up to 5 mb locally. Anomalies extend to 45°N over North America and Eurasia but not to lower latitudes, and with limited changes in circulation above the boundary layer. No statistically significant impact on climate variability was simulated, in terms of the North Atlantic Oscillation. Results suggest that the uncertainty in summer sea ice prescription is not critical but that winter values require greater accuracy, with the caveats that the influences of ocean–sea ice feedbacks were not included in this study.