982 resultados para 040105 Climatology (excl. Climate Change Processes)


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Agriculture and food security are key sectors for intervention under climate change. Agricultural production is highly vulnerable even to 2C (low-end) predictions for global mean temperatures in 2100, with major implications for rural poverty and for both rural and urban food security. Agriculture also presents untapped opportunities for mitigation, given the large land area under crops and rangeland, and the additional mitigation potential of aquaculture. This paper presents a summary of current knowledge on options to support farmers, particularly smallholder farmers, in achieving food security through agriculture under climate change. Actions towards adaptation fall into two broad overlapping areas: (1) accelerated adaptation to progressive climate change over decadal time scales, for example integrated packages of technology, agronomy and policy options for farmers and food systems, and (2) better management of agricultural risks associated with increasing climate variability and extreme events, for example improved climate information services and safety nets. Maximization of agriculture’s mitigation potential will require investments in technological innovation and agricultural intensification linked to increased efficiency of inputs, and creation of incentives and monitoring systems that are inclusive of smallholder farmers. Food systems faced with climate change need urgent, broad-based action in spite of uncertainties.

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Targets for stabilizing climate change are often based on considerations of the impacts of different levels of global warming, usually assessing the time of reaching a particular level of warming. However, some aspects of the Earth system, such as global mean temperatures1 and sea level rise due to thermal expansion2 or the melting of large ice sheets3, continue to respond long after the stabilization of radiative forcing. Here we use a coupled climate–vegetation model to show that in turn the terrestrial biosphere shows significant inertia in its response to climate change. We demonstrate that the global terrestrial biosphere can continue to change for decades after climate stabilization. We suggest that ecosystems can be committed to long-term change long before any response is observable: for example, we find that the risk of significant loss of forest cover in Amazonia rises rapidly for a global mean temperature rise above 2 °C. We conclude that such committed ecosystem changes must be considered in the definition of dangerous climate change, and subsequent policy development to avoid it.

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We investigate ozone changes from preindustrial times to the present using a chemistry-climate model. The influence of changes in physical climate, ozone-depleting substances, N2O, and tropospheric ozone precursors is estimated using equilibrium simulations with these different factors set at either preindustrial or present-day values. When these effects are combined, the entire decrease in total column ozone from preindustrial to present day is very small (–1.8 DU) in the global annual average, though with significant decreases in total column ozone over large parts of the Southern Hemisphere during austral spring and widespread increases in column ozone over the Northern Hemisphere during boreal summer. A significant contribution to the total ozone column change is the increase in lower stratospheric ozone associated with the increase in ozone precursors (5.9 DU). Also noteworthy is the near cancellation of the global average climate change effect on ozone (3.5 DU) by the increase in N2O (–3.9 DU).

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Increasing cereal yield is needed to meet the projected increased demand for world food supply of about 70% by 2050. Sirius, a process-based model for wheat, was used to estimate yield potential for wheat ideotypes optimized for future climatic projections (HadCM3 global climate model) for ten wheat growing areas of Europe. It was predicted that the detrimental effect of drought stress on yield would be decreased due to enhanced tailoring of phenology to future weather patterns, and due to genetic improvements in the response of photosynthesis and green leaf duration to water shortage. Yield advances could be made through extending maturation and thereby improve resource capture and partitioning. However the model predicted an increase in frequency of heat stress at meiosis and anthesis. Controlled environment experiments quantify the effects of heat and drought at booting and flowering on grain numbers and potential grain size. A current adaptation of wheat to areas of Europe with hotter and drier summers is a quicker maturation which helps to escape from excessive stress, but results in lower yields. To increase yield potential and to respond to climate change, increased tolerance to heat and drought stress should remain priorities for the genetic improvement of wheat.

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An online national survey among the Spanish population (n = 602) was conducted to examine the factors underlying a person’s support for commitments to global climate change reductions. Multiple hierarchical regression analysis was conducted in four steps and a structural equations model was tested. A survey tool designed by the Yale Project on Climate Change Communication was applied in order to build scales for the variables introduced in the study. The results show that perceived consumer effectiveness and risk perception are determinant factors of commitment to mitigating global climate change. However, there are differences in the influence that other factors, such as socio-demographics, view of nature and cultural cognition, have on the last predicted variable.

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Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

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Ninety-four sites worldwide have sufficient resolution and dating to document the impact of millennial-scale climate variability on vegetation and fire regimes during the last glacial period. Although Dansgaard–Oeschger (D–O) cycles all show a basically similar gross structure, they vary in the magnitude and the length of the warm and cool intervals. We illustrate the geographic patterns in the climate-induced changes in vegetation by comparing D–O 6, D–O 8 and D–O 19. There is a strong response to both D–O warming events and subsequent cooling, most marked in the northern extratropics. Pollen records from marine cores from the northern extratropics confirm that there is no lag between the change in climate and the vegetation response, within the limits of the dating resolution (50–100 years). However, the magnitude of the change in vegetation is regionally specific and is not a simple function of either the magnitude or the duration of the change in climate as registered in Greenland ice cores. Fire regimes also show an initial immediate response to climate changes, but during cooling intervals there is a slow recovery of biomass burning after the initial reduction, suggesting a secondary control through the recovery of vegetation productivity. In the extratropics, vegetation changes are largely determined by winter temperatures while in the tropics they are largely determined by changes in plant-available water. Tropical vegetation records show changes corresponding to Heinrich Stadials but the response to D–O warming events is less marked than in the northern extratropics. There are very few high-resolution records from the Southern Hemisphere extratropics, but these records also show both a vegetation and fire response to millennial-scale climate variability. It is not yet possible to determine unequivocally whether terrestrial records reflect the asynchroneity apparent in the ice-core records.

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This paper compares the effects of two indicative climate mitigation policies on river flows in six catchments in the UK with two scenarios representing un-mitigated emissions. It considers the consequences of uncertainty in both the pattern of catchment climate change as represented by different climate models and hydrological model parameterisation on the effects of mitigation policy. Mitigation policy has little effect on estimated flow magnitudes in 2030. By 2050 a mitigation policy which achieves a 2oC temperature rise target reduces impacts on low flows by 20-25% compared to a business-as-usual emissions scenario which increases temperatures by 4oC by the end of the 21st century, but this is small compared to the range in impacts between different climate model scenarios. However, the analysis also demonstrates that an early peak in emissions would reduce impacts by 40-60% by 2080 (compared with the 4oC pathway), easing the adaptation challenge over the long term, and can delay by several decades the impacts that would be experienced from around 2050 in the absence of policy. The estimated proportion of impacts avoided varies between climate model patterns and, to a lesser extent, hydrological model parameterisations, due to variations in the projected shape of the relationship between climate forcing and hydrological response.

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A holistic perspective on changing rainfall-driven flood risk is provided for the late 20th and early 21st centuries. Economic losses from floods have greatly increased, principally driven by the expanding exposure of assets at risk. It has not been possible to attribute rain-generated peak streamflow trends to anthropogenic climate change over the past several decades. Projected increases in the frequency and intensity of heavy rainfall, based on climate models, should contribute to increases in precipitation-generated local flooding (e.g. flash flooding and urban flooding). This article assesses the literature included in the IPCC SREX report and new literature published since, and includes an assessment of changes in flood risk in seven of the regions considered in the recent IPCC SREX report—Africa, Asia, Central and South America, Europe, North America, Oceania and Polar regions. Also considering newer publications, this article is consistent with the recent IPCC SREX assessment finding that the impacts of climate change on flood characteristics are highly sensitive to the detailed nature of those changes and that presently we have only low confidence1 in numerical projections of changes in flood magnitude or frequency resulting from climate change.

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We present a Bayesian image classification scheme for discriminating cloud, clear and sea-ice observations at high latitudes to improve identification of areas of clear-sky over ice-free ocean for SST retrieval. We validate the image classification against a manually classified dataset using Advanced Along Track Scanning Radiometer (AATSR) data. A three way classification scheme using a near-infrared textural feature improves classifier accuracy by 9.9 % over the nadir only version of the cloud clearing used in the ATSR Reprocessing for Climate (ARC) project in high latitude regions. The three way classification gives similar numbers of cloud and ice scenes misclassified as clear but significantly more clear-sky cases are correctly identified (89.9 % compared with 65 % for ARC). We also demonstrate the poetential of a Bayesian image classifier including information from the 0.6 micron channel to be used in sea-ice extent and ice surface temperature retrieval with 77.7 % of ice scenes correctly identified and an overall classifier accuracy of 96 %.