Climatic Change

This study demonstrates that IPCC Third Assessment Report is strongly dominated by Natural sciences, especially the Earth sciences. The Social sciences are dominated by Economics. The IPCC assessment also results in the separation of the Earth, Biological and Social sciences. The integration that occurs is mainly between closely related scientific fields. The research community consequently imposes a physical and economic bias and a separation of scientific fields that the IPCC reproduces in the policy sphere. It is argued that this physical and economic bias distorts a comprehensive understanding of climate change and that the weak integration of scientific fields hinders climate change from being fully addressed as an integral environmental and social problem. If climate change is to be understood, evaluated and responded to in its fullness, the IPCC must broaden its knowledge base and challenge the anthropocentric worldview that places humans outside of nature.

Explaining the recent slowdown in the rise of global mean surface temperature (the hiatus in warming) has become a major focus of climate research. Efforts to identify the causes of the hiatus that compare simulations from experiments run by climate models raise several statistical issues. Specifically, it is necessary to identify whether an experiment’s inability to simulate the hiatus is unique to this period or reflects a more systematic failure throughout the sample period. Furthermore, efforts to attribute the hiatus to a particular factor by including that mechanism in an experimental treatment must improve the model’s performance in a statistically significant manner at the time of the hiatus. Sample-wide assessments of simulation errors can provide an accurate assessment of whether or not the control experiment uniquely fails at the hiatus, and can identify its causes using experimental treatments. We use this approach to determine if the hiatus constitutes a unique failure in simulated climate models and to re-examine the conclusion that the hiatus is uniquely linked to episodes of La Niña-like cooling (Kosaka and Xie 2013). Using statistical techniques that do not define the hiatus a priori, we find no evidence that the slowdown in temperature increases are uniquely tied to episodes of La Niña-like cooling.

Chúng tôi đã khảo sát các xu hướng về nhiệt độ lạnh tích lũy trong vùng trồng trái cây ở trung tâm California và các thung lũng ven biển nội địa của nó. Chúng tôi đã thử nghiệm giả thuyết rằng hiện tượng nóng lên toàn cầu đang diễn ra ở California và đang gây ra sự giảm nhiệt độ lạnh tích lũy trong các vùng trồng cây ăn quả và hạt của bang này. Việc phát hiện các xu hướng tiềm tàng trong nhiệt độ lạnh tích lũy (từ 0 đến 7.2°C) được xác định bằng cách sử dụng hai bộ dữ liệu khí hậu bổ sung cho nhau. Hệ thống Thông tin Quản lý Tưới tiêu California (CIMIS) chứa dữ liệu khí hậu theo giờ và phù hợp để tính toán giờ lạnh tích lũy và độ lạnh tính theo giờ. Tuy nhiên, các bản ghi dữ liệu dài nhất của nó chỉ kéo dài đến những năm 1980. Bản ghi khí hậu của Dịch vụ Thời tiết Quốc gia Coop dài hơn, kéo dài ra ngoài những năm 1950 tại nhiều địa điểm. Nhưng bộ dữ liệu của nó chỉ chứa thông tin về nhiệt độ tối đa và tối thiểu hàng ngày. Để đánh giá các xu hướng dài hạn trong việc tích lũy nhiệt độ lạnh mùa đông, chúng tôi đã phát triển một thuật toán mà chuyển đổi thông tin từ nhiệt độ tối đa và tối thiểu hàng ngày thành số giờ lạnh tích lũy và tổng hợp các giờ độ lạnh. Những tính toán suy diễn này về việc tích lũy giờ lạnh đã được thử nghiệm và xác nhận bởi các phép đo trực tiếp từ dữ liệu tính theo giờ của mạng lưới CIMIS. Với các bộ dữ liệu khí hậu kết hợp, chúng tôi nhận thấy rằng việc tích lũy các giờ lạnh mùa đông và giờ độ lạnh đang giảm dần trong các vùng trồng trái cây và hạt của California. Các xu hướng quan sát được trong nhiệt độ lạnh dao động từ -50 đến -260 giờ lạnh mỗi thập kỷ. Chúng tôi cũng đã áp dụng thuật toán phân tích của mình để dự đoán thay đổi nhiệt độ lạnh mùa đông bằng cách sử dụng các dự đoán khí hậu khu vực về nhiệt độ cho ba vùng ở Thung lũng Trung tâm. Tỷ lệ giảm nhiệt độ lạnh dự đoán, cho giai đoạn từ 1950 đến 2100, khoảng -40 giờ mỗi thập kỷ. Đến cuối thế kỷ 21, các vườn cây ăn trái ở California dự kiến sẽ trải qua ít hơn 500 giờ lạnh mỗi mùa đông. Sự giảm liên tục và ổn định trong nhiệt độ lạnh mùa đông này được kỳ vọng sẽ có ảnh hưởng kinh tế và ẩm thực bất lợi đến sản xuất trái cây và hạt ở California vào cuối thế kỷ 21.

Spatial models of present-day mountain permafrost probability were perturbed to examine potential climate change impacts. Mean annual air temperature (MAAT) changes were simulated by adjusting elevation in the models, and cloud cover changes were examined by altering the partitioning of direct beam and diffuse radiation within the calculation for potential incoming solar radiation (PISR). The effects of changes in MAAT on equilibrium permafrost distribution proved to be more important than those due to cloud cover. Under a −2 K scenario (approximating Little Ice Age conditions), permafrost expanded into an additional 22–43% of the study areas as zonal boundaries descended by 155–290 m K − 1. Under warming scenarios, permafrost probabilities progressively declined and zonal boundaries rose in elevation. A MAAT change of +5 K, caused two of the areas to become essentially permafrost-free. The absolute values of these predictions were affected up to ±10% when lapse rates were altered by ±1.5 K km − 1 but patterns and trends were maintained. A higher proportion of diffuse radiation (greater cloud cover) produced increases in permafrost extent of only 2–4% while decreases in the diffuse radiation fraction had an equal but opposite effect. Notwithstanding the small change in overall extent, permafrost probabilities on steep south-facing slopes were significantly impacted by the altered partitioning. Combined temperature and PISR partitioning scenarios produced essentially additive results, but the impact of changes in the latter declined as MAAT increased. The modelling illustrated that mountain permafrost in the discontinuous zone is sensitive spatially to long-term climate change and identified those areas where changes may already be underway following recent atmospheric warming.

The objective of the present study was to develop a simulation model of the growth of sweet orange (Citrus sinensis L. Osbeck) cv. Natal in response to climate change based on system dynamics principles. The model was developed based on a system analysis of the factors that affect crop biomass formation. The main variables considered were atmospheric carbon dioxide (CO2), air temperature, transpiration, rainfall, water deficit, irrigation depth, canopy volume, and the respective interrelationships. Simulations were performed for the period from 2010 to 2100. Overall, the model results indicate that the increase in atmospheric CO2 concentrations predicted in the Intergovernmental Panel on Climate Change (IPCC) report, combined with air temperatures higher, lower, or equal to those generally occurring in natural environments, will result in higher water use efficiency by orange trees. When other factors, such as the soil water deficit, were included in the model, the water productivity was predicted to be lower in 2100 without irrigation than when irrigation was included. It is concluded that the model is suitable for determination of the effects of climate change on water use efficiency of sweet orange cv. Natal. Increased atmospheric CO2 concentrations will result in higher CO2 assimilation in orange trees and therefore in increased biomass production (g) per unit of water transpired (mm). However, this positive effect may be masked by other effects of atmospheric CO2 increases, mainly those associated with temperature.

Pattern scaling is a computationally efficient method to generate global projections of future climate changes, such as temperature and precipitation, under various emission scenarios. In this study, we apply the pattern-scaling method to project future changes of potential evapotranspiration (PET), a metric highly relevant to hydroclimate research. While doing so, this study tests the basic assumption of pattern-scaling methods, which is that the underlying scaling pattern is largely identical across all emission scenarios. We use a pair of the large-ensemble global climate model (GCM) simulations and obtain the two separate scaling patterns, one due to greenhouse gasses (GHGs) and the other due to aerosols, which show substantial regional differences. We also derive a single combined pattern, encapsulating the effects of both forcings. Using an energy balance climate model, future changes in temperature, precipitation, and PET are projected by combining the separate GHGs and aerosols scaling patterns (“hybrid-pattern” approach) and the performance of this “hybrid-pattern” approach is compared to the conventional approach (“single-pattern”) by evaluating both approaches against the GCM direct output. We find that both approaches provide reasonably good emulations for the long-term projection (end of the twenty-first century). However, the “hybrid-pattern” approach provides better emulations for the near-term climate changes (2020–2040) when the large changes in aerosol emissions occur.

Recent results from an enhanced greenhouse-gas scenario over Europe suggest that climate change might not only imply a general mean warming at the surface, but also a pronounced increase in interannual surface temperature variability during the summer season (Schär et al., Nature 427:332–336, 2004). It has been proposed that the underlying physical mechanism is related to land surface-atmosphere interactions. In this study we expand the previous analysis by including results from a heterogeneous ensemble of 11 high-resolution climate models from the PRUDENCE project. All simulations considered comprise 30-year control and enhanced greenhouse-gas scenario periods. While there is considerable spread in the models’ ability to represent the observed summer variability, all models show some increase in variability for the scenario period, confirming the main result of the previous study. Averaged over a large-scale Central European domain, the models simulate an increase in the standard deviation of summer mean temperatures between 20 and 80%. The amplification occurs predominantly over land points and is particularly pronounced for surface temperature, but also evident for precipitation. It is also found that the simulated changes in Central European summer conditions are characterized by an emergence of dry and warm years, with early and intensified depletion of root-zone soil moisture. There is thus some evidence that the change in variability may be linked to the dynamics of soil-moisture storage and the associated feedbacks on the surface energy balance and precipitation.