scholarly journals Simulation Skills of the SST-Forced Global Climate Variability of the NCEP–MRF9 and the Scripps–MPI ECHAM3 Models

2000 ◽  
Vol 13 (20) ◽  
pp. 3657-3679 ◽  
Author(s):  
Peitao Peng ◽  
Arun Kumar ◽  
Anthony G. Barnston ◽  
Lisa Goddard
2021 ◽  
Vol 13 (11) ◽  
pp. 6495
Author(s):  
Yayan Apriyana ◽  
Elza Surmaini ◽  
Woro Estiningtyas ◽  
Aris Pramudia ◽  
Fadhlullah Ramadhani ◽  
...  

Climate change and its variability are some of the most critical threats to sustainable agriculture, with potentially severe consequences on Indonesia’s agriculture, such as changes in rainfall patterns, especially the onset of the wet season and the temporal distribution of rainfall. Most Indonesian farmers receive support from agricultural extension services, and therefore, design their agricultural calendar based on personal experience without considering global climate phenomena, such as La Niña and El Niño, which difficult to interpret on a local scale. This paper describes the Integrated Cropping Calendar Information System (ICCIS) as a mechanism for adapting to climate variability. The ICCIS contains recommendations on planting time, cropping pattern, planting area, varieties, fertilizers, agricultural machinery, potential livestock feed, and crop damage due to climate extremes for rice, maize, and soybean. To accelerate the dissemination of information, the ICCIS is presented in an integrated web-based information system. The ICCIS is disseminated to extension workers and farmers by Task Force of the Assessment Institute for Agricultural Technology (AIAT) located in each province. Based on the survey results, it is known that the ICCIS adoption rate is moderate to high. The AIAT must actively encourage and support the ICCIS Task Force team in each province. Concerning the technological recommendations, it is necessary to update the recommendations for varieties, fertilizer, and feed to be more compatible with local conditions. More accurate information and more intensive dissemination can enrich farmers’ knowledge, allowing for a better understanding of climate hazards and maintaining agricultural production.


Author(s):  
Jiban Mani Poudel

In the 21st century, global climate change has become a public and political discourse. However, there is still a wide gap between global and local perspectives. The global perspective focuses on climate fluctuations that affect the larger region; and their analysis is based on long-term records over centuries and millennium. By comparison, local peoples’ perspectives vary locally, and local analyses are limited to a few days, years, decades and generations only. This paper examines how farmers in Kirtipur of Kathmandu Valley, Nepal, understand climate variability in their surroundings. The researcher has used a cognized model to understand farmers’ perception on weather fluctuations and climate change. The researcher has documented several eyewitness accounts of farmers about weather fluctuations which they have been observing in a lifetime. The researcher has also used rainfall data from 1970-2009 to test the accuracy of perceptions. Unlike meteorological analyses, farmers recall and their understanding of climatic variability by weather-crop interaction, and events associating with climatic fluctuations and perceptions are shaped by both physical visibility and cultural frame or belief system.DOI: http://dx.doi.org/10.3126/hn.v11i1.7200 Hydro Nepal Special Issue: Conference Proceedings 2012 pp.30-34


Author(s):  
Cynthia Rosenzweig ◽  
Daniel Hillel

The climate system envelops our planet, with swirling fluxes of mass, momentum, and energy through air, water, and land. Its processes are partly regular and partly chaotic. The regularity of diurnal and seasonal fluctuations in these processes is well understood. Recently, there has been significant progress in understanding some of the mechanisms that induce deviations from that regularity in many parts of the globe. These mechanisms include a set of combined oceanic–atmospheric phenomena with quasi-regular manifestations. The largest of these is centered in the Pacific Ocean and is known as the El Niño–Southern Oscillation. The term “oscillation” refers to a shifting pattern of atmospheric pressure gradients that has distinct manifestations in its alternating phases. In the Arctic and North Atlantic regions, the occurrence of somewhat analogous but less regular interactions known as the Arctic Oscillation and its offshoot, the North Atlantic Oscillation, are also being studied. These and other major oscillations influence climate patterns in many parts of the globe. Examples of other large-scale interactive ocean–atmosphere– land processes are the Pacific Decadal Oscillation, the Madden-Julian Oscillation, the Pacific/North American pattern, the Tropical Atlantic Variability, the West Pacific pattern, the Quasi-Biennial Oscillation, and the Indian Ocean Dipole. In this chapter we review the earth’s climate system in general, define climate variability, and describe the processes related to ENSO and the other major systems and their interactions. We then consider the possible connections of the major climate variability systems to anthropogenic global climate change. The climate system consists of a series of fluxes and transformations of energy (radiation, sensible and latent heat, and momentum), as well as transports and changes in the state of matter (air, water, solid matter, and biota) as conveyed and influenced by the atmosphere, the ocean, and the land masses. Acting like a giant engine, this dynamic system is driven by the infusion, transformation, and redistribution of energy.


2020 ◽  
Vol 24 (6) ◽  
pp. 3251-3269 ◽  
Author(s):  
Chao Gao ◽  
Martijn J. Booij ◽  
Yue-Ping Xu

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.


2000 ◽  
Vol 22 (4) ◽  
pp. 6-10 ◽  
Author(s):  
Donald Nelson ◽  
Timothy Finan

Climate studies have traditionally fallen within the purview of the natural sciences where cause and predictable pattern are sought for such phenomena as climate change and climate variability. In the past, social scientists had little occasion to cross disciplinary paths with atmospheric or oceanographic scientists. Not that social science has ignored climate, for anthropology and geography claim a rich literature on the impacts of climate variability, particularly drought, on human populations (e.g., Franke and Chasin 1980; Watts 1983; Langworthy and Finan 1997). New theoretical ground, fertilized by an increasing number of empirical studies, now promises to bear the fruit we call climate anthropology. The expanding social science agenda has responded to two relatively recent advances in the natural sciences. The first has been the widening scientific consensus regarding global climate change and its anthropogenic causes. Global change cannot be adequately characterized without understanding the human-environment interactions that have contributed to the phenomenon, forcing social and natural scientists to pursue common research objectives. The second influence on climate anthropology has been the improvement in scientific understanding of oceanic/atmospheric interactions, thus allowing for more refined predictability of climatic events, particularly extreme ones. It is with this advance in climate predictability that climate anthropology is beginning to reap an exceedingly bountiful harvest in both theory and application.


2017 ◽  
Vol 10 (2) ◽  
pp. 344-359 ◽  
Author(s):  
Jie Yang ◽  
Jianxia Chang ◽  
Jun Yao ◽  
Yimin Wang ◽  
Qiang Huang ◽  
...  

Abstract Studying the impact of climate variability is important for the rational utilization of water resources, especially in the case of intensified global climate variability. Climate variability can be caused by natural climate variability or human-caused climate variability. The analysis of Jinghe River Basin (JRB) may not be comprehensive because few studies have concentrated on natural climate variability. Therefore, the primary goal is to explore the impact of natural climate variability on runoff. A modified Mann–Kendall test method was adopted to analyze the aberrance point to determine the natural condition period during which runoff was only influenced by natural climate variability. Then, the Monte Carlo method was employed to extract segments of monthly runoff in the natural condition period and combine them to construct a long series to reduce the instability. Results indicate that the percentage of runoff variability affected by natural climate variability is 30.52% at a confidence level of 95%. Next, a topography-based hydrological model and climate elasticity method were used to simulate runoff after the aberrance point without considering the impact caused by local interference. Through a comparison of the measured and simulated runoff, we discovered that local interference has the greatest impact on runoff in the JRB.


2002 ◽  
Vol 15 (4) ◽  
pp. 757-770 ◽  
Author(s):  
Erin K. Lipp ◽  
Anwar Huq ◽  
Rita R. Colwell

SUMMARY Recently, the role of the environment and climate in disease dynamics has become a subject of increasing interest to microbiologists, clinicians, epidemiologists, and ecologists. Much of the interest has been stimulated by the growing problems of antibiotic resistance among pathogens, emergence and/or reemergence of infectious diseases worldwide, the potential of bioterrorism, and the debate concerning climate change. Cholera, caused by Vibrio cholerae, lends itself to analyses of the role of climate in infectious disease, coupled to population dynamics of pathogenic microorganisms, for several reasons. First, the disease has a historical context linking it to specific seasons and biogeographical zones. In addition, the population dynamics of V. cholerae in the environment are strongly controlled by environmental factors, such as water temperature, salinity, and the presence of copepods, which are, in turn, controlled by larger-scale climate variability. In this review, the association between plankton and V. cholerae that has been documented over the last 20 years is discussed in support of the hypothesis that cholera shares properties of a vector-borne disease. In addition, a model for environmental transmission of cholera to humans in the context of climate variability is presented. The cholera model provides a template for future research on climate-sensitive diseases, allowing definition of critical parameters and offering a means of developing more sophisticated methods for prediction of disease outbreaks.


2020 ◽  
Author(s):  
Raphael Hébert ◽  
Ulrike herzschuh ◽  
Thomas Laepple

<p>Multidecadal to millenial timescale climate variability has been investigated over the ocean</p><p>using extensive proxy data and it was found to yield coherent interproxy estimates of global and regional sea-surface temperature (SST) climate variability (Laepple and Huybers, 2014). Global Climate Model (GCM) simulations on the other hand, were found to exhibit an increasingly large deficit of regional SST climate variability for increasingly longer timescales.</p><p>Further investigation is needed to better quantify terrestrial climate variability for long</p><p>timescales and validate climate models.</p><p>Vegetation related proxies such as tree rings and pollen records are the most widespread</p><p>types of archives available to investigate terrestrial climate variability. Tree ring records are</p><p>particularly useful for short time scales estimates due to their annual resolution, while pollen-based reconstructions are necessary to cover the longer timescales. In the present work, we use a large database of 1873 pollen records covering the northern hemisphere in order to quantify Holocene vegetation and climate variability for the first time at centennial to multi-millenial timescales.</p><p>To ensure the robustness of our results, we are particularly interested in the spatio-temporal representativity of the archived signal in pollen records after taking into account the effective spatial scale, the intermittent and irregular sampling, the age-uncertainty and the sediment mixing effect. A careful treatment of the proxy formation allows us to investigate the spatial correlation structure of the pollen-based climate reconstructions as a function of timescales. The pollen data results are then contrasted with the analysis replicated using transient Holocene simulations produced with state-of-the-art climate models as well as stochastic climate model simulations.Our results indicate a substantial gap in terrestrial climate variability between the climate model simulations and the pollen reconstructions at centennial to multi-millenial timescales, mirroring the variability gap found in the marine domain. Finally, we investigate how future climate model projections with greater internal variability would be affected, and how this increases the uncertainty of regional land temperature projections.</p>


2011 ◽  
Vol 38 (3) ◽  
pp. 252-262 ◽  
Author(s):  
Beatrice B. Yung ◽  
Bryan A. Tolson ◽  
Donald H. Burn

A model is developed to assess risk to a municipal water supply system under the influence of population growth and climate variability. To incorporate the uncertainty in water use, a model that combines time series Monte Carlo simulations and a deterministic artificial neural network (ANN) is developed to simulate the daily water demand under climate variation. The model is first applied to assess how climate change alters the risk of a current water supply system and is then used to estimate the effects of demand management programs and system expansion. The model quantifies water supply system risk in terms of reliability, resiliency, and vulnerability (RRV). The model evaluates 11 scenarios defined by combining various population growth forecasts, demand management programs, system expansions, and global climate model (GCM) scenarios. The simulation results suggest that a rise in temperature and a change in precipitation magnitude will negatively impact the performance of the case study system.


2014 ◽  
Vol 456 (2) ◽  
pp. 745-748 ◽  
Author(s):  
N. V. Vakulenko ◽  
V. M. Kotlyakov ◽  
D. M. Sonechkin

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