We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems ...(7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models' MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial-temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80-90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model's slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.
Given observed initial conditions, how well do coupled atmosphere-ocean models predict precipitation climatology with 1-month lead forecast? And how do the models' biases in climatology in turn ...affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981-2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models' ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian-Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.
DECADAL CLIMATE PREDICTION Meehl, Gerald A.; Goddard, Lisa; Boer, George ...
Bulletin of the American Meteorological Society,
02/2014, Volume:
95, Issue:
2
Journal Article
Peer reviewed
Open access
This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users ...of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.
The diatom composition of natural substrates in streams of different water qualities was compared among samples collected by researchers and samples collected from the intestine contents of three ...species of Cyprinid fishes: Campostoma anomalum, Pimephales notatus, and Semotilus atromaculatus. Campostoma and Pimephales were found to be robust samplers that efficiently collected diverse, representative diatom samples. Semotilus were adequate diatom samplers but collected the most diverse samples. In no instance were water-quality indices calculated from Pimephales samples significantly different from human-collected composite samples, whereas Campostoma and Semotilus samples diverged slightly from human-collected composite samples. Internal similarities of fish-collected samples were not significantly higher than those of human-collected samples, indicating that the fish were indiscriminately foraging on diatoms. Furthermore, samples clustered primarily by stream, indicating that fish-collected samples of diatoms were as representative of the stream as those collected by human researchers. By all standards measured in this study, these three fish species sample the benthic diatom community of mid-order streams with a facility equal to that of trained ecologists.
Several operational centers routinely issue seasonal forecasts of Earth's climate using coupled ocean-atmosphere models, which require near-real-time knowledge of the state of the global ocean. This ...paper reviews existing ocean analysis efforts aimed at initializing seasonal forecasts. We show that ocean data assimilation improves the skill of seasonal forecasts in many cases, although its impact can be overshadowed by errors in the coupled models. The current practice, known as "uncoupled" initialization, has the advantage of better knowledge of atmospheric forcing fluxes, but it has the shortcoming of potential initialization shock. In recent years, the idea of obtaining truly "coupled" initialization, where the different components of the coupled system are well balanced, has stimulated several research activities that will be reviewed in light of their application to seasonal forecasts.
We successfully demonstrated a fully-managed, field-deployed, three-node QKD ring network with L1-OTNsec encryption, that employs a hybrid scheme of QKD and classical yet quantum-safe ...centrally-generated symmetric keys to support point-to-point and relay consumers.
The temperature record of the last 150 years is characterized by a long-term warming trend, with strong multidecadal variability superimposed. The multidecadal variability is also seen in other ...(societal important) parameters such as Sahel rainfall or Atlantic hurricane activity. The existence of the multidecadal variability makes climate change detection a challenge, since Global Warming evolves on a similar timescale. The ongoing discussion about a potential anthropogenic signal in the Atlantic hurricane activity is an example. A lot of work was devoted during the last years to understand the dynamics of the multidecadal variability, and external as well as internal mechanisms were proposed. This White Paper focuses on the internal mechanisms relevant to the Atlantic Multidecadal Oscillation/Variability (AMO/V) and the Pacific Decadal Oscillation/Variability (PDO/V). Specific attention is given to the role of the Meridional Overturning Circulation (MOC) in the Atlantic. The implications for decadal predictability and prediction are discussed.
Worldwide, poultry is considered the main source of food-related human campylobacteriosis, which is generally associated with the consumption of raw or undercooked chicken meat. Furthermore, Cam- ...pylobacter develops biofilms that are resistant to environmental stress, antibiotics, and disinfectants and are becoming a major issue for the food industry, especially the poultry industry. This study investigated the antimicrobial and anti-biofilm properties of polyphenols found in spray-dried olive mill wastewater (OMWW--SD) against Campylobacter strains isolated from chicken meat.
OMWW-SD was produced by dehydration of olive mill wastewater polyphenolic extract. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) for OMWW-SD were determined by microdilution method whereas the inhibitory effect of the OMWW-SD on biofilm formation and biofilm disaggregation was tested through crystal violet assay on polystyrene plates.
The phenolic profile of OMWW-SD mainly consisted of secoiridoid and hydroxycinnamic acid derivatives. Oleuropein-aglycone di-aldehyde (a secoiridoid derivative) was the major constituent, representing 72.5% of the total identified phenolic compounds. OMWW-SD showed a MIC ranging from 0.15 mg/mL to 0.3 mg/mL and a MBC of 0.3 mg/mL for all Campylobacter strains tested. The olive by-product extract tested was able, in vitro, to inhibit biofilm formation and to promote biofilm dispersion even at sub-MIC concentra- tions, with values ranging from 6% to 92% and from 4% to 83% at varying extract dilutions, respectively.
OMWW-SD could be developed as a new anti-biofilm agent with potential to control Campylo- bacter in the food chain, especially in the poultry industry, thereby enhancing food safety.
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