The interpretation and use of biases in decadal climate predictions

Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities

Characterizing the Ocean Economies of Guam, American Samoa, and the Commonwealth of the Northern Mariana Islands

The National Oceanic and Atmospheric Administration’s (NOAA’s) Economics: National Ocean Watch (ENOW) provides an annual time series of select employment, establishment, wage, and gross domestic product data for all 30 U.S. coastal and Great Lakes states as far back as 2005. As detailed in Section 4 of this report, ENOW covers 47 six-digit NAICS industries across the following six ocean- and Great Lakes– dependent sectors of the economy: Living resources Marine construction Marine transportation Offshore mineral resources • Ship and boat building Tourism and recreation ENOW data play an important role in characterizing and determining the relative importance of the ocean economies of the U.S. states and sub-state regions, as well as enhancing our understanding of the economic impacts of natural and human-made disasters, such as hurricanes and oil spills. Most importantly, ENOW allows NOAA and other stakeholders to clearly describe the importance of the ocean and coastal economies and to access such information for policy development. This report characterizes the ocean economies of Guam, American Samoa, and the Commonwealth of the Northern Mariana Islands (CNMI) and assesses what information would be needed to develop an ENOW dataset for each of these Pacific Island Territories, none of which ENOW currently covers. Due to data availability issues similar to those faced in a prior NOAA effort to characterize the ocean economies of Puerto Rico and the U.S. Virgin Islands (NOAA OCM 2016), and additional issues unique to these Pacific Island Territories, this study relied primarily on U.S. Census County Business Patterns (CBP) data, local datasets, and information from interviews to describe these three ocean economies. Methods The ERG team, under contract to NOAA, performed in-person interviews in Guam, American Samoa, and CNMI in January and February 2018 to better estimate the size of the ocean economy in each territory. Using a combination of U.S. Census CBP data, local data, and information from interviews, ERG developed establishment and employment estimates for industries in the six ENOW sectors as well as other related industries in these sectors that we deemed ocean-dependent in an island setting (referred to as ENOW+ in this report)

Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion

Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems

Uncertainties in the timing of unprecedented climates

The question of when the signal of climate change will emerge from the background noise of climate variability—the ‘time of emergence’—is potentially important for adaptation planning. Mora et al.1 presented precise projections of the time of emergence of unprecedented regional climates. However, their methodology produces artificially early dates at which specific regions will permanently experience unprecedented climates and artificially low uncertainty in those dates everywhere. This overconfidence could impair the effectiveness of climate risk management decisions 2. There is a Reply to this Brief Communication Arising by Mora, C. et al. Nature 511, http://dx.doi.org/10.1038/nature13524 (2014)

What guidance are researchers given on how to present network meta-analyses to end-users such as policymakers and clinicians? A systematic review

© 2014 Sullivan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: Network meta-analyses (NMAs) are complex methodological approaches that may be challenging for non-technical end-users, such as policymakers and clinicians, to understand. Consideration should be given to identifying optimal approaches to presenting NMAs that help clarify analyses. It is unclear what guidance researchers currently have on how to present and tailor NMAs to different end-users. Methods: A systematic review of NMA guidelines was conducted to identify guidance on how to present NMAs. Electronic databases and supplementary sources were searched for NMA guidelines. Presentation format details related to sample formats, target audiences, data sources, analysis methods and results were extracted and frequencies tabulated. Guideline quality was assessed following criteria developed for clinical practice guidelines. Results: Seven guidelines were included. Current guidelines focus on how to conduct NMAs but provide limited guidance to researchers on how to best present analyses to different end-users. None of the guidelines provided reporting templates. Few guidelines provided advice on tailoring presentations to different end-users, such as policymakers. Available guidance on presentation formats focused on evidence networks, characteristics of individual trials, comparisons between direct and indirect estimates and assumptions of heterogeneity and/or inconsistency. Some guidelines also provided examples of figures and tables that could be used to present information. Conclusions: Limited guidance exists for researchers on how best to present NMAs in an accessible format, especially for non-technical end-users such as policymakers and clinicians. NMA guidelines may require further integration with end-users' needs, when NMAs are used to support healthcare policy and practice decisions. Developing presentation formats that enhance understanding and accessibility of NMAs could also enhance the transparency and legitimacy of decisions informed by NMAs.The Canadian Institute of Health Research (CIHR) Drug Safety and Effectiveness Network (Funding reference number – 116573)

Project Report No. 43, Site Index Equations for Loblolly and Slash Pine Plantations in East Texas, Update: 1996

In this update, after combining the data from the two subplots comprising each ETPPRP plot, the number of age-height pairs available for this analysis is 1,520 loblolly and 658 slash. the It is anticipated that the equations in this :996 update may productivity of East Texas loblolly and slash pine plantations quantify in a more accurate and reliable manner than the four previous sets 0: equations

An International Quiet Ocean Experiment

Author Posting. © Oceanography Society, 2011. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 24, no. 2 (2011): 174–181, doi:10.5670/oceanog.2011.37.The effect of noise on marine life is one of the big unknowns of current marine science. Considerable evidence exists that the human contribution to ocean noise has increased during the past few decades: human noise has become the dominant component of marine noise in some regions, and noise is directly correlated with the increasing industrialization of the ocean. Sound is an important factor in the lives of many marine organisms, and theory and increasing observations suggest that human noise could be approaching levels at which negative effects on marine life may be occurring. Certain species already show symptoms of the effects of sound. Although some of these effects are acute and rare, chronic sublethal effects may be more prevalent, but are difficult to measure. We need to identify the thresholds of such effects for different species and be in a position to predict how increasing anthropogenic sound will add to the effects. To achieve such predictive capabilities, the Scientific Committee on Oceanic Research (SCOR) and the Partnership for Observation of the Global Oceans (POGO) are developing an International Quiet Ocean Experiment (IQOE), with the objective of coordinating the international research community to both quantify the ocean soundscape and examine the functional relationship between sound and the viability of key marine organisms. SCOR and POGO will convene an open science meeting to gather community input on the important research, observations, and modeling activities that should be included in IQOE

Skunk River Review Autumn 1994, vol 6

https://openspace.dmacc.edu/skunkriver/1009/thumbnail.jp