Pattern scaling is a simple way to produce climate projections beyond the scenarios run with expensive global climate models (GCMs). The simplest technique has known limitations and assumes that a ...spatial climate anomaly pattern obtained from a GCM can be scaled by the global mean temperature (GMT) anomaly. We propose alternatives and assess their skills and limitations. One approach which avoids scaling is to consider a period in a different scenario with the same GMT change. It is attractive as it provides patterns of any temporal resolution that are consistent across variables, and it does not distort variability. Second, we extend the traditional approach with a land‐sea contrast term, which provides the largest improvements over the traditional technique. When interpolating between known bounding scenarios, the proposed methods significantly improve the accuracy of the pattern scaled scenario with little computational cost. The remaining errors are much smaller than the Coupled Model Intercomparison Project Phase 5 model spread.
Key Points
Improved pattern scaling approaches are proposed and tested with CMIP5 models
Skill improves when land‐sea temperature contrast is considered
Pattern scaling error is significantly smaller than the CMIP5 model spread
The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates; therefore, a ...range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near-duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognised by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.
Uncertainties in climate projections exist due to natural variability, scenario uncertainty, and model uncertainty. It has been argued that model uncertainty can be decreased by giving more weight to ...those models in multimodel ensembles that are more skillful and realistic for a specific process or application. In addition, some models in multimodel ensembles are not independent. We use a weighting approach proposed recently that takes into account both model performance and interdependence and apply it to investigate projections of summer maximum temperature climatology over North America in two regions of different sizes. We quantify the influence of predicting diagnostics included in the method, look at ways how to choose them, and assess the influence of the observational data set used. The trend in shortwave radiation, mean precipitation, sea surface temperature variability, and variability and trend in maximum temperature itself are the most promising constraints on projections of summer maximum temperature over North America. The influence of the observational data sets is large for summer temperature climatology, since the observational and reanalysis products used for absolute maximum temperatures disagree. Including multiple predicting diagnostics leads to more similar results for different data sets. We find that the weighted multimodel mean reduces the change in summer daily temperature maxima compared to the nonweighted mean slightly (0.05–0.45 °C) over the central United States. We show that it is essential to have reliable observations for key variables to be able to constrain multimodel ensembles of future projections.
Key Points
Model weighting slightly reduces summer warming signal over central North America
More than one predicting diagnostics should be used to inform the weighting
Shortwave radiation trend, mean precipitation, and SST variability are possible constraints on projections of summer maximum temperature
End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily ...determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history.Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.
Future climate is typically projected using multi-model ensembles, but the ensemble mean is unlikely to be optimal if models’ skill at reproducing historical climate is not considered. Moreover, ...individual climate models are not independent. Here, we examine the interplay between the benefits of optimising an ensemble for the performance of its mean and the the effect this has on ensemble spread as an uncertainty estimate. Using future Australian precipitation change as a case study, we perform optimal subset selection based on present-day precipitation, sea surface temperature and/or 500 hPa eastward wind climatologies. We use either one, two, or all three variables as predictors. Out-of-sample projection skill is assessed using a model-as-truth approach (rather than observations). For multiple variables, multi-objective optimisation is used to obtain Pareto-optimal subsets (an ensemble of model subsets), to gauge the uncertainty in optimisation arising from the multiple constraints. We find that the spread of climate model subset averages typically under-represents the true projection uncertainty (overconfidence), but that the situation can be significantly improved using mixture distributions for uncertainty estimation. The single best predictor, present-day precipitation, gives the most accurate results but is still overconfident—a consequence of calibrating too specifically. It is only when all three constraints are used that projection skill is improved and overconfidence is eliminated, but at the cost of a poorer best estimate relative to one predictor. We thus identify an important trade-off between accuracy and precision, depending on the number of predictors, which is likely relevant for any subset selection or weighting strategy.
Climate models serve as indispensable tools to investigate the effect of anthropogenic emissions on current and future climate, including extremes. However, as low‐dimensional approximations of the ...climate system, they will always exhibit biases. Several attempts have been made to correct for biases as they affect extremes prediction, predominantly focused on correcting model‐simulated distribution shapes. In this study, the effectiveness of a recently published quantile‐based bias correction scheme, as well as a new subset selection method introduced here, are tested out‐of‐sample using model‐as‐truth experiments. Results show that biases in the shape of distributions tend to persist through time, and therefore, correcting for shape bias is useful for past and future statements characterizing the probability of extremes. However, for statements characterized by a ratio of the probabilities of extremes between two periods, we find that correcting for shape bias often provides no skill improvement due to the dominating effect of bias in the long‐term trend. Using a toy model experiment, we examine the relative importance of the shape of the distribution versus its position in response to long‐term changes in radiative forcing. It confirms that the relative position of the two distributions, based on the trend, is at least as important as the shape. We encourage the community to consider all model biases relevant to their metric of interest when using a bias correction procedure and to construct out‐of‐sample tests that mirror the intended application.
Key Points
Out‐of‐sample testing when introducing bias correction approaches is critical
Biases in trends can dominate uncertainty in estimates of anthropogenic effect on extreme weather
Calibration on distribution shapes does not guarantee improved skill of attribution statements
Named Entity Recognition (NER) is the task of identifying and classifying named entities in unstructured text. In the legal domain, named entities of interest may include the case parties, judges, ...names of courts, case numbers, references to laws etc. We study the problem of legal NER with noisy text extracted from PDF files of filed court cases from US courts. The "gold standard" training data for NER systems provide annotation for each token of the text with the corresponding entity or non-entity label. We work with only partially complete training data, which differ from the gold standard NER data in that the exact location of the entities in the text is unknown and the entities may contain typos and/or OCR mistakes. To overcome the challenges of our noisy training data, e.g. text extraction errors and/or typos and unknown label indices, we formulate the NER task as a text-to-text sequence generation task and train a pointer generator network to generate the entities in the document rather than label them. We show that the pointer generator can be effective for NER in the absence of gold standard data and outperforms the common NER neural network architectures in long legal documents.
Modic type 1 changes (MC1) are vertebral bone marrow lesions and associate with low back pain. Increased serum C‐reactive protein (CRP) has inconsistently been associated with MC1. We aimed to ...provide evidence for the role of CRP in the tissue pathophysiology of MC1 bone marrow. From 13 MC1 patients undergoing spinal fusion at MC1 levels, vertebral bone marrow aspirates from MC1 and intrapatient control bone marrow were taken. Bone marrow CRP, interleukin (IL)‐1, and IL‐6 were measured with enzyme‐linked immunosorbent assays; lactate dehydrogenase (LDH) was measured with a colorimetric assay. CRP, IL‐1, and IL‐6 were compared between MC1 and control bone marrow. Bone marrow CRP was correlated with blood CRP and with bone marrow IL‐1, IL‐6, and LDH. CRP expression by marrow cells was measured with a polymerase chain reaction. Increased CRP in MC1 bone marrow (mean difference: +0.22 mg CRP/g, 95% confidence interval CI −0.04, 0.47, p = 0.088) correlated with blood CRP (r = 0.69, p = 0.018), with bone marrow IL‐1β (ρ = 0.52, p = 0.029) and IL‐6 (ρ = 0.51, p = 0.031). Marrow cells did not express CRP. Increased LDH in MC1 bone marrow (143.1%, 95% CI 110.7%, 175.4%, p = 0.014) indicated necrosis. A blood CRP threshold of 3.2 mg/L detected with 100% accuracy increased CRP in MC1 bone marrow. In conclusion, the association of CRP with inflammatory and necrotic changes in MC1 bone marrow provides evidence for a pathophysiological role of CRP in MC1 bone marrow.
Background
Vertebral endplate signal intensity changes visualized by magnetic resonance imaging termed Modic changes (MC) are highly prevalent in low back pain patients. Interconvertibility between ...the three MC subtypes (MC1, MC2, MC3) suggests different pathological stages. Histologically, granulation tissue, fibrosis, and bone marrow edema are signs of inflammation in MC1 and MC2. However, different inflammatory infiltrates and amount of fatty marrow suggest distinct inflammatory processes in MC2.
Aims
The aims of this study were to investigate (i) the degree of bony (BEP) and cartilage endplate (CEP) degeneration in MC2, (ii) to identify inflammatory MC2 pathomechanisms, and (iii) to show that these marrow changes correlate with severity of endplate degeneration.
Methods
Pairs of axial biopsies (n = 58) spanning the entire vertebral body including both CEPs were collected from human cadaveric vertebrae with MC2. From one biopsy, the bone marrow directly adjacent to the CEP was analyzed with mass spectrometry. Differentially expressed proteins (DEPs) between MC2 and control were identified and bioinformatic enrichment analysis was performed. The other biopsy was processed for paraffin histology and BEP/CEP degenerations were scored. Endplate scores were correlated with DEPs.
Results
Endplates from MC2 were significantly more degenerated. Proteomic analysis revealed an activated complement system, increased expression of extracellular matrix proteins, angiogenic, and neurogenic factors in MC2 marrow. Endplate scores correlated with upregulated complement and neurogenic proteins.
Discussion
The inflammatory pathomechanisms in MC2 comprises activation of the complement system. Concurrent inflammation, fibrosis, angiogenesis, and neurogenesis indicate that MC2 is a chronic inflammation. Correlation of endplate damage with complement and neurogenic proteins suggest that complement system activation and neoinnervation may be linked to endplate damage. The endplate‐near marrow is the pathomechanistic site, because MC2 occur at locations with more endplate degeneration.
Conclusion
MC2 are fibroinflammatory changes with complement system involvement which occur adjacent to damaged endplates.
Modic type 2 changes (MC2) are fibroinflammatory changes with complement system involvement which occur adjacent to damaged endplates. Endplate degeneration correlates with complement and neurogenic proteins, which suggests that complement system activation and neoinnervation may be linked to endplate injuries. The endplate‐near bone marrow is the pathomechanistic site, because MC2 occur at locations with more endplate degeneration.
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