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Climate data portals are essential tools for climate change adaptation. This study analyses differences between two Canadian portals providing bias-adjusted CMIP6 simulations: Climate Data Canada and Portraits Climatiques. The study evaluates three core variables (daily maximum temperature, daily minimum temperature and precipitation) as well as assesses five case studies, taken from the agriculture, transport and health sectors, that relied on climate indicators available through the portals. The underlying datasets vary in multiple ways (bias-adjustment methodology, climate of reference, ensemble composition, emissions scenarios) and, in general, the climatology of variables and indicators tends to be statistically different between portals towards the end of the century. Differences are significantly reduced when comparing projected changes with respect to present climate conditions, highlighting the important role played by the dataset used as a reference for the bias-adjustment procedure. When considered from the point of view of practical applications, the discrepancies between the portals are generally, although not always, sufficiently small that they do not impact the resulting decisions. Finally, indicators based on a fixed threshold were found to be strongly influenced by the reference used for the bias adjustment.

As a leading mode of sea surface temperature (SST) variability over the North Atlantic in both observations and model simulations, the Atlantic Multidecadal Oscillation (AMO) can have a substantial influence on regional and global climate. By using Low-Frequency Component Analysis, we explore the uncertainties of the resulting AMO indices and the corresponding spatial patterns derived from three observational SST datasets. We found that the known coherent spatial pattern of the AMO at the basin scale over the North Atlantic appears in two out of the three datasets. Further analysis indicates that both the warming trend and the different techniques used to construct these observed gridded SSTs contribute to the AMO’s spatial coherence over the North Atlantic, especially during periods of sparse data sampling. The SST in the Extended Reconstructed SST dataset version 5 (ERSSTv5), changes from being systematically below the other datasets during the dense sampling periods on either side of the Second World War (WWII), to systematically above the other datasets during WWII, thereby introducing an artificial 10–20-year variability that affects the AMO’s spatial coherence. This coherence in the AMO’s spatial pattern is also affected by bias adjustment in ERSSTv5 at relative cool (i.e., non-summer) seasons, and by the heterogeneous North Atlantic warming pattern. The different AMO patterns can induce the different effects of wind, surface heat fluxes, and then drive ocean circulation and its heat transport convergence, especially for some seasons. For AMO indices, both the different detrending methods and different observational data result in uncertainty for the period 1935–1950. Such SST uncertainty is important to detect the relative role of the atmosphere and ocean in shaping the AMO.

We present a dataset of daily, bias-adjusted temperature and precipitation projections for continental Africa based on a large ensemble of regional climate model simulations, which can be useful for climate change impact studies in several sectors. We provide guidance on the benefits and caveats of using the dataset by investigating the effect of bias-adjustment on impact-relevant indices (both their future absolute value and change). Extreme threshold-based temperature indices show large differences between original and bias-adjusted values at the end of the century due to the general underestimation of temperature in the present climate. These results indicate that when biases are accounted for, projected risks of extreme temperature-related hazards are higher than previously found, with possible consequences for the planning of adaptation measures. Bias-adjusted results for precipitation indices are usually consistent with the original results, with the median change preserved for most regions and indices. The interquartile and full range of the original model ensemble is usually well preserved by bias-adjustment, with the exception of maximum daily precipitation, whose range is usually greatly reduced by the bias-adjustment. This is due to the poor simulation and extremely large model range for this index over the reference period; when the bias is reduced, most models converge in projecting a similar change. Finally, we provide a methodology to select a small subset of simulations that preserves the overall uncertainty in the future projections of the large model ensemble. This result can be useful in practical applications when process-based impact models are too expensive to be run with the full ensemble of model simulations. [ABSTRACT FROM AUTHOR]

A cross-validated model output statistics (MOS) approach is applied to precipitation data from the high-resolution regional climate model CCLM for Europe. The aim is to remove systematic errors of simulated precipitation in decadal climate predictions. We developed a two-step bias-adjustment approach. In step one, we estimate model errors based on a long-term 'CCLM assimilation run' (regionalizing data from a global assimilation run) and observational data. In step two, the resulting transfer function is applied to the complete set of decadal hindcast simulations (285 individual runs). In contrast to lead-time-dependent bias-adjustment approaches, this one is designed for variables with poor decadal prediction skill and without dominant lead-time-dependent bias. In terms of the CCLM assimilation run, MOS is shown to be effective in predictor selection, model skill improvement, and model bias reduction. Yet, the positive effect of MOS correction is accompanied with an underestimation of precipitation variability. After MOS application, an estimated mean square skill score of more than 0.5 is observed regionally. Simulated precipitation in decadal hindcasts is further improved when the MOS is trained on the basis of other decadal hindcasts from the same regional climate model but with a large underestimation in forecast uncertainty. Our results suggest that the MOS system derived from the assimilation run is less effective but allows the potential climate predictability in decadal hindcasts and forecasts to be retained. Using hindcasts itself for training is recommended unless a statistical method is capable of distinguishing biases and predictions within a hindcasts dataset. [ABSTRACT FROM AUTHOR]

This study promotes the critical use of air pollution modelling results for health and agriculture impacts, with the primary goal of providing more reliable estimates to decision makers. To date, the accuracy of air quality (AQ) models and the effects of model-to-model result variability (which we will refer to as model uncertainty) on impact assessment studies have been often ignored, thus undermining the robustness of the information used in the decision making process and the confidence in the results obtained. A suite of twelve PM 2.5 and ozone concentration fields produced by regional-scale chemistry transport Air Quality (AQ) models during the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) has been used to calculate the impact of air pollution on premature deaths and crop yields. An innovative technique is applied to bias-adjust the models to available observations. The model results for ozone and PM 2.5 are combined in a multi-model (MM) ensemble, which is used to estimate the damage and economic cost to human health and crop yields, as well as the associated uncertainties. The MM ensemble quantifies directly the uncertainty introduced by AQ models into the air pollution impact assessment chain, while the indirect use of experimental information through a bias adjustment, reduces the uncertainty in the ozone and PM 2.5 fields and subsequently the uncertainty of the final impact assessment and cost valuation. The analysis over the European countries analysed in this study shows a mean number of premature deaths due to exposure to PM 2.5 and ozone of approximately 370,000 (inter-quantile range between 260,000 and 415,000) and a relative yield loss of approximately 7% to 9% (depending on the exposure metrics used, for wheat and maize together). Furthermore, the results indicate that a reduction in the uncertainty of the modelled ozone by 61% and by 80% (depending on the aggregation metric used) and by 46% for PM 2.5 , produces a reduction in the uncertainty in premature mortality and crop loss of >60%, and of an equivalent percentage in the final uncertainty of cost valuation, providing decision makers with more accurate estimations for more targeted interventions. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a copula-based ensemble simulation method for analyzing the uncertainty and adjusting the bias of two high resolution satellite precipitation products (PERSIANN and TMPA-3B42). First, a set of sixty daily rainfall events that each of them occurs concurrently over twenty 0.25°×0.25° pixels (corresponding to both PERSIANN and TMPA spatial resolution) is determined to perform the simulations and validations. Next, for a number of fifty-four out of sixty (90%) selected events, the differences between rain gauge measurements as reference surface rainfall data and satellite rainfall estimates (SREs) are considered and termed as observed biases. Then, a multivariate Gaussian copula constructed from the multivariate normal distribution is fitted to the observed biases. Afterward, the copula is employed to generate multiple bias fields randomly based on the observed biases. In fact, copula is invariant to monotonic transformations of random variables and thus the generated bias fields have the same spatial dependence structure as that of the observed biases. Finally, the simulated biases are imposed over the original satellite rainfall estimates in order to obtain an ensemble of bias-adjusted rainfall realizations of satellite estimates. The study area selected for the implementation of the proposed methodology is a region in the southwestern part of Iran. The reliability and performance of the developed model in regard to bias correction of SREs are examined for a number of six out of those sixty (10%) daily rainfall events. Note that these six selected events have not participated in the steps of bias generation. In addition, three statistical indices including bias, root mean square error (RMSE), and correlation coefficient (CC) are used to evaluate the model. The results indicate that RMSE is improved by 35.42% and 36.66%, CC by 17.24% and 14.89%, and bias by 88.41% and 64.10% for bias-adjusted PERSIANN and TMPA-3B42 estimates, respectively. [ABSTRACT FROM AUTHOR]

Abstract: The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA’s North American Mesoscale (NAM) meteorological model with EPA’s Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O3) and experimental fine particular matter (PM2.5) forecasts over the continental United States (CONUS) during 2008. This paper describes the implementation of a real-time Kalman Filter (KF) bias-adjustment technique to improve the accuracy of O3 and PM2.5 forecasts at discrete monitoring locations. The operational surface-level O3 and PM2.5 forecasts from the NAQFC system were post-processed by the KF bias-adjusted technique using near real-time hourly O3 and PM2.5 observations obtained from EPA’s AIRNow measurement network. The KF bias-adjusted forecasts were created daily, providing 24-h hourly bias-adjusted forecasts for O3 and PM2.5 at all AIRNow monitoring sites within the CONUS domain. The bias-adjustment post-processing implemented in this study requires minimal computational cost; requiring less than 10 min of CPU on a single processor Linux machine to generate 24-h hourly bias-adjusted forecasts over the entire CONUS domain. The results show that the real-time KF bias-adjusted forecasts for both O3 and PM2.5 have performed as well as or even better than the previous studies when the same technique was applied to the historical O3 and PM2.5 time series from archived AQF in earlier years. Compared to the raw forecasts, the KF forecasts displayed significant improvement in the daily maximum 8-h O3 and daily mean PM2.5 forecasts in terms of both discrete (i.e., reduced errors, increased correlation coefficients, and index of agreement) and categorical (increased hit rate and decreased false alarm ratio) evaluation metrics at almost all locations during the study period in 2008. [Copyright &y& Elsevier]

Combination of forecasts from survey data is complicated by the frequent entry and exit of individual forecasters which renders conventional least squares regression approaches infeasible. We explore the consequences of this issue for existing combination methods and propose new methods for bias-adjusting the equal-weighted forecast or applying combinations on an extended panel constructed by back-filling missing observations using an EM algorithm. Through simulations and an application to a range of macro- economic variables we show that the entry and exit of forecasters can have a large effect on the real-time performance of conventional combination methods. The bias-adjusted combination method is found to work well in practice. [ABSTRACT FROM AUTHOR]

The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models. In this paper, we present an efficacious and plain bias-adjustment scheme using chronological age as a covariate through the training set for downgrading the prediction bias in a Brain-age estimation framework. We applied proposed bias-adjustment scheme coupled by a machine learning-based brain age framework on a large set of metabolic brain features acquired from 675 cognitively unimpaired adults through fluorodeoxyglucose positron emission tomography data as the training set to build a robust Brain-age estimation framework. Then, we tested the reliability of proposed bias-adjustment scheme on 75 cognitively unimpaired adults, 561 mild cognitive impairment patients as well as 362 Alzheimer's disease patients as independent test sets. Using the proposed method, we gained a strong R 2 of 0.81 between the chronological age and brain estimated age, as well as an excellent mean absolute error of 2.66 years on 75 cognitively unimpaired adults as an independent set; whereas an R 2 of 0.24 and a mean absolute error of 4.71 years was achieved without bias-adjustment. The simulation results demonstrated that the proposed bias-adjustment scheme has a strong capability to diminish prediction error in brain age estimation frameworks for clinical settings., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)