Data-rich Regions Research Articles

CCTV image‐based classification of blocked trash screens

AbstractThis study introduces image‐based classification techniques to identify whether trash screens in urban rivers are blocked. The study obtained 755 images from a CCTV camera surveying a trash screen located on an urban river at Tongwynlais in Cardiff. Manual quality control reduced the dataset to 577 images, labelled as either blocked (80%) or unblocked (20%). The performance of a logistic regression for classification of images was investigated using three different subsets of the labelled images: (1) the original dataset, (2) a balanced but under‐sampled dataset with equal number of blocked and unblocked images, and (3) an augmented dataset with an equal number of blocked and unblocked images using Gaussian noise augmentation to increase the number of unblocked images. Results show that our data‐augmentation method enhanced model accuracy by 8%, successfully classifying images as blocked or unblocked with an accuracy of 88%; by overcoming the bias in the dataset these results also highlight potential solutions to overcome the challenges of operating this methodology across a network of cameras. This enables authorities in both data rich and data scarce regions the ability to take advantage of machine learning to open up the next generation of a distributed, data‐driven flood warning systems, protecting people, infrastructure and the environment.

A framework on utilizing of publicly availability stream gauges datasets and deep learning in estimating monthly basin-scale runoff in ungauged regions

This study introduces a framework that strategically applies a Long Short-Term Memory (LSTM)-based approach for monthly runoff prediction in South Africa and Central Asia. The framework is distinct in its utilization of newly developed Global Land Data Assimilation System (GLDAS)-derived climate dynamics variables and Global Streamflow Indices and Metadata Archives (GSIM)-derived static descriptors, ensuring a consistent evaluation of Deep Learning (DL) performance across multiple continents, including North and South America, and Western Europe. Seven LSTM models were trained using seven different datasets, each representing a combination of these data-rich regions. We assessed the sensitivity of these seven training data sets to LSTM models by testing these trained models in predicting monthly basin-scale runoff across 214 test catchments located in South Africa and Central Asia. Our results show that runoff predictions generated by LSTM within the test domain could exhibit better prediction skills compared to those derived from GLDAS datasets. The performance of the trained LSTMs appears to be linked to hydrological similarities between the data-rich regions and the test basins. Also, our results indicate the importance of selecting the appropriate input sources for the LSTM models to achieve accurate runoff predictions at the test region. We emphasize the possibility of utilizing LSTM models that are leveraging on either North American catchments or a combination of South American and Western European catchments to predict basin-scale runoff in the test regions. To this end, this study harnesses the burgeoning availability of publicly stream-gauge datasets and DL to enhance water information prediction in ungauged regions, responding to the challenge of geographically unbalanced stream gauge instruments.

High-Resolution Regional Ocean Climatologies with the Northwest Atlantic as an Example: A Review

The advantages of high-resolution oceanographic data analysis stems from an increased ability to capture sharp gradients in frontal zones and across mesoscale entities, especially in the coastal regions and along ocean jet-like currents, relative to coarser resolution analyses. In a sense, the finer-resolution analysis pursues the same goal as the usage of progressively reduced grid sizes in succeeding from coarse-resolution to eddy-permitting and then to eddy-resolving in numerical models of ocean circulation. Thus, the high-resolution regional climatologies are now closing the gaps existing between observations and model simulations allowing meaningful data-model comparisons in critical data-rich regions, such as the Northwestern Atlantic, Northeast Pacific Ocean, and several others. In this paper, we briefly review the locations, timing, purpose, and specificity of the regional ocean climatologies developed at the National Center for Environmental Information of the National Oceanic and Atmospheric Administration. We describe the Northwestern Atlantic Regional Climatology, version 2 in more details and demonstrate how this regional climatology contributes to a better understanding of the long-term climate state and variability

Backbone Ground-Motion Models for Crustal, Interface, and Slab Earthquakes in New Zealand from Equivalent Point-Source Concepts

ABSTRACT A ground-motion model (GMM) that strikes a balance between empirical and simulation-based approaches is developed in support of the 2022 update of the New Zealand National Seismic Hazard Model. The development follows the backbone approach, comprising a central model to express the median ground motions for earthquakes in New Zealand (NZ), along with upper and lower alternatives to describe its epistemic uncertainty. Aleatory variability of ground-motion amplitudes about the median is also characterized. Separate GMMs are developed for crustal, interface, and in-slab earthquakes. The approach taken is to perform a regression analysis of the NZ response spectra database employing a functional form, concepts, and constraints that are drawn from equivalent point-source simulations. The model parameters that control the scaling of the GMM with magnitude and distance describe source effects (seismic moment and stress parameter), path effects (geometric and anelastic attenuation), and site effects (site shear-wave velocity). The NZ database provides constraints on the model for M ∼ 4–7, for frequencies from 0.2 to 100 Hz, at distances to ∼400 km. Extension of the GMM to larger magnitudes (M 7–9) is constrained by the Hassani and Atkinson seismological model, which was developed for application to events of M 3–9 and validated in data-rich regions (California for crustal earthquakes, Japan for interface and slab earthquakes).

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980-2019.

Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71-0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32-0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment.

Deep learning models of the discrete component of the Galactic interstellar γ -ray emission

A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies on observations of rare gas tracers only available in limited regions of the sky. Identifying this contribution is important to discriminate gamma-ray point sources from interstellar gas, and to better characterize extended gamma-ray sources. We design and train convolutional neural networks to predict this emission where observations of these rare tracers do not exist and discuss the impact of this component on the analysis of the Fermi-LAT data. In particular, we evaluate prospects to exploit this methodology in the characterization of the Fermi-LAT Galactic center excess through accurate modeling of point-like structures in the data to help distinguish between a point-like or smooth nature for the excess. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions, supporting prospects to employ these methods in yet unobserved regions.

Probabilistic prediction of ground-motion intensity for regions lacking strong ground-motion records

Probabilistic prediction of ground-motion intensity in regions lacking strong ground-motion records is a vital issue for seismic structural design. Several approaches have been suggested for this purpose, and nearly all of them directly import and adjust the ground-motion prediction equation (GMPE) of ground-motion measures such as the peak ground acceleration (PGA) or spectral acceleration (SA) from data-rich regions. However, as the transmissions of PGA and SA from the source to the site correspond to nonlinear processes, this import and adjustment may lead to an unrealistic evaluation of ground motion. In this study, a novel probabilistic prediction method of ground-motion intensity for such regions is proposed. In contrast to the current approach wherein the GMPE of ground-motion measures such as PGA or SA is directly used, a Fourier amplitude spectral (FAS) model is suggested to express the seismic transmission process from the source to the site. The ground-motion intensities of PGA or SA are obtained from the FAS model using the random vibration theory. The exceedance probability of ground-motion intensity is calculated based on Monte Carlo simulations. As the FAS conforms to the linear system theory and the determination of FAS model does not require too many ground-motion records, the proposed method should be convenient for the probabilistic prediction of ground-motion intensity in regions lacking strong ground-motion records.

Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions.

Terrestrial export of nitrogen is a critical Earth system process, but its global dynamics remain difficult to predict at a high spatiotemporal resolution. Here, we use deep learning (DL) to model daily riverine nitrogen export in response to hydrometeorological and anthropogenic drivers. Long short-term memory (LSTM) models for the daily concentration and flux of dissolved inorganic nitrogen (DIN) were built in a coastal watershed in southeastern China with a typical subtropical monsoon climate. The DL models exhibited excellent accuracy for both DIN concentration and flux, with Nash-Sutcliffe efficiency coefficients (NSEs) up to 0.67 and 0.92, respectively, a performance unlikely to be achieved by generic process-based models with comparable data quality. The flux model ensemble, without retraining, performed well (mean NSE = 0.32-0.84) in seven distinct watersheds in Asia, Europe, and North America, and retraining with multi-watershed data further improved the lowest NSE from 0.32 to 0.68. DL interpretation confirmed that interbasin consistency of riverine nitrogen export exists across different continents, which stems from the similarities in rainfall-runoff relationships. The multi-watershed flux model projects 0.60-12.4% increases in the nitrogen export to oceans from the studied watersheds under a 20% increase in fertilizer consumption, which rises to 6.7-20.1% with a 10% increase in runoff, indicating the synergistic effect of human activities and climate change. The DL-based method represents a successful case of explainable artificial intelligence in environmental science, providing a potential shortcut to a consistent understanding of the global daily-resolution dynamics of riverine nitrogen export under the currently limited data conditions.

One planet: one health. A call to support the initiative on a global science\u2013policy body on chemicals and waste

The chemical pollution crisis severely threatens human and environmental health globally. To tackle this challenge the establishment of an overarching international science–policy body has recently been suggested. We strongly support this initiative based on the awareness that humanity has already likely left the safe operating space within planetary boundaries for novel entities including chemical pollution. Immediate action is essential and needs to be informed by sound scientific knowledge and data compiled and critically evaluated by an overarching science–policy interface body. Major challenges for such a body are (i) to foster global knowledge production on exposure, impacts and governance going beyond data-rich regions (e.g., Europe and North America), (ii) to cover the entirety of hazardous chemicals, mixtures and wastes, (iii) to follow a one-health perspective considering the risks posed by chemicals and waste on ecosystem and human health, and (iv) to strive for solution-oriented assessments based on systems thinking. Based on multiple evidence on urgent action on a global scale, we call scientists and practitioners to mobilize their scientific networks and to intensify science–policy interaction with national governments to support the negotiations on the establishment of an intergovernmental body based on scientific knowledge explaining the anticipated benefit for human and environmental health.

A New Method to Estimate Heat Exposure Days and Its Impacts in China

Understanding the spatiotemporal trends of temperature in the context of global warming is significant for public health. Although many studies have examined changes in temperature and the impacts on human health over the past few decades in many regions, they have often been carried out in data-rich regions and have rarely considered acclimatization explicitly. The most frequent temperature (MFT) indicator provides us with the ability to solve this problem. MFT is defined as the longest period of temperature throughout the year to which a human is exposed and therefore acclimates. In this study, we propose a new method to estimate the number of heat exposure days from the perspective of temperature distribution and MFT, based on the daily mean temperature readings of 2142 weather stations in eight major climate zones in China over the past 20 years. This method can be used to calculate the number of heat exposure days in terms of heat-related mortality risk without the need for mortality data. We estimated the distribution and changes of annual mean temperature (AMT), minimum mortality temperature (MMT), and the number of heat exposure days in different climate zones in China. The AMT, MMT, and number of heat exposure days vary considerably across China. They all tend to decrease gradually from low to high latitudes. Heat exposure days are closely related to the risk of heat-related mortality. In addition, we utilized multiple linear regression (MLR) to analyze the association between the risk of heat-related mortality and the city and its climatic characteristics. Results showed that the number of heat exposure days, GDP per capita, urban population ratio, proportion of elderly population, and climate zone were found to modify the estimate on heat effect, with an R2 of 0.71. These findings will be helpful for the creation of public policies protecting against high-temperature-induced mortalities.

A Suite of Alternative Ground-Motion Models (GMMs) for Israel

ABSTRACTModels for estimating earthquake ground motions are a key component in seismic hazard analysis. In data-rich regions, these models are mostly empirical, relying on the ever-increasing ground-motion databases. However, in areas in which strong-motion data are scarce, other approaches for ground-motion estimates are sought, including, but not limited to, the use of simulations to replace empirical data. In Israel, despite a clear seismic hazard posed by the active plate boundary on its eastern border, the instrumental record is sparse and poor, leading to the use of global models for hazard estimation in the building code and all other engineering applications. In this study, we develop a suite of alternative ground-motion models for Israel, based on an empirical database from Israel as well as on four data-calibrated synthetic databases. Two host models are used to constrain model behavior, such that the epistemic uncertainty is captured and characterized. Despite the lack of empirical data at large magnitudes and short distances, constraints based on the host models or on the physical grounds provided by simulations ensure these models are appropriate for engineering applications. The models presented herein are cast in terms of the Fourier amplitude spectra, which is a linear, physical representation of ground motions. The models are suitable for shallow crustal earthquakes; they include an estimate of the median and the aleatory variability, and are applicable in the magnitude range of 3–8 and distance range of 1–300 km.

Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density

Abstract. The setup of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activities for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to estimate the model parameters based on the hydrometric information available in the region. The informative content of the dataset (i.e. which and how many gauged river stations are available) plays an essential role in the assessment of the best regionalisation method. This study analyses how the performances of regionalisation approaches are influenced by the “information richness” of the available regional dataset, i.e. the availability of potential donors, and in particular by the gauging density and by the presence of nested donor catchments, which are expected to be hydrologically very similar to the target section. The research is carried out over a densely gauged dataset covering the Austrian country, applying two rainfall-runoff models and different regionalisation approaches. The regionalisation techniques are first implemented using all the gauged basins in the dataset as potential donors and then re-applied, decreasing the informative content of the dataset. The effect of excluding nested basins and the status of “nestedness” is identified based on the position of the closing section along the river or the percentage of shared drainage area. Moreover, the impact of reducing station density on regionalisation performance is analysed. The results show that the predictive accuracy of parameter regionalisation techniques strongly depends on the informative content of the dataset of available donor catchments. The “output-averaging” approaches, which exploit the information of more than one donor basin and preserve the correlation structure of the parameter, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions. Moreover, with the use of an optimised set of catchment descriptors as a similarity measure, rather than the simple geographical distance, results are more robust to the deterioration of the informative content of the set of donors.

Artificial neural network based hybrid modeling approach for flood inundation modeling

Flood inundation models are important tools in flood management. Commonly used flood inundation models, such as hydrodynamic or simplified conceptual models, are either computationally intensive or cannot simulate the temporal behavior of floods. Therefore, emulation models based on data-driven methods, such as artificial neural networks (ANNs), have been developed. However, the performance of ANN models, like any other data-driven models, is limited by available data and will not perform well in data-sparse regions. In this study, we developed an ANN-based hybrid modeling approach to improve model performance in data-sparse regions by leveraging better model performance in data-rich regions. We applied our proposed hybrid modeling approach with three ANN models, including the traditional point-based ANN and two newly proposed block-based ANN models. The results demonstrate that all three ANN models have better performance in data-rich regions compared to data-sparse regions as expected, with the block-based ANN with the most complicated model structure performing better in data-rich regions and the simplest point-based ANN performing better in data-sparse regions. The hybrid modeling approach can significantly improve model performance in data-sparse regions, with the hybrid model based on the most complex block-based ANN performing the best. Our results show the importance of considering the trade-offs between data availability and model complexity in developing data-driven models, and demonstrate the potential for improving performance in data-sparse regions by using a hybrid modeling approach that optimizes model complexity based on data availability.

Understanding customer regional differences from online opinions: a hierarchical Bayesian approach

A large volume of customer reviews is generated from time to time and customer requirements are presented between lines of online opinions. Many studies about online opinions mainly focus on the extraction of customer sentiment, but practical concerns regarding the integration into new product design are far from extensively discussed. To enlighten designers about how consumers differ geographically in terms of their preferences, which is possessing important research significance and practical values, is not well investigated. Specifically, in this study, online reviews are invited to explore market regional heterogeneity. With identified product feature related subjective sentences from online reviews, a straightforward applied approach is to assume the ratio of the number of satisfied customers to the total number of customers as the expected percentage of satisfied customers across different regions. However, such frequency based approach becomes unreliable in case that the number of reviews do not distribute evenly. Accordingly, the Bayesian school of thought is utilized in which statistics of data-rich regions are invited to help to analyze that of data-poor regions. Then, a hierarchical Bayesian model is proposed and it assumes that the expected percentages of customer satisfaction in different regions follow a certain probability distribution. Finally, taking 9541 mobile phone online reviews on Amazon as an example, categories of experiments were conducted. It informs the significance to product designers about the value of online concerns on analyzing market regional heterogeneity and presents the effectiveness of the proposed approach in terms of discovering customer regional differences.

Mapping the increased minimum mortality temperatures in the context of global climate change

Minimum mortality temperature (MMT) is an important indicator to assess the temperature–mortality relationship. It reflects human adaptability to local climate. The existing MMT estimates were usually based on case studies in data rich regions, and limited evidence about MMT was available at a global scale. It is still unclear what the most significant driver of MMT is and how MMT will change under global climate change. Here, by analysing MMTs in 420 locations covering six continents (Antarctica was excluded) in the world, we found that although the MMT changes geographically, it is very close to the local most frequent temperature (MFT) in the same period. The association between MFT and MMT is not changed when we adjust for latitude and study year. Based on the MFT~MMT association, we estimate and map the global distribution of MMTs in the present (2010s) and the future (2050s) for the first time.

Global drivers of minimum mortality temperatures in cities

Human mortality shows a pronounced temperature dependence. The minimum mortality temperature (MMT) as a characteristic point of the temperature-mortality relationship is influenced by many factors. As MMT estimates are based on case studies, they are sporadic, limited to data-rich regions, and their drivers have not yet been clearly identified across case studies. This impedes the elaboration of spatially comprehensive impact studies on heat-related mortality and hampers the temporal transfer required to assess climate change impacts. Using 400 MMTs from cities, we systematically establish a generalised model that is able to estimate MMTs (in daily apparent temperature) for cities, based on a set of climatic, topographic and socio-economic drivers. A sigmoid model prevailed against alternative model setups due to having the lowest Akaike Information Criterion (AICc) and the smallest RMSE. We find the long-term climate, the elevation, and the socio-economy to be relevant drivers of our MMT sample within the non-linear parametric regression model. A first model application estimated MMTs for 599 European cities (>100 000 inhabitants) and reveals a pronounced decrease in MMTs (27.8–16 °C) from southern to northern cities. Disruptions of this pattern across regions of similar mean temperatures can be explained by socio-economic standards as noted for central eastern Europe. Our alternative method allows to approximate MMTs independently from the availability of daily mortality records. For the first time, a quantification of climatic and non-climatic MMT drivers has been achieved, which allows to consider changes in socio-economic conditions and climate. This work contributes to the comparability among MMTs beyond location-specific and regional limits and, hence, towards a spatially comprehensive impact assessment for heat-related mortality.

The sensitivity of satellite-based PM2.5 estimates to its inputs: Implications to model development in data-poor regions

Exposure to fine particulate matter (PM2.5) has been associated with a wide range of negative health outcomes. The overwhelming majority of the epidemiological studies that helped establish such associations was conducted in regions with sufficient ground observations and other supporting data, i.e., the data-rich regions. However, air pollution health effects research in the data-poor regions, where pollution levels are often the highest, is still very limited due to the lack of high-quality exposure estimates. To improve our understanding of the desired input datasets for the application of satellite-based PM2.5 exposure models in data-poor areas, we applied a Bayesian ensemble model in the southeast U.S. that was selected as a representative data-rich region. We designed four groups of sensitivity tests to simulate various data-poor scenarios. The factors considered that would influence the model performance included the temporal sampling frequency of the monitors, the number of ground monitors, the accuracy of the chemical transport model simulation of PM2.5 concentrations, and different combinations of the additional predictors. While our full model achieved a 10-fold cross-validated (CV) R2 of 0.82, we found that when reducing the sampling frequency from the current 1-in-3 day to 1-in-9 day, the CV R2 decreased to 0.58, and the predictions could not capture the daily variations of PM2.5. Half of the current stations (i.e., 30 monitors) could still support a robust model with a CV R2 of 0.79. With 20 monitors, the CV R2 decreased from 0.71 to 0.55 when 100% additional random errors were added to the original CMAQ simulations. However, with a sufficient number of ground monitors (e.g., 30 monitors), our Bayesian ensemble model had the ability to tolerate CMAQ errors with only a slight decrease in CV R2 (from 0.79 to 0.75). With fewer than 15 monitors, our full model collapsed and failed to fit any covariates, while the models with only time-varying variables could still converge even with only five monitors left. A model without the land use parameters lacked fine spatial details in the prediction maps, but could still capture the daily variability of PM2.5 (CV R2 ≥ 0.67) and might support a study of the acute health effects of PM2.5 exposure.

Arctic hydroclimate variability during the last 2000 years: current understanding and research challenges

Abstract. Reanalysis data show an increasing trend in Arctic precipitation over the 20th century, but changes are not homogenous across seasons or space. The observed hydroclimate changes are expected to continue and possibly accelerate in the coming century, not only affecting pan-Arctic natural ecosystems and human activities, but also lower latitudes through the atmospheric and ocean circulations. However, a lack of spatiotemporal observational data makes reliable quantification of Arctic hydroclimate change difficult, especially in a long-term context. To understand Arctic hydroclimate and its variability prior to the instrumental record, climate proxy records are needed. The purpose of this review is to summarise the current understanding of Arctic hydroclimate during the past 2000 years. First, the paper reviews the main natural archives and proxies used to infer past hydroclimate variations in this remote region and outlines the difficulty of disentangling the moisture from the temperature signal in these records. Second, a comparison of two sets of hydroclimate records covering the Common Era from two data-rich regions, North America and Fennoscandia, reveals inter- and intra-regional differences. Third, building on earlier work, this paper shows the potential for providing a high-resolution hydroclimate reconstruction for the Arctic and a comparison with last-millennium simulations from fully coupled climate models. In general, hydroclimate proxies and simulations indicate that the Medieval Climate Anomaly tends to have been wetter than the Little Ice Age (LIA), but there are large regional differences. However, the regional coverage of the proxy data is inadequate, with distinct data gaps in most of Eurasia and parts of North America, making robust assessments for the whole Arctic impossible at present. To fully assess pan-Arctic hydroclimate variability for the last 2 millennia, additional proxy records are required.

A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments

Abstract. Rainfall–runoff modelling has long been a special subject in hydrological sciences, but identifying behavioural parameters in ungauged catchments is still challenging. In this study, we comparatively evaluated the performance of the local calibration of a rainfall–runoff model against regional flow duration curves (FDCs), which is a seemingly alternative method of classical parameter regionalisation for ungauged catchments. We used a parsimonious rainfall–runoff model over 45 South Korean catchments under semi-humid climate. The calibration against regional FDCs was compared with the simple proximity-based parameter regionalisation. Results show that transferring behavioural parameters from gauged to ungauged catchments significantly outperformed the local calibration against regional FDCs due to the absence of flow timing information in the regional FDCs. The behavioural parameters gained from observed hydrographs were likely to contain intangible flow timing information affecting predictability in ungauged catchments. Additional constraining with the rising limb density appreciably improved the FDC calibrations, implying that flow signatures in temporal dimensions would supplement the FDCs. As an alternative approach in data-rich regions, we suggest calibrating a rainfall–runoff model against regionalised hydrographs to preserve flow timing information. We also suggest use of flow signatures that can supplement hydrographs for calibrating rainfall–runoff models in gauged and ungauged catchments.

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

Abstract Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice–ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981–2010: National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP–U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice–ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R = 0.82). The trend in the total ice volume in September is greatest with MERRA (−4.1 × 103 km3 decade−1) and least with CFSR (−2.7 × 103 km3 decade−1).