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Several observational precipitation products that provide high temporal (≤3 h) and spatial (≤0.25°) resolution gridded estimates are available, although no single product can be assumed worldwide to be closest to the (unknown) "reality." Here, we propose and apply a methodology to quantify the uncertainty of a set of precipitation products and to identify, at individual grid points, the products that are likely wrong (i.e., outliers). The methodology is applied over eastern North America for the 2015–2019 period for eight high‐resolution gridded precipitation products: CMORPH, ERA5, GSMaP, IMERG, MSWEP, PERSIANN, STAGE IV and TMPA. Four difference metrics are used to quantify discrepancies in different aspects of the precipitation time series, such as the total accumulation, two characteristics of the intensity‐frequency distribution, and the timing of precipitating events. Large regional and seasonal variations in the observational uncertainty are found across the ensemble. The observational uncertainty is higher in Canada than in the United States, reflecting large differences in the density of precipitation gauge measurements. In northern midlatitudes, the uncertainty is highest in winter, demonstrating the difficulties of satellite retrieval algorithms in identifying precipitation in snow‐covered areas. In southern midlatitudes, the uncertainty is highest in summer, probably due to the more discontinuous nature of precipitation. While the best product cannot be identified due to the lack of an absolute reference, our study is able to identify products that are likely wrong and that should be excluded depending on the specific application. [ABSTRACT FROM AUTHOR]

The Jenkinson–Collison weather typing scheme (JC-WT) is an automated method used to classify regional sea-level pressure into a reduced number of typical recurrent patterns. Originally developed for the British Isles in the early 1970's on the basis of expert knowledge, the method since then has seen many applications. Encouraged by the premise that the JC-WT approach can in principle be applied to any mid-to-high latitude region, the present study explores its global extra-tropical applicability, including the Southern Hemisphere. To this aim, JC-WT is applied at each grid-box of a global 2.5 ∘ regular grid excluding the inner tropics (± 5 ∘ band). Thereby, 6-hourly JC-WT catalogues are obtained for 5 distinct reanalyses, covering the period 1979–2005, which are then applied to explore (1) the limits of method applicability and (2) observational uncertainties inherent to the reanalysis datasets. Using evaluation criteria, such as the diversity of occurring circulation types and the frequency of unclassified situations, we extract empirically derived applicability thresholds which suggest that JC-WT can be generally used anywhere polewards of 23.5 ∘ , with some exceptions. Seasonal fluctuations compromise this finding along the equatorward limits of the domain. Furthermore, unreliable reanalysis sea-level pressure estimates in elevated areas with complex orography (such as the Tibetan Plateau, the Andes, Greenland and Antarctica) prevent the application of the method in these regions. In some other regions, the JC-WT classifications obtained from the distinct reanalyses substantially differ from each other, which may bring additional uncertainties when the method is used in model evaluation experiments. [ABSTRACT FROM AUTHOR]

Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.

Study region: The Danube River Basin. Study focus: Hydrological modelling of large, heterogeneous watersheds requires appropriate meteorological forcing data. The global meteorological reanalysis ERA5 and the global forcing dataset WFDE5 were evaluated for driving an uncalibrated setup of the mechanistic hydrological model PROMET (0.00833333°/1 h resolution) for the period 1980–2016. Different climatologies were used for linear bias correction of ERA5: the global WorldClim 2 temperature and precipitation climatologies and the regional GLOWA and PRISM Alpine precipitation climatologies. Simulations driven with the uncorrected ERA5 reanalysis, the WFDE5 forcing dataset, ERA5 bias-corrected with WorldClim 2 and ERA5 bias-corrected with a GLOWA-PRISM-WorldClim 2 mosaic were evaluated regarding percent bias of discharge and model efficiency. New hydrological insights for the region: Simulations yielded good model efficiencies and low percent biases of discharge at selected gauges. Uncalibrated model efficiencies corresponded with previous hydrological modelling studies. ERA5 and WFDE5 were well suited to drive PROMET in the hydrologically complex Danube basin, but bias correction of precipitation was essential for ERA5. The ERA5-driven simulation bias-corrected with a GLOWA-PRISM-WorldClim 2 mosaic performed best. Bias correction with GLOWA and PRISM outperformed WorldClim 2 in the Alps due to more realistic small-scale Alpine precipitation patterns resulting from higher station densities. In mountainous terrain, we emphasize the need for regional high-resolution precipitation climatologies and recommend them for bias correction of precipitation rather than global datasets.

The performance of multiple empirical statistical downscaling (ESD) methods was assessed for simulating daily precipitation during 1979–2017 over southeastern South America (SESA), a region where extremes are remarkable. Meteorological stations were used as reference and three gridded precipitation products were included to account for observational uncertainty. The set of ESD models involved different configurations of the analogue method (ANs), deterministic and stochastic versions of neural networks (NNs) and generalized linear models (GLMs) and circulation‐conditioned GLMs (GLM_WTs). The years with the largest number of extreme events (wet years) were calibrated separately. The spatio‐temporal variability of extremes was assessed by analysing their intensity, spatial extent, frequency and interannual variability. An overall good performance of the ESD models was found for several aspects of daily precipitation. ESD performance dispersion was usually contained in the observational spread. No particular model configuration was found to perform best in all aspects, indicating the advantage of considering a multi‐model ensemble. The ANs tended to follow the stations, satisfactorily simulating daily precipitation and its extremes. The deterministic GLMs strongly underestimated precipitation estimates and were not able to represent extreme frequencies and intensities, but this was alleviated by employing a stochastic version of the method. Furthermore, the use of weather types to condition the GLMs (GLM_WTs) considerably improved model performance, particularly for the annual cycle and the spatial structure of extreme precipitation. The NNs adequately reproduced the spatial behaviour and intra‐annual variability of extreme precipitation, although they underestimated its intensity in their deterministic version. An analysis of the regional time series of extremes showed consistency among datasets and evidenced the influence of the ENSO teleconnection on the wet years, which were commonly well‐simulated by the statistical models. ESD models presented good skills in simulating a wetter climate over SESA, which is of particular importance in a climate change scenario. [ABSTRACT FROM AUTHOR]

The performance of multiple empirical statistical downscaling (ESD) methods was assessed for simulating daily precipitation during 1979-2017 over southeastern South America (SESA), a region where extremes are remarkable. Meteorological stations were used as reference and three gridded precipitation products were included to account for observational uncertainty. The set of ESD models involved different configurations of the analogue method (ANs), deterministic and stochastic versions of neural networks (NNs) and generalized linear models (GLMs) and circulation-conditioned GLMs (GLM_WTs). The years with the largest number of extreme events (wet years) were calibrated separately. The spatio-temporal variability of extremes was assessed by analysing their intensity, spatial extent, frequency and interannual variability. An overall good performance of the ESD models was found for several aspects of daily precipitation. ESD performance dispersion was usually contained in the observational spread. No particular model configuration was found to perform best in all aspects, indicating the advantage of considering a multi-model ensemble. The ANs tended to follow the stations, satisfactorily simulating daily precipitation and its extremes. The deterministic GLMs strongly underestimated precipitation estimates and were not able to represent extreme frequencies and intensities, but this was alleviated by employing a stochastic version of the method. Furthermore, the use of weather types to condition the GLMs (GLM_WTs) considerably improved model performance, particularly for the annual cycle and the spatial structure of extreme precipitation. The NNs adequately reproduced the spatial behaviour and intra-annual variability of extreme precipitation, although they underestimated its intensity in their deterministic version. An analysis of the regional time series of extremes showed consistency among datasets and evidenced the influence of the ENSO teleconnection on the wet years, which were commonly well-simulated by the statistical models. ESD models presented good skills in simulating a wetter climate over SESA, which is of particular importance in a climate change scenario. [ABSTRACT FROM AUTHOR]

Observational records are more often than not influenced by residual non‐climatic factors which must be detected and adjusted for prior to their usage. In this work, we present a novel approach, named Radiosounding HARMonization (RHARM), providing a homogenized data set of temperature, humidity and wind profiles along with an estimation of the measurement uncertainties for 697 radiosounding stations globally. The RHARM method has been used to adjust twice daily (0000 and 1200 UTC) radiosonde data holdings at 16 pressure levels in the range 1,000–10 hPa, from 1978 to present, provided by the Integrated Global Radiosonde Archive. Relative humidity (RH) data are limited to 250 hPa. The applied adjustments are interpolated to all reported levels. RHARM is the first data set to provide homogenized time series with an estimation of the observational uncertainty at each sounding pressure level. By construction, RHARM adjusted fields are not affected by cross‐contamination of biases across stations and are fully independent of reanalysis data. Analysis of trends for temperature, RH and winds highlights increased geographical coherency of trends over 1978–2000 globally, but especially in the Northern Hemisphere and South America. RHARM shows warming trends of 0.39 K/decade at 300 hPa in the Northern Hemisphere and of 0.25 K/decade in the tropics. The RHARM adjustments also reduce differences with the European Centre for Medium‐Range Weather Forecast ERA5 reanalysis, with the strongest effect in the Northern Hemisphere for temperature and relative humidity. For wind speed, the comparison indicates a good agreement with ERA5 in the troposphere. Plain Language Summary: Systematic observations of atmospheric parameters by balloon soundings have been collected regularly across the globe since decades. However, changes in the instrumentation, calculation algorithms, station re‐locations and other factors can influence the measurement time series, making the identification of climate signals difficult. The availability of measurement metadata, cross‐comparison with (sparser) reference networks and the application of statistical methods allows to greatly improve the physical consistency of the records. We developed the Radiosounding HARMonization (RHARM) methodology to provide users with homogenized radiosonde profiles of temperature, humidity, and wind, along with measurement uncertainties, for 697 locations globally and twice daily based on the Integrated Global Radiosonde Archive. Key features of RHARM are the provision of multiple fundamental atmospheric parameters, adjusted for spurious biases and the inclusion of measurement uncertainties estimated following the standards of the metrological community when available metadata permits. The current version of the RHARM data set can be used to investigate tropospheric weather and climate for the last four decades. Key Points: The Radiosounding HARMonization (RHARM) data set provides homogenized time series of temperature, relative humidity and wind profiles for hundreds of radiosonde stationsMetrologically accurate estimates of the observational uncertainty are provided together with each observation and variableFor all variables, RHARM increases the geographical coherency of estimated trends and the agreement with a modern atmospheric reanalysis [ABSTRACT FROM AUTHOR]

The presence of error in water quality and hydrologic variables can significantly impair the calibration of water quality models. Precise and reliable identification of observational errors can have a significant impact on improving model parameter estimation. This study develops the Bayesian error analysis with reordering (BEAR) method to accommodate multiple sources of observational errors in the calibration of a water quality model. It realizes this goal by sampling the errors for input and output data from their respective error distributions and reordering them with inferred ranks via the secant method. This approach is demonstrated in the case of total suspended solids (TSS) simulated via a conceptual water quality model. Based on case studies using synthetic data, the new algorithm successfully quantifies one source of observational error when the error model of another source of observational error can be estimated accurately in advance. The results of a real case study also illustrate that considering observational errors in both model inputs and outputs, rather than in just the inputs or outputs, can improve the parameter calibration and error characterization. The improvements depend on the precision of the prior information for the error model. The application of this new algorithm in TSS simulation can be an example to understand how the BEAR algorithm works in other water quality models. The core idea of the BEAR algorithm is flexible and can be extended to the calibration of other environmental models. [ABSTRACT FROM AUTHOR]

Several temperature and precipitation indices, with special focus on extremes, were analysed in different sub‐regions of southern South America during 1979–2017 using multiple reanalyses, the CPC gridded data set and the most extended network of meteorological stations employed in regional climate studies up to date. Reanalyses generally well represented the spatial patterns of the indices, although they showed some differences in extreme indices over large portions of southern South America and tended to overestimate precipitation maximums, especially in southern Chile. Furthermore, ERA‐Interim presented clear difficulties in reproducing precipitation near the Andes Mountains, exhibiting the largest overestimations. This seemed to be improved in the new generation of ERA reanalyses (ERA5). When evaluating the long‐term changes, most of the data sets agreed in general warming conditions, stronger and more homogeneous for the maximum temperature. NCEP1 and NCEP2 reanalyses showed contrary temporal changes in almost all the temperature indices. Precipitation indices exhibited less consistent changes among reanalyses, although significant upward trends were detected for precipitation extremes in southeastern South America and downward trends were detected in southern Chile in the observational data sets. In addition, most of the data sets agreed in drier conditions in the arid diagonal region of Argentina as reflected by significant positive trends for dry spells and negative trends for the total annual precipitation. In terms of the inter‐annual correspondence, reanalyses usually presented good correlations to the stations reference in the regional averaged series, mainly for temperature indices and more variable for precipitation indices. Overall, no reanalysis was found to perform best. The use of reanalyses data to perform regional climate studies should consider the existent differences among them and with observational data. Moreover, using multiple sources of information is strongly recommended to account for observational uncertainty, especially in regions like southern South America, where data availability and its resolution are often limited. [ABSTRACT FROM AUTHOR]

The main features of daily extreme precipitation and circulation types in southern South America (SSA) were evaluated and compared in both multiple observational datasets (rain gauges, CHIRPS, CPC and MSWEP) and simulations from four regional climate models (RCMs) driven by ERA-Interim during 1980–2010. The inter-comparison of extreme events, characterised in terms of their intensity, frequency and spatial coverage, varied across SSA showing large differences among observational datasets and RCMs and reflecting the current observational uncertainty when evaluating precipitation extremes at a daily scale. The spread between observational datasets was smaller than for the RCMs. Most of the RCMs successfully captured the spatial pattern of extreme precipitation across SSA, although RCA4 (REMO) usually underestimated (overestimated) precipitation intensities, particularly the maximum amounts in southeastern South America (SESA), where the extremes are remarkable. The synoptic circulation was described by a classification of circulation types (CTs) using Self-Organizing Maps (SOM). Specific CTs were found to significantly enhance the occurrence of extreme precipitation events in sectorized areas of SESA. The RCMs adequately reproduced the SOM node frequencies, although they tended to simplify the predominant CTs into a more reduced number of configurations. They appropriately represented the extreme precipitation frequencies conditioned by each CT, exhibiting some limitations in the location and intensity of the resulting precipitation systems. These sorts of evaluations contribute to a better understanding of the physical mechanisms responsible for extreme precipitation and of their future projections in a climate change scenario. [ABSTRACT FROM AUTHOR]

Landscape evolution models (LEMs) have the capability to characterize key aspects of geomorphological and hydrological processes. However, their usefulness is hindered by model equifinality and paucity of available calibration data. Estimating uncertainty in the parameter space and resultant model predictions is rarely achieved as this is computationally intensive and the uncertainties inherent in the observed data are large. Therefore, a limits‐of‐acceptability (LoA) uncertainty analysis approach was adopted in this study to assess the value of uncertain hydrological and geomorphic data. These were used to constrain simulations of catchment responses and to explore the parameter uncertainty in model predictions. We applied this approach to the River Derwent and Cocker catchments in the UK using a LEM CAESAR‐Lisflood. Results show that the model was generally able to produce behavioural simulations within the uncertainty limits of the streamflow. Reliability metrics ranged from 24.4% to 41.2% and captured the high‐magnitude low‐frequency sediment events. Since different sets of behavioural simulations were found across different parts of the catchment, evaluating LEM performance, in quantifying and assessing both at‐a‐point behaviour and spatial catchment response, remains a challenge. Our results show that evaluating LEMs within uncertainty analyses framework while taking into account the varying quality of different observations constrains behavioural simulations and parameter distributions and is a step towards a full‐ensemble uncertainty evaluation of such models. We believe that this approach will have benefits for reflecting uncertainties in flooding events where channel morphological changes are occurring and various diverse (and yet often sparse) data have been collected over such events. [ABSTRACT FROM AUTHOR]

Abstract Human activities, such as human water use, have been shown to directly influence terrestrial water fluxes and states. Simulations of soil moisture, river discharge, evapotranspiration, and groundwater storage are significantly improved, if human interactions, such as irrigation and groundwater abstraction, are incorporated. Yet improvements through the incorporation of human water use on the simulation of local and remote precipitation are rarely studied but may contribute to the skill of land surface fluxes. In this study, we evaluate the impact of human water use on the skill of evapotranspiration and precipitation in a fully coupled bedrock‐to‐atmosphere modeling platform. The results show that human water use can potentially increase the skill of the simulations across scales. However, observational uncertainty at the watershed scale limits the identification of model deficiencies and added value related to human water use. Locally, daily precipitation statistics potentially benefit from the incorporation of human water use. Although the incorporation of human water use does not remove the wet bias, it can increase the model skill.

The aim of this work is to present preliminary results of the statistical and dynamical simulations carried out within the framework of the Flagship Pilot Study in southeastern South America (FPS-SESA) endorsed by the Coordinated Regional Climate Downscaling Experiments (CORDEX) program. The FPS-SESA initiative seeks to promote inter-institutional collaboration and further networking with focus on extreme rainfall events. The main scientific aim is to study multi-scale processes and interactions most conducive to extreme precipitation events through both statistical and dynamical downscaling techniques, including convection-permitting simulations. To this end, a targeted experiment was designed considering the season October 2009 to March 2010, a period with a record number of extreme precipitation events within SESA. Also, three individual extreme events within that season were chosen as case studies for analyzing specific regional processes and sensitivity to resolutions. Four dynamical and four statistical downscaling models (RCM and ESD respectively) from different institutions contributed to the experiment. In this work, an analysis of the capability of the set of the FPS-SESA downscaling methods in simulating daily precipitation during the selected warm season is presented together with an integrated assessment of multiple sources of observations and available CORDEX Regional Climate Model simulations. Comparisons among all simulations reveal that there is no single model that performs best in all aspects evaluated. The ability in reproducing the different features of daily precipitation depends on the model. However, the evaluation of the sequence of precipitation events, their intensity and timing suggests that FPS-SESA simulations based on both RCM and ESD yield promising results. Most models capture the extreme events selected, although with a considerable spread in accumulated values and the location of heavy precipitation. [ABSTRACT FROM AUTHOR]

Changes in the tropical atmospheric overturning circulation can strongly influence the global weather pattern via affecting the location, extent, and strength of tropical convective heating. However, due to limitations in obtaining reliable global data before the satellite era, there are still uncertainties on long‐term changes in the tropical overturning features, such as the Walker Circulation. By analysing the 20th century reanalysis products, ship‐based observations and gauge‐based land precipitation data, robust intensification of convection is found over the western equatorial Pacific and Maritime Continent (WEP‐MC; 90°E–150°E/15°S–15°N), where rainfall and total cloud cover increased by 15–20% in boreal spring during 1901–2010. The signal is noticeably seasonally dependent, and both rainfall and cloud cover show consistent and significant increasing trends only in the boreal spring season. General circulation model (GCM) experiments were conducted using two different reconstructed SST data sets as forcing; results from both runs indicate springtime intensification of WEP‐MC convection, even with relatively uniform SST warming. Further, numerical experiments using a simplified model show that the Pacific trade wind can be accelerated by intensifying cumulus heating; however, this can be sensitive to the location of convection, and only occurs if the latter is within ±5° in latitude along the equator. Our findings suggest that the secular change in the Walker Circulation is potentially discovered from the current reanalysis and observational data sets if "regional‐up" approaches are applied. [ABSTRACT FROM AUTHOR]

Simulated historical precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices. The model indices are evaluated against corresponding indices from the CPC unified gauge-based analyses of precipitation over seven geographical regions across the contiguous US (CONUS). The regions assessed match those in recent US National Climate Assessment Reports. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Most CMIP6 models overestimate the mean and variability of wet spell durations and underestimate the mean and variability of dry spell durations across the CONUS. Biases in interannual variability of most of the indices have similar patterns to those in corresponding mean biases. The median and interquartile model spreads in CMIP6 model biases are clearly smaller than those in CMIP5 model biases for wet spell durations. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. Depending on the index, extreme precipitation is slightly better in parts of the eastern half of the CONUS in CMIP6-MMM, otherwise, the biases in climatology and variability are similar to CMIP5-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.

Abstract Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state‐of‐the‐art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend‐preservation) for a case study in the Iberian Peninsula. The quantile trend‐preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP‐ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high‐quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions.

Spotted wing drosophila (SWD) is an invasive pest with devastating effects on soft‐skinned fruit crops. Due to its high economic impacts, current SWD management strategies usually focus on preventive calendar‐based insecticide sprays. The industry is calling for adoption of monitoring‐based integrated pest management (IPM) strategies to reduce unnecessary insecticide applications. However, because traps are costly and do not provide perfect observations of the population size, most growers do not monitor. We develop a Bayesian state‐space bioeconomic framework to inform the optimal SWD management strategy when observational uncertainty exists. We use Bayesian inference via the Markov Chain Monte Carlo method to generate the posterior distribution of population model parameters, which we then use to simulate the economic performance of alternative SWD management strategies. We find that one of the monitoring‐based IPM strategies has a slightly lower total cost than the calendar‐based spray strategy. We also find evidence of misalignments between public and private incentives in the adoption of IPM strategies. Profit‐maximizing growers who ignore the negative externalities of insecticide spray have little incentive to adopt the IPM strategies. However, for environmentally conscious growers who take into account the external costs of insecticide sprays, IPM strategies are superior to calendar‐based spray strategy. Our results indicate that IPM strategies become more appealing to both types of growers as the trapping efficiency improves. Extension efforts, support for research to improve trapping efficiency, and monetary incentives can be used to encourage grower adoption of monitoring‐based IPM strategies to control SWD infestation. [ABSTRACT FROM AUTHOR]

Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state‐of‐the‐art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend‐preservation) for a case study in the Iberian Peninsula. The quantile trend‐preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP‐ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high‐quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions. [ABSTRACT FROM AUTHOR]

This work evaluates the daily precipitation and mean temperature of eight CORDEX‐EUR11 ERA‐Interim‐driven simulations of EURO‐CORDEX over the Iberian Peninsula (IP) for the period 1989–2008. To this aim, three observational data sets (Iberia01, E‐OBS‐v19e, and MESAN‐0.11) were considered as reference and compared with the models by means of several indices reflecting the mean and extreme regimes over the IP. For precipitation the Lamb weather types were considered to identify synoptic conditions related with higher observational uncertainty. RCMs are able to reproduce the spatial pattern and the variability observed in the IP. However, there is a higher agreement between models and observations for mean temperature than for precipitation, decreasing when extremes are analyzed. For the observational uncertainty analysis, also extreme daily temperatures were considered to obtain a wider picture of this topic. A higher dependence on the observational data set has been found for precipitation than for temperature. This uncertainty is particularly significant when the 50‐year return value is considered for which the observational uncertainty doubles the model uncertainty. Only the wet‐day frequency presents values lower than 0.5 for all seasons, with most of the rest of values reflecting a similar contribution of both components to the uncertainty. In the case of temperatures, the main contribution of the observations has been found when the lower (MAE01) and upper (MAE99) extremes are considered, with values lower than 0.5. For precipitation the observational uncertainty increases when synoptic patterns affecting the Mediterranean Basin are considered, reflecting the difficulty to properly capture the Mediterranean precipitation regimes. Key Points: EURO‐CORDEX verication over Iberian PeninsulaObservational uncertainty analysis of the evaluation of the EURO‐CORDEX RCMsSynoptic patterns and observational uncertainty of precipitation regimes [ABSTRACT FROM AUTHOR]

This paper investigates the performance of 10 Regional Climate Models (RCMs) hindcasts from the Coordinated Regional Climate Downscaling Experiments (CORDEX) over Central Africa, covering the period 1998–2008 and performed over a common model grid spacing 0.44° (∼50 km). Multiple observational data sets are used to evaluate model performances over four targeted subregions. Throughout the work, a measure of observational uncertainty is made and we discuss whether or not the models are found within or outside the range of observational uncertainty. Results indicate that RCMs generally capture rainfall and temperature basic features, though important biases exist and vary for models and seasons. Dry (wet) biases are common features over the Congo basin (northern and southern part of the domain). In terms of precipitation and temperature in both seasonal and annual scale, most RCMs along with their ensemble mean generally fall in the range of observational uncertainty. Furthermore, most RCMs show a good spread of grid points where the added value of RCMs is found although the added value in temperature is not as great as with precipitation. UC‐WRF is among models adding less value on ERAINT and this could explain why whatever the time scale of variability, UC‐WRF outputs are generally out from the observational uncertainty. The multimodel ensemble mean is generally found within observational range when most models are there as well. This highlights the fact that the ensemble mean, built from the equal treatment of RCMs, does not generally outperform individual RCMs realization as it is reported in several previous studies. Key Points: Most RCMs along with their ensemble mean generally fall in the range of observational uncertaintyThe ensemble mean of models is generally found within the observational range when most models are there as wellThe ensemble mean of models does not generally outperform individual RCMs realization as it is reported in several previous studies [ABSTRACT FROM AUTHOR]

Human activities, such as human water use, have been shown to directly influence terrestrial water fluxes and states. Simulations of soil moisture, river discharge, evapotranspiration, and groundwater storage are significantly improved, if human interactions, such as irrigation and groundwater abstraction, are incorporated. Yet improvements through the incorporation of human water use on the simulation of local and remote precipitation are rarely studied but may contribute to the skill of land surface fluxes. In this study, we evaluate the impact of human water use on the skill of evapotranspiration and precipitation in a fully coupled bedrock‐to‐atmosphere modeling platform. The results show that human water use can potentially increase the skill of the simulations across scales. However, observational uncertainty at the watershed scale limits the identification of model deficiencies and added value related to human water use. Locally, daily precipitation statistics potentially benefit from the incorporation of human water use. Although the incorporation of human water use does not remove the wet bias, it can increase the model skill. Plain Language Summary: Precipitation forecasts exhibit large uncertainties, arising from unknown initial conditions and states, and limitations of models to represent processes at various scales. In order to improve simulations of the terrestrial water cycle, studies seek to improve the realism of the applied models through, for example, increased spatial resolution or the incorporation of additional processes across the Earth system. Here we analyze how the incorporation of additional processes in a European modeling system affects the accuracy of the simulated water fluxes, that is, evapotranspiration and precipitation. In particular, we improve the representation of groundwater in a continental‐scale, atmospheric model and include groundwater pumping and irrigation, as major components of human water use. Results indicate that large‐scale averages of precipitation, for example, over watersheds, are not necessarily improved if human water use is considered. However, significance of this finding is difficult to establish, because observational data sets exhibit large uncertainties, arising from, for example, the lack of observations in space and time, and miscalibration of the measuring devices. However, our results indicate that the accuracy of simulated daily precipitation is locally improved, which suggests that the incorporation of human water use may increase the accuracy of precipitation forecasts and advance our understanding of water cycle processes. Key Points: Observation‐based products of evapotranspiration and precipitation exhibit a large uncertainty at the watershed scaleModel biases at the watershed scale are subject to observational uncertaintyIncorporation of human water use improves the simulation of evapotranspiration and precipitation at the local scale [ABSTRACT FROM AUTHOR]

This work analyses three uncertainty sources affecting the observation‐based gridded data sets: station density, interpolation methodology and spatial resolution. For this purpose, we consider precipitation in two countries, Poland and Spain, three resolutions (0.11, 0.22 and 0.44°), three interpolation methods, both areal‐ and point‐representative implementations, and three different densities of the underlying station network (high/medium/low density). As a result, for each resolution and interpolation approach, nine different grids have been obtained for each country and inter‐compared using a variance decomposition methodology. Results indicate larger differences among the data sets for Spain than for Poland, mainly due to the larger spatial variability and complex orography of the former region. The variance decomposition points out to station density as the most influential factor, independent of the season, the areal‐ or point‐representative implementation and the country considered, and slightly increasing with the spatial resolution. In contrast, the decomposition is stable when extreme precipitation indices are considered, in particular for the 50‐year return value. Finally, the uncertainty due to station sub‐sampling inside a particular grid box decreases with the number of stations used in the averaging/interpolation. In the case of spatially homogeneous grid boxes, the interpolation approach obtains similar results for all the parameters, excepting the wet day frequency, independently of the number of stations. When there is a more significant internal variability in the grid box, the interpolation is more sensitive to the number of stations, pointing out to a minimum stations' density for the target resolution (six to seven stations). [ABSTRACT FROM AUTHOR]

Abstract: Assessing the accuracy of gridded climate data sets is highly relevant to climate change impact studies, since evaluation, bias correction, and statistical downscaling of climate models commonly use these products as reference. Among all impact studies those addressing hydrological fluxes are the most affected by errors and biases plaguing these data. This paper introduces a framework, coined Hydrological Coherence Test (HyCoT), for assessing the hydrological coherence of gridded data sets with hydrological observations. HyCoT provides a framework for excluding meteorological forcing data sets not complying with observations, as function of the particular goal at hand. The proposed methodology allows falsifying the hypothesis that a given data set is coherent with hydrological observations on the basis of the performance of hydrological modeling measured by a metric selected by the modeler. HyCoT is demonstrated in the Adige catchment (southeastern Alps, Italy) for streamflow analysis, using a distributed hydrological model. The comparison covers the period 1989–2008 and includes five gridded daily meteorological data sets: E‐OBS, MSWEP, MESAN, APGD, and ADIGE. The analysis highlights that APGD and ADIGE, the data sets with highest effective resolution, display similar spatiotemporal precipitation patterns and produce the largest hydrological efficiency indices. Lower performances are observed for E‐OBS, MESAN, and MSWEP, especially in small catchments. HyCoT reveals deficiencies in the representation of spatiotemporal patterns of gridded climate data sets, which cannot be corrected by simply rescaling the meteorological forcing fields, as often done in bias correction of climate model outputs. We recommend this framework to assess the hydrological coherence of gridded data sets to be used in large‐scale hydroclimatic studies. [ABSTRACT FROM AUTHOR]

A new ensemble of climate and climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km, from the WCRP CORDEX COmmon Regional Experiment (CORE) Framework, has been established in support of the growing demands for climate services. The main objective of this study is to assess the quality of the simulated climate and its fitness for climate change projections by REMO (REMO2015), a regional climate model of Climate Service Center Germany (GERICS) and one of the RCMs used in the CORDEX-CORE Framework. The CORDEX-CORE REMO2015 simulations were driven by the ECMWF ERA-Interim reanalysis and the simulations were evaluated in terms of biases and skill scores over ten CORDEX Domains against the Climatic Research Unit (CRU) TS version 4.02, from 1981 to 2010, according to the regions defined by the Köppen−Trewartha (K−T) Climate Classification types. The REMO simulations have a relatively low mean annual temperature bias (about ± 0.5 K) with low spatial standard deviation (about ± 1.5 K) in the European, African, North and Central American, and Southeast Asian domains. The relative mean annual precipitation biases of REMO are below ± 50 % in most domains; however, spatial standard deviation varies from ± 30 % to ± 200 %. The REMO results simulated most climate types relatively well with lowest biases and highest skill score found in the boreal, temperate, and subtropical regions. In dry and polar regions, the REMO results simulated a relatively high annual biases of precipitation and temperature and low skill. Biases were traced to: missing or misrepresented processes, observational uncertainty, and uncertainties due to input boundary forcing.

Monitoring animal populations can be difficult. Limited resources often force monitoring programs to rely on unadjusted or smoothed counts as an index of abundance. Smoothing counts is commonly done using a moving-average estimator to dampen sampling variation. These indices are commonly used to inform management decisions, although their reliability is often unknown. We outline a process to evaluate the biological plausibility of annual changes in population counts and indices from a typical monitoring scenario and compare results with a hierarchical Bayesian time series (HBTS) model. We evaluated spring and fall counts, fall indices, and model-based predictions for the Rocky Mountain population {RMP} of Sandhill Cranes {Antigone canadensis} by integrating juvenile recruitment, harvest, and survival into a stochastic stage-based population model. We used simulation to evaluate population indices from the HBTS model and the commonly used 3-yr moving average estimator. We found counts of the RMP to exhibit biologically unrealistic annual change, while the fall population index was largely biologically realistic. HBTS model predictions suggested that the RMP changed little over 31 yr of monitoring, but the pattern depended on assumptions about the observational process. The HBTS model fall population predictions were biologically plausible if observed crane harvest mortality was compensatory up to natural mortality, as empirical evidence suggests. Simulations indicated that the predicted mean of the HBTS model was generally a more reliable estimate of the true population than population indices derived using a moving 3-yr average estimator. Practitioners could gain considerable advantages from modeling population counts using a hierarchical Bayesian autoregressive approach. Advantages would include; (1) obtaining measures of uncertainty; (2) incorporating direct knowledge of the observational and population processes; (3) accommodating missing years of data; and (4) forecasting population size. [ABSTRACT FROM AUTHOR]

Hypothesis testing about catchment functioning with conceptual hydrological models is affected by uncertainties in the model representation of reality as well as in the observed data used to drive and evaluate the model. We formulated a learning framework to investigate the role of observational uncertainties in hypothesis testing using conceptual models and applied it to the relatively data-scarce tropical Sarapiqui catchment in Costa Rica. Observational uncertainties were accounted for throughout the framework that incorporated different choices of model structures to test process hypotheses, analyses of parametric uncertainties and effects of likelihood choice, a posterior performance analysis and (iteratively) formulation of new hypotheses. Estimated uncertainties in precipitation and discharge were linked to likely non-linear near-surface runoff generation and the potentially important role of soils in mediating the hydrological response. Some model-structural inadequacies could be identified in the posterior analyses (supporting the need for an explicit soil-moisture routine to match streamflow dynamics), but the available information about the observational uncertainties prevented conclusions about other process representations. The importance of epistemic data errors, the difficulty in quantifying them and their effect on model simulations was illustrated by an inconsistent event with long-term effects. Finally we discuss the need for new data, new process hypotheses related to deep groundwater losses, and conclude that observational uncertainties need to be accounted for in hypothesis testing to reduce the risk of drawing incorrect conclusions. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Benchmarking the quality of river discharge data and understanding its information content for hydrological analyses is an important task for hydrologic science. There is a wide variety of techniques to assess discharge uncertainty. However, few studies have developed generalized approaches to quantify discharge uncertainty. This study presents a generalized framework for estimating discharge uncertainty at many gauging stations with different errors in the stage-discharge relationship. The methodology utilizes a nonparametric LOWESS regression within a novel framework that accounts for uncertainty in the stage-discharge measurements, scatter in the stage-discharge data and multisection rating curves. The framework was applied to 500 gauging stations in England and Wales and we evaluated the magnitude of discharge uncertainty at low, mean and high flow points on the rating curve. The framework was shown to be robust, versatile and able to capture place-specific uncertainties for a number of different examples. Our study revealed a wide range of discharge uncertainties (10-397% discharge uncertainty interval widths), but the majority of the gauging stations (over 80%) had mean and high flow uncertainty intervals of less than 40%. We identified some regional differences in the stage-discharge relationships, however the results show that local conditions dominated in determining the magnitude of discharge uncertainty at a gauging station. This highlights the importance of estimating discharge uncertainty for each gauging station prior to using those data in hydrological analyses. [ABSTRACT FROM AUTHOR]

Structural uncertainty arises when key features of the behavior of a system are not well understood. Observational uncertainty arises when key variables in a system are not directly observed. Both types of uncertainty lead to problems for standard dynamic optimization approaches. The replacement of uncertainties by belief distributions over those uncertainties is one approach to addressing the problem. The use of this and other approaches are reviewed, with an emphasis on applications to environmental and resource management problems. [ABSTRACT FROM AUTHOR]

This review and commentary sets out the need for authoritative and concise information on the expected error distributions and magnitudes in observational data. We discuss the necessary components of a benchmark of dominant data uncertainties and the recent developments in hydrology which increase the need for such guidance. We initiate the creation of a catalogue of accessible information on characteristics of data uncertainty for the key hydrological variables of rainfall, river discharge and water quality (suspended solids, phosphorus and nitrogen). This includes demonstration of how uncertainties can be quantified, summarizing current knowledge and the standard quantitative results available. In particular, synthesis of results from multiple studies allows conclusions to be drawn on factors which control the magnitude of data uncertainty and hence improves provision of prior guidance on those uncertainties. Rainfall uncertainties were found to be driven by spatial scale, whereas river discharge uncertainty was dominated by flow condition and gauging method. Water quality variables presented a more complex picture with many component errors. For all variables, it was easy to find examples where relative error magnitudes exceeded 40%. We consider how data uncertainties impact on the interpretation of catchment dynamics, model regionalization and model evaluation. In closing the review, we make recommendations for future research priorities in quantifying data uncertainty and highlight the need for an improved 'culture of engagement' with observational uncertainties. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A framework for the verification of ensemble forecasts of near-surface wind speed is described. It is based on existing scores and diagnostic tools, though considering observations from synoptic stations as reference instead of the analysis. This approach is motivated by the idea of having a user-oriented view of verification, for instance with the wind power applications in mind. The verification framework is specifically applied to the case of ECMWF ensemble forecasts and over Europe. Dynamic climatologies are derived at the various stations, serving as a benchmark. The impact of observational uncertainty on scores and diagnostic tools is also considered. The interest of this framework is demonstrated from its application to the routine evaluation of ensemble forecasts and to the assessment of the quality improvements brought in by the recent change in horizontal resolution of the ECMWF ensemble prediction system. The most important conclusions cover (1) the generally high skill of these ensemble forecasts of near-surface wind speed when evaluated at synoptic stations, (2) the noteworthy improvement of scores brought by the change of horizontal resolution, and, (3) the scope for further improvements of reliability and skill of wind speed ensemble forecasts by appropriate post-processing. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: The physical processes involved in air quality modeling are governed by dynamically-generated meteorological model fields. This research focuses on reducing the uncertainty in the horizontal transport in the lower troposphere by improving the four dimensional data assimilation (FDDA) strategy in retrospective meteorological modeling. In particular, characterization of winds in the nocturnal low-level jet and overlying residual layer is crucial to accurately model regional-scale ozone transport in the key airsheds of the US. Since model errors in wind speed and direction lead to spatial displacements of pollution plumes, observations not routinely used in previous retrospective modeling are introduced through FDDA in an effort to help reduce this transport uncertainty. Prior to the main modeling sensitivity, an observational uncertainty analysis was pursued to identify uncertainties in the wind speed and direction in the lower 1-km of the troposphere that are inherent in the observational data sets used in FDDA. Comparisons of observations among various platforms (radar wind profilers, radiosonde soundings and weather radar profiles) taken in close proximity revealed that an uncertainty of approximately 1.8 m s−1 for wind speed and about 20° for wind direction was intrinsic to the measurements. In the modeling sensitivities, some minimal improvement of modeled winds within the convective planetary boundary layer (PBL) was found when surface analysis nudging of wind was eliminated. Improvements in the nocturnal jet and residual layer winds at night are demonstrated as a reaction to the use of new observations in the data assimilation in layers above the stable PBL. There is also evidence that the assimilated observations above the convective PBL during the day led to improvements of winds within the PBL, which may relieve the need of nudging within the PBL, including surface analysis nudging. [Copyright &y& Elsevier]