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To better understand and to skillfully predict climate variability, accurate and physically coherent observation-based data sets are needed. Towards this goal, continuous efforts are made to develop and produce reanalyses. Because of the convenience of their use, these datasets are often treated as a proxy for “observations” and are used for initialization and verification. Thus, assessing the fidelity of these data is crucial for their application in different aspects of climate variability and prediction. In this analysis, we validate the basic features of coupled air-sea feedback related to El Niño-Southern Oscillation (ENSO), including dynamic feedback between sea surface temperature (SST) and surface wind stress, thermodynamic feedbacks between SST and surface heat flux, as well as the extratropical influences of ENSO in seven atmospheric reanalyses. Overall, the modern reanalyses are superior to their earlier generation counterpart in capturing the dynamic feedback in the Niño3.4 region. However, the improvement in the thermodynamic feedback is vague. We note that Climate Forecast System Reanalysis, the only partially coupled data assimilation system, does not show superiority in reproducing the atmospheric feedbacks associated with ENSO. Intriguingly, all the reanalyses are highly consistent in the evolutions of equatorial Southern Oscillation and Pacific-North American indices, along with the ENSO-North American winter precipitation teleconnection. [ABSTRACT FROM AUTHOR]

Near-surface air temperature is an essential climate variable for the study of many biophysical phenomena, yet is often only available as a daily mean or extrema (minimum, maximum). While many applications require sub-diurnal dynamics, temporal interpolation methods have substantial limitations and atmospheric reanalyses are complex models that typically have coarse spatial resolution and may only be periodically updated. To overcome these issues, we developed an hourly air temperature product for Australia with spatial interpolation of hourly observations from 621 stations between 1990 and 2019. The model was validated with hourly observations from 28 independent stations, compared against empirical temporal interpolation methods, and both regional (BARRA-R) and global (ERA5-Land) reanalysis outputs. We developed a time-varying (i.e., time-of-day and day-of-year) coastal distance index that corresponds to the known dynamics of sea breeze systems, improving interpolation performance by up to 22.4% during spring and summer in the afternoon and evening hours. Cross-validation and independent validation (n = 24/4 OzFlux/CosmOz field stations) statistics of our hourly output showed performance that was comparable with contemporary Australian interpolations of daily air temperature extrema (climatology/hourly/validation: R2 = 0.99/0.96/0.92, RMSE = 0.75/1.56/1.78 °C, Bias = -0.00/0.00/-0.03 °C). Our analyses demonstrate the limitations of temporal interpolation of daily air temperature extrema, which can be biased due to the inability to represent frontal systems and assumptions regarding rates of temperature change and the timing of minimum and maximum air temperature. Spatially interpolated hourly air temperature compared well against both BARRA-R and ERA5-Land, and performed better than both reanalyses when evaluated against the 28 independent validation stations. Our research demonstrates that spatial interpolation of sub-diurnal meteorological fields, such as air temperature, can mitigate the limitations of alternative data sources for studies of near-surface phenomena and plays an important ongoing role in supporting numerous scientific applications. [ABSTRACT FROM AUTHOR]

Trends in temperature variability are often referred to have higher effect on temperature extremes than trends in the mean. We investigate trends in three complementary measures of intraseasonal temperature variability: (a) standard deviation of mean daily temperature (SD), (b) mean absolute value of day‐to‐day temperature change (DTD) and (c) 1‐day lagged temporal autocorrelation of temperature (LAG). It is a well‐established fact that different types of data (station, gridded, reanalyses) provide different temperature characteristics and particularly their trends. Moreover, we have uncovered that trends in measures of variability are considerably sensitive to data inhomogeneities. Therefore, we use five different datasets, one station based (ECA&D), one gridded (EOBS) and three reanalyses (JRA‐55, NCEP/NCAR, 20CR), and compare them. The period from 1961 to 2014 where all datasets overlap is examined, and the linear regression method is utilized to calculate trends of investigated measures in summer and winter. Intraseasonal temperature variability tends to decrease in winter, especially in eastern and northern Europe, where trends below −7%·decade−1 are detected for all measures. Decreases in DTD and LAG (indicating increase in persistence) prevail also in summer while summer SD tends to increase. The increase in the width of temperature distribution and the simultaneous increase in persistence indicate a tendency towards the rise in the frequency of extended extreme events in summer. Unlike previous studies, our results imply that reanalyses are not the least accurate in determining trends. JRA‐55 appears to be the least diverging from other datasets, while the largest discrepancies are detected for DTD at station data. [ABSTRACT FROM AUTHOR]

Reanalysis datasets have been widely used in oceanography and climate change studies. We evaluate the applicability of eight reanalysis datasets in the Yellow and Bohai seas (YBS), including ERA5, SODA3.4.2, GREPv2, C-GLORSv7, GLORYS2v4, ORAS5, CORAv1.0, and CORAv2.0, compared to the observation data from the oceanic stations and an observation-based dataset of global instantaneous 3D thermohaline fields (ARMOR3D). The results show that the sea surface temperature (SST) of ERA5, ORAS5, and GREPv2 agrees with the observations, while the temperature profiles of C-GLORSv7 and GREPv2 are in good agreement with the ARMOR3D observation. In terms of ocean salinity, the salinity profile of the CORA series is in better agreement than other reanalysis datasets. Overall, GREPv2 is more consistent with seawater temperature observations in the YBS than others, while the CORA series reproduces the salinity variation better. GREPv2, a multi-model ensemble product, delivers a better depiction of seawater temperature and salinity than that of the individual member dataset. Most reanalysis datasets can reproduce the interannual variation of SST in the YBS well with improved performance in the last decade. The spatial distribution differences occur mostly in offshore waters with a warmer but less salinity bias. [ABSTRACT FROM AUTHOR]

Cyclonic low-pressure systems (LPS) produce abundant rainfall in South Asia, where they are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department (IMD) has tracked monsoon depressions for over a century, finding a large decline in their number in recent decades, but their methods have changed over time and do not include monsoon lows. This study presents a fast, objective algorithm for identifying monsoon LPS and uses it to assess interannual variability and trends in reanalyses. Variables and thresholds used in the algorithm are selected to best match a subjectively analyzed LPS data set while minimizing disagreement between four reanalyses in a training period. The stream function of 850 hPa horizontal wind is found to be optimal in this sense; it is less noisy than vorticity and represents the complete nondivergent wind, even when flow is not geostrophic. Using this algorithm, LPS statistics are computed for five reanalyses, and none show a detectable trend in monsoon depression counts since 1979. Both the Japanese 55-year Reanalysis (JRA-55) and the IMD data set show a step-like reduction in depression counts when they began using geostationary satellite data, in 1979 and 1982, respectively; the 1958–2018 linear trend in JRA-55, however, is smaller than in the IMD data set, and its error bar includes 0. There are more LPS in seasons with above-average monsoon rainfall and in La Niña years, but few other large-scale modes of interannual variability are found to modulate LPS counts, lifetimes, or track length consistently across reanalyses.

As the linkages between extreme weather events, changes in climatic conditions and health impacts in exposed populations become clearer, so does the need for climate‐smart decisions aimed at making the public health sector more responsive and resilient. By integrating climate and health information, climate services for health provide robust decision‐support tools. The Lancet Countdown monitoring system uses global climate reanalyses products to track annual changes in a set of health‐related outcomes. In the monitoring system, multiple variables from reanalysis datasets such as ERA5 and ERA5‐Land are retrieved and processed to capture heatwaves, precipitation extremes, wildfires, droughts, warming and ecosystem changes across the globe and over multiple decades. This reanalysis‐derived information is then input into a hazard–exposure–vulnerability framework that delivers, as outcomes, indicators tracking the year‐by‐year impacts of climate‐related hazards on human mortality, labour capacity, physical activity, sentiment, infectious disease transmission, and food security and undernutrition. Building on the reanalysis gridded format, the indicators create worldwide 'maps without gaps' of climate–health linkages. Our experience shows that reanalysis datasets allow standardization across the climate information used in the framework, making the system potentially adaptable to multiple geographical scales. An ongoing challenge is to quantify how the inherent bias of global reanalyses influences indicator outcomes. We foresee the health sector as a key user of reanalysis products. Therefore, public health professionals and health impact modellers should be involved in the co‐development of future iterations of reanalysis datasets, to reach finer spatial resolutions and provide a wider set of health‐relevant climate variables. [ABSTRACT FROM AUTHOR]

Water table elevation is a key feature for identifying the groundwater behaviour. Accordingly, appropriate measurements—in terms of both frequency and spatial distribution—play a crucial role for capturing the aquifer response to recharge and withdrawals. However, numerical models simulating the main features of the behaviour of the water table elevation may help groundwater management, as an additional tool. In this article, soil moisture data from three well‐established global reanalyses (ERA5, CFS, and JRA‐55) are used for evaluating the flux in the vadose zone towards shallow unconfined aquifers, Fg$$ {F}_g $$, in the Umbria region (central Italy). Then, according to the methodology proposed in Bongioannini Cerlini et al. (2021), where for the considered aquifers most of the recharge derives from the unsaturated zone, Fg$$ {F}_g $$ is used for simulating the water table evolution in time. With the aim of assessing which reanalysis is the most appropriate in simulating the evolution of groundwater levels, the properties of the correspondent land surface models (LSM) are examined, as they provide Fg$$ {F}_g $$. For the considered aquifers, the analysis of the performance of the selected reanalyses confirms the validity of the proposed approach. Moreover, it points out the crucial role of the spreading of the water table elevation with respect to its mean value, as a significant parameter for selecting the most adequate reanalysis to use. In addition, the role in the LSM of the explored soil depth, hydraulic conductivity curve, and spatial resolution is highlighted. These results, in line with recent literature on the performance of the reanalyses, suggest to extend future work to other regions of the world. [ABSTRACT FROM AUTHOR]

State‐of‐the‐art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse‐input reanalysis products which are constrained by observed sea surface temperatures, sea‐ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse‐input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour‐based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long‐term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post‐2015 when the NOAA‐CIRES‐DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases. [ABSTRACT FROM AUTHOR]

Radiosonde data are important for understanding and monitoring the upper troposphere and lower stratosphere (UTLS) region. Over much of Africa, however, such data are lacking; consequently, the African UTLS is understudied, and potential proxies such as climate models and reanalysis products fail to fully capture the behavior of the UTLS. This study pioneers the use of Global Navigation Satellite System‐Radio Occultation (GNSS‐RO) data from 2001 to 2020 to address the radiosonde data gap over Africa and contributes to a better understanding of the tropopause (TP) characteristics under the influence of global and regional climate drivers over the continent. As a first step to using GNSS‐RO for infilling the radiosonde data gap over Africa, we analyzed the performance of GNSS‐RO (2001–2020) and reanalysis products (European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5) and Modern‐Era Retrospective analysis for Research and Applications version 2 (MERRA‐2)) against radiosonde observations applying the Kling‐Gupta Efficiency metric. The analyses show that GNSS‐RO data from Challenging Mini‐satellite Payload, Gravity Recovery and Climate Experiment, Meteorological Operational, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), and COSMIC‐2 are in good agreement with radiosonde measurements with differences being smaller than 1 K in the UTLS, thereby enabling infilling of missing radiosonde data in Africa during 2001–2020. By contrast, the smoothed vertical temperature profiles of reanalysis products lead to a warm bias of +0.8 K in ERA5 and +1.2 K in MERRA‐2 and these biases alter some vertical and temporal structure details, with possible implications on climate change detection and attribution. Furthermore, the analysis of GNSS‐RO data over Africa revealed: (a) the teleconnections of El Niño‐Southern Oscillation (ENSO), Quasi‐Biennial Oscillation (QBO), Indian Ocean Dipole (IOD), Madden‐Julian Oscillation (MJO), North Atlantic Oscillation (NAO) and Southern Annular Mode (SAM) at the tropopause boundary; (b) multiple coupled global climate drivers such as ENSO‐IOD, ENSO‐MJO, ENSO‐NAO, QBO‐IOD, and ENSO‐NAO‐MJO; (c) coupled global and regional climate drivers that influence the TP variability, for example, ENSO‐Inter Tropical Convergence Zone; and (d), the deep convection associated with the Asian Summer Monsoon and Tropical/African Easterly Jet also locally influence TP height. In conclusion, this study demonstrates the capability of GNSS‐RO to fill the vast radiosonde data gap over Africa. This opens the opportunity for further detailed studies toward a better understanding of the tropopause characteristics including localization, quantification of trends, and influences of global, regional, and coupled climate drivers. Key Points: Global Navigation Satellite System‐Radio Occultation (GNSS‐RO) is capable of infilling the vast radiosonde data gap over AfricaGNSS‐RO captures global and regional climate drivers of tropopause behaviorGNSS‐RO reveals multiple coupled global and regional climate drivers [ABSTRACT FROM AUTHOR]

A proposal to characterize seasons based on the annual cycle of daily mean temperature over extratropical regions is presented. Four metrics are computed, based on the dates along the year when the maximum and minimum temperature values and the maximum warming and cooling (when the rate of temperatura change is maximum or minimum) are obtained. These four metrics are computed for 1980–2014 period for ERA‐Interim, JRA‐55 and NCEP2 reanalysis products. Results indicate that these four metrics are able to represent, with geographical coherence over any extratropical region, the start and end of subperiods along the year and that can be seen and used as a consistent procedure to define seasons. The two more extreme metrics (dates for the maximum and minimum temperature) are closely influenced by the variability of the net radiation and near‐surface circulation. Regions with different climatic regimes, defined in terms of land–atmosphere coupling conditions, have different contributing factors influencing the interannual variability of the metrics. In humid regions the net surface radiation is critical for the timing of the maximum temperature date, while the variability of the atmospheric circulation is relevant for the minimum temperature metric. In transitional regions, land–atmosphere interaction plays a key role in the timing of the annual cycle, both in summer and winter. Variations in net radiation are relevant for dry regions. Therefore, a robust methodology to establish seasons based on the annual temperature cycle is presented. We also inspect how the metrics timing responds in terms of the interannual variations of different relevant atmospheric variables. [ABSTRACT FROM AUTHOR]

Earth system modelling is currently playing an increasing role in weather forecasting and understanding climate change, however, the operation, deployment and development of numerical Earth system models are extremely demanding in terms of computational resources and human effort. Merging synergies has become a natural process by which national meteorological services assess and contribute to the development of such systems. With the advent of joining synergies at the national level, the second edition of the workshop on Numerical Weather Prediction in Portugal was promoted by the Portuguese Institute for the Sea and Atmosphere, I.P. (IPMA), in cooperation with several Portuguese Universities. The event was hosted by the University of Évora, during the period of 11–12 of November 2021. It was dedicated to surface–atmosphere interactions and allowed the exchange of experiences between experts, students and newcomers. The workshop provided a refreshed overview of ongoing research and development topics in Portugal on surface–atmosphere interaction modelling and its applications and an opportunity to revisit some of the concepts associated with this area of atmospheric sciences. This article reports on the main aspects discussed and offers guidance on the many technical and scientific modelling platforms currently under study. [ABSTRACT FROM AUTHOR]

Monitoring changes in climate extremes is vitally important in order to provide context for both our current and possible future climates. Datasets based on climate extremes indices from in situ observations and climate reanalyses are often used for this purpose. We assess the spatial and temporal consistency between these two classes of dataset on a global basis to understand where they agree or are complementary. As expected, the temperature time series expressed as anomalies, or self-normalizing indices, agree well. While there is sometimes a large spread in absolute values between products, both long-term trends and inter-annual variability are also in agreement. Spatially the temperature indices show high correlations, but comparisons between the cumulative distributions at each grid box show differences in regions at high altitude or where interpolation has been performed across climatic zones. The agreement is lower between the time series from observation-based and reanalysis datasets for precipitation indices. Trends in these indices show larger spatial heterogeneity, and inter-annual variation in the global averages is often larger than the magnitude of the long-term trend. These indices show larger spatial heterogeneity in the trends, which results in comparatively small long-term trends in the global averages, which are also small compared to the inter-annual variation. Spatially these indices show on average smaller correlations than for the temperature indices, but large regions show strong positive correlations for some precipitation indices. A subset of the reanalyses has higher correlations with the latest in situ-based dataset, HadEX3, and also have smaller differences in the per-grid box cumulative distributions, indicating close agreement to the observation-based dataset. Also, we outline how the comparisons herein suggest that the gridding method used when creating HadEX3 may need to be updated for future versions of this dataset, in order to retain detail arising from topographic features, for example.

[Display omitted] • Models based on kriging with external drift on elevation were the optimal choices. • Additional RMI stations improved the accuracy but not the robustness of the models. • Meteorological reanalysis used as predictor did not improve spatial interpolation. • Spatial interpolation is a promising option to improve current agricultural DSSs. • Agromet.be is an application for spatial interpolation of weather data in Wallonia. Food production will have to increase in the future to face the growing world population. Agricultural decision support systems (DSSs) are part of the solution since they aim at protecting crops against fungal diseases, a significant contributor to yield losses, while minimising pesticide use. DSSs are mainly driven by weather data which, currently, are usually obtained from the nearest available weather station. Since the latter is sometimes located far away from a farmer's field, this can lead to inaccurate recommendations. In order to provide better local weather data, spatial interpolation is a solution. However, since it must be delivered in near real-time, integrating a spatial interpolation process into an operational application necessitates addressing four constraints: Accuracy , Robustness , Reliability and Latency. This study aimed at developing an operational application for a near real-time spatial interpolation of air temperature and relative humidity at hourly and daily timescales. The first objective was to select the best spatial interpolation models among five algorithms: nearest neighbour, inverse distance weighting, multiple linear regression, ordinary kriging and kriging with external drift. The best models were based on kriging with elevation as external drift. They largely reduced the mean absolute error (MAE) compared to using the nearest station: for hourly air temperature MAE dropped from 0.93 °C to 0.59 °C. It performed also better than multiple linear regression (MAE = 0.68 °C). The second objective was to evaluate the impact of increasing station density by adding stations from the Belgian synoptic network. Additional stations improved Accuracy (MAE = 0.57 °C) but to a lesser extent than expected and had no clear impact on Robustness. The third objective was to assess the interest of using reanalyses (i.e. climate model outputs) as dynamic predictor variables. Reanalyses did not improve Accuracy (MAE = 0.62 °C) because, compared to elevation, they did not provide useful additional information that can be leveraged by the interpolation models. Using such a dynamical input also impacted Reliability negatively due to potential availability issues. Kriging models presented the highest computing times. However, Latency caused by the interpolation process itself was very small compared to the entirety of data processing. The selected models were implemented on an online application " Agromet.be ". Near real-time dissemination of interpolated weather data enables to produce local warnings helping farmers to take better decisions about spraying schedules. As a future improvement of spatial interpolation, integrating numerous personal weather stations owned by farmers seems promising. [ABSTRACT FROM AUTHOR]

Temporal evolution of relationships between surface temperature and modes of low‐frequency circulation variability is compared between five reanalyses (20CRv3, 20CRv2c, ERA‐20C, JRA‐55, and NCEP‐1) in winter from 1958 to 2010 over the northern Extratropics. The relationships are evaluated using 15‐year running correlations between temperature anomalies (from the CRU TS v. 4.03 data set) and the intensity of circulation modes (detected in 500 hPa heights by rotated principal component analysis). The analysis, utilizing mean absolute differences between time series of running correlations, points to the large agreement between ERA‐20C, JRA‐55, and NCEP‐1. Circulation modes in those reanalyses are highly similar, which in turn lead to the agreement in temporal development of correlations. In contrast, relationships of some circulation modes with temperature in 20CRv3 and 20CRv2c differ due to differences in the position, strength, and shape of the action centres. This concerns circulation modes located over Eurasia and the Atlantic, mainly North Atlantic Oscillation and Eurasian mode type 1 (EU1). Composite maps, calculated for all running periods, indicate dissimilar temporal evolution of action centres in both 20CR reanalyses. Increased differences in correlations occur mainly during periods when the position and strength of action centres diverge the most. Relationships of circulation modes located over North America and the Pacific with temperature share large resemblance between all reanalyses, including those from the 20CR family. Differences appear to be smaller in 20CRv3 compared to the preceding version, 20CRv2(c), suggesting that the development of the 20CR reanalysis has succeeded in correcting and diminishing biases. [ABSTRACT FROM AUTHOR]

Perfect prognosis statistical downscaling relies on the statistical relationships established using observational data for predictands and predictors. Predictors are often retrieved from reanalyses, which are considered pseudo‐observations. The impact of the choice of a reanalysis dataset on the performance of the downscaling method is usually overlooked, as global reanalyses are frequently assumed to be equivalent for the last few decades and data‐rich regions such as Europe. However, it was recently shown that the reanalysis dataset can have a bigger impact on the method skill than the choice of predictor variables. Generally, reanalyses processed by more recent atmospheric models assimilate more data and perform best. This work is aimed at assessing the extent of potential gains from the use of ERA5, following its release, compared to other global reanalyses. The assessment was carried out using six variants of analogue methods, which are statistical downscaling techniques, to predict daily precipitation at 301 stations across Switzerland. ERA5 proved to be one of the best performing reanalyses across the different analogue methods. Due to data availability, we recommend using 20CR for applications starting between 1851 and 1900, CERA‐20C for those between 1900 and 1950, and ERA5 for applications after 1950. However, ERA5 high spatial resolution (0.25°) turned out to be a trap for simple calibration techniques. The domains over which the predictor fields are compared need to be optimized, and high‐resolution grids come along with numerous sub‐optimal local solutions. An enhanced calibration procedure, thus, must be used. Besides the risk of poorly‐calibrated domains, the high resolution also requires much higher computational time with no gain in skill, provided that the predictors considered are relevant at a synoptic scale. Although ERA5 should be the dataset of choice, its use at a lower resolution to predict daily precipitation should provide equivalent performance. [ABSTRACT FROM AUTHOR]

While the evidence for anthropogenic climate change continues to strengthen, and concerns about severe weather events are increasing, global projections of regional climate change are still uncertain due to model‐dependent changes in large‐scale atmospheric circulation, including over North Atlantic and Europe. Here, the Jenkinson–Collison classification of daily circulation patterns is used to evaluate past and future changes in their seasonal frequencies over Central Europe for the 1900–2100 period. Three reanalyses and eight global climate models from the Coupled Model Intercomparison Project phase 6, were used based on daily mean sea‐level pressure data. Best agreement in deriving relative frequencies of the synoptic types was found between the reanalyses. Global models can generally capture the interannual variability of circulation patterns and their climatological state, especially for the less frequent synoptic types. Based on historical data and the shared socioeconomic pathway 5 scenario, the evaluated trends show more robust signals during summer, given their lesser internal variability. Increasing frequencies were found for circulation types characterized by weak pressure gradients, mainly at the expense of decreasing frequencies of westerlies. Our findings indicate that given a high‐emission scenario, these signals will likely emerge from past climate variability towards the mid‐21st century for most altered circulation patterns. [ABSTRACT FROM AUTHOR]

Surface water stress remote sensing indices can be very helpful to monitor the impact of drought on agro-ecosystems, and serve as early warning indicators to avoid further damages to the crop productivity. In this study, we compare indices from three different spectral domains: the plant water use derived from evapotranspiration retrieved using data from the thermal infrared domain, the root zone soil moisture at low resolution derived from the microwave domain using the Soil Water Index (SWI), and the active vegetation fraction cover deduced from the Normalized Difference Vegetation Index (NDVI) time series. The thermal stress index is computed from a dual-source model Soil Plant Atmosphere and Remote Evapotranspiration (SPARSE) that relies on meteorological variables and remote sensing data. In order to extend in time the available meteorological series, we compare the use of a statistical downscaling method applied to reanalysis data with the use of the unprocessed reanalysis data. Our study shows that thermal indices show comparable performance overall compared to the SWI at better resolution. However, thermal indices are more sensitive for a drought period and tend to react quickly to water stress. [ABSTRACT FROM AUTHOR]

The annular mode at the surface, referred to as the Arctic Oscillation (AO) in the Northern Hemisphere and the Antarctic Oscillation (AAO) in the Southern Hemisphere, is statistically defined by the leading mode of variability of sea level pressure (SLP) or geopotential height. Its principal component time series is set as the AO or AAO index. Although this metric is widely used, it needs an empirical orthogonal function analysis which introduces the complexity in the multi-model analysis. As an alternative measure, the zonal index (ZI), which is traditionally defined as the zonal-mean SLP difference between the two reference latitudes, has also been used. Here we re-evaluate the interannual and trend relationships between the ZI and the AO/AAO index using the two reanalysis datasets and 35 climate model simulations for both the present and future climate. For all datasets, the spatio-temporal variability of the Southern-Hemisphere ZI is almost identical to that of the AAO index. The ZI is still useful to examine the AO-related circulation variability and change in the Northern Hemisphere but mostly in the cold season. The data-dependent ZI, optimized for each data, exhibits a rather weak relationship with the AO index. This result suggests that the interannual variability and long-term trend of the midlatitude circulation can be concisely quantified by computing the fixed-latitude ZI in all seasons in the Southern Hemisphere and in the cold season in the Northern Hemisphere, not only for reanalysis data but also for climate model datasets which have significant biases varying from model to model. [ABSTRACT FROM AUTHOR]

The utility of a forecast depends on its skill as demonstrated by past performance. For most forecasts errors rapidly become large compared to uncertainties in the observation-based state of the system and, for this reason, it is usually deemed adequate to assess predictions against a single verification dataset. Eleven reanalyses and station-based analyses of annual mean surface air temperature are compared as are basic skill measures obtained when using them to verify decadal prediction hindcasts from the Canadian Centre for Climate Modelling and Analysis forecasting system. There are differences between reanalysis and station-based analyses which translate also into differences in basic skill scores. In an average sense, using station-based verification data results in somewhat better correlation skill. The spread between the locally best and worst scores is obtained for individual forecast ensemble members and for ensemble mean forecasts compared to individual analyses. The comparison of ensemble mean forecasts against different analyses can result in apparent skill differences, and using a “favorable” analysis for verification can improve apparent forecast skill. These differences may be more pertinent for longer time averages and should be considered when verifying decadal predictions and when comparing the skill of decadal prediction systems as part of a model intercomparison project. Either a particular analysis could be recommended by the decadal prediction community, if such could be agreed on, or the ensemble average of a subset of recent analyses could be used, assuming that ensemble averaging will act to average out errors.

The role of eddies in the North Atlantic decadal variability is investigated in this study by using two ocean reanalyses, including an eddy permitting (or eddy poor) reanalysis with horizontal resolution of 0.25 degree and 75 vertical levels and an eddy resolving (or eddy rich) reanalysis with horizontal resolution of 1/12 degree and 50 vertical levels. The prominent mid-1990s warming and post-2005 cooling trend as part of the North Atlantic decadal variability is well displayed in both reanalyses with no significant difference between them. The main driver of the mid-1990s warming and post-2005 cooling trend is the increase and reduction of the meridional ocean heat transport showing similar patterns in both reanalyses. The relative contribution of the heat transport anomalies from eddies to the total heat transport anomalies is slightly larger in eddy resolving than in eddy permitting ocean reanalysis. However, the total mean ocean meridional heat transport increases by 10% in eddy resolving reanalysis with respect to eddy permitting reanalysis and is mainly due to the associated increase of the mean states (temperature and velocity). Therefore, the increase of eddy population due to the increase of horizontal resolution, found by comparing the two datasets, does not affect the MHT anomalies significantly and, consequently, the North Atlantic decadal variability. It is found that the importance of the model horizontal resolution for the North Atlantic decadal variability depends on the interaction between the eddies (small scale) and the mean state (large scale) at decadal time scales. Although the fast increase of computational power will allow soon for eddy-resolving predictions, the need to use high resolution modeling tools for decadal predictions depends on the importance of initialization methods and the interaction between small scale and large-scale variabilities. This study has pivotal implications for the development of North Atlantic decadal prediction systems.

Despite the implication of aerosols for the radiation budget, there are persistent differences in data for the aerosol optical depth (τ) for 1998–2019. This study presents a comprehensive evaluation of the large‐scale spatio‐temporal patterns of mid‐visible τ from modern data sets. In total, we assessed 94 different global data sets from eight satellite retrievals, four aerosol‐climate model ensembles, one operational ensemble product, two reanalyses, one climatology and one merged satellite product. We include the new satellite data SLSTR and aerosol‐climate simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the Aerosol Comparisons between Observations and Models Phase 3 (AeroCom‐III). Our intercomparison highlights model differences and observational uncertainty. Spatial mean τ for 60°N – 60°S ranges from 0.124 to 0.164 for individual satellites, with a mean of 0.14. Averaged τ from aerosol‐climate model ensembles fall within this satellite range, but individual models do not. Our assessment suggests no systematic improvement compared to CMIP5 and AeroCom‐I. Although some regional biases have been reduced, τ from both CMIP6 and AeroCom‐III are for instance substantially larger along extra‐tropical storm tracks compared to the satellite products. The considerable uncertainty in observed τ implies that a model evaluation based on a single satellite product might draw biased conclusions. This underlines the need for continued efforts to improve both model and satellite estimates of τ, for example, through measurement campaigns in areas of particularly uncertain satellite estimates identified in this study, to facilitate a better understanding of aerosol effects in the Earth system. Plain Language Summary: Aerosols are known to affect atmospheric processes. For instance, particles emitted during dust storms, biomass burning and anthropogenic activities affect air quality and influence the climate through effects on solar radiation and clouds. Although many studies address such aerosol effects, there is a persistent difference in current estimates of the amount of aerosols in the atmosphere across observations and complex climate models. This study documents the data differences for aerosol amounts, including new estimates from climate‐model simulations and satellite products. We quantify considerable differences across aerosol amount estimates as well as regional and seasonal variations of extended and new data. Further, this study addresses the question to what extent complex climate models have improved over the past decades in light of observational uncertainty. Key Points: Present‐day patterns in aerosol optical depth differ substantially between 94 modern global data setsThe range in spatial means from individual satellites is −11% to +17% of the multi‐satellite meanSpatial means from climate model intercomparison projects fall within the satellite range but strong regional differences are identified [ABSTRACT FROM AUTHOR]

Actual evapotranspiration (AE) is a crucial processes in terrestrial ecosystems. Global warming is expected to increase AE; however, various AE estimation methods or models give inconsistent trends. This study analyzed AE variability in China during 1982–2015 based on the Budyko framework (AE_Budyko), a complementary-relationship-based product (AE_CR), and the weighted average of six reanalyses (AE_WAR). Because the response of AE to driving factors and the performances of AE datasets are both scale-dependent, China has been categorized into six distinct climatic areas. From a regional perspective, the X-12-ARIMA method was used to decompose monthly AE into the trend, seasonal, and irregular components. We examined the main characteristics of these components and the relationships of climate factors with AE. The results indicate that the trend component of AE increased from the mid-1990s to the early 2000s and more recently in the hyper-arid and arid areas. Increasing AE was observed from 1982 to the early 1990s in the semi-arid and dry sub-humid areas. AE increased significantly and had substantial interannual variability for the entire period in the sub-humid and humid areas. Increased precipitation and water supply from terrestrial water storage contributed significantly to increasing AE in the drylands. The simultaneous occurrence of increasing precipitation and wet-day frequency caused increasing AE in the dry sub-humid area. Increased AE could be explained by the increased energy supply and precipitation in the sub-humid and humid areas. Precipitation had the strongest influence on the irregular component of AE in drylands. AE and potential evapotranspiration had a strong positive correlation in the sub-humid and humid areas. Regarding data availability, a discrepancy existed in the trend component of AE_CR because soil moisture was not explicitly considered, whereas the irregular component of AE_Budyko contained distinct variations in humid and sub-humid areas.

Abstract Recent high‐resolution dropsonde observations from the 2010 Concordiasi field campaign in austral spring season show that surface‐based inversions (SBIs) over Antarctica are frequently eroded, with well‐mixed boundary layers occurring 33% and 18% of the time in West and East Antarctica, respectively. In this study, using the dropsonde observations, we evaluate the performance of the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA2) in representing the Antarctic boundary layer thermodynamic structure. Results show that MERRA2 has a good overall representation of the Antarctic surface stability and correctly predicts 82% of the SBIs. However, an underprediction of less stable boundary layer occurrence, especially over the elevated East Antarctic plateau, is favored during conditions of increased lower tropospheric stability associated with model dynamics, indicating difficulty in parameterizing turbulence in very stable boundary layers. In addition, a lower tropospheric cool bias (first model level and above) is observed in the MERRA2 reanalysis, especially over West Antarctica, which amplifies in the boundary layer during mixed conditions. The near‐surface cold bias is most pronounced when the model fails to predict mixed layers over West Antarctica and is expected to negatively impact the representation of surface energy budget and melt processes.

Several studies have been carried out on the tropopause, stratopause, and mesopause (collectively termed as ‘pauses’) independently; however, all the pauses have not been studied together. We present global distribution of altitudes and temperatures of these pauses observed with long-term space borne high-resolution measurements of Global Positioning System (GPS) Radio Occultation (RO) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) aboard Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite. Here we study the commonality and differences observed in the variability of all the pauses. We also examined how good other datasets will represent these features among (and in between) different satellite measurements, re-analysis, and model data. Hemispheric differences observed in all the pauses are also reported. In addition, we show that asymmetries between northern and southern hemispheres continue up to the mesopause. We analyze inter and intra-seasonal variations and long-term trends of these pauses at different latitudes. Finally, a new reference temperature profile is shown from the ground to 110 km for tropical, mid-latitudes, and polar latitudes for both northern and southern hemispheres.

Observations from the historical meteorological observing network contain many artefacts of non‐climatic origin which must be accounted for prior to using these data in climate applications. State‐of‐the‐art homogenisation approaches use various flavours of pairwise comparison between a target station and candidate neighbour station series. Such approaches require an adequate number of neighbours of sufficient quality and comparability – a condition that is met for most station series since the mid‐20th Century. However, pairwise approaches have challenges where suitable neighbouring stations are sparse, as remains the case in vast regions of the globe and is common almost everywhere prior to the early 20th Century. Modern sparse‐input centennial reanalysis products continue to improve and offer a potential alternative to pairwise comparison, particularly where and when observations are sparse. They do not directly ingest or use land‐based surface temperature observations, so they are a formally independent estimate. This may be particularly helpful in cases where structurally similar changes exist across broad networks, which challenges current techniques in the absence of metadata. They also potentially offer a valuable methodologically distinct method, which would help explore structural uncertainty in homogenisation techniques. The present study compares the potential of spatially‐interpolated sparse‐input reanalysis products to neighbour‐based approaches to perform homogenisation of global monthly land surface air temperature records back to 1850 based upon the statistical properties of station‐minus‐reanalysis and station‐minus‐neighbour series. This shows that neighbour‐based approaches likely remain preferable in data dense regions and epochs. However, the most recent reanalysis product, NOAA‐CIRES‐DOE 20CRv3, is potentially preferable in cases where insufficient neighbours are available. This may in particular affect long‐term global average estimates where a small number of long‐term stations in data sparse regions will make substantial contributions to global estimates and may contain missed data artefacts in present homogenisation approaches. [ABSTRACT FROM AUTHOR]

This study describes the performance of five gridded data sets in reproducing precipitation and/or temperature over the complex terrain in the high Chilean Andes. The relationship of instrumental observations and the gridded data sets with climate modes of variability and the trends of indices of climate extremes are also explored between the period 1980–2015. The mismatches between gridded data sets are larger in northern and southern regions in relation to precipitation, while for temperature, disagreement is higher in central region. However, better results are delivered by the Climatic Research Unit and Global Precipitation Climatology Centre followed by Re‐Analysis Interim Project. The El Niño Southern Oscillation and Pacific Decadal Oscillation indices are well correlated with precipitation in North and South Chile. Additional, trend analyses reveal a significant downward (upward) tendency for precipitation (temperature), especially in central region, delivered by observed and the majority of gridded data sets. Furthermore, the consecutive number of dry days is increasing in all regions at the annual scale. This study allows a better understanding of the capacity of global data sets and thus contributes to further climate research within this Andean region. [ABSTRACT FROM AUTHOR]

The monthly variation of upper tropospheric water vapor (UTWV) simulated by six reanalysis data sets is evaluated with homogenized water vapor radiance observations from international geostationary (GEO) weather satellites by using a profile‐to‐radiance approach over 45°N to 45°S regions for the period 2015–2017. Results show that reanalysis data sets have an overall good agreement with observations. However, a widespread wet bias is found in all reanalyses and is more dominant in large‐scale subsidence regions. JRA55 has the smallest wet bias while MERRA‐2 exhibits the most humid upper troposphere. The temporal variation of brightness temperatures in response to the warm phase of El Niño‐Southern Oscillation (ENSO) in 2015–2016 indicates that the UTWV field is regulated by both ascending and descending branches of the large‐scale circulation. All six reanalyses roughly capture the temporal variation of UTWV in the developing and decay year of this ENSO event. However, they tend to overestimate the eastward propagation of high UTWV in the developing year, especially MERRA‐2. The UTWV gradient over the tropical Pacific in the decay year is underestimated, with a dry bias over the convective western Pacific and a wet bias over the eastern Pacific in reanalysis data sets. These results may provide a useful tool for the climate modeling community for identifying and solving problems associated with UTWV simulation. Key Points: Homogenized 6.5‐μm WV band radiance from GEO satellites is used for evaluating the monthly variation of UTWV nearly globally in reanalysesReanalyses exhibit a dominant wet bias. ERA5, JRA55, and CRA are closer to the observations whereas MERRA‐2 has the largest wet biasReanalyses capture the pattern of UTWV well during the El Niño but underestimate the UTWV gradient over tropical Pacific in the decay year [ABSTRACT FROM AUTHOR]

Intense sub‐daily summer rainfall is linked to flooding impacts in the United Kingdom. Characterizing the atmospheric conditions prior to the rainfall event can improve understanding of the large‐scale mechanisms involved. The most intense sub‐daily rainfall intensity data generated from rain gauge records across the United Kingdom over the period 1979–2014 are combined with fields from the ERA Interim reanalysis to characterize atmospheric conditions prior to heavy rainfall events. The 200 most intense 3‐hourly events for six UK regions are associated with negative anomalies in sea level pressure (<−2 hPa) and 200 hPa geopotential height (<−60 m) to the west or south west of the United Kingdom 1 day earlier, with above average moisture, evaporation and dew point temperature over North West Europe. Atmospheric precursors are more intense but less coherent between regions for composites formed of the 25 heaviest rainfall events but all display substantial moisture transport from the south or south east prior to their occurrence. Composites for the heaviest events are characterized by a tripole geopotential anomaly pattern across the North Atlantic. Above average geopotential height and dew point temperature over Newfoundland and below average geopotential height but elevated evaporation in the North Atlantic are found to be weakly associated with an increased chance of the most intense sub‐daily rainfall events 5–9 days later. [ABSTRACT FROM AUTHOR]

The clear knowledge of decadal variability of surface solar radiation (SSR) is of vitally significant for understanding hydrological and biological processes and climate prediction. However, existing studies have shown observed SSR over China may have large discrepancies and inhomogeneity in decadal variability due to sensitivity drift, inaccurate calibrations and instrument replacement. Therefore, a new procedure of station selection was proposed to eliminate errors and to derive "true" SSR values in this study. Afterward, two satellite retrieves of SSR, including Clouds and the Earth's Radiant Energy System‐energy balanced and filled product (CERES‐EBAF) (edition 4) and Global Energy and Water Cycle Experiment‐Surface Radiation Budget (GEWEX‐SRB) (Version 3.0), and three reanalysis products, including National Centers for Environmental Prediction‐National Center for Atmospheric Research (NCEP‐NCAR), national centers for environmental prediction‐/department of energy (NCEP‐DOE) and Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA‐2) were evaluated using "true" SSR values at 39 homogeneous stations from the China Meteorological Administration and it was found that although all five products overestimated SSR, two satellite retrieves showed better accuracy with an overall R of 0.95, an root mean squared error (RMSE) of 20.4 W m−2 and mean absolute bias error (MAE) of 14.9 W m−2 for CERES‐EBAF and an overall R of 0.92, an RMSE of 27.7 W m−2 and MAE of 21.2 W m−2 for GEWEX‐SRB across China. Meanwhile, inter‐comparisons between trends of observations and trends of two satellite retrieves in this study showed that the new trends derived from two satellite retrieves (+0.78 W m−2 decade−1) were good agreement with trends of observation (+0.92 W m−2 decade−1) from 1994 to 2015. However, trends of SSR (+5.8 W m−2 decade−1) in situ measurements were still in disagreement with the trends of SSR (−3.7 W m−2 decade−1) derived from two satellite retrieves from 1984 to 1993 because of the sensitivity drift and instrument replacement in this period. The possible reasons for decadal variability of SSR in China were detected and it was found that variations in aerosol optical depth (AOD) and aerosol‐cloud interaction, rather than cloud, were suggested to be likely the main influencing factor of decadal variability of SSR across China from 1984 to 2015. [ABSTRACT FROM AUTHOR]

Atmospheric reanalyses are the only source of spatial and temporal gridded wind information at wind turbine height providing data over several decades in the past. The application potential of reanalyses in the renewable energy sector depends strongly on the quality of the meteorological quantities. While global reanalyses have a resolution of typically 50 km, new regional reanalyses COSMO-REA6 and COSMO-REA2 have about 6 km and 2 km horizontal grid spacing, respectively. Here, we investigate the added value of the new regional reanalyses for the renewable energy sector, especially their application potential for site assessment. Four well established wind towers in Europe are used as reference for this purpose. We find regional reanalyses performing significantly better or at least similar to global reanalyses. Especially marginal distributions show significant improvements e.g. the most extreme temporal wind changes (ramp rates) at typical hub-heights are underrepresented by global reanalyses between −80 and −43% while COSMO-REA2 represents them with relative errors between −14 and +9%. Considering biases, mean absolute errors, and correlations most significant improvements occur close to ground and in areas with complex terrain. Moreover, vertically extrapolated wind measurements which are commonly used for site assessment show a stronger site dependency in their performance than reanalyses. • Reanalyses (REA) are a valuable tool for providing gridded wind speed at hub-height. • Low level wind profiles from regional reanalyses outperform global reanalyses. • COSMO-REA2 represents extreme ramp rate occurrences at hub-height best. • The performance of REA is more robust than those of vertical extrapolation methods. [ABSTRACT FROM AUTHOR]

In this study, new estimates of monthly freshwater discharge from continents, drainage regions, and global land for the period of 2003–05 are presented. The method uses observed terrestrial water storage change estimates from the Gravity Recovery and Climate Experiment (GRACE) and reanalysis-based atmospheric moisture divergence and precipitable water tendency in a coupled land–atmosphere water mass balance. The estimates of freshwater discharge are analyzed within the context of global climate and compared with previously published estimates. Annual cycles of observed streamflow exhibit stronger correlations with the computed discharge compared to those with precipitation minus evapotranspiration (P − E) in several of the world’s largest river basins. The estimate presented herein of the mean monthly discharge from South America (846 km3 month−1) is the highest among the continents and that flowing into the Atlantic Ocean (1382 km3 month−1) is the highest among the drainage regions. The volume of global freshwater discharge estimated here is 30 354 ± 1212 km3 yr−1. Monthly variations of global freshwater discharge peak between August and September and reach a minimum in February. Global freshwater discharge is also computed using a global ocean–atmosphere mass balance in order to validate the land–atmosphere water balance estimates and as a measure of global water budget closure. Results show close proximity between the two estimates of global discharge at monthly (RMSE = 329 km3 month−1) and annual time scales (358 km3 yr−1). Results and comparisons to observations indicate that the method shows important potential for global-scale monitoring of combined surface water and submarine groundwater discharge at near-real time, as well as for contributing to contemporary global water balance studies and for constraining global hydrologic model simulations.

Ability tilts refer to within-individual differences between two abilities, e.g. math ability – verbal ability. Coyle et al. (2023) found ability tilts to be genetically heritable and concluded that ability tilts are genuine and, presumably, genetically coded individual characteristics. Moreover, Coyle et al. found a large portion of variance in ability tilts to be attributable to non-shared environmental factors (i.e. environmentability), which they interpreted to indicate that ability tilts are potentially generated by niche-picking. However, through simulations we show that heritability and environmentability of X-Y tilts are spurious consequences of heritability and environmentability of the constituent variables X and Y. Furthermore, we reanalyzed data used by Coyle et al. and show that the logic of their arguments would lead to the conclusions, for example, that the human genome codes for a difference between head circumference and verbal ability and that some individuals have picked a niche that includes a long nose at the expense of spatial ability. We do not find these conclusions tenable and propose, instead, that heritability and environmentability of tilts are spurious consequences of heritability and environmentability of the constituent variables. • GH = genetic heritability; NSE = non-shared environmental factors • Ability tilts (ATs) refer to within-individual differences between two abilities. • ATs have been found to be affected by GH and by NSE. • ATs have been claimed to be genuine human characteristics and due to niche-picking. • Effects of GH/NSE on tilts are spurious consequences of the constituent variables. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) plays a vital role in climate research, especially for Antarctica in which meteorological observations are insufficient due to the adverse climate and topography therein. Reanalysis data sets provide a great opportunity for Antarctic water vapor research. This study investigates the climatological PWV means, variability and trends over Antarctica from four reanalyses, including the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2), Japanese 55-year Reanalysis (JRA-55) and National Centers for Environmental Prediction/Department of Energy (NCEP/DOE), in the period of 2001–2018 based on radiosonde and GNSS observations. PWV data from the ERA5, MERRA-2, JRA-55 and NCEP/DOE have been evaluated by radiosonde and GNSS observations, showing that ERA5 and MERRA-2 perform better than JRA-55 and NCEP/DOE with mean root mean square (RMS) errors below 1.2 mm. The climatological PWV mean distribution over Antarctica roughly shows a decreasing trend from west to east, with the highest content in summer and the lowest content in winter. The PWV variability is generally small over Antarctica, showing a seasonal dependence that is larger in the cold season and smaller in the warm season. PWV trends for all reanalyses at most Antarctic regions are insignificant and most reanalyses present overall drying trends from 2001 to 2018, except for ERA5 exhibiting a moistening trend. PWV trends also show seasonal and regional dependence. All reanalyses are generally consistent with radiosonde and GNSS observations in reproducing the PWV means (mean differences within 1.1 mm), variability (mean differences within 3%) and trends (mean differences within 6.4% decade−1) over Antarctica, except for NCEP/DOE showing spurious variability and trends in East Antarctica. Results can help us further understand these four reanalysis PWV products and promote climate research in Antarctica.

This study is conducted over the data-poor Limpopo basin centered over southern Africa using reanalysis downscaled to useful resolution. Reanalysis products are of limited value in hydrological applications due to the coarse spatial scales they are available at. Dynamical downscaling of these products over a domain of interest offers a means to convert them to finer spatial scales in a dynamically consistent manner. Additionally, this downscaling also offers a way to resolve dominantatmospheric processes, leading to improved accuracy in the atmospheric variables derived. This study thus evaluates high-resolution downscaling of an objectively chosen reanalysis (ERA-I) over the Limpopo basin using Weather Research and Forecasting (WRF) as a regional climate model. The model generally under-estimates temperature and over-estimates precipitation over the basin, although reasonably consistent with observations. The model does well in simulating observed sustained hydrological extremes as assessed using the Standardized Precipitation Index (SPI) although it consistently under-estimates the severity ofmoisture deficit for the wettest part of the year during the dry years. The basin's aridity index (I) is above the severe drought threshold during summer and is more severe in autumn. This practically restricts rain-fed agriculture to around 3 months in a year over the basin. This study presents possible beneficial use of the downscaled simulations foroptimal hydrologic design and water resources planning in data scarce parts of the world.

Abstract We compare the consistency between eight reanalyses: CERA20C, ERA5, ERA‐Interim, ERA20C, NCEP‐DOE, MERRA2, JRA55, and 20CRV2c. This comparison uses daily surface winds near Antarctica to classify synoptic patterns using the self‐organizing map technique. The relative frequency of occurrence (RFO) of these patterns are very similar during the satellite era in each reanalysis. The three most common patterns are the same in each reanalysis and changes between the reanalyses only display a 12% relative variation. Examination of the RFOs over time highlights that the CERA20C and 20CRV2c reanalyses display large changes previous to 1957. These changes are likely connected to model relaxation toward their climatology because of a lack of observational constraints. Primarily, we introduce the entropy coefficient (U) which quantifies the consistency between reanalyses in their representation of synoptic patterns. Examination of U shows current reanalyses (ERA5, ERA‐Interim, JRA55, and MERRA2) are highly consistent in the satellite era likely due to good observational coverage. However, centennial reanalyses (CERA20C, ERA20C, and 20CRV2c) show two upward step changes in consistency as measured by U at around 1957 and to a lesser extent 1979. Low values of U before 1957 suggest that centennial reanalyses are of limited use before this date, but may be useful after 1957 in this region. We also show that the entropy coefficient displays an inverse relationship with ensemble spread metrics of individual reanalyses. We conclude that the entropy coefficient provides a powerful quantification of the influence of changes in observation density on reanalysis quality in data sparse regions.

Recent high‐resolution dropsonde observations from the 2010 Concordiasi field campaign in austral spring season show that surface‐based inversions (SBIs) over Antarctica are frequently eroded, with well‐mixed boundary layers occurring 33% and 18% of the time in West and East Antarctica, respectively. In this study, using the dropsonde observations, we evaluate the performance of the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA2) in representing the Antarctic boundary layer thermodynamic structure. Results show that MERRA2 has a good overall representation of the Antarctic surface stability and correctly predicts 82% of the SBIs. However, an underprediction of less stable boundary layer occurrence, especially over the elevated East Antarctic plateau, is favored during conditions of increased lower tropospheric stability associated with model dynamics, indicating difficulty in parameterizing turbulence in very stable boundary layers. In addition, a lower tropospheric cool bias (first model level and above) is observed in the MERRA2 reanalysis, especially over West Antarctica, which amplifies in the boundary layer during mixed conditions. The near‐surface cold bias is most pronounced when the model fails to predict mixed layers over West Antarctica and is expected to negatively impact the representation of surface energy budget and melt processes. Key Points: MERRA2 has good skill in representing the frequency of less stable and stable Antarctic boundary layersThe model underpredicts turbulence and the frequency of less stable boundary layers especially over elevated regions of East AntarcticaRepercussions of missing boundary layer turbulent processes are more severe for the representation of West Antarctic surface processes [ABSTRACT FROM AUTHOR]

We assess the ability of the regional circulation model MAR to represent the recent negative surface mass balance (SMB) observed over the Kerguelen Islands ( 49 ∘ S , 69 ∘ E ) and evaluate the uncertainties in SMB projections until the end of the century. The MAR model forced by ERA-Interim reanalysis shows a good agreement with meteorological observations at Kerguelen, particularly after slight adjustment of the forcing fields (+ 10% humidity, + 0.8 ∘ C , all year round) to improve precipitation occurrence and intensity. The modeled SMB and surface energy balance (SEB) are also successfully evaluated with observations, and spatial distributions are explained as being largely driven by the elevation gradient and by the strong west to east foehn effect occurring on the ice cap. We select five general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5) by evaluating their ability to represent temperature and humidity in the southern mid-latitudes over 1980–1999 with respect to ERA-Interim and use them to force the MAR model. These simulations fail to replicate SMB observations even when outputs from the best CMIP5 model (ACCESS1-3) are used as forcing because all GCMs fail in accurately reproducing the circulation changes observed at Kerguelen since the mid-1970s. Global models chosen to represent extreme values of SMB drivers also fail in producing extreme values of SMB, suggesting that more rigorous modeling of present and future circulation changes with GCMs is still needed to accurately assess future changes of the cryosphere in this area. [ABSTRACT FROM AUTHOR]

The Arctic region has experienced significant warming during the past two decades with major implications on the cryosphere. The causes of Arctic amplification are still an open question within the scientific community, attracting recent interest. The goal of this study is to quantify the contribution of atmospheric circulation on temperature variability in the Atlantic–Arctic region at decadal to intra‐annual timescales from 1951 to 2014. Daily 20th Century reanalyses geopotential height anomalies at 500 hPa were clustered into different weather regimes to assess their contribution to observed temperature variability. The results show that in winter, 25% of the warming (cooling) in the North Atlantic Ocean (northeastern Canada) is due to temporal decreases of high geopotential anomalies in Greenland. This regime influences air mass migration patterns, bringing less cold (warm) air masses into these regions. Additionally, atmospheric warming or cooling has been attributed to a change in nearby oceanic basin surface conditions because of sea ice decline. In summer, about 15% of the warming observed in Norwegian/Greenland Seas is related to an increase in temporal anticyclonic patterns. This ratio reaches 37% in Norway due to an amplification from downwards solar radiation. This study allows for better understanding how natural climate variability modulates the regional signature of climate change and estimating the uncertainties in climate projections. [ABSTRACT FROM AUTHOR]

Statistical downscaling based on a perfect prognosis approach often relies on global reanalyses to infer the statistical relationship between synoptic predictors and the local variable of interest, here daily precipitation. Nowadays, many reanalyses are available and their impact on the downscaled variable is not often considered. The present work assessed the impact of ten reanalyses on the performance of seven variants of analogue methods for statistical precipitation downscaling at 301 stations in Switzerland. Even though the study location is in a data-rich region, significant differences were found between reanalyses and their impact on the performance of the method can be even higher than the choice of the predictor variables. There was no single overall winner, but a selection of recommended reanalyses resulting in higher skill scores depending on the considered predictor variables. The impact of the output spatial resolution was assessed for different types of variables. Output resolutions below one degree were found to be often of low to no interest. Reanalyses with longer archives allow the pool of potential analogues to be increased, resulting in better performance. However, when adding variables affected by errors in a more distant past, the skill score decreased again. The use of multiple members from two reanalyses was also tested over a recent and a past period. The benefit of using members to increase the performance by better incorporating the uncertainties was found to be limited, and even problematic with methods using multiple analogy levels. [ABSTRACT FROM AUTHOR]

Abstract In the United States, Central California has gained significant interest in offshore wind energy due to its strong winds and proximity to existing grid connections. This study provides a comprehensive evaluation of near-surface wind datasets in this region, including satellite-based observations (QuikSCAT, ASCAT, and CCMP V2.0), reanalysis (NARR and MERRA), and regional atmospheric models (WRF and WIND Toolkit). This work highlights spatiotemporal variations in the performance of the respective datasets in relation to in-situ buoy measurements using error metrics over both seasonal and diurnal time scales. The two scatterometers (QuikSCAT and ASCAT) showed the best overall performance, albeit with significantly less spatial and temporal resolution relative to other datasets. These datasets only slightly outperformed the next best dataset (WIND Toolkit), which has significantly greater temporal and spatial resolution as well as estimates of winds aloft. Considering tradeoffs between spatiotemporal resolution of the underlying datasets, error metrics relative to in-situ measurements, and the availability of data aloft, the WIND Toolkit appears to be the best dataset for this region. The framework and tradeoff analysis this research developed and demonstrated to assess offshore wind datasets can be applied in other regions where offshore wind energy is being considered. Highlights • Comprehensive assessment of near-surface wind datasets for Central California. • Seasonal and diurnal error metrics and spatial differences analyzed. • Framework for assessing wind data products for offshore wind energy. [ABSTRACT FROM AUTHOR]

Global and regional ocean and sea ice reanalysis products (ORAs) are increasingly used in polar research, but their quality remains to be systematically assessed. To address this, the Polar ORA Intercomparison Project (Polar ORA-IP) has been established following on from the ORA-IP project. Several aspects of ten selected ORAs in the Arctic and Antarctic were addressed by concentrating on comparing their mean states in terms of snow, sea ice, ocean transports and hydrography. Most polar diagnostics were carried out for the first time in such an extensive set of ORAs. For the multi-ORA mean state, we found that deviations from observations were typically smaller than individual ORA anomalies, often attributed to offsetting biases of individual ORAs. The ORA ensemble mean therefore appears to be a useful product and while knowing its main deficiencies and recognising its restrictions, it can be used to gain useful information on the physical state of the polar marine environment. [ABSTRACT FROM AUTHOR]

The stratosphere and its dynamics are a very important part of atmospheric circulation. We need to analyze its climatology, as well as long-term trends. A long-term trend study needs homogenous datasets without significant artificial discontinuities. The analysis is based on the two newest released reanalyses, Modern Era-Retrospective Analysis (MERRA2) and European Center for Medium-Range Weather Forecast Reanalysis (ERA5). The aim of this study is to detect discontinuities in the temperature time series from the above reanalyses with the help of the Pettitt homogeneity test for pressure layers above 500 hPa up to 1 hPa in January and February, and show a comparison of temperature trends from the studied reanalyses and GPS radio occultation (GPS RO). We search for individual grid points where these discontinuities occur, and also for the years when they occur (geographical and temporal distribution). As expected, the study confirms better results for the Northern Hemisphere due to the denser data coverage. A high number of grid points with jumps on the Southern Hemisphere is found, especially at higher pressure levels (from 50 hPa). The spatial and vertical distribution of discontinuities is also presented. The vertical distribution reveals the reduction of the number of jumps around 10 hPa, especially for ERA5 reanalysis. The results show that ERA5 has significantly less jumps than MERRA2. We also study temperature trends from reanalyses and GPS RO and our analysis shows that the agreement between the reanalyses and observations are very good for the period 2006−2018.