Atmospheric Reanalysis Products Research Articles

Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)

Abstract Shallow landslides are often triggered during rainfall events, which can increase subsurface soil water pressure and destabilize hillslopes. The likelihood of regional shallow landslide initiation is often assessed through a comparison of rainfall intensity and duration to pre-established thresholds. While informative for landslide warning, this exclusive focus on rainfall exceeding thresholds does not consider the meteorological conditions producing the rainfall. Here, we ask the question, are there common meteorological characteristics that lead to landslide triggering precipitation? We develop a catalog of 18 post-1995 widespread, impactful shallow landslide events occurring within 13 storms across California, USA, where initiation time could be constrained to a <=6-hour window. We examine storm characteristics during the landslide initiation window using atmospheric reanalysis products, radar observations, and quantitative precipitation estimates. We find that, while there are some common atmospheric characteristics across landslide events, they can occur under a range of atmospheric conditions. For example, all northern California landslide events assessed are associated with moderate to strong atmospheric rivers (ARs) while southern California landslides feature non-AR to strong AR conditions. The storm events evaluated herein share many characteristics of hydrologically important storms in California that did not necessarily result in landslides, thus atmospheric characteristics alone may not be sufficient to determine whether landslides will occur. However, documenting the characteristics of landslide-triggering storms defines the conditions under which landslides tend to occur, provides analog events that can be useful in forecast applications, helps to define future research directions relating atmospheric conditions and landslides, and supports interdisciplinary research efforts.

Sea ice cover in the Copernicus Arctic Regional Reanalysis

Abstract. The Copernicus Arctic Regional Reanalysis (CARRA) is a novel regional high-resolution atmospheric reanalysis product that covers a considerable part of the European Arctic including substantial amounts of ice-covered areas. Sea ice in CARRA is modelled by means of a one-dimensional thermodynamic sea ice parameterisation scheme, which also explicitly resolves the evolution of the snow layer over sea ice. In the present study, we assess the representation of sea ice cover in CARRA and validate it against a wide set of satellite products and observations from ice mass balance buoys. We show that CARRA adequately represents general interannual trends towards thinner and warmer ice in the Arctic. Compared to ERA5, sea ice in CARRA shows a reduced warm bias in the ice surface temperature. The strongest improvement was observed for winter months over the central Arctic and the Greenland and Barents seas where a 4.91 °C median ice surface temperature error in ERA5 is reduced to 1.88 °C in CARRA on average. Over Baffin Bay, intercomparisons suggest the presence of a cold winter-time ice surface temperature bias in CARRA. No improvement over ERA5 was found in the ice surface albedo with spring-time errors in CARRA being up to 0.08 higher on average than those in ERA5 when computed against the CLARA-A2 satellite retrieval product. Summer-time ice surface albedos are comparable in CARRA and ERA5. Sea ice thickness and snow depth in CARRA adequately resolve the annual cycle of sea ice cover in the Arctic and bring added value compared to ERA5. However, limitations of CARRA indicate potential benefits of utilising more advanced approaches for representing sea ice cover in next-generation reanalyses.

Remote influence on regional scale intraseasonal rainfall variability over Vietnam

AbstractRemote influences on intraseasonal anomalous rainfall over regions that encompass North and South Vietnam are explored using a 38‐year (1979–2016) global dataset over the extended summer (May–October). Wet and Dry composites in filtered daily data with lags of up to 2 weeks are assembled for various rainfall indices over the two subregions, including atmospheric reanalysis products and in‐situ rainfall data. On the regional scale, the moisture flux convergence correlates well with reanalysed rainfall. The large‐scale dynamics associated with these composites are described. Rainfall composites of opposing signs show asymmetrical large‐scale precursors and different pathways of influence. Wet and Dry anomalies in North Vietnam are seen to originate from Europe and propagate at high latitudes. The exact nature of the precursors is sensitive to the definition of the composite index. There is also a pathway of influence along the Asian jet, which impacts South Vietnam, especially for Wet composites which often coincide with Dry conditions in the North. South Vietnam is also influenced by tropical divergent precursors, which are again asymmetric between Wet and Dry composites.

A rapid ray tracing method to evaluate the performances of ERA5 and MERRA2 in retrieving global tropospheric delay

Atmospheric reanalysis plays an important role in retrieving the atmospheric tropospheric delays with ray tracing for space geodetic techniques. In order to represent the complex weather and climate conditions better, the spatiotemporal resolutions of the newly developed atmospheric reanalysis products are improved significantly. The increased spatiotemporal resolution provides a great opportunity to improve the accuracy of the tropospheric delays derived from ray tracing, but it remains a challenge due to the highly increased computation costs. In this paper, we develop a rapid ray tracing method with refined height interval determination to accommodate the increased spatiotemporal resolution of the atmospheric reanalysis products. The accuracy of this method was validated by the 2010 International Association of Geodesy Working Group 4.3.3 ray tracing Comparison Campaign reference results. Zenith and slant delays were obtained by tracing 342 global International Global Navigation Satellite System Service (IGS) stations. Compared to the traditional method, this reduced memory footprint by 16.1%, global refractivity field construction time by 13.6%, and per ray trace time by 22.5% while maintaining accuracy. Based on this methodology, ray tracing using state-of-the-art fifth-generation European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA5) and second Modern-Era Retrospective Analysis for Research and Applications (MERRA2) at 342 IGS stations assessed tropospheric delay performance in 2021. Results showed significant ERA5 and MERRA2 slant delay and mapping factor differences up to the decimeter level, especially for the wet component. Additionally, using IGS zenith total delay (ZTD) as a reference, ERA5 ZTD bias and root mean square error (RMSE) were 2.3 and 11.9 mm, versus that of 1.8 and 16.2 mm for MERRA2 ZTD. At extreme weather-affected AIRA stations over August 5–9, 2021, ERA5 ZTD mean and RMSE differences were −3.0 and 19.8 mm, and −5.3 and 21.7 mm for MERRA2 ZTD. Tropospheric delays and models derived from ERA5 can support space geodetic applications given improved performance and temporal resolution.

A decadal atmospheric ammonia reanalysis product in China

Atmospheric ammonia has great environmental implications due to its important role in ecosystem and nitrogen cycle, as well as contribution to formation of secondary particles. China is recognized as a hotspot of NH3 pollution owing to agricultural and livestock intensification. In the quest to achieve a comprehensive understanding of atmospheric ammonia load and to quantify its environmental impacts in China, relying solely either on existing measurements or on model simulations falls short. Their limitations, either in spatial coverage and integrity or in data quality, fails to meet the needs. Available reanalysis products exhibit a marked deficiency in ammonia data. We therefore aim to propose an integrated ammonia reanalysis product in China, adeptly melding satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI) NH3 retrievals with chemical transport model simulation, capitalizing on the robust Ensemble Kalman Filter (EnKF) data assimilation methodology. The product is validated in high quality via the comparison against independent measurements from ground monitoring stations. Spanning a decade from 2013 to 2022, our reanalysis uncovers not just the spatial intricacies of NH3 concentrations but also their temporal dynamics. Our findings pinpointed the spatial disparities in atmospheric ammonia intensities, highlighting regional hotspots in the NCP, SCB, and Northeast China, and identified annual and seasonal patterns. Our research provides crucial insights for shaping future NH3 pollution prevention and control strategies in China.

Antecedent snowmelt and orographic precipitation contributions to water supply of Pakistan disastrous floods, 2022

In 2022, the Pakistan witnessed the hottest spring and wettest summer in history. And devastating floods inundated a large portion of Pakistan and caused enormous damages. However, the primary water source and its contributions to these unprecedented floods remain unclear. Based on the reservoir inflow measurements, Multi-Source Weighted-Ensemble Precipitation (MSWEP), the fifth generation ECMWF atmospheric reanalysis (ERA5) products, this study quantified the contributions of monsoon precipitation, antecedent snowmelts, and orographic precipitation enhancement to floods in Pakistan. We found that the Indus experienced at least four inflow uprushes, which was mainly supplied by precipitation and snowmelt; In upper Indus, abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation. Before July, the snowmelt has higher contributions than the precipitation to the streamflow of Indus River, with contribution value of more than 60%. Moreover, the snowmelt could still supply 20%–40% water to the lower Indus in July and August; The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation, where terrain disturbance induced precipitation account to approximately 33% over the southern Pakistan. The results help to understand the mechanisms of flood formation, and to better predict future flood risks over complex terrain regions.

Dynamics of Spring Snow Cover Variability over Northeast China

Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms of spring snow cover variability over NEC during 1983–2018 based on the satellite-derived snow cover data and atmospheric reanalysis products. The empirical orthogonal function (EOF) analysis showed that the first EOF mode (EOF1) explains about 50% of the total variances and characterizes a coherent snow cover variability pattern over NEC. Further analyses suggested that the formation of the EOF1 mode is jointly affected by the atmospheric internal variability and the sea surface temperature (SST) anomaly at the interannual timescale. Specifically, following a negative phase of the atmospheric teleconnection of the Polar–Eurasian pattern, a prominent cyclonic circulation appears over NEC, which increases the snowfall over the east of NEC by enhancing the water vapor transport and decreases the air temperature through reducing the solar radiation and intensifying the cold advection. As a result, the snow cover has increased over NEC. Additionally, the tripole structure of the North Atlantic spring SST anomaly could excite a wave-train-type anomalous circulation propagating to NEC that further regulates the snow cover variability by altering the atmospheric dynamic and thermodynamic conditions and the resultant air temperature and snowfall. Our results have important implications on the understanding of the spring snow cover anomaly over NEC and the formulation of the local agricultural production plan.

Intercomparison of tropospheric and stratospheric mesoscale kinetic energy resolved by the high‐resolution global reanalysis datasets

AbstractWith the development of advanced data assimilation and computing techniques, many modern global reanalysis datasets aim to resolve the atmospheric mesoscale spectrum. However, large uncertainties remain with respect to the representation of mesoscale motions in these reanalysis datasets, for which a clear understanding is lacking. The aforementioned challenges have served as a strong motivation to reveal and quantify their mesoscale differences. This study presents the first comprehensive global intercomparison of the tropospheric and stratospheric mesoscale kinetic energy and its spectra over two selected periods of summer and winter events among six leading high‐resolution atmospheric reanalysis products: European Centre for Medium‐Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), China Meteorological Administration Reanalysis, Modern‐Era Retrospective Analysis for Research and Applications version 2, National Centers for Environmental Prediction's Climate Forecast System version 2 (CFSv2), Japanese 55‐year Reanalysis, and ECMWF Reanalysis‐Interim. A state‐of‐the‐art global operational model is adopted as a supplementary reference. Although all reanalysis datasets can reproduce broad distribution characteristics that are grossly consistent with the 9 km model, there are substantial discrepancies among them in magnitudes. The ability to capture mesoscale signals is closely linked to their resolutions, but it is also impacted by other factors, including, but not limited to, the selected types of energy, seasons, altitudes, latitudes, model diffusions, parametrization schemes, moist condition, assimilation methods, and observation inputs. Moreover, all datasets illustrate conclusive behaviors for the prevalence of the rotational component in the troposphere, whereas only very few products fail to exhibit the dominance of the divergent component in the stratosphere. Overall, stratospheric ERA5 and CFSv2 outperform the other reanalysis datasets, and only these two can reproduce the feature of the canonical kinetic energy spectrum with a distinct shift from a steeper slope (approximately −3) at lower wave numbers to a shallower slope (approximately −5/3) at higher wave numbers. In addition, the relative disparities among datasets increase dramatically with height, and they are more pronounced in the divergent component. It is also found that the correlations among these datasets are much weaker in the Tropics.

Distribution and Trend of Wind Power Input to Near‐Inertial Motions in the Southern Ocean

AbstractWind power input to near‐inertial motions is an important energy source for generating diapycnal mixing in the ocean. However, the distribution and long‐term trend of this input over the Southern Ocean have yet to be quantified. In this study, we investigate the near‐inertial wind power input (WPIi) to the Southern Ocean using a global eddy‐permitting coupled ocean‐sea ice model forced by a high‐resolution atmospheric reanalysis product. Our results reveal a zonally asymmetric distribution of WPIi in the Southern Ocean, with the strongest input in the South Indian Ocean and the weakest in the South Pacific. The integrated WPIi between 30°S and 60°S exhibits a significant positive trend over the past four decades due to the intensification of mesoscale weather systems. The surface mixed‐layer depth is found to modulate the spatial pattern and trend of WPIi by altering the surface near‐inertial currents.

Evaluation of ERA5-Simulated Temperature and Its Extremes for Australia

Atmospheric reanalysis products offer high-resolution and long-term gridded datasets that can often be used as an alternative or a supplement to observational data. Although more accessible than typical observational data and deemed fit for climate change studies, reanalysis data can show biases resulting from data assimilation approaches. Thus, a thorough evaluation of the reanalysis product over the region and metric of study is critical. Here, we evaluate the performance of the latest generation of ECMWF reanalysis, ERA5, in simulating mean and extreme temperatures over Australia for 1979–2020 versus high-quality gridded observations. We find ERA5 generally simulates maximum and minimum temperatures reasonably well (mean bias ~1.5 °C), even though it underestimates/overestimates the daily maximum/minimum temperatures, leading to a cold bias for Tmax and a warm bias for Tmin. ERA5 also underestimates the decadal warming trend in both Tmax and Tmin compared to the observations. Furthermore, ERA5 struggles to simulate the temporal variability of Tmin, leading to a markedly worse skill in Tmin than Tmax. In terms of extreme indices, ERA5 is skilled at capturing the spatial and temporal patterns and trends of extremes, albeit with the presence of biases in each index. This can partially be attributed to the warm bias in the minimum temperature. Overall, ERA5 captures the mean and extreme temperature indices over the Australian continent reasonably well, warranting its potential to supplement observations in aiding climate change-related studies, downscaling for boundary conditions, and climate model evaluation.

Impact of atmospheric forcing uncertainties on Arctic and Antarctic sea ice simulations in CMIP6 OMIP models

Abstract. Atmospheric reanalyses are valuable datasets for driving ocean–sea ice general circulation models and for proposing multidecadal reconstructions of the ocean–sea ice system in polar regions. However, these reanalyses exhibit biases in these regions. It was previously found that the representation of Arctic and Antarctic sea ice in models participating in the Ocean Model Intercomparison Project Phase 2 (OMIP2, using the updated Japanese 55-year atmospheric reanalysis, JRA55-do) was significantly more realistic than in OMIP1 (forced by the atmospheric state from the Coordinated Ocean-ice Reference Experiments version 2, CORE-II). To understand why, we study the sea ice concentration budget and its relations to surface heat and momentum fluxes as well as the connections between the simulated ice drift and the ice concentration, the ice thickness and the wind stress in a subset of three models (CMCC-CM2-SR5, MRI-ESM2-0 and NorESM2-LM). These three models are representative of the ensemble and are the only ones to provide the surface fluxes and the tendencies of ice concentrations attributed to dynamic and thermodynamic processes required for the ice concentration budget analysis. The sea ice simulations of two other models (EC-Earth3 and MIROC6) forced by both CORE-II and JRA55-do reanalysis are also included in the analysis. It is found that negative summer biases in high-ice-concentration regions and positive biases in the Canadian Arctic Archipelago (CAA) and central Weddell Sea (CWS) regions are reduced from OMIP1 to OMIP2 due to surface heat flux changes. Net shortwave radiation fluxes provide key improvements in the Arctic interior, CAA and CWS regions. There is also an influence of improved surface wind stress in OMIP2 giving better winter Antarctic ice concentration and the Arctic ice drift magnitude simulations near the ice edge. The ice velocity direction simulations in the Beaufort Gyre and the Pacific and Atlantic sectors of the Southern Ocean in OMIP2 are also improved owing to surface wind stress changes. This study provides clues on how improved atmospheric reanalysis products influence sea ice simulations. Our findings suggest that attention should be paid to the radiation fluxes and winds in atmospheric reanalyses in polar regions.

Extreme sea level changes over the tropical western Pacific in 1.5 °C and 2.0 °C warmer climates

The tropical western Pacific and the adjacent South China Sea are home to many low-lying islands and coastal zones that are vulnerable to flood hazards resulting from extreme sea level (ESL) changes. Based on the hourly sea level recorded by 15 tide gauges during the period 1980-2018, this study evaluates the historical trend of ESLs over this region. On this basis, a regression model for hourly future sea-level prediction is established by combining the atmospheric reanalysis products, the tidal harmonics, and the outputs of three climate models archived by the Coupled Model Intercomparison Project Phase 6 (CMIP6) to evaluate the future ESL changes in 1.5 °C and 2.0 °C warmer climates. The historical trend of ESLs show that the ESLs along the coasts and islands of the tropical western Pacific have significantly risen during the past decades, which is mainly contributed by the mean sea level rise. And results from the historical observations and the prediction model show that in a warming climate from 1980 to 2050, both the mean sea levels and ESLs rise with fluctuations. The mean sea level change plays an important role in the secular trend of ESLs, while the interannual-to-decadal variability of ESLs is significantly affected by tides and extreme weather events. Under the warming scenario of 1.5°C, the changes in the return levels of ESL relative to the historical period are generally small at most tide gauge sites. Compared with the situations under 1.5°C warming, the return levels of ESL at most selected tide gauges will rise more significantly under the 2.0°C warming scenario, so the frequency of the current 100-year return level will reduce to less than 10 years at most stations. The above results suggest that this additional 0.5°C warming will cause a huge difference in the ESLs along the coasts and islands of the tropical western Pacific. As proposed in the Paris climate agreement, it is very necessary to limit anthropogenic warming to 1.5°C instead of 2.0°C, which will substantially reduce the potential risk of flood disasters along the coasts and islands of the tropical western Pacific.

Optimized sea ice simulation in MITgcm-ECCO2 forced by ERA5

Coupled ice–ocean models require an accurate performance of sea ice dynamics and thermodynamics to realistically simulate sea ice circulation in the polar regions. However, atmospheric surface variables from reanalysis products that force the models differ in spatial and temporal resolutions, and there are also substantial inconsistencies in the distributions and magnitudes of these variables between the products. Therefore, several key parameters in the models need to be adjusted for the models forced by different atmospheric reanalysis products. This study compares the sea ice properties simulated by the previously optimized MITgcm-ECCO2 model forced by JRA25 and forced by the recently released ERA5. The comparisons with high-resolution satellite sea ice data reveal that the simulations forced by these two datasets significantly overestimate the Arctic sea ice concentration in summer, whereas the simulation forced by ERA5 underestimates the winter Antarctic sea ice. It is likely that the downward longwave radiation and wind stress regime in ERA5 is responsible for the underestimated Antarctic sea ice cover. We then optimize MITgcm-ECCO2 model forced by ERA5 by using Green functions. We consider six key parameters: sea ice strength, ice-atmospheric drag coefficient, ice–ocean drag coefficient, ice and snow albedos, and sea ice salinity. The optimized simulation significantly improves the sea ice distribution in both regions using a more realistic set of sea ice parameters. The most significant improvement is in the Antarctic, where winter sea ice extent misfit decreases by 48%. The optimized simulation reveals that ERA5 reproduces a closer representation of the Arctic sea ice extent and thickness than JRA25. In the southern hemisphere, ERA5 simulates the sea ice drift better, whereas the JRA25 have the closest match to the sea ice extent. Despite the improvements, further work is necessary for the Antarctic, where the lack of reliable observational data and limitations of the ocean model hinders the optimization.

Statistics of precipitable water vapour above the sites of the 6-m Big Telescope Alt-azimuthal and new 3-m Large Solar Telescope using ERA5 data

ABSTRACTAtmospheric conditions have a major impact on the quality of ground-based astronomy observations. Among atmospheric molecular gases, water vapour presents a significant challenge in ground-based astronomical observations in spectral bands ranging from radio to infrared. The impact of water vapour on astronomical observations is expressed in terms of precipitable water vapour (PWV). Water vapour is a greenhouse gas on Earth that plays an important role in various atmospheric processes, including global climate change. In this work, we analyse the long-term (1975–2021) statistics of PWV above two mid-latitude sites: the 6-m Big Telescope Alt-azimuthal in the Caucasus Mountains and the new 3-m Large Solar Telescope in the Sayan Mountains. Both astronomical sites were established over 45 years ago and are located at an altitude of 2000 m above sea level. We use ERA5 – a new-generation European Centre for Medium-Range Weather Forecasts (ECMWF) retrieved atmospheric reanalysis product. First, we evaluate the reliability of the PWV variable provided in the ERA5 data using global navigation satellite system (GNSS) measurement data. We present the analysis of PWV statistics, including seasonal behaviour above the sites in the infrared astronomical observations context. Then we provide long-term changes of PWV for the period 1975–2021 and examine the data set for the statistical significance of trends. Finally we discuss PWV correlation with other atmospheric parameters: surface temperature and total cloud cover in the regions of both sites.

How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling

Multi-satellite, gauge-based and atmospheric reanalysis precipitation and temperature datasets with increasing spatiotemporal resolution are proliferating in recent decades. Their estimation accuracy has been evaluated in several areas, but their applications in global hydrological monitoring and modelling have been poorly understood so far. Here, we focus on the performance of the recently released Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2) and the atmospheric reanalysis ERA5 temperature in modelling daily river discharge over 10,596 global catchments. Four lumped models including XAJ, GR4J, HMETS, and HBV are employed to take different model conceptual schemes into consideration. Moreover, different streamflow conditions including the peak timing and volumes of both high and low flows are evaluated to reveal further dynamics. The results show that: (a) the MSWEP V2 and ERA5 combination yields satisfactory performance in global streamflow simulation with at least one candidate model exhibiting Kling-Gupta efficiency (KGE) > 0.5 at both calibration and validation periods for each catchment; (b) better simulation skills are achieved in temperate, cold, polar, and tropical Köppen-Geiger climate zones, whereas the arid zone has poorer performance; (c) the XAJ, GR4J, and HMETS models with different degree of realisms have their own merits in simulating both daily mean flow and wet flow conditions but perform less satisfactorily in low flow conditions, while the HBV exhibits consistently poor performance in various flow conditions over most catchments. Our findings highlight the importance of selecting appropriate hydrological models by considering both climatic and geophysical conditions and targeting streamflow metrics, and suggest the potential of using multi-satellite and atmospheric reanalysis datasets as observed alternatives for quasi-global hydrological modelling.

Meteotsunamis in the northern Baltic Sea and their relation to synoptic patterns

Low-tidal coastal regions, such as the Baltic Sea, are known to be particularly vulnerable to exceptional high-frequency sea level oscillations such as meteotsunamis. Possibilities of studying sub-hourly sea level variations have recently improved, owing to advancement in temporal resolution of tide gauge observations. In this work, we study high-frequency (period <6 h) sea level oscillations – strongest of which we consider to be meteotsunamis – on the coast of Finland, in the northern Baltic Sea, using quality-checked 1-min observations collected between 2004 and 2015 at 13 tide gauge stations. The intensity of the oscillations varies substantially between stations due to local coastal morphologies. The most intense oscillations predominantly occur in late summer and autumn, although the seasonality may differ between sub-regions. Measured atmospheric data and reanalysis products related to the strongest events reveal two distinct types of atmospheric processes and governing synoptic patterns that are mostly associated with warmer and colder period of year. Consequently, meteotsunamis are classified as summer-type or winter-type events. Most of the summer-type events are caused by surface atmospheric pressure jumps associated with mesoscale convective systems, which are advancing northward over the sea and are embedded into a mid-troposphere jet overtopping an inflow of warm low-troposphere air. At the surface, weak air pressure gradients due to a high-pressure area to the east and a low-pressure area to the west of the Baltic are usually found during summer-type events. The winter-type events, on the contrary, are mostly related to cold fronts and strong northerly-northwesterly-westerly winds at the surface layer. Contrary to summer-type events, surface atmospheric pressure jumps are not necessarily detected during the strongest winter-type events. Deep lows and extratropical cyclones are commonly centered to the north of the Baltic Sea and, at the mid-troposphere level, there is a pronounced westerly jet stream. A hypothesis about the generation mechanism of intense high-frequency sea level oscillations is given: Proudman resonance appears to be the main driver of summer-type events, whereas the main driver of winter-type events is less clear.

A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval

Abstract. As changes to Earth's polar climate accelerate, the need for robust, long–term sea ice thickness observation datasets for monitoring those changes and for verification of global climate models is clear. By linking an algorithm for retrieving snow–ice interface temperature from passive microwave satellite data to a thermodynamic sea ice energy balance relation known as Stefan's law, we have developed a retrieval method for estimating thermodynamic sea ice thickness growth from space: Stefan's Law Integrated Conducted Energy (SLICE). With an initial condition at the beginning of the sea ice growth season, the method can model basin-wide absolute sea ice thickness by combining the one-dimensional SLICE retrieval with an ice motion dataset. The advantages of the SLICE retrieval method include daily basin-wide coverage, lack of atmospheric reanalysis product input requirement, and a potential for use beginning in 1987. Validation of the retrieval against measurements from 10 ice mass balance buoys shows a mean correlation of 0.89 and a mean bias of 0.06 m over the course of an entire sea ice growth season. Despite its simplifications and assumptions relative to models like the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS), basin-wide SLICE performs nearly as well as PIOMAS when compared against CryoSat-2 and Operation IceBridge using a linear correlation between collocated points.

Evaluation of meteorological reanalysis data over the tropical Western Indian Ocean based on buoy observations in 2022

The Indian Ocean is the least observed of the three oceans, and a large amount of reanalysis data has to be used to study the Indian Ocean. Observations of sea surface winds, temperature, and sea level pressure, measured from Bailong real-time buoy system deployed in the western Indian Ocean, are being used to assess the capabilities of major high-frequency atmospheric reanalysis products in the western Indian Ocean region. During the study period, the atmospheric reanalysis data can accurately reproduce the temperature and sea level pressure fields, but the surface wind fields, especially for the meridional winds, are poorly described. ERA5 provides a better description of temperature and sea level pressure among all datasets. For the surface wind field, the reanalysis dataset overestimate/underestimate the intensity of the zonal/meridional winds, respectively, while the R-2 presents a worse description. Apart from the comparison of raw data, the diurnal variation of the three variables was also evaluated, and the results indicate a significant gap between the observation and reanalysis data. This study suggests that the current high-frequency reanalysis data still have shortcomings in describing atmospheric parameters in the tropical western Indian Ocean region.

Can Satellite and Atmospheric Reanalysis Products Capture Compound Moist Heat Stress-Floods?

Satellite-retrieved and model-based reanalysis precipitation products with high resolution have received increasing attention in recent decades. Their hydrological performance has been widely evaluated. However, whether they can be applied in characterizing the novel category of extreme events, such as compound moist heat-flood (CMHF) events, has not been fully investigated to date. The CMHF refers to the rapid transition from moist heat stress to devastating floods and has occurred increasingly frequently under the current warming climate. This study focuses on the applicability of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth generation of European Reanalysis (ERA5-Land) in simulating CMHF events over 120 catchments in China. Firstly, the precipitation accuracy of IMERG and ERA5-Land products is appraised for each catchment, using the gridded in situ meteorological dataset (CN05.1) as a baseline. Then, the ability of IMERG and ERA5-Land datasets in simulating the fraction, magnitude, and decade change of floods and CMHFs is comprehensively evaluated by forcing the XAJ and GR4J hydrological models. The results show that: (a) the IMERG and ERA5-Land perform similarly in terms of precipitation occurrences and intensity; (b) the IMERG yields discernably better performance than the ERA5-Land in streamflow simulation, with 71.7% and 50.8% of catchments showing the Kling–Gupta efficiency (KGE) higher than 0.5, respectively; (c) both datasets can roughly capture the frequency, magnitude, and their changes of floods and CMHFs in recent decades, with the IMERG exhibiting more satisfactory accuracy. Our results indicate that satellite remote sensing and atmospheric reanalysis precipitation can not only simulate individual hydrological extremes in most regions, but monitor compound events such as CMHF episodes, and especially, the IMERG satellite can yield better performance than the ERA5-Land reanalysis.

Intercomparison of Atmospheric Upper-Air Temperature From Recent Global Reanalysis Datasets

Atmospheric temperature is a key variable to detect and attribute climate change. Due to the relative sparseness of ground-based observations and heterogeneity of satellite data, global atmospheric reanalysis products are considered valuable datasets for studying and monitoring the climate, since these usually ensure spatially complete and continuous temporal coverage. Consequently, evaluating differences among the existing reanalyses is key to identifying inconsistencies. To this aim, the current study intercompares the climatological mean, variability, and linear trends for upper air temperature provided from four recent atmospheric reanalysis products (ERA5, ERA-Interim, MERRA-2, and JRA-55) The Reanalysis Multi-Model Ensemble-mean (RMME) is used as a comparator. Radiosonde observations are included for comparison on the regional scale (tropics). The results reveal that all evaluated reanalyses provide a consistent reproduction of the upper-air temperature profile. Temperature differences from RMME within ±0.25 K are found in both the troposphere and lower stratosphere, except for a few specific regions. Larger differences (&amp;gt;± 1.5 K) and discrepancies among the datasets are found in the upper stratosphere. Agreement between reanalyses increased after 1998. Differences in the temperature time series and seasonal cycle at the regional scales are smaller in the Northern Hemisphere mid-latitudes than in the tropics and Southern Hemisphere. A robust cooling trend in the lower stratosphere during the period of largest ozone depletion (1980-1997) and a warming trend in the troposphere for the period 1998-2018 are clearly found, at the global level, in all the datasets. Temperature trends and variability in the tropics are consistent in all reanalysis datasets with the homogenized radiosonde records from the lower to middle troposphere and in the lower stratosphere. However, large differences are found in the upper troposphere, tropical tropopause layer (TTL), and middle stratosphere. The well-known temperature variability in the lower stratosphere associated with Quasi-Biennial Oscillation (QBO) is captured in both reanalyses and observational datasets. Among the reanalyses, ERA5 shows the smallest temperature difference with respect to homogenized upper-air radiosoundings records.