Global Atmospheric Reanalysis Research Articles

Perspective of explosive cyclones with three shapes of upper-level potential vorticity over the northern Atlantic Ocean

The characteristics of explosive cyclones (ECs) with three shapes of upper-level potential vorticity (PV), namely “streamer”, “hook”, and “treble clef” shapes were jointly examined by composite and diagnostic approaches, by using the fifth-generation global atmospheric reanalysis data (ERA5) issued by European Centre for Medium-Range Weather Forecasts (ECMWF). Totally, 48 ECs over the northern Atlantic Ocean with typical evolution from “streamer” to “hook”, and then to “treble clef” shape of PV were selected for composite analyses. It turns out that the upper-level PV exhibits “streamer”, “hook” and “treble clef” features at early, rapid deepening and mature stages of EC, respectively. The intrinsic essences of three shapes of upper-level PV associated with different stages of EC were linked. In the upper troposphere, positive PV tendency is mainly derived from positive horizontal advection of PV (HADV), which is essentially generated by high-PV air trapped in upper-level jet and cyclonic circulation. Positive HADV is the main factor driving the evolution of upper-level PV from “streamer” to “hook”, and then to “treble clef”. In the middle troposphere, positive PV tendency is primarily associated with positive vertical advection of PV (VADV). This is due to the upper-level high-PV anomalies from stratospheric intrusion and the lower-level high-PV anomalies generated by diabatic heating both extend vertically to the middle troposphere. In the lower troposphere, diabatic heating (DIAB) overwhelmingly produce positive PV tendency. These three PV anomalies are usually vertically overlain and connected to form PV “tower” that runs through the whole troposphere. The formation of PV “tower” may strengthen cyclonic circulation, which is in turn conducive to the explosive deepening of cyclone.

Development of the CMA-ChemRA: China Regional Weakly Coupled Chemical-Weather Reanalysis System with product since 2007.

The CMA-ChemRA (China Regional Weakly Coupled Chemical-Weather Reanalysis System) was developped using China's first-generation global atmospheric reanalysis product (CRA-40) as initial fields and boundary conditions, coupled with the WRF-Chem atmospheric chemical model and the WRFDA/3DVar assimilation system. By constructing a joint background error covariance matrix, CMA-ChemRA achieves weak coupling between atmospheric chemistry and meteorological variables, enabling simultaneous assimilation of diverse data sources, including hourly observations from ground stations, wind profilers, upper-air soundings, aircraft reports, and atmospheric composition measurements. To extend the dataset to periods before 2013 when China lacked PM2.5 observations, the system incorporates a reconstructed PM2.5 dataset derived by AI from visibility inversion alongside various emission inventories. The CMA-ChemRA system produces a reanalysis product from 2007 to the present, with a spatial resolution of 15km and an hourly temporal resolution. It includes three-dimensional isobaric and near-surface layers for 6 key elements PM2.5, PM10, O3, SO2, NO2, and CO, as well as meteorological variables. This product is updated in near real-time, with a 50-min lag for forecast updates. Evaluation of the system shows substantial improvements in accuracy, with significant reductions in root mean square error (RMSE) for the six elements in the near-surface atmospheric layer post-assimilation. The model's depiction of ground-level PM2.5 concentrations aligns well with independent observational data across five urban regions, showing a narrow RMSE range of 15.5 to 32.8μg/m3. Additionally, CMA-ChemRA demonstrates strong performance in capturing the evolution of dust storms and pollution events, particularly in accurately modeling PM2.5 concentrations during severe pollution episodes. Our innovative approach in constructing a joint background error covariance matrix and the resulting high-resolution, real-time updating CMA-ChemRA product. This represents significant advancement in the field of atmospheric and chemical weather reanalysis. The product serves as an crucial tool for environmental monitoring and forecasting in China.

Evaluation of Fengyun-4B Satellite Temperature Profile Products Using Radiosonde Observations and ERA5 Reanalysis over Eastern Tibetan Plateau

The latest-generation geostationary meteorological satellite, Fengyun-4B (FY-4B), equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), offers high-spatiotemporal-resolution three-dimensional temperature structures. Its deployment serves as a critical complement to atmospheric temperature profile (ATP) observation in the Tibetan Plateau (TP). Based on radiosonde observation (RAOB) and the fifth-generation ECMWF global climate atmospheric reanalysis (ERA5), this study validates the availability and representativeness of FY-4B/GIIRS ATP products in the eastern TP region. Due to the issue of satellite zenith, this study focuses solely on examining the eastern TP region. Under a clear sky, FY-4B/GIIRS ATP exhibits good consistency with RAOB compared to cloudy conditions, with an average root mean square error (RMSE) of 2.57 K. FY-4B/GIIRS tends to underestimate temperatures in the lower layers while overestimating temperatures in the upper layers. The bias varies across seasons. Except for summer, the horizontal and vertical bias distribution patterns are similar, though there are slight differences in values. Despite the presence of bias, FY-4B/GIIRS ATP maintains a good consistency with observations and reanalysis data, indicating commendable product quality. These results demonstrate that it can play a vital role in augmenting the ATP observation network limited by sparse radiosonde stations in the eastern TP, offering crucial data support for numerical weather prediction, weather monitoring, and related meteorological research in this region.

Should We Conserve Entropy or Energy when Computing CAPE with Mixed-Phase Precipitation Physics?

Abstract The rapidly increasing resolution of global atmospheric reanalysis and climate model datasets necessitates finding methods for computing convective available potential energy (CAPE) both efficiently and accurately. To this end, this article compares two common methods for computing CAPE which conserve either energy or entropy. Inaccuracies in these computations arise from both physical and numerical errors. For instance, computing CAPE with entropy conserved results in physical errors from nonequilibrium phase transitions but minimizes numerical errors because solutions are analytic at each height. In contrast, computing CAPE with energy conserved avoids these physical errors, but accumulates numerical errors that are grid-resolution-dependent because the numerical integration of a differential equation is required. Analysis of CAPE computed with large databases of soundings from the tropical Amazon and midlatitude storm environments shows that physical errors from the entropy method are typically 1%–3% as large as CAPE, which is comparable to the numerical errors from conserving energy with grid spacing of 25 and 250 m using explicit first-order and second-order integration schemes, respectively. Errors in entropy-based CAPE calculations are also insensitive to vertical grid spacing, in contrast to energy-based calculations whose error strongly scales with the grid spacing. It is shown that entropy-based methods are advantageous when intercomparing datasets with differing vertical resolution because they produce accurate and reasonably fast results that are insensitive to grid resolution, whereas a second-order energy-based method is advantageous when analyzing data with a consistent vertical resolution because of its superior computational efficiency. Significance Statement Convective available potential energy (CAPE) is a measure of instability in the atmosphere that helps forecasters and researchers understand when and where thunderstorms will form. The purpose of this article is to identify the most efficient and accurate methods for computing CAPE. Two methods are considered here, one that relates to the entropy (a measure of thermodynamic disorder) of an air parcel and one that relates to the energy of an air parcel. Results indicate that the entropy method is most accurate and insensitive to the resolution of the data used for the calculation (which can vary considerably), whereas the energy method uses the least computation time.

Fidelity of global tropical cyclone activity in a new reanalysis dataset (CRA40)

AbstractThis study systematically evaluated global tropical cyclone (TC) activity in a new global atmospheric reanalysis dataset named the “40‐year Global Reanalysis” (CRA40) against the best track data. For comparison, four state‐of‐the‐art reanalyses—ERA5, JRA55, CFSR, and MERRA2—were also assessed. The results showed that there is a general underestimation of global TC genesis frequency and intensity in both CRA40 and other reanalyses. A detailed investigation of spatial distribution, seasonality, interannual variation, and long‐term trend for TC genesis frequency, as well as pressure–wind relationship for TC intensity, revealed similarities and differences among these reanalyses datasets. Overall, CRA40 does not exhibit clear advantages over other reanalyses in these aspects, but its biases are also not more pronounced. However, regarding TC translation speed, CRA40 outpeforms other reanalyses, evident by its high level of consistency with the observation in the zonal average pattern, meridional distribution at peak latitudes, and interannual variation, suggesting its reasonable capability in capturing large‐scale atmospheric characteristics. Our findings indicate that the use of CRA40 is appropriate for conducting TC‐related studies, within the scope of its limitations.

Climatology of the terms and variables of transformed Eulerian-mean (TEM) equations from multiple reanalyses: MERRA-2, JRA-55, ERA-Interim, and CFSR

Abstract. A 30-year (1980–2010) climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations is constructed by using four global atmospheric reanalyses: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); the Japanese 55-year Reanalysis (JRA-55); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Climate Forecast System Reanalysis (CFSR). Both the reanalysis ensemble mean (REM) and the differences in each reanalysis from the REM are investigated in the latitude–pressure domain for December–January–February and for June–July–August. For the REM investigation, two residual vertical velocities (the original one and one evaluated from residual meridional velocity) and two mass streamfunctions (from meridional and vertical velocities) are compared. Longwave (LW) radiative heating and shortwave (SW) radiative heating are also shown and discussed. For the TEM equations, the residual terms are also calculated and investigated for their potential usefulness, as the residual term for the momentum equation should include the effects of parameterized processes such as gravity waves, while that for the thermodynamic equation should indicate the analysis increment. Inter-reanalysis differences are investigated for the mass streamfunction, LW and SW heating, the two major terms of the TEM momentum equation (the Coriolis term and the Eliassen–Palm flux divergence term), and the two major terms of the TEM thermodynamic equation (the vertical temperature advection term and the total diabatic heating term). The spread among reanalysis TEM momentum balance terms is around 10 % in Northern Hemisphere winter and up to 50 % in Southern Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are found in the vertical advection, for which the structure is inconsistent with the differences in heating. The results shown in this paper provide basic information on the degree of agreement among recent reanalyses in the stratosphere and upper troposphere in the TEM framework.

Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)

Abstract. We assess the ability of two modelling chains to reproduce, over the last century (1902–2009) and from large-scale atmospheric information only, the temporal variations in river discharges, low-flow sequences and flood events observed at different locations of the upper Rhône River catchment, an alpine river straddling France and Switzerland (10 900 km2). The two modelling chains are made up of a downscaling model, either statistical (Sequential Constructive Atmospheric Analogues for Multivariate weather Predictions – SCAMP) or dynamical (Modèle Atmosphérique Régional – MAR), and the Glacier and SnowMelt SOil CONTribution (GSM-SOCONT) model. Both downscaling models, forced by atmospheric information from the global atmospheric reanalysis ERA-20C, provide time series of daily scenarios of precipitation and temperature used as inputs to the hydrological model. With hydrological regimes ranging from highly glaciated ones in its upper part to mixed ones dominated by snow and rain downstream, the upper Rhône River catchment is ideal for evaluating the different downscaling models in contrasting and demanding hydro-meteorological configurations where the interplay between weather variables in both space and time is determinant. Whatever the river sub-basin considered, the simulated discharges are in good agreement with the reference ones, provided that the weather scenarios are bias-corrected. The observed multi-scale variations in discharges (daily, seasonal, and interannual) are reproduced well. The low-frequency hydrological situations, such as annual monthly discharge minima (used as low-flow proxy indicators) and annual daily discharge maxima (used as flood proxy indicators), are reproduced reasonably well. The observed increase in flood activity over the last century is also reproduced rather well. The observed low-flow activity is conversely overestimated, and its variations from one sub-period to another are only partially reproduced. Bias correction is crucial for both precipitation and temperature and for both downscaling models. For the dynamical one, a bias correction is also essential for getting realistic daily temperature lapse rates. Uncorrected scenarios lead to irrelevant hydrological simulations, especially for the sub-basins at high elevation, due mainly to irrelevant snowpack dynamic simulations. The simulations also highlight the difficulty in simulating precipitation dependency on elevation over mountainous areas.

Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses

Abstract. A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 W m−2 for the latent heat flux and over 800 W m−2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 W m−2 for the latent heat flux and 600 W m−2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes.

Seasonally dependent increases in subweekly temperature variability over Southern Hemisphere landmasses detected in multiple reanalyses

Abstract. The inter-dataset agreement of trends in subweekly near-surface (850 hPa) temperature variability over Southern Hemisphere midlatitude land masses is assessed among 12 global atmospheric reanalysis datasets. A comparison of the climatological temperature variance and dominant sources and sinks of the variance reveals that, except for NCEP-NCAR (R1) and NCEP-DOE (R2), there is a relatively good agreement for their magnitudes and spatial distributions during the satellite era (1980–2022), which indicates that the key features of subweekly variability are sufficiently well represented. A good agreement is noted for the positive trends found in subweekly variability over the satellite era affecting South Africa in September–October–November (SON) and South America in December–January–February (DJF). Although there is agreement in most of the reanalyses concerning the positive trend affecting Australia in SON, this has not yet emerged from the noise associated with interannual variability when considering only the satellite era. It is significant, however, when the period is extended (1954–2022) or limited to the most recent decades (1990–2022). The trends are explained primarily by a more efficient generation of subweekly temperature variance by horizontal temperature advection. This generation is also identified as a source of biases among the datasets. The trends are found to be reproduced even in those reanalyses that do not assimilate satellite data (JRA-55C) or that assimilate surface observations only (ERA-20C, 20CRv2c, and 20CRv3).

Climate change alone cannot explain boreal caribou range recession in Quebec since 1850.

The contraction of species range is one of the most significant symptoms of biodiversity loss worldwide. While anthropogenic activities and habitat alteration are major threats for several species, climate change should also be considered. For species at risk, differentiating the effects of human disturbances and climate change on past and current range transformations is an important step towards improved conservation strategies. We paired historical range maps with global atmospheric reanalyses from different sources to assess the potential effects of recent climate change on the observed northward contraction of the range of boreal populations of woodland caribou (Rangifer tarandus caribou) in Quebec (Canada) since 1850. We quantified these effects by highlighting the discrepancies between different southern limits of the caribou's range (used as references) observed in the past and reconstitutions obtained through the hindcasting of the climate conditions within which caribou are currently found. Hindcasted southern limits moved ~105 km north over time under all reanalysis datasets, a trend drastically different from the ~620 km reported for observed southern limits since 1850. The differences in latitudinal shift through time between the observed and hindcasted southern limits of distribution suggest that caribou range recession should have been only 17% of what has been observed since 1850 if recent climate change had been the only disturbance driver. This relatively limited impact of climate reinforces the scientific consensus stating that caribou range recession in Quebec is mainly caused by anthropogenic drivers (i.e. logging, development of the road network, agriculture, urbanization) that have modified the structure and composition of the forest over the past 160 years, paving the way for habitat-mediated apparent competition and overharvesting. Our results also call for a reconsideration of past ranges in models aiming at projecting future distributions, especially for endangered species.

Active and break spells of summer monsoon over Bangladesh

This study examines the intraseasonal variability of the southwest summer monsoon over Bangladesh using rainfall data from rain gauge stations of the Bangladesh Meteorological Department (BMD) collected over 30 years (1988–2017). In this paper, active and break spells are defined as periods during the peak monsoon months of July and August, in which the daily precipitation lasts for three or more days at 0.5 above and below the daily climatological cycle. The active and break phases of the monsoon over a period of 10 years (2008–2017) were also analyzed by The Weather Research and Forecasting (WRF) model simulation for analysing synoptic conditions. The model simulations for each year were done for the period of 1 May to 30 September with 1 May as the initial condition with a single domain of 30 km resolution and 19 vertical levels. The final operational global analysis data from the Global Forecasting System of National Centers for Environment Prediction (NCEP-FNL) with resolution 1° × 1° is used for model simulation. The model-simulated daily rainfall, Sea Level Pressure (SLP), wind pattern at 850 hPa, 200 hPa, and Outgoing Longwave Radiation (OLR) are compared with the observations from Tropical Rainfall Measuring Mission (TRMM), ERA5 (ERA5 is the European Centre for Medium-Range Weather Forecasts fifth-gen global atmospheric reanalysis data), and Kalpana-1. This study also finds that the increase in rainfall is concurrent with a southwesterly wind and the decrease of rainfall simultaneously occurs with a southeasterly wind. Active days were found to have lower OLR values and lower SLP than break days.

A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance

Abstract. Ice sheet surface mass balance describes the net snow accumulation at the ice sheet surface. On the Antarctic ice sheet, winds redistribute snow, resulting in a surface mass balance that is variable in both space and time. Representing wind-driven snow redistribution processes in models is critical for local assessments of surface mass balance, repeat altimetry studies, and interpretation of ice core accumulation records. To this end, we have adapted Alpine3D, an existing distributed snow modeling framework, to downscale Antarctic surface mass balance to horizontal resolutions up to 1 km. In particular, we have introduced a new two-dimensional advection-based wind-driven snow redistribution module that is driven by an offline coupling between WindNinja, a wind downscaling model, and Alpine3D. We then show that large accumulation variability can be at least partially explained by terrain-induced wind speed variations which subsequently redistribute snow around rolling topography. By comparing Alpine3D to airborne-derived snow accumulation measurements within a testing domain over Pine Island Glacier in West Antarctica, we demonstrate that our Alpine3D downscaling approach improves surface mass balance estimates when compared to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), a global atmospheric reanalysis which we use as atmospheric forcing. In particular, when compared to MERRA-2, Alpine3D reduces simulated surface mass balance root mean squared error by 23.4 mmw.e.yr-1 (13 %) and increases variance explained by 24 %. Despite these improvements, our results demonstrate that considerable uncertainty stems from the employed saltation model, confounding simulations of surface mass balance variability.

The role of wind and buoyancy forcing on mixed layer depths and productivity on the Chatham Rise from ocean glider and ship-based observations

ABSTRACT The Subtropical Front (STF) and associated biological productivity are topographically locked to the Chatham Rise. Year-to-year differences of upper ocean dynamics and associated responses of phytoplankton are valuable for understanding variability in fisheries biomass as well as providing new knowledge of environmental condition for potential seabed mining in this keystone ecosystem. In-situ observations collected over three consecutive years are used to examine the oceanographic variability on Chatham Rise and its potential causal mechanisms are evaluated from satellite observations and a global atmospheric reanalysis. The water column over Chatham Rise consists of a three-layer structure comprised of a stable intermediate layer sandwiched between a well-mixed surface and bottom layer. The in-situ data showed that different water masses can dominate the surface mixed layer at different times. This variability in surface water masses appears to be related to the position of the STF modulated by wind forcing. Spatial and temporal variability was also observed in the mixed layer depth with the temporal variability driven mainly by wind forcing. A strong relationship was observed between phytoplankton biomass and the depth of the mixed layer with implications for the distribution of biological communities and fisheries at the year-to-year timescale on the Chatham Rise.

The effects of intraseasonal oscillations on landfalling tropical cyclones in the Philippines during the boreal summer

As a kind of weather phenomenon with destructive wind and heavy rainfall, tropical cyclones (TCs), especially the landing TCs, can cause severer economic damage and losses of life. Philippines is one of the countries mostly affected by Tropical cyclones (TCs). Based on the best-track TC data and global atmospheric and oceanic reanalysis data, the present paper investigates the isolated and combined effects of two intraseasonal oscillations, the Madden-Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO), on landfall of TCs in the Philippines during boreal summer (May-September) in 1979-2019. The results show that both the MJO and the QBWO can significantly affect the frequency, landfall intensity, location, and translation speed of TCs that make landfall in the Philippines. During the convective (non-convective) phases of the MJO and the QBWO, more (less) frequent and stronger (weaker) TCs make landfall in the Philippines. This is due to the increased (decreased) frequency of TCs formation in the NWP and environmental factors in the region east of the Philippines that are favorable (unfavorable) for the development of TCs. With the northward propagation of the convective signals of the MJO and QBWO, the Western Pacific Subtropical High (WPSH) shifts eastward, and the steering flow is unfavorable for westward movement of NWP TCs. This, in turn, causes a northward shift in the landfall locations and a decrease in the translation speed of TCs. These results are helpful for the prediction of the TCs affecting the Philippines.

Evaluation of the weighted mean temperature over China using multiple reanalysis data and radiosonde

Global Navigation Satellite Systems (GNSS) meteorology has received extensive attention for weather research in recent years. As a crucial conversion parameter, weighted mean temperature (Tm) is used to calculate precipitable water vapor (PWV) by the signal path wet delay in GNSS meteorology. Comparing with Tm calculated by radiosonde, Tm from reanalysis data is more convenient due to higher spatiotemporal resolution, such as the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), the fifth-generation ECMWF Reanalysis (ERA5), Japanese 55-year Reanalysis Project (JRA-55), the global atmospheric reanalysis of China Meteorological Administration (CRA40). To investigate their different performances, a comprehensive assessment of multiple Tm datasets from 2011 to 2019 was conducted using the radiosonde data over China as references, in which the root mean square error (RMSE) and the mean absolute error (MAE) were selected as statistics. The results showed that the Tm derived from multiple reanalysis data exhibited excellent applicability in China, especially in the southeast region. The RMSE and MAE were 1.69/1.36, 1.70/1.36, 1.79/1.45, 1.99/1.61 K for ERA5, JRA-55, CRA40, MERRA-2, respectively. By comparing Tm from ERA5 with three different resolutions, it's found that the accuracy of Tm was getting better with the increase of the spatial resolution. Additionally, the accuracy of these Tm was of great relevance to season, moment, latitude, and elevation. By exploring the intercomparison of Tm from reanalysis data, their correlation coefficients were all above 0.98. This study indicated that the Tm derived from various reanalysis data can be utilized for climate research in China, and the ERA5 data with high resolution is recommended first.

Surface temperature comparison of the Arctic winter MOSAiC observations, ERA5 reanalysis, and MODIS satellite retrieval

Atmospheric model systems, such as those used for weather forecast and reanalysis production, often have significant and systematic errors in their representation of the Arctic surface energy budget and its components. The newly available observation data of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (2019/2020) enable a range of model analyses and validation in order to advance our understanding of potential model deficiencies. In the present study, we analyze deficiencies in the surface radiative energy budget over Arctic sea ice in the ERA5 global atmospheric reanalysis by comparing against the winter MOSAiC campaign data, as well as, a pan-Arctic level-2 MODIS ice surface temperature remote sensing product. We find that ERA5 can simulate the timing of radiatively clear periods, though it is not able to distinguish the two observed radiative Arctic winter states, radiatively clear and opaquely cloudy, in the distribution of the net surface radiative budget. The ERA5 surface temperature over Arctic sea ice has a conditional error with a positive bias in radiatively clear conditions and a negative bias in opaquely cloudy conditions. The mean surface temperature error is 4°C for radiatively clear situations at MOSAiC and up to 15°C in some parts of the Arctic. The spatial variability of the surface temperature, given by 4 observation sites at MOSAiC, is not captured by ERA5 due to its spatial resolution but represented in the level-2 satellite product. The sensitivity analysis of possible error sources, using satellite products of snow depth and sea ice thickness, shows that the positive surface temperature errors during radiatively clear events are, to a large extent, caused by insufficient sea ice thickness and snow depth representation in the reanalysis system. A positive bias characterizes regions with ice thickness greater than 1.5 m, while the negative bias for thinner ice is partly compensated by the effect of snow.

A change in the relation between the Subtropical Indian Ocean Dipole and the South Atlantic Ocean Dipole indices in the past four decades

Abstract. We utilized the global atmospheric reanalysis ERA5 and reconstructed sea surface temperature (SST) data from 1979 through 2020 to examine the stability of the relationship between the SST oscillations in the southern Indian Ocean and the Atlantic Ocean, as described by the Subtropical Indian Ocean Dipole (SIOD) and South Atlantic Ocean Dipole (SAOD) indices, respectively. We note a significant positive correlation between the two indices prior to the year 2000 but practically no correlation afterwards. We show that in the two decades prior to 2000, a positive phase of the SAOD is associated with more convective activities over the subtropical southern Atlantic Ocean and eastern Brazil, which trigger a stronger upper-atmosphere wavetrain. This produces stronger southern subtropical highs and surface anti-cyclonic circulations and therefore a stronger correlation between the two indices. The situation is reversed after 2000. Our results are potentially applicable to predictions of precipitation in southern Africa and South America.

Dynamic Downscaling the South Asian Summer Monsoon From a Global Reanalysis Using a Regional Coupled Ocean‐Atmosphere Model

AbstractIn this study, we present the results of a regional model (regional spectral model‐regional ocean model [(RSM‐ROMS]) simulation of the South Asian Summer Monsoon (SASM). The RSM‐ROMS integration is carried out at 20 km grid spacing over a period of 25 years (1986–2010). The simulation is forced by global atmospheric and oceanic reanalysis. The RSM‐ROMS simulation shows a realistic alignment of the simulated rainfall along the orographic features of the domain. Furthermore, the RSM‐ROMS simulates the observed feature of convection over continental SASM region being more vigorous with dominance of mixed warm and cold phase hydrometeors in contrast to the dominance of the warm rain process in the neighboring tropical oceans. Similarly, the upper ocean features of contrasting mixed layer and thermocline depths between the northern and equatorial Indian Ocean are also simulated in the RSM‐ROMS. Intra‐Seasonal Oscillation (ISO) of the SASM at 10–20 and 20–70 days are also simulated in the RSM‐ROMS with many of its features verifying with observations. For example, the 20–70 days ISO are of higher amplitude and its meridional propagation is slower in Bay of Bengal compared to that over Arabian Sea. Additionally, RSM‐ROMS shows 12.3 Monsoon Low Pressure Systems (LPSs) per season that is comparable to 14.6 per season from observations. Furthermore, the observed intraseasonal contrasts of LPS between the wet and dry spells of ISO is also reproduced in the RSM‐ROMS.

Comparison of Weighted Mean Temperature in Greenland Calculated by Four Reanalysis Data

The weighted mean temperature ( Tm) is a critical parameter for precipitable water vapor (PWV) retrieval in global navigation satellite system (GNSS) meteorology. Reanalysis data are an important data source for Tm calculation and Tm empirical model establishment. This study uses radiosonde data to evaluate the accuracy and the spatiotemporal variation of Tm that is derived from four reanalysis data, namely, the release of the fifth-generation accurate global atmospheric reanalysis (ERA5), the modern-era retrospective analysis for research and applications version 2 (MERRA-2), the NCEP/DOE, and the NCEP/NCAR, from 2005 to 2019 in Greenland, due to the paucity of research on the performance of Tm in the polar region that is derived from reanalysis data, particularly on a long temporal scale. The results were as follows: (1) The 15-year mean bias errors (MBEs) and root mean square errors (RMSEs) of Tm that were obtained from the four reanalysis data are 0.267 and 0.691 K for the ERA5, −0.247 and 0.962 K for the MERRA-2, 0.192 and 1.148 K for the NCEP/DOE, and −0.069 and 1.37 K for the NCEP/NCAR. The Tm that was derived from the ERA5 (ERA5 Tm) has the highest accuracy, followed by the MERRA-2 Tm, the NCEP/DOE Tm, and the NCEP/NCAR Tm. (2) In the inter-annual stability of the Tm precision compared with the radiosonde data, the results of the ERA5 are the most stable, followed by the NCEP/DOE Tm, the NCEP/NCAR Tm, and the MERRA-2 Tm. The ERA5 Tm have improved from 2005 to 2019. (3) The Tm accuracy that was computed by the four reanalysis data exhibits significant seasonal variation characteristics in Greenland, as follows: the summer and the autumn accuracy is higher than that in the winter and the spring, which may be related to the variation of the surface temperature (Ts) accuracy. (4) The Tm that was estimated from the four reanalysis data exhibits a consistent spatial distribution, as follows: the Tm is smaller in the middle region of Greenland and is greater at the island’s edge. The comparative study of Tm that is obtained from the four reanalysis data can serve as a reference for future research on Tm model development and water vapor retrieval in polar regions by utilizing reanalysis data.

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 (>± 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.