Variation and comparison of cloud cover in MODIS and four reanalysis datasets of ERA-interim, ERA5, MERRA-2 and NCEP

Wu, Haopeng; Xu, Xin; Luo, Tianyang; Yang, Yudi; Xiong, Zixu; Wang, Yuan

doi:10.1016/j.atmosres.2022.106477

Cited by 18 publications

(10 citation statements)

References 40 publications

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“…1); signaling accurate representations of water vapor, temperature, and surface albedo in ERA5. Second, ERA5's climatological clouds are highly similar to observations in their spatial pattern (Wu et al, 2022;Yao et al, 2020).…”

Section: Cloud-masking Contributionsmentioning

confidence: 65%

Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record

et al. 2023

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Satellite observations show a near-zero trend in the top-of-atmosphere global-mean net cloud radiative effect (CRE), suggesting that clouds did not further cool nor heat the planet over the last two decades. The causes of this observed trend are unknown and can range from effective radiative forcing (ERF), cloud feedbacks, cloud masking, to internal variability. We find that the near-zero NetCRE trend is a result of a significant negative trend in the longwave (LW) CRE and a significant positive trend in the shortwave (SW) CRE, cooling and heating the climate system, respectively. We find that it is exceptionally unlikely (<1% probability) that internal variability can explain the observed LW and SW CRE trends. Instead, the majority of the observed LWCRE trend arises from cloud masking wherein increases in greenhouse gases reduce OLR in all-sky conditions less than in clear-sky conditions. In SWCRE, rapid cloud adjustments to greenhouse gases, aerosols, and natural forcing agents (ERF) explain a majority of the observed trend. Over the Northeast Pacific, we show that ERF, hitherto an ignored factor, contributes as much as cloud feedbacks to the observed SWCRE trend. Large contributions from ERF and cloud masking to the global-mean LW and SW CRE trends are supplemented by negative LW and positive SW cloud feedback trends, which are detectable at 80-95% confidence depending on the observational uncertainty assumed. The large globalmean LW and SW cloud feedbacks cancel, leaving a small net cloud feedback that is unconstrained in sign, implying that clouds could amplify or dampen global warming.

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Section: Cloud-masking Contributionsmentioning

confidence: 65%

Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record

et al. 2023

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“…and are then situated approximately every 20 m, with an increasing spacing upwards. Several studies note the high performance of ERA5 in the Arctic region (Graham et al, 2019a;Wu et al, 2023), specifically in the Fram Strait region (Graham et al, 2019b). Thus, numerous authors performing trajectory analysis in the Arctic rely on wind and meteorological data fields from ERA5 (e.g., Papritz and Spengler, 2017;Papritz, 2020;Dahlke et al, 2022;You et al, 2021a;Kirbus et al, 2023a, b;Svensson et al, 2023).…”

Section: Reanalysis Productsmentioning

confidence: 99%

Thermodynamic and cloud evolution in a cold-air outbreak during HALO-(AC)³: quasi-Lagrangian observations compared to the ERA5 and CARRA reanalyses

Kirbus,

Schirmacher,

Klingebiel

et al. 2024

Atmos. Chem. Phys.

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Abstract. Arctic air masses undergo intense transformations when moving southward from closed sea ice to warmer open waters in marine cold-air outbreaks (CAOs). Due to the lack of measurements of diabatic heating and moisture uptake rates along CAO flows, studies often depend on atmospheric reanalysis output. However, the uncertainties connected to those datasets remain unclear. Here, we present height-resolved airborne observations of diabatic heating, moisture uptake, and cloud evolution measured in a quasi-Lagrangian manner. The investigated CAO was observed on 1 April 2022 during the HALO-(AC)3 campaign. Shortly after passing the sea-ice edge, maximum diabatic heating rates over 6 K h−1 and moisture uptake over 0.3 gkg-1h-1 were measured near the surface. Clouds started forming and vertical mixing within the deepening boundary layer intensified. The quasi-Lagrangian observations are compared with the fifth-generation global reanalysis (ERA5) and the Copernicus Arctic Regional Reanalysis (CARRA). Compared to these observations, the mean absolute errors of ERA5 versus CARRA data are 14 % higher for air temperature over sea ice (1.14 K versus 1.00 K) and 62 % higher for specific humidity over ice-free ocean (0.112 g kg−1 versus 0.069 g kg−1). We relate these differences to issues with the representation of the marginal ice zone and corresponding surface fluxes in ERA5, as well as the cloud scheme producing excess liquid-bearing, precipitating clouds, which causes a too-dry marine boundary layer. CARRA's high spatial resolution and demonstrated higher fidelity towards observations make it a promising candidate for further studies on Arctic air mass transformations.

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“…The budget analysis could have been performed by using all of the aerosol and meteorological fields from a particular reanalysis model, thereby avoiding this model discrepancy. Numerous studies have shown the ERA5 to better simulate the PBL (Guo et al., 2021; Johnston et al., 2021; Taszarek et al., 2021), clouds (Urraca et al., 2018; Wu et al., 2023), and precipitation (Hassler & Lauer, 2021) as compared to MERRA‐2. However, it is unclear how the aerosol fields simulated by EAC4 compare with those simulated by MERRA‐2, with some studies demonstrating both reanalysis models to be similarly biased (e.g., Ali et al., 2022; Lacima et al., 2023).…”

Section: Monthly and Sub‐monthly Ccn Variabilitymentioning

confidence: 99%

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

Ghate,

Surleta,

Magaritz‐Ronen

et al. 2023

JGR Atmospheres

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Seven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site.

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Variation and comparison of cloud cover in MODIS and four reanalysis datasets of ERA-interim, ERA5, MERRA-2 and NCEP

Cited by 18 publications

References 40 publications

Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record

Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record

Thermodynamic and cloud evolution in a cold-air outbreak during HALO-(AC)³: quasi-Lagrangian observations compared to the ERA5 and CARRA reanalyses

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

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Variation and comparison of cloud cover in MODIS and four reanalysis datasets of ERA-interim, ERA5, MERRA-2 and NCEP

Cited by 18 publications

References 40 publications

Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record

Forcing, Cloud Feedbacks, Cloud Masking, and Internal Variability in the Cloud Radiative Effect Satellite Record

Thermodynamic and cloud evolution in a cold-air outbreak during HALO-(AC)3: quasi-Lagrangian observations compared to the ERA5 and CARRA reanalyses

Drivers of Cloud Condensation Nuclei in the Eastern North Atlantic as Observed at the ARM Site

Contact Info

Product

Resources

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Thermodynamic and cloud evolution in a cold-air outbreak during HALO-(AC)³: quasi-Lagrangian observations compared to the ERA5 and CARRA reanalyses