Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

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Simulations of 20th and 21st century Arctic cloud amount in the global climate models assessed in the IPCC AR4

Climate Dynamics ◽

10.1007/s00382-008-0475-6 ◽

2008 ◽

Vol 33 (7-8) ◽

pp. 1099-1115 ◽

Cited By ~ 75

Author(s):

Steve Vavrus ◽

Duane Waliser ◽

Axel Schweiger ◽

Jennifer Francis

Keyword(s):

21St Century ◽

Climate Models ◽

Global Climate ◽

Cloud Amount ◽

Global Climate Models ◽

Ipcc Ar4

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Simulation of Present-Day and Twenty-First-Century Energy Budgets of the Southern Oceans

Journal of Climate ◽

10.1175/2009jcli3152.1 ◽

2010 ◽

Vol 23 (2) ◽

pp. 440-454 ◽

Cited By ~ 262

Author(s):

Kevin E. Trenberth ◽

John T. Fasullo

Keyword(s):

Southern Hemisphere ◽

Cloud Cover ◽

Climate Models ◽

Global Climate ◽

Strong Relationship ◽

Global Climate Models ◽

Storm Tracks ◽

Energy Budgets ◽

Poleward Shift ◽

Projected Changes

Abstract The energy budget of the modern-day Southern Hemisphere is poorly simulated in both state-of-the-art reanalyses and coupled global climate models. The ocean-dominated Southern Hemisphere has low surface reflectivity and therefore its albedo is particularly sensitive to cloud cover. In modern-day climates, mainly because of systematic deficiencies in cloud and albedo at mid- and high latitudes, too much solar radiation enters the ocean. Along with too little radiation absorbed at lower latitudes because of clouds that are too bright, unrealistically weak poleward transports of energy by both the ocean and atmosphere are generally simulated in the Southern Hemisphere. This implies too little baroclinic eddy development and deficient activity in storm tracks. However, projections into the future by coupled climate models indicate that the Southern Ocean features a robust and unique increase in albedo, related to clouds, in association with an intensification and poleward shift in storm tracks that is not observed at any other latitude. Such an increase in cloud may be untenable in nature, as it is likely precluded by the present-day ubiquitous cloud cover that models fail to capture. There is also a remarkably strong relationship between the projected changes in clouds and the simulated current-day cloud errors. The model equilibrium climate sensitivity is also significantly negatively correlated with the Southern Hemisphere energy errors, and only the more sensitive models are in the range of observations. As a result, questions loom large about how the Southern Hemisphere will actually change as global warming progresses, and a better simulation of the modern-day climate is an essential first step.

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Could the cloud cover in future climate models be improved by incorporating the role of sea spray in surface heat fluxes?

10.5194/egusphere-egu2020-6316 ◽

2020 ◽

Author(s):

Yajuan Song ◽

Fangli Qiao ◽

Qi Shu ◽

Jiping Liu ◽

Ying Bao ◽

...

Keyword(s):

Latent Heat ◽

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Heat Fluxes ◽

Global Climate Models ◽

Shortwave Radiation ◽

Sea Spray ◽

Low Level

<p>Accurate cloud cover and radiative effect simulation remains a long-standing challenge for global climate models (GCMs). The Southern Ocean (SO) cloud cover is substantially underestimated by most GCMs. Therefore, too much shortwave radiation is absorbed by oceans, which causes an overly warm sea surface temperature (SST) bias over the SO. For the first time, sea spray effects on latent and sensible heat fluxes are considered in a climate model. The most notable sea spray impacts on heat fluxes occur over the SO, with anomalous latent heat fluxes up to -7.74 W m<sup>-2</sup>. Enhanced latent heat release lead to SST cooling. In addition, more clouds are formed over the SO to reflect excessive downward shortwave radiation, especially low-level clouds at 1.51% increments. Our results provide a feasible solution to mitigate the lack of low-level clouds and overly warm SST biases over the SO in GCMs.</p>

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Understanding the Varied Influence of Midlatitude Jet Position on Clouds and Cloud Radiative Effects in Observations and Global Climate Models

Journal of Climate ◽

10.1175/jcli-d-16-0295.1 ◽

2016 ◽

Vol 29 (24) ◽

pp. 9005-9025 ◽

Cited By ~ 22

Author(s):

Kevin M. Grise ◽

Brian Medeiros

Keyword(s):

Vertical Velocity ◽

Climate Models ◽

Global Climate ◽

Storm Track ◽

Cloud Amount ◽

Global Climate Models ◽

Radiative Effects ◽

Velocity Anomalies ◽

Cooling Effects ◽

Cloud Radiative Effects

Abstract This study examines the dynamical mechanisms responsible for changes in midlatitude clouds and cloud radiative effects (CRE) that occur in conjunction with meridional shifts in the jet streams over the North Atlantic, North Pacific, and Southern Oceans. When the midlatitude jet shifts poleward, extratropical cyclones and their associated upward vertical velocity anomalies closely follow. As a result, a poleward jet shift contributes to a poleward shift in high-topped storm-track clouds and their associated longwave CRE. However, when the jet shifts poleward, downward vertical velocity anomalies increase equatorward of the jet, contributing to an enhancement of the boundary layer estimated inversion strength (EIS) and an increase in low cloud amount there. Because shortwave CRE depends on the reflection of solar radiation by clouds in all layers, the shortwave cooling effects of midlatitude clouds increase with both upward vertical velocity anomalies and positive EIS anomalies. Over midlatitude oceans where a poleward jet shift contributes to positive EIS anomalies but downward vertical velocity anomalies, the two effects cancel, and net observed changes in shortwave CRE are small. Global climate models generally capture the observed anomalies associated with midlatitude jet shifts. However, there is large intermodel spread in the shortwave CRE anomalies, with a subset of models showing a large shortwave cloud radiative warming over midlatitude oceans with a poleward jet shift. In these models, midlatitude shortwave CRE is sensitive to vertical velocity perturbations, but the observed sensitivity to EIS perturbations is underestimated. Consequently, these models might incorrectly estimate future midlatitude cloud feedbacks in regions where appreciable changes in both vertical velocity and EIS are projected.

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Calibration of global MODIS cloud amount using CALIOP cloud profiles

10.5194/amt-2020-111 ◽

2020 ◽

Author(s):

Andrzej Z. Kotarba

Keyword(s):

Cloud Amount ◽

Satellite Observation ◽

Cloud Fraction ◽

Orthogonal Polarization ◽

Polar Regions ◽

Cloud Detection ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Mask

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOV) as either confident cloudy, probably cloudy, probably clear, or confident clear. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by NASA assumes that confident clear and probably clear IFOV are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this best guess approach is unreliable, especially on a regional/ local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with MODIS/ Aqua IFOV. Based on 33,793,648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in the polar regions at night, and in selected locations over the northern hemisphere. Applications of MODIS data at ~10 degrees resolution (or finer) should first assess the extent of the error. Uncertainties were related to the efficiency of the cloud masking algorithm. Until the algorithm can be significantly modified, our method is a robust way to calibrate (correct) MODIS estimates. It can be also used for MODIS/ Terra data, and other missions where the footprint is collocated with CALIPSO.

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Comparison of CMIP6 and CMIP5 Model Performance in Simulating Historical Precipitation and Temperature in Bangladesh: A Preliminary Study

10.21203/rs.3.rs-233789/v1 ◽

2021 ◽

Author(s):

Mohammad Kamruzzaman ◽

Shamsuddin Shahid ◽

ARM Towfiqul Islam ◽

Syewoon Hwang ◽

Jaepil Cho ◽

...

Keyword(s):

Climate Models ◽

Global Climate ◽

Model Performance ◽

Coupled Model ◽

Skill Score ◽

Global Climate Models ◽

Added Value ◽

Lower Performance ◽

Mean Square Errors ◽

Cmip5 Gcms

Abstract The relative performance of global climate models (GCMs) of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. The multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skilful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, a significant improvement in CMIP6 MME compared to CMIP5 MME was noticed in simulating rainfall over Bangladesh at annual and seasonal scales. CMIP6 MME also showed significant reduction in maximum and minimum temperature biases over Bangladesh. However, systematic wet and cold biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlation with observed data compared to CMIP5 GCMs, but higher difference in terms of standard deviations and centered root mean square errors, indicating better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables for different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature compared to CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is incompatible with the climate models used in this research.

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Stratocumulus Clouds in Southeastern Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models

Journal of Climate ◽

10.1175/jcli-d-13-00376.1 ◽

2014 ◽

Vol 27 (8) ◽

pp. 3000-3022 ◽

Cited By ~ 25

Author(s):

Jia-Lin Lin ◽

Taotao Qian ◽

Toshiaki Shinoda

Keyword(s):

Cloud Cover ◽

Large Scale ◽

Climate Models ◽

Global Climate ◽

Cloud Feedback ◽

Global Climate Models ◽

Temperature Structure ◽

Model Intercomparison ◽

Stratocumulus Clouds ◽

Intercomparison Project

Abstract This study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific (SEP) simulated by eight global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing (CRF), sea surface temperature (SST), and large-scale atmosphere environment. The results show that the state-of-the-art global climate models still have significant difficulty in simulating the SEP stratocumulus clouds and associated cloud feedback. Comparing with observations, the models tend to simulate significantly less cloud cover, higher cloud top, and a variety of unrealistic cloud albedo. The insufficient cloud cover leads to overly weak shortwave CRF and net CRF. Only two of the eight models capture the observed positive cloud feedback at subannual to decadal time scales. The cloud and radiation biases in the models are associated with 1) model biases in large-scale temperature structure including the lack of temperature inversion, insufficient lower troposphere stability (LTS), and insufficient reduction of LTS with local SST warming, and 2) improper model physics, especially insufficient increase of low cloud cover associated with larger LTS. The two models that arguably do best at simulating the stratocumulus clouds and associated cloud feedback are the only ones using cloud-top radiative cooling to drive boundary layer turbulence.

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Ranking general circulation models for India using TOPSIS

Journal of Water and Climate Change ◽

10.2166/wcc.2014.074 ◽

2014 ◽

Vol 6 (2) ◽

pp. 288-299 ◽

Cited By ~ 24

Author(s):

K. Srinivasa Raju ◽

D. Nagesh Kumar

Keyword(s):

General Circulation ◽

Climate Models ◽

Performance Indicator ◽

Global Climate ◽

Evaluation Criteria ◽

General Circulation Models ◽

Skill Score ◽

Global Climate Models ◽

Climate Conditions ◽

Grid Points

Eleven general circulation models/global climate models (GCMs) – BCCR-BCCM2.0, INGV-ECHAM4, GFDL2.0, GFDL2.1, GISS, IPSL-CM4, MIROC3, MRI-CGCM2, NCAR-PCMI, UKMO-HADCM3 and UKMO-HADGEM1 – are evaluated for Indian climate conditions using the performance indicator, skill score (SS). Two climate variables, temperature T (at three levels, i.e. 500, 700, 850 mb) and precipitation rate (Pr) are considered resulting in four SS-based evaluation criteria (T500, T700, T850, Pr). The multicriterion decision-making method, technique for order preference by similarity to an ideal solution, is applied to rank 11 GCMs. Efforts are made to rank GCMs for the Upper Malaprabha catchment and two river basins, namely, Krishna and Mahanadi (covered by 17 and 15 grids of size 2.5° × 2.5°, respectively). Similar efforts are also made for India (covered by 73 grid points of size 2.5° × 2.5°) for which an ensemble of GFDL2.0, INGV-ECHAM4, UKMO-HADCM3, MIROC3, BCCR-BCCM2.0 and GFDL2.1 is found to be suitable. It is concluded that the proposed methodology can be applied to similar situations with ease.

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Evaluation of the Global Climate Models in CMIP6 over Uganda

10.20944/preprints202012.0782.v1 ◽

2020 ◽

Author(s):

Hamida Ngoma ◽

Wang Wen ◽

Brian Ayugi ◽

Hassen Babaousmail ◽

Riwzan Karim ◽

...

Keyword(s):

Climate Models ◽

Lake Victoria ◽

Global Climate ◽

Skill Score ◽

Global Climate Models ◽

Mean Bias Error ◽

Bias Error ◽

Lake Victoria Basin ◽

Taylor Diagram ◽

Pattern Correlation

This study employed 15 CMIP6 GCMs and evaluated their ability to simulate rainfall over Uganda during 1981-2019. The models and the ensemble mean were assessed based on the ability to reproduce the annual climatologyseasonal rainfall distribution, trend, and statistical metrics, including mean bias error, root mean square error, and pattern correlation coefficient. The Taylor diagram and Taylor skill score (TSS) were used in ranking the models. The models performance varies greatly from one season to the other. The models reproduced the observed bimodal rainfall pattern of March to May (MAM) and September to November (SON) rains occurring over the region. Some models slightly overestimated, while some slightly underestimated, the MAM rainfall. However, there was a high rainfall overestimation during SON by most models. The models showed a positive spatial correlation with observed dataset, whereas a low correlation was shown interannually. Some models could not capture the rainfall patterns around local-scale features, for example, around the Lake Victoria basin and mountainous areas. The best performing models identified in the study include GFDL-ESM4, BCC-CMC-MR, IPSL-CM6A-LR, CanESM5, GDFL-CM4-gr1, and GFDL-CM4-gr2. The models CNRM-CM6-1 and CNRM-ESM2 underestimated rainfall throughout the annual cycle and mean climatology. However, these two models better reproduced the spatial trends of rainfall during both MAM and SON. The model spread in CMIP6 over the study area calls for further investigation on the attributions and possible implementation of robust approaches of Machine learning to minimize the biases.

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Evaluation of ERA-Interim and MERRA Cloudiness in the Southern Ocean

Journal of Climate ◽

10.1175/jcli-d-13-00432.1 ◽

2014 ◽

Vol 27 (5) ◽

pp. 2109-2124 ◽

Cited By ~ 69

Author(s):

Catherine M. Naud ◽

James F. Booth ◽

Anthony D. Del Genio

Keyword(s):

Southern Ocean ◽

Cloud Cover ◽

Warm Season ◽

Cloud Amount ◽

Thermodynamic Characteristics ◽

Cumulus Clouds ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Modern Era

Abstract The Southern Ocean cloud cover modeled by the Interim ECMWF Re-Analysis (ERA-Interim) and Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses are compared against Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) observations. ERA-Interim monthly mean cloud amounts match the observations within 5%, while MERRA significantly underestimates the cloud amount. For a compositing analysis of clouds in warm season extratropical cyclones, both reanalyses show a low bias in cloud cover. They display a larger bias to the west of the cyclones in the region of subsidence behind the cold fronts. This low bias is larger for MERRA than for ERA-Interim. Both MODIS and MISR retrievals indicate that the clouds in this sector are at a low altitude, often composed of liquid, and of a broken nature. The combined CloudSat–Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud profiles confirm these passive observations, but they also reveal that low-level clouds in other parts of the cyclones are also not properly represented in the reanalyses. The two reanalyses are in fairly good agreement for the dynamic and thermodynamic characteristics of the cyclones, suggesting that the cloud, convection, or boundary layer schemes are the problem instead. An examination of the lower-tropospheric stability distribution in the cyclones from both reanalyses suggests that the parameterization of shallow cumulus clouds may contribute in a large part to the problem. However, the differences in the cloud schemes and in particular in the precipitation processes, which may also contribute, cannot be excluded.

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