the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Jan 2025
General Circulation Models evaluation at different time scales over tropical region using ESA-CCI satellite data records: a case study of water vapour and cloud cover
Abstract. Water vapour and cloud cover are two essential components of the earth's atmosphere. General circulation models (GCM) are used to study the long term evolution of the Earth's climate over past and future periods. The present work consists of assessing the representation of total column water vapor (TCWV) and total cloud cover (TCC) in the Atmospheric Model Intercomparison Project Phase 6 (AMIP6), the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF), and satellite data records from the European Space Agency Climate Change Initiative (ESA-CCI). ESA-CCI is used as the reference for the common observation period with AMIP6, spanning from July 2003 to December 2014, to calibrate the framework. For the period prior to the observational period, from January 1981 to June 2003, ERA5 serves as the reference. This study is carried out over the tropical region which has been splitted in two sub-regions: the tropical oceans and tropical lands. The assessment of TCWV and TCC at different time-frequency is performed using a mathematical tool called "multi-resolution analysis" (MRA). By applying the MRA decomposition, we found that the AMIP6 models produce consistent evolution of TCWV and TCC at seasonal to interannual scales (from 2 months to 5.6 years) in the tropical region, even if the representation of the amplitude of TCC remains sometimes challenging. The evaluation of ESA-CCI TCWV and TCC variability in AMIP6 models reveals that the models do not perform well at daily and subseasonal scales. At seasonal to interannual scales, the models reproduce more accurate variability of TCWV and TCC with respect to ESA-CCI. However, AMIP6 models do not capture the trend in the evolution of ESA-CCI TCWV and TCC. The co-variations between TCWV and TCC were analyzed in the Nino3.4 region, revealing a significant positive correlation at the subseasonal scale, with a value of 0.7 for ESA-CCI and 0.3 for AMIP6. At seasonal to annual scales, we found a strong positive correlation between TCWV and TCC, with the exception of the CanESM5 and IPSL models, which showed a negative but significant correlation around -0.5.
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RC1: 'Comment on egusphere-2024-3481', Anonymous Referee #1, 05 Jun 2025
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Overview
This study compared evolutions of the total column water vapor (TCWV) and total cloud cover (TCC) from AMIP6, ERA-5, and ESA-CCI over tropical ocean and land areas. The comparison was made for different time scales, using the multi-resolution analysis (MRA) decomposition method. TCWV signals from the AMIP6 are relatively well agreed with those of ESA observations, while larger differences are shown for the TCC signals. AMIP6 models also reproduced the variability of TCWV and TCC at seasonal to annual time scales. The authors also tried to attempt to select the best AMIP6 model group, based on the RMSE, and this group shows slight improvement compared to the entire AMIP6 group shown in this study.
This study was thoroughly performed, and the manuscript is well organized. The methodology used for the comparison for various time scales is solid, and the cited references are relevant. The display of figures, tables, and their corresponding captions is clear. As the authors mentioned, identifying the processes that are not well captured by models will give useful information to the satellite and climate modeling community. I recommend a minor revision before the publication in the Egusphere.
General commentsThe TCWV and TCC from ESA are served as references throughout this study. The two observations are from the independent measurements. If there have been changes in the retrieval algorithm, sensor calibration, or operation of the satellite, there is a risk of the artifact in the long-term trends obtained from ESA products. I do not feel that the authors must investigate these factors specifically in this study. However, including relevant information in the methodology will give insight on the stability of the ESA products in analyzing long-term trends from the satellite products.
While the decomposition method shown in this study is very relevant, it could be improved if the physical reasons are discussed more. This discussion will serve as a key point for the modeling group to figure out why the models have disagreements with the observations.
Specific commentsThe ESA-CCI data was used as a reference in this study. Therefore, it would be necessary to mention the uncertainty estimates of ESA-CCI TCWV and TCC for over ocean and land, around Table 1.
The ESA-CCI TCC parameter was obtained from multiple satellite sensors. Considering different spectral response functions, how consistent are the TCCs from those satellites? In other words, were any discontinuities or inconsistencies noted across satellite platforms? Including related references would be also useful.The ESA-CCI TCC was from polar orbit satellites, meaning that the TCC was sampled from specific local times for the region. In the longer scale of time series analysis, this may not impact the results, but further discussions might be necessary, particularly for the short time scale analysis.
Line 88: I guessed that WV_cci TCWV data are from the clear sky cases, according to the following paragraph about AMIP6 (line 113). If so, please mention here that WV_cci TCWV is from clear sky observations. Since the clear-sky sampling causes a dry-biased condition, it is worth mentioning here.
Lines 114 and 130: For AMIP6, a threshold of cloud mask of 50% was applied. In contrast, a threshold of cloud cover of 95% was applied to the ERA5 dataset. Please give a reason for this and discuss the impact of the threshold.
Line 127: Isn’t the resolution 0.05 degree?
Line 170: The authors might have meant the power of a multiple of 2?
Figure 2: It seems that S12 shows the water vapor increase according to the global warming by following the Clausius-Clapeyron relation. Does the D12 signal represent the impact of ENSO signals? It would be great if the main contributor for the D12.
Line 216: AMIP6 models produce a revolution of TCWV close to the ESA-CCI, in terms of amplitude and phase, for the seasonal to annual time scales. In contrast, TCC from the AMIP6 models are quite different from ESA-CCI in terms of the phase and amplitude. The low-level cloud amounts are strongly tied to the SST anomalies. Therefore, I am wondering if the differences shown in TCC anomalies are mainly related to the mid- and high-level cloud evolution. A similar discussion is applied to the annual to decadal time scale. TCWV signals are relatively well agreed upon across ESA, ERA5, and AMIP6. However, larger differences are shown for the TCC signals (Fig. 5c). I noticed that a short discussion is given in line 276 about the possible reasons for the larger differences in the TCC signals, but it would be great if the authors could elaborate further discussions or could include more references.
Citation: https://doi.org/10.5194/egusphere-2024-3481-RC1 -
AC1: 'Reply on RC1', Cedric Gacial Ngoungue Langue, 01 Sep 2025
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-3481/egusphere-2024-3481-AC1-supplement.pdf
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AC1: 'Reply on RC1', Cedric Gacial Ngoungue Langue, 01 Sep 2025
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