Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
Scientists from Monash University have, for the first time, identified and tracked “moisture modes” and related systems as discrete, trackable weather objects, showing that these less dramatic systems collectively contribute more to tropical rainfall than tropical cyclones.
The paper introduces a practical framework for defining and tracking synoptic-scale tropical weather systems (other than tropical cyclones).
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Scientists from Monash University have, for the first time, identified and tracked “moisture modes” and related systems as discrete, trackable weather objects, showing that these less dramatic systems collectively contribute more to tropical rainfall than tropical cyclones.
The study, published 6 November 2026 in Weather and Climate Dynamics (Respati, Singh & Jakob), uses more than three decades of satellite observations (1984- 2016). Researchers identified large areas of high, cold cloud linked to organised tropical convection and tracked them as individual objects, classifying them mainly by propagation speed:
Moisture modes: relatively slow- moving systems that organise thunderstorms and rainfall over several days; strongly coupled to moisture perturbations.
Inertio- gravity (IG) waves and gravity- wave systems: faster- moving systems with stronger temperature perturbations.
Mixed systems: intermediate speeds and characteristics.
This turns moisture modes from largely theoretical concepts into trackable objects analogous to tropical cyclones.
Key findings include:
Moisture modes are especially important near the northern and southern margins of the humid tropics (including areas near tropical Australia), where they are linked to 45- 75% of extreme three- hourly rainfall events in some regions.
Overall, the defined systems (including TCs) account for a large share of total tropical rainfall and an even larger share of heavy rainfall.
Mixed systems tend to matter more in equatorial regions such as the Pacific ITCZ; IG waves and TCs each contribute less due to lower frequency and spatial coverage.
Professor Christian Jakob noted: “Tropical cyclones are the divas of the tropics… But less dramatic systems occur much more frequently, and collectively they are very important for rainfall.”
Lead author Muhamad Reyhan Respati emphasised that defining moisture modes as objects allows them to be tracked like tropical cyclones. The approach helps fill a longstanding gap: mid- latitude weather is routinely described via discrete systems (fronts, highs, lows), while the tropics previously lacked a comparable framework between individual thunderstorms and planetary- scale waves.
Source press release: Monash University, 11 November 2026. Paper: “The moisture mode-to-gravity wave spectrum as a framework to define tropical weather systems,” Weather Clim. Dynam., 7, 2171- 2192, 2026.
The moisture mode- to- gravity wave spectrum as a framework to define tropical weather systems
The paper introduces a practical framework for defining and tracking synoptic-scale tropical weather systems (other than tropical cyclones).
It bridges two previous approaches:
Object tracking of mesoscale convective systems (MCSs)
Spectral analysis of planetary- scale waves (e.g., MJO, equatorial waves)
Using theory on the moisture mode- to- gravity wave spectrum, the authors classify convectively coupled systems by zonal phase speed:
Category
Phase speed (approx.)
Dominant physics
Moisture modes
Slow (≲ 9 m s⁻¹)
Strong moisture anomalies; weak temperature gradients
Mixed systems
Intermediate (~9- 27 m s⁻¹)
Hybrid moisture and buoyancy characteristics
Inertio- gravity (IG) waves
Fast (≳ 27 m s⁻¹)
Stronger temperature and buoyancy anomalies
Key findings
Consistent with theory, the ratio of low- level temperature to moisture anomalies increases with phase speed (moisture variations remain non-negligible even for IG waves).
Moisture modes and mixed systems show distinct dynamical structures near vs. off the equator; IG waves are less clearly differentiated.
Characteristics are broadly consistent across the tropics, with some regional variations.
Rainfall importance:
Moisture modes account for up to 75% of extreme rainfall events along climatological moist margins (including regions near tropical Australia).
Mixed systems dominate rainfall contributions in the Intertropical Convergence Zone (ITCZ).
Together with tropical cyclones, these systems explain a large fraction of total and especially heavy tropical rainfall.
Methods (high- level)
Satellite observations (primarily outgoing longwave radiation / high cold cloud areas associated with organised convection, 1984- 2016).
Lagrangian object tracking of cloud systems combined with spectral filtering by phase speed.
Composites of thermodynamic and dynamic fields from reanalysis (ERA5) and precipitation data (GPM- IMERG).
Significance
This framework treats moisture modes as discrete, trackable “weather objects” analogous to mid- latitude cyclones or fronts. It opens new possibilities for:
Improved understanding of day- to- day tropical rainfall organisation
Conditional forecasting of extremes
Attribution of rainfall changes under global warming (frequency vs. intensity of different system types)
Code and data: Linked on the article page (Zenodo for analysis code; ERA5 for atmospheric fields).
This is the scientific paper behind the recent Monash University press release about systems “more important than tropical cyclones” for tropical rainfall.
Paper:The moisture mode- to- gravity wave spectrum as a framework to define tropical weather systems
Journal:Weather and Climate Dynamics, 7, 2171- 2192, 2026
Open access: Full PDF and supplement available on the Copernicus site.
Authors: Muhamad Reyhan Respati, Martin S. Singh, and Christian Jakob
Abstract
While mid-latitude weather is commonly described in terms of discrete “weather systems”, such as cyclones and fronts, an equivalent framework is less established in the tropics due to the wide range of relevant scales. Instead, tropical weather is often analysed either in Lagrangian terms, through the tracking of mesoscale convective systems, or via spectral decomposition into planetary-scale wave-like disturbances. Here, we use both spectral analysis and Lagrangian object tracking to develop a novel framework for analysing tropical weather systems. We apply recent theoretical advances to classify convectively coupled tropical motions into three types based on their zonal phase speed: moisture modes, inertio-gravity waves, and mixed systems. We show that, consistent with theory, the observed ratio of low-level temperature to moisture anomalies increases with the system’s phase speed, although moisture variations may not be negligible for inertio-gravity waves. Moisture modes and mixed systems show distinct dynamical structures near and off the equator, while inertio-gravity waves are less clearly differentiated. These characteristics are broadly consistent across the tropics, with some regional variations. We find that moisture modes account for up to 75 % of extreme rainfall events along climatological moist margins, whereas mixed systems dominate in the intertropical convergence zone. Our tropical weather systems framework offers a new way to link large-scale tropical dynamics with precipitation.
Global warming is projected to substantially weaken the South Asian summer monsoon’s remote influence on Mediterranean summer climate, according to a study by researchers at the Chinese Academy of Sciences’ Institute of Atmospheric Physics.
The South Asian summer monsoon (SASM) and Mediterranean summer climate are linked by a well-established large- scale atmospheric teleconnection known as the “monsoon- desert mechanism.”
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Global warming is projected to substantially weaken the South Asian summer monsoon’s remote influence on Mediterranean summer climate, according to a study by researchers at the Chinese Academy of Sciences’ Institute of Atmospheric Physics.
The paper, “Reduced Asian monsoon influence on Mediterranean summers in a warmer climate” (Nature Geoscience, published around 23 September 2026), was led by Hanzhao Yu, with corresponding author Tianjun Zhou and co-author Zhun Guo.
The Mediterranean’s hot, dry summers are strongly shaped by large- scale atmospheric subsidence (descending air) linked to the South Asian summer monsoon (SASM) via a well- known monsoon, desert teleconnection. Stronger monsoon heating tends to enhance this subsidence, suppressing clouds and rainfall over the Mediterranean.
Main findings (from large- ensemble simulations under a high- emission scenario)
The interannual correlation between SASM heating and Mediterranean summer mid- level circulation/subsidence drops from an ensemble- mean value of about 0.41 (statistically significant, P < 0.001) in the late 20th- century climate to near zero by the end of the 21st century.
The monsoon’s contribution to Mediterranean summer precipitation variability falls from ~14.2% explained variance to ~5.1%.
In one large CESM1 ensemble (40 members), 39 members (~97.5%) showed weakened monsoon influence on Mediterranean subsidence, with explained variance of subsidence variability dropping from ~22% to under 3%.
Mechanisms
Two main dynamical changes drive the weakening:
Elevated monsoon convection (linked to increased tropospheric stability and a rising tropopause under warming) causes the monsoon- induced mid- tropospheric warm anomaly and zonal thermal structure to expand westward. This flattens isentropic surfaces and weakens the adiabatic descent over the Mediterranean.
Local dynamical feedbacks reduce the meridional wind anomalies that help transmit the monsoon signal westward.
The interannual coupling between SASM strength and Mediterranean summer circulation and rainfall is expected to largely disappear. This has consequences for predicting Mediterranean summer heatwaves, droughts, and wildfires, as monsoon conditions will become a much weaker precursor. Mediterranean precipitation variability will be controlled by other factors in a warmer climate.
The results come from large- ensemble climate model simulations, reanalysis data, CMIP6 models, and supporting theoretical and numerical experiments. They highlight how changing atmospheric dynamics under global warming can reshape major climate teleconnections.
Projected changes in SASM-related mid-level circulation over the CEM. Credit: Nature Geoscience (2026). DOI: 10.1038/s41561-026-02091-1
The South Asian summer monsoon (SASM) and Mediterranean summer climate are linked by a well-established large- scale atmospheric teleconnection known as the “monsoon- desert mechanism.”
Intense convective rainfall and associated diabatic (latent heat) heating over South Asia and the Tibetan Plateau during the SASM (roughly June- September) generate westward- propagating Rossby waves (often described as a Gill- type response). These waves interact with the mid- latitude westerlies and produce:
Large- scale subsidence (descending air) over the eastern and central Mediterranean (and parts of the Middle East and North Africa).
Strengthened mid- tropospheric anticyclonic circulation and the northerly Etesian winds.
Suppression of clouds and rainfall, reinforcing the region’s characteristically hot, dry summers.
Stronger SASM years tend to enhance this subsidence, leading to drier and sometimes hotter conditions over parts of the Mediterranean. Weaker monsoon years have the opposite effect. This link operates on interannual timescales and contributes meaningfully to Mediterranean summer climate variability (including precipitation, circulation, evaporation, heatwave and drought risk).
Classic foundational work includes Rodwell & Hoskins (1996, 2001). Later observational and modeling studies (e.g., Tyrlis et al., Cherchi et al. 2014, Rizou et al. 2015) have confirmed and refined the mechanism using reanalyses and CMIP models.
Large- ensemble simulations (e.g., CESM1) show an ensemble-mean correlation of ~0.41 between SASM heating and Mediterranean summer mid- level circulation/subsidence (P < 0.001). The monsoon explains roughly 14% of Mediterranean summer land precipitation variability (and higher fractions, ~22%, of subsidence variability in some ensembles).
A 2026 Nature Geoscience study (Yu, Zhou & Guo, Chinese Academy of Sciences) using CESM1 large ensembles, CMIP6 models, reanalyses, and linear baroclinic model experiments under a high- emission scenario finds that this teleconnection weakens substantially:
Correlation between SASM and Mediterranean summer circulation drops from ~0.41 to near zero by the late 21st century.
Explained variance of Mediterranean summer precipitation falls from ~14.2% to ~5.1%.
Nearly all ensemble members (e.g., 39/40 in CESM1) show the weakening.
Broader implications
Mediterranean summer heatwaves, droughts, and wildfires are already a major climate-change hotspot. Loss of the monsoon teleconnection means year- to- year predictability that currently draws on SASM conditions will diminish; other drivers (local factors, mid- latitude teleconnections such as the summer North Atlantic Oscillation, etc.) will become relatively more important.
Mean-state changes (e.g., overall drying or circulation shifts) may still occur, but the interannual coupling itself is projected to fade.
The finding underscores how warming can reshape remote climate teleconnections through changes in atmospheric dynamics and vertical structure, not only through thermodynamic effects.
In short, the SASM currently exerts a remote “steering” influence that helps maintain and modulate the dry Mediterranean summer. Under strong warming this influence is expected to largely disappear, altering both the variability and the predictability of the region’s summer climate.
Reduced Asian monsoon influence on Mediterranean summers in a warmer climate
The Mediterranean’s summer climate is strongly influenced by large-scale subsidence linked to the South Asian summer monsoon (SASM). Using large- ensemble simulations under a high- emission scenario, the study shows that this teleconnection weakens substantially under global warming:
Ensemble- mean correlation between SASM and Mediterranean summer circulation falls from 0.41 (P < 0.001) to near zero.
Mechanism: westward expansion of the monsoon- induced zonal thermal structure (tied to elevated monsoon convection) and reduction in meridional wind anomalies via local dynamical feedback.
Result: monsoon-related Mediterranean summer precipitation variability drops (explained variance from 14.2% to 5.1%).
Global warming is projected to weaken the interannual coupling between the SASM and Mediterranean summer climate by altering atmospheric dynamics.
Main figures (titles)
Projected changes in SASM- related mid- level circulation over the Central- Eastern Mediterranean (CEM).
Future change of SASM- related CCF (cross- equatorial flow / circulation component) and its decomposition.
Westward expansion of zonal thermal structure and the role of the monsoon heating profile.
Reduction in SASM- related meridional wind and underlying mechanism.
Weakened control of SASM on Mediterranean summer land precipitation.
Data and code availability
ERA5 reanalysis (single- level and pressure- level).
CESM1 Large Ensemble simulations.
CMIP6 models.
LBM experiment output, analysis and visualization scripts (NCL 6.6.2 and Python 3.11.6): available on Zenodo https://doi.org/10.5281/zenodo.21500988. Source data are provided with the paper.
The paper includes extensive supporting analyses validating the diabatic heating index for SASM strength, observed relationships in ERA5, temporal evolution of the response, consistency across CMIP6 models, roles of elevated heating and thermal structure, LBM sensitivity experiments to heat- source height, and links between circulation and precipitation changes.
Note: The full article text is behind a paywall and subscription. The above is drawn from the publicly available abstract, author information, figure captions, data/code statements, and extended- data descriptions on the Nature page. For the complete methods, results, and discussion, institutional access or purchase is required.
Journal information:Nature Geoscience (2026), published 23 September 2026
Contributions: T.Z. conceived and designed the study (with support from H.Y.). H.Y. performed the analysis, drafted the paper, and ran the linear baroclinic model (LBM) simulations. T.Z. and Z.G. provided comments and revisions. All authors contributed to interpretation.
Funding: National Natural Science Foundation of China (grants 42588201, 42575166, 42175164).
Abstract
The Mediterranean is highly vulnerable to summer heatwaves and wildfires, with its climate strongly influenced by large-scale subsidence linked to the South Asian summer monsoon. However, how this Mediterranean–monsoon teleconnection will respond to global warming remains poorly understood. Here we investigate the future evolution of the South Asian summer monsoon’s remote influence on Mediterranean summer climate under global warming using large ensemble simulations under a high-emission scenario. The monsoon influence on Mediterranean summer circulation weakens substantially under global warming, with the ensemble mean of correlation coefficient decreasing from 0.41 (P < 0.001) to near zero. This weakening results from the westward expansion of the monsoon-induced zonal thermal structure associated with elevated monsoon convection and a reduction in meridional wind anomalies through local dynamical feedbacks. The weakened monsoon-induced subsidence response further reduces the monsoon-related Mediterranean summer precipitation variability, with explained variance decreasing from 14.2% to 5.1%. Our results suggest that global warming will weaken the interannual coupling between the South Asian summer monsoon and Mediterranean summer climate, emphasizing the role of changing atmospheric dynamics in reshaping large-scale climate teleconnections.
The reanalysis models assimilate surface measurements and satellite data in addition to radiosonde data and blend the measurements together into a global or regional grid using a general circulation atmospheric model.
My new paper (May, 2025) emphasizes that while many of the underlying observations used to build weather reanalysis datasets, such as ERA5 (European Centre for Medium-Range Weather Forecasts or ECMWF Reanalysis v5) (Soci et al., 2024) or MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) (Gelaro et al., 2017), are from radiosondes, weather reanalysis models are still models and have the same problems that other models have.
Thus, they are not observations or measurements, like those in radiosonde data repositories such as IGRA2, and should not be treated as such. The reanalysis models assimilate surface measurements and satellite data in addition to radiosonde data and blend the measurements together into a global or regional grid using a general circulation atmospheric model.
Weather reanalysis models produce reasonably consistent, physics-based periodic (usually every 6 to 12 hours) estimates of the global atmospheric state (Bloom et al., 1996), but they are not observations.
Dr. Hans Hersbach of ECMWF (European Centre for Medium-Range Weather Forecasts) provides us with figure 1 below which is an illustration of the data assimilation process in ERA5.
Figure 1. Data assimilation process for ERA5. Illustration by Dr. Hans Hersbach of ECMWF, public domain image.
Although like climate models in their structure, weather reanalysis models deal with shorter time periods, and they are constantly being updated with new data. This is good in that each new data item assimilated forces the model to be more realistic. However, data does not come into the model evenly, it arrives in uneven batches. There are several ways of dealing with this problem, but the most common is called incremental analysis updating or “IAU” (Bloom et al., 1996). IAU introduces new data to the model in a gradual manner.
There are numerous problems with adding new data to a model, one of the main problems is the new data will almost always differ from the computed model state at the time of the measurement to some degree. This causes “data shock” where the model must make a sudden, and non-physical, adjustment to accommodate the new data. Because the shock is unphysical and it doesn’t match the physics programmed into the model, the model can wander into an unstable state that takes time to recover from. MERRA-2 uses an IAU system to add new data (Gelaro et al., 2017).
ERA5 uses incremental “4D-Var” data assimilation (Lavers et al., 2024) to minimize new data shock. At each analysis point, usually every 12 hours, 4D-Var simulates both forward and backward in time to find the central 12-hour analysis state that minimizes total error. It inserts all the new data at once and at each data item’s precise measurement time and not gradually like the IAU process. It uses the model physics to choose the appropriate 12-hour analysis, thus the model physical state suffers less than it does with IAU, although it still has some spin-up data shock (Bernsen et al., 2008) to recover from after each 12-hour analysis just like all models do. In short, neither ERA5 nor MERRA-2 exactly honor all the measurements, but no model approximation can.
Conservation of Mass and Energy
Due primarily to the data assimilation processes, model approximations (or parameterizations), and finite resolution, reanalysis models do not perfectly conserve global or domain-wide energy and mass over long integrations. Model parameterizations are algorithms that estimate average critical climate quantities for the grid cells that cannot be modeled at the scale required, like cloud cover. ERA5 has a grid cell size of about 31 km and MERRA-2 about 50 km. The parameterized quantities, like cloud cover, require a much smaller scale (millimeters or less) to model using first principles.
As a result, over long periods of time these reanalysis climate models do not “close.” Closure refers to the degree to which the integrated budgets (sum of mass and energy fluxes, storage changes, and divergences) balance over time, ensuring the initial totals match the final totals after a long simulation run, after accounting for external forcings and inputs. Perfect closure is theoretically required to honor mass and energy conservation laws but never achieved in practice.
The error in atmospheric mass from the beginning of simulation runs to the end can be measured in terms of mm of water because the total dry mass of the atmosphere does not change with time, only the water content. Early models sometimes lost or gained dry atmospheric mass, but ERA5 and MERRA-2 are constrained so that dry mass is conserved (Gelaro et al., 2017), (Mayer et al., 2021), and (Wild & Bosilovich, 2024).
ERA5 generally exhibits tighter closure globally due to its higher resolution and advanced assimilation, while MERRA-2 shows improvements over its predecessor (MERRA) but larger residuals in some cases, particularly from cloud and aerosol effects.
Most modern studies of the troposphere rely mostly on weather reanalysis grids, thus any weaknesses in the weather reanalysis models carry over into these studies. Weather reanalysis models do not capture precipitation, radiation in and out of the climate system, cloud cover, wind speed, cyclones, extreme temperatures, or the double tropopause frequency very well, among other problems. For a summary of the problems with ERA5 and MERRA-2 compiled by Grok, download this report. Yet, even with all these problems, most published studies use weather reanalysis models because they are spatially and temporally continuous and they incorporate global quality-controlled radiosonde data (Xian & Homeyer, 2019).
ERA5 Imbalances
The ERA5 global TOA mean radiative energy imbalance from 2000-2020 is 0.68 ± 0.6 Wm-2, versus the CERES EBAF satellite observations, after they have been corrected to match the ocean heat content changes, of 0.73 ± 0.7 Wm-2 (Loeb et al., 2022). Positive values are a net input of energy into the climate system and negative values are net outgoing (cooling). The agreement between the two global numbers is better than the regional agreement, CERES and ERA5 differ substantially in the eastern Pacific off North America.
The ERA5 model does a good job with the total global water vapor balance and loses almost none over the period (2001-2014). It does less well with precipitation, it overestimates total precipitation by 0.1 to 0.2 mm and most of the error is in the tropics (Lavers et al., 2024). Reanalysis models often have a precipitation “wet bias” that is most pronounced in the tropics. Lavers, et al. mention that the precipitation match has been improved in the latest versions of ERA5.
MERRA2 Imbalances
MERRA-2 is produced by NASA’s Global Modeling and Assimilation Office (GMAO). It is a global atmospheric reanalysis spanning 1980 to the present at ~50 km horizontal resolution with 72 vertical levels. MERRA-2 enforces a global dry atmospheric mass constraint, ensuring near-perfect dry mass conservation (~0.01% error per cycle), but its energy budget does not balance well.
MERRA-2’s all-sky reflected shortwave flux is ~7 Wm-2 higher than CERES EBAF’s, resulting in a net TOA flux imbalance of -4 Wm-2 for 2001-2015 (Hinkelman, 2019). This is a substantial bias, after all the entire human impact on climate from 1750-2019 is estimated to be only 2.72 Wm-2 (IPCC, 2021, p. 959). While MERRA-2 performs better on trends and variability, this absolute difference should worry everyone.
At the surface, high clear-sky downward shortwave fluxes indicate that the MERRA-2 atmosphere is too transmissive. MERRA-2’s all-sky shortwave radiation down and longwave radiation up energy flux terms agree reasonably well with CERES EBAF, but its net flux agreement is poor (-4.7 Wm-2) mainly because it overestimates cloud reflectivity. Analysis by region and surface type gives mixed outcomes. Clouds are overrepresented over the tropical oceans in MERRA-2, and somewhat underrepresented in marine stratocumulus areas. MERRA-2 also overestimates cloudiness in the Southern Ocean. The model also has problems in the polar regions, where the effects of snow and ice cover are important (Hinkelman, 2019).
Discussion
In general, ERA5 is a better weather reanalysis model than MERRA-2, but both have problems. Generally, ERA5 has a better resolution and a better methodology of integrating new data, but neither model has the resolution required to model clouds properly and, as a result, they do not estimate either cloudiness or precipitation well in key areas, like the tropics. Both models are poor at modeling tropical cyclones due to their large grid sizes, but ERA5 is a bit better at this than MERRA-2. Temperature prediction is very poor in the Arctic and Antarctic regions.
Weather reanalysis model products are very useful, but they cannot be considered data or observations, or used as such, as they often are, for example in AR6 figure TS.2 (IPCC, 2021, p. 50). There are places in AR6 that concede weather reanalysis model products are not observations, but some parts of the report treat them as observations. There is poor agreement between various models of radiosonde data in tropospheric temperature trends as shown in figure 2, from AR6 figure 2.12.
Figure 2. ERA5 temperature trend profiles (blue dashed line) compared to various other estimates of decadal temperature trends in the troposphere. Source: (IPCC, 2021, p. 328).
Figure 2 shows the tropospheric warming trend by altitude from radio occultation, using data from the infrared satellite instrument AIRS (Qian et al., 2025), and corrected and homogenized radiosonde data (RICH and RAOBCORE). All the measurements in figure 2 are from models, there are no direct measurements in the plot, although RICH and RAOBCORE (Haimberger et al., 2012) are the least removed from the actual radiosonde measurements (Haimberger et al., 2012). As figure 2 makes clear, the models match one another fairly well below about 10 km, but above 10 km there is little agreement and the match in the tropics from 10-17 km is poor.
Figure 3 compares the CMIP6 climate models to ERA5 and the RICH and RAOBCORE homogenized radiosonde results in the tropics (20°S to 20°N). The model results are in red, with the mean and the model spread indicated. Model runs with the actual sea surface temperature (SST) forced are in blue. The black and grey are the ERA5, RICH, and RAOBCORE model results.
Figure 3. CMIP6 climate models compared to RICH, RAOBCORE, and ERA5 model results. Source: (IPCC, 2021, p. 444).
Figure 3 demonstrates that not only is the match between ERA5 and the CMIP6 climate models poor, it is getting worse with time, since the post 1998 match is worse than the other time periods shown. The difference between forcing the SSTs (blue) and not forcing them (red) suggests that the models are not doing a very good job over the oceans, which cover 71% of Earth’s surface.
The greenhouse effect is strongest in the tropics due to the higher humidity in this region. Some researchers have suggested that the CMIP6 climate models have overestimated the climate sensitivity to CO2 and greenhouses in general, and this is the cause of the mismatch in figure 3 in the tropical middle and upper troposphere (Mauritsen & Stevens, 2015), (McKitrick & Christy, 2020), and (Po-Chedley et al., 2022). McKitrick and Christy present evidence that the most straightforward way to reduce the mismatch in tropospheric warming rates in figure 3 is to lower the climate sensitivity to CO2 in the climate models.
We would also suggest that a necessary validation step would be to select the best radiosonde data and check the nearest climate model grid cells against the actual radiosonde data, rather than the RICH, RAOBCORE, and ERA5 models. Just as the best way to compare the present to that past is to compare modern data to well selected proxies, the best way to compare radiosonde measurements to climate models is to find the best radiosonde data and compare the models to it, at the location of the radiosonde data.
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