Space lidar observations constrain longwave cloud feedback

Observational evidence of changing cloud macro-physical properties under warming climate over the Indian summer monsoon region

The cloud responses to global warming are captured in various global climate models with distinct inferences on changes in cloud vertical structure as function of surface warming. However, long term observational evidences are scarce to validate the model outputs. Here, we have studied the changes in radiosonde derived cloud macro-physical properties and their association with other atmospheric variables during the period 2000-2019 in response to warming climate over the Indian summer monsoon region. We have observed a statistically significant increase in the frequency of cloudy days (∼13 % decade-1), high-level clouds (HLCs ∼11 % decade-1) and simultaneous decrease in low-level clouds (LLCs ∼8 % decade-1) over the Indian region during the monsoon season. The multiple linear regression, principle component analyses and further correlation analyses suggest significant associations between cloud vertical structure variations and large-scale climate indicators, such as global warming and El Niño-Southern Oscillation. The vertical extension of the tropospheric column and the upward shift of clouds, attributed to global warming, explain the changes observed in both HLCs and LLCs. These results contribute to a deeper understanding of the dynamic interplay between global climate change and regional cloud dynamics, with implications for weather and climate modeling.

Intra-seasonal contrasting trends in clouds due to warming induced circulation changes

Quantification of long term changes in cloud distribution and properties is critical for the proper assessment of future climate. We show contrasting trends in cloud properties and cloud radiative effects over Northwest Indian Ocean (NWIO) in south Asian summer monsoon. Cloud top height (CTH) decreases in June (− 69 ± 3 myr⁻¹) and July (− 44 ± 3 myr⁻¹), whereas it increases in August (106 ± 2 myr⁻¹) and September (37 ± 1 myr⁻¹). These contrasting trends are investigated to be due to the changes in upper tropospheric winds and atmospheric circulation pattern. Strengthening of upper tropospheric easterlies and changes in vertical wind dampen the vertical development of clouds in June and July. In contrast, weakening of upper tropospheric winds over NWIO and strengthening of updraft favour the vertical growth of clouds in August. Further, changes in horizontal winds at 450–350 hPa and strengthening of Indian Ocean Walker cell favour the westward spread of high level clouds, contributing to the increase in CTH over NWIO in August. Decrease of cloud cover and altitude in June and July and increase of the same in subsequent months would affect the monsoon rainfall over the Indian region. Proper representation of these intra-seasonal contrasting trends of clouds in climate models is important for the better prediction of regional weather.

Observational evidence that cloud feedback amplifies global warming

Global warming drives changes in Earth's cloud cover, which, in turn, may amplify or dampen climate change. This "cloud feedback" is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)-the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W⋅m^-2⋅K^-1 (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.

An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence

We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6-3.9 K for our Baseline calculation and remains within 2.3-4.5 K under the robustness tests; corresponding 5-95% ranges are 2.3-4.7 K, bounded by 2.0-5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.