Mean Surface Temperature Change Research Articles

Factors determining tropical upper-level cloud radiative effect in the radiative-convective equilibrium framework

Investigation of the major factors determining tropical upper-level cloud radiative effect (TUCRE) is crucial for understanding cloud feedback mechanisms. We examined the TUCRE inferred from the outputs of historical runs and AMIP runs from CMIP6 models employing a radiative-convective equilibrium (RCE). In this study, we incorporated the RCE model configurations of atmospheric dynamics and thermodynamics from the climate models, while simplifying the intricate systems. Using the RCE model, we adjusted the global mean surface temperature to achieve energy balance, considering variations in tropical cloud fraction, regional reflectivity, and emission temperature corresponding to each climate model. Subsequently, TUCRE was calculated as a unit of K/%, representing the change in global mean surface temperature (K) in response to an increment in the tropical upper-level clouds (%). Our RCE model simulation indicates that the major factors determining the TUCRE are the emission temperatures of tropical moist-cloudy and moist-clear regions, as well as the fraction of tropical upper-level clouds. The higher determination coefficients between TUCRE and both the emission temperature of tropical moist regions and the upper-level cloud fraction are attributable to their contribution to the trapping effect on the outgoing longwave radiations, which predominantly determines TUCRE. Consequently, the results of this study underscore the importance of accurately representing the upper-level cloud fraction and emission temperature in tropical moist regions to enhance the representation of TUCRE in climate models.

ENSO Disrupts Boreal Winter CRE Feedback

Abstract Twenty years of satellite-based cloud and radiation observations allow us to examine the observed cloud radiative effect (CRE) feedback (i.e., CRE change per unit change in global mean surface temperature). By employing a decomposition method to separate the contribution of “internal changes” and “relative-frequency-of-occurrence (RFO) changes” of distinct cloud regime (CR) groups, notable seasonal contrasts of CRE feedback characteristics emerge. Boreal winter CRE feedback is dominated by the positive shortwave CRE (SWCRE) feedback of oceanic low-thick clouds, due to their decreasing RFO as temperature rises. This signal is most likely due to El Niño–Southern Oscillation (ENSO) activity. When ENSO signals are excluded, boreal winter CRE feedback becomes qualitatively similar to the boreal summer feedback, where several CR groups contribute to the total CRE feedback more evenly. Most CR groups’ CRE feedbacks largely come from changing RFO (e.g., the predominant transition from oceanic cumulus to broken clouds and more occurrences of higher convective clouds with warming temperature). At the same time, low-thick and broken clouds experience optical thinning and decreasing cloud fraction, and these features are more prominent in boreal summer than winter. Overall, the seasonally asymmetric patterns of CRE feedback, primarily due to ENSO, introduce complexity in assessments of CRE feedback.

Tropical mountain ice core δ18O: A Goldilocks indicator for global temperature change.

Very high tropical alpine ice cores provide a distinct paleoclimate record for climate changes in the middle and upper troposphere. However, the climatic interpretation of a key proxy, the stable water oxygen isotopic ratio in ice cores (δ18Oice), remains an outstanding problem. Here, combining proxy records with climate models, modern satellite measurements, and radiative-convective equilibrium theory, we show that the tropical δ18Oice is an indicator of the temperature of the middle and upper troposphere, with a glacial cooling of -7.35° ± 1.1°C (66% CI). Moreover, it severs as a "Goldilocks-type" indicator of global mean surface temperature change, providing the first estimate of glacial stage cooling that is independent of marine proxies as -5.9° ± 1.2°C.Combined with all estimations available gives the maximum likelihood estimate of glacial cooling as -5.85° ± 0.51°C.

Steady global surface warming from 1973 to 2022 but increased warming rate after 1990

AbstractThe change in global mean surface temperature is a crucial and broadly used indicator of the evolution of climate change. Any decadal scale changes in warming rate are however obfuscated by internal variability. Here we show that the surface temperature increase through the recent La Nina influenced years (2022) is consistent with the 50-year trend of 0.18 °C/decade. We use an Earth System Model based tool to filter out modulations to the warming rate by sea-surface temperature patterns and find consistent warming rates in four major global temperature data series. However, we also find clear indications, in all observational series, of a step-up in warming rate since around 1990. CMIP6 models generally do not capture this observed combination of long-term warming rate and recent increase.

A transient coupled general circulation model (CGCM) simulation of the past 3 million years

Abstract. Driven primarily by variations in the earth's axis wobble, tilt, and orbit eccentricity, our planet experienced massive glacial/interglacial reorganizations of climate and atmospheric CO2 concentrations during the Pleistocene (2.58 million years ago (Ma)–11.7 thousand years ago (ka)). Even after decades of research, the underlying climate response mechanisms to these astronomical forcings have not been fully understood. To further quantify the sensitivity of the earth system to orbital-scale forcings, we conducted an unprecedented quasi-continuous coupled general climate model simulation with the Community Earth System Model version 1.2 (CESM1.2, ∼3.75∘ horizontal resolution), which covers the climatic history of the past 3 million years (3 Myr). In addition to the astronomical insolation changes, CESM1.2 is forced by estimates of CO2 and ice-sheet topography which were obtained from a simulation previously conducted with the CLIMBER-2 earth system model of intermediate complexity. Our 3 Ma simulation consists of 42 transient interglacial/glacial simulation chunks, which were partly run in parallel to save computing time. The chunks were subsequently merged, accounting for spin-up and overlap effects to yield a quasi-continuous trajectory. The computer model data were compared against a plethora of paleo-proxy data and large-scale climate reconstructions. For the period from the Mid-Pleistocene Transition (MPT, ∼1 Ma) to the late Pleistocene we find good agreement between simulated and reconstructed temperatures in terms of phase and amplitude (−5.7 ∘C temperature difference between Last Glacial Maximum and Holocene). For the earlier part (3–1 Ma), differences in orbital-scale variability occur between model simulation and the reconstructions, indicating potential biases in the applied CO2 forcing. Our model-proxy data comparison also extends to the westerlies, which show unexpectedly large variance on precessional timescales, and hydroclimate variables in major monsoon regions. Eccentricity-modulated precessional variability is also responsible for the simulated changes in the amplitude and flavors of the El Niño–Southern Oscillation. We further identify two major modes of planetary energy transport, which played a crucial role in Pleistocene climate variability: the first obliquity and CO2-driven mode is linked to changes in the Equator-to-pole temperature gradient; the second mode regulates the interhemispheric heat imbalance in unison with the eccentricity-modulated precession cycle. During the MPT, a pronounced qualitative shift occurs in the second mode of planetary energy transport: the post-MPT eccentricity-paced variability synchronizes with the CO2 forced signal. This synchronized feature is coherent with changes in global atmospheric and ocean circulations, which might contribute to an intensification of glacial cycle feedbacks and amplitudes. Comparison of this paleo-simulation with greenhouse warming simulations reveals that for an RCP8.5 greenhouse gas emission scenario, the projected global mean surface temperature changes over the next 7 decades would be comparable to the late Pleistocene glacial-interglacial range; but the anthropogenic warming rate will exceed any previous ones by a factor of ∼100.

The Magic of Environmental Detection and Future Prediction by Satellite

Climate change is one of the important issues that face the world in this technological era. We have used data collected from the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS). The data consisted of the mean surface temperature change with respect to baseline climatology corresponding to the period 1951-1980. The data covers the time period of 1961-2019. We studied the change in temperature in the countries like Greenland and India using the regression models. All the regression graphs were plotted in the Spyder software using python. From the regression models we observed the significant rise in temperature in these countries caused due to global warming. These models will help us to predict the change in temperature in future

Critical Review on Radiative Forcing and Climate Models for Global Climate Change since 1970

This review identifies a critical problem in the fundamental physics of current climate models. The large greenhouse effect of rising CO2 assumed in climate models is assessed by six key observations from ground- and satellite-based measurements. This assessment is enhanced by statistical analyses and model calculations of global or regional mean surface temperature changes by conventional climate models and by a conceptual quantum physical model of global warming due to halogen-containing greenhouse gases (halo-GHGs). The postulated large radiative forcing of CO2 in conventional climate models does not agree with satellite observations. Satellite-observed warming pattern resembles closely the atmospheric distribution of chlorofluorocarbons (CFCs). This review helps understand recent remarkable observations of reversals from cooling to warming in the lower stratosphere over most continents and in the upper stratosphere at high latitudes, surface warming cessations in the Antarctic, North America, UK, and Northern-Hemisphere (NH) extratropics, and the stabilization in NH or North America snow cover, since the turn of the century. The complementary observation of surface temperature changes in 3 representative regions (Central England, the Antarctic, and the Arctic) sheds new light on the primary mechanism of global warming. These observations agree well with not CO2-based climate models but the CFC-warming quantum physical model. The latter offers parameter-free analytical calculations of surface temperature changes, exhibiting remarkable agreement with observations. These observations overwhelmingly support an emerging picture that halo-GHGs made the dominant contribution to global warming in the late 20th century and that a gradual reversal in warming has occurred since ~2005 due to the phasing out of halo-GHGs. Advances and insights from this review may help humans make rational policies to reverse the past warming and maintain a healthy economy and ecosystem.

Climate Change: Linear and Nonlinear Causality Analysis

The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. Monthly data on potential climate change causal factors, including greenhouse gas concentrations, sunspot numbers, humidity, ice sheets mass, and sea ice coverage, from January 2003 to December 2021, have been utilized in the analysis. We first applied the vector autoregressive model (VAR) and Granger causality test to gauge the linear Granger causal relationships among climate factors. We then adopted the vector error correction model (VECM) as well as the autoregressive distributed lag model (ARDL) to quantify the linear long-run equilibrium and the linear short-term dynamics. Cointegration analysis has also been adopted to examine the dual directional Granger causalities. Furthermore, in this work, we have presented a novel pipeline based on the artificial neural network (ANN) and the VAR and ARDL models to detect nonlinear causal relationships embedded in the data. The results in this study indicate that the global sea level rise is affected by changes in ice sheet mass (both linearly and nonlinearly), global mean temperature (nonlinearly), and the extent of sea ice coverage (nonlinearly and weakly); whereas the global mean temperature is affected by the global surface mean specific humidity (both linearly and nonlinearly), greenhouse gas concentration as measured by the global warming potential (both linearly and nonlinearly) and the sunspot number (only nonlinearly and weakly). Furthermore, the nonlinear neural network models tend to fit the data closer than the linear models as expected due to the increased parameter dimension of the neural network models. Given that the information criteria are not generally applicable to the comparison of neural network models and statistical time series models, our next step is to examine the robustness and compare the forecast accuracy of these two models using the soon-available 2022 monthly data.

Why Is Climate Sensitivity for Solar Forcing Smaller than for an Equivalent CO2 Forcing?

Abstract Previous studies have shown that climate sensitivity, defined as the global mean surface temperature change per unit radiative forcing, is smaller for solar radiative forcing compared to an equivalent CO2 radiative forcing. We investigate the causes for this difference using the NCAR CAM4 model. The contributions to the climate feedback parameter, which is inversely related to climate sensitivity, are estimated for water vapor, lapse rate, Planck, albedo, and cloud feedbacks using the radiative kernel technique. The total feedback estimated for CO2 and solar radiative forcing from our model simulations is −1.23 and −1.45 W m−2 K−1, respectively. We find that the difference in feedback between the two cases is primarily due to differences in lapse rate, water vapor, and cloud feedbacks, which together explain 65% of the difference in total feedback. The rest comes from Planck and albedo feedbacks. The differences in feedbacks arise mainly from differences in the horizontal (meridional) structure of forcing and the consequent warming. Our study provides important insights into the effects of the meridional structure of forcing on climate feedback, which is important for evaluating global climate change from different forcing agents. Significance Statement An increase in atmospheric CO2 concentration or an increase in incoming solar radiation leads to a rise in the radiative budget and consequent climate warming, which is amplified by the presence of multiple climate feedbacks. These feedbacks, from changes in surface albedo, combined effect of water vapor and the vertical lapse rate of temperature, and changes in clouds, differ between solar and CO2 forcing. Using radiative kernels, this study quantifies these individual feedbacks for an equivalent radiative change caused by an increase in CO2 or incoming solar radiation, showing how the differences arise from differences in the meridional patterns of warming. In agreement with prior studies, these differences can explain the smaller efficacy of solar forcing compared to CO2 forcing.

Radiative effects of observationally constrained tropical upper-level clouds in a radiative-convective equilibrium model

Tropical upper-level clouds (TUCs) control the radiation budget in a climate system and strongly influence surface temperatures. This study examines global mean surface temperature changes due to the percent change in TUC cover, which is referred to as the tropical upper-level cloud radiative effect (TUCRE, in units of Kelvin per %). We use a radiative-convective equilibrium model that can control both upper- and lower-level cloud layers separately in three idealized regions (extratropics, tropical moist, and tropical dry regions) and two sub-regions (clear-moist and cloudy-moist regions) within the tropical moist regions. In the simulation, tropical reflectivity based on the TUC fraction assumes a primary role in determining the TUCRE. Accurate estimate of the TUCRE requires careful prescriptions according to actual satellite observations. We use the extent of TUC fraction and reflectivity obtained from 18 years (2003–2020) of satellite data on daily MODIS cloud properties. Our results show that the estimated net TUCRE ranges from 0.19 to 0.33 K/%, with a higher TUC fraction leading to higher temperatures (a warming effect) in the climate system. This means that a longwave TUCRE dominates over a shortwave TUCRE. When upper- and lower-level clouds interplay in the model, the range of the TUCRE was greater with a combination of two cloud layers, although all values were positive. The TUCRE is greater by 0.22 to 0.40 K/% when upper- and lower-level clouds are negatively coupled, because the Earth warms due to a decline in the reflectance of solar radiation. When upper- and lower-level clouds are positively coupled, the TUCRE is lower by 0.14 to 0.30 K/%, as less radiation reaches the Earth through combined cloud layers. Finally, we test the sensitivity of the TUCRE with five TUC fractions and 15 combinations of tropical reflectivity. Comparing our results with the TUCREs estimated from climate models will help us understand how TUC cover affects climate, and should greatly reduce uncertainty associated with cloud feedback.

Prescribing stratospheric chemistry overestimates southern hemisphere climate change during austral spring in response to quadrupled CO2

The interaction of stratospheric chemistry with a changing climate from an abrupt CO2 quadrupling is assessed using the coupled atmosphere–ocean Goddard Earth Observing System Chemistry-Climate Model (GEOSCCM). Two abrupt 4 × CO2 experiments were performed, one with interactive stratospheric chemistry and the other with a prescribed stratospheric chemistry that does not simulate stratospheric ozone response to 4 × CO2. The interactive and prescribed chemistry experiments simulate similar global mean surface temperature change. Nevertheless, interactive chemistry is critical to capture the Southern Hemisphere tropospheric midlatitude jet response to 4 × CO2. When stratospheric ozone response to 4 × CO2 is neglected, GEOSCCM overestimates Southern Hemisphere tropospheric circulation change. This stratospheric chemistry-induced climate impact has large seasonal variability. During the austral spring season September–October–November (SON), prescribed chemistry yields a stronger poleward shift and intensification of the Southern Hemisphere midlatitude tropospheric jet, surface wind stress, and the Southern Ocean meridional overturning circulation than occurs with interactive chemistry. In other seasons interactive and prescribed chemistry have similar effects on the Southern Hemisphere circulation. The seasonality of stratospheric chemistry-induced climate impact is related to the seasonality of Antarctic lower stratospheric ozone response to 4 × CO2. In contrast to this stratospheric ozone response to 4 × CO2, stratospheric ozone recovery from decline of the ozone depleting substances has its largest impact on the Southern Hemisphere tropospheric circulation in austral summer (December–January–February), but no effects in SON. It is found that the different seasonality for these two stratospheric ozone layer change scenarios is related to the different seasonality of tropopause meridional temperature gradient response.

The climate impact of hydrogen-powered hypersonic transport

Abstract. Hypersonic aircraft flying at Mach 5 to 8 are a means for traveling very long distances in extremely short times and are even significantly faster than supersonic transport (Mach 1.5 to 2.5). Fueled with liquid hydrogen (LH2), their emissions consist of water vapor (H2O), nitrogen oxides (NOx), and unburned hydrogen. If LH2 is produced in a climate- and carbon-neutral manner, carbon dioxide does not have to be included when calculating the climate footprint. H2O that is emitted near the surface has a very short residence time (hours) and thereby no considerable climate impact. Super- and hypersonic aviation emit at very high altitudes (15 to 35 km), and H2O residence times increase with altitude from months to several years, with large latitudinal variations. Therefore, emitted H2O has a substantial impact on climate via high altitude H2O changes. Since the (photo-)chemical lifetime of H2O largely decreases at altitudes above 30 km via the reaction with O(1D) and via photolysis, the question is whether the H2O climate impact from hypersonics flying above 30 km becomes smaller with higher cruise altitude. Here, we use two state-of-the-art chemistry–climate models and a climate response model to investigate atmospheric changes and respective climate impacts as a result of two potential hypersonic fleets flying at 26 and 35 km, respectively. We show, for the first time, that the (photo-)chemical H2O depletion of H2O emissions at these altitudes is overcompensated by a recombination of hydroxyl radicals to H2O and an enhanced methane and nitric acid depletion. These processes lead to an increase in H2O concentrations compared to a case with no emissions from hypersonic aircraft. This results in a steady increase with altitude of the H2O perturbation lifetime of up to 4.4±0.2 years at 35 km. We find a 18.2±2.8 and 36.9±3.4 mW m−2 increase in stratosphere-adjusted radiative forcing due to the two hypersonic fleets flying at 26 and 35 km, respectively. On average, ozone changes contribute 8 %–22 %, and water vapor changes contribute 78 %–92 % to the warming. Our calculations show that the climate impact, i.e., mean surface temperature change derived from the stratosphere-adjusted radiative forcing, of hypersonic transport is estimated to be roughly 8–20 times larger than a subsonic reference aircraft with the same transport volume (revenue passenger kilometers) and that the main contribution stems from H2O.

Climate change impacts on flood peaks in Britain for a range of global mean surface temperature changes

AbstractAn increase in extreme weather events is leading to increased flood occurrence and risk in many areas. Although climate mitigation strategies are being implemented, it is widely accepted that societies must adapt to climate variability and climate change. Traditional climate change impact studies have used projections for future time‐slices, often for a range of possible emissions scenarios. Recently however, there has been a move to instead consider climate change impacts relative to global mean surface temperature (GMST) change, to try to encourage action to avoid the more severe impacts from higher GMST changes. To support adaptation planning, more localised information on impacts is required. Here, data on the potential range of changes in flood peaks is generated by combining flood response surfaces and the new UK Climate Projections 2018, for every river cell on a 1 km grid across Britain, for GMST changes of 1–4.5°C. The results show significant spatial variation, with impacts typically higher in the west than the east, and generally increasing with GMST change. Some southern regions show flood peak changes accelerating with GMST change. The changes in flood peaks can be translated into changes in flood inundation and associated flood risk under alternative adaptation assumptions.

Quantifying CMIP6 model uncertainties in extreme precipitation projections

Projected changes in precipitation extremes and their uncertainties are evaluated using an ensemble of global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP). They are scaled by corresponding changes either in global mean surface temperature (ΔGSAT) or in local surface temperature (ΔT) and are expressed in terms of 20-yr return values (RV20) of annual maximum one-day precipitation. Our main objective is to quantify the model response uncertainty and to highlight the regions where changes may not be consistent with the widely used assumption of a Clausius–Clapeyron (CC) rate of ≈7%/K. When using a single realization for each model, as in the latest report from the Intergovernmental Panel on Climate Change (IPCC), the assessed inter-model spread includes both model uncertainty and internal variability, which can be however assessed separately using a large ensemble. Despite the overestimated inter-model spread, our results show a robust enhancement of extreme precipitation with more than 90% of models simulating an increase of RV20. Moreover, this increase is consistent with the CC rate of ≈7%/K over about 83% of the global land domain when scaled by (ΔGSAT). Our results also advocate for producing multiple single model initial condition ensembles in the next CMIP projections, to better filter internal variability out in estimating the response of extreme events.

Evaluating the Nature and Extent of Changes to Climate Sensitivity Between FGOALS‐g2 and FGOALS‐g3

AbstractEquilibrium climate sensitivity (ECS) and its related feedbacks are important metrics that can be used to measure the global mean surface temperature change in future climate projections, and the cloud feedback (CF) is considered to be the main contributor to ECS uncertainties. However, the use of different CF methods significantly affects the CF amplitudes, and the sign of the CF may also change. Combining the radiative kernel approach with a simplified CF method, the differences in the ECS and associated feedbacks between two versions of the Flexible Global Ocean–Atmosphere–Land System model (i.e., FGOALS‐g2 and FGOALS‐g3) were analyzed. Results show that the ECS of FGOALS‐g3 is smaller than that of FGOALS‐g2 (2.8 vs. 3.3 K). The main feedback processes that contribute to the ECS change in FGOALS‐g3 are the weaker surface albedo feedback and stronger negative shortwave CF. The reduced surface albedo feedback in FGOALS‐g3 can be attributed mainly to the differences in the simulations of sea ice area, surface temperature, and the Atlantic Meridional Overturning Circulation, as well as their interactions, compared with FGOALS‐g2. The enhanced negative shortwave CF in FGOALS‐g3 is directly related to the strengthened feedback of cloud area fraction and liquid water path. Furthermore, these changes can be traced back to the different atmospheric moist processes, parameter tuning, ocean grid, and external forcings used in FGOALS‐g3, as these all affect the mean climate state of the model.

Detection and attribution of climate change: A deep learning and variational approach

Abstract Twelve climate models and observations are used to attribute the global mean surface temperature (GMST) changes from 1900 to 2014 to external climate forcings. The external forcings are decomposed into the effects of the well-mixed greenhouse gas concentration variation, the effects of anthropogenic aerosol concentration changes, and the effects of natural forcings. First, a convolutional neural network (CNN) is trained to estimate the simulated historical GMST from single-forcing experiments using outputs from the multi-model ensemble. We then use this CNN to solve the attribution problem using an original variational inversion approach. The variational inversion is first validated using historical climate simulations as pseudo-observations. Then we perform an inversion from observations. This provides a distribution of the GMST resulting from the three forcings. For 2014, inversions estimate that the greenhouse gases changes are responsible for a GMST anomaly within [0.8 $ {}^{\circ } $ C,1.9 $ {}^{\circ } $ C], while anthropogenic aerosols and natural forcings anomalies are within [−0.7 $ {}^{\circ } $ C,−0.1 $ {}^{\circ } $ C] and [−0.1 $ {}^{\circ } $ C,0.3 $ {}^{\circ } $ C], respectively. The method designed here can be adapted and extended to attribute the changes of other variables or to focus on the regional scale.

Similar patterns of tropical precipitation and circulation changes under solar and greenhouse gas forcing

Theory and model evidence indicate a higher global hydrological sensitivity for the same amount of surface warming to solar as to greenhouse gas (GHG) forcing, but regional patterns are highly uncertain due to their dependence on circulation and dynamics. We analyse a multi-model ensemble of idealized experiments and a set of simulations of the last millennium and we demonstrate similar global signatures and patterns of forced response in the tropical Pacific, of higher sensitivity for the solar forcing. In the idealized simulations, both solar and GHG forcing warm the equatorial Pacific, enhance precipitation in the central Pacific, and weaken and shift the Walker circulation eastward. Centennial variations in the solar forcing over the last millennium cause similar patterns of enhanced equatorial precipitation and slowdown of the Walker circulation in response to periods with stronger solar forcing. Similar forced patterns albeit of considerably weaker magnitude are identified for variations in GHG concentrations over the 20th century, with the lower sensitivity explained by fast atmospheric adjustments. These findings differ from previous studies that have typically suggested divergent responses in tropical precipitation and circulation between the solar and GHG forcings. We conclude that tropical Walker circulation and precipitation might be more susceptible to solar variability rather than GHG variations during the last-millennium, assuming comparable global mean surface temperature changes.

Suppressed Late‐20th Century Warming in CMIP6 Models Explained by Forcing and Feedbacks

AbstractFor the 1960–2000 period, the latest generation of climate models (Coupled Model Intercomparison Project Phase 6 [CMIP6]) shows less global mean surface temperature change relative to pre‐industrial than that seen in observations. In contrast, the previous generation of models (CMIP5) performed well over this period. It has been hypothesized that this suppressed late‐20th Century warming seen in CMIP6 is caused by a stronger aerosol forcing. However, we find this to be only part of the story. Not only is the aerosol forcing marginally more negative in CMIP6 compared to CMIP5, the greenhouse gas forcing in CMIP6 is also weaker than in CMIP5. These forcing differences are amplified by differences in climate sensitivity between the CMIP5 and CMIP6 ensemble, which leads to both a stronger aerosol cooling over 1960–1990 and a stronger greenhouse gas induced warming from 1990, returning the warming post‐2000 toward the observed level.

The impacts of cloud-radiative changes on poleward atmospheric and oceanic energy transport in a warmer climate

AbstractBased on theory and climate model experiments, previous studies suggest most of the uncertainties in projected future changes in meridional energy transport and zonal mean surface temperature can be attributed to cloud feedback. To investigate how radiative and dynamical adjustments modify the influence of cloud-radiative changes on energy transport, this study applies a cloud-locking technique in a fully-coupled climate model, CESM. Under global warming, the impacts of cloud-radiative changes on the meridional energy transport are asymmetric in the two hemispheres. In the Northern Hemisphere, the cloud-radiative changes have little impact on energy transport, because 89% of the cloud-induced heating is balanced locally by increasing outgoing longwave radiation. In the Southern Hemisphere, on the other hand, cloud-induced dynamical changes in the atmosphere and the ocean cause enhanced poleward energy transport, accounting for most of the increase in energy transport under warming. Our experiments highlight that the local longwave radiation adjustment induced by temperature variation can partially offset the impacts of cloud-radiative changes on energy transport, making the estimated impacts smaller than those obtained from directly integrating cloud-radiative changes in previous studies. It is also demonstrated that the cloud-radiative impacts on temperature and energy transport can be significantly modulated by the oceanic circulation, suggesting the necessity of considering atmospheric-oceanic coupling when estimating the impacts of cloud-radiative changes on the climate system.

CMIP5 Intermodel Relationships in the Baseline Southern Ocean Climate System and With Future Projections

AbstractClimate models exhibit a broad range in the simulated properties of the climate system. In the early historical period, the absolute global mean surface air temperature in Coupled Model Intercomparison Project, Phase 5 (CMIP5) models spans a range of ∼12°C – 15°C. Other climate variables may be linked to global mean temperature, and so accurate representation of the baseline climate state is crucial for meaningful future climate projections. In CMIP5 baseline climate states, statistically significant intermodel correlations between Southern Ocean surface temperature, outgoing shortwave radiation, cloudiness, the position of the mid‐latitude eddy‐driven jet, and Antarctic sea ice area are found. The baseline temperature relationships extend to projected future changes in the same set of variables, impacting on the projected global mean surface temperature change. Models with initially cooler Southern Ocean tend to exhibit more global warming, and vice versa for initially warmer models. These relationships arise due to a “capacity for change”. For example, cold‐biased models tend to have more cloud cover, sea ice, and equatorward jet initially, and thus a greater capacity to lose cloud cover and sea ice, and for the jet to shift poleward under global warming. A first look at emerging data from CMIP6 reveals a shift of the relationship from the Southern Ocean towards the Antarctic region, possibly due to reductions in Southern Ocean biases, such as in westerly wind representation.