The dependence of mean climate state on shortwave absorption by water vapor

State-of-the-art climate models exhibit significant spread in the climatological value of atmospheric shortwave absorption (SWA). This study investigates both the possible causes and climatic impacts of this SWA inter-model spread. The inter-model spread of global-mean SWA largely originates from the inter-model difference in water vapor shortwave absorptivity. Hence, we alter the water vapor shortwave absorptivity in the Community Earth System Model, version 1, with Atmosphere Model, version 4 (CESM1-CAM4). Increasing the water vapor shortwave absorptivity leads to a reduction in global-mean precipitation and a La Niña-like cooling over the tropical Pacific. The global-mean atmospheric energy budget suggests that the precipitation is suppressed as a way to compensate for the increased SWA. The precipitation reduction is driven by the weakened surface winds, stabilized planetary boundary layer, and surface cooling. The La Niña-like cooling over the tropical Pacific is attributed to the zonal asymmetry of climatological evaporative damping efficiency and the low cloud enhancement over the eastern basin. Complementary fixed SSTs simulations suggest that the latter is more fundamental and that it primarily arises from atmospheric processes. Consistent with our experiments, the CMIP5/6 models with a higher global-mean SWA tend to exhibit the tropical Pacific toward a more La Niña-like mean state, highlighting the possible role of water vapor shortwave absorptivity for shaping the mean-state climate patterns.

Indian Ocean Dynamic Sea Level, Variability And Projections In CMIP6 Models

The regional sea level variability and its projection amidst the global sea level rise is one of the major concerns for coastal communities. The dynamic sea level plays a major role in the observed spatial deviations in regional sea level rise from the global mean. The present study evaluates 27 climate model simulations from the sixth phase of the coupled model intercomparison project (CMIP6) for their representation of the historical mean states, variability and future projections for the Indian Ocean. Most models reproduce the observed mean state of the dynamic sea level realistically, however consistent positive bias is evident across the latitudinal range of the Indian Ocean. The strongest sea level bias is seen along the Antarctic Circumpolar Current (ACC) regime owing to the stronger than observed south Indian Ocean westerlies and its equatorward bias. Further, this equatorward shift of the wind field resulted in stronger positive windstress curl across the southeasterly trade winds in the southern tropical basin and easterly wind bias along the equatorial waveguide. These anomalous easterly equatorial winds cause upwelling in the eastern part of the basin and keeps the thermocline shallower in the model than observed, resulted in enhanced variability for the dipole zonal mode or Indian Ocean dipole in the tropics. In the north Indian Ocean, the summer monsoon winds are weak in the model causing weaker upwelling and positive sea level bias along the western Arabian Sea. The high-resolution models compare better in simulating the sea level variability, particularly in the eddy dominated regions like the ACC regime in interannual timescale. However, these improved variabilities do not necessarily produce a better mean state likely due to the enhanced mixing driven by parametrizations set in these high-resolution models. Finally, the overall pattern of the projected dynamic sea level rise is found to be similar for the mid (SSP2-4.5) and high-end (SSP5-8.5) scenarios, except that the magnitude is higher under the high emission situation. Notably, the projected dynamic sea level change is found to be milder when only the best performing models are used compared to the full ensemble.

Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration

A single column version of the CNRM-CM6-1 global climate model has been developed to ease development and validation of the boundary layer physics and air-sea coupling in a simplified environment. This framework is then used to assess the ability of the coupled model to represent the sea surface temperature (SST) diurnal cycle. To this aim, the atmospheric-ocean single column model (AOSCM), called CNRM-CM6-1D, is implemented on a case study derived from the Cindy-Dynamo field campaign over the Indian Ocean, where large diurnal SST variabilities have been well documented. Comparing the AOSCM and its uncoupled components (atmospheric SCM and oceanic SCM, called OSCM) highlights that the impact of coupling in the atmosphere results both from the possibility to take in to account the diurnal variability of SST, not usually available in forcing products, and from the change in mean state SST as simulated by the OSCM, the ocean mean state not being heavily impacted by the coupling. This suggests that coupling feedbacks are more due to advection processes in the 3D model than to the model physics. Additionally, a sub-daily coupling frequency is needed to represent the SST diurnal variability but the choice of the coupling time-step between 15 min and 3 h does not impact much on the diurnal temperature range simulated. The main drawback of a 3-h coupling being to delay the SST diurnal cycle by 5 h in asynchronous coupled models. Overall, the diurnal SST variability is reasonably well represented in the CNRM-CM6-1 with a 1 h coupling time-step and the upper ocean model resolution of 1 m. This framework is shown to be a very valuable tool to develop and validate the boundary layer physics and the coupling interface. It highlights the interest to develop other atmosphere-ocean coupling case studies.

Ubiquity of human-induced changes in climate variability

While climate change mitigation targets necessarily concern maximum mean state changes, understanding impacts and developing adaptation strategies will be largely contingent on how climate variability responds to increasing anthropogenic perturbations. Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño–Southern Oscillation. However, our knowledge of forced changes in the overall spectrum of climate variability and higher-order statistics is relatively limited. Here we present a new 100-member large ensemble of climate change projections conducted with the Community Earth System Model version 2 over 1850–2100 to examine the sensitivity of internal climate fluctuations to greenhouse warming. Our unprecedented simulations reveal that changes in variability, considered broadly in terms of probability distribution, amplitude, frequency, phasing, and patterns, are ubiquitous and span a wide range of physical and ecosystem variables across many spatial and temporal scales. Greenhouse warming in the model alters variance spectra of Earth system variables that are characterized by non-Gaussian probability distributions, such as rainfall, primary production, or fire occurrence. Our modeling results have important implications for climate adaptation efforts, resource management, seasonal predictions, and assessing potential stressors for terrestrial and marine ecosystems.

Observed Quantile Features of Summertime Temperature Trends over China: Non-Negligible Role of Temperature Variability

Changes in temperature variability can have more serious social and ecological impacts than changes in the mean state of temperature, especially when they are concurrent with global warming. The present study examines the summertime temperatures’ trends over China from the quantile perspective. Through fully investigating the quantile trends (QTs) of the maximum (Tmax) and minimum temperature (Tmin) using the homogenized observation data and quantile regression analysis, we identify evident region-specific quantile features of summertime temperature trends. In most of northern China, the QTs in Tmax and Tmin for all percentiles generally show strong uniform warmings, which are dominated by a warm shift in mean state temperatures. In contrast, the QTs of Tmax in the Yangtze River Basin show distinguishable inter-quantile features, i.e., an increasing tendency of QTs from cooling trends in the lower percentile to warming trends in the higher percentile. Further investigations show that such robust growing QTs of Tmax across quantiles are dominated by the temperature variance. Our results highlight that more attention should be paid to the region-specific dominance of temperature variability in trends and the related causes.

Reduced El Niño variability in the mid-Pliocene according to the PlioMIP2 ensemble

The mid-Pliocene warm period (3.264–3.025 Ma) is the most recent geological period during which atmospheric CO2 levels were similar to recent historical values (∼400 ppm). Several proxy reconstructions for the mid-Pliocene show highly reduced zonal sea surface temperature (SST) gradients in the tropical Pacific Ocean, indicating an El Niño-like mean state. However, past modelling studies do not show these highly reduced gradients. Efforts to understand mid-Pliocene climate dynamics have led to the Pliocene Model Intercomparison Project (PlioMIP). Results from the first phase (PlioMIP1) showed clear El Niño variability (albeit significantly reduced) and did not show the greatly reduced time-mean zonal SST gradient suggested by some of the proxies. In this work, we study El Niño–Southern Oscillation (ENSO) variability in the PlioMIP2 ensemble, which consists of additional global coupled climate models and updated boundary conditions compared to PlioMIP1. We quantify ENSO amplitude, period, spatial structure and “flavour”, as well as the tropical Pacific annual mean state in mid-Pliocene and pre-industrial simulations. Results show a reduced ENSO amplitude in the model-ensemble mean (−24 %) with respect to the pre-industrial, with 15 out of 17 individual models showing such a reduction. Furthermore, the spectral power of this variability considerably decreases in the 3–4-year band. The spatial structure of the dominant empirical orthogonal function shows no particular change in the patterns of tropical Pacific variability in the model-ensemble mean, compared to the pre-industrial. Although the time-mean zonal SST gradient in the equatorial Pacific decreases for 14 out of 17 models (0.2 ∘C reduction in the ensemble mean), there does not seem to be a correlation with the decrease in ENSO amplitude. The models showing the most “El Niño-like” mean state changes show a similar ENSO amplitude to that in the pre-industrial reference, while models showing more “La Niña-like” mean state changes generally show a large reduction in ENSO variability. The PlioMIP2 results show a reasonable agreement with both time-mean proxies indicating a reduced zonal SST gradient and reconstructions indicating a reduced, or similar, ENSO variability.

The Role of Continental Topography in the Present-Day Ocean’s Mean Climate

Climate models of varying complexity have been used for decades to investigate the impact of mountains on the atmosphere and surface climate. Here, the impact of removing the continental topography on the present-day ocean climate is investigated using three different climate models spanning multiple generations. An idealized study is performed where all present-day land surface topography is removed and the equilibrium change in the oceanic mean state with and without the mountains is studied. When the mountains are removed, changes found in all three models include a weakening of the Atlantic Meridional Overturning Circulation and associated SST cooling in the subpolar North Atlantic. The SSTs also warm in all the models in the western North Pacific Ocean associated with a northward shift of the atmospheric jet and Kuroshio current. In the ocean interior, the magnitude of the temperature and salinity response to removing the mountains is relatively small and the sign and magnitude of the changes generally varies among the models. These different interior ocean responses are likely related to differences in the mean state of the control integrations due to differences in resolution and associated sub-grid scale mixing parametrizations. Compared to the results from 4xCO2 simulations, the interior ocean temperature changes caused by mountain removal are relatively small, however, the oceanic circulation response and Northern Hemisphere near-surface temperature changes are of a similar magnitude to the response to such radiative forcing changes.

Assessing the impact of suppressing Southern Ocean SST variability in a coupled climate model

The Southern Ocean exerts a strong influence on global climate, regulating the storage and transport of heat, freshwater and carbon throughout the world’s oceans. While the majority of previous studies focus on how wind changes influence Southern Ocean circulation patterns, here we set out to explore potential feedbacks from the ocean to the atmosphere. To isolate the role of oceanic variability on Southern Hemisphere climate, we perform coupled climate model experiments in which Southern Ocean variability is suppressed by restoring sea surface temperatures (SST) over 40°–65°S to the model’s monthly mean climatology. We find that suppressing Southern Ocean SST variability does not impact the Southern Annular Mode, suggesting air–sea feedbacks do not play an important role in the persistence of the Southern Annular Mode in our model. Suppressing Southern Ocean SST variability does lead to robust mean-state changes in SST and sea ice. Changes in mixed layer processes and convection associated with the SST restoring lead to SST warming and a sea ice decline in southern high latitudes, and SST cooling in midlatitudes. These results highlight the impact non-linear processes can have on a model’s mean state, and the need to consider these when performing simulations of the Southern Ocean.

Structure of the Pacific Walker Circulation Depicted by the Reanalysis and CMIP6

The Pacific Walker circulation (PWC) is one of the most important components of large-scale tropical atmospheric circulations. The PWC and its influences have been studied extensively by numerical models and reanalysis. The newly released ERA5 and NCEP2 are the most widely used reanalysis datasets and serve as benchmarks for evaluation of model simulations. If the results of these datasets differ significantly, this could lead to a bias in projected long-term climate knowledge. For better understanding of future climate change, it is necessary to evaluate PWC reanalysis productions. As a result, we compared the PWC structures between the ERA5 and NCEP2 datasets from month to seasonal time scales. We used the zonal mass streamfunction (ZMS) over the equatorial Pacific to indicate the strength of the PWC. The PWC’s average monthly or seasonal cycle peaks around July. From February to June, the NCEP2 shows a higher PWC intensity, whereas the ERA5 shows greater intensity from July to December. The circulation center in the NCEP2 is generally stronger and wider than in the ERA5. The ERA5, however, revealed that the PWC’s west edge (zero line of ZMS over the western Pacific) had moved 10 degrees westward in comparison to the NCEP2. In addition, we compared the PWC mean state in the reanalysis and CMIP6 models; the mean state vertical structures of the tropical PWC in the CMIP6 multi-model ensemble (MME) are similar to those of the reanalyses in structure but weaker and wider than in the two reanalysis datasets. The PWC is broader in CMIP6, and the western boundary is 7 and 17 degrees farther west than in the ERA5 and NCEP2, respectively. This study suggests that, when using reanalysis datasets to evaluate PWC structural changes in intensity and western edge, extreme caution should be exercised.