Chengxing Zhai

Weakening and strengthening structures in the Hadley Circulation change under global warming and implications for cloud response and climate sensitivity

It has long been recognized that differences in climate model-simulated cloud feedbacks are a primary source of uncertainties for the model-predicted surface temperature change induced by increasing greenhouse gases such as CO_2. Large-scale circulation broadly determines when and where clouds form and how they evolve. However, the linkage between large-scale circulation change and cloud radiative effect (CRE) change under global warming has not been thoroughly studied. By analyzing 15 climate models, we show that the change of the Hadley Circulation exhibits meridionally varying weakening and strengthening structures, physically consistent with the cloud changes in distinct cloud regimes. The regions that experience a weakening (strengthening) of the zonal-mean circulation account for 54% (46%) of the multimodel-mean top-of-atmosphere (TOA) CRE change integrated over 45°S–40°N. The simulated Hadley Circulation structure changes per degree of surface warming differ greatly between the models, and the intermodel spread in the Hadley Circulation change is well correlated with the intermodel spread in the TOA CRE change. This correlation underscores the close interactions between large-scale circulation and clouds and suggests that the uncertainties of cloud feedbacks and climate sensitivity reside in the intimate coupling between large-scale circulation and clouds. New model performance metrics proposed in this work, which emphasize how models reproduce satellite-observed spatial variations of zonal-mean cloud fraction and relative humidity associated with the Hadley Circulation, indicate that the models closer to the satellite observations tend to have equilibrium climate sensitivity higher than the multimodel mean.

An assessment of upper‐troposphere and lower‐stratosphere water vapor in MERRA, MERRA2 and ECMWF reanalyses using Aura MLS observations

Global water vapor (H2O) measurements from Microwave Limb Sounder (MLS) are used to evaluate upper tropospheric (UT) and lower stratospheric (LS) H2O products produced by NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA), its newest release MERRA2, and European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalyses. Focusing on the H2O amount and transport from UT to LS, we show that all reanalyses overestimate annual global mean UT H2O by up to ~150% compared to MLS observations. Substantial differences in H2O transport are also found between the observations and reanalyses. Vertically, H2O transport across the tropical tropopause (16–20 km) in the reanalyses is faster by up to ~86% compared to MLS observations. In the tropical LS (21–25 km), the mean vertical transport from ECMWF is 168% faster than the MLS estimate, while MERRA and MERRA2 have vertical transport velocities within 10% of MLS values. Horizontally at 100 hPa, both observation and reanalyses show faster poleward transport in the Northern Hemisphere (NH) than in the Southern Hemisphere (SH). Compared to MLS observations, the H2O horizontal transport for both MERRA and MERRA2 is 106% faster in the NH but about 42–45% slower in the SH. ECMWF horizontal transport is 16% faster than MLS observations in both hemispheres. The ratio of northward to southward transport velocities for ECMWF is 1.4, which agrees with MLS observation, while the corresponding ratios for MERRA and MERRA2 are about 3.5 times larger.

Long‐term cloud change imprinted in seasonal cloud variation: More evidence of high climate sensitivity

The large spread of model equilibrium climate sensitivity (ECS) is mainly caused by the differences in the simulated marine boundary layer cloud (MBLC) radiative feedback. We examine the variations of MBLC fraction in response to the changes of sea surface temperature (SST) at seasonal and centennial time scales for 27 climate models that participated in the Coupled Model Intercomparison Project phase 3 and phase 5. We find that the intermodel spread in the seasonal variation of MBLC fraction with SST is strongly correlated with the intermodel spread in the centennial MBLC fraction change per degree of SST warming and that both are well correlated with ECS. Seven models that are consistent with the observed seasonal variation of MBLC fraction with SST at a rate −1.28 ± 0.56%/K all have ECS higher than the multimodel mean of 3.3 K yielding an ensemble-mean ECS of 3.9 K and a standard deviation of 0.45 K.

Evaluating the diurnal cycle of upper-tropospheric ice clouds in climate models using SMILES observations

AbstractUpper-tropospheric ice cloud measurements from the Superconducting Submillimeter Limb Emission Sounder (SMILES) on the International Space Station (ISS) are used to study the diurnal cycle of upper-tropospheric ice cloud in the tropics and midlatitudes (40°S–40°N) and to quantitatively evaluate ice cloud diurnal variability simulated by 10 climate models. Over land, the SMILES-observed diurnal cycle has a maximum around 1800 local solar time (LST), while the model-simulated diurnal cycles have phases differing from the observed cycle by −4 to 12 h. Over ocean, the observations show much smaller diurnal cycle amplitudes than over land with a peak at 1200 LST, while the modeled diurnal cycle phases are widely distributed throughout the 24-h period. Most models show smaller diurnal cycle amplitudes over ocean than over land, which is in agreement with the observations. However, there is a large spread of modeled diurnal cycle amplitudes ranging from 20% to more than 300% of the observed over both lan...

Tightening of tropical ascent and high clouds key to precipitation change in a warmer climate

The change of global-mean precipitation under global warming and interannual variability is predominantly controlled by the change of atmospheric longwave radiative cooling. Here we show that tightening of the ascending branch of the Hadley Circulation coupled with a decrease in tropical high cloud fraction is key in modulating precipitation response to surface warming. The magnitude of high cloud shrinkage is a primary contributor to the intermodel spread in the changes of tropical-mean outgoing longwave radiation (OLR) and global-mean precipitation per unit surface warming (dP/dTs) for both interannual variability and global warming. Compared to observations, most Coupled Model Inter-comparison Project Phase 5 models underestimate the rates of interannual tropical-mean dOLR/dTs and global-mean dP/dTs, consistent with the muted tropical high cloud shrinkage. We find that the five models that agree with the observation-based interannual dP/dTs all predict dP/dTs under global warming higher than the ensemble mean dP/dTs from the ∼20 models analysed in this study.

Evaluation of CMIP5 upper troposphere and lower stratosphere geopotential height with GPS radio occultation observations

We present a detailed comparison of geopotential height fields between the Coupled Model Inter-Comparison Project phase 5 (CMIP5) models and satellite observations from GPS radio occultation (RO). Our comparison focuses on the annual mean, seasonal cycle, and interannual variability of 200 hPa geopotential height in the years 2002–2008. Using a wide sample of atmosphere-only model runs (AMIP) from the CMIP5 archive, we find that most models agree well with the observations and weather reanalyses in the tropics in both the annual means and interannual variabilities. However, the agreement is poor over the extratropics with the largest model spreads in the high latitudes and the largest bias in the southern middle to high latitudes that persist all seasons. The models also show excessive seasonal variability over the Northern midlatitude land areas as well as the Southern Ocean but insufficient variability over the tropics and Antarctica. While the underlying causes for the model discrepancies require further analyses, this study demonstrates that global observations from GPS RO provide accurate benchmark-quality measurements in the upper troposphere and lower stratosphere through which biases in climate models as well as weather reanalyses can be identified.

Using Deep Space Climate Observatory Measurements to Study the Earth as an Exoplanet

Even though it was not designed as an exoplanetary research mission, the Deep Space Climate Observatory (DSCOVR) has been opportunistically used for a novel experiment, in which Earth serves as a proxy exoplanet. More than two years of DSCOVR Earth images were employed to produce time series of multi-wavelength, single-point light sources, in order to extract information on planetary rotation, cloud patterns, surface type, and orbit around the Sun. In what follows, we assume that these properties of the Earth are unknown, and instead attempt to derive them from first principles. These conclusions are then compared with known data about our planet. We also used the DSCOVR data to simulate phase angle changes, as well as the minimum data collection rate needed to determine the rotation period of an exoplanet. This innovative method of using the time evolution of a multi-wavelength, reflected single-point light source, can be deployed for retrieving a range of intrinsic properties of an exoplanet around a distant star.

On the Emergent Constraints of Climate Sensitivity

AbstractDifferences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificia...