Engineering topics
Cai, Ming
Publications and source records attributed to Cai, Ming.
Unmasking the Negative Greenhouse Effect over the Antarctic Plateau
A paradoxical negative greenhouse effect has been found over the Antarctic Plateau, indicating that greenhouse gases enhance energy loss to space. Using 13 years of NASA satellite observations, we verify the existence of the negative greenhouse effect and find that the magnitude and sign of the effect varies seasonally and spectrally. A previous explanation attributes this effect solely to stratospheric CO2; however, we surprisingly find that the negative greenhouse effect is predominantly caused by tropospheric water vapor. A recently developed principle-based concept is used to provide a complete account of the Antarctic Plateau’s negative greenhouse effect indicating that it is controlled by the vertical variation of temperature and greenhouse gas absorption. Our findings indicate that unique climatological conditions over the Antarctic Plateau—a strong surface-based temperature inversion and scarcity of free tropospheric water vapor—cause the negative greenhouse effect.
Decadal Evolution of the Surface Energy Budget During the Fast Warming and Global Warming Hiatus Periods in the ERA-Interim
The global-mean surface temperature has experienced a rapid warming from the 1980s to early-2000s but a muted warming since, referred to as the global warming hiatus in the literature. Decadal changes in deep ocean heat uptake are thought to primarily account for the rapid warming and subsequent slowdown. Here, we examine the role of ocean heat uptake in establishing the fast warming and warming hiatus periods in the ERA-Interim through a decomposition of the global-mean surface energy budget. We find the increase of carbon dioxide alone yields a nearly steady increase of the downward longwave radiation at the surface from the 1980s to the present, but neither accounts for the fast warming nor warming hiatus periods. During the global warming hiatus period, the transfer of latent heat energy from the ocean to atmosphere increases and the total downward radiative energy flux to the surface decreases due to a reduction of solar absorption caused primarily by an increase of clouds. The reduction of radiative energy into the ocean and the surface latent heat flux increase cause the ocean heat uptake to decrease and thus contribute to the slowdown of the global-mean surface warming. Our analysis also finds that in addition to a reduction of deep ocean heat uptake, the fast warming period is also driven by enhanced solar absorption due predominantly to a decrease of clouds and by enhanced longwave absorption mainly attributed to the air temperature feedback.
Covariance Between Arctic Sea Ice and Clouds Within Atmospheric State Regimes at the Satellite Footprint Level
Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and mid-tropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.
Diagnosis of Middle Atmosphere Climate Sensitivity by the Climate Feedback Response Analysis Method
We present a new method to diagnose the middle atmosphere climate sensitivity by extending the Climate Feedback-Response Analysis Method (CFRAM) for the coupled atmosphere-surface system to the middle atmosphere. The Middle atmosphere CFRAM (MCFRAM) is built on the atmospheric energy equation per unit mass with radiative heating and cooling rates as its major thermal energy sources. MCFRAM preserves the CFRAM unique feature of an additive property for which the sum of all partial temperature changes due to variations in external forcing and feedback processes equals the observed temperature change. In addition, MCFRAM establishes a physical relationship of radiative damping between the energy perturbations associated with various feedback processes and temperature perturbations associated with thermal responses. MCFRAM is applied to both measurements and model output fields to diagnose the middle atmosphere climate sensitivity. It is found that the largest component of the middle atmosphere temperature response to the 11-year solar cycle (solar maximum vs. solar minimum) is directly from the partial temperature change due to the variation of the input solar flux. Increasing CO2 always cools the middle atmosphere with time whereas partial temperature change due to O3 variation could be either positive or negative. The partial temperature changes due to different feedbacks show distinctly different spatial patterns. The thermally driven globally averaged partial temperature change due to all radiative processes is approximately equal to the observed temperature change, ranging from 0.5 K near 70 km from the near solar maximum to the solar minimum.
ENSO Bred Vectors in Coupled Ocean-Atmosphere General Circulation Models
The breeding method has been implemented in the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Coupled General Circulation Model (CGCM) with the goal of improving operational seasonal to interannual climate predictions through ensemble forecasting and data assimilation. The coupled instability as cap'tured by the breeding method is the first attempt to isolate the evolving ENSO instability and its corresponding global atmospheric response in a fully coupled ocean-atmosphere GCM. Our results show that the growth rate of the coupled bred vectors (BV) peaks at about 3 months before a background ENSO event. The dominant growing BV modes are reminiscent of the background ENSO anomalies and show a strong tropical response with wind/SST/thermocline interrelated in a manner similar to the background ENSO mode. They exhibit larger amplitudes in the eastern tropical Pacific, reflecting the natural dynamical sensitivity associated with the presence of the shallow thermocline. Moreover, the extratropical perturbations associated with these coupled BV modes reveal the variations related to the atmospheric teleconnection patterns associated with background ENSO variability, e.g. over the North Pacific and North America. A similar experiment was carried out with the NCEP/CFS03 CGCM. Comparisons between bred vectors from the NSIPP CGCM and NCEP/CFS03 CGCM demonstrate the robustness of the results. Our results strongly suggest that the breeding method can serve as a natural filter to identify the slowly varying, coupled instabilities in a coupled GCM, which can be used to construct ensemble perturbations for ensemble forecasts and to estimate the coupled background error covariance for coupled data assimilation.