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Xiaoming Sun
and
Guiling Wang

Abstract

Although the intensity of extreme precipitation is predicted to increase with climate warming, at the weather scale precipitation extremes over most of the globe decrease when temperature exceeds a certain threshold, and the spatial extent of this negative scaling is projected to increase as the climate warms. The nature and cause of the negative scaling at high temperature and its implications remain poorly understood. Based on subdaily data from observations, a reanalysis product, and output from a coarse-resolution (∼200 km) global model and a fine-resolution (4 km) convection-permitting regional model, we show that the negative scaling is primarily a reflection of high temperature suppressing precipitation over land and storm-induced temperature variations over the ocean. We further identify the high temperature–induced increase of saturation deficit as a critical condition for the negative scaling of extreme precipitation over land. A large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation. The heat-induced suppression of precipitation, both for its mean and extremes, provides one mechanism for the co-occurrence of drought and heatwaves. As the saturation deficit over land is expected to increase in a warmer climate, our results imply a growing prevalence of negative scaling, potentially increasing the frequency of compound drought and heat events. Understanding the physical mechanisms underlying the negative scaling of precipitation at high temperature is, therefore, essential for assessing future risks of extreme events, including not only flood due to extreme precipitation but also drought and heatwaves.

Significance Statement

Negative scaling, a decrease of extreme precipitation at high local temperature, is a poorly understood phenomenon. It was suggested that the negative scaling may be a reflection of precipitation’s influence on temperature. Here we show based on observational data, a reanalysis product, and climate models that the negative scaling results primarily from the impact of the high temperature–induced saturation deficit on precipitation over land and from storm-induced temperature variations over the ocean. In hot weather when moisture is limited (as is over land), a large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation, leading to a negative scaling. The same mechanism can also contribute to increased compound drought and heat events.

Free access
Xiaoming Sun
and
Ana P. Barros

Abstract

Confidence in the estimation of variations in the frequency of extreme events, and specifically extreme precipitation, in response to climate variability and change is key to the development of adaptation strategies. One challenge to establishing a statistical baseline of rainfall extremes is the disparity among the types of datasets (observations versus model simulations) and their specific spatial and temporal resolutions. In this context, a multifractal framework was applied to three distinct types of rainfall data to assess the statistical differences among time series corresponding to individual rain gauge measurements alone—National Climatic Data Center (NCDC), model-based reanalysis [North America Regional Reanalysis (NARR) grid points], and satellite-based precipitation products [Global Precipitation Climatology Project (GPCP) pixels]—for the western United States (west of 105°W). Multifractal analysis provides general objective metrics that are especially adept at describing the statistics of extremes of time series. This study shows that, as expected, multifractal parameters estimated from the NCDC rain gauge dataset map the geography of known hydrometeorological phenomena in the major climatic regions, including the strong orographic gradients from west to east; whereas the NARR parameters reproduce the spatial patterns of NCDC parameters, but the frequency of large rainfall events, the magnitude of maximum rainfall, and the mean intermittency are underestimated. That is, the statistics of the NARR climatology suggest milder extremes than those derived from rain gauge measurements. The spatial distributions of GPCP parameters closely match the NCDC parameters over arid and semiarid regions (i.e., the Southwest), but there are large discrepancies in all parameters in the midlatitudes above 40°N because of reduced sampling. This study provides an alternative independent backdrop to benchmark the use of reanalysis products and satellite datasets to assess the effect of climate change on extreme rainfall.

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Xiaoming Sun
and
Ana P. Barros
Full access
Xiaoming Sun
and
Ana P. Barros

Abstract

The contribution of surface evapotranspiration (ET) to moist convection, cloudiness, and precipitation along the eastern flanks of the tropical Andes (EADS) was investigated using the Weather Research and Forecasting (WRF) Model with nested simulations of selected weather conditions down to 1.2-km grid spacing. To isolate the role of surface ET, numerical experiments were conducted using a quasi-idealized approach whereby, at every time step, the surface sensible heat effects are exactly the same as in the reference simulations, whereas the surface latent heat fluxes are prevented from entering the atmosphere. Energy balance analysis indicates that surface ET influences moist convection primarily through its impact on conditional instability, because it acts as an important source of moist entropy in this region. The energy available for convection decreases by up to approximately 60% when the ET contribution is withdrawn. In contrast, when convective motion is not thermally driven or under conditionally stable conditions, the role of latent heating from the land surface becomes secondary. At the scale of the Andes proper, removal of surface ET weakens upslope flows by increasing static stability of the lower troposphere, as the vertical gradient of water vapor mixing ratio tends to be less negative. Consequently, moisture convergence is reduced over the EADS. In the absence of surface ET, this process operates in concert with damped convective energy, suppressing cloudiness and decreasing daily precipitation by up to around 50% in the simulations presented here.

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Xiaoming Sun
and
Ana P. Barros

Abstract

The influence of large-scale forcing on the high-resolution simulation of Tropical Storm Ivan (2004) in the southern Appalachians was investigated using the Weather Research and Forecasting model (WRF). Two forcing datasets were employed: the North American Regional Reanalysis (NARR; 32 km × 32 km) and the NCEP Final Operational Global Analysis (NCEP FNL; 1° × 1°). Simulated fields were evaluated against rain gauge, radar, and satellite data; sounding observations; and the best track from the National Hurricane Center (NHC). Overall, the NCEP FNL forced simulation (WRF_FNL) captures storm structure and evolution more accurately than the NARR forced simulation (WRF_NARR), benefiting from the hurricane initialization scheme in the NCEP FNL. Further, the performance of WRF_NARR is also negatively affected by a previously documented low-level warm bias in NARR. These factors lead to excessive precipitation in the Piedmont region, delayed rainfall in Alabama, as well as spatially displaced and unrealistically extreme rainbands during its passage over the southern Appalachians. Spatial filtering of the simulated precipitation fields confirms that the storm characteristics inherited from the forcing are critical to capture the storm’s impact at local places. Compared with the NHC observations, the storm is weaker in both NARR and NCEP FNL (up to Δp ~ 5 hPa), yet it is persistently deeper in all WRF simulations forced by either dataset. The surface wind fields are largely overestimated. This is attributed to the underestimation of surface roughness length over land, leading to underestimation of surface drag, reducing low-level convergence, and weakening the dissipation of the simulated cyclone.

Full access
Qingyuan Wu
,
Qingquan Li
,
Xiaoming Hu
, and
Xiaoting Sun

Abstract

Using the ERA5 reanalysis data and the Climate Feedback Response Analysis Method (CFRAM), we attribute the mechanism of summer upper-tropospheric temperature (UTT) variations in the South Asian summer monsoon (SASM) region to several external forcing and internal feedback processes. The summer UTT in the SASM region is dominated by two modes. The first empirical orthogonal function (EOF) mode (EOF1) is a monopolar warming pattern, and the second EOF mode (EOF2) shows a meridional dipole pattern. CFRAM results show that summer UTT anomalies are mainly attributed to cloud feedback and nonradiative processes of atmospheric dynamics (ATD) and surface-related processes. For EOF1, ocean heat storage and partial cloud feedback processes contribute most UTT anomalies over the Indian Ocean. The ATD increases the UTT over East Asia through the adiabatic warming caused by anomalous anticyclone in the upper troposphere. The formation of EOF2 is closely linked to the ATD, while the cloud process partially compensates for the excessive changes in UTT caused by the ATD. The South Asian high and its circulation in the midlatitude region are significantly enhanced. The anomalous anticyclone over northern East Asia along with the anomalous easterly wind on the south side of the South Asian high favors increased warm advection and adiabatic heating, contributing to the warming of UTT. Meanwhile, adiabatic cooling resulting from the atmospheric ascent in the middle and upper troposphere leads to UTT cooling over the Indian Ocean. The quantitative attribution of UTT has great implications for better understanding future SASM variation.

Significance Statement

The purpose of this study is to understand the physical mechanisms of upper-tropospheric temperature (UTT) changes in the South Asian summer monsoon (SASM). Although previous studies have examined temperature variation from the perspective of atmospheric circulation, there has been limited investigation into the influence of various feedback processes. Our study reveals that the summer UTT in the SASM region is dominated by monopolar and meridional dipole modes. Utilizing a climate feedback–response analysis method, we attribute the UTT anomalies in the SASM region to the cloud feedback, oceanic heat storage, and atmospheric dynamics processes, and explore the physical mechanisms of their effects. These results have important implications for the better prediction of monsoon variability.

Restricted access
Xiaoming Sun
,
Kerry H. Cook
, and
Edward K. Vizy

Abstract

ERA-Interim and JRA-55 reanalysis products are analyzed to document the annual cycle of the South Atlantic subtropical high (SASH) and examine how its interannual variability relates to regional and large-scale climate variability. The annual cycle of the SASH is found to have two peaks in both intensity and size. The SASH is strongest and largest during the solstitial months when its center is either closest to the equator and on the western side of the South Atlantic basin during austral winter or farthest poleward and in the center of the basin in late austral summer. Although interannual variations in the SASH’s position are larger in the zonal direction, the intensity of the high decreases when it is positioned to the north. This relationship is statistically significant in every month. Seasonal composites and EOF analysis indicate that meridional changes in the position of the SASH dominate interannual variations in austral summer. In particular, the anticyclone tends to be displaced poleward in La Niña years when the southern annular mode (SAM) is in its positive phase and vice versa. Wave activity flux vectors suggest that ENSO-related convective anomalies located in the central-eastern tropical Pacific act as a remote forcing for the meridional variability of the summertime SASH. In southern winter, multiple processes operate in concert to induce interannual variability, and none of them appears to dominate like ENSO does during the summer.

Full access
Ming Cai
,
Xiaoming Hu
,
Jie Sun
,
Feng Ding
, and
Jing Feng

Abstract

This paper introduces a climate feedback kernel, referred to as the “energy gain kernel” (EGK). EGK allows for separating the net longwave radiative energy perturbations given by a Planck feedback matrix explicitly into thermal energy emission perturbations of individual layers, and thermal radiative energy flux convergence perturbations at individual layers resulting from the coupled atmosphere-surface temperature changes in response to the unit forcing in individual layers. The former is represented by the diagonal matrix of a Planck feedback matrix and the latter by EGK. Elements of EGK are all positive, representing amplified energy perturbations at a layer where forcing is imposed and energy gained at other layers, both of which are achieved through radiative thermal coupling within an atmosphere-surface column.

Applying EGK to input energy perturbations, whether external or internal due to responses of non-temperature feedback processes to external energy perturbations, such as water vapor and albedo feedbacks, yields their total energy perturbations amplified through radiative thermal coupling within an atmosphere-surface column.

As the strength of EGK depends exclusively on climate mean states, it offers a solution for effectively and objectively separating control climate state information from climate perturbations for climate feedback studies. Given that an EGK comprises critical climate mean state information on mean temperature, water vapor, clouds, and surface pressure, we envision that the diversity of EGK across different climate models could provide insight into the inquiry of why, under the same anthropogenic greenhouse gas increase scenario, different models yield varying degrees of global mean surface warming.

Open access
Ming Cai
,
Jie Sun
,
Feng Ding
,
Wanying Kang
, and
Xiaoming Hu

Abstract

The slope of the quasi-linear relation between planetary outgoing longwave radiation (OLR) and surface temperature (TS ) is an important parameter measuring the sensitivity of Earth’s climate system. The primary objective of this study is to seek a general explanation for the quasi-linear OLR–TS relation that remains valid regardless of the strength of the atmospheric window’s narrowing effect on planetary thermal emission at higher temperatures. The physical understanding of the quasi-linear OLR–TS relation and its slope is gained from observation analysis, climate simulations with radiative–convective equilibrium and general circulation models, and a series of online feedback suppression experiments. The observed quasi-linear OLR–TS relation manifests a climate footprint of radiative (such as the greenhouse effect) and nonradiative processes (poleward energy transport). The former acts to increase the meridional gradient of surface temperature and the latter decreases the meridional gradient of atmospheric temperatures, causing the flattening of the meridional profile of the OLR. Radiative processes alone can lead to a quasi-linear OLR–TS relation that is more steeply sloped. The atmospheric poleward energy transport alone can also lead to a quasi-linear OLR–TS relation by rerouting part of the OLR to be emitted from a warmer place to a colder place. The combined effects of radiative and nonradiative processes make the quasi-linear OLR–TS relation less sloped with a higher degree of linearity. In response to anthropogenic radiative forcing, the slope of the quasi-linear OLR–TS relation is further reduced via stronger water vapor feedback and enhanced poleward energy transport.

Significance Statement

The slope of the quasi-linear relation between planetary outgoing longwave radiation (OLR) and surface temperature (TS ) is an important parameter measuring the sensitivity of Earth’s climate system. The observed quasi-linear OLR–TS relation manifests a climate footprint of radiative (greenhouse effect) and nonradiative processes (poleward energy transport). Radiative processes alone can lead to a quasi-linear OLR–TS relation that is more steeply sloped. The atmospheric poleward energy transport alone can also lead to a quasi-linear OLR–TS relation by rerouting part of the OLR to be emitted from a warmer place to a colder place. The combined effects of radiative and nonradiative processes make the quasi-linear OLR–TS relation less sloped with a higher degree of linearity.

Open access
Benzhi Zhou
,
Lianhong Gu
,
Yihui Ding
,
Lan Shao
,
Zhongmin Wu
,
Xiaosheng Yang
,
Changzhu Li
,
Zhengcai Li
,
Xiaoming Wang
,
Yonghui Cao
,
Bingshan Zeng
,
Mukui Yu
,
Mingyu Wang
,
Shengkun Wang
,
Honggang Sun
,
Aiguo Duan
,
Yanfei An
,
Xu Wang
, and
Weijian Kong

Abstract

Extreme events often expose vulnerabilities of socioeconomic infrastructures and point to directions of much-needed policy change. Integrated impact assessment of such events can lead to finding of sustainability principles. Southern and central China has for decades been undergoing a breakneck pace of socioeconomic development. In early 2008, a massive ice storm struck this region, immobilizing millions of people. The storm was a consequence of sustained convergence between tropical maritime and continental polar air masses, caused by an anomalously stable atmospheric general circulation pattern in both low and high latitudes. Successive waves of freezing rain occurred during a month period, coating southern and central China with a layer of ice 50–160 mm in thickness. We conducted an integrated impact assessment of this event to determine whether and how the context of socioeconomic and human-disturbed natural systems may affect the transition of natural events into human disasters. We found that 1) without contingency plans, advanced technologies dependent on interrelated energy supplies can create worse problems during extreme events, 2) the weakest link in disaster response lies between science and decision making, 3) biodiversity is a form of long-term insurance for sustainable forestry against extreme events, 4) sustainable extraction of nontimber goods and services is essential to risk planning for extreme events in forest resources use, 5) extreme events can cause food shortage directly by destroying crops and indirectly by disrupting food distribution channels, 6) concentrated economic development increases societal vulnerability to extreme events, and 7) formalized institutional mechanisms are needed to ensure that unexpected opportunities to learn lessons from weather disasters are not lost in distracting circumstances.

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