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Ying Sun
,
Susan Solomon
,
Aiguo Dai
, and
Robert W. Portmann

Abstract

Daily precipitation data from climate change simulations using the latest generation of coupled climate system models are analyzed for potential future changes in precipitation characteristics. For the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) B1 (a low projection), A1B (a medium projection), and A2 (a high projection) during the twenty-first century, all the models consistently show a shift toward more intense and extreme precipitation for the globe as a whole and over various regions. For both SRES B1 and A2, most models show decreased daily precipitation frequency and all the models show increased daily precipitation intensity. The multimodel averaged percentage increase in the precipitation intensity (2.0% K−1) is larger than the magnitude of the precipitation frequency decrease (−0.7% K−1). However, the shift in precipitation frequency distribution toward extremes results in large increases in very heavy precipitation events (>50 mm day−1), so that for very heavy precipitation, the percentage increase in frequency is much larger than the increase in intensity (31.2% versus 2.4%). The climate model projected increases in daily precipitation intensity are, however, smaller than that based on simple thermodynamics (∼7% K−1). Multimodel ensemble means show that precipitation amount increases during the twenty-first century over high latitudes, as well as over currently wet regions in low- and midlatitudes more than other regions. This increase mostly results from a combination of increased frequency and intensity. Over the dry regions in the subtropics, the precipitation amount generally declines because of decreases in both frequency and intensity. This indicates that wet regions may get wetter and dry regions may become drier mostly because of a simultaneous increase (decrease) of precipitation frequency and intensity.

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Xi Cao
,
Renguang Wu
,
Liangtao Xu
,
Zhibiao Wang
,
Ying Sun
,
Yifeng Dai
, and
Sheng Chen

Abstract

The present study investigates the relationship of tropical cyclone (TC) genesis between the western North Pacific (WNP) and South China Sea (SCS) from 1979 to 2020. A significantly out-of-phase variation is found between spring [March–May (MAM)] TC genesis over the WNP and the following summer–fall [June–November (JJASON)] TC genesis over the SCS. More TCs over the WNP in MAM are followed by fewer TCs over the SCS in the succeeding JJASON. Composite analysis and numerical model experiments show that negative sea surface temperature (SST) anomalies during MAM in the tropical central-eastern Pacific (CEP) and southeastern Indian Ocean work together to induce a lower-level cyclonic circulation over the WNP, with the latter more important. The positive specific humidity and ascending motion favor the TC genesis over the WNP in MAM. In the following JJASON, the SST anomalies are reversed in the tropical CEP. The positive precipitation anomalies over the western-central Pacific induced by positive SST anomalies further stimulate an anomalous zonal overturning circulation with anomalous descending motion and boundary layer divergence over the SCS. In addition, the persistent negative SST anomalies around the Maritime Continent (MC) induce an anomalous anticyclone to the west. Both processes lead to negative genesis potential index (GPI) anomalies and thus inhibit the TC genesis over the SCS. This out-of-phase relationship of TC genesis between the WNP and SCS also exists when the El Niño–Southern Oscillation (ENSO) transition years are removed. This finding may be helpful to improve the seasonal prediction of the SCS TC activity over the peak TC season.

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Chuanhao Wu
,
Pat J.-F. Yeh
,
Jiali Ju
,
Yi-Ying Chen
,
Kai Xu
,
Heng Dai
,
Jie Niu
,
Bill X. Hu
, and
Guoru Huang

Abstract

Drought projections are accompanied with large uncertainties due to varying estimates of future warming scenarios from different modeling and forcing data. Using the standardized precipitation index (SPI), this study presents a global assessment of uncertainties in drought characteristics (severity S and frequency Df) projections based on the simulations of 28 general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). A hierarchical framework incorporating a variance-based global sensitivity analysis was developed to quantify the uncertainties in drought characteristics projections at various spatial (global and regional) and temporal (decadal and 30-yr) scales due to 28 GCMs, three representative concentration pathway scenarios (RCP2.6, RCP4.5, RCP8.5), and two bias-correction (BC) methods. The results indicated that the largest uncertainty contribution in the globally projected S and Df is from the GCM uncertainty (>60%), followed by BC (<35%) and RCP (<16%) uncertainty. Spatially, BC reduces the spreads among GCMs particularly in Northern Hemisphere (NH), leading to smaller GCM uncertainty in the NH than the Southern Hemisphere (SH). In contrast, the BC and RCP uncertainties are larger in the NH than the SH, and the BC uncertainty can be larger than GCM uncertainty for some regions (e.g., southwest Asia). At the decadal and 30-yr time scales, the contributions for three uncertainty sources show larger variability in S than Df projections, especially in the SH. The GCM and BC uncertainties show overall decreasing trends with time, while the RCP uncertainty is expected to increase over time and even can be larger than BC uncertainty for some regions (e.g., northern Asia) by the end of this century.

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Zhiguo Yue
,
Daniel Rosenfeld
,
Guihua Liu
,
Jin Dai
,
Xing Yu
,
Yannian Zhu
,
Eyal Hashimshoni
,
Xiaohong Xu
,
Ying Hui
, and
Oliver Lauer

Abstract

The advent of the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi NPP (SNPP) satellite made it possible to retrieve a new class of convective cloud properties and the aerosols that they ingest. An automated mapping system of retrieval of some properties of convective cloud fields over large areas at the scale of satellite coverage was developed and is presented here. The system is named Automated Mapping of Convective Clouds (AMCC). The input is level-1 VIIRS data and meteorological gridded data. AMCC identifies the cloudy pixels of convective elements; retrieves for each pixel its temperature T and cloud drop effective radius r e ; calculates cloud-base temperature T b based on the warmest cloudy pixels; calculates cloud-base height H b and pressure P b based on T b and meteorological data; calculates cloud-base updraft W b based on H b ; calculates cloud-base adiabatic cloud drop concentrations N d,a based on the T–r e relationship, T b , and P b ; calculates cloud-base maximum vapor supersaturation S based on N d,a and W b ; and defines N d,a /1.3 as the cloud condensation nuclei (CCN) concentration N CCN at that S. The results are gridded 36 km × 36 km data points at nadir, which are sufficiently large to capture the properties of a field of convective clouds and also sufficiently small to capture aerosol and dynamic perturbations at this scale, such as urban and land-use features. The results of AMCC are instrumental in observing spatial covariability in clouds and CCN properties and for obtaining insights from such observations for natural and man-made causes. AMCC-generated maps are also useful for applications from numerical weather forecasting to climate models.

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