Precipitation Probability and Its Future Changes From a Global Cloud‐Resolving Model and CMIP6 Simulations

Abstract

This study investigates precipitation probability and its future changes using simulations from the Non‐hydrostatic Icosahedral Atmospheric Model (NICAM), a global cloud‐resolving model, and Coupled Model Intercomparison Project Phase 6 (CMIP6) models. For the precipitation probability comparison between models and Tropical Rainfall Measuring Mission (TRMM) during 1998–2008, the difference is greatly reduced by regridding to a common coarser resolution, but the difference signs largely remain. Both NICAM and CMIP6 model ensemble mean show less global‐mean nonprecipitation fractions than observations, but NICAM agrees better with observations. For global‐mean light precipitation probability, NICAM (CMIP6 model ensemble mean) is smaller (larger) than TRMM. NICAM shows higher heavy precipitation probability than TRMM in native resolutions, but it is close to TRMM and falls in the CMIP6 model range after regridding. For the precipitation probability changes from 1979–2008 to 2075–2104, NICAM predicts an increase in nonprecipitation and extreme heavy (>100 mm/day) precipitation probability but a slight decrease in the probability for precipitation <100 mm/day. After regridding to 2.5o, the precipitation intensity where the probability starts to increase becomes ~20 mm/day and the relative increases in nonprecipitation and heavy precipitation probability become much larger. Future heavy precipitation probability relative increases from CMIP6 models are much larger than NICAM at their native resolutions, but NICAM falls in the CMIP6 model range after regridding, indicating robust future increase (decrease) in heavy (light) precipitation probability regardless of underlying convection treatments. Discrepancy, however, exists in the projected nonprecipitation fraction changes: NICAM shows that nonprecipitation probability will increase over most of low and middle latitudes where CMIP6 models show less increases and even decreases.

Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High‐Resolution Global Nonhydrostatic Model

“…NICAM can well simulate the spatiotemporal variations of convective clouds over Tibetan Plateau (Sato et al., 2007, 2008), and diurnal precipitation cycle and nocturnal precipitation over southern slope of the Tibetan Plateau (Dirmeyer et al., 2011). It has been used to investigate the response of global precipitation and monsoon precipitation to climate change in the future (e.g., Na et al., 2020; Takahashi et al., 2020). Herein, we focus on the Tibetan Plateau by using the NICAM AMIP‐type simulation for the historical period from 1979 to 2008 and future period from 2075 to 2104 under A1B scenario.…”

The Nonhydrostatic ICosahedral Atmospheric Model for CMIP6 HighResMIP simulations (NICAM16-S): experimental design, model description, and impacts of model updates

“…A modified version of the Mellor-Yamada level 2 scheme (Nakanishi and Niino, 2006;Noda et al, 2010) is used to simulate the planetary boundary layer. The radiation scheme, mstrnX (Sekiguchi and Nakajima, 2008), is a broadband model with 29 radiation bands as used here.…”

“…In addition, the coupling provides model developers with a better understanding of the origins of model biases (Hashino et al, 2016). The mstrnX scheme requires a database of singlescattering properties of hydrometeors (RADPARA), including parameters such as the volume extinction coefficient, absorption coefficient, asymmetry factor, and second moment of phase function (Nakajima et al, 2000). In NICAM16-S we used the RADPARA database revised by .…”

Characteristics of Meiyu Seen From Multiple Observational Analyses and Reanalyses

“…research and in evaluation of the climate model simulations (e.g., Demory et al, 2013;Giorgi et al, 2014;Su et al, 2015;Wehner et al, 2010). However, the uncertainty in derived precipitation probability varies from different satellite data sets (e.g., Gebremicael et al, 2019;Giorgi et al, 2014;Liang et al, 2019;Na et al, 2020;Sun et al, 2018;Sylla et al, 2013;Xu et al, 2014). It is useful to use multiple analysis datasets to provide insight to the observational uncertainties.…”

“…Compared with TRMM, the GPCP has a significantly smaller variance. This is because GPCP significantly underestimates the heavy precipitation (e.g., Na et al, 2020). Na et al (2020) suggested that it should be cautious to use the GPCP for the model evaluation, especially for the heavy precipitation (see their Figure 2).…”