Impacts of cloud superparameterization on projected daily rainfall intensity climate changes in multiple versions of the Community Earth System Model

Abstract

Changes in the character of rainfall are assessed using a holistic set of statistics based on rainfall frequency and amount distributions in climate change experiments with three conventional and superparameterized versions of the Community Atmosphere Model (CAM and SPCAM). Previous work has shown that high‐order statistics of present‐day rainfall intensity are significantly improved with superparameterization, especially in regions of tropical convection. Globally, the two modeling approaches project a similar future increase in mean rainfall, especially across the Inter‐Tropical Convergence Zone (ITCZ) and at high latitudes, but over land, SPCAM predicts a smaller mean change than CAM. Changes in high‐order statistics are similar at high latitudes in the two models but diverge at lower latitudes. In the tropics, SPCAM projects a large intensification of moderate and extreme rain rates in regions of organized convection associated with the Madden Julian Oscillation, ITCZ, monsoons, and tropical waves. In contrast, this signal is missing in all versions of CAM, which are found to be prone to predicting increases in the amount but not intensity of moderate rates. Predictions from SPCAM exhibit a scale‐insensitive behavior with little dependence on horizontal resolution for extreme rates, while lower resolution (∼2°) versions of CAM are not able to capture the response simulated with higher resolution (∼1°). Moderate rain rates analyzed by the “amount mode” and “amount median” are found to be especially telling as a diagnostic for evaluating climate model performance and tracing future changes in rainfall statistics to tropical wave modes in SPCAM.

Sensitivity of Coupled Tropical Pacific Model Biases to Convective Parameterization in CESM1

“…The enhanced southeast Pacific rainfall seen in the CTRL and NODC simulations as compared to observations is also present in our 1981 superparameterized hindcast though the bias amplitude is slightly reduced (Figure d). The SP SPCZ is also zonally elongated with precipitation in excess of 2 mm/d present in the southeast Pacific for the 1986 simulations (supporting information Figure S1) though the enhancement is absent from the 1991 simulation (supporting information Figure S2); such similarities between SP and NOSP double‐ITCZ rainfall patterns have also been noted in long coupled simulations (Kooperman et al, ). Notably, unlike in CTRL or NODC, near equatorial precipitation in excess of 2 mm/d reaches the dateline in all SP simulations.…”

“…This suggests the shallow convection and planetary boundary layer physics are sufficient to produce the proper vertical wind profile, but it is the large scale pressure forcing that results in an overly strong mean surface wind, which in turn drives the rapidly developing cold tongue bias. The superparameterized simulations are unable to accurately simulate the vertical wind profile near the equator, serendipitously yielding a weaker surface wind stress that reduces the cold tongue bias and supports the improved central Pacific rainfall climatology noted in Kooperman et al () but for nonphysical reasons. Global rainfall statistics were improved by inclusion of a convective momentum transport parameterization in a superparameterized version of the Weather Research and Forecast model (Tulich, ).…”

Plant Physiological Responses to Rising CO2 Modify Simulated Daily Runoff Intensity With Implications for Global‐Scale Flood Risk Assessment

“…The intensification of precipitation associated with the Madden Julian Oscillation and monsoons (Kooperman et al, 2016), radiatively and physiologically driven increases in mean precipitation from enhanced moisture convergence (Kooperman et al, 2018), and rising soil moisture from reduced stomatal conductance may make the Maritime Continent and Southeast Asia particularly vulnerable to future flooding. Therefore, improving projections of future flood risk requires reducing uncertainty in the representation of plant processes (e.g., plant growth and stomatal conductance responses to rising CO 2 ) as well as the effects of climate change on precipitation.…”

“…With logarithmic spacing, ΔlnR is a constant value (equal to 0.1 here) with units of mm·day À1 /mm·day À1 and is thus a unitless scaling term; seeKooperman et al (2016) for more details. Runoff is shown as a (d) conditional amount distribution, which represents the amount of runoff as a function of the precipitation rate.…”

Simultaneous characterization of mesoscale and convective‐scale tropical rainfall extremes and their dynamical and thermodynamic modes of change

“…Compared to traditional GCMs, SPCAM has been shown to improve mesoscale precipitation statistics both regionally [ Li et al ., ; Kooperman et al ., ] and globally [ Kooperman et al ., ], due to its enhanced ability to represent various hydrological features such as monsoon systems [ McCrary et al ., , ], the MJO [ Benedict et al ., ] and mesoscale convective systems [ Pritchard et al ., ]. Other additional processes that matter to tropical precipitation are still misrepresented or missing in SPCAM, such as terrain effects, orographic precipitation and a detailed representation of surface fluxes.…”