The Impact of Horizontal Resolution on North American Monsoon Gulf of California Moisture Surges in a Suite of Coupled Global Climate Models

Abstract

The impact of atmosphere and ocean horizontal resolution on the climatology of North American monsoon Gulf of California (GoC) moisture surges is examined in a suite of global circulation models (CM2.1, FLOR, CM2.5, CM2.6, and HiFLOR) developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These models feature essentially the same physical parameterizations but differ in horizontal resolution in either the atmosphere ('200, 50, and 25 km) or the ocean ('18, 0.258, and 0.18). Increasing horizontal atmospheric resolution from 200 to 50 km results in a drastic improvement in the model's capability of accurately simulating surge events. The climatological near-surface flow and moisture and precipitation anomalies associated with GoC surges are overall satisfactorily simulated in all higher-resolution models. The number of surge events agrees well with reanalyses, but models tend to underestimate July-August surge-related precipitation and overestimate September surge-related rainfall in the southwestern United States. Large-scale controls supporting the development of GoC surges, such as tropical easterly waves (TEWs), tropical cyclones (TCs), and trans-Pacific Rossby wave trains (RWTs), are also well captured, although models tend to underestimate the TEW and TC magnitude and number. Near-surface GoC surge features and their large-scale forcings (TEWs, TCs, and RWTs) do not appear to be substantially affected by a finer representation of the GoC at higher ocean resolution. However, the substantial reduction of the eastern Pacific warm sea surface temperature bias through flux adjustment in the Forecast-Oriented Low Ocean Resolution (FLOR) model leads to an overall improvement of tropical-extratropical controls on GoC moisture surges and the seasonal cycle of precipitation in the southwestern United States.

Changes in the future summer Mediterranean climate: contribution of teleconnections and local factors

“…3). Increasing the horizontal atmospheric resolution from 200 km in CM2.1 (approximately the average of CMIP5 models) to 50 km in CM2.5 improves the representation of mountains and coastlines (Kapnick et al, 2014;Delworth et al, 2012), which is necessary to improve regional precipitation, land-ocean dynamics, and regional circulation (Pascale et al, 2016).…”

Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change

“…These experiments can be used to connect the statistics of rainfall extremes to the detailed history of SSTs that occurred over the past 45 years, part of which was a response to radiative forcing changes and part of which emerged from internal climate variations. Furthermore, by construction, these experiments have a substantially smaller SST bias than the free-running versions of HiFLOR, as the statistics of weather extremes and their connection to larger-scale climate can be substantially affected by SST biases (e.g., Vecchi et al, 2014;Pascale et al, 2016). These experiments are described in more detail in Murakami et al (2015) and Van der Wiel et al (2016).…”

“…We assume that the FLOR-FA and HiFLOR modeled responses to changes in radiative forcing are meaningful estimates of the sensitivity of precipitation extremes in the real climate system, since these models capture multiple physical factors affecting precipitation extremes in a physically based and internally consistent framework. This assumption is motivated in part because of the ability of these models to simulate large-scale precipitation and temperature over land (e.g., Van der Wiel et al, 2016;Delworth et al, 2015;Jia et al, 2015Jia et al, , 2016, precipitation extremes over the US (Van der Wiel et al, 2016), modes of climate variability (e.g., Vecchi et al, 2014;Murakami et al, 2015), the meteorological phenomena that led to precipitation extremes and their relationship to modes of climate variability (e.g., Vecchi et al, 2014;Krishnamurthy et al, 2015;Zhang et al, 2015Zhang et al, , 2016Pascale et al, Hydrol. Earth Syst.…”

“…These simulations, obviously, necessitated the long-term research aimed at developing these high-resolution models (e.g., Putman and Lin, 2007;Delworth et al, 2012;Vecchi et al, 2014;. Furthermore, this work was enabled by a body of work using these models that provided the necessary understanding of the characteristics and fidelity of these models to simulate large-scale and regional climate and weather events over a broad range of scales and phenomena (e.g., Msadek et al, 2014;Delworth et al, 2015;Murakami et al, 2015Murakami et al, , 2016Krishnamurthy et al, 2015;Zhang et al, 2015Zhang et al, , 2016Pascale et al, 2016;Van der Wiel et al, 2016).…”

Tropical cyclone sensitivities to CO2 doubling: roles of atmospheric resolution, synoptic variability and background climate changes

“…These models are derived from the National Oceanic and Atmospheric Administration's Geophysical Fluid Dynamics Laboratory (NOAA/GFDL) Coupled Model version 2.1 (CM2.1, Delworth et al 2006) and version 2.5 (CM2.5Delworth et al 2012), and are the Low Ocean Atmosphere Resolution version of CM2.5 (LOAR; Van der Wiel et al 2016a), the Forecast-oriented Low Ocean Resolution version of CM2.5 (FLOR; Vecchi et al 2014) and the high atmospheric resolution version of FLOR (HiFLOR; Murakami et al 2015), which have, respectively, ~ 2°, ~ 0.5° and ~ 0.25° atmospheric and land horizontal grid spacings. The changes in atmospheric resolution, land model and ocean parameterizations from CM2.1 to LOAR, to FLOR and then HiFLOR result in a general improvement to the simulation of large-scale near-surface climate and modes of variability (e.g., Vecchi et al 2014;Delworth et al 2015;Jia et al 2015;Yang et al 2015;Murakami et al 2015;Baldwin and Vecchi 2016;Zhang et al 2016;Van der Wiel et al 2016a;Pascale et al 2016Pascale et al , 2017Muñoz et al 2017;Kapnick 2018;Ng et al 2018;Wittenberg et al 2018;Ray et al 2018), and general improvements in seasonal prediction skill (e.g., Vecchi et al 2014;Jia et al 2015;Murakami et al 2015Murakami et al , 2016aZhang et al 2019), although for some quantities (e.g., snowpack in the Western U.S.; Kapnick et al 2018) the seasonal prediction skill does not improve (and can degrade in places) between FLOR and HiFLOR.…”