Role of Troposphere‐Convection‐Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model Version 1 (CESM1)

Sakaguchi, Kôichi; Leung, L. Ruby; Burleyson, Casey D.; Xiao, Heng; Wan, Hui

doi:10.1029/2018jd028999

Cited by 20 publications

(24 citation statements)

References 91 publications

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“…Even with excessive latent heat flux to provide moisture flux to the boundary layer, CAM5 still simulates less precipitation, which is consistent with the findings with CESM1 in Sakaguchi et al. (2018).…”

Section: Resultssupporting

confidence: 88%

Evaluation of the Causes of Wet‐Season Dry Biases Over Amazonia in CAM5

Zhang

Tang

et al. 2021

JGR Atmospheres

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This study investigates the causes of pronounced low precipitation bias over Amazonia in the Community Atmosphere Model version 5 (CAM5), a common feature in many global climate models. Our analysis is based on a suite of 3‐day long hindcasts starting every day at 00Z from 1997 to 2012 and an AMIP simulation for the same period. The Amazonia dry bias appears by the second day in the hindcasts and is very robust for all the seasons with the largest bias magnitude during the wet season (December–February). The bias pattern and magnitude do not change much during different dynamical wind regimes on sub‐seasonal time scales. We further classify the diurnal cycle of precipitation near the LBA sites from observations and hindcasts into three convective regimes: no precipitation, late afternoon deep convection, and nighttime deep convection. CAM5 can only simulate the late afternoon convective regime and completely fails to simulate the nighttime convection, which is mostly from propagating convective systems originating from remote locations. CAM5 mainly underestimates precipitation in the late afternoon and nighttime convective regimes, which occur during ∼67% of wet season days and account for ∼75% of accumulated precipitation amount in observations. The persistent warm temperature bias and slightly higher moisture below 850 mb likely trigger deep convection too frequently, resulting in an earlier but weaker rainfall peak in the diurnal cycle. Furthermore, shallow convection may not effectively transport moisture from boundary layer to the free atmosphere, which also leads to weaker deep convection events.

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Section: Resultssupporting

confidence: 88%

Evaluation of the Causes of Wet‐Season Dry Biases Over Amazonia in CAM5

Zhang

Tang

et al. 2021

JGR Atmospheres

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“…Other processes, such as better‐resolved orographic precipitation and other regional circulation features in finer grids, may also play a role. Besides, several aspects of the convective parameterization can affect total precipitation partition (Sakaguchi et al, ; Williamson, ; Yang et al, ), notably time step changes associated with resolution change. Williamson () suggests that when the model time step is lengthened, convective parameterization will be more active hence more convective precipitation is produced.…”

Section: Resultsmentioning

confidence: 99%

Precipitation Characteristics in the Community Atmosphere Model and Their Dependence on Model Physics and Resolution

Chen

Dai

2019

J Adv Model Earth Syst

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Precipitation amount (A), frequency (F), intensity (I), and duration (D) are important properties of precipitation, but their estimates are sensitive to data resolution. This study investigates this resolution dependence, and the influences of different model physics, by analyzing simulations by the Community Atmospheric Model (CAM) version 4 (CAM4) and version 5 (CAM5) with varying grid sizes from~0.25 to 2.0°. Results show that both CAM4 and CAM5 greatly overestimate F and D but underestimate I at all resolutions, despite realistic A. These biases partly result from too much parameterized (convective) precipitation with high F and D but low I. Different cloud microphysics schemes contribute to the precipitation differences between CAM4 and CAM5. The A, F, I, and D of convective and nonconvective precipitation react differently to grid-size decreases, leading to the large decreases in F and D but increases in the I for total precipitation as model resolution increases. This resolution dependence results from the increased probability of precipitation over a larger area (area aggregation effect, which is smaller than in observations) and the varying performance of model physics under changing resolution (model adjustment effect), which roughly enhances the aggregation-induced dependence. Finer grid sizes not only increase resolved precipitation, which has higher intensity and thus improves overall precipitation intensity in CAM, but also reduce the area aggregation effect. Thus, the long-standing drizzling problem in climate models may be mitigated by increasing model resolution and modifying model physics to suppress parameterized convective precipitation and enhance resolved nonconvective precipitation.

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“…It has been shown that the precipitation deficits over the Amazon in CESM1 likely had major contributions from deficiencies in the sensitivity of modeled deep convection to lower tropospheric moisture (Sakaguchi et al, 2018). Thus, an increased sensitivity to stable layers in the deep convection formulation McFarlane, 1995, andNeale et al, 2008), based on a reduction in the number of allowable negative…”

Section: Journal Of Advances In Modeling Earth Systemsmentioning

confidence: 99%

Intraseasonal, Seasonal, and Interannual Characteristics of Regional Monsoon Simulations in CESM2

Meehl

Shields

Arblaster

et al. 2020

J Adv Model Earth Syst

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A survey of intraseasonal, seasonal, and interannual precipitation and 850 hPa winds for various monsoon regimes around the world is presented for the Community Earth System Model Version 2 (CESM2) compared to observations and the previous generation CESM1. In CESM2 the south Asian monsoon has a reduction of excessive precipitation in the western Indian Ocean and an increase of precipitation in the eastern Bay of Bengal and land areas of Vietnam, Cambodia, and Laos. The seasonal timing of the south Asian monsoon, monsoon-ENSO connections, and monsoon intraseasonal variability all are improved compared to CESM1. For the Australian monsoon, deficient precipitation over the Maritime Continent has been improved in CESM2 with increases of precipitation over the large tropical islands of Borneo, Celebes, and Papua New Guinea and decreases over southwestern Australia. In the West African monsoon, May-June seasonal rainfall occurs more preferentially over the African coast in CESM2 as in observations, and excessive rainfall over the Ethiopian region is reduced. During July-September in the West African monsoon, deficient precipitation over equatorial Africa in CESM1 has been lessened in CESM2, and there are increases in precipitation over the Guinean coast, though there is little overall improvement in the South African monsoon. In the South American monsoon, precipitation in CESM2 is improved with increased precipitation over the Amazon in central and western Brazil. CESM2 simulates a reduction of excessive precipitation seen in CESM1 over coastal Mexico extending up into the U.S. Great Plains in the North American monsoon. Plain Language Summary A survey of simulations of regional precipitation and 850 hPa winds is presented for the Community Earth System Model Version 2 (CESM2) compared to observations and the previous generation CESM1 for the south Asian and Australian monsoons, the West African and South African monsoons, the North American monsoon, and the South American monsoon. There are mostly improvements in the seasonal timing, monsoon-ENSO connections, distribution of seasonal mean rainfall, and intraseasonal variability in CESM2 compared to CESM1 in the monsoon regimes.

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Role of Troposphere‐Convection‐Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model Version 1 (CESM1)

Cited by 20 publications

References 91 publications

Evaluation of the Causes of Wet‐Season Dry Biases Over Amazonia in CAM5

Evaluation of the Causes of Wet‐Season Dry Biases Over Amazonia in CAM5

Precipitation Characteristics in the Community Atmosphere Model and Their Dependence on Model Physics and Resolution

Intraseasonal, Seasonal, and Interannual Characteristics of Regional Monsoon Simulations in CESM2

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