Land surface albedo bias in climate models and its association with tropical rainfall

Levine, Xavier J.; Boos, William R.

doi:10.1002/2017gl072510

Cited by 31 publications

(23 citation statements)

References 61 publications

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“…The multi-model mean summer stationary wave climatology in CMIP5 models is in good agreement with the ERA-Interim reanalysis [110] ( Supplementary Fig. S4); however, there remains poor agreement between models on present-day climatologies at the regional scale, due to their differing representations of key processes such as surface albedo [111], moist physics [112], and subgrid-scale topography [113].…”

Section: Summer Stationary Wavesmentioning

confidence: 58%

Northern Hemisphere Stationary Waves in a Changing Climate

Wills

White

Levine

2019

Curr Clim Change Rep

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Purpose of Review Stationary waves are planetary-scale longitudinal variations in the time-averaged atmospheric circulation. Here, we consider the projected response of Northern Hemisphere stationary waves to climate change in winter and summer. We discuss how the response varies across different metrics, identify robust responses, and review proposed mechanisms. Recent Findings Climate models project shifts in the prevailing wind patterns, with corresponding impacts on regional precipitation, temperature, and extreme events. Recent work has improved our understanding of the links between stationary waves and regional climate and identified robust stationary wave responses to climate change, which include an increased zonal lengthscale in winter, a poleward shift of the wintertime circulation over the Pacific, a weakening of monsoonal circulations, and an overall weakening of stationary wave circulations, particularly their divergent component and quasi-stationary disturbances. Summary Numerous factors influence Northern Hemisphere stationary waves, and mechanistic theories exist for only a few aspects of the stationary wave response to climate change. Idealized studies have proven useful for understanding the climate responses of particular atmospheric circulation features and should be a continued focus of future research. Keywords Stationary waves. Climate change. Rossby waves. Climate dynamics. Atmospheric general circulation This article is part of the Topical Collection on Mid-latitude Processes and Climate Change

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Section: Summer Stationary Wavesmentioning

confidence: 58%

Northern Hemisphere Stationary Waves in a Changing Climate

Wills

White

Levine

2019

Curr Clim Change Rep

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“…Understanding the causes of surface albedo biases in climate models is challenging since different models may use different surface albedo parameterizations. According to studies from Li et al () and Levine and Boos (), large source of land surface albedo error comes from the representation of land use type (i.e., vegetation and soil properties). The intermodel correlation coefficients between bias in summertime T2 m and three NetSW err components are the following: 0.65, 0.39, and 0.09 for cloud, clear sky, and surface albedo errors, respectively.…”

Section: Resultsmentioning

confidence: 99%

CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site

et al. 2018

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All the weather and climate models participating in the Clouds Above the United States and Errors at the Surface project show a summertime surface air temperature (T2 m) warm bias in the region of the central United States. To understand the warm bias in long‐term climate simulations, we assess the Atmospheric Model Intercomparison Project simulations from the Coupled Model Intercomparison Project Phase 5, with long‐term observations mainly from the Atmospheric Radiation Measurement program Southern Great Plains site. Quantities related to the surface energy and water budget, and large‐scale circulation are analyzed to identify possible factors and plausible links involved in the warm bias. The systematic warm season bias is characterized by an overestimation of T2 m and underestimation of surface humidity, precipitation, and precipitable water. Accompanying the warm bias is an overestimation of absorbed solar radiation at the surface, which is due to a combination of insufficient cloud reflection and clear‐sky shortwave absorption by water vapor and an underestimation in surface albedo. The bias in cloud is shown to contribute most to the radiation bias. The surface layer soil moisture impacts T2 m through its control on evaporative fraction. The error in evaporative fraction is another important contributor to T2 m. Similar sources of error are found in hindcast from other Clouds Above the United States and Errors at the Surface studies. In Atmospheric Model Intercomparison Project simulations, biases in meridional wind velocity associated with the low‐level jet and the 500 hPa vertical velocity may also relate to T2 m bias through their control on the surface energy and water budget.

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“…The underestimated high vegetation extent can weaken the changes of reduced albedo or increased canopy water storage. The former is suggested to induce insufficient rainfall feedback (Kutzbach et al, ; Levine & Boos, ), and the latter leads to deficient local moisture recycling (Braconnot et al, ). Therefore, our results indicate that a more realistic soil texture or the inclusion of dynamic soil composition or/and albedo schemes (Vamborg et al, ) may be recommended in future ESM studies of the Green Sahara period.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Vegetation Simulations of the Mid‐Holocene Green Sahara

Miller

Zhang

et al. 2018

Geophysical Research Letters

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The “Green Sahara” is a period when North Africa was characterized by vegetation cover and wetlands. To qualitatively identify the orbital‐climatic causation of the Green Sahara regime, we performed dynamic vegetation model (LPJ‐GUESS) simulations, driven by climate forcings from coupled general circulation model (EC‐Earth) simulations for the mid‐Holocene, in which the vegetation is prescribed to be either modern desert or artificially vegetated with a reduced dust load. LPJ‐GUESS simulates a vegetated Sahara covered by both herbaceous and woody vegetation types consistent with proxy reconstructions only in the latter scenario. Sensitivity experiments identify interactions required to capture the northward extension of vegetation. Increased precipitation is the main driver of the vegetation extent changes, and the temperature anomalies determine the plant functional types mainly through altered fire disturbance. Furthermore, the simulated vegetation composition also depends on the correct representation of soil texture in a humid environment like Green Sahara.

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Land surface albedo bias in climate models and its association with tropical rainfall

Cited by 31 publications

References 61 publications

Northern Hemisphere Stationary Waves in a Changing Climate

Northern Hemisphere Stationary Waves in a Changing Climate

CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site

Dynamic Vegetation Simulations of the Mid‐Holocene Green Sahara

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