Mechanisms for Global Warming Impacts on Madden–Julian Oscillation Precipitation Amplitude

Abstract

Mechanisms that cause changes in Madden–Julian oscillation (MJO) precipitation amplitude under global warming are examined in models from phase 5 of the Coupled Model Intercomparison Project. Under global warming in representative concentration pathway 8.5, MJO precipitation intensifies in most models relative to current climate while MJO wind circulations increase at a slower rate or weaken. Changes in MJO precipitation intensity are partially controlled by changes in moisture profiles and static stability. The vertical moisture gradient increases in the lower half of the troposphere in response to the surface warming, while the vertical static stability gradient increases due to preferential warming in the upper troposphere. A nondimensional quantity called α has been defined that gives the efficiency of vertical advective moistening associated with diabatic processes in the free troposphere, and has been hypothesized by previous studies to regulate MJO amplitude. The term α is proportional to the vertical moisture gradient and inversely proportional to static stability. Under global warming, the increased vertical moisture gradient makes α larger in models, despite increased static stability. Although α increases in all models, MJO precipitation amplitude decreases in some models, contrary to expectations. It is demonstrated that in these models more top-heavy MJO diabatic heating with warming overwhelms the effect of increased α to make vertical moisture advection less efficient.

Transient Response of MJO Precipitation and Circulation to Greenhouse Gas Forcing

“…Given these impacts of the MJO in current climate, an increasing interest exists in understanding how the MJO might change under global warming (Maloney et al, ). Global climate models (GCMs) generally find that MJO precipitation amplitude change ranges from −10% to +20% while MJO circulation strength increases at a slower rate or even weakens (by 1–10%) in the presence of substantial global mean temperature warming (Adames et al, , ; Arnold et al, , ; Bui & Maloney, , ; Chang et al, ; Rushley et al, ; Takahashi et al, ). Bui and Maloney () documented end of the 21st century (2081–2100) MJO precipitation and wind anomaly amplitude changes in several Coupled Model Intercomparison Project phase 5 (CMIP5) models forced with Representative Concentration Pathway 8.5 (RCP8.5) and explained the systematic weakening of MJO wind amplitude or lower rate of increase relative to precipitation amplitude through increases in tropical dry static stability using weak temperature gradient (WTG) theory (e.g., Sobel & Bretherton, ).…”

“…In particular, WTG theory indicates weakening large‐scale vertical motion per unit apparent heating given increases in tropical dry static stability in a warming climate (e.g., Wolding et al, ). A follow‐up study suggested that changes in MJO precipitation amplitude at the end of the 21st century are regulated by competing effects from an increased lower tropospheric moisture gradient and more top‐heavy MJO diabatic heating profile with warming (Bui & Maloney, ). How the MJO changes in earlier decades of the 21st century (2021–2080) remains less thoroughly investigated, in particular how soon MJO changes in response to anthropogenic greenhouse gas (GHG) increases can be detected relative to the historical period given the nonlinear and chaotic nature of the climate system (e.g., Cassou et al, ; Kirtman, ).…”

A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

“…The area with the highest SST was concentrated in the central area of the Pacific Ocean and the Indian Ocean. In the eastern part of Africa and the southern part of Asia and the Indian Ocean, the average SST has increased from [22,27] to [27,30]. The increase in SST near the west coast of Canada was consistent with the rise in the average temperature in the coastal area, thereby the contribution of SST to climate change cannot be neglected.…”

“…Using the existing literature and official government documents as a reference, some original indicators affecting climate change can be selected [26,27]. However, the quality of the information varies greatly, therefore information from the academic literature is dominant, which is supplemented by information obtained from other channels.…”

“…The surface temperature of the sea https://www.esrl.noaa.gov/psd/data/gridded [28][29][30][31] Ice coating https://www.ecmwf.int/en/research/modellingand-prediction/marine [32][33][34] Monthly average long-wave radiation https://climatedataguide.ucar.edu/climatedata/outgoing-longwave-radiation-olr-hirs [14,35,36] Monthly average near-infrared beam downward sun flux https://climatedataguide.ucar.edu/climatedata/outgoing-longwave-radiation-olr-hirs [37,38] Average monthly precipitation https://www.esrl.noaa.gov/psd/cgi-bin/data/ getpage.pl [32,39] Monthly average evaporation rate https://www.esrl.noaa.gov/psd/data/gridded [27,[39][40][41][42][43] Earth surface wind speed https://www.esrl.noaa.gov/psd/data/gridded [6,43] Earth surface cloud amount https: //www.ecmwf.int/en/forecasts/datasets/set-i [44] Average temperature https://climate.weather.gc.ca [5,45,46] Relative humidity https: //www.ecmwf.int/en/forecasts/datasets/set-i [7,45] Total carbon dioxide emissions https://www.ecmwf.int/en/annual-report-2014/ developing-european-infrastructure [47] Soil moisture https://www.esrl.noaa.gov/psd/data/gridded [48,49] Land-sea mask https://www.esrl.noaa.gov/psd/data/gridded [38] Mean sea level pressure https://www.esrl.noaa.gov/psd/data/gridded [50] Snow density https: //www.ecmwf.int/en/forecasts/datasets/set-i [38] Total column ozone https: //www.ecmwf.int/en/forecasts/datasets/set-i [51] Cooling degree-days https://climatedataguide.ucar.edu/climatedata/outgoing-longwave-radiation-olr-hirs …”