Outsize Influence of Central American Orography on Global Climate

Abstract

Global Climate Models (GCMs) exhibit substantial biases in their simulation of tropical climate. One particularly problematic bias exists in GCMs' simulation of the tropical rainband known as the Intertropical Convergence Zone (ITCZ). Much of the precipitation on Earth falls within the ITCZ, which plays a key role in setting Earth's temperature by affecting global energy transports, and partially dictates dynamics of the largest interannual mode of climate variability: The El Niño‐Southern Oscillation (ENSO). Most GCMs fail to simulate the mean state of the ITCZ correctly, often exhibiting a “double ITCZ bias,” with rainbands both north and south rather than just north of the equator. These tropical mean state biases limit confidence in climate models' simulation of projected future and paleoclimate states, and reduce the utility of these models for understanding present climate dynamics. Adjusting GCM parameterizations of cloud processes and atmospheric convection can reduce tropical biases, as can artificially correcting sea surface temperatures through modifications to air‐sea fluxes (i.e., “flux adjustment”). Here, we argue that a significant portion of these rainfall and circulation biases are rooted in orographic height being biased low due to assumptions made in fitting observed orography onto GCM grids. We demonstrate that making different, and physically defensible, assumptions that raise the orographic height significantly improves model simulation of climatological features such as the ITCZ and North American rainfall as well as the simulation of ENSO. These findings suggest a simple, physically based, and computationally inexpensive method that can improve climate models and projections of future climate.The Sierra Madre mountain range stretches north to south in Central America. These narrow mountains are important for climate due to their location. They block tropical winds that flow east to west, making winds slower and sea surface temperatures warmer in the tropical East Pacific. This affects tropical rainbands and a pattern of year‐to‐year climate variability in the tropical Pacific Ocean, called the El Niño‐Southern Oscillation, which has impacts across the entire globe. Climate models break the earth up into grid boxes to simulate atmosphere and ocean circulations. Since mountain peaks are smaller than these grid boxes, mountains in climate models, including the Sierra Madre, are shorter than in reality. The low bias in these mountains makes the simulation of climate in the tropical East Pacific different than that observed on earth. We show that these differences can be resolved by making mountains in climate models as high as in reality. Making mountains higher in climate models is also helpful in other places, including North America where the Rockies have a big impact on the atmosphere. Resolving these mountain‐related biases can help improve climate models, and our confidence in their simulation of future changes such as global warming. Central American orography blocks easterlies and warms sea surface temperatures in the northern tropical East Pacific, shaping precipitation and the El Niño‐Southern Oscillation Low biases in these mountains' height in climate models are partially responsible for pervasive tropical climate simulation biases These and other model biases are improved by alternative interpolation of topography onto model grids to better retain mountain height