Projected Changes in Climate, Elevation-dependent Warming, and Extreme Events over Continental Ecuador for the Period 2041-2070

The climate over Ecuador is complex due to several interacting factors, such as its location at the equator, the Andean topography, and several modes of internal variability, including the El Niño–Southern Oscillation (ENSO), affecting the region. In addition, the rapid increase in greenhouse gas concentrations will continue to affect both the mean state and climate variability in Ecuador over the coming decades. Hence, a thorough understanding of both natural and anthropogenic forcings and how they combine to influence Ecuadorian climate is a necessity for decision-making and implementation of adequate adaptation measures. However, the lack of observational data, both in space and time, severely limits our ability to study climate changes that affect Ecuador today. Employing a high-resolution regional climate model (RCM) can help to better diagnose the mechanisms and feedbacks that lead to climate changes and how they differ in space and time, as long as the model is able to adequately reproduce what is observed in the limited observational data. With the purpose of contributing to a better understanding of how and why Ecuador’s climate will change in the coming decades, three topics of specific relevance for this country are addressed in this dissertation: a) how well can a RCM simulate the mean climate state and its variability over a region of complex topography such as Ecuador under different parameterization schemes? b) what feedbacks are involved in producing elevation-dependent warming (EDW) in the Ecuadorian Andes? And c) how are the characteristics of climate extreme events (CEEs) over Ecuador projected to change by the middle of the 21st century? These three questions are addressed by use of observations and simulations using the Weather Research and Forecasting Model (WRF) configured as a RCM with a high-resolution of 10 km horizontal grid spacing and 51 vertical levels. Sensitivity test runs were performed to choose a proper combination of parameterization schemes for conducting four WRF simulations comprising the territory of Ecuador and spanning 30 years. The first simulation was driven by the Climate Forecast System Reanalysis (CFSR) for the period 1980–2010 and used to evaluate the model’s ability to realistically portray present-day climate over the region. The other three simulations used the output from the Community Climate System Model version 4 (CCSM4) as the boundary conditions to produce a baseline simulation (1976–2005) and two future simulations (2041– 2070) following the moderate-emissions scenario RCP 4.5 and the high-emissions scenario RCP 8.5. EDW over the Ecuadorian Andes is studied by analyzing observations and the present-day WRF-simulation, while the future simulations were used to test the contribution to this effect caused by future changes in feedback mechanisms. Surface net radiation changes due to future changes in cloudiness were identified as the most important mechanisms leading to EDW over the Ecuadorian Andes, with future reductions in cloudiness dominating at high elevations. The model results also indicate different future warming signals on both sides of the Andes, with higher warming rates at the high elevations of the western Andes, likely due to enhanced subsidence and adiabatic warming in the mid-troposphere. CEEs are analyzed by using annual climatic indices. First the present-day relationship between CEEs and Pacific (ENSO) and Atlantic modes of variability are investigated in both models and observations. Results confirm the dominant role played by ENSO in governing the occurrence of many CCEs over Ecuador, while calling for more studies on the potential influence of Atlantic modes over Ecuador’s CEEs. The model projections suggest significant future changes in CEEs, with large increases in warm and wet extremes over most regions, but the simulations also highlight significant spatial heterogeneity, which suggests that it is important to study changes in extreme events using high-spatial resolution data