Understanding the Role of Convection on Global Land-Atmosphere Coupling and Remote Control of Amazonian Hydroclimate by the Andes

This dissertation aims to better understand the role of moist convection (clouds and turbulence) in the complex dynamic coupling that occurs between components (land and atmosphere) as well as between subregions (Andes and Amazon) of the Earth system, using modern numerical modeling approaches. First, climate model representation of land-atmosphere coupling is compared between standard techniques that use conventionally parameterized convection versus prototype next-generation simulations that can include explicit convection. That is, numerical experiments are designed to isolate the global hydrologic land-atmosphere coupling on weekly-to-subseasonal and seasonal timescales using both approaches to representing convection in climate simulation. Initial analysis focuses on hydrologic coupling dynamics, and then the effects of explicit convection on thermal land-atmosphere coupling are highlighted over the southwestern U.S. and the Arabian Peninsula. Implications on global land surface Bowen ratio and its climate sensitivity are also discovered and discussed. Finally, a separate topic is analyzed, related to clarifying ongoing issues in capturing realistic regional hydroclimate over the Amazon. In this case the coupling dynamics are not between atmosphere and land, but between convective heating over a mountain chain (the Andes) and associated modulations of the atmospheric thermodynamic environment over an upstream rainforest (the Amazon). These dynamics are elucidated using mechanism denial techniques within an ensemble hindcast experiment, which sheds light on a previously underappreciated Andean control on Amazonian rainfall. Key results from these analyses are summarized below.The first theme (Chapter 2) is to investigate the effects of explicit convection on global hydrologic land-atmosphere coupling across multiple timescales. Over weekly-to-subseasonal timescales, based on experiments following the Global Land-Atmosphere Coupling Experiment (GLACE) protocol, explicit convection is found to systematically mute the soil moisture–precipitation coupling strength on a global scale which is primarily linked to the atmospheric segment (evapotranspiration–precipitation) of the land-atmosphere feedback. Whereas this sensitivity is pronounced on short timescales relevant to synoptically driven drought and hydrologic weather extremes, over longer (seasonal) timescales, based on simulations following the Atmospheric Model Intercomparison Project (AMIP) protocol, the effects of explicit convection on seasonal hydrologic land-atmosphere coupling are undetectable.The second topic (Chapter 3) is to uncover the effects of explicit convection on global thermal land-atmosphere coupling. In both the GLACE- and AMIP-type simulations, a range of complementary metrics all suggest that explicit convection robustly changes the thermal coupling within specific subregions of the globe. Over the Arabian Peninsula, explicit convection produces less but more realistic time-mean rainfall, pushing local soil moisture regime away from the optimal transitional regime, which is required for strong coupling. As a result, local thermal coupling strength is significantly reduced. Over the southwestern U.S. and northern Mexico, explicit convection substantially enhances the thermal coupling strength independent of soil moisture or precipitation. Instead, such enhancement is related to increases in the local surface Bowen ratio. Based on a variety of simulations across multiple model version pairs of using parameterized vs. explicit convection, I conclude that the use of explicit convection has a systematic and robust effect of increasing the land surface Bowen ratio. Furthermore, under climate change scenarios, comparing to parameterized convection, explicit convection exhibits a remarkably stronger amplification of the land surface Bowen ratio. This opens up new questions in the era of explicitly simulated convection in climate models pertaining to surface climate simulation.The third subject matter (Chapter 4) of the thesis is motivated by the ubiquitous “Wet Andes – Dry Amazon” (WADA) dipole pattern of rainfall biases seen in the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive. Using the Community Earth System Model version 1 (CESM v1) and focusing on the wet season, mechanism denial experiments are conducted to investigate the remote influence of Andean convection on Amazonian rainfall. It is shown that Andean convective heating reduces rainfall over the Amazon with a maximum of -1 mm/day, and that this response sets up rapidly, within 15 hours. Column moisture budget analysis shows that the total advection of moisture over the Amazon due to Andean convective forcing is controlled by the vertical advective term in ways that can be traced back to a mountain-forced component of the vertical velocity field. Meanwhile, other causative pathways that could control Amazon precipitation through the surface moist static energy, gross moist stability, or column energetics are found to be either irrelevant or secondary

Representing cloud mesoscale variability in superparameterized climate models

In atmospheric modeling, superparameterization (SP) has gained popularity as a technique to improve cloud and convection representations in large-scale models by coupling them locally to cloud-resolving models. We show how the different representations of cloud water in the local and the global models in SP lead to a suppression of cloud advection and ultimately to a systematic underrepresentation of the cloud amount in the large-scale model. We demonstrate this phenomenon in a regional SP experiment with the global model OpenIFS coupled to the local model Dutch Atmospheric Large Eddy Simulation, as well as in an idealized setup, where the large-scale model is replaced by a simple advection scheme. As a starting point for mitigating the problem of suppressed cloud advection, we propose a scheme where the spatial variability of the local model's total water content is enhanced in order to match the global model's cloud condensate amount. The proposed scheme enhances the cloud condensate amount in the test cases, however a large discrepancy remains, caused by rapid dissipation of the clouds added by the proposed scheme

Multiple‐instance superparameterization, part 2: the effects of stochastic convection on the simulated climate

The cloud-permitting model (CPM) of the super-parameterized Community Atmosphere Model (SP-CAM) is a stochastic parameterization. As reported in a companion paper, we have created a variant of SP-CAM, called MP-CAM, that uses the averaged feedback of ten independent two-dimensional CPMs in each global model column, in place of the single CPM of SP-CAM. This ensemble-averaged feedback is interpreted as an approximation to the feedback from a deterministic parameterization. We present evidence that MP-CAM is indeed more deterministic than SP-CAM. The climates of the SP and MP configurations are compared, giving particular attention to extreme precipitation events and convectively coupled large-scale tropical weather systems, such as the Madden-Julian Oscillation (MJO). A number of small but significant changes in the mean-state climate are uncovered, and the deterministic parameterization slightly degrades the MJO simulation