Energy Balance

Energy Balance Introduction

The Earth's climate is determined by flows of energy that smooth out differences in the amount of energy the different locations receive from the sun radiate back to space. Excess heat in the tropics must be moved to cold polar regions that continuously lose energy to space. This difference in temperature between tropical and polar regions causes the pattern of rising moist air in the tropics, sinking dry air in the sub-tropics, storminess at the middle latitudes, and strong ocean currents that is responsible for the weather across the planet. This system that governs exchanges of water between the atmosphere, land, and oceans and, in turn, the exchange of energy between the atmosphere and the surface is known as the global energy and water cycle (E&WC). Accurate observational estimates of this cycle is a critical first step toward predicting future climate.

The L’Ecuyer Research Group is conducting research on various components of Earth’s energy balance, highlights of which are described below. The majority of our work in this area is funded by NASA. We also actively contribute to the World Climate Research Programme’s Global Energy and Water Exchanges (GEWEx) and Ocean-Variability Predictability and Change (CLIVAR) activities.

Cloud Impact on Earth’s Energy Balance Using Satellite Observations

Our group is actively engaged in the development and application of satellite observations to provide state-of-the art estimates on the impact on clouds on the Earth’s energy balance and water cycle, its extremes, and its variability. The products are essential to understand the response of radiation and atmospheric heating related to variability in clouds clouds, aerosol, and environmental properties on climate. One such product utilizes a multi-sensor dataset leveraging cloud information from CloudSat, CALIPSO, and MODIS observations to provide radiative fluxes with high vertical and spatial resolution, which is required to resolve the radiative effects of individual cloud systems at the top of the atmosphere and the surface. An example of this is shown below where the fluxes and heating rate products have been used to derive the individual components of the Earth’s Radiation Budget as well as separate the cloud radiative effect of various cloud types and their respective impacts on atmospheric transport and the global circulation.

Using observations to evaluate climate models

Clouds cool the surface by reflecting shortwave radiation back to space as well as warm the surface by trapping longwave radiation in the atmosphere below the cloud. Due to a lack of physical observations of cloud properties in the arctic, the impact of these two radiative impacts are not well known; This creates uncertainty in how clouds could either accelerate or delay ice melt in the arctic over time. To facilitate this, we utilize NASA satellite output and have developed a methodology to map where clouds have a heating/cooling influence on Earth’s surface using the Cloud Impact on Surface Radiation Ratio (CISRR). Using this methodology, we observe that the warming influence of clouds outweigh their cooling effect in the ice-covered polar regions, the Himalayas, the Sahara Desert and the SW coasts of Africa, North America and South America. This is visually displayed where regions in red are where clouds increase the amount of energy that the surface receives (warms); regions in blue are where clouds overall reduce the radiative energy that reaches the surface (cools).

Cloud impact on Earth’s Energy balance in reanalyses and models

Clouds play a significant role in modulating the Earth’s global energy balance. Our group utilizes the state-of-the-art cloud and energy observations discussed above to provide benchmarks for climate models and reanalyses to demonstrate where models and observations agree and where improvements are needed. In this project we introduce five Cloud Impact Parameters (CIPs) to describe the coupled impact of clouds systems on regional energy balance and hydrology. The CIPs are used to detect and attribute cloud biases that impact the Earth's Energy balance in reanalyses and we are currently developing methods to implement these impact parameters with climate models.

Applications to Solar Energy Forecasting

Satellite observations and models also have numerous practical uses for benefiting society. As part of a collaborative grant through the University of Wisconsin (UW2020), for example, our gorup is implementing from geostationary satellites (GOES-16) and CloudSat to improve the accuracy in model-derived solar energy forecasts. By correcting cloud and radiation forecast biases we seek to improve forecasts of solar power availability across the continental US and implement energy planning models to optimize solar operation. Corrections are applied to real data from the power grid to estimate the impact of forecast uncertainty on power cost and efficiency. Improvement in model-derived forecast of solar energy reaching the surface using observational data from geostationary satellites are shown on the left.