Public Abstract

DE-SC0025539: Understanding the polar cloud longwave feedback and its confounding factors through a spectral lens

Award Status: Active

Institution: Regents of the University of Michigan, Ann Arbor, MI
UEI: GNJ7BBP73WE9
DUNS: 073133571

Most Recent Award Date: 09/04/2024
Number of Support Periods: 1
PM: Joseph, Renu

Current Budget Period: 09/01/2024 - 02/28/2025
Current Project Period: 09/01/2024 - 08/31/2027
PI: Huang, Xianglei

Supplement Budget Period: N/A

Public Abstract

Understanding the polar cloud longwave feedback and its confounding factors through a spectral lens Prof. Xianglei Huang, the University of Michigan at Ann Arbor (Principal Investigator) Dr. Hailong Wang, Pacific Northwest National Laboratory (Co-Investigator) Dr. Wuyin Lin, Brookhaven National Laboratory (Co-Investigator) Motivations: Clouds play an essential role in the climate system due to their close connections with dynamics, radiation, and precipitations. Cloud feedback is the most uncertain among all types of radiative feedback in the Arctic, and it intrinsically affects the complicated cryosphere-atmospheric interaction in the polar regions. For decades, how to represent cloud processes in the earth system models (ESMs), objectively evaluate cloud properties against observations, and understand the model-observation discrepancies have been central topics in any ESM diagnostics. A unique challenge for the polar regions is the lack of high-quality ground observations with enough spatial and temporal coverage, making satellite measurements a primary choice for model evaluation. Regarding satellite cloud observation, active cloud remote sensing suffers from sampling shortage due to its curtain view, and shortwave passive cloud remote sensing cannot provide cloud information during polar night, making infrared cloud remote sensing the attractive choice for all seasons. While broadband radiative flux, as measured from ERBE and CERES, is widely used in ESM development and validation, many offset factors can make simulated broadband flux seemingly right but for the wrong reasons. A unique way to meaningfully enhance cloud diagnostics capability and connect it with broadband diagnostics is to utilize hyperspectral infrared radiance observations such as AIRS spectra, which have been available since 2002. The PI’s group has developed and validated algorithms to derive the spectrally resolved flux from NASA AIRS observations, which has become part of the official AIRS L3 product. Such spectral flux enables the model-observation evaluation of the flux and cloud radiative effect (CRE) in the spectral dimension. The PI’s group has also developed longwave (LW) spectral radiative kernels so all LW spectral radiative feedbacks, including cloud feedback, can be diagnosed using the standard monthly mean output fields from the model simulations. Objectives and technical approaches: We propose to use the AIRS LW spectral flux and the spectral radiative kernels of the PI’s group, in conjunction with the existing E3SM diagnostics tools, to evaluate the polar cloud climatology and cloud radiative feedback as simulated by the E3SM. We will focus on the Arctic first and then the Antarctic. We propose the following subtasks: (1) Integrate the AIRS spectral flux (2003 to 2022) into the existing E3SM diagnostics package, enabling model-observation comparison for the TOA flux and CRE for each RRTMG LW band (referred to as band-by-band comparison). This will make our spectral flux usable by all E3SM users. (2) Integrate our spectral radiative kernels into the PMP (PCMDI metrics package) or similar diagnostics package so all E3SM users can use it with standard monthly-mean output to diagnose spectrally resolved LW radiative feedback. (3) We will use band-by-band diagnostics to evaluate the polar climate simulations in the E3SM, focusing on connecting biases in cloud simulation with biases in the radiation field through band-by-band analyses and interpretations. (4) We will use spectral radiative kernels to diagnose E3SM short-term (i.e., year-to-year) radiative feedback in the polar regions, including cloud feedback, in the last two decades (2002 to 2023) and compare them with observational counterparts and other CMIP6 model results. The focuses are (a) to understand how well E3SM can simulate the year-to-year polar cloud variations and (b) how discrepancies identified in (a) can affect the simulated long-term climate change, e.g., the strength of Arctic amplifications in response to increases of greenhouse gases. Relevance to the FOA and Potential Impact: The proposed studies aim to integrate spectral flux observations and spectral radiative kernels developed by the PI’s group to the E3SM and PMP diagnostics and metrics packages and use such tools to scrutinize polar cloud simulations. It provides a unique, physically meaningful perspective to analyze E3SM cloud simulations for both its seasonal climatology and its long-term change. It connects such diagnostics directly with TOA radiation budget analysis. More and more high-quality, spectrally resolved observations are expected to be available from NASA and ESA this decade and the next. The project will also prepare the E3SM community well for ingesting such future observations into model evaluation practices.

Understanding the polar cloud longwave feedback and its confounding factors through a spectral lens

Prof. Xianglei Huang, the University of Michigan at Ann Arbor (Principal Investigator)

Dr. Hailong Wang, Pacific Northwest National Laboratory (Co-Investigator)

Dr. Wuyin Lin, Brookhaven National Laboratory (Co-Investigator)

Motivations: Clouds play an essential role in the climate system due to their close connections with dynamics, radiation, and precipitations. Cloud feedback is the most uncertain among all types of radiative feedback in the Arctic, and it intrinsically affects the complicated cryosphere-atmospheric interaction in the polar regions. For decades, how to represent cloud processes in the earth system models (ESMs), objectively evaluate cloud properties against observations, and understand the model-observation discrepancies have been central topics in any ESM diagnostics. A unique challenge for the polar regions is the lack of high-quality ground observations with enough spatial and temporal coverage, making satellite measurements a primary choice for model evaluation. Regarding satellite cloud observation, active cloud remote sensing suffers from sampling shortage due to its curtain view, and shortwave passive cloud remote sensing cannot provide cloud information during polar night, making infrared cloud remote sensing the attractive choice for all seasons. While broadband radiative flux, as measured from ERBE and CERES, is widely used in ESM development and validation, many offset factors can make simulated broadband flux seemingly right but for the wrong reasons. A unique way to meaningfully enhance cloud diagnostics capability and connect it with broadband diagnostics is to utilize hyperspectral infrared radiance observations such as AIRS spectra, which have been available since 2002. The PI’s group has developed and validated algorithms to derive the spectrally resolved flux from NASA AIRS observations, which has become part of the official AIRS L3 product. Such spectral flux enables the model-observation evaluation of the flux and cloud radiative effect (CRE) in the spectral dimension. The PI’s group has also developed longwave (LW) spectral radiative kernels so all LW spectral radiative feedbacks, including cloud feedback, can be diagnosed using the standard monthly mean output fields from the model simulations.

Objectives and technical approaches: We propose to use the AIRS LW spectral flux and the spectral radiative kernels of the PI’s group, in conjunction with the existing E3SM diagnostics tools, to evaluate the polar cloud climatology and cloud radiative feedback as simulated by the E3SM. We will focus on the Arctic first and then the Antarctic. We propose the following subtasks:

(1) Integrate the AIRS spectral flux (2003 to 2022) into the existing E3SM diagnostics package, enabling model-observation comparison for the TOA flux and CRE for each RRTMG LW band (referred to as band-by-band comparison). This will make our spectral flux usable by all E3SM users.

(2) Integrate our spectral radiative kernels into the PMP (PCMDI metrics package) or similar diagnostics package so all E3SM users can use it with standard monthly-mean output to diagnose spectrally resolved LW radiative feedback.

(3) We will use band-by-band diagnostics to evaluate the polar climate simulations in the E3SM, focusing on connecting biases in cloud simulation with biases in the radiation field through band-by-band analyses and interpretations.

(4) We will use spectral radiative kernels to diagnose E3SM short-term (i.e., year-to-year) radiative feedback in the polar regions, including cloud feedback, in the last two decades (2002 to 2023) and compare them with observational counterparts and other CMIP6 model results. The focuses are (a) to understand how well E3SM can simulate the year-to-year polar cloud variations and (b) how discrepancies identified in (a) can affect the simulated long-term climate change, e.g., the strength of Arctic amplifications in response to increases of greenhouse gases.

Relevance to the FOA and Potential Impact: The proposed studies aim to integrate spectral flux observations and spectral radiative kernels developed by the PI’s group to the E3SM and PMP diagnostics and metrics packages and use such tools to scrutinize polar cloud simulations. It provides a unique, physically meaningful perspective to analyze E3SM cloud simulations for both its seasonal climatology and its long-term change. It connects such diagnostics directly with TOA radiation budget analysis. More and more high-quality, spectrally resolved observations are expected to be available from NASA and ESA this decade and the next. The project will also prepare the E3SM community well for ingesting such future observations into model evaluation practices.