Earth Systems

How uncertainty in field measurements of ice nucleating particles influences modeled cloud forcing

Cirrus clouds are composed mainly of ice crystals, many of which form on dust and other particles which have been lofted to high altitudes. To better understand how different particles contribute to the formation of ice crystals in these clouds, scientists have attempted to recreate the temperature and humidity conditions in which they form in controlled laboratory experiments. A key research challenge is to translate the uncertain results of those experiments to climate models, where one could try to estimate how changes in the amount and type of particles in the atmosphere impact clouds and climate. The goal of this study was to explore how the range of uncertainty we derive from experimental results influences our estimates of how much these particles warm or cool the climate.

The study found that depending on just how strong the connection one estimates between particles and their ability to form ice crystals, increasing the amount of these particles could lead to either climate warming or cooling. Because many different experimental and modeling setups yield different estimates of the strength of this particle-ice connection, it is critical that the climate science community work to better fine-tune its measurements of this important physical process, and thoroughly account for the diversity of estimates when studying aerosol impacts on climate.

Phytoplankton play key roles as the base of the marine food web and as a crucial component in the Earth’s carbon cycle. Understanding how ocean dynamics influence plankton ecology will add to basic knowledge and inform studies of higher-level marine organism habitat. This project will examine how ocean dynamics across an unprecedented range of scales set, transport and re-organize phytoplankton communities. An interdisciplinary team of researchers will combine satellite data, extensive flow cytometry observations, other existing in-situ measurements, and modeling to achieve these goals.

Signatures of the Multiple Scales of Motion in Shaping Marine Phytoplankton Biogeography
Uncertainty in counting ice nucleating particles with continuous flow diffusion chambers

This study investigates the measurement of ice nucleating particle (INP) concentrations and sizing of crystals using continuous flow diffusion chambers (CFDCs). CFDCs have been deployed for decades to measure the formation of INPs under controlled humidity and temperature conditions in laboratory studies and by ambient aerosol populations. These measurements have, in turn, been used to construct parameterizations for use in models by relating the formation of ice crystals to state variables such as temperature and humidity as well as aerosol particle properties such as composition and number. We show here that assumptions of ideal instrument behavior are not supported by measurements made with a commercially available CFDC, the SPectrometer for Ice Nucleation (SPIN), and the instrument on which it is based, the Zurich Ice Nucleation Chamber (ZINC). Non-ideal instrument behavior, which is likely inherent to varying degrees in all CFDCs, is caused by exposure of particles to different humidities and/or temperatures than predicated from instrument theory of operation. This can result in a systematic, and variable, underestimation of reported INP concentrations. We find here variable correction factors from 1.5 to 9.5, consistent with previous literature values. We use a machine learning approach to show that non-ideality is most likely due to small-scale flow features where the aerosols are combined with sheath flows. Machine learning is also used to minimize the uncertainty in measured INP concentrations. We suggest that detailed measurement, on an instrument-by-instrument basis, be performed to characterize this uncertainty.

Improving constraints on climate system properties with additional data and new methods

Future climate change depends on properties of the climate system and the external forcing factors that drive the global energy budget. Among those properties are climate sensitivity, the rate at which heat is mixed into the deep ocean, and the aerosol forcing on the planet. In this dissertation, we use the newly updated Massachusetts Institute of Technology Earth System Model (MESM) to derive the joint probability distribution function (PDF) for model parameters that represent the aforementioned climate system properties. Climate sensitivity (ECS) in the model is set through an adjustment to the cloud feedback parameter. The vertical diffusion coefficient, Kv, represents the mixing of heat into the deep ocean by all mixing processes. The net anthropogenic aerosol forcing parameter, Faer, estimates the contribution of aerosol cooling to the global energy budget. Using an 1800-member ensemble of MESM runs where the model parameters have been systematically varied, we derive PDFs for the model parameters by comparing the model output against historical observations of surface temperature and global mean ocean heat content. In particular, we answer four main research questions: (1) How are the parameter PDFs derived using the MESM ensemble different from those using a previous version of the model?, (2) How do the estimates change when recent surface temperature and ocean heat content observations are included in the model diagnostics used to evaluate model performance?, (3) How does internal climate variability lead to uncertainty in the parameter estimates?, and (4) What impact do the changes in PDFs have on estimates of future warming, namely estimates of transient climate response (TCR)? We show that estimates of climate sensitivity increase and the aerosol forcing is less negative when using MESM. These shifts are the result of a new forcing suite used to drive the model. By extending the length of the model diagnostics one decade at a time, we show that recent temperature patterns impact our estimates of the climate system properties. The continued rise in surface temperature leads to higher values of ECS, while the increased rate of heat storage in the ocean leads to lower estimates of ECS and higher estimates of Kv. We show that the parameter distributions are sensitive to the internal variability in the climate system and that using a single variability estimate can lead to PDFs that are too narrow. Throughout the dissertation, we show that estimates of transient climate response are correlated with ECS and Kv. Namely, higher ECS and weaker Kv lead to higher values of TCR. When considering all of these factors, we arrive at our best estimate for the climate system properties. We estimate the 90-percent confidence interval for climate sensitivity to be 2.7 to 5.4 degrees C with a mode of 3.5 degrees C. Our estimate for Kv is 1.9 to 23.0 cm2s−1 with a mode of 4.41 cm2s−1. Faer is estimated to be between -0.4 and -0.04 Wm−2 with a mode of –0.25 Wm−2. Lastly, we estimate TCR to be between 1.4 and 2.1 degrees C with a mode of 1.8 degrees C.

 

Pages

Subscribe to Earth Systems