Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.Comment: 32 pages, 3 figure

Modeling the gas-particle partitioning of secondary organic aerosol: the importance of liquid-liquid phase separation

The partitioning of semivolatile organic compounds between the gas phase and aerosol particles is an important source of secondary organic aerosol (SOA). Gas-particle partitioning of organic and inorganic species is influenced by the physical state and water content of aerosols, and therefore ambient relative humidity (RH), as well as temperature and organic loading levels. We introduce a novel combination of the thermodynamic models AIOMFAC (for liquid mixture non-ideality) and EVAPORATION (for pure compound vapor pressures) with oxidation product information from the Master Chemical Mechanism (MCM) for the computation of gas-particle partitioning of organic compounds and water. The presence and impact of a liquid-liquid phase separation in the condensed phase is calculated as a function of variations in relative humidity, organic loading levels, and associated changes in aerosol composition. We show that a complex system of water, ammonium sulfate, and SOA from the ozonolysis of α-pinene exhibits liquid-liquid phase separation over a wide range of relative humidities (simulated from 30% to 99% RH). Since fully coupled phase separation and gas-particle partitioning calculations are computationally expensive, several simplified model approaches are tested with regard to computational costs and accuracy of predictions compared to the benchmark calculation. It is shown that forcing a liquid one-phase aerosol with or without consideration of non-ideal mixing bears the potential for vastly incorrect partitioning predictions. Assuming an ideal mixture leads to substantial overestimation of the particulate organic mass, by more than 100% at RH values of 80% and by more than 200% at RH values of 95%. Moreover, the simplified one-phase cases stress two key points for accurate gas-particle partitioning calculations: (1) non-ideality in the condensed phase needs to be considered and (2) liquid-liquid phase separation is a consequence of considerable deviations from ideal mixing in solutions containing inorganic ions and organics that cannot be ignored. Computationally much more efficient calculations relying on the assumption of a complete organic/electrolyte phase separation below a certain RH successfully reproduce gas-particle partitioning in systems in which the average oxygen-to-carbon (O:C) ratio is lower than ~0.6, as in the case of α-pinene SOA, and bear the potential for implementation in atmospheric chemical transport models and chemistry-climate models. A full equilibrium calculation is the method of choice for accurate offline (box model) computations, where high computational costs are acceptable. Such a calculation enables the most detailed predictions of phase compositions and provides necessary information on whether assuming a complete organic/electrolyte phase separation is a good approximation for a given aerosol system. Based on the group-contribution concept of AIOMFAC and O:C ratios as a proxy for polarity and hygroscopicity of organic mixtures, the results from the α-pinene system are also discussed from a more general point of view

Numerical simulation of clouds and precipitation depending on different relationships between aerosol and cloud droplet spectral dispersion

The aerosol effects on clouds and precipitation in deep convective cloud systems are investigated using the Weather Research and Forecast (WRF) model with the Morrison two-moment bulk microphysics scheme. Considering positive or negative relationships between the cloud droplet number concentration (Nc) and spectral dispersion (ɛ), a suite of sensitivity experiments are performed using an initial sounding data of the deep convective cloud system on 31 March 2005 in Beijing under either a maritime (‘clean’) or continental (‘polluted’) background. Numerical experiments in this study indicate that the sign of the surface precipitation response induced by aerosols is dependent on the ɛ−Nc relationships, which can influence the autoconversion processes from cloud droplets to rain drops. When the spectral dispersion ɛ is an increasing function of Nc, the domain-average cumulative precipitation increases with aerosol concentrations from maritime to continental background. That may be because the existence of large-sized rain drops can increase precipitation at high aerosol concentration. However, the surface precipitation is reduced with increasing concentrations of aerosol particles when ɛ is a decreasing function of Nc. For the ɛ−Nc negative relationships, smaller spectral dispersion suppresses the autoconversion processes, reduces the rain water content and eventually decreases the surface precipitation under polluted conditions. Although differences in the surface precipitation between polluted and clean backgrounds are small for all the ɛ−Nc relationships, additional simulations show that our findings are robust to small perturbations in the initial thermal conditions. Keywords: aerosol indirect effects, cloud droplet spectral dispersion, autoconversion parameterization, deep convective systems, two-moment bulk microphysics schem

Understanding climate: A strategy for climate modeling and predictability research, 1985-1995

The emphasis of the NASA strategy for climate modeling and predictability research is on the utilization of space technology to understand the processes which control the Earth's climate system and it's sensitivity to natural and man-induced changes and to assess the possibilities for climate prediction on time scales of from about two weeks to several decades. Because the climate is a complex multi-phenomena system, which interacts on a wide range of space and time scales, the diversity of scientific problems addressed requires a hierarchy of models along with the application of modern empirical and statistical techniques which exploit the extensive current and potential future global data sets afforded by space observations. Observing system simulation experiments, exploiting these models and data, will also provide the foundation for the future climate space observing system, e.g., Earth observing system (EOS), 1985; Tropical Rainfall Measuring Mission (TRMM) North, et al. NASA, 1984

The implementation and validation of improved landsurface hydrology in an atmospheric general circulation model

Landsurface hydrological parameterizations are implemented in the NASA Goddard Institute for Space Studies (GISS) General Circulation Model (GCM). These parameterizations are: (1) runoff and evapotranspiration functions that include the effects of subgrid scale spatial variability and use physically based equations of hydrologic flux at the soil surface, and (2) a realistic soil moisture diffusion scheme for the movement of water in the soil column. A one dimensional climate model with a complete hydrologic cycle is used to screen the basic sensitivities of the hydrological parameterizations before implementation into the full three dimensional GCM. Results of the final simulation with the GISS GCM and the new landsurface hydrology indicate that the runoff rate, especially in the tropics is significantly improved. As a result, the remaining components of the heat and moisture balance show comparable improvements when compared to observations. The validation of model results is carried from the large global (ocean and landsurface) scale, to the zonal, continental, and finally the finer river basin scales