An official website of the United States government
Abstract The variability of the summer North Pacific Subtropical High (NPSH) has substantial socioeconomic impacts. However, state‐of‐the‐art climate models significantly disagree on the response of the NPSH to anthropogenic warming. Inter‐model spread in NPSH projections originates from models' inconsistency in simulating tropical precipitation changes. This inconsistency in precipitation changes is partly due to inter‐model spread in tropical sea surface temperature (SST) changes, but it can also occur independently of uncertainty in SST changes. Here, we show that both types of precipitation uncertainty influence the NPSH via the Matsuno‐Gill wave response, but their relative impact varies by region. Through the modulation of low cloud fraction, inter‐model spread of the NPSH can have a further impact on extra‐tropical land surface temperature. The teleconnection between tropical precipitation and the NPSH is examined through a series of numerical experiments.
more »
« less
Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.
more »
« less
- Award ID(s):
- 2003725
- PAR ID:
- 10218025
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Cirrus ice crystals are produced heterogeneously on ice‐nucleating particles (INPs) and homogeneously in supercooled liquid solution droplets. They grow by uptake of water molecules from the ice‐supersaturated vapor. The precursor particles, characterized by disparate ice nucleation abilities and number concentrations, compete for available vapor during ice formation events. We investigate cirrus formation events systematically in different temperature and updraft regimes, and for different INP number concentrations and time‐independent nucleation efficiencies. We consider vertical air motion variability due to mesoscale gravity waves and effects of supersaturation‐dependent deposition coefficients for water molecules on ice surfaces. We analyze ice crystal properties to better understand the dynamics of competing nucleation processes. We study the reduction of ice crystal numbers produced by homogeneous freezing due to INPs in both, individual simulations assuming constant updraft speeds and in ensemble simulations based on a stochastic representation of vertical wind speed fluctuations. We simulate and interpret probability distributions of total nucleated ice crystal number concentrations, showing signatures of homogeneous and heterogeneous nucleation. At typically observed, mean updraft speeds (≈15 cm s−1) competing nucleation should occur frequently, even at rather low INP number concentrations (<10 L−1). INPs increase cirrus occurrence and may alter cirrus microphysical properties without entirely suppressing homogeneous freezing events. We suggest to improve ice growth models, especially for low cirrus temperatures (<220 K) and low ice supersaturation (<0.3).more » « less
-
Abstract An unprecedented heatwave impacted East Antarctica in March 2022, peaking at 39°C above climatology, the largest temperature anomaly ever recorded globally. We investigate the causes of the heatwave, the impact of climate change, and a climate model's ability in simulating such an event. The heatwave, which was skillfully forecast, resulted from a highly anomalous large‐scale circulation pattern that advected an Australian airmass to East Antarctica in 4 days and produced record atmospheric heat fluxes. Southern Ocean sea surface temperatures anomalies had a minimal impact on the heatwave's amplitude. Simulations from a climate model fail to simulate such a large temperature anomaly mostly due to biases in its large‐scale circulation variability, showcasing a pathway for future model improvement in simulating extreme heatwaves. The heatwave was made 2°C warmer by climate change, and end of 21st century heatwaves may be an additional 5–6°C warmer, raising the prospect of near‐melting temperatures over the interior of East Antarctica.more » « less
-
Abstract Satellite precipitation products, as all quantitative estimates, come with some inherent degree of uncertainty. To associate a quantitative value of the uncertainty to each individual estimate, error modeling is necessary. Most of the error models proposed so far compute the uncertainty as a function of precipitation intensity only, and only at one specific spatiotemporal scale. We propose a spectral error model that accounts for the neighboring space–time dynamics of precipitation into the uncertainty quantification. Systematic distortions of the precipitation signal and random errors are characterized distinctively in every frequency–wavenumber band in the Fourier domain, to accurately characterize error across scales. The systematic distortions are represented as a deterministic space–time linear filtering term. The random errors are represented as a nonstationary additive noise. The spectral error model is applied to the IMERG multisatellite precipitation product, and its parameters are estimated empirically through a system identification approach using the GV-MRMS gauge–radar measurements as reference (“truth”) over the eastern United States. The filtering term is found to be essentially low-pass (attenuating the fine-scale variability). While traditional error models attribute most of the error variance to random errors, it is found here that the systematic filtering term explains 48% of the error variance at the native resolution of IMERG. This fact confirms that, at high resolution, filtering effects in satellite precipitation products cannot be ignored, and that the error cannot be represented as a purely random additive or multiplicative term. An important consequence is that precipitation estimates derived from different sources shall not be expected to automatically have statistically independent errors. Significance StatementSatellite precipitation products are nowadays widely used for climate and environmental research, water management, risk analysis, and decision support at the local, regional, and global scales. For all these applications, knowledge about the accuracy of the products is critical for their usability. However, products are not systematically provided with a quantitative measure of the uncertainty associated with each individual estimate. Various parametric error models have been proposed for uncertainty quantification, mostly assuming that the uncertainty is only a function of the precipitation intensity at the pixel and time of interest. By projecting satellite precipitation fields and their retrieval errors into the Fourier frequency–wavenumber domain, we show that we can explicitly take into account the neighboring space–time multiscale dynamics of precipitation and compute a scale-dependent uncertainty.more » « less
-
Abstract The physical processes of heat exchange between lakes and the surrounding atmosphere are important in simulating and predicting terrestrial surface energy balance. Latent and sensible heat fluxes are the dominant physical process controlling ice growth and decay on the lake surface, as well as having influence on regional climate. While one-dimensional lake models have been used in simulating environmental changes in ice dynamics and water temperature, understanding the seasonal to daily cycles of lake surface energy balance and its relationship to lake thermal properties, atmospheric conditions, and how those are represented in models is still an open area of research. We evaluated a pair of one-dimensional lake models, Freshwater Lake (FLake) and the General Lake Model (GLM), to compare modeled latent and sensible heat fluxes against observed data collected by an eddy covariance tower during a 1-yr period in 2017, using Lake Mendota in Madison, Wisconsin, as our study site. We hypothesized transitional periods of ice cover as a leading source of model uncertainty, and we instead found that the models failed to simulate accurate values for large positive heat fluxes that occurred from late August into late December. Our results ultimately showed that one-dimensional models are effective in simulating sensible heat fluxes but are considerably less sensitive to latent heat fluxes than the observed relationships of latent heat flux to environmental drivers. These results can be used to focus future improvement of these lake models especially if they are to be used for surface boundary conditions in regional numerical weather models. Significance Statement While lakes consist of a small amount of Earth’s surface, they have a large impact on local climate and weather. A large amount of energy is stored in lakes during the spring and summer, and then removed from lakes before winter. The effect is particularly noticeable in high latitudes, when the seasonal temperature difference is larger. Modeling this lake energy exchange is important for weather models and measuring this energy exchange is challenging. Here we compare modeled and observed energy exchange, and we show there are large amounts of energy exchange happening in the fall, which models struggle to capture well. During periods of partial ice coverage in early winter, lake behavior can change rapidly.more » « less
