About

Deconinck, Bernard Boeing Professor of Applied Mathematics, Adjunct Professor of Mathematics, is a faculty member at the University of Washington. His role indicates a distinguished position within the department, emphasizing his expertise and leadership in applied mathematics. The page does not provide specific details about his research focus, background, or key contributions, only his titles and contact information.

Research topics

• Environmental science
• Meteorology
• Climatology
• Geology
• Physics
• Geography
• Atmospheric sciences
• Oceanography
• Computer Science
• Astrobiology
• Engineering
• Remote sensing
• Geophysics

Selected publications

• ai2cm/ACE2.1-ERA5-AIMIP: ACE2.1-ERA5: AIMIP Phase 1 submission

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-27

  otherOpen accessSenior author

  Describes the training, evaluation, inference, and postprocessing of the ACE2.1-ERA5 submission made to AIMIP Phase 1.

  PublisherDOI

• AIMIP Phase 1: systematic evaluations of AI weather and climate models

  ArXiv.org · 2026-05-07

  articleOpen access

  We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a common experiment, output data format, and training constraints (namely, training against historical reanalysis data) for AIMIP Phase 1 models. We aim to identify differences in modeling frameworks and AI architectural choices that influence model behavior, and build trust in AI weather and climate models through open data and evaluation. AIMIP Phase 1 models must simulate the atmosphere given specified historical sea surface temperatures over 1979-2024. We evaluate the models' performance using five major evaluation criteria: biases, trends, response to El Niño-related sea surface temperature anomalies, temporal variability, and out-of-sample generalization tests. We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests. We describe the AIMIP Phase 1 dataset that is publicly available for additional evaluations.

  PublisherOA PDF

• ai2cm/ACE2.1-ERA5-AIMIP: ACE2.1-ERA5: AIMIP Phase 1 submission

  Open MIND · 2026-04-27

  otherOpen accessSenior author

  Describes the training, evaluation, inference, and postprocessing of the ACE2.1-ERA5 submission made to AIMIP Phase 1.

  PublisherDOI

• AIMIP Phase 1: systematic evaluations of AI weather and climate models

  arXiv (Cornell University) · 2026-05-07

  preprintOpen access

  We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a common experiment, output data format, and training constraints (namely, training against historical reanalysis data) for AIMIP Phase 1 models. We aim to identify differences in modeling frameworks and AI architectural choices that influence model behavior, and build trust in AI weather and climate models through open data and evaluation. AIMIP Phase 1 models must simulate the atmosphere given specified historical sea surface temperatures over 1979-2024. We evaluate the models' performance using five major evaluation criteria: biases, trends, response to El Niño-related sea surface temperature anomalies, temporal variability, and out-of-sample generalization tests. We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests. We describe the AIMIP Phase 1 dataset that is publicly available for additional evaluations.

  PublisherDOI

• Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model

  Journal of Geophysical Research Machine Learning and Computation · 2025-09-01 · 2 citations

  articleOpen accessSenior author

  Abstract Green's functions are a useful technique for interpreting atmospheric state responses to changes in the spatial pattern of sea surface temperature (SST). Here, we train version two of the Ai2 Climate Emulator (ACE2) on reference historical SST simulations of the US Department of Energy's EAMv3 global atmosphere model. We compare how well the SST Green's functions generated by ACE2 match those of EAMv3, following the protocol of the Green's Function Model Intercomparison Project (GFMIP). The spatial patterns of top‐of‐atmosphere (TOA) radiative response from the individual GFMIP SST patch simulations are similar for ACE and the EAMv3 reference. The derived sensitivity of global net TOA radiation sensitivity to SST patch location is qualitatively similar in ACE as in EAMv3, but there are statistically significant discrepancies for some SST patches, especially over the subtropical northeast Pacific. These discrepancies may reflect insufficient diversity in the SST patterns sampled over the course of the EAMv3 AMIP simulation used for training ACE. Both ACE and EAMv3 Green's functions reconstruct the historical record of the global annual‐mean TOA radiative flux from a reference EAMv3 AMIP simulation reasonably well. Notably, under our configuration and compute resources, ACE achieves these results approximately 100 times faster in wall‐clock time compared with EAMv3, highlighting its potential as a powerful and efficient tool for tackling other computationally intensive problems in climate science.

  PublisherDOI

• Detecting changes in large-scale metrics of climate in short integrations of a global storm-resolving model of the atmosphere

  Environmental Research Climate · 2025-05-08

  articleOpen access

  Abstract Recent advances have allowed for integration of global storm resolving models (GSRMs) to a timescale of several years. These short simulations are sufficient for studying aggregated statistics of short-timescale and small spatial-scale phenomena; however, it is questionable what we can learn from these integrations about the large-scale climate response to perturbations. To address this question, we use the response of X-SHiELD (a GSRM) to uniform sea surface temperature warming and CO 2 increase in two-year integrations and compare it to similar CMIP experiments. Specifically, we assess the statistical meaning of having two years in one model outside the spread of another model or model ensemble. This is of particular interest because X-SHiELD shows a distinct response of the global-mean precipitation to uniform warming and the northern hemisphere jet shift response to isolated CO 2 increase. To estimate the probability of X-SHiELD’s and the CMIP models having different means, we take the approach of Bayesian inference. We derive a posterior distribution for the differences in the mean between X-SHiELD and the CMIP models taking into account the X-SHiELD values for the global-mean precipitation response to uniform warming and the response of the norther hemisphere jet latitude to isolated CO 2 increase. We find that the most probable value for the difference between X-SHiELD and the CMIP mean is larger than one standard deviation, representing both internal variability and inter-model spread of the CMIP models. We also find that there is an important base-state dependence for some large-scale metrics that, when taken into account, can qualitatively change the interpretation of the results. We note that a year-to-year comparison is meaningful due to the use of prescribed sea-surface-temperature simulations.

  PublisherDOI

• HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model

  ArXiv.org · 2025-12-20

  articleOpen access

  Kilometer-scale simulations of the atmosphere are an important tool for assessing local weather extremes and climate impacts, but computational expense limits their use to small regions, short periods, and limited ensembles. Machine learning offers a pathway to efficiently emulate these high-resolution simulations. Here we introduce HiRO-ACE, a two-stage AI modeling framework combining a stochastic version of the Ai2 Climate Emulator (ACE2S) with diffusion-based downscaling (HiRO) to generate 3 km precipitation fields over arbitrary regions of the globe. Both components are trained on data derived from a decade of atmospheric simulation by X-SHiELD, a 3 km global storm-resolving model. HiRO performs a 32x downscaling--generating 3 km 6-hourly precipitation from coarse 100 km inputs by training on paired high-resolution and coarsened X-SHiELD outputs. ACE2S is a $1^\circ \times 1^\circ$ ($\sim$100 km) stochastic autoregressive global atmosphere emulator that maintains grid-scale precipitation variability consistent with coarsened X-SHiELD, enabling its outputs to be ingested by HiRO without additional tuning. HiRO-ACE reproduces the distribution of extreme precipitation rates through the 99.99th percentile, with time-mean precipitation biases below 10% almost everywhere. The framework generates plausible tropical cyclones, fronts, and convective events from poorly resolved coarse inputs. Its computational efficiency allows generation of 6-hourly high-resolution regional precipitation for decades of simulated climate within a single day using one H100 GPU, while the probabilistic design enables ensemble generation for quantifying uncertainty. This establishes an AI-enabled pathway for affordably leveraging the realism of expensive km-scale simulations to support local climate adaptation planning and extreme event risk assessment.

  PublisherOA PDF

• Applying Corrective Machine Learning in the E3SM Atmosphere Model in C++ (EAMxx)

  2025-09-17

  preprintOpen access

  Abstract. The Simplified Cloud-Resolving E3SM Atmosphere Model (SCREAM) is the newest addition to the family of earth system models capable of explicitly resolving convective systems. SCREAM is a kilometer-scale configuration of the advanced E3SM Atmosphere Model (EAMxx), designed for heterogeneous systems. While the enhanced accuracy of kilometer-scale modeling offers significant benefits, it comes with a substantial computational cost, limiting feasible simulation durations to only a few years, even on the fastest supercomputers. Machine learning presents an opportunity for scientists to achieve the high accuracy of storm-resolving models at a significantly reduced cost. Building on the previous success of applying corrective machine learning (ML) to the FV3 model, this study explores the effects of implementing corrective ML in EAMxx-SCREAM. We also address the computational challenges of integrating our implementation of corrective ML, which is written in Python, with the C++/Kokkos EAMxx driver, as well as potential reasons why this approach has not proved as effective for EAMxx-SCREAM as for the FV3 model.

  PublisherOA PDFDOI

• ACE2‐SOM: Coupling an ML Atmospheric Emulator to a Slab Ocean and Learning the Sensitivity of Climate to Changed CO ₂

  Journal of Geophysical Research Machine Learning and Computation · 2025-09-30 · 2 citations

  articleOpen accessSenior author

  Abstract Although autoregressive machine learning‐based emulators have been trained to produce stable and accurate rollouts in the climate of the present‐day and recent past, none so far have been trained to emulate the sensitivity of climate to substantial changes in or other greenhouse gases. As an initial step we couple the Ai2 Climate Emulator version 2 to a slab ocean model (hereafter ACE2‐SOM) and train it on output from a collection of equilibrium‐climate physics‐based reference simulations with varying levels of . We test it in equilibrium and non‐equilibrium climate scenarios with concentrations seen and unseen in training. ACE2‐SOM performs well in equilibrium‐climate inference with both in‐sample and out‐of‐sample concentrations, accurately reproducing the emergent time‐mean spatial patterns of surface temperature and precipitation change with doubling, tripling, or quadrupling. In addition, the vertical profile of atmospheric warming and change in extreme precipitation rates up to the 99.9999th percentile closely agree with the reference model. Non‐equilibrium‐climate inference is more challenging. With increasing gradually at a rate of 2% year −1 , ACE2‐SOM can accurately emulate the global annual mean trends of surface and lower‐to‐middle atmosphere fields but produces unphysical jumps in stratospheric fields. With an abrupt quadrupling of , ML‐controlled fields transition unrealistically quickly to the regime. In doing so they violate global energy conservation and exhibit unphysical sensitivities of surface and top of atmosphere radiative fluxes to instantaneous changes in . Future emulator development needed to address these issues should improve its generalizability to diverse climate change scenarios.

  PublisherDOI

• Resolving Low Cloud Feedbacks Globally With E3SM High‐Res MMF: Agreement With LES but Stronger Shortwave Effects

  Journal of Advances in Modeling Earth Systems · 2025-06-01

  articleOpen access

  Abstract This study investigates low cloud feedback in a warmer climate using global simulations from the High‐Resolution Multi‐scale Modeling Framework (HR‐MMF), which explicitly simulates small‐scale eddies globally. Two 5‐year simulations—one with present‐day sea surface temperatures (SSTs) and a second with SSTs warmed uniformly by 4 K—reveal a positive global shortwave cloud radiative effect (SWCRE = 0.3 W//K), comparable to estimates from CMIP models. As the climate warms, significant reductions in low cloud cover occur over stratocumulus regions. This study is the first attempt to compare HR‐MMF results with predictions from idealized large‐eddy simulations from the CGILS intercomparison. Despite different underlying assumptions, we find qualitative agreement in SWCRE and inversion height changes between HR‐MMF and CGILS predictions. This suggests reasonable credibility for the CGILS framework in predicting cloud responses under the out‐of‐sample conditions found in HR‐MMF. However, the HR‐MMF exhibits stronger SWCRE changes than predicted by CGILS. We explore potential causes for this discrepancy, examining variations in cloud‐controlling factors (CCFs) and cloud conditions. Our results show a fairly homogeneous SWCRE response, with little systematic variation tied to the variations in CCFs. This reveals a dominant role for SST forcing in modulating SWCRE.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: EaSM-3: Understanding the Development of Precipitation Biases in CESM and the Superparameterized CESM on Seasonal to Decadal Timescales

  NSF · $480k · 2014–2019

• Collaborative Research: Using SOCRATES Datasets to Improve Simulations of Clouds, Aerosols and their Climate Impacts

  NSF · $531k · 2017–2021

• Collaborative Research: Climate Process Team on Low-Latitude Cloud Feedbacks on Climate Sensitivity

  NSF · $159k · 2003–2007

Frequent coauthors

• Robert Wood

  University of Washington

  135 shared

• Peter N. Blossey

  Allen Institute for Artificial Intelligence

  130 shared

• Spencer K. Clark

  NOAA Geophysical Fluid Dynamics Laboratory

  93 shared

• Jeremy McGibbon

  Allen Institute for Artificial Intelligence

  63 shared

• Noah Brenowitz

  58 shared

• Oliver Watt‐Meyer

  Allen Institute for Artificial Intelligence

  56 shared

• Isabel L. McCoy

  56 shared

• K. Comstock

  University of Washington

  52 shared

Education

• Ph.D., Mathematics

  Massachusetts Institute of Technology

  1984

Awards & honors

• Jule G. Charney Award (2012)
• Fellow of the American Meteorological Society
• Fellow of the American Geophysical Union
• Bernhard Haurwitz Memorial Lecturer (2018)