Virtual Earth System Research Institute (VESRI)

VESRI aims to improve the accuracy and credibility of major climate models by addressing some of the hardest problems that challenge them.



VESRI provides sustained, multi-year funding and embedded technical expertise towards transformational climate modeling research by jointly exploiting advances in models of the Earth systems, Earth observations, computational tools, and bringing tools and approaches from outside the climate sciences to bear within it. VESRI aims to improve climate modeling, change the direction of multiple models globally, and to ultimately accelerate the pace of earth systems and climate research.


Led by a scientific advisory board of world-leading climate scientists, research projects within VESRI were selected from the most promising research proposals arising from Schmidt Sciences’ global, multi-stage competition to address current weaknesses in climate models. Scientific teams submitted innovative proposals that addressed principal shortcomings in current climate modeling – well-known and stubborn biases in the modeling of present-day climate, reliability in the simulation of climate extremes and extreme events, shortcomings in the modeling of past climates, as well as abrupt historical transitions in climate. These are among the aspects of the climate system that continue to challenge our models in terms of resolution, fidelity, complexity of representation, and teasing apart the roles of natural and forced variability. From this global competition we selected four highly innovative, transdisciplinary, and cross-national research projects traditionally not supported through current funding agency structures. 


Schmidt Sciences leverages its philanthropic model and technical expertise to fund and coordinate high-impact, high-risk research that falls outside traditional disciplinary boundaries. Through VESRI, we coordinate hundreds of climate and data scientists across eleven countries, four scientific research consortiums and fifty research institutions to tackle some of the most difficult scientific and computational problems in climate modeling. Further accelerating VESRI is the Schmidt Sciences-funded Institute of Computing for Climate Science  (one of four Virtual Institute of Scientific Software (VISS)) at the University of Cambridge, which advances climate sciences through computer science, software engineering, data science, and AI. Through these efforts, VESRI seeks to drive large-scale, catalytic, and transformative impact in climate modeling.


Schmidt Sciences creates Virtual Institutes of Science where a distributed network of carefully selected scientific and technical talent is more likely to solve important problems of scientific knowledge by working across institutions and disciplines than by concentrating the research in a single field or at a single place. This approach allows for high-risk bets that apply new ideas, advanced computing and innovative technologies to STEM R&D.


Our impact

Schmidt Sciences’ impact in climate modeling includes:

>75 scientific publications to improve climate models and predictions
1st climate model being delivered that automatically learns from diverse data
1000x accelerated Bayesian learning in select climate models

VESRI Members

DataWave: Collaborative Gravity Wave Research

The DataWave project is an international consortium focused on improving our modeling capability for gravity waves and large scale circulation, particularly to lead novel observationally constrained and data-driven gravity wave parameterization schemes. Results from this project will enable better predictions of how atmospheric circulation responds to global warming and impacts subseasonal-to-seasonal forecasts.

Read our external DataWave Project Review

LEMONTREE: Land Ecosystem Models based On New Theory, obseRvations, and ExperimEnts

LEMONTREE is an international consortium developing a next-generation model of the terrestrial biosphere and its interactions with the carbon cycle, water cycle and climate. Their approach is to create ecosystem models that rest on firm theoretical and empirical foundations, and eventually, more reliable projections of future climates and a newfound ability to address issues in sustainability.

For more information

M²LInES: Multiscale Machine Learning In Coupled Earth System Modeling

M²LInES is a large international collaborative project with the goal of improving climate projections, using scientific and interpretable Machine Learning (ML) to capture unaccounted physical processes at the air-sea-ice interface. ML will guide the development of innovative, physics-guided, and interpretable representations of these complex processes directly from data for use in global climate simulations.

SASIP: The Scale-Aware Sea Ice Project

SASIP is an international consortium that is developing a scale-aware continuum sea ice model for climate research; one that faithfully represents sea ice dynamics and thermodynamics and that is physically sound, data-adaptive, highly parallelized and computationally efficient. SASIP is using machine learning and data assimilation to exploit large datasets obtained from both simulations and remote sensing.

For more information

Fate, Emissions, and Transport of CH₄ (FETCH₄)

Led by the University of Washington and the University of Rochester, FETCHaims to improve understanding of the historic and modern methane cycle. The consortium utilizes unique chemical fingerprints, satellite observations, and machine learning models to enhance data representation of methane in global climate models. Measuring methane is important to mitigating and understanding climate change, as it traps heat in the atmosphere, exacerbating warming and contributing to air pollution.

Learn more

Carbon Loss in Plants, Soils, and Oceans (CALIPSO)

Led by research teams at the Université de Versailles Saint-Quentin-en-Yvelines, University of East Anglia, and University of Exeter, CALIPSO quantifies vulnerabilities of terrestrial and oceanic carbon stocks under climate change. By integrating novel observations, theoretical understanding, and machine learning tools, the project assesses risks associated with carbon cycle tipping points and provides refined emissions reduction strategies.

In addition to these ongoing projects, VESRI builds upon the following Schmidt Sciences-supported philanthropic investments in the climate sciences.

CliMA: Climate Modeling Alliance

CliMA is building a new Earth system model that leverages recent advances in the computational and data sciences to learn directly from a wealth of Earth observations from space, from the ground, as well as high-resolution simulations spun off on the fly. It will couple atmosphere, ocean, and land models to a single global climate model that is calibrated through the Earth observations and simulations. This model will harness more data than ever before, providing a new level of accuracy to predictions of droughts, heat waves, and rainfall extremes. The model will serve as a backbone for a suite of end-user applications that can be used to plan for and respond to climate change.

Read our external CliMA project review

Keeling Curve: Measuring Global CO2

Schmidt Sciences supports measurements of CO2 concentration and its stable isotopes from an array of ten stations distributed from the Arctic to the Antarctic — sustaining the iconic Mauna Loa continuous record, known as “the Keeling curve”. These records are the longest continuous measurement of CO2 and its isotopes, and thus among the first places to look for evidence of large-scale changes in the global carbon cycle of relevance to climate and sustainability.

For more information

Work we’re doing for science

We build networks of brilliant researchers at different career stages. We lead Virtual Institutes of Science to solve hard problems across locations and fields using modern tools.