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Dr

Sarah Sparrow DPhil

Deputy Course Director, MSc in Energy Systems

Member of Faculty

CPDN Programme Coordinator

Biography

Dr Sarah Sparrow is Deputy Course Director for the MSc in Energy Systems, and is climateprediction.net (CPDN) Programme Coordinator at Oxford e-Research Centre. She is experienced in data driven coupling of climate model output to impact models and lead research proposals in this area. Her responsibilities include application development, support and data management. 

Sarah has extensive experience in preparing and analysing large ensembles of climate model output. She has tutored at several international attribution workshops and summer schools. Her research has also included analysis of atmospheric dynamics having modelled solar climate influences on tropospheric dynamics and studied modes of stratospheric variability.

Previously, she has worked in industry as a software developer and implementation manager of business management systems.

ORCID
Google Scholar
ResearchGate
climateprediction.net

Most Recent Publications

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

Assessing the impact of climate change on the cost of production of green ammonia from offshore wind

Assessing the impact of climate change on the cost of production of green ammonia from offshore wind

Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone

Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone

Physically based equation representing the forcing-driven precipitation in climate models

Physically based equation representing the forcing-driven precipitation in climate models

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

View all

Research Interests

  • Extreme weather events and their extended impacts
  • Climate modelling and dynamics
  • Machine learning in the context of climate
  • Software system development

Current Projects

Attributing Amazon Forest fires from Land-use Alteration and Meteorological Extremes (AFLAME)

Sarah leads this project looking at the sensitivity of wildfire outbreaks in Brazil to human induced climate change, land use change and natural climate variations.

National Trust Visitor Impact from Expected Weather (NT-VIEW)

Sarah leads this project in collaboration with the National Trust to assess the impact of extreme weather events on visitor numbers at National Trust sites.

Globally Observed Teleconnections in Hierarchies of Atmospheric Models (GOTHAM)

This project looks at remote drivers of atmospheric extremes.

Drivers of Change in Mid-Latitude Weather Events (DOCILE)

This project looks at dynamical drivers of extreme weather in Europe using a new higher resolution distributed computing model

OpenIFS@home

Project bringing the European Centre for Medium Range Weather Forecasting (ECMWF) OpenIFS model to run in a distributed computing environment.

The Nature Conservancy (TNC) project

Looks at how the risk of droughts in the Amazon will change in the future and aims to inform resilient land use planning in a changing world.

The Children's Investment Fund Foundation (CIFF) project

Aims to attribute drought events in West Africa to human induced climate change.

East Asian Heatwave Attribution (EASHA)

This project focuses on attributing current and future changes of heatwaves in East Asia.

Most Recent Publications

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

Assessing the impact of climate change on the cost of production of green ammonia from offshore wind

Assessing the impact of climate change on the cost of production of green ammonia from offshore wind

Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone

Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone

Physically based equation representing the forcing-driven precipitation in climate models

Physically based equation representing the forcing-driven precipitation in climate models

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

View all

Publications

View a full list of Sarah's publications via ORCID or Google Scholar.

Most Recent Publications

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

Response of winter climate and extreme weather to projected Arctic sea-ice loss in very large-ensemble climate model simulations

Assessing the impact of climate change on the cost of production of green ammonia from offshore wind

Assessing the impact of climate change on the cost of production of green ammonia from offshore wind

Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone

Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone

Physically based equation representing the forcing-driven precipitation in climate models

Physically based equation representing the forcing-driven precipitation in climate models

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

Using Deep Learning for an Analysis of Atmospheric Rivers in a High‐Resolution Large Ensemble Climate Data Set

View all