An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter
We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus o...
Published in: | Climate Dynamics |
---|---|
Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
2004
|
Subjects: | |
Online Access: | http://nora.nerc.ac.uk/id/eprint/124075/ http://www.metapress.com/media/7ped06ptqn0uwmfynwul/contributions/0/j/t/a/0jtavcecm5cl3ulx_html/fulltext.html https://doi.org/10.1007/s00382-004-0471-4 |
id |
ftnerc:oai:nora.nerc.ac.uk:124075 |
---|---|
record_format |
openpolar |
spelling |
ftnerc:oai:nora.nerc.ac.uk:124075 2023-05-15T17:36:04+02:00 An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter Hargreaves, J.C. Annan, J.D. Edwards, N.R. Marsh, R. 2004 http://nora.nerc.ac.uk/id/eprint/124075/ http://www.metapress.com/media/7ped06ptqn0uwmfynwul/contributions/0/j/t/a/0jtavcecm5cl3ulx_html/fulltext.html https://doi.org/10.1007/s00382-004-0471-4 unknown Hargreaves, J.C.; Annan, J.D.; Edwards, N.R.; Marsh, R. 2004 An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter. Climate Dynamics, 23 (7-8). 745-760. https://doi.org/10.1007/s00382-004-0471-4 <https://doi.org/10.1007/s00382-004-0471-4> Publication - Article PeerReviewed 2004 ftnerc https://doi.org/10.1007/s00382-004-0471-4 2023-02-04T19:34:32Z We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO2 increase. The equilibrium surface air temperature rise for this CO2 increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial ... Article in Journal/Newspaper North Atlantic North atlantic Thermohaline circulation Natural Environment Research Council: NERC Open Research Archive Climate Dynamics 23 7-8 745 760 |
institution |
Open Polar |
collection |
Natural Environment Research Council: NERC Open Research Archive |
op_collection_id |
ftnerc |
language |
unknown |
description |
We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO2 increase. The equilibrium surface air temperature rise for this CO2 increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial ... |
format |
Article in Journal/Newspaper |
author |
Hargreaves, J.C. Annan, J.D. Edwards, N.R. Marsh, R. |
spellingShingle |
Hargreaves, J.C. Annan, J.D. Edwards, N.R. Marsh, R. An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter |
author_facet |
Hargreaves, J.C. Annan, J.D. Edwards, N.R. Marsh, R. |
author_sort |
Hargreaves, J.C. |
title |
An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter |
title_short |
An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter |
title_full |
An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter |
title_fullStr |
An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter |
title_full_unstemmed |
An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter |
title_sort |
efficient climate forecasting method using an intermediate complexity earth system model and the ensemble kalman filter |
publishDate |
2004 |
url |
http://nora.nerc.ac.uk/id/eprint/124075/ http://www.metapress.com/media/7ped06ptqn0uwmfynwul/contributions/0/j/t/a/0jtavcecm5cl3ulx_html/fulltext.html https://doi.org/10.1007/s00382-004-0471-4 |
genre |
North Atlantic North atlantic Thermohaline circulation |
genre_facet |
North Atlantic North atlantic Thermohaline circulation |
op_relation |
Hargreaves, J.C.; Annan, J.D.; Edwards, N.R.; Marsh, R. 2004 An efficient climate forecasting method using an intermediate complexity Earth system model and the ensemble Kalman Filter. Climate Dynamics, 23 (7-8). 745-760. https://doi.org/10.1007/s00382-004-0471-4 <https://doi.org/10.1007/s00382-004-0471-4> |
op_doi |
https://doi.org/10.1007/s00382-004-0471-4 |
container_title |
Climate Dynamics |
container_volume |
23 |
container_issue |
7-8 |
container_start_page |
745 |
op_container_end_page |
760 |
_version_ |
1766135434749935616 |