Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical Models
Abstract An essential step toward meeting agreed climate targets and policies is the ability to understand and predict near‐term changes in global carbon cycle, and importantly, ocean carbon uptake. Initialized climate model simulations have proven skillful for near‐term predictability of the key ph...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Earth's Future |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023EF004204 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846110213069864960 |
|---|---|
| author | P. Gooya N. C. Swart P. Landschützer |
| author_facet | P. Gooya N. C. Swart P. Landschützer |
| author_sort | P. Gooya |
| collection | DOAJ |
| description | Abstract An essential step toward meeting agreed climate targets and policies is the ability to understand and predict near‐term changes in global carbon cycle, and importantly, ocean carbon uptake. Initialized climate model simulations have proven skillful for near‐term predictability of the key physical climate variables, for example, temperature, precipitation, etc. By comparison, predictions of biogeochemical fields like ocean carbon flux, are still emerging. Initial studies indicate skillful predictions are possible for lead‐times up to 6 years at global scale for some CMIP6 models. However, unlike core physical variables, biogeochemical variables are not directly initialized in existing decadal prediction systems, and extensive empirical parametrization of ocean‐biogeochemistry in Earth System Models introduces a significant source of uncertainty. Here we propose a new approach for improving the skill of decadal ocean carbon flux predictions using observationally‐constrained statistical models, as alternatives to the ocean‐biogeochemistry models. We use observations to train multi‐linear and neural‐network models to predict the ocean carbon flux. To account for observational uncertainties, we train using six different observational estimates of the flux. We then apply these trained statistical models using input predictors from the Canadian Earth System Model (CanESM5) decadal prediction system to produce new decadal predictions. Our hybrid GCM‐statistical approach significantly improves prediction skill, relative to the raw CanESM5 hindcast predictions over 1990–2019. Our hybrid‐model skill is also larger than that obtained by any available CMIP6 model. Using bias‐corrected CanESM5 predictors, we make forecasts for ocean carbon flux over 2020–2029. Both statistical models predict increases in the ocean carbon flux larger than the changes predicted from CanESM5 forecasts. Our work highlights the ability to improve decadal ocean carbon flux predictions by using observationally‐trained statistical models together with robust input predictors from GCM‐based decadal predictions. |
| format | Article |
| id | doaj-art-b1bc0f7b7bcb46d3a7c06b60fff3c48e |
| institution | Kabale University |
| issn | 2328-4277 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj-art-b1bc0f7b7bcb46d3a7c06b60fff3c48e2024-12-24T14:06:58ZengWileyEarth's Future2328-42772024-12-011212n/an/a10.1029/2023EF004204Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical ModelsP. Gooya0N. C. Swart1P. Landschützer2Canadian Centre for Climate Modeling and Analysis (CCCma) Victoria BC CanadaCanadian Centre for Climate Modeling and Analysis (CCCma) Victoria BC CanadaFlanders Marine Institute (VLIZ) Ostend BelgiumAbstract An essential step toward meeting agreed climate targets and policies is the ability to understand and predict near‐term changes in global carbon cycle, and importantly, ocean carbon uptake. Initialized climate model simulations have proven skillful for near‐term predictability of the key physical climate variables, for example, temperature, precipitation, etc. By comparison, predictions of biogeochemical fields like ocean carbon flux, are still emerging. Initial studies indicate skillful predictions are possible for lead‐times up to 6 years at global scale for some CMIP6 models. However, unlike core physical variables, biogeochemical variables are not directly initialized in existing decadal prediction systems, and extensive empirical parametrization of ocean‐biogeochemistry in Earth System Models introduces a significant source of uncertainty. Here we propose a new approach for improving the skill of decadal ocean carbon flux predictions using observationally‐constrained statistical models, as alternatives to the ocean‐biogeochemistry models. We use observations to train multi‐linear and neural‐network models to predict the ocean carbon flux. To account for observational uncertainties, we train using six different observational estimates of the flux. We then apply these trained statistical models using input predictors from the Canadian Earth System Model (CanESM5) decadal prediction system to produce new decadal predictions. Our hybrid GCM‐statistical approach significantly improves prediction skill, relative to the raw CanESM5 hindcast predictions over 1990–2019. Our hybrid‐model skill is also larger than that obtained by any available CMIP6 model. Using bias‐corrected CanESM5 predictors, we make forecasts for ocean carbon flux over 2020–2029. Both statistical models predict increases in the ocean carbon flux larger than the changes predicted from CanESM5 forecasts. Our work highlights the ability to improve decadal ocean carbon flux predictions by using observationally‐trained statistical models together with robust input predictors from GCM‐based decadal predictions.https://doi.org/10.1029/2023EF004204decadal predictabilityocean carbon fluxstatistical modelingCMIP6bias correctionneural netwrok |
| spellingShingle | P. Gooya N. C. Swart P. Landschützer Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical Models Earth's Future decadal predictability ocean carbon flux statistical modeling CMIP6 bias correction neural netwrok |
| title | Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical Models |
| title_full | Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical Models |
| title_fullStr | Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical Models |
| title_full_unstemmed | Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical Models |
| title_short | Improving GCM‐Based Decadal Ocean Carbon Flux Predictions Using Observationally‐Constrained Statistical Models |
| title_sort | improving gcm based decadal ocean carbon flux predictions using observationally constrained statistical models |
| topic | decadal predictability ocean carbon flux statistical modeling CMIP6 bias correction neural netwrok |
| url | https://doi.org/10.1029/2023EF004204 |
| work_keys_str_mv | AT pgooya improvinggcmbaseddecadaloceancarbonfluxpredictionsusingobservationallyconstrainedstatisticalmodels AT ncswart improvinggcmbaseddecadaloceancarbonfluxpredictionsusingobservationallyconstrainedstatisticalmodels AT plandschutzer improvinggcmbaseddecadaloceancarbonfluxpredictionsusingobservationallyconstrainedstatisticalmodels |