Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture
The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/9/10/186 |
| _version_ | 1797472918293184512 |
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| author | Francisco Andree Ramírez Casas Laxmi Sushama Bernardo Teufel |
| author_facet | Francisco Andree Ramírez Casas Laxmi Sushama Bernardo Teufel |
| author_sort | Francisco Andree Ramírez Casas |
| collection | DOAJ |
| description | The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest (RF) model, trained on observed data of the climate variable of interest and related predictors. The second step involves emulations of the climate variable of interest with the RF model developed in step one by replacing the observed predictors with those from the climate model one at a time. The assumption is that comparing these emulations with that of a reference emulation driven by all observed predictors can shed light on the contribution of respective predictor biases to the biases in the climate model simulation. The proposed framework is used to understand the biases in the Global Environmental Multiscale (GEM) model simulated surface soil moisture (SSM) for the April–September period, over a domain covering part of north-east Canada. The grid cell-based RF model, trained on daily SSM and related climate predictors (water availability, 2 m temperature, relative humidity, snowmelt, maximum snow water equivalent) from the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), demonstrates great skill in emulating SSM, with root mean square error of 0.036. Comparison of the five RF emulations based on GEM predictors with that based on ERA5 predictors suggests that the biases in the mean April–September SSM can be attributed mainly to biases in three predictors: water availability, 2 m temperature and relative humidity. The regions where these predictors contribute to biases in SSM are mostly collocated with the regions where they are shown to be the among the top three influential predictors through the predictor importance analysis, i.e., 2 m temperature in the southern part of the domain, relative humidity in the northern part of the domain and water availability over rest of the domain. The framework, without having to undertake expensive simulations with the climate model, thus successfully identifies the main causes for SSM biases, albeit with slightly reduced skill for heavily perturbed simulations. Furthermore, identification of the causes for biases, by informing targeted climate model improvements, can lead to additional reductions in computational costs. |
| first_indexed | 2024-03-09T20:07:57Z |
| format | Article |
| id | doaj.art-91f62cfe10fb47a99c4ca9a85645657d |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-09T20:07:57Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-91f62cfe10fb47a99c4ca9a85645657d2023-11-24T00:25:23ZengMDPI AGHydrology2306-53382022-10-0191018610.3390/hydrology9100186Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil MoistureFrancisco Andree Ramírez Casas0Laxmi Sushama1Bernardo Teufel2Department of Civil Engineering, Trottier Institute for Sustainability in Engineering and Design, McGill University, Montreal, QC H3A OC3, CanadaDepartment of Civil Engineering, Trottier Institute for Sustainability in Engineering and Design, McGill University, Montreal, QC H3A OC3, CanadaDepartment of Civil Engineering, Trottier Institute for Sustainability in Engineering and Design, McGill University, Montreal, QC H3A OC3, CanadaThe development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest (RF) model, trained on observed data of the climate variable of interest and related predictors. The second step involves emulations of the climate variable of interest with the RF model developed in step one by replacing the observed predictors with those from the climate model one at a time. The assumption is that comparing these emulations with that of a reference emulation driven by all observed predictors can shed light on the contribution of respective predictor biases to the biases in the climate model simulation. The proposed framework is used to understand the biases in the Global Environmental Multiscale (GEM) model simulated surface soil moisture (SSM) for the April–September period, over a domain covering part of north-east Canada. The grid cell-based RF model, trained on daily SSM and related climate predictors (water availability, 2 m temperature, relative humidity, snowmelt, maximum snow water equivalent) from the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), demonstrates great skill in emulating SSM, with root mean square error of 0.036. Comparison of the five RF emulations based on GEM predictors with that based on ERA5 predictors suggests that the biases in the mean April–September SSM can be attributed mainly to biases in three predictors: water availability, 2 m temperature and relative humidity. The regions where these predictors contribute to biases in SSM are mostly collocated with the regions where they are shown to be the among the top three influential predictors through the predictor importance analysis, i.e., 2 m temperature in the southern part of the domain, relative humidity in the northern part of the domain and water availability over rest of the domain. The framework, without having to undertake expensive simulations with the climate model, thus successfully identifies the main causes for SSM biases, albeit with slightly reduced skill for heavily perturbed simulations. Furthermore, identification of the causes for biases, by informing targeted climate model improvements, can lead to additional reductions in computational costs.https://www.mdpi.com/2306-5338/9/10/186machine learning-based climate model validationRandom Forestsurface soil moisturebias assessmentEastern Canada |
| spellingShingle | Francisco Andree Ramírez Casas Laxmi Sushama Bernardo Teufel Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture Hydrology machine learning-based climate model validation Random Forest surface soil moisture bias assessment Eastern Canada |
| title | Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture |
| title_full | Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture |
| title_fullStr | Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture |
| title_full_unstemmed | Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture |
| title_short | Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture |
| title_sort | development of a machine learning framework to aid climate model assessment and improvement case study of surface soil moisture |
| topic | machine learning-based climate model validation Random Forest surface soil moisture bias assessment Eastern Canada |
| url | https://www.mdpi.com/2306-5338/9/10/186 |
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