Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects

Abstract. The complexity of the state-of-the-art climate models requires high computational resources and imposes rather simplified parameterization of inland waters. The effect of lakes and reservoirs on the local and regional climate is commonly parameterized in regional or global climate modeling as a function of surface water temperature estimated by atmosphere-coupled one-dimensional lake models. The latter typically neglect one of the major transport mechanisms specific to artificial reservoirs: heat and mass advection due to inflows and outflows. Incorporation of these essentially two-dimensional processes into lake parameterizations requires a trade-off between computational efficiency and physical soundness, which is addressed in this study. We evaluated the performance of the two most used lake parameterization schemes and a machine-learning approach on high-resolution historical water temperature records from 24 reservoirs. Simulations were also performed at both variable and constant water level to explore the thermal structure differences between lakes and reservoirs. Our results highlight the need to include anthropogenic inflow and outflow controls in regional and global climate models. Our findings also highlight the efficiency of the machine-learning approach, which may overperform process-based physical models in both accuracy and computational requirements if applied to reservoirs with long-term observations available. Overall, results suggest that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with a retention time shorter than ∼ 100 d, if simulated without inflow and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.

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Modeling reservoir surface temperatures for regional and global climate models: a multi-model study on the inflow and level variation effects

10.5194/gmd-2021-64 ◽

2021 ◽

Author(s):

Manuel Celestino Vilela Teixeira Almeida ◽

Yurii Shevchuk ◽

Georgiy Kirillin ◽

Pedro Matos Soares ◽

Rita Margarida Antunes de Paula Cardoso ◽

...

Keyword(s):

Machine Learning ◽

Surface Water ◽

Water Temperature ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Learning Approach ◽

Surface Temperatures ◽

Machine Learning Approach ◽

Lakes And Reservoirs

Abstract. The complexity of the state-of-the-art climate models requires high computational resources and imposes rather simplified parameterization of inland waters. The effect of lakes and reservoirs on the local and regional climate is commonly parameterized in regional or global climate modeling as a function of surface water temperature estimated by atmosphere-coupled one-dimensional lake models. The latter typically neglect one of the major transport mechanisms specific to artificial reservoirs: heat and mass advection due to in- and outflows. Incorporation of these essentially two-dimensional processes into lake parameterizations requires a trade-off between computational efficiency and physical soundness, which is addressed in this study. We evaluated the performance of the two most used lake parameterization schemes and a machine learning approach on high-resolution historical water temperature records from 24 reservoirs. Simulations were also performed at both variable and constant water level to explore the thermal structure differences between lakes and reservoirs. Our results highlight that surface water temperatures in reservoirs differ significantly from those found in lakes, reinforcing the need to include anthropogenic inflow and outflow controls in regional and global climate models. Our findings also highlight the efficiency of the machine learning approach, which may overperform process-based physical models both in accuracy and in computational requirements, if applied to reservoirs with long-term observations available. A relationship between mean water retention times and the importance of inflows and outflows is established: reservoirs with the retention time shorter than ~100 days, if simulated without in- and outflow effects, tend to exhibit a statistically significant deviation in the computed surface temperatures regardless of their morphological characteristics.

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IMPROVING THE NORTH AMERICAN MULTI-MODEL ENSEMBLE (NMME) PRECIPITATION FORECASTS AT SEASONAL SCALE OVER THE HIMALAYAN REGION USING MACHINE LEARNING

International Journal of Big Data Mining for Global Warming ◽

10.1142/s263053482150008x ◽

2021 ◽

Author(s):

SOURABH SHRIVASTAVA ◽

RAM AVTAR ◽

PRASANTA KUMAR BAL

Keyword(s):

Machine Learning ◽

North American ◽

Climate Models ◽

Global Climate ◽

Horizontal Resolution ◽

Summer Monsoon Rainfall ◽

Global Climate Models ◽

Himalayan Region ◽

Model Ensemble ◽

The North

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

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Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

10.21203/rs.3.rs-227967/v1 ◽

2021 ◽

Author(s):

Gavin D. Madakumbura ◽

Chad W. Thackeray ◽

Jesse Norris ◽

Naomi Goldenson ◽

Alex Hall

Keyword(s):

Machine Learning ◽

Model Uncertainty ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Internal Variability ◽

Global Climate Models ◽

Human Influence ◽

Anthropogenic Forcing ◽

Global Datasets

Abstract Global climate models produce large increases in extreme precipitation when subject to anthropogenic forcing, but detecting this human influence in observations is challenging. Large internal variability makes the signal difficult to characterize. Models produce diverse precipitation responses to anthropogenic forcing, mirroring a variety of parameterization choices for subgrid-scale processes. And observations are inhomogeneously sampled in space and time, leading to multiple global datasets, each produced with a different homogenization technique. Thus, previous attempts to detect human influence on extreme precipitation have not incorporated internal variability or model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods, we find a physically interpretable anthropogenic signal that is detectable in all global datasets. Detection occurs even when internal variability and model uncertainty are taken into account. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in extreme precipitation.

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Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

Nature Communications ◽

10.1038/s41467-021-24262-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Gavin D. Madakumbura ◽

Chad W. Thackeray ◽

Jesse Norris ◽

Naomi Goldenson ◽

Alex Hall

Keyword(s):

Machine Learning ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Internal Variability ◽

Global Climate Models ◽

Sampled Data ◽

Anthropogenic Forcing ◽

Precipitation Response ◽

Observational Record

Abstract The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.

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