The Application Of Machine Learning In The Lower Boundary Conditions For Improving Climate Prediction

Climate prediction is a challenging task due to the complexity of the climate system,and climate models are crucial for accurate predictions.However,obtaining precise initial fields and boundary conditions for these models is a basic yet difficult scientific problem.The initial fields and boundary conditions of atmospheric models require essential information such as sea surface temperature,snow cover,sea ice,and soil moisture on the underlying surface.These external forcings play a significant role in climate change and have a massive impact on regional and global climate on different time scales.Of these factors,sea surface temperature is particularly important as it directly affects the interaction between the ocean and atmosphere,making it a necessary lower boundary condition.Obtaining accurate sea surface temperature is critical for generating precise initial and boundary conditions for numerical models,which in turn improves short-term climate predictions.Soil moisture also plays a vital role in the climate system,ranking second only to sea surface temperature.In some regions far from the ocean,soil moisture’s role even exceeds that of sea surface temperature,making it an essential factor in climate prediction.Therefore,this paper uses the convolutional neural network algorithm in machine learning to predict sea surface temperature and soil moisture on a seasonal scale.On this basis,the sea surface temperature and soil temperature predicted by machine learning are updated to the WRF model as the lower boundary conditions and initial conditions,and the prediction test and verification of the short-term summer climate in 2022 are carried out.The preliminary conclusions are as follows:(1)SST has significant interannual variability at depths of 5.02 m,15.08 m,25.16m,35.28 m,45.45 m,and 76.55 m.The mean standard deviation of temperature in each layer is 1.57 K、1.50 K、1.40 K,1.28 K,1.18 K,0.94 K respectively.As the depth increases,the sea temperature changes gradually become smaller.The lagged autocorrelation coefficient of the surface sea temperature is the highest,and the autocorrelation coefficient with a one-month lag reaches 0.7.As the depth deepens,the lagged correlation coefficient gradually decreases.The correlation coefficient between the surface sea temperature and the one-month lag at 76 m is lower than 0.4,but the correlation coefficient between the two can still pass the 99%significance test with a lag of 12 months.The persistence of subsurface sea temperature anomalies varies greatly in different months.The persistence of sea salt anomalies does not change with the month,and the persistence of sea temperature anomalies is significantly weakened in spring,showing a"spring barrier".Overall,the Shanghai temperature and sea-salt anomaly signal can last for 6 months or more in the global ocean.(2)The convolutional neural network can effectively extract the temporal and spatial distribution characteristics of deep sea temperature and sea salt to predict the global sea surface temperature over a long period of time.The deep sea temperature and sea salt in the past six months are used as predictors,and the average deviation of the predicted sea surface temperature in the next six months is about 0-0.8K.The prediction deviation is larger near the coastline,while it is less than 0.5K in the ocean far away from the coastline.At the same time,the convolutional neural network model constructed using deep sea temperature and sea salt signals can reproduce the main anomalous changes in sea surface temperature,such as IPO,AMO,IOD,and El Ni(?)o/La Ni(?)a events,providing a new basis for sea temperature prediction.(3)The convolutional neural network can effectively extract the spatiotemporal characteristics of soil temperature and humidity and can be used to predict the global soil moisture for a long time.Use the soil temperature and humidity of four layers,0～7cm,7～28 cm,28～100 cm,and 100～289 cm,in the first six months as factors to predict the soil moisture in the next six months.The deviation of shallow soil moisture is less than 0.05m~3/m~3,and the deep soil moisture deviation is within 0.02m~3/m~3.The convolutional neural network model established using soil temperature and humidity signals can be used to predict soil moisture in different dry and wet areas and can reproduce its main abnormal change characteristics.The average deviation of soil moisture prediction in arid and humid areas is within 0.02m~3/m~3,and the prediction effect of the wet area is slightly better than that of the dry area.(4)The ability of the WRF model to predict global short-term climate still needs improvement,and the simulated spatial distribution of precipitation is quite different from the observation,especially for the simulated precipitation near the equator.The air temperature simulated by WRF has an obvious deviation,and the average deviation can reach up to 4.5 K.The sea surface temperature predicted by machine learningis input into the WRF model as an external forcing,which can effectively improve the short-term climate prediction.The relative deviation of precipitation is reduced to less than 50%,and the absolute deviation of air temperature is reduced to within 3.5 K.