Optimal estimation of sea surface temperature from AMSR-E

The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST

Surface Temperatures of the Arctic Oceans

The Arctic is warming faster than any other region of the world (known as Arctic amplification) leading to rapid and widespread changes, which transform the Arctic environment with far-reaching consequences. Despite much attention, existing observational datasets, reanalyses and climate models show large uncertainties in Arctic surface temperatures and limited consensus on the magnitude of the Arctic amplification. The difference and uncertainties mainly arise in the Arctic oceans where many clouds, the mix of open water and sea ice, and the sparse in situ network challenge an accurate and absolute surface temperature estimation.Each of these challenges are considered in this PhD study with the overall aim to provide more accurate and consistent sea surface temperature (SST) and sea ice surface temperature (IST) estimates in the Arctic, and thereby improving the understanding, characterization and monitoring of the Arctic warming and amplification.The frequent and persistent cloud cover in the Arctic limits the extent to which SST can be retrieved from thermal infrared satellite sensors. Therefore, this PhD study explores the capability of using passive microwave (PMW) observations to retrieve SST and improve the SST estimates in the Arctic. Multiple PMW SST retrieval algorithms have been developed, analysed and validated and the first European PMW SST climate data record has been generated. To prepare for the future Copernicus Imaging Microwave Radiometer (CIMR) satellite mission, this study also investigates the impact of using different frequency channels in SST retrievals, with promising results for the proposed CIMR constellation. The impact of including the PMW SST observations in the Arctic surface temperature estimation has been evaluated and substantial improvements are seen. The results are expected to become even better in the future with the launch of CIMR, which will enable SST retrievals at lower uncertainties and much closer to the coasts and sea ice. Due to the mix of open ocean and sea ice (and the temporal varying sea ice coverage) the most consistent way to monitor the Arctic surface temperature change is by integrating SST and IST estimates. This PhD study presents the first gap-free infrared satellite-based climate data record (1982-2021) of combined sea and sea-ice surface temperatures in the Arctic (>58◦N), which can be used as a consistent indicator for climate monitoring. It shows that the combined sea and sea-ice surface temperature has increased by ∼4.5◦C from 1982 to 2021, with a peak warming of ∼10◦C in the northeastern Barents Sea. To supplement the sparse in situ network, the satellite-observed ISTs have been used to estimate near-surface air temperature (T2m) over sea ice. The satellite-derived T2m estimates provide much better spatial coverage than the in situ observations and show improved performance compared to ECMWF’s most recent reanalysis (ERA5). The satellitederived IST and T2m estimates provide an important supplement to the existing in situ observations and have a large potential to be used for assimilation, evaluating and improving global surface temperature reconstructions, atmospheric reanalyses and climate models in the Arctic. Initial efforts show that the satellite-derived surface temperatures can improve our physical understanding and guide future developments in global climate models and atmospheric reanalyses in the Arctic

Optimal Estimation of Sea Surface Temperature from AMSR-E

The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST

Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements

Two machine learning (ML) models are investigated for retrieving sea surface temperature (SST) from passive microwave (PMW) satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and auxiliary data, such as ERA5 reanalysis data. The first model is the Extreme Gradient Boosting (XBG) model and the second is a multilayer perceptron neural network (NN). The performance of the two ML algorithms is compared to that of an existing state-of-the-art regression (RE) retrieval algorithm. The performance of the three algorithms is assessed using independent in situ SSTs from drifting buoys. Overall, the three models have similar biases; 0.01, 0.01 and −0.02 K for the XGB, NN and RE, respectively. The XGB model performs best with respect to standard deviation; 0.36 K. While the NN model performs slightly better than the RE model with respect to standard deviation, 0.50 and 0.55 K, respectively, the RE model is found to be more sensitive to changes in the in situ SST. Moreover, the XGB model is the least sensitive with an overall sensitivity of 0.78, compared to 0.90 for the RE model and 0.88 for the NN model. The good performance of the two ML algorithms compared to the state-of-the-art RE algorithm in this initial study demonstrates that there is a large potential in the use of ML algorithms for the retrieval of SST from PMW satellite observations

Exploring machine learning techniques to retrieve sea surface temperatures from passive microwave measurements

Two machine learning (ML) models are investigated for retrieving sea surface temperature (SST) from passive microwave (PMW) satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and auxiliary data, such as ERA5 reanalysis data. The first model is the Extreme Gradient Boosting (XBG) model and the second is a multilayer perceptron neural network (NN). The performance of the two ML algorithms is compared to that of an existing state-of-the-art regression (RE) retrieval algorithm. The performance of the three algorithms is assessed using independent in situ SSTs from drifting buoys. Overall, the three models have similar biases; 0.01, 0.01 and −0.02 K for the XGB, NN and RE, respectively. The XGB model performs best with respect to standard deviation; 0.36 K. While the NN model performs slightly better than the RE model with respect to standard deviation, 0.50 and 0.55 K, respectively, the RE model is found to be more sensitive to changes in the in situ SST. Moreover, the XGB model is the least sensitive with an overall sensitivity of 0.78, compared to 0.90 for the RE model and 0.88 for the NN model. The good performance of the two ML algorithms compared to the state-of-the-art RE algorithm in this initial study demonstrates that there is a large potential in the use of ML algorithms for the retrieval of SST from PMW satellite observations