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Abstract
The purpose of this study is to present a satellite-derived sea surface temperature (SST) climatology based on Pathfinder Advanced Very High Resolution Radiometer (AVHRR) data and to evaluate it and several other climatologies for their usefulness in the determination of SST trends. The method of evaluation uses two long-term observational collections of in situ SST measurements: the 1994 World Ocean Atlas (WOA94) and the Comprehensive Ocean–Atmosphere Data Set (COADS). Each of the SST climatologies being evaluated is subtracted from each raw SST observation in WOA94 and COADS to produce several separate long-term anomaly datasets. The anomaly dataset with the smallest standard deviation is assumed to identify the climatology best able to represent the spatial and seasonal SST variability and therefore be most capable of reducing the uncertainty in SST trend determinations.
The satellite SST climatology was created at a resolution of 9.28 km using both day and night satellite fields generated with the version 4 AVHRR Pathfinder algorithm and cloud-masking procedures, plus an erosion filter that provides additional cloud masking in the vicinity of cloud edges. Using the statistical comparison method, the performance of this “Pathfinder + erosion” climatology is compared with the performances of the WOA94 1° in situ climatology, the Reynolds satellite and in situ blended 1° analysis, version 2.2 of the blended 1° Global Sea-Ice and Sea Surface Temperature (GISST) climatology, and the in situ 5° Global Ocean Surface Temperature Atlas (GOSTA).
The standard deviation of the anomalies produced using the raw WOA94 in situ observations and the reference SST climatologies indicate that the 9.28-km Pathfinder + erosion climatology is more representative of spatial and seasonal SST variability than the traditional in situ and blended SST climatologies. For the anomalies created from the raw COADS observations, the Pathfinder + erosion climatology is also found to minimize variance more than the other climatologies. In both cases, the 5° GOSTA climatology exhibits the largest anomaly standard deviations.
Regional characteristics of the climatologies are also examined by binning the anomalies by climatological temperature classes and latitudinal bands. Generally, the Pathfinder + erosion climatology yields lower anomaly variances in the mid- and high latitudes and the Southern Hemisphere, but larger variances than the 1° climatologies in the warm, Northern Hemisphere low-latitude regions.
Abstract
The purpose of this study is to present a satellite-derived sea surface temperature (SST) climatology based on Pathfinder Advanced Very High Resolution Radiometer (AVHRR) data and to evaluate it and several other climatologies for their usefulness in the determination of SST trends. The method of evaluation uses two long-term observational collections of in situ SST measurements: the 1994 World Ocean Atlas (WOA94) and the Comprehensive Ocean–Atmosphere Data Set (COADS). Each of the SST climatologies being evaluated is subtracted from each raw SST observation in WOA94 and COADS to produce several separate long-term anomaly datasets. The anomaly dataset with the smallest standard deviation is assumed to identify the climatology best able to represent the spatial and seasonal SST variability and therefore be most capable of reducing the uncertainty in SST trend determinations.
The satellite SST climatology was created at a resolution of 9.28 km using both day and night satellite fields generated with the version 4 AVHRR Pathfinder algorithm and cloud-masking procedures, plus an erosion filter that provides additional cloud masking in the vicinity of cloud edges. Using the statistical comparison method, the performance of this “Pathfinder + erosion” climatology is compared with the performances of the WOA94 1° in situ climatology, the Reynolds satellite and in situ blended 1° analysis, version 2.2 of the blended 1° Global Sea-Ice and Sea Surface Temperature (GISST) climatology, and the in situ 5° Global Ocean Surface Temperature Atlas (GOSTA).
The standard deviation of the anomalies produced using the raw WOA94 in situ observations and the reference SST climatologies indicate that the 9.28-km Pathfinder + erosion climatology is more representative of spatial and seasonal SST variability than the traditional in situ and blended SST climatologies. For the anomalies created from the raw COADS observations, the Pathfinder + erosion climatology is also found to minimize variance more than the other climatologies. In both cases, the 5° GOSTA climatology exhibits the largest anomaly standard deviations.
Regional characteristics of the climatologies are also examined by binning the anomalies by climatological temperature classes and latitudinal bands. Generally, the Pathfinder + erosion climatology yields lower anomaly variances in the mid- and high latitudes and the Southern Hemisphere, but larger variances than the 1° climatologies in the warm, Northern Hemisphere low-latitude regions.
Abstract
Individual sea surface temperature (SST) anomalies are calculated using a satellite-based climatology and observations from the World Ocean Atlas 1994 (WOA94) and the Comprehensive Ocean–Atmosphere Data Set (COADS) to characterize global and regional changes in ocean surface temperature since 1942. For each of these datasets, anomaly trends are computed using a new method that groups individual anomalies into climatological temperature classes. These temperature class anomaly trends are compared with trends estimated using a technique representative of previous studies based on 5° latitude–longitude bins.
Global linear trends in the data-rich period between 1960 and 1990 calculated from the WOA94 data are found to be 0.14° ± 0.04°C decade−1 for the temperature class approach and 0.13° ± 0.04°C decade−1 for the 5° bin approach. The corresponding results for the COADS data are 0.10° ± 0.03°C and 0.09° ± 0.03°C decade−1. These trends are not statistically different at the 95% confidence level. Additionally, they agree closely with both SST and land–air temperature trends estimated from results reported by the Intergovernmental Panel on Climate Change. The similarity between the COADS trends and the trends calculated from the WOA94 dataset provides confirmation of previous SST trend studies, which are based almost exclusively on volunteer observing ship datasets like COADS.
Regional linear trends reveal a nonuniformity in the SST rates between 1945–70 and 1970–95. Intensified warming during the later period is observed in the eastern equatorial Pacific, the North Atlantic subtropical convergence, and in the vicinity of the Kuroshio extension. Also, despite close agreement globally, localized differences between COADS and WOA94 trends are observed.
Abstract
Individual sea surface temperature (SST) anomalies are calculated using a satellite-based climatology and observations from the World Ocean Atlas 1994 (WOA94) and the Comprehensive Ocean–Atmosphere Data Set (COADS) to characterize global and regional changes in ocean surface temperature since 1942. For each of these datasets, anomaly trends are computed using a new method that groups individual anomalies into climatological temperature classes. These temperature class anomaly trends are compared with trends estimated using a technique representative of previous studies based on 5° latitude–longitude bins.
Global linear trends in the data-rich period between 1960 and 1990 calculated from the WOA94 data are found to be 0.14° ± 0.04°C decade−1 for the temperature class approach and 0.13° ± 0.04°C decade−1 for the 5° bin approach. The corresponding results for the COADS data are 0.10° ± 0.03°C and 0.09° ± 0.03°C decade−1. These trends are not statistically different at the 95% confidence level. Additionally, they agree closely with both SST and land–air temperature trends estimated from results reported by the Intergovernmental Panel on Climate Change. The similarity between the COADS trends and the trends calculated from the WOA94 dataset provides confirmation of previous SST trend studies, which are based almost exclusively on volunteer observing ship datasets like COADS.
Regional linear trends reveal a nonuniformity in the SST rates between 1945–70 and 1970–95. Intensified warming during the later period is observed in the eastern equatorial Pacific, the North Atlantic subtropical convergence, and in the vicinity of the Kuroshio extension. Also, despite close agreement globally, localized differences between COADS and WOA94 trends are observed.
Abstract
Two Pathfinder sea surface temperature (SST) datasets—version 5.0 (V50) and version 4.1 (V41)—were compared in two test areas: 1) the Gulf Stream (GS) between 35° and 43°N, 75° and 60°W and 2) the California coast (CC) between 30° and 45°N, 130° and 120°W. Using a nearest-neighbor approach, V50 data were regridded to the lower resolution V41 9-km data. The V50 and V41 versions were also independently compared with data from the World Ocean Database (WOD). Climatological monthly rms differences between V50 and V41 were calculated as well as seasonal differences between V50, V41, and the WOD.
Maximum rms differences of 0.8°C between the V50 and V41 were seen in June for the GS. In the CC maximum differences of 0.4°C were seen in July. Significant seasonal trends were evident in rms differences between V41 and the WOD, with a maximum of 1.5°C occurring in the GS in June and in the CC in July. No seasonal peaks occurred in the rms differences between V50 and the WOD. SST gradients were calculated using both V50 and V41 datasets. Maximum climatological SST gradients were seen in the June time frame for the GS and July for the CC, consistent with the largest rms differences compared to the WOD. Results indicate the importance of projects such as the Group for High-Resolution Sea Surface Temperature (GHRSST) and the creation of high-resolution SST datasets for resolving air–sea interactions, specifically in areas of strong SST gradients.
Abstract
Two Pathfinder sea surface temperature (SST) datasets—version 5.0 (V50) and version 4.1 (V41)—were compared in two test areas: 1) the Gulf Stream (GS) between 35° and 43°N, 75° and 60°W and 2) the California coast (CC) between 30° and 45°N, 130° and 120°W. Using a nearest-neighbor approach, V50 data were regridded to the lower resolution V41 9-km data. The V50 and V41 versions were also independently compared with data from the World Ocean Database (WOD). Climatological monthly rms differences between V50 and V41 were calculated as well as seasonal differences between V50, V41, and the WOD.
Maximum rms differences of 0.8°C between the V50 and V41 were seen in June for the GS. In the CC maximum differences of 0.4°C were seen in July. Significant seasonal trends were evident in rms differences between V41 and the WOD, with a maximum of 1.5°C occurring in the GS in June and in the CC in July. No seasonal peaks occurred in the rms differences between V50 and the WOD. SST gradients were calculated using both V50 and V41 datasets. Maximum climatological SST gradients were seen in the June time frame for the GS and July for the CC, consistent with the largest rms differences compared to the WOD. Results indicate the importance of projects such as the Group for High-Resolution Sea Surface Temperature (GHRSST) and the creation of high-resolution SST datasets for resolving air–sea interactions, specifically in areas of strong SST gradients.
Abstract
Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses.
The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.
Abstract
Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses.
The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.
Abstract
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
Abstract
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
