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Abstract
This study provides an assessment of the uncertainty in ocean surface (OS) freshwater budgets and variability using evaporation E and precipitation P from 10 atmospheric reanalyses, two combined satellite-based E − P products, and two observation-based salinity products. Three issues are examined: the uncertainty level in the OS freshwater budget in atmospheric reanalyses, the uncertainty structure and association with the global ocean wet/dry zones, and the potential of salinity in ascribing the uncertainty in E − P. The products agree on the global mean pattern but differ considerably in magnitude. The OS freshwater budgets are 129 ± 10 (8%) cm yr−1 for E, 118 ± 11 (9%) cm yr−1 for P, and 11 ± 4 (36%) cm yr−1 for E − P, where the mean and error represent the ensemble mean and one standard deviation of the ensemble spread. The E − P uncertainty exceeds the uncertainty in E and P by a factor of 4 or more. The large uncertainty is attributed to P in the tropical wet zone. Most reanalyses tend to produce a wider tropical rainband when compared to satellite products, with the exception of two recent reanalyses that implement an observation-based correction for the model-generated P over land. The disparity in the width and the extent of seasonal migrations of the tropical wet zone causes a large spread in P, implying that the tropical moist physics and the realism of tropical rainfall remain a key challenge. Satellite salinity appears feasible to evaluate the fidelity of E − P variability in three tropical areas, where the uncertainty diagnosis has a global indication.
Abstract
This study provides an assessment of the uncertainty in ocean surface (OS) freshwater budgets and variability using evaporation E and precipitation P from 10 atmospheric reanalyses, two combined satellite-based E − P products, and two observation-based salinity products. Three issues are examined: the uncertainty level in the OS freshwater budget in atmospheric reanalyses, the uncertainty structure and association with the global ocean wet/dry zones, and the potential of salinity in ascribing the uncertainty in E − P. The products agree on the global mean pattern but differ considerably in magnitude. The OS freshwater budgets are 129 ± 10 (8%) cm yr−1 for E, 118 ± 11 (9%) cm yr−1 for P, and 11 ± 4 (36%) cm yr−1 for E − P, where the mean and error represent the ensemble mean and one standard deviation of the ensemble spread. The E − P uncertainty exceeds the uncertainty in E and P by a factor of 4 or more. The large uncertainty is attributed to P in the tropical wet zone. Most reanalyses tend to produce a wider tropical rainband when compared to satellite products, with the exception of two recent reanalyses that implement an observation-based correction for the model-generated P over land. The disparity in the width and the extent of seasonal migrations of the tropical wet zone causes a large spread in P, implying that the tropical moist physics and the realism of tropical rainfall remain a key challenge. Satellite salinity appears feasible to evaluate the fidelity of E − P variability in three tropical areas, where the uncertainty diagnosis has a global indication.
Abstract
The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m−2 for downward shortwave and −2.5 (7.1) W m−2 for downward longwave irradiances over ocean and −1.7 (7.8) W m−2 for downward shortwave and −1.0 (7.6) W m−2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.
Abstract
The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m−2 for downward shortwave and −2.5 (7.1) W m−2 for downward longwave irradiances over ocean and −1.7 (7.8) W m−2 for downward shortwave and −1.0 (7.6) W m−2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.
Abstract
Interannual variability in the volumetric water mass distribution within the North Atlantic Subtropical Gyre is described in relation to variability in the Atlantic meridional overturning circulation. The relative roles of diabatic and adiabatic processes in the volume and heat budgets of the subtropical gyre are investigated by projecting data into temperature coordinates as volumes of water using an Argo-based climatology and an ocean state estimate (ECCO version 4). This highlights that variations in the subtropical gyre volume budget are predominantly set by transport divergence in the gyre. A strong correlation between the volume anomaly due to transport divergence and the variability of both thermocline depth and Ekman pumping over the gyre suggests that wind-driven heave drives transport anomalies at the gyre boundaries. This wind-driven heaving contributes significantly to variations in the heat content of the gyre, as do anomalies in the air–sea fluxes. The analysis presented suggests that wind forcing plays an important role in driving interannual variability in the Atlantic meridional overturning circulation and that this variability can be unraveled from spatially distributed hydrographic observations using the framework presented here.
Abstract
Interannual variability in the volumetric water mass distribution within the North Atlantic Subtropical Gyre is described in relation to variability in the Atlantic meridional overturning circulation. The relative roles of diabatic and adiabatic processes in the volume and heat budgets of the subtropical gyre are investigated by projecting data into temperature coordinates as volumes of water using an Argo-based climatology and an ocean state estimate (ECCO version 4). This highlights that variations in the subtropical gyre volume budget are predominantly set by transport divergence in the gyre. A strong correlation between the volume anomaly due to transport divergence and the variability of both thermocline depth and Ekman pumping over the gyre suggests that wind-driven heave drives transport anomalies at the gyre boundaries. This wind-driven heaving contributes significantly to variations in the heat content of the gyre, as do anomalies in the air–sea fluxes. The analysis presented suggests that wind forcing plays an important role in driving interannual variability in the Atlantic meridional overturning circulation and that this variability can be unraveled from spatially distributed hydrographic observations using the framework presented here.
THE PIRATA PROGRAM
History, Accomplishments, and Future Directions *
The Pilot Research Moored Array in the tropical Atlantic (PIRATA) was developed as a multinational observation network to improve our knowledge and understanding of ocean-atmosphere variability in the tropical Atlantic. PIRATA was motivated by fundamental scientific issues and by societal needs for improved prediction of climate variability and its impact on the economies of West Africa, northeastern Brazil, the West Indies, and the United States. In this paper the implementation of this network is described, noteworthy accomplishments are highlighted, and the future of PIRATA in the framework of a sustainable tropical Atlantic observing system is discussed. We demonstrate that PIRATA has advanced beyond a “Pilot” program and, as such, we have redefined the PIRATA acronym to be “Prediction and Research Moored Array in the Tropical Atlantic.”
The Pilot Research Moored Array in the tropical Atlantic (PIRATA) was developed as a multinational observation network to improve our knowledge and understanding of ocean-atmosphere variability in the tropical Atlantic. PIRATA was motivated by fundamental scientific issues and by societal needs for improved prediction of climate variability and its impact on the economies of West Africa, northeastern Brazil, the West Indies, and the United States. In this paper the implementation of this network is described, noteworthy accomplishments are highlighted, and the future of PIRATA in the framework of a sustainable tropical Atlantic observing system is discussed. We demonstrate that PIRATA has advanced beyond a “Pilot” program and, as such, we have redefined the PIRATA acronym to be “Prediction and Research Moored Array in the Tropical Atlantic.”
Abstract
From 11 April to 11 June 2018 a new type of ocean observing platform, the Saildrone surface vehicle, collected data on a round-trip, 60-day cruise from San Francisco Bay, down the U.S. and Mexican coast to Guadalupe Island. The cruise track was selected to optimize the science team’s validation and science objectives. The validation objectives include establishing the accuracy of these new measurements. The scientific objectives include validation of satellite-derived fluxes, sea surface temperatures, and wind vectors and studies of upwelling dynamics, river plumes, air–sea interactions including frontal regions, and diurnal warming regions. On this deployment, the Saildrone carried 16 atmospheric and oceanographic sensors. Future planned cruises (with open data policies) are focused on improving our understanding of air–sea fluxes in the Arctic Ocean and around North Brazil Current rings.
Abstract
From 11 April to 11 June 2018 a new type of ocean observing platform, the Saildrone surface vehicle, collected data on a round-trip, 60-day cruise from San Francisco Bay, down the U.S. and Mexican coast to Guadalupe Island. The cruise track was selected to optimize the science team’s validation and science objectives. The validation objectives include establishing the accuracy of these new measurements. The scientific objectives include validation of satellite-derived fluxes, sea surface temperatures, and wind vectors and studies of upwelling dynamics, river plumes, air–sea interactions including frontal regions, and diurnal warming regions. On this deployment, the Saildrone carried 16 atmospheric and oceanographic sensors. Future planned cruises (with open data policies) are focused on improving our understanding of air–sea fluxes in the Arctic Ocean and around North Brazil Current rings.
ABSTRACT
Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of essential climate variables (ECVs), many related to the water cycle, required to systematically monitor Earth’s climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, and resolution, to consistently characterize water cycle variability at multiple spatial and temporal scales. Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle. We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity. An advanced closure of the water cycle requires improved model–data synthesis capabilities, particularly at regional to local scales.
ABSTRACT
Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of essential climate variables (ECVs), many related to the water cycle, required to systematically monitor Earth’s climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, and resolution, to consistently characterize water cycle variability at multiple spatial and temporal scales. Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle. We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity. An advanced closure of the water cycle requires improved model–data synthesis capabilities, particularly at regional to local scales.
