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ArticleChallenges and prospects for reducing coupled climate model SST biases in the eastern tropical Atlantic and Pacific Oceans : the U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group(American Meteorological Society, 2017-01-12) Zuidema, Paquita ; Chang, Ping ; Medeiros, Brian ; Kirtman, Benjamin ; Mechoso, Roberto ; Schneider, Edwin K. ; Toniazzo, Thomas ; Richter, Ingo ; Small, R. Justin ; Bellomo, Katinka ; Brandt, Peter ; de Szoeke, Simon ; Farrar, J. Thomas ; Jung, Eunsil ; Kato, Seiji ; Li, Mingkui ; Patricola, Christina ; Wang, Zaiyu ; Wood, Robert ; Xu, ZhaoWell-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.
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ArticleSurface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances(American Meteorological Society, 2013-05-01) Kato, Seiji ; Loeb, Norman G. ; Rose, Fred G. ; Doelling, David R. ; Rutan, David A. ; Caldwell, Thomas E. ; Yu, Lisan ; Weller, Robert A.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.
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ArticleMeasuring global ocean heat content to estimate the Earth energy Imbalance(Frontiers Media, 2019-08-20) Meyssignac, Benoit ; Boyer, Tim ; Zhao, Zhongxiang ; Hakuba, Maria Z. ; Landerer, Felix ; Stammer, Detlef ; Kohl, Armin ; Kato, Seiji ; L’Ecuyer, Tristan S. ; Ablain, Michaël ; Abraham, John Patrick ; Blazquez, Alejandro ; Cazenave, Anny ; Church, John A. ; Cowley, Rebecca ; Cheng, Lijing ; Domingues, Catia M. ; Giglio, Donata ; Gouretski, Viktor ; Ishii, Masayoshi ; Johnson, Gregory C. ; Killick, Rachel E. ; Legler, David ; Llovel, William ; Lyman, John ; Palmer, Matthew D. ; Piotrowicz, Stephen R. ; Purkey, Sarah G. ; Roemmich, Dean ; Roca, Rémy ; Savita, Abhishek ; von Schuckmann, Karina ; Speich, Sabrina ; Stephens, Graeme ; Wang, Gongjie ; Wijffels, Susan E. ; Zilberman, NathalieThe energy radiated by the Earth toward space does not compensate the incoming radiation from the Sun leading to a small positive energy imbalance at the top of the atmosphere (0.4–1 Wm–2). This imbalance is coined Earth’s Energy Imbalance (EEI). It is mostly caused by anthropogenic greenhouse gas emissions and is driving the current warming of the planet. Precise monitoring of EEI is critical to assess the current status of climate change and the future evolution of climate. But the monitoring of EEI is challenging as EEI is two orders of magnitude smaller than the radiation fluxes in and out of the Earth system. Over 93% of the excess energy that is gained by the Earth in response to the positive EEI accumulates into the ocean in the form of heat. This accumulation of heat can be tracked with the ocean observing system such that today, the monitoring of Ocean Heat Content (OHC) and its long-term change provide the most efficient approach to estimate EEI. In this community paper we review the current four state-of-the-art methods to estimate global OHC changes and evaluate their relevance to derive EEI estimates on different time scales. These four methods make use of: (1) direct observations of in situ temperature; (2) satellite-based measurements of the ocean surface net heat fluxes; (3) satellite-based estimates of the thermal expansion of the ocean and (4) ocean reanalyses that assimilate observations from both satellite and in situ instruments. For each method we review the potential and the uncertainty of the method to estimate global OHC changes. We also analyze gaps in the current capability of each method and identify ways of progress for the future to fulfill the requirements of EEI monitoring. Achieving the observation of EEI with sufficient accuracy will depend on merging the remote sensing techniques with in situ measurements of key variables as an integral part of the Ocean Observing System.
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ArticleAir-sea fluxes with a focus on heat and momentum(Frontiers Media, 2019-07-31) Cronin, Meghan F. ; Gentemann, Chelle L. ; Edson, James B. ; Ueki, Iwao ; Bourassa, Mark A. ; Brown, Shannon ; Clayson, Carol A. ; Fairall, Christopher W. ; Farrar, J. Thomas ; Gille, Sarah T. ; Gulev, Sergey ; Josey, Simon A. ; Kato, Seiji ; Katsumata, Masaki ; Kent, Elizabeth ; Krug, Marjolaine ; Minnett, Peter J. ; Parfitt, Rhys ; Pinker, Rachel T. ; Stackhouse, Paul W., Jr. ; Swart, Sebastiaan ; Tomita, Hiroyuki ; Vandemark, Douglas ; Weller, Robert A. ; Yoneyama, Kunio ; Yu, Lisan ; Zhang, DongxiaoTurbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m–2 and a bias of less than 5 W m–2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500–1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1–3 measurement platforms in each nominal 10° by 10° box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development, product validation, and for improving satellite-based, NWP and blended flux products. In addition, some of these flux platforms will also measure direct turbulent fluxes, which can be used to improve algorithms for computation of air-sea exchange of heat and momentum in flux products and models. With these improved air-sea fluxes, the ocean’s influence on the atmosphere will be better quantified and lead to improved long-term weather forecasts, seasonal-interannual-decadal climate predictions, and regional climate projections.