Exploring Atmosphere–Ocean Coupling Using Principal Component and Redundancy Analysis

The July–August–September (JAS) seasonal predictability of the Atlantic Warm Pool (AWP) is examined in the extensive set of seasonal hindcasts of the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). We conduct both deterministic and probabilistic skill analyses of the NCEP CFS Seasonal Hindcasts (SHs). This study reveals that the SHs have a reasonable climatology of the AWP. The presence of robust decadal variability in sea surface temperature (SST) observations, while absent in the SHs, does seem to indicate its influence on the seasonal errors of the AWP in the NCEP CFS. However, after filtering out the observed SST for variability greater than 8 years, there is no systematic relation of AWP seasonal errors in the NCEP CFS with lead time. The signal to noise ratio of the area of the AWP in the SHs decreases with lead time, largely due to increases in the ensemble spread (noise). It is sobering to note that there is barely any probabilistic skill in the NCEP CFS SHs for the prediction of the anomalous AWP events (occurring either in the upper or lower terciles) at nearly all lead times. However if we examine the SHs from 1981–1996, when the observed SST in the AWP region is in one uniform phase of the prevalent decadal variation, then the SHs display useful skill in the prediction of anomalously large AWP events at all lead times. Furthermore, these truncated SHs also show significant probabilistic skill in the prediction of small (lower tercile) and normal (middle tercile) AWP events up to a lead time of 1 month.

Interannual variations of tropical tropospheric temperatures are closely related to sea surface temperature (SST) changes in the tropical eastern Pacific (TEP). This study investigated the physical mechanisms for such an air-sea interrelationship. SSTs and latent heat flux were analyzed to find the unique properties of their variations during El Nino. A Gill-type model was used to investigate how a local heat source communicates with the entire tropics. Radiative fluxes in the tropics were evaluated to search for the factors limiting air temperature increases when warm SSTs remain in the TEP. We found that interannual variabilities of SST and latent heat flux are dominated by the variations in the TEP region. The SST variations there have three unique properties that allow the ocean to influence the atmosphere effectively: large magnitude, long persistence, and spatial coherence. The Gill-type model shows that a local heat source can warm the entire tropical troposphere when the heat source is near the equator. Released latent heat in the heat source region and forced adiabatic subsidence elsewhere in the tropics warm the atmosphere. As a result, a local heat source warms the entire tropical strip. The forced subsidence depresses clouds, allowing more infrared radiation to leave the atmosphere and preventing further atmospheric warming when warm SSTs remain in the TEP. This finding is verified by reanalysis data from the National Centers for Environmental Prediction.

The database of evaporation ducts characteristics and meteorological parameters is constructed with about 0.2° spatial-temporal resolution in the South China Sea (SCS) between 2011 and 2020.By combining the National Centers for Environmental Prediction (NCEP) reanalysis data and the Naval Postgraduate School (NPS) model, the variation of modified refractivity M with height can be predicted. While analyzing the characteristics of the evaporation duct, five meteorological parameters need to be taken into account: the pressure and temperature at the surface, the relative humidity (RH) and temperature at 2 m above the ground and sea surface, and the wind speed data at 10 m above the surface. Meteorological datasets derived from NCEP are used as input to the algorithm; the Radio refractivity N is calculated with atmospheric temperature, pressure, and water vapor pressure. The modified refractivity M can be expressed as dM / dh=dN / dh + 0.157, where h is the height above the earth, and characteristics of the evaporation duct can be achieved. The evaporation duct height (EDH) will also be determined.We provided fifteen files include ‘Hourly Average EDH.xlsx’, ‘Monthly Average EDH.xlsx’, ‘Yearly Average EDH.xlsx’; ‘Hourly Average ASTD.xlsx’, ‘Monthly Average ASTD.xlsx’, ‘Yearly Average ASTD.xlsx’; ‘Hourly Average WS.xlsx’, ‘Monthly Average WS.xlsx’, ‘Yearly Average WS.xlsx’; ‘Hourly Average RH.xlsx’, ‘Monthly Average RH.xlsx’, ‘Yearly Average RH.xlsx’; ‘Hourly Average Meteorological Parameters In Typical Position.xlsx’, ‘Monthly Average Meteorological Parameters In Typical Position.xlsx’, and ‘Yearly Average Meteorological Parameters In Typical Position.xlsx’ in excel worksheets, users can open them with Microsoft office (office 2010 and later version).‘Hourly Average EDH.xlsx’, ‘Monthly Average EDH.xlsx’, and ‘Yearly Average EDH.xlsx’ files show the spatial and temporal statistical distribution of the average EDH as a function of hours, months, and years over the SCS (105° E ~125° E, 5° N~25° N), separately. The EDH in the SCS was divided in the grid form with 0.2° spatial resolution; the value at each grid point is derived from hourly meteorological parameters between 2011 and 2020 by NCEP databases and the NPS model. The database was constructed with hourly, monthly, and yearly statistical average values from 87672 hours of data at each grid point. And statistical results in different hours (from 0 o’clock to 23 o’clock in Beijing time, UTC +08:00), months (from Jan. to Dec.), and years (from 2011 to 2020) are classified with different sheets. The data size in each sheet is 101*101; the corresponding longitudes and latitudes are displayed in the first row and column.‘Hourly Average ASTD.xlsx’, ‘Monthly Average ASTD.xlsx’, and ‘Yearly Average ASTD.xlsx’ files show the spatial and temporal statistical distribution of the average air-sea temperature difference (ASTD) as a function of hours, months, and years over the SCS, separately. Consistent with the EDH statistical results, the data in each sheet shows corresponding ASTD in different longitudes and latitudes.Similarly, the wind speed (WS) dataset is recorded with ‘Hourly Average WS.xlsx’, ‘Monthly Average WS.xlsx’, and ‘Yearly Average WS.xlsx’ files.And the relative humidity (RH) dataset is recorded with ‘Hourly Average RH.xlsx’, ‘Monthly Average RH.xlsx’, and ‘Yearly Average RH.xlsx’ files.In addition, ‘Hourly Average Meteorological Parameters In Typical Position.xlsx’, ‘Monthly Average Meteorological Parameters In Typical Position.xlsx’, and ‘Yearly Average Meteorological Parameters In Typical Position.xlsx’ files show the average meteorological parameters (including sea surface temperature (SST), ASTD, WS, and RH) at five typical positions (including Land-sea interface, The coastal land, Taiwan Strait, Central SCS, Hainan Island) as a function of hours, months, and years, separately. The data of SST, ASTD, WS, and RH are classified with different sheets. The data size in each sheet is X*5, representing X statistical types (24 for hour, 12 for month, and 10 for year) and five typical positions. And the corresponding typical positions and statistical types are displayed in the first row and column.

The subsurface ocean temperature response to El Niño–Southern Oscillation (ENSO) is examined based on 31-yr (1981–2011) simulations with the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) coupled model. The model sea surface temperature (SST) in the tropical Pacific is relaxed to observations to ensure realistic ENSO variability in the simulations. In the tropical Pacific, the subsurface temperature response to the ENSO SST is closely related to the variability of thermocline. The subsurface response is stronger and deeper in the tropical Indian Ocean than in the tropical Atlantic. The analysis at three selected locations reveals that the peak response of the subsurface temperature to ENSO lags the Niño-3.4 SST by 3, 6, and 6 months, respectively, in the southern tropical Indian Ocean, the northern tropical Atlantic, and the North Pacific, where SSTs are also known to be strongly influenced by ENSO. The ENSO-forced temperature anomalies gradually penetrate to the deeper ocean with time in the North Pacific and the tropical Atlantic, but not in the tropical Indian Ocean where the subsurface response at different depths peaks almost at the same time (i.e., at about 3–4 months following ENSO). It is demonstrated that the ENSO-induced surface wind stress plays an important role in determining the time scale and strength of the subsurface temperature response to ENSO in the North Pacific and the northern tropical Atlantic. Additionally, the ENSO-related local surface latent heat flux also contributes to the subsurface response to ENSO in these two regions.

The role of subsurface temperature variability in modulating El Niño–Southern Oscillation (ENSO) properties is examined using an intermediate coupled model (ICM), consisting of an intermediate dynamic ocean model and a sea surface temperature (SST) anomaly model. An empirical procedure is used to parameterize the temperature of subsurface water entrained into the mixed layer (Te) from sea level (SL) anomalies via a singular value decomposition (SVD) analysis for use in simulating sea surface temperature anomalies (SSTAs). The ocean model is coupled to a statistical atmospheric model that estimates wind stress anomalies also from an SVD analysis. Using the empirical Te models constructed from two subperiods, 1963–79 (T63–79e) and 1980–96 (T80–96e), the coupled system exhibits strikingly different properties of interannual variability (the oscillation period, spatial structure, and temporal evolution). For the T63–79e model, the system features a 2-yr oscillation and westward propagation of SSTAs on the equator, while for the T80–96e model, it is characterized by a 5-yr oscillation and eastward propagation. These changes in ENSO properties are consistent with the behavior shift of El Niño observed in the late 1970s. Heat budget analyses further demonstrate a controlling role played by the vertical advection of subsurface temperature anomalies in determining the ENSO properties.

Performance of CCCma and GFDL climate models using remote sensing and surface data for the state of Rio de Janeiro-Brazil

It is well recognized that sea surface temperature (SST) plays a dominant role in the formation and intensification of tropical cyclones. A number of observational/empirical studies were conducted at different basins to investigate the influence of SST on the intensification of tropical cyclones and in turn, modification in SST by the cyclone itself. Although a few modeling studies confirmed the sensitivity of model simulation/forecast to SST, it is not well quantified, particularly for Bay of Bengal cyclones. The present study is designed to quantify the sensitivity of SST on mesoscale simulation of an explosively deepening storm over the Bay of Bengal, i.e., Orissa super cyclone (1999). Three numerical experiments are conducted with climatological SST, NCEP (National Center for Environmental Prediction) skin temperature as SST, and observed SST (satellite derived) toward 5-day simulation of the storm using mesoscale model MM5. At model initial state, NCEP skin temperature and observed SST over the Bay of Bengal are 1–2°C warmer than climatological SST, but cooler by nearly 1°C along the coastline. Observed SST shows a number of warm patches in the Bay of Bengal compared with NCEP skin temperature. The simulation results indicate that the sea surface temperature has a significant impact on model-simulated track and intensity of the cyclonic storm. The track and intensity of the storm is better simulated with the use of satellite-observed SST.

A method for seasonal predictions of U.S. precipitation based on tropical Pacific sea surface temperature (SST) anomalies is developed in this letter with the singular value decomposition (SVD). This method was applied to the 1997/98 El Niño. The 1997 summer and 1997/98 winter precipitation over the United States were predicted using forecast SST from the National Centers for Environmental Prediction (NCEP) coupled ocean‐atmosphere model. A cross validation, based on the percentages of hit between the hindcast and observed precipitation for the past El Niño and La Niña events, indicates a certain degree of predictability in both winter and summer seasons. During El Niño, significant predictability of summer precipitation is found over the Northern Plains and Atlantic States, while during La Niña it is in the Midwest. Above normal precipitation in the Northern Plains and the Midwest and below normal precipitation in the Atlantic States is associated with a warm phase of the El Niño‐Southern Oscillation (ENSO) in summer. For winter precipitation, significant predictability is detected over the Gulf Coast States, the Southern Plains and California. Over these regions, wetter conditions are generally associated with the warm phase of ENSO.

The Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation (JCSDA) is used in conjunction with a daily sea surface temperature (SST) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) atmospheric data and surface wind to calculate clear-sky top-of-atmosphere (TOA) brightness temperatures (BTs) in three Advanced Very High Resolution Radiometer (AVHRR) thermal infrared channels over global oceans. CRTM calculations are routinely performed by the sea surface temperature team for four AVHRR instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites NOAA-16, NOAA-17, and NOAA-18 and the Meteorological Operation (MetOp) satellite MetOp-A, and they are compared with clear-sky TOA BTs produced by the operational AVHRR Clear-Sky Processor for Oceans (ACSPO). It was observed that the model minus observation (M−O) bias in the NOAA-16 AVHRR channel 3b (Ch3b) centered at 3.7 μm experienced a discontinuity of ∼0.3 K when a new CRTM version 1.1 (v.1.1) was implemented in ACSPO processing in September 2008. No anomalies occurred in any other AVHRR channel or for any other platform. This study shows that this discontinuity is caused by the out-of-band response in NOAA-16 AVHRR Ch3b and by using a single layer to the NCEP GFS temperature profiles above 10 hPa for the alpha version of CRTM. The problem has been solved in CRTM v.1.1, which uses one of the six standard atmospheres to fill in the missing data above the top pressure level in the input NCEP GFS data. It is found that, because of the out-of-band response, the NOAA-16 AVHRR Ch3b has sensitivity to atmospheric temperature at high altitudes. This analysis also helped to resolve another anomaly in the absorption bands of the High Resolution Infrared Radiation Sounder (HIRS) sensor, whose radiances and Jacobians were affected to a much greater extent by this CRTM inconsistency.

Nonlinear principal component analysis (NLPCA), via a neural network (NN) approach, was applied to an ensemble of six 47-yr simulations conducted by the Canadian Centre for Climate Modelling and Analysis (CCCma) second-generation atmospheric general circulation model (AGCM2). Each simulation was forced with the observed sea surface temperature [from the Global Sea Ice and Sea Surface Temperature dataset (GISST)] from January 1948 to November 1994. The NLPCA modes reveal nonlinear structures in both the winter 500-mb geopotential height (Z500) anomalies and surface air temperature (SAT) anomalies over North America, with asymmetric spatial anomaly patterns during the opposite phases of an NLPCA mode. Only during its negative phase is the first NLPCA mode related to the El Niño–Southern Oscillation (ENSO); the positive phase is related to a weakened jet stream. Spatial patterns of the NLPCA mode for the Z500 anomalies generally agree with those for the SAT anomalies. Nonlinear canonical correlation analysis (NLCCA), also via an NN approach, was then applied to the midlatitude winter GCM data and the observed SST of the tropical Pacific. Nonlinearity was detected in both the forcing field (SST) and the response field (Z500 or SAT) at zero time lag. The leading NLCCA mode for the SST anomalies is a nonlinear ENSO mode, with a 30°–40° eastward shift of the positive SST anomalies during El Niño relative to the negative SST anomalies during La Niña. The leading NLCCA mode for the Z500 anomaly field is a nonlinear Pacific–North American (PNA) teleconnection pattern. The ray path of the Rossby waves induced during El Niño is 10°–15° east of that induced during La Niña. The nonlinear atmospheric response to ENSO is also found in the leading NLCCA mode for the SAT anomalies.

Crop yield as a bioclimatic index of El Niño impact in Europe: Crop forecast implications

A detailed analysis and validation of ocean thermodynamic variables, simulated, using an Ocean general circulation Model (OGCM) forced by QuikSCAT scatterometer (QS-R) has been carried out in this study. The results are discussed in terms of comparison of vertical profiles of temperature against TRITON observations for the year 2004. Root mean square error (RMSE) of Sea Surface Temperature (SST) simulated by QS-R showed an error of the order of 0.6°C. QS-R SST also has a cooler bias. Another OGCM simulation was carried out in which the National Center for Environmental Prediction (NCEP) reanalysis winds (NCEP-R) were used to force the OGCM. The simulated SST resulted in lesser RMSE (~0.5°C) and there was no bias. However, variabilities in SST were captured more realistically in scatterometer forced solution. However the RMSE in SST was higher which could be partly due to physical inconsistency between scatterometer winds and other air-sea exchange parameters used from NCEP. A detailed statistics of the model simulated temperatures at different depths show excellent performance of scatterometer forced simulations as compared to NCEP-R. Maximum error in the temperature profile was in the thermocline region (~1.5°C) in QS-R as against 3.5°C in NCEP-R. Possible causes of these errors in relation to air-sea exchange parameters used in forcing the OGCM are described in this work.

Chapter 11 - Canonical analysis

A singular value decomposition (SVD) analysis between the Northern Hemisphere winter-mean 500-hPa geopotential height (Z500) and near-global surface temperature (land-surface air temperature + sea surface temperature) fields was conducted for the recent three decades. The leading mode, referred to as Polar/Eurasia (PEA), is considered as a hemispheric-wide atmospheric internal variability rather than a local teleconnection pattern. Winter mean Z500 of the leading mode is closely related to Eurasian surface air temperature from autumn to spring throughout the period. Both interannual and interdecadal time scales are dominant during the three decades in the time scales. No linear relationship between the tropical SST anomalies is found in this mode. The second and third modes can be identified as the Pacific/North American (PNA) and Western Pacific (WP)-type teleconnection patterns, respectively. Although they are also considered as internal modes of atmospheric variabilities, their interannual variabilities are also influenced by the tropical Pacific SST anomalies. These results are compared with the more popular SVDanalysis between geopotential height and SST, for which the leading mode exhibits the so-called "ENSO (El Niño/Southern Oscillation) related" winter circulation pattern. It is shown that this ENSO-related mode can be reconstructed from the linear combination of the second (PNA) and third (WP) modes of the present SVD mentioned above. These results suggest that inter-El Niño differences of atmospheric circulation in midlatitudes can be interpreted as the different strengths of the PNA and WP patterns on one hand, and on the other hand by the different magnitude of the PEA, which is linearly independent of ENSO. The characteristics of the vertical structure of the atmospheric variability were further examined by conducting a SVD analysis of 100-hPa temperature and Z500. The PEA and PNA patterns in the Z500 field extracted as the first and second SVD modes, respectively, exhibit contrasting features in association with lower stratospheric temperature. The PNA pattern is confined to the troposphere, while PEA extends further into the stratosphere. This suggests they are produced by different mechanisms.

A coupled atmosphere–ocean–sea ice–land surface climate system model developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) is used to investigate the response to glacial ice-sheet topography and decreased CO2 representative of the last glacial maximum (LGM, roughly 21,000 years before present). Imposing these glacial boundary conditions leads to a strong and rapid climate adjustment, first in the atmosphere, and then in the ocean. We describe the model and boundary conditions and analyze the initial transient response. In a subsequent paper we analyze the near-equilibrium response. During the transient phase, surface air temperature rapidly decreases with time. The rapid cooling over the Laurentide and Fennoscandian ice sheets introduces a strong inter-hemispheric asymmetry. Sea surface temperature also decreases and after 80 years of integration, ocean surface temperature has decreased by 3 °C and global surface air temperature by 5 °C. The expected overall cooling is contrasted by marked regions of unexpected localized ocean surface warming and the retreat of sea ice in the northern North Atlantic and in the high latitude Southern Ocean. This strong transient behavior, associated with more vigorous oceanic convection at these high latitudes, represents the localization of the initial response in areas remote from the direct forcing change. The strength of the North Atlantic (Southern Ocean) overturning stream function, associated with the formation of North Atlantic Deep Water (Antarctic Bottom Water), increases from 12 to 36 Sv (2 to 26 Sv). There is a marked increase in the Antarctic Circumpolar Current transport as well (from 80 to 140 Sv), even though the wind stress decreases over the Southern Ocean, presumably as a consequence of the enhanced bottom pressure torque due to an increase in the density and the formation of Antarctic Bottom Water. Both precipitation and evaporation decrease on average with time and the change in the hydrological cycle modifies sea surface salinity (SSS). SSS increases in the Arctic Ocean due to a decrease in river runoff while it decreases in the North Atlantic due to an increase in river runoff and an increase in precipitation minus evaporation. The initial (and near equilibrium) climate response to LGM forcing in the fully coupled atmosphere–ocean climate model is quite different from that in the associated atmosphere-mixed layer ocean model due to very strong oceanic feedbacks. This is especially evident where oceanic convection is active. The anomalously warm surface temperature, especially in the Southern Ocean, and the marked increase in the overturning circulation are transient features of the response to imposed LGM boundary conditions which moderate or reverse as the system approaches equilibrium (as described in the companion paper). Other differences with the mixed layer simulation include a considerably colder tropical Pacific and the development of a La Nina-like response both of which are absent in the mixed layer simulations.