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This study investigates the influence of oceanic and atmospheric processes in extratropical thermodynamic air‐sea interactions resolved by satellite observations (OBS) and by two climate model simulations run with eddy‐resolving high‐resolution (HR) and eddy‐parameterized low‐resolution (LR) ocean components. Here, spectral methods are used to characterize the sea surface temperature (SST) and turbulent heat flux (THF) variability and co‐variability over scales between 50 and 10,000 km and 60 days to 80 years in the Pacific Ocean. The relative roles of the ocean and atmosphere are interpreted using a stochastic upper‐ocean temperature evolution model forced by noise terms representing intrinsic variability in each medium, defined using climate model data to produce realistic rather than white spectral power density distributions. The analysis of all datasets shows that the atmosphere dominates the SST and THF variability over zonal wavelengths larger than ∼2,000–2,500 km. In HR and OBS, ocean processes dominate the variability of both quantities at scales smaller than the atmospheric first internal Rossby radius of deformation (R1, ∼600–2,000 km) due to a substantial ocean forcing coinciding with a weaker atmospheric modulation of THF (and consequently of SST) than at larger scales. The ocean forcing also induces oscillations in SST and THF with periods ranging from intraseasonal to multidecadal, reflecting a red spectrum response to ocean forcing similar to that driven by atmospheric forcing. Such features are virtually absent in LR due to a weaker ocean forcing relative to HR. Plain Language Summary: This study investigates the importance of atmospheric processes (weather) and ocean currents in driving variations in sea surface temperature (SST) and the air‐sea heat exchange at mid‐latitudes. Our analysis uses satellite observations, a high‐resolution (HR) climate model that resolves ocean currents with dimensions of tens of km, and a low‐resolution model (LR) that can only simulate ocean currents with hundreds of km in size. We specifically examine the SST and heat exchange variability resolved by these datasets at horizontal scales between 50 and 10,000 km and time scales from 2 months to 80 years in the Pacific Ocean. Using a simple mathematical model to interpret the results, we find that variability at scales larger than 2,000 km is driven predominantly by weather. At smaller scales, SST and heat exchange are more variable in HR than in LR and agree better with satellite observations. We also find that ocean processes drive variability in SST with time scales ranging from 2 months to several decades, similar to those caused by weather, which in turn induces slow variations in the air‐sea heat exchange. Key Points: We use spectral methods to examine the role of oceanic and atmospheric processes in Pacific sea surface temperature (SST) and turbulent heat flux variabilityAt mid‐latitudes, the atmosphere controls variability with scales larger than 2,000 km while ocean processes dominate at smaller scalesOcean phenomena drive a red spectrum SST response similar to that induced by the atmosphere, which is mirrored by the turbulent fluxes [ABSTRACT FROM AUTHOR]

Ocean variability is a dominant source of remote rainfall predictability, but in many cases the physical mechanisms driving this predictability are not fully understood. This study examines how ocean mesoscales (i.e., the Gulf Stream SST front) affect decadal Southeast US (SEUS) rainfall, arguing that the local imprint of large‐scale teleconnections is sensitive to resolved mesoscale features. Based on global coupled model experiments with eddying and eddy‐parameterizing ocean, we find that a resolved Gulf Stream improves localized rainfall and remote circulation response in the SEUS. The eddying model generally improves the air‐sea interactions in the Gulf Stream and the North Atlantic Subtropical High that modulate SEUS rainfall over decadal timescales. The eddy‐parameterizing simulation fails to capture the sharp SST gradient associated with the Gulf Stream and overestimates the role of tropical Pacific SST anomalies in the SEUS rainfall. Plain Language Summary: Current global climate models (GCMs) typically fail to fully resolve mesoscale ocean features (with length scales on the order of 10 km) such as western boundary currents, which potentially limit rainfall predictability over decadal timescales. Improvements in high‐performance climate modeling enable us to incorporate high‐resolution ocean models (0.1°) that capture these important mesoscale features with increased fidelity. Here we show that the inclusion of mesoscale ocean processes produces a more realistic Gulf Stream and improves both localized rainfall patterns and large‐scale teleconnections. The high‐resolution model shows a better representation of the air‐sea interactions between the sea surface temperature and low‐level atmosphere over the Gulf Stream, thus improving low‐frequency rainfall variations over the Southeast US. The results further imply that high‐resolution GCMs with increased ocean model resolution may be needed in future climate prediction systems. Key Points: Decadal rainfall pattern and associated mechanism in the Southeast US are examined from an eddying global coupled modelEddying CCSM4 improves the air‐sea interactions in the Gulf Stream and the North Atlantic Subtropical High, modulating Southeast US rainfallEddy‐parameterizing CCSM4 and CMIP5 models may overestimate the role of tropical sea surface temperature in decadal Southeast US rainfall [ABSTRACT FROM AUTHOR]

The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models. [ABSTRACT FROM AUTHOR]

Decadal climate predictability has received considerable scientific interest in recent years, yet the limits and mechanisms for decadal predictability are currently not well known. It is widely accepted that noise due to internal atmospheric dynamics at the air‐sea interface influences predictability. The purpose of this paper is to use the interactive ensemble (IE) coupling strategy to quantify how internal atmospheric noise at the air‐sea interface impacts decadal predictability. The IE technique can significantly reduce internal atmospheric noise and has proven useful in assessing seasonal‐to‐interannual variability and predictability. Here we focus on decadal timescales and apply the nonlinear local Lyapunov exponent method to the Community Climate System Model comparing control simulations with IE simulations. This is the first time the nonlinear local Lyapunov exponent has been applied to the state‐of‐the‐art coupled models. The global patterns of decadal predictability are discussed from the perspective of internal atmospheric noise and ocean dynamics. Key Points: For the first time, the nonlinear local Lyapunov exponent method has been applied to the state‐of‐the‐art coupled climate modelsDepending on region reducing internal atmospheric noise reduces decadal variability but either increases or decreases decadal predictabilityDecadal‐scale subsurface predictability is examined for the North Atlantic region [ABSTRACT FROM AUTHOR]

We investigate the interannual variability of Agulhas leakage in an ocean-eddy-resolving coupled simulation and characterize its influence on regional climate. Many observational leakage estimates are based on the study of Agulhas rings, whereas recent model studies suggest that rings and eddies carry less than half of leakage transport. While leakage variability is dominated by eddies at seasonal time scales, the noneddy leakage transport is likely to be constrained by large-scale forcing at longer time scales. To investigate this, leakage transport is quantified using an offline Lagrangian particle tracking approach. We decompose the velocity field into eddying and large-scale fields and then recreate a number of total velocity fields by modifying the eddying component to assess the dependence of leakage variability on the eddies. We find that the resulting leakage time series show strong coherence at periods longer than 1000 days and that 50% of the variance at interannual time scales is linked to the smoothed, large-scale field. As shown previously in ocean models, we find Agulhas leakage variability to be related to a meridional shift and/or strengthening of the westerlies. High leakage periods are associated with east–west contrasting patterns of sea surface temperature, surface heat fluxes, and convective rainfall, with positive anomalies over the retroflection region and negative anomalies within the Indian Ocean to the east. High leakage periods are also related to reduced inland convective rainfall over southeastern Africa in austral summer. [ABSTRACT FROM AUTHOR]

In this study, we examine the relationship between the number of tropical cyclones (TCs) in the western North Pacific and the tropical Pacific sea surface temperature (SST) during the main TC season (July–November) for the period of 1965–2006. Results show that there are periods when TC frequency and the tropical Pacific SST are well correlated and periods when the relationship breaks down. Therefore, decadal variation is readily apparent in the relationship between the TC frequency and the SST variations in the tropical Pacific. We further examine the oceanic and atmospheric states in the two periods (i.e., 1979–1989 vs. 1990–2000) when the marked contrast in the correlation between the TC frequency and the tropical Pacific SST is observed. Before 1990, the analysis indicates that oceanic conditions largely influenced anomalous TC frequency, whereas atmospheric conditions had little impact. After 1990, there the reverse appears to be the case, i.e., atmospheric conditions drive anomalous TC frequency and oceanic conditions are relatively unimportant. A role of atmosphere and ocean in relation to the TC development in the western North Pacific changes, which is consistent with the change of the correlations between the TC frequency and the tropical Pacific SST. [ABSTRACT FROM AUTHOR]

The U.S. Climate Variability and Predictability (CLIVAR) working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land–atmosphere feedbacks on regional drought. The runs were carried out with five different atmospheric general circulation models (AGCMs) and one coupled atmosphere–ocean model in which the model was continuously nudged to the imposed SST forcing. This paper provides an overview of the experiments and some initial results focusing on the responses to the leading patterns of annual mean SST variability consisting of a Pacific El Niño–Southern Oscillation (ENSO)-like pattern, a pattern that resembles the Atlantic multidecadal oscillation (AMO), and a global trend pattern. One of the key findings is that all of the AGCMs produce broadly similar (though different in detail) precipitation responses to the Pacific forcing pattern, with a cold Pacific leading to reduced precipitation and a warm Pacific leading to enhanced precipitation over most of the United States. While the response to the Atlantic pattern is less robust, there is general agreement among the models that the largest precipitation response over the United States tends to occur when the two oceans have anomalies of opposite signs. Further highlights of the response over the United States to the Pacific forcing include precipitation signal-to-noise ratios that peak in spring, and surface temperature signal-to-noise ratios that are both lower and show less agreement among the models than those found for the precipitation response. The response to the positive SST trend forcing pattern is an overall surface warming over the world’s land areas, with substantial regional variations that are in part reproduced in runs forced with a globally uniform SST trend forcing. The precipitation response to the trend forcing is weak in all of the models. It is hoped that these early results, as well as those reported in the other contributions to this special issue on drought, will serve to stimulate further analysis of these simulations, as well as suggest new research on the physical mechanisms contributing to hydroclimatic variability and change throughout the world. [ABSTRACT FROM AUTHOR]

The present study documents the so-called spring prediction and persistence barriers in association with El Niño–Southern Oscillation (ENSO) in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) retrospective forecasts. It is found that the spring prediction and persistence barriers in the eastern equatorial Pacific sea surface temperature (SST) are preceded by a boreal winter barrier in the western equatorial Pacific zonal wind stress. The time of the persistence barrier is closely related to the time of the ENSO phase transition, but may differ from the time of the lowest variance. The seasonal change of the signal-to-noise ratio cannot explain the persistence barrier. While the noise may lead to a drop of skill around boreal spring in the western equatorial Pacific zonal wind stress, its impacts on the skill of eastern equatorial Pacific SST is small. The equatorial Pacific zonal winds display an excessive response to ENSO-related SST anomalies, which leads to a longer persistence in the equatorial Pacific thermocline depth anomalies and a delayed transition of the eastern equatorial Pacific SST anomalies. This provides an interpretation for the prediction skill drop in boreal spring in the eastern equatorial Pacific SST. The results suggest that improving the atmospheric model wind response to SST anomalies may reduce the spring prediction barrier. [ABSTRACT FROM AUTHOR]

The impact of coupled air–sea feedbacks on the simulation of tropical intraseasonal variability is investigated in this study using the National Centers for Environmental Prediction Climate Forecast System. The simulation of tropical intraseasonal variability in a freely coupled simulation is compared with two simulations of the atmospheric component of the model. In one experiment, the uncoupled model is forced with the daily sea surface temperature (SST) from the coupled run. In the other, the uncoupled model is forced with climatological SST from the coupled run. Results indicate that the overall intraseasonal variability of precipitation is reduced in the coupled simulation compared to the uncoupled simulation forced by daily SST. Additionally, air–sea coupling is responsible for differences in the simulation of the tropical intraseasonal oscillation between the coupled and uncoupled models, specifically in terms of organization and propagation in the western Pacific. The differences between the coupled and uncoupled simulations are due to the fact that the relationships between precipitation and SST and latent heat flux and SST are much stronger in the coupled model than in the uncoupled model. Additionally, these relationships are delayed by about 5 days in the uncoupled model compared to the coupled model. As demonstrated by the uncoupled simulation forced with climatological SST, some of the intraseasonal oscillation can be simulated by internal atmospheric dynamics. However, the intraseasonally varying SST appears to be important to the amplitude and propagation of the oscillation beyond the Maritime Continent. [ABSTRACT FROM AUTHOR]

This study investigates whether air–sea interactions contribute to differences in the predictability of the boreal winter tropical intraseasonal oscillation (TISO) using the NCEP operational climate model. A series of coupled and uncoupled, “perfect” model predictability experiments are performed for 10 strong model intraseasonal events. The uncoupled experiments are forced by prescribed SST containing different types of variability. These experiments are specifically designed to be directly comparable to actual forecasts. Predictability estimates are calculated using three metrics, including one that does not require the use of time filtering. The estimates are compared between these experiments to determine the impact of coupled air–sea interactions on the predictability of the tropical intraseasonal oscillation and the sensitivity of the potential predictability estimates to the different SST forcings. Results from all three metrics are surprisingly similar. They indicate that predictability estimates are longest for precipitation and outgoing longwave radiation (OLR) when the ensemble mean from the coupled model is used. Most importantly, the experiments that contain intraseasonally varying SST consistently predict the control events better than those that do not for precipitation, OLR, 200-hPa zonal wind, and 850-hPa zonal wind after the first 10 days. The uncoupled model is able to predict the TISO with similar skill to that of the coupled model, provided that an SST forecast that includes these intraseasonal variations is used to force the model. This indicates that the intraseasonally varying SSTs are a key factor for increased predictability and presumably better prediction of the TISO. [ABSTRACT FROM AUTHOR]

A negative correlation is observed between interannual variations of the Australian summer monsoon (ASM) and El Niño–Southern Oscillation (ENSO). This negative relationship is well simulated in the Center for Ocean–Land–Atmosphere (COLA) interactive ensemble coupled general circulation model (CGCM). The present study investigates roles of the Indian Ocean in the ASM–ENSO relationship through controlled numerical experiments with the COLA CGCM. It is found that air–sea coupling in the Indian Ocean plays an important role in maintaining the negative ASM–ENSO relationship. When the Indian Ocean is decoupled from the atmosphere, the ASM–ENSO relationship is significantly weakened or even masked by the internal atmospheric variability. This change in the ASM–ENSO relationship is related to complementary roles of Indian Ocean sea surface temperature (SST) anomalies in the ASM variability and feedbacks from the Indian Ocean on ENSO. Without a coupled Indian Ocean, the ENSO amplitude is reduced, leading to a decrease in the ENSO-induced ASM variability, and the constructive impacts of the Indian Ocean SST anomalies on the ASM variability are substantially reduced. This reduces the ASM variability related to ENSO. Consistent with the change in the ASM–ENSO relationship, the local air–sea relationship in the ASM region displays important differences with and without a coupled Indian Ocean. The long-term change in the ASM–ENSO relationship is related to that in ENSO amplitude in the interactive ensemble coupled model. A relatively higher (lower) negative correlation occurs in periods of larger (smaller) ENSO amplitude. This relationship, however, is not clear in the anomaly coupled model with only one atmospheric realization. This difference is attributed to changes in the signal-to-noise ratio in the ASM variability. A comparison is made with observations and the long-term change in the Indian summer monsoon (ISM)–ENSO relationship in the model. [ABSTRACT FROM AUTHOR]

A newly developed Atmospheric General Circulation Model (AGCM) at T62 spectral truncation with 28 terrain-following levels coupled to the Modular Ocean Model version 3.0 (MOM3.0) is evaluated for its simulation of El Niño and the Southern Oscillation (ENSO). It is also compared with an older version of the AGCM coupled to the same ocean model. A dozen features of ENSO are validated. These characteristics of ENSO highlight its influence on global climate at seasonal to interannual scales. The major improvements of the ENSO simulation from this new coupled climate model are the seasonal phase locking of the ENSO variability to a realistic annual cycle of the eastern equatorial Pacific Ocean, the duration of the ENSO events and its evolution that is comparable to the ocean data assimilation. The two apparent drawbacks of this new model are its relatively weak ENSO variability and the presence of erroneous split ITCZ. The improvement of the ENSO simulation in the new coupled model is attributed to realistic thermocline variability and wind stress simulation. [ABSTRACT FROM AUTHOR]

Different measures of wind influence the ocean in different ways. In particular, the time-averaged mixed layer turbulent energy production rate is proportional to 〈u3*〉, where u* is the “oceanic friction velocity” that is based on wind stress. Estimating 〈u3*〉 from monthly averages of wind stress or wind speed may introduce large biases due to the day-to-day variability of the direction and magnitude of the wind. The authors create monthly climatologies of 〈u3*〉 from daily wind stress measurements obtained from the Goddard Satellite-based Surface Turbulent Fluxes version 2 (GSSTF2; based on satellite microwave measurements), the Quick Scatterometer (QuikSCAT; based on satellite scatterometry measurements), and the National Centers for Environmental Prediction (NCEP) reanalysis wind. The differences among zonal averages of these climatologies and of a similar climatology based on the da Silva version of the Comprehensive Ocean–Atmosphere Data Set (COADS) have a complex dependence on latitude. These differences are typically 10%–30% of the climatological values. The GSSTF2 data confirm that 〈u3*〉 is much larger than estimates from monthly averaged wind stress or wind speed, especially outside the Tropics. [ABSTRACT FROM AUTHOR]

The present study compares the local simultaneous correlation between rainfall–evaporation and sea surface temperature (SST)–SST tendency among observations, coupled general circulation model (CGCM) simulations, and stand-alone atmospheric general circulation model (AGCM) simulations. The purpose is to demonstrate to what extent the model simulations can reproduce the observed air–sea relationship. While the model-simulated correlation agrees with the observations in the tropical eastern Pacific, large discrepancies are found in the subtropics, midlatitudes, and tropical Indo-western Pacific Ocean regions. In tropical Indo-western Pacific Ocean regions and the midlatitudes where the atmosphere contributes to the observed SST changes, the specified SST simulations produce excessive SST forcing, whereas the CGCM captures the atmospheric feedback on the SST, but with somewhat of an overestimation. In the subtropics, both the AGCM and CGCM produce unrealistic positive rainfall–SST correlations. In the tropical western-central Pacific and the North Indian Ocean, the CGCM-simulated evaporation–SST correlation is opposite to that observed because of an excessive dependence of the sea–air humidity difference on the SST. [ABSTRACT FROM AUTHOR]

Equatorial Pacific sea surface temperature (SST) anomalies in the Center for Ocean–Land–Atmosphere Studies (COLA) interactive ensemble coupled general circulation model show near-annual variability as well as biennial El Niño–Southern Oscillation (ENSO) variability. There are two types of near-annual modes: a westward propagating mode and a stationary mode. For the westward propagating near-annual mode, warm SST anomalies are generated in the eastern equatorial Pacific in boreal spring and propagate westward in boreal summer. Consistent westward propagation is seen in precipitation, surface wind, and ocean current. For the stationary near-annual mode, warm SST anomalies develop near the date line in boreal winter and decay locally in boreal spring. Westward propagation of warm SST anomalies also appears in the developing year of the biennial ENSO mode. However, warm SST anomalies for the westward propagating near-annual mode occur about two months earlier than those for the biennial ENSO mode and are quickly replaced by cold SST anomalies, whereas warm SST anomalies for the biennial ENSO mode only experience moderate weakening. Anomalous zonal advection contributes to the generation and westward propagation of warm SST anomalies for both the westward propagating near-annual mode and the biennial ENSO mode. However, the role of mean upwelling is markedly different. The mean upwelling term contributes to the generation of warm SST anomalies for the biennial ENSO mode, but is mainly a damping term for the westward propagating near-annual mode. The development of warm SST anomalies for the stationary near-annual mode is partially due to anomalous zonal advection and upwelling, similar to the amplification of warm SST anomalies in the equatorial central Pacific for the biennial ENSO mode. The mean upwelling term is negative in the eastern equatorial Pacific for the stationary near-annual mode, which is opposite to the ENSO mode. The development of cold SST anomalies in the aftermath of warm SST anomalies for the westward propagating near-annual mode is coupled to large easterly wind anomalies, which occur between the warm and cold SST anomalies. The easterly anomalies contribute to the cold SST anomalies through anomalous zonal, meridional, and vertical advection and surface evaporation. The cold SST anomalies, in turn, enhance the easterly anomalies through a Rossby-wave-type response. The above processes are most effective during boreal spring when the mean near-surface-layer ocean temperature gradient is the largest. It is suggested that the westward propagating near-annual mode is related to air–sea interaction processes that are limited to the near-surface layers. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of the Indian Ocean on El Niño–Southern Oscillation (ENSO) variability through numerical simulations with a coupled atmosphere–ocean general circulation model, composite analyses with the coupled model output, and simple atmosphere model experiments with specified sea surface temperature (SST) forcing. It is found that, when the Indian Ocean is decoupled from the atmosphere, the ENSO variability in the coupled model is significantly reduced. Conditional SST distributions indicate that the warm (cold) ENSO state is stronger and occurs more frequently when the Indian Ocean SST in summer is relatively cold (warm), whereas it is weaker and occurs less frequently when the Indian Ocean is relatively warm (cold). The impacts of the Indian Ocean are suggested by a comparison of SST composites under warm, normal, and cold Indian Ocean SST conditions in the developing stage of ENSO. It is demonstrated that the Indian Ocean affects the ENSO variability through modulating convective heating over the Indian Ocean and the Walker circulation over the tropical Indian and Pacific Oceans. Warmer (colder) Indian Ocean SST induces easterly (westerly) surface wind anomalies over the eastern Indian Ocean and western-central equatorial Pacific. Numerical experiments of a simple atmosphere model with specified SST forcing support the roles of imposed Indian Ocean SST anomalies. The applicability of the model results to nature is discussed. It is shown that the observed SST anomalies in the Indian Ocean were out of phase with those in the Pacific Ocean in some ENSO developing years. As such, the Indian Ocean SST anomalies could contribute to the intensity of ENSO. This impact is significant for the cold ENSO events, and there is some evidence for this impact during some warm ENSO events. [ABSTRACT FROM AUTHOR]

Describes results from a series of 40 retrospective forecasts of tropical Pacific SST, starting January 1 and July 1 1980-1999, performed with several coupled ocean-atmosphere general circulation models sharing the same ocean model and the same initial conditions. Indication that the systematic errors of the mean evolution in the coupled models depend strongly on the atmospheric model; Suggestion of a spring prediction barrier.

Results are described from a large sample of coupled ocean–atmosphere retrospective forecasts during 1980–99. The prediction system includes a global anomaly coupled general circulation model and a state-of-the-art ocean data assimilation system. The retrospective forecasts are initialized each January, April, July, and October of each year, and ensembles of six forecasts are run for each initial month, yielding a total of 480 1-yr predictions. In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective. The probabilistic approach is used to quantify the uncertainty in any given forecast. The deterministic measures of skill for eastern tropical Pacific SST anomalies (SSTAs) suggest that the ensemble mean forecasts are useful up to lead times of 7–9 months. At somewhat shorter leads, the forecasts capture some aspects of the variability in the tropical Indian and Atlantic Oceans. The ensemble mean precipitation anomaly has disappointingly low correlation with observed rainfall. The probabilistic measures of skill (relative operating characteristics) indicate that the distribution of the ensemble provides useful forecast information that could not easily be gleaned from the ensemble mean. In particular, the prediction system has more skill at forecasting cold ENSO events compared to warm events. Despite the fact that the ensemble mean rainfall is not well correlated with the observed, the ensemble distribution does indicate significant regions where there is useful information in the forecast ensemble. In fact, it is possible to detect that droughts over land are more predictable than floods. It is argued that probabilistic verification is an important complement to any deterministic verification, and provides a useful and quantitative way to measure uncertainty. [ABSTRACT FROM AUTHOR]

The Center for Ocean-Land-Atmosphere Studies (COLA) global coupled and anomaly coupled ocean-atmosphere GCM models are described. The ocean and atmosphere components of these coupled models are identical. The only difference between them is in the coupling strategy. The anomaly coupling strategy guarantees that the climatology of the coupled model is close to the observed climatology, whereas the global coupled model suffers from serious climate drift. This climate drift is largest in the eastern tropical Pacific where the coupled model is too warm by as much as 5°C. The climate drift in the coupled model can also be seen by the predominance of an erroneous double intertopical convergence zone (ITCZ) in the eastern tropical Pacific. Despite the climate drift, both models exhibit robust interannual variability in the tropical Pacific. Composite analysis, however, reveals that the characteristics of interannual variability in the coupled and the anomaly coupled models are markedly different. For example, the coupled model exhibits a distinct eastward migration of the ENSO events, whereas the anomaly coupled model is dominated by a standing mode, which is too strongly phase-locked to the annual cycle. Based on diagnostic ocean model simulations, it is shown that an erroneous eastward migration of the annual cycle of thermocline depth and upwelling is responsible for the eastward migration of the ENSO events in the coupled model. The anomaly coupled model has a comparatively weak annual cycle in the therm/02 [ABSTRACT FROM AUTHOR]

In order to identify and quantify intrinsic errors in the atmosphere–land and ocean–sea ice model components of the Community Earth System Model version 1 (CESM1) and their contributions to the tropical Atlantic sea surface temperature (SST) bias in CESM1, we propose a new method of diagnosis and apply it to a set of CESM1 simulations. Our analyses of the model simulations indicate that both the atmosphere–land and ocean–sea ice model components of CESM1 contain large errors in the tropical Atlantic. When the two model components are fully coupled, the intrinsic errors in the two components emerge quickly within a year with strong seasonality in their growth rates. In particular, the ocean–sea ice model contributes significantly in forcing the eastern equatorial Atlantic warm SST bias in early boreal summer. Further analysis shows that the upper thermocline water underneath the eastern equatorial Atlantic surface mixed layer is too warm in a stand-alone ocean–sea ice simulation of CESM1 forced with observed surface flux fields, suggesting that the mixed layer cooling associated with the entrainment of upper thermocline water is too weak in early boreal summer. Therefore, although we acknowledge the potential importance of the westerly wind bias in the western equatorial Atlantic and the low-level stratus cloud bias in the southeastern tropical Atlantic, both of which originate from the atmosphere–land model, we emphasize here that solving those problems in the atmosphere–land model alone does not resolve the equatorial Atlantic warm bias in CESM1. [ABSTRACT FROM AUTHOR]

Coupling between the atmosphere and ocean is scale-dependent. For example, in the mid-latitudes and at oceanic mesoscales (spatial scales between 10 and hundreds of kilometers), the air–sea interactions are driven by the oceanic variability, and the atmosphere responds to the changes in the sea surface temperature anomalies (SSTA), which are created by fast oceanic advection. This study explores these interactions, using a regional high-resolution atmosphere–ocean coupled model with a realistic atmospheric component and a semi-idealized oceanic model of a zonal flow. The atmospheric component consists of two nested domains: the inner domain fully coupled with the ocean model, and the outer domain one-way coupled with the observed SST. Two 2-year simulations are discussed here: one in which the oceanic isopycnals are steep and ocean currents are strong ("Strong Currents" or SC) and another with less steep isopycnals and weaker currents ("Weak Currents" or WC). Simulated mesoscale variability occurs on a wide range of spatial scales, and we distinguish large-mesoscale (hundreds of kilometers and shorter) and small-mesoscale (tens of kilometers and shorter) anomalies. The model is most applicable to the mid-latitude Southern Ocean far from any boundaries. Relationships between atmospheric variables and SSTA are studied using temporal correlations and coupling coefficients, and for both large-mesoscale and small-mesoscale anomalies. Significant positive correlations between large-mesoscale anomalies are found for following pairs of variables: equivalent neutral stability (ENS) 10-meter winds and SSTA, wind stress and SSTA, and wind stress divergence/curl and SSTA downwind/crosswind gradients. The temporal correlations are smaller for the small-mesoscale anomalies. The correlation coefficients are also higher for the model SC region, whereas the corresponding coupling coefficients are higher in the model WC region. Among all pairs, the coupling coefficients for the ENS winds and SSTA are the most consistent in time and various environmental conditions. Coupling coefficients for the wind stress and SSTA show a nearly linear dependence on the ENS wind speed. As a result, the reported variability in these coupling coefficients indicates a complex, nonlinear relationship between the wind and SST anomalies. Our numerical analysis indicates strong presence of the Vertical Mixing Mechanism involving the downward momentum mixing on both small- and large-mesoscales. In contrast, the active presence of the Pressure Adjustment Mechanism in the Marine Boundary Layer could not be confirmed on the spatial and time scales considered in this study. • A regional model combines a realistic atmosphere and an idealized ocean component. • Two Southern Ocean regions with very different densities and velocities are studied. • The relationship between the winds and SST is studied using coupling coefficients. • The coupling coefficients depend on the region and scales of the SST anomalies. • Vertical Mixing Mechanism is important at both small- and large-mesoscales. • The importance of the Pressure Adjustment Mechanism is not confirmed. [ABSTRACT FROM AUTHOR]