Ocean Variability Research Articles

Role of Oceanic Internal Instability in the Generation of Low‐Frequency Variability in the Indian Ocean

AbstractLow‐frequency (Interannual and longer timescale) variability in sea surface temperature (SST) of the Indian Ocean plays a crucial role in affecting the regional climate. Using a high‐resolution global model simulation, we show that internal oceanic variability is an important cause of the observed low‐frequency variability in the subtropical‐midlatitude south Indian Ocean (SIO) between 20° and 40°S, a marked southward shift in the latitude band of active internal variability for the low‐frequency compared to earlier estimates based on coarser Indian Ocean regional models. Notably, we show that internal variability does not contribute to the observed low‐frequency variability in the Seychelles−Chagos thermocline ridge region. Energy budget analysis shows that baroclinic instability is the primary cause for the internal variability. The slowly growing baroclinic instabilities at low frequency and longer length scale favor Rossby waves' generation, propagating the SST and sea level anomaly signals westward.

Western South Atlantic seasonal variability recorded in a mid-deglacial bivalve from the outer Uruguayan continental shelf

The oceanographic dynamics on the continental shelf off southeastern South America are primarily controlled by the southward-flowing warm Brazil Current, converging with the northward-directed cold Malvinas (Falkland) Current, and interacting with the continental discharge of the Plata River. The seasonally reversing regional wind field together with the seasonal cycle of riverine discharge, determines which of these three components provides the dominant forcing. The Uruguayan shelf is thus located in a transitional zone that extends from the region influenced by the Brazil-Malvinas Confluence (BMC) in the open ocean to the Subtropical Shelf Front (STSF) on the continental shelf. Understanding how the resulting oceanic seasonal variability responded to different climatic boundary conditions may shed light on its future behavior. This study presents the first reconstruction of mid-deglacial seasonal hydrographic variability on the continental shelf off southeastern South America in seasonal resolution based on stable oxygen and carbon isotopes (δ18O, δ13C) from a thick-walled shell of a long-living bivalve. The mid-deglacial (14.3 cal ka BP; Bølling-Allerød interstadial) Retrotapes exalbidus bivalve shows a mean δ18O of 3.27 ± 0.42‰ (2.50 ± 0.42‰ when corrected for changes in global ice volume) and a seasonal δ18O amplitude of 1.69‰ for raw isotopic excursions. Moreover, the δ13C exhibits abrupt negative peaks coincident with more negative δ18O values that indicate seasons of elevated freshwater discharge. Finally, the growth rate of the bivalve suggests that the specimen was closer to the metabolically optimum than modern individual of this species from southern South America. Combining biogeographic and ecologic information with these isotopic data, the results point to colder waters and a slightly lower mid-deglacial seasonal amplitude in temperature compared with modern conditions at this shelf site. Because of the northward-displaced Plata River mouth during deglacial times, negative δ13C peaks are expected to reflect an influence of non-point freshwater sources in the form of small fluvial distributaries along the paleo-coast. Most of this signal may, however, be driven by seasonal metabolic effects associated with low ambient water temperatures related to a shallow-water environment located closer to the respective paleo-coastline due to the low sea level at those times.

Synoptic-to-intraseasonal atmospheric modulation of phytoplankton biomass in the inner sea of Chiloé, Northwest Patagonia (42.5°-43.5°S, 72.5°-74°W), Chile

The Inner Sea of Chiloé (ISC) in northwestern Patagonia has experienced large harmful algal blooms in the past decade, impacting human health and affecting the large aquaculture industry of the region. Thus, the investigation of factors favouring regional phytoplankton growth are of particular interest. Analysing the synoptic-to-intraseasonal variability, we explore changes in phytoplankton biomass in southern ISC (S-ISC, 42.5°-43.5°S, 72.5°-74°W) and their concurrent mesoscale and large-scale meteorological and oceanographic conditions. We use high-resolution satellite normalized fluorescence line height (nFLH) and chlorophyll-a (CHL-A) from the MODIS-Aqua sensor as proxies for phytoplankton biomass, besides oceanic and atmospheric variables derived from various remote-sensing sources and atmospheric fields from the ERA5 reanalysis. Specifically, we focus on high phytoplankton biomass events HBEs, which are defined as those cases when intraseasonal nFLH anomaly (nFLH’) exceeds the 95th percentile threshold. Each event was characterised by its first date of occurrence (called day 0). We detected 16 HBE between 2003 and 2019 in S-ISC. HBEs tend to occur under the influence of a mid-latitude migratory anticyclone that induce persistent cloudless conditions preceding day 0, leading to enhanced photosynthetically active radiation (PAR) starting around day -8, and positive sea surface temperature (SST) anomalies between days -4 and +4. We hypothesise that HBEs are mainly modulated by i) mixing and advection that could contribute to a greater availability of nutrients in the upper sea layers before the onset of the anticyclonic anomalies; and ii) increased thermal stratification related to positive PAR and SST anomalies that would promote phytoplankton growth during the anticyclonic regime. Furthermore, we show that the Madden-Julian Oscillation modulates the frequency of nFLH’ and thus of HBEs, a result that suggests an enhanced predictability of these cases.

Quantifying the Benefits of Altimetry Assimilation in Seasonal Forecasts of the Upper Ocean

AbstractSatellite altimetry measurements of sea surface height provide near‐global ocean state observations on sub‐monthly time scales, which are not always utilized by seasonal climate forecasting systems. As early as the mid‐1990s, attempts were made to assimilate altimetry observations to initialize climate models. These experiments demonstrated improved ocean forecasting skill, especially compared to experiments that did not assimilate subsurface ocean temperature information. Nowadays, some operational climate forecasting models utilize altimetry in their assimilation systems, whereas others do not. Here, we assess the impact of altimetry assimilation on seasonal prediction skill of ocean variables in two climate forecasting systems that are from the European Centre for Medium‐Range Weather Forecasts (SEAS5) and the Australian Bureau of Meteorology (ACCESS‐S). We show that assimilating altimetry improves the initialization of subsurface ocean temperatures, as well as seasonal forecasts of monthly variability in upper‐ocean heat content and sea level. Skill improvements are largest in the subtropics, where there are typically less subsurface ocean observations available to initialize the forecasts. In the tropics, there are no noticeable improvements in forecast skill. The positive impact of altimetry assimilation on forecast skill related to the subsurface ocean does not seem to affect predictions of sea surface temperature. Whether this is because current forecasting systems are close to the potential predictability limit for the ocean surface, or perhaps altimetry observations are not fully exploited, remains a question. In summary, we find that utilizing altimetry observations improves the overall global ocean forecasting skill, at least for upper‐ocean heat content and sea level.

Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT

IntroductionWhile crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented.MethodsWith a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (<20 laboratories) and multivariate datasets. Finally, the expanded uncertainty of measurement for 20 environmental variables routinely measured by SOMLIT from discrete sampling—including Essential Ocean Variables—is provided.Results, Discussion, ConclusionThe examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.

A status assessment of selected data synthesis products for ocean biogeochemistry

Ocean data synthesis products for specific biogeochemical essential ocean variables have the potential to facilitate today’s biogeochemical ocean data usage and comply with the Findable Accessible Interoperable and Reusable (FAIR) data principles. The products constitute key outputs from the Global Ocean Observation System, laying the observational foundation for information and services regarding climate and environmental status of the ocean. Using the Framework of Ocean Observing (FOO) readiness level concept, we present an evaluation framework for biogeochemical data synthesis products, which enables a systematic assessment of each product’s maturity. A new criteria catalog provides the foundation for assigning scores to the nine FOO readiness levels. As an example, we apply the assessment to four existing biogeochemical essential ocean variables data products. In descending readiness level order these are: The Surface Ocean CO2 Atlas (SOCAT); the Global Ocean Data Analysis Project (GLODAP); the MarinE MethanE and NiTrous Oxide (MEMENTO) data product and the Global Ocean Oxygen Database and ATlas (GO2DAT). Recognizing that the importance of adequate and comprehensive data from the essential ocean variables will grow, we recommend using this assessment framework to guide the biogeochemical data synthesis activities in their development. Moreover, we envision an overarching cross-platform FAIR biogeochemical data management system that sustainably supports the products individually and creates an integrated biogeochemical essential ocean variables data synthesis product; in short a system that provides truly comparable and FAIR data of the entire biogeochemical essential ocean variables spectrum.

Classification of Interannual Surface Layer Salinity Variability

AbstractSince surface layer salinity (SLS) mainly reflects sea surface freshwater flux and affects ocean circulation and upper ocean stratification by modifying seawater density, knowledge of SLS variability is important to better understand the global hydrological cycle and ocean and climate variability. The global pattern of SLS interannual variability has not received as much attention compared to the seasonal variability. The SLS interannual variability in the global ocean based on Argo profiling float data over 2003–2020 was classified into seven individual clusters. SLS interannual variability was found to be regionally different even within each ocean basin and primarily corresponded to large‐scale climate mode variability. The spatial distribution of the classified SLS clusters provides the first map of SLS interannual variability in the global ocean that largely reflects the variability of the global hydrological cycle.

Low-frequency variability enhancement of the midlatitude climate in an eddy-resolving coupled ocean–atmosphere model. Part I: anatomy

This study investigated the coupling of the wind-driven ocean gyres with the atmospheric westerly jet using an idealised, eddy-resolving, coupled model. An empirical orthogonal function analysis of the low-pass filtered data showed that the ocean gyre variability is dominated by meridional shifts of the western boundary current extension (WBCE) and changes in the strength of the subtropical inertial recirculation zone. On the other hand, the atmospheric potential vorticity (PV) variability is dominated by the growth of standing Rossby wave patterns, while its pressure variability is dominated by a zonally-asymmetric meridional shift of the atmospheric jet. Damping sea surface temperature (SST) variability in the atmosphere was shown to weaken its PV variability and reduce the zonal asymmetry of the jet-shift mode. Singular value decompositions revealed a positive feedback between meridional shifts of the WBCE and the growth of standing Rossby wave disturbances in the atmospheric jet. The atmosphere’s response is controlled by shifts in the meridional eddy heat flux over the SST front which triggers the growth of baroclinic instabilities. This instability growth eventually leads to a large-scale, barotropic pressure response over the eastern ocean basin, or an aforementioned meridional shift of the atmospheric jet. Reduction in the atmospheric resolution inhibits the ability of atmospheric eddies to resolve length scales associated with meridional shifts of the SST front and WBCE. The lack of resolution consequently weakens the influence of ocean gyre variability on the atmospheric jet and reduces the strength of the positive feedback.

Strong subsurface meridional current forced by monsoon intraseasonal oscillation in the southern Bay of Bengal during summer 2020

The meridional current in the southern Bay of Bengal (BOB) exhibits prominent intraseasonal variability (ISV), which exerts a critical influence on meridional mass and energy exchange. However, its relationship with the northward propagating monsoon intraseasonal oscillation (MISO), which is the predominant variability in the tropical Indian Ocean during summer, is not well understood. Using a one-year mooring deployed at 5.5 °N, 90 °E, a strong ISV of the meridional current is observed between 150 and 250 m during summer, exhibiting amplitudes exceeding 0.4 ms−1 and periods of 30–70 d. Further analysis shows that the summer ISV is forced by a strong MISO event with the following dynamic processes. The MISO first drives the zonal wind stress at the equator, leading to the equatorial Kelvin wave and the reflected equatorial Rossby wave at 5 °N. Then, the MISO propagates northward and generates local wind stress curl-induced Ekman pumping near the mooring site, leading to the subsequent off-equatorial Rossby wave at 8 °N. As a result, the synergy of the remotely- and locally-forced Rossby wave causes strong subsurface ISV in southern BOB. This study provides a new insight into the dynamic relationship between the MISO and the meridional current in the southern BOB, which has important implications for regional climate research and prediction.

Intraseasonal variability of the South Vietnam upwelling, South China Sea: influence of atmospheric forcing and ocean intrinsic variability

Abstract. The South Vietnam upwelling (SVU) develops off the Vietnamese coast (South China Sea, SCS) during the southwest summer monsoon over four main areas: the northern coastal upwelling (NCU), the southern coastal upwelling (SCU), the offshore upwelling (OFU) and the shelf off the Mekong River mouth (MKU). An ensemble of 10 simulations with perturbed initial conditions were run with the fine-resolution SYMPHONIE model (1 km inshore) to investigate the daily to intraseasonal variability of the SVU and the influence of the ocean intrinsic variability (OIV) during the strong SVU of summer 2018. The intraseasonal variability is similar for the SCU, MKU and OFU, driven to the first order by the wind variability. The MKU and SCU are induced by stable ocean dynamics (the northeastward then eastward boundary current) and have very little chaotic variability. The OIV has a stronger influence on OFU. In July, OFU mainly develops along the northern flank of the eastward jet. The influence of the OIV is strongest and related to the chaotic variability of the meridional position of the jet. In August, this position is stable and OFU develops mainly in the area of positive wind curl and cyclonic eddies north of the jet. The influence of the OIV, weaker than in July, is related to the organization of this mesoscale circulation. The NCU shows a behavior different from that observed in the other areas. In the heart of summer, a large-scale circulation formed by the eastward jet and eddy dipole is well established with an alongshore current preventing the NCU development. In early and late summer, this circulation is weaker, allowing a mesoscale circulation of strongly chaotic nature to develop in the NCU area. During those periods, the OIV influence on the NCU is very strong and related to the organization of this mesoscale circulation: the NCU is favored (annihilated) by offshore-oriented (alongshore) structures.

Global-scale random bottom pressure fluctuations from oceanic intrinsic variability

Global-scale random bottom pressure fluctuations from oceanic intrinsic variability

Upper-Ocean Circulation and Tropical Atlantic Interannual Modes

Abstract The impact of tropical Atlantic Ocean variability modes in the variability of the upper-ocean circulation has been investigated. For this purpose, we use three oceanic reanalyses, an interannual forced-ocean simulation, and satellite data for the period 1982–2018. We have explored the changes in the main surface and subsurface ocean currents during the emergence of Atlantic meridional mode (AMM), Atlantic zonal mode (AZM), and AMM–AZM connection. The developing phase of the AMM is associated with a boreal spring intensification of North Equatorial Countercurrent (NECC) and a reinforced summer Eastern Equatorial Undercurrent (EEUC) and north South Equatorial Current (nSEC). During the decaying phase, the reduction of the wind forcing and zonal sea surface height gradient produces a weakening of surface circulation. For the connected AMM–AZM, in addition to the intensified NECC, EEUC, and nSEC in spring, an anomalous north-equatorial wind curl excites an oceanic Rossby wave (RW) that is boundary-reflected into an equatorial Kelvin wave (KW). The KW reverses the thermocline slope, weakening the nSEC and EUC in boreal summer and autumn, respectively. During the developing spring phase of the AZM, the nSEC is considerably reduced with no consistent impact at subsurface levels. During the autumn decaying phase, the upwelling RW-reflected mechanism is activated, modifying the zonal pressure gradient that intensifies the nSEC. The NECC is reduced in boreal spring–summer. Our results reveal a robust alteration of the upper-ocean circulation during AMM, AZM, and AMM–AZM, highlighting the decisive role of ocean waves in connecting the tropical and equatorial ocean transport.

Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India

The role of ocean variability is at a focal point in improving the weather and climate forecasts at different spatial and temporal scales. We study the effect of antecedent southwestern Indian Ocean mean sea level anomaly (MSLA) and sea surface temperature anomalies (SSTA) as a proxy to upper ocean heat capacitance on all India summer monsoon rainfall (AISMR) during 1993–2019. SSTA and MSLA over the southwestern Indian Ocean (SWIO) have been influenced by El Niño-Southern Oscillation (ENSO), the impact of ENSO-induced SWIO variability was low on rainfall variability over several homogeneous regions. Rainfall over northeast (NE) and North India (EI) has been modulated by ENSO-induced SSTA and MSLA over SWIO, thus effecting the total AISMR magnitude. The ENSO-induced changes in heat capacitance (SSTA and MSLA) over SWIO during antecedent months has less impact on west coast of India, central India and North India (NI) rainfall variability. The long-term trend in pre-monsoonal SSTA and MSLA over SWIO shows decreasing rainfall trend over NI, NE, and EI in the recent time. Furthermore, the cooler (warmer) anomaly over the western Indian Ocean affects rainfall variability adversely (favourably) due to the reversal of the wind pattern during the pre-monsoon period. While SSTA and MSLA are increasing in the SWIO, large-scale variability of these parameters during preceding winter and pre-monsoon months combined with surface winds could impact the inter-annual AISMR variability over homogeneous regions of India. Similarly, from an oceanic perspective, the antecedent heat capacitance over SWIO on an inter-annual time scale has been the key to the extreme monsoon rainfall variability.

Monthly Arctic Sea‐Ice Prediction With a Linear Inverse Model

AbstractWe evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea‐ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. We find that more than 500 years of training data and 100 years of validation data are needed to reliably estimate LIM forecast skill. The best LIM has skill up to 8 months lead time and outperforms an autoregressive model of order one (AR1) forecast at all locations, with particularly large outperformance near the ice edge. However, for out‐of‐sample validation tests using data from various different model simulations and reanalysis products, they underperform an AR1 model due to differences in the location of the sea‐ice edge from the training data. We present a metric for predicting LIM forecast skill, based on the spatial correlation of the variance in the training and validation data sets.

Ocean Mesoscale and Frontal-Scale Ocean–Atmosphere Interactions and Influence on Large-Scale Climate: A Review

Abstract Two decades of high-resolution satellite observations and climate modeling studies have indicated strong ocean–atmosphere coupled feedback mediated by ocean mesoscale processes, including semipermanent and meandrous SST fronts, mesoscale eddies, and filaments. The air–sea exchanges in latent heat, sensible heat, momentum, and carbon dioxide associated with this so-called mesoscale air–sea interaction are robust near the major western boundary currents, Southern Ocean fronts, and equatorial and coastal upwelling zones, but they are also ubiquitous over the global oceans wherever ocean mesoscale processes are active. Current theories, informed by rapidly advancing observational and modeling capabilities, have established the importance of mesoscale and frontal-scale air–sea interaction processes for understanding large-scale ocean circulation, biogeochemistry, and weather and climate variability. However, numerous challenges remain to accurately diagnose, observe, and simulate mesoscale air–sea interaction to quantify its impacts on large-scale processes. This article provides a comprehensive review of key aspects pertinent to mesoscale air–sea interaction, synthesizes current understanding with remaining gaps and uncertainties, and provides recommendations on theoretical, observational, and modeling strategies for future air–sea interaction research. Significance Statement Recent high-resolution satellite observations and climate models have shown a significant impact of coupled ocean–atmosphere interactions mediated by small-scale (mesoscale) ocean processes, including ocean eddies and fronts, on Earth’s climate. Ocean mesoscale-induced spatial temperature and current variability modulate the air–sea exchanges in heat, momentum, and mass (e.g., gases such as water vapor and carbon dioxide), altering coupled boundary layer processes. Studies suggest that skillful simulations and predictions of ocean circulation, biogeochemistry, and weather events and climate variability depend on accurate representation of the eddy-mediated air–sea interaction. However, numerous challenges remain in accurately diagnosing, observing, and simulating mesoscale air–sea interaction to quantify its large-scale impacts. This article synthesizes the latest understanding of mesoscale air–sea interaction, identifies remaining gaps and uncertainties, and provides recommendations on strategies for future ocean–weather–climate research.

Deriving Sea Subsurface Temperature Fields From Satellite Remote Sensing Data Using a Generative Adversarial Network Model

AbstractAccurately inverting global and regional subsurface temperature (ST) by multisource satellite observations is a challenging but hot topic. This study proposes a new method to invert daily ST from the sea surface information in China's marginal seas based on generative adversarial network (GAN) model. The proposed GAN‐based model can project the STs from sea surface information (SLA, SSTA, SST) with a high resolution of 1/12°. A traditional regression‐based model, Modular Ocean Data Assimilation System (MODAS), is set up same experiments for comparison. The results show that the averaged root mean square error results are less than 1.45°C in the upper 200 m and the highest averaged R2 of 0.97 at the 70 m level, which is better than that of MODAS. Errors analysis and typical oceanographic phenomena analysis results show the superiority of the proposed GAN‐based model in this study. This study can provide high‐precision daily ST data from sea surface information, which can be expanded to further studies on the interior ocean variation characteristics.

Mesoscale Variability Linked to Interannual Displacement of Gulf Stream

AbstractThe impacts of interannual oscillations of the Gulf Stream (GS) on oceanic mesoscale variability are investigated using satellite observations of sea surface height (SSH) and sea surface temperature (SST) from 1993 to 2018. We show that variations in GS position, strength, and meandering status are the dominant spatiotemporal modes in regional SSH variability as they explain over 50% of the total variance. In particular, meridional shift of the GS associated with the large‐scale wind variation over the North Atlantic contributes to approximately 30% of SSH variability. We further find that this path displacement mode can drive approximately 15% of regional mesoscale variability in eddy kinetic energy and divergent eddy heat flux. This observational‐based evidence of ocean mesoscale response to GS shift infers a potentially important forcing mechanism that could drive eddy‐scale ocean variability and has far‐reaching implications for regional ocean and ecosystem dynamics in response to climate variation.

Ocean drifter velocity data assimilation, Part 1: Formulation and diagnostic results

Numerical ocean circulation forecasts diverge from reality due to non-deterministic processes. Through data assimilation, ocean forecasting systems routinely make corrections to scalar ocean variables of temperature and salinity derived from ocean observations. Velocity observations, however, are vector quantities that require different processing. To assimilate existing ocean velocity observations, which generally are depth limited, vertical error covariances are needed to relate the velocity errors throughout the water column. In this paper we show a velocity assimilation approach that relies on historical observations and geostrophy to relate velocity, temperature, and salinity. Geopotential calculations transform temperature and salinity covariances into corresponding geopotential covariances. This paper describes the methods and examines details of the solution process for ocean velocity data assimilation. The approach employs two separate three-dimensional variational (3DVAR) analyses combined to create one set of initial conditions during each daily forecast cycle. The first 3DVAR analysis utilizes temperature, salinity, and sea surface height anomaly data and reduces the background error variance based on these observations. The second 3DVAR analysis uses the reduced error variance and performs velocity data assimilation using surface drifter data, and these observations further reduce the error variance of temperature and salinity for the next cycle. The second 3DVAR analysis for velocity data assimilation utilizes the new full depth error covariances. We show diagnostic results from a Gulf of Mexico simulation during extensive drifter deployments in the summer of 2020. Results reveal that the two-step data assimilation cycle applies vector velocity innovations to create increments that correct the background field towards a geostrophic balance in temperature and salinity.

Current status and progress in the seasonal prediction of the Asian summer monsoon

The Asian summer monsoon (ASM) has a considerable impact on human lives in the most populated region in the world. Thus, its seasonal prediction is a high-profile application in Earth Science. However, the prediction skill of the regional ASM variability has long been limited due to a formidable difficulty in accurately simulating the complex interactions of the atmosphere-ocean variability and its remote influence on regional climate in numerical models. This study updates the current status and assesses progress in the ASM seasonal prediction performance. This study evaluated the seasonal prediction skill of two generations of models in hindcast data archived by the WCRP Climate-system Historical Forecast Project (CHFP) and Copernicus Climate Change Service (C3S). A special focus was put on the representation of the predominant teleconnections associated with the ENSO and Indian Ocean variability. It was found that the latest seasonal prediction systems (C3S) generally outperform previous-generation systems (CHFP) in terms of the reproducibility of the observed precipitation climatology and the prediction skill of the interannual variability of seasonal precipitation over the ASM region. Furthermore, the results suggested that the improvement of the prediction skill of the ASM likely stems from the improved representation of the monsoon climatology and teleconnections in the models. These analyses highlight the steady progress of the atmosphere-ocean coupled modelling and promise future improvements in the seasonal ASM prediction.

Indian Ocean variability changes in the Paleoclimate Modelling Intercomparison Project

Abstract. The Indian Ocean exhibits multiple modes of interannual climate variability, whose future behaviour is uncertain. Recent analysis of glacial climates has uncovered an additional El Niño-like equatorial mode in the Indian Ocean, which could also emerge in future warm states. Here we explore changes in the tropical Indian Ocean simulated by the Paleoclimate Model Intercomparison Project (PMIP4). These simulations are performed by an ensemble of models contributing to the Coupled Model Intercomparison Project 6 and over four coordinated experiments: three past periods – the mid-Holocene (6000 years ago), the Last Glacial Maximum (21 000 years ago), the last interglacial (127 000 years ago) – and an idealized forcing scenario to examine the impact of greenhouse forcing. The two interglacial experiments are used to characterize the role of orbital variations in the seasonal cycle, whilst the other pair focus on responses to large changes in global temperature. The Indian Ocean Basin Mode (IOBM) is damped in both the mid-Holocene and last interglacial, with the amount related to the damping of the El Niño–Southern Oscillation in the Pacific. No coherent changes in the strength of the IOBM are seen with global temperature changes; neither are changes in the Indian Ocean Dipole (IOD) nor the Niño-like mode. Under orbital forcing, the IOD robustly weakens during the mid-Holocene experiment, with only minor reductions in amplitude during the last interglacial. Orbital changes do impact the SST pattern of the Indian Ocean Dipole, with the cold pole reaching up to the Equator and extending along it. Induced changes in the regional seasonality are hypothesized to be an important control on changes in the Indian Ocean variability.