Global Mean Sea Surface Research Articles

Enhanced ocean heat storage efficiency during the last deglaciation

Proxy reconstructions suggest that increasing global mean sea surface temperature (GMSST) during the last deglaciation was accompanied by a comparable or greater increase in global mean ocean temperature (GMOT), corresponding to a large heat storage efficiency (HSE; ∆GMOT/∆GMSST). An increased GMOT is commonly attributed to surface warming at sites of deepwater formation, but winter sea ice covered much of these source areas during the last deglaciation, which would imply an HSE much less than 1. Here, we use climate model simulations and proxy-based reconstructions of ocean temperature changes to show that an increased deglacial HSE is achieved by warming of intermediate-depth waters forced by mid-latitude surface warming in response to greenhouse gas and ice sheet forcing as well as by reduced Atlantic meridional overturning circulation associated with meltwater forcing. These results, which highlight the role of surface warming and oceanic circulation changes, have implications for our understanding of long-term ocean heat storage change.

Exploring the contribution of low‐frequency internal variability modes to global mean sea surface temperature variability based on large ensembles

AbstractBased on three large ensemble (LE) historical simulations, this study explored the contribution of low‐frequency internal variability (IV) modes to global mean sea surface temperature anomaly (GmSST) during 1920–2010. The dominant contributions of low‐frequency IV modes to GmSST in the three large ensembles are quite different. The low‐frequency IV modes in the Indo‐Pacific Ocean (i.e., IPO and IOBW) dominate the low‐frequency GmSST signal for the CESM2 and MPI LEs (the ensemble median contribution value reaches 70%–80%), while the low‐frequency IV modes in the North Atlantic dominate for the FGOALS‐g3 LE (contribute more than 50%). CESM2 LE and MPI LE can reasonably reproduce the impact of low‐frequency modes in the Indo‐Pacific ocean as observed, but FGOALS‐g3 LE underestimates this impact and overestimates the extratropical impact (on GmSST) of the North Atlantic. After removing the external forcing, it still retains the excessive signs in low‐frequency IV modes in FGOALS‐g3 LE over the North Atlantic. Furthermore, we used a pattern adjustment approach to revise the surplus effect in the North Atlantic. After revision, the contribution of decadal IVs in the North Atlantic to GmSST is reduced by 30%. Meanwhile, the tropical contribution in the Indo‐Pacific Ocean is increased and that is closer to the observed one. This approach can be employed to revise the weak or strong IV signals in other regions or LEs, which is meaningful for reducing the uncertainty of IV signals.

Tropical Atmospheric Response of Atlantic Niños to Changes in the Ocean Background State

AbstractSince the 1970s, Atlantic Niños during boreal summer have been linked to Pacific La Niñas the following winter. Earlier studies have explained the appearance of the Atlantic‐Pacific teleconnection with changing Atlantic Niño configurations. Here we find that the non‐stationarity of this teleconnection can also be explained by changes in the ocean background state, without changing the Atlantic Niño configuration. Experiments with different atmospheric general circulation models are performed where the same Atlantic Niño pattern is prescribed to different global ocean background states. The 1975–1985 global mean sea surface temperature forces a Walker Circulation response and low‐level convergence over the Maritime Continent, increasing the chance of triggering a La Niña‐like event in the Pacific. These results suggest that ENSO‐predictions could be improved in certain periods by considering tropical Atlantic variability.

ICESat-2 for Coastal MSS Determination—Evaluation in the Norwegian Coastal Zone

Radar satellite altimeters enable the determination of the mean sea surface to centimeter accuracy, which can be degraded in coastal areas because of the lack of valid altimetry observations due to land contamination and the altimeter footprint size. In 2018, the National Aeronautics and Space Administration launched ICESat-2, a laser altimetry mission equipped with the Advanced Topographic Laser Altimeter System, providing measurements every 0.7 m in the along-track direction. Taking into account the complexity of the Norwegian coastline, this study aims to evaluate coastal observations from ICESat-2 in order to use it to update the existing mean sea surface for Norway, NMBU18. We, therefore, determined the mean sea surface using only ICESat-2 observations and compared it with mean sea level observations from 23 permanent tide gauges along the entire coast and 21 temporary tide gauges in Norway’s largest and deepest fjord, Sognefjorden. We also included two global mean sea surface models and NMBU18 for comparison. The results have shown that ICESat-2 is indeed able to provide more valid observations in the coastal zone, which can be used to improve the mean sea surface model, especially along the coast.

SDUST2020 MSS: a global 1′ × 1′ mean sea surface model determined from multi-satellite altimetry data

Abstract. This study focuses on the determination and validation of a new global mean sea surface (MSS) model, named the Shandong University of Science and Technology 2020 (SDUST2020), with a grid size of 1′×1′. This new model was established with a 19-year moving average method and fused multi-satellite altimetry data over a 27-year period (from January 1993 to December 2019). The data of HaiYang-2A, Jason-3, and Sentinel-3A were first ingested in the SDUST2020 MSS but not in any other global MSS model, such as the CLS15 and DTU18 MSS models. Validations, including comparisons with the CLS15 and DTU18 MSS models, GPS-leveled tide gauges, and altimeter data, were performed to evaluate the quality of the SDUST2020 MSS model, all of which showed that the SDUST2020 MSS model is accurate and reliable. The SDUST2020 MSS dataset is freely available at the site (data DOI: https://doi.org/10.5281/zenodo.6555990, Yuan et al., 2022).

A deep learning model for forecasting global monthly mean sea surface temperature anomalies

Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Niño-Southern Oscillation (ENSO) regarded as a major source of interannual climate variability at the global scale. The ability to make long-range forecasts of SST variations and extreme marine heatwave events have potentially significant economic and societal benefits, especially in a warming climate. We have developed a deep learning time series prediction model (Unet-LSTM), based on more than 70 years (1950–2021) of ECMWF ERA5 monthly mean SST and 2-m air temperature data, to predict global 2-dimensional SSTs up to a 24-month lead. Model prediction skills are high in the equatorial and subtropical Pacific. We have assessed the ability of the model to predict SST anomalies in the Niño3.4 region, an ENSO index in the equatorial Pacific, and the Blob marine heatwave events in the northeast Pacific in detail. An assessment of the predictions of the 2019–2020 El Niño and the 2016–2017 and 2017–2018 La Niña show that the model has skill up to 18 months in advance. The prediction of the 2015–2016 extreme El Niño is less satisfactory, which suggests that subsurface ocean information may be crucial for the evolution of this event. Note that the model makes predictions of the 2-d monthly SST field and Nino 3.4 is just one region embedded in the global field. The model also shows long lead prediction skills for the northeast Pacific marine heatwave, the Blob. However, the prediction of the marine heatwaves in the southeast Indian Ocean, the Ningaloo Niño, shows a short lead prediction. These results indicate the significant potential of data-driven methods to yield long-range predictions of SST anomalies.

Evolving CO2 Rather Than SST Leads to a Factor of Ten Decrease in GCM Convergence Time.

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO2 mixing ratio to equilibrate climate, has been implemented in a cloud‐resolving model. In this article, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global‐mean SST. We find that InvCM produces the same climate as normal slab‐ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO2 for SSTs ranging from 290 to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

Evaluation and projection of the AMO and PDO variabilities in the CMIP5 models under different warming scenarios part1: Evaluation

The capabilities of the CMIP5 models to simulate the two most important interdecadal modes, the Atlantic Multi-decadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO), are evaluated by comparing the observed sea surface temperature data from 1880 to 2009 and the CMIP5 simulations under the historical scenario. The multi-model ensemble (MME) historical simulations show deficiency in reproducing characteristics of the PDO mode in the tropics but well simulated the spatial patterns of the AMO and PDO in the high latitudes. The amplitude of the two modes is obviously weaker in the historical simulations than the observation. In terms of periodic simulations, the MME can simulate the 60–70 year period of the AMO and the double periods, namely 20 and 60–70 years, of the PDO. The MME show more realistic simulations of observed spatial and temporal features of AMO and PDO than most models. The CESM1-CAM5, FGOALS-g2, GISS-E2-R-CC, GISS-E2-H-CC and MIROC-ESM-CHEM are the top-performing models. By comparing the individual models, the variances of the AMO and PDO modes decrease when the simulated global mean sea surface temperatures are higher.

Quantifying the precision of retracked Jason-2 sea level data in the 0–5 km Australian coastal zone

This paper aims to quantify the precision of retracked Jason-2 altimeter sea level data (2008–2016) in the 0–5 km Australian coastal zone. A new retracking strategy has been developed to achieve the highest possible precision of coastal sea-level data. Coastal waveforms were classified into four groups according to the adaptability of dedicated coastal retrackers, from which each group was reprocessed using an optimal retracker. A modified Brown model was introduced to reprocess the quasi-specular waveforms over calm waters. A seamless transition of sea surface heights from different retrackers was achieved by estimating and removing the significant wave height dependent height differences. In order to generate the 20-Hz sea level anomaly (SLA) and total water level envelope (TWLE) at the coast, the regional along-track mean sea surface (MSS) with a high spatial resolution (~300 m) was estimated. Compared to the global MSS model, the use of the along-track MSS has dramatically reduced the SLA variance by 140 cm2 in the 0–15 km coastal strip.The results from the evaluation of data precision for Jason-2 ground tracks and the validation against tide gauges show that this new retracking strategy can retrieve more reliable sea level data nearshore. The precision of 20-Hz sea level data in the 0–5 km Australian coastal strip ranges typically from 5 cm to 6 cm in most coastal areas, which is only ~1 cm lower than that beyond 5 km off the coastline. However, in Northwest Australian and some coastal regions, the data precision drops to ≥7 cm due partly to the specific coastal sea states and topography. In addition, it is found that the altimeter and tide gauge TWLE time series have a high correlation (>0.8) in the 0–5 km distance band, indicating that coastal sea-level data are useful for applications such as estimation of river plumes, long-term variation of water level in lakes and rivers, and the analysis of storm surges.

Sea Surface Heights and Marine Gravity Determined from SARAL/AltiKa Ka-band Altimeter Over South China Sea

SARAL/AltiKA (SRL) is the first altimetry satellite with a Ka-band altimeter. To validate the advantages of the Ka-band altimeter over traditional Ku-band altimeters in marine geodetic applications, a comprehensive analysis is carried out over the South China Sea (SCS) (0–30° N, 105–125° E) from three aspects, namely the influence of load on waveforms, the precision of sea surface heights (SSHs), and the precision of altimeter-derived marine gravity field. Coastal waveforms of SRL, Jason-2, and CryoSat-2 are separately compared with corresponding ocean-type waveforms. The radius of coastal influence on SSHs of SRL/exact repeat mission (SRL/ERM) is the smallest, being about 3 km. Crossover discrepancies, global mean sea surface models, and tide gauge data are used to assess the precision of altimetric SSHs. Compared with the SSH precision of Ku-band Jason-2/ERM, the SSH precision of Ka-band SRL/ERM is 4.6% higher over the SCS and 10% higher in offshore areas. Gridded gravity anomalies are derived from measurements of SRL/drifting phase (SRL/DP) and CryoSat-2 through the inverse Vening-Meinesz formula, respectively. According to the assessment by shipborne gravity data and global marine gravity models, the precision of SRL/DP-derived gravity is higher than that of CryoSat-2-derived gravity over the SCS, especially in offshore areas. In some cycles, ground tracks of SRL/ERM have large drifting of more than 10 km from nominal tracks. The results show that the Ka-band altimeter of SRL has better precision in SSHs and marine gravity recovery than the Ku-band altimeter over the SCS, particularly in offshore areas.

The Unique Role of the Jason Geodetic Missions for high Resolution Gravity Field and Mean Sea Surface Modelling

The resolutions of current global altimetric gravity models and mean sea surface models are around 12 km wavelength resolving 6 km features, and for many years it has been difficult to improve the resolution further in a systematic way. For both Jason 1 and 2, a Geodetic Mission (GM) has been carried out as a part of the Extension-of-Life phase. The GM for Jason-1 lasted 406 days. The GM for Jason-2 was planned to provide ground-tracks with a systematic spacing of 4 km after 2 years and potentially 2 km after 4 years. Unfortunately, the satellite ceased operation in October 2019 after 2 years of Geodetic Mission but still provided a fantastic dataset for high resolution gravity recovery. We highlight the improvement to the gravity field which has been derived from the 2 years GM. When an Extension-of-Life phase is conducted, the satellite instruments will be old. Particularly Jason-2 suffered from several safe-holds and instrument outages during the GM. This leads to systematic gaps in the data-coverage and degrades the quality of the derived gravity field. For the first time, the Jason-2 GM was “rewound” to mitigate the effect of the outages, and we evaluate the effect of “mission rewind” on gravity. With the recent successful launch of Sentinel-6 Michael Freilich (S6-MF, formerly Jason CS), we investigate the possibility creating an altimetric dataset with 2 km track spacing as this would lead to fundamental increase in the spatial resolution of global altimetric gravity fields. We investigate the effect of bisecting the ground-tracks of existing GM to create a mesh with twice the resolution rather than starting all over with a new GM. The idea explores the unique opportunity to inject Jason-3 GM into the same orbital plane as used for Jason-2 GM but bisecting the existing Jason-2 tracks. This way, the already 2-years Jason-2 GM could be used to create a 2 km grid after only 2 years of Jason-3 GM, rather than starting all over with a new GM for Jason-3.

Kinematics of global mean thermosteric sea level during 1993–2019

Abstract Because oceans cover 71% of Earth’s surface, ocean warming, consequential for thermal expansion of sea water, has been the largest contributor to the global mean sea level rise averaged over the 20 th and the early 21 st century. This study first generates quasi-observed monthly globally averaged thermosteric sea level time series by removing the contributions of global mean sea level budget components, namely, Glaciers, Greenland, Antarctica, and Terrestrial Water Storage from satellite altimetry measured global sea level changes during 1993–2019. A baseline kinematic model with global mean thermosteric sea level trend and a uniform acceleration is solved to evaluate the performance of a rigorous mixed kinematic model. The model also includes coefficients of monthly lagged 60 yearlong cumulative global mean sea surface temperature gradients and control variables of lunisolar origins and representations for first order autoregressive disturbances. The mixed kinematic model explains 94% (Adjusted R 2)1 of the total variability in quasi-observed monthly and globally averaged thermosteric time series compared to the 46% of the baseline kinematic model’s Adjusted R 2. The estimated trend, 1.19±0.03 mm/yr., is attributed to the long-term ocean warming. Whereas eleven statistically significant (α = 0.05) monthly lagged cumulative global mean sea surface temperature gradients each having a memory of 60 years explain the remainder transient global mean thermosteric sea level changes due to the episodic ocean surface warming and cooling during this period. The series also exhibit signatures of a statistically significant contingent uniform global sea level acceleration and periodic lunisolar forcings.

A global mean sea surface temperature dataset for the Last Interglacial (129–116 ka) and contribution of thermal expansion to sea level change

Abstract. A valuable analogue for assessing Earth's sensitivity to warming is the Last Interglacial (LIG; 129–116 ka), when global temperatures (0 to +2 ∘C) and mean sea level (+6 to 11 m) were higher than today. The direct contribution of warmer conditions to global sea level (thermosteric) is uncertain. We report here a global network of LIG sea surface temperatures (SST) obtained from various published temperature proxies (e.g. faunal and floral plankton assemblages, Mg ∕ Ca ratios of calcareous organisms, and alkenone U37K′). We summarize the current limitations of SST reconstructions for the LIG and the spatial temperature features of a naturally warmer world. Because of local δ18O seawater changes, uncertainty in the age models of marine cores, and differences in sampling resolution and/or sedimentation rates, the reconstructions are restricted to mean conditions. To avoid bias towards individual LIG SSTs based on only a single (and potentially erroneous) measurement or a single interpolated data point, here we report average values across the entire LIG. Each site reconstruction is given as an anomaly relative to 1981–2010, corrected for ocean drift, and where available seasonal estimates are provided (189 annual, 99 December–February, and 92 June–August records). To investigate the sensitivity of the reconstruction to high temperatures, we also report maximum values during the first 5 millennia of the LIG (129–124 ka). We find mean global annual SST anomalies of 0.2 ± 0.1 ∘C averaged across the LIG and an early maximum peak of 0.9 ± 0.1 ∘C, respectively. The global dataset provides a remarkably coherent pattern of higher SST increases at polar latitudes than in the tropics (demonstrating the polar amplification of surface temperatures during the LIG), with comparable estimates between different proxies. Polewards of 45∘ latitude, we observe annual SST anomalies averaged across the full LIG of > 0.8 ± 0.3 ∘C in both hemispheres with an early maximum peak of > 2.1 ± 0.3 ∘C. Using the reconstructed SSTs suggests a mean LIG global thermosteric sea level rise of 0.08 ± 0.1 m and a peak contribution of 0.39 ± 0.1 m, respectively (assuming warming penetrated to 2000 m depth). The data provide an important natural baseline for a warmer world, constraining the contributions of Greenland and Antarctic ice sheets to global sea level during a geographically widespread expression of high sea level, and can be used to test the next inter-comparison of models for projecting future climate change. The dataset described in this paper, including summary temperature and thermosteric sea level reconstructions, is available at https://doi.org/10.1594/PANGAEA.904381 (Turney et al., 2019).

Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two different vintages of each of the annual time series, covering the periods 1880–2001 and 1880–2013. We find that temperature and sea level updates and revisions have a substantial influence both on the magnitude of the estimated coefficients of influence (differences of up to 50%) and therefore on long-term projections of sea level rise following the RCP4.5 and RCP6 scenarios (differences of up to 40 cm by the year 2100). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to have access to and to work with the data vintages used at the time.

A joint role for forced and internally-driven variability in the decadal modulation of global warming

Despite the observed monotonic increase in greenhouse-gas concentrations, global mean temperature displays important decadal fluctuations typically attributed to both external forcing and internal variability. Here, we provide a robust quantification of the relative contributions of anthropogenic, natural, and internally-driven decadal variability of global mean sea surface temperature (GMSST) by using a unique dataset consisting of 30-member large initial-condition ensembles with five Earth System Models (ESM-LE). We present evidence that a large fraction (~29–53%) of the simulated decadal-scale variance in individual timeseries of GMSST over 1950–2010 is externally forced and largely linked to the representation of volcanic aerosols. Comparison with the future (2010–2070) period suggests that external forcing provides a source of additional decadal-scale variability in the historical period. Given the unpredictable nature of future volcanic aerosol forcing, it is suggested that a large portion of decadal GMSST variability might not be predictable.

Using Late Pleistocene sea surface temperature reconstructions to constrain future greenhouse warming

Future greenhouse warming projections conducted with coupled climate models still exhibit a substantial spread in response to a given anthropogenic greenhouse gas concentration scenario. In order to constrain this spread and to provide robust warming projections, our understanding of Earth's global-mean surface temperature response to radiative forcing (referred to as climate sensitivity) needs to be further refined. Here we estimate an averaged glacial/interglacial climate sensitivity using 25 transient Earth system model simulations of the Last Glacial Cycle and a global-mean sea surface temperature (SST) reconstruction derived from 64 globally-distributed paleo-proxies of SST. Our results document that Earth's averaged Late Pleistocene equilibrium climate sensitivity is in the order of ∼4.2 K per CO2 doubling. Using the Representative Concentration Pathway 8.5 for future greenhouse radiative forcing, this value translates into a global-mean surface warming of ∼5.0 K by the year 2100 relative to pre-industrial levels. This estimate is in excellent agreement with the ensemble-mean projection of climate simulations conducted as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Our uncertainty analysis reveals further that the lack of robust reconstructions of glacial aerosol forcing is a key contributor to the overall uncertainty of paleo-based estimates of climate sensitivity.

Comparative Assessment of the Recent Global Mean Sea Surface Models using Tidal Bench Mark Observation Data around Korea

Lee, D.H.; Acharya, T.D., and Jang, W.H., 2019. Comparative assessment of the recent global mean sea surface models using tidal bench mark observation data around Korea. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp.401-405. Coconut Creek (Florida), ISSN 0749-0208.Mean Sea Surface (MSS) provides the basic information of mean height of the water level above a reference surface over a year. These are used to understand the characters of the undersea topography and are widely used in geodesy, geophysics, oceanography (operational oceanography, modeling, ocean variability studies). MSS can be obtained from range of techniques such as LiDAR, altimetry, etc. Depending upon the data source and processing method the quality is varied. In the past decade, significant advances in global MSS modelling have been made with the release of the altimetry satellites. These data cover most of the oceans and are freely available, which benefits areas with poor geospatial infrastructure or inaccessible areas. Since, their applications depend on the accuracy of global MSS, their evaluation should be done as preliminary step. MSS models for specific regions can be analyzed based on comparison with the coastal Tidal Bench Marks (TBMs) observations. This paper aims to assess the accuracy of the recent global MSS models in comparison with TBM observation data around Korea. First data were collected, and necessary statistics were calculated. As the characteristics of the sea around Korea are different, there different cases of East, West and South seas were also calculated along with profile plots. Result shows WHU13 shows less difference with TBM while CNESCLS15 and DTU13 are more correlated. In case of Korean sea, combined used of TBM near coast and WHU13 far in sea can be used.

A coastal mean sea surface with associated errors in Norway based on new-generation altimetry

Abstract The coastal mean sea surface (MSS) has applications within oceanography as well as geodesy. Together with a geoid model, it forms an important component for geodetic mapping of ocean surface currents that are in geostrophic balance. Furthermore, it forms a bridge between open ocean MSS and in situ measurements of mean sea level at or close to land, it contributes to the mapping of the geoid and the marine gravity field, and it is essential for connecting tidal nautical chart datums to physical height systems or global geodetic reference frames. In this study, we determine a coastal MSS with an associated error field for Norway. The MSS is solely based on new-generation altimetry data, i.e., SAR(In) data from Sentinel-3A and CryoSat-2, as well as Ka-band data from SARAL/AltiKa. The data sets partly overlap in time and cover the time period from 2010 to 2017 inclusive. We have chosen these altimeters because they represent evolutions of conventional altimetry, with reduced footprint sizes as a main benefit. This is especially advantageous in the coastal zone, as a smaller footprint reduces the probability of radar pulses being contaminated by energy backscattered from land areas. The satellite missions were harmonized by applying inter-mission biases determined in a regional crossover analysis. Furthermore, in a zone closer to land than 25 km, we have replaced the global ocean tide model with a regional ocean tide model provided by the Norwegian Mapping Authority (NMA). We use an optimal interpolation technique to determine a coastal MSS grid and discuss it in context of the estimated error field. We assess our coastal MSS by comparison to state-of-the-art MSS products along three sections perpendicular to the coast, as well as ellipsoidal mean sea level as observed by an array of permanent tide gauges within the study area. In addition, we assess a higher-resolution version of our MSS in the NMA testbed for vertical datums, by comparison with temporary tide gauges. We find that the coastal MSS outperforms the global MSS models directly at the coast, with standard deviations of differences of ~ 8 cm to the tide gauges, compared to 14–22 cm, obtained with the global MSS models. All MSS models largely agree along three sections perpendicular to the coast, with standard deviations of differences of 2–4 cm. The higher-resolution version of the coastal MSS performs similarly to the coastal MSS in comparison with the temporary tide gauges (standard deviation of differences of ~ 8 cm), but its formal error field also quantifies large uncertainties at the coast and in the fjords, mainly due to the lack of altimetry observations. A trustworthy error field is decisive for the combination of altimetry with other sea-level observations.

A Multivariate AMV Index and Associated Discrepancies Between Observed and CMIP5 Externally Forced AMV

AbstractAtlantic Multidecadal Variability (AMV) is a multivariate phenomenon. Here for the first time we obtain a multivariate AMV index and associated patterns using Multivariate Empirical Orthogonal Function (MEOF) analysis to explore the multivariate nature of AMV. Coherent multidecadal variability that is unique to the Atlantic is found in the observed MEOF‐extracted AMV, various AMV‐related indices, and an Atlantic Meridional Overturning Circulation fingerprint. When the signal associated with global mean sea surface temperature is removed from both observations and Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, the residual CMIP5 forced basin‐wide sea surface temperature‐based AMV index disagrees strongly with the observed residual. Only the observed residual basin‐wide sea surface temperature‐based AMV index retains a strong AMV signal. The MEOF approach still extracts a residual CMIP5 forced AMV signal that is unique to the Atlantic, although very different from observations. Our findings suggest that the observed AMV is not dominated by external forcing.

Phytoplankton calcifiers control nitrate cycling and the pace of transition in warming icehouse and cooling greenhouse climates

Abstract. Phytoplankton calcifiers contribute to global carbon cycling through their dual formation of calcium carbonate and particulate organic carbon (POC). The carbonate might provide an efficient export pathway for the associated POC to the deep ocean, reducing the particles' exposure to biological degradation in the upper ocean and increasing the particle settling rate. Previous work has suggested ballasting of POC by carbonate might increase in a warming climate, in spite of increasing carbonate dissolution rates, because calcifiers benefit from the widespread nutrient limitation arising from stratification. We compare the biogeochemical responses of three models containing (1) a single mixed phytoplankton class, (2) additional explicit small phytoplankton and calcifiers, and (3) additional explicit small phytoplankton and calcifiers with a prognostic carbonate ballast model, to two rapid changes in atmospheric CO2. The first CO2 scenario represents a rapid (151-year) transition from a stable icehouse climate (283.9 ppm) into a greenhouse climate (1263 ppm); the second represents a symmetric rapid transition from a stable greenhouse climate into an icehouse climate. We identify a slope change in the global net primary production response with a transition point at about 3.5 ∘C global mean sea surface temperature change in all models, driven by a combination of physical and biological changes. We also find that in both warming and cooling scenarios, the application of a prognostic carbonate ballast model moderates changes in carbon export production, suboxic volume, and nitrate sources and sinks, reducing the long-term model response to about one-third that of the calcifier model without ballast. Explicit small phytoplankton and calcifiers, and carbonate ballasting, increase the physical separation of nitrate sources and sinks through a combination of phytoplankton competition and lengthened remineralization profile, resulting in a significantly higher global nitrate inventory in this model compared to the single phytoplankton type model (15 % and 32 % higher for icehouse and greenhouse climates). Higher nitrate inventory alleviates nitrate limitation, increasing phytoplankton sensitivity to changes in physical limitation factors (light and temperature). This larger sensitivity to physical forcing produces stronger shifts in ocean phosphate storage between icehouse and greenhouse climates. The greenhouse climate is found to hold phosphate and nitrate deeper in the ocean, despite a shorter remineralization length scale than the icehouse climate, because of the longer residence times of the deep water masses. We conclude the global biogeochemical impact of calcifiers extends beyond their role in global carbon cycling, and that the ecological composition of the global ocean can affect how ocean biogeochemistry responds to climate forcing.