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Riverine transport of nutrients and carbon from inland waters to the coastal and finally the open ocean alters marine primary production (PP) and carbon (C) uptake regionally and globally. So far, this process has not been fully represented and evaluated in the state-of-the-art Earth system models. Here we assess changes in marine PP and C uptake projected under the Representative Concentration Pathway 4.5 climate scenario using the Norwegian Earth system model, with four riverine transport configurations for nutrients (nitrogen, phosphorus, silicon, and iron), carbon, and total alkalinity: deactivated, fixed at a recent-past level, coupled to simulated freshwater runoff, and following four plausible future scenarios. The inclusion of riverine nutrients and carbon at the 1970 level improves the simulated contemporary spatial distribution of annual mean PP and air–sea CO2 fluxes relative to observations, especially on the continental margins (5.4 % reduction in root mean square error (RMSE) for PP) and in the North Atlantic region (7.4 % reduction in RMSE for C uptake). While the riverine nutrients and C input is kept constant, its impact on projected PP and C uptake is expressed differently in the future period from the historical period. Riverine nutrient inputs lessen nutrient limitation under future warmer conditions as stratification increases and thus lessen the projected decline in PP by up to 0.66 ± 0.02 Pg C yr−1 (29.5 %) globally, when comparing the 1950–1999 with the 2050–2099 period. The riverine impact on projected C uptake depends on the balance between the net effect of riverine-nutrient-induced C uptake and riverine-C-induced CO2 outgassing. In the two idealized riverine configurations the riverine inputs result in a weak net C sink of 0.03–0.04 ± 0.01 Pg C yr−1, while in the more plausible riverine configurations the riverine inputs cause a net C source of 0.11 ± 0.03 Pg C yr−1. It implies that the effect of increased riverine C may be larger than the effect of nutrient inputs in the future on the projections of ocean C uptake, while in the historical period increased nutrient inputs are considered the largest driver. The results are subject to model limitations related to resolution and process representations that potentially cause underestimation of impacts. High-resolution global or regional models with an adequate representation of physical and biogeochemical shelf processes should be used to assess the impact of future riverine scenarios more accurately.

We evaluated the ¼° model NorESM1.3 in which the well-known “double-ITCZ problem” in the Pacific is mitigated. However, excessive precipitation is produced in the northern branch of the ITCZ. The excessive precipitation is consistent with overevaluated latent heat flux in the tropical ocean. Further analysis shows that in NorESM1.3, the latent heat flux is too sensitive to the surface wind. The increased sensitivity in the ¼° model is partly contributed by small-scale air-sea interaction. The sensitivity of latent heat flux to surface wind, with the scale finer than 2.5°, is up to 40 (Wm-2 / ms-1), which is almost twice of that with scale coarser than 2.5°. This study helps to understand extra air-sea interaction resolved by higher-resolution models, and helps to tune and correct the related model bias.

Although both physical and biological marine changes are required to explain the 100 ppm lower atmospheric pCO2 of the Last Glacial Maximum (LGM, ~ 21 ka) as compared to pre-industrial (PI) times, their exact contributions are debated. Proxies of past marine carbon cycling (such as δ13C) document these changes, and thus provide constraints for quantifying the drivers of long-term carbon cycle variability. This modelling study explores the relative roles of physical and biological changes in the ocean needed to simulate an LGM ocean in satisfactory agreement with proxy data, and here especially δ13C. We prepared a PI and LGM ocean model state (NorESM-OC) with full biogeochemistry (including the carbon isotopes δ13C and radiocarbon) and dynamic sea ice. The modelled LGM-PI differences are evaluated against a wide range of physical and biogeochemical proxy data, and show agreement for key aspects of the physical ocean state within the data uncertainties. However, the lack of a simulated increase of regenerated nutrients for the LGM indicates that additional biogeochemical changes are required to simulate an LGM ocean in agreement with proxy data. In order to examine these changes, we explore the theoretical effects of different global mean biological pump efficiencies on the simulated marine biogeochemical tracer distributions. We estimate that (besides changes in ocean circulation) an approximate doubling of the global mean biological pump efficiency from 38 % (PI) to 75 % (LGM) reduces model-proxy biases the most. The remaining absolute model-proxy error in δ13C (which is 0.07 ‰ larger than the 0.19 ‰ data uncertainty) indicates that additional changes in ocean dynamics are needed to simulate an LGM ocean in agreement with proxy data, such as increased aging or volume of Southern Source Waters. Besides that, our theoretical approach of increasing the biological pump efficiency may be too simplified to capture the vertical redistribution of regenerated nutrients – also suggested by a too weak chemocline. Our results underline that only those coupled climate models that contain the processes and/or components that realistically change both ocean circulation and biogeochemistry will be able to simulate an LGM ocean in satisfactory agreement with proxy data – and hence be reliable for use in climate projections. Therefore, future research should aim to identify the exact physical and biogeochemical processes that could have doubled the global mean biological pump efficiency (i.e., the interior regenerated signature) between the PI and LGM, with a likely central role for Southern Source Waters.

The predictability of phytoplankton abundance in the Barents Sea is explored in the CMIP6 decadal prediction runs with the Norwegian Climate Prediction Model (NorCPM1), together with satellite data and in situ measurements. The model successfully predicts a maximum in the observed phytoplankton abundance in 2007 up to five years in advance, which is associated with a strong predictive skill of 2007 minimum extent of the summer sea ice concentration. The underlying mechanism is an event of anomalously high heat transport into the Barents Sea that is seen both in the model and in situ observations. These results are an important step towards marine ecosystem predictions.

In the last decade, high demands from stakeholders and policymakers have driven unprecedented research efforts directed to improve climate predictability. Nevertheless, attempts to get operational climate predictions on seasonal time scales have been far from skillful for a long time. Based on sources of predictability from the ocean, atmosphere and land processes, current state-of-the-art prediction systems are approaching operational predictability. This work examines and compares the ability of different prediction systems to simulate the variability of sea surface temperatures (SSTs) associated with El Niño-Southern Oscillation (ENSO) and the ENSO-forced response of hydroclimate variability in the North Atlantic-Europe (NAE) region. Seasonal hindcasts derived from two generations of the Norwegian Earth System Model (NorESM1-ME and NorESM2-MM) are used in addition to C3S data to generate time series of year-to-year variability that are validated against observational data. Our results reveal both the advantages and the limitations of these prediction systems to simulate ENSO-related variability, identifying model biases that prevent skillful predictability. Further efforts must be aimed at mitigating these biases in order to achieve fully operational predictions of paramount importance for the benefit of society.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015.

El Niño–Southern Oscillation (ENSO) is a main driver of climate variability worldwide, but the presence of atmospheric internal variability makes accurate assessments of its atmospheric teleconnections a challenge. Here, we use a multimodel large ensemble of simulations to investigate the ENSO teleconnection response to a low global warming scenario that represents Paris Agreement targets. The ensemble comprises five atmospheric general circulation models with two experiments (present-day and +2°C) in which the same set of ENSO events is prescribed, which allows for quantification of the uncertainty in the ENSO response due to internal variability. In winter, the teleconnection during the positive ENSO phase features a strong negative anomaly in sea level pressure over the northeast Pacific (and vice versa for the negative phase); this anomaly shifts northeastward and strengthens in the warming experiment ensemble. At least 50–75 ENSO events are required to detect a significant shift or strengthening, emphasizing the need to adequately sample the internal variability to isolate the forced response of the ENSO teleconnection under a low warming scenario. Even more events may be needed if one includes other sources of uncertainty not considered in our experimental setup, such as changes in ENSO itself. Over North America, precipitation changes are generally more robust than temperature changes for the regions considered, despite large internal variability, and are shaped primarily by changes in atmospheric circulation. These results suggest that the observational period is likely too short for assessing changes in the ENSO teleconnection under Paris Agreement warming targets.

The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) – for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic-Atlantic region – focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8-10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is relatively higher skill with respect to SSS than for SST. A systematic benefit from more complex data assimilation approach can not be identified for this region. Somewhat surprisingly, skill deteriorates quite consistently for both the Subpolar North Atlantic and the Norwegian Sea when going from CMIP5 to corresponding CMIP6 versions. We find this to relate to change in the regional performance of the underlying physical model that dominates the benefit from initialization.

Differences between a 1.5 and 2.0 ∘C warmer climate than 1850 pre-industrial conditions are investigated using a suite of uncoupled (Atmospheric Model Intercomparison Project; AMIP), fully coupled, and slab-ocean experiments performed with Norwegian Earth System Model (NorESM1)-Happi, an upgraded version of NorESM1-M. The data from the AMIP-type runs with prescribed sea-surface temperatures (SSTs) and sea ice were provided to a model intercomparison project (HAPPI – Half a degree Additional warming, Prognosis and Projected Impacts; http://www.happimip.org/, last access date: 14 September 2019). This paper compares the AMIP results to those from the fully coupled version and the slab-ocean version of the model (NorESM1-HappiSO) in which SST and sea ice are allowed to respond to the warming, focusing on Arctic amplification of the global change signal. The fully coupled and the slab-ocean runs generally show stronger responses than the AMIP runs in the warmer worlds. The Arctic polar amplification factor is stronger in the fully coupled and slab-ocean runs than in the AMIP runs, both in the 1.5 ∘C warming run and with the additional 0.5 ∘C warming. The low-level Equator-to-pole temperature gradient consistently weakens more between the present-day climate and the 1.5 ∘C warmer climate in the experiments with an active ocean component. The magnitude of the upper-level Equator-to-pole temperature gradient increases in a warmer climate but is not systematically larger in the experiments with an active ocean component. Implications for storm tracks and blocking are investigated. We find considerable reductions in the Arctic sea-ice cover in the slab-ocean model runs; while ice-free summers are rare under 1.5 ∘C warming, they occur 18 % of the time in the 2.0 ∘C warming simulation. The fully coupled model does not, however, reach ice-free conditions as it is too cold and has too much ice in the present-day climate. Differences between the experiments with active ocean and sea-ice models and those with prescribed SSTs and sea ice can be partially due to ocean and sea-ice feedbacks that are neglected in the latter case but can also in part be due to differences in the experimental setup.

An equilibrium simulation of Marine Isotope Stage 3 (MIS3) climate with boundary conditions characteristic of Greenland Interstadial 8 (GI-8; 38 kyr BP) is carried out with the Norwegian Earth System Model (NorESM). A computationally efficient configuration of the model enables long integrations at relatively high resolution, with the simulations reaching a quasi-equilibrium state after 2500 years. We assess the characteristics of the simulated large-scale atmosphere and ocean circulation, precipitation, ocean hydrography, sea ice distribution, and internal variability. The simulated MIS3 interstadial near-surface air temperature is 2.9 ∘C cooler than the pre-industrial (PI). The Atlantic meridional overturning circulation (AMOC) is deeper and intensified by ∼13 %. There is a decrease in the volume of Antarctic Bottom Water (AABW) reaching the Atlantic. At the same time, there is an increase in ventilation of the Southern Ocean, associated with a significant expansion of Antarctic sea ice and concomitant intensified brine rejection, invigorating ocean convection. In the central Arctic, sea ice is ∼2 m thicker, with an expansion of sea ice in the Nordic Seas during winter. Attempts at triggering a non-linear transition to a cold stadial climate state, by varying atmospheric CO2 concentrations and Laurentide Ice Sheet height, suggest that the simulated MIS3 interstadial state in the NorESM is relatively stable, thus underscoring the role of model dependency, and questioning the existence of unforced abrupt transitions in Greenland climate in the absence of interactive ice sheet–meltwater dynamics.

A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g., 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multimillennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speed-up is primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean–sea ice horizontal grid configuration that allows an increase of the ocean–sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasizes model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F in general agree well with observational estimates and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain.

Future climate projections for the 21st century generally do not include the effects of volcanic eruptions. While some attempt has been made to account for the integrated effect of multiple eruptions by incorporating a small continuous volcanic forcing, a recent study (http://nature.com/articles/doi:10.1038/nclimate3394) has already shown that this approach is insufficient to resolve the increased climate variance caused by individual eruptions, especially on decadal timescales. Increased climate variance exerts stresses on ecosystems and society, thus resolving the impacts of plausible future volcanic eruptions is of importance for certain adaptation and mitigation decisions. While previous work has used a modelling approach to address this problem, in this talk we demonstrate a computationally inexpensive method to incorporate the effects of plausible volcanic eruptions into future climate projections. This method uses stochastic volcanic emulators based on 2,500 years of past volcanic activity and the characterization of the response of the climate system to individual eruptions. We will demonstrate not only this methodology, but also describe the requirements and potential for its application to the wider future projections of CMIP6.

Society's need for operational climate forecast on seasonal to decadal time scales means an increased effort to improve climate prediction models. One way to address this issue is to investigate how initialization techniques affect the predictive skill in these systems.Considering this, three implementations and two versions of the Norwegian Climate Prediction Model (NorCPM) are analyzed concerning the effects of different initialization methods on the predictive skill in the Arctic-Atlantic region from interannual to decadal time scales. We consider aspects as data assimilation (DA) in the surface vs subsurface, DA update of sea-ice, CMIP5 vs CMIP6 NorCPM versions, ensemble size, and initialization frequency. Besides that, a comparison between the predictive skill in the Norwegian Sea (NS) and the Subpolar North Atlantic Ocean (SPNA) is performed to identify characteristics that can help to improve predictions in these areas.The additional assimilation of subsurface data increases the predictive skill in the SPNA at all lead times (1-10 years). In contrast, in the NS the skill is increased just at medium lead times (4-7 years). The strongly coupled DA, updating both ocean and sea ice, increases the predictive skill in the SPNA at all lead times, whereas the weakly coupled DA method, only updating ocean, results in higher skill in the NS at shorter (1-3 years) and medium (4-7 years) lead times. With respect to the NorCPM versions, the CMIP5 versions show higher predictive skill in both areas than the CMIP6 ones. In this comparison, besides the differences in the climate forcings, the new NorCPM version contributing to CMIP6 has minor code modifications, addition of interactive aerosol-cloud schemes, and an ocean component with biogeochemistry. Because of that, it is not possible to isolate just the effect of the climate forcings on the skill. Regarding the ensemble size and initialization frequency, NorCPM had a non-linear response; the skill varies with the area, variable, and lead times.Considering the results, no single version was superior to the others with respect to the skill. In the SPNA, the CMIP5 version, assimilating both surface and subsurface observations, and using strongly coupled DA, shows the highest skill. In the NS, we find the similar except that the highest skill is shown for the weakly coupled DA. Further investigation about technical aspects and the representation of dynamical process are necessary to better understand why the sea ice updating in the strongly coupled method is not beneficial to the NS.

In this study we assess to what extent seven different dynamical prediction systems can retrospectively predict the winter sea surface temperature (SST) in the subpolar North Atlantic and the Nordic Seas in the time period 1970-2005. We focus in particular on the region where warm water flows poleward, i.e., the Atlantic water pathway, and on interannual-to-decadal time scales. To better understand why dynamical prediction systems have predictive skill or lack thereof, we confront them with a mechanism identified from observations – propagation of oceanic anomalies from low to high latitudes – on different forecast lead times. This observed mechanism shows that warm and cold anomalies propagate along the Atlantic water pathway within a certain time frame. A key result from this study is that most models have difficulty representing this mechanism, resulting in an overall poor prediction skill after 1-2 years lead times (after applying a band-pass filter to focus on interannual-to-decadal time scales). There is a link, although not very strong, between predictive skill and the representation of the SST propagation. Observational studies demonstrate predictability several years in advance in this region, thus suggesting a great potential for improvement of dynamical climate predictions by resolving the causes for the misrepresentation of the oceanic link. Inter model differences in simulating surface velocities along the Atlantic water pathway suggest that realistic velocities are important to better circulate anomalies poleward, and hence, increase predictive skill on interannual-to-decadal time scales in the oceanic gateway to the Arctic.

Recent operational systems are able to predict sea surface temperature (SST) on seasonal timescales in the extra-tropical North Atlantic and Nordic Seas to a high-degree and as high as in the tropical Pacific. While prediction on multi-year timescales is well documented, the source of the high skill on seasonal timescales is unclear and somewhat unexpected. Here, using the Norwegian Climate Prediction model, we show that the skill on seasonal timescales is associated primarily with low-frequency variability (timescales longer than five years). Consistently, there is high skill in predicting SST anomalies six seasons in advance, although there is a skill drop across boreal summer that seems associated with reduced vertical mixing. External forcing and initialized ocean variability contribute similarly to skill on seasonal timescales, as assessed through a heat budget analysis. Skill on these timescales can benefit fisheries and aqua culture.

The global monsoon system is of exceptional socioeconomic importance owing to its impacts on two-thirds of the globe’s population. Major volcanic eruptions strongly influence global land monsoon (GLM) precipitation change. By using 60 plausible eruption scenarios sampled from reconstructed volcanic proxies over the past 2,500 years, 21st century volcanic influences on GLM precipitation projections are examined with an Earth system model under a moderate emission scenario. The decadal-scale ensemble spread with realistic eruptions (VOLC) increases by 17.5% and 20.1% compared to no-volcanic (NO-VOLC) and constant background-volcanic (VOLC-CONST) scenarios, respectively. Compared with NO-VOLC, the centennial mean VOLC GLM precipitation shows a 10% overall reduction and regionally, Asia is the most impacted. Changes in atmospheric circulation in the aftermath of large volcanic eruptions match the global warming response patterns well with opposite sign, with the North American monsoon precipitation enhanced following large volcanic eruptions, which is in sharp contrast to the robust decrease in Asian monsoon rainfall. Volcanic activity could delay the time of emergence of anthropogenic influence by five years on average over about 60% of the GLM area. Our results demonstrate the importance of statistical representation of potential volcanism for the projections of future monsoon variability. Quantifying volcanic impacts on regional climate projections and their socioeconomic influences on infrastructure planning, food security, and disaster management should be a priority of future work. publishedVersion

We investigate the impact of large climatological biases in the tropical Atlantic on reanalysis and seasonal prediction performance using the Norwegian Climate Prediction Model (NorCPM) in a standard and an anomaly coupled configuration. Anomaly coupling corrects the climatological surface wind and sea surface temperature (SST) fields exchanged between oceanic and atmospheric models, and thereby significantly reduces the climatological model biases of precipitation and SST. NorCPM combines the Norwegian Earth system model (NorESM) with the Ensemble Kalman Filter and assimilates SST and hydrographic profiles. We perform a reanalysis for the period 1980-2010 and a set of seasonal predictions for the period 1985-2010 with both model configurations. Anomaly coupling improves the accuracy and the reliability of the reanalysis in the tropical Atlantic, because the corrected model enables a dynamical reconstruction that satisfies better the observations and their uncertainty. Anomaly coupling also enhances seasonal prediction skill in the equatorial Atlantic to the level of the best models of the North American multi-model ensemble, while the standard model is among the worst. However, anomaly coupling slightly damps the amplitude of Atlantic Niño and Niña events. The skill enhancements achieved by anomaly coupling are largest for forecast started from August and February. There is strong spring predictability barrier, with little skill in predicting conditions in June. The anomaly coupled system show some skill in predicting the secondary Atlantic Niño-II SST variability that peaks in November-December from August 1st.

Instrumental records suggest multidecadal variability in Arctic surface temperature throughout the twentieth century. This variability is caused by a combination of external forcing and internal variability, but their relative importance remains unclear. Since the early twentieth century Arctic warming has been linked to decadal variability in the Pacific, we hypothesize that the Pacific could impact decadal temperature trends in the Arctic throughout the twentieth century. To investigate this, we compare two ensembles of historical all-forcing twentieth century simulations with the Norwegian Earth System Model (NorESM): (1) a fully coupled ensemble and (2) an ensemble where momentum flux anomalies from reanalysis are prescribed over the Indo-Pacific Ocean to constrain Pacific sea surface temperature variability. We find that the combined effect of tropical and extratropical Pacific decadal variability can explain up to ~ 50% of the observed decadal surface temperature trends in the Arctic. The Pacific-Arctic connection involves both lower tropospheric horizontal advection and subsidence-induced adiabatic heating, mediated by Aleutian Low variations. This link is detected across the twentieth century, but the response in Arctic surface temperature is moderated by external forcing and surface feedbacks. Our results also indicate that increased ocean heat transport from the Atlantic to the Arctic could have compensated for the impact of a cooling Pacific at the turn of the twenty-first century. These results have implications for understanding the present Arctic warming and future climate variations.

The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profile observations using the ensemble Kalman filter (EnKF). We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations. We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products. The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. NorESM2 is based on the second version of the Community Earth System Model (CESM2) and shares with CESM2 the computer code infrastructure and many Earth system model components. However, NorESM2 employs entirely different ocean and ocean biogeochemistry models. The atmosphere component of NorESM2 (CAM-Nor) includes a different module for aerosol physics and chemistry, including interactions with cloud and radiation; additionally, CAM-Nor includes improvements in the formulation of local dry and moist energy conservation, in local and global angular momentum conservation, and in the computations for deep convection and air–sea fluxes. The surface components of NorESM2 have minor changes in the albedo calculations and to land and sea-ice models. We present results from simulations with NorESM2 that were carried out for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Two versions of the model are used: one with lower (∼ 2∘) atmosphere–land resolution and one with medium (∼ 1∘) atmosphere–land resolution. The stability of the pre-industrial climate and the sensitivity of the model to abrupt and gradual quadrupling of CO2 are assessed, along with the ability of the model to simulate the historical climate under the CMIP6 forcings. Compared to observations and reanalyses, NorESM2 represents an improvement over previous versions of NorESM in most aspects. NorESM2 appears less sensitive to greenhouse gas forcing than its predecessors, with an estimated equilibrium climate sensitivity of 2.5 K in both resolutions on a 150-year time frame; however, this estimate increases with the time window and the climate sensitivity at equilibration is much higher. We also consider the model response to future scenarios as defined by selected Shared Socioeconomic Pathways (SSPs) from the Scenario Model Intercomparison Project defined under CMIP6. Under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the warming in the period 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.0, and 3.9 K in NorESM2-LM, and 1.3, 2.1, 3.1, and 3.9 K in NorESM-MM, robustly similar in both resolutions. NorESM2-LM shows a rather satisfactory evolution of recent sea-ice area. In NorESM2-LM, an ice-free Arctic Ocean is only avoided in the SSP1-2.6 scenario.

Although both physical and biological marine changes are required to explain the 100 ppm lower atmospheric pCO2 of the Last Glacial Maximum (LGM, ~ 21 ka) as compared to pre-industrial (PI) times, their exact contributions are debated. Proxies of past marine carbon cycling (such as δ13C) document these changes, and thus provide constraints for quantifying the drivers of long-term carbon cycle variability. This modelling study explores the relative roles of physical and biological changes in the ocean needed to simulate an LGM ocean in satisfactory agreement with proxy data, and here especially δ13C. We prepared a PI and LGM ocean model state (NorESM-OC) with full biogeochemistry (including the carbon isotopes δ13C and radiocarbon) and dynamic sea ice. The modelled LGM-PI differences are evaluated against a wide range of physical and biogeochemical proxy data, and show agreement for key aspects of the physical ocean state within the data uncertainties. However, the lack of a simulated increase of regenerated nutrients for the LGM indicates that additional biogeochemical changes are required to simulate an LGM ocean in agreement with proxy data. In order to examine these changes, we explore the theoretical effects of different global mean biological pump efficiencies on the simulated marine biogeochemical tracer distributions. We estimate that (besides changes in ocean circulation) an approximate doubling of the global mean biological pump efficiency from 38 % (PI) to 75 % (LGM) reduces model-proxy biases the most. The remaining absolute model-proxy error in δ13C (which is 0.07 ‰ larger than the 0.19 ‰ data uncertainty) indicates that additional changes in ocean dynamics are needed to simulate an LGM ocean in agreement with proxy data, such as increased aging or volume of Southern Source Waters. Besides that, our theoretical approach of increasing the biological pump efficiency may be too simplified to capture the vertical redistribution of regenerated nutrients – also suggested by a too weak chemocline. Our results underline that only those coupled climate models that contain the processes and/or components that realistically change both ocean circulation and biogeochemistry will be able to simulate an LGM ocean in satisfactory agreement with proxy data – and hence be reliable for use in climate projections. Therefore, future research should aim to identify the exact physical and biogeochemical processes that could have doubled the global mean biological pump efficiency (i.e., the interior regenerated signature) between the PI and LGM, with a likely central role for Southern Source Waters.

The second version of the fully coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. NorESM2 is based on the second version of the Community Earth System Model (CESM2), but has entirely different ocean and ocean biogeochemistry models; a new module for aerosols in the atmosphere model along with aerosol-radiation-cloud interactions and changes related to the moist energy formulation, deep convection scheme and angular momentum conservation; modified albedo and air-sea turbulent flux calculations; and minor changes to land and sea ice models. We show results from low (∼2°) and medium (∼1°) atmosphere-land resolution versions of NorESM2 that have both been used to carry out simulations for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The stability of the pre-industrial climate and the sensitivity of the model to abrupt and gradual quadrupling of CO2 is assessed, along with the ability of the model to simulate the historical climate under the CMIP6 forcings. As compared to observations and reanalyses, NorESM2 represents an improvement over previous versions of NorESM in most aspects. NorESM2 is less sensitive to greenhouse gas forcing than its predecessors, with an equilibrium climate sensitivity of 2.5 K in both resolutions on a 150 year frame. We also consider the model response to future scenarios as defined by selected shared socioeconomic pathways (SSP) from the Scenario Model Intercomparison Project defined under CMIP6. Under the four scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, the warming in the period 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.0, and 3.9 K in NorESM2-LM, and 1.3, 2.1, 3.1, and 3.9 K in NorESM–MM, robustly similar in both resolutions. NorESM2-LM shows a rather satisfactorily evolution of recent sea ice area. In NorESM2-LM an ice free Arctic Ocean is only avoided in the SSP1-2.6 scenario.

The ocean carbon cycle is a key player in the climate system through its role in regulating the atmospheric carbon dioxide concentration and other processes that alter the Earth's radiative balance. In the second version of the Norwegian Earth System Model (NorESM2), the oceanic carbon cycle component has gone through numerous updates that include, amongst others, improved process representations, increased interactions with the atmosphere, and additional new tracers. Oceanic dimethyl sulfide (DMS) is now prognostically simulated and its fluxes are directly coupled with the atmospheric component, leading to a direct feedback to the climate. Atmospheric nitrogen deposition and additional riverine inputs of other biogeochemical tracers have recently been included in the model. The implementation of new tracers such as “preformed” and “natural” tracers enables a separation of physical from biogeochemical drivers as well as of internal from external forcings and hence a better diagnostic of the simulated biogeochemical variability. Carbon isotope tracers have been implemented and will be relevant for studying long-term past climate changes. Here, we describe these new model implementations and present an evaluation of the model's performance in simulating the observed climatological states of water-column biogeochemistry and in simulating transient evolution over the historical period. Compared to its predecessor NorESM1, the new model's performance has improved considerably in many aspects. In the interior, the observed spatial patterns of nutrients, oxygen, and carbon chemistry are better reproduced, reducing the overall model biases. A new set of ecosystem parameters and improved mixed layer dynamics improve the representation of upper-ocean processes (biological production and air–sea CO2 fluxes) at seasonal timescale. Transient warming and air–sea CO2 fluxes over the historical period are also in good agreement with observation-based estimates. NorESM2 participates in the Coupled Model Intercomparison Project phase 6 (CMIP6) through DECK (Diagnostic, Evaluation and Characterization of Klima) and several endorsed MIP simulations.

Based on the large ensembles of the half a degree additional warming, prognosis, and projected impacts historical, +1.5 and +2 °C experiments, we quantify changes in the magnitude of water availability (i.e., precipitation minus actual evapotranspiration; a function of monthly precipitation flux, latent heat flux, and surface air temperature) below normal conditions (less than median, e.g., 20th percentile water availability). We found that, relative to the historical experiment, water availability below normal conditions of the +1.5 and +2 °C experiments would decrease in the midlatitudes and the tropics, indicating that hydrological drought is likely to increase in warmer worlds. These cause more (less) people in East Asia, Central Europe, South Asia, and Southeast Asia (West Africa and Alaska/Northwest Canada) to be exposed to water shortage. Stabilizing warming at 1.5 °C instead of 2 °C would limit population impact in most of the regions, less effective in Alaska/Northwest Canada, Southeast Asia, and Amazon. Globally, this reduced population impact is ~117 million people.

We develop an efficient data assimilation system that aims at quantifying the uncertainties of various biogeochemical states and parameters. We explore the use of four different ensemble estimation techniques for tuning poorly constrained ecosystem parameters using a one-dimensional configuration of the Ocean Biogeochemical General Circulation Model. The schemes are all EnKF-based operating sequentially in time but have different correction equations. The 1D model is used to simulate the biogeochemical cycle at three different stations in mid and high latitudes. We assimilate monthly climatological profiles of nitrate, silicate, phosphate and oxygen in addition to seasonal surface pCO 2 data, between 2006 and 2010. We use the data to optimize eleven ecosystem parameters in addition to all state variables of the model, describing the dynamical processes of the water column. Our assimilation results suggest the following: (1) Among all tested schemes, the one-step-ahead smoothing-based ensemble Kalman filter (OSA-EnKF) is robust and the most accurate, providing consistent and reliable state-parameter ensemble realizations. (2) Given the large uncertainties associated with the ecosystem parameters, estimating only the state variables is generally inconclusive and biased. (3) The OSA-EnKF successfully recovers the observed seasonal variability of the ecosystem dynamics at all stations and helps optimizing the parameters, eventually reducing the prediction errors of the nutrients’ concentrations. (4) The estimates of the parameters may have some temporally correlated features and they can also vary spatially between different regions depending on the magnitude of the bias in the observed variables and other factors such as the intensity of the bloom period. We further show that the presented assimilation system has the potential to be used in global models.

There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model that combines the fully coupled Norwegian Earth System Model and the Ensemble Kalman filter, we present a system that performs both, weakly coupled data assimilation (wCDA) when assimilating ocean hydrography (by updating the ocean alone) and strongly coupled data assimilation when assimilating sea ice concentration (SIC) (by jointly updating the sea ice and ocean). We assess the seasonal prediction skill of this version of the Norwegian Climate Prediction Model, the first climate prediction system using strongly coupled data assimilation, by performing retrospective predictions (hindcasts) for the period 1985 to 2010. To better understand the origins of the prediction skill of Arctic sea ice, we compare this version with a version that solely performs wCDA of ocean hydrography. The reanalysis that assimilates just ocean data exhibits skillful hydrography in the upper Arctic Ocean and features an improved sea ice state, such as improved summer SIC in the Barents Sea, or reduced biases in sea ice thickness. Skillful prediction of SIE up to 10–12 lead months are only found during winter in regions of a relatively deep ocean mixed layer outside the Arctic basin. Additional DA of SIC data notably further corrects the initial seaice state, confirming the applicability of the results of Kimmritz et al. (2018) in a historical setting. The resulting prediction skill of SIE is widely enhanced compared to predictions initialized through wCDA of only ocean data. Particularly high skill is found for July‐initialized autumn SIE predictions.

A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g. 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multi-millennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speedup is primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean-sea ice horizontal grid configuration that allows an increase of the ocean-sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasises model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F in general agree well with observational estimates, and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain.

This study investigates the impacts of global warming of 1.5 °C and 2.0 °C above pre-industrial conditions (Paris Agreement target temperatures) on the South Asian and East Asian monsoon rainfall using five atmospheric global climate models participating in the 'Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) project. Mean and extreme precipitation is projected to increase under warming over the two monsoon regions, more strongly in the 2.0 °C warmer world. Moisture budget analysis shows that increases in evaporation and atmospheric moisture lead to the additional increases in mean precipitation with good inter-model agreement. Analysis of daily precipitation characteristics reveals that more-extreme precipitation will have larger increase in intensity and frequency responding to the half a degree additional warming, which is more clearly seen over the South Asian monsoon region, indicating non-linear scaling of precipitation extremes with temperature. Strong inter-model relationship between temperature and precipitation intensity further demonstrates that the increased moisture with warming (Clausius-Clapeyron relation) plays a critical role in the stronger intensification of more-extreme rainfall with warming. Results from CMIP5 coupled global climate models under a transient warming scenario confirm that half a degree additional warming would bring more frequent and stronger heavy precipitation events, exerting devastating impacts on the human and natural system over the Asian monsoon region., Environmental Research Letters, 13 (4), ISSN:1748-9326, ISSN:1748-9318

This study investigates the global response of the midlatitude atmospheric circulation to 1.5 °C and 2.0 °C of warming using the HAPPI Half a degree Additional warming, Projections, Prognosis and Impacts ensemble, with a focus on the winter season. Characterizing and understanding this response is critical for accurately assessing the near-term regional impacts of climate change and the benefits of limiting warming to the 1.5 °C above pre-industrial levels, as advocated by the Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC). The HAPPI experimental design allows an assessment of uncertainty in the circulation response due to model dependence and internal variability. Internal variability is found to dominate the multi-model mean response of the jet streams, storm tracks and stationary waves across most of the midlatitudes; larger signals in these features are mostly consistent with those seen in more strongly forced warming scenarios. Signals that emerge in the 1.5 °C experiment are a weakening of storm activity over North America, an inland shift of the North American stationary ridge, an equatorward shift of the North Pacific jet exit, and an equatorward intensification of the South Pacific jet. Signals that emerge under an additional 0.5 °C of warming include a poleward shift of the North Atlantic jet exit, an eastward extension of the North Atlantic storm track, and an intensification on the flanks of the Southern Hemisphere storm track. Case studies explore the implications of these circulation responses for precipitation impacts in the Mediterranean, western Europe and the North American west coast, paying particular attention to possible outcomes at the tails of the response distributions. For example, the projected weakening of the Mediterranean storm track emerges in the 2.0 °C world, though the ensemble spread still allows for both wetting and drying responses.

Arctic surface temperature warmed more than twice as fast as global temperature during the early twentieth century, similar to that during the recent global warming. This Arctic warming has been attributed to both external forcing1 and internal variability associated with atmospheric dynamics2,3 and Atlantic Ocean temperature4 in combination with Pacific variability5. Here we show, through coupled climate model experiments that superpose externally forced and dynamically driven changes, that Pacific decadal variability alone was a key contributor to the early twentieth century Arctic warming. Sea surface temperatures in the model are phased to observations by prescribing historical wind variations over the Pacific, which drive thermodynamically consistent decadal variations. During the early twentieth century, the Pacific Decadal Oscillation (PDO) transitioned to a positive phase with a concomitant deepening of the Aleutian Low that warms the Arctic by poleward low-level advection of extratropical air. In addition, our experiments revealed that the implemented Pacific surface changes weaken the polar vortex, which leads to subsidence-induced adiabatic heating of the Arctic surface. Thus, our results suggest that the observed recent shift to the positive PDO phase6 will intensify Arctic warming in the forthcoming decades. In the early twentieth century, the Arctic warmed faster than the global average. Pacific Ocean interdecadal variability, specifically wind-driven sea surface temperatures, drove the Arctic warming through enhanced heat transport.

Abstract. The global NorESM1-M model that produced results for CMIP5 (http://cmip-pcmdi.llnl.gov/cmip5/index.html) has been slightly upgraded to NorESM1-Happi, and has been run with double resolution (~ 1° in the atmosphere and the land surface) to provide model simulations to address the differences between a 1.5 °C and a 2.0 °C warmer climate than the 1850 pre-industrial. As a part of the validation of temperature-targeted model simulations, the atmosphere and land models have been run fully coupled with deep ocean and sea-ice as an extension of the NorESM1-M which produced CMIP5-results. Selected results from a standard set of validation experiments are discussed: a 500-year 1850 pre-industrial control run, three runs for the historical period 1850–2005, three detection and attribution runs, and three future projection runs based on RCPs. NorESM1-Happi has a better representation of sea-ice, improved Northern Hemisphere (NH) extratropical cyclone and blocking activity, and a fair representation of the Madden-Julian oscillation. The amplitude of ENSO signals is reduced and is too small, although the frequency is improved. The strength of the AMOC is larger and probably too large. Modern era global near-surface temperatures and the cloudiness are considerably under-estimated, while the precipitation and the intensity of the hydrological cycle are over-estimated, although the atmospheric residence time of water-vapour appears satisfactory. An ensemble of AMIP-type runs with prescribed SSTs and sea-ice from observations at present-day and a set of global CMIP5 models for a 1.5 °C and a 2.0 °C world (i.e. AMIP) has been provided by the model to a multi-model project (HAPPI, http://www.happimip.org/). This paper concentrates on the results from the NorESM1-Happi AMIP runs, which are compared to results from a slab-ocean version of the model (NorESM1-HappiSO) designed to emulate the AMIP simulation allowing SST and sea-ice to respond. The paper discusses the Arctic Amplification of the global change signal. The slab-ocean results generally show stronger response than the AMIP results to a global change, such as reduced NH extratropical cyclone activity, and different changes in the occurrence of blocking. A considerable difference in the reduction of sea-ice in the Arctic between a 1.5 °C and a 2.0 °C world is simulated. Ice-free summer conditions in the Arctic is estimated to be very rare for the 1.5 °C case, but to occur 40 % of the time for the 2.0 °C case. These results agree with some fully coupled models, but need to be further confirmed.

We have investigated the causes of the sea surface temperature (SST) bias in the Angola–Benguela Frontal Zone (ABFZ) of the southeastern Atlantic Ocean simulated by the Norwegian Earth System Model (NorESM). Similar to other coupled-models, NorESM has a warm SST bias in the ABFZ of up to 8 °C in the annual mean. Our analysis of NorESM reveals that a cyclonic surface wind bias over the ABFZ drives a locally excessively strong southward (0.05 m/s (relative to observation)) Angola Current displacing the ABFZ southward. A series of uncoupled stand-alone atmosphere and ocean model simulations are performed to investigate the cause of the coupled model bias. The stand-alone atmosphere model driven with observed SST exhibits a similar cyclonic surface circulation bias; while the stand-alone ocean model forced with the reanalysis data produces a warm SST in the ABFZ with a magnitude approximately half of that in the coupled NorESM simulation. An additional uncoupled sensitivity experiment shows that the atmospheric model’s local negative surface wind curl generates anomalously strong Angola Current at the ocean surface. Consequently, this contributes to the warm SST bias in the ABFZ by 2 °C (compared to the reanalysis forced simulation). There is no evidence that local air-sea feedbacks among wind stress curl, SST, and sea level pressure (SLP) affect the ABFZ SST bias. Turbulent surface heat flux differences between coupled and uncoupled experiments explain the remaining 2 °C warm SST bias in NorESM. Ocean circulation, upwelling and turbulent heat flux errors all modulate the intensity and the seasonality of the ABFZ errors. publishedVersion

International audience; Hydrographic profiles are crucial observational datasets for constraining ocean models and their verti-cal structure. In this study, we investigate a key implementation setup for optimising their assimilationinto isopycnal ocean models. For this purpose, we use the Norwegian Climate Prediction Model (Nor-CPM), which is a fully-coupled climate prediction system based on the Norwegian Earth System Modeland the ensemble Kalman filter. First, we revisit whether it is more accurate to assimilate observationsin their original coordinate (z-level coordinate) or to transform them into isopycnal coordinates prior toassimilation. The analysis is performed with a single assimilation step using synthetic observations thatmimic the characteristic properties of hydrographic profiles: varying vertical resolutions, profiles of onlytemperature and observations only in the top 1000 m. Assimilating profiles in their native coordinate (z-level coordinates) performs best because converting observations into isopycnal coordinates is stronglynon-linear which reduces the efficiency of the assimilation. Secondly, we investigate how to set the hori-zontal localisation radius for our system. A radius that varies with latitude following a bimodal Gaussianfunction fits the system well. Thirdly, we estimate observation error, which consists of both instrumen-tal error and representativeness error. In the proposed formulation only the instrumental error decreaseswith the number of observations during superobing, because the representativeness error is dominatedby model limitation. Finally, we demonstrate the impact of assimilating hydrographic profiles from theobservational EN4 dataset into NorCPM. An analysis of 10 years with monthly assimilation is performedwith special focus on assessing the accuracy and the reliability of our analysis. The assimilation of hydro-graphic profiles into NorCPM is found to efficiently reduce the model bias and error, and the ensemblespread is found to be a reliable estimator for the forecast error in most regions.

Projections of future climate do not typically include the effects of volcanic activity. By incorporating a range of volcanic futures into a coupled model, it is shown that volcanic forcing has quantifiable impacts on the time at which anthropogenic signatures emerge across various climate metrics. Volcanic activity plays a strong role in modulating climate variability1. Most model projections of the twenty-first century, however, under-sample future volcanic effects by not representing the range of plausible eruption scenarios2,3,4. Here, we explore how sixty possible volcanic futures, consistent with ice-core records5, impact climate variability projections of the Norwegian Earth System Model (NorESM)6 under RCP4.5 (ref. 7). The inclusion of volcanic forcing enhances climate variability on annual-to-decadal timescales. Although decades with negative global temperature trends become ∼50% more commonplace with volcanic activity, these are unlikely to be able to mitigate long-term anthropogenic warming. Volcanic activity also impacts probabilistic projections of global radiation, sea level, ocean circulation, and sea-ice variability, the local-scale effects of which are detectable when quantifying the time of emergence8. These results highlight the importance and feasibility of representing volcanic uncertainty in future climate assessments.