On dynamical downscaling of ENSO-induced oceanic anomalies off Baja California Peninsula, Mexico: role of the air-sea heat flux

The El Niño Southern Oscillation (ENSO) phenomenon is responsible for important physical and biogeochemical anomalies in the Northeastern Pacific Ocean. The event of 1997-98 has been one of the most intense in the last decades and it had large implications for the waters off Baja California (BC) Peninsula with a pronounced warm sea surface temperature (SST) anomaly adjacent to the coast. Downscaling of reanalysis products was carried out using a mesoscale-resolving numerical ocean model to reproduce the regional SST anomalies. The nested model has a 9 km horizontal resolution that extend from Cabo Corrientes to Point Conception. A downscaling experiment that computes surface fluxes online with bulk formulae achieves a better representation of the event than a version with prescribed surface fluxes. The nested system improves the representation of the large scale warming and the localized SST anomaly adjacent to BC Peninsula compared to the reanalysis product. A sensitivity analysis shows that air temperature and to a lesser extent wind stress anomalies are the primary drivers of the formation of BC temperature anomaly. The warm air-temperature anomalies advect from the near-equatorial regions and the central north Pacific and is associated with sea-level pressure anomalies in the synoptic-scale atmospheric circulation. This regional warm pool has a pronounced signature on sea level anomaly in agreement with observations, which may have implications for biogeochemistry.publishedVersio

Forecasting harmful algae blooms: Application to Dinophysis acuminata in northern Norway

Dinophysis acuminata produces Diarrhetic Shellfish Toxins (DST) that contaminate natural and farmed shellfish, leading to public health risks and economically impacting mussel farms. For this reason, there is a high interest in understanding and predicting D. acuminata blooms. This study assesses the environmental conditions and develops a sub-seasonal (7 - 28 days) forecast model to predict D. acuminata cells abundance in the Lyngen fjord located in northern Norway. A Support Vector Machine (SVM) model is trained to predict future D. acuminata cells abundance by using the past cell concentration, sea surface temperature (SST), Photosynthetic Active Radiation (PAR), and wind speed. Cells concentration of Dinophysis spp. are measured in-situ from 2006 to 2019, and SST, PAR, and surface wind speed are obtained by satellite remote sensing. D. acuminata only explains 40% of DST variability from 2006 to 2011, but it changes to 65% after 2011 when D. acuta prevalence reduced. The D. acuminata blooms can reach concentration up to 3954 cells l−1 and are restricted to the summer during warmer waters, varying from 7.8 to 12.7 °C. The forecast model predicts with fair accuracy the seasonal development of the blooms and the blooms amplitude, showing a coefficient of determination varying from 0.46 to 0.55. SST has been found to be a useful predictor for the seasonal development of the blooms, while the past cells abundance is needed for updating the current status and adjusting the blooms timing and amplitude. The calibrated model should be tested operationally in the future to provide an early warning of D. acuminata blooms in the Lyngen fjord. The approach can be generalized to other regions by recalibrating the model with local observations of D. acuminata blooms and remote sensing data.publishedVersio

Seasonal predictability of Kiremt rainfall in coupled general circulation models

The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June–September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985–2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.publishedVersio

Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic

There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model (NorCPM) 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 hydrogaphy (by updating the ocean alone) and strongly-coupled data assimilation (sCDA) when assimilating sea ice concentration (SIC) (by jointly updating the sea ice and ocean). We assess the seasonal prediction skill of this version of NorCPM, the first climate prediction system using sCDA, by performing retrospective predictions (hindcasts) for the period 1985 to 2010. To better understand 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 a 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 sea ice 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 initialised through wCDA of only ocean data. Particularly high skill is found for July-initialised autumn SIE predictions.publishedVersio

Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF

This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6- and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño–Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content.publishedVersio

Phytoplankton abundance in the Barents Sea is predictable up to five years in advance

The Barents Sea is a highly biologically productive Arctic shelf sea with several commercially important fish stocks. Interannual-to-decadal predictions of its ecosystem would therefore be valuable for marine resource management. Here, we demonstrate that the abundance of phytoplankton, the base of the marine food web, can be predicted up to five years in advance in the Barents Sea with the Norwegian Climate Prediction Model. We identify two different mechanisms giving rise to this predictability; 1) in the southern ice-free Atlantic Domain, skillful prediction is a result of the advection of waters with anomalous nitrate concentrations from the Subpolar North Atlantic; 2) in the northern Polar Domain, phytoplankton predictability is a result of the skillful prediction of the summer ice concentration, which influences the light availability. The skillful prediction of the phytoplankton abundance is an important step forward in the development of numerical ecosystem predictions of the Barents Sea.publishedVersio

Causes of the large warm bias in the Angola–Benguela Frontal Zone in the Norwegian Earth System Model

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.publishedVersio

Framework for an Ocean-Connected Supermodel of the Earth System

A supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non-interactive multi-model ensembles (NI), which combines model outputs a-posteriori. Supermodels with Earth system models (ESMs) has not been developed because it is technically challenging to combine models with different state space. Here, we formulate the first supermodel framework for ESMs and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated by them on a common grid to handle discrepancies in grid and resolution. We compare the performance of two supermodel approaches to that of the NI. In the first (EW), the models are connected to the equal-weight multi-model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in the ocean and in the atmosphere, where the ocean drives the variability. The time variability of the supermodel multi-model mean SST is reduced compared to observations, most where synchronisation is not achieved and is lower-bounded by NI. The damping is larger in EW, for which variability in the individual models is also damped. Hence, under partial synchronisation, the unsynchronized variability gets damped in the multi-model average pseudo-observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi-model mean in the synchronised regions. A trained supermodel remains to be tested.publishedVersio

An assessment of the added value from data assimilation on modelled Nordic Seas hydrography and ocean transports

-The Nordic Seas is a hotspot both in terms of climate related processes, such as Atlantic–Arctic heat exchange, and natural marine resources. A sustainable management of the marine resources within the Nordic Seas, including the co-existence between fisheries and petroleum industries, requires detailed information on the state of the ocean within an operational framework and beyond what is obtainable from observations only. Numerical ocean models applying data assimilation techniques are utilized to address this need. Subsequently, comprehensive comparisons between model results and observations are required in order to assess the model performance. Here, we apply a set of objective statistics to quantitatively assess the added value of data assimilation in numerical ocean models that are currently used operationally. The results indicate that the inclusion of data assimilation improves the model performance both in terms of hydrographic properties and volume and heat transports. Furthermore, we find that increasing the resolution towards eddy resolving resolution performs similarly to coarser resolution models applying data assimilation in shelf areas