Jet Stream Poleward Migration Leads to Marine Primary Production Decrease

doi:10.21203/rs.3.rs-5164046/v1

Download PDF

Article

Jet Stream Poleward Migration Leads to Marine Primary Production Decrease

https://doi.org/10.21203/rs.3.rs-5164046/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Jet streams (JS) are high-speed upper-tropospheric winds that have significant influence on weather and climate. As anthropogenic climate change alters temperature gradients, subtropical JS are projected to shift poleward, which can have unforeseen impacts on midlatitude Earth systems. Here, we demonstrate for the first time the impact of the steady poleward migration of the Northern Hemisphere subtropical JS on marine primary production (MPP). This northward migration lines up with a consistent decrease in MPP over the last two decades in the Northwestern Mediterranean Sea. While the primary influence of JS position on MPP is seasonal, its impact extends to non-seasonal components as well. These findings highlight the direct consequences of JS latitudinal shifts on marine ecosystems, indicating potential cascading effects driven by climate change.

Earth and environmental sciences/Climate sciences/Atmospheric science

Earth and environmental sciences/Climate sciences/Climate change

Earth and environmental sciences/Climate sciences/Ocean sciences

Earth and environmental sciences/Ocean sciences/Physical oceanography

Jet streams (JSs) are strong meandering upper-troposphere fast air currents that play a significant role in shaping weather patterns and climate dynamics¹. There are two main types of near-tropopause JSs: the Polar jets, 9-12 km above the sea surface, which are embedded in the Polar front; and the subtropical jets, found at the poleward edge of the Hadley cell, at 10-16 km altitude^2,3. The latter is one of the major drivers of daily weather and long-term climate in the midlatitudes^4-7.

Due to the alteration of midlatitude meridional temperature gradients because of anthropogenic climate change⁸, subtropical JSs are expected to shift poleward^8-11. Storm tracks, global weather including temperature and precipitation patterns, and the hydrologic cycle can all be impacted by variations in JS positions and intensity⁵. Therefore, there is a pressing need to gain a comprehensive understanding of JS dynamics and assess its current and future influence on different Earth systems.

Subtropical JSs have been related to the onset of optimal upwelling wind patterns on a seasonal scale, and to impacts on sea surface temperature and the shallow pelagic ecosystem^12-14. However, a major knowledge gap exists concerning the impact of JS dynamics on MPP across marine regions, especially over extended temporal scales, thus prompting the need for devoted research. Moreover, it remains unclear how variations in JS positions associated to climate change can alter atmosphere-ocean interactions and feedback, potentially leading to multiple impacts over the marine ecosystem.

Our study directly addresses the above questions using the NW Mediterranean Sea as test area, with the focus in the subregion between 40º–44ºN and 2º–7ºE (referred to as “extended Gulf of Lion”, EGoL from here onwards) (Supp. Fig. 1). Recurrent intense atmospheric-ocean interactions impacting the entire ecosystem functioning occur in that area^15-19, with dense shelf water cascading, open-ocean deep winter convection, and upwelling events being commonplace²⁰. EGoL is also one of the most productive regions in the entire Mediterranean Basin²¹. All the above makes the selected test area perfectly suited to examine the consequences of JS shifts on MPP, thus easing the likelihood of obtaining new insight on the intricate relationship between atmospheric dynamics and marine ecosystems, and specifically on climate-driven impacts over productivity at regional scale.

Multidecadal Connection Between Key Variables and Jet Stream

To investigate the potential relationship between JS position and MPP, we analyzed key variables including geopotential height (HGT) to obtain the JS position, northern wind stress (NWS), sea surface temperature (SST), and surface chlorophyll (CHL) concentration, the latter serving as a proxy for MPP (cf. Methods).

The time-series of the different variables averaged across the EGoL subregion alongside their correlation coefficients show the strongest correlation to occur between SST and CHL concentration (-0.82), followed by SST and JS position (0.67), and JS position and CHL concentration (-0.54) (Fig. 1). Moreover, the variables under consideration show a strong seasonal pattern (Fig. 1).

A deseasoning and detrending process of JS position and CHL concentration (monthly means for both) highlights to which extent the relationship between these two variables is influenced by the seasonality of the driving processes, namely atmospheric dynamics and MPP (Fig. 2A-C), which results in a correlation coefficient of -0.91 for the seasonal component (Fig. 2B). The residual component of JS position and CHL concentrations (Fig. 2C) shows a correlation coefficient of 0.31, which indicates some degree of connection beyond seasonality, though rather weak, amongst these two variables.

Yearly Variability of Coupling Amongst Variables

A year-by-year examination of the data reveals that in specific years the coupling between variables is tighter than in other years (Fig. 3). While the relationships between JS position and SST, and amongst CHL concentration and SST, remain consistent throughout the 2000–2023 study period, this is not the case for JS position and NWS, and for JS position and CHL concentration. For the latter relationship, some years (i.e. 2000, 2003, 2004, 2007, 2009, 2012, 2014, 2015, 2016, and 2019) show a stronger inverse link (≤ -0.6) than the rest of the years, with 5 years ≤ -0,7 (i.e. 2003, 2012, 2014, 2015 and 2016). Other years have much weaker inverse links, with values amongst − 0.36 and − 0.15 (Fig. 3).

The detrended and deseasonalized time series again reveal some remarkable, year-specific correlations amongst variables (Fig. 3). Namely, in some years where the JS position correlates well with NWS (e.g. 2005, 2012, and 2020), a good correlation with CHL concentration is also observed, whereas no equivalent pattern is found with SST. Therefore, these two variables —CHL concentration and SST— do not respond similarly to JS position.

In the test area, 2012 is one of the most well-known years for intense dense shelf-water cascading and open-sea convection events¹⁷. Our analysis reveals, for this particular year, a high correlation with key variables, both before and after deseasoning and detrending (Fig. 3). Moreover, upon deseasonalizing and detrending, we detect a robust connection among JS position-related variables (NWS, CHL and SST), whereas CHL and SST exhibit a lower cross-correlation (Fig. 3).

Dominant Frequencies and Variance Patterns

Fast Fourier Transforms (FFTs) of JS position, NWS and CHL concentration primary variables demonstrate highly similar spectra amongst them with nearly identical frequencies, as illustrated by an intraseasonal oscillation or cyclicity of about 10–12 days (Supp. Figure 2).

Computing time-lagged cross-correlations between the position of the JS and CHL concentrations shows that the highest correlation involves a 10–12 day-lag (Supp. Figure 3). Therefore, the CHL concentration response to oscillations in the position of the JS is about 10–12 days, which aligns with the frequencies observed in the FFT analysis (Supp. Figure 2).

The variance patterns of JS position and CHL concentration beyond seasonal cyclicity was further investigated by means of an Empirical Orthogonal Functions (EOFs) analysis of the deseasoned and detrended HGT and NWS time series. This method decomposes the data into independent modes (or dominant patterns) that are ranked according to the amount of variance in the dataset, therefore revealing underlying spatial structures within the relationships between different variables.

The EOFs modes for both HGT and NWS highlight the distinctive behaviour of these two variables in the NW Mediterranean Sea (Fig. 4). In particular, the maximum latitudinal gradient of the HGT EOFs reveals that Mode 1 (55% covariance) crosses the EGoL subregion, while Mode 3 (7% covariance) is located quite closely to the south (Fig. 4A).

The Principal Component Analysis (PCA) of the monthly average JS position and monthly average CHL concentration (Supp. Figure 4A-C) yields a 0.84 correlation coefficient amongst these variables for the first mode (PCA Mode 1 in Supp. Figure 4A), thus underscoring the pronounced seasonal pattern behind their relationship while also suggesting a potential link between HGT and CHL concentration dynamics. The cycles in PCA modes 2 and 3 do not show any correlation although they exhibit some visual coherence (Supp. Figure 4B and C). Consequently, we performed another FFT analysis, which results highlight the near-perfect correlation (r = 0.97) between the FFTs of the first PCA mode for both variables, alongside with a correlation of 0.43 in the second mode (FFT PCA modes 1 and 2 in Supp. Figure 5).

Temporal trends and Anomalies

A remarkable result from our analysis is the striking contrast in the trends of JS position and CHL concentration through time. Whereas during the investigated period, and despite interannual variability, the JS position exhibits a consistent overall northward trend, corresponding to a poleward shift of 75 km in the two last decades or so (Fig. 5A), also coinciding with an increase of 1.41ºC of SST in the test area (Fig. 5B), CHL concentration conversely displays an equally consistent overall declining trend within the NW Mediterranean Sea region (Fig. 5C). Notably, these two trends display a perfect negative correlation, therefore underlining the inverse relationship between JS position and CHL concentration in the study area.

Subsequent statistical analysis of the time series of JS position, SST, and CHL concentration further illustrates that the rolling standard deviation (STD) of the subtropical JS position displays a significant positive trend too (Fig. 6A). This points —in addition to its consistent northward shift— to an increase in the variability of the latitudinal position of the JS. Conversely, SST and CHL concentration do not show such behaviour in the test area (Fig. 6B-C).

An analysis of the JS position anomaly relative to the climatology during the study period was also conducted, with no particularly clear patterns emerging beyond (i) a high concentration of positive anomalies, and (ii) the two strongest positive anomalies (exceeding 6º) occurring in the last five years of the time series (Supp. Figure 6).

Our findings stress the critical role of atmospheric dynamics in shaping MPP patterns, with implications for ecosystem dynamics and biogeochemical cycles. They also evidence the major potential implications of climate change on marine ecosystems at regional scales, as demonstrated by the persistent declining trend in CHL concentrations in the NW Mediterranean Sea region²² driven by the steady northward shift of the JS position, which is robustly documented here beyond the masking effect that interannual variability might have over general trends.

Contrary to previous findings pointing to an intensification of upwelling favourable winds in a context of climate change²³, our study documents the contrary behaviour marked by a reduction in CHL concentration associated to a decrease in upwelling events in marine regions such as the NW Mediterranean Sea. This suggests that, unlike other marine regions, the prevailing influence of JS position patterns outweighs that of wind patterns in the test area, as it could be the case in other regions as well. Moreover, we observe that in our test area, SST and CHL do not exhibit similar responses to JS position shifts. This finding is significant because it is generally assumed that SST and CHL behave similarly²⁴, at least over shorter time scales.

The similar periods on FFT frequencies suggests a shared underlying forcing mechanism or a strong interdependence among JS, NWS, and CHL variables, while the 10–12 day cross-correlation alignment endorse that changes in the JS position are a driving factor in the observed CHL concentration variations, thus reinforcing the idea of a direct link between atmospheric dynamics and marine biogeochemical processes in the area. These findings underscore the importance of considering such temporal lags when analyzing the impact of atmospheric processes on marine ecosystems, as expressed by their indicator variables. The consistent cyclicity identified across different variables highlights the potential predictability of CHL concentration changes based on JS position patterns, providing valuable insights for forecasting and managing marine ecological responses to atmospheric variations.

Moreover, the high correlation between JS position and key variables observed in particular years (e.g. 2012) in the EGoL subregion demonstrates the persistent linkages associated with JS position beyond seasonal variations. The strong correlation between JS and CHL found in 2012 suggests that the JS position could play a role in creating favourable conditions for convection and cascading events in the NW Mediterranean Sea.

The observed increase in the variability of the JS position, indicated by the significant positive trend in its rolling STD, suggests that climate change is not only shifting the JS northward but also making its position more erratic^25,26. This finding supports the hypothesis that a weakened JS tends to exhibit “wavier” patterns⁷. Such increased variability can lead to more unpredictable atmospheric conditions, further complicating the already complex relationship between JS dynamics and MPP. Additionally, the analysis of JS position anomalies shows a notable concentration of positive anomalies in recent years, which could be indicative of more frequent or intense disruptions in atmospheric patterns^26–28 that could affect marine ecosystems.

Overall, while contributing to a deeper understanding of the complex interactions between large-scale atmospheric circulation patterns, including the upper-troposphere, and marine biogeochemistry under the current climate change scenario, our work provides insight that can help enhancing preparedness for a warmer future. The findings here emphasize the importance of continuing multidisciplinary and modelling research to better understand atmosphere-ocean coupling feedbacks and their effects on a variety of spatial and time scales. Our study also opens a new door for research, with novel questions emerging from the results achieved so far, such as understanding why JS position, NWS and CHL are better coupled in some years than in others, or which are the processes that intimately connect them.

Materials

The ERA5 reanalysis products on single levels²⁹ and on pressure levels³⁰ are available from 1940 to present in the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (https://cds.climate.copernicus.eu/). The datasets have a temporal and spatial resolution of 1 hour and 0.25 degrees, respectively. In this study, daily data from January 1, 2000, to November 25, 2024, were used to obtain the geopotential height (HGT), northward wind stress (NWS), and sea surface temperature (SST).

Variations in marine primary production (MPP) were obtained by examining daily Level-4 chlorophyll as a proxy, available from the Mediterranean Sea Biogechemistry Reanalysis (CMEMS MED-Biogeochemistry, MedBFM3 system at 1/24° of horizontal resolution, or around 4 km, version 1)³¹ of the European Union (EU) Copernicus Marine Service Information.

North Atlantic Oscillation (NAO) indices are available at the National Centers for Environmental Information webpage³², whereas the Western Mediterranean Oscillation (WeMO) indices³³ are available at the Climate Data repository from the Climatic Research Unit (University of East Anglia) webpage³⁴.

Methods

The successive locations of the subtropical jet stream (JS) in the Northern Hemisphere were determined by computing the maximum meridional gradient of HGT at 250 hectopascals³⁵ over the 30°–50°N latitudinal window along the central longitude (4ºE) of the EGoL (Supp. Figure 1), following Bane et al. (2005) methodology³⁶.

SST, and wind components at 10 meters above the sea surface (10 meters u- and v-components of wind, with u being the zonal or eastward wind and v being the meridional or northward wind) were examined as well. The NWS, averaged over the 30°–50°N latitudinal window (Supp. Figure 1), was calculated using the following equations:

\(\:Ws=\:\sqrt{{\text{u10}}^{2}+{v10}^{2}\:}\) [1]

where Ws is the wind speed, and

\(\:{\tau\:}_{v}=\:{C}_{d}·\:\rho\:·Ws·v\) [2]

where an air density (ρ) value of 1.2 km m^− 3 and a non-dimensional drag coefficient (C_d) of 1.3·10^{− 3 (37)} were considered to obtain the NWS (τ_v)³⁸.

CHL was computed by summing the chlorophyll levels from the surface layer to the 5.46-meter depth layer, and then it was averaged across the EGoL region.

In the current study, all time-series were filtered with 8-days and 35-days rolling windows to remove the weather band.

To identify patterns connecting JS position and surface CHL we proceeded in successive steps. First, we obtained a two-dimensional time-evolving mean of the filtered variables JS position, NWS, CHL, and SST, which captures the essential variability and the dynamics of the variables. Then, correlations and cross-correlations between them were computed using the scipy.signal package. Second, to determine the variables’ significant frequencies, discrete FFT were calculated using the numpy.fft package. Third, the temporal and spatial means, variances, trends, and annual cycles were computed as a preliminary approach to the data statistics. Linear trends were calculated using the numpy.polyfit and numpy.poly1d packages. Then, the 8- and 35-day data was detrended and deseasoned using the seasonal.decompose from the statsmodels.tsa.seasonal package. After removing the trend and seasonality from our variables, PCAs were performed on correlation matrices for HGT, NWS, CHL, and SST to examine the functionality of the whole environmental system in the test area. The PCAs were computed for both the original data and the detrended and deseasoned data using the xarray.eofs package. Fourth, following the same procedure, we removed the trend and seasonal cycles for each grid point of the original data in three dimensions to compute EOFs of the variables HGT, NWS and CHL again using the xarray.eofs package. To determine the PCAs significant frequencies, discrete FFT were again calculated using the numpy.fft package.

Finally, since the time-series had both inter-annual and intra-annual variability, a monthly time-series contour plot was performed to highlight the seasonal cycle and intra-annual variability. In addition, because of their reported ability to represent environmental conditions in the NW Mediterranean Sea, NAO³² and WeMO³³ indices were also analysed. However, no direct relationship was identified between these indices and the key variables under study (Supp. Figure 7).

COMPETING INTERESTS

The authors declare no competing interests of any kind.

Reprints and permissions

Reprints and permissions information is available at www.nature.com/reprints.

Author Contribution

J. C., J. S., and M. C. conceptualized the study. Methodology was developed by J. C. Investigation and analysis were performed by J. C., J. S., and M. C. Figures were prepared by J. C. The original manuscript text was written by J. C., with review and editing contributions from all authors.

Acknowledgement

JC benefits from a grant for the recruitment of researchers in training (FI-SDUR) by the Catalan Government Generalitat de Catalunya. MC acknowledges Tecnoambiente for supporting the Sustainable Blue Economy Chair of University of Barcelona. CRG Marine Geosciences is funded by the Catalan Government within its excellence research groups program (ref. 2021 SGR 01195) (JC, MC). JS acknowledges Catalan Government Generalitat de Catalunya contract PYMEDEASCAT, prospectiva d'emissions a Catalunya: pymedeascat_pro, and grant CEX2019-000928-S funded by AEI10.13039/501100011033.Hersbach, H. et al., (2023) were downloaded from the Copernicus Climate Change Service (2023). The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. This study has been conducted using E.U. Copernicus Marine Service Information; doi: 10.25423/cmcc/medsea_multiyear_bgc_006_008_medbfm3.

Data Availability

All data used in this study are freely available from the following repositories: Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (https://cds.climate.copernicus.eu/datasets/) for the ERA5 datasets (10.24381/cds.adbb2d47 and 10.24381/cds.bd0915c6); EU Copernicus Marine Service Information for the CMEMS MED-Biogeochemistry dataset (https://data.marine.copernicus.eu/product/MEDSEA_MULTIYEAR_BGC_006_008); National Centers for Environmental Information (NCEI) for the North Atlantic Oscillation (NAO) indices (https://www.ncei.noaa.gov/access/monitoring/nao/); and the Climate Data repository from the Climatic Research Unit (University of East Anglia) webpage for the Western Mediterranean Oscillation (WeMO) indices (https://crudata.uea.ac.uk/cru/data/moi/).

Woollings, T., Hannachi, A. & Hoskins, B. Variability of the North Atlantic eddy-driven jet stream: Variability of the North Atlantic Jet Stream. Quart. J. R Met. Soc. 136, 856–868. 10.1002/qj.625 (2010).
Holton, J. R. & Staley, D. O. An Introduction to Dynamic Meteorology. Am. J. Phys. 41, 752–754. 10.1119/1.1987371 (1973).
Bluestein, H. B. Synoptic-dynamic meteorology in midlatitudes. Volume II. Observations and theory of weather systems (Oxford University Press, 1993).
Archer, C. L. & Caldeira, K. Historical trends in the jet streams. Geophys. Res. Lett. 35, 2008GL033614. 10.1029/2008GL033614 (2008).
Hudson, R. D. Measurements of the movement of the jet streams at mid-latitudes, in the Northern and Southern Hemispheres, 1979 to 2010. Atmos. Chem. Phys. 12, 7797–7808. 10.5194/acp-12-7797-2012 (2012).
Barnes, E. A. & Screen, J. A. The impact of Arctic warming on the midlatitude jet-stream: Can it? Has it? Will it? WIREs Clim. Ch. 6, 277–286. 10.1002/wcc.337 (2015).
Stendel, M., Francis, J., White, R., Williams, P. D. & Woollings, T. Chapter 15 - The jet stream and climate change. In: Climate Change. Observed Impacts on Planet Earth, Third Edition (ed. Letcher, T. M.) 327–357 (Elsevier, 2021). 10.1016/B978-0-12-821575-3.00015-3
Vallis, G. K., Zurita-Gotor, P., Cairns, C. & Kidston, J. Response of the large‐scale structure of the atmosphere to global warming. Quart. J. R Met. Soc. 141, 1479–1501. 10.1002/qj.2456 (2015).
Meehl, G. A. et al. Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M. & Miller, H. L.) 747–845 (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, (2007).
Woollings, T. & Blackburn, M. The North Atlantic Jet Stream under climate change and its relation to the NAO and EA patterns. J. Clim. 25, 886–902. 10.1175/JCLI-D-11-00087.1 (2012).
Williams, P. D. Transatlantic flight times and climate change. Env Res. Lett. 11, 024008. 10.1088/1748-9326/11/2/024008 (2016).
Bane, J. M., Spitz, Y. H., Letelier, R. M. & Peterson, W. T. Jet stream intraseasonal oscillations drive dominant ecosystem variations in Oregon’s summertime coastal upwelling system. Proc. Natl. Acad. Sci. U.S.A. 104, 13262–13267 Doi: (2007). 10.1073/pnas.0700926104
Barth, J. A. et al. Delayed upwelling alters nearshore coastal ocean ecosystems in the northern California current. Proc. Natl. Acad. Sci. U.S.A. 104, 3719–3724 Doi: (2007). 10.1073/pnas.0700462104
Le, C., Wu, M., Sun, H., Long, S. & Beck, M. W. Linking phytoplankton variability to atmospheric blocking in an eastern boundary upwelling system. J. Geophys. Res. Oceans. 127, e2021JC017348. 10.1029/2021JC017348 (2022).
Canals, M. et al. Flushing submarine canyons. Nature. 444, 354–357. 10.1038/nature05271 (2006).
Renault, L. et al. Coupled atmosphere-ocean‐wave simulations of a storm event over the Gulf of Lion and Balearic Sea. J. Geophys. Res. 117, 2012JC007924. 10.1029/2012JC007924 (2012).
De Durrieu, X. et al. Interaction of dense shelf water cascading and open-sea convection in the northwestern Mediterranean during winter 2012. Geophys. Res. Lett. 40, 1379–1385. 10.1002/grl.50331 (2013).
Tamburini, C. et al. Deep-sea bioluminescence blooms after dense water formation at the ocean surface. PLoS ONE. 8, e67523. 10.1371/journal.pone.0067523 (2013).
Touratier, F. et al. Role of deep convection on anthropogenic CO₂ sequestration in the Gulf of Lions (northwestern Mediterranean Sea). Deep Sea Res. Part. I: Oceanogr. Res. Pap. 113, 33–48. 10.1016/j.dsr.2016.04.003 (2016).
Many, G., Ulses, C., Estournel, C. & Marsaleix, P. Particulate organic carbon dynamics in the Gulf of Lion shelf (NW Mediterranean) using a coupled hydrodynamic–biogeochemical model. Biogeosc. 18, 5513–5538. 10.5194/bg-18-5513-2021 (2021).
Bănaru, D. et al. Trophic structure in the Gulf of Lions marine ecosystem (north-western Mediterranean Sea) and fishing impacts. J. Mar. Sys. 111–112, 45–68. 10.1016/j.jmarsys.2012.09.010 (2013).
Gómez-Jakobsen, F., Ferrera, I., Yebra, L. & Mercado, J. M. Two decades of satellite surface chlorophyll a concentration (1998–2019) in the Spanish Mediterranean marine waters (Western Mediterranean Sea): Trends, phenology and eutrophication assessment. Rem. Sens. Appl. : Soc. Env. 28, 100855. 10.1016/j.rsase.2022.100855 (2022).
Sydeman, W. J. et al. Climate change and wind intensification in coastal upwelling ecosystems. Science. 345, 77–80. 10.1126/science.1251635 (2014).
Nieves, V. et al. Common turbulent signature in sea surface temperature and chlorophyll maps. Geophys. Res. Lett. 34, 2007GL030823. 10.1029/2007GL030823 (2007).
Trouet, V., Babst, F. & Meko, M. Recent enhanced high-summer North Atlantic Jet variability emerges from three-century context. Nat. Commun. 9, 180 (2018).
Mann, M. E. et al. Influence of Anthropogenic Climate Change on Planetary Wave Resonance and Extreme Weather Events. Sci. Rep. 7, 45242 (2017).
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change. 2, 491–496 (2012).
Lehmann, J. & Coumou, D. The influence of mid-latitude storm tracks on hot, cold, dry and wet extremes. Sci. Rep. 5, 17491 (2015).
Hersbach, H. et al. ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) Doi: (2023). 10.24381/cds.adbb2d47
Hersbach, H. et al. ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) Doi: (2023). 10.24381/cds.bd0915c6
Teruzzi, A. et al. Mediterranean Sea biogeochemical reanalysis (CMEMS MED-Biogeochemistry, MedBFM3 system): MEDSEA_MULTIYEAR_BGC_006_008. Preprint at (2021). https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_BGC_006_008_MEDBFM3
North Atlantic Oscillation (NAO). National Centers for Environmental Information (NCEI). (2024). https://www.ncei.noaa.gov/access/monitoring/nao/
Martin-Vide, J. & Lopez‐Bustins, J. The Western Mediterranean Oscillation and rainfall in the Iberian Peninsula. Int. J. Clim. 26, 1455–1475. 10.1002/joc.1388 (2006).
Climate Data. Climatic Research Unit, University of East Anglia. (2023). https://crudata.uea.ac.uk/cru/data/moi/
Lv, Y. et al. Increased turbulence in the Eurasian upper-level jet stream in winter: Past and future. Earth and Space Sc. 8, e2020EA001556 Doi: (2021). 10.1029/2020EA001556
Bane, J. M. et al. Atmospheric forcing of the Oregon coastal ocean during the 2001 upwelling season. J. Geophys. Res. 110 10.1029/2004JC002653 (2005).
Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. BAMS. 77, 437–472 (1996).
Taylor, G. I. Skin friction of the wind on the Earth’s surface. Proc. R. Soc. Lond. A 92, 196–199 Doi: (1916). 10.1098/rspa.1916.0005

No competing interests reported.

CrespinSciRepSuppInf.pdf

Download PDF

Reviewers agreed at journal
11 Nov, 2024
Reviewers agreed at journal
07 Nov, 2024
Reviewers invited by journal
23 Oct, 2024
Editor assigned by journal
23 Oct, 2024
Editor invited by journal
13 Oct, 2024
Submission checks completed at journal
10 Oct, 2024
First submitted to journal
27 Sep, 2024

You are reading this latest preprint version

Jet Stream Poleward Migration Leads to Marine Primary Production Decrease

Status:

Version 1

Abstract

Figures

INTRODUCTION

RESULTS

Multidecadal Connection Between Key Variables and Jet Stream

Yearly Variability of Coupling Amongst Variables

Dominant Frequencies and Variance Patterns

Temporal trends and Anomalies

DISCUSSION

METHODS

Materials

Methods

Declarations

COMPETING INTERESTS

Reprints and permissions

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1