Factors governing pelagic production in polar oceans

Historical changes in fresh water transport from sub-tropical to sub-polar oceans

10.5194/egusphere-egu21-9630 ◽

2021 ◽

Author(s):

Taimoor Sohail ◽

Jan Zika ◽

Damien Irving ◽

John Church

Keyword(s):

Fresh Water ◽

Climate Model ◽

Water Cycle ◽

Tropical Ocean ◽

Freshwater Transport ◽

Run Off ◽

Polar Oceans ◽

S Curve ◽

Polar Ocean ◽

Different Response

Warming-induced global water cycle changes pose a significant threat to biodiversity and humanity. &#160;The atmosphere transports freshwater from the sub-tropical ocean to the tropics and poles in two distinct branches.&#160;The resulting air-sea fluxes of fresh water and river run-off imprint on ocean salinity (S) at different temperatures (T), creating a characteristic `T-S curve' of mean salinity as a function of temperature.&#160;Using a novel tracer-percentile framework, we quantify changes in the observed T-S curve from 1970 to 2014. &#160;The warming ocean has been characterised by freshening tropical and sub-polar oceans and salinifying sub-tropical oceans. Over the 44 year period investigated, a net poleward freshwater transport out of the sub-tropical ocean is quantified, implying an amplification of the net poleward atmospheric freshwater transport.&#160;Historical reconstructions from the 6th Climate Model Intercomparison Project (CMIP6) exhibit a different response, underestimating the peak salinification of the ocean by a factor of 4, and showing a weak freshwater transport into the sub-polar ocean.&#160;Results indicate this discrepancy between the observations and models may be attributed to consistently biased representations of evaporation and precipitation patterns, which lead to the the weaker amplification seen in CMIP6 models.

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CryoSat-2’s contribution to the complete sea level records from the Polar Oceans

10.5194/egusphere-egu21-15978 ◽

2021 ◽

Author(s):

Stine Kildegaard Rose ◽

Ole Baltazar Andersen ◽

Sara Fleury ◽

Carsten Ludwigsen ◽

Michel Tsamados ◽

...

Keyword(s):

Sea Level ◽

Polar Oceans

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Microbiology in Polar Oceans

Oceanography ◽

10.5670/oceanog.2007.59 ◽

2007 ◽

Vol 20 (2) ◽

pp. 140-145 ◽

Cited By ~ 12

Author(s):

James Hollibaugh ◽

Connie Lovejoy ◽

Alison Murray

Keyword(s):

Polar Oceans

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Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Atmospheric Measurement Techniques ◽

10.5194/amt-8-4025-2015 ◽

2015 ◽

Vol 8 (10) ◽

pp. 4025-4041 ◽

Cited By ~ 4

Author(s):

H.-J. Kang ◽

J.-M. Yoo ◽

M.-J. Jeong ◽

Y.-I. Won

Keyword(s):

Sea Ice ◽

The Arctic ◽

Data Sets ◽

High Latitudes ◽

Ice Surface ◽

Moderate Resolution ◽

Microwave Sounding ◽

Polar Oceans ◽

Moderate Resolution Imaging Spectroradiometer ◽

Ice Surface Temperature

Abstract. Uncertainties in the satellite-derived surface skin temperature (SST) data in the polar oceans during two periods (16–24 April and 15–23 September) 2003–2014 were investigated and the three data sets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice boundary and colder down to −2.04 K in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a less accurate GCM forecast over the seasonally varying frozen oceans than the microwave data. The three data sets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ~ 2.8 ± 1.9 K decade−1) in the northern high regions (70–80° N) as expected from the ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

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