scholarly journals Local cold adaption increases the thermal window of temperate mussels in the Arctic

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
J Thyrring ◽  
R Tremblay ◽  
M K Sejr

Abstract Species expand towards higher latitudes in response to climate warming, but the pace of this expansion is related to the physiological capacity to resist cold stress. However, few studies exist that have quantified the level of inter-population local adaptation in marine species freeze tolerance, especially in the Arctic. We investigated the importance of cold adaptation and thermal window width towards high latitudes from the temperate to the Arctic region. We measured upper and lower lethal air temperatures (i.e. LT and LT50) in temperate and Arctic populations of blue mussels (Mytilus edulis), and analysed weather data and membrane fatty acid compositions, following emersion simulations. Both populations had similar upper LT (~38 °C), but Arctic mussels survived 4°C colder air temperatures than temperate mussels (−13 vs. −9°C, respectively), corresponding to an 8% increase in their thermal window. There were strong latitudinal relationships between thermal window width and local air temperatures, indicating Arctic mussels are highly adapted to the Arctic environment where the seasonal temperature span exceeds 60°C. Local adaptation and local habitat heterogeneity thus allow leading-edge M. edulis to inhabit high Arctic intertidal zones. This intraspecific pattern provides insight into the importance of accounting for cold adaptation in climate change, conservation and biogeographic studies.

2018 ◽  
Author(s):  
Pia Nielsen-Englyst ◽  
Jacob L. Høyer ◽  
Kristine S. Madsen ◽  
Gorm Dybkjær ◽  
Rasmus Tonboe ◽  
...  

Abstract. To facilitate the combined use of traditional 2 m air temperature (T2m) observations from weather stations in the Arctic and skin temperature (Tskin) observations from satellites the relationship between high latitude snow and ice Tskin and T2m is quantified. Multiyear data records of simultaneous Tskin and T2m from 20 different in situ sites have been analysed, covering the Greenland Ice Sheet (GrIS), sea ice in the Arctic Ocean, and coastal snow covered land in North Alaska. The diurnal and seasonal temperature variabilities and the impacts from clouds and wind on the T2m-Tskin differences are quantified. Considering all stations, T2m is on average 1.37 °C warmer than Tskin, with the largest differences at the GrIS stations (mean of diff. of 1.64 °C). Tskin and T2m are often highly correlated, and the two temperatures are almost identical (


2016 ◽  
Author(s):  
Liang Chang ◽  
Lixin Guo ◽  
Guiping Feng ◽  
Xuerui Wu ◽  
Guoping Gao

Abstract. Air temperature is one of the most important parameters used for monitoring Arctic climate change. The Constellation Observing System for Meteorology, Ionosphere, and Climate and Formosa Satellite mission 3 (COSMIC/FORMOSAT-3) radio occultation (RO) "wet" temperature product (i.e., "wetPrf") was introduced to analyze the Arctic air temperature profiles at 925–200 hPa in 2007–2012. The "wet" temperatures were further compared with radiosonde (RS) observations. Results from the spatially and temporally synchronized RS and COSMIC observations showed that their temperatures were matched well with each other, especially at 400 hPa. Comparisons of seasonal temperatures and anomalies from COSMIC and homogenized RS observations suggested the limited number of COSMIC observations during the spatial matchup may be insufficient to describe the small-scale spatial structure of temperature variations. Furthermore, comparisons of seasonal temperature anomalies from RS and 5 × 5 degree gridded COSMIC observations at 400 hPa during the sea ice minimum (SIM) of 2007 and 2012 were also made. Results revealed that the widely covered COSMIC observations can provide more details than RS observations in describing the Arctic temperature variations. Therefore, despite COSMIC observations being unsuitable to describe the Arctic temperatures in the lowest level, they provide a complementary data source to study the Arctic upper-air temperature variations and related climate change.


2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.


2020 ◽  
Vol 50 (2) ◽  
pp. 161-169 ◽  
Author(s):  
O. Alejandro Aleuy ◽  
Stephanie Peacock ◽  
Eric P. Hoberg ◽  
Kathreen E. Ruckstuhl ◽  
Taylor Brooks ◽  
...  

2018 ◽  
Vol 37 (1) ◽  
pp. 30-39
Author(s):  
Liang Chang ◽  
Lixin Guo ◽  
Guiping Feng ◽  
Xuerui Wu ◽  
Guoping Gao ◽  
...  

2017 ◽  
Vol 30 (22) ◽  
pp. 8913-8927 ◽  
Author(s):  
Svenja H. E. Kohnemann ◽  
Günther Heinemann ◽  
David H. Bromwich ◽  
Oliver Gutjahr

The regional climate model COSMO in Climate Limited-Area Mode (COSMO-CLM or CCLM) is used with a high resolution of 15 km for the entire Arctic for all winters 2002/03–2014/15. The simulations show a high spatial and temporal variability of the recent 2-m air temperature increase in the Arctic. The maximum warming occurs north of Novaya Zemlya in the Kara Sea and Barents Sea between March 2003 and 2012 and is responsible for up to a 20°C increase. Land-based observations confirm the increase but do not cover the maximum regions that are located over the ocean and sea ice. Also, the 30-km version of the Arctic System Reanalysis (ASR) is used to verify the CCLM for the overlapping time period 2002/03–2011/12. The differences between CCLM and ASR 2-m air temperatures vary slightly within 1°C for the ocean and sea ice area. Thus, ASR captures the extreme warming as well. The monthly 2-m air temperatures of observations and ERA-Interim data show a large variability for the winters 1979–2016. Nevertheless, the air temperature rise since the beginning of the twenty-first century is up to 8 times higher than in the decades before. The sea ice decrease is identified as the likely reason for the warming. The vertical temperature profiles show that the warming has a maximum near the surface, but a 0.5°C yr−1 increase is found up to 2 km. CCLM, ASR, and also the coarser resolved ERA-Interim data show that February and March are the months with the highest 2-m air temperature increases, averaged over the ocean and sea ice area north of 70°N; for CCLM the warming amounts to an average of almost 5°C for 2002/03–2011/12.


2020 ◽  
Author(s):  
Eric Samakinwa ◽  
Christian Stepanek ◽  
Gerrit Lohmann

Abstract. In this study, we compare results obtained from modelling the mid-Pliocene warm period using the Community Earth System Models (COSMOS, version: COSMOS-landveg r2413, 2009) with the two different modelling methodologies and sets of boundary conditions prescribed for the two phases of the Pliocene Model Intercomparison Project (PlioMIP), tagged PlioMIP1 and PlioMIP2. Boundary conditions, model forcing, and modelling methodology for the two phases of PlioMIP differ considerably in palaeogeography, in particular with regards to the state of ocean gateways, ice-masks, treatment of vegetation and topography. Further differences between model setups as suggested for PlioMIP1 and PlioMIP2 consider updates to the concentration of trace gases: atmospheric carbon dioxide (CO2), is specified as 405 and 400 parts per million by volume (ppmv) for PlioMIP1 and PlioMIP2, respectively. There are also minor differences in the concentrations of methane (CH4) and nitrous oxide (N2O) due to changes in the protocol of the Paleoclimate Model Intercomparison Project (PMIP) from phase 3 to phase 4. Employing a single model across two phases of PlioMIP enables a better understanding of the impact that the various differences in modelling methodology between PlioMIP1 and PlioMIP2 have on model output. Yet, a dedicated comparison of COSMOS model output of PlioMIP1 and PlioMIP2 is not in the curriculum of model analyses proposed in PlioMIP2. Here, we bridge the gap between our contributions to PlioMIP1 (Stepanek and Lohmann, 2012) and PlioMIP2 (Stepanek et al., 2020). We highlight some of the effects that differences in the chosen mid-Pliocene model setup (PlioMIP2 vs. PlioMIP1) have on the climate state as derived with the COSMOS, as this information will be valuable in the framework of the model-model and model-data-comparison within PlioMIP2. We evaluate the model sensitivity to improved mid-Pliocene boundary conditions using PlioMIP's core mid-Pliocene experiments for PlioMIP1 and PlioMIP2, and present further simulations where we test model sensitivity to variations in palaeogeography, orbit and concentration of CO2. Firstly, we highlight major changes in boundary conditions from PlioMIP1 to PlioMIP2 and also the challenges recorded from the initial effort. The results derived from our simulations show that COSMOS simulates a mid-Pliocene climate state that is 0.29 K colder in PlioMIP2, if compared to PlioMIP1 (17.82 °C in PlioMIP1, 17.53 °C in PlioMIP2, values based on simulated surface skin temperature). On one hand, high-latitude warming, which is supported by proxy evidence of the mid-Pliocene, is underestimated in simulations of both PlioMIP1 and PlioMIP2. On the other hand, spatial variations in surface air temperature (SAT), sea surface temperature (SST) as well as the distribution of sea ice suggest improvement of simulated SAT and SST in PlioMIP2 if employing the updated palaeogeography. Our PlioMIP2 mid-Pliocene simulation produces warmer SSTs in the Arctic and North Atlantic Ocean than derived from the respective PlioMIP1 climate state. The difference in prescribed CO2 accounts for 1.1 K of warming in the Arctic, leading to an ice-free summer in the PlioMIP1 simulation, and a quasi ice-free summer in PlioMIP2. Beyond the official set of PlioMIP2 simulations, we present further simulations and analyses that sample the phase space of potential alternative orbital forcings that have acted during the Pliocene and may have impacted on geological records. Employing orbital forcing, which differ from that proposed for PlioMIP2 (i.e. corresponding to Pre-Industrial conditions) but falls into the Mid-Pliocene time period targeted in the PlioMIP, leads to pronounced annual and seasonal temperature variations, which are not directly retrievable from the marine and terrestrial reconstruction of the time-slice.


2011 ◽  
Vol 7 (1) ◽  
pp. 463-483 ◽  
Author(s):  
N. Fischer ◽  
J. H. Jungclaus

Abstract. Changes in the Earth's orbit lead to changes in the seasonal and meridional distribution of insolation. We quantify the influence of orbitally induced changes on the seasonal temperature cycle in a transient simulation of the last 6000 years – from the mid-Holocene to today – using a coupled atmosphere-ocean general circulation model (ECHAM5/MPI-OM) including a land surface model (JSBACH). The seasonal temperature cycle responds directly to the insolation changes almost everywhere. In the Northern Hemisphere, its amplitude decreases according to an increase in winter insolation and a decrease in summer insolation. In the Southern Hemisphere, the opposite is true. Over the Arctic Ocean, however, decreasing summer insolation leads to an increase of sea-ice cover. The insulating effect of sea ice between the ocean and the atmosphere favors more continental conditions over the Arctic Ocean in winter, resulting in strongly decreasing temperatures. Consequently, there are two competing effects: the direct response to insolation changes and a sea-ice dynamics feedback. The sea-ice feedback is stronger, and thus an increase in the amplitude of the seasonal cycle over the Arctic Ocean occurs. This increase is strongest over the Barents Shelf and influences the temperature response over northern Europe. We compare our modelled seasonal temperatures over Europe to paleo reconstructions. We find better agreements in winter temperatures than in summer temperatures and better agreements in northern Europe than in southern Europe, since the model does not reproduce the southern European Holocene summer cooling inferred from the paleo data. The temperature reconstructions for northern Europe support the notion of the influence of the sea-ice effect on the evolution of the seasonal temperature cycle.


Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1494
Author(s):  
Fernanda Casagrande ◽  
Francisco A. B. Neto ◽  
Ronald B. de Souza ◽  
Paulo Nobre

One of the most visible signs of global warming is the fast change in the polar regions. The increase in Arctic temperatures, for instance, is almost twice as large as the global average in recent decades. This phenomenon is known as the Arctic Amplification and reflects several mutually supporting processes. An equivalent albeit less studied phenomenon occurs in Antarctica. Here, we used numerical climate simulations obtained from CMIP5 and CMIP6 to investigate the effects of +1.5, 2 and 3 °C warming thresholds for sea ice changes and polar amplification. Our results show robust patterns of near-surface air-temperature response to global warming at high latitudes. The year in which the average air temperatures brought from CMIP5 and CMIP6 models rises by 1.5 °C is 2024. An average rise of 2 °C (3 °C) global warming occurs in 2042 (2063). The equivalent warming at northern (southern) high latitudes under scenarios of 1.5 °C global warming is about 3 °C (1.8 °C). In scenarios of 3 °C global warming, the equivalent warming in the Arctic (Antarctica) is close to 7 °C (3.5 °C). Ice-free conditions are found in all warming thresholds for both the Arctic and Antarctica, especially from the year 2030 onwards.


2016 ◽  
Author(s):  
Libo Wang ◽  
Peter Toose ◽  
Ross Brown ◽  
Chris Derksen

Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snowpit surveys, and a surface-based passive microwave radiometer. The results of running the algorithm over the pan-Arctic region (north of 50º N) for the 1988–2013 period show that winter melt days are relatively rare averaging less than 7 melt days per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g. central Quebec and Labrador, southern Alaska, and Scandinavia). The observed spatial pattern was similar to winter melt events inferred with surface air temperatures from ERA-interim and MERRA reanalysis datasets. There was little evidence of trends in winter melt frequency except decreases over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false increasing trends from shifts in the timing of the snow cover season.


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