Researches On Arctic Sea Ice Surface Temperature At Multiple Scales

Arctic sea ice has been undergoing continuous loss in thickness,extent,and volume since the satellite era,together with surface temperature rising.The declining sea ice cover has significant climate effects far beyond the Arctic region.Among those essential climate variables of the Arctic Ocean,sea ice surface temperature(IST)is a crucial thermodynamic parameter relating to the longwave radiation of the Arctic Ocean,and thus has a significant influence on the surface mass and energy balance.A precise and accurate IST product will benefit studies on retrieving other key sea ice parameters as well as the development and upgrade of regional and global climate models.To better describe the spatial-temporal character of sea ice in the Arctic Ocean,consistent IST records from multiple technologies is required.IST can be acquired through in situ measurement,airborne observation,satellite remote sensing,as well as model output reanalysis.In general,the amount of in situ and airborne measurements are not sufficient in the Arctic Ocean due to the extremely cold environment,which decreases their overall accuracies.Thermal infrared remote sensing could acquire large-scale IST,but the coarse spatial resolution and invalid data caused by cloud cover hamper their usage in the polar region,particularly in leads and marginal ice zones.ERA5 reanalysis temperature products provide spatial-temporal continuous surface temperature products at the time resolution of 1 hour that could date back to 1959,with even coarser spatial resolution and worse accuracy.Thus,it is important to take the advantage of these existing IST products to analyzed the spatial-temporal variation of surface temperature in the Arctic Ocean,and produce new IST products to fill the scale gap of these existing IST products.Thus,this thesis is designated to contribute to the subject in three aspects:Ⅰ.Evaluating the relationships and uncertainties of multi-source ISTs on different scales against remotely sensed temperature records.Different error sources and spatial resolutions of aforementioned ISTs make it questionable whether they are consistent or comparable with each other.As the remotely sensed IST products are well validated and have better spatial coverage,in this study,the relative performance of four mainstream in situ & airborne IST records,i.e.,airborne IST,infrared radiometer measured IST(IR IST),longwave radiation derived IST(LWR IST),and snow and ice mass balance array buoy derived IST(Buoy IST),together with ERA5 reanalysis surface skin temperature was evaluated against the MODIS IST product.Bias,standard deviation(STD),and root mean square error(RMSE)were used to evaluate the data quality.Our results show that IST data from various measuring principles are biased against each other and hold different uncertainties.Airborne IST yielded the best overall accuracy and smallest uncertainty,and was nearly unbiased with MODIS IST,with a cold bias of 0.21 K,low STD of 1.46 K,and RMSE of 1.47 K,partly because the scale of airborne IST is closer to satellite IST data.All ground-based ISTs were warmer than MODIS ISTs.Among them,the IR IST had the best overall accuracy and was more stable than other ISTs,with bias of 0.55 K,STD of 1.52 K,and RMSE of 1.61 K,while the LWR IST tended to suffer from gross errors.Besides,concurrent and co-located IR and LWR IST records coincided better with each other than any ground or airborne IST record with MODIS IST(r =0.99,bias = 0.75 K,STD = 0.73 K,RMSE = 1.04 K).The Buoy IST had similar precision compared to other IST records(STD = 1.62 K),however,the large bias(1.74K)and consequent RMSE(2.37 K)decreased its overall accuracy.However,ERA5 reanalysis temperature tends to significantly overestimate MODIS IST(bias = 4.88 K;STD = 4.80 K),particularly in the cold environment.The Airborne and IR ISTs are thus the premier choice for monitoring the rapidly changing Arctic sea ice,together with satellite observations,while the ERA5 temperature product should be only used in longterm and large-scale IST variation analysis.Ⅱ.High resolution IST products retrieval based on Landsat-8 TIRS data in the Arctic Ocean.Accurate and high-resolution IST data is of great importance for Arctic climate studies.However,high-resolution IST products is still lacking in the polar sea ice regions,as the temperature retrieval methods were usually designed and validated in the mid-and low-latitude regions.This study assesses the accuracy of three splitwindow(SW)and two single-channel(SC)methods,based on Landsat 8 thermal infrared imagery at 100 m spatial resolution over Arctic sea ice regions.The SW methods are proposed by Jin et al.(2015)(SW-Jin),Jiménez-Mu(?)oz et al.(2014)(SWJM),and Du et al.(2015)(SW-Du).The SC methods are proposed by Jiménez-Mu(?)oz et al.(2014)(SC-JM)and Barsi et al.(2003,2005)(SC-Barsi).IST data derived from58 scenes of the Landsat 8 images were compared with coincident in situ ice skin temperatures and near-surface air temperatures,as measured by a combination of Ice Mass Balance(IMB)buoys,Snow and Ice Mass Balance Array(SIMBA)buoys,and automatic weather stations measured infrared surface skin temperature.SW-Du offers the best accuracy when compared with the skin temperature(bias:-1.06 K;RMSE: 2.08K)and near-surface air temperature(bias:-0.98 K;RMSE: 2.17 K).SC-Barsi ranks second,with a bias of-1.55 K and RMSE of 2.40 K.As for precision,IST from the MODIS has best performance(STD: 1.69 K),followed by SW-Du,SW-JM,and SCBarsi(STD: 1.80 K,1.82 K,and 1.85 K,respectively).SW-JM and SC-Barsi methods agree best with the MODIS IST in leads and marginal ice zone scenes,respectively.Moreover,Landsat IST outperforms the MODIS IST in narrow lead areas and broken sea ice of MIZ,owing to its finer spatial resolution.Besides,as all three SW methods are constrained by banding effects with different degrees in a lead scene,they are not recommended to be applied on an image scene with severe banding artifacts.Ⅲ.Evaluating the long-term spatial-temporal variation of surface temperature in the whole Arctic Ocean based on ERA5 reanalysis.The trends and variability of surface and air temperature tend to be larger in the Arctic region than that of the whole globe.Thus,it is of great importance to monitor the detailed tendency and spatial distribution of the warming trend in the whole Arctic Ocean.Based on ERA5 reanalysis surface skin temperature(SKT)and 2 m air temperature(SAT)products that cover both open water and sea ice of the Arctic Ocean,we study the spatial-temporal variation of SKT and SAT in the whole Arctic Ocean during the years of 1959–2021 and 2001–2021.In general,SKT and SAT are well correlated with the Pearson correlation better than 0.9 in the Arctic Ocean except for the warm Norwegian Sea and Greenland Sea.In addition,SKT is generally colder than SAT in the cold central Arctic Ocean,and warmer in warm the marginal sea area.Both SKT and SAT shows an accelerated warm trend in the Arctic Ocean.During 1959–2021and 2001–2021,the SKT is warming by the rate of 0.066 K/year and 0.084 K/year,whereas the change rates of SAT is 0.062 K/year and 0.083 K/year,respectively.The warm trend of SKT is faster than that of SAT,and faster in the Arctic Ocean than the land region of Arctic.The Kara Sea exhibits the most pronounced warm trend during the both time ranges(0.096 K/year during 1959–2021 and 0.187 K/year during 2001–2021).The slowest warm trend was found in Canadian Arctic Archipelago during 1959–2021(0.051 K/year)and Baffin Bay during 2001–2021(0.018 K/year,not statistically significant).Besides,significant cooling tendency was found in Kane Basin that located in Nares Strait of Baffin Bay under the warming Arctic.Moreover,temperature change in the Arctic Ocean is seasonal-dependency.From 1959 to 2021,the warming trend of the Arctic Ocean is most pronounced in November,with a positive change rate of SKT of 0.108 K/year and SAT of 0.099 K/year,around 10 times larger than those of July(SKT: 0.017 K/year;SAT: 0.022 K/year).While from 2001 to 2021,the warming change rate is most pronounced in February(SKT: 0.163 K/year;SAT: 0.165 K/year),and slowest in July(SKT: 0.017 K/year;SAT: 0.022 K/year).