인공위성 수동 관측자료를 활용한 겨울철 북극 해빙지역 적설깊이 산출 및 장기변동성 분석

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2021.8. 손병주.A new satellite retrieval algorithm for wintertime snow depth on Arctic sea ice was developed based on the hydrostatic balance and thermodynamic steady-state of a snow-ice system. In this algorithm, snow depth is estimated from the passive infrared and microwave measurements, with the use of sea ice freeboard, snow surface temperature, and snow-ice interface temperature as inputs. The algorithm was validated against NASA's Operation IceBridge (OIB) measurements, and results indicate that the snow depth on the Arctic sea ice can be estimated with a high level of accuracy. To produce a long-term snow depth record in the Arctic basin-scale, sea ice freeboard was estimated from the satellite passive microwave (PMW) measurements. To do so, the snow-ice scattering optical depth from satellite PMW measurements was used as a predictor for the estimation of the total freeboard. Estimated PMW total freeboards were found to be in good agreement with OIB total freeboards. The wintertime snow depth records for the 2003-2020 period were produced by combining the PMW freeboard and satellite-derived temperatures. It was found that snow depth is highly dependent on sea ice type, likely due to the snow accumulation timing and period. The snow depth and its variability were greater on multiyear ice than on first-year ice. Besides, a significant reduction in mean snow depth was found, compared to the snow depth climatology for the 1954-1991 period. Regarding the temporal variations over the 2003-2020 period, regionally different snow depth trends are found; negative and positive snow depth trends were noted over the eastern and western parts of the Arctic Ocean, respectively. It is thought that the negative trends are related to sea ice type transition and delayed freeze onset, while the positive trends are related to increased precipitation amount.겨울철 북극 해빙지역 적설깊이 산출을 위해 적설-해빙 시스템의 정역학적 평형 및 열역학적 정상상태(steady state)를 기반으로 한 새로운 인공위성 산출 알고리즘이 개발되었다. 개발된 알고리즘은 수동 마이크로파/적외선 관측자료로부터 얻어진 해빙건현(freeboard), 적설표면온도와 적설-해빙경계층온도를 입력자료로 사용하여 적설깊이 산출을 수행한다. 적설깊이 산출물은 NASA의 OIB(Operation IceBridge) 항공기 관측자료를 기준으로 검증되었으며 높은 정확도를 보였다. 새로운 알고리즘을 활용한 북극해 규모의 장기간 적설깊이 자료 생산을 위해 인공위성 수동 마이크로파 관측자료로부터 해빙건현을 추정하였다. 이를 위해 수동 마이크로파 관측에서 얻은 적설-해빙 산란 광학 깊이를 예측 변수로 사용하였다. 추정된 수동 마이크로파 해빙건현은 OIB 관측치와 높은 일치성을 보였다. 2003-2020년 기간 겨울철 적설깊이 자료를 인공위성 온도자료와 수동마이크로파 해빙건현자료를 함께 사용하여 생산하고 분석하였다. 북극 해빙지역 적설깊이는 눈이 쌓이는 시기 및 기간과 관련하여 해빙의 종류에 크게 의존하는 것으로 나타났으며, 적설깊이와 그 변동성은 단년빙보다 다년빙에서 큰 값을 보였다. 또한, 1954-1991년 기간 동안의 현장관측 기반 적설깊이 기후값과 비교하여 현대 적설깊이의 상당한 감소가 발견되었다. 시계열 분석 결과 2003-2020년 기간동안 지역적으로 다른 적설깊이 경향성이 보였다. 북극해의 동쪽지역에서는 감소, 서쪽지역에서는 증가 경향이 나타났다. 감소 경향은 다년빙에서 단년빙으로의 해빙 종류 변화 및 결빙시점의 지연과 관련이 있으며, 증가 경향은 강수량의 증가와 관련이 있는 것으로 보인다.1. Introduction 1 2. Previous studies in obtaining Arctic snow depth 11 2.1. In situ measurements 11 2.1.1. Snow depth climatology 11 2.1.2. Arctic buoy programs 14 2.2. Remote sensing 17 2.2.1. Operation IceBridge 17 2.2.2. Satellite passive microwave (PMW) measurements 20 2.2.3. Dual-frequency satellite altimetry 22 3. Used data 23 3.1. Snow-ice temperature profiles 23 3.2. Satellite data 25 3.2.1. PMW brightness temperature 25 3.2.2. Snow surface temperature 28 3.2.3. Total freeboard 29 3.3. Auxiliary data 31 4. Methods 34 4.1. Algorithm development 34 4.1.1. New method using thickness ratio (TR) 34 4.1.2. Theoretical background of TR 38 4.1.3. Strategy for obtaining TR 41 4.1.4. Buoy data preprocessing 43 4.1.5. Snow depth retrieval procedure 46 4.2. Sea ice parameters from satellite PMW measurements 49 4.2.1. Simplified radiative transfer model 49 4.2.2. Snow-ice scattering optical depth 52 4.2.3. Sea ice type 55 5. Results 59 5.1. Snow depth retrieval algorithm 59 5.1.1. TR-temperature equation 59 5.1.2. Snow depth retrieval and validation 63 5.2. Long-term snow depth record 67 5.2.1. PMW total freeboard 67 5.2.2. Snow depth from satellite passive measurements 74 5.2.3. Uncertainty estimation and sensitivity test 78 5.3. Analysis of Arctic snow depth during 2003-2020 period 85 5.3.1. Geographical distribution 85 5.3.2. Temporal variation 91 6. Conclusions and discussion 97 References 106 국문 초록 120박

Auxiliary data from SIMBA-type sea ice mass balance buoy 2014T33

Temperature and heating-induced temperature differences were measured along a chain of thermistors. SIMBA 2014T33 (a.k.a. FMI_15) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the expedition Polarstern PS87 (ARK28/4,ALEX) in 2014. The thermistor chain was 5 m long and included 240 sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature and temperature differences after two heating cycles of 30 and 120 s as a function of location, depth and time between 2014-08-26 16:30:00 and 2015-04-21 14:17:00. Sample intervals are commonly between 1 and 24 hours, but most frequently hit intervals of 6 hours for temperature and 24 hours for temperature differences. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, position: The geographic position is flagged +1 if the drift velocity, as derived from the GPS longitude and latitude, exceeds a threshold of 10 deg latitude or 50 deg longitude per time step; +2 if the position exceeds extreme values, such as longitude > 360 deg; +4 if the position is exactly 0.0. This instrument was deployed as part of the project FMI

Temperature measurements from SIMBA-type sea ice mass balance buoy 2014T33

Temperature and heating-induced temperature differences were measured along a chain of thermistors. SIMBA 2014T33 (a.k.a. FMI_15) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the expedition Polarstern PS87 (ARK28/4,ALEX) in 2014. The thermistor chain was 5 m long and included 240 sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature and temperature differences after two heating cycles of 30 and 120 s as a function of location, depth and time between 2014-08-26 16:30:00 and 2015-04-21 14:17:00. Sample intervals are commonly between 1 and 24 hours, but most frequently hit intervals of 6 hours for temperature and 24 hours for temperature differences. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, position: The geographic position is flagged +1 if the drift velocity, as derived from the GPS longitude and latitude, exceeds a threshold of 10 deg latitude or 50 deg longitude per time step; +2 if the position exceeds extreme values, such as longitude > 360 deg; +4 if the position is exactly 0.0. This instrument was deployed as part of the project FMI

Snow depth, sea ice thickness and interface temperatures derived from measurements of SIMBA buoy 2014T33

The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environmental temperature SIMBA-ET and a temperature change around the thermistors after a weak heating is applied to each sensor (SIMBA-HT). SIMBA 2014T33 (a.k.a. FMI_15) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean (Polarstern PS87 (ARK28/4, ALEX) in 2014) as part of the project FMI. Its thermistor chain is 5 m long, and equipped with 240 thermistors (Maxim Integrated DS28EA00) at a spacing of 2 cm. Based on a manual classification method, the SIMBA-ET and SIMBA-HT were processed to obtain snow depth and ice thickness (smoothed with a 3-day running mean), as well as the thermistor number, the vertical position Z relative to the snow-ice interface and the measured SIMBA-ET at each detected interface (atmosphere-snow, snow-ice and ice-ocean) for the period between 2014-08-27T08:00:39 and 2015-04-08T08:00:39. To do this, we combined two derivatives of measured temperatures (the ET vertical gradient and HT rise ratio) to reduce the detection uncertainty of all interfaces considered. The snow or ice surface, consequentially the snow depth, is determined by the ET vertical gradient. Potential formation of snow ice is not explicitly considered in this data set, but may occur as depicted by vertical changes of the snow-ice interface position. The ice-ocean interface is usually determined using the HT rise ratio and serves as the lower limit for ice thickness. Overall, the accumulated error is 2 to 4 times the sensor spacing for both the snow depth and ice thickness. For interface temperatures, individual sensors in the chain measure with a temperature resolution of 0.0625°C, with the overall accuracy landing in the range of ± 2°C (Jackson et al., 2013). After the snow cover has melted, negative values for snow depth may indicate the onset of ice surface melt. Due to a malfunction of the on-board GPS unit on 02 November 2014 (12 UTC), all position values after that date are based on cleaned and smoothed (3-day running mean) position readings derived from the Iridium satellite network system

Temperature and heating induced temperature difference measurements from the sea ice mass balance SIMBA 2014T33

Temperature and heating-induced temperature differences were measured along a chain of thermistors. SIMBA 2014T33 (a.k.a. FMI_15) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the expedition Polarstern PS87 (ARK28/4,ALEX) in 2014. The thermistor chain was 5 m long and included 240 sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature and temperature differences after two heating cycles of 30 and 120 s as a function of location, depth and time between 2014-08-26 16:30:00 and 2015-04-21 14:17:00. Sample intervals are commonly between 1 and 24 hours, but most frequently hit intervals of 6 hours for temperature and 24 hours for temperature differences. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, position: The geographic position is flagged +1 if the drift velocity, as derived from the GPS longitude and latitude, exceeds a threshold of 10 deg latitude or 50 deg longitude per time step; +2 if the position exceeds extreme values, such as longitude > 360 deg; +4 if the position is exactly 0.0. This instrument was deployed as part of the project FMI

Heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2014T33: 30 s after the heating cycle

Temperature and heating-induced temperature differences were measured along a chain of thermistors. SIMBA 2014T33 (a.k.a. FMI_15) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the expedition Polarstern PS87 (ARK28/4,ALEX) in 2014. The thermistor chain was 5 m long and included 240 sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature and temperature differences after two heating cycles of 30 and 120 s as a function of location, depth and time between 2014-08-26 16:30:00 and 2015-04-21 14:17:00. Sample intervals are commonly between 1 and 24 hours, but most frequently hit intervals of 6 hours for temperature and 24 hours for temperature differences. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, position: The geographic position is flagged +1 if the drift velocity, as derived from the GPS longitude and latitude, exceeds a threshold of 10 deg latitude or 50 deg longitude per time step; +2 if the position exceeds extreme values, such as longitude > 360 deg; +4 if the position is exactly 0.0. This instrument was deployed as part of the project FMI

Heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2014T33: 120 s after the heating cycle

Temperature and heating-induced temperature differences were measured along a chain of thermistors. SIMBA 2014T33 (a.k.a. FMI_15) is an autonomous instrument that was installed on drifting sea ice in the Arctic Ocean during the expedition Polarstern PS87 (ARK28/4,ALEX) in 2014. The thermistor chain was 5 m long and included 240 sensors with a regular spacing of 2 cm. The resulting time series describes the evolution of temperature and temperature differences after two heating cycles of 30 and 120 s as a function of location, depth and time between 2014-08-26 16:30:00 and 2015-04-21 14:17:00. Sample intervals are commonly between 1 and 24 hours, but most frequently hit intervals of 6 hours for temperature and 24 hours for temperature differences. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, position: The geographic position is flagged +1 if the drift velocity, as derived from the GPS longitude and latitude, exceeds a threshold of 10 deg latitude or 50 deg longitude per time step; +2 if the position exceeds extreme values, such as longitude > 360 deg; +4 if the position is exactly 0.0. This instrument was deployed as part of the project FMI