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Zmiany zlodzenia Morza Karskiego w latach 1979-2015. Podejście systemowe

Styszyńska A., Marsz A. A.

Problemy Klimatologii Polarnej

2016

Nr 26

109--156

Praca omawia zmiany powierzchni lodów na Morzu Karskim i mechanizmy tych zmian. Scharakteryzowano przebieg zmian zlodzenia, ustalając momenty skokowego zmniejszenia się letniej powierzchni lodów. Rozpatrzono wpływ cyrkulacji atmosferycznej, zmian temperatury powietrza i zmian zasobów ciepła w wodach na zmiany zlodzonej tego morza. Analizy wykazały, że wszystkie zmienne opisujące zarówno stan zlodzenia jak i stan elementów klimatycznych są ze sobą wzajemnie powiązane przez różnego rodzaju sprzężenia zwrotne. W rezultacie tworzy się rekurentny system, w którym zmiany powierzchni lodów, wpływając na przebieg innych elementów systemu (temperaturę powietrza, temperaturę wody powierzchniowej) w znacznej części same sterują swoim rozwojem. Zmiennością całego tego systemu sterują zmiany intensywności cyrkulacji termohalinowej (THC) na Atlantyku Północnym, dostarczając do niego zmienne ilości energii (ciepła). Reakcja systemu zlodzenia Morza Karskiego na zmiany natężenia THC następuje z 6.letnim opóźnieniem.

The work discusses the changes in the ice extent on the Kara Sea in the years 1979-2015, i.e. in the period for which there are reliable satellite data. The analysis is based on the average monthly ice extent taken from the database AANII (RF, St. Peterburg). 95% of the variance of average annual ice extent explains the variability of the average of ice extent in ‘warm' season (July-October). Examination of features of auto-regressive course of changes in ice extent shows that the extent of the melting ice area between June and July (marked in the text RZ07-06) can reliably predict the ice extent on the Kara Sea in August, September, October and November as well as the average ice extent in a given year. Thus the changes in ice extent can be treated as a result of changes occurring within the system. Analysis of the relationship of changes in ice extent and variable RZ07-06 with the features of atmospheric circulation showed that only changes in atmospheric circulation in the Fram Strait (Dipole Fram Strait; variable DCF03-08) have a statistically significant impact on changes in ice extent on the Kara Sea and variable RZ07-06. The analysis shows no significant correlation with changes in ice extent or AO (Arctic Oscillation), or NAO (North Atlantic Oscillation). Variable RZ07-06 and variable DCF03-08 are strongly correlated and their changes follow the same pattern. Analysis of the relationship of changes in ice extent and variable RZ07-06 with changes in air temperature (the SAT) showed the presence of strong relationships. These correlations differ significantly depending on the region; they are much stronger with changes in air temperature in the north than in the south of the Kara Sea. Temperature of cold period (average temperature from November to April over the Kara Sea, marked 6ST11-04) has a significant effect on the thickness of the winter ice and in this way the thickness of ice in the next melting season becomes part of the "memory" (retention) of past temperature conditions. The thickness of the winter ice has an impact on the value of the variable RZ07-06 and on changes in ice extent during the next ‘warm’ season. As a result, 6ST11-04 explains 62% of the observed variance of the annual ice extent on the Kara Sea. SAT variability in the warm period over the Kara Sea (the average of the period July-October, marked 6ST07-10) explains 73% of the variance of annual ice extent. SAT variability of the N part of the Kara Sea (Ostrov Vize, Ostrov Golomjannyj), which explains 72-73% of the variance ice extent during this period, has particularly strong impact on changes in ice extent during warm period. These stations are located in the area where the transformed Atlantic Waters import heat to the Kara Sea. Analysis of the impact of changes in sea surface temperature (SST) variability on sea ice extent indicated that changes in SST are the strongest factor that has influence on ice extent. The variability of annual SST explains 82% of the variance of annual ice extent and 58% of the variance of the variable RZ07-06. Further analysis showed that the SAT period of warm and annual SAT on the Kara Sea are functions of the annual SST (water warmer than the air) but also ice extent. On the other hand, it turns out that the SST is in part a function of ice extent. All variables describing the ice extent and its changes as well as variables describing the nature of the elements of hydro-climatic conditions affecting the changes in ice extent (atmospheric circulation, SAT, SST) are strongly and highly significantly related (Table 9) and change in the same pattern. In this way, the existence of recursion system is detected where the changes in ice extent eventually have influence on ‘each other’ with some time shift. The occurrence of recursion in the system results in very strong autocorrelation in the course of inter-annual changes in ice extent. Despite the presence of recursion, factors most influencing change in ice extent, i.e. the variability in SST (83% of variance explanations) and variability in SAT were found by means of multiple regression analysis and analysis of variance. Their combined impact explains 89% of the variance of the annual ice extent on the Kara Sea and 85% of the variance of ice extent in the warm period. The same rhythm of changes suggests that the system is controlled by an external factor coming from outside the system. The analyses have shown that this factor is the variability in the intensity of the thermohaline circulation (referred to as THC) on the North Atlantic, characterized by a variable marked by DG3L acronym. Correlation between the THC signal and the ice extent and hydro-climatic variables are stretched over long periods of time (Table 10). The system responds to changes in the intensity of THC with a six-year delay, the source comes from the tropical North Atlantic. Variable amounts of heat (energy) supplied to the Arctic by ocean circulation change heat resources in the waters and in SST. This factor changes the ice extent and sizes of heat flux from the ocean to the atmosphere and the nature of the atmospheric circulation, as well as the value of the RZ07-06 variable, which determines the rate of ice melting during the ‘warm’ season. A six-year delay in response of the Kara Sea ice extent to the THC signal, compared to the known values of DG3L index to the year 2016, allows the approximate estimates of changes in ice extent of this sea by the year 2023. In the years 2017 to 2020 a further rapid decrease in ice extent will be observed during the ‘warm' period (July-October), in this period in the years 2020-2023 ice free conditions on the Kara Sea will prevail. Ice free navigation will continue from the last decade of June to the last decade of October in the years 2020-2023. Since the THC variability includes the longterm, 70-year component of periodicity, it allows to assume that by the year 2030 the conditions of navigation in the Kara Sea will be good, although winter ice cover will reappear.

Model zmian powierzchni lodów morskich Arktyki (1979-2013) – zmienne sterujące w modelu „minimalistycznym” i ich wymowa klimatyczna

Marsz A. A.

Problemy Klimatologii Polarnej

2015

Nr 25

s. 249--334

Praca omawia model zmian powierzchni zlodzonej Arktyki typu „białej skrzynki”, opierający się na dwu zmiennych niezależnych – wskaźniku oznaczonym jako DG3L, który charakteryzuje intensywność cyrkulacji termohalinowej (THC) na Atlantyku Północnym i wskaźniku D, który charakteryzuje cyrkulację atmosferyczną nad Arktyką. Objaśnienie konstrukcji obu wskaźników i wartości ich szeregów czasowych przedstawione jest w załącznikach Z1 i Z2. Okres opracowania obejmuje lata 1979-2013 i jest limitowany dostępnością danych o zmianach powierzchni lodów morskich w Arktyce. Model liniowy opierający się na tych zmiennych objaśnia ~72% wariancji rocznej powierzchni zlodzonej w Arktyce i powyżej 65% wariancji powierzchni zlodzonej w marcu (maksimum rozwoju powierzchni lodów) i wrześniu (minimum). Główną rolę w kształtowaniu tej zmienności odgrywa zmienność cyrkulacji termohalinowej, rola cyrkulacji atmosferycznej jest niewielka i wykazuje silną zmienność sezonową. Analiza tego modelu wykazała, że rzeczywiste zależności są nieliniowe, a zmiany pokrywy lodowej zachodzą w dwu odrębnych reżimach – „ciepłym” i „chłodnym”. Reżim „ciepły” funkcjonuje w sytuacji, gdy THC jest bardziej intensywna niż przeciętnie (wskaźnik DG3L > 0). Dochodzi wtedy do szybkiego spadku powierzchni lodów w okresie ciepłym – zwłaszcza we wrześniu i powolnego spadku rozmiarów pokrywy lodowej w marcu, cyrkulacja atmosferyczna w tym reżimie odgrywa istotną rolę w kształtowaniu zmian powierzchni lodów. Spadek natężenia THC poniżej przeciętnej (DG3L ≤ 0), z opóźnieniem około 6.letnim prowadzi, do przejścia do reżimu „chodnego”. W reżimie chłodnym następuje szybki przyrost powierzchni lodów w okresie ciepłym i bardzo powolny wzrost powierzchni lodów w marcu, rola cyrkulacji atmosferycznej w kształtowaniu zmienności pokrywy lodowej staje się nikła. Po dalszych kilku latach utrzymywania się reżimu „chłodnego” międzyroczne zmiany powierzchni zlodzonej stają się małe. Analizy związków między zmiennymi z przesunięciami czasowymi wykazały, że cyrkulacja atmosferyczna nad Arktyką stanowi funkcję THC. W rezultacie, za główną przyczynę zmian powierzchni zlodzonej Arktyki należy uznać rozciągnięte w czasie działanie zmian intensywności THC, które w rozpatrywanym okresie objaśnia ~90% wariancji rocznej powierzchni zlodzonej.

The paper presents the assumptions and structure of statistical model reproducing the changes in sea ice extent in the Arctic, using the minimum number of steering variables. The data set of NASA's Goddard Space Flight Center (GSFC) nsidc0192_seaice_trends_climo/total-area-ice-extent/nasateam/ (Total Ice-Covered Area and Extent) was used as starting data in the calibration of this model. Its subsets characterizing the sea ice extent of the Arctic Ocean (ArctOcn), Greenland Sea (Grnland), Barents and Kara seas (BarKara) were used. Their sums create a new variable known as the ‘Proper Arctic’. This model also used the following subsets: Archipelago Canadian (CanArch), Bay and Strait Hudson (Hudson), and Baffin Bay and Labrador Sea (Baffin), the sum of which creates another variable the ‘American Arctic’. The sum of all the above mentioned subsets creates a variable defined as the ‘entire Arctic’. The study covered the period 1979-2013, for which the said data set is made up of uniform and reliable data based on satellite observations. The model was developed for moments of maximum (March) and minimum (September) development of sea ice extent as well as for the annual average sea ice extent. After presenting the assumptions of the model (model type ‘White box’), formal analysis of the type and characteristics of the model, the choice of steering variables (independent; Chapters 3 and 4) was made. The index characterizing the intensity of thermohaline circulation (THC) in the North Atlantic, referred to as DG3L and an index characterizing atmospheric circulation having significant influence on changes in sea ice extent, marked as D, were used as independent variables in this model. Physical fundamentals and rules for calculating the DG3L index are discussed in detail in Annex 1, and the D index in Annex 2. These Annexes also include time series of both indexes (DG3L – 1880-2015; D – 1949-2015). Research into delays between the impact of variables and changes in sea ice extent indicated that sea ice extent showed maximum strength of the correlation with the DG3L variable with a three-year delay and with D variable with zero delay. The final form of the model is a simple equation of multiple regression (equation [1]). The following equations are used for estimating the regression parameters for individual sea areas in those time series: the Proper Arctic – equation [1a, 1b, 1c]; the American Arctic – equations [2a, 2b, 2c] and for the entire Arctic - equation [3a, 3b, 3c]. Statistical characteristics of each model are presented in Tables 3, 4 and 5, and Figures 2, 3 and 4 respectively and show the scattering of values estimated by means of each model in relation to the observed values. All models show high statistical significance. The best results, both in terms of explanation of the variance of the observed sea ice extent, as well as the size of the standard errors of estimation of sea ice extent are obtained for changes in the sea ice extent of the entire Arctic. The reasons for this may be traced back to the fact that errors in the estimation of partial models ([1a, 1b, 1c] and [2a, 2b, 2c]) have different signs, which in a synthetic model partially cancel out each other. Moreover, if the variable DG3L three years before shows strong and evenly distributed in time action, the D variable characterizing atmospheric circulation shows clearly seasonal activity – it is marked only during the minimum development of sea ice extent (September), when the degree of ice concentration is reduced, allowing its relatively free drift. The model for the annual average of sea ice extent of the entire Arctic (in the accepted limits) explains 71.5% of the variance, in September 68%, and in March 65% of the variance (Table 5). The lowest values are obtained for the American Arctic, where the D variable, characterizing atmospheric circulation does not appear to have significant influence, so the model is a linear equation with one variable (DG3L). Nevertheless, also in this case, the variance of the annual sea ice extent in the American Arctic is explained exceeding 50%. Variability of THC (described by the DG3L index) explains ~67% of the variance of annual sea ice extent and variability of atmospheric circulation (described by the D index) explains ~6% of the variance of annual sea ice extent of the entire Arctic. It allows claiming that THC and atmospheric circulation are the essential factors that influence the variability of sea ice extent of the Arctic. Both of these factors are natural factors. Further analysis of the results presented by various models and especially those affected by the DG3L variable (Fig. 5) delayed by three years suggests that the linear model is not the most appropriate model reflecting the changes in the sea ice extent of the entire Arctic and its parts. The action of DG3L variable, accumulated over several years, is saved and this causes that a strong significant correlation with the sea ice extent is prolonged. The analysis carried out by means of the segmented regression showed that the variability of sea ice extent was different where THC is lower than the average (DG3L ≤ 0), or different where THC is stronger than average (DG3L> 0; see equation [4a, 4b]). When the index is zero or less than zero, the impact of THC on the increase in sea ice extent is limited and the influence of changes in atmospheric circulation on sea ice extent is very small. Conversely, when the THC becomes intense and imports increased amounts of heat to the Arctic, the influence of DG3L index on the decrease in sea ice extent rises, like growing impact of atmospheric circulation on variation of sea ice extent (see equations [5a, 5b]. The segmented regression equations with these two variables explain 88.76% of the observed annual variation of sea ice extent of the entire Arctic (equations [5a, 5b]).This means that the sea ice extent of the Arctic is variable in two distinct regimes – ‘warm’, when the DG3L> 0 and ‘cold’, when the DG3L ≤ 0. This is similar to the results of Proshutinsky and Johnson (1997), Polyakov et al. (1999) and Polyakov and Johnson (2000) and their LFO oscillation. Time limits of the transition intensity of the THC phases from the positive to negative and vice versa correspond to similar limits of LFO, suggesting that the two different systems have the same cause. Polyakov and Johnson (2000) and Polyakov et al. (2002, 2003, 2004, 2005) can see the main reason for the change in the LFO regime in the transition of atmospheric circulation from anticyclonic regime to cyclonic regime and vice versa. The analysis of the reason for the transition of regime of changes in sea ice extent from ‘warm’ to ‘cold’ and vice versa – THC or atmospheric circulation – has shown that the D index is a function of previous changes in DG3L index. Atmospheric circulation over the Arctic shows a greater delay in response to changes in THC than the sea ice extent – this occurs with a 6-year delay (see Table 6, Equation 6). This allows replacing the D variable in the equations describing the change in sea ice extent, directly by DG3L variable from 6 years before (see Equation [7a, 7b]).These simultaneous equations explain about 90% of the observed annual variance of the sea ice extent of the entire Arctic in the years 1979-2013. Most importantly, however, it can be stated, with a high degree of certainty, that the variability of THC of the North Atlantic steers both the changes in sea ice extent and Basic features of atmospheric circulation over the Arctic. The effects of other factors than THC, having influence on variability of sea ice extent and the basic processes of the climate in the Arctic, in the short time scales, leave not too much space/place. The transition from ‘cold’ to ‘warm’ regime in the development of the sea ice extent in the Arctic requires an increase in the intensity of THC. If the values of DG3L index are greater than 0 for a period not shorter than three years, the decrease in the sea ice extent will start, initially in the period of its minimum development (August, September). If the resultant values of the DG3L index have positive values for further three years, the atmospheric circulation will transform into a cyclonic circulation (D index goes to positive values). The role of atmospheric circulation during the ‘warm’ season in the Arctic having influence on the change (reduction) of the sea ice extent becomes significant. The ‘warm’ regime will remain as long as long after its start the situation in which the algebraic sum of DG3L values is greater than 0. If such a situation lasts long, or in case of accumulation of high values of DG3L index, the sea ice cover can disappear almost completely in the warm period. The transition from the ‘warm’ regime to the ‘cold’ regime demands fulfillment of reverse conditions – a consistent decrease in the values of DG3L index into negative values for at least another three year period. After three years this will result in rapid increase in sea ice extent during warm period, thereby increasing the annual average of sea ice extent. If in subsequent years the value of DG3L index remains lower than zero, after the next 3-4 years, the atmospheric circulation will become the anticyclonic circulation. After that there will be gradual, slow growth in sea ice extent, decrease in air temperature, increase in ice thickness and change in the age of the ice structure towards the increase in the multi-year ice. The ice cover in the Arctic will become "self-sustaining", reducing interannual variability. Major changes will occur in the ‘warm’ season, minor in other seasons. The maximum sea ice extent of the Arctic in the cold season, with current conditions in the ‘cold’ regime, can reach ~13.5-14.5 million km2, the average annual sea ice extent should be ~12 (± 0.5) million km2. This area, especially in the winter season, may be in fact higher, since the weakening of the THC must also lead to a decrease in air temperature in the hemisphere.