Freeze-up Dates Research Articles

Bowhead and beluga whale acoustic detections in the western Beaufort Sea 2008-2018.

The Distributed Biological Observatory (DBO) was established to detect environmental changes in the Pacific Arctic by regular monitoring of biophysical responses in each of 8 DBO regions. Here we examine the occurrence of bowhead and beluga whale vocalizations in the western Beaufort Sea acquired by acoustic instruments deployed from September 2008-July 2014 and September 2016-October 2018 to examine inter-annual variability of these Arctic endemic species in DBO Region 6. Acoustic data were collected on an oceanographic mooring deployed in the Beaufort shelfbreak jet at ~71.4°N, 152.0°W. Spectrograms of acoustic data files were visually examined for the presence or absence of known signals of bowhead and beluga whales. Weekly averages of whale occurrence were compared with outputs of zooplankton, temperature and sea ice from the BIOMAS model to determine if any of these variables influenced whale occurrence. In addition, the dates of acoustic whale passage in the spring and fall were compared to annual sea ice melt-out and freeze-up dates to examine changes in phenology. Neither bowhead nor beluga whale migration times changed significantly in spring, but bowhead whales migrated significantly later in fall from 2008–2018. There were no clear relationships between bowhead whales and the environmental variables, suggesting that the DBO 6 region is a migratory corridor, but not a feeding hotspot, for this species. Surprisingly, beluga whale acoustic presence was related to zooplankton biomass near the mooring, but this is unlikely to be a direct relationship: there are likely interactions of environmental drivers that result in higher occurrence of both modeled zooplankton and belugas in the DBO 6 region. The environmental triggers that drive the migratory phenology of the two Arctic endemic cetacean species likely extend from Bering Sea transport of heat, nutrients and plankton through the Chukchi and into the Beaufort Sea.

Delay in Arctic Sea Ice Freeze-Up Linked to Early Summer Sea Ice Loss: Evidence from Satellite Observations

The past decades have witnessed a rapid loss of the Arctic sea ice and a significant lengthening of the melt season. The years with the lowest summertime sea ice minimum were found to be accompanied by the latest freeze-up onset on record. Here, a synthetic approach is taken to examine the connections between sea ice melt timing and summer sea ice evolution from the remote sensing perspective. A 40-year (1979–2018) satellite-based time-series analysis shows that the date of autumn sea ice freeze-up is significantly correlated with the sea ice extent in early summer (r = −0.90, p < 0.01), while the spring melt onset is not a promising predictor of summer sea ice evolution. The delay in Arctic sea ice freeze-up (0.61 days year−1) in the Arctic was accompanied by a decline in surface albedo (absolute change of −0.13% year−1), an increase in net short-wave radiation (0.21 W m−2 year−1), and an increase in skin temperature (0.08 °C year−1) in summer. Sea ice loss would be the key reason for the delay in autumn freeze-up, especially in the Laptev, East-Siberian, Chukchi and Beaufort Seas, where sea ice has significantly declined throughout the summer, and strong correlations were found between the freeze-up onset and the solar radiation budget since early summer. This study highlights a connection between the summer sea ice melting and the autumn refreezing process through the ice-albedo feedback based on multisource satellite-based observations.

Geographic variation and temporal trends in ice phenology in Norwegian lakes during the period 1890–2020

Abstract. Long-term observations of ice phenology in lakes are ideal for studying climatic variation in time and space. We used a large set of observations from 1890 to 2020 of the timing of freeze-up and break-up, and the length of ice-free season, for 101 Norwegian lakes to elucidate variation in ice phenology across time and space. The dataset of Norwegian lakes is unusual, covering considerable variation in elevation (4–1401 m a.s.l.) and climate (from oceanic to continental) within a substantial latitudinal and longitudinal gradient (58.2–69.9∘ N, 4.9–30.2∘ E). The average date of ice break-up occurred later in spring with increasing elevation, latitude and longitude. The average date of freeze-up and the length of the ice-free period decreased significantly with elevation and longitude. No correlation with distance from the ocean was detected, although the geographical gradients were related to regional climate due to adiabatic processes (elevation), radiation (latitude) and the degree of continentality (longitude). There was a significant lake surface area effect as small lakes froze up earlier due to less volume. There was also a significant trend that lakes were completely frozen over later in the autumn in recent years. After accounting for the effect of long-term trends in the large-scale North Atlantic Oscillation (NAO) index, a significant but weak trend over time for earlier ice break-up was detected. An analysis of different time periods revealed significant and accelerating trends for earlier break-up, later freeze-up and completely frozen lakes after 1991. Moreover, the trend for a longer ice-free period also accelerated during this period, although not significantly. An understanding of the relationship between ice phenology and geographical parameters is a prerequisite for predicting the potential future consequences of climate change on ice phenology. Changes in ice phenology will have consequences for the behaviour and life cycle dynamics of the aquatic biota.

The Impact of Climate Warming on Lake Surface Heat Exchange and Ice Phenology of Different Types of Lakes on the Tibetan Plateau

Increasing air temperature is a significant feature of climate warming, and is cause for some concern, particularly on the Tibetan Plateau (TP). A lack of observations means that the impact of rising air temperatures on TP lakes has received little attention. Lake surfaces play a unique role in determining local and regional climate. This study analyzed the effect of increasing air temperature on lake surface temperature (LST), latent heat flux (LE), sensible heat flux (H), and ice phenology at Lake Nam Co and Lake Ngoring, which have mean depths of approximately 40 m and 25 m, respectively, and are in the central and eastern TP, respectively. The variables were simulated using an adjusted Fresh-water Lake (FLake) model (FLake_α_ice = 0.15). The simulated results were evaluated against in situ observations of LST, LE and H, and against LST data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for 2015 to 2016. The simulations show that when the air temperature increases, LST increases, and the rate of increase is greater in winter than in summer; annual LE increases; H and ice thickness decrease; ice freeze-up date is delayed; and the break-up date advances. The changes in the variables in response to the temperature increases are similar at the two lakes from August to December, but are significantly different from December to July.

Development and Calibration of Seasonal Probabilistic Forecasts of Ice-Free Dates and Freeze-Up Dates

AbstractDynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.

Changing ice and changing light: trends in solar heat input to the upper Arctic ocean from 1988 to 2014

AbstractThe Arctic sea-ice cover has undergone a significant decline in recent decades. The melt season is starting earlier, ice is thinner and seasonal ice dominates. Here we examine the effects of these changes on the solar heat input to the upper ocean in ice-covered Arctic waters from 1985 to 2014. Satellite observations of ice concentration, onset dates of melt and freeze-up and ice age, are combined with ice thicknesses from the PIOMAS model and incident solar irradiance from reanalysis products to calculate the contributions of open ocean and ice to the solar heat input in the upper ocean. Of the total, 86% of the area has positive trends for solar heat input to the ocean through leads due to decreases in ice concentration. Only 62% of the area shows positive trends of solar heat input to the ocean explicitly through the ice. Positive trends are due to thinning ice, while negative trends occur in regions where the ice-free season has lengthened. The annual total solar heat input to the ocean exhibits positive trends in 82% of the area. The spatial pattern of the cumulative annual total solar heat input is similar to the pattern of solar heat input directly to leads.

MODIS-observed variations of lake ice phenology in Xinjiang, China

Lakes sensitively respond to global and regional climate change, especially in arid areas. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the lake ice phenology of 23 lakes in the Xinjiang Uygur Autonomous Region of China from 2001 to 2018 was extracted based on thresholds of ice/water pixel numbers, and their change trends over 18 years were calculated. The results of MODIS-derived lake ice phenology showed consistent variations with existing ice phenology data sets derived from passive microwave data. Generally, lakes in Xinjiang begin to freeze from October to December every year, and their ice cover periods end from March to June. The average ice cover duration for the 23 lakes is 167 days, of which 16 lakes have an average shortening rate of − 1.08 days/year and seven lakes have an average extending rate of 1.18 days/year. The majority of lakes experienced later freeze-up (17 lakes) and earlier break-up (18 lakes) from 2001 to 2018. Lake ice phenology is affected by both climatic factors and lake physicochemical characteristics, in which freeze-up dates are more easily affected by lake-specific factors such as lake area (r = 0.535), while climatic factors especially water surface temperature have greater impacts on lake break-up dates (r = − 0.874). Compared to air temperature, water surface temperature changes have a more direct influence on the variations in lake ice phenology, 1° increase in water surface temperature may cause the ice cover duration to decrease by 12 days, while precipitation changes have almost no effect on the lakes in Xinjiang. In some cases, lake changes such as changes to the area and mineralization may also have dominant impacts on lake ice phenology.

Influence of meteorological conditions on the formation of ice regime of the Lake Onega

The formation of the ice regime on large lakes is mostly determined by the complex meteorological processes occurring over the water area. Previous studies of the air temperature influence on the ice regime formation on the Lake Onega were based on observational data on the state of the ice cover in the Petrozavodsk Bay, as well as the materials of the rare missions of aerial reconnaissance performed in the second half of the XX century. So, it was necessary to revise the previously established dependencies using the present-day satellite data for 2000-2018, and to determine the nature of influence of other meteorological factors, among which a thickness of the snow cover on the Lake area. The authors obtained regularities of changes in the characteristic dates (complete freeze-up – ±5 days/°C and final clearing of ice – ±3 days/°C) of the ice regime on the Lake Onega, depending on changes in the average air temperature that preceded these dates (autumn and spring) for the period of two months. The regression equations to calculate these dates on the basis of the previous three - and four-month periods from the data of 2000-2018 are also given. Relative to the above mentioned results for the previous period, a certain shift by a month ahead of two- and three- period exerting influence on the date of the complete freezeup is noted. Dependencies to calculate the cumulative daily temperatures for the period of the ice regime formation (the beginning of the formation of ice phenomena, complete freeze-up phase, the beginning of the breakup phase, clearing of the ice) were also deduced. Together with the data on the expected air temperature over the Lake these dates provide a potential possibility to estimate (predict) the characteristic dates of the ice regime. The significant influence of the average snow cover thickness on the process of destruction of the ice cover was shown, and the nature of the statistical relationship (along with the air temperature) was also determined.

Evolution From Past to Future Conditions of Fast Ice Coverage in James Bay

Fast ice is often used by coastal communities in the James Bay area for transportation in winter using snowmobiles. Therefore, the extent of fast ice along the James Bay coastline is important for land use and any changes to these extents may have significant impacts on the lifestyles of local communities. The eastern coastline has experienced changes in recent decades that might have affected the ice processes, namely hydrologic modifications due to hydroelectric development by Hydro-Québec along with climatic changes that have been observed worldwide. A statistical analysis in the form of summarized ice charts of the ice extents in the middle of winter have been compiled for the past four decades to highlight any recent changes in ice coverage using data from satellite imagery and ice charts produced by the Canadian Ice Service. A statistical analysis has also been carried out on the freeze-up and breakup dates using historical data. Moreover, a statistical analysis of hydrological data and climatic data has been carried out to determine the long-term and short-term trends in the parameters influencing the ice processes. After testing sensitivity of the past ice regime to climatic and hydrological parameters, the trends detected in the extents of fast ice, as well as freeze-up and breakup conditions, have been correlated with climatic parameters. Using the dominant parameters, a simplified model of the extents of fast ice has been developed, as well as criteria to determine freeze-up and breakup dates. Air temperature projections have also been obtained for two greenhouse gas emissions scenarios using global climate model results to establish future climatic conditions. Finally, projections of the fast ice regime around the year 2050 have been developed to determine a long-term trend covering both historical changes and future conditions. We expect around the year 2050 a recession of the landfast ice coverage of several kilometers, a delay of one to three weeks of the freeze-up dates and an advance of two to ten days of the breakup dates in comparison to the period of 1998-2016 in the James Bay area.

Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland

Abstract. The High Resolution Limited Area Model (HIRLAM), used for the operational numerical weather prediction in the Finnish Meteorological Institute (FMI), includes prognostic treatment of lake surface state since 2012. Forecast is based on the Freshwater Lake (FLake) model integrated into HIRLAM. Additionally, an independent objective analysis of lake surface water temperature (LSWT) combines the short forecast of FLake to observations from the Finnish Environment Institute (SYKE). The resulting description of lake surface state – forecast FLake variables and analysed LSWT – was compared to SYKE observations of lake water temperature, freeze-up and break-up dates, and the ice thickness and snow depth for 2012–2018 over 45 lakes in Finland. During the ice-free period, the predicted LSWT corresponded to the observations with a slight overestimation, with a systematic error of +0.91 K. The colder temperatures were underrepresented and the maximum temperatures were too high. The objective analysis of LSWT was able to reduce the bias to +0.35 K. The predicted freeze-up dates corresponded well to the observed dates, mostly within the accuracy of a week. The forecast break-up dates were far too early, typically several weeks ahead of the observed dates. The growth of ice thickness after freeze-up was generally overestimated. However, practically no predicted snow appeared on lake ice. The absence of snow, presumably due to an incorrect security coefficient value, is suggested to be also the main reason for the inaccurate simulation of the lake ice melting in spring.

Remote Sensing of Lake Ice Phenology across a Range of Lakes Sizes, ME, USA

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km2 and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km2.

Modulation of the Kara Sea Ice Variation on the Ice Freeze-Up Time in Lake Qinghai

Abstract Lake ice phenology, as an indicator for climate variability and change, exerts a great influence on regional climate and hydrometeorology. In this study, the changing characteristics of lake ice phenology at Lake Qinghai (LQH) are investigated using retrieved historical datasets during 1979–2016. The results show that the variation of the lake freeze-up date over LQH is characterized by a strong interannual variability. Further analysis has revealed that November sea ice concentration (SIC) variation in the Kara Sea can exert a great impact on the freeze-up date at LQH. During the low sea ice years, the open sea serves as a strong diabatic heating source, largely contributing to the enhanced Arctic Eliassen–Palmer flux, which then results in the deceleration of zonal wind in the middle and high latitudes. In addition to this, accompanied with the decreasing Kara SIC, the enhanced stationary Rossby wave flux propagating along the high-latitude regions may further exert remarkable influences in deepening the East Asian trough, which provides a favorable atmospheric circulation pattern for cold air intrusion from the Arctic and Siberian regions to mainland China. The decreased surface air temperature would thus advance the freezing date over LQH. Furthermore, the close relationship between atmospheric circulation anomalies and Kara SIC variations is validated by a large ensemble of simulations from the Community Earth System Model, and the atmospheric circulation patterns induced by the SIC anomalies are reproduced to some extent. Therefore, the November Kara Sea ice anomaly might be an important predictor for the variation in the freeze-up date at LQH.

Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record

The seasonal evolution of Arctic sea ice can be described by the timing of key dates of sea ice concentration (SIC) change during its annual retreat and advance cycle. Here, we use SICs from a satellite passive microwave climate data record to identify the sea ice dates of opening (DOO), retreat (DOR), advance (DOA), and closing (DOC) and the periods of time between these events. Regional variability in these key dates, periods, and sea ice melt onset and freeze-up dates for 12 Arctic regions during the melt seasons of 1979–2016 is investigated. We find statistically significant positive trends in the length of the melt season (outer ice-free period) for most of the eastern Arctic, the Bering Sea, and Hudson and Baffin Bays with trends as large as 11.9 d decade−1 observed in the Kara Sea. Trends in the DOR and DOA contribute to statistically significant increases in the length of the open water period for all regions within the Arctic Ocean ranging from 3.9 to 13.8 d decade−1. The length of the ice retreat period (DOR−DOO) ranges from 17.1 d in the Sea of Okhotsk to 41 d in the Greenland Sea. The length of the ice advance period (DOC−DOA) is generally much shorter and ranges from 17.9 to 25.3 d in the Sea of Okhotsk and Greenland Sea, respectively. Additionally, we derive the extent of the seasonal ice zone (SIZ) and find statistically significant negative trends (SIZ is shrinking) in the Sea of Okhotsk, Baffin Bay, Greenland Sea, and Barents Sea regions, which are geographically open to the oceans and influenced by reduced winter sea ice extent. Within regions of the Arctic Ocean, statistically significant positive trends indicate that the extent of the SIZ is expanding as Arctic summer sea ice declines.

Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data

AbstractLake ice is a robust indicator of climate change. The availability of information contained in Moderate Resolution Imaging Spectroradiometer daily snow products from 2000 to 2017 could be greatly improved after cloud removal by gap filling. Thresholds based on open water pixel numbers are used to extract the freezeup start and breakup end dates for 58 lakes on the Tibetan Plateau (TP); 18 lakes are also selected to extract the freezeup end and breakup start dates. The lake ice durations are further calculated based on freezeup and breakup dates. Lakes on the TP begin to freezeup in late October and all the lakes start the ice cover period in mid‐January of the following year. In late March, some lakes begin to break up, and all the lakes end the ice cover period in early July. Generally, the lakes in the northern Inner‐TP have earlier freezeup dates and later breakup dates (i.e., longer ice cover durations) than those in the southern Inner‐TP. Over 17 years, the mean ice cover duration of 58 lakes is 157.78 days, 18 (31%) lakes have a mean extending rate of 1.11 day/year, and 40 (69%) lakes have a mean shortening rate of 0.80 day/year. Geographical location and climate conditions determine the spatial heterogeneity of the lake ice phenology, especially the ones of breakup dates, while the physico‐chemical characteristics mainly affect the freezeup dates of the lake ice in this study. Ice cover duration is affected by both climatic and lake specific physico‐chemical factors, which can reflect the climatic and environmental change for lakes on the TP.

CHANGEABILITY OF THE ICE COVER ON THE LAKES OF NORTHERN POLAND IN THE LIGHT OF CLIMATIC CHANGES

The study is based upon instrumental observations of ice covers which formed on the lakes in northern Poland in the period 1956–2005 and records of air temperature measured at 9 meteorological stations in the period 1960–2005. Relations between mean dates of ice cover freeze-up, ice cover duration, maximum ice thickness, and also other properties of ice regime indicate obvious dependency upon air temperatures in winter months (December-February). Both air temperatures and main properties of ice covers revealed definite trends, showing the increase in air temperature in winter (0.04–0.06°C year-1), earlier disappearance of ice cover (0.5–0.6 day year-1), its shorter duration (0.6–0.7 day year-1), and decreases in maximum thickness of the ice cover (0.2–0.25 cm year-1). The author shows considerable statistical relations between main properties of the course of the ice cover, air temperatures in winter and the NAO winter indexes. Therefore, changeability of the ice covers on the lakes in northern Poland in the latter half of the twentieth century may be treated as another proof and an indirect indicator of climatic changes undergoing in this part of Europe.

The lake scheme of the Common Land Model and its performance evaluation

The Common Land Model (CoLM) has been updated to a new version, but the lake scheme in CoLM (CoLM-Lake) has never been evaluated. This paper introduces the structure and physical processes of CoLM-Lake, and evaluates its simulation performance through the observations from 10 lakes over different regions. It also discusses the sensitivities of simulated results to some important parameters in the model. Results show that CoLM-Lake performs very well over the three shallow lakes (Kossenblatter, Taihu and Sparkling Lake) where the model accurately captures the magnitudes and seasonal variations of lake surface temperature, turbulent fluxes, and vertical thermal structure. The freeze-thaw cycle of Sparkling Lake is reproduced at a reasonable level as well. Also, CoLM-Lake show acceptable performance in simulating vertical structures and their variations for deep lakes, although the vertical mixing strength remains underestimated. The biases of surface temperatures in the Great Lakes of North America are relatively larger, but the magnitudes and seasonal variations in temperature fall within a reasonable range. In general, CoLM-Lake is suitable for the simulation of lake physical processes on the global scale. The simulated results have strong sensitivities to some parameters with values that have large uncertainties. The surface roughness lengths determine the amounts of turbulent fluxes which are emitted into the atmosphere, and thus affecting the simulated lake surface temperature. Although the surface roughness based on the dynamic diagnosis of lake properties in the model has made the simulated turbulent fluxes very accurate, the further adjustment of surface roughness can improve the realism of the simulated turbulent fluxes and temperature on lake surface. Lake depth, optical extinction coefficient and thermal diffusivity affect the simulated lake thermal structure from different angles. Lake depth determines the amount of water in vertical mixing; optical extinction coefficient determines the distribution of solar radiation at different lake depths; and thermal diffusivity determines the simulated vertical mixing strength that a lake can achieve. Either a larger lake depth, a smaller extinction coefficient, or a larger thermal diffusivity enables a lake to transfer and store more heat internally, thereby increasing lake thermal inertia, reducing daily and seasonal variations of lake water temperatures, and causing late freeze-up dates in winter. Therefore, the biases of simulated lake thermal structure can be corrected by adjusting the above three parameters. Note that lake depth and optical extinction coefficient are lake inherent attributes, and thus it is important to collect high-resolution and high-precision data for these two parameters in the future. The modification of thermal diffusivity should focus on increasing simulated vertical mixing strength for deep lakes, while the relatively accurate simulation for shallow lakes should be maintained, and thus more complex processes in deep lakes need to be considered in the model than simply multiplying the background thermal diffusivity of entire lake. The temperature at which lake water reaches maximum density is affected by lake salinity, and adjusting this temperature also improves simulated results. Hence, the effects of lake salinity on lake water physical properties should be considered in the development of future lake schemes. As three-dimensional lake schemes become more sophisticated, it is increasingly important to couple them into regional and global climate models. However, the description of important processes such as lake vertical mixing, phase change and snow hydrology are still too simple in one-dimensional schemes, and thus the upper limit of accuracy that one-dimensional schemes can achieve in deep lake simulations remains unclear, and the necessity of coupling the three-dimensional schemes with climate models is under dispute. If one-dimensional schemes are found to have better performance in simulating the characteristics of deep lakes, then the large amount of computational resources that need to be consumed to run three-dimensional models can be saved. Therefore, developing one-dimensional lake schemes at current stage is extremely important.

Sea-ice cover timing in the Pacific Arctic: The present and projections to mid-century by selected CMIP5 models

With the sea-ice cover in the Arctic fast declining, changes to the timing of sea-ice break-up and freeze-up is an urgent economic, social, and scientific concern. Based on daily sea-ice concentration data we assess three variables: the dates of sea-ice break-up and freeze-up, and the annual sea-ice duration in the Pacific Arctic. The simulation results from the coupled Atmosphere-Ocean General Circulation Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are the source for this study. Compared with observations, CMIP5 models simulate all three variables well. The length of sea-ice duration is shrinking, with the strongest trend occurring for the period 1990–2014; this downward trend is projected to continue at least through mid-century by the CMIP5 models. Comparisons made at eight Chukchi Sea mooring sites and eight Distributed Biological Observatory (DBO) regions show consistent results. The 30-year averaged trend for annual sea-ice duration in the southern Chukchi Sea is projected to be −0.68 (−0.74) days/year to −1.20 (−1.17) days/year for 2015–2044 under RCP8.5 (RCP4.5) emissions scenarios. This is equivalent to a reduction of 20–36 days in the annual sea-ice duration. A similar negative trend is also found at all eight DBO regions. The reduction in annual sea-ice duration will include both earlier break-up dates and later freeze-up dates. However, models project that a later freeze-up contributes more than earlier break-up to the overall shortening of annual sea-ice duration. Around the Bering Strait area, future changes are the smallest, with less than 20 days change in duration during the next 30 years. In contrast, up to a 60-day reduction of the sea-ice duration in the East Siberian, Chukchi and Beaufort Seas is projected near the middle of the 21st century, when averaged over the period of 2030–2044.

Lake ice phenology of Nam Co, Central Tibetan Plateau, China, derived from multiple MODIS data products

Abstract Lake ice phenology, including freeze-up and break-up dates, is a sensitive indicator of climate change. However, uniformly created lake ice phenology records are still required on the Tibetan Plateau (TP) for a more accurate analysis of climate change. In this study, we present a new automated method to extend the interpretation of existing in-situ lake ice records, using multiple MODIS data products. Our results showed strong agreement with the in-situ data at Nam Co (Co means lake in Tibetan language) on the central TP. The multiple MODIS based method is therefore well-suited to the detection of lake ice phenology. The result showed that in 2000–2015, the mean lake ice duration (ID) in the middle of Nam Co was ~ 87 d; the freeze-up start (FUS) date delayed 0.58 d/yr and the break-up start (BUS) date delayed 0.09 d/yr, giving a total decrease in ID at the rate of 0.49 d/yr. We suggest the delayed freeze-up dates became more marked in response to climate warming. Changes in lake ice coverage in Nam Co are affected by a number of local and regional climatic factors, such as air temperature and wind speed.

Effect of roughness lengths on surface energy and the planetary boundary layer height over high-altitude Ngoring Lake

The special climate environment creates a distinctive air-lake interaction characteristic in the Tibetan Plateau (TP) lakes, where the variations of surface roughness lengths also differ somewhat from those of other regions. However, how different categories of roughness lengths affect the lake surface energy exchange and the planetary boundary layer height (PBLH) remains unclear in the TP lakes. In this study, we used a tuned Weather Research and Forecasting (WRF) model version 3.6.1 to investigate the responses of the freeze-up date, turbulent fluxes, meteorological variables, and PBLH to surface roughness length variations in Ngoring Lake. Of all meteorological variables, the lake surface temperature responded to roughness length variations most sensitively; increasing roughness lengths can put the lake freeze-up date forward. The effect of momentum roughness length on wind speed was significantly affected by the fetch length. The increase in the roughness length for heat can induce the increment of the nightly PBLH in most months, especially for the central lake area in autumn. The primary factors that contribute to sensible heat flux (H) and latent heat flux (LE) were the roughness lengths for heat and momentum during the ice-free period, respectively. Increasing roughness length for heat can increase the nightly PBLH, and decreasing roughness length for moisture can also promote growth of the PBLH, but there was no obvious correlation between the momentum roughness length and the PBLH.

The role of snow in the thickening processes of lake ice at Lake Abashiri, Hokkaido, Japan

To clarify the properties of lake ice at mid-latitudes subject to moderate air temperature, heavy snow and abundant solar radiation even in winter, we conducted field observations at Lake Abashiri in Japan for three winters and developed a one-dimensional (1-D) thermo-dynamical ice growth model. Using this model with meteorological data-sets, we examined the role of snow in the ice thickening process, as well as the Lake Abashiri ice phenology (including the interannual trend) for the past 55 years to compare with high latitude lakes. The ice was composed of two distinct layers: a snow ice (SI) layer and a congelation ice layer. The SI layer occupied a much greater fraction of total ice thickness than that at high latitude lakes. In-situ observations served to demonstrate the validity of the model. Freeze-up and break-up dates supplied by satellite imagery enabled further model validation prior to the availability of field data (2000/01–2015/16). Based on both observations and numerical experiments, it was found that one important role of snow is to moderate the variability of ice thickness caused by changes in meteorological conditions. Furthermore, ice thickness is more sensitive to snow depth than air temperature. When applied to an extended 55-year period (1961/62–2015/16) for which local meteorological observations are available, the mean dates of freeze-up and break-up, ice cover duration and ice thickness in February were estimated to be 12 December (no significant trend), 17 April (−1.7 d/decade), 127 d (−2.4 d/decade) and 43 cm (−1.4 cm/decade). For this long-term period, while snow still played an important role in ice growth, the surface air temperature warming trend was found to be a strong factor influencing ice growth, as reported for the high latitude lakes.