Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model

The freshwater 1-D FLake lake model was coupled to the ORCHIDEE land surface model to simulate lake energy balance at the global scale. A multi-tile approach has been chosen to allow the modelling of various types of lakes within the ORCHIDEE grid cell. The different categories have been defined according to lake depth which is the most influential parameter of FLake, but other properties could be considered in the future. Several depth parameterization strategies have been compared, differing by the way to aggregate the depth of the subgrid lakes, i.e., arithmetical, geometrical, harmonical mean and median. Five atmospheric reanalysis datasets available at 0.5° or 0.25° resolution, have been used to force the model and assess model systematic errors. Simulations have been performed, evaluated and intercompared against observations of lake water temperatures provided by the GloboLakes database over about 1000 lakes and ice phenology derived from the Global Lake and River Ice Phenology database. The results highlighted the large impact of the atmospheric forcing on the lake energy budget simulations and the improvements brought by the highest resolution products (ERA5 and E2OFD). The median of the Root Square Mean Errors (RMSE) calculated at global scale range between 3.2 K and 2.7 K among the forcings, CRUJRA and ERA5 leading respectively to the best and worst results. Depth parameterization strategy appeared to be less influent, with RMSE differences less than 0.1 K for the four aggregation scenarios tested. The simulation of ice phenology presented systematic errors whatever the forcing used and the depth parameterization. Freezing onset was shown to be the less sensitive to forcing and depth parameterization with median of the errors ranging between 10 and 14 days. Larger errors were observed on the simulation of the end of the freezing period significantly influenced by the atmospheric forcing used. Such errors already highlighted in previous works, could be the result of deficiencies in the modeling of snow/ice parameterization processes. Various pathways are drawn to improve the model results, including the use of remote sensing data to better constrain the lake radiative parameters (albedo and extinction coefficient) as well as the lake depth thanks to the recent and forthcoming high resolution satellite missions.

River ice phenology and thickness from satellite altimetry: potential for ice bridge road operation and climate studies

River ice is a key component of the cryosphere. Satellite monitoring of river ice is a rapidly developing area of scientific enquiry, which has wide-ranging implications for climate, environmental and socioeconomic applications. Spaceborne radar altimetry is widely used for monitoring river water regimes; however, its potential for the observation of river ice processes and properties has not been demonstrated yet. Using Ku-band backscatter measurements from the Jason-2 and Jason-3 satellite missions (2008–2019), we demonstrate the potential of radar altimetry for the retrieval of river ice phenology dates and ice thickness for the first time. The altimetric measurements were determined to be sensitive enough to detect the first appearance of ice and the beginning of thermal breakup on the lower Ob River (Western Siberia). The uncertainties in the retrieval of ice event timing were within the 10 d repeat cycle of Jason-2 and Jason-3 in 88 %–90 % of the cases analysed. The uncertainties in the river ice thickness retrievals made via empirical relations between the satellite backscatter measurements and in situ observations, expressed as the root mean square error (RMSE), were of 0.07–0.18 m. A novel application of radar altimetry is the prediction of ice bridge road operations, which is demonstrated herein. We established that the dates of ferry closing and ice road opening and closing in the city of Salekhard can be predicted with an accuracy (expressed as RMSE) of 3–5 d.

An Automatic Method to Detect Lake Ice Phenology Using MODIS Daily Temperature Imagery

Lake ice phenology is a climate-sensitive indicator. However, ground-based monitoring suffers from the limitations of human vision and the difficulty of its implementation in harsh environments. Remote sensing provides great potential to detect lake ice phenology. In this study, a new automated method was developed to extract lake ice phenology parameters by capturing the temporal pattern of the transitional water/ice phase using a parameterized time function. The method is based on Moderate-Resolution Imaging Spectroradiometer (MODIS) daily temperature products, which have unique potential for monitoring lake ice cover as a result of providing four observations per day at 1 km spatial resolution from 2002 to 2016. Three seasonally ice-covered lakes with different characteristics in different climate regions were selected to test the method during the period of 2002–2016. The temporal pattern of water/ice transition phase was determined on the basis of unfrozen water cover fraction extracted from the MODIS daily temperature data, and was compared with the MODIS snow and reflectance products and Landsat images. A good agreement with an R2 of above 0.8 was found when compared with the MODIS snow product. The annual variation of extracted ice phenology dates showed good consistency with the MODIS reflectance and AMSR-E/2 products. The approach was then applied to nine seasonally ice-covered lakes in northern China from 2002 to 2016. The strongest tendency towards a later freeze-up start date was revealed in Lake Qinghai (6.31 days/10 yr) among the lakes in Tibetan plateau, and the break-up start and end dates rapidly shifted towards earlier dates in Lake Hulun (−3.73 days/10 yr; −5.02 days/10 yr). The method is suitable for estimating and monitoring ice phenology on different types of lakes over large scales and has a strong potential to provide valuable information on the responses of ice processes to climate change.

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

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.

Climate change and Northern Hemisphere lake and river ice phenology from 1931–2005

At high latitudes and altitudes one of the main controls on hydrological and biogeochemical processes is the breakup and freeze-up of lake and river ice. This study uses 3510 time series from across 678 Northern Hemisphere lakes and rivers to explore historical patterns in lake and river ice phenology across five overlapping time periods (1931–1960, 1946–1975, 1961–1990, 1976–2005, and 1931–2005). These time series show that the number of annual open-water days increased by 0.63 d per decade from 1931–2005 across the Northern Hemisphere, with trends for breakup and, to a lesser extent, freeze-up closely correlating with regionally averaged temperature. Breakup and freeze-up trends display a spatiotemporally complex evolution and reveal considerable caveats with interpreting the implications of ice phenology changes at lake and river sites that may only have breakup or freeze-up data, rather than both. These results provide an important contribution by showing regional variation in ice phenology trends through time that can be hidden by longer-term trends. The overlapping 30-year time periods also show evidence for an acceleration in warming trends through time. Understanding the changes on both long- and short-term timescales will be important for determining the causes of this change, the underlying biogeochemical processes associated with it, and the wider climatological significance as global temperatures rise.

Spatial-Temporal Distribution of the Freeze–Thaw Cycle of the Largest Lake (Qinghai Lake) in China Based on Machine Learning and MODIS from 2000 to 2020

The lake ice phenology variations are vital for the land–surface–water cycle. Qinghai Lake is experiencing amplified warming under climate change. Based on the MODIS imagery, the spatio-temporal dynamics of the ice phenology of Qinghai Lake were analyzed using machine learning during the 2000/2001 to 2019/2020 ice season, and cloud gap-filling procedures were applied to reconstruct the result. The results showed that the overall accuracy of the water–ice classification by random forest and cloud gap-filling procedures was 98.36% and 92.56%, respectively. The annual spatial distribution of the freeze-up and break-up dates ranged primarily from DOY 330 to 397 and from DOY 70 to 116. Meanwhile, the decrease rates of freeze-up duration (DFU), full ice cover duration (DFI), and ice cover duration (DI) were 0.37, 0.34, and 0.13 days/yr., respectively, and the duration was shortened by 7.4, 6.8, and 2.6 days over the past 20 years. The increased rate of break-up duration (DBU) was 0.58 days/yr. and the duration was lengthened by 11.6 days. Furthermore, the increase in temperature resulted in an increase in precipitation after two years; the increase in precipitation resulted in the increase in DBU and decrease in DFU in corresponding years, and decreased DI and DFI after one year.