A tuning method for FLake, a one-dimensional (1-D) freshwater lake model, is applied for the individual tuning of 244 globally distributed large lakes using observed lake surface water temperatures (LSWTs) derived from along-track scanning radiometers (ATSRs). The model, which was tuned using only three lake properties (lake depth, snow and ice albedo and light extinction coefficient), substantially improves the measured mean differences in various features of the LSWT annual cycle, including the LSWTs of saline and high altitude lakes, when compared to the observed LSWTs. Lakes whose lake-mean LSWT persists below 1 °C for part of the annual cycle are considered to be seasonally ice-covered. For trial seasonally ice-covered lakes (21 lakes), the daily mean and standard deviation (2σ) of absolute differences between the modelled and observed LSWTs are reduced from 3.07 °C ± 2.25 °C to 0.84 °C ± 0.51 °C by tuning the model. For all other trial lakes (14 non-ice-covered lakes), the improvement is from 3.55 °C ± 3.20 °C to 0.96 °C ± 0.63 °C. The post tuning results for the 35 trial lakes (21 seasonally ice-covered lakes and 14 non-ice-covered lakes) are highly representative of the post-tuning results of the 244 lakes. For the 21 seasonally ice-covered lakes, the modelled response of the summer LSWTs to changes in snow and ice albedo is found to be statistically related to lake depth and latitude, which together explain 0.50 (R2adj, p = 0.001) of the inter-lake variance in summer LSWTs. Lake depth alone explains 0.35 (p = 0.003) of the variance. Lake characteristic information (snow and ice albedo and light extinction coefficient) is not available for many lakes. The approach taken to tune the model, bypasses the need to acquire detailed lake characteristic values. Furthermore, the tuned values for lake depth, snow and ice albedo and light extinction coefficient for the 244 lakes provide some guidance on improving FLake LSWT modelling.

The GloboLakes project, a global observatory of lake responses to environmental change, aims to exploit current satellite missions and long remote-sensing archives to synoptically study multiple lake ecosystems, assess their current condition, reconstruct past trends to system trajectories, and assess lake sensitivity to multiple drivers of change. Here we describe the selection protocol for including lakes in the global observatory based upon remote-sensing techniques and an initial pool of the largest 3721 lakes and reservoirs in the world, as listed in the Global Lakes and Wetlands Database. An 18-year-long archive of satellite data was used to create spatial and temporal filters for the identification of waterbodies that are appropriate for remote-sensing methods. Further criteria were applied and tested to ensure the candidate sites span a wide range of ecological settings and characteristics; a total 960 lakes, lagoons, and reservoirs were selected. The methodology proposed here is applicable to new generation satellites, such as the European Space Agency Sentinel-series.

FLake, a 1-dimensional freshwater lake model, is tuned for 244 globally distributed large lakes using lake surface water temperatures (LSWTs) derived from Along-Track Scanning Radiometers (ATSRs). The model, tuned using only 3 lake properties; lake depth, albedo (snow and ice) and light extinction co-efficient, substantially improves the measured biases in various features of the LSWT annual cycle, including the LSWTs of saline and high altitude lakes. The daily mean absolute differences (MAD) and the spread of differences (±2 standard deviations) across the trial seasonally ice covered lakes (lakes with a lake-mean LSWT remaining below 1 °C for part of the annual cycle) is reduced from 3.01± 2.25 °C (pre-tuning) to 0.84 ± 0.51 °C (post-tuning). For non-seasonally ice-covered trial lakes (lakes with a lake-mean LSWT remaining above 1 °C throughout its annual cycle), the average daily mean absolute difference (MAD) is reduced from 3.55 ± 3.20 °C to 0.96 ± 0.63 °C. The post tuning results for the trial lakes (35 lakes) are highly representative of the post tuning results of the 244 lakes. The sensitivity of the summer LSWTs of deeper lakes to changes in the timing of ice-off is demonstrated. The modelled summer LSWT response to changes in ice-off timing is found to be strongly affected by lake depth and latitude, explaining 0.50 (R2adj, p = 0.001) of the inter-lake variance in summer LSWTs. Lake depth alone explains 0.35 (p =0.003) of the variance. The tuning approach undertaken in this study, overcomes the obstacle of the lack of available lake characteristic information (snow and ice albedo and light extinction co-efficient) for individual lakes. Furthermore, the tuned values for lake depth, snow and ice albedo and light extinction co-efficient for the 244 lakes provide guidance for improving LSWTs modelling in FLake.

ABSTRACTLake surface water temperatures (LSWTs) of 246 globally distributed large lakes were derived from Along‐Track Scanning Radiometers (ATSR) for the period 1991–2011. The climatological cycles of mean LSWT derived from these data quantify on a global scale the responses of large lakes' surface temperatures to the annual cycle of forcing by solar radiation and the ambient meteorological conditions. LSWT cycles reflect the twice annual peak in net solar radiation for lakes between 1°S to 12°N. For lakes without a lake‐mean seasonal ice cover, LSWT extremes exceed air temperatures by 0.5–1.7 °C for maximum and 0.7–1.9 °C for minimum temperature. The summer maximum LSWTs of lakes from 25°S to 35°N show a linear decrease with increasing altitude; −3.76 ± 0.17 °C km−1 ( = 0.95), marginally lower than the corresponding air temperature decrease with altitude −4.15 ± 0.24 °C km−1 ( = 0.95). Lake altitude of tropical lakes account for 0.78–0.83 () of the variation in the March to June LSWT–air temperature differences, with differences decreasing by 1.9 °C as the altitude increases from 500 to 1800 m above sea level (a.s.l.) We define an ‘open water phase’ as the length of time the lake‐mean LSWT remains above 4 °C. There is a strong global correlation between the start and end of the lake‐mean open water phase and the spring and fall 0 °C air temperature transition days, ( = 0.74 and 0.80, respectively), allowing for a good estimation of timing and length of the open water phase of lakes without LSWT observations. Lake depth, lake altitude and distance from coast further explain some of the inter‐lake variation in the start and end of the open water phase.

Optimal estimation (OE) and probabilistic cloud screening were developed to provide lake surface water temperature (LSWT) estimates from the series of (advanced) along-track scanning radiometers (ATSRs). Variations in physical properties such as elevation, salinity, and atmospheric conditions are accounted for through the forward modelling of observed radiances. Therefore, the OE retrieval scheme developed is generic (i.e., applicable to all lakes). LSWTs were obtained for 258 of Earth's largest lakes from ATSR-2 and AATSR imagery from 1995 to 2009. Comparison to in situ observations from several lakes yields satellite in situ differences of -0.2 +/- 0.7 K for daytime and -0.1 +/- 0.5 K for nighttime observations (mean +/- standard deviation). This compares with -0.05 +/- 0.8 K for daytime and -0.1 +/- 0.9 K for nighttime observations for previous methods based on operational sea surface temperature algorithms. The new approach also increases coverage (reducing misclassification of clear sky as cloud) and exhibits greater consistency between retrievals using different channel-view combinations. Empirical orthogonal function (EOF) techniques were applied to the LSWT retrievals (which contain gaps due to cloud cover) to reconstruct spatially and temporally complete time series of LSWT. The new LSWT observations and the EOF-based reconstructions offer benefits to numerical weather prediction, lake model validation, and improve our knowledge of the climatology of lakes globally. Both observations and reconstructions are publically available from http://hdl.handle.net/10283/88.

Large-scale climate patterns influenced temperature and weather patterns around the globe in 2011. In particular, a moderate-to-strong La Nina at the beginning of the year dissipated during boreal spring but reemerged during fall. The phenomenon contributed to historical droughts in East Africa, the southern United States, and northern Mexico, as well the wettest two-year period (2010-11) on record for Australia, particularly remarkable as this follows a decade-long dry period. Precipitation patterns in South America were also influenced by La Nina. Heavy rain in Rio de Janeiro in January triggered the country's worst floods and landslides in Brazil's history.The 2011 combined average temperature across global land and ocean surfaces was the coolest since 2008, but was also among the 15 warmest years on record and above the 1981-2010 average. The global sea surface temperature cooled by 0.1 degrees C from 2010 to 2011, associated with cooling influences of La Nina. Global integrals of upper ocean heat content for 2011 were higher than for all prior years, demonstrating the Earth's dominant role of the oceans in the Earth's energy budget. In the upper atmosphere, tropical stratospheric temperatures were anomalously warm, while polar temperatures were anomalously cold. This led to large springtime stratospheric ozone reductions in polar latitudes in both hemispheres. Ozone concentrations in the Arctic stratosphere during March were the lowest for that period since satellite records began in 1979. An extensive, deep, and persistent ozone hole over the Antarctic in September indicates that the recovery to pre-1980 conditions is proceeding very slowly.Atmospheric carbon dioxide concentrations increased by 2.10 ppm in 2011, and exceeded 390 ppm for the first time since instrumental records began. Other greenhouse gases also continued to rise in concentration and the combined effect now represents a 30% increase in radiative forcing over a 1990 baseline. Most ozone depleting substances continued to fall. The global net ocean carbon dioxide uptake for the 2010 transition period from El Nino to La Nina, the most recent period for which analyzed data are available, was estimated to be 1.30 Pg C yr(-1), almost 12% below the 29-year long-term average.Relative to the long-term trend, global sea level dropped noticeably in mid-2010 and reached a local minimum in 2011. The drop has been linked to the La Nina conditions that prevailed throughout much of 2010-11. Global sea level increased sharply during the second half of 2011.Global tropical cyclone activity during 2011 was well-below average, with a total of 74 storms compared with the 1981-2010 average of 89. Similar to 2010, the North Atlantic was the only basin that experienced above-normal activity. For the first year since the widespread introduction of the Dvorak intensity-estimation method in the 1980s, only three tropical cyclones reached Category 5 intensity level-all in the Northwest Pacific basin.The Arctic continued to warm at about twice the rate compared with lower latitudes. Below-normal summer snowfall, a decreasing trend in surface albedo, and above-average surface and upper air temperatures resulted in a continued pattern of extreme surface melting, and net snow and ice loss on the Greenland ice sheet. Warmer-than-normal temperatures over the Eurasian Arctic in spring resulted in a new record-low June snow cover extent and spring snow cover duration in this region. In the Canadian Arctic, the mass loss from glaciers and ice caps was the greatest since GRACE measurements began in 2002, continuing a negative trend that began in 1987. New record high temperatures occurred at 20 m below the land surface at all permafrost observatories on the North Slope of Alaska, where measurements began in the late 1970s. Arctic sea ice extent in September 2011 was the second-lowest on record, while the extent of old ice (four and five years) reached a new record minimum that was just 19% of normal.On the opposite pole, austral winter and spring temperatures were more than 3 degrees C above normal over much of the Antarctic continent. However, winter temperatures were below normal in the northern Antarctic Peninsula, which continued the downward trend there during the last 15 years. In summer, an all-time record high temperature of -12.3 degrees C was set at the South Pole station on 25 December, exceeding the previous record by more than a full degree. Antarctic sea ice extent anomalies increased steadily through much of the year, from briefly setting a record low in April, to well above average in December. The latter trend reflects the dispersive effects of low pressure on sea ice and the generally cool conditions around the Antarctic perimeter.

Optimal estimation (OE) and probabilistic cloud screening have been developed to provide lake surface water temperature (LSWT) estimates from the series of (Advanced) Along-Track Scanning Radiometers (ATSRs). Variations in physical properties such as elevation, salinity, and atmospheric conditions are accounted for through the forward modelling of observed radiances. Therefore, the OE retrieval scheme developed is generic – i.e., applicable to all lakes. LSWTs have been obtained for 258 of Earth’s largest lakes from ATSR-2 and AATSR imagery from 1995 to 2009. Comparison to in situ observations from several lakes yields satellite-in situ differences of 0.3 ± 0.9 K for day-time and -0.3 ± 0.8 K for night-time observations (mean ± standard deviation). This compares to 0.6 ± 1.1 K for day-time and -0.2 ± 1.0 K for night-time observations for previous methods based on operational sea surface temperature algorithms. The new approach also increases coverage (reducing misclassification of clear-sky as cloud) and exhibits greater consistency between retrievals using different channel/view combinations. Empirical orthogonal function (EOF) techniques have been applied to the LSWT retrievals (which contain gaps due to cloud cover) to reconstruct spatially and temporally complete time series of LSWT. Visible reflectance channels are used to provide day-time observations of lake ice cover (LIC). Preliminary assessment of the LIC observations, through comparison with ice chart data, yields positive agreement for over 75% of cases assessed. The new LSWT observations, EOF-based reconstructions, and LIC observations offer benefits to numerical weather prediction (NWP), lake model validation, and improve our knowledge of the climatology of lakes globally.

Lakes are a vital component of the Earth's fresh water resources, and are of fundamental importance for terrestrial life. Lake water temperature is one of the key parameters determining ecological conditions within a lake, as it influences both chemical and biological processes. In addition to the impact on lake ecology, lake water temperatures determine air-water heat and moisture exchanges, and are therefore vital for understanding the hydrological cycle. Lake surface temperature (Lake ST) and lake ice cover (LIC) observations therefore have potential environmental and meteorological applications for inland water management and numerical weather prediction (NWP). The series of (Advanced) Along Track Scanning Radiometers, (A)ATSRs provide exceptional radiometric qualities and dual-view scanning capability, making them suitable instruments for providing observations of Lake ST and LIC globally. The (A)ATSRs have previously been exploited for sea surface temperature (SST) observations in the ATSR Reprocessing for Climate (ARC) project. However, attempts to deliver Lake ST as a by-product of SST retrieval or land surface temperature (LandST) retrieval have not delivered sufficiently convincing results from ATSRs or other satellite borne instruments. The ARCLake project aims to unlock the potential of the ATSRs for observations of Lake ST and LIC, and demonstrate the usefulness of these observations to climate science. Phase one of the ARC-Lake project focuses on large natural lakes, with surface area greater than 500 km2. A global database of over 250 such lakes has been constructed and the ARC SST retrieval scheme developed to provide improved observations of Lake ST and LIC for these lakes, over the ATSR-2/AATSR lifetime of 1995 to present day. A number of these lakes have been the subject of in situ measurement campaigns, over varying time periods, and of varying measurement type (buoy, ship, and radiometer). These in situ observations are used in validation studies of the new ARC-Lake observations of Lake ST and LIC. Further assessment of the new observations is carried out through comparisons with alternative satellite retrieval schemes. In this presentation, we demonstrate the quality of the new observations through results from these comparisons with in situ and alternative satellite observations.

This paper describes the techniques used to obtain sea surface temperature (SST) retrievals from the Geostationary Operational Environmental Satellite 12 (GOES-12) at the National Oceanic and Atmospheric Administration’s Office of Satellite Data Processing and Distribution. Previous SST retrieval techniques relying on channels at 11 and 12 mm are not applicable because GOES-12 lacks the latter channel. Cloud detection is performed using a Bayesian method exploiting fast-forward modeling of prior clear-sky radiances using numerical weather predictions. The basic retrieval algorithm used at nighttime is based on a linear combination of brightness temperatures at 3.9 and 11 mm. In comparison with traditional split window SSTs (using 11- and 12-mm channels), simulations show that this combination has maximum scatter when observing drier colder scenes, with a comparable overall performance. For daytime retrieval, the same algorithm is applied after estimating and removing the contribution to brightness temperature in the 3.9-mm channel from solar irradiance. The correction is based on radiative transfer simulations and comprises a parameterization for atmospheric scattering and a calculation of ocean surface reflected radiance. Potential use of the 13-mm channel for SST is shown in a simulation study: in conjunction with the 3.9-mm channel, it can reduce the retrieval error by 30%. Some validation results are shown while a companion paper by Maturi et al. shows a detailed analysis of the validation results for the operational algorithms described in this present article.