ALBERT

Description: The effect of diurnal variations in sea surface temperature (SST) on the air-sea flux of CO2 over the central Atlantic ocean and Mediterranean Sea is evaluated for 2005–2006. We use high resolution hourly satellite SST data to determine the diurnal warming (ΔSST). The CO2 flux is then computed using three different temperature fields – a foundation temperature (Tf, measured at a depth where there is no diurnal variation), Tf plus the hourly ΔSST and Tf plus monthly-averaged ΔSST. This is done in conjunction with a physically-based parameterisation for the gas transfer velocity (NOAA-COARE). The differences between the fluxes evaluated for these three different temperature fields quantifies the effects of both diurnal warming and diurnal covariations. We find that including diurnal warming increases the CO2 flux out of the Atlantic for 2005–2006 from 9.6 Tg C a−1 to 30.4 Tg C a−1 (hourly ΔSST) and 31.2 Tg C a−1 (monthly ΔSST). Diurnal warming, therefore, has a large impact on the annual net CO2 flux but diurnal covariations in variables are negligible implying that CO2 fluxes may be adequately computed using monthly averaged ΔSSTs along with a suitable foundation temperature.

Description: The effect of diurnal variations in sea surface temperature (SST) on the air-sea flux of CO2 over the central Atlantic ocean and Mediterranean Sea (60 S–60 N, 60 W–45 E) is evaluated for 2005–2006. We use high spatial resolution hourly satellite ocean skin temperature data to determine the diurnal warming (ΔSST). The CO2 flux is then computed using three different temperature fields – a foundation temperature (Tf, measured at a depth where there is no diurnal variation), Tf, plus the hourly ΔSST and Tf, plus the monthly average of the ΔSSTs. This is done in conjunction with a physically-based parameterisation for the gas transfer velocity (NOAA-COARE). The differences between the fluxes evaluated for these three different temperature fields quantify the effects of both diurnal warming and diurnal covariations. We find that including diurnal warming increases the CO2 flux out of this region of the Atlantic for 2005–2006 from 9.6 Tg C a−1 to 30.4 Tg C a−1 (hourly ΔSST) and 31.2 Tg C a−1 (monthly average of ΔSST measurements). Diurnal warming in this region, therefore, has a large impact on the annual net CO2 flux but diurnal covariations are negligible. However, in this region of the Atlantic the uptake and outgassing of CO2 is approximately balanced over the annual cycle, so although we find diurnal warming has a very large effect here, the Atlantic as a whole is a very strong carbon sink (e.g. −920 Tg C a−1 Takahashi et al., 2002) making this is a small contribution to the Atlantic carbon budget.

Description: Long-term temporal averaging of meteorological data, such as wind speed and air pressure, can cause large errors in air-sea carbon flux estimates. Other researchers have already shown that time averaging of wind speed data creates large errors in flux due to the non-linear dependence of the gas transfer velocity on wind speed (Bates and Merlivat, 2001). However, in general, wind speed is negatively correlated with air pressure, and a given fractional change in the pressure of dry air produces an equivalent fractional change in the atmospheric partial pressure of carbon dioxide (pCO2air). Thus low pressure systems cause a drop in pCO2air, which together with the associated high winds, promotes outgassing/reduces uptake of CO2 from the ocean. Here we quantify the errors in global carbon flux estimates caused by using monthly or climatological pressure data to calculate pCO2air (and thus ignoring the covariance of wind and pressure) over the period 1990-1999, using two common parameterisations for gas transfer velocity. Results show that on average, compared with estimates made using 6 hourly pressure data, the global oceanic sink is systematically overestimated by 7% (W92) and 10% (WM99) when monthly mean pressure is used, and 9% (W92) and 12% (WM99) when climatological pressure is used.

Description: The sensitivity of the biological parameters in a nutrient-phytoplankton-zooplankton-detritus (NPZD) model in the calculation of the air-sea CO2 flux, primary production and detrital export is analysed. The NPZD model is the Hadley Centre Ocean Carbon Cycle model (HadOCC) from the UK Met Office, used in the Hadley Centre Coupled Model 3 (HadCM3) and FAst Met Office and Universities Simulator (FAMOUS) GCMs. Here, HadOCC is coupled to the 1-D General Ocean Turbulence Model (GOTM) and forced with European Centre for Medium-Range Weather Forecasting meteorology to undertake a sensitivity analysis of its twenty biological parameters. Analyses are performed at three sites in the EuroSITES European Ocean Observatory Network: the Central Irminger Sea (60° N 40° W), the Porcupine Abyssal Plain (49° N 16° W) and the European Station for Time series in the Ocean Canary Islands (29° N 15° W) to assess variability in parameter sensitivities at different locations in the North Atlantic Ocean. Reasonable changes to the values of key parameters are shown to have a large effect on the calculation of the air-sea CO2 flux, primary production, and export of biological detritus to the deep ocean. Changes in the values of key parameters have a greater effect in more productive regions than in less productive areas. We perform the analysis using one-at-a-time perturbations and using a statistical emulator, and compare results. The most sensitive parameters are generic to many NPZD ocean ecosystem models. The air-sea CO2 flux is most influenced by variation in the parameters that control phytoplankton growth, detrital sinking and carbonate production by phytoplankton (the rain ratio). Primary production is most sensitive to the parameters that define the shape of the photosythesis-irradiance curve. Export production is most sensitive to the parameters that control the rate of detrital sinking and the remineralisation of detritus.

Description: The sensitivity of the biological parameters in a nutrient-phytoplankton-zooplankton-detritus (NPZD) model in the calculation of the air-sea CO2 flux, primary production and detrital export is analysed. We explore the effect on these outputs of variation in the values of the twenty parameters that control ocean ecosystem growth in a 1-D formulation of the UK Met Office HadOCC NPZD model used in GCMs. We use and compare the results from one-at-a-time and all-at-a-time perturbations performed at three sites in the EuroSITES European Ocean Observatory Network: the Central Irminger Sea (60° N 40° W), the Porcupine Abyssal Plain (49° N 16° W) and the European Station for Time series in the Ocean Canary Islands (29° N 15° W). Reasonable changes to the values of key parameters are shown to have a large effect on the calculation of the air-sea CO2 flux, primary production, and export of biological detritus to the deep ocean. Changes in the values of key parameters have a greater effect in more productive regions than in less productive areas. The most sensitive parameters are generally found to be those controlling well-established ocean ecosystem parameterisations widely used in many NPZD-type models. The air-sea CO2 flux is most influenced by variation in the parameters that control phytoplankton growth, detrital sinking and carbonate production by phytoplankton (the rain ratio). Primary production is most sensitive to the parameters that define the shape of the photosynthesis-irradiance curve. Export production is most sensitive to the parameters that control the rate of detrital sinking and the remineralisation of detritus.

Description: A statistical model is derived relating the diurnal variation of sea surface temperature (SST) to the net surface heat flux and surface wind speed from a numerical weather prediction (NWP) model. The model is derived using fluxes and winds from the European Centre for Medium-Range Weather Forecasting (ECMWF) NWP model and SSTs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In the model, diurnal warming has a linear dependence on the net surface heat flux integrated since (approximately) dawn and an inverse quadratic dependence on the maximum of the surface wind speed in the same period. The model coefficients are found by matching, for a given integrated heat flux, the frequency distributions of the maximum wind speed deceedance and the observed warming exceedance. Diurnal cooling, where it occurs, is modelled as proportional to the integrated heat efflux divided by the heat capacity of the seasonal mixed layer. The model reproduces the statistics (mean, standard deviation, and 95-percentile) of the diurnal variation of SST seen by SEVIRI and reproduces the geographical pattern of mean warming seen by the Advanced Microwave Scanning Radiometer (AMSR-E). We use the functional dependencies in the statistical model to test the behaviour of two physical model of diurnal warming that display contrasting systematic errors.

Description: A statistical model is derived relating the diurnal variation of sea surface temperature (SST) to the net surface heat flux and surface wind speed from a numerical weather prediction (NWP) model. The model is derived using fluxes and winds from the European Centre for Medium-Range Weather Forecasting (ECMWF) NWP model and SSTs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In the model, diurnal warming has a linear dependence on the net surface heat flux integrated since (approximately) dawn and an inverse quadratic dependence on the maximum of the surface wind speed in the same period. The model coefficients are found by matching, for a given integrated heat flux, the frequency distributions of the maximum wind speed and the observed warming. Diurnal cooling, where it occurs, is modelled as proportional to the integrated heat flux divided by the heat capacity of the seasonal mixed layer. The model reproduces the statistics (mean, standard deviation, and 95-percentile) of the diurnal variation of SST seen by SEVIRI and reproduces the geographical pattern of mean warming seen by the Advanced Microwave Scanning Radiometer (AMSR-E). We use the functional dependencies in the statistical model to test the behaviour of two physical model of diurnal warming that display contrasting systematic errors.

Description: Long-term temporal averaging of meteorological data, such as wind speed and air pressure, can cause large errors in air-sea carbon flux estimates. Other researchers have already shown that time averaging of wind speed data creates large errors in flux due to the non-linear dependence of the gas transfer velocity on wind speed (Bates and Merlivat, 2001). However, in general, wind speed is negatively correlated with air pressure, and a given fractional change in the pressure of dry air produces an equivalent fractional change in the atmospheric partial pressure of carbon dioxide (pCO2air). Thus low pressure systems cause a drop in pCO2air, which together with the associated high winds, promotes outgassing/reduces uptake of CO2 from the ocean. Here we quantify the errors in global carbon flux estimates caused by using monthly or climatological pressure data to calculate pCO2air (and thus ignoring the covariance of wind and pressure) over the period 1990–1999, using two common parameterisations for gas transfer velocity (Wanninkhof, 1992 (W92) and Wanninkhof and McGillis, 1999 (WM99)). Results show that on average, compared with estimates made using 6 hourly pressure data, the global oceanic sink is systematically overestimated by 7% (W92) and 10% (WM99) when monthly mean pressure is used, and 9% (W92) and 12% (WM99) when climatological pressure is used.

Description: 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.

Description: Surface temperature is a key aspect of weather and climate, but the term may refer to different quantities that play interconnected roles and are observed by different means. In a community-based activity in June 2012, the EarthTemp Network brought together 55 researchers from five continents to improve the interaction between scientific communities who focus on surface temperature in particular domains, to exploit the strengths of different observing systems and to better meet the needs of different communities. The workshop identified key needs for progress towards meeting scientific and societal requirements for surface temperature understanding and information, which are presented in this community paper. A "whole-Earth" perspective is required with more integrated, collaborative approaches to observing and understanding Earth's various surface temperatures. It is necessary to build understanding of the relationships between different surface temperatures, where presently inadequate, and undertake large-scale systematic intercomparisons. Datasets need to be easier to obtain and exploit for a wide constituency of users, with the differences and complementarities communicated in readily understood terms, and realistic and consistent uncertainty information provided. Steps were also recommended to curate and make available data that are presently inaccessible, develop new observing systems and build capacities to accelerate progress in the accuracy and usability of surface temperature datasets.