Assessment of current methods of positive degree-day calculation using in situ observations from glaciated regions

The continued use of the positive degree-day (PDD) method to predict ice-sheet melt is generally favoured over surface energy-balance methods partly due to the computational efficiency of the algorithm and the requirement of only one input variable (temperature). In this paper, we revisit some of the assumptions governing the application of the PDD method. Using hourly temperature data from the GC-Net network we test the assumption that monthly PDD total (PDDM) can be represented by a Gaussian distribution with fixed standard deviation of monthly temperature (σM). The results presented here show that the common assumption of fixed σM does not hold, and that σM may be represented more accurately as a quadratic function of average monthly temperature. For Greenland, the mean absolute error in predicting PDDM using our methodology is 3.9°C d, representing a significant improvement on current methods (7.8°C d, when σM = 4.5°C). Over a range of glaciated settings, our method reproduces PDDM, on average, to within 1.5–8.5°C d, compared to 4.4–15.7°C d when σM = 4.5°C. The improvement arises because we capture the systematic reduction in temperature variance that is observed over melting snow and ice, when surface temperatures cannot warm above 0°C.

Modeling Leaf Development of the African Violet (Saintpaulia ionantha Wendl.)

Leaf unfolding rate (LUR) was determined for `Utah' African violet plants grown in growth chambers under 20 combinations of temperature and photosynthetic photon flus (PPF). A nonlinear model was used to predict LUR as a function of shoot temperature and daily integrated PPF. The maximum predicted LUR was 0.27 leaves/day, which occurred at 25C and a daily integrated PPF of 10 mol/m2 per day. The optimum temperature for leaf unfolding decreased to 23C, and the maximum rate decreased to 0.18 leaves/day as the daily integrated PPF decreased from 10 to 1 mol/m2 per day. A greenhouse experiment using 12 combinations of air temperature and daily integrated PPF was conducted to validate the LUR model. Plant temperatures used in the model predicted leaf development more accurately than did air temperatures, but using average hourly temperature data was no more accurate than using average daily temperature data.