Positive Degree Days Research Articles

Rapid loss of the Ayles Ice Shelf, Ellesmere Island, Canada

On August 13, 2005, almost the entire Ayles Ice Shelf (87.1 km2) calved off within an hour and created a new 66.4 km2 ice island in the Arctic Ocean. This loss of one of the six remaining Ellesmere Island ice shelves reduced their overall area by ∼7.5%. The ice shelf was likely weakened prior to calving by a long‐term negative mass balance related to an increase in mean annual temperatures over the past 50+ years. The weakened ice shelf then calved during the warmest summer on record in a period of high winds, record low sea ice conditions and the loss of a semi‐permanent landfast sea ice fringe. Climate reanalysis suggests that a threshold of >200 positive degree days year−1 is important in determining when ice shelf calving events occur on N. Ellesmere Island.

Daily Microwave-Derived Surface Temperature over Canada/Alaska

Abstract The land surface temperature variation over northern high latitudes in response to the increase in greenhouse gases is challenging because of the lack of meteorological stations. A new method to derive the surface temperature from satellite microwave measurements that improves the frequency of measurements relative to that of infrared data is presented. The daily Special Sensor Microwave Imager 25 km × 25 km Equal-Area Scalable Earth Grid (EASE-Grid) dataset provided by the National Snow and Ice Data Center in Boulder, Colorado, is processed to derive the surface temperature using the method proposed by Fily et al. A normalization approach based on the 40-yr ECMWF reanalysis (ERA-40; 2.5°) temperature diurnal cycle fitted for each pixel is applied to overcome the time acquisition variation of measurements as well as to interpolate missing data. An adaptive mask for discriminating between ice-free pixels and snow-free pixels is also applied. The resulting database is thus a new consistent hourly series of near-surface air temperatures during the summer (without snow). The mean accuracy is on the order of 2.5–3 K when compared with the synchronous in situ air temperature and different gridded datasets over Canada and Alaska. The trend over the last 10 yr confirms observed climate evolution: an increase in summer surface temperature of +0.09° ± 0.04°C yr−1, at the 90% confidence level, for Canada between 1992 and 2002, whereas a decrease of −0.15° ± 0.05°C yr−1, at the 95% confidence level, is observed for Alaska. Spatial and temporal anomalies show regional impacts of meteorological phenomena such as the El Niño extreme warm summer episode of 1998, the decrease in temperatures in 1992 in Canada following the volcanic eruption of Mount Pinatubo in June 1991, and the strong drought in the prairies in 2001. The annual sum of positive degree-days (thawing index) has been related to the permafrost distribution. The lower values of the derived thawing index (<1400 degree-days) are related well to the presence of continuous and dense discontinuous permafrost. The observed increase in the thawing index during the 1992–2002 period represents a decrease of classified permafrost area of 7%.

Recent Trends in Melting Conditions on the Antarctic Peninsula and Their Implications for Ice-sheet Mass Balance and Sea Level

Long-term records from meteorological stations on the Antarctic Peninsula show strong rising trends in the annual duration of melting conditions. In each case, the trend is statistically significant and represents a major increase in the potential for melting; for example, between 1950 and 2000 the record from Faraday/Vernadsky Station showed a 74% increase in the number of positive degree-days (PDDs). A simple parameterization of the likely effects of the warming on the rate of snow melt suggests an increase across the Antarctic Peninsula ice sheet from 28 ± 12 Gt a−1 in 1950, to 54 ± 26 Gt a−1 by 2000. Given a similar rate of warming over the next 50 years this may reach 100 ± 46 Gt a−1. The majority of this increased meltwater does not drain into the sea but is refrozen in the ice sheet, and it is difficult to predict the fraction of ablation that will become runoff; however, a calculation based on an established criterion for runoff indicates that the contribution from the Antarctic Peninsula, as a direct and immediate response to climate warming is significant, equivalent to (0.008–0.055) mm a−1 of global sea level rise. Given future warming this could easily treble in the coming 50 years. This contribution due to increased runoff could be augmented by any dynamic imbalance in the glaciers draining the ice sheet. This finding appears to contradict the conclusions of previous assessments, including the Intergovernmental Panel on Climate Change, which considered the contribution of runoff from Antarctica to sea level rise would be insignificant.

Surface melting on Larsen Ice Shelf, Antarctica

AbstractThe disintegration of Larsen A and B ice shelves in 1995 and 2002, respectively, was preceded by intense surface melting during the summer of ice-shelf collapse and previous summers. To understand the transition of the ice-shelf surface from dry to wet conditions, we developed a one-dimensional model, describing the mass, heat and force balances of water and firn within the ice-shelf surface layer. The model is run using atmospheric data from an automatic weather station on Larsen C ice shelf (World Meteorological Organization station ‘Larsen Ice Site’) located south of Larsen A and B. The model’s derived melting rate is greater than melting predicted by the positive degree-day (PDD) approach, common in studies of ablating ice sheets, such as Greenland. The model shows that the years of ice-shelf break-up (1995 and 2002) are distinguished from previous years by local maxima in the number of melting days. When the PDD approach is considered, however, a maximum in the number of positive degree-days appears in the 2002 break-up year, but not in 1995.

Implications of increased Greenland surface melt under global-warming scenarios: ice-sheet simulations

The Greenland ice sheet is likely to make a faster contribution to sea-level rise in a warming world than previously believed, based on numerical modelling using a parameterization of recent results showing surface-meltwater lubrication of ice flow. Zwally et al. (Science 297(557) (2002) 218) documented correlation between increased ice velocity and increased surface melt (as parameterized by positive degree days). They argued that surface water is piped directly to the bed with little delay, causing increased basal-water pressures and basal-sliding velocities, an effect not included in recent Greenland ice-sheet models known to the authors. Using the Pennsylvania State University/University of Chicago thermomechanical flowline model, numerous simulations were conducted to test a wide range of parameter space linking surface melt with a new sliding law based on the Zwally et al. data under three different global warming scenarios (2×CO 2, 4×CO 2, and 8×CO 2). Comparisons to reconstructions generated with a traditional sliding parameterization illustrate an enhanced sensitivity of the ice sheet to surface warming resulting in higher ablation rates, additional thinning and retreat of the margin, and a reduction in ice volume leading to an increased contribution to global sea-level rise.

Melting of small Arctic ice caps observed from ERS scatterometer time series

Time series of active microwave backscatter data from the ERS Wind Scatterometers are used to demonstrate (1) the timing of melt onset can be observed over small ice caps, as well as the major ice sheets and multi‐year sea ice; and (2) temporally integrated backscatter reduction (R) is strongly correlated with total annual positive‐degree‐days (PDD), as measured at four automated weather stations on the surface of the Greenland Ice Sheet. The latter is particularly interesting because unlike melt onset, R is a measure of melt processes integrated over an entire ablation season. Both methods work owing to the well‐known sharp decrease in radar backscatter triggered by the appearance of liquid water in a glacier snowpack. Melt onset and R are determined for 14 small Arctic ice caps from 1992–2000. Interannual and regional variability in the timing of melt onset are comparable, averaging 33 ± 9 and 38 ± 13 days, respectively. Regional variability in R exceeds interannual variability, averaging 906 ± 164 and 309 ± 130 dB·d, respectively. The brevity of the derived nine‐year records limits their statistical testing for trend. However, their qualitative interpretation suggests that the timing of melt onset is more variable for Arctic ice caps than for sea ice, and the intensity of summer surface melting increased by about 20% in the 1990s.

Ablation Rate Estimates over the Greenland Ice Sheet from Microwave Radiometric Data

Scanning Multichannel Microwave Radiometer (SMMR) data are used to estimate the annual melt duration (number of days with melt) for elevation transects over the Greenland ice sheet during the period from 1979-1986. The annual melt duration is used to estimate the number of positive degree days (PDDs), which are used in a degree-day mass balance model to determine ablation rates and the equilibrium line altitude (ELA). The annual melt duration along two transects estimated with SMMR data compares favorably, particularly above the ELA, to melt duration calculated from surface temperature data for the same locations. The mass balance estimates and ELA locations along eight transects agree reasonably well with measurements reported in previous studies using surface temperature data. ELAs were within 10m of published values along two transects, and the root mean square error of SMMR-derived versus surface mass balance measurements was 43mm yr−1. The estimated error in SMMR-derived ablation is between ±15% and ±50%, but could be reduced substantially by using daily microwave data available from the Special Sensor Microwave/Imager (SSM/I). This research shows the feasibility of using passive microwave data to estimate the ablation rate in order to determine ELA, which can be used to monitor the mass balance of the ice sheet.

Relationship Between Ambient and Leaf Litter Temperatures in Overwintering Habitats of Boll Weevil (Coleoptera: Curculionidae)

Temperature data are unavailable for most boll weevil overwintering habitats. As a result, ambient data often are used to estimate overwintering survival and emergence. In this study, relationship of ambient and overwintering habitat temperatures was determined for 6 habitats in the Rolling Plains of Texas. The amplitude of the daily temperature cycle in the leaf litter of each habitat was dampened compared with that recorded for ambient. Average minimum daily leaf litter temperature was 3.19°C higher in the shelterbelt habitat, followed by mesquite–grass pasture (3.54°C), tall shinnery motte (3.64°C), low shinnery brush (3.81°C), lovegrass (4.01°C), and pecan (6.01°C). Likewise, the average maximum daily leaf litter temperature was 3.77°C lower in shelterbelt, followed by pecan (3.86°C), lovegrass (3.87°C), mesquite-grass pasture (4.07°C), low shinnery brush (5.51°C), and tall shinnery motte (6.12°C). Stepwise multiple regression of hourly leaf litter temperatures explained 93% of the variability, with hourly ambient temperature and week of year being significant variables. Similarly, multiple regressions of daily leaf litter minimum temperatures and daily leaf litter maximum temperatures explained 96 and 90% of the variability, respectively, with ambient temperatures and week of year being significant variables for both regressions. The effect of temperature quality (ambient versus leaf litter) and sampling frequency (hourly versus daily) on positive (>6.1°C) and negative (<0.0°C) degree-days experienced by overwintering boll weevils was quantified for each of the 6 habitats for 4 yr of independent data, representing 2 mild and 2 severe winters. Degree-day calculations based on fitting minimum–maximum data to an asymmetrical double sine curve overestimated positive degree-day accumulation by 2.5%. Ambient temperature positive degree-days were similar to that for mesquite–grass pasture habitat, but the ambient temperatures overestimated positive degree-days experienced by overwintering weevils in the pecan by 6.5%, followed by low shinnery brush (6.7%), lovegrass (8.7%), tall shinnery motte (9.2%), and shelterbelt (15.7%). All habitats had significantly fewer cumulative negative degree-days compared with that calculated for ambient. Pecan leaf litter accumulated 99% fewer negative degree-days, followed by shelterbelt (95%), tall shinnery motte (88%), low shinnery brush (82%), lovegrass (71%), and mesquite–grass pasture (50%).

Natural Sludge Dewatering. II: Thawing-Drying Process in Full-Scale Sludge Freezing Ditches

The thawing and drying process in full-scale sludge freezing ditches was studied. The ditches are located in northern Sweden. The treated sludge derived from a small wastewater-treatment plant with mechanical and chemical treatment. Due to the relatively warm winter of 1994–1995, it became possible to study the effect of incomplete freezing. The results show that frozen and thawed sludge has a porous and humus-like structure, whereas unfrozen sludge has a compact and smeary consistency. The study also shows that incomplete freezing decreases the dewaterability of the sludge, but that the result measured as solid content is satisfactory even if all the sludge is not well frozen. Moreover, the study indicates that the thawing depth could be estimated by a simple equation based on the number of positive degree-days.

Climatic Data-Based Analysis of Boll Weevil (Coleoptera: Curculionidae) Overwintering Survival and Spring Emergence

Analysis of data from a 15-yr study, composed of 97 boll weevil cohorts from overwintering habitats in the Rolling Plains of Texas, showed that overwintering survivorship and spring/summer emergence were influenced by the day of the year ( DOY ) when the weevils entered overwintering habitat ( DOY in), the amount of rainfall, and habitat temperatures experienced by the weevils during diapause. The earlier the date that weevils entered overwintering habitat or the greater the amount of rain occurring before emergence of the 1st weevil, the greater the positive degree-days (>6.1°C) required for weevils to emerge from overwintering habitat. There was no significant correlation between the timings of weevil emergence and the winter severity, measured in terms of negative degree-days(<0.0°C). Multiple least squares regression analyses describing the positive degree-days required for the weevils to start emergence ( DD start) and the time required for all weevils to emerge (duration of emergence) ( DD emerg) explained 88 and 77% of the variability in the data, respectively. With independent data, 75 and 36% of the variability were explained for DD start and DD emerg, respectively. Overwintering survival ranged from 0 to 50% in the 15-yr study. Survivorship was positively correlated with DOY in, and negatively correlated with NDD start and DD start. Multiple regression analysis with DOY in, negative degree-days, rainfall, positive degree-days, and their 1st order interactions explained 99% of the variability in overwintering survival. With independent data, 74% of the variability was explained by the survival function. A chi-square test for goodness-of-fit showed that 74% of the survival estimates did not differ significantly from their observed values. In contrast, when compared with independent data, 37% of the survival estimates did not differ significantly from their observed values. The spring/summer emergence pattern for the overwintering weevils was described by a sigmoid function that explained 95 and 92% of the variability for the verification data set and the independent data set, respectively. Boll weevil overwintering emergence patterns can be predicted with considerable accuracy, but greater knowledge of factors affecting boll weevil overwintering survival and the time to completion of emergence is required for these to be predicted accurately.

Climatic Variables and Mass Balance Of Alpine Glaciers During A Period Of Climate Fluctuation

Parameterisation of relationships between climate and glacier mass balance is of considerable importance in understanding and modelling how temporal variability in climate affects the quantity of perennial snow and ice stored in glaciers, and the runoff from glacierised areas. Influences of year-to-year variations in air temperatures are pertinent in the absence of long records of measured energy balance and in view of predictions of future climate scenarios in terms of temperature. Measurements of temperature and precipitation from several stations in Alpine valleys in the Rhone basin, Wallis, Switzerland have been analysed to indicate trends in climate from 1930 to 1988. Actual measurements of mass balance of Griesgletscher, ablation calculated from runoff and net accumulation estimated from totalising rain gauges for Findelengletscher and Gornergletscher beginning in the late 1960s, and runoff from Aletschgletscher since 1930, were taken as annual glaciological responses to climatic variation. Variables to represent climatic elements and interactions between precipitation and temperature were selected according to degree of correlation with glacier response variables, and climate-glacier response relationships were assessed by multiple regression. Subsets of the data representing the coolest (1972–81) and warmest (1943–52) decades were also analysed to indicate whether relationships amongst climatic variables and between climate and mass balance remain the same under contrasting climatic conditions.Overall, mean summer air temperature variables for the months May through September and June through August provide the highest levels of explanation of variance of ablation and mass balance respectively (75–82%). Addition of a precipitation variable (winter, spring or summer) in multiple regression increases explanation to a maximum of 91%. Spring and summer precipitation variables are negatively correlated with ablation. Positive degree days and temperature-summer snow functions provide alternatives to temperature. Event-based analysis of the coolest and warmest years selected by rank order invokes high precipitation in May and low May-June temperatures and summer snowfall events as significant variables.Relationships between climatic variables indicate that warmer-than-average winters have higher precipitation, but at summer and annual time scales precipitation is slightly negatively associated with temperature. At the decadal level, warmer periods appear to be influenced by increased frequency of continental anticyclonic conditions, in an area subject to both maritime and continental influences. These analyses of climatic variables indicate that summer energy inputs dominate glacier mass balance. Relationships between precipitation and temperature are complex and were changeable during a fluctuation of about 1° over 40 years. Effects of a potentially warmer future on the form of precipitation in spring, summer and autumn are not clear, so estimates of changes of mass balance have been calculated for contrasting precipitation regimes.

Climatic Variables and Mass Balance Of Alpine Glaciers During A Period Of Climate Fluctuation

Parameterisation of relationships between climate and glacier mass balance is of considerable importance in understanding and modelling how temporal variability in climate affects the quantity of perennial snow and ice stored in glaciers, and the runoff from glacierised areas. Influences of year-to-year variations in air temperatures are pertinent in the absence of long records of measured energy balance and in view of predictions of future climate scenarios in terms of temperature. Measurements of temperature and precipitation from several stations in Alpine valleys in the Rhone basin, Wallis, Switzerland have been analysed to indicate trends in climate from 1930 to 1988. Actual measurements of mass balance of Griesgletscher, ablation calculated from runoff and net accumulation estimated from totalising rain gauges for Findelengletscher and Gornergletscher beginning in the late 1960s, and runoff from Aletschgletscher since 1930, were taken as annual glaciological responses to climatic variation. Variables to represent climatic elements and interactions between precipitation and temperature were selected according to degree of correlation with glacier response variables, and climate-glacier response relationships were assessed by multiple regression. Subsets of the data representing the coolest (1972–81) and warmest (1943–52) decades were also analysed to indicate whether relationships amongst climatic variables and between climate and mass balance remain the same under contrasting climatic conditions. Overall, mean summer air temperature variables for the months May through September and June through August provide the highest levels of explanation of variance of ablation and mass balance respectively (75–82%). Addition of a precipitation variable (winter, spring or summer) in multiple regression increases explanation to a maximum of 91%. Spring and summer precipitation variables are negatively correlated with ablation. Positive degree days and temperature-summer snow functions provide alternatives to temperature. Event-based analysis of the coolest and warmest years selected by rank order invokes high precipitation in May and low May-June temperatures and summer snowfall events as significant variables. Relationships between climatic variables indicate that warmer-than-average winters have higher precipitation, but at summer and annual time scales precipitation is slightly negatively associated with temperature. At the decadal level, warmer periods appear to be influenced by increased frequency of continental anticyclonic conditions, in an area subject to both maritime and continental influences. These analyses of climatic variables indicate that summer energy inputs dominate glacier mass balance. Relationships between precipitation and temperature are complex and were changeable during a fluctuation of about 1° over 40 years. Effects of a potentially warmer future on the form of precipitation in spring, summer and autumn are not clear, so estimates of changes of mass balance have been calculated for contrasting precipitation regimes.

Quantifying how climatic factors affect variation in plant disease severity: A general method using a new way to analyze meteorological data

A general method is presented for analyzing how climatic conditions affect plant disease severity. An example of its application is given for the analysis of stripe rust (caused by Puccinia striiformis) data on winter wheat cultivar Gaines and climatic data collected at Pullman, WA. for 1968–1986. A computer program WINDOW was written to identify the climatic factors most highly correlated with disease. This program is designed to utilize meteorological data for an entire growing season of a crop as well as to include climatic conditions preceding planting. This program uses an iterative process to examine variable-length segments of meteorological data in a more exhaustive analysis than previously possible. Climatic factors considered include: mean maximum, minimum, and average temperature; total and frequency of precipitation; consecutive days with and without precipitation; accumulation of negative and positive degree days; and number of days with extreme temperature events. Variables that were highly correlated with disease were the basis for regression models that were developed to predict disease severity index for each of the three cultivars. Two- and three-variable models explained, respectively, 75 and 76% of the variation in disease from year to year. Predictions (which could be made early enough in the growing season to allow application of chemical control) were evaluated on the basis of whether years with severe disease were accurately predicted. Models were validated using Allen's PRESS statistic and by application to new data. The method is potentially applicable to studies of how climatic conditions affect the populations or productivity of other types of organisms.

Development of Regional Models that Use Meteorological Variables for Predicting Stripe Rust Disease on Winter Wheat

Meteorological variables can be used to predict stripe rust, a disease of wheat caused by Puccinia striiformis West., at Lind, Pullman, and Walla Walla, Washington and Pendleton, Oregon in the Pacific Northwest of the United States. Regional models developed using different methodologies are described and evaluated for accuracy. Disease intensity data, collected from 1968 to 1981, were converted to a 0–9 disease index (DI) and were used as the dependent variable in regression analysis. Meteorological data were expressed as standardized negative degree days (NDDZ) accumulated during December and January, the Julian date of spring (JDS) [defined as the date when 40 or more positive degree days (PDD) accumulated during the subsequent 14 days] and PDD for the 80-day period after the JDS. In one of the regional models, NDDZ was accumulated for adjusted time periods at sites other than Pullman. Mallow's Cp criterion was used to evaluate the regression equations with different numbers of independent variables. The most accurate model uses NDDZ and JDS as the independent variables. The models were cross-validated by randomly removing 2 years' data and reformulating the model based on the remaining data; the new model was then used to compare actual and predicted DI. Predicted DI was within one standard error of the actual DI 60% of the time. Incorrect predictions occurred during years when spring was unusually favorable or unfavorable for disease development. The methodology described is applicable to developing statistical models relating other pest occurrences to meteorological conditions.

Ablation and heat balance of Biafo Glacier in Karakorum.

In the summer of 1977, surface ablation and relevant meteorological parameters were measured on the bare ice and moraine-covered ice of the Biafo Glacier in the Karakorum. The ablation rate of the bare ice was 6.8 cm/d in water equivalent while it was 2.1 cm/d for the 6 cm thick moraine-covered ice. The total amount of ablation had a close correlation with the positive degree-days of air temperature. Ablation experiments on ice with different moraine cover from 2 to 18 cm showed a decrease in ablation with an increase of moraine thickness. This result was similar to that which was obtained in the Hidden Valley in Nepal Himalaya, but slightly different from results obtained in Europe and Japan. Ablation of bare ice under two plastic sheets, having 44% and 94% in transmittivities of solar radiation, showed that the ablation was due mainly to the solar radiation; this was confirmed by a heat balance calculation. It is concluded that short-wave radiation is the dominant factor in the ablation of the Biafo Glacier.

Limatic variables that control development of stripe rust disease on winter wheat

The frequency and severity of the stripe rust disease (caused byPuccinia striiformis) on winter wheat in the Pacific Northwest of the U.S.A. has increased since 1958 in association with climatic variation. From 1968–1979, rust intensities on ‘Gaines’ wheat were most highly correlated with accumulated negative degree days (NDD) between 1 December and 31 January and positive degree days (PDD) between 1 April and 30 June. NDD and PDD were calculated from a 7 °C base. Linear regression equations using NDD alone accounted for 76% of the variation in stripe rust. When NDD and PDD were combined, 88% of the variation in stripe rust was explained. When a growth index (GI), and NDD plus PDD were used as independent variables in a multiple regression analysis, 91% of the variation in disease was explained. Frequency of precipitation in June was correlated with stripe rust intensity, but when it was added to the multiple regression analysis, it explained less than an additional 1% of the variation. The relationships between NDD, PDD, and disease index help to explain why stripe rust was not severe from 1941 to 1957. Methods used in this research should be applicable to similar studies of the effect of climatic variation on other pests.