A method for generating daily surfaces of temperature, precipitation, humidity, and radiation over large regions of complex terrain is presented. Required inputs include digital elevation data and observations of maximum temperature, minimum temperature and precipitation from ground-based meteorological stations. Our method is based on the spatial convolution of a truncated Gaussian weighting filter with the set of station locations. Sensitivity to the typical heterogeneous distribution of stations in complex terrain is accomplished with an iterative station density algorithm. Spatially and temporally explicit empirical analyses of the relationships of temperature and precipitation to elevation were performed, and the characteristic spatial and temporal scales of these relationships were explored. A daily precipitation occurrence algorithm is introduced, as a precursor to the prediction of daily precipitation amount. Surfaces of humidity (vapor pressure deficit) are generated as a function of the predicted daily minimum temperature and the predicted daily average daylight temperature. Daily surfaces of incident solar radiation are generated as a function of Sun-slope geometry and interpolated diurnal temperature range. The application of these methods is demonstrated over an area of approximately 400000 km 2 in the northwestern USA, for I year, including a detailed illustration of the parameterization process. A cross-validation analysis was performed, comparing predicted and observed daily and annual average values. Mean absolute errors (MAE) for predicted annual average maximum and minimum temperature were 0.7°C and 1.2°C, with biases of +0. loC and -0. loC, respectively. MAE for predicted annual total precipitation was 13.4 cm, or, expressed as a percentage of the observed annual totals, 19.3%. The success rate for predictions of daily precipitation occurrence was 83.3%. Particular attention was given to the predicted and observed relationships between precipitation frequency and intensity, and they were shown to be similar. We tested the sensitivity of these methods to prediction grid-point spacing, and found that areal averages were unchanged for grids ranging in spacing from 500 m to 32 km. We tested the dependence of the results on timestep, and found that the temperature prediction algorithms scale perfectly in this respect. Temporal scaling of precipitation predictions was complicated by the daily * Corresponding author.0022-1694/97/$17.00 @ 1997-Elsevier Science B.V. All rights reserved PIl SOO22-1694(96)03128-9Journal of P.E. Thornton et al./Journal of Hydrology 190 ( 1997) 214-251 215 occurrence predictions, but very nearly the same predictions were obtained at daily and annual timesteps. @ 1997 Elsevier Science B.V.

Abstract. Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km x 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biorne and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls.

From 1950 to 1999 the majority of the world's highest quality wine-producing regions experienced growing season warming trends. Vintage quality ratings during this same time period increased significantly while year-to-year variation declined. While improved winemaking knowledge and husbandry practices contributed to the better vintages it was shown that climate had, and will likely always have, a significant role in quality variations. This study revealed that the impacts of climate change are not likely to be uniform across all varieties and regions. Currently, many European regions appear to be at or near their optimum growing season temperatures, while the relationships are less defined in the New World viticulture regions. For future climates, model output for global wine producing regions predicts an average warming of 2 • C in the next 50 yr. For regions producing highquality grapes at the margins of their climatic limits, these results suggest that future climate change will exceed a climatic threshold such that the ripening of balanced fruit required for existing varieties and wine styles will become progressively more difficult. In other regions, historical and predicted climate changes could push some regions into more optimal climatic regimes for the production of current varietals. In addition, the warmer conditions could lead to more poleward locations potentially becoming more conducive to grape growing and wine production.

Ecosystem simulation models use descriptive input parame ters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations it is possible to measure required data, but as spatial resolution increases, so too does data unavailability. Generalized biome parameterizations are then re quired. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. The authors present documented input parameters for a process-based ecosystem simulation model, BIOME-BGC, for major natural temperate biomes. Parameter groups include the fol lowing: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf mor phology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (ribulose bisphosphate-l,5-carboxylase/oxygenase) (PLNR). Using climatic and site de

Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by AE 60 days and in standard deviation by AE 20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground-or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS Correspondence: Michael A. White, tel. 1 1 435 797 3794, fax 1 1 435 797 187, trends could be detected for only 12% of North America and were divided between trends towards both earlier and later spring.

Premium wine production is limited to regions climatically conducive to growing grapes with balanced composition and varietal typicity. Three central climatic conditions are required: (i) adequate heat accumulation; (ii) low risk of severe frost damage; and (iii) the absence of extreme heat. Although wine production is possible in an extensive climatic range, the highest-quality wines require a delicate balance among these three conditions. Although historical and projected average temperature changes are known to influence global wine quality, the potential future response of wineproducing regions to spatially heterogeneous changes in extreme events is largely unknown. Here, by using a high-resolution regional climate model forced by the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios A2 greenhouse gas emission scenario, we estimate that potential premium winegrape production area in the conterminous United States could decline by up to 81% by the late 21st century. While increases in heat accumulation will shift wine production to warmer climate varieties and͞or lower-quality wines, and frost constraints will be reduced, increases in the frequency of extreme hot days (>35°C) in the growing season are projected to eliminate winegrape production in many areas of the United States. Furthermore, grape and wine production will likely be restricted to a narrow West Coast region and the Northwest and Northeast, areas currently facing challenges related to excess moisture. Our results not only imply large changes for the premium wine industry, but also highlight the importance of incorporating fine-scale processes and extreme events in climate-change impact studies.climate change ͉ enology ͉ grape ͉ viticulture ͉ winegrape A number of observations indicate that warming has occurred during the late 20th and early 21st centuries at the Earth's surface (1), in the troposphere (2-4), and in the oceans (5). The majority of this warming has likely been caused by anthropogenic greenhouse gas (GHG) emissions (6), and if such emissions continue unabated, global mean temperatures are likely to rise by 2-6°C over the next century (1). This mean global warming will likely manifest itself over a range of spatial and temporal scales, altering mean seasonal climate (e.g., ref. 7), interannual climate variability (e.g., ref. 8), and the frequency and magnitude of extreme events (e.g., refs. 9-11).Such climatic changes could have a wide variety of important impacts on sectors such as human health (12), biological invasions (13), species extinctions (14), and water (15) and energy (16) resources. Because the quality and production of cultivated crops are directly influenced by local climate variables, agricultural systems may be particularly susceptible to climate change. For at least five reasons, the cultivation of grapes for the production of premium wine provides an optimal case for assessing potential impacts of climate change. First, premium wines are produced conterminously with human habitation an...

Recent research suggests that increases in growing-season length (GSL) in mid-northern latitudes may be partially responsible for increased forest growth and carbon sequestration. We used the BIOME-BGC ecosystem model to investigate the impacts of including a dynamically regulated GSL on simulated carbon and water balance over a historical 88-year record (1900-1987) for 12 sites in the eastern USA deciduous broadleaf forest. For individual sites, the predicted GSL regularly varied by more than 15 days. When grouped into three climatic zones, GSL variability was still large and rapid. There is a recent trend in colder, northern sites toward a longer GSL, but not in moderate and warm climates. The results show that, for all sites, prediction of a long GSL versus using the mean GSL increased net ecosystem production (NEP), gross primary production (GPP), and evapotranspiration (ET); conversely a short GSL is predicted to decrease these parameters. On an absolute basis, differences in GPP between the dynamic and mean GSL simulations were larger than the differences in NEP. As a percentage difference, though, NEP was much more sensitive to changes in GSL than were either GPP or ET. On average, a 1-day change in GSL changed NEP by 1.6%, GPP by 0.5%, and ET by 0.2%. Predictions of NEP and GPP in cold climates were more sensitive to changes in GSL than were predictions in warm climates. ET was not similarly sensitive. First, our results strongly agree with field measurements showing a high correlation between NEP and dates of spring growth, and second they suggest that persistent increases in GSL may lead to long-term increases in carbon storage.

The authors note that on page 13064, right column, first full paragraph, lines 4-7, "Some of the climatic events linked to such concerns are the severe droughts experienced by the Amazon basin in 2005 and 2010, which reportedly reduced tropical forest NPP by 1.6 to 2.2 PgC/y and increased tree mortality (50, 51)" should instead appear as "Some of the climatic events linked to such concerns are the severe droughts experienced by the Amazon basin in 2005 and 2010, which reportedly reduced tropical forest NPP and increased tree mortality by a total biomass carbon loss of 1.6 to 2.2 PgC (50, 51)."On page 13064, right column, first full paragraph, lines 12-17, "Assuming that the large reductions of tropical NPP reported previously (50, 51) are realistic, the absence of marked variations of the global CO 2 growth rate after the 2005 and 2010 Amazon droughts may imply that the drought conditions also coincidently reduced tropical Rh along with NPP, resulting in (relatively) small NEE anomalies" should instead appear as "Assuming that the large reductions of tropical forest biomass reported previously (50, 51) are realistic, the absence of marked variations of the global CO 2 growth rate after the 2005 and 2010 Amazon droughts may imply that the drought conditions also coincidently reduced tropical Rh along with NPP, resulting in (relatively) small NEE anomalies."These changes do not affect the conclusions of the article.