Climate Station Data Research Articles

Is the data collection period of the Large‐Scale Biosphere‐Atmosphere Experiment in Amazonia representative of long‐term climatology?

The Large‐Scale Biosphere‐Atmosphere Experiment in Amazonia (LBA) sampled surface‐atmosphere flux exchanges and related ecohydrometeorological processes at several flux tower sites in the Amazonian region between 1999 and 2006. This extensive database is now being analyzed to investigate, for example, the carbon balance of the Amazon basin and the effect of land use change in the basin on climate. It is therefore important to establish whether the period during which these data were collected is representative of the long‐term climatology for the region. This study analyzed long‐term climate station data for stations located nearby the LBA eddy flux tower sites. Measurements taken during the period of data collection were compared with the long‐term station climatology using the Kolmogorov‐Smirnov test and analysis of histogram from random samples from the long‐term climatological record. In terms of precipitation, the LBA data collection period is statistically consistent with the climatology for all LBA study sites. In terms of temperature, the same result is true for most flux station sites; the main exception being the Bananal Island site where the temperature during the LBA period is significantly warmer by about 1°C. There were some short periods when temperature in the region of other LBA flux sites was also statistically different (higher) during the LBA data collection period and an average but not statistically significant tendency toward higher temperatures across the whole region during the LBA period relative to previous years. This is probably because there has been significant land cover change near some of the LBA study sites, but a contribution from climate warming cannot be ruled out.

Application of Landsat-7 ETM+ and MODIS products in mapping seasonal accumulation of growing degree days at an enhanced resolution

This paper describes a procedure for mapping long-term average, growing season- accumulated growing degree days at an enhanced spatial resolution of 28.5 m. GDD-product enhancement is based on augmenting a previously developed 1 km resolution map of GDD described in Hassan et al. (J. Applied Remote Sens., 1, 013511, 12p (2007)) using data from a series of scene- and date-specific Landsat-7 ETM+ images (at 28.5 m resolution) from the 1999-2002 data collection period and a chronological series of standard MODIS 16-day composites of enhanced vegetation index (EVI; at 250 m resolution) spanning the 2003-2005 growing periods (April-October). Surface reflectances from the Landsat-7 ETM+ images are used to derive fine-scale estimates of EVI, which are then transformed into long-term averages by taking into account growing-season specific, temporal trends in the series of MODIS-EVI images. As values from the 8-day accumulated GDD and 16-day composites of EVI have been shown to be strongly correlated, a new data-fusion method based on the mean and instantaneous values of fine-grain long-term average EVI is used to augment the resolution of the initial GDD map. As a demonstration, we apply the procedure to satellite and climate station data for the Canadian Province of Nova Scotia.

Validation of climate model output using Bayesian statistical methods

The growing interest in and emphasis on high spatial resolution estimates of future climate has demonstrated the need to apply regional climate models (RCMs) to that problem. As a consequence, the need for validation of these models, an assessment of how well an RCM reproduces a known climate, has also grown. Validation is often performed by comparing RCM output to gridded climate datasets and/or station data. The primary disadvantage of using gridded climate datasets is that the spatial resolution is almost always different and generally coarser than climate model output. We have used a Bayesian statistical model derived from observational data to validate RCM output. We used surface air temperature (SAT) data from 109 observational stations in California, all with records of approximately 50 years in length, and created a statistical model based on this data. The statistical model takes into account the elevation of the station, distance from coastline, and the NOAA climate region in which the station resides. Analysis indicates that the statistical model provides reliable estimates of the mean monthly SAT at any given station. In our method, the uncertainty in the estimates produced by the statistical model are directly determined by obtaining probability density functions for predicted SATs. This statistical model is then used to estimate average SATs corresponding to each of the climate model grid cells. These estimates are compared to the output of the RCM to assess how well the RCM matches the observed climate as defined by the statistical model. Overall, the match between the RCM output and the statistical model is good, with some deficiencies likely due in part to the representation of topography in the RCM.

Estimation of potential evapotranspiration in the mountainous Panama Canal watershed

AbstractSpatially distributed hydrometeorological and plant information within the mountainous tropical Panama Canal watershed is used to estimate parameters of the Penman–Monteith evapotranspiration formulation. Hydrometeorological data from a few surface climate stations located at low elevations in the watershed are complemented by (a) typical wet‐ and dry‐season fields of temperature, wind, water vapour and pressure produced by a mesoscale atmospheric model with a 3 × 3 km2 spatial and hourly temporal resolution, and (b) leaf area index fields estimated over the watershed during a few years using satellite data with two different spatial and temporal resolutions. The mesoscale model estimates of spatially distributed surface hydrometeorological variables provide the basis for the extrapolation of the surface climate station data to produce input for the Penman–Monteith equation. The satellite information and existing digital spatial databases of land use and land cover form the basis for the estimation of Penman–Monteith spatially distributed parameter values. Spatially distributed 3 × 3 km2 potential evapotranspiration estimates are obtained for the 3300 km2 Panama Canal watershed. Estimates for Gatun Lake within the watershed are found to reproduce well the monthly and annual lake evaporation obtained from submerged pans. Sensitivity analysis results of potential evapotranspiration estimates with respect to cloud cover, dew formation, leaf area index distribution and mesoscale model estimates of surface climate are presented and discussed. The main conclusion is that even the limited spatially distributed hydrometeorological and plant information used in this study contributes significantly toward explaining the substantial spatial variability of potential evapotranspiration in the watershed. These results also allow the determination of key locations within the watershed where additional surface stations may be profitably placed. Copyright © 2006 John Wiley & Sons, Ltd.

One-Way Coupling of an Atmospheric and a Hydrologic Model in Colorado

Abstract This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them.

The development of 1901–2000 historical monthly climate models for Canada and the United States

Here we present the results of an intensive modelling effort that generated 100 years (1901–2000) of monthly precipitation, and minimum and maximum temperature grids for Canada and the United States—a total of 3600 grids. From these primary climate variables, a further 2900 grids were generated for 29 annual bioclimatic indices. Models were generated using the ANUSPLIN software package, which employs thin plate smoothing splines to develop elevation dependent spatially continuous climate surfaces from noisy weather station data. As a rigorous test of the predictive accuracy of the historical surfaces, we carried out validation runs in which climate station data were withheld from the model building process. The magnitude of the withheld errors appear reasonable, ranging from about 1 to 1.5 °C for temperature and 20–40% for precipitation on average. Predictive accuracy of the surfaces varied over time, with a modest improvement as the century progressed and the climate station network expanded. There was also considerable spatial variation in predictive accuracy across the continent. Not surprisingly, mountainous and coastal regions exhibited the highest errors for all climate variables, and error magnitude was closely related to the mean of the underlying climate variable. Though not developed specifically for climate change analysis, continental-scale trends calculated from our grids were comparable to those reported in the literature for several climate variables. The grids have already been used in a variety of applications and are available by contacting the senior author.

GLOBAL MODELS FOR PREDICTING WOODY PLANT RICHNESS FROM CLIMATE: DEVELOPMENT AND EVALUATION

There have been few attempts to generate global models of climate–richness relationships, and fewer still that aim to predict richness rather than fitting a model to data. One such model, grounded on theory (biological relativity to water–energy dynamics) is the interim general model (IGM1) of the climatic potential for woody plant richness. Here we present a second-generation model (IGM2), and genus and family versions of both models. IGM1 describes horizontal climate–richness relationships based on climate station data and systematic species range maps, with IGM2 additionally incorporating vertical changes in climate due to topographic relief. The IGMs are mathematical transformations of empirical relationships obtained for the southern subcontinent of Africa, whereby the re-described regression models apply to the full range of global variation in all independent climate parameters. We undertake preliminary validation of the new IGMs, first by mapping the distribution and relative spatial variation in forecasted richness (per 25 000 km2) across the continent of Africa, then by evaluating the precision of forecasted values (actual vs. predicted) for an independent study system, the woody plants of Kenya. We also compare the IGMs with a recent example of purely statistical regression models of climate–richness relationships; namely, the “global” model of A. P. Francis and D. J. Currie for angiosperm family richness. We conclude that the IGMs are globally applicable and can provide a fundamental baseline for systematically estimating differences in (woody) plant richness and for exploring the hierarchy of subordinate relationships that should also contribute to differences in realized richness (mostly at more discrete scales of analysis). Further, we found that the model of Francis and Currie is useful for predicting angiosperm richness in Africa, on a conditional basis (somewhere, sometime); we examined the relationship that it describes between climate and richness. Lastly, we found that indices of available soil water used in “water-budget” or “water-balance” analyses are not proxies for available liquid water as a function of climatological dynamics.

Predicted short-term radial-growth changes of trees based on past climate on Vancouver Island, British Columbia

Tree-ring radial expansion estimator (TREE) is an integrated radial growth model that allows users to define short-term climate change scenarios to anticipate the impact upon mature trees found growing at high elevation on Vancouver Island, British Columbia. Five individualistic models were built to represent the radial growth behaviour of mountain hemlock ( Tsuga mertensiana (Bong.) Carr), yellow-cedar ( Chamaecyparis nootkatensis (D. Don) Spach), western red-cedar ( Thuja plicata Donn), Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco), and western hemlock ( Tsuga heterophylla (Raf.) Sarg.) trees. The models were developed on climate-radial growth relationships incorporating Nanaimo climate station data, and were able to explain from 55 to 68 per cent of the variance in radial growth. The models can be run with modifications to yearly precipitation and temperature variables, giving the user the ability to investigate the radial-growth impacts of a wide range of possible climate change scenarios. Results from eight such scenarios show that species growing within their ecological limits illustrate a limited change in radial growth to forecasted climate, while species growing at an ecotonal boundary are usually very sensitive to a specific climate variables (e.g., July temperature). A forecasted alteration to this key variable will then radically alter the radial-growth rate of the species.

Three‐Dimensional Neurointerpolation of Annual Mean Precipitation and Temperature Surfaces for China

Climatic variables such as annual mean precipitation and temperature display complex and nonlinear variation with latitude, longitude, and elevation. Neural networks are universal approximators and very good at detecting and representing nonlinear relationships between dependent and independent variables. In this paper we use resilient backpropagation (Rprop) neural networks to interpolate annual mean precipitation and temperature surfaces for China. Climate surfaces are interpolated from a total of 288 long‐term climate station data points using latitude, longitude, and elevation derived from a 5‐kilometer resolution digital elevation model. Initial trials of Rprop suggested very fast learning, insensitivity to selection of learning parameters, and a tendency not to overtrain. Cross‐validation was used to determine the best network structure and assess the error inherent in climate interpolation. With the error explicit, the final neurointerpolations of annual mean precipitation and temperature were constructed using all 288 climate station data points. Maps of residuals are also presented. The neurointerpolation of temperature was very successful and captures most of the regional trends found in established climate maps of China as well as significant topographically defined detail. For annual mean temperature the Rprop neural network was found to be an accurate and robust global spatial interpolator. However, the precipitation surface captures only the major latitudinally and continentally defined trends and misses many subregional rainfall features probably because of the influence of other nonparameterized atmospheric and topographic factors.

Climate and the limits of permafrost: a zonal analysis

AbstractThis paper defines the climatic and environmental conditions that determine the limits and continuity of permafrost occurrence, in the Canadian context. The analysis utilizes a functional model that links air, surface and permafrost temperature through seasonal surface transfer functions and subsurface thermal properties. The temperature of permafrost (TTOP) results from the interplay between the air temperature, the nival (snow) offset and the thermal offset. These offset values vary systematically and geographically with freezing and thawing indices, snow cover conditions and ground thermal properties. These effects are analysed by calculating offset and TTOP values using Canadian climate station data for air temperature and snowfall. Whilst permafrost is ultimately a climatic phenomenon, the ground thermal conductivity ratio, via the thermal offset, is shown to be the critical factor in determining the southernmost extent of (discontinuous) permafrost. In contrast, snow cover, via the nival offset, is the critical factor in determining the northern limit of discontinuous permafrost (i.e. southern limit of continuous permafrost). Calculated TTOP values increase gradually southwards towards the limit of permafrost occurrence, as the effect of a rising mean annual air temperature (MAAT) is counteracted by an increasing thermal offset. This results in a diffuse geographical transition in the disappearance of permafrost. In contrast, there is a more abrupt transition to continuous permafrost at the northern limit of the discontinuous zone, associated with geographical changes in snow cover and the associated nival offset. The transition from discontinuous to continuous permafrost occurs between a MAAT of −6° to −8°C. This may explain the air temperature limit for continuous permafrost cited by previous authors. Copyright © 2002 John Wiley & Sons, Ltd.

Quantitative and Qualitative Assessment of the Accuracy of Neurointerpolated Annual Mean Precipitation and Temperature Surfaces for China

This study quantitatively and qualitatively compares new annual mean precipitation and temperature surfaces for China produced using neurointerpolation (Bryan and Adams, in press) with Gestalt climate maps (Editorial Committee of the Physical Geography of China, 1995) at a variety of scales. The new mean precipitation and temperature maps display good broad scale correspondence with the Gestalt maps. However, correspondence decreases at increasingly finer scales. The new climate maps capture substantial fine-scale spatial variation seemingly driven by topographic influences and continentality that is not present on the Gestalt maps. The precipitation map however, lacks important subregional scale variation in several areas. It is concluded that whilst climate maps may quite readily be produced by neurointerpolation, it is still imperative to validate the new maps with Gestalt maps which consider a more complex set of indicators than climate station and topographic data alone.

A modified pool permutation procedure for short regional climate model simulations nested within analyzed fields

Short regional climate model simulations are routinely compared with observations, though because of extremely small sample sizes (often only a single season or year is simulated) it is generally difficult to establish whether any model‐reality differences are statistically significant. In the following, a permutation technique is proposed to estimate the statistical significance of similarities between spatial fields as simulated by a regional climate model, and observations, when the climate model is nested within operational analyses (i.e. “perfect” lateral boundary conditions). To illustrate the technique, we consider a short simulation over the Mackenzie River Basin of northwestern Canada made using the Canadian Regional Climate Model, and focus our attention on accumulated monthly precipitation and monthly average screen temperature. For comparison we have a 45 year gridded, monthly climate dataset produced by the Meteorological Service of Canada over the same region, based on adjusted operational climate station data.

Utilizing Multiple Datasets for Snow-Cover Mapping

Snow-cover maps generated from surface data are based on direct measurements. However, they are prone to interpolation errors where climate stations are sparsely distributed. Snow cover is clearly discernible using satellite-obtained optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Satellite-obtained passive microwave data, compared with optical data, is relatively unaffected by clouds; however, the snow-cover signature is significantly affected by melting snow and the microwaves may be transparent to thin snow (<3 cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies. This paper describes a method that combines ground and satellite-derived optical and passive microwave data to produce a multiple-dataset snow-cover product. Comparisons with current snow-cover products show that the multiple-dataset product draws together the advantages of each of its component products while minimizing the potential errors. Improved estimates of the snow-covered area are derived through the addition of two snow-cover classes (“thin or patchy” and “high elevation” snow cover) and from the analysis of the climate station data within each class. The compatibility of this method for use with Moderate Resolution Imaging Spectroradiometer data, which will be available in 1999, and with Advanced Microwave Scanning Radiometer data, available in 2000, is also discussed. With the assimilation of these data, the resolution of the multiple-dataset product would be improved both spatially and temporally and the analysis would become completely automated.

Slopes of glacier ELAs in the Southern Alps of New Zealand in relation to atmospheric circulation patterns

The links between climate and glaciers of the Southern Alps are investigated by examining the relationships between atmospheric circulation and glacier mass balance changes, as measured by end-of-summer snowlines (ELAs) over a 21-year period on 48 index glaciers. Trend surfaces are fitted to the ELAs data for each mass balance year, and the elevation and slope of each surface is computed. These are compared to atmospheric pressure anomaly maps generated for the southwest Pacific from long-period climate station data. The results show that atmospheric circulation patterns exert a strong control on elevation and slope of the trend surfaces. A simple “sloping roof” model is suggested to represent the ELAs surface, with its tilt across the Southern Alps varying from year to year, depending upon the nature of the atmospheric circulation. Steeper sloping trend surfaces across the Southern Alps are associated with anomalous southwest to westerly flow, whereas less steep slopes are associated with anomalous airflow from the south, southeast, and easterly directions.

Global analysis of empirical relations between annual climate and seasonality of NDVI

This paper describes the use of satellite data to calibrate a new climate vegetation greenness relation for global change studies. We examined statistical relations between annual climate indexes (temperature, precipitation, and surface radiation) and seasonal attributes of the AVHRR Normalized Difference Vegetation Index (NDVI) time series for the mid-1980s in order to refine our understanding of intra-annual patterns and global controls on natural vegetation dynamics. Multiple linear regression results using global 1 gridded data sets suggest that three climate indexes: degree days (growing/chilling), annual precipitation total, and an annual moisture index together can account to 70-80% of the geographical variation in the NDVI seasonal extremes (maximum and minimum values) for the calibration year 1984. Inclusion of the same annual climate index values from the previous year explains no substantial additional portion of the global scale variation in NDVI seasonal extremes. The monthly timing of NDVI extremes is closely associated with seasonal patterns in maximum and minimum temperature and rainfall, with lag times of 1 to 2 months. We separated well-drained areas from 1 grid cells mapped as greater than 25% inundated coverage for estimation of both the magnitude and timing of seasonal NDVI maximum values. Predicted monthly NDVI, derived from our climate-based regression equations and Fourier smoothing algorithms, shows good agreement with observed NDVI for several different years at a series of ecosystem test locations from around the globe. Regions in which NDVI seasonal extremes are not accurately predicted are mainly high latitude zones, mixed and disturbed vegetation types, and other remote locations where climate station data are sparse.

Climatic relationships of some New Zealand forest tree species

Abstract. A dataset of some 10 000 plots was used to describe the climatic relationships of 33 widespread New Zealand tree species. Estimates of mean annual temperature, temperature seasonality, mean annual solar radiation, and moisture balance were derived from mathematical surfaces fitted to climate station data. Plots were also categorized into five lithological classes and three drainage classes. Generalized additive models were used to examine species/environment relationships. Mean annual temperature and mean annual solar radiation are most strongly correlated with current tree distributions, followed by moisture balance, temperature seasonality, lithology, and drainage. Most broad‐leaved tree species other than Nothofagus spp. reach their greatest levels of occurrence in warm, moist environments with high solar radiation. In contrast, Nothofagus spp. generally reach their greatest levels of occurrence in cooler and/or lower insolation environments, and all have lower levels of occurrence on rhyolitic substrates which have resulted from large‐scale geomorphic disturbance, mostly over the past few thousand years. Although coniferous species have widely differing climatic optima, many are biased towards lithological classes characterized either by large‐scale geomorphic disturbance or harsh edaphic conditions. The relevance of these results to particular synecological questions is briefly discussed. Continuing adjustments in the range of slow‐dispersing Nothofagus spp. are strongly suggested, and the climatic suitability of extensive rhyolitic basins in the central North Island, from which these species are largely absent, is confirmed.

Forest pattern, climate and vulcanism in central North Island, New Zealand

Abstract. A quantitative study of relationships between forest pattern and environment in the central North Island, New Zealand, is based on forest composition data from ca. 2000 existing plots distributed throughout the forests of the region. Estimates of mean annual temperature, rainfall, and solar radiation are derived for each plot from mathematical surfaces fitted to climate station data. Estimates of the depth of the last major rhyolitic eruption, (Taupo Pumice, ca. 130 AD) are derived from isopach maps. A classification procedure is used to identify broad compositional groups. Generalised linear models are used to examine relationships between major species and climatic and other physical factors. Significant relationships are identified between the distributions of both plot groups and species, and climate, vulcanism, topography and drainage. Among these factors, temperature and/or solar radiation are indicated as major determinants of the regional forest pattern, with rainfall, topography, and drainage acting at a secondary level. The role of the Taupo Pumice eruption is more difficult to interpret, and its effects seem to have been greatly influenced by topography. Deep extensive deposits of tephra on flat‐to‐rolling sites close to the eruption centre have probably favoured the current dominance of these sites by more rapidly dispersing conifers. In contrast, on adjacent steep sites where forest destruction was likely to be less severe, slow‐dispersing Nothofagus species are largely dominant. Further work is needed to understand the factors favouring conifer dominance of the central basins and the degree to which Nothofagus species might expand their range in the future.

Modelling soil temperature in forest clearcuts using climate station data

A physically based numerical model was developed to estimate the time courses of soil temperature in forest clearcuts from measured solar irradiance, air temperature and wind speed. The model is based upon a finite difference solution of the one-dimensional soil heat flow equation with a surface boundary condition determined from aerodynamic heat transfer and energy balance theory. Modelled soil temperatures were compared with data from two other studies reported in the literature and with temperatures measured in a forest clearcut during 6 and 16 day periods in the summer. The model calculated surface and subsurface soil temperatures accurately over these long intervals. Modelled soil temperatures were relatively insensitive to air temperature, soil thermal properties and the lower boundary soil temperature but quite sensitive to solar irradiance, wind speed and surface roughness.