data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1 ° latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5 ° latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reducedspace optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well,

We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a

Abstract. We review the surface air temperature record of the past 150 years, considering the homogene-ity of the basic data and the standard errors of estima-tion of the average hemispheric and global estimates. We present global fields of surface temperature change over the two 20-year periods of greatest warming this century, 1925-1944 and 1978-1997. Over these periods, global temperatures rose by 0.37 ø and 0.32øC, respec-tively. The twentieth-century warming has been accom-panied by a decrease in those areas of the world affected by exceptionally cool temperatures and to a lesser extent by increases in areas affected by exceptionally warm temperatures. In recent decades there have been much greater increases in night minimum temperatures than in day maximum temperatures, so that over 1950-1993 the diurnal temperature range has decreased by 0.08øC per decade. We discuss the recent divergence of surface and satellite temperature measurements of the lower troposphere and consider the last 150 years in the con-text of the last millennium. We then provide a globally complete absolute surface air temperature climatology on a 1 ø x 1 ø grid. This is primarily based on data for 1961-1990. Extensive interpolation had to be under-taken over both polar regions and in a few other regions where basic data are scarce, but we believe the climatol-ogy is the most consistent and reliable of absolute sur-face air temperature conditions over the world. The climatology indicates that the annual average surface temperature of the world is 14.0øC (14.6øC in the North-ern Hemisphere (NH) and 13.4øC for the Southern Hemisphere). The annual cycle of global mean temper-atures follows that of the land-dominated NH, with a maximum in July of 15.9øC and a minimum in January of 12.2øC. 1.

We study the problem of modeling species geographic distributions, a critical problem in conservation biology. We propose the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features. We describe experiments comparing maxent with a standard distribution-modeling tool, called GARP, on a dataset containing observation data for North American breeding birds. We also study how well maxent performs as a function of the number of training examples and training time, analyze the use of regularization to avoid overfitting when the number of examples is small, and explore the interpretability of models constructed using maxent. 1.

Freshwater availability has been recognized as a global issue, and its consistent quantification not only in individual river basins but also at the global scale is required to support the sustainable use of water. The WaterGAP Global Hydrology Model WGHM, which is a submodel of the global water use and availability model WaterGAP 2, computes surface runoff, groundwater recharge and river discharge at a spatial resolution of 0.58. WGHM is based on the best global data sets currently available, and simulates the reduction of river discharge by human water consumption. In order to obtain a reliable estimate of water availability, it is tuned against observed discharge at 724 gauging stations, which represent 50 % of the global land area and 70 % of the actively discharging area. For 50 % of these stations, the tuning of one model parameter was sufficient to achieve that simulated and observed long-term average discharges agree within 1%. For the rest, however, additional corrections had to be applied to the simulated runoff and discharge values. WGHM not only computes the long-term average water resources of a country or a drainage basin but also water availability indicators that take into account the interannual and seasonal variability of runoff and discharge. The reliability of the modeling results is assessed by comparing observed and simulated discharges at the tuning stations and at selected other stations. The comparison shows that WGHM is able to calculate reliable and meaningful indicators of water availability at a high spatial resolution. In particular, the 90 % reliable monthly discharge is simulated well. Therefore, WGHM is suited for application in global assessments related to water security, food security and freshwater ecosystems.

Ecoclimap, a new complete surface parameter global dataset at a 1-km resolution, is presented. It is intended to be used to initialize the soil–vegetation–atmosphere transfer schemes (SVATs) in meteorological and climate models (at all horizontal scales). The database supports the ‘‘tile’ ’ approach, which is utilized by an increasing number of SVATs. Two hundred and fifteen ecosystems representing areas of homogeneous vegetation are derived by combining existing land cover maps and climate maps, in addition to using Advanced Very High Resolution Radiometer (AVHRR) satellite data. Then, all surface parameters are derived for each of these ecosystems using lookup tables with the annual cycle of the leaf area index (LAI) being constrained by the AVHRR information. The resulting LAI is validated against a large amount of in situ ground observations, and it is also compared to LAI derived from the International Satellite Land Surface Climatology Project (ISLSCP-2) database and the Polarization and Directionality of the Earth’s Reflectance (POLDER) satellite. The comparison shows that this new LAI both reproduces values coherent at large scales with other datasets, and includes the high spatial variations owing to the input land cover data at a 1-km resolution. In terms of climate modeling studies, the use of this new database is shown to improve the surface climatology of the ARPEGE climate model. 1.

Abstract. We consider the problem of estimating an unknown probability distribution from samples using the principle of maximum entropy (maxent). To alleviate overfitting with a very large number of features, we propose applying the maxent principle with relaxed constraints on the expectations of the features. By convex duality, this turns out to be equivalent to finding the Gibbs distribution minimizing a regularized version of the empirical log loss. We prove nonasymptotic bounds showing that, with respect to the true underlying distribution, this relaxed version of maxent produces density estimates that are almost as good as the best possible. These bounds are in terms of the deviation of the feature empirical averages relative to their true expectations, a number that can be bounded using standard uniform-convergence techniques. In particular, this leads to bounds that drop quickly with the number of samples, and that depend very moderately on the number or complexity of the features. We also derive and prove convergence for both sequential-update and parallel-update algorithms. Finally, we briefly describe experiments on data relevant to the modeling of species geographical distributions. 1

A land surface scheme that may be run with or without a tiled representation of subgrid heterogeneity and includes an implicit atmospheric coupling scheme is described. Simulated average surface air temperatures and diurnal temperature ranges in a GCM using this surface model are compared with climatology. Surface tiling is not found to give a clear improvement in the simulated climate but offers more flexibility in the representation of heterogeneous land surface processes. Using the same meteorological forcing in offline simulations using versions of the surface model with and without tiling, the tiled model gives slightly lower winter temperatures at high latitudes and higher summer temperatures at midlatitudes. When the surface model is coupled to a GCM, reduced evaporation in the tiled version leads to changes in cloud cover and radiation at the surface that enhance these differences. 1.

climate change

Spatial climate data are often key drivers of computer models and statistical analyses, which form the basis for scientific conclusions, management decisions, and other important outcomes. The recent availability of very high-resolution climate data sets raises important questions about the tendency to equate resolution with realism. This paper discusses the relationship between scale and spatial climate-forcing factors, and provides background and advice on assessing the suitability of data sets. Spatial climate patterns are most affected by terrain and water bodies, primarily through the direct effects of elevation, terrain-induced climate transitions, cold air drainage and inversions, and coastal effects. The importance of these factors is generally lowest at scales of 100 km and greater, and becomes greatest at less than 10 km. Except in densely populated regions of developed countries, typical station spacing is on the order of 100 km. Regions without major terrain features and which are at least 100 km from climatically important coastlines can be handled adequately by most interpolation techniques. Situations characterized by significant terrain features, but with no climatically important coastlines, no rain shadows, and a well-mixed atmosphere can be reasonably handled by methods that explicitly account for elevation effects. Regions having significant terrain features, and also significant coastal effects, rain shadows, or cold air drainage and inversions are best handled by sophisticated systems that are configured and evaluated by experienced climatologists. There is no one satisfactory method for quantitatively estimating errors in