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 higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright  2005 Royal Meteorological Society.

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Very high resolution interpolated climate surfaces for global land areas.

Basics of qualitative research: Grounded theory procedures and techniques

https://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf

Maximum entropy modeling of species geographic distributions

[1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) 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 reduced-space 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, containing SST fields with more uniform variance through time and better month-to-month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2002JD002670

Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century

Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

A co-evolutionary approach to climate change impact assessment (II): A scenario-based case study in the UK

On the 1 January 2021 a new World Meteorological Organisation (WMO) climatological standard normal comes into effect. The ‘present-day’ climate will formally be represented by the meteorological statistics of the period 1991-2020, replacing those from 1961-1990. National Meteorological Agencies in member states are instructed to issue new standard normals for observing stations and for associated climatological products. Climate will ‘change’, one might say, in an instant; the world’s climate will ‘suddenly’ become nearly 0.5°C warmer. It is somewhat equivalent to re-setting Universal Time or adjusting the exact definition of a metre.

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Climates Multiple: Three Baselines, Two Tolerances, One Normal

Bridges is the free, online magazine of the OSTA published by Office of Science & Technology Austria, located in Washington D.C.

Climate change and the assessment of expert knowledge: Does the ipcc model need updating?

Changes in extremes are important indicators of climate change. Zhang et al. (2001) show that a change in the mean of a Gaussian distribution results in an increase in the total number of extremes. So if there is an increase in the total of extreme values, such as values more extreme than the 1st and 99th percentiles, then it does not necessarily follow that the standard deviation of the distribution has changed. We support Zhang et al.’s hypothesis for Gaussian distributions, but show, in addition, that a change in the mean of a skewed distribution can either increase or decrease the total number of extremes. For a Gaussian distribution the total percentage of values classed as extreme increases continuously as the mean is increased. This is because the Gaussian distribution is symmetrical about p = 50%. For any value of p, f( p)= f( 100 − p). On increasing the mean of the sample by q, the frequency of low values decreases by

The Effect of Increasing the Mean on the Percentage of Extreme Values in Gaussian and Skew Distributions. Response to X. Zhang et al.

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Mike Hulme

Papers

A co-evolutionary approach to climate change impact assessment (II): A scenario-based case study in the UK

Climates Multiple: Three Baselines, Two Tolerances, One Normal

Climate change and the assessment of expert knowledge: Does the ipcc model need updating?

The Effect of Increasing the Mean on the Percentage of Extreme Values in Gaussian and Skew Distributions. Response to X. Zhang et al.

WIREs Climate Change 2018: An editorial essay