Mike Hulme

Bio: Mike Hulme is an academic researcher from University of Cambridge. The author has contributed to research in topics: Climate change & Global warming. The author has an hindex of 84, co-authored 300 publications receiving 35436 citations. Previous affiliations of Mike Hulme include Ludwig Maximilian University of Munich & Norwich University.

A high-resolution data set of surface climate over global land areas

Abstract: We describe the construction of a 10' latitude/longitude data set of mean monthly sur- face climate over global land areas, excluding Antarctica The climatology includes 8 climate ele- ments —precipitation, wet-day frequency, temperature, diurnal temperature range, relative humid- ity, sunshine duration, ground frost frequency and windspeed—and was interpolated from a data set of station means for the period centred on 1961 to 1990 Precipitation was first defined in terms of the parameters of the Gamma distribution, enabling the calculation of monthly precipitation at any given return period The data are compared to an earlier data set at 05o latitude/longitude resolution and show added value over most regions The data will have many applications in applied climatology, biogeochemical modelling, hydrology and agricultural meteorology and are available through the International Water Management Institute World Water and Climate Atlas (http://wwwiwmiorg) and the Climatic Research Unit (http://wwwcruueaacuk)

Are there social limits to adaptation to climate change

Abstract: While there is a recognised need to adapt to changing climatic conditions, there is an emerging discourse of limits to such adaptation. Limits are traditionally analysed as a set of immutable thresholds in biological, economic or technological parameters. This paper contends that limits to adaptation are endogenous to society and hence contingent on ethics, knowledge, attitudes to risk and culture. We review insights from history, sociology and psychology of risk, economics and political science to develop four propositions concerning limits to adaptation. First, any limits to adaptation depend on the ultimate goals of adaptation underpinned by diverse values. Second, adaptation need not be limited by uncertainty around future foresight of risk. Third, social and individual factors limit adaptation action. Fourth, systematic undervaluation of loss of places and culture disguises real, experienced but subjective limits to adaptation. We conclude that these issues of values and ethics, risk, knowledge and culture construct societal limits to adaptation, but that these limits are mutable.

Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate

Abstract: The authors describe the construction of a 0.58 lat‐long gridded dataset of monthly terrestrial surface climate for the period of 1901‐96. The dataset comprises a suite of seven climate elements: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapor pressure, cloud cover, and ground frost frequency. The spatial coverage extends over all land areas, including oceanic islands but excluding Antarctica. Fields of monthly climate anomalies, relative to the 1961‐90 mean, were interpolated from surface climate data. The anomaly grids were then combined with a 1961‐90 mean monthly climatology (described in Part I) to arrive at grids of monthly climate over the 96-yr period. The primary variables—precipitation, mean temperature, and diurnal temperature range—were interpolated directly from station observations. The resulting time series are compared with other coarser-resolution datasets of similar temporal extent. The remaining climatic elements, termed secondary variables,were interpolated from merged datasets comprising station observations and, in regions where there were no station data, synthetic data estimated using predictive relationships with the primary variables. These predictive relationships are described and evaluated. It is argued that this new dataset represents an advance over other products because (i) it has higher spatial resolution than other datasets of similar temporal extent, (ii) it has longer temporal coverage than other products of similar spatial resolution, (iii) it encompasses a more extensive suite of surface climate variables than available elsewhere, and (iv) the construction method ensures that strict temporal fidelity is maintained. The dataset should be of particular relevance to a number of applications in applied climatology, including large-scale biogeochemical and hydrological modeling, climate change scenario construction, evaluation of regional climate models, and comparison with satellite products. The dataset is available from the Climatic Research Unit and is currently being updated to 1998.

Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology

Abstract: The construction of a 0.5° lat × 0.5° long surface climatology of global land areas, excluding Antarctica, is described. The climatology represents the period 1961–90 and comprises a suite of nine variables: precipitation, wet-day frequency, mean temperature, diurnal temperature range, vapor pressure, sunshine, cloud cover, ground frost frequency, and wind speed. The climate surfaces have been constructed from a new dataset of station 1961–90 climatological normals, numbering between 19 800 (precipitation) and 3615 (wind speed). The station data were interpolated as a function of latitude, longitude, and elevation using thin-plate splines. The accuracy of the interpolations are assessed using cross validation and by comparison with other climatologies. This new climatology represents an advance over earlier published global terrestrial climatologies in that it is strictly constrained to the period 1961–90, describes an extended suite of surface climate variables, explicitly incorporates elevation...

Why we disagree about climate change : understanding controversy, inaction and opportunity

Abstract: Climate change is not 'a problem' waiting for 'a solution' It is an environmental, cultural and political phenomenon which is re-shaping the way we think about ourselves, our societies and humanity's place on Earth Drawing upon twenty-five years of professional work as an international climate change scientist and public commentator, Mike Hulme provides a unique insider's account of the emergence of this phenomenon and the diverse ways in which it is understood He uses different standpoints from science, economics, faith, psychology, communication, sociology, politics and development to explain why we disagree about climate change In this way he shows that climate change, far from being simply an 'issue' or a 'threat', can act as a catalyst to revise our perception of our place in the world Why We Disagree About Climate Change is an important contribution to the ongoing debate over climate change and its likely impact on our lives

Very high resolution interpolated climate surfaces for global land areas.

Abstract: 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.

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

Abstract: [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.

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

Abstract: 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