Willem A. Landman

Bio: Willem A. Landman is an academic researcher from University of Pretoria. The author has contributed to research in topics: Forecast skill & Downscaling. The author has an hindex of 34, co-authored 125 publications receiving 8453 citations. Previous affiliations of Willem A. Landman include Lamont–Doherty Earth Observatory & South African Weather Service.

Climate change 2007: the physical science basis

Abstract: S. Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H. LeRoy Miller, Jr. and Z. Chen, Cambridge, Cambridge University Press, 2007, 996 pp. (paperback), ISBN-978-1-57718-033-3 This...

Climate change 2007 : the physical science basis, S. Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H. LeRoy Miller, Jr. and Z. Chen (Eds.) : book review

Abstract: This contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change is the most comprehensive scientific assessments of climate change during the past (the climate during periods before the development of measuring instruments, including historic and geological time), the present (the average weather over a number of recent decades) and the future (projected long-term average weather changes due to changes in atmospheric composition or other factors). The book also provides an excellent overview on how the science of climate change has progressed, including the methods used, and also shows the recent advances made in the modelling of regional climate change over the African continent. Moreover, the scientific understanding of anthropogenic effects on global climate has improved since the Third Assessment Report, which has led to very high confidence that the global average net effect of human activities over the past 250 years has been one of warning.

Climate and southern Africa's water-energy-food nexus

Abstract: In southern Africa, the connections between climate and the water–energy–food nexus are strong. Physical and socioeconomic exposure to climate is high in many areas and in crucial economic sectors. Spatial interdependence is also high, driven, for example, by the regional extent of many climate anomalies and river basins and aquifers that span national boundaries. There is now strong evidence of the effects of individual climate anomalies, but associations between national rainfall and gross domestic product and crop production remain relatively weak. The majority of climate models project decreases in annual precipitation for southern Africa, typically by as much as 20% by the 2080s. Impact models suggest these changes would propagate into reduced water availability and crop yields. Recognition of spatial and sectoral interdependencies should inform policies, institutions and investments for enhancing water, energy and food security. Three key political and economic instruments could be strengthened for this purpose: the Southern African Development Community, the Southern African Power Pool and trade of agricultural products amounting to significant transfers of embedded water.

Seasonal to Decadal Prediction of Southern African Climate and Its Links with Variability of the Atlantic Ocean

Abstract: A review of the interannual to interdecadal variability of the southern African region and its links with the Atlantic is given. Emphasis is placed on modes such as the Benguela Nĩno that develop within the Atlantic and may have some predictability. Seasonal forecasting and climate prediction efforts within the region are discussed. Most southern African countries rely on a combination of products obtained overseas and simple statistical methods. GCM based forecasts and statistical downscaling of their outputs are used operationally in South Africa and also applied to some neighboring countries. A review of these downscaling efforts and their various applications is given. Research is also taking place into the predictability of quantities such as the onset of the rainy season (which appears to be associated with anomalous South Atlantic anticyclonic ridging) and dry spell frequencies within it. These parameters are often more useful to farmers in the region than forecasting above- or belowaverage seasona...

Multi-scale climate modelling over Southern Africa using a variable-resolution global model

Abstract: Evidence is provided of the successful application of a single atmospheric model code at time scales ranging from shortrange weather forecasting through to projections of future climate change, and at spatial scales that vary from relatively low-resolution global simulations, to ultra-high resolution simulations at the micro-scale. The model used for these experiments is a variable-resolution global atmospheric model, the conformal-cubic atmospheric model (CCAM). It is shown that CCAM may be used to obtain plausible projections of future climate change, as well as skilful forecasts at the seasonal and short-range time scales, over the Southern African region. The model is additionally applied for extended simulations of present-day climate at spatial scales ranging from global simulations at relatively low horizontal resolution, to the micro-scale at ultra-high (1 km) resolution. Applying the atmospheric model at the shorter time scales provides the opportunity to test its physical parameterisation schemes and its response to fundamental forcing mechanisms (e.g. ENSO). The existing skill levels at the shorter time scales enhance the confidence in the model projections of future climate change, whilst the related verification studies indicate opportunities for future model improvement.

Planetary boundaries: Guiding human development on a changing planet

Abstract: The planetary boundaries framework defines a safe operating space for humanity based on the intrinsic biophysical processes that regulate the stability of the Earth system. Here, we revise and update the planetary boundary framework, with a focus on the underpinning biophysical science, based on targeted input from expert research communities and on more general scientific advances over the past 5 years. Several of the boundaries now have a two-tier approach, reflecting the importance of cross-scale interactions and the regional-level heterogeneity of the processes that underpin the boundaries. Two core boundaries—climate change and biosphere integrity—have been identified, each of which has the potential on its own to drive the Earth system into a new state should they be substantially and persistently transgressed.

Updated high‐resolution grids of monthly climatic observations – the CRU TS3.10 Dataset

Abstract: This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). © 2013 Royal Meteorological Society

Global food demand and the sustainable intensification of agriculture

Abstract: Global food demand is increasing rapidly, as are the environmental impacts of agricultural expansion. Here, we project global demand for crop production in 2050 and evaluate the environmental impacts of alternative ways that this demand might be met. We find that per capita demand for crops, when measured as caloric or protein content of all crops combined, has been a similarly increasing function of per capita real income since 1960. This relationship forecasts a 100–110% increase in global crop demand from 2005 to 2050. Quantitative assessments show that the environmental impacts of meeting this demand depend on how global agriculture expands. If current trends of greater agricultural intensification in richer nations and greater land clearing (extensification) in poorer nations were to continue, ∼1 billion ha of land would be cleared globally by 2050, with CO2-C equivalent greenhouse gas emissions reaching ∼3 Gt y−1 and N use ∼250 Mt y−1 by then. In contrast, if 2050 crop demand was met by moderate intensification focused on existing croplands of underyielding nations, adaptation and transfer of high-yielding technologies to these croplands, and global technological improvements, our analyses forecast land clearing of only ∼0.2 billion ha, greenhouse gas emissions of ∼1 Gt y−1, and global N use of ∼225 Mt y−1. Efficient management practices could substantially lower nitrogen use. Attainment of high yields on existing croplands of underyielding nations is of great importance if global crop demand is to be met with minimal environmental impacts.

A statistical explanation of MaxEnt for ecologists

Abstract: MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence-only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south-west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.