Willem A. Landman

Climate change 2007: the physical science basis

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. The book is divided into a summary for policymakers, a detailed technical summary, followed by technical chapters that deal with various aspects of climate change science, including a historical overview, observed changes in the atmosphere, cryosphere and oceans, the climate models used and global climate projections, understanding and attributing climate change and regional climate projections. The summary for policymakers contains easily understandable illustrations and tables and makes clear statements referring to the latest findings. For example, based on the observations of increases in global average air and ocean temperatures, sea level rise, etc., the warming of the climate system is unequivocal. The 73-page technical summary is a comprehensive overview of the detailed technical chapters. A very useful component of this part of the book is a listing and description of the robust findings as well as the key uncertainties of the current understanding of the complexities of global climate change. The chapters that follow contain a large number of excellent illustrations and tables, and each chapter is concluded with several pages of references. Summary statements, including regional responses to climate change, the definitions of circulation indices such as the Southern Annular Mode, etc., are presented in ‘boxes’ of text in the chapters. The chapters also contain answers to frequently asked questions, such as whether or not sea levels are rising, and whether or not the warming of the twentieth century can be explained by natural variability. The book provides strong evidence to support statements such as: (a) global atmospheric concentrations of greenhouse gasses and aerosols have increased markedly due to human activities, (b) warming of the climate system is unequivocal, and at continental, regional and ocean basin scales, numerous long-term changes in climate have

Statistical Recalibration of GCM Forecasts over Southern Africa Using Model Output Statistics

A technique for producing regional rainfall forecasts for southern Africa is developed that statistically maps or ‘‘recalibrates’’ large-scale circulation features produced by the ECHAM3.6 general circulation model (GCM) to observed regional rainfall for the December‐February (DJF) season. The recalibration technique, model output statistics (MOS), relates archived records of GCM fields to observed DJF rainfall through a set of canonical correlation analysis (CCA) equations. After screening several potential predictor fields, the 850-hPa geopotential height field is selected as the single predictor field in the CCA equations that is subsequently used to produce MOS-recalibrated rainfall patterns. The recalibrated forecasts outscore area-averaged GCM-simulated rainfall anomalies, as well as forecasts produced using a simple linear forecast model. The MOS recalibration is applied to two sets of GCM experiments: for the ‘‘simulation’’ experiment, simultaneous observed sea surface temperature (SST) serves as the lower boundary forcing; for the ‘‘hindcast’’ experiment, the prescribed SSTs are obtained by persisting the previous month’s SST anomaly through the forecast period. Pattern analyses performed on the predictor‐predictand pairs confirm a robust relationship between the GCM 850-hPa height fields and the rainfall fields. The structure and variability of the large-scale circulation is well characterized by the GCM in both simulation and hindcast mode. Measures of retroactive skill for a 9-yr independent period (1991/92‐1999/2000) using the hindcast MOS are obtained for both deterministic and probabilistic forecasts, suggesting that a probabilistic representation of MOS forecasts is potentially more valuable. Finally, MOS is employed to investigate its potential to downscale the GCM large-scale circulation to more specific forecasts of land surface characteristics such as streamflow.

The Effect of Regional Climate Model Domain Choice on the Simulation of Tropical Cyclone-Like Vortices in the Southwestern Indian Ocean

A regional climate model is tested for several domain configurations over the southwestern Indian Ocean to examine the ability of the model to reproduce observed cyclones and their landfalling tracks. The interaction between large-scale and local terrain forcing of tropical storms approaching and transiting the island landmass of Madagascar makes the southwestern Indian Ocean a unique and interesting study area. In addition, tropical cyclones across the southern Indian Ocean are likely to be significantly affected by the large-scale zonal flow. Therefore, the effects of model domain size and the positioning of its lateral boundaries on the simulation of tropical cyclone–like vortices and their tracks on a seasonal time scale are investigated. Four tropical cyclones, which occurred over the southwestern Indian Ocean in January of the years 1995–97, are studied, and four domains are tested. The regional climate model is driven by atmospheric lateral boundary conditions that are derived from large-scale meteorological analyses. The use of analyzed boundary forcing enables comparison with observed cyclones in these tests. Simulations are performed using a 60-km horizontal resolution and for an extended time integration of about 6 weeks. Results show that the positioning of the eastern boundary of the regional model domain is of major importance in the life cycle of simulated tropical cyclone–like vortices: a vortex entering through the eastern boundary of the regional model is generally well simulated. The size of the domain also has a bearing on the ability of the regional model to simulate vortices in the Mozambique Channel, and the island landmass of Madagascar additionally influences storm tracks. These results show that the regional model can produce cyclonelike vortices and their tracks (with some deficiencies) given analyzed lateral boundary forcing. Statistical analyses of GCM-driven nested model ensemble integrations are now required to further address predictive skill of cyclones in the southwestern Indian Ocean and to test if the model can realistically simulate tropical storm genesis as opposed to advecting existing tropical disturbances entering through the model boundaries.

Operational long-lead prediction of South African rainfall using canonical correlation analysis

A statistically based technique is used to study the variability and predictability of South African summer rainfall. The country is divided into homogeneous regions on the basis of the interannual rainfall variability. Canonical variates are then used to make 3-month aggregate precipitation forecasts for October–November–December and January–February–March for South Africa from global-scale sea-surface temperatures. Four consecutive 3-month mean periods of sea-surface temperatures are used to incorporate evolutionary features as well as steady-state conditions in the global oceans. Levels and possible origins of forecast skill are investigated for up to 5-month lead-times. Modest skill (correlation >0.5) is found over mainly the central and western interiors of the country, but the skill is poor over the north-eastern regions. The most important contribution of the prediction skill comes from the equatorial Pacific Ocean, with weaker predictability from the equatorial Indian and Atlantic oceans. Sea-surface temperatures in the Atlantic and Indian oceans have important influences on the atmospheric circulation and moisture fluxes over southern Africa, and therefore provide useful predictability, at least for the October–December rainfall. When forecasting South African rainfall, it is insufficient to consider only the El Nino–Southern Oscillation (ENSO) phenomenon, because it does not occur every year and because the sea-surface temperatures of the adjacent oceans modify the ENSO forcing on South African rainfall. Unfortunately, the predictability during years not associated with the ENSO is weak. Copyright

Seasonal Rainfall Prediction Skill over South Africa: One- versus Two-Tiered Forecasting Systems

Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface temperature (SST) anomalies are used to force the atmospheric general circulation model. Two coupled models and one two-tiered model are considered here, and they are, respectively, the ECHAM4.5–version 3 of the Modular Ocean Model (MOM3-DC2), the ECHAM4.5-GML–NCEP Coupled Forecast System (CFSSST), and the ECHAM4.5 atmospheric model that is forced with SST anomalies predicted by a statistical model. The 850-hPa geopotential height fields of the three models are statistically downscaled to South African Weather Service district rainfall data by retroactively predicting 3-month seasonal rainfall totals over the 14-yr period from 1995/96 to 2008/09. Retroactive forecasts are produced for lead times of up to 4 months, and probabilistic forecast performance is evaluated for three categories with the outer two categories, respectively, defined by the 25th and 75th percentile values of the climatological record. The resulting forecast skill levels are also compared with skill levels obtained by downscaling forecasts produced by forcing the atmospheric model with simultaneously observed SST in order to produce a reference forecast set. Downscaled forecasts from the coupled systems generally outperform the downscaled forecasts from the twotiered system, but neither of the two systems outscores the reference forecasts, suggesting that further improvement in operational seasonal rainfall forecast skill for South Africa is still achievable.

Forecasts of Near-Global Sea Surface Temperatures Using Canonical Correlation Analysis

The skill of global-scale sea surface temperature forecasts using a statistically based linear forecasting technique is investigated. Canonical variates are used to make monthly sea surface temperature anomaly forecasts using evolutionary and steady-state features of antecedent sea surface temperatures as predictors. Levels of forecast skill are investigated over several months’ lead time by comparing the model performance with a simple forecast strategy involving the persistence of sea surface temperature anomalies. Forecast skill is investigated over an independent test period of 18 yr (1982/83‐1999/2000), for which the model training period was updated after every 3 yr. Forecasts for the equatorial Pacific Ocean are a significant improvement over a strategy of random guessing, and outscore forecasts of persisted anomalies beyond lead times of about one season during the

Statistical downscaling of monthly forecasts

Canonical correlation analysis (CCA) is used to downscale large-scale circulation forecasts by the Centre for Ocean‐Land‐Atmosphere studies (COLA) T30 general circulation model (GCM) statistically to regional rainfall in South Africa. Monthly GCM ensemble forecasts available from 1979 to 1995 have been generated using NCEP reanalysis data as initial input and globally observed sea-surface temperature (SST) data at the lower boundary. Altogether, 51 30-day cases of GCM simulations, spanning 17 years, within the target season of December‐February (DJF), are produced. This period is very important for agriculture and maize, in particular. A model output statistics (MOS) procedure is used to downscale GCM forecast sea-level pressure and 500 hPa height fields to regional rainfall for 30-day periods over South Africa. The CCA model is trained on the first 31 cases (up to February 1989) and forecasts are subsequently made for the remaining 20 cases. These retro-active real-time forecasts have a high potential (correlations \0.5) over most of the interior of South Africa and, furthermore, the prediction of extreme events seems feasible. CCA diagnostics of the GCM-output against rainfall reveal that favourable rainfall over most of the interior is associated with low pressure systems at the surface over the west coast, with an associated ridging high. This is supported by other observational studies. Copyright

Projected changes in tropical cyclone climatology and landfall in the Southwest Indian Ocean region under enhanced anthropogenic forcing

The conformal-cubic atmospheric model, a variable-resolution global model, is applied at high spatial resolution to perform simulations of present-day and future climate over southern Africa and over the Southwest Indian Ocean. The model is forced with the bias-corrected sea-surface temperatures and sea-ice of six coupled global climate models that contributed to Assessment Report 4 of the Intergovernmental Panel on Climate Change. All six simulations are for the period 1961–2100, under the A2 emission scenario. Projections for the latter part of the 21st century indicate a decrease in the occurrence of tropical cyclones over the Southwest Indian Ocean adjacent to southern Africa, as well as a northward shift in the preferred landfall position of these systems over the southern African subcontinent. A concurrent increase in January to March rainfall is projected for northern Mozambique and southern Tanzania, with decreases projected further south over semi-arid areas such as the Limpopo River Basin where these systems make an important contribution as main cause of widespread heavy rainfall. It is shown that the projected changes in tropical cyclone attributes and regional rainfall occur in relation to changes in larger scale atmospheric temperature, pressure and wind profiles of the southern African region and adjacent oceans.

Dynamical Seasonal Climate Prediction Using an Ocean–Atmosphere Coupled Climate Model Developed in Partnership between South Africa and the IRI

AbstractThe recent increase in availability of high-performance computing (HPC) resources in South Africa allowed the development of an ocean–atmosphere coupled general circulation model (OAGCM). The ECHAM4.5-South African Weather Service (SAWS) Modular Oceanic Model version 3 (MOM3-SA) is the first OAGCM to be developed in Africa for seasonal climate prediction. This model employs an initialization strategy that is different from previous versions of the model that coupled the same atmosphere and ocean models. Evaluation of hindcasts performed with the model revealed that the OAGCM is successful in capturing the development and maturity of El Nino and La Nina episodes up to 8 months ahead. A model intercomparison also indicated that the ECHAM4.5-MOM3-SA has skill levels for the Nino-3.4 region SST comparable with other coupled models administered by international centers. Further analysis of the coupled model revealed that La Nina events are more skillfully discriminated than El Nino events. However, as ...

Regional Projections of Extreme Apparent Temperature Days in Africa and the Related Potential Risk to Human Health

Regional climate modelling was used to produce high resolution climate projections for Africa, under a “business as usual scenario”, that were translated into potential health impacts utilizing a heat index that relates apparent temperature to health impacts. The continent is projected to see increases in the number of days when health may be adversely affected by increasing maximum apparent temperatures (AT) due to climate change. Additionally, climate projections indicate that the increases in AT results in a moving of days from the less severe to the more severe Symptom Bands. The analysis of the rate of increasing temperatures assisted in identifying areas, such as the East African highlands, where health may be at increasing risk due to both large increases in the absolute number of hot days, and due to the high rate of increase. The projections described here can be used by health stakeholders in Africa to assist in the development of appropriate public health interventions to mitigate the potential health impacts from climate change.