Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UKMeteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is underestimated (over-estimated) over wet (dry) regions of southern Africa.

Multimodel climate and variability of the stratosphere

The stratospheric climate and variability from simulations of sixteen chemistry‐climate models is evaluated. On average the polar night jet is well reproduced though its variability is less well reproduced with a large spread between models. Polar temperature biases are less than 5 K except in the Southern Hemisphere (SH) lower stratosphere in spring. The accumulated area of low temperatures responsible for polar stratospheric cloud formation is accurately reproduced for the Antarctic but underestimated for the Arctic. The shape and position of the polar vortex is well simulated, as is the tropical upwelling in the lower stratosphere. There is a wide model spread in the frequency of major sudden stratospheric warnings (SSWs), late biases in the breakup of the SH vortex, and a weak annual cycle in the zonal wind in the tropical upper stratosphere. Quantitatively, “metrics” indicate a wide spread in model performance for most diagnostics with systematic biases in many, and poorer performance in the SH than in the Northern Hemisphere (NH). Correlations were found in the SH between errors in the final warming, polar temperatures, the leading mode of variability, and jet strength, and in the NH between errors in polar temperatures, frequency of major SSWs, and jet strength. Models with a stronger QBO have stronger tropical upwelling and a colder NH vortex. Both the qualitative and quantitative analysis indicate a number of common and long‐standing model problems, particularly related to the simulation of the SH and stratospheric variability.

High frequency variability of the Atlantic meridional overturning circulation

We compare the variability of the Atlantic meridional overturning circulation (AMOC) as simulated by the coupled climate models of the RAPID project, which cover a wide range of resolution and complexity, and observed by the RAPID/MOCHA array at about 26N. We analyse variability on a range of timescales, from five-daily to interannual. In models of all resolutions there is substantial variability on timescales of a few days; in most AOGCMs the amplitude of the variability is of somewhat larger magnitude than that observed by the RAPID array, while the time-mean is within about 10% of the observational estimate. The amplitude of the simulated annual cycle is similar to observations, but the shape of the annual cycle shows a spread among the models. A dynamical decomposition shows that in the models, as in observations, the AMOC is predominantly geostrophic (driven by pressure and sea-level gradients), with both geostrophic and Ekman contributions to variability, the latter being exaggerated and the former underrepresented in models. Other ageostrophic terms, neglected in the observational estimate, are small but not negligible. The time-mean of the western boundary current near the latitude of the RAPID/MOCHA array has a much wider model spread than the AMOC does, indicating large differences among models in the simulation of the wind-driven gyre circulation, and its variability is unrealistically small in the models. In many RAPID models and in models of the Coupled Model Intercomparison Project Phase 3 (CMIP3), interannual variability of the maximum of the AMOC wherever it lies, which is a commonly used model index, is similar to interannual variability in the AMOC at 26N. Annual volume and heat transport timeseries at the same latitude are well-correlated within 15--45N, indicating the climatic importance of the AMOC. In the RAPID and CMIP3 models, we show that the AMOC is correlated over considerable distances in latitude, but not the whole extent of the north Atlantic; consequently interannual variability of the AMOC at 50N, where it is particularly relevant to European climate, is not well-correlated with that of the AMOC at 26N, where it is monitored by the RAPID/MOCHA array.

Multi-satellite observed responses of precipitation and its extremes to interannual climate variability

The consistency of precipitation variability estimated from the multiple satellite-based observing systems is assessed. There is generally good agreement between TRMM TMI, SSM/I, GPCP and AMSRE datasets for the inter-annual variability of precipitation since 1997 but the HOAPS dataset appears to overestimate the magnitude of variability. Over the tropical ocean the TRMM 3B42 dataset produces unrealistic variabilitys. Based upon deseasonalised GPCP data for the period 1998-2008, the sensitivity of global mean precipitation (P) to surface temperature (T) changes (dP/dT) is about 6%/K, although a smaller sensitivity of 3.6%/K is found using monthly GPCP data over the longer period 1989-2008. Over the tropical oceans dP/dT ranges from 10-30%/K depending upon time-period and dataset while over tropical land dP/dT is -8 to -11%/K for the 1998-2008 period. Analyzing the response of the tropical ocean precipitation intensity distribution to changes in T we find the wetter area P shows a strong positive response to T of around 20%/K. The response over the drier tropical regimes is less coherent and varies with datasets, but responses over the tropical land show significant negative relationships over an interannual time-scale. The spatial and temporal resolutions of the datasets strongly influence the precipitation responses over the tropical oceans and help explain some of the discrepancy between different datasets. Consistency between datasets is found to increase on averaging from daily to 5-day time-scales and considering a 1o (or coarser) spatial resolution. Defining the wet and dry tropical ocean regime by the 60th percentile of P intensity, the 5-day average, 1o TMI data exhibits a coherent drying of the dry regime at the rate of -20%/K and the wet regime becomes wetter at a similar rate with warming.

Resolving the spatial variability of soil N using fractions of soil organic matter

The spatial variability of soil nitrogen (N) mineralisation has not been extensively studied, which limits our capacity to make N fertiliser recommendations. Even less attention has been paid to the scale-dependence of the variation. The objective of this research was to investigate the scale-dependence of variation of mineral N (MinN, N–NO3− plus N–NH4+) at within-field scales. The study was based on the spatial dependence of the labile fractions of SOM, the key fractions for N mineralisation. Soils were sampled in an unbalanced nested design in a 4-ha arable field to examine the distribution of the variation of SOM at 30, 10, 1, and 0.12 m. Organic matter in free and intra-aggregate light fractions (FLF and IALF) was extracted by physical fractionation. The variation occurred entirely within 0.12 m for FLF and at 10 m for IALF. A subsequent sampling on a 5-m grid was undertaken to link the status of the SOM fractions to MinN, which showed uncorrelated spatial dependence. A uniform application of N fertiliser would be suitable in this case. The failure of SOM fractions to identify any spatial dependence of MinN suggests that other soil variables, or crop indicators, should be tested to see if they can identify different N supply areas within the field for a more efficient and environmentally friendly N management.

Evidence for the chaotic origin of Northern Annular Mode variability

Exponential spectra are found to characterize variability of the Northern Annular Mode (NAM) for periods less than 36 days. This corresponds to the observed rounding of the autocorrelation function at lags of a few days. The characteristic persistence timescales during winter and summer is found to be ∼5 days for these high frequencies. Beyond periods of 36 days the characteristic decorrelation timescale is ∼20 days during winter and ∼6 days in summer. We conclude that the NAM cannot be described by autoregressive models for high frequencies; the spectra are more consistent with low-order chaos. We also propose that the NAM exhibits regime behaviour, however the nature of this has yet to be identified.

Holocene palaeoenvironmental history and the impact of prehistoric salt production in the Seille Valley, Eastern France

The Seille Valley in eastern France was home to one of Europe’s largest Iron Age salt industries. Sedimentology, palynology and geochronology have been integrated within ongoing archaeological investigations to reconstruct the Holocene palaeoenvironmental history of the Seille Valley and to elucidate the human–environment relationship of salt production. A sedimentary model of the valley has been constructed from a borehole survey of the floodplain and pollen analyses have been undertaken to reconstruct the vegetation history. Alluvial records have been successfully dated using optically stimulated luminescence and radiocarbon techniques, thereby providing a robust chronological framework. The results have provided an insight into the development of favourable conditions for salt production and there is evidence in the sedimentary record to suggest that salt production may have taken place during the mid-to-late Bronze Age. The latter has yet to be identified in the archaeological record and targeted excavation is therefore underway to test this finding. The development of the Iron Age industry had a major impact on the hydrological regime of the valley and its sedimentological history, with evidence for accelerated alluviation arising from floodplain erosion at salt production sites and modification of the local fluvial regime due to briquetage accumulation on the floodplain. This research provides an important insight into the environmental implications of early industrial activities, in addition to advancing knowledge about the Holocene palaeoenvironmental and social history of this previously poorly studied region of France.