<p>East Africa has high rates of soil erosion which negatively impact agricultural yields. Climate projections suggest that rainfall intensity will increase in East Africa, which may increase soil erosion. Soil erosion estimates require information on rainfall erosivity, which is calculated using sub-daily storm characteristics that are known to be biased in traditional parameterized convection climate models. Convection-permitting climate models, which are run at higher resolution to negate the need for convection parameterisation, generally better represent rainfall intensity and frequency. We use a novel convection-permitting pan-Africa regional climate model (CP4A) to estimate rainfall erosivity in Tanzania and Malawi, and compare it to its parameterized counterpart (P25), to determine if there is a benefit to using convection permitting climate models to look at rainfall erosivity. We use 8-year historical and end-of-century RCP8.5 simulations to examine the impact of climate change on rainfall erosivity. We then apply the Revised Universal Soil Loss Equation (RUSLE), using the rainfall erosivity estimates from CP4A and P25, to calculate soil erosion in Tanzania and Malawi. The distribution of rainfall intensity and duration was closer to the TRMM rainfall observations in the convection permitting model than in the parameterized model before and after bias-correction. We found that rainfall erosivity was lower in the parameterized convection model than in CP4A due to differences in storm characteristics, even after bias-correction. These results suggest that parameterized convection regional and global climate models might under-estimate rainfall erosivity, and the associated soil erosion. We found high values of present day erosion associated with mountainous regions in Tanzania and Malawi in CP4A. Under climate change, areas at high risk of soil erosion expanded due to increases in rainfall intensity in CP4A. The levels of soil erosion were high enough to negatively impact on agricultural yields.  Soil management was less effective in the future at reducing soil erosion risk than in the present day, and more extensive soil management may be required in the future to manage soil erosion and reduce the negative impacts of soil erosion on agriculture.</p>

Abstract. Orbital forcing is a key climate driver over multi-millennial timescales. In particular, monsoon systems are thought to be driven by orbital cyclicity, especially by precession. Here we analyse the impact of orbital forcing on global climate with a particular focus on the North African monsoon, by carrying out a ensemble of 22 atmosphere-ocean-vegetation simulations, equally-spaced in time and covering one full late Miocene precession cycle (~ 6.5 Ma). Orbital parameters vary realistically for the selected time slice. Our results highlight the high sensitivity of the North African summer monsoon to orbital forcing, with strongly intensified precipitation during the precession minimum, leading to a northward penetration of vegetation up to ~ 21° N. The summer monsoon is also moderately sensitive to palaeogeography changes, but has a low sensitivity to atmospheric CO2 levels between 280 and 400 ppm. Our ensemble of simulations allows us to explore the climatic response to orbital forcing not only for the precession extremes, but also on sub-precessional timescales. We demonstrate the importance of including orbital variability in model-data comparison studies, because doing so partially reduces the mismatch between the late Miocene terrestrial proxy record and model results. Failure to include orbital variability could also lead to significant miscorrelations in temperature-based proxy reconstructions for this time period, because of the asynchronicity between maximum (minimum) surface air temperatures and minimum (maximum) precession in several areas around the globe. This is of particular relevance for the North African regions, which have previously been identified as optimal areas to target for late Miocene palaeodata acquisition.
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<p><strong>Abstract.</strong> Understanding natural and anthropogenic climate change processes involves using computational models that represent the main components of the Earth system: the atmosphere, ocean, sea-ice and land surface. These models have become increasingly computationally expensive as resolution is increased and more complex process representations are included. However, to gain robust insight into how climate may respond to a given forcing, and to meaningfully quantify the associated uncertainty, it is often required to use either or both of ensemble approaches and very long integrations. For this reason, more computationally efficient models can be very valuable tools. Here we provide a comprehensive overview of the suite of climate models based around the coupled general circulation model HadCM3. This model was originally developed at the UK Met Office and has been heavily used during the last 15 years for a range of future (and past) climate change studies but is now largely being replaced by more recent models. However, it continues to be extensively used by the BRIDGE (Bristol Research Initiative for the Dynamic Global Environment) research group at the University of Bristol and elsewhere. Over time, adaptations have been made to the base HadCM3 model. These adaptations mean that the original documentation is not entirely representative, and several other configurations are in use which now differ from the originally described model versions. We therefore describe the key features of a number of configurations of the HadCM3 climate model family, including the atmosphere-only model (HadAM3), the coupled model with a low resolution ocean (HadCM3L), the high resolution atmosphere only model (HadAM3H), the regional model (HadRM3) and a fast coupled model (FAMOUS), which together make up HadCM3@Bristol version 1.0. These also include three versions of the land surface scheme. By comparing with observational datasets, we show that these models produce a good representation of many aspects of the climate system, including the land and sea surface temperatures, precipitation, ocean circulation and vegetation. This evaluation, combined with the relatively fast computational speed (up to 2000× faster than some CMIP6 models), motivates continued development and scientific use of the HadCM3 family of coupled climate models, particularly for quantifying uncertainty and for long multi-millennial scale simulations.</p>
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<p>Sub-Saharan Africa is one of the most food-insecure regions in the world, and is particularly vulnerable to the impacts of extreme climate events and climate change.  to gain a better understanding of the present-day likelihood of extreme seasonal temperature and rainfall events, and their joint occurrence, we apply the UNprecedented Simulated Extremes using ENsembles (UNSEEN) approach  to a large ensemble of high-resolution initialised climate simulations in three countries of Sub-Saharan Africa: Tanzania, Zambia and South Africa. We assess the annual likelihood of unprecedented seasonal temperature and precipitation extremes during the maize growing season (October-April), as key variables for maize productivity , and investigate the large-scale dynamics of the climate system that govern their occurrence. We estimate that there is a 3-4% chance per year of exceeding the present-day seasonal temperature records in the maize growing regions of these countries, and a 1-3.5% chance per year of exceeding the present-day seasonal precipitation records.  Conversely, whilst we find a 2% and 5% chance per year of subceeding the present-day seasonal precipitation records in Zambia and Tanzania respectively, we find a very low chance (0-1% per year) of subceeding the present-day seasonal precipitation records in South Africa.  We also use the large ensemble to investigate the large-scale dynamics of the climate extremes, finding that high temperature extremes tend to be associated with El Niño and positive IOD/SIOD events and low temperature extremes with La Niña and negative IOD/SIOD events. The drivers of precipitation extremes, however, differ between the countries. In South Africa, high (low) precipitation extremes are associated with La Niña (El Niño) events but otherwise the influence on extremes of ENSO, and even more so the IOD/SIOD, is weak or not seen in the ensemble, which invites further investigation. To explore implications for growing maize in these regions, we convert our unprecedented seasonal temperature estimates to daily maximum temperatures and our seasonal precipitation estimates to monthly precipitation indices and compare to climatic thresholds for maize. Combined with projected changes to crop suitability in much of sub-Saharan Africa, our analysis suggests the need for significant adaptation strategies that build food system resilience in the near and longer term.</p>

We projected changes in weather extremes, hydrological impacts and vulnerability to food insecurity at global warming of 1.5°C and 2°C relative to pre-industrial, using a new global atmospheric general circulation model HadGEM3A-GA3.0 driven by patterns of sea-surface temperatures and sea ice from selected members of the 5th Coupled Model Intercomparison Project (CMIP5) ensemble, forced with the RCP8.5 concentration scenario. To provide more detailed representations of climate processes and impacts, the spatial resolution was N216 (approx. 60 km grid length in mid-latitudes), a higher resolution than the CMIP5 models. We used a set of impacts-relevant indices and a global land surface model to examine the projected changes in weather extremes and their implications for freshwater availability and vulnerability to food insecurity. Uncertainties in regional climate responses are assessed, examining ranges of outcomes in impacts to inform risk assessments. Despite some degree of inconsistency between components of the study due to the need to correct for systematic biases in some aspects, the outcomes from different ensemble members could be compared for several different indicators. The projections for weather extremes indices and biophysical impacts quantities support expectations that the magnitude of change is generally larger for 2°C global warming than 1.5°C. Hot extremes become even hotter, with increases being more intense than seen in CMIP5 projections. Precipitation-related extremes show more geographical variation with some increases and some decreases in both heavy precipitation and drought. There are substantial regional uncertainties in hydrological impacts at local scales due to different climate models producing different outcomes. Nevertheless, hydrological impacts generally point towards wetter conditions on average, with increased mean river flows, longer heavy rainfall events, particularly in South and East Asia with the most extreme projections suggesting more than a doubling of flows in the Ganges at 2°C global warming. Some areas are projected to experience shorter meteorological drought events and less severe low flows, although longer droughts and/or decreases in low flows are projected in many other areas, particularly southern Africa and South America. Flows in the Amazon are projected to decline by up to 25%. Increases in either heavy rainfall or drought events imply increased vulnerability to food insecurity, but if global warming is limited to 1.5°C, this vulnerability is projected to remain smaller than at 2°C global warming in approximately 76% of developing countries. At 2°C, four countries are projected to reach unprecedented levels of vulnerability to food insecurity.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Understanding natural and anthropogenic climate change processes involves using computational models that represent the main components of the Earth system: the atmosphere, ocean, sea ice, and land surface. These models have become increasingly computationally expensive as resolution is increased and more complex process representations are included. However, to gain robust insight into how climate may respond to a given forcing, and to meaningfully quantify the associated uncertainty, it is often required to use either or both ensemble approaches and very long integrations. For this reason, more computationally efficient models can be very valuable tools. Here we provide a comprehensive overview of the suite of climate models based around the HadCM3 coupled general circulation model. This model was developed at the UK Met Office and has been heavily used during the last 15 years for a range of future (and past) climate change studies, but has now been largely superseded for many scientific studies by more recently developed models. However, it continues to be extensively used by various institutions, including the BRIDGE (Bristol Research Initiative for the Dynamic Global Environment) research group at the University of Bristol, who have made modest adaptations to the base HadCM3 model over time. These adaptations mean that the original documentation is not entirely representative, and several other relatively undocumented configurations are in use. We therefore describe the key features of a number of configurations of the HadCM3 climate model family, which together make up HadCM3@Bristol version 1.0. In order to differentiate variants that have undergone development at BRIDGE, we have introduced the letter B into the model nomenclature. We include descriptions of the atmosphere-only model (HadAM3B), the coupled model with a low-resolution ocean (HadCM3BL), the high-resolution atmosphere-only model (HadAM3BH), and the regional model (HadRM3B). These also include three versions of the land surface scheme. By comparing with observational datasets, we show that these models produce a good representation of many aspects of the climate system, including the land and sea surface temperatures, precipitation, ocean circulation, and vegetation. This evaluation, combined with the relatively fast computational speed (up to 1000 times faster than some CMIP6 models), motivates continued development and scientific use of the HadCM3B family of coupled climate models, predominantly for quantifying uncertainty and for long multi-millennial-scale simulations.

The impact of rising CO. on future climate remains uncertain but the evidence for high CO. in the palaeorecord suggests that past climates could provide a potentially quantifiable indication of climate in a high-CO. world. One such past time period is the late Miocene (11.6-5.3Ma), for which CO. reconstructions indicate higher levels than those of preindustrial, and similar to the present atmospheric level (~400ppm). The late Miocene palaeorecord suggests a much warmer and wetter Northern Hemisphere than preindustrial. However, vegetation feedbacks are an important component of the climate system and vegetation distribution reconstructions from the palaeorecord have been shown to be very different to the present vegetation distribution. We examine the roles that different vegetation and palaeogeography play in climate sensitivity for the late Miocene and consider the implications for potential future climate change. To do this we use coupled atmosphere-ocean-vegetation simulations of late Miocene and potential modern climates forced by three different CO. concentrations with vegetation perturbation experiments and make quantitative comparisons to the palaeorecord. Optimal regions to target late Miocene palaeodata acquisition for the purposes of informing about future climate include North America, northern Africa, Australia, Paraguay and southern Brazil, and northeastern Asia. These regions are those which the model results predict to be most sensitive to CO. forcing, but where the local temperature response to CO. forcing is similar between the simulated potential modern and late Miocene climates. The model results suggest that climate sensitivity to CO. forcing is directly affected by the palaeogeographic configuration and that the inferred climate sensitivity for doubled CO. is 0.5-0.8°C higher for the late Miocene than we might expect for future climate because of differences in synergy. The greater land mass at high northern latitudes during the late Miocene and the differences in vegetation distribution predictions that result, combined with differences in ocean circulation and the effect of sea ice, make the late Miocene boundary conditions more sensitive to CO. forcing than the modern boundary conditions. 2 2 2 2 2 2 2 2 2 2

Abstract. Orbital forcing is a key climate driver over multi-millennial timescales. In particular, monsoon systems are thought to be driven by orbital cyclicity, especially by precession. Here, we analyse the impact of orbital forcing on global climate with a particular focus on the North African monsoon, by carrying out an ensemble of 22 equally spaced (one every 1000 years) atmosphere–ocean–vegetation simulations using the HadCM3L model, covering one full late Miocene precession-driven insolation cycle with varying obliquity (between 6.568 and 6.589 Ma). The simulations only differ in their prescribed orbital parameters, which vary realistically for the selected time period. We have also carried out two modern-orbit control experiments, one with late Miocene and one with present-day palaeogeography, and two additional sensitivity experiments for the orbital extremes with varying CO2 forcing. Our results highlight the high sensitivity of the North African summer monsoon to orbital forcing, with strongly intensified precipitation during the precession minimum, leading to a northward penetration of vegetation up to ~ 21° N. The modelled summer monsoon is also moderately sensitive to palaeogeography changes, but it has a low sensitivity to atmospheric CO2 concentration between 280 and 400 ppm. Our simulations allow us to explore the climatic response to orbital forcing not only for the precession extremes but also on sub-precessional timescales. We demonstrate the importance of including orbital variability in model–data comparison studies, because doing so partially reduces the mismatch between the late Miocene terrestrial proxy record and model results. Failure to include orbital variability could also lead to significant miscorrelations in temperature-based proxy reconstructions for this time period, because of the asynchronicity between maximum (minimum) surface air temperatures and minimum (maximum) precession in several areas around the globe. This is of particular relevance for the North African regions, which have previously been identified as optimal areas to target for late Miocene palaeodata acquisition.
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Abstract. The late Miocene palaeorecord provides evidence for a warmer and wetter climate than that of today, and there is uncertainty in the palaeo-CO2 record of at least 200 ppm. We present results from fully coupled atmosphere-ocean-vegetation simulations for the late Miocene that examine the relative roles of palaeogeography (topography and ice sheet geometry) and CO2 concentration in the determination of late Miocene climate through comprehensive terrestrial model-data comparisons. Assuming that these data accurately reflect the late Miocene climate, and that the late Miocene palaeogeographic reconstruction used in the model is robust, then results indicate that:. 1. Both palaeogeography and atmospheric CO2 contribute to the proxy-derived precipitation differences between the late Miocene and modern reference climates. However these contributions exibit synergy and so do not add linearly. 2. The vast majority of the proxy-derived temperature differences between the late Miocene and modern reference climates can only be accounted for if we assume a palaeo-CO2 concentration towards the higher end of the range of estimates.
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