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The first experiment studying the effect of irrigation on pre-monsoon rainfall in India using a high-resolution convection-permitting model has been carried out. This study includes both short (3-day) experiments and month-long free-running simulations, enabling investigation of the effect of irrigation on mesoscale circulations and associated rainfall. In the pre-monsoon, it is found that irrigation increases rainfall in our simulations. Intriguingly, the rainfall increase found in the high-resolution model mostly occurs on the mountains near the irrigation rather than over the irrigated region itself. This is because our applied irrigation is in low-lying regions, and so it enhances the mountain-valley flows leading to enhancement of diurnally driven orographic rainfall. Because Ganges basin irrigation occurs near mountains which already have some of the highest rainfall rates in the world, and which are subject to flash flooding and landslides, this has significant implications for hazards in mountainous regions during the pre-monsoon and early monsoon period.

Tropical cyclones (TCs) in the Bay of Bengal can be extremely destructive when they make landfall in India and Bangladesh. Accurate prediction of their track and intensity is essential for disaster management. This study evaluates simulations of Bay of Bengal TCs using a regional convection-permitting atmosphere-ocean coupled model. The Met Office Unified Model atmosphere-only configuration (4.4 km horizontal grid spacing) is compared with a configuration coupled to a three-dimensional dynamical ocean model (2.2 km horizontal grid spacing). Simulations of six TCs from 2016–2019 show that both configurations produce accurate TC tracks for lead times of up to 6 days before landfall. Both configurations underestimate high wind speeds and high rain rates, and overestimate low wind speeds and low rain rates. The ocean-coupled configuration improves landfall timing predictions and reduces wind speed biases relative to observations outside the eyewall but underestimates maximum wind speeds in the eyewall for the most intense TCs. The coupled configuration produces weaker TCs than the atmosphere-only configuration, consistent with lower sea surface temperatures in the coupled model and an overestimated cooling response in TC wakes. Both model configurations accurately predict rain rate asymmetry, suggesting a good representation of TC dynamics. Much of the rain rate asymmetry variation in the simulations is related to wind shear variations, with a preference for higher rain rates in the down-shear left quadrant.

Seasonal forecasts of rainfall are considered the priority timescale by many users in the tropics. In East Africa, the primary operational seasonal forecast for the region is produced by the Greater Horn of Africa Climate Outlook Forum (GHACOF), and issued ahead of each rainfall season. This study evaluates and compares the GHACOF consensus forecasts with dynamical model forecasts from the UK Met Office GloSea5 seasonal prediction system for the two rainy seasons. GloSea demonstrates positive skill (r = 0.69) for the short rains at 1 month lead. In contrast, skill is low for the long rains due to lack of predictability of driving factors. For both seasons GHACOF forecasts show generally lower levels of skill than GloSea. Several systematic errors within the GHACOF forecasts are identified; the largest being the tendency to over-estimate the likelihood of near normal rainfall, with over 70% (80%) of forecasts giving this category the highest probability in the short (long) rains. In a more detailed evaluation of GloSea, a large wet bias, increasing with forecast lead time, is identified in the short rains. This bias is attributed to a developing cold SST bias in the eastern Indian Ocean, driving an easterly wind bias across the equatorial Indian Ocean. These biases affect the mean state moisture availability, and could act to reduce the ability of the dynamical model in predicting interannual variability, which may also be relevant to predictions from coupled models on longer timescales.

Tropical cyclones (TCs) forming over the Bay of Bengal can cause devastation when they make landfall in India and Bangladesh; accurate prediction of their track and intensity is essential for disaster management. TC intensity is moderated by heat, momentum and moisture exchanges between the atmosphere and ocean. In recent years there have been significant improvements in the skill of TC forecasts due to the implementation of coupled atmosphere-ocean models and high-resolution models capable of explicitly resolving small-scale physical processes influencing storm development. This study evaluates the representation of six TCs in the Bay of Bengal from 2016 to 2019, using both a Met Office Unified Model atmosphere-only configuration (ATM) with 4.4 km grid spacing, and coupled to a 2.2 km resolution NEMO (Nucleus for European Models of the Ocean) ocean model (CPL). To determine the impact of coupling on wind-driven mixing and ocean-atmosphere heat exchange, forecast sea surface temperature (SST) is compared to observations. The impact of coupling on track position and storm intensity is evaluated using predictions of minimum sea level pressure (MSLP) and 10 m maximum sustained winds (MSW). Representation of TC dynamics is assessed by analysing storm structure, using radius of maximum winds and rain rate asymmetry. Results from the three most intense TC case studies (Fani, Titli, and Vardah) show that SSTs in ATM are too high, while SSTs in CPL are slightly too low, with an overestimation of the cooling response in TC wakes. TC track position errors are small, but intensity error metrics for MSLP and MSW show biases relative to observations. Peak intensity is overestimated for Titli and Vardah in the ATM model configuration; the CPL model configuration generally produces weaker storms than the ATM model configuration. Wind speeds outside the storm centre are high compared to observations, with a greater bias in the ATM model configuration. Both model configurations produce accurate predictions of radius of maximum winds and rain rate asymmetry, suggesting a good representation of TC dynamics. Much of the variation in rain rate asymmetry in the forecasts can be explained by variations in wind shear.

Mean temperatures and their extremes have increased over Africa since the latter half of the 20th century and this trend is projected to continue, with very frequent, intense and often deadly heatwaves likely to occur very regularly over much of Africa by 2100. It is crucial that we understand the scale of the future increases in extremes and the driving mechanisms. We diagnose daily maximum wet bulb temperature heatwaves, which allows for both the impact of temperature and humidity, both critical for human health and survivability. During wet bulb heatwaves, humidity and cloud cover increase, which limits the surface shortwave radiation flux but increases longwave warming. It is found from observations and ERA5 reanalysis that approximately 30% of wet bulb heatwaves over Africa are associated with daily rainfall accumulations of more than 1 mm/day on the first day of the heatwave. The first ever pan-African convection-permitting climate model simulations of present-day and RCP8.5 future climate are utilised to illustrate the projected future change in heatwaves, their drivers and their sensitivity to the representation of convection. Compared to ERA5, the convection-permitting model better represents the frequency and magnitude of present-day wet bulb heatwaves than a version of the model with more traditional parameterised convection. The future change in heatwave frequency, duration and magnitude is also larger in the convective-scale simulation, suggesting CMIP-style models may underestimate the future change in wet bulb heat extremes over Africa. The main reason for the larger future change appears to be the ability of the model to produce larger anomalies relative to its climatology in precipitation, cloud and the surface energy balance.

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.

Convection-permitting ensemble prediction systems (CP-ENS) have been implemented in themid-latitudes for weather forecasting timescales over the past decade, enabled by the increase incomputational resources. Recently, efforts are being made to study the benefits of CP-ENS fortropical regions. This study examines CP-ENS forecasts produced by the UK Met Office overtropical East Africa, for 24 cases in the period April-May 2019. The CP-ENS, an ensemble withparametrized convection (Glob-ENS), and their deterministic counterparts are evaluated againstrainfall estimates derived from satellite observations (GPM-IMERG). The CP configurations havethe best representation of the diurnal cycle, although heavy rainfall amounts are overestimatedcompared to observations. Pairwise comparisons between the different configurations reveal thatthe CP-ENS is generally the most skilful forecast for both 3-h and 24-h accumulations of heavyrainfall (97th percentile), followed by the CP deterministic forecast. More precisely, probabilisticforecasts of heavy rainfall, verified using a neighbourhood approach, show that the CP-ENS isskilful at scales greater than 100 km, significantly better than the Glob-ENS, although not as goodas found in the mid-latitudes. Skill decreases with lead time and varies diurnally, especially forCP forecasts. The CP-ENS is under-spread both in terms of forecasting the locations of heavyrainfall and in terms of domain-averaged rainfall. This study demonstrates potential benefits inusing CP-ENS for operational forecasting of heavy rainfall over tropical Africa and gives specificsuggestions for further research and development, including probabilistic forecast guidance.

A novel technique to produce reasonable worst‐case rainfall scenarios from ensemble forecasts is presented. This type of scenario is relevant for predicting the risk of localized, intense rainfall events with a duration between 15 min and several hours. Such rainfall events can cause surface‐water (pluvial) flooding. Producing useful forecasts of these events at lead times of more than a few hours is challenging due to the precision and accuracy in rainfall intensity, duration and location that is required. The technique described here addresses these challenges by constructing appropriate scenarios using a neighbourhood technique in combination with ensemble forecasting. It is similar to the distance‐dependent depth–duration analysis described in earlier studies, but it introduces an additional post‐processing step based on probability distribution functions of rainfall accumulation near a location of interest. This additional step makes the reasonable worst‐case scenarios less dependent on grid‐scale behaviour, and helps to generate scenarios with a consistent interpretation. The method is used to compare forecasts with a lead time of 6–36 hr to radar data for several case studies that occurred in Yorkshire. These comparisons also introduce new techniques to present maps of the reasonable worst‐case rainfall accumulation at each location.

East Africa is highly reliant on agriculture and has high rates of soil erosion which negatively impact agricultural yields. Climate projections suggest that rainfall intensity will increase in East Africa, which is likely to 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 parameterization, 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 eight year historical and end-of-century (RCP8.5) climate simulations to examine the impact of climate change on soil erosion in Tanzania and Malawi based on rainfall erosivity estimates from CP4A and P25 applied to the Revised Universal Soil Loss Equation. The effectiveness of soil conservation measures was also evaluated. Rainfall erosivity was lower in P25 than in CP4A and was a poorer match to observational 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 in 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. Terracing was less effective at reducing soil erosion risk in the future than in the present day, and more extensive soil management may be required to manage soil erosion and reduce the negative impacts of soil erosion on agriculture.

Cold pools are integral components of squall-line mesoscale convective systems and the West African monsoon, but are poorly represented in operational global models. Observations of 38 cold pools made at Niamey, Niger, during the 2006 African Monsoon Multidisciplinary Analysis (AMMA) campaign (1 June–30 September 2006), are used to generate a seasonal characterization of cold pool properties by quantifying related changes in surface meteorological variables. Cold pools were associated with temperature decreases of 2°–14°C, pressure increases of 0–8 hPa, and wind gusts of 3–22 m s−1. Comparison with published values of similar variables from the U.S. Great Plains showed comparable differences. The leading part of most cold pools had decreased water vapor mixing ratios compared to the environment, with moister air, likely related to precipitation, approximately 30 min behind the gust front. A novel diagnostic used to quantify how consistent observed cold pool temperatures are with saturated or unsaturated descent from midlevels [fractional evaporational energy deficit (FEED)] shows that early season cold pools are consistent with less saturated descents. Early season cold pools were relatively colder, windier, and wetter, consistent with drier midlevels, although this was only statistically significant for the change in moisture. Late season cold pools tended to decrease equivalent potential temperature from the pre–cold pool value, whereas earlier in the season changes were smaller, with more increases. The role of cold pools may therefore change through the season, with early season cold pools more able to feed subsequent convection.

Due to high present-day temperatures and reliance on rainfed agriculture, sub-Saharan Africa is highly vulnerable to climate change. We use a comprehensive set of global (CMIP5) and regional (CORDEX-Africa) climate projections and a new convection-permitting pan-Africa simulation (and its parameterized counterpart) to examine changes in rainfall and temperature and the impact on crop suitability of maize, cassava and soybean in sub-Saharan Africa by 2100 (RCP8.5). This is the first time an explicit-convection simulation has been used to examine crop suitability in Africa. Increasing temperatures and declining rainfall led to large parts of sub-Saharan Africa becoming unsuitable for multiple staple crops, which may necessitate a transition to more heat and drought resistant crops to ensure food and nutrition security. Soybean was resilient to temperature increases, however maize and cassava were not, leading to declines in crop suitability. Inclusion of sensitivity to extreme temperatures led to larger declines in maize suitability than when this was excluded. The results were explored in detail for Tanzania, Malawi, Zambia and South Africa. In each country the range of projections included wetting and drying, but the majority of models projected rainfall declines leading to declines in crop suitability, except in Tanzania. Explicit-convection was associated with more high temperature extremes, but had little systematic impact on average temperature and total rainfall, and the resulting suitability analysis. Global model uncertainty, rather than convection parameterizations, still makes up the largest part of the uncertainty in future climate. Explicit-convection may have more impact if suitability included a more comprehensive treatment of extremes. This work highlights the key uncertainty from global climate projections for crop suitability projections, and the need for improved information on sensitivities of African crops to extremes, in order to give better predictions and make better use of the new generation of explicit-convection models.

Forecasting convective rainfall in the tropics is a major challenge for numerical weather prediction. The use of convection-permitting (CP) forecast models in the tropics has lagged behind the midlatitudes, despite the great potential of such models in this region. In the scientific literature, there is very little evaluation of CP models in the tropics, especially over an extended time period. This paper evaluates the prediction of convective storms for a period of 2 years in the Met Office operational CP model over East Africa and the global operational forecast model. A novel localized form of the fractions skill score is introduced, which shows variation in model skill across the spatial domain. Overall, the CP model and the global model both outperform a 24-h persistence forecast. The CP model shows greater skill than the global model, in particular on subdaily time scales and for storms over land. Forecasts over Lake Victoria are also improved in the CP model, with an increase in hit rate of up to 20%. Contrary to studies in the midlatitudes, the skill of both models shows a large dependence on the time of day and comparatively little dependence on the forecast lead time within a 48-h forecast. Although these results provide more motivation for forecasters to use the CP model to produce subdaily forecasts with increased detail, there is a clear need for more in situ observations for data assimilation into the models and for verification. A move toward ensemble forecasting could have further benefits.

Global climate and weather models are a key tool for the prediction of future crop productivity, but they all rely on parameterizations of atmospheric convection, which often produce significant biases in rainfall characteristics over the tropics. The authors evaluate the impact of these biases by driving the General Large Area Model for annual crops (GLAM) with regional-scale atmospheric simulations of one cropping season over West Africa at different resolutions, with and without a parameterization of convection, and compare these with a GLAM run driven by observations. The parameterization of convection produces too light and frequent rainfall throughout the domain, as compared with the short, localized, high-intensity events in the observations and in the convection-permitting runs. Persistent light rain increases surface evaporation, and much heavier rainfall is required to trigger planting. Planting is therefore delayed in the runs with parameterized convection and occurs at a seasonally cooler time, altering the environmental conditions experienced by the crops. Even at high resolutions, runs driven by parameterized convection underpredict the small-scale variability in yields produced by realistic rainfall patterns. Correcting the distribution of rainfall frequencies and intensities before use in crop models will improve the process-based representation of the crop life cycle, increasing confidence in the predictions of crop yield. The rainfall biases described here are a common feature of parameterizations of convection, and therefore the crop-model errors described are likely to occur when using any global weather or climate model, thus remaining hidden when using climate-model intercomparisons to evaluate uncertainty.

Cold pool outflows, generated by downdrafts from moist convection, can generate strong winds and therefore uplift of mineral dust. These so-called haboob convective dust storms occur over all major dust source areas worldwide and contribute substantially to emissions in northern Africa, the world’s largest source. Most large-scale models lack convective dust storms because they do not resolve moist convection, relying instead on convection schemes. The authors suggest a parameterization of convective dust storms to account for their contribution in such large-scale models. The parameterization is based on a simple conceptual model, in which the downdraft mass flux from the convection scheme spreads out radially in a cylindrical cold pool. The parameterization is tested with a set of Met Office Unified Model runs for June and July 2006 over West Africa. It is calibrated with a convection-permitting run and applied to a convection-parameterized run. The parameterization successfully produces the extensive area of dust-generating winds from cold pool outflows over the southern Sahara. However, this area extends farther to the east and dust-generating winds occur earlier in the day than in the convection-permitting run. These biases are caused by biases in the convection scheme. It is found that the location and timing of dust-generating winds are weakly sensitive to the parameters of the conceptual model. The results demonstrate that a simple parameterization has the potential to correct a major and long-standing limitation in global dust models.

The organization of deep convection and its misrepresentation in many global models is the focus of much current interest. A new method is presented for quantifying convective organization based on the identification of convective objects and subsequent derivation of object numbers, areas, and separation distances to describe the degree of convective organization. These parameters are combined into a “convection organization potential” based on the physical principle of an interaction potential between pairs of convective objects. This technique is applied to simulated and observed fields of outgoing longwave radiation (OLR) over the West African monsoon region using data from Met Office Unified Model simulations and satellite observations made by the Geostationary Earth Radiation Budget (GERB) instrument. The method is evaluated by using it to quantify differences between models with different horizontal grid lengths and representations of convection. Distributions of OLR, precipitation and organization parameters, the diurnal cycle of convection, and relationships between the meteorology in different states of organization are compared. Switching from a configuration with parameterized convection to one that allows the model to resolve convective processes at the model grid scale is the leading-order factor improving some aspects of model performance, while increased model resolution is the dominant factor for others. However, no single model configuration performs best compared to observations, indicating underlying deficiencies in both model scaling and process understanding.

Loss of summertime Arctic sea ice will lead to a large increase in the emission of aerosols and precursor gases from the ocean surface. It has been suggested that these enhanced emissions will exert substantial aerosol radiative forcings, dominated by the indirect effect of aerosol on clouds. Here, we investigate the potential for these indirect forcings using a global aerosol microphysics model evaluated against aerosol observations from the Arctic Summer Cloud Ocean Study (ASCOS) campaign to examine the response of Arctic cloud condensation nuclei (CCN) to sea-ice retreat. In response to a complete loss of summer ice, we find that north of 70° N emission fluxes of sea salt, marine primary organic aerosol (OA) and dimethyl sulfide increase by a factor of ~ 10, ~ 4 and ~ 15 respectively. However, the CCN response is weak, with negative changes over the central Arctic Ocean. The weak response is due to the efficient scavenging of aerosol by extensive drizzling stratocumulus clouds. In the scavenging-dominated Arctic environment, the production of condensable vapour from oxidation of dimethyl sulfide grows particles to sizes where they can be scavenged. This loss is not sufficiently compensated by new particle formation, due to the suppression of nucleation by the large condensation sink resulting from sea-salt and primary OA emissions. Thus, our results suggest that increased aerosol emissions will not cause a climate feedback through changes in cloud microphysical and radiative properties.

A suite of 40 day UK Met Office Unified Model simulations over West Africa during summer 2006 are analyzed to investigate the causes of biases in the position of the rainbelt and to understand the role of convection in the regional water budget. The simulations include climate, global operational, and limited area runs (grid spacings from 1.5 to 40 km), including two 12 km runs, one with parameterized and one with explicit convection. The most significant errors in the water cycle terms occur in the simulations with parameterized convection, associated with the diurnal cycle and the location of the convection. Errors in the diurnal cycle increase the northward advection of moisture out of the Sahel toward the Sahara but decrease the advection of moisture into the Sahel from further south, which limits the availability of moisture for Sahelian rainfall. These biases occur within the first 24 h, showing that they originate from the representation of fast physical processes, specifically, the convection scheme. Once these rainfall regimes have been established, the terms of the water budgets act to reinforce the biases, effectively locking the rainbelt's latitude. One of the simulations with parameterized convection does, however, produce a better latitudinal distribution of rainfall because on the first day it is better able to trigger convection in the Sahel. Accurate representation of the diurnal cycle of convection and the ability to trigger convection in a high convective inhibition environment is key to capturing the water cycle of the region and will improve the representation of the West African Monsoon.

State-of-the-art regional climate model simulations that are able to resolve key mesoscale circulations are used, for the first time, to understand the interaction between the large-scale convective environment of the MJO and processes governing the strong diurnal cycle over the islands of the Maritime Continent (MC). Convection is sustained in the late afternoon just inland of the coasts because of sea breeze convergence. Previous work has shown that the variability in MC rainfall associated with the MJO is manifested in changes to this diurnal cycle; land-based rainfall peaks before the active convective envelope of the MJO reaches the MC, whereas oceanic rainfall rates peak while the active envelope resides over the region. The model simulations show that the main controls on oceanic MC rainfall in the early active MJO phases are the large-scale environment and atmospheric stability, followed by high oceanic latent heat flux forced by high near-surface winds in the later active MJO phases. Over land, rainfall peaks before the main convective envelope arrives (in agreement with observations), even though the large-scale convective environment is only moderately favorable for convection. The causes of this early rainfall peak are strong convective triggers from land–sea breeze circulations that result from high surface insolation and surface heating. During the peak MJO phases cloud cover increases and surface insolation decreases, which weakens the strength of the mesoscale circulations and reduces land-based rainfall, even though the large-scale environment remains favorable for convection at this time. Hence, scale interactions are an essential part of the MJO transition across the MC.

There are some long-established biases in atmospheric models that originate from the representation of tropical convection. Previously, it has been difficult to separate cause and effect because errors are often the result of a number of interacting biases. Recently, researchers have gained the ability to run multiyear global climate model simulations with grid spacings small enough to switch the convective parameterization off, which permits the convection to develop explicitly. There are clear improvements to the initiation of convective storms and the diurnal cycle of rainfall in the convection-permitting simulations, which enables a new process-study approach to model bias identification. In this study, multiyear global atmosphere-only climate simulations with and without convective parameterization are undertaken with the Met Office Unified Model and are analyzed over the Maritime Continent region, where convergence from sea-breeze circulations is key for convection initiation. The analysis shows that, although the simulation with parameterized convection is able to reproduce the key rain-forming sea-breeze circulation, the parameterization is not able to respond realistically to the circulation. A feedback of errors also occurs: the convective parameterization causes rain to fall in the early morning, which cools and wets the boundary layer, reducing the land–sea temperature contrast and weakening the sea breeze. This is, however, an effect of the convective bias, rather than a cause of it. Improvements to how and when convection schemes trigger convection will improve both the timing and location of tropical rainfall and representation of sea-breeze circulations.

Despite the importance of dust aerosol in the Earth system, state-of-the-art models show a large variety for North African dust emission. This study presents a systematic evaluation of dust emitting-winds in 30 years of the historical model simulation with the UK Met Office Earth-system model HadGEM2-ES for the Coupled Model Intercomparison Project Phase 5. Isolating the effect of winds on dust emission and using an automated detection for nocturnal low-level jets (NLLJs) allow an in-depth evaluation of the model performance for dust emission from a meteorological perspective. The findings highlight that NLLJs are a key driver for dust emission in HadGEM2-ES in terms of occurrence frequency and strength. The annually and spatially averaged occurrence frequency of NLLJs is similar in HadGEM2-ES and ERA-Interim from the European Centre for Medium-Range Weather Forecasts. Compared to ERA-Interim, a stronger pressure ridge over northern Africa in winter and the southward displaced heat low in summer result in differences in location and strength of NLLJs. Particularly the larger geostrophic winds associated with the stronger ridge have a strengthening effect on NLLJs over parts of West Africa in winter. Stronger NLLJs in summer may rather result from an artificially increased mixing coefficient under stable stratification that is weaker in HadGEM2-ES. NLLJs in the Bodélé Depression are affected by stronger synoptic-scale pressure gradients in HadGEM2-ES. Wintertime geostrophic winds can even be so strong that the associated vertical wind shear prevents the formation of NLLJs. These results call for further model improvements in the synoptic-scale dynamics and the physical parametrization of the nocturnal stable boundary layer to better represent dust-emitting processes in the atmospheric model. The new approach could be used for identifying systematic behavior in other models with respect to meteorological processes for dust emission. This would help to improve dust emission simulations and contribute to decreasing the currently large uncertainty in climate change projections with respect to dust aerosol.

Feedback between soil moisture and precipitation influence climate variability in semiarid regions. However, serious concerns exist about the ability of coarse-scale global atmospheric models to depict one key aspect of the feedback loop, namely the sensitivity of daytime convection to soil moisture. Here we compare regional simulations using a single model, run at different spatial resolutions, and with convective parameterizations switched on or off against Sahelian observations. Convection-permitting simulations at 4 and 12 km capture the observed relationships between soil moisture and convective triggering, emphasizing the importance of surface-driven mesoscale dynamics. However, with the inclusion of the convection scheme at 12 km, the behavior of the model fundamentally alters, switching from negative to positive feedback. Similar positive feedback is found in 9 out of 10 Regional Climate Models run at 50 km. These results raise questions about the accuracy of the feedback in regional models based on current convective parameterizations.

Loss of summertime Arctic sea ice will lead to a large increase in the emission of aerosols and precursor gases from the ocean surface. It has been suggested that these enhanced emissions will exert substantial aerosol radiative forcings, dominated by the indirect effect of aerosol on clouds. Here, we investigate the potential for these indirect forcings using a global aerosol microphysics model evaluated against aerosol observations from the ASCOS campaign to examine the response of Arctic cloud condensation nuclei (CCN) to sea-ice retreat. In response to a complete loss of summer ice, we find that north of 70° N emission fluxes of sea-salt, marine primary organic aerosol (OA) and dimethyl sulphide increase by a factor of ~10, ~4 and ~15, respectively. However, the CCN response is weak, with negative changes over the central Arctic ocean. The weak response is due to the efficient scavenging of aerosol by extensive drizzling stratocumulus clouds. In the scavenging-dominated Arctic environment, the production of condensable vapour from oxidation of dimethyl sulphide grows particles to sizes where they can be scavenged. This loss is not sufficiently compensated by new particle formation, due to the suppression of nucleation by the large condensation sink resulting from sea-salt and primary OA emissions. Thus, our results suggest that increased aerosol emissions will not cause a climate feedback through changes in cloud microphysical and radiative properties.

Understanding the rapidly changing climate in the Arctic is limited by a lack of understanding of underlying strong feedback mechanisms that are specific to the Arctic. Progress in this field can only be obtained by process-level observations; this is the motivation for intensive ice-breaker-based campaigns such as the Arctic Summer Cloud-Ocean Study (ASCOS), described here. However, detailed field observations also have to be put in the context of the larger-scale meteorology, and short field campaigns have to be analysed within the context of the underlying climate state and temporal anomalies from this. To aid in the analysis of other parameters or processes observed during this campaign, this paper provides an overview of the synoptic-scale meteorology and its climatic anomaly during the ASCOS field deployment. It also provides a statistical analysis of key features during the campaign, such as key meteorological variables, the vertical structure of the lower troposphere and clouds, and energy fluxes at the surface. In order to assess the representativity of the ASCOS results, we also compare these features to similar observations obtained during three earlier summer experiments in the Arctic Ocean: the AOE-96, SHEBA and AOE-2001 expeditions. We find that these expeditions share many key features of the summertime lower troposphere. Taking ASCOS and the previous expeditions together, a common picture emerges with a large amount of low-level cloud in a well-mixed shallow boundary layer, capped by a weak to moderately strong inversion where moisture, and sometimes also cloud top, penetrate into the lower parts of the inversion. Much of the boundary-layer mixing is due to cloud-top cooling and subsequent buoyant overturning of the cloud. The cloud layer may, or may not, be connected with surface processes depending on the depths of the cloud and surface-based boundary layers and on the relative strengths of surface-shear and cloud-generated turbulence. The latter also implies a connection between the cloud layer and the free troposphere through entrainment at cloud top.

Understanding the rapidly changing climate in the Arctic is limited by a lack of understanding of underlying strong feedback mechanisms that are specific to the Arctic. Progress in this field can only be obtained by process-level observations; this is the motivation for intensive ice-breaker-based campaigns such as that described in this paper: the Arctic Summer Cloud-Ocean Study (ASCOS). However, detailed field observations also have to be put in the context of the larger-scale meteorology, and short field campaigns have to be analysed within the context of the underlying climate state and temporal anomalies from this. To aid in the analysis of other parameters or processes observed during this campaign, this paper provides an overview of the synoptic-scale meteorology and its climatic anomaly during the ASCOS field deployment. It also provides a statistical analysis of key features during the campaign, such as some key meteorological variables, the vertical structure of the lower troposphere and clouds, and energy fluxes at the surface. In order to assess the representativity of the ASCOS results, we also compare these features to similar observations obtained during three earlier summer experiments in the Arctic Ocean, the AOE-96, SHEBA and AOE-2001 expeditions. We find that these expeditions share many key features of the summertime lower troposphere. Taking ASCOS and the previous expeditions together, a common picture emerges with a large amount of low-level cloud in a well-mixed shallow boundary layer, capped by a weak to moderately strong inversion where moisture, and sometimes also cloud top, penetrate into the lower parts of the inversion. Much of the boundary-layer mixing is due to cloud-top cooling and subsequent buoyant overturning of the cloud. The cloud layer may, or may not, be connected with surface processes depending on the depths of the cloud and surface-based boundary layers and on the relative strengths of surface-shear and cloud-buoyancy turbulence generation. The latter also implies a connection between the cloud layer and the free troposphere through entrainment at cloud top.

Observations made during late summer in the central Arctic Ocean, as part of the Arctic Summer Cloud Ocean Study (ASCOS), are used to evaluate cloud and vertical temperature structure in the Met Office Unified Model (MetUM). The observation period can be split into 5 regimes; the first two regimes had a large number of frontal systems, which were associated with deep cloud. During the remainder of the campaign a layer of low-level cloud occurred, typical of central Arctic summer conditions, along with two periods of greatly reduced cloud cover. The short-range operational NWP forecasts could not accurately reproduce the observed variations in near-surface temperature. A major source of this error was found to be the temperature-dependant surface albedo parameterisation scheme. The model reproduced the low-level cloud layer, though it was too thin, too shallow, and in a boundary-layer that was too frequently well-mixed. The model was also unable to reproduce the observed periods of reduced cloud cover, which were associated with very low cloud condensation nuclei (CCN) concentrations (−3). As with most global NWP models, the MetUM does not have a prognostic aerosol/cloud scheme but uses a constant CCN concentration of 100 cm−3 over all marine environments. It is therefore unable to represent the low CCN number concentrations and the rapid variations in concentration frequently observed in the central Arctic during late summer. Experiments with a single-column model configuration of the MetUM show that reducing model CCN number concentrations to observed values reduces the amount of cloud, increases the near-surface stability, and improves the representation of both the surface radiation fluxes and the surface temperature. The model is shown to be sensitive to CCN only when number concentrations are less than 10–20 cm−3.

The first measurements of bubble size spectra within the near-surface waters of open leads in the central Arctic pack ice were obtained during the Artic Summer Cloud-Ocean Study (ASCOS) in August 2008 at 87–87.6° N, 1–11° W. A significant number of small bubbles (30–100 μm diameter) were present, with concentration decreasing rapidly with size from 100–560 μm; no bubbles larger than 560 μm were observed. The bubbles were present both during periods of low wind speed (U < 6 m/s) and when ice covered the surface of the lead. The low wind and short open-water fetch precludes production of bubbles by wave breaking suggesting that the bubbles are generated by processes below the surface. When the surface water was open to the atmosphere bubble concentrations increased with increasing heat loss to the atmosphere. The presence of substantial numbers of bubbles is significant because the bursting of bubbles at the surface provides a mechanism for the generation of aerosol and the ejection of biological material from the ocean into the atmosphere. Such a transfer has previously been proposed as a potential climate feedback linking marine biology and Arctic cloud properties.

Measurements of turbulent fluxes, clouds, radiation, and profiles of mean meteorological parameters, obtained over an ice floe in the central Arctic Ocean during the Arctic Ocean Experiment 2001, are used to evaluate the performance of U.K. Met Office Unified Model (MetUM) and Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) in the lower atmosphere during late summer. Both the latest version of the MetUM and the version operational in 2001 are used in the comparison to gain an insight as to whether updates to the model have improved its performance over the Arctic region. As with previous model evaluations over the Arctic, the pressure, humidity, and wind fields are satisfactorily represented in all three models. The older version of the MetUM underpredicts the occurrence of low-level Arctic clouds, and the liquid and ice cloud water partitioning is inaccurate compared to observations made during SHEBA. In the newer version, simulated ice and liquid water paths are improved, but the occurrence of low-level clouds are overpredicted. Both versions overestimate the amount of radiative heat absorbed at the surface, leading to a significant feedback of errors involving the surface albedo, which causes a large positive bias the surface temperature. Cloud forcing in COAMPS produces similar biases in the downwelling shortwave and longwave radiation fluxes to those produced by UM(G25). The surface albedo parameterization is, however, more realistic, and thus, the total heat flux and surface temperature are more accurate for the majority of the observation period.