Rising atmospheric CO2 concentrations are impacting the global terrestrial biosphere through indirect climate effects and direct effects on plant performance1-3. In tropical forests, long-term monitoring indicates a substantial CO2-driven carbon sink4. C4-grass-dominated tropical and subtropical savannas contribute approximately 30% of terrestrial net primary production5, and yet equivalent long-term analyses of CO2 responses are lacking. Here we show a clear and consistent result across a meta-analysis of 70 CO2-addition experiments and 32 years of in situ field observations from southern Africa: CO2 fertilization of wild C4 grasses is widespread in dry conditions. In experiments, grasses reduced stomatal conductance under higher levels of CO2, limiting water loss while increasing carbon gain. In the field, improved water use efficiency translated into increased C4 grass biomass production across three decades of observations. Finally, simulations via the Community Land Model6 suggest that CO2 fertilization of C4 grass aboveground productivity may continue to increase under future conditions. Together, these results challenge the view that C4 grasses are unresponsive to increasing levels of CO2, demonstrating instead that annual aboveground production of grasses in the field in southern Africa has increased by 28% over three decades (a CO2-driven increase of 75.1 g m-2 (95% confidence interval of 74.5-75.8 g m-2) or 0.37 tons C ha-1 of annual production). Although the fate of this carbon is uncertain (depending on feedbacks with fire, herbivory and woody vegetation), effects on the global carbon cycle may be profound.

Urban air pollution from traffic poses serious public health risks. Pollution exposure can be minimised through traffic routing systems; these currently rely on detailed local environmental information, which is often difficult to collect or generalise within and across cities. Here, we introduce a new data-driven approach for ready application to different urban road networks by directly relating NO2 to traffic density in a time-dependent and weather-corrected manner. We demonstrate this application by comparing pollution-optimal routings, using our novel direct NO2/density approach, to the conventional traffic assignment minimising user travel time, in a case study of Sheffield, UK. There, we find user-optimal traffic flows result in 21% higher total NO2 concentrations than pollution-optimal routings, while saving only 9% in total travel time: an average of 0.3 min per road. Our generalisable framework offers a practical alternative to current emissions-based models for air-quality-aware traffic control and environmental zone planning.

Large-scale reforestation, afforestation, and forest restoration schemes have gained global support as climate change mitigation strategies due to their significant carbon dioxide removal (CDR) potential. However, there has been limited research into the unintended consequences of forestation from a biophysical perspective. In the Community Earth System Model version 2 (CESM2), we apply a global forestation scenario, within a Paris Agreement-compatible warming scenario, to investigate the land surface and hydroclimate response. Compared to a control scenario where land use is fixed to present-day levels, the forestation scenario is up to 2 °C cooler at low latitudes by 2100, driven by a 10 % increase in evaporative cooling in forested areas. However, afforested areas where grassland or shrubland are replaced lead to a doubling of plant water demand in some tropical regions, causing significant decreases in soil moisture (∼ 5 % globally, 5 %–10 % regionally) and water availability (∼ 10 % globally, 10 %–15 % regionally) in regions with increased forest cover. While there are some increases in low cloud and seasonal precipitation over the expanded tropical forests, with enhanced negative cloud radiative forcing, the impacts on large-scale precipitation and atmospheric circulation are limited. This contrasts with the precipitation response to simulated large-scale deforestation found in previous studies. The forestation scenario demonstrates local cooling benefits without major disruption to global hydrodynamics beyond those already projected to result from climate change, in addition to the cooling associated with CDR. However, the water demands of extensive forestation, especially afforestation, have implications for its viability, given the uncertainty in future precipitation changes.

Forestation is widely proposed for carbon dioxide (CO2) removal, but its impact on climate through changes to atmospheric composition and surface albedo remains relatively unexplored. We assessed these responses using two Earth system models by comparing a scenario with extensive global forest expansion in suitable regions to other plausible futures. We found that forestation increased aerosol scattering and the greenhouse gases methane and ozone following increased biogenic organic emissions. Additionally, forestation decreased surface albedo, which yielded a positive radiative forcing (i.e., warming). This offset up to a third of the negative forcing from the additional CO2 removal under a 4°C warming scenario. However, when forestation was pursued alongside other strategies that achieve the 2°C Paris Agreement target, the offsetting positive forcing was smaller, highlighting the urgency for simultaneous emission reductions.

Forestation is widely proposed for carbon dioxide (CO 2 ) removal, but its impact on climate through changes to atmospheric composition and surface albedo remains relatively unexplored. We assessed these responses using two Earth system models by comparing a scenario with extensive global forest expansion in suitable regions to other plausible futures. We found that forestation increased aerosol scattering and the greenhouse gases methane and ozone following increased biogenic organic emissions. Additionally, forestation decreased surface albedo, which yielded a positive radiative forcing (i.e., warming). This offset up to a third of the negative forcing from the additional CO 2 removal under a 4°C warming scenario. However, when forestation was pursued alongside other strategies that achieve the 2°C Paris Agreement target, the offsetting positive forcing was smaller, highlighting the urgency for simultaneous emission reductions.

Model data and analysis code supporting the Geoscientific Model Development manuscript "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "

Reforestation is widely proposed for carbon dioxide (CO2) removal but the impact on climate, via atmospheric composition and surface albedo changes, remains relatively unexplored. Using two Earth System models, UKESM1 and CESM2, we compare scenarios where existing forests expand to a near biophysical limit (with croplands fixed at 2015 to preserve food production) with SSP1-2.6 and SSP3-7.0 at 2050 and 2095. In the reforestation scenario, global BVOC emissions are 18% (35%) higher than SSP3-7.0 at 2050 (2095) and 8% (12%) higher than SSP1-2.6. The resulting increases to secondary organic aerosols and aerosol scattering, from BVOC emission changes, drive a negative radiative forcing (RF). However, this is outweighed by the positive RF from increases to methane and ozone and decreases to surface albedo.The net RF is equivalent to CO2 increases of 13 (32) ppm relative to SSP3-7.0 at 2050 (2095) and 3 (8) ppm relative to SSP1-2.6. These indirect factors offset ~25% of the additional CO2 removal arising from reforestation relative to SSP3-7.0 and ~10% relative to SSP1-2.6. This highlights the importance of assessing the full response of the Earth System to reforestation, rather than just the potential CO2 removal.

Reliable climate change projections over East Africa are vital because of regional vulnerability to precipitation changes. However, global climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) display significant biases in their representation of key East African rainfall seasons, which call into question the reliability of projected climate change. We investigate the links between models' representation of rainfall over Kenya during the long and short rains and the proximate Walker circulation. There is a strong correlation in the short rains between model biases in Kenyan rainfall and in the mid-to-upper tropospheric vertical velocity associated with this circulation. The overturning Indian Ocean Walker cell at the equator is absent in 5/25 models during the short rains - these models exhibit wet biases. In the long rains, dry biased models overestimate the strength of the descending limb of the circulation over East Africa. Omega biases over the Congo Basin are linked to broader Walker circulation biases. During the long rains, models overestimate equatorial descent more generally across the Western Hemisphere Tropics (0 degrees E-200 degrees E). A significant correlation is obtained across the model ensemble between model rainfall over Kenya and Western Hemisphere equatorial ascent during November. Atmosphere-only models display some improvements over coupled models, but biases of a similar magnitude remain. We therefore propose Indian Ocean Walker circulation errors as a key source of bias in CMIP5 East African rainfall. The results add to recent work on CMIP5 biases in this region, demonstrating that the Indian Ocean Walker circulation should be a focus for future model improvement and a consideration when assessing the reliability of climate projections over East Africa. Further work is needed on the causes of Walker circulation biases (in particular the role of SST), and on understanding the impact of Walker circulation biases on modelled tropical rainfall elsewhere in the world.

The complex topography of East Africa poses challenges for accurate modeling of regional climate. The Turkana Channel in northwestern Kenya is an important feature because of a persistent low‐level jet (LLJ) that blows through it, which has complex interactions with local and regional rainfall. We establish the annual cycle and interannual variability of the LLJ in the ERA5, MERRA‐2, and JRA‐55 reanalyses. The jet is strongest during wet seasons in the surrounding region. Results suggest a statistically significant weakening of the LLJ over the last 30–40 years in two out of the three reanalyses. We propose an explanation based on the jet's relationship with regional warming patterns and zonal surface pressure gradients, which link the jet to larger‐scale climate dynamics including the Walker Circulation. If these changes continue in the future, there may be significant implications for rainfall including increases in northwest Kenya and decreases further inland. However, the global models used to produce climate projections vary in their simulations of the LLJ in part because they represented topography. Consequently, it is not possible to assess how future LLJ changes will affect regional climate using CMIP5 models alone. Differences between the reanalyses preclude their direct use for model evaluation. Improving the processes by which topographical observations are mapped onto model grids could lead to improvements in the simulation of the East African climate. A field campaign to measure the LLJ directly could resolve uncertainties in the literature, help constrain reanalyses, and determine which models have the most realistic LLJ representation.

East Africa is vulnerable to hydroclimatic variability and change, and therefore reliable projections of future rainfall are important for climate change adaptation planning. However, the region’s climate is affected by complex multi-scalar processes and poorly represented in climate models, leading to uncertainty surrounding rainfall change. The importance of circulation features in controlling long-term rainfall variability provides an opportunity to constrain projections. We use a process-based climate model evaluation methodology to demonstrate links between Coupled Model Intercomparison Project phase 5 (CMIP5) rainfall biases over Kenya and circulation biases in the Indian Ocean Walker Circulation (IOWC). During both the long and short rains, models with wet biases in historical runs continue to be wet in future. Wet future projections are associated with enhanced easterly winds over the equatorial Indian Ocean, as well as decreasing vertical velocity over Kenya and increasing vertical velocity over the Maritime Continent. We demonstrate that models with a simulated IOWC which is close to reanalysis in historical runs project different changes to Kenyan rainfall than those which do not. In particular, the projected rainfall increase during the long rains is confined to a single month (April) in these models. We call for a renewed focus on the Walker Circulation as a way to constrain uncertain rainfall projections elsewhere in the tropics.

The long rains occurring in March-April-May (MAM) in East Africa have been notoriously difficult to capture in climate models and the CMIP5 ensemble. This is highlighted by the fact that CMIP5 models show increasing MAM rainfall in East Africa while measurements show a drying at the end of the 20th century. Evaluating models with a process-based analysis is key to selecting which models to use for projections on a regional scale, but the MAM season poses an extra challenge in this regard since the processes governing the rainfall are hard to pinpoint in the historical period.