Old-Growth Forest Disturbance in the Ukrainian Carpathians

Human activity has greatly reduced the area of old-growth forest in Europe, with some of the largest remaining fragments in the Carpathian Mountains of south-western Ukraine. We used satellite image analysis to calculate old-growth forest disturbance in this region from 2010 to 2019. Over this period, we identified 1335 ha of disturbance in old-growth forest, equivalent to 1.8% of old-growth forest in the region. During 2015 to 2019, the average annual disturbance rate was 0.34%, varying with altitude, distance to settlements and location within the region. Disturbance rates were 7–8 times lower in protected areas compared to outside of protected areas. Only one third of old-growth forest is currently within protected areas; expansion of the protected area system to include more old-growth forests would reduce future loss. A 2017 law that gave protection to all old-growth forest in Ukraine had no significant impact on disturbance rates in 2018, but in 2019 disturbance rates reduced to 0.19%. Our analysis is the first indication that this new legislation may be reducing loss of old-growth forest in Ukraine

Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians

Old-growth forests are an important, rare and endangered habitat in Europe. The ability to identify old-growth forests through remote sensing would be helpful for both conservation and forest management. We used data on beech, Norway spruce and mountain pine old-growth forests in the Ukrainian Carpathians to test whether Sentinel-2 satellite images could be used to correctly identify these forests. We used summer and autumn 2017 Sentinel-2 satellite images comprising 10 and 20 m resolution bands to create 6 vegetation indices and 9 textural features. We used a Random Forest classification model to discriminate between dominant tree species within old-growth forests and between old-growth and other forest types. Beech and Norway spruce were identified with an overall accuracy of around 90%, with a lower performance for mountain pine (70%) and mixed forest (40%). Old-growth forests were identified with an overall classification accuracy of 85%. Adding textural features, band standard deviations and elevation data improved accuracies by 3.3%, 2.1% and 1.8% respectively, while using combined summer and autumn images increased accuracy by 1.2%. We conclude that Random Forest classification combined with Sentinel-2 images can provide an effective option for identifying old-growth forests in Europe

Determination of Structural Characteristics of Old-Growth Forest in Ukraine Using Spaceborne LiDAR

A forest’s structure changes as it progresses through developmental stages from establishment to old-growth forest. Therefore, the vertical structure of old-growth forests will differ from that of younger, managed forests. Free, publicly available spaceborne Laser Range and Detection (LiDAR) data designed for the determination of forest structure has recently become available through NASA’s General Ecosystem and Development Investigation (GEDI). We use this data to investigate the structure of some of the largest remaining old-growth forests in Europe in the Ukrainian Carpathian Mountains. We downloaded 18489 cloud-free shots in the old-growth forest (OGF) and 20398 shots in adjacent non-OGF areas during leaf-on, snow-free conditions. We found significant differences between OGF and non-OGF over a wide range of structural metrics. OGF was significantly more open, with a more complex vertical structure and thicker ground-layer vegetation. We used Random Forest classification on a range of GEDI-derived metrics to classify OGF shapefiles with an accuracy of 73%. Our work demonstrates the use of spaceborne LiDAR for the identification of old-growth forests

Synergistic Use of Sentinel-1 and Sentinel-2 to Map Natural Forest and Acacia Plantation and Stand Ages in North-Central Vietnam

Many remote sensing studies do not distinguish between natural and planted forests. We combine C-Band Synthetic Aperture Radar (Sentinel-1, S-1) and optical satellite imagery (Sentinel-2, S-2) and examine Random Forest (RF) classification of acacia plantations and natural forest in North-Central Vietnam. We demonstrate an ability to distinguish plantation from natural forest, with overall classification accuracies of 87% for S-1, and 92.5% and 92.3% for S-2 and for S-1 and S-2 combined respectively. We found that the ratio of the Short-Wave Infrared Band to the Red Band proved most effective in distinguishing acacia from natural forest. We used RF on S-2 imagery to classify acacia plantations into 6 age classes with an overall accuracy of 70%, with young plantation consistently separated from older. However, accuracy was lower at distinguishing between the older age classes. For both distinguishing plantation and natural forest, and determining plantation age, a combination of radar and optical imagery did nothing to improve classification accuracy

Effects of windscape on three-dimensional foraging behaviour in a wide-ranging marine predator, the northern gannet

Marine birds are strongly exposed to weather conditions at sea but to date, few studies have investigated the influences of wind or rainfall on their time-activity budgets or foraging routines. Here, we used data from GPS and pressure loggers to investigate the effects of wind speed and direction and rainfall on the 3-dimensional foraging behaviour of gannets Morus bassanus breeding at Bass Rock, Scotland. We found that birds spent more time actively foraging during stronger winds, but there was no subsequent increase in overall trip duration because individuals compensated by decreasing the time they spent on the water during stronger winds. Birds returned more quickly from distant foraging grounds, and those encountering head winds spent less time on the water and so were able to compensate to some extent for an adverse effect of head winds on speed of travel over the return leg. These data strongly suggest that by reducing time spent on the water, birds were able to buffer trip durations against adverse effects of strong winds encountered during both commuting and active foraging. Birds also commuted at greater heights with increasing tail wind speed and at lower heights with increasing head wind speed, potentially providing an additional behavioural buffer against the adverse effects of strong head winds during foraging trips. There was no discernible effect of rain on foraging, but the behavioural flexibility recorded here is likely to be critical to maintaining nest attendance patterns and food provisioning rates of chicks across variable environmental conditions encountered at sea

Observations of increased cloud cover over irrigated agriculture in an arid environment

Irrigated agriculture accounts for 20% of global cropland area and may alter climate locally and globally, but feedbacks on clouds and rainfall remain highly uncertain, particularly in arid regions. Non-renewable groundwater in arid regions accounts for 20% of global irrigation water demand, and quantifying these feedbacks is crucial for the prediction of long-term water use in a changing climate. Here we use satellite data to show how irrigated crops in an arid environment alter land-surface properties, cloud cover and rainfall patterns. Land surface temperatures (LST) over the cropland are 5-7 K lower than their surroundings, despite a lower albedo, suggesting that Bowen ratio is strongly reduced (and latent heat fluxes increased) over the irrigated cropland. Daytime cloud cover is increased by up to 15% points (a relative increase of 60%), with increased cloud development in the morning, and a greater afternoon peak in cloud. Cloud cover is significantly correlated with interannual variations in vegetation and LST. Afternoon rainfall also appears to be enhanced around the irrigation. The cloud feedback is the opposite of what has been previously observed in tropical and semiarid regions, suggesting different processes drive land-atmosphere feedbacks in very dry environments. Increased cloud and rainfall, and associated increases in diffuse radiation and reductions in temperature, are likely to benefit vegetation growth. Predictions of changes in crop productivity due to climate change and the impacts of global land-use change on climate and the use of water-resources would therefore benefit from including these effects

Divergent Representation of Precipitation Recycling in the Amazon and the Congo in CMIP6 Models

Moisture evaporated from the land contributing to precipitation in a given area is known as precipitation recycling and needs to be accurately represented in climate models. The Amazon and Congo basins are reported to have the highest precipitation recycling rates globally, but model representation has not yet been assessed over these regions. We evaluated recycling over the Amazon and Congo in 45 Coupled Model Intercomparison Project Phase 6 models. Regional annual means from models and reanalyzes agreed well over both basins. Models captured seasonal variation in recycling over the Congo but there was a large-scale underestimation of recycling during the Amazon dry-to-wet transition season relative to ERA5, caused by models underestimating Amazon evapotranspiration and overestimating incoming wind speed and associated water vapor imports. Both regions show robust declines in precipitation recycling over the next century under future climate-change scenarios. Our results suggest models may underestimate impacts of deforestation on regional precipitation in the Amazon

The impact of COVID-19 control measures on air quality in China

The outbreak of Coronavirus Disease 2019 (COVID-19) in China in January 2020 prompted substantial control measures including social distancing measures, suspension of public transport and industry, and widespread cordon sanitaires ('lockdowns'), that have led to a decrease in industrial activity and air pollution emissions over a prolonged period. We use a 5 year dataset from China's air quality monitoring network to assess the impact of control measures on air pollution. Pollutant concentration time series are decomposed to account for the inter-annual trend, seasonal cycles and the effect of Lunar New Year, which coincided with the COVID-19 outbreak. Over 2015–2019, there were significant negative trends in particulate matter (PM2.5, −6% yr−1) and sulphur dioxide (SO2, −12% yr−1) and nitrogen dioxide (NO2, −2.2% yr−1) whereas there were positive trends in ozone (O3, + 2.8% yr−1). We quantify the change in air quality during the LNY holiday week, during which pollutant concentrations increase on LNY's day, followed by reduced concentrations in the rest of the week. After accounting for interannual trends and LNY we find NO2 and PM concentrations were significantly lower during the lockdown period than would be expected, but there were no significant impacts on O3. Largest reductions occurred in NO2, with concentrations 27.0% lower on average across China, during the lockdown. Average concentrations of PM2.5 and PM10 across China were respectively 10.5% and 21.4% lower during the lockdown period. The largest reductions were in Hubei province, where NO2 concentrations were 50.5% lower than expected during the lockdown. Concentrations of affected pollutants returned to expected levels during April, after control measures were relaxed

The contribution of fungal spores and bacteria to regional and global aerosol number and ice nucleation immersion freezing rates

Primary biological aerosol particles (PBAPs) may play an important role in aerosol-climate interactions, in particular by affecting ice formation in mixed phase clouds. However, the role of PBAPs is poorly understood because the sources and distribution of PBAPs in the atmosphere are not well quantified. Here we include emissions of fungal spores and bacteria in a global aerosol microphysics model and explore their contribution to concentrations of supermicron particle number, cloud condensation nuclei (CCN) and immersion freezing rates. Simulated surface annual mean concentrations of fungal spores are ∼ 2.5 × 104 mg-3 over continental midlatitudes and 1 × 105 mg-3 over tropical forests. Simulated surface concentrations of bacteria are 2.5 × 104 mg-3 over most continental regions and 5 × 104 mg-3 over grasslands of central Asia and North America. These simulated surface number concentrations of fungal spores and bacteria are broadly in agreement with the limited available observations. We find that fungal spores and bacteria contribute 8 and 5% respectively to simulated continental surface mean supermicron number concentrations, but have very limited impact on CCN concentrations, altering regional concentrations by less than 1%. In agreement with previous global modelling studies, we find that fungal spores and bacteria contribute very little (3 × 10g-3%, even when we assume upper limits for ice nucleation activity) to global average immersion freezing ice nucleation rates, which are dominated by soot and dust. However, at lower altitudes (400 to 600 hPa), where warmer temperatures mean that soot and dust may not nucleate ice, we find that PBAP controls the immersion freezing ice nucleation rate. This demonstrates that PBAPs can be of regional importance for IN formation, in agreement with case study observations

Importance of tropospheric volcanic aerosol for indirect radiative forcing of climate

Observations and models have shown that continuously degassing volcanoes have a potentially large effect on the natural background aerosol loading and the radiative state of the atmosphere. We use a global aerosol microphysics model to quantify the impact of these volcanic emissions on the cloud albedo radiative forcing under pre-industrial (PI) and present-day (PD) conditions. We find that volcanic degassing increases global annual mean cloud droplet number concentrations by 40% under PI conditions, but by only 10% under PD conditions. Consequently, volcanic degassing causes a global annual mean cloud albedo effect of −1.06 W m−2 in the PI era but only −0.56 W m−2 in the PD era. This non-equal effect is explained partly by the lower background aerosol concentrations in the PI era, but also because more aerosol particles are produced per unit of volcanic sulphur emission in the PI atmosphere. The higher sensitivity of the PI atmosphere to volcanic emissions has an important consequence for the anthropogenic cloud radiative forcing because the large uncertainty in volcanic emissions translates into an uncertainty in the PI baseline cloud radiative state. Assuming a −50/+100% uncertainty range in the volcanic sulphur flux, we estimate the annual mean anthropogenic cloud albedo forcing to lie between −1.16 W m−2 and −0.86 W m−2. Therefore, the volcanically induced uncertainty in the PI baseline cloud radiative state substantially adds to the already large uncertainty in the magnitude of the indirect radiative forcing of climate