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The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow–vegetation–atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land–atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined 3 years (2019–2021) of continuous eddy covariance flux measurements of the net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a 1-month delay in melt-out date, which falls in the 92nd percentile in the distribution of yearly melt-out dates in the period 2001–2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to 1 month later than the other ecosystem types. While the summertime average normalized difference vegetation index (NDVI) was reduced considerably during the extreme-snow year 2020, it reached the same maximum as in the other years for all but one of the ecosystem types (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model, as well as by the high correlation of flux magnitudes (correlation coefficient r=0.92 for NEE and r=0.89 for ET) between both areas. The 1-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021 and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (−31 to −6 gC m−2 yr−1) to a source (34 to 20 gC m−2 yr−1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.

As temperatures decrease in autumn, vegetation of temperate and boreal ecosystems increases its tolerance to freezing. This process, known as hardening, results in a set of physiological changes at the molecular level that initiate modifications of cell membrane composition and the synthesis of anti-freeze proteins. Together with the freezing of extracellular water, anti-freeze proteins reduce plant water potentials and xylem conductivity. To represent the responses of vegetation to climate change, land surface schemes increasingly employ “hydrodynamic” models that represent the explicit fluxes of water from soil and through plants. The functioning of such schemes under frozen soil conditions, however, is poorly understood. Nonetheless, hydraulic processes are of major importance in the dynamics of these systems, which can suffer from, e.g., winter “frost drought” events. In this study, we implement a scheme that represents hardening into CLM5.0-FATES-Hydro. FATES-Hydro is a plant hydrodynamics module in FATES, a cohort model of vegetation physiology, growth, and dynamics hosted in CLM5.0. We find that, in frozen systems, it is necessary to introduce reductions in plant water loss associated with hardening to prevent winter desiccation. This work makes it possible to use CLM5.0-FATES-Hydro to model realistic impacts from frost droughts on vegetation growth and photosynthesis, leading to more reliable projections of how northern ecosystems respond to climate change.

We assess the quality of regional and global ozone reanalysis data for vegetation modeling and ozone (O3) risk mapping over subarctic Europe where monitoring is sparse. Reanalysis data can be subject to systematic errors originating from, for example, quality of assimilated data, distribution and strength of precursor sources, incomprehensive atmospheric chemistry or land–atmosphere exchange, and spatiotemporal resolution. Here, we evaluate two selected global products and one regional ozone reanalysis product. Our analysis suggests that global reanalysis products do not reproduce observed ground-level ozone well in the subarctic region. Only the Copernicus Atmosphere Monitoring Service Regional Air Quality (CAMSRAQ) reanalysis ensemble sufficiently captures the observed seasonal cycle. We also compute the root mean square error (RMSE) by season. The RMSE variation between (2.6–6.6) ppb suggests inherent challenges even for the best reanalysis product (CAMSRAQ). O3 concentrations in the subarctic region are systematically underestimated by (2–6) ppb compared to the ground-level background ozone concentrations derived from observations. Spatial patterns indicate a systematical underestimation of ozone abundance by the global reanalysis products on the west coast of northern Fennoscandia. Furthermore, we explore the suitability of CAMSRAQ for gap-filling at one site in northern Norway with a long-term record but not belonging to the observational network. We devise a reconstruction method based on Reynolds decomposition and adhere to recommendations by the United Nations Economic Commission for Europe (UNECE) Long-Range Transboundary Air Pollution (LRTAP) convention. The thus reconstructed data for 2 weeks in July 2018 are compared with CAMSRAQ evaluated at the nearest-neighbor grid point. Our reconstruction method's performance (76 % accuracy) is comparable with CAMSRAQ (80 % accuracy), but diurnal extremes are underestimated by both.

Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

It has long been recognized that the amplitude of the seasonal cycle can substantially modify climate features in distinct timescales. This study evaluates the impact of the enhanced seasonality characteristic of the Marine Isotope Stage 31 (MIS31) on the El Niño–Southern Oscillation (ENSO). Based upon coupled climate simulations driven by present-day (CTR) and MIS31 boundary conditions, we demonstrate that the CTR simulation shows a significant concentration of power in the 3–7-year band and on the multidecadal timescale between 15 and 30 years. However, the MIS31 simulation shows drastically modified temporal variability of the ENSO, with stronger power spectrum at interannual timescales but the absence of decadal periodicity. Increased meridional gradient of sea surface temperature (SST) and wind stress in the Northern Hemisphere subtropics are revealed to be the primary candidates responsible for changes in the equatorial variability. The oceanic response to the MIS31 ENSO extends to the extratropics, and fits nicely with SST anomalies delivered by paleoreconstructions. The implementation of the MIS31 conditions results in a distinct global monsoon system and its link to the ENSO in respect to current conditions. In particular, the Indian monsoon intensified but no correlation with ENSO is found in the MIS31 climate, diverging from conditions delivered by our current climate in which this monsoon is significantly correlated with the NIÑO34 index. This indicates that monsoonal precipitation for this interglacial is more closely connected to hemispherical features than to the tropical–extratropical climate interaction.

The Indo-Gangetic Plain (IGP) is one of the main wheat-production regions in India and the world. With climate change, wheat yields in this region will be affected through changes in temperature and precipitation and decreased water availability for irrigation, raising major concerns for national and international food security. Here we use a regional climate model and a crop model to better understand the direct (via changes in temperature and precipitation) and indirect (via a decrease in irrigation availability) impacts of climate change on wheat yields at four sites spread across different states of the IGP: Punjab, Haryana, Uttar Pradesh and Bihar. The results show an increase in mean temperature and precipitation as well as maximum temperature during the growing season or Rabi season (November–April). The direct impact of climate change, via changes in temperature and precipitation, leads to wheat yield losses between −1% and −8% depending on the site examined. Then, the indirect impact of climate change is examined, considering the impact of climate change on water availability leading to a decrease in irrigation. In this case, the yield losses become significant and much higher, reaching −4% to −36% depending on the site examined and the irrigation regime chosen (6, 5, 3 or 1 irrigations). This work shows that the indirect impacts of climate change may be more detrimental than the direct climatic effects for the future wheat yields in the IGP. It also emphasizes the complexity of climatic risk and the necessity of integrating indirect impacts of climate change to fully assess how it affects agriculture and choose the adequate adaptation response.

A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 °C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.

Increased shrub and tree cover in high latitudes is a widely observed response to climate change that can lead to positive feedbacks to the regional climate. In this study we evaluate the sensitivity of the near-surface atmosphere to a potential increase in shrub and tree cover in the northern Fennoscandia region. We have applied the Weather Research and Forecasting (WRF) model with the Noah-UA land surface module in evaluating biophysical effects of increased shrub cover on the near-surface atmosphere at a fine resolution (5.4 km × 5.4 km). Perturbation experiments are performed in which we prescribe a gradual increase in taller vegetation in the alpine shrub and tree cover according to empirically established bioclimatic zones within the study region. We focus on the spring and summer atmospheric response. To evaluate the sensitivity of the atmospheric response to inter-annual variability in climate, simulations were conducted for two contrasting years, one warm and one cold. We find that shrub and tree cover increase leads to a general increase in near-surface temperatures, with the highest influence seen during the snowmelt season and a more moderate effect during summer. We find that the warming effect is stronger in taller vegetation types, with more complex canopies leading to decreases in the surface albedo. Counteracting effects include increased evapotranspiration, which can lead to increased cloud cover, precipitation, and snow cover. We find that the strength of the atmospheric feedback is sensitive to snow cover variations and to a lesser extent to summer temperatures. Our results show that the positive feedback to high-latitude warming induced by increased shrub and tree cover is a robust feature across inter-annual differences in meteorological conditions and will likely play an important role in land–atmosphere feedback processes in the future.

Marine Isotope Stage 31 (MIS31, between 1085 and 1055 ka) was characterized by higher extratropical air temperatures and a substantial recession of polar glaciers compared to today. Paleoreconstructions and model simulations have increased the understanding of the MIS31 interval, but questions remain regarding the role of the Atlantic and Pacific oceans in modifying the climate associated with the variations in Earth's orbital parameters. Multi-century coupled climate simulations, with the astronomical configuration of the MIS31 and modified West Antarctic Ice Sheet (WAIS) topography, show an increase in the thermohaline flux and northward oceanic heat transport (OHT) in the Pacific Ocean. These oceanic changes are driven by anomalous atmospheric circulation and increased surface salinity in concert with a stronger meridional overturning circulation (MOC). The intensified northward OHT is responsible for up to 85 % of the global OHT anomalies and contributes to the overall reduction in sea ice in the Northern Hemisphere (NH) due to Earth's astronomical configuration. The relative contributions of the Atlantic Ocean to global OHT and MOC anomalies are minor compared to those of the Pacific. However, sea ice changes are remarkable, highlighted by decreased (increased) cover in the Ross (Weddell) Sea but widespread reductions in sea ice across the NH.

Amplified warming at high latitudes over the past few decades has led to changes in the boreal and Arctic climate system such as structural changes in high-latitude ecosystems and soil moisture properties. These changes trigger land–atmosphere feedbacks through altered energy partitioning in response to changes in albedo and surface water fluxes. Local-scale changes in the Arctic and boreal zones may propagate to affect large-scale climatic features. In this study, MODIS land surface data are used with the Weather Research and Forecasting model (WRF V3.5.1) and Noah land surface model (LSM), in a series of experiments to investigate the sensitivity of the overlying atmosphere to perturbations in the structural vegetation in the northern European boreal ecosystem. Emphasis is placed on surface energy partitioning and near-surface atmospheric variables, and their response to observed and anticipated land cover changes. We find that perturbations simulating northward migration of evergreen needleleaf forest into tundra regions cause an increase in latent rather than sensible heat fluxes during the summer season. Shrub expansion in tundra areas has only small effects on surface fluxes. Perturbations simulating the northward migration of mixed forest across the present southern border of the boreal forest, have largely opposite effects on the summer latent heat flux, i.e., they lead to a decrease and act to moderate the overall mean regional effects of structural vegetation changes on the near-surface atmosphere.

The international research project RECONCILE has addressed central questions regarding polar ozone depletion, with the objective to quantify some of the most relevant yet still uncertain physical and chemical processes and thereby improve prognostic modelling capabilities to realistically predict the response of the ozone layer to climate change. This overview paper outlines the scope and the general approach of RECONCILE, and it provides a summary of observations and modelling in 2010 and 2011 that have generated an in many respects unprecedented dataset to study processes in the Arctic winter stratosphere. Principally, it summarises important outcomes of RECONCILE including (i) better constraints and enhanced consistency on the set of parameters governing catalytic ozone destruction cycles, (ii) a better understanding of the role of cold binary aerosols in heterogeneous chlorine activation, (iii) an improved scheme of polar stratospheric cloud (PSC) processes that includes heterogeneous nucleation of nitric acid trihydrate (NAT) and ice on non-volatile background aerosol leading to better model parameterisations with respect to denitrification, and (iv) long transient simulations with a chemistry-climate model (CCM) updated based on the results of RECONCILE that better reproduce past ozone trends in Antarctica and are deemed to produce more reliable predictions of future ozone trends. The process studies and the global simulations conducted in RECONCILE show that in the Arctic, ozone depletion uncertainties in the chemical and microphysical processes are now clearly smaller than the sensitivity to dynamic variability.

Satellite measurements of atmospheric trace gases have proved to be an invaluable tool for monitoring the Earth system. When these measurements are to be used for assessing tropospheric emissions and pollution, as for example in the case of nadir measurements of nitrogen dioxide (NO2), it is necessary to separate the stratospheric from the tropospheric signal. The SCIAMACHY instrument offers the unique opportunity to combine its measurements in limb- and nadir-viewing geometries into a tropospheric data product, using the limb measurements of the stratospheric NO2 abundances to correct the nadir measurements' total columns. In this manuscript, we present a novel approach to limb/nadir matching, calculating one stratospheric NO2 value from limb measurements for every single nadir measurement, abandoning global coverage for the sake of spatial accuracy. For comparison, modelled stratospheric NO2 columns from the Oslo CTM2 are also evaluated for stratospheric correction. Our study shows that stratospheric NO2 columns from SCIAMACHY limb measurements very well reflect stratospheric conditions. The zonal variability of the stratospheric NO2 field is captured by our matching algorithm, and the quality of the resulting tropospheric NO2 columns improves considerably. Both stratospheric datasets need to be adjusted to the level of the nadir measurements, because a time- and latitude-dependent bias to the measured nadir columns can be observed over clean regions. After this offset is removed, the two datasets agree remarkably well, and both stratospheric correction methods provide a significant improvement to the retrieval of tropospheric NO2 columns from the SCIAMACHY instrument.

The future impact of traffic emissions on atmospheric ozone and OH has been investigated separately for the three sectors AIRcraft, maritime SHIPping and ROAD traffic. To reduce uncertainties we present results from an ensemble of six different atmospheric chemistry models, each simulating the atmospheric chemical composition in a possible high emission scenario (A1B), and with emissions from each transport sector reduced by 5% to estimate sensitivities. Our results are compared with optimistic future emission scenarios (B1 and B1 ACARE), presented in a companion paper, and with the recent past (year 2000). Present-day activity indicates that anthropogenic emissions so far evolve closer to A1B than the B1 scenario. As a response to expected changes in emissions, AIR and SHIP will have increased impacts on atmospheric O3 and OH in the future while the impact of ROAD traffic will decrease substantially as a result of technological improvements. In 2050, maximum aircraft-induced O3 occurs near 80° N in the UTLS region and could reach 9 ppbv in the zonal mean during summer. Emissions from ship traffic have their largest O3 impact in the maritime boundary layer with a maximum of 6 ppbv over the North Atlantic Ocean during summer in 2050. The O3 impact of road traffic emissions in the lower troposphere peaks at 3 ppbv over the Arabian Peninsula, much lower than the impact in 2000. Radiative forcing (RF) calculations show that the net effect of AIR, SHIP and ROAD combined will change from a marginal cooling of −0.44 ± 13 mW m−2 in 2000 to a relatively strong cooling of −32 ± 9.3 (B1) or −32 ± 18 mW m−2 (A1B) in 2050, when taking into account RF due to changes in O3, CH4 and CH4-induced O3. This is caused both by the enhanced negative net RF from SHIP, which will change from −19 ± 5.3 mW m−2 in 2000 to −31 ± 4.8 (B1) or −40 ± 9 mW m−2 (A1B) in 2050, and from reduced O3 warming from ROAD, which is likely to turn from a positive net RF of 12 ± 8.5 mW m−2 in 2000 to a slightly negative net RF of −3.1 ± 2.2 (B1) or −3.1 ± 3.4 (A1B) mW m−2 in the middle of this century. The negative net RF from ROAD is temporary and induced by the strong decline in ROAD emissions prior to 2050, which only affects the methane cooling term due to the longer lifetime of CH4 compared to O3. The O3 RF from AIR in 2050 is strongly dependent on scenario and ranges from 19 ± 6.8 (B1 ACARE) to 61 ± 14 mW m−2 (A1B). There is also a considerable span in the net RF from AIR in 2050, ranging from −0.54 ± 4.6 (B1 ACARE) to 12 ± 11 (A1B) mW m−2 compared to 6.6 ± 2.2 mW m−2 in 2000.

We present here the global chemical transport model Oslo CTM3, an update of the Oslo CTM2. The update comprises a faster transport scheme, an improved wet scavenging scheme for large scale rain, updated photolysis rates and a new lightning parameterization. Oslo CTM3 is better parallelized and allows for stable, large time steps for advection, enabling more complex or high spatial resolution simulations. A new treatment of the horizontal distribution of lightning is presented and found to compare well with measurements. The vertical distribution of lightning is updated and found to be a large contributor to CTM2–CTM3 differences, producing more NOx in the tropical middle troposphere, and less at the surface and at high altitudes. Compared with Oslo CTM2, Oslo CTM3 is faster, more capable and has better conceptual models for scavenging, vertical transport and fractional cloud cover. CTM3 captures stratospheric O3 better than CTM2, but shows minor improvements in terms of matching atmospheric observations in the troposphere. Use of the same meteorology to drive the two models shows that some features related to transport are better resolved by the CTM3, such as polar cap transport, while features like transport close to the vortex edge are resolved better in the Oslo CTM2 due to its required shorter transport time step. The longer transport time steps in CTM3 result in larger errors, e.g., near the jets, and when necessary the errors can be reduced by using a shorter time step. Using a time step of 30 min, the new transport scheme captures both large-scale and small-scale variability in atmospheric circulation and transport, with no loss of computational efficiency. We present a version of the new transport scheme which has been specifically tailored for polar studies, resulting in more accurate polar cap transport than the standard CTM3 transport, confirmed by comparison to satellite observations. Inclusion of tropospheric sulfur chemistry and nitrate aerosols in CTM3 is shown to be important to reproduce tropospheric O3, OH and the CH4 lifetime well.

In order to explore future air quality in Europe at the 2030 horizon, two emission scenarios developed in the framework of the Global Energy Assessment including varying assumptions on climate and energy access policies are investigated with an ensemble of six regional and global atmospheric chemistry transport models. A specific focus is given in the paper to the assessment of uncertainties and robustness of the projected changes in air quality. The present work relies on an ensemble of chemistry transport models giving insight into the model spread. Both regional and global scale models were involved, so that the ensemble benefits from medium-resolution approaches as well as global models that capture long-range transport. For each scenario a whole decade is modelled in order to gain statistical confidence in the results. A statistical downscaling approach is used to correct the distribution of the modelled projection. Last, the modelling experiment is related to a hind-cast study published earlier, where the performances of all participating models were extensively documented. The analysis is presented in an exposure-based framework in order to discuss policy relevant changes. According to the emission projections, ozone precursors such as NOx will drop down to 30% to 50% of their current levels, depending on the scenario. As a result, annual mean O3 will slightly increase in NOx saturated areas but the overall O3 burden will decrease substantially. Exposure to detrimental O3 levels for health (SOMO35) will be reduced down to 45% to 70% of their current levels. And the fraction of stations where present-day exceedences of daily maximum O3 is higher than 120 μg m−3 more than 25 days per year will drop from 43% down to 2 to 8%. We conclude that air pollution mitigation measures (present in both scenarios) are the main factors leading to the improvement, but an additional cobenefit of at least 40% (depending on the indicator) is brought about by the climate policy.

The hot summer of 2007 in southeast Europe has been studied using two regional atmospheric chemistry models; WRF-Chem and EMEP MSC-W. The region was struck by three heat waves and a number of forest fire episodes, greatly affecting air pollution levels. We have focused on ozone and its precursors using state-of-the-art inventories for anthropogenic, biogenic and forest fire emissions. The models have been evaluated against measurement data, and processes leading to ozone formation have been quantified. Heat wave episodes are projected to occur more frequently in a future climate, and therefore this study also makes a contribution to climate change impact research. The plume from the Greek forest fires in August 2007 is clearly seen in satellite observations of CO and NO2 columns, showing extreme levels of CO in and downwind of the fires. Model simulations reflect the location and influence of the fires relatively well, but the modelled magnitude of CO in the plume core is too low. Most likely, this is caused by underestimation of CO in the emission inventories, suggesting that the CO/NOx ratios of fire emissions should be re-assessed. Moreover, higher maximum values are seen in WRF-Chem than in EMEP MSC-W, presumably due to differences in plume rise altitudes as the first model emits a larger fraction of the fire emissions in the lowermost model layer. The model results are also in fairly good agreement with surface ozone measurements. Biogenic VOC emissions reacting with anthropogenic NOx emissions are calculated to contribute significantly to the levels of ozone in the region, but the magnitude and geographical distribution depend strongly on the model and biogenic emission module used. During the July and August heat waves, ozone levels increased substantially due to a combination of forest fire emissions and the effect of high temperatures. We found that the largest temperature impact on ozone was through the temperature dependence of the biogenic emissions, closely followed by the effect of reduced dry deposition caused by closing of the plants' stomata at very high temperatures. The impact of high temperatures on the ozone chemistry was much lower. The results suggest that forest fire emissions, and the temperature effect on biogenic emissions and dry deposition, will potentially lead to substantial ozone increases in a warmer climate.

The RegCM-CHEM4 is a new online climate-chemistry model based on the International Centre for Theoretical Physics (ICTP) regional climate model (RegCM4). Tropospheric gas-phase chemistry is integrated into the climate model using the condensed version of the Carbon Bond Mechanism (CBM-Z; Zaveri and Peters, 1999) with a fast solver based on radical balances. We evaluate the model over continental Europe for two different time scales: (1) an event-based analysis of the ozone episode associated with the heat wave of August 2003 and (2) a climatological analysis of a six-year simulation (2000–2005). For the episode analysis, model simulations show good agreement with European Monitoring and Evaluation Programme (EMEP) observations of hourly ozone over different regions in Europe and capture ozone concentrations during and after the summer 2003 heat wave event. For long-term climate simulations, the model captures the seasonal cycle of ozone concentrations with some over prediction of ozone concentrations in non-heat wave summers. Overall, the ozone and ozone precursor evaluation shows the feasibility of using RegCM-CHEM4 for decadal-length simulations of chemistry-climate interactions.

We discuss the capability of current state-of-the-art chemistry and transport models to reproduce air quality trends and interannual variability. Documenting these strengths and weaknesses on the basis of historical simulations is essential before the models are used to investigate future air quality projections. To achieve this, a coordinated modelling exercise was performed in the framework of the CityZEN European Project. It involved six regional and global chemistry-transport models (BOLCHEM, CHIMERE, EMEP, EURAD, OSLOCTM2 and MOZART) simulating air quality over the past decade in the Western European anthropogenic emissions hotspots. Comparisons between models and observations allow assessing the skills of the models to capture the trends in basic atmospheric constituents (NO2, O3, and PM10). We find that the trends of primary constituents are well reproduced (except in some countries – owing to their sensitivity to the emission inventory) although capturing the more moderate trends of secondary species such as O3 is more challenging. Apart from the long term trend, the modelled monthly variability is consistent with the observations but the year-to-year variability is generally underestimated. A comparison of simulations where anthropogenic emissions are kept constant is also investigated. We find that the magnitude of the emission-driven trend exceeds the natural variability for primary compounds. We can thus conclude that emission management strategies have had a significant impact over the past 10 yr, hence supporting further emission reductions.

The impact of future emissions from aviation and shipping on the atmospheric chemical composition has been estimated using an ensemble of six different atmospheric chemistry models. This study considers an optimistic emission scenario (B1) taking into account e.g. rapid introduction of clean and resource-efficient technologies, and a mitigation option for the aircraft sector (B1 ACARE), assuming further technological improvements. Results from sensitivity simulations, where emissions from each of the transport sectors were reduced by 5%, show that emissions from both aircraft and shipping will have a larger impact on atmospheric ozone and OH in near future (2025; B1) and for longer time horizons (2050; B1) compared to recent time (2000). However, the ozone and OH impact from aircraft can be reduced substantially in 2050 if the technological improvements considered in the B1 ACARE will be achieved. Shipping emissions have the largest impact in the marine boundary layer and their ozone contribution may exceed 4 ppbv (when scaling the response of the 5% emission perturbation to 100% by applying a factor 20) over the North Atlantic Ocean in the future (2050; B1) during northern summer (July). In the zonal mean, ship-induced ozone relative to the background levels may exceed 12% near the surface. Corresponding numbers for OH are 6.0 × 105 molecules cm−3 and 30%, respectively. This large impact on OH from shipping leads to a relative methane lifetime reduction of 3.92 (±0.48) on the global average in 2050 B1 (ensemble mean CH4 lifetime is 8.0 (±1.0) yr), compared to 3.68 (±0.47)% in 2000. Aircraft emissions have about 4 times higher ozone enhancement efficiency (ozone molecules enhanced relative to NOx molecules emitted) than shipping emissions, and the maximum impact is found in the UTLS region. Zonal mean aircraft-induced ozone could reach up to 5 ppbv at northern mid- and high latitudes during future summer (July 2050; B1), while the relative impact peaks during northern winter (January) with a contribution of 4.2%. Although the aviation-induced impact on OH is lower than for shipping, it still causes a reduction in the relative methane lifetime of 1.68 (±0.38)% in 2050 B1. However, for B1 ACARE the perturbation is reduced to 1.17 (±0.28)%, which is lower than the year 2000 estimate of 1.30 (±0.30)%. Based on the fully scaled perturbations we calculate net radiative forcings from the six models taking into account ozone, methane (including stratospheric water vapour), and methane-induced ozone changes. For the B1 scenario, shipping leads to a net cooling with radiative forcings of −28.0 (±5.1) and −30.8 (±4.8) mW m−2 in 2025 and 2050, respectively, due to the large impact on OH and, thereby, methane lifetime reductions. Corresponding values for the aviation sector shows a net warming effect with 3.8 (±6.1) and 1.9 (±6.3) mW m−2, respectively, but with a small net cooling of -0.6 (±4.6) mW m−2 for B1 ACARE in 2050.

Methyl chloride (CH3Cl) is a chlorine-containing trace gas in the atmosphere contributing significantly to stratospheric ozone depletion. Large uncertainties in estimates of its source and sink magnitudes and temporal and spatial variations currently exist. GEIA inventories and other bottom-up emission estimates are used to construct a priori maps of the surface fluxes of CH3Cl. The Model of Atmospheric Transport and Chemistry (MATCH), driven by NCEP interannually varying meteorological data, is then used to simulate CH3Cl mole fractions and quantify the time series of sensitivities of the mole fractions at each measurement site to the surface fluxes of various regional and global sources and sinks. We then implement the Kalman filter (with the unit pulse response method) to estimate the surface fluxes on regional/global scales with monthly resolution from January 2000 to December 2004. High frequency observations from the AGAGE, SOGE, NIES, and NOAA/ESRL HATS in situ networks and low frequency observations from the NOAA/ESRL HATS flask network are used to constrain the source and sink magnitudes. The inversion results indicate global total emissions around 4100 ± 470 Gg yr−1 with very large emissions of 2200 ± 390 Gg yr−1 from tropical plants, which turn out to be the largest single source in the CH3Cl budget. Relative to their a priori annual estimates, the inversion increases global annual fungal and tropical emissions, and reduces the global oceanic source. The inversion implies greater seasonal and interannual oscillations of the natural sources and sink of CH3Cl compared to the a priori. The inversion also reflects the strong effects of the 2002/2003 globally widespread heat waves and droughts on global emissions from tropical plants, biomass burning and salt marshes, and on the soil sink.

The response of clouds to sudden decreases in the flux of galactic cosmic rays (GCR) – Forbush decrease events – has been investigated using cloud products from the space-borne MODIS instrument, which has been in operation since 2000. By focusing on pristine Southern Hemisphere ocean regions we examine areas where we believe that a cosmic ray signal should be easier to detect than elsewhere. While previous studies have mainly considered cloud cover, the high spatial and spectral resolution of MODIS allows for a more thorough study of microphysical parameters such as cloud droplet size, cloud water content and cloud optical depth, in addition to cloud cover. Averaging the results from the 22 Forbush decrease events that were considered, no statistically significant correlations were found between any of the four cloud parameters and GCR, when autocorrelations were taken into account. Splitting the area of study into six domains, all of them have a negative correlation between GCR and cloud droplet size, in agreement with a cosmic ray – cloud coupling, but in only one of the domains (eastern Atlantic Ocean) was the correlation statistically significant. Conversely, cloud optical depth is mostly negatively correlated with GCR, and in the eastern Atlantic Ocean domain that correlation is statistically significant. For cloud cover and liquid water path, the correlations with GCR are weaker, with large variations between the different domains. When only the six Forbush decrease events with the largest amplitude (more than 10% decrease) were studied, the correlations fit the hypothesis slightly better, with 16 out of 24 correlations having the expected sign, although many of the correlations are quite weak. Introducing a time lag of a few days for clouds to respond to the cosmic ray signal the correlations tend to become weaker and even to change sign.

We have used the MODIS satellite data and two global aerosol models to investigate the relationships between aerosol optical depth (AOD) and cloud parameters that may be affected by the aerosol concentration. The relationships that are studied are mainly between AOD, on the one hand, and cloud cover, cloud liquid water path, and water vapour, on the other. Additionally, cloud droplet effective radius, cloud optical depth, cloud top pressure and aerosol Ångström exponent, have been analysed in a few cases. In the MODIS data we found, as in earlier studies, an enhancement in the cloud cover with increasing AOD. We find it likely that most of the strong increase in cloud cover with AOD, at least for AOD0.2 can be explained by larger water uptake close to the clouds since relative humidity is higher in regions with higher cloud cover. The efficiency of the hygroscopic growth depends on aerosol type, the hygroscopic nature of the aerosol, the relative humidity, and to some extent the cloud screening. By analysing the Ångström exponent we find that the hygroscopic growth of the aerosol is not likely to be a main contributor to the cloud cover increase with AOD. Since the largest increase in cloud cover with AOD is for low AOD (~0.2) and thus also for low cloud cover, we argue that cloud contamination is not likely to play a large role. However, interpretation of the complex relationships between AOD and cloud parameters should be made with great care and further work is clearly needed.

The indirect effect of aerosols via liquid clouds is investigated by comparing aerosol and cloud characteristics from the Global Climate Model CAM-Oslo to those observed by the MODIS instrument onboard the TERRA and AQUA satellites http://modis.gsfc.nasa.gov). The comparison is carried out for 15 selected regions ranging from remote and clean to densely populated and polluted. For each region, the regression coefficient and correlation coefficient for the following parameters are calculated: Aerosol Optical Depth vs. Liquid Cloud Optical Thickness, Aerosol Optical Depth vs. Liquid Cloud Droplet Effective Radius and Aerosol Optical Depth vs. Cloud Liquid Water Path. Modeled and observed correlation coefficients and regression coefficients are then compared for a 3-year period starting in January 2001. Additionally, global maps for a number of aerosol and cloud parameters crucial for the understanding of the aerosol indirect effect are compared for the same period of time. Significant differences are found between MODIS and CAM-Oslo both in the regional and global comparison. However, both the model and the observations show a positive correlation between Aerosol Optical Depth and Cloud Optical Depth in practically all regions and for all seasons, in agreement with the current understanding of aerosol-cloud interactions. The correlation between Aerosol Optical Depth and Liquid Cloud Droplet Effective Radius is variable both in the model and the observations. However, the model reports the expected negative correlation more often than the MODIS data. Aerosol Optical Depth is overall positively correlated to Cloud Liquid Water Path both in the model and the observations, with a few regional exceptions.

Trends in cirrus cloud cover have been estimated based on 16 years of data from ISCCP (International Satellite Cloud Climatology Project). The results have been spatially correlated with aircraft density data to determine the changes in cirrus cloud cover due to aircraft traffic. The correlations are only moderate, as many other factors have also contributed to changes in cirrus. Still we regard the results to be indicative of an impact of aircraft on cirrus amount. The main emphasis of our study is on the area covered by the METEOSAT satellite to avoid trends in the ISCCP data resulting from changing satellite viewing geometry. In Europe, which is within the METEOSAT region, we find indications of a trend of about 1-2% cloud cover per decade due to aircraft, in reasonable agreement with previous studies. The positive trend in cirrus in areas of high aircraft traffic contrasts with a general negative trend in cirrus. Extrapolation in time to cover the entire period of aircraft operations and in space to cover the global scale yields a mean estimate of 0.03 Wm-2 (lower limit 0.01, upper limit 0.08 Wm-2) for the radiative forcing due to aircraft induced cirrus. The mean is close to the value given by IPCC (1999) as an upper limit.

Monthly mean aerosol optical depth (AOD) over ocean is compared from a total of 9 aerosol retrievals during a 40 months period. Comparisons of AOD have been made both for the entire period and sub periods. We identify regions where there is large disagreement and good agreement between the aerosol satellite retrievals. Significant differences in AOD have been identified in most of the oceanic regions. Several analyses are performed including spatial correlation between the retrievals as well as comparison with AERONET data. During the 40 months period studied there have been several major aerosol field campaigns as well as events of high aerosol content. It is studied how the aerosol retrievals compare during such circumstances. The differences found in this study are larger than found in a previous study where 5 aerosol retrievals over an 8 months period were compared. Part of the differences can be explained by limitations and deficiencies in some of the aerosol retrievals. In particular, results in coastal regions are promising especially for aerosol retrievals from satellite instruments particularly suited for aerosol research. In depth analyses explaining the differences between AOD obtained in different retrievals are clearly needed. We limit this study to identifying differences and similarities and indicating possible sources that affect the quality of the retrievals. This is a necessary first step towards understanding the differences and improving the retrievals.

Temperature anomalies and changes in the diurnal temperature range (DTR) are expected to pose physiological challenges to biota; hence, both spatial and temporal variations in DTR provide important insights into temperature-induced stress in humans, animals, and vegetation. Furthermore, vegetation could dampen temperature variability. Here, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data of Land Surface Temperature (LST) to evaluate the global variation in DTR and its rate of change in spatial and temporal scales for the two decades spanning from 2001 to 2020. We show that North America, Africa, and Antarctica, as well as the global mean, experienced statistically significant DTR rates of change over the last 20 years in either summer, winter, or the annual mean. The rates were all negative, indicating the day-night temperature differences are decreasing in those regions because night temperatures are increasing at a faster rate than day temperatures. MODIS data of the Normalized Difference Vegetation Index (NDVI) revealed a strongly negative correlation with DTR, with a spatial correlation coefficient of -0.61. This correlation demonstrates a prominent dampening effect of vegetation on diurnal temperature oscillations. For future DTR projections, we used 19 models in the Coupled Model Intercomparison Project 6 (CMIP6) to predict global DTR trends from 2021 to 2050 with low and high CO 2 concentration scenarios. The high CO 2 emission scenario projects significant decreases in DTR in circumpolar regions, central Africa, and India compared to the low CO 2 scenario. This difference in the two scenarios underscores the substantial influence of increased global temperatures and elevated CO 2 concentration on DTR and, consequently, on the ecosystems in certain regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines., (© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Net Ecosystem Production (NEP) of forests is the net carbon dioxide (CO 2 ) fluxes between land and the atmosphere due to forests' biogeochemical processes. NEP varies with natural drivers such as precipitation, air temperature, solar radiation, plant functional type (PFT), and soil texture, which affect the gross primary production and ecosystem respiration, and thus the net C sequestration. It is also known that deposition of sulphur and nitrogen influences NEP in forest ecosystems. These drivers' respective, unique effects on NEP, however, are often difficult to be individually identified by conventional bivariate analysis. Here we show that by analyzing 22 forest sites with 231 site-year data acquired from FLUXNET database across Europe for the years 2000-2014, the individual, unique effects of these drivers on annual forest CO 2 fluxes can be disentangled using Generalized Additive Models (GAM) for nonlinear regression analysis. We show that S and N deposition have substantial impacts on NEP, where S deposition above 5 kg S ha -1 yr -1 can significantly reduce NEP, and N deposition around 22 kg N ha -1 yr -1 has the highest positive effect on NEP. Our results suggest that air quality management of S and N is crucial for maintaining healthy biogeochemical functions of forests to mitigate climate change. Furthermore, the empirical models we developed for estimating NEP of forests can serve as a forest management tool in the context of climate change mitigation. Potential applications include the assessment of forest carbon fluxes in the REDD+ framework of the UNFCCC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

In north-western Europe, the common tick, Ixodes ricinus, is widely established, its distribution appears to be increasing and the spread of tick-borne diseases is of increasing concern. The project 'Flått i Nord' (Ticks in northern Norway) commenced in spring 2009 with the intention of studying the tick's distribution and that of its pathogens in northern Norway. Several methods were used: cloth-dragging, collecting from trapped small mammals, and collecting from pets. Since 2010, the occurrence of ticks in the region of northern Norway was determined directly by cloth-dragging 167 times in 109 separate locations between the latitudes of 64 °N and 70 °N (included seven locations in the northern part of Trøndelag County). The northernmost location of a permanent I. ricinus population was found to be Nordøyvågen (66.2204 °N, 12.59 °E) on the Island of Dønna. In a sample of 518 nymphal and adult ticks, the Borrelia prevalence collected close to this distribution limit varied but was low (1-15 %) compared with the locations in Trøndelag, south of the study area (15-27 %). Five specimens (1 %) were positive for Rickettsia helvetica. The length of the vegetation growing season (GSL) can be used as an approximate index for the presence of established populations of I. ricinus. The present study suggests that the threshold GSL for tick establishment is about 170 days, because the median GSL from 1991 to 2015 was 174-184 days at sites with permanent tick populations, showing a clear increase compared with the period 1961-1990. This apparent manifestation of climate change could explain the northward extension of the range of I. ricinus., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2020 The Authors. Published by Elsevier GmbH.. All rights reserved.)

The Hess Brezowsky Großwetterlagen (HBGWL) European weather classification system, accumulated over a long period (more than 130 years), provides a rare opportunity to examine the impact of various factors on regional atmospheric flow. We have used these data to examine changes in the frequency (days/month) of given weather systems direction (WSD) during peak phases in the North Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO), solar cycle (SC) and peaks in stratospheric aerosol optical depth (AOD) with superposed epoch analysis and Monte Carlo significance testing. We found highly significant responses to the NAO consistent with expectations: this signal confirmed the utility of the HBGWL data for this type of analysis and provided a benchmark of a clear response. WSD changes associated with ENSO, SC and AOD were generally within the ranges expected from random samples. When seasonal restrictions were added the results were similar, however, we found one clearly significant result: an increase in southerly flow of 2.6±0.8 days/month (p=1.9×10(-4)) during boreal summertime in association with El Niño. This result supports the existence of a robust teleconnection between the ENSO and European weather.

Small water bodies in cold climate respond fast to global warming, and species adapted to such habitats may be valuable indicators for climate change. We investigated the geographical and physiological temperature limits of the Arctic fairy shrimp (Branchinecta paludosa), which is common in cold water arctic ponds, but at present retracts its range in alpine areas along its southern outreach of Norway. Seasonal logging of water temperatures along an altitudinal transect revealed an upper temperature limit of 12.7°C for its presence, which closely matched a calculated upper temperature limit of 12.9°C throughout its entire Norwegian range. Field data hence point to cold stenotherm features, which would be consistent with its Arctic, circumpolar distribution. Lab experiments, on the other hand, revealed a linear increase in respiration over 10-20°C. When fed ad libitum somatic growth increased with temperature, as well, without negative physiological impacts of higher temperatures. The absence of Branchinecta paludosa in ponds warmer than 13°C could still be due to a mismatch between temperature dependent metabolism and limited energy supply in these ultraoligotrophic water bodies. We discuss the concept of cold stenothermy in this context, and the impacts of regional warming on the future distribution of the Arctic fairy shrimp., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

This is the first study to determine the density of questing Ixodes ricinus in northern Norway. It was performed at two sites in Brønnøy, which has been known for its tick permissive habitats for decades and is one of the northernmost habitats with an abundant I. ricinus population in the world. From April to November 2011, all stages of host-seeking I. ricinus were collected from the two sites. The overall prevalence of nymphs infected with Borrelia burgdorferi sensu lato was 21% and that of adult ticks 46%. The rates of the genospecies Borrelia afzelii, Borrelia garinii, and Borrelia valaisiana were similar to findings in most other studies in Scandinavia, with B. afzelii by far the most prevalent at 76%. The high Borrelia-infection prevalence in ticks from Brønnøy may explain the high incidence rate of reported Lyme borreliosis in the municipality., (Copyright © 2015 The Authors. Published by Elsevier GmbH.. All rights reserved.)