Past and future trends of diurnal temperature range and their correlation with vegetation assessed by MODIS and CMIP6

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 CO2 concentration scenarios. The high CO2 emission scenario projects significant decreases in DTR in circumpolar regions, central Africa, and India compared to the low CO2 scenario. This difference in the two scenarios underscores the substantial influence of increased global temperatures and elevated CO2 concentration on DTR and, consequently, on the ecosystems in certain regions.

Climate-ecosystem modelling made easy: The Land Sites Platform

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.

Disentangling effects of natural and anthropogenic drivers on forest net ecosystem production

Net Ecosystem Production (NEP) of forests is the net carbon dioxide (CO₂) 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 CO₂ 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.

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes

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.

The distribution limit of the common tick, Ixodes ricinus, and some associated pathogens in north-western Europe

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.

Are there statistical links between the direction of European weather systems and ENSO, the solar cycle or stratospheric aerosols?

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.

Is distribution of cold stenotherms constrained by temperature? The case of the Arctic fairy shrimp (Branchinecta paludosa O.F. Müller 1788)

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.